Abbasi, MH, Arjmandzadeh, Z, Zhang, J, Krovi, VN, Xu, B & Mishra, DK 2024, 'A Coupled Game Theory and Lyapunov Optimization Approach to Electric Vehicle Charging at Fast Charging Stations', IEEE Transactions on Vehicular Technology, vol. 73, no. 10, pp. 14224-14235.
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Abbasnejad, B, Nasirian, A, Duan, S, Diro, A, Prasad Nepal, M & Song, Y 2024, 'Measuring BIM Implementation: A Mathematical Modeling and Artificial Neural Network Approach', Journal of Construction Engineering and Management, vol. 150, no. 5.
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Abdul-Kader, HA, Shakor, ZM, Sherhan, BY, Al-Humairi, ST, Aboughaly, M, Hazrat, MA & Fattah, IMR 2024, 'Biodiesel production from waste cooking oil using KOH/HY-type nano-catalyst derived from silica sand', Biofuels, vol. 15, no. 5, pp. 527-543.
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Abedini, A, Abedin, B & Zowghi, D 2024, 'A Framework of Environmental, Personal, and Behavioral Factors of Adult Learning in Online Communities of Practice', Information Systems Frontiers, vol. 26, no. 3, pp. 1201-1218.
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AbstractAdult learning is a complex phenomenon that takes place over an adult’s lifetime and is not limited to a particular age. It includes a set of activities to enhance life through improving skills, knowledge and capabilities. The foundational theories of adult learning, such as andragogy theory, place the individual adult centre stage and differentiate adult learning from formal learning. They also shift the focus from the individuals to the environment in which adult learning takes place. In line with this movement, online communities of practice (OCOPs) have evolved from being considered as online environments for learning to specialised forums that allow practitioners to collaborate around a project of mutual interest. The principles of adult learning are directly applicable to engagement in OCOPs because they include practical methods founded on the belief that adults are self-directed, autonomous learners and that learning is most effective when the environment plays the role of a facilitator, rather than being just a supportive and traditional setting for learning. However, how individual adults engage in OCOPs and benefit from them is not well understood. This paper draws on social cognitive theory to examine: how environmental, personal and behavioural factors shape engagement in OCOPs. To answer this question, twenty-one interviews were conducted with members of GitHub, a large online community of practice for IT professionals. The findings revealed that adults’ engagement in OCOPs involves project-based activities on mutual interests and willingness to help others. The findings also show that engaging in online communities does not only satisfy intrinsic, well-defined, expected outcomes and shape adults’ engagement, but also has an impact on adults’ lifelong learning achievements, such as professional experience and credit recognition. Based on these findings, a revised framework for adults’ engagement in...
Aboutorab, H, Hussain, OK, Saberi, M, Hussain, FK & Prior, D 2024, 'Adaptive identification of supply chain disruptions through reinforcement learning', Expert Systems with Applications, vol. 248, pp. 123477-123477.
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Abraham, MT, Satyam, N & Pradhan, B 2024, 'A novel approach for quantifying similarities between different debris flow sites using field investigations and numerical modelling', Terra Nova, vol. 36, no. 2, pp. 138-147.
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AbstractDebris flows are geomorphological processes that affect the landscape evolution process of any region. In this study, an integrated methodology is proposed to identify the chance of further debris flows and quantify the similarities between debris flow locations, materials and rheology, using field and laboratory investigations and remote sensing data. The method was tested for four failure‐triggered debris flow sites in the Western Ghats of India, using dimensionless parametric similarity values ranging from 0 to 1. The maximum parametric similarity was observed as 0.84 when comparing the flow accumulation values of Sites 3 and 4, and the maximum overall site similarity was 0.68. The calibrated rheological parameters of one site were found to be satisfactory in modelling the shape of debris flow at all other sites. The findings can be used to identify similar hotspots in the region and to simulate debris flows for quantitative hazard assessment.
ABTAHI, H, KARIMI, M & MAXIT, L 2024, 'A vibration-based method to estimate the low-wavenumber wall pressure field in a turbulent boundary layer', INTER-NOISE and NOISE-CON Congress and Conference Proceedings, vol. 270, no. 11, pp. 604-613.
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When an elastic structure is excited by a low-speed turbulent flow, it generates flow-induced vibration which is primarily due to the low-wavenumber components of the wall pressure field (WPF) beneath the turbulent boundary layer (TBL). Therefore, to accurately predict the vibration response of the structure, an accurate estimation of the low-wavenumber WPF is needed. While existing TBL models for WPF well predict the convective region, they exhibit significant discrepancies in predicting the low-wavenumber levels. This numerical study aims to explore the feasibility of estimation of the low-wavenumber WPF by analysing vibration data obtained from a structure excited by a TBL. An inverse method is proposed based on the relationship between the TBL forcing function and structural vibrations in the wavenumber domain. The random TBL force is simulated with deterministic loading using the uncorrelated wall plane wave technique. A model of a simply supported plate under a TBL excitation is developed to demonstrate the proposed method. The plate's acceleration data is then used to estimate the WPF in the low-wavenumber range. It is shown how using multiple discrete frequencies in the analysis can reduce the required snapshots for accurate estimation of the WPF.
Abtahi, H, Karimi, M & Maxit, L 2024, 'Identification of low-wavenumber wall pressure field beneath a turbulent boundary layer using vibration data', Journal of Fluids and Structures, vol. 127, pp. 104135-104135.
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Abtahi, H, Karimi, M & Maxit, L 2024, 'On the challenges of estimating the low-wavenumber wall pressure field beneath a turbulent boundary layer using a microphone array', Journal of Sound and Vibration, vol. 574, pp. 118230-118230.
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Abualhamayl, A, Almalki, M, Al-Doghman, F, Alyoubi, A & Hussain, FK 2024, 'Blockchain for real estate provenance: an infrastructural step toward secure transactions in real estate E-Business', Service Oriented Computing and Applications, vol. 18, no. 4, pp. 333-347.
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AbstractIn the rapidly evolving digital era, the growing trend of conducting real estate e-business transactions through online platforms has led to escalated challenges in ensuring transactional security and trust. These challenges underscore the importance of balancing transparency with data privacy and enhancing accountability in this field. As an extension of our previously published work (Abualhamayl AJ, Almalki MA, Al-Doghman F, Alyoubi AA, Hussain FK (2023) Towards fractional NFTs for joint ownership and provenance in real estate. In: 2023 IEEE international conference on e-business engineering (ICEBE), p. 143–8. 10.1109/ICEBE59045.2023.00022.), this paper introduces the Global Real Estate Platform (GREP), a novel hybrid blockchain system that utilizes real estate provenance to establish a secure and trustworthy environment for real estate e-business, specifically focusing on two key challenges: ensuring data authenticity and effectively managing access rights. Integral to GREP's design is the involvement of government entities, which is essential for maintaining the required balance between transparency, privacy, and high levels of accountability. This proposed framework is explained conceptually and demonstrated practically, offering an innovative perspective on the integration of hybrid blockchain technology in the real estate system. Furthermore, our research encompasses a detailed implementation, using various tools, and an in-depth examination of three use cases. This combined analysis effectively demonstrates GREP's efficacy in addressing the targeted challenges in the field. While acknowledging the system's limitations, including challenges in user adoption and performance variability under different network conditions, our findings open new avenues for further exploration, such as landlords' payment histories and utility bills, and using blockchain as a secondary user identifier. These features collective...
Adak, C, Chattopadhyay, S & Saqib, M 2024, 'Deep Analysis of Visual Product Reviews', IEEE Transactions on Emerging Topics in Computational Intelligence, pp. 1-6.
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Adeoti, OS, Kandasamy, J & Vigneswaran, S 2024, 'Sustainability framework for water infrastructure development in Nigeria: a modelling approach', Water Supply, vol. 24, no. 8, pp. 2933-2945.
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ABSTRACT This study introduces the Predictive Iterative Sustainability Model (PISM), a tailored framework designed to enhance water infrastructure sustainability evaluations in Nigeria. PISM addresses the lack of localised, adaptable frameworks by integrating three key components: a Viability Rating (VR), a Sustainability Rating (SR), and a conceptual formula within a predictive iterative process. This integrated approach optimises project evaluation and planning. Empirical data were derived by evaluating responses to a survey with 70 Likert-scale questions covering 265 sustainability challenges. This data was used to assess community viability for sustainable water infrastructure in five Nigerian communities facing significant water poverty. The results reveal VR scores ranging from 63.95 to 67.91%, establishing a benchmark for viability. SR scores, on the other hand, vary substantially from 179 to 424%, illustrating the model's capacity to evaluate sustainability under diverse conditions and identify critical, high-impact projects that can mitigate infrastructure failure risks. As a dynamic and adaptable framework, PISM holds significant potential to improve water infrastructure sustainability in Nigeria and similar regions globally.
Adeoti, OS, Kandasamy, J & Vigneswaran, S 2024, 'Water infrastructure sustainability challenge in Nigeria: A detailed examination of infrastructure failures and potential solutions', Water Supply, vol. 24, no. 6, pp. 2066-2076.
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ABSTRACT Achieving Sustainable Development Goal 6.1 – universal and equitable access to safe and affordable drinking water – is a critical global challenge. This study contributes to this aim by analyzing the functionality and sustainability of rural water boreholes in Nigeria. It employs GIS mapping, Spearman's rho correlation analysis, and interviews across 1,696 communities to investigate borehole failure dynamics, the impact of multidimensional poverty index (MPI) on water access, technical failure causes, and the influence of ownership on functionality. Findings show that while 49.8% of communities lack improved water sources, 25.5 benefit from functional boreholes, and 24.5 grapple with failures. This study reveals a complex relationship between MPI and water access, with community ownership associated with better functionality. Consequently, the study proposes holistic strategies, emphasizing community mapping and smart infrastructure, to enhance water system sustainability. Although the study is centered in Nigeria, its insights are applicable to regions with similar socio-economic conditions, contributing to the global pursuit of sustainable water access in alignment with SDG 6.1.
Adhiatma, A, Hakim, A, Fachrunnisa, O & Hussain, FK 2024, 'The role of social media business and organizational resources for successful digital transformation', Journal of Media Business Studies, vol. 21, no. 1, pp. 23-50.
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Adhikari, S, Alsadoon, A, Bekhit, M, Jerew, OD, Siddiqi, M & Ali, A 2024, 'Optimizing Real-Time Video Transmission for Surgical Tele-Education Through ERV Algorithm', IEEE Access, vol. 12, pp. 98707-98722.
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Aditya, L, Vu, HP, Johir, MAH, Mao, S, Ansari, A, Fu, Q & Nghiem, LD 2024, 'Synthesizing cationic polymers and tuning their properties for microalgae harvesting', Science of The Total Environment, vol. 917, pp. 170423-170423.
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Aedan, Y, Altaee, A, Zhou, JL & Shon, HK 2024, 'Perfluorooctanoic acid-contaminated wastewater treatment by forward osmosis: Performance analysis', Science of The Total Environment, vol. 934, pp. 173368-173368.
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Perfluorooctanoic acid (PFOA) is a persistent compound, raising considerable global apprehension due to its resistance to breakdown and detrimental impacts on human health and aquatic environments. Pressure-driven membrane technologies treating PFAS-contaminated water are expensive and prone to fouling. This study presented a parametric investigation of the effectiveness of cellulose triacetate membrane in the forward osmosis (FO) membrane for removing PFOA from an aqueous solution. The study examined the influence of membrane orientation modes, feed pH, draw solution composition and concentration, and PFOA concentration on the performance of FO. The experimental results demonstrated that PFOA rejection was 99 % with MgCl2 and slightly >98 % with NaCl draw solutions due to the mechanism of PFOA binding to the membrane surface through Mg2+ ions. This finding highlights the crucial role of the draw solution's composition in PFOA treatment. Laboratory results revealed that membrane rejection of PFOA was 99 % at neutral and acidic pH levels but decreased to 95 % in an alkaline solution at pH 9. The decrease in membrane rejection is attributed to the dissociation of the membrane's functional groups, consequently causing pore swelling. The results were confirmed by calculating the average pore radius of the CTA membrane, which increased from 27.94 nm at pH 5 to 30.70 nm at pH 9. Also, variations in the PFOA concentration from 5 to 100 mg/L did not significantly impact the membrane rejection, indicating the process's capability to handle a wide range of PFOA concentrations. When seawater was the draw solution, the FO membrane rejected 99 % of PFOA concentrations ranging from 5 mg/L to 100 mg/L. The CTA FO treating PFOA-contaminated wastewater from soil remediation achieved a 90 % recovery rate and water flux recovery of 96.5 % after cleaning with DI water at 40 °C, followed by osmotic backwash. The results suggest the potential of using abundant and cost-effec...
Aghajani, S, Wu, C, Li, Q & Fang, J 2024, 'Additively manufactured composite lattices: A state-of-the-art review on fabrications, architectures, constituent materials, mechanical properties, and future directions', Thin-Walled Structures, vol. 197, pp. 111539-111539.
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Aguilera, RP, Acuna, P, Rojas, C, Pou, J, Konstantinou, G & Watanabe, EH 2024, 'An Instantaneous Power Theory Extension for the Interphase Power Imbalance Problem', IEEE Transactions on Power Electronics, vol. 39, no. 10, pp. 12261-12270.
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Ahadi, A, Bower, M, Lai, J, Singh, A & Garrett, M 2024, 'Evaluation of teacher professional learning workshops on the use of technology - a systematic review', Professional Development in Education, vol. 50, no. 1, pp. 221-237.
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Ahamed, E, Keshavarz, R, Franklin, D & Shariati, N 2024, 'Software-Defined Programmable Metamaterial Lens System for Dynamic Wireless Power Transfer Applications', IEEE Transactions on Antennas and Propagation, vol. 72, no. 7, pp. 5840-5851.
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Ahmad Zamri, MFM, Hassan, SHA, Tiong, SK, Milano, J, Bahru, R, Fattah, IMR & Mahlia, TMI 2024, 'Progress and challenges of mesoporous catalysts in upgraded pyrolysis of biomass for biofuel production', Journal of Analytical and Applied Pyrolysis, vol. 182, pp. 106651-106651.
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Ahmed, A, Al-Dweik, A, Iraqi, Y & Damiani, E 2024, 'Integrated Terrestrial-Wired and LEO Satellite With Offline Bidirectional Cooperation for 6G IoT Networks', IEEE Internet of Things Journal, vol. 11, no. 9, pp. 15767-15782.
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Ahmed, F, Afzal, MU, Esselle, KP & Thalakotuna, DN 2024, 'Novel Dual-Band Phase-Gradient Metascreen and Dual-Band Near-Field Meta-Steering Antennas', IEEE Transactions on Antennas and Propagation, vol. 72, no. 3, pp. 2202-2216.
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Ahmed, SF, Alam, MSB, Afrin, S, Rafa, SJ, Rafa, N & Gandomi, AH 2024, 'Insights into Internet of Medical Things (IoMT): Data fusion, security issues and potential solutions', Information Fusion, vol. 102, pp. 102060-102060.
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Ahmed, SF, Alam, MSB, Afrin, S, Rafa, SJ, Taher, SB, Kabir, M, Muyeen, SM & Gandomi, AH 2024, 'Toward a Secure 5G-Enabled Internet of Things: A Survey on Requirements, Privacy, Security, Challenges, and Opportunities', IEEE Access, vol. 12, pp. 13125-13145.
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Ahmed, SF, Islam, N, Tasannum, N, Mehjabin, A, Momtahin, A, Chowdhury, AA, Almomani, F & Mofijur, M 2024, 'Microplastic removal and management strategies for wastewater treatment plants', Chemosphere, vol. 347, pp. 140648-140648.
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Discharging microplastics into the environment with treated wastewater is becoming a major concern around the world. Wastewater treatment plants (WWTPs) release microplastics into terrestrial and aquatic habitats, mostly from textile, laundry, and cosmetic industries. Despite extensive research on microplastics in the environment, their removal, and WWTP management strategies, highlighting their environmental effects, little is known about microplastics' fate and behaviour during various treatment processes. Microplastics interact with treatment technologies differently due to their diverse physical and chemical characteristics, resulting in varying removal efficiency. Microplastics removed from WWTPs may accumulate in soil and harm terrestrial ecosystems. Few studies have examined the cost, energy use, and trade-offs of large-scale implementation of modern treatment methods for the removal of microplastics. To safeguard aquatic and terrestrial habitats from microplastics' contamination, focused and efficient management techniques must bridge these knowledge gaps. This review summarizes microplastic detection, collection, removal and management strategies. A compilation of treatment process studies on microplastics' removal efficiency and their destiny and transit paths shows recent improvement. Bioremediation, membrane bioreactor (MBR), electrocoagulation, sol-gel technique, flotation, enhanced filtering, and AOPs are evaluated for microplastic removal. The fate and behaviour of microplastics in WWTPs suggest they may be secondary suppliers of microplastics to receiving ecosystems. Innovative microplastic removal strategies and technologies such as nanoparticles, microorganism-based remediation, and tertiary treatment raise issues. These new WWTP technologies are examined for feasibility, limitations, and implementation issues. Pretreatment modifies microplastic size, adsorption potential, and surface morphology to remove microplastics from WWTPs. Memb...
Ahmed, SF, Kuldeep, SA, Rafa, SJ, Fazal, J, Hoque, M, Liu, G & Gandomi, AH 2024, 'Enhancement of traffic forecasting through graph neural network-based information fusion techniques', Information Fusion, vol. 110, pp. 102466-102466.
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Ahmed, SF, Kumar, PS, Ahmed, B, Mehnaz, T, Shafiullah, GM, Nguyen, VN, Duong, XQ, Mofijur, M, Badruddin, IA & Kamangar, S 2024, 'Carbon-based nanomaterials: Characteristics, dimensions, advances and challenges in enhancing photocatalytic hydrogen production', International Journal of Hydrogen Energy, vol. 52, pp. 424-442.
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Ai, J, Abdelraheem, WHM, Peng, S, Guo, W, Duan, X, Peng, S, Zhang, W, Wang, Q & Dionysiou, DD 2024, 'AsIII-enhanced oxidation by coexisting MnIII-phenolic complexes during arsenic contaminated groundwater treatment by MnO2', Separation and Purification Technology, vol. 344, pp. 127254-127254.
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Ai, J, Wang, K, Fu, Q, Dong, T, Li, L, Peng, S, Wang, D, Wang, Q & Zhang, W 2024, 'Novel insights into the biopolymers transformation under wastewater sludge drying process at different temperatures in relation to drying behavior', Chemical Engineering Journal, vol. 486, pp. 150376-150376.
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Akbari, A, Rajabi Jaghargh, M, Abu Samah, A, Peter Cox, J, Gholamzadeh, M, Araghi, A, Saco, PM & Khosravi, K 2024, 'Utilization of the Google Earth Engine for the evaluation of daily soil temperature derived from Global Land Data Assimilation System in two different depths over a semiarid region', Meteorological Applications, vol. 31, no. 4.
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AbstractThe Google Earth Engine (GEE) was used to investigate the performance of the Global Land Data Assimilation System (GLDAS) soil temperature (ST) data against observed ST from 13 synoptic stations over a semiarid region in Iran. Three‐hourly ST data were collected and analyzed in two depths (0–10 cm; 40–100 cm) and 5 years. In each depth, GLDAS‐Noah ST data were evaluated for daily minimum, maximum, and average ST (i.e., Tmin, Tmax, and Tavg). Based on the correlation coefficient, Kling–Gupta Efficiency, and Nash–Sutcliffe Efficiency the overall performance of the GLDAS‐Noah is 0.96, 0.66, and 0.79 for Tmin; 0.97, 0.84, and 0.89 for Tavg; and 0.95, 0.89, and 0.89 for Tmax, respectively in the first layer. Likewise, 0.97, 0.85, and 0.86 for Tmin; 0.97, 0.77, and 0.80 for Tavg; and 0.97, 0.69, and 0.69 for Tmax are obtained in the second layer. However, there is a significant negative bias which tends to underestimate ST in the two investigated layers, given by an average bias over all the stations analyzed of −24%, −12%, and −5% for Tmin, Tavg, and Tmax in the first layer, and average bias of −8%, −13%, and −17% for Tmin, Tavg, and Tmax in the second layer. This study reveals that GLDAS‐Noah‐derived ST can be ...
Akter, A, Zafir, EI, Dana, NH, Joysoyal, R, Sarker, SK, Li, L, Muyeen, SM, Das, SK & Kamwa, I 2024, 'A review on microgrid optimization with meta-heuristic techniques: Scopes, trends and recommendation', Energy Strategy Reviews, vol. 51, pp. 101298-101298.
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Akter, MM, Surovy, IZ, Sultana, N, Faruk, MO, Gilroyed, BH, Tijing, L, Arman, Didar-ul-Alam, M, Shon, HK, Nam, SY & Kabir, MM 2024, 'Techno-economics and environmental sustainability of agricultural biomass-based energy potential', Applied Energy, vol. 359, pp. 122662-122662.
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Alaminos, D, Guillén-Pujadas, M, Vizuete-Luciano, E & Merigó, JM 2024, 'What is going on with studies on financial speculation? Evidence from a bibliometric analysis', International Review of Economics & Finance, vol. 89, pp. 429-445.
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AL-Attabi, R, Merenda, A, Hsia, T, Sriramoju, B, Dumée, LF, Thang, SH, Pham, H, Yang, X & Kong, L 2024, 'Morphology engineering of nanofibrous poly(acrylonitrile)-based strong anion exchange membranes for enhanced protein adsorption and recovery', Journal of Water Process Engineering, vol. 65, pp. 105750-105750.
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Alcaide, AM, Poblete, P, Vazquez, S, Aguilera, RP, Leon, JI, Kouro, S & Franquelo, LG 2024, 'Generalized Feed-Forward Sampling Method for Multilevel Cascaded H-Bridge Converters', IEEE Transactions on Industrial Electronics, vol. 71, no. 8, pp. 8259-8267.
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Algayyim, SJM, Saleh, K, Wandel, AP, Fattah, IMR, Yusaf, T & Alrazen, HA 2024, 'Influence of natural gas and hydrogen properties on internal combustion engine performance, combustion, and emissions: A review', Fuel, vol. 362, pp. 130844-130844.
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Alharbi, F, Luo, S, Zhao, S, Yang, G, Wheeler, C & Chen, Z 2024, 'Belt Conveyor Idlers Fault Detection Using Acoustic Analysis and Deep Learning Algorithm With the YAMNet Pretrained Network', IEEE Sensors Journal, vol. 24, no. 19, pp. 31379-31394.
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Alharbi, S, Abdulrhman, A, Alahmadi, K, Alhosaini, H, Zhu, Y & Wang, X 2024, 'Exploring oversampling techniques for fraud detection with imbalanced classes', International Journal of Computer Vision and Signal Processing, vol. 14, no. 1, pp. 26-33.
Alhosaini, H, Alharbi, S, Wang, X & Xu, G 2024, 'API Recommendation For Mashup Creation: A Comprehensive Survey', The Computer Journal, vol. 67, no. 5, pp. 1920-1940.
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AbstractMashups are web applications that expedite software development by reusing existing resources through integrating multiple application programming interfaces (APIs). Recommending the appropriate APIs plays a critical role in assisting developers in building such web applications easily and efficiently. The proliferation of publicly available APIs on the Internet has inspired the community to adopt various models to accomplish the recommendation task. Until present, considerable efforts have been made to recommend the optimal set of APIs, delivering fruitful results and achieving varying recommendation performance. This paper presents a timely review on the topic of API recommendations for mashup creation. Specifically, we investigate and compare not only traditional data mining approaches and recommendation techniques but also more recent approaches based on network representation learning and deep learning techniques. By analyzing the merits and pitfalls of existing approaches, we pinpoint a few promising directions to address the remaining challenges in the current research. This survey provides a timely comprehensive review of the API recommendation research and could be a useful reference for relevant researchers and practitioners.
Al-Hunaity, SA & Far, H 2024, 'Vibration Performance Evaluation of Cold-Formed Steel and Plywood Composite Floors', International Journal of Structural Stability and Dynamics, vol. 24, no. 24.
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In this study, experimental natural frequencies and mid-span deflections were used to assess the tested composite floors against vibration serviceability limit states adopted by different standards such as AS3623. It was found that existing design criteria have predicted inconsistent results. Further, the finite element (FE) model was created, updated, and validated against the experimental modal parameters. The validated numerical model examined how various design parameters, such as material properties, cross-section geometry, and achieved a degree of composite action, have influenced the vibration properties such as natural frequencies and mode shapes. It was shown that as the added mass became more dominant than the stiffness enhancement, the fundamental natural frequency of the floor system dropped. This was evident from the results of the plywood slab thickness and the added permanent load parameters. On the other hand, the fundamental natural frequency increased with thicker and deeper joist sections since they made the floor system stiffer.
Al-Hunaity, SA & Far, H 2024, 'Vibration performance of cold-formed steel and plywood composite floors – Experimental studies', Journal of Constructional Steel Research, vol. 219, pp. 108793-108793.
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Ali Ijaz Malik, M, Kalam, MA, Mujtaba Abbas, M, Susan Silitonga, A & Ikram, A 2024, 'Recent advancements, applications, and technical challenges in fuel additives-assisted engine operations', Energy Conversion and Management, vol. 313, pp. 118643-118643.
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Ali, MY, Lalbakhsh, A, Singh, K, Koziel, S & Golunski, L 2024, '3-D Printable Metal-Dielectric Metasurface for Risley Prism-Based Beam-Steering Antennas', IEEE Access, vol. 12, pp. 165143-165154.
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Ali, U, Wu, L, Müller, A, Sukkar, F, Kaupp, T & Vidal-Calleja, T 2024, 'Interactive Distance Field Mapping and Planning to Enable Human-Robot Collaboration', IEEE Robotics and Automation Letters, vol. 9, no. 12, pp. 10850-10857.
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Alkhalaf, A & Hussain, FK 2024, 'EleVMate — A data-driven approach for ‘on-the-fly’ horizontal small datacentre scalability and VM starvation', Future Generation Computer Systems, vol. 159, pp. 91-101.
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Al-Maliki, S, Qayyum, A, Ali, H, Abdallah, M, Qadir, J, Hoang, DT, Niyato, D & Al-Fuqaha, A 2024, 'Adversarial Machine Learning for Social Good: Reframing the Adversary as an Ally', IEEE Transactions on Artificial Intelligence, vol. 5, no. 9, pp. 4322-4343.
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Alnahhal, MF, Hamdan, A, Hajimohammadi, A, Castel, A & Kim, T 2024, 'Hydrothermal synthesis of sodium silicate from rice husk ash: Effect of synthesis on silicate structure and transport properties of alkali-activated concrete', Cement and Concrete Research, vol. 178, pp. 107461-107461.
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Al-Ruzouq, R, Shanableh, A, Jena, R, Mukherjee, S, Ali Khalil, M, Gibril, MBA, Pradhan, B & Atalla Hammouri, N 2024, 'Hybrid deep learning and remote sensing for the delineation of artificial groundwater recharge zones', The Egyptian Journal of Remote Sensing and Space Sciences, vol. 27, no. 2, pp. 178-191.
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Alsaka, L, Ibrar, I, Altaee, A, Zhou, J, Chowdhury, MH, AL-Ejji, M & Hawari, AH 2024, 'Performance and analysis of kappa-carrageenan hydrogel for PFOA-contaminated soil remediation wastewater treatment', Chemosphere, vol. 365, pp. 143371-143371.
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AlSarji, AH, Al-Humairi, ST, AlMukhtar, RS, Alardhi, SM, Sulyman, M & Fattah, IMR 2024, 'Response surface methodology approach for optimization of biosorption process for removal of Hg(II) ions by immobilized Algal biomass Coelastrella sp.', Polish Journal of Chemical Technology, vol. 26, no. 2, pp. 57-68.
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Abstract Currently, adsorption stands as a viable technique for the effective removal of pollutants such as heavy metals from water. Within this research endeavor, adapted green algae (Coelastrella sp.) have been harnessed as a sustainable and environmentally conscious adsorbent, employed in the removal of Hg(II) ions from a simulated aqueous solution via employment of an Airlift bioreactor. The analysis of the attributes of adsorbent was conducted through the utilization of Fourier transform infrared (FTIR) spectroscopy. The examination of residual concentrations of Hg(II) ions in the treated solution was accomplished through the utilization of atomic absorption spectroscopy (AAS). The impact of various experimental factors, including the duration of contact (ranging from 10 to 90 minutes), initial concentrations of Hg(II) ions (ranging from 500 to 2000 μg/l), quantity of adsorbent introduced (ranging from 0.1 to 0.7 g per 250 ml), temperature variations (ranging from 20 to 40 °C), and airflow velocity (ranging from 200 to 300 ml/min), was systematically examined. For the optimization of adsorption efficiency, MINITAB 18 software was employed. The equilibrium data was subjected to analysis using the Langmuir, Freundlich, and Temkin isotherm models. Employing the framework recommended by MINITAB 18, the optimal parameters for adsorption were identified as 2000 μg/l for initial concentration, 90 minutes for contact time, 40 °C for temperature, and 300 ml/min for airflow rate. The Langmuir equation yielded the highest adsorption capacity, measuring 750 μg/g at a temperature of 40 °C.
Alshuaibi, EA, Hamdi, AM & Hussain, FK 2024, 'Volunteer Computing for fog scalability: A systematic literature review', Internet of Things, vol. 25, pp. 101072-101072.
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Altoe, F, Moreira, C, Pinto, HS & Jorge, JA 2024, 'Online Fake News Opinion Spread and Belief Change: A Systematic Review', Human Behavior and Emerging Technologies, vol. 2024, pp. 1-20.
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Fake news has been linked to the rise of psychological disorders, the increased disbelief in science, and the erosion of democracy and freedom of speech. Online social networks are arguably the main vehicle of fake news spread. Educating online users with explanations is one way of preventing this spread. Understanding how online belief is formed and changed may offer a roadmap for such education. The literature includes surveys addressing online opinion formation and polarization; however, they usually address a single domain, such as politics, online marketing, health, and education, and do not make online belief change their primary focus. Unlike other studies, this work is the first to present a cross-domain systematic literature review of user studies, methodologies, and opinion model dimensions. It also includes the orthogonal polarization dimension, focusing on online belief change. We include peer-reviewed works published in 2020 and later found in four relevant scientific databases, excluding theoretical publications that did not offer validation through dataset experimentation or simulation. Bibliometric networks were constructed for better visualization, leading to the organization of the papers that passed the review criteria into a comprehensive taxonomy. Our findings show that a person’s individuality is the most significant influential force in online belief change. We show that online arguments that balance facts with emotionally evoking content are more efficient in changing their beliefs. Polarization was shown to be cross-correlated among multiple subjects, with politics being the central polarization pole. Polarized online networks start as networks with high opinion segregation, evolve into subnetworks of consensus, and achieve polarization around social network influencers. Trust in the information source was demonstrated to be the chief psychological construct that drives online users to polarization. This shows that chang...
Al-Waaly, AAY, Paul, AR, Saha, G & Saha, SC 2024, 'Entropy generation analysis of natural convection flow in porous diamond-shaped cavity', International Journal of Thermofluids, vol. 23, pp. 100801-100801.
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Al-Zainati, N, Ibrar, I, Altaee, A, Subbiah, S & Zhou, J 2024, 'Multiple staging of pressure retarded osmosis: Impact on the energy generation', Desalination, vol. 573, pp. 117199-117199.
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AlZainati, N, Ibrar, I, Braytee, A, Altaee, A, Chowdhury, MH, Subbiah, S, Zhou, J, Alanezi, AA & Samal, AK 2024, 'Machine learning to predict the intrinsic membrane parameters in pressure retarded osmosis for an economic salinity gradient power plant', Journal of Water Process Engineering, vol. 64, pp. 105674-105674.
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Ambrosio, L, Vadalà, G, Tavakoli, J, Scaramuzzo, L, Brodano, GB, Lewis, SJ, Kato, S, Cho, SK, Yoon, ST, Kim, H-J, Gary, MF & Denaro, V 2024, 'Surgeon Preference Regarding Wound Dressing Management in Lumbar Fusion Surgery: An AO Spine Global Cross-Sectional Study', Neurospine, vol. 21, no. 1, pp. 204-211.
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Objective: To evaluate the global practice pattern of wound dressing use after lumbar fusion for degenerative conditions.Methods: A survey issued by AO Spine Knowledge Forums Deformity and Degenerative was sent out to AO Spine members. The type of postoperative dressing employed, timing of initial dressing removal, and type of subsequent dressing applied were investigated. Differences in the type of surgery and regional distribution of surgeons’ preferences were analyzed.Results: Right following surgery, 60.6% utilized a dry dressing, 23.2% a plastic occlusive dressing, 5.7% glue, 6% a combination of glue and polyester mesh, 2.6% a wound vacuum, and 1.2% other dressings. The initial dressing was removed on postoperative day 1 (11.6%), 2 (39.2%), 3 (20.3%), 4 (1.7%), 5 (4.3%), 6 (0.4%), 7 or later (12.5%), or depending on drain removal (9.9%). Following initial dressing removal, 75.9% applied a dry dressing, 17.7% a plastic occlusive dressing, and 1.3% glue, while 12.1% used no dressing. The use of no additional coverage after initial dressing removal was significantly associated with a later dressing change (p < 0.001). Significant differences emerged after comparing dressing management among different AO Spine regions (p < 0.001).Conclusion: Most spine surgeons utilized a dry or plastic occlusive dressing initially applied after surgery. The first dressing was more frequently changed during the first 3 postoperative days and replaced with the same type of dressing. While dressing policies tended not to vary according to the type of surgery, regional differences suggest that actual practice may be based on personal experience rather than available evidence.
Amin, M, Umar, H, Ginting, SF, Amir, F, Rizal, TA, Septiadi, WN & Mahlia, TMI 2024, 'Enhancing solar distillation through beeswax-infused tubular solar still with a heat exchanger using parabolic trough collector', Journal of Energy Storage, vol. 86, pp. 111262-111262.
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Amini, E, Marsooli, R & Neshat, M 2024, 'A multi-faceted methodology for calibration of coastal vegetation drag coefficient', Ocean Modelling, vol. 190, pp. 102391-102391.
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An, Y, Wong, J & Ling, SH 2024, 'Development of real-time brain-computer interface control system for robot', Applied Soft Computing, vol. 159, pp. 111648-111648.
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Ansari, M, Zetterstrom, O, Fonseca, NJG, Quevedo-Teruel, O & Guo, YJ 2024, 'A Lightweight Spherical Generalized Luneburg Lens Antenna With Low Cross-Polarization Over a Wide Range in Azimuth and Elevation', IEEE Open Journal of Antennas and Propagation, vol. 5, no. 1, pp. 58-66.
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Arafat, MY, Hossain, MJ & Alam, MM 2024, 'Machine learning scopes on microgrid predictive maintenance: Potential frameworks, challenges, and prospects', Renewable and Sustainable Energy Reviews, vol. 190, pp. 114088-114088.
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Arango, E, Jiménez, P, Nogal, M, Sousa, HS, Stewart, MG & Matos, JC 2024, 'Enhancing infrastructure resilience in wildfire management to face extreme events: Insights from the Iberian Peninsula', Climate Risk Management, vol. 44, pp. 100595-100595.
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Arango, E, Nogal, M, Sousa, HS, Matos, JC & Stewart, MG 2024, 'Improving societal resilience through a GIS-based approach to manage road transport networks under wildfire hazards', Transportation Engineering, vol. 15, pp. 100219-100219.
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Aref, A, Martin, A, Aboulkheyr Es, H, Krzesaj, P, Adler, V, Khardenavis, K, Sarafraz-Yazdi, E & Perricone, MA 2024, 'Abstract LB218: Advanced ex vivo platform for drug response analysis: Evaluating the potent effects of NMC-521 mAb on mouse-and patient-derived organotypic tumor spheroids', Cancer Research, vol. 84, no. 7_Supplement, pp. LB218-LB218.
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Abstract In response to the high failure rate of new drugs in clinical trials, we have developed an innovative organotypic tumor spheroids platform, designed to mitigate the risks associated with advancing drugs to clinical stages. Utilizing patient-derived organotypic tumor spheroids (PDOTS), this platform uniquely captures both the phenotype and genotype of individual patient tumor microenvironments. This approach not only enhances the precision of preclinical models but also bridges the gap between laboratory research and clinical applicability. In this study, we assess the effectiveness of the first-in-class monoclonal antibody NMC-521 (NomoCan Pharmaceuticals, NY) using our ex vivo platform. Targeting the novel cancer-specific antigen NMC-1, NMC-521 exhibited significant growth inhibition of CT-26 tumors in syngeneic BALB/c mice, reducing tumor size by approximately 55% compared to the control. Similarly, in ex vivo settings with freshly excised CT-26 tumors (n=18), NMC-521 demonstrated a dose-dependent cytotoxic effect, achieving about a 65% reduction in tumor viability with an effective dose (ED50) of 10 µg/ml. Further insights were gained into NMC-521's mechanism of action using our ex vivo platform. The drug’s ability to induce tumor cell death in CT26 ex vivo was significantly reversed (p<0.01) when co-treated with α-NKG2D but not α-CD8, indicating a critical role for natural killer (NK) cells in its therapeutic effect. Complementing these findings, our analyses on freshly excised patient tumors (n=15) corroborated the cytotoxic effects of this drug on colorectal cancer but not to the same magnitude as in the CT-26 model. Furthermore, unlike the syngeneic murine tumor model, the response rate was (8 of 15 responsive patient tumors. Gene expression profiling indicated that NK cell-associated RNAs were elevated in NMC-521 treated PDOTS. Additionally, combination therapy studies using NMC-521...
Armaghani, DJ, Yang, P, He, X, Pradhan, B, Zhou, J & Sheng, D 2024, 'Toward Precise Long-Term Rockburst Forecasting: A Fusion of SVM and Cutting-Edge Meta-heuristic Algorithms', Natural Resources Research, vol. 33, no. 5, pp. 2037-2062.
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Arqam, M, Jahangiri, A, Mitchell, M, Bennett, NS & Woodfield, P 2024, 'Second law efficiency of air-cooled refrigeration compressors', Progress in Engineering Science, vol. 1, no. 1, pp. 100002-100002.
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Arsad, SR, Arsad, AZ, Ker, PJ, Hannan, MA, Tang, SGH, Goh, SM & Mahlia, TMI 2024, 'Recent advancement in water electrolysis for hydrogen production: A comprehensive bibliometric analysis and technology updates', International Journal of Hydrogen Energy, vol. 60, pp. 780-801.
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Arsad, SR, Ker, PJ, Hannan, MA, Tang, SGH, R S, N, Chau, CF & Mahlia, TMI 2024, 'Patent landscape review of hydrogen production methods: Assessing technological updates and innovations', International Journal of Hydrogen Energy, vol. 50, pp. 447-472.
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Asheralieva, A, Niyato, D & Miyanaga, Y 2024, 'Efficient Dynamic Distributed Resource Slicing in 6G Multi-Access Edge Computing Networks With Online ADMM and Message Passing Graph Neural Networks', IEEE Transactions on Mobile Computing, vol. 23, no. 4, pp. 2614-2638.
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Askari, M, Rajabzadeh, S, Tijing, L & Shon, HK 2024, 'Advances in capacitive deionization (CDI) systems for nutrient recovery from wastewater: Paving the path towards a circular economy', Desalination, vol. 583, pp. 117695-117695.
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Assaf, AR, Sidhu, GS, Soni, A, Cappelleri, JC, Draica, F, Herbert, C, Arham, I, Bader, M, Jimenez, C, Bois, M, Silvester, E, Meservey, J, Eng, V, Nelson, M, Cai, Y, Nangarlia, A, Tian, Z, Liu, Y & Watt, S 2024, 'Cross-Sectional Survey of Factors Contributing to COVID-19 Testing Hesitancy Among US Adults at Risk of Severe Outcomes from COVID-19', Infectious Diseases and Therapy, vol. 13, no. 7, pp. 1683-1701.
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Asteris, PG, Gandomi, AH, Armaghani, DJ, Kokoris, S, Papandreadi, AT, Roumelioti, A, Papanikolaou, S, Tsoukalas, MZ, Triantafyllidis, L, Koutras, EI, Bardhan, A, Mohammed, AS, Naderpour, H, Paudel, S, Samui, P, Ntanasis-Stathopoulos, I, Dimopoulos, MA & Terpos, E 2024, 'Prognosis of COVID-19 severity using DERGA, a novel machine learning algorithm', European Journal of Internal Medicine, vol. 125, pp. 67-73.
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Asteris, PG, Gandomi, AH, Armaghani, DJ, Tsoukalas, MZ, Gavriilaki, E, Gerber, G, Konstantakatos, G, Skentou, AD, Triantafyllidis, L, Kotsiou, N, Braunstein, E, Chen, H, Brodsky, R, Touloumenidou, T, Sakellari, I, Alkayem, NF, Bardhan, A, Cao, M, Cavaleri, L, Formisano, A, Guney, D, Hasanipanah, M, Khandelwal, M, Mohammed, AS, Samui, P, Zhou, J, Terpos, E & Dimopoulos, MA 2024, 'Genetic justification ofCOVID‐19 patient outcomes usingDERGA, a novel data ensemble refinement greedy algorithm', Journal of Cellular and Molecular Medicine, vol. 28, no. 4.
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AbstractComplement inhibition has shown promise in various disorders, including COVID‐19. A prediction tool including complement genetic variants is vital. This study aims to identify crucial complement‐related variants and determine an optimal pattern for accurate disease outcome prediction. Genetic data from 204 COVID‐19 patients hospitalized between April 2020 and April 2021 at three referral centres were analysed using an artificial intelligence‐based algorithm to predict disease outcome (ICU vs. non‐ICU admission). A recently introduced alpha‐index identified the 30 most predictive genetic variants. DERGA algorithm, which employs multiple classification algorithms, determined the optimal pattern of these key variants, resulting in 97% accuracy for predicting disease outcome. Individual variations ranged from 40 to 161 variants per patient, with 977 total variants detected. This study demonstrates the utility of alpha‐index in ranking a substantial number of genetic variants. This approach enables the implementation of well‐established classification algorithms that effectively determine the relevance of genetic variants in predicting outcomes with high accuracy.
Asteris, PG, Gavriilaki, E, Kampaktsis, PN, Gandomi, AH, Armaghani, DJ, Tsoukalas, MZ, Avgerinos, DV, Grigoriadis, S, Kotsiou, N, Yannaki, E, Drougkas, A, Bardhan, A, Cavaleri, L, Formisano, A, Mohammed, AS, Murlidhar, BR, Paudel, S, Samui, P, Zhou, J, Sarafidis, P, Virdis, A & Gkaliagkousi, E 2024, 'Revealing the nature of cardiovascular disease using DERGA, a novel data ensemble refinement greedy algorithm', International Journal of Cardiology, vol. 412, pp. 132339-132339.
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Ataei, M, Divsalar, A & Saberi, M 2024, 'The bi-objective orienteering problem with hotel selection: an integrated text mining optimisation approach', Information Technology and Management, vol. 25, no. 3, pp. 247-275.
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Atapattu, S, Indraratna, B & Rujikiatkamjorn, C 2024, 'Influence of periodic cyclic loading and rest period on soft clay consolidation', Proceedings of the Institution of Civil Engineers - Ground Improvement, vol. 177, no. 1, pp. 30-43.
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Railways are often subjected to periodic cyclic loading and intermittent rest periods. Excessive consolidation settlements can affect the performance of railway tracks built on the soft subgrade. The consolidation behaviour under railway loading conditions with rest periods has not been evaluated thoroughly. In this study, laboratory testing was conducted to investigate the influence of periodic cyclic loading and rest periods on the consolidation of Holocene soft clay from Ballina NSW. The specimens were subjected to a loading frequency of 1 Hz for 54 h with multiple rest periods. The recorded settlements and excess pore-water pressures (EPWP) during cyclic consolidation were employed to determine the corresponding hydraulic gradient, void ratio, resilient (dynamic) modulus and damping ratio. The settlement and accumulated EPWP can be observed during cyclic loading. In contrast, settlements do not occur within a rest period, despite the rapidly dissipating EPWP at the start of a given rest period. The maximum EPWP and settlements decrease as the number of resting period increases. An analytical model capturing the effect of cyclic loading and rest period is proposed where the unique relationships between the hydraulic gradient and the flow rate are established.
Atgur, V, Manavendra, G, Rao, BN, Veza, I & Fattah, IMR 2024, 'Thermal and combustion characteristics of honge, jatropha, and honge‐jatropha mixed biodiesels', Environmental Progress & Sustainable Energy, vol. 43, no. 1.
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AbstractThermal characteristics of biodiesels are useful in system design, modeling, and operation. Such investigations are extensively being carried out in combustors, engine, and process industries. This article examines the thermal characteristics of jatropha (Jatropha curcas), honge (Pongamia pinnata), and their equal mixing from thermogravitometry and differential scanning calorimetry (TG‐DSC) curves for the specific 10°C/min heating rate under atmospheric air. Fuel properties are measured following ASTM standards to compare with diesel properties. Each experiment was repeated three times, and the properties showed insignificant scatter. The average properties of the repeated tests are presented. Two phases of decomposition were observed for diesel, whereas three (viz., devolatilization of aqueous fractions, combustion of methyl esters, and combustion of carbonaceous residues) in biodiesels. Jatropha oil methyl ester (JOME) is thermally stable compared to honge oil methyl ester (HOME). Mixed biodiesel (JOME+HOME) is prone to oxidation due to the high content of oleic and linoleic acids. Recorded onset and offset temperatures of mixed biodiesel are low compared to pure biodiesel. Mixed biodiesel exhibited high volatility resembling diesel characteristics. It exhibited an enthalpy of 240 J/g, whereas the enthalpy of diesel, jatropha, and honge exhibited enthalpies of 130, 321, and 570 J/g, respectively. The combustion index (S) of diesel, jatropha, honge, and mixed biodiesel was 41.6, 82.8, 77.74, and 64.6, respectively. Mixed biodiesel reduces the intensity of combustion (Hf), promising better combustion characteristics. Thus, mixed biodiesel shows the potential of an efficient alternative energy source.
Athuraliya, S, Indraratna, B, Medawela, S, Rowe, RK & Thamwattana, N 2024, 'Modelling biogeochemical clogging affecting piezometers in acid sulfate soil terrain', Canadian Geotechnical Journal, vol. 61, no. 1, pp. 149-164.
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This study offers an analytical solution for radial consolidation that captures the biogeochemical clogging effect in acid sulfate soils. Field sites and personal communication with industry practitioners have provided evidence of piezometers exhibiting retarded pore pressure readings that do not follow conventional soil consolidation and seepage principles when installed in coastal acidic floodplains. This retarded response together with a variation in pH, ion concentrations, and piezometric heads provided evidence of clogging at and around the piezometers. This paper uses the proposed biogeochemical clogging model, which is an analytically derived system of equations to estimate the excess pore water pressure dissipation of piezometers installed in clogging-prone acid sulfate soils. The inclusion of the total porosity reduction attributed to biological and geochemical clogging improves the predictions of the retarded dissipation of excess pore pressure, especially after about 1 year. This method is validated for two previously identified acidic field sites in coastal Australia, where piezometers measured a very slow rate of dissipation. It is concluded that this model has potential to accurately monitor the performance of critical infrastructure, such as dams and embankment foundations built on acidic terrain.
Athuraliya, S, Indraratna, B, Medawela, S, Rowe, RK & Thamwattana, N 2024, 'Modelling biogeochemical clogging affecting piezometers in acid sulfate soil terrain', CANADIAN GEOTECHNICAL JOURNAL, vol. 61, no. 1, pp. 149-164.
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Atmakuru, A, Chakraborty, S, Faust, O, Salvi, M, Datta Barua, P, Molinari, F, Acharya, UR & Homaira, N 2024, 'Deep learning in radiology for lung cancer diagnostics: A systematic review of classification, segmentation, and predictive modeling techniques', Expert Systems with Applications, vol. 255, pp. 124665-124665.
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Ayres, LB, Bandara, M, McMillen, CD, Pennington, WT & Garcia, CD 2024, 'eutXG: A Machine-Learning Model to Understand and Predict the Melting Point of Novel X-Bonded Deep Eutectic Solvents', ACS Sustainable Chemistry & Engineering, vol. 12, no. 30, pp. 11260-11273.
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Azadi, M, Moghaddas, Z, Farzipoor Saen, R & Hussain, FK 2024, 'Financing manufacturers for investing in Industry 4.0 technologies: internal financing vs. External financing', International Journal of Production Research, vol. 62, no. 22, pp. 8056-8072.
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Supply chain finance (SCF) as a crucial approach plays a key role in improving commitment, trust, financial flows, and profitability in a supply chain (SC). Many industrial organisations finance their SC through two resources: internal financing (buyer) and external financing (bank). The main objective of this paper is to develop an advanced data envelopment analysis (DEA) model for measuring the sustainability of financing resources of Industry 4.0 technologies. To do so, for the first time a non-radial DEA model in the presence of both zero inputs and ratio data is proposed. In this paper, the sustainability factors, including economic, environmental, and social factors are incorporated into the proposed approach. The developed DEA model, for the first time, is applied in SCF. The results show the most sustainable financial resource for investing in Industry 4.0 technologies. Also, the inputs and outputs’ inefficiencies are determined.
Azadi, M, Toloo, M, Ramezani, F, Saen, RF, Hussain, FK & Farnoudkia, H 2024, 'Evaluating efficiency of cloud service providers in era of digital technologies', Annals of Operations Research, vol. 342, no. 2, pp. 1049-1078.
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Azizivahed, A, Gholami, K, Arefi, A, Li, L, Arif, MT & Haque, ME 2024, 'Stochastic scheduling of energy sharing in reconfigurable multi-microgrid systems in the presence of vehicle-to-grid technology', Electric Power Systems Research, vol. 231, pp. 110285-110285.
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Babakian, A, Huston, G, Braun, R & Lipman, J 2024, 'Internet Identifiers: A Survey of History, Challenges, and Future Perspectives', IEEE Access, vol. 12, pp. 51919-51941.
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Badeti, U, Jiang, J, Kumarasingham, S, Almuntashiri, A, Pathak, NK, Chanan, A, Freguia, S, Ang, WL, Ghaffour, N, Shon, HK & Phuntsho, S 2024, 'Source separation of urine and treatment: Impact on energy consumption, greenhouse gas emissions, and decentralised wastewater treatment process', Desalination, vol. 583, pp. 117633-117633.
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Badiee, SH, Masoudi, M, Abtahi, SA & Pradhan, B 2024, 'Assessment of desertification hazard using RS and GIS to compare the efficiency of FAO-UNEP and MEDALUS models', Spatial Information Research, vol. 32, no. 5, pp. 641-650.
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Bai, K, Zhang, W, Wen, S, Zhao, C, Meng, W, Zeng, Y & Jia, D 2024, 'A data-knowledge-driven interval type-2 fuzzy neural network with interpretability and self-adaptive structure', Information Sciences, vol. 660, pp. 120133-120133.
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Bailey, B, Latulipe, C & Ferguson, S 2024, 'Welcome to ACM Creativity & Cognition 2024', ACM International Conference Proceeding Series, p. III.
Balaji, J, Seikh, AH, Kalam, MA & Venkatesh, R 2024, 'Influences of Rotational Speed on Friction Stir Welding Quality, Mechanical and Fatigue Behaviour of AA6061/SiC Composite', Silicon, vol. 16, no. 1, pp. 323-329.
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Banerjee, S, Huang, Z, Lyu, J, Leung, FHF, Lee, T, Yang, D, Zheng, Y, McAviney, J & Ling, SH 2024, 'Automatic Assessment of Ultrasound Curvature Angle for Scoliosis Detection Using 3-D Ultrasound Volume Projection Imaging', Ultrasound in Medicine & Biology, vol. 50, no. 5, pp. 647-660.
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Bano, M, Hoda, R, Zowghi, D & Treude, C 2024, 'Large language models for qualitative research in software engineering: exploring opportunities and challenges', Automated Software Engineering, vol. 31, no. 1.
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Bao, S, Lee, HH, Yang, Q, Remedios, LW, Deng, R, Cui, C, Cai, LY, Xu, K, Yu, X, Chiron, S, Li, Y, Patterson, NH, Wang, Y, Li, J, Liu, Q, Lau, KS, Roland, JT, Coburn, LA, Wilson, KT, Landman, BA & Huo, Y 2024, 'Alleviating tiling effect by random walk sliding window in high-resolution histological whole slide image synthesis.', Proc Mach Learn Res, vol. 227, pp. 1406-1422.
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Multiplex immunofluorescence (MxIF) is an advanced molecular imaging technique that can simultaneously provide biologists with multiple (i.e., more than 20) molecular markers on a single histological tissue section. Unfortunately, due to imaging restrictions, the more routinely used hematoxylin and eosin (H&E) stain is typically unavailable with MxIF on the same tissue section. As biological H&E staining is not feasible, previous efforts have been made to obtain H&E whole slide image (WSI) from MxIF via deep learning empowered virtual staining. However, the tiling effect is a long-lasting problem in high-resolution WSI-wise synthesis. The MxIF to H&E synthesis is no exception. Limited by computational resources, the cross-stain image synthesis is typically performed at the patch-level. Thus, discontinuous intensities might be visually identified along with the patch boundaries assembling all individual patches back to a WSI. In this work, we propose a deep learning based unpaired high-resolution image synthesis method to obtain virtual H&E WSIs from MxIF WSIs (each with 27 markers/stains) with reduced tiling effects. Briefly, we first extend the CycleGAN framework by adding simultaneous nuclei and mucin segmentation supervision as spatial constraints. Then, we introduce a random walk sliding window shifting strategy during the optimized inference stage, to alleviate the tiling effects. The validation results show that our spatially constrained synthesis method achieves a 56% performance gain for the downstream cell segmentation task. The proposed inference method reduces the tiling effects by using 50% fewer computation resources without compromising performance. The proposed random sliding window inference method is a plug-and-play module, which can be generalized for other high-resolution WSI image synthesis applications. The source code with our proposed model are available at https://github.com/MASILab/RandomWalkSlidingWindow.git.
Baral, B, Altaee, A, Simeonidis, K & Samal, AK 2024, 'Editorial: Shape and size dependent nanostructures for environmental applications', Frontiers in Chemistry, vol. 12.
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Barra, ME, Solt, K, Yu, X & Edlow, BL 2024, 'Restoring consciousness with pharmacologic therapy: Mechanisms, targets, and future directions', Neurotherapeutics, vol. 21, no. 4, pp. e00374-e00374.
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Barua, PD, Keles, T, Kuluozturk, M, Kobat, MA, Dogan, S, Baygin, M, Tuncer, T, Tan, R-S & Acharya, UR 2024, 'Automated asthma detection in a 1326-subject cohort using a one-dimensional attractive-and-repulsive center-symmetric local binary pattern technique with cough sounds', Neural Computing and Applications, vol. 36, no. 27, pp. 16857-16871.
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AbstractAsthma is a common disease. The clinical diagnosis is usually confirmed on a pulmonary function test, which is not always readily accessible. We aimed to develop a computationally lightweight handcrafted machine learning model for asthma detection based on cough sounds recorded using mobile phones. Toward this aim, we proposed a novel feature extractor based on a one-dimensional version of the published attractive-and-repulsive center-symmetric local binary pattern (1D-ARCSLBP), which we tested on a new cough sound dataset. We prospectively recorded cough sounds from 511 asthmatics and 815 non-asthmatic subjects (comprising mostly healthy volunteers), which yielded 1875 one-second cough sound segments for analysis. Our model comprised four steps: (i) preprocessing, in which speech signals and stop times (silent zones between coughs) were removed, leaving behind analyzable cough sound segments; (ii) feature extraction, in which tunable q-factor wavelet transformation was used to perform multilevel signal decomposition into wavelet subbands, allowing 1D-ARCSLBP to extract local low- and high-level features; (iii) feature selection, in which neighborhood component analysis was used to select the most discriminative features; and (iv) classification, in which a standard shallow cubic support vector machine was deployed to calculate binary classification results (asthma versus non-asthma) using tenfold and leave-one-subject-out cross-validations. Our model attained 98.24% and 96.91% accuracy rates with tenfold and leave-one-subject-out cross-validation strategies, respectively, and obtained a low-time complexity. The excellent results confirmed the feature extraction capability of 1D-ARCSLBP and the feasibility of the model being developed into a real-world application for asthma screening.
Barua, PD, Tuncer, T, Baygin, M, Dogan, S & Acharya, UR 2024, 'N-BodyPat: Investigation on the dementia and Alzheimer's disorder detection using EEG signals', Knowledge-Based Systems, vol. 304, pp. 112510-112510.
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Barua, PD, Vicnesh, J, Lih, OS, Palmer, EE, Yamakawa, T, Kobayashi, M & Acharya, UR 2024, 'Artificial intelligence assisted tools for the detection of anxiety and depression leading to suicidal ideation in adolescents: a review', Cognitive Neurodynamics, vol. 18, no. 1, pp. 1-22.
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Baygin, M, Barua, PD, Dogan, S, Tuncer, T, Hong, TJ, March, S, Tan, R-S, Molinari, F & Acharya, UR 2024, 'Automated anxiety detection using probabilistic binary pattern with ECG signals', Computer Methods and Programs in Biomedicine, vol. 247, pp. 108076-108076.
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Beigi, S & Tomamichel, M 2024, 'Lower Bounds on Error Exponents via a New Quantum Decoder', IEEE Transactions on Information Theory, vol. 70, no. 11, pp. 7882-7891.
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Berta, M & Tomamichel, M 2024, 'Entanglement Monogamy via Multivariate Trace Inequalities', Communications in Mathematical Physics, vol. 405, no. 2.
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AbstractEntropy is a fundamental concept in quantum information theory that allows to quantify entanglement and investigate its properties, for example its monogamy over multipartite systems. Here, we derive variational formulas for relative entropies based on restricted measurements of multipartite quantum systems. By combining these with multivariate matrix trace inequalities, we recover and sometimes strengthen various existing entanglement monogamy inequalities. In particular, we give direct, matrix-analysis-based proofs for the faithfulness of squashed entanglement by relating it to the relative entropy of entanglement measured with one-way local operations and classical communication, as well as for the faithfulness of conditional entanglement of mutual information by relating it to the separably measured relative entropy of entanglement. We discuss variations of these results using the relative entropy to states with positive partial transpose, and multipartite setups. Our results simplify and generalize previous derivations in the literature that employed operational arguments about the asymptotic achievability of information-theoretic tasks.
Berta, M, Brandão, FGSL, Gour, G, Lami, L, Plenio, MB, Regula, B & Tomamichel, M 2024, 'The tangled state of quantum hypothesis testing', Nature Physics, vol. 20, no. 2, pp. 172-175.
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Bérubé, C, Nißen, M, Vinay, R, Geiger, A, Budig, T, Bhandari, A, Pe Benito, CR, Ibarcena, N, Pistolese, O, Li, P, Sawad, AB, Fleisch, E, Stettler, C, Hemsley, B, Berkovsky, S, Kowatsch, T & Kocaballi, AB 2024, 'Proactive behavior in voice assistants: A systematic review and conceptual model', Computers in Human Behavior Reports, vol. 14, pp. 100411-100411.
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Best, G, Garg, R, Keller, J, Hollinger, GA & Scherer, S 2024, 'Multi-robot, multi-sensor exploration of multifarious environments with full mission aerial autonomy', The International Journal of Robotics Research, vol. 43, no. 4, pp. 485-512.
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We present a coordinated autonomy pipeline for multi-sensor exploration of confined environments. We simultaneously address four broad challenges that are typically overlooked in prior work: (a) make effective use of both range and vision sensing modalities, (b) perform this exploration across a wide range of environments, (c) be resilient to adverse events, and (d) execute this onboard teams of physical robots. Our solution centers around a behavior tree architecture, which adaptively switches between various behaviors involving coordinated exploration and responding to adverse events. Our exploration strategy exploits the benefits of both visual and range sensors with a generalized frontier-based exploration algorithm and an OpenVDB-based map processing pipeline. Our local planner utilizes a dynamically feasible trajectory library and a GPU-based Euclidean distance transform map to allow fast and safe navigation through both tight doorways and expansive spaces. The autonomy pipeline is evaluated with an extensive set of field experiments, with teams of up to three robots that fly up to 3 m/s and distances exceeding 1 km in confined spaces. We provide a summary of various field experiments and detail resilient behaviors that arose: maneuvering narrow doorways, adapting to unexpected environment changes, and emergency landing. Experiments are also detailed from the DARPA Subterranean Challenge, where our proposed autonomy pipeline contributed to us winning the “Most Sectors Explored” award. We provide an extended discussion of lessons learned, release software as open source, and present a video that illustrates our extensive field trials.
Beydoun, G, Low, G, Gill, A, Moniruzzaman, M & Shen, J 2024, 'Tailoring ontology retrieval for supporting requirements analysis', Advanced Engineering Informatics, vol. 59, pp. 102231-102231.
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It is well accepted that domain ontologies can support requirement analysis activities, particularly in detecting inconsistencies and incompleteness of requirement models. These benefits critically depend on the provision of a suitable ontology. We observe the context of supporting requirement analysis provides both opportunities and restrictions when choosing the most appropriate ontology retrieval mechanisms. Requirement models are the basis for retrieving the most influential ontologies and are not the typical retrieval domain ontologies. For instance, a retrieval ontology derived from the requirement is not expected only to be a hierarchical taxonomy, nor is it limited to the boundaries of a single domain, nor does it cover any particular domain completely. Hence, retrieval methods cannot be based on classes only and computational constraints do not necessarily apply as the retrieval process is expected to run only once at the outset of the analysis phase. It is also important to assume that the retrieval in this context is targeting multiple ontologies describing multiple but related domains. In this paper, we deduce that avoiding structural based retrieval mechanisms in fact benefits to the requirement models. Instead, we formulate a new retrieval method based on the PageRank algorithm that takes into account the indirect influences of various concepts within plausible supporting ontologies. This paper provides an empirical analysis that evidences the strength of our retrieval algorithm in supporting the identification of ontologies to support requirement analysis.
Beyhan, B, Akcomak, IS & Cetindamar, D 2024, 'How do technology-based accelerators build their legitimacy as new organizations in an emerging entrepreneurship ecosystem?', Journal of Entrepreneurship in Emerging Economies, vol. 16, no. 4, pp. 954-976.
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PurposeThis paper aims to understand technology-based accelerators’ legitimation efforts in an emerging entrepreneurship ecosystem.Design/methodology/approachThis research is based on qualitative inductive methodology using ten Turkish technology-based accelerators.FindingsThe analysis indicates that accelerators’ legitimation efforts are shaped around crafting a distinctive identity and mobilizing allies around this identity; and establishing new collaborations to enable collective action. Further, the authors observe two types of technology-based accelerators, namely, “deal flow makers” and “welfare stimulators” in Turkey. These variations among accelerators affect how they build their legitimacy. Different types of accelerators make alliances with different actors in the entrepreneurship ecosystem. Accelerators take collective action to build a collective identity and simultaneously imply how they are distinguished from other organizations in the same category and the ones in the old category.Originality/valueThis study presents a framework to understand how accelerators use strategies and actions to legitimize themselves as new organizations and advocate new norms, values and routines in an emerging entrepreneurship ecosystem. The framework also highlights how different accelerators support legitimacy building by managing the judgments of diverse audiences and increasing the variety of resources these audiences provide to the ecosystem.
Beyhan, B, Akçomak, S & Cetindamar, D 2024, 'The Startup Selection Process in Accelerators: Qualitative Evidence from Turkey', Entrepreneurship Research Journal, vol. 14, no. 1, pp. 27-51.
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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.
Bhandari, S, Fatahi, B, Peellage, WH, Khabbaz, H, Rasekh, H & Hsi, J 2024, 'Dynamic characteristics of compacted landfill waste material from cyclic triaxial tests', International Journal of Fatigue, vol. 189, pp. 108550-108550.
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Bhat, IK, Qadir, F, Neshat, M & Gandomi, AH 2024, 'Exploring Cellular Automata Learning: An Innovative Approach for Secure and Imperceptible Digital Image Watermarking', IEEE Access, vol. 12, pp. 159748-159774.
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Bhuvaneswari Ramakrishnan, A, Sridevi, M, Vasudevan, SK, Manikandan, R & Gandomi, AH 2024, 'Optimizing brain tumor classification with hybrid CNN architecture: Balancing accuracy and efficiency through oneAPI optimization', Informatics in Medicine Unlocked, vol. 44, pp. 101436-101436.
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Bi, S, Li, K, Hu, S, Ni, W, Wang, C & Wang, X 2024, 'Detection and Mitigation of Position Spoofing Attacks on Cooperative UAV Swarm Formations', IEEE Transactions on Information Forensics and Security, vol. 19, pp. 1883-1895.
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Bidwai, P, Gite, S, Pradhan, B, Gupta, H & Alamri, A 2024, 'Harnessing deep learning for detection of diabetic retinopathy in geriatric group using optical coherence tomography angiography-OCTA: A promising approach', MethodsX, vol. 13, pp. 102910-102910.
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Booth, E, Lee, J, Rizoiu, M-A & Farid, H 2024, 'Conspiracy, Misinformation, Radicalisation: Understanding the Online Pathway to Indoctrination and Opportunities for Intervention', Journal of Sociology, vol. 60, no. 2, pp. 440-457.
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In response to the rise of various fringe movements in recent years, from anti-vaxxers to QAnon, there has been increased public and scholarly attention to misinformation and conspiracy theories and the online communities that produce them. However, efforts at understanding the radicalisation process largely focus on those who go on to commit violent crimes. This article draws on three waves of research exploring the experiences of individuals currently or formerly involved in fringe communities, including the different stages of investment they progressed through, and ultimately, what made people leave. We propose a pathway model for understanding contemporary online radicalisation, including potential interventions that could be safely made at each stage. Insight into the experience of being immersed in these communities is essential for engaging with these people empathetically, and therefore preventing both the emergence of violent terrorists and protecting vulnerable people from being drawn into these communities.
Bosco Mofatto, PM, Cosenza, A, Di Trapani, D, Wu, L, Ni, B-J & Mannina, G 2024, 'Carbon footprint reduction by coupling intermittent aeration with submerged MBR: A pilot plant study', Journal of Environmental Chemical Engineering, vol. 12, no. 4, pp. 113115-113115.
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Bouzidi, M, Alimi, F, Alasmari, A, Islam, MS, Talebizadehsardari, P, Shafi, J & Ghalambaz, M 2024, 'Artificial intelligence and numerical study of the heat transfer and entropy generation analysis of NEPCM-MWCNTs-Water Hybrid Nanofluids inside a quadrilateral enclosure', Case Studies in Thermal Engineering, vol. 63, pp. 105258-105258.
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Boye, T 2024, 'Investigating the experience of students with disabilities in Australian engineering and information technology work placements', International Journal of Work-Integrated Learning, vol. 25, no. 1, pp. 109-125.
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Work-integrated learning (WIL) placements seek to improve employability for all, but increasing evidence suggests equity groups see significant barriers in accessing WIL, in part due to existing barriers to work and study. This project sought to investigate the experiences of students with disabilities in engineering and IT WIL through a participatory research approach. Students with disabilities were invited to join a series of workshops to investigate WIL experiences through shared reflection and critique. The group was led through a Design Thinking process using numerous tools including empathy mapping, journey mapping, and yarning, to help elicit and frame the experiences. Participants identified significant discrimination and a lack of connection, community, and support as key issues. To address these, participants recommended developing community and connection among students, providing workshops on employment tailored for disability, greater support from universities, and greater training for university and industry staff on accessibility, inclusion, and legal requirements.
Brahmachari, S, Lumbreras, J & Tomamichel, M 2024, 'Quantum contextual bandits and recommender systems for quantum data', Quantum Machine Intelligence, vol. 6, no. 2.
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Brandhofer, S, Myers, CR, Devitt, S & Polian, I 2024, 'Multiplexed pseudo-deterministic photon source with asymmetric switching elements', Research Directions: Quantum Technologies, vol. 2.
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Abstract The reliable, deterministic production of trustworthy high-quality single photons is a critical component of discrete variable, optical quantum technology. For single-photon based fully error-corrected quantum computing systems, it is estimated that photon sources will be required to produce a reliable stream of photons at rates exceeding 1 GHz (Vigliar et al., 2021). Photon multiplexing, where low probability sources are combined with switching networks to route successful production events to an output, are a potential solution but requires extremely fast single-photon switching with ultra-low-loss rates. In this paper, we examine the specific properties of the switching elements and present a new design that exploits the general one-way properties of common switching elements such as thermal pads. By introducing multiple switches to a basic, temporal multiplexing device, we can use slow switching elements in a multiplexed source being pumped at much faster rates. We model this design under multiple error channels and show that anticipated performance is now limited by the intrinsic loss rate of the optical waveguides within integrated photonic chipsets. While the developed design does not achieve the necessary 1 GHz photon rate, we demonstrate design elements that could become useful when underlying technology improves.
Braytee, A & Liu, W 2024, 'Robust multi-label feature learning-based dual space', International Journal of Data Science and Analytics, vol. 17, no. 4, pp. 373-387.
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AbstractMulti-label learning handles instances associated with multiple class labels. The original label space is a logical matrix with entries from the Boolean domain $$\in \left\{ 0,1 \right\} $$ ∈ 0 , 1 . Logical labels cannot show the relative importance of each semantic label to the instances. Most existing methods map the input features to the label space using linear projections considering the label dependencies using a logical label matrix. However, the discriminative features are learned using one-way projection from the feature representation of an instance into a logical label space. There is no manifold in the learning space of logical labels, which limits the potential of learned models. We propose a novel method in multi-label learning to learn the projection matrix from the feature space to the semantic label space and project it back to the original feature space using encoder–decoder deep learning architecture. The key intuition which guides our method is that the discriminative features are identified due to mapping the features back and forth using two linear projections. To the best of our knowledge, this is one of the first attempts to study the ability to reconstruct the original features from the label manifold in multi-label learning. We show that the learned projection matrix identifies a subset of discriminative features across multiple semantic labels. Extensive experiments on real-world datasets show the superiority of the...
Bryant, L, Sedlarevic, N, Stubbs, P, Bailey, B, Nguyen, V, Bluff, A, Barnett, D, Estela, M, Hayes, C, Jacobs, C, Kneebone, I, Lucas, C, Mehta, P, Power, E & Hemsley, B 2024, 'Collaborative co-design and evaluation of an immersive virtual reality application prototype for communication rehabilitation (DISCOVR prototype)', Disability and Rehabilitation: Assistive Technology, vol. 19, no. 1, pp. 90-99.
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PURPOSE: Virtual reality (VR) lends itself to communication rehabilitation by creating safe, replicable, and authentic simulated environments in which users learn and practice communication skills. The aim of this research was to obtain the views of health professionals and technology specialists on the design characteristics and usability of a prototype VR application for communication rehabilitation. MATERIALS AND METHODS: Nine professionals from different health and technology disciplines participated in an online focus group or individual online interview to evaluate the application and use of the VR prototype. Data sources were analysed using a content thematic analysis. RESULTS: Four main themes relating to VR design and implementation in rehabilitation were identified: (i) designing rehabilitation-focused virtual worlds; (ii) understanding and using VR hardware; (iii) making room for VR in rehabilitation and training; and (iv) implementing VR will not replace the health professional's role. DISCUSSION: Health professionals and technology specialists engaged in co-design while evaluating the VR prototype. They identified software features requiring careful consideration to ensure improved usability, client safety, and success in communication rehabilitation outcomes. Continuing inclusive co-design, engaging health professionals, clients with communication disability, and their families will be essential to creating useable VR applications and integrating these successfully into rehabilitation. Implications for rehabilitationHealth and technology professionals, along with clients, are integral to the co-design of new VR technology applications.Design of VR applications needs to consider the client's communication, physical, cognitive, sensory, psychosocial, and emotional needs for greater usability of these programs.Realism and authenticity of interactions, characters, and environments are considered important factors to allow users to be fully immersed in v...
Bryant, L, Stubbs, P, Bailey, B, Nguyen, V, Bluff, A & Hemsley, B 2024, 'Interacting with virtual characters, objects and environments: investigating immersive virtual reality in rehabilitation', Disability and Rehabilitation: Assistive Technology, pp. 1-11.
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PURPOSE: This pilot study aimed to (a) investigate opportunities for immersive Virtual Reality (VR) technology in communication, physical, and visual rehabilitation by examining the interaction of people without disabilities in a range of structured virtual environments; and (b) validate research protocols that might be used to evaluate the physical, visual, and verbal interaction of users in virtual worlds, and their safety while using the technology. METHODS: Thirteen adults identifying as people without disability were exposed to VR via a head-mounted display. A video-review method was used to qualitatively code and analyse each participant's communication, movement, orientation, and support needs. RESULTS: All participants oriented to their virtual environments sufficiently to use applications. Their spoken language was effective for interaction, although unconventional social behaviours were also observed. Two participants reported minor adverse reactions consistent with mild cybersickness. CONCLUSION: The results provide insight into the types of environments and characters that support the greatest communicative, physical, and visual interaction in immersive VR. The tested protocols are useful to assess safety when using VR, and to observe communicative, physical, and visual interaction with virtual environments and characters. Implications for future research and use of VR with people with communication, physical and visual disability are discussed.
Bui, TA, Mei, H, Sang, R, Ortega, DG & Deng, W 2024, 'Advancements and challenges in developing in vivo CAR T cell therapies for cancer treatment', eBioMedicine, vol. 106, pp. 105266-105266.
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Bukhari, AA & Hussain, FK 2024, 'Fuzzy logic trust-based fog node selection', Internet of Things, vol. 27, pp. 101293-101293.
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Cai, X, Shi, K, Sun, Y, Cao, J, Wen, S & Tian, Z 2024, 'Intelligent Event-Triggered Control Supervised by Mini-Batch Machine Learning and Data Compression Mechanism for T-S Fuzzy NCSs Under DoS Attacks', IEEE Transactions on Fuzzy Systems, vol. 32, no. 3, pp. 804-815.
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Cai, X, Shi, K, Sun, Y, Cao, J, Wen, S, Chen, P & Tian, Z 2024, 'Dual-Channel NCSs Performance Error Estimation Under DoS Attacks and Intelligent Control Supervised by Machine Learning to AGV Application', IEEE Transactions on Transportation Electrification, vol. 10, no. 3, pp. 4882-4893.
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Cai, X, Shi, K, Sun, Y, Cao, J, Wen, S, Qiao, C & Tian, Z 2024, 'Stability Analysis of Networked Control Systems Under DoS Attacks and Security Controller Design With Mini-Batch Machine Learning Supervision', IEEE Transactions on Information Forensics and Security, vol. 19, pp. 3857-3865.
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Cai, X, Shi, K, Sun, Y, Wen, S, Yan, H & Xie, Y 2024, 'Fuzzy Memory Controller Design Based-Machine Learning Algorithm and Stability Analysis for Nonlinear NCSs Under Asynchronous Cyber Attacks', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 54, no. 2, pp. 1082-1093.
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Cai, Y, Che, H, Pan, B, Leung, M-F, Liu, C & Wen, S 2024, 'Projected cross-view learning for unbalanced incomplete multi-view clustering', Information Fusion, vol. 105, pp. 102245-102245.
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Camaya, I, Hill, M, Sais, D, Tran, N, O’Brien, B & Donnelly, S 2024, 'The Parasite‐Derived Peptide, FhHDM‐1, Selectively Modulates miRNA Expression in β‐Cells to Prevent Apoptotic Pathways Induced by Proinflammatory Cytokines', Journal of Diabetes Research, vol. 2024, no. 1.
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We have previously identified a parasite‐derived peptide, FhHDM‐1, that prevented the progression of diabetes in nonobese diabetic (NOD) mice. Disease prevention was mediated by the activation of the PI3K/Akt pathway to promote β‐cell survival and metabolism without inducing proliferation. To determine the molecular mechanisms driving the antidiabetogenic effects of FhHDM‐1, miRNA:mRNA interactions and in silico predictions of the gene networks were characterised in β‐cells, which were exposed to the proinflammatory cytokines that mediate β‐cell destruction in Type 1 diabetes (T1D), in the presence and absence of FhHDM‐1. The predicted gene targets of miRNAs differentially regulated by FhHDM‐1 mapped to the biological pathways that regulate β‐cell biology. Six miRNAs were identified as important nodes in the regulation of PI3K/Akt signaling. Additionally, IGF‐2 was identified as a miRNA gene target that mediated the beneficial effects of FhHDM‐1 on β‐cells. The findings provide a putative mechanism by which FhHDM‐1 positively impacts β‐cells to permanently prevent diabetes. As β‐cell death/dysfunction underlies diabetes development, FhHDM‐1 opens new therapeutic avenues.
Canales, M, Castilla-Rho, J, Rojas, R, Vicuña, S & Ball, J 2024, 'Agent-based models of groundwater systems: A review of an emerging approach to simulate the interactions between groundwater and society', Environmental Modelling & Software, vol. 175, pp. 105980-105980.
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Cang, M, Zhang, L, Wang, Y, Fu, J, Luo, Z, Kang, Z, Fu, MW & Wang, MY 2024, 'An efficient method for design of lattice core sandwich structures with superior buckling strength under compression', Engineering Optimization, vol. 56, no. 4, pp. 506-524.
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Cao, J, Kristanto, AB & Gu, Z 2024, 'Evolution of Research Streams and Future Research Directions in Accounting Education: Quantitative Systematic Literature Review', Issues in Accounting Education, vol. 39, no. 4, pp. 19-53.
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ABSTRACT This study comprehensively analyzes the landscape of accounting education research and constructs the agenda for future studies. We are specifically interested in investigating the current state of accounting education research and identifying areas that require further attention for its development. We employ a quantitative systematic literature review focusing on 673 academic articles from top-tier accounting journals. The study applies various bibliometric analyses, including co-citation, bibliographic coupling, keyword co-occurrence, topic burstiness, and thematic mapping, using HistCite, VOSviewer, CiteSpace, and R Bibliometrix software. Through rigorous examination, we identify three prominent research streams: (1) accounting pedagogy, (2) competencies, and (3) ethics. Furthermore, each stream discusses specific distinctive themes. This study suggests future investigations on leveraging the accounting education role in pursuing environmental sustainability and embracing technology to improve student engagement in post-pandemic learning. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: M49; I20; I23.
Cao, J, Xiong, W, Lu, J, Chen, P, Wang, J, Lai, J & Huang, M 2024, 'An optimized EEGNet processor for low-power and real-time EEG classification in wearable brain–computer interfaces', Microelectronics Journal, vol. 145, pp. 106134-106134.
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Cao, MX, Ramakrishnan, N, Berta, M & Tomamichel, M 2024, 'Channel Simulation: Finite Blocklengths and Broadcast Channels', IEEE Transactions on Information Theory, vol. 70, no. 10, pp. 6780-6808.
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Cao, X & Tsang, IW 2024, 'Distribution Matching for Machine Teaching', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 9, pp. 12316-12329.
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Cao, Y, Ni, Q, Jia, M, Zhao, X & Yan, X 2024, 'Online Knowledge Distillation for Machine Health Prognosis Considering Edge Deployment', IEEE Internet of Things Journal, vol. 11, no. 16, pp. 27828-27839.
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Cao, Y, Yao, L, Pan, L, Sheng, QZ & Chang, X 2024, 'Guided Image-to-Image Translation by Discriminator-Generator Communication', IEEE Transactions on Multimedia, vol. 26, pp. 1528-1538.
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Cao, Y, Zhao, L, Zhong, Q, Shi, K, Xiao, J & Wen, S 2024, 'Synchronization of coupled neural networks with multiple switching topologies via adaptive control techniques', International Journal of Robust and Nonlinear Control, vol. 34, no. 13, pp. 8661-8675.
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AbstractThis research paper primarily focuses on the synchronization of a specific class of multi‐weighted coupled neural networks (MWCNNs) with switching topology, employing two adaptive control methods. In complex environments and under the influence of communication disturbances, the topology structures of coupled neural networks with multiple weights inevitably undergo time‐varying changes. To address this, we propose a novel type of MWCNNs with switching topologies. To ensure synchronization within this network, we develop sufficient conditions based on Lyapunov functional and inequality techniques. These conditions guarantee the achievement of synchronization. Moreover, we address the synchronization problem by employing node‐based and edge‐based adaptive controllers. Finally, we provide a numerical example to demonstrate the effectiveness of the obtained results. This example serves as empirical evidence showcasing the successful application of the proposed synchronization approach in practical scenarios.
Cao, Z, Shi, Y, Chang, Y-C, Yao, X & Lin, C-T 2024, 'Twin Fuzzy Networks With Interpolation Consistency Regularization for Weakly Supervised Anomaly Detection', IEEE Transactions on Fuzzy Systems, vol. 32, no. 9, pp. 5086-5097.
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Carbajal Piña, CA, Acur, N & Cetindamar, D 2024, 'An activity theory analysis of digital innovation orchestration in Industry 4.0', Journal of Manufacturing Technology Management, vol. 35, no. 5, pp. 962-983.
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PurposeThis paper explores the orchestration of digital innovation in Industry 4.0 organisations.Design/methodology/approachThe study applies the activity theory to explorative multiple case studies. Observations of innovation activities in five business cases take place at two large international organisations.FindingsThe results underline five logics of action that drive digital innovation: (1) digital transformation, (2) technology translation, (3) catalyst agents, (4) digital thread and (5) empowerment. Further, the case study organisations highlight the importance of developing a sustainable culture capable of continuously adopting new technologies, processes and infrastructure that will allow the management of digital innovations.Originality/valueThe study empirically shows the motivations and challenges in orchestrating digital innovation in Industry 4.0 organisations.
Cartland, SP, Patil, MS, Kelland, E, Le, N, Boccanfuso, L, Stanley, CP, Cholan, PM, Dona, MI, Patrick, R, McGrath, J, Su, QP, Alwis, I, Ganss, R, Powell, JE, Harvey, RP, Pinto, AR, Griffith, TS, Loa, J, Aitken, SJ, Robinson, DA, Patel, S & Kavurma, MM 2024, 'The generation of stable microvessels in ischemia is mediated by endothelial cell derived TRAIL', Science Advances, vol. 10, no. 40.
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Reversal of ischemia is mediated by neo-angiogenesis requiring endothelial cell (EC) and pericyte interactions to form stable microvascular networks. We describe an unrecognized role for tumor necrosis factor–related apoptosis-inducing ligand (TRAIL) in potentiating neo-angiogenesis and vessel stabilization. We show that the endothelium is a major source of TRAIL in the healthy circulation compromised in peripheral artery disease (PAD). EC deletion of TRAIL in vivo or in vitro inhibited neo-angiogenesis, pericyte recruitment, and vessel stabilization, resulting in reduced lower-limb blood perfusion with ischemia. Activation of the TRAIL receptor (TRAIL-R) restored blood perfusion and stable blood vessel networks in mice. Proof-of-concept studies showed that Conatumumab, an agonistic TRAIL-R2 antibody, promoted vascular sprouts from explanted patient arteries. Single-cell RNA sequencing revealed heparin-binding EGF-like growth factor in mediating EC-pericyte communications dependent on TRAIL. These studies highlight unique TRAIL-dependent mechanisms mediating neo-angiogenesis and vessel stabilization and the potential of repurposing TRAIL-R2 agonists to stimulate stable and functional microvessel networks to treat ischemia in PAD.
Cassim, A, Dun, MD, Gallego-Ortega, D & Valdes-Mora, F 2024, 'EZHIP’s role in diffuse midline glioma: echoes of oncohistones?', Trends in Cancer.
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Cedieu, S, Grigoletto, FB, Lee, SS, Barzegarkhoo, R & Siwakoti, YP 2024, 'A Five-Level Common-Ground Inverter With Reduced Switch Count for Transformerless Grid-Tied PV Applications', IEEE Transactions on Industry Applications, vol. 60, no. 5, pp. 7061-7075.
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Cetindamar, D, Abedin, B & Shirahada, K 2024, 'The Role of Employees in Digital Transformation: A Preliminary Study on How Employees’ Digital Literacy Impacts Use of Digital Technologies', IEEE Transactions on Engineering Management, vol. 71, pp. 7837-7848.
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Even though digital technologies such as cloud technologies are prevalent in transforming businesses, the role of employees and their digital skills in the process is, to a large extent, neglected. This study brings forward the novel concept of digital literacy to explore the role of employees in understanding the wide variety of opportunities of digital technologies and their actualization. By treating digital literacy as the antecedent of cognitive behavior of employees in utilizing cloud technology at companies, we apply the Theory of Planned Behavior (TPB) for analyzing preliminary empirical data collected from 124 Australian employees’ technology use intentionality and behavior. The quantitative analysis shows that the TPB holds for the utilization of cloud technology and there is a positive relationship between employees' digital literacy and the utilization of cloud technology at companies. Overall, the study contributes to the technology management literature by offering a workable construct to measure the digital skills of employees in the form of digital literacy. Further, it expands the TPB framework by introducing digital literacy as a perceived behavior control variable that helps to examine the role of employees in digital transformation. The paper ends with implications and limitations of our preliminary study, followed with suggestions for future studies.
Cetindamar, D, Abedin, B, Gerdsri, N & Shirahada, K 2024, 'Editorial Overview of Digital Literacy of Employees and Organizational Transformation and Innovation', IEEE Transactions on Engineering Management, vol. 71, pp. 7832-7836.
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This Special Issue (SI) aims to provide organizations with a theoretical, conceptual, and applied grounded discussion of the Digital Literacy of Employees and organizational Transformation and Innovation to aid in innovative, sustainable development and effective decision-making. By doing so, our SI hopes to expand the technology management discipline in understanding the human side of technological innovations.
Cetindamar, D, Kitto, K, Wu, M, Zhang, Y, Abedin, B & Knight, S 2024, 'Explicating AI Literacy of Employees at Digital Workplaces', IEEE Transactions on Engineering Management, vol. 71, pp. 810-823.
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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, Renando, C, Bliemel, M & Klerk, SD 2024, 'The Evolution of the Australian Start-up and Innovation Ecosystem: Mapping Policy Developments, Key Actors, Activities, and Artefacts', Science, Technology and Society, vol. 29, no. 1, pp. 13-33.
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This study maps the evolution of the Australian start-up and innovation ecosystem by exploring policy developments and mapping the key actors, activities, and artefacts. This study unpacks policy developments over the past two decades to show the government’s role in shaping the innovation ecosystem and the implications for start-ups. We outline the ecosystem’s key actors, including start-ups, scale-ups, support organisations, investors, research institutions, and their growth over time. We examine the artefacts of the ecosystem to understand start-up and innovation performance in a global context. We also explore the activities of the ecosystem in terms of collaboration, research, and development. The study concludes with a discussion of policy gaps.
Ceylan, O, Neshat, M & Mirjalili, S 2024, 'Cascaded H-bridge multilevel inverters optimization using adaptive grey wolf optimizer with local search', Electrical Engineering, vol. 106, no. 2, pp. 1765-1779.
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Chacon, A, Rutherford, H, Hamato, A, Nitta, M, Nishikido, F, Iwao, Y, Tashima, H, Yoshida, E, Akamatsu, G, Takyu, S, Kang, HG, Franklin, DR, Parodi, K, Yamaya, T, Rosenfeld, A, Guatelli, S & Safavi-Naeini, M 2024, 'A quantitative assessment of Geant4 for predicting the yield and distribution of positron-emitting fragments in ion beam therapy', Physics in Medicine & Biology, vol. 69, no. 12, pp. 125015-125015.
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Abstract Objective. To compare the accuracy with which different hadronic inelastic physics models across ten Geant4 Monte Carlo simulation toolkit versions can predict positron-emitting fragments produced along the beam path during carbon and oxygen ion therapy. Approach. Phantoms of polyethylene, gelatin, or poly(methyl methacrylate) were irradiated with monoenergetic carbon and oxygen ion beams. Post-irradiation, 4D PET images were acquired and parent 11C, 10C and 15O radionuclides contributions in each voxel were determined from the extracted time activity curves. Next, the experimental configurations were simulated in Geant4 Monte Carlo versions 10.0 to 11.1, with three different fragmentation models—binary ion cascade (BIC), quantum molecular dynamics (QMD) and the Liege intranuclear cascade (INCL++) - 30 model-version combinations. Total positron annihilation and parent isotope production yields predicted by each simulation were compared between simulations and experiments using normalised mean squared error and Pearson cross-correlation coefficient. Finally, we compared the depth of the maximum positron annihilation yield and the distal point at which the positron yield decreases to 50% of peak between each model and the experimental results. Main results. Performance varied considerably across versions and models, with no one version/model combination providing the best prediction of all positron-emitting fragments in all evaluated target materials and irradiation conditions. BIC in Geant4 10.2 provided the best overall agreement with experimental results in the largest number of test cases. QMD consistently provided the best estimates of both the depth of peak positron yield (10.4 and 10.6) and the distal 50%-of-peak point (10.2), while...
Chai, S, Xiao, Y, Liu, F, Zhu, J & Zhou, Y 2024, 'Securing Multi-Source Domain Adaptation With Global and Domain-Wise Privacy Demands', IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 12, pp. 9235-9248.
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Chakraborty, S, Khurana, N, Kaur, J, Mehta, M & Sharma, N 2024, 'Gamma Oryzanol: A natural compound with potential for treating polycystic ovary syndrome', Pharmacological Research - Modern Chinese Medicine, vol. 13, pp. 100506-100506.
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Chandan, B, Kumar Pal, P, Chandra Jana, K & Siwakoti, YP 2024, 'Performance Evaluation of a New Transformerless Grid-Connected Six-Level Inverter With Integrated Voltage Boosting', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 12, no. 5, pp. 4361-4376.
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Chang, X, Sato, Y & Zhang, C 2024, 'Multi-peak Solutions of a Class of Fractional p-Laplacian Equations', The Journal of Geometric Analysis, vol. 34, no. 1.
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Chaturvedi, K, Dhiman, C & Vishwakarma, DK 2024, 'Fight detection with spatial and channel wise attention‐based ConvLSTM model', Expert Systems, vol. 41, no. 1.
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AbstractAn automated detection of aggressive and violent behaviour in videos has immense potential. It enables efficient online content filtering by restricting access to extreme content and also, when integrated with security systems, helps to monitor violence in surveillance videos. In this work, a convolutional neural network is combined with the proposed Spatial and Channel wise Attention‐based ConvLSTM encoder (SCan‐ConvLSTM). The proposed architecture performs an efficient spatiotemporal fusion of the features extracted from the video sequences containing fight scenes. In order to focus selectively on regions of utmost importance, this blended attention mechanism adjusts the weights of outputs in different locations and across different channels. This recurrent attention mechanism enhances the sequential refinement of activation maps and boosts the model performance. Finally, the experimental results have been presented that show the proposed architecture achieves superior results on the benchmark datasets (RWF‐2000, Violent‐flow, Hockey‐fights, and Movies).
Che, H, Li, C, Leung, M-F, Ouyang, D, Dai, X & Wen, S 2024, 'Robust Hypergraph Regularized Deep Non-Negative Matrix Factorization for Multi-View Clustering', IEEE Transactions on Emerging Topics in Computational Intelligence, pp. 1-13.
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Chen, B, Cao, Z & Bai, Q 2024, 'SATF: A Scalable Attentive Transfer Framework for Efficient Multiagent Reinforcement Learning', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15.
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Chen, B, Li, F, Lin, Y, Yang, L, Wei, W, Ni, B-J & Chen, X 2024, 'Degradation of Chloroquine by Ammonia-Oxidizing Bacteria: Performance, Mechanisms, and Associated Impact on N2O Production', Environmental Science & Technology, vol. 58, no. 10, pp. 4662-4669.
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Chen, CX, Koskue, V, Duan, H, Gao, L, Shon, HK, Martin, GJO, Chen, GQ & Freguia, S 2024, 'Impact of nutrient deficiency on biological sewage treatment – Perspectives towards urine source segregation', Science of The Total Environment, vol. 946, pp. 174174-174174.
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Chen, D, Li, J, Wang, L & Wu, C 2024, 'A generic approach for modelling hydrogen-methane-air detonation in hydrocode', Journal of Cleaner Production, vol. 465, pp. 142840-142840.
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Chen, D, Zhang, H, Li, J, Liu, K, Wang, Y, Huang, Y, Mao, Z & Wu, C 2024, 'A full-scale experimental investigation of natural gas explosion in a 710-m long utility tunnel with multiple pipelines', Tunnelling and Underground Space Technology, vol. 153, pp. 106049-106049.
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Chen, DY, Di, X, Yu, X & Biswal, BB 2024, 'The significance and limited influence of cerebrovascular reactivity on age and sex effects in task- and resting-state brain activity', Cerebral Cortex, vol. 34, no. 2.
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Abstract Functional MRI measures the blood-oxygen-level dependent signals, which provide an indirect measure of neural activity mediated by neurovascular responses. Cerebrovascular reactivity affects both task-induced and resting-state blood-oxygen-level dependent activity and may confound inter-individual effects, such as those related to aging and biological sex. We examined a large dataset containing breath-holding, checkerboard, and resting-state tasks. We used the breath-holding task to measure cerebrovascular reactivity, used the checkerboard task to obtain task-based activations, and quantified resting-state activity with amplitude of low-frequency fluctuations and regional homogeneity. We hypothesized that cerebrovascular reactivity would be correlated with blood-oxygen-level dependent measures and that accounting for these correlations would result in better estimates of age and sex effects. We found that cerebrovascular reactivity was correlated with checkerboard task activations in the visual cortex and with amplitude of low-frequency fluctuations and regional homogeneity in widespread fronto-parietal regions, as well as regions with large vessels. We also found significant age and sex effects in cerebrovascular reactivity, some of which overlapped with those observed in amplitude of low-frequency fluctuations and regional homogeneity. However, correcting for the effects of cerebrovascular reactivity had very limited influence on the estimates of age and sex. Our results highlight the limitations of accounting for cerebrovascular reactivity with the current breath-holding task.
Chen, F & Long, G 2024, 'FedGE: Break the Scalability Limitation of Graph Neural Network With Federated Graph Embedding', IEEE Transactions on Big Data, vol. 10, no. 6, pp. 965-974.
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Neighborhood aggregation algorithms, represented as graph convolutional networks, have attained non-negligible success in numerous topological structure-based scenarios with the assumption that the topological structure of the given graph is pre-defined and relatively small. However, a real-world graph generally is a super large graph consisting of many small graphs that are interconnected and overlapping. Which makes graph embedding in real-life industries, by nature, fall into the federated learning scheme. While current graph-based algorithms are only able to capture the individual topology of each natural graph, learning the complete structural information of the merged large graph remains challenging due to the unsustainable computational cost of graph convolutional operations. We propose a tailored federated graph embedding framework to learn the intact structural information of the numerous inherently linked small-scale graphs and the embedding of each node. We leverage graphs with around two and a half million nodes to validate the effectiveness and the correctness of the proposed framework.
Chen, H, Wang, H, Chen, H, Zhang, Y, Zhang, W & Lin, X 2024, 'Denoising Variational Graph of Graphs Auto-Encoder for Predicting Structured Entity Interactions', IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 3, pp. 1016-1029.
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Chen, H, Zhu, T, Liu, B, Zhou, W & Yu, PS 2024, 'Fine-tuning a Biased Model for Improving Fairness', IEEE Transactions on Big Data, pp. 1-15.
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Chen, H, Zhu, T, Liu, C, Yu, S & Zhou, W 2024, 'High-Frequency Matters: Attack and Defense for Image-Processing Model Watermarking', IEEE Transactions on Services Computing, vol. 17, no. 4, pp. 1565-1579.
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Chen, J, Wang, S, Liu, C, Ng, DWK, Xu, C, Hao, Q & Lu, H 2024, 'Road Supervised Federated Learning with Bug-Aware Sensor Placement', IEEE Transactions on Vehicular Technology, pp. 1-6.
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Chen, J, Wu, K, Niu, J, Li, Y, Xu, P & Andrew Zhang, J 2024, 'Spectral and Energy Efficient Waveform Design for RIS-Assisted ISAC', IEEE Transactions on Communications, pp. 1-1.
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Chen, J, Zhu, K, Wu, K, Niu, J & Zhang, JA 2024, 'Introducing User Grouping to Counteract Channel Correlation in IRS-Assisted ISAC', IEEE Communications Letters, vol. 28, no. 4, pp. 808-812.
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Chen, K & Ying, M 2024, 'Automatic Test Pattern Generation for Robust Quantum Circuit Testing', ACM Transactions on Design Automation of Electronic Systems, vol. 29, no. 6, pp. 1-36.
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Quantum circuit testing is essential for detecting potential faults in realistic quantum devices, while the testing process itself also suffers from the inexactness and unreliability of quantum operations. This article alleviates the issue by proposing a novel framework of automatic test pattern generation (ATPG) for robust testing of logical quantum circuits. We introduce the stabilizer projector decomposition (SPD) for representing the quantum test pattern and construct the test application (i.e., state preparation and measurement) using Clifford-only circuits, which are rather robust and efficient as evidenced in the fault-tolerant quantum computation. However, it is generally hard to generate SPDs due to the exponentially growing number of the stabilizer projectors. To circumvent this difficulty, we develop an SPD generation algorithm, as well as several acceleration techniques that can exploit both locality and sparsity in generating SPDs. The effectiveness of our algorithms are validated by (1) theoretical guarantees under reasonable conditions and (2) experimental results on commonly used benchmark circuits, such as Quantum Fourier Transform (QFT), Quantum Volume (QV), and Bernstein-Vazirani (BV) in IBM Qiskit.
Chen, K, Bai, F, Huang, S & Sun, Y 2024, 'iMCB-PGO: Incremental Minimum Cycle Basis Construction and Application to Online Pose Graph Optimization', IEEE Robotics and Automation Letters, vol. 9, no. 11, pp. 10185-10192.
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Chen, K, He, X, Liang, F & Sheng, D 2024, 'Critical state behaviour of an unsaturated kaolin mixture', Engineering Geology, vol. 338, pp. 107606-107606.
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Chen, K, He, X, Liang, F & Sheng, D 2024, 'Influences of ink-bottle effect evolution on water retention hysteresis of unsaturated soils: An experimental investigation', Engineering Geology, vol. 330, pp. 107409-107409.
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Chen, K, Qu, F, Huang, Y, Cai, J, Wu, F & Li, W 2024, 'Advancing photocatalytic concrete technologies in design, performance and application for a sustainable future', Advanced Nanocomposites, vol. 1, no. 1, pp. 180-200.
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Chen, L, Li, H, Su, Y, Yang, Z, He, Z, Wang, D, Li, JJ & Xing, D 2024, 'Using A Google Web Search Analysis to Assess the Utility of ChatGPT in Stem Cell Therapy', Stem Cells Translational Medicine, vol. 13, no. 1, pp. 60-68.
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Abstract Objective Since its introduction, the use of ChatGPT has increased significantly for medically related purposes. However, current research has not captured its applications in providing information on stem cell therapy. To address this gap, the present study compared the effectiveness of ChatGPT to Google in answering medical questions related to stem cell therapy. Methods The search term “stem cell therapy” was used to perform a Google web search, and the top 20 frequently asked questions along with answers were recorded together with relevant website sources. Of these questions, the top 10 questions were separately entered into ChatGPT, and the answers and the sources were recorded. Then, the following statement was entered into ChatGPT: “Do a Google search with the search term ‘stem cell therapy’ and record 20 common questions related to the search term.” After obtaining these questions, each question was separately entered into ChatGPT for an answer and source. Results A majority of the top 20 questions provided by Google were related to fact, whereas a majority of the questions provided by ChatGPT were related to policy. The answer sources used by Google were mostly drawn from medical practice, while those used by ChatGPT were mostly drawn from academic information. Conclusion Compared to Google, ChatGPT exhibits stronger capabilities in promoting awareness of stem cell therapy. ChatGPT has the ability to eliminate misleading informatio...
Chen, L, Liu, Y, Ban, Y-L, Yang, S & Guo, YJ 2024, 'Synthesis of Large-Scale Planar Isophoric Sparse Arrays Using Iterative Least Squares With Nonredundant Constraints (ILS-NRC)', IEEE Transactions on Antennas and Propagation, vol. 72, no. 5, pp. 4232-4245.
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Chen, N, Zhang, X, Du, Q, Wang, H, Wang, Z, Ren, J, Li, H, Guo, W & Ngo, HH 2024, 'An in-situ biochar-enhanced anaerobic membrane bioreactor for swine wastewater treatment under various organic loading rates', Journal of Environmental Sciences, vol. 146, pp. 304-317.
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Chen, Q, Choo, Y, Akther, N, Shon, HK & Naidu, G 2024, 'Generating sustainable cement material from seawater with low-cost ultrafiltration (UF) membrane electrolysis', Chemical Engineering Journal, vol. 494, pp. 153007-153007.
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Chen, Q, Wan, S, Tao, G, Nimbalkar, S, Tian, Z & Yu, R 2024, 'Characterization of nano-SiO 2 cemented soil under the coupled effects of dry-wet cycles and chlorination', Marine Georesources & Geotechnology, vol. 42, no. 11, pp. 1560-1572.
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Chen, Q, Wan, S, Tao, G, Tian, Z, Yu, R & Nimbalkar, S 2024, 'Improved mechanical response of Nano-SiO2 powder cemented soil under the coupling effect of dry and wet cycles and seawater corrosion', Acta Geotechnica, vol. 19, no. 9, pp. 5915-5931.
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Chen, Q, Xie, K, Tao, G, Nimbalkar, S & Zhang, H 2024, 'Laboratory assessment of impact of nano-SiO2 on different soil types in onshore and offshore environment', Acta Geotechnica, vol. 19, no. 8, pp. 5065-5087.
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Chen, Q, Xiong, Z, Tao, G, Nimbalkar, S & Wang, C 2024, 'Laboratory investigation on static and dynamic properties, durability, and microscopic structure of Nano-SiO2 and polypropylene fiber cemented soil under coupled seawater corrosion and cyclic loading', Construction and Building Materials, vol. 440, pp. 137449-137449.
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Chen, S-L, Liu, Y, Li, M, Jones, B & Guo, YJ 2024, 'Analysis, Design, and Measurement of Continuous Frequency-Scanning Polarization-Rotating Antenna', IEEE Transactions on Antennas and Propagation, vol. 72, no. 2, pp. 1911-1916.
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Chen, S-L, Zheng, D, Wu, G-B, Guo, YJ, Wu, K & Chan, CH 2024, 'Guest Editorial Special Section on Advanced Beam-Forming Antennas for Beyond 5G and 6G', IEEE Open Journal of Antennas and Propagation, vol. 5, no. 4, pp. 803-809.
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Chen, W, Hussain, W, Cauteruccio, F & Zhang, X 2024, 'Deep Learning for Financial Time Series Prediction: A State-of-the-Art Review of Standalone and Hybrid Models', Computer Modeling in Engineering & Sciences, vol. 139, no. 1, pp. 187-224.
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Chen, W-H, Maheshwaran, S, Park, Y-K & Ong, HC 2024, 'Iron-based electrode material composites for electrochemical sensor application in the environment: A review', Science of The Total Environment, vol. 953, pp. 176128-176128.
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Chen, W-H, Wu, D-R, Chang, M-H, Rajendran, S, Ong, HC & Lin, K-YA 2024, 'Modeling of hydrogen separation through Pd membrane with vacuum pressure using Taguchi and machine learning methods', International Journal of Hydrogen Energy.
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Chen, X, Feng, Z, Zhang, JA, Gao, F, Yuan, X, Yang, Z & Zhang, P 2024, 'Complex CNN CSI Enhancer for Integrated Sensing and Communications', IEEE Journal of Selected Topics in Signal Processing, pp. 1-15.
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Chen, X, Feng, Z, Zhang, JA, Wei, Z, Yuan, X, Zhang, P & Peng, J 2024, 'Downlink and Uplink Cooperative Joint Communication and Sensing', IEEE Transactions on Vehicular Technology, vol. 73, no. 8, pp. 11318-11332.
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Chen, X, Feng, Z, Zhang, JA, Yuan, X & Zhang, P 2024, 'Kalman Filter-Based Sensing in Communication Systems With Clock Asynchronism', IEEE Transactions on Communications, vol. 72, no. 1, pp. 403-417.
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Chen, X, Liu, T, Fournier-Viger, P, Zhang, B, Long, G & Zhang, Q 2024, 'A fine-grained self-adapting prompt learning approach for few-shot learning with pre-trained language models', Knowledge-Based Systems, vol. 299, pp. 111968-111968.
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Chen, X, Pan, Y, Tsang, I & Zhang, Y 2024, 'Learning node representations against perturbations', Pattern Recognition, vol. 145, pp. 109976-109976.
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Chen, Y, Guo, W, Ngo, HH, Wei, W, Ding, A, Ni, B, Hoang, NB & Zhang, H 2024, 'Ways to mitigate greenhouse gas production from rice cultivation', Journal of Environmental Management, vol. 368, pp. 122139-122139.
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Chen, Y, Hu, R, Li, Z, Yang, C, Wang, X, Long, G & Xu, G 2024, 'Exploring explicit and implicit graph learning for multivariate time series imputation', Engineering Applications of Artificial Intelligence, vol. 127, pp. 107217-107217.
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Chen, Y, Jia, W & Wu, Q 2024, 'Fine-scale deep learning model for time series forecasting', Applied Intelligence, vol. 54, no. 20, pp. 10072-10083.
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AbstractTime series data, characterized by large volumes and wide-ranging applications, requires accurate predictions of future values based on historical data. Recent advancements in deep learning models, particularly in the field of time series forecasting, have shown promising results by leveraging neural networks to capture complex patterns and dependencies. However, existing models often overlook the influence of short-term cyclical patterns in the time series, leading to a lag in capturing changes and accurately tracking fluctuations in forecast data. To overcome this limitation, this paper introduces a new method that utilizes an interpolation technique to create a fine-scaled representation of the cyclical pattern, thereby alleviating the impact of the irregularity in the cyclical component and hence enhancing prediction accuracy. The proposed method is presented along with evaluation metrics and loss functions suitable for time series forecasting. Experiment results on benchmark datasets demonstrate the effectiveness of the proposed approach in effectively capturing cyclical patterns and improving prediction accuracy.
Chen, Y, Li, G, An, P, Liu, Z, Huang, X & Wu, Q 2024, 'Light Field Salient Object Detection With Sparse Views via Complementary and Discriminative Interaction Network', IEEE Transactions on Circuits and Systems for Video Technology, vol. 34, no. 2, pp. 1070-1085.
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Chen, Y, Ren, L, Li, X & Zhou, JL 2024, 'Competitive adsorption and bioaccumulation of sulfamethoxazole and roxithromycin by sediment and zebrafish (Danio rerio) during individual and combined exposure in water', Journal of Hazardous Materials, vol. 464, pp. 132894-132894.
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Chen, Y, Tuan, HD, Fang, Y, Yu, H, Poor, HV & Hanzo, L 2024, 'Enhancing the Downlink Rate Fairness of Low-Resolution Active RIS-Aided Signaling by Closed-Form Expression-Based Iterative Optimization', IEEE Transactions on Vehicular Technology, vol. 73, no. 6, pp. 8013-8029.
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Chen, Y, Tuan, HD, Yu, H, Poor, HV & Hanzo, L 2024, 'Active RIS-Assisted Multi-User Multi-Stream Transmit Precoding Relying on Scalable-Complexity Iterations', IEEE Transactions on Communications, vol. 72, no. 9, pp. 5796-5809.
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This is the first investigation focused on delivering multi-stream information to multiple multi-antenna users employing an active reconfigurable intelligent surface (aRIS)-assisted system. We conceive the joint design of the transmit precoders and of the aRIS’s power-amplified reconfigurable elements (APRES) to enhance the log-det rate objective functions for all users, which poses large-scale mixed discrete continuous problems. We develop a max-min log-det solver, which iterates quadratic-solvers of cubic complexity to maximize the nonsmooth function representing the minimum of the users’ log-det rate functions. To mitigate the computational burden associated with cubically escalating complexity in large-scale scenarios, we introduce a pair of alternative problems aimed at maximizing the smooth functions representing the sum of the users’ log-det rate function (sum log-det) and the soft minimum of the users’ log-det rate function (soft min log-det). We develop sum log-det and soft max-min solvers, leveraging closed-form expressions of scalable (linear) complexity for efficient computation. This approach ensures practicality in addressing large-scale scenarios. Furthermore, the soft min log-det enables us to enhance the log-det rates for all users and their sum, ultimately improving the quality of delivering multi-user multi-stream information.
Chen, Y, Zhu, S, Shen, M, Liu, X & Wen, S 2024, 'Event-Based Output Quantized Synchronization Control for Multiple Delayed Neural Networks', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 1, pp. 428-438.
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This article concentrates on the global exponential synchronization problem of multiple neural networks with time delay by the event-based output quantized coupling control method. In order to reduce the signal transmission cost and avoid the difficulty of obtaining the systems' full states, this article adopts the event-triggered control and output quantized control. A new dynamic event-triggered mechanism is designed, in which the control parameters are time-varying functions. Under weakened coupling matrix conditions, by using a Halanay-type inequality, some simple and easily verified sufficient conditions to ensure the exponential synchronization of multiple neural networks are presented. Moreover, the Zeno behaviors of the system are excluded. Some numerical examples are given to verify the effectiveness of the theoretical analysis in this article.
Chen, Y, Zhu, S, Yan, H, Shen, M, Liu, X & Wen, S 2024, 'Event-Based Global Exponential Synchronization for Quaternion-Valued Fuzzy Memristor Neural Networks With Time-Varying Delays', IEEE Transactions on Fuzzy Systems, vol. 32, no. 3, pp. 989-999.
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Chen, Z, Han, G-F, Mahmood, A, Hou, J, Wei, W, Kyong Shon, H, Wang, G, David Waite, T, Baek, J-B & Ni, B-J 2024, 'Mechanosynthesized electroactive materials for sustainable energy and environmental applications: A critical review', Progress in Materials Science, vol. 145, pp. 101299-101299.
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Chen, Z, Ji, J, Yu, W, Ni, Q, Lu, G & Chang, X 2024, 'A multi-scale graph convolutional network with contrastive-learning enhanced self-attention pooling for intelligent fault diagnosis of gearbox', Measurement, vol. 230, pp. 114497-114497.
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Chen, Z, Ji, JC, Chen, K, Ni, Q, Ding, X & Yu, W 2024, 'Adaptive Topology-Aware Siamese Network for Cross-Domain Fault Diagnosis With Small Samples', IEEE Sensors Journal, vol. 24, no. 15, pp. 24438-24451.
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Chen, Z, Wei, W, Chen, X, Liu, Y, Shen, Y & Ni, B-J 2024, 'Upcycling of plastic wastes for hydrogen production: Advances and perspectives', Renewable and Sustainable Energy Reviews, vol. 195, pp. 114333-114333.
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Chen, Z, Yan, R, Ma, Y, Sui, Y & Xue, J 2024, 'A Smart Status Based Monitoring Algorithm for the Dynamic Analysis of Memory Safety', ACM Transactions on Software Engineering and Methodology, vol. 33, no. 4, pp. 1-47.
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C is a dominant programming language for implementing system and low-level embedded software. Unfortunately, the unsafe nature of its low-level control of memory often leads to memory errors. Dynamic analysis has been widely used to detect memory errors at runtime. However, existing monitoring algorithms for dynamic analysis are not yet satisfactory, as they cannot deterministically and completely detect some types of errors, such as segment confusion errors, sub-object overflows, use-after-frees and memory leaks. We propose a new monitoring algorithm, namely Smatus , short for smart status , that improves memory safety by performing comprehensive dynamic analysis. The key innovation is to maintain at runtime a small status node for each memory object. A status node records the status value and reference count of an object, where the status value denotes the liveness and segment type of this object, and the reference count tracks the number of pointer variables pointing to this object. Smatus maintains at runtime a pointer metadata for each pointer variable, to record not only the base and bound of a pointer’s referent but also the address of the referent’s status node. All the pointers pointing to the same referent share the same status node in their pointer metadata. A status node is smart in the sense that it is automatically deleted when it becomes useless (indicated by its reference count reaching zero). To the best of our knowledge, Smatus represents the most comprehensive approach of its kind. ...
Chen, Z, Zong, Z, Li, J, Li, J, Yan, Y & Wu, C 2024, 'Experimental and numerical study on damage behavior of air-backed steel-concrete-steel composite panels subjected to underwater contact explosion', Engineering Structures, vol. 318, pp. 118744-118744.
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Chen, Z, Zuo, W, Zhou, K, Li, Q, Yi, Z & Huang, Y 2024, 'Numerical investigation on the performance enhancement of PEMFC with gradient sinusoidal-wave fins in cathode channel', Energy, vol. 288, pp. 129894-129894.
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Cheng, H, Guo, Y, Wang, T, Li, Q, Chang, X & Nie, L 2024, 'Voice-Face Homogeneity Tells Deepfake', ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 20, no. 3, pp. 1-22.
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Detecting forgery videos is highly desirable due to the abuse of deepfake. Existing detection approaches contribute to exploring the specific artifacts in deepfake videos and fit well on certain data. However, the growing technique on these artifacts keeps challenging the robustness of traditional deepfake detectors. As a result, the development of these approaches has reached a blockage. In this article, we propose to perform deepfake detection from an unexplored voice-face matching view. Our approach is founded on two supporting points: first, there is a high degree of homogeneity between the voice and face of an individual (i.e., they are highly correlated), and second, deepfake videos often involve mismatched identities between the voice and face due to face-swapping techniques. To this end, we develop a voice-face matching method that measures the matching degree between these two modalities to identify deepfake videos. Nevertheless, training on specific deepfake datasets makes the model overfit certain traits of deepfake algorithms. We instead advocate a method that quickly adapts to untapped forgery, with a pre-training then fine-tuning paradigm. Specifically, we first pre-train the model on a generic audio-visual dataset, followed by the fine-tuning on downstream deepfake data. We conduct extensive experiments over three widely exploited deepfake datasets: DFDC, FakeAVCeleb, and DeepfakeTIMIT. Our method obtains significant performance gains as compared to other state-of-the-art competitors. For instance, our method outperforms the baselines by nearly 2%, achieving an AUC of 86.11% on FakeAVCeleb. It is also worth noting that our method already achieves competitive results when fine-tuned on limited deepfake data.
Cheng, X, Nie, X, Li, N, Wang, H, Zheng, Z & Sui, Y 2024, 'How About Bug-Triggering Paths? - Understanding and Characterizing Learning-Based Vulnerability Detectors', IEEE Transactions on Dependable and Secure Computing, vol. 21, no. 2, pp. 542-558.
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Machine learning and its promising branch deep learning have proven to be effective in a wide range of application domains. Recently, several efforts have shown success in applying deep learning techniques for automatic vulnerability discovery, as alternatives to traditional static bug detection. In principle, these learning-based approaches are built on top of classification models using supervised learning. Depending on the different granularities to detect vulnerabilities, these approaches rely on learning models which are typically trained with well-labeled source code to predict whether a program method, a program slice, or a particular code line contains a vulnerability or not. The effectiveness of these models is normally evaluated against conventional metrics including precision, recall and F1 score. In this paper, we show that despite yielding promising numbers, the above evaluation strategy can be insufficient and even misleading when evaluating the effectiveness of current learning-based approaches. This is because the underlying learning models only produce the classification results or report individual/isolated program statements, but are unable to pinpoint bug-triggering paths, which is an effective way for bug fixing and the main aim of static bug detection. Our key insight is that a program method or statement can only be stated as vulnerable in the context of a bug-triggering path. In this work, we systematically study the gap between recent learning-based approaches and conventional static bug detectors in terms of fine-grained metrics called BTP metrics using bug-triggering paths. We then characterize and compare the quality of the prediction results of existing learning-based detectors under different granularities. Finally, our comprehensive empirical study reveals several key issues and challenges in developing classification models to pinpoint bug-triggering paths and calls for more advanced learning-based bug detection techniques.
Cheng, X-Y, Ding, C & Ziolkowski, RW 2024, 'Dual-Band Shared-Aperture Dielectric Resonator Antenna (DRA) With Suppressed Cross-Band Interactions', IEEE Transactions on Antennas and Propagation, vol. 72, no. 7, pp. 5694-5704.
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Cheniti, M, Akhtar, Z, Adak, C & Siddique, K 2024, 'An Approach for Full Reinforcement-Based Biometric Score Fusion', IEEE Access, vol. 12, pp. 49779-49790.
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Chenrayan, V, Kanaginahal, G, Shahapurkar, K, Soudagar, MEM, Fouad, Y & Kalam, MA 2024, 'Analytical modeling and experimental estimation of the dynamic mechanical characteristics of green composite: Caesalpinia decapetala seed reinforcement', Polymer Engineering & Science, vol. 64, no. 3, pp. 1096-1109.
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AbstractThe emerging need for a sustainable environment prompts the research community to develop functional materials with bio‐ and organic waste. This research advocates biodegradable waste management and its performance evaluation. The involvement of Caesalpinia decapetala (CD) as a potential reinforcement in the epoxy matrix and its analytical evaluation of thermal stability are novel ideas for disposing of bio and organic waste. Three different variants (10, 20, and 30 wt%) of CD seed particles are used to develop the epoxy composite, and further, their influence on dynamic mechanical characteristics such as damping type, loss modulus, and storage modulus has been investigated. The results corroborate that the higher CD seed content (30 wt%) in the epoxy matrix enhances the storage modulus, loss modulus, and damping on a scale of 1.14, 1.25, and 1.07 times that of the neat epoxy matrix. The reason behind the improved dynamic properties has been validated through theoretical modeling. A substantial increment in the degree of entanglement and activation energy in the band of 8.33 × 10−3 moles/m3 and 20.201 kJ/mol, respectively, in comparison with neat epoxy, is considered to be good authentication for the thermal stability of the CD 30 specimen. The analytical prediction of storage modulus is executed with five different models, whereas damping behavior is executed with two different models. The analytically estimated results are matched with the experimental ones, and we conclude that they are in fair agreement with the experimental findings.
Chenrayan, V, Palanisamy, D, Mani, K, Shahapurkar, K, Elahi M. Soudagar, M, Fouad, Y, Kalam, MA, Ali, MM & Nasir Bashir, M 2024, 'Mitigation of bio-corrosion characteristics of coronary artery stent by optimising fs-laser micromachining parameters', Heliyon, vol. 10, no. 6, pp. e28057-e28057.
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Cheung, NW, McElduff, P, Fulcher, G, Middleton, S, Chen, R, Depczynski, B, Flack, J, Kinsella, J, Layton, M, McLean, M, Poynten, A, Tonks, K, White, C, Wong, V & Chipps, DR 2024, 'Glucose levels at hospital admission are associated with 5 year mortality', Diabetes Research and Clinical Practice, vol. 217, pp. 111840-111840.
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Chi, H, Yang, W, Liu, F, Lan, L, Qin, T & Han, B 2024, 'Does Confusion Really Hurt Novel Class Discovery?', International Journal of Computer Vision, vol. 132, no. 8, pp. 3191-3207.
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Chi, K, Li, J & Wu, C 2024, 'Behavior of Reinforced Ultra-High Performance Concrete Slabs Under Impact Loading After Exposure to Elevated Temperatures', International Journal of Computational Methods, vol. 21, no. 08.
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Steel fiber-reinforced ultra-high performance concrete (UHPC) material is prone to explosive spalling under elevated temperatures. With the addition of polypropylene (PP) fiber, thermal spalling of UHPC can be mitigated and its fire resistance can be improved. This research investigates the impact resistance of steel and PP fiber-reinforced UHPC slabs after exposure to elevated temperatures, and the structural behavior and damage were compared against normal strength concrete (NSC) slabs. Karagozian & Case concrete (KCC) model was adopted to simulate both NSC and UHPC materials. With consideration of thermal hazards, the material damage, equation of state and strain rate sensitivity were adapted. The validity of this numerical model was evaluated against available experimental results. The numerical model was used to investigate the impact resistance of the reinforced UHPC slabs after exposure to fire hazards. The effect of fire exposure time, impact velocity and impact mass on the resistance of the reinforced NSC and UHPC slabs were analyzed. The simulation results revealed that punching shear failure areas in the NSC slabs were 2.5 times, 3.4 times, 3.0 times and 1.2 times larger than the UHPC slabs after exposure to international standardization ISO-834 standard fire for 1[Formula: see text]h, 2[Formula: see text]h, 3[Formula: see text]h and 4[Formula: see text]h, respectively. After exposure to the standard fire ISO-834 for 2 h, the punching shear failure on the bottom side of NSC increased 90.9% with the increase in falling height from 1[Formula: see text]m to 7[Formula: see text]m, while for the UHPC slabs, the increment was around 67.9%. After exposure to the standard fire ISO-834 for 2[Formula: see text]h, the punching shear damage of the NSC slabs increased by 72.9% with the punch weight increased from 100[Formula: see text]kg to 700[Formula: see text]kg, whereas the damage in the UHPC slabs increased by 53.8%.
Chi, K, Li, J & Wu, C 2024, 'Behaviour of reinforced concrete panels under impact loading after cryogenic freeze-thaw cycles', Construction and Building Materials, vol. 414, pp. 135058-135058.
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Chi, K, Li, J, Shao, R, Chen, L, Xu, S & Wu, C 2024, 'Experimental study on impact behaviour of normal strength mortar at cryogenic temperatures and after freeze-thaw cycles', Construction and Building Materials, vol. 440, pp. 137497-137497.
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Choi, S, Hike, D, Pohmann, R, Avdievich, N, Gomez-Cid, L, Man, W, Scheffler, K & Yu, X 2024, 'Alpha-180 spin-echo-based line-scanning method for high-resolution laminar-specific fMRI in animals', Imaging Neuroscience, vol. 2, pp. 1-14.
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Abstract Laminar-specific functional magnetic resonance imaging (fMRI) has been widely used to study circuit-specific neuronal activity by mapping spatiotemporal fMRI response patterns across cortical layers. Hemodynamic responses reflect indirect neuronal activity given the limitation of spatial and temporal resolution. Previously, a gradient-echo-based line-scanning fMRI (GELINE) method was proposed with high temporal (50 ms) and spatial (50 µm) resolution to better characterize the fMRI onset time across cortical layers by employing two saturation RF pulses. However, the imperfect RF saturation performance led to poor boundary definition of the reduced region of interest (ROI) and aliasing problems outside of the ROI. Here, we propose an α (alpha)-180 spin-echo-based line-scanning fMRI (SELINE) method in animals to resolve this issue by employing a refocusing 180˚ RF pulse perpendicular to the excitation slice (without any saturation RF pulse) and also achieve high spatiotemporal resolution. In contrast to GELINE signals which peaked at the superficial layer, we detected varied peaks of laminar-specific BOLD signals across deeper cortical layers using the SELINE method, indicating the well-defined exclusion of the large draining-vein effect using the spin-echo sequence. Furthermore, we applied the SELINE method with a 200 ms repetition time (TR) to sample the fast hemodynamic changes across cortical layers with a less draining vein effect. In summary, this SELINE method provides a novel acquisition scheme to identify microvascular-sensitive laminar-specific BOLD responses across cortical depth.
Choi, S-H, Im, GH, Choi, S, Yu, X, Bandettini, PA, Menon, RS & Kim, S-G 2024, 'No replication of direct neuronal activity–related (DIANA) fMRI in anesthetized mice', Science Advances, vol. 10, no. 13.
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Direct imaging of neuronal activity (DIANA) by functional magnetic resonance imaging (fMRI) could be a revolutionary approach for advancing systems neuroscience research. To independently replicate this observation, we performed fMRI experiments in anesthetized mice. The blood oxygenation level–dependent (BOLD) response to whisker stimulation was reliably detected in the primary barrel cortex before and after DIANA experiments; however, no DIANA–like fMRI peak was observed in individual animals’ data with the 50 to 300 trials. Extensively averaged data involving 1050 trials in six mice showed a flat baseline and no detectable neuronal activity–like fMRI peak. However, spurious, nonreplicable peaks were found when using a small number of trials, and artifactual peaks were detected when some outlier-like trials were excluded. Further, no detectable DIANA peak was observed in the BOLD-responding thalamus from the selected trials with the neuronal activity–like reference function in the barrel cortex. Thus, we were unable to replicate the previously reported results without data preselection.
Chowdhury, S, Sais, D, Donnelly, S & Tran, N 2024, 'The knowns and unknowns of helminth–host miRNA cross-kingdom communication', Trends in Parasitology, vol. 40, no. 2, pp. 176-191.
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Chu, NH, Hoang, DT, Nguyen, DN, Phan, KT, Dutkiewicz, E, Niyato, D & Shu, T 2024, 'MetaSlicing: A Novel Resource Allocation Framework for Metaverse', IEEE Transactions on Mobile Computing, vol. 23, no. 5, pp. 4145-4162.
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Creating and maintaining the Metaverse requires enormous resources that have never been seen before, especially computing resources for intensive data processing to support the Extended Reality, enormous storage resources, and massive networking resources for maintaining ultra high-speed and low-latency connections. Therefore, this work aims to propose a novel framework, namely MetaSlicing, that can provide a highly effective and comprehensive solution in managing and allocating different types of resources for Metaverse applications. In particular, by observing that Metaverse applications may have common functions, we first propose grouping applications into clusters, called MetaInstances. In a MetaInstance, common functions can be shared among applications. As such, the same resources can be used by multiple applications simultaneously, thereby enhancing resource utilization dramatically. To address the real-time characteristic and resource demand's dynamic and uncertainty in the Metaverse, we develop an effective framework based on the semi-Markov decision process and propose an intelligent admission control algorithm that can maximize resource utilization and enhance the Quality-of-Service for end-users. Extensive simulation results show that our proposed solution outperforms the Greedy-based policies by up to 80% and 47% in terms of long-term revenue for Metaverse providers and request acceptance probability, respectively.
Chu, NH, Huynh, NV, Nguyen, DN, Hoang, DT, Gong, S, Shu, T, Dutkiewicz, E & Phan, KT 2024, 'Countering Eavesdroppers With Meta- Learning-Based Cooperative Ambient Backscatter Communications', IEEE Transactions on Wireless Communications, vol. 23, no. 10, pp. 13678-13693.
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Chu, NH, Nguyen, HQ, Nguyen, DN, Hoang, DT, Phan, KT, Dutkiewicz, E, Niyato, D & Shu, T 2024, 'Dynamic Multi-tier Resource Allocation Framework for Metaverse', IEEE Network, pp. 1-1.
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Chu, Y, Zhao, S, Niu, F, Dong, Y & Zhao, Y 2024, 'A New Diffusion Filtered-X Affine Projection Algorithm: Performance Analysis and Application in Windy Environment', IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 32, pp. 1596-1608.
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Colino-Sanguino, Y, Rodriguez de la Fuente, L, Gloss, B, Law, AMK, Handler, K, Pajic, M, Salomon, R, Gallego-Ortega, D & Valdes-Mora, F 2024, 'Performance comparison of high throughput single-cell RNA-Seq platforms in complex tissues', Heliyon, vol. 10, no. 17, pp. e37185-e37185.
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Cong, J, He, M, Jang, JS, Huang, J, Privat, K, Chen, Y, Li, J, Yang, L, Green, MA, Kim, JH, Cairney, JM & Hao, X 2024, 'Unveiling the Role of Ge in CZTSSe Solar Cells by Advanced Micro‐To‐Atom Scale Characterizations', Advanced Science, vol. 11, no. 15, p. e2305938.
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AbstractKesterite is an earth‐abundant energy material with high predicted power conversion efficiency, making it a sustainable and promising option for photovoltaics. However, a large open circuit voltage Voc deficit due to non‐radiative recombination at intrinsic defects remains a major hurdle, limiting device performance. Incorporating Ge into the kesterite structure emerges as an effective approach for enhancing performance by manipulating defects and morphology. Herein, how different amounts of Ge affect the kesterite growth pathways through the combination of advanced microscopy characterization techniques are systematically investigated. The results demonstrate the significance of incorporating Ge during the selenization process of the CZTSSe thin film. At high temperature, the Ge incorporation effectively delays the selenization process due to the formation of a ZnSe layer on top of the metal alloys through decomposition of the Cu‐Zn alloy and formation of Cu‐Sn alloy, subsequently forming of Cu‐Sn‐Se phase. Such an effect is compounded by more Ge incorporation that further postpones kesterite formation. Furthermore, introducing Ge mitigates detrimental “horizontal” grain boundaries by increasing the grain size on upper layer. The Ge incorporation strategy discussed in this study holds great promise for improving device performance and grain quality in CZTSSe and other polycrystalline chalcogenide solar cells.
Craig, L & Hastings, C 2024, 'Intersectionality of gender and age (‘gender*age’): a critical realist approach to explaining older women’s increased homelessness', Journal of Critical Realism, vol. 23, no. 4, pp. 361-383.
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Crowther, CA, Ashwood, P, Middleton, PF, McPhee, A, Tran, T & Harding, JE 2024, 'Prenatal Intravenous Magnesium at 30–34 Weeks' Gestation and Neurodevelopmental Outcomes in Offspring: The MAGENTA Randomized Clinical Trial', Obstetrical & Gynecological Survey, vol. 79, no. 2, pp. 78-80.
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(Abstracted from JAMA 2023;330(7):603–614) Preterm birth is a focus of many studies and interventions but remains the leading cause of global neonatal morbidity and mortality. One particular risk that is elevated in preterm infants is cerebral palsy, which affects movement and/or posture, causing health problems and high health care costs for children and their families.
Cui, H, Han, M, Xu, F, Liu, Q & Saha, SC 2024, 'Scaling analysis of intrusion flow and thermal plume for Pr > 1 in the triangular cavity', International Journal of Thermal Sciences, vol. 195, pp. 108616-108616.
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Cui, J, Ji, JC, Zhang, T, Cao, L, Chen, Z & Ni, Q 2024, 'A novel dual-branch Transformer with gated cross-attention for remaining useful life prediction of bearings', IEEE Sensors Journal, pp. 1-1.
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Cui, Z, Sun, X, Chen, H, Pan, L, Cui, L, Liu, S & Xu, G 2024, 'Dynamic Recommendation Based on Graph Diffusion and Ebbinghaus Curve', IEEE Transactions on Computational Social Systems, vol. 11, no. 2, pp. 2755-2764.
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Cuzmar, R, Montenegro, A, Mora, A, Pereda, J & Aguilera, RP 2024, 'Constrained MPC for Intercluster Energy Control of Modular Multilevel Matrix Converters', IEEE Transactions on Industrial Electronics, vol. 71, no. 7, pp. 7766-7776.
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Cuzmar, RH, Mora, A, Pereda, J & Aguilera, RP 2024, 'An Improved Reference Generator Based on MPC of Circulating Currents and Common-Mode Voltage for Modular Multilevel Matrix Converters', IEEE Transactions on Industrial Electronics, pp. 1-11.
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Cuzmar, RH, Mora, A, Pereda, J, Poblete, P & Aguilera, RP 2024, 'Long-Horizon Sequential FCS-MPC Approaches for Modular Multilevel Matrix Converters', IEEE Transactions on Industrial Electronics, vol. 71, no. 5, pp. 5137-5147.
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Czuba, M, Nurek, M, Serwata, D, Qiu, Y-X, Jia, M, Musial, K, Michalski, R & Bródka, P 2024, 'Network Diffusion Framework to Simulate Spreading Processes in Complex Networks', Big Data Mining and Analytics, vol. 7, no. 3, pp. 637-654.
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Dadol, GC, Tijing, LD, Agapay, RC, Lobarbio, CFY & Tan, NPB 2024, 'Solution blow-spun polyacrylonitrile–polyamide thin-film nanofibrous composite membrane for the removal of fermentation inhibitors', Nanocomposites, vol. 10, no. 1, pp. 351-362.
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Dai, J, Liu, Z, Song, Y, Liu, Y, Nghiem, LD, Wang, Q, Liu, W, Sun, X & Cai, Z 2024, 'Heterocyclic polymerization modified g-C3N4 nanotube with advanced charge separation for solar light driven degradation of ciprofloxacin', Separation and Purification Technology, vol. 348, pp. 127692-127692.
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Dai, S, He, X, Tong, C, Gao, F, Zhang, S & Sheng, D 2024, 'Stability of sandy soils against internal erosion under cyclic loading and quantitatively examination of the composition and origin of eroded particles', Canadian Geotechnical Journal, vol. 61, no. 4, pp. 732-747.
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Internal erosion refers to the movement of fine particles within soil framework due to subsurface water seepage. Existing criteria for assessing internal erosion usually are based on static loading, and the effect of cyclic load is not considered. Additionally, there are limited studies to examine the particle-size distribution and origin of eroded fine particles. This study presents an experimental investigation that examines the impact of cyclic loading on internal stability through a series of seepage tests. The composition and origin of lost particles are quantitatively studied using particle staining and image recognition techniques. With increasing hydraulic gradient, particle erosion progresses from top layer to bottom layer, with a gradual increase in the maximum particle size of eroded particles from each layer. After significant loss of particles, the specimens reach a state of transient equilibrium, resulting in a gradual slowdown of both particle loss rate and average flow velocity. The results indicate that cyclic loading promotes massive particle loss and causes erosion failure of specimens that are considered stable according to existing criteria. The reason is that under cyclic loading, local hydraulic gradients is oscillating, and a larger than average hydraulic gradient may occur, which is responsible for the internal instability. The analysis suggests that existing criteria can provide a reasonable assessment of the relative stabilities of specimens under static loads but fail to capture the stabilities under cyclic loading conditions.
Dai, S, Zhang, S, Gao, F, He, X & Sheng, D 2024, 'Investigation of particle segregation in a vertically vibrated binary mixture: Segregation process and mechanism', Computers and Geotechnics, vol. 169, pp. 106236-106236.
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Dalai, SK, Panda, KP, Siwakoti, YP & Panda, G 2024, 'Three-Phase Switched-Capacitor Boost Self-Balanced Multilevel Inverter for Photovoltaic Applications', IEEE Transactions on Energy Conversion, vol. 39, no. 3, pp. 1818-1827.
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Dang, KB, Nguyen, CQ, Tran, QC, Nguyen, H, Nguyen, TT, Nguyen, DA, Tran, TH, Bui, PT, Giang, TL, Nguyen, DA, Lenh, TA, Ngo, VL, Yasir, M, Nguyen, TT & Ngo, HH 2024, 'Comparison between U-shaped structural deep learning models to detect landslide traces', Science of The Total Environment, vol. 912, pp. 169113-169113.
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Landslides endanger lives and public infrastructure in mountainous areas. Monitoring landslide traces in real-time is difficult for scientists, sometimes costly and risky because of the harsh terrain and instability. Nowadays, modern technology may be able to identify landslide-prone locations and inform locals for hours or days when the weather worsens. This study aims to propose indicators to detect landslide traces on the fields and remote sensing images; build deep learning (DL) models to identify landslides from Sentinel-2 images automatically; and apply DL-trained models to detect this natural hazard in some particular areas of Vietnam. Nine DL models were trained based on three U-shaped architectures, including U-Net, U2-Net, and U-Net3+, and three options of input sizes. The multi-temporal Sentinel-2 images were chosen as input data for training all models. As a result, the U-Net, using an input image size of 32 × 32 and a performance of 97 % with a loss function of 0.01, can detect typical landslide traces in Vietnam. Meanwhile, the U-Net (64 × 64) can detect more considerable landslide traces. Based on multi-temporal remote sensing data, a different case study in Vietnam was chosen to see landslide traces over time based on the trained U-Net (32 × 32) model. The trained model allows mountain managers to track landslide occurrences during wet seasons. Thus, landslide incidents distant from residential areas may be discovered early to warn of flash floods.
Dang, Z, Luo, M, Jia, C, Dai, G, Wang, J, Chang, X & Wang, J 2024, 'Disentangled Representation Learning with Transmitted Information Bottleneck', IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1.
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Dang, Z, Luo, M, Wang, J, Jia, C, Yan, C, Dai, G, Chang, X & Zheng, Q 2024, 'Disentangled Generation With Information Bottleneck for Enhanced Few-Shot Learning', IEEE Transactions on Image Processing, vol. 33, pp. 3520-3535.
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Das, A, Im, KS, Kabir, MM, Shon, HK & Nam, SY 2024, 'Polybenzimidazole (PBI)-based membranes for fuel cell, water electrolysis and desalination', Desalination, vol. 579, pp. 117500-117500.
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Dashti, A, Navidpour, AH, Amirkhani, F, Zhou, JL & Altaee, A 2024, 'Application of machine learning models to improve the prediction of pesticide photodegradation in water by ZnO-based photocatalysts', Chemosphere, vol. 362, pp. 142792-142792.
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Dayioglu, M, Küskü, F & Cetindamar, D 2024, 'The Impact of Business Environmental Factors on Performance Through Strategic Agility and Business Model Innovation: An Analysis Based on Dynamic Capabilities Theory', IEEE Transactions on Engineering Management, vol. 71, pp. 3656-3670.
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Delle-Vergini, S, Eacersall, D, Dann, C, Ally, M & Chakraborty, S 2024, 'Teaching project management to primary school children: a scoping review', The Australian Educational Researcher, vol. 51, no. 4, pp. 1035-1062.
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AbstractTeachers have used projects in children’s education for over a century. More recently, project management knowledge and skills have become essential when students manage technological solutions from inception to presentation. This paper presents the first scoping literature review on teaching project management to primary school students. A total of 33 publications between 2000 and 2022 were analysed and presented both descriptively and thematically. While the review did not identify any empirical studies of teaching project management to primary school students, it did reveal several examples of suggested teaching approaches, project management activity, and common elements associated with project management. The review concludes with a recommendation for researchers, educators, and project management practitioners to build upon this research by exploring the effectiveness of comprehensive approaches to teaching project management to primary school students. This paper represents a significant area of research as project management is one of the most critical skills for students to achieve success in the twenty-first century.
Deng, F, Sang, R, Li, Y, Yang, D, Mckinnirey, F, Deng, W & Goldys, EM 2024, 'Towards understanding Trans-cleavage of natural and synthetic nucleic acids by Cas12a for sensitive CRISPR biosensing', Microchemical Journal, vol. 207, pp. 111850-111850.
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Deng, S, Ji, J, Wen, G & Yin, S 2024, 'Global dynamics of a hexagonal governor system with two time delays in the parameter and state spaces', Chaos, Solitons & Fractals, vol. 185, pp. 115018-115018.
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Deng, Z, Bao, D, Jiang, L, Zheng, W & Xu, X 2024, 'One-step prepared of e-Gelatin-CNTs nano-composite fiber modified carbon fiber microelectrode for continuous dopamine monitoring: Characterization, biocompatibility evaluation and in vivo experiment', Microchemical Journal, vol. 197, pp. 109876-109876.
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Deng, Z, Fu, Z, Wang, L, Yang, Z, Bai, C, Zhou, T, Wang, Z & Jiang, J 2024, 'False Correlation Reduction for Offline Reinforcement Learning', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 2, pp. 1199-1211.
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Deng, Z, Mahmood, AH, Dong, W, Sheng, D, Lin, X & Li, W 2024, 'Piezoresistive performance of self-sensing bitumen emulsion-cement mortar with multi-walled carbon nanotubes', Cement and Concrete Composites, vol. 153, pp. 105718-105718.
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Deng, Z-H, Jiang, J, Long, G & Zhang, C 2024, 'Causal Reinforcement Learning: A Survey', Transactions on Machine Learning Research.
Deshpande, NM, Gite, S & Pradhan, B 2024, 'Explainable AI for binary and multi-class classification of leukemia using a modified transfer learning ensemble model', International Journal on Smart Sensing and Intelligent Systems, vol. 17, no. 1.
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Abstract In leukemia diagnosis, automating the process of decision-making can reduce the impact of individual pathologists' expertise. While deep learning models have demonstrated promise in disease diagnosis, combining them can yield superior results. This research introduces an ensemble model that merges two pre-trained deep learning models, namely, VGG-16 and Inception, using transfer learning. It aims to accurately classify leukemia subtypes using real and standard dataset images, focusing on interpretability. Therefore, the use of Local Interpretable Model-Agnostic Explanations (LIME) is employed to achieve interpretability. The ensemble model achieves an accuracy of 83.33% in binary classification, outperforming individual models. In multi-class classification, VGG-16 and Inception reach accuracies of 83.335% and 93.33%, respectively, while the ensemble model reaches an accuracy of 100%.
Deshpande, NM, Gite, S, Pradhan, B, Alamri, A & Lee, C-W 2024, 'A New Method for Diagnosis of Leukemia Utilizing a Hybrid DL-ML Approach for Binary and Multi-Class Classification on a Limited-Sized Database', Computer Modeling in Engineering & Sciences, vol. 139, no. 1, pp. 593-631.
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Deutsch, F, Tran, NH, Pham, DX, Hien, ND, Tuan, VN, Sais, D & Tran, N 2024, 'Trends in head and neck cancer incidence in Ho Chi Minh City, Vietnam between 1996 and 2015', Cancer Epidemiology, vol. 93, pp. 102686-102686.
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Devaisy, S, Jeong, S, Kandasamy, J, Nguyen, TV, Ratnaweera, H & Vigneswaran, S 2024, 'Membrane Hybrid System for Sustainable Removal of Organic Micropollutants and Biofoulants from Reverse-Osmosis Concentrate', Journal of Environmental Engineering, vol. 150, no. 10.
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Devi, A, Palmer, EE, Ganguly, R & Barua, PD 2024, 'Teachers’ Educational Experiences and Preparedness in Teaching Students with Autism', The Asia-Pacific Education Researcher, vol. 33, no. 1, pp. 71-81.
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Dhandapani, Y, Machner, A, Wilson, W, Kunther, W, Afroz, S, Kim, T, Zunino, F, Joseph, S, Kanavaris, F, Castel, A, Thienel, K-C, Irassar, EF, Bishnoi, S, Martirena, F & Santhanam, M 2024, 'Performance of cementitious systems containing calcined clay in a chloride-rich environment: a review by TC-282 CCL', Materials and Structures, vol. 57, no. 7.
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AbstractIn this review by TC- 282 CCL, a comprehensive examination of various facets of chloride ingress in calcined clay-based concrete in aggressive chloride-rich environments is presented due to its significance in making reinforced concrete structures susceptible to chloride-induced corrosion damages. The review presents a summary of available literature focusing on materials characteristics influencing the chloride resistance of calcined clay-based concrete, such as different clay purity, kaolinite content and other clay minerals, underscoring the significance of pore refinement, pore solution composition, and chloride binding mechanisms. Further, the studies dealing with the performance at the concrete scale, with a particular emphasis on transport properties, curing methods, and mix design, are highlighted. Benchmarking calcined clay mixes with fly ash or slag-based concrete mixes that are widely used in aggressive chloride conditions instead of OPC is recommended. Such comparison could extend the usage of calcined clay as a performance-enhancing mineral admixture in the form of calcined clay or LC2 (limestone-calcined clay). The chloride diffusion coefficient in calcined clay concrete is reported to be significantly lower (about 5–10 times in most literature available so far) compared to OPC, and even lower compared to fly ash and slag-based concrete at early curing ages reported across recent literature made with different types of cements and concrete mixes. Limited studies dealing with reinforcement corrosion point out that calcined clay delays corrosion initiation and reduces corrosion rates despite the reduction in critical chloride threshold. Most of these results on corrosion performance are mainly from laboratory studies and warrant field evaluation in future. Finally, two case studies demonstrating the application of calcined clay-based concrete in real-world marine exposure conditions are discussed to s...
Dhruva, S, Krishankumar, R, Zavadskas, EK, Ravichandran, KS & Gandomi, AH 2024, 'Selection of Suitable Cloud Vendors for Health Centre: A Personalized Decision Framework with Fermatean Fuzzy Set, LOPCOW, and CoCoSo', Informatica, pp. 65-98.
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Cloud computing has emerged as a transformative technology in the healthcare industry, but selecting the most suitable CV (“cloud vendor”) remains a complex task. This research presents a decision framework for CV selection in the healthcare industry, addressing the challenges of uncertainty, expert hesitation, and conflicting criteria. The proposed framework incorporates FFS (“Fermatean fuzzy set”) to handle uncertainty and data representation effectively. The importance of experts is attained via the variance approach, which considers hesitation and variability. Furthermore, the framework addresses the issue of extreme value hesitancy in criteria through the LOPCOW (“logarithmic percentage change-driven objective weighting”) method, which ensures a balanced and accurate assessment of criterion importance. Personalized grading of CVs is done via the ranking algorithm that considers the formulation of CoCoSo (“combined compromise solution”) with rank fusion, providing a compromise solution that balances conflicting criteria. By integrating these techniques, the proposed framework aims to enhance the rationale and reduce human intervention in CV selection for the healthcare industry. Also, valuable insights are gained from the framework for making informed decisions when selecting CVs for efficient data management and process implementation. A case example from Tamil Nadu is presented to testify to the applicability, while sensitivity and comparison analyses reveal the pros and cons of the framework.
Dhull, P, Schreurs, D, Paolini, G, Costanzo, A, Abolhasan, M & Shariati, N 2024, 'Multitone PSK Modulation Design for Simultaneous Wireless Information and Power Transfer', IEEE Transactions on Microwave Theory and Techniques, vol. 72, no. 1, pp. 446-460.
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Di, X, Wang, D, Shan, X, Ding, L, Zhong, Z, Chen, C, Wang, D, Song, Z, Wang, J, Su, QP, Yue, S, Zhang, M, Cheng, F & Wang, F 2024, 'Probing the Nanonewton Mitotic Cell Deformation Force by Ion-Resonance-Enhanced Photonics Force Microscopy', Nano Letters, vol. 24, no. 44, pp. 14004-14011.
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Dikshit, A, Pradhan, B, Matin, SS, Beydoun, G, Santosh, M, Park, H-J & Maulud, KNA 2024, 'Artificial Intelligence: A new era for spatial modelling and interpreting climate-induced hazard assessment', Geoscience Frontiers, vol. 15, no. 4, pp. 101815-101815.
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Ding, H, Zhang, H, Fu, G, Jiang, C, Luo, F, Xiao, C & Xu, M 2024, 'Towards High-Quality Photorealistic Image Style Transfer', IEEE Transactions on Multimedia, vol. 26, pp. 9892-9905.
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Ding, L, Chen, C, Shan, X, Liu, B, Wang, D, Du, Z, Zhao, G, Su, QP, Yang, Y, Halkon, B, Tran, TT, Liao, J, Aharonovich, I, Zhang, M, Cheng, F, Fu, L, Xu, X & Wang, F 2024, 'Optical Nonlinearity Enabled Super‐Resolved Multiplexing Microscopy', Advanced Materials, vol. 36, no. 2.
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AbstractOptical multiplexing for nanoscale object recognition is of great significance within the intricate domains of biology, medicine, anti‐counterfeiting, and microscopic imaging. Traditionally, the multiplexing dimensions of nanoscopy are limited to emission intensity, color, lifetime, and polarization. Here, a novel dimension, optical nonlinearity, is proposed for super‐resolved multiplexing microscopy. This optical nonlinearity is attributable to the energy transitions between multiple energy levels of the doped lanthanide ions in upconversion nanoparticles (UCNPs), resulting in unique optical fingerprints for UCNPs with different compositions. A vortex beam is applied to transport the optical nonlinearity onto the imaging point‐spread function (PSF), creating a robust super‐resolved multiplexing imaging strategy for differentiating UCNPs with distinctive optical nonlinearities. The composition information of the nanoparticles can be retrieved with variations of the corresponding PSF in the obtained image. Four channels multiplexing super‐resolved imaging with a single scanning, applying emission color and nonlinearity of two orthogonal imaging dimensions with a spatial resolution higher than 150 nm (1/6.5λ), are demonstrated. This work provides a new and orthogonal dimension – optical nonlinearity – to existing multiplexing dimensions, which shows great potential in bioimaging, anti‐counterfeiting, microarray assays, deep tissue multiplexing detection, and high‐density data storage.
Ding, L, Chen, C, Shan, X, Liu, B, Wang, D, Du, Z, Zhao, G, Su, QP, Yang, Y, Halkon, B, Tran, TT, Liao, J, Aharonovich, I, Zhang, M, Cheng, F, Fu, L, Xu, X & Wang, F 2024, 'Optical Nonlinearity Enabled Super‐Resolved Multiplexing Microscopy (Adv. Mater. 2/2024)', Advanced Materials, vol. 36, no. 2.
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Ding, W, Fan, X, Zhou, X, Liu, R, Chen, C, Jin, W, Sun, J, Li, X, Jiang, G & Liu, H 2024, 'Performance and mechanisms of zero valent iron enhancing short-chain fatty acids production during thermophilic anaerobic fermentation of waste activated sludge', Science of The Total Environment, vol. 912, pp. 169025-169025.
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Ding, W, Geng, Y, Huang, J, Ju, H, Wang, H & Lin, C-T 2024, 'MGRW-Transformer: Multigranularity Random Walk Transformer Model for Interpretable Learning', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15.
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Ding, W, Sun, Y, Huang, J, Ju, H, Zhang, C, Yang, G & Lin, C-T 2024, 'RCAR-UNet: Retinal vessel segmentation network algorithm via novel rough attention mechanism', Information Sciences, vol. 657, pp. 120007-120007.
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Ding, W, Sun, Y, Li, M, Liu, J, Ju, H, Huang, J & Lin, C-T 2024, 'A Novel Spark-Based Attribute Reduction and Neighborhood Classification for Rough Evidence', IEEE Transactions on Cybernetics, vol. 54, no. 3, pp. 1470-1483.
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Neighborhood classification (NEC) algorithms have been widely used to solve classification problems. Most traditional NEC algorithms employ the majority voting mechanism as the basis for final decision making. However, this mechanism hardly considers the spatial difference and label uncertainty of the neighborhood samples, which may increase the possibility of the misclassification. In addition, the traditional NEC algorithms need to load the entire data into memory at once, which is computationally inefficient when the size of the dataset is large. To address these problems, we propose a novel Spark-based attribute reduction and NEC for rough evidence in this article. Specifically, we first construct a multigranular sample space using the parallel undersampling method. Then, we evaluate the significance of attribute by neighborhood rough evidence decision error rate and remove the redundant attribute on different samples subspaces. Based on this attribute reduction algorithm, we design a parallel attribute reduction algorithm which is able to compute equivalence classes in parallel and parallelize the process of searching for candidate attributes. Finally, we introduce the rough evidence into the classification decision of traditional NEC algorithms and parallelize the classification decision process. Furthermore, the proposed algorithms are conducted in the Spark parallel computing framework. Experimental results on both small and large-scale datasets show that the proposed algorithms outperform the benchmarking algorithms in the classification accuracy and the computational efficiency.
Ding, W, Zhou, T, Huang, J, Jiang, S, Hou, T & Lin, C-T 2024, 'FMDNN: A Fuzzy-Guided Multigranular Deep Neural Network for Histopathological Image Classification', IEEE Transactions on Fuzzy Systems, vol. 32, no. 8, pp. 4709-4723.
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Dinh, PV, Nguyen, QU, Hoang, DT, Nguyen, DN, Bao, SP & Dutkiewicz, E 2024, 'Constrained Twin Variational Auto-Encoder for Intrusion Detection in IoT Systems', IEEE Internet of Things Journal, vol. 11, no. 8, pp. 14789-14803.
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Dinh, TQ, Dau, SH, Lagunas, E, Chatzinotas, S, Nguyen, DN & Hoang, DT 2024, 'Quantum Annealing for Complex Optimization in Satellite Communication Systems', IEEE Internet of Things Journal, pp. 1-1.
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Dios, KD, Huynh, N, Tran, TS, Center, JR & Nguyen, TV 2024, 'Association between Fat Mass and Obesity-Related Transcript Polymorphisms and Osteoporosis Phenotypes', Journal of Bone Metabolism, vol. 31, no. 1, pp. 48-55.
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Background: Common variants in the fat mass and obesity-related transcript (<i>FTO</i>) gene are related to body mass index and obesity, suggesting its potential association with bone mineral density (BMD) and fracture risk. This study sought to define the association between <i>FTO</i> gene variants and the following phenotypes: (1) BMD; (2) bone loss; and (3) fracture risk.Methods: This analysis was based on the Dubbo Osteoporosis Epidemiology Study that included 1,277 postmenopausal women aged ≥60 years living in Dubbo, Australia. BMD at the femoral neck and lumbar spine was measured biennially by dual energy X-ray absorptiometry (GE Lunar). Fractures were radiologically ascertained. Six single nucleotide polymorphisms (SNPs; rs1421085, rs1558902, rs1121980, rs17817449, rs9939609, and rs9930506) of the <i>FTO</i> gene were genotyped using TaqMan assay.Results: Women homozygous for the minor allele (GG) of rs9930506 had a significantly higher risk of hip fracture (adjusted hazard ratio, 1.93; 95% confidence interval, 1.15–3.23) than those homozygous for the major allele (AA) after adjusting for potential confounding effects. Similar associations were also observed for the minor allele of rs1121980. However, there was no significant association between the <i>FTO</i> SNPs and BMD or the rate of bone loss.Conclusions: Common variations in the <i>FTO</i> gene are associated with a hip fracture risk in women, and the association is not mediated through BMD or bone loss.
Doan, S & Fatahi, B 2024, 'Simplified analytical solution for time dependent deformation of soft soil improved with pervious column considering load transfer between column and soil', Computers and Geotechnics, vol. 166, pp. 105988-105988.
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Dogan, A, Barua, PD, Baygin, M, Tuncer, T, Dogan, S, Yaman, O, Dogru, AH & Acharya, RU 2024, 'Automated accurate emotion classification using Clefia pattern-based features with EEG signals', International Journal of Healthcare Management, vol. 17, no. 1, pp. 32-45.
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Dogan, S, Barua, PD, Baygin, M, Tuncer, T, Tan, R-S, Ciaccio, EJ, Fujita, H, Devi, A & Acharya, UR 2024, 'Lattice 123 pattern for automated Alzheimer’s detection using EEG signal', Cognitive Neurodynamics, vol. 18, no. 5, pp. 2503-2519.
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AbstractThis paper presents an innovative feature engineering framework based on lattice structures for the automated identification of Alzheimer's disease (AD) using electroencephalogram (EEG) signals. Inspired by the Shannon information entropy theorem, we apply a probabilistic function to create the novel Lattice123 pattern, generating two directed graphs with minimum and maximum distance-based kernels. Using these graphs and three kernel functions (signum, upper ternary, and lower ternary), we generate six feature vectors for each input signal block to extract textural features. Multilevel discrete wavelet transform (MDWT) was used to generate low-level wavelet subbands. Our proposed model mirrors deep learning approaches, facilitating feature extraction in frequency and spatial domains at various levels. We used iterative neighborhood component analysis to select the most discriminative features from the extracted vectors. An iterative hard majority voting and a greedy algorithm were used to generate voted vectors to select the optimal channel-wise and overall results. Our proposed model yielded a classification accuracy of more than 98% and a geometric mean of more than 96%. Our proposed Lattice123 pattern, dynamic graph generation, and MDWT-based multilevel feature extraction can detect AD accurately as the proposed pattern can extract subtle changes from the EEG signal accurately. Our prototype is ready to be validated using a large and diverse database.
Dogan, S, Barua, PD, Tuncer, T & Acharya, UR 2024, 'An accurate hypertension detection model based on a new odd-even pattern using ballistocardiograph signals', Engineering Applications of Artificial Intelligence, vol. 133, pp. 108306-108306.
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Dogan, S, Tuncer, T, Barua, PD & Acharya, UR 2024, 'Automated EEG-based language detection using directed quantum pattern technique', Applied Soft Computing, vol. 167, pp. 112301-112301.
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Dolmark, T, Sohaib, O, Beydoun, G & Taghikhah, F 2024, 'Agent-based modelling of individual absorptive capacity for effective knowledge transfer', Journal of Ambient Intelligence and Humanized Computing, vol. 15, no. 9, pp. 3479-3492.
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AbstractThe importance of knowledge for organizational success is widely recognized, leading managers to leverage knowledge actively. Within knowledge transfer, the Absorptive Capacity (ACAP) of Knowledge Recipients (KR) emerges as an unresolved barrier. ACAP is the dynamic capability to absorb knowledge and surpass the aggregation of individual ACAP within an organization. However, more research is needed on individual-level ACAP and its implications for bridging the gap between individual and organizational knowledge transfer. To address this gap, this study employs Agent-Based Modeling (ABM) as a simulation method to replicate individual ACAP within an organization, facilitating the examination of knowledge transfer dynamics. ABM allows for the detailed analysis of interactions between individual KRs and the organizational environment, revealing how uninterrupted time and other factors influence knowledge absorption. The implications of the study are that ABM provides specific insights into how individual ACAP affects organizational learning and performance, emphasizing the importance of uninterrupted time for KR to achieve optimal knowledge exploitation and highlighting the need for organizational practices and policies that foster environments conducive to knowledge absorption.
Dong, C, Weng, J, Li, M, Liu, J-N, Liu, Z, Cheng, Y & Yu, S 2024, 'Privacy-Preserving and Byzantine-Robust Federated Learning', IEEE Transactions on Dependable and Secure Computing, vol. 21, no. 2, pp. 889-904.
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Dong, J, Cong, Y, Sun, G, Fang, Z & Ding, Z 2024, 'Where and How to Transfer: Knowledge Aggregation-Induced Transferability Perception for Unsupervised Domain Adaptation', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 3, pp. 1664-1681.
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Dong, L, Yang, Y, Liu, Z, Yang, T, Xue, C, Shao, R & Wu, C 2024, 'Effect of chloride ion migration behaviour on the microstructure and mechanical properties of ultra-high performance concrete: A review', Journal of Building Engineering, vol. 82, pp. 108233-108233.
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Dong, L, Yang, Y, Liu, Z, Zhang, Y & Wu, C 2024, 'Interface bonding characteristics of 3D printed ultra-high performance concrete after elevated temperatures', Journal of Building Engineering, vol. 93, pp. 109801-109801.
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Dong, T, Ai, J, Zong, Y, Zhang, Y, Li, L, Zhou, H, Peng, S, He, H, Zhang, Z & Wang, Q 2024, 'Novel multiplexed alkali enzyme lysis coupled with EDTA pretreatment for RNA virus extraction from wastewater sludge: Optimization, recovery, and detection', Journal of Environmental Management, vol. 352, pp. 120102-120102.
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Dong, W, Gao, S, Peng, S, Shi, L, Shah, SP & Li, W 2024, 'Graphene reinforced cement-based triboelectric nanogenerator for efficient energy harvesting in civil infrastructure', Nano Energy, vol. 131, pp. 110380-110380.
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Dong, X, Kedziora, DJ, Musial, K & Gabrys, B 2024, 'Automated Deep Learning: Neural Architecture Search Is Not the End', Foundations and Trends® in Machine Learning, vol. 17, no. 5, pp. 767-920.
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Dong, Y, Liang, CJ, Chen, Y & Hua, J 2024, 'A visual modeling method for spatiotemporal and multidimensional features in epidemiological analysis: Applied COVID-19 aggregated datasets', Computational Visual Media, vol. 10, no. 1, pp. 161-186.
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AbstractThe visual modeling method enables flexible interactions with rich graphical depictions of data and supports the exploration of the complexities of epidemiological analysis. However, most epidemiology visualizations do not support the combined analysis of objective factors that might influence the transmission situation, resulting in a lack of quantitative and qualitative evidence. To address this issue, we developed a portrait-based visual modeling method called +msRNAer. This method considers the spatiotemporal features of virus transmission patterns and multidimensional features of objective risk factors in communities, enabling portrait-based exploration and comparison in epidemiological analysis. We applied +msRNAer to aggregate COVID-19-related datasets in New South Wales, Australia, combining COVID-19 case number trends, geo-information, intervention events, and expert-supervised risk factors extracted from local government area-based censuses. We perfected the +msRNAer workflow with collaborative views and evaluated its feasibility, effectiveness, and usefulness through one user study and three subject-driven case studies. Positive feedback from experts indicates that +msRNAer provides a general understanding for analyzing comprehension that not only compares relationships between cases in time-varying and risk factors through portraits but also supports navigation in fundamental geographical, timeline, and other factor comparisons. By adopting interactions, experts discovered functional and practical implications for potential patterns of long-standing community factors regarding the vulnerability faced by the pandemic. Experts confirmed that +msRNAer is expected to deliver visual modeling benefits with spatiotemporal and multidimensional features in other epidemiological...
Dong, Z-L, Cheng, YP, Tong, C-X, Liu, H, Zhang, S & Sheng, D 2024, 'DEM modelling of particle crushing of single carbonate sand using the improved bonded particle model', Powder Technology, vol. 445, pp. 120121-120121.
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Dong, Z-L, Tong, C-X, Zhang, S, Teng, J-D & Sheng, D 2024, 'A Comparative Study on Shear Behavior of Uniform-, Gap-, and Fractal-Graded Carbonate Soils', Journal of Geotechnical and Geoenvironmental Engineering, vol. 150, no. 1.
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Dou, G, Guo, W, Kong, L, Sun, J, Guo, M & Wen, S 2024, 'Operant Conditioning Neuromorphic Circuit With Addictiveness and Time Memory for Automatic Learning', IEEE Transactions on Biomedical Circuits and Systems, vol. 18, no. 5, pp. 1166-1177.
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Dou, J, Xie, G, Tian, Z, Cui, L & Yu, S 2024, 'Modeling and Analyzing the Spatial–Temporal Propagation of Malware in Mobile Wearable IoT Networks', IEEE Internet of Things Journal, vol. 11, no. 2, pp. 2438-2452.
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Douville, C, Lahouel, K, Kuo, A, Grant, H, Avigdor, BE, Curtis, SD, Summers, M, Cohen, JD, Wang, Y, Mattox, A, Dudley, J, Dobbyn, L, Popoli, M, Ptak, J, Nehme, N, Silliman, N, Blair, C, Romans, K, Thoburn, C, Gizzi, J, Schoen, RE, Tie, J, Gibbs, P, Ho-Pham, LT, Tran, BNH, Tran, TS, Nguyen, TV, Goggins, M, Wolfgang, CL, Wang, T-L, Shih, I-M, Lennon, AM, Hruban, RH, Bettegowda, C, Kinzler, KW, Papadopoulos, N, Vogelstein, B & Tomasetti, C 2024, 'Machine learning to detect the SINEs of cancer', Science Translational Medicine, vol. 16, no. 731.
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We previously described an approach called RealSeqS to evaluate aneuploidy in plasma cell-free DNA through the amplification of ~350,000 repeated elements with a single primer. We hypothesized that an unbiased evaluation of the large amount of sequencing data obtained with RealSeqS might reveal other differences between plasma samples from patients with and without cancer. This hypothesis was tested through the development of a machine learning approach called Alu Profile Learning Using Sequencing (A-PLUS) and its application to 7615 samples from 5178 individuals, 2073 with solid cancer and the remainder without cancer. Samples from patients with cancer and controls were prespecified into four cohorts used for model training, analyte integration, and threshold determination, validation, and reproducibility. A-PLUS alone provided a sensitivity of 40.5% across 11 different cancer types in the validation cohort, at a specificity of 98.5%. Combining A-PLUS with aneuploidy and eight common protein biomarkers detected 51% of the cancers at 98.9% specificity. We found that part of the power of A-PLUS could be ascribed to a single feature—the global reduction of AluS subfamily elements in the circulating DNA of patients with solid cancer. We confirmed this reduction through the analysis of another independent dataset obtained with a different approach (whole-genome sequencing). The evaluation of Alu elements may therefore have the potential to enhance the performance of several methods designed for the earlier detection of cancer.
Du, A, Zhou, T, Pang, S, Wu, Q & Zhang, J 2024, 'PCL: Point Contrast and Labeling for Weakly Supervised Point Cloud Semantic Segmentation', IEEE Transactions on Multimedia, vol. 26, pp. 8902-8914.
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Du, G, Cui, C, Li, L, Li, N, Lei, G & Zhu, J 2024, 'Comprehensive Performance Analysis of High-Speed PM Motors With Layered Rotor Structures', IEEE Transactions on Industrial Electronics, pp. 1-11.
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Du, G, Yan, H, Li, N, Li, L, Chen, Y, Lei, G & Zhu, J 2024, 'Four Rotor Structures for High Speed Interior Permanent Magnet Motor Considering Mechanical, Electromagnetic and Thermal Performance', IEEE Transactions on Transportation Electrification, pp. 1-1.
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Du, H, Liu, C, Liu, H, Ding, X & Huo, H 2024, 'An efficient federated learning framework for graph learning in hyperbolic space', Knowledge-Based Systems, vol. 289, pp. 111438-111438.
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Du, J, Liu, B, Hu, Y & Castro-Lacouture, D 2024, 'Analyzing Prefabricated Components Supply Chain Cooperation Patterns with Multiparty Dynamics Using Evolutionary Game Theory', Journal of Construction Engineering and Management, vol. 150, no. 12.
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Du, L, Chen, Z, Hu, H, Liu, X & Guo, Y 2024, 'An improved uncooperative space target de-tumbling method using electromagnetic de-tumbling devices with AC excitation', Advances in Space Research.
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Duan, W, Xuan, J, Qiao, M & Lu, J 2024, 'Graph Convolutional Neural Networks With Diverse Negative Samples via Decomposed Determinant Point Processes', IEEE Transactions on Neural Networks and Learning Systems, vol. PP, no. 99, pp. 1-12.
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Graph convolutional neural networks (GCNs) have achieved great success in graph representation learning by extracting high-level features from nodes and their topology. Since GCNs generally follow a message-passing mechanism, each node aggregates information from its first-order neighbor to update its representation. As a result, the representations of nodes with edges between them should be positively correlated and thus can be considered positive samples. However, there are more non-neighbor nodes in the whole graph, which provide diverse and useful information for the representation update. Two non-adjacent nodes usually have different representations, which can be seen as negative samples. Besides the node representations, the structural information of the graph is also crucial for learning. In this article, we used quality-diversity decomposition in determinant point processes (DPPs) to obtain diverse negative samples. When defining a distribution on diverse subsets of all non-neighboring nodes, we incorporate both graph structure information and node representations. Since the DPP sampling process requires matrix eigenvalue decomposition, we propose a new shortest-path-base method to improve computational efficiency. Finally, we incorporate the obtained negative samples into the graph convolution operation. The ideas are evaluated empirically in experiments on node classification tasks. These experiments show that the newly proposed methods not only improve the overall performance of standard representation learning but also significantly alleviate over-smoothing problems.
Eggler, AM, Falque, R, Liu, M, Vidal‐Calleja, T, Sorkine‐Hornung, O & Pietroni, N 2024, 'Digital Garment Alteration', Computer Graphics Forum, vol. 43, no. 7.
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AbstractGarment alteration is a practical technique to adapt an existing garment to fit a target body shape. Typically executed by skilled tailors, this process involves a series of strategic fabric operations—removing or adding material—to achieve the desired fit on a target body. We propose an innovative approach to automate this process by computing a set of practically feasible modifications that adapt an existing garment to fit a different body shape. We first assess the garment's fit on a reference body; then, we replicate this fit on the target by deriving a set of pattern modifications via a linear program. We compute these alterations by employing an iterative process that alternates between global geometric optimization and physical simulation. Our method utilizes geometry‐based simulation of woven fabric's anisotropic behavior, accounts for tailoring details like seam matching, and incorporates elements such as darts or gussets. We validate our technique by producing digital and physical garments, demonstrating practical and achievable alterations.
Entezari, A, Wu, Q, Mirkhalaf, M, Lu, Z, Roohani, I, Li, Q, Dunstan, CR, Jiang, X & Zreiqat, H 2024, 'Unraveling the influence of channel size and shape in 3D printed ceramic scaffolds on osteogenesis', Acta Biomaterialia, vol. 180, pp. 115-127.
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Errey, N, Liang, CJ, Leong, TW, Chen, Y, Vally, H & Bennett, CM 2024, 'Nudging with Narrative Visualization: Communicating to a Young Adult Audience in the Pandemic', Proceedings of the ACM on Human-Computer Interaction, vol. 8, no. CSCW2, pp. 1-21.
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Effective narrative visualization communicates information by integrating story-telling and data visualization in a comprehensible, compelling manner. The compelling aspect of effective narrative visualization consequentially results in the potential to shift the attitude of an audience. However, there is much to understand about how narrative visualization can best be designed to influence target audiences. This paper focuses on an empirical experiment where we examined the effects of two communication strategies - anthropomorphism and personal identification - on a young adult audience. In particular, we wanted to understand which strategy, when integrated into narrative visualization, can nudge a specific audience's attitude towards greater consideration in the context of the COVID-19 pandemic. Our results indicated that the personal identification communication strategy was the most successful in nudging participants. This study contributes a better grasp of how technologies such as narrative visualization, using different communication strategies, can deliver more targeted messaging.
Errey, N, Liang, J, Leong, TW & Zowghi, D 2024, 'Evaluating narrative visualization: a survey of practitioners', International Journal of Data Science and Analytics, vol. 18, no. 1, pp. 19-34.
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Erten, M, Aydemir, E, Barua, PD, Baygin, M, Dogan, S, Tuncer, T, Tan, R-S, Hafeez-Baig, A & Rajendra Acharya, U 2024, 'Novel tiny textural motif pattern-based RNA virus protein sequence classification model', Expert Systems with Applications, vol. 242, pp. 122781-122781.
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Etaati, B, Neshat, M, Dehkordi, AA, Pargoo, NS, El-Abd, M, Sadollah, A & Gandomi, AH 2024, 'Shape and sizing optimisation of space truss structures using a new cooperative coevolutionary-based algorithm', Results in Engineering, vol. 21, pp. 101859-101859.
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Fallahpoor, M, Chakraborty, S, Pradhan, B, Faust, O, Barua, PD, Chegeni, H & Acharya, R 2024, 'Deep learning techniques in PET/CT imaging: A comprehensive review from sinogram to image space', Computer Methods and Programs in Biomedicine, vol. 243, pp. 107880-107880.
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Fan, L, Zhang, X, Zhao, Y, Sood, K & Yu, S 2024, 'Online Training Flow Scheduling for Geo-Distributed Machine Learning Jobs Over Heterogeneous and Dynamic Networks', IEEE Transactions on Cognitive Communications and Networking, vol. 10, no. 1, pp. 277-291.
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Fan, W, Li, H, Jiang, W, Hao, M, Yu, S & Zhang, X 2024, 'Stealthy Targeted Backdoor Attacks Against Image Captioning', IEEE Transactions on Information Forensics and Security, vol. 19, pp. 5655-5667.
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Fan, W, Xiao, F, Lv, M, Han, L & Yu, S 2024, 'Efficient Fault-Tolerant Path Embedding for 3D Torus Network Using Locally Faulty Blocks', IEEE Transactions on Computers, vol. 73, no. 9, pp. 2305-2319.
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Fan, Y, Xu, B, Zhang, L, Tan, G, Yu, S, Li, K-C & Zomaya, A 2024, 'psvCNN: A Zero-Knowledge CNN Prediction Integrity Verification Strategy', IEEE Transactions on Cloud Computing, vol. 12, no. 2, pp. 359-369.
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Fan, Y, Yi, B & Liu, D 2024, 'An overview of stiffening approaches for continuum robots', Robotics and Computer-Integrated Manufacturing, vol. 90, pp. 102811-102811.
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Fan, Z, Yan, Z, Cao, Y, Yang, Y & Wen, S 2024, 'Enhancing skeleton-based human motion recognition with Lie algebra and memristor-augmented LSTM and CNN', AIMS Mathematics, vol. 9, no. 7, pp. 17901-17916.
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<abstract><p>Lately, as a subset of human-centric studies, vision-oriented human action recognition has emerged as a pivotal research area, given its broad applicability in fields like healthcare, video surveillance, autonomous driving, sports, and education. This brief applies Lie algebra and standard bone length data to represent human skeleton data. A multi-layer long short-term memory (LSTM) recurrent neural network and convolutional neural network (CNN) are applied for human motion recognition. Finally, the trained network weights are converted into the crossbar-based memristor circuit, which can accelerate the network inference, reduce energy consumption, and obtain an excellent computing performance.</p></abstract>
Fang, W & Ying, M 2024, 'Symbolic Execution for Quantum Error Correction Programs', Proceedings of the ACM on Programming Languages, vol. 8, no. PLDI, pp. 1040-1065.
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We define QSE, a symbolic execution framework for quantum programs by integrating symbolic variables into quantum states and the outcomes of quantum measurements. The soundness of QSE is established through a theorem that ensures the correctness of symbolic execution within operational semantics. We further introduce symbolic stabilizer states, which symbolize the phases of stabilizer generators, for the efficient analysis of quantum error correction (QEC) programs. Within the QSE framework, we can use symbolic expressions to characterize the possible discrete Pauli errors in QEC, providing a significant improvement over existing methods that rely on sampling with simulators. We implement QSE with the support of symbolic stabilizer states in a prototype tool named QuantumSE.jl. Our experiments on representative QEC codes, including quantum repetition codes, Kitaev's toric codes, and quantum Tanner codes, demonstrate the efficiency of QuantumSE.jl for debugging QEC programs with over 1000 qubits. In addition, by substituting concrete values in symbolic expressions of measurement results, QuantumSE.jl is also equipped with a sampling feature for stabilizer circuits. Despite a longer initialization time than the state-of-the-art stabilizer simulator, Google's Stim, QuantumSE.jl offers a quicker sampling rate in the experiments.
Fang, W, Ying, M & Wu, X 2024, 'Differentiable Quantum Programming with Unbounded Loops.', ACM Trans. Softw. Eng. Methodol., vol. 33, pp. 19:1-19:1.
Fang, XS, Wang, X, Sheng, QZ & Yao, L 2024, 'Generalizing truth discovery by incorporating multi-truth features', Computing, vol. 106, no. 5, pp. 1557-1583.
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Fang, Z, Li, Y, Liu, F, Han, B & Lu, J 2024, 'On the Learnability of Out-of-distribution Detection', Journal of Machine Learning Research, vol. 25.
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Supervised learning aims to train a classifier under the assumption that training and test data are from the same distribution. To ease the above assumption, researchers have studied a more realistic setting: out-of-distribution (OOD) detection, where test data may come from classes that are unknown during training (i.e., OOD data). Due to the unavailability and diversity of OOD data, good generalization ability is crucial for effective OOD detection algorithms, and corresponding learning theory is still an open problem. To study the generalization of OOD detection, this paper investigates the probably approximately correct (PAC) learning theory of OOD detection that fits the commonly used evaluation metrics in the literature. First, we find a necessary condition for the learnability of OOD detection. Then, using this condition, we prove several impossibility theorems for the learnability of OOD detection under some scenarios. Although the impossibility theorems are frustrating, we find that some conditions of these impossibility theorems may not hold in some practical scenarios. Based on this observation, we next give several necessary and sufficient conditions to characterize the learnability of OOD detection in some practical scenarios. Lastly, we offer theoretical support for representative OOD detection works based on our OOD theory.
Fang, Z, Lu, J & Zhang, G 2024, 'An Extremely Simple Algorithm for Source Domain Reconstruction', IEEE Transactions on Cybernetics, vol. 54, no. 3, pp. 1921-1933.
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Fani, A, Golroo, A, Naseri, H, Mirhassani, SA & Gandomi, AH 2024, 'Risk-based pavement maintenance planning considering budget and pavement deterioration uncertainty', Structure and Infrastructure Engineering, vol. 20, no. 10, pp. 1437-1450.
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Farah, N, Lei, G, Zhu, J & Guo, Y 2024, 'Robust Model-Free Reinforcement Learning Based Current Control of PMSM Drives', IEEE Transactions on Transportation Electrification, pp. 1-1.
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Farhangi, M, Barzegarkhoo, R, Lee, SS, Aguilera, RP, Lu, D & Siwakoti, YP 2024, 'An Interleaved Single-Stage Switched-Boost Common-Ground Multilevel Inverter: Design, Control, and Experimental Validation', IEEE Transactions on Industry Applications, vol. 60, no. 3, pp. 4168-4182.
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Faro, B, Abedin, B, Cetindamar, D & Daneshgar, F 2024, 'Dynamic capabilities for nimbleness and resilience in a continuous digital transformation: action design research in an Australian financial services organisation', Journal of Enterprise Information Management, vol. 37, no. 4, pp. 1206-1226.
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PurposeThe research aims to understand the co-existence of nimbleness and resilience in a continuous digital transformation, along with the dynamic capabilities needed to balance the challenges of their co-existence.Design/methodology/approachThe current study draws on dialogical action design research (D-ADR) to investigate interactions among practitioners and executives. Data are collected from a major Australian financial services organisation (FSO) and many international experts.FindingsThe study presents a framework, the continuous transformation model (CTM), to describe digital transformation within an FSO context, emphasising nimbleness and resilience as its foundational pillars. This framework facilitates the identification of the critical role of organisational capabilities in managing continuous digital transformation, supported by dynamic IT capabilities. More importantly, the findings underscore how these capabilities enable managers to effectively balance the coexistence of nimbleness and resilience.Research limitations/implicationsThe CTM contributes to the enterprise information systems literature by offering a coherent understanding of balancing resilience and nimbleness to succeed in digital transformation. In particular, the research model elucidates the relationship between dynamic capabilities and continuous digital transformations.Practical implicationsDigital transformations are not a one-off exercise. Managers in the FSO context must cultivate their organisational capab...
Farooq, H & Nimbalkar, S 2024, 'Rheological modelling of train-track-ground: A review covering core concepts, materials and applications', Applications in Engineering Science, vol. 20, pp. 100194-100194.
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Farooq, H, Nimbalkar, S, Punetha, P & Sheng, D 2024, 'Viscoelastic Rheological Modelling of the Lateral Dynamic Response of Ballasted Railway Tracks', Transportation Infrastructure Geotechnology, vol. 11, no. 5, pp. 3667-3693.
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AbstractThis article presents a novel methodology for evaluating the response of ballasted railway tracks under train-induced loading along lateral directions. The main focus of this study is the development of a computational technique that can capture the lateral response of ballasted railway tracks, which has been ignored in past studies. The proposed approach employs a viscoelastic rheological track model in which three substructure layers are simulated using discrete masses, Hooke’s (springs) and Newtonian elements (dampers). The methodology is successfully validated against the data from experimental and analytical investigations published in the literature. Subsequently, parametric investigations are conducted to study the influence of axle load, train speed, and granular layer thickness on the track response. The results indicate that as the axle load (20 to 40 t) and train speed (70 to 200 km/h) are increased, there is a corresponding increase in track displacements (both lateral and vertical) by 100% and 26.2%, respectively. However, an increase in the granular layer thickness (0.1 to 0.75 m) reduces ballast top displacement (lateral and vertical) by 20–30%. The results demonstrate the capability of the proposed computational approach to capture the transient response of railway tracks and the influence of neighbouring layer properties on the track response. The proposed methodology can be helpful to practising railway engineers for assessing the performance of ballasted railway tracks along lateral directions.
Farooq, MA & Nimbalkar, S 2024, 'Laboratory and numerical analyses on polyurethane–scrap rubber-reinforced base layer', Canadian Geotechnical Journal, vol. 61, no. 10, pp. 2266-2285.
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Previous studies have explored using scrap rubber in constructing the ballasted track and showed tremendous potential to mitigate noise and vibration. However, its application for slab tracks has not been extensively investigated. This study intends to utilise scrap rubber in the base layer of the slab track; however, the high stress below base layer of the slab track may render its use unsuitable. The addition of scrap rubber would improve the damping performance but reduce the elastic modulus and cause excessive settlement of the track. This paper utilises an experimental programme comprising static and cyclic triaxial testing and numerical analyses to assess the suitability of four mixes, e.g., mix-A (soil), mix-B (soil mixed with rubber), mix-C (polyurethane-treated soil), and mix-D (polyurethane-treated soil mixed with rubber), as a base layer in slab tracks. The laboratory investigations reveal that the best performance in terms of improved damping ratio and resilient modulus, and lowered excess pore water pressure and vertical strains are shown by mix-D. These experimental test findings were supplemented with the results from three-dimensional full-scale finite element analyses, which showed a drastic reduction in the vibration levels of the track with mix-D as a base layer instead of conventional lean-mix concrete.
Farooq, MA & Nimbalkar, S 2024, 'Monotonic and cyclic triaxial testing of untreated and polyurethane-treated soil and soil–rubber mixtures', Acta Geotechnica, vol. 19, no. 2, pp. 605-630.
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AbstractThe present research focuses on developing alternate sustainable base materials for a high-speed slab track. In this study, a series of monotonic triaxial, cyclic triaxial and permeability tests were conducted on four types of materials, viz. mix-A (gravel soil), mix-B (soil mixed with rubber), mix-C (polyurethane foam adhesive (PFA)-treated soil), and mix-D (PFA-treated soil–rubber mixture). The influence of cyclic loading frequency, effective confining pressure, drainage condition and relative density on the deformation, excess pore water pressure, resilient modulus and damping ratio of these different mixes is evaluated. The monotonic triaxial test results indicate that the PFA treatment of mix-A and mix-B increased their shear strength and critical state strength. In contrast, incorporating rubber into mix-A and mix-C helped enhance their ductility. The cyclic triaxial test results show that the PFA treatment of mix-A and mix-B significantly reduced the magnitude of deformation and generation of excess pore water pressure, which caused these untreated mixes to fail prematurely under lower confinement to which a typical base layer is subjected. The influence of cyclic loading frequency and effective confining pressure on the material's response differed for untreated and treated soil. The permeability test results indicate good drainage for mix-D comparable to mix-A.
Farooq, MA & Nimbalkar, S 2024, 'Static and cyclic performance of polyurethane foam adhesive bound soil–rubber mixtures under drained conditions', Acta Geotechnica, vol. 19, no. 2, pp. 561-589.
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AbstractThe major drawbacks of a railway track include noise, vibration, and aggravated track degradation. Resilient mats and asphalt have been increasingly used in recent years to mitigate this noise and vibration. However, these materials are quite expensive. Conventional asphalt is very stiff and brittle, making it more prone to cracking. The present work aims to develop a novel material that can be used as a base layer in ballasted and slab tracks. The current research proposes a sustainable and resilient base course layer comprising ground rubber (GR) and polyurethane foam adhesive (PFA). In this study, the performance of GR embedded in the sand is investigated. The use of PFA-treated sand with and without GR is then explored. The optimum dosage of PFA for soil and GR for treated and untreated soil is recommended based on static direct simple shear (SDSS) and cyclic direct simple shear (CDSS) tests. SDSS tests were performed to evaluate the monotonic performance of all mixtures. CDSS tests were performed to assess the long-term performance of these different mixes under repeated cyclic loading (50,000 load cycles) and varying cyclic shear stress amplitude. It is shown that PFA helps reduce the settlement and enhance soil shear strength, while GR increases the damping ratio of the soil. The optimum dosage of PFA is recommended 10%. The optimum GR content for untreated and PFA-treated soil is recommended 5 and 10%, respectively.
Farooq, MU, Fritz, T, Haapasalo, E & Tomamichel, M 2024, 'Matrix Majorization in Large Samples', IEEE Transactions on Information Theory, vol. 70, no. 5, pp. 3118-3144.
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Farooq, MU, Sadiq, K, Anis, M, Hussain, G, Usman, M, Fouad, Y, Mujtaba, MA, Fayaz, H & Silitonga, AS 2024, 'Turning trash into treasure: Torrefaction of mixed waste for improved fuel properties. A case study of metropolitan city', Heliyon, vol. 10, no. 7, pp. e28980-e28980.
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Fatahi, B 2024, 'Uncertainty, modeling, and decision making in geotechnics', Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, vol. 18, no. 1, pp. 314-316.
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FathollahZadeh Aghdam, R, Al-Kharusi, S, Ahmad, N, Al-Said, A & Azar, BB 2024, 'Performance of power generation: A dynamic productivity and efficiency analysis', Heliyon, vol. 10, no. 13, pp. e33112-e33112.
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Fattah, IMR, Farhan, ZA, Kontoleon, KJ, kianfar, E & Hadrawi, SK 2024, 'Retraction Note: Hollow fiber membrane contactor based carbon dioxide absorption − stripping: a review', Macromolecular Research, vol. 32, no. 8, pp. 823-823.
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Fauzi, MA, Pradhan, B, Sapuan, NM & Kusumastuti, RD 2024, 'The application of knowledge management in disaster management: past, present and future trends', Journal of Knowledge Management, vol. 28, no. 4, pp. 1141-1163.
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PurposeThe purpose of this study is to review the role of knowledge management (KM) in disaster management and crisis. Disaster causes many detrimental impacts on human lives through loss of life and damage to properties. KM has been shown to dampen the impact of the disaster on the utilization of knowledge among agencies involved and the local communities impacted by disasters.Design/methodology/approachThrough a bibliometric methodology (co-citation, bibliographic coupling and co-word analysis), this study presents significant themes in the past, current and future predictions on the role of KM in disaster management. In this review paper, 437 publications were retrieved from the Web of Science and analyzed through VOSviewer software to visualize and explore the knowledge map on the subject domain.FindingsFindings suggest that the significant themes derived are centralized to disaster preparedness during disaster and disaster postrecovery. This review presents a state-of-art bibliometric analysis of the crucial role of KM in building networks and interconnection among relevant players and stakeholders involved in disaster management.Research limitations/implicationsThe main implication of this study is how the authorities, stakeholders and local community can integrate the KM system within the three stages of disasters and the crucial role of technologies and social media in facilitating disaster management.Originality/valueTo the best of the authors’ knowledge, this is the first study to p...
Fei, Z, Tang, S, Wang, X, Xia, F, Liu, F & Andrew Zhang, J 2024, 'Revealing the Trade-Off in ISAC Systems: The KL Divergence Perspective', IEEE Wireless Communications Letters, vol. 13, no. 10, pp. 2747-2751.
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Feng, A, Shi, Y, Onggowarsito, C, Zhang, XS, Mao, S, Johir, MAH, Fu, Q & Nghiem, LD 2024, 'Structure‐Property Relationships of Hydrogel‐based Atmospheric Water Harvesting Systems', ChemSusChem, vol. 17, no. 11.
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AbstractAtmospheric water harvesting (AWH) is considered one of the promising technologies to alleviate the uneven‐distribution of water resources and water scarcity in arid regions of the world. Hydrogel‐based AWH materials are currently attracting increasing attention due to their low cost, high energy efficiency and simple preparation. However, there is a knowledge gap in the screening of hydrogel‐based AWH materials in terms of structure‐property relationships, which may increase the cost of trial and error in research and fabrication. In this study, we synthesised a variety of hydrogel‐based AWH materials, characterized their physochemcial properties visualized the electrostatic potential of polymer chains, and ultimately established the structure‐property‐application relationships of polymeric AWH materials. Poly(2‐acrylamido‐2‐methyl‐1‐propanesulfonic acid) (PAMPS) hydrogel is able to achieve an excellent water adsorption capacity of 0.62 g g−1 and a high water desorption efficiency of more than 90 % in relatively low‐moderate humidity environments, which is regarded as one of the polymer materials with potential for future AWH applications.
Feng, S, Liu, F, Zhu, S, Xu, Z, Qin, L, Feng, P, Wang, Z, Chen, H, Guo, W & Hao Ngo, H 2024, 'Role of hydraulic retention time in integration of microalgae and activated sludge process for nutrient recycle from diluted dairy liquid digestate', Chemical Engineering Journal, vol. 484, pp. 149538-149538.
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Feng, S, Zhang, G, Yang, Y, Zhou, X & Hong, J 2024, '3-D Printed Multiband Filtering Crossovers Based on Mixed Spherical Cavity Resonators', IEEE Microwave and Wireless Technology Letters, vol. 34, no. 7, pp. 903-906.
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Feng, W, Rao, P, Cui, J, Ouyang, P, Chen, Q & Nimbalkar, S 2024, 'Multiphysics Multicoupled Modeling of Rock Fragmentation under High-Voltage Electrical Pulse', International Journal of Geomechanics, vol. 24, no. 9.
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Feng, Y, Alamdari, MM, Wu, D, Luo, Z, Ruan, D, Egbelakin, T, Chen, X & Gao, W 2024, 'Virtual modelling aided safety assessment for ductile structures against high-velocity impact', Engineering Structures, vol. 301, pp. 117373-117373.
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Ferrazzo, ST, Tonini de Araújo, M, Consoli, NC & da Rocha, CG 2024, 'Geotech social impacts: Development, application, and comparative analysis', Environmental Impact Assessment Review, vol. 108, pp. 107577-107577.
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Ferreira, FB & Nimbalkar, S 2024, 'Guest Editorial for the Special Issue on “Geosynthetic-Reinforced Sustainable Transport Infrastructures”', International Journal of Geosynthetics and Ground Engineering, vol. 10, no. 3.
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Fu, B, Boutros, F, Lin, C-T & Damer, N 2024, 'A Survey on Drowsiness Detection – Modern Applications and Methods', IEEE Transactions on Intelligent Vehicles, pp. 1-23.
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Fu, Z, Zuo, W, Li, Q, Zhou, K, Huang, Y & Li, Y 2024, 'Numerical investigations on liquid cooling plate partially filled with porous medium for thermal management of lithium-ion battery pack', Energy, vol. 313, pp. 133926-133926.
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Ganbat, N, Altaee, A, Hamdi, FM, Zhou, J, Chowdhury, MH, Zaidi, SJ, Samal, AK, Almalki, R & Tapas, MJ 2024, 'PFOA remediation from kaolinite soil by electrokinetic process coupled with activated carbon/iron coated activated carbon - permeable reactive barrier', Journal of Contaminant Hydrology, vol. 267, pp. 104425-104425.
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Gandhi, H, Tandon, K, Gite, S, Pradhan, B & Alamri, A 2024, 'Navigating the Complexity of Money Laundering: Anti–money Laundering Advancements with AI/ML Insights', International Journal on Smart Sensing and Intelligent Systems, vol. 17, no. 1.
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Abstract This study explores the fusion of artificial intelligence (AI) and machine learning (ML) methods within anti–money laundering (AML) frameworks using data from the US Treasury’s Financial Crimes Enforcement Network (FinCEN). ML and deep learning (DL) algorithms—such as random forest classifier, elastic net regressor, least absolute shrinkage and selection operator (LASSO) regression, gradient boosting regressor, linear regression, multilayer perceptron (MLP) classifier, convolutional neural network (CNN), random forest regressor, and K-nearest neighbor (KNN)—were used to forecast variables such as state, year, and transaction types (credit card and debit card). Hyperparameter tuning through grid search and randomized search was used to optimize model performance. The results demonstrated the efficacy of AI/ML algorithms in predicting temporal, spatial, and industry-specific money-laundering patterns. The random forest classifier achieved 99.99% average accuracy in state prediction, while the gradient boosting regressor and random forest classifier excelled in predicting year and state simultaneously, and credit card transactions, respectively. MLP and CNN showed promise in the context of debit card transactions. The gradient boosting regressor performed competitively with low mean squared error (MSE) (2.9) and the highest R-squared (R 2) value of 0.24, showcasing its pattern-capturing proficiency. Logistic regression and random forest classifier performed well in predicting credit card transactions, with area under the receiver operating characteristic curve (ROC_AUC) scores of 0.55 and 0.53, respectively. For debit card prediction, MLP achieved a precision of 0.55 and recall of 0.42, while CNN showed a precision of 0.6 and recall of 0.54, highlighting their effectiveness. The study recommends interpretabil...
Gao, C, Yang, Q, Kim, ME, Khairi, NM, Cai, LY, Newlin, NR, Kanakaraj, P, Remedios, LW, Krishnan, AR, Yu, X, Yao, T, Zhang, P, Schilling, KG, Moyer, D, Archer, DB, Resnick, SM, Landman, BA, Disease Neuroimaging Initiative, FTA & Study team, TBIOCARD 2024, 'Characterizing patterns of diffusion tensor imaging variance in aging brains', Journal of Medical Imaging, vol. 11, no. 04.
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Gao, F, Zhang, J, Zhang, S, Zheng, J & Sheng, D 2024, 'Experimental study on migration and deposition of particles under alternating dynamic and static loads', Yantu Gongcheng Xuebao/Chinese Journal of Geotechnical Engineering, vol. 46, no. 5, pp. 1057-1066.
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The study on the migration and deposition of suspended particles in subgrade is important to reveal the generation and disaster-causing mechanism of mud pumping. The effects of magnitude of dynamic loads, intermittent duration of static loads and repetition number of alternating dynamic and static loads on the migration and deposition of particles are studied by carrying out the mud pumping tests on the layered gravel-sandy silt column. According to the hydrodynamic response characteristics of specimens, the driving mechanism of particle migration is analyzed. The test results show that the suspended particles migrate upward under hydrodynamic force and settle under gravity during the interval of static loads, which results in the fluctuating growth of slurry turbidity in the gravel layer. The particle suspension increment tends to decrease with the increasing repetition of alternating dynamic and static loads, and these particles gradually clog gravel pores and inhibit mud pumping to a certain extent. Increasing the dynamic frequency and stress can further reduce the internal stability of the sandy silt surface layer. However, compared with increasing the dynamic stress, increasing the loading frequency is more beneficial to increase the particle migration mass and vertical migration distance.
Gao, L, Qin, Y, Zhou, X, Jin, W, He, Z, Li, X & Wang, Q 2024, 'Microalgae as future food: Rich nutrients, safety, production costs and environmental effects', Science of The Total Environment, vol. 927, pp. 172167-172167.
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Gao, S, Wang, X, Song, B, Liu, R, Yao, S, Zhou, W & Yu, S 2024, 'Exploiting Type I Adversarial Examples to Hide Data Information: A New Privacy-Preserving Approach', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 8, no. 3, pp. 2518-2528.
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Gao, X, Xu, Z, Shi, T, Qi, C, Nghiem, LD, Li, G & Luo, W 2024, 'Role of Lignocellulosic Biomass Composition to Regulate Microbial Mutualism for Organic Mineralization and Humification during Digestate Composting', ACS ES&T Engineering, vol. 4, no. 4, pp. 771-782.
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Gao, Y, Chen, L, Han, J, Yu, S & Fang, H 2024, 'Similarity-Based Secure Deduplication for IIoT Cloud Management System', IEEE Transactions on Dependable and Secure Computing, vol. 21, no. 4, pp. 2242-2256.
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Gao, Y, Yu, J, Hu, C, Wen, S & Kong, F 2024, 'Fixed/preassigned-time output synchronization for T–S fuzzy complex networks via quantized control', Nonlinear Analysis: Hybrid Systems, vol. 51, pp. 101434-101434.
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Garcia, C, Mora, A, Norambuena, M, Rodriguez, J, Aly, M, Carnielutti, F, Pereda, J, Acuna, P, Aguilera, R & Tarisciotti, L 2024, 'Model Predictive Control in Multilevel Inverters Part I: Basic Strategy and Performance Improvement', IEEE Open Journal of Industry Applications, vol. 5, pp. 428-441.
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García-Díaz, JA, Beydoun, G & Valencia-García, R 2024, 'Evaluating Transformers and Linguistic Features integration for Author Profiling tasks in Spanish', Data & Knowledge Engineering, vol. 151, pp. 102307-102307.
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Ge, H, Beydoun, G & Qu, H 2024, 'Preface', ACM International Conference Proceeding Series, p. viii.
Ge, H, Dai, G, Wang, F, Yu, Y & Liu, W 2024, 'Theoretical solution for bond-slip behavior of composite structures consisting of H-shape beam and concrete based on experiment, numerical simulation, and theoretical derivation', Engineering Structures, vol. 302, pp. 117456-117456.
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Ge, Y, Gao, Y, Li, X, Cai, B, Xi, J & Yu, S 2024, 'EMTD-SSC: An Enhanced Malicious Traffic Detection Model Using Transfer Learning Under Small Sample Conditions in IoT', IEEE Internet of Things Journal, vol. 11, no. 19, pp. 30725-30741.
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Geng, C, Lian, J-W, Guo, YJ & Ding, D 2024, 'Millimeter-Wave Three-Layer Substrate-Integrated 9 × 9 Butler Matrix and Its Application to Wide-Angle Endfire Multibeam Metasurface Antenna', IEEE Transactions on Microwave Theory and Techniques, vol. 72, no. 4, pp. 2253-2266.
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Geng, X-F, Ding, H, Ji, J-C, Wei, K-X, Jing, X-J & Chen, L-Q 2024, 'A state-of-the-art review on the dynamic design of nonlinear energy sinks', Engineering Structures, vol. 313, pp. 118228-118228.
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Ghalambaz, M, Ayoubi Ayoubloo, K, Mozaffari, M, Yusaf, T, Islam, MS, Shah, NA & Baro, M 2024, 'Mixed convection of nano-encapsulated phase change suspensions in a wavy wall lid-driven trapezoid cavity', Journal of Thermal Analysis and Calorimetry, vol. 149, no. 9, pp. 4195-4207.
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Ghannam, S & Hussain, F 2024, 'Investigating the influence of Australia Day and Christmas Day on water demand in the Greater Sydney region', Water Supply, vol. 24, no. 10, pp. 3540-3567.
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ABSTRACT The objective of this research is to investigate whether particular occasions, such as Australia Day and Christmas Day, have a notable impact on water demand in the Greater Sydney region. By examining water demand during these events, the study aims to enhance understanding of water consumption patterns and contribute to the development of effective demand management strategies. Multivariate time series data from several water plants in the Greater Sydney region were analyzed using three methods: correlation heatmaps, t-tests, and descriptive statistics. The findings indicate that neither Australia Day nor Christmas Day has a significant impact on water demand at different water plants in the Greater Sydney region. These results suggest that public holidays may not need to be a critical factor in short-term water demand forecasting models for this area.
Gharoun, H, Momenifar, F, Chen, F & Gandomi, AH 2024, 'Meta-learning Approaches for Few-Shot Learning: A Survey of Recent Advances', ACM Computing Surveys, vol. 56, no. 12, pp. 1-41.
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Despite its astounding success in learning deeper multi-dimensional data, the performance of deep learning declines on new unseen tasks mainly due to its focus on same-distribution prediction. Moreover, deep learning is notorious for poor generalization from few samples. Meta-learning is a promising approach that addresses these issues by adapting to new tasks with few-shot datasets. This survey first briefly introduces meta-learning and then investigates state-of-the-art meta-learning methods and recent advances in: (i) metric-based, (ii) memory-based, (iii), and learning-based methods. Finally, current challenges and insights for future researches are discussed.
Ghasemi Jouneghani, H, Nouri, Y, Mortazavi, M, Haghollahi, A & Memarzadeh, P 2024, 'Seismic Performance Factors of Elliptic-Braced Frames with Rotational Friction Dampers through IDA', Practice Periodical on Structural Design and Construction, vol. 29, no. 4.
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Ghasemi, Z, Neshat, M, Aldrich, C, Karageorgos, J, Zanin, M, Neumann, F & Chen, L 2024, 'An integrated intelligent framework for maximising SAG mill throughput: Incorporating expert knowledge, machine learning and evolutionary algorithms for parameter optimisation', Minerals Engineering, vol. 212, pp. 108733-108733.
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Ghimire, S, Deo, RC, Casillas-Pérez, D, Sharma, E, Salcedo-Sanz, S, Barua, PD & Rajendra Acharya, U 2024, 'Half-hourly electricity price prediction with a hybrid convolution neural network-random vector functional link deep learning approach', Applied Energy, vol. 374, pp. 123920-123920.
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Gholami, K, Azizivahed, A, Arefi, A, Rahman, MM, Islam, MR, Li, L, Arif, MT & Haque, ME 2024, 'Hybrid uncertainty approach for management of energy storage-embedded soft open points in distribution grids', Journal of Energy Storage, vol. 87, pp. 111394-111394.
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Ghosh, D, Karande, H, Gite, S & Pradhan, B 2024, 'Psychological disorder detection: A multimodal approach using a transformer-based hybrid model', MethodsX, vol. 13, pp. 102976-102976.
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Gomes, SDC, Nguyen, QD, Li, W & Castel, A 2024, 'Effects of mix composition on the mechanical, physical and durability properties of alkali-activated calcined clay/slag concrete cured under ambient condition', Construction and Building Materials, vol. 453, pp. 139064-139064.
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Gong, C, Lin, W, Ding, X, Liu, X, He, X, Nan, J, Li, G, Ma, J, Hao Ngo, H & Ding, A 2024, 'A three-stage process of Mn(VII)-Fe(III)/PDS system for enhancing sludge dewaterability: Effective driving of Fe(II)/Fe(III) cycle and adequate assurance of ROS', Separation and Purification Technology, vol. 330, pp. 125377-125377.
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Gong, S, Guo, Z, Ou, S, Wen, S & Huang, T 2024, 'Synchronization Control for T-S Fuzzy Neural Networks With Time Delay: A Novel Event-Triggered Mechanism', IEEE Transactions on Fuzzy Systems, vol. 32, no. 2, pp. 586-594.
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Gong, X, Wang, H, Wang, X, Chen, C, Zhang, W & Zhang, Y 2024, 'Influence maximization on hypergraphs via multi-hop influence estimation', Information Processing & Management, vol. 61, no. 3, pp. 103683-103683.
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Gong, Z, Ba, X, Zhang, C & Guo, Y 2024, 'Enhanced Maximum Torque per Ampere Control With Predictable Core Loss for the Interior Permanent Magnet Synchronous Motor', IEEE Transactions on Applied Superconductivity, vol. 34, no. 8, pp. 1-4.
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Gong, Z, Shen, L, Zhang, Y, Zhang, LY, Wang, J, Bai, G & Xiang, Y 2024, 'AgrAmplifier: Defending Federated Learning Against Poisoning Attacks Through Local Update Amplification', IEEE Transactions on Information Forensics and Security, vol. 19, pp. 1241-1250.
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Goodarzimehr, V, Talatahari, S, Shojaee, S & Gandomi, AH 2024, 'Computer-aided dynamic structural optimization using an advanced swarm algorithm', Engineering Structures, vol. 300, pp. 117174-117174.
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Goss, DM, Vasilescu, SA, Vasilescu, PA, Cooke, S, Kim, SHK, Sacks, GP, Gardner, DK & Warkiani, ME 2024, 'Evaluation of an artificial intelligence-facilitated sperm detection tool in azoospermic samples for use in ICSI', Reproductive BioMedicine Online, vol. 49, no. 1, pp. 103910-103910.
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Grigoletto, FB, Cedieu, S, Chaves, DB, Sing Lee, S & Siwakoti, YP 2024, 'A five‐level common‐ground inverter with step‐up/step‐down dual‐mode operation for transformerless grid‐connected PV application', International Journal of Circuit Theory and Applications, vol. 52, no. 3, pp. 1210-1230.
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AbstractStep‐up multilevel inverters with common‐ground feature are attractive for transformerless photovoltaic systems. However, their performance deteriorates at step‐down voltage range. Considering a five‐level inverter with double voltage gain, the number of output voltage levels decreases from 5 to 3 for a modulation index smaller than 0.5, declining the quality of the output currents. This paper proposes a new dual‐mode five‐level common‐grounded inverter with a reduced number of switches. The proposed topology has the ability to operate as step‐up or step‐down making it well suited for application with wide input voltage range. The proposed inverter consists of five unidirectional switches, one bidirectional switch, two diodes, and two capacitors. In addition, the description of the topology, the design guidelines, and a comparison among the main topologies are given in detail. Experimental results are obtained to attest the practicability of the proposed solution.
Grigorev, A, Mihăiţă, A-S, Saleh, K & Chen, F 2024, 'Automatic Accident Detection, Segmentation and Duration Prediction Using Machine Learning', IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 2, pp. 1547-1568.
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Grochow, JA & Qiao, Y 2024, 'On p -Group Isomorphism: Search-to-Decision, Counting-to-Decision, and Nilpotency Class Reductions via Tensors', ACM Transactions on Computation Theory, vol. 16, no. 1, pp. 1-39.
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In this article, we study some classical complexity-theoretic questions regarding Group Isomorphism ( GpI ). We focus on p -groups (groups of prime power order) with odd p , which are believed to be a bottleneckcase for GpI , and work in the model of matrix groups over finite fields. Our main results are as follows: • Although search-to-decision and counting-to-decision reductions have been known for more than four decades for Graph Isomorphism , they had remained open for GpI , explicitly asked by Arvind and Torán ( EATCS Bull. , 2005). Extending methods from Tensor Isomorphism (TI) (Grochow and Qiao, ITCS 2021), we show moderately exponential-time such reductions within p -groups of class 2 and exponent p . • Despite the widely held belief that p -groups of class 2 and exponent p are the hardest cases of GpI , there was no reduction to these groups from ...
Grossman, H, Dickson-Deane, C & Lunga, M 2024, 'Valuing Diverse Learning Ecologies and Climates: Through a strength-based lens', TechTrends, vol. 68, no. 3, pp. 559-560.
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Gu, P, Hu, H & Xu, G 2024, 'Modeling multi-behavior sequence via HyperGRU contrastive network for micro-video recommendation', Knowledge-Based Systems, vol. 295, pp. 111841-111841.
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Gu, Z, He, X, Yu, P, Jia, W, Yang, X, Peng, G, Hu, P, Chen, S, Chen, H & Lin, Y 2024, 'Automatic quantitative stroke severity assessment based on Chinese clinical named entity recognition with domain-adaptive pre-trained large language model', Artificial Intelligence in Medicine, vol. 150, pp. 102822-102822.
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Guan, L, Laporte, G, Merigó, JM, Nickel, S, Rahimi, I & Saldanha-da-Gama, F 2024, '50 years of computers & operations research: A bibliometric analysis', Computers & Operations Research, pp. 106910-106910.
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Guan, S, Yu, X, Huang, W, Fang, G & Lu, H 2024, 'DMMG: Dual Min-Max Games for Self-Supervised Skeleton-Based Action Recognition', IEEE Transactions on Image Processing, vol. 33, pp. 395-407.
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Guan, W, Ansari, AJ, Yin, R, Qi, C & Song, X 2024, 'Optimizing feedstock organic composition to regulate humification and heavy metal passivation during solid-state anaerobic digestion', Chemical Engineering Journal, vol. 499, pp. 156071-156071.
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Guan, Y, Zou, S, Peng, H, Ni, W, Sun, Y & Gao, H 2024, 'Cooperative UAV Trajectory Design for Disaster Area Emergency Communications: A Multiagent PPO Method', IEEE Internet of Things Journal, vol. 11, no. 5, pp. 8848-8859.
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Gudigar, A, Kadri, NA, Raghavendra, U, Samanth, J, Maithri, M, Inamdar, MA, Prabhu, MA, Hegde, A, Salvi, M, Yeong, CH, Barua, PD, Molinari, F & Acharya, UR 2024, 'Automatic identification of hypertension and assessment of its secondary effects using artificial intelligence: A systematic review (2013–2023)', Computers in Biology and Medicine, vol. 172, pp. 108207-108207.
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Guertler, M & Sick, N 2024, 'Action research: Combining research and problem solving for socio-technical engineering and innovation management research', CERN IdeaSquare Journal of Experimental Innovation, vol. 8, no. 1, pp. 5-8.
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Guertler, MR, Schneider, D, Heitfeld, J & Sick, N 2024, 'Analysing Industry 4.0 technology-solution dependencies: a support framework for successful Industry 4.0 adoption in the product generation process', Research in Engineering Design, vol. 35, no. 2, pp. 115-136.
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AbstractIndustry 4.0 (i4.0) is central to advanced manufacturing. Building on novel digital technologies, it enables smart and flexible manufacturing with systems connected across company boundaries and product lifecycle phases. Despite its benefits, the adoption of i4.0 has been limited, especially in small and medium-sized enterprises. A key challenge is the technological complexity of i4.0. While advanced functionality requires technological complexity, it complicates an understanding of which enabling technologies are particularly useful and required. This article presents a framework to support successful i4.0 adoption across the entire product generation process through a systematic matrix-based dependency analysis of i4.0 solutions and underlying i4.0 technologies. Through increasing transparency around technological complexity of i4.0 solutions, this research contributes to a better understanding of which technologies are required for specific i4.0 solutions and which technologies could be strategic enablers for a broad variety of i4.0 applications. Knowing these technological dependencies supports both, the systematic adoption of existing i4.0 solutions and the development of new i4.0 solutions. This also sets the basis for a future socio-technical investigation.
Guillén-Pujadas, M, Alaminos, D, Vizuete-Luciano, E, Callejón-Gil, ÁM & Merigó-Lindahl, JM 2024, 'Mapping the evolution of ethical standards in trading: A bibliometric analysis', International Review of Economics & Finance, vol. 96, pp. 103639-103639.
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Guleria, S, Chawla, P, Relhan, A, Kumar, A, Bhasin, A & Zhou, JL 2024, 'Antibacterial and photocatalytic potential of bioactive compounds extracted from freshwater microalgae species (Spirogyra and Ocillatoria): A comparative analysis', Science of The Total Environment, vol. 912, pp. 169224-169224.
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Gulied, M, Zavahir, S, Elmakki, T, Park, H, Gago, GH, Shon, HK & Han, DS 2024, 'Efficient lithium recovery from simulated brine using a hybrid system: Direct contact membrane distillation (DCMD) and electrically switched ion exchange (ESIX)', Desalination, vol. 572, pp. 117127-117127.
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Seawater reverse osmosis (SWRO) brine is a readily available resource hub in many countries, fulfilling the country's freshwater need by SWRO, yet lower in a concentration of high-demand elements like Li. This study outlines developing a novel hybrid system that combines direct contact membrane distillation (DCMD) and electrically switched ion exchange (ESIX) to facilitate simultaneous SWRO brine enrichment followed by selective Li recovery. The DCMD process concentrates the SWRO brine utilizing electrospun nanofiber membranes (ENMs) composed of polyvinylidene fluoride (PVDF). Incorporating reduced graphene oxide (rGO) nanoparticles augments the morphological, thermal, and mechanical stability of the PVDF ENMs. The water contact angle (WCA) of the 1-rGO/PVDF ENM stands at 142.08°, a testament to an enhanced hydrophobic property which resulted in a 12 % freshwater recovery from simulated SWRO brine and a 2.4-fold increase in Li+ concentration. The durability of the 1-rGO/PVDF ENM is evident in its minimal 11 % reduction in WCA after 15 h of brine concentration. In the context of hybrid operation, a Li-selective LiAlO2 electrode, coupled with an activated carbon counter electrode, demonstrated remarkable Li recovery for Li capture solutions enriched by the rGO-PVDF membrane in the DCMD phase. Compared to the Li concentration in the DCMD feed, sequential Li capture and release cycles recovered 91.8 % of Li, thereby underlining the critical role of the hybrid mode operation in concentrating Li from simulated brine solutions.
Gunatilake, A & Miro, JV 2024, 'Multimodel Neural Network for Live Classification of Water Pipe Leaks From Vibro-Acoustic Signals', IEEE Sensors Journal, vol. 24, no. 9, pp. 14825-14832.
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Gunawan, R, Tran, Y, Zheng, J, Nguyen, H, Carrigan, A, Mills, MK & Chai, R 2024, 'Combining Multistaged Filters and Modified Segmentation Network for Improving Lung Nodules Classification', IEEE Journal of Biomedical and Health Informatics, vol. 28, no. 9, pp. 5519-5527.
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Guo, B, Li, H, Dong, H, Han, T, Sun, Y, Hou, J, Jiang, Z & Ni, Q 2024, 'A novel cross-domain adaption network based on Se-Sk-DenseNet for remaining useful life prediction of rolling bearings under different working conditions', Measurement Science and Technology, vol. 35, no. 7, pp. 076114-076114.
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Abstract Effectively predicting the remaining useful life (RUL) of rolling bearings can ensure reliability and safety, minimize machine downtime, and reduce the operation and maintenance costs of enterprises. To solve the problems of data distribution discrepancy caused by different working conditions and the collected signals containing a lot of useless information and noise, a novel cross-domain adaption network (CDAN) is proposed in this study. Firstly, a novel feature extractor, squeeze-and-excitation (Se)-selective kernel (Sk)-DenseNet, is developed to extract useful critical features from the input data and remove the ineffective features by embedding Se and Sk attention blocks; besides, a new objective loss function consist of the RUL loss, the multi-kernel maximum mean discrepancy loss, the contrastive loss, and the Kullback–Leibler divergence loss, is proposed to solve the problem of data distribution shift; finally, the effectiveness and superiority of CDAN are proved on the PHM2012 bearings dataset. The results demonstrate that CDAN can extract deep critical features and achieve the high cross-domain RUL prediction accuracy under different working conditions.
Guo, B, Qiao, Z, Dong, H, Wang, Z, Huang, S, Xu, Z, Wu, F, Huang, C & Ni, Q 2024, 'Temporal convolutional approach with residual multi-head attention mechanism for remaining useful life of manufacturing tools', Engineering Applications of Artificial Intelligence, vol. 128, pp. 107538-107538.
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Guo, CA, Guo, YJ & Yuan, J 2024, 'Multibeam Receiving Antennas Employing Generalized Joined Coupler Matrix', IEEE Transactions on Antennas and Propagation, vol. 72, no. 1, pp. 424-432.
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Owing to the characteristics of directional couplers, series fed multibeam receiving antenna arrays require different treatment from their transmitting counterparts. In this paper, the theory and strategies for the feed network synthesis of multibeam receiving antennas employing the generalized joined coupler matrix (GJC) are presented. Given M incident waves in any set of directions, we first derive the output signal matrix as a function of the incident signal matrix. This serves as an important tool for both synthesizing and analysing GJC receiving matrices. Then, we present three different synthesis strategies and show how they are related to the array patterns. We also reveal how the receiving power efficiency of the GJC matrix changes with the antenna beam pattern. We further demonstrate that, despite the employment of matched loads, high receiving power efficiencies can be achieved using the GJC matrix.
Guo, F, Cui, Q, Zhang, H, Wang, Y, Zhang, H, Zhu, X & Chen, J 2024, 'A new deep learning-based approach for concrete crack identification and damage assessment', Advances in Structural Engineering, vol. 27, no. 13, pp. 2303-2318.
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Concrete building structures are prone to cracking as they are subjected to environmental temperatures, freeze-thaw cycles, and other operational environmental factors. Failure to detect cracks in the key building structure at the early stage can result in serious accidents and associated economic losses. A new method using the SE-U-Net model based on a conditional generative adversarial network (CGAN) has been developed to identify small cracks in concrete structures in this paper. This proposed method was a pixel-level U-Net model based on a generative network, that was integrated the original convolutional layer with an attention mechanism, and an SE module in the jump connection section was added to improve the identifiability of the model. The discriminative network compared the generated images with real images using the PatchGAN model. Through the adversarial training of generator and discriminator, the performance of generator in crack image segmentation task is improved, and the trained generation network is used to segment cracks. In damage assessments, the crack skeleton was represented by the individual pixel width and recognized using the binary morphological crack skeleton method, in which the final length, area, and average width of the crack could be determined through the geometric correction index. The results showed that compared with other methods, the proposed method could better identify subtle pixel-level cracks, and the identification accuracy is 98.48%. These methods are of great significance for the identification of cracks and the damage assessment of concrete structures in practice.
Guo, H, Miao, Z, Ji, JC & Pan, Q 2024, 'An effective collaboration evolutionary algorithm for multi-robot task allocation and scheduling in a smart farm', Knowledge-Based Systems, vol. 289, pp. 111474-111474.
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Guo, K & Guo, Y 2024, 'Design and Optimization of Linear Rotary Drilling Motor', IEEE Transactions on Industrial Electronics, vol. 71, no. 9, pp. 11195-11205.
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Guo, K, Cheng, A, Li, Y, Li, J, Duffield, R & Su, SW 2024, 'Cooperative Markov Decision Process model for human–machine co-adaptation in robot-assisted rehabilitation', Knowledge-Based Systems, vol. 291, pp. 111572-111572.
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Guo, K, Guo, Y, Fang, S, Li, C & Xue, W 2024, 'Design and Analysis of a Permanent Magnet Frameless Motor', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 12, no. 3, pp. 3124-3134.
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Guo, K, Li, C & Guo, Y 2024, 'Control System Analysis of a Linear Rotary Motor With Single Directional Magnetic Circuit Structure', IEEE Transactions on Applied Superconductivity, vol. 34, no. 8, pp. 1-5.
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Guo, K, Liu, C, Guo, Y & Li, C 2024, 'Design and Analysis of Two Doubly Salient Permanent Magnet Machines', IEEE Transactions on Applied Superconductivity, vol. 34, no. 8, pp. 1-5.
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Guo, M, Sun, Y, Zhu, Y, Han, M, Dou, G & Wen, S 2024, 'Pruning and quantization algorithm with applications in memristor-based convolutional neural network', Cognitive Neurodynamics, vol. 18, no. 1, pp. 233-245.
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The human brain’s ultra-low power consumption and highly parallel computational capabilities can be accomplished by memristor-based convolutional neural networks. However, with the rapid development of memristor-based convolutional neural networks in various fields, more complex applications and heavier computations lead to the need for a large number of memristors, which makes power consumption increase significantly and the network model larger. To mitigate this problem, this paper proposes an SBT-memristor-based convolutional neural network architecture and a hybrid optimization method combining pruning and quantization. Firstly, SBT-memristor-based convolutional neural network is constructed by using the good thresholding property of the SBT memristor. The memristive in-memory computing unit, activation unit and max-pooling unit are designed. Then, the hybrid optimization method combining pruning and quantization is used to improve the SBT-memristor-based convolutional neural network architecture. This hybrid method can simplify the memristor-based neural network and represent the weights at the memristive synapses better. Finally, the results show that the SBT-memristor-based convolutional neural network reduces a large number of memristors, decreases the power consumption and compresses the network model at the expense of a little precision loss. The SBT-memristor-based convolutional neural network obtains faster recognition speed and lower power consumption in MNIST recognition. It provides new insights for the complex application of convolutional neural networks.
Guo, Y, Canning, J, Chaczko, Z & Peng, G-D 2024, 'Compact, remote optical waveguide magnetic field sensing using double-pass Faraday rotation-induced optical attenuation', Applied Optics, vol. 63, no. 14, pp. D35-D35.
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Compact, magnetic field, B sensing is proposed and demonstrated by combining the two Faraday rotation elements and beam displacement crystals within a micro-optical fiber circulator with a fiber reflector and ferromagnets to allow high contrast attenuation in an optical fiber arm. Low optical noise sensing is measured at λ=1550nm as a change in attenuation, α, of optical light propagating through the optical noise sensing rotators and back. The circulator’s double-pass configuration, using a gold mirror as a reflector, achieves a magnetic field sensitivity s=Δα/ΔB=(0.26±0.02)dB/mT with a resolution of ΔB=0.01mT, over a detection range B=0−89mT. The circulator as a platform provides direct connectivity to the Internet, allowing remote sensing to occur. The method described here is amenable to multisensor combinations, including with other sensor technologies, particularly in future integrated waveguide Faraday optical circuits and devices, extending its utility beyond point magnetic field sensing applications.
Guo, Y, Yu, H, Ma, L, Luo, X & Xie, S 2024, 'DIE-CDK: A Discriminative Information Enhancement Method with Cross-modal Domain Knowledge for Fine-grained Ship Detection', IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1.
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Habaraduwa Peellage, W, Fatahi, B & Rasekh, H 2024, 'Stiffness and damping characteristics of jointed rocks under cyclic triaxial loading subjected to prolonged cyclic loading', International Journal of Fatigue, vol. 181, pp. 108121-108121.
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Habib, MA, Hossain, MJ, Alam, MM & Islam, MT 2024, 'A hybrid optimized data-driven intelligent model for predicting short-term demand of distribution network', Sustainable Energy Technologies and Assessments, vol. 67, pp. 103818-103818.
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Haddad, H, Fatahi, B, Khabbaz, H, Hsi, J & Li, I 2024, 'Effects of stress history on compressibility characteristics of undisturbed landfill waste material', Construction and Building Materials, vol. 422, pp. 135725-135725.
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Hajiabadi, ME, Samadi, M, Nikkhah, MH, Lotfi, H & Li, L 2024, 'Determining the optimal bid direction of a generation company using the gradient vector of the profit function in the network constraints of the electricity market', IET Generation, Transmission & Distribution, vol. 18, no. 21, pp. 3339-3349.
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AbstractOne of the primary challenges faced by generation companies (GenCos), which operate multiple generation units within the electricity market, is the determination of the optimal bid price for these units to maximize profit. This paper proposes a novel approach to ascertain the optimal bid price direction for GenCos by leveraging the gradient vector of the profit function within the constraints of the electricity market. First, the Jacobian matrix of unit profits is computed using the electricity market structural decomposition method. This matrix highlights how the profit of generation units is affected by market input parameters, including the bid prices of the units. Then, the gradient vector of the GenCos' profit function and the optimal bid price direction are derived from the Jacobian matrix. The methodology is applied to a 24‐bus IEEE network, with results validated against those from a simulation method to confirm the efficacy of the proposed approach. The simulation results show that the highest and lowest profit changes with a step increase of 0.1$/MWh are observed for GenCo 4 and GenCo 6 with values of 60.28 and 2.20 $/h, respectively. The proposed approach can be effective in the changes of bid direction of the units of a GenCo to achieve the highest possible profit.
Halder, A, Shivakumara, P, Pal, U, Blumenstein, M & Ghosal, P 2024, 'A Locally Weighted Linear Regression-Based Approach for Arbitrary Moving Shaky and Nonshaky Video Classification', International Journal of Pattern Recognition and Artificial Intelligence, vol. 38, no. 01.
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Classification and identification of objects are complex and challenging in pattern recognition and artificial intelligence if a shaky and nonshaky camera captures the videos at different distances during the day and nighttime. This work presents a model for classifying a given video as a static, uniform, or arbitrarily moving videos so that the complexity of the problem can be reduced. To avoid the threat of different distances between the objects and the camera, the proposed work introduces new steps for estimating the depth of the objects in the video frames. We explore locally weighted linear regression for feature extraction from depth information based on the notion that the regression line fits almost all the points for uniformity and does not fit for arbitrary moving. The extracted features are fed to a random forest classifier to classify static, uniform, or arbitrary moving video. The results on a large dataset, which includes videos captured day and night, show that the proposed method successfully classifies static, uniform and arbitrary videos with 0.86, 1.00 and 0.67 F-measures, respectively. Overall, our method obtains 87% accuracy for classification of static, uniform and arbitrary video, which is superior to the state-of-the-art methods.
Hamdi, FM, Altaee, A, Alsaka, L, Ibrar, I, AL-Ejji, M, Zhou, J, Samal, AK & Hawari, AH 2024, 'Iron slag/activated carbon-electrokinetic system with anolyte recycling for single and mixture heavy metals remediation', Science of The Total Environment, vol. 930, pp. 172516-172516.
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Han, M, Wang, Y, Li, M, Chang, X, Yang, Y & Qiao, Y 2024, 'Progressive Frame-Proposal Mining for Weakly Supervised Video Object Detection', IEEE Transactions on Image Processing, vol. 33, pp. 1560-1573.
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Han, S, Ansari, AJ, Zhang, N, Wu, C, Chen, X, Peng, Y & Song, X 2024, 'Role of biochar addition to improve anaerobic membrane bioreactors to resist oil stress in synthetic food wastewater treatment', Environmental Technology & Innovation, vol. 35, pp. 103665-103665.
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Han, Z, Zhao, G, Hu, Y, Xu, C, Cheng, K & Yu, S 2024, 'Dynamic Bond Percolation-Based Reliable Topology Evolution Model for Dynamic Networks', IEEE Transactions on Network and Service Management, vol. 21, no. 4, pp. 4197-4212.
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Hanna, B, Xu, G, Wang, X & Hossain, J 2024, 'Integrating UN Sustainable Development Goals into family business practices: a perspective article', Journal of Family Business Management, vol. 14, no. 6, pp. 1203-1211.
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PurposeThis paper explores the potential for family businesses (FBs) to play a pivotal role in advancing the United Nations (UN) Sustainable Development Goals (SDGs). It seeks to elucidate how FBs' inherent strengths and values can be harnessed to integrate sustainable practices within their operational paradigms.Design/methodology/approachThe authors employed a literature review to synthesize all the information and identify how FBs' desire to pass on a healthy company to future generations encourages sustainable practices.FindingsFBs have the potential to contribute significantly to not only their own sustainability but also the broader well-being of society by aligning with the SDGs.Originality/valueThis paper provides practical insights for stakeholders, policymakers and business leaders seeking to foster a more inclusive and environmentally responsible economic landscape.
Hannigan, IP, Rosengren, SM, Bharathy, GK, Prasad, M, Welgampola, MS & Watson, SRD 2024, 'Subjective and objective responses to caloric stimulation help separate vestibular migraine from other vestibular disorders', Journal of Neurology, vol. 271, no. 2, pp. 887-898.
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Abstract Background Nystagmus generated during bithermal caloric test assesses the horizontal vestibulo-ocular-reflex. Any induced symptoms are considered unwanted side effects rather than diagnostic information. Aim We hypothesized that nystagmus slow-phase-velocity (SPV) and subjective symptoms during caloric testing would be higher in vestibular migraine (VM) patients compared with peripheral disorders such as Meniere’s disease (MD) and non-vestibular dizziness (NVD). Methods Consecutive patients (n = 1373, 60% female) referred for caloric testing were recruited. During caloric irrigations, patients scored their subjective sensations. We assessed objective-measures, subjective vertigo (SVS), subjective nausea (SNS), and test completion status. Results Nystagmus SPV for VM, MD (unaffected side), and NVD were 29 ± 12.8, 30 ± 15.4, and 28 ± 14.2 for warm irrigation and 24 ± 8.9, 22 ± 10.0, and 25 ± 12.8 for cold-irrigation. The mean SVS were 2.5 ± 1.1, 1.5 ± 1.33, and 1.5 ± 1.42 for warm irrigation and 2.2 ± 1.1, 1.1 ± 1.19, and 1.1 ± 1.16 for cold-irrigation. Age was significantly correlated with SVS and SNS, (p < 0.001) for both. The SVS and SNS were significantly higher in VM compared with non-VM groups (p < 0.001), and there was no difference in nystagmus SPV. VM patients SVS was significantly different to the SVS of migraineurs in the other diagnostic groups (p < 0.001). Testing was incomplete for 34.4% of VM an...
Hasan, MM, Rasul, MG, Jahirul, MI & Mofijur, M 2024, 'Fuelling the future: Unleashing energy and exergy efficiency from municipal green waste pyrolysis', Fuel, vol. 357, pp. 129815-129815.
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Hasanpour, S, Siwakoti, YP & Blaabjerg, F 2024, 'A new soft‐switching high gain DC/DC converter with bipolar outputs', IET Power Electronics, vol. 17, no. 1, pp. 144-156.
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AbstractThis paper introduces a new single‐input multi‐output step‐up DC/DC converter with soft‐switching performance and low input current for renewable energy applications. The proposed topology uses a three‐winding coupled‐inductor (TWCI) and voltage multiplier circuits to achieve high voltage gains. The bipolar output voltages of the proposed converter can be varied independently by tuning the turns ratios of the TWCI. Due to the semi‐trans‐inverse specification of the suggested topology, high voltage gains can be obtained under a lower number of turns ratio in the magnetic device. Furthermore, a regenerative passive clamp technique mitigates the voltage stress on the single power switch. Additionally, the power dissipations are further reduced by considering a resonant tank in the circuit. In the converter, the parasitic leakage inductances of the TWCI windings help to provide the soft‐switching conditions for the switch and also to eliminate the reverse‐recovery loss for all converter diodes. The operating mode of the presented converter has been introduced and the steady state, along with the main operating equations have also been derived. Finally, the theoretical analysis is verified by a sample prototype 235 W at the input voltage 25 V and outputs of 200 V and −200 V.
Hashmi, A, Papachristos, A, Sidhu, S & Hutvagner, G 2024, 'AGO2 protein: a key enzyme in the miRNA pathway as a novel biomarker in adrenocortical carcinoma', Endocrine-Related Cancer, vol. 31, no. 12.
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Adrenocortical carcinoma (ACC) is a rare and aggressive malignancy characterized by diagnostic challenges, high recurrence rates, and poor prognosis. This study explored the role of miRNA processing genes in ACC and their potential role as diagnostic and prognostic biomarkers. We analyzed the mRNA expression levels of miRNA machinery components (DROSHA, DGCR8, XPO5, RAN, DICER, TARBP2, and AGO2) utilizing mRNA-Seq data from The Cancer Genome Atlas (TCGA) and The Genotype-Tissue Expression (GTEx) projects. Additionally, protein levels were quantified in tissue samples from the Kolling Institute of Medical Research’s tumor bank. Our results demonstrated that among all miRNA processing components, AGO2 exhibited significant overexpression in ACC compared to the normal adrenal cortex and benign adrenal adenoma (P < 0.001). Kaplan–Meier survival analysis indicated that higher AGO2 expression correlated with significantly worse overall survival in ACC patients (HR: 7.07, P < 0.001). Among 32 cancer types in TCGA, the prognostic significance of AGO2 was most prominent in ACC. This study is the first to report AGO2's potential as a diagnostic and prognostic biomarker in ACC, emphasizing its significance in ACC pathogenesis and potential application as a non-invasive liquid biopsy biomarker.
Hashmi, A, Sidhu, S & Hutvagner, G 2024, 'AGO2 protein: identified as a potential multi-cancer detection biomarker by integrative transcriptome and proteome analyses', Pathology, vol. 56, pp. S7-S7.
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Hassan, M, Kennard, M, Yoshitake, S, Ishac, K, Takahashi, S, Kim, S, Matsui, T, Hirokawa, M & Suzuki, K 2024, 'Augmenting the Sense of Social Presence in Online Video Games Through the Sharing of Biosignals', IEEE Access, vol. 12, pp. 98977-98989.
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Hassani, S, Dackermann, U, Mousavi, M & Li, J 2024, 'A systematic review of data fusion techniques for optimized structural health monitoring', Information Fusion, vol. 103, pp. 102136-102136.
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Hassani, S, Dackermann, U, Mousavi, M & Li, J 2024, 'Enhanced damage detection for noisy input signals using improved reptile search algorithm and data analytics techniques', Computers & Structures, vol. 296, pp. 107293-107293.
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Hastings, C 2024, 'Homelessness: A Critical Introduction Homelessness: A Critical Introduction , by Cameron Parsell, 2023, Polity Press, ISBN 978-1-5095-5450-8(pb). pp. 245', International Journal of Housing Policy, vol. 24, no. 2, pp. 391-394.
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Hastings, C 2024, 'Why do some disadvantaged Australian families become homeless? Resources, disadvantage, housing and welfare', Housing Studies, vol. 39, no. 10, pp. 2479-2503.
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Hastings, C, Overgaard, C, Wilson, S, Ramia, G, Morris, A & Mitchell, E 2024, 'Crowded house: accommodation precarity and self-reported academic performance of international students', Compare: A Journal of Comparative and International Education, vol. 54, no. 7, pp. 1190-1209.
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This article draws on two surveys of international students in Sydney and Melbourne, undertaken in 2019 and during the 2020 COVID-19 lockdowns. Using the concept of bounded agency, we identify how the challenges of living in one of the world’s most expensive rental housing markets impact students’ perceptions of their academic attainment. We find housing insecurity, unaffordability and condition, amplified by financial stress, contribute significantly to student anxiety about their studies. These relationships differ by student background and education. We argue students’ agency to meet their educational ambitions in Australia is constrained by the cost of housing and the housing choices they consequently make to mitigate financial stress. Our findings suggest the importance of ‘town’ or non-institutional aspects of the international student experience on their satisfaction and academic outcomes. We call for further research to explore these relationships in other global contexts.
He, B, Armaghani, DJ, Lai, SH, He, X, Asteris, PG & Sheng, D 2024, 'A deep dive into tunnel blasting studies between 2000 and 2023—A systematic review', Tunnelling and Underground Space Technology, vol. 147, pp. 105727-105727.
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He, B, Jahed Armaghani, D, Hin Lai, S, Samui, P & Tonnizam Mohamad, E 2024, 'Applying data augmentation technique on blast-induced overbreak prediction: Resolving the problem of data shortage and data imbalance', Expert Systems with Applications, vol. 237, pp. 121616-121616.
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He, H, Zhang, Q, Wang, S, Yi, K, Niu, Z & Cao, L 2024, 'Learning Informative Representation for Fairness-Aware Multivariate Time-Series Forecasting: A Group-Based Perspective', IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 6, pp. 2504-2516.
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He, H, Zhang, Q, Yi, K, Shi, K, Niu, Z & Cao, L 2024, 'Distributional Drift Adaptation With Temporal Conditional Variational Autoencoder for Multivariate Time Series Forecasting', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15.
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He, J, Zhu, Y, Hua, B, Xu, Z, Zhang, Y, Chu, L, Shi, Q, Braun, R & Shi, J 2024, 'Non-Motorized Lane Target Behavior Classification Based on Millimeter Wave Radar With P-Mrca Convolutional Neural Network', IEEE Transactions on Biometrics, Behavior, and Identity Science, pp. 1-1.
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He, K, Vu, TX, Hoang, DT, Nguyen, DN, Chatzinotas, S & Ottersten, B 2024, 'Risk-Aware Antenna Selection for Multiuser Massive MIMO Under Incomplete CSI', IEEE Transactions on Wireless Communications, vol. 23, no. 9, pp. 11001-11014.
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He, L, Shi, K, Wang, D, Wang, X & Xu, G 2024, 'A topic‐controllable keywords‐to‐text generator with knowledge base network', CAAI Transactions on Intelligence Technology, vol. 9, no. 3, pp. 585-594.
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AbstractWith the introduction of more recent deep learning models such as encoder‐decoder, text generation frameworks have gained a lot of popularity. In Natural Language Generation (NLG), controlling the information and style of the output produced is a crucial and challenging task. The purpose of this paper is to develop informative and controllable text using social media language by incorporating topic knowledge into a keyword‐to‐text framework. A novel Topic‐Controllable Key‐to‐Text (TC‐K2T) generator that focuses on the issues of ignoring unordered keywords and utilising subject‐controlled information from previous research is presented. TC‐K2T is built on the framework of conditional language encoders. In order to guide the model to produce an informative and controllable language, the generator first inputs unordered keywords and uses subjects to simulate prior human knowledge. Using an additional probability term, the model increases the likelihood of topic words appearing in the generated text to bias the overall distribution. The proposed TC‐K2T can produce more informative and controllable senescence, outperforming state‐of‐the‐art models, according to empirical research on automatic evaluation metrics and human annotations.
He, R, Han, Z, Nie, X, Yin, Y & Chang, X 2024, 'Visual Out-of-Distribution Detection in Open-Set Noisy Environments', International Journal of Computer Vision, vol. 132, no. 11, pp. 5453-5470.
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He, W, Zhao, J, Yang, L & Guo, Y 2024, 'Magnetic Field Coupling Analysis in Integrated Magnetic Suspension Spherical Induction Motors', IEEE Transactions on Applied Superconductivity, vol. 34, no. 8, pp. 1-4.
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He, W-N, Huang, X-L, Xu, Z, Hu, F & Yu, S 2024, 'Robust Localization for Mobile Targets Along a Narrow Path With LoS/NLoS Interference', IEEE Internet of Things Journal, vol. 11, no. 11, pp. 20853-20866.
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He, Y, Ding, C, Chang, C, Wei, G & Guo, YJ 2024, 'A Bowl-Shaped Filtering Antenna With Wideband Cross-Band Scattering Mitigation for Dual-Band Base Stations', IEEE Transactions on Antennas and Propagation, vol. 72, no. 8, pp. 6723-6728.
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He, Y, Wei, G, Ziolkowski, RW & Jay Guo, Y 2024, 'An Ultrawideband Frequency-Reconfigurable Tightly Coupled Dipole Array With Wide Beam-Scanning Capability', IEEE Transactions on Antennas and Propagation, vol. 72, no. 11, pp. 8488-8500.
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He, Z, Zheng, G, Luo, Q, Li, Q & Sun, G 2024, 'Fatigue damage tolerance of CFRP/Al adhesive joints with thermal effects', International Journal of Mechanical Sciences, vol. 281, pp. 109543-109543.
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He, Z, Zheng, G, Luo, Q, Li, Q & Sun, G 2024, 'Fatigue life improvement mechanisms of CFRP/Al hybrid joints – Load sharing study using a digital image correlation technique', Composite Structures, vol. 327, pp. 117625-117625.
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He, Z, Zhou, X, Mei, N, Jin, W, Sun, J, Yin, S & Wang, Q 2024, 'Effectiveness of cyclic treatment of municipal wastewater by Tetradesmus obliquus – Loofah biofilm, its internal community changes and potential for resource utilization', Water Research X, vol. 24, pp. 100254-100254.
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He, Z, Zhou, X, Qu, L, Jin, W, Li, X, Liu, H & Wang, Q 2024, 'Integrating electrochemical pretreatment with microalgae treatment for nitrogen and phosphorus removal and resource recovery from swine wastewater', Bioresource Technology, vol. 414, pp. 131559-131559.
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Hellmann, A, Scagnelli, SD, Ang, L & Sood, S 2024, 'Exploring impression management through eye-tracking: A study on the influence of photographs in financial reporting', Journal of Behavioral and Experimental Finance, vol. 44, pp. 100987-100987.
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Hemsley, B, Dann, S, Reddacliff, C, Smith, R, Given, F, Gay, V, Leong, TW, Josserand, E, Skellern, K, Bull, C, Palmer, S & Balandin, S 2024, 'Views on the usability, design, and future possibilities of a 3D food printer for people with dysphagia: outcomes of an immersive experience', Disability and Rehabilitation: Assistive Technology, vol. 19, no. 3, pp. 527-536.
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PURPOSE: Although 3D food printing is expected to enable the creation of visually appealing pureed food for people with disability and dysphagia, little is known about the user experience in engaging with 3D food printing or the feasibility of use with populations who need texture-modified foods. The aim of this study was to explore the feasibility and usability of using domestic-scale 3D food printer as an assistive technology to print pureed food into attractive food shapes for people with dysphagia. MATERIALS AND METHODS: In total, 16 participants engaged in the unfamiliar, novel process of using a domestic-scale 3D food printer (choosing, printing, tasting), designed for printing pureed food, and discussed their impressions in focus group or individual interviews. RESULTS AND CONCLUSIONS: Overall, results demonstrated that informed experts who were novice users perceived the 3D food printing process to be fun but time consuming, and that 3D food printers might not yet be suitable for people with dysphagia or their supporters. Slow response time, lack of user feedback, scant detail on the appropriate recipes for the pureed food to create a successful print, and small font on the user panel interface were perceived as barriers to accessibility for people with disability and older people. Participants expected more interactive elements and feedback from the device, particularly in relation to resolving printer or user errors. This study will inform future usability trials and food safety research into 3D printed foods for people with disability and dysphagia. IMPLICATIONS FOR REHABILITATION3D food printers potentially have a role as an assistive technology in the preparation of texture-modified foods for people with disability and dysphagia.To increase feasibility, 3D food printers should be co-designed with people with disability and their supporters and health professionals working in the field of dysphagia and rehabilitation.Experts struggled to be ...
Henderson, H, Grace, K, Gulbransen-Diaz, N, Klaassens, B, Leong, TW & Tomitsch, M 2024, 'From Parking Meters to Vending Machines: A Study of Usability Issues in Self-Service Technologies', International Journal of Human–Computer Interaction, vol. 40, no. 16, pp. 4365-4379.
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Hernandez Moreno, V, Jansing, S, Polikarpov, M, Carmichael, MG & Deuse, J 2024, 'Obstacles and opportunities for learning from demonstration in practical industrial assembly: A systematic literature review', Robotics and Computer-Integrated Manufacturing, vol. 86, pp. 102658-102658.
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Herse, S, Vitale, J & Williams, M-A 2024, 'Simulation Evidence of Trust Calibration: Using POMDP with Signal Detection Theory to Adapt Agent Features for Optimised Task Outcome During Human-Agent Collaboration', International Journal of Social Robotics, vol. 16, no. 6, pp. 1381-1403.
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AbstractAppropriately calibrated human trust is essential for successful Human-Agent collaboration. Probabilistic frameworks using a partially observable Markov decision process (POMDP) have been previously employed to model the trust dynamics of human behavior, optimising the outcomes of a task completed with a collaborative recommender system. A POMDP model utilising signal detection theory to account for latent user trust is presented, with the model working to calibrate user trust via the implementation of three distinct agent features: disclaimer message, request for additional information, and no additional feature. A simulation experiment is run to investigate the efficacy of the proposed POMDP model compared against a random feature model and a control model. Evidence demonstrates that the proposed POMDP model can appropriately adapt agent features in-task based on human trust belief estimates in order to achieve trust calibration. Specifically, task accuracy is highest with the POMDP model, followed by the control and then the random model. This emphasises the importance of trust calibration, as agents that lack considered design to implement features in an appropriate way can be more detrimental to task outcome compared to an agent with no additional features.
Hertrampf, T & Oberst, S 2024, 'Recurrence Rate spectrograms for the classification of nonlinear and noisy signals', Physica Scripta, vol. 99, no. 3, pp. 035223-035223.
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Abstract Time series analysis of real-world measurements is fundamental in natural sciences and engineering, and machine learning has been recently of great assistance especially for classification of signals and their understanding. Yet, the underlying system’s nonlinear response behaviour is often neglected. Recurrence Plot (RP) based Fourier-spectra constructed through τ-Recurrence Rate (RR τ ) have shown the potential to reveal nonlinear traits otherwise hidden from conventional data processing. We report a so far disregarded eligibility for signal classification of nonlinear time series by training RESnet-50 on spectrogram images, which allows recurrence-spectra to outcompete conventional Fourier analysis. To exemplify its functioning, we employ a simple nonlinear physical flow of a continuous stirred tank reactor, able to exhibit exothermic, first order, irreversible, cubic autocatalytic chemical reactions, and a plethora of fast-slow dynamics. For dynamics with noise being ten times stronger than the signal, the classification accuracy was up to ≈ 75% compared to ≈ 17% for the periodogram. We show that an increase in entropy only detected by the RR τ allows differentiation. This shows that RP power spectra, combined with off-the-shelf machine learning techniques, have the potential to significantly improve the detection of nonlinear and noise contaminated signals.
Heusdens, R & Zhang, G 2024, 'Distributed Optimisation With Linear Equality and Inequality Constraints Using PDMM', IEEE Transactions on Signal and Information Processing over Networks, vol. 10, pp. 294-306.
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Hieu, NQ, Hoang, DT, Nguyen, DN, Nguyen, V-D, Xiao, Y & Dutkiewicz, E 2024, 'Enhancing Immersion and Presence in the Metaverse with Over-the-Air Brain-Computer Interface', IEEE Transactions on Wireless Communications, pp. 1-1.
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Hieu, NQ, Thai Hoang, D, Nguyen, DN & Abu Alsheikh, M 2024, 'Reconstructing Human Pose From Inertial Measurements: A Generative Model-Based Compressive Sensing Approach', IEEE Journal on Selected Areas in Communications, vol. 42, no. 10, pp. 2674-2687.
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Higashide, N, Zhang, Y, Asatani, K, Miura, T & Sakata, I 2024, 'Quantifying advances from basic research to applied research in material science', Technovation, vol. 135, pp. 103050-103050.
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Hirayama, R, Tashima, H, Hamato, A, Howell, N, Sierro, F, Kielly, M, Caracciolo, A, Franklin, D, Guatelli, S, Yamaya, T, Rosenfeld, A, Fiorini, C, Carminati, M & Safavi-Naeini, M 2024, 'ADVANCEMENTS IN NCEPT: ANIMAL STUDY OUTCOMES AND TECHNOLOGICAL DEVELOPMENTS TOWARD CLINICAL APPLICATION', International Journal of Particle Therapy, vol. 12, pp. 100603-100603.
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Hirche, C & Tomamichel, M 2024, 'Quantum Rényi and f-Divergences from Integral Representations', Communications in Mathematical Physics, vol. 405, no. 9.
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Hoang, DK, Doan, MC, Le, NM, Nguyen, HG, Ho‐Pham, LT & Nguyen, TV 2024, 'Prevalence of and risk factors for sarcopenia in community‐dwelling people: The Vietnam Osteoporosis Study', Journal of Cachexia, Sarcopenia and Muscle, vol. 15, no. 1, pp. 380-386.
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AbstractBackgroundSarcopenia is a geriatric disease characterized by the progressive and generalized loss of skeletal lean mass and strength with age. The prevalence of sarcopenia in the Vietnamese population is unknown. This study sought to estimate the prevalence of and risk factors for sarcopenia among community‐dwelling individuals in Vietnam.MethodsThis cross‐sectional study is part of the ongoing Vietnam Osteoporosis Study project. The study involved 1308 women and 591 men aged 50 years and older as at 2015 (study entry). Whole‐body dual‐energy X‐ray absorptiometry was used to measure the appendicular skeletal lean mass. Anthropometric and clinical data were collected using a structured questionnaire. Sarcopenia was defined according to the criteria proposed by the Asian Working Group for Sarcopenia in 2019. Logistic regression analysis was used to determine the association between potential risk factors and sarcopenia.ResultsThe prevalence of sarcopenia in women and men was 14% (n = 183) and 16% (n = 83), respectively. Age (odds ratio [OR] per 10 years = 1.37; 95% confidence interval [CI] 1.26–1.48) and being underweight (OR = 1.61; 95% CI 1.00–2.58) were independently associated with increased risk of sarcopenia. The combination of low physical activity, being underweight and advancing age accounted for ~27% of sarcopenic patients. However, most of the attributable fraction was due to ageing.ConclusionsSarcopenia is common in community‐dwelling Vietnamese adults, particularly those with advancing age, who are underweight and with low physical activity.
Hoang, TM, Vahid, A, Tuan, HD & Hanzo, L 2024, 'Physical Layer Authentication and Security Design in the Machine Learning Era', IEEE Communications Surveys & Tutorials, vol. 26, no. 3, pp. 1830-1860.
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Security at the physical layer (PHY) is a salient research topic in wireless systems, and machine learning (ML) is emerging as a powerful tool for providing new data-driven security solutions. Therefore, the application of ML techniques to the PHY security is of crucial importance in the landscape of more and more data-driven wireless services. In this context, we first summarize the family of bespoke ML algorithms that are eminently suitable for wireless security. Then, we review the recent progress in ML-aided PHY security, where the term “PHY security” is classified into two different types: i) PHY authentication and ii) secure PHY transmission. Moreover, we treat NNs as special types of ML and present how to deal with PHY security optimization problems using NNs. Finally, we identify some major challenges and opportunities in tackling PHY security challenges by applying carefully tailored ML tools.
Hossain, MAM, Hannan, MA, Ker, PJ, Tiong, SK, Salam, MA, Abdillah, M & Mahlia, TMI 2024, 'Silicon-based nanosphere anodes for lithium-ion batteries: Features, progress, effectiveness, challenges, and prospects', Journal of Energy Storage, vol. 99, pp. 113371-113371.
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Hossain, MAM, Tiong, SK, Hannan, MA, Ker, PJ, Fattah, IMR & Mahlia, TMI 2024, 'Recent advances in silicon nanomaterials for lithium-ion batteries: Synthesis approaches, emerging trends, challenges, and opportunities', Sustainable Materials and Technologies, vol. 40, pp. e00964-e00964.
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Hossain, MS, Bacaoco, M, Mai, TNA, Ponchon, G, Chen, C, Ding, L, Chen, Y, Ekimov, E, Xu, X, Solntsev, AS & Tran, TT 2024, 'Fiber-Based Ratiometric Optical Thermometry with Silicon Vacancy in Microdiamonds', ACS Applied Optical Materials, vol. 2, no. 1, pp. 97-107.
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Hosseini, S, Armaghani, DJ, He, X, Pradhan, B, Zhou, J & Sheng, D 2024, 'Fuzzy Cognitive Map for Evaluating Critical Factors Causing Rockbursts in Underground Construction: A Fundamental Study', Rock Mechanics and Rock Engineering, vol. 57, no. 11, pp. 9713-9738.
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AbstractThe rockburst phenomenon in excavation endeavours reveals a multitude of complexities and obstacles that significantly impact both the technical and financial dimensions of project execution. Investigating critical rockburst factors in underground excavations is of considerable importance for addressing pivotal safety issues and operational complexities within the field of underground excavation projects. This research proposes an innovative approach based on an expert-based fuzzy cognitive map (FCM) framework, aiming to identify and prioritize the key critical rockburst factors prevalent in underground excavations and tunnelling. A tailored cognitive map of the parameters of problem was constructed, integrating 56 critical and critical factors meticulously curated by a team of seasoned managers, engineers, deputy managers, trainee engineers and assistant managers. The structured cognitive map was meticulously developed, considering the relative weights of the identified critical factors and their intricate interrelationships—all informed by the invaluable insights and expertise of seasoned engineers in the field. Subsequently, the cognitive map underwent a systematic solution process, whereby the causal relationships and influences amongst the identified critical factors were analysed and factored in. The outcomes of the comprehensive analysis unveiled several critical factors: lack of rockburst risk assessments, high in situ stress, presence of rock seams and weak layers, rock quality variations, and geological heterogeneity as the most paramount concerns demanding immediate attention and strategic intervention. By adopting the proposed FCM approach and leveraging the collective expertise of industry professionals, this research offers a robust and systematic framework for comprehensively assessing and addressing the key challenges associated with rockburst events in underground excavations and tunnelling proje...
Hou, J, Zhao, Z, Wang, Z, Lu, W, Jin, G, Wen, D & Du, X 2024, 'AeonG: An Efficient Built-in Temporal Support in Graph Databases', Proceedings of the VLDB Endowment, vol. 17, no. 6, pp. 1515-1527.
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Real-world graphs are often dynamic and evolve over time. It is crucial for storing and querying a graph's evolution in graph databases. However, existing works either suffer from high storage overhead or lack efficient temporal query support, or both. In this paper, we propose AeonG, a new graph database with built-in temporal support. AeonG is based on a novel temporal graph model. To fit this model, we design a storage engine and a query engine. Our storage engine is hybrid, with one current storage to manage the most recent versions of graph objects, and another historical storage to manage the previous versions of graph objects. This separation makes the performance degradation of querying the most recent graph object versions as slight as possible. To reduce the historical storage overhead, we propose a novel anchor+delta strategy, in which we periodically create a complete version (namely anchor) of a graph object, and maintain every change (namely delta) between two adjacent anchors of the same object. To boost temporal query processing, we propose an anchor-based version retrieval technique in the query engine to skip unnecessary historical version traversals. Extensive experiments are conducted on both real and synthetic datasets. The results show that AeonG achieves up to 5.73× lower storage consumption and 2.57× lower temporal query latency against state-of-the-art approaches, while introducing only 9.74% performance degradation for supporting temporal features.
Hou, J, Zhu, Y, Liu, J, Lin, L, Zheng, M, Yang, L, Wei, W, Ni, B-J & Chen, X 2024, 'Competitive enrichment of comammox Nitrospira in floccular sludge', Water Research, vol. 251, pp. 121151-121151.
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Hou, S, Zheng, J, Pan, H, Feng, K, Liu, Q & Ni, Q 2024, 'Multivariate multi-scale cross-fuzzy entropy and SSA-SVM-based fault diagnosis method of gearbox', Measurement Science and Technology, vol. 35, no. 5, pp. 056102-056102.
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Abstract Fuzzy entropy (FuzzyEn) is widely recognized as a powerful tool for analyzing nonlinear dynamics and measuring the complexity of time series data. It has been utilized as an effective indicator to capture nonlinear fault features in gearbox vibration signals. However, FuzzyEn only measures complexity at a single scale, ignoring the valuable information contained in large-scale features of the time series. Furthermore, FuzzyEn does not account for coupling characteristics between related or synchronized time series. To address these limitations, a novel entropy-based approach called multivariate multi-scale cross-fuzzy entropy (MvMCFE) is proposed in this paper for measuring the complexity and mutual predictability of two multivariate time series. Relying on the advantages of MvMCFE in nonlinear feature extraction, a new fault diagnosis method for gearboxes is proposed based on MvMCFE and an optimized support vector machine (SVM) using the salp swarm algorithm (SSA-SVM). Ultimately, the proposed gearbox diagnostic method is employed to analyze the gearbox experimental data and a comparison with existing fault diagnosis approaches is conducted. The comparison results indicate that the proposed method can effectively extract nonlinear fault features of gearboxes and achieve the highest recognition rate compared to the other methods.
Howell, N, Middleton, RJ, Sierro, F, Fraser, BH, Wyatt, NA, Chacon, A, Bambery, KR, Livio, E, Dobie, C, Bevitt, JJ, Davies, J, Dosseto, A, Franklin, DR, Garbe, U, Guatelli, S, Hirayama, R, Matsufuji, N, Mohammadi, A, Mutimer, K, Rendina, LM, Rosenfeld, AB & Safavi-Naeini, M 2024, 'Neutron Capture Enhances Dose and Reduces Cancer Cell Viability in and out of Beam During Helium and Carbon Ion Therapy', International Journal of Radiation Oncology*Biology*Physics, vol. 120, no. 1, pp. 229-242.
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Hu, K, Li, L, Xie, Q, Liu, J, Tao, X & Xu, G 2024, 'Decoupled Progressive Distillation for Sequential Prediction with Interaction Dynamics', ACM Transactions on Information Systems, vol. 42, no. 3, pp. 1-35.
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Sequential prediction has great value for resource allocation due to its capability in analyzing intents for next prediction. A fundamental challenge arises from real-world interaction dynamics where similar sequences involving multiple intents may exhibit different next items. More importantly, the character of volume candidate items in sequential prediction may amplify such dynamics, making deep networks hard to capture comprehensive intents. This article presents a sequential prediction framework with Decoupled Progressive Distillation (DePoD), drawing on the progressive nature of human cognition. We redefine target and non-target item distillation according to their different effects in the decoupled formulation. This can be achieved through two aspects: (1) Regarding how to learn, our target item distillation with progressive difficulty increases the contribution of low-confidence samples in the later training phase while keeping high-confidence samples in the earlier phase. And, the non-target item distillation starts from a small subset of non-target items from which size increases according to the item frequency. (2) Regarding whom to learn from, a difference evaluator is utilized to progressively select an expert that provides informative knowledge among items from the cohort of peers. Extensive experiments on four public datasets show DePoD outperforms state-of-the-art methods in terms of accuracy-based metrics.
Hu, R, Wang, X, Chang, X, Zhang, Y, Hu, Y, Liu, X & Yu, S 2024, 'CStrCRL: Cross-View Contrastive Learning Through Gated GCN With Strong Augmentations for Skeleton Recognition', IEEE Transactions on Circuits and Systems for Video Technology, vol. 34, no. 8, pp. 6674-6685.
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Hu, R, Wang, X, Ding, X, Zhang, Y, Xin, X, Pang, W & Yu, S 2024, 'Unsupervised Domain Adaptation for Skeleton Recognition with Fourier Analysis', IEEE Internet of Things Journal, pp. 1-1.
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Hu, S, Yuan, X, Ni, W, Wang, X, Hossain, E & Vincent Poor, H 2024, 'OFDMA-F²L: Federated Learning With Flexible Aggregation Over an OFDMA Air Interface', IEEE Transactions on Wireless Communications, vol. 23, no. 7, pp. 6793-6807.
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Hu, X, Liu, T, Shu, T & Nguyen, D 2024, 'Spoofing Detection for LiDAR in Autonomous Vehicles: A Physical-Layer Approach', IEEE Internet of Things Journal, vol. 11, no. 11, pp. 20673-20689.
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Hu, X, Wang, L, Chen, G, Zhu, S & Wen, S 2024, 'Intermittent boundary control for robust fixed‐time stabilization of uncertain reaction‐diffusion systems', International Journal of Robust and Nonlinear Control, vol. 34, no. 4, pp. 2457-2471.
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AbstractAchieving the robust fixed‐time stabilization (FxTS) of uncertain reaction‐diffusion systems (RDSs) utilizing intermittent boundary control is a valuable and unresolved problem. This paper simultaneously considers the influence of the uncertainty of non‐diffusion and diffusion coefficients on RDSs and provides an effective solution for the treatment of the uncertainty of diffusion coefficient in the investigation of FxTS. Then, a fixed‐time control scheme is presented to stabilize uncertain RDSs within a fixed time, which incorporates the merits of intermittent and boundary control and reduces control costs in both the spatial and temporal domains. Besides, some sufficient conditions in the form of linear matrix inequalities are deduced to guarantee the robust FxTS of uncertain RDSs with Neumann and mixed boundary conditions, respectively, which contain and generalize some existing results. Finally, the validity of the intermittent boundary control scheme and the FxTS criteria developed in this paper are verified by numerical simulations.
Hu, Y, Deng, W, Zhang, JA & Guo, YJ 2024, 'Resource Optimization for Delay Estimation in Perceptive Mobile Networks', IEEE Wireless Communications Letters, vol. 13, no. 1, pp. 223-227.
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Hu, Y, Jin, P, Guo, Y, Lei, G & Zhu, J 2024, 'A New SVM Strategy to Suppress Total Harmonic Distortion and Current Stress in HFLMCs', IEEE Transactions on Industrial Electronics, vol. 71, no. 9, pp. 10512-10522.
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Hu, Y, Jung, C, Qin, Q, Han, J, Liu, Y & Li, M 2024, 'HDVC: Deep Video Compression With Hyperprior-Based Entropy Coding', IEEE Access, vol. 12, pp. 17541-17551.
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HU, Y, LI, H, ZHANG, JA, HUANG, X & CHENG, Z 2024, 'Optimal Design of Wideband mmWave LoS MIMO Systems Using Hybrid Arrays with Beam Squint', IEICE Transactions on Communications, vol. E107.B, no. 1, pp. 244-252.
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Hu, Y, Wu, K, Zhang, JA, Deng, W & Guo, YJ 2024, 'Performance Bounds and Optimization for CSI-Ratio-Based Bi-Static Doppler Sensing in ISAC Systems', IEEE Transactions on Wireless Communications, vol. 23, no. 11, pp. 17461-17477.
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Hu, Y, Zhang, JA, Wu, K, Deng, W & Guo, YJ 2024, 'Anchor Points Assisted Uplink Sensing in Perceptive Mobile Networks', IEEE Transactions on Communications, pp. 1-1.
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Huang, H, Chang, X & Yao, L 2024, 'Accelerating one-shot neural architecture search via constructing a sparse search space', Knowledge-Based Systems, vol. 305, pp. 112620-112620.
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Huang, J, Gao, J, Gao, J, Huang, Y, Wang, X, Wang, S, Qi, M & Tian, G 2024, 'Insight into the mechanism of solution organic fractions on soot oxidation activity enhancement', Journal of Hazardous Materials, vol. 479, pp. 135606-135606.
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Huang, J, Gong, Y, Shi, Y, Zhang, X, Zhang, J & Yin, Y 2024, 'Focusing on Subtle Differences: A Feature Disentanglement Model for Series Photo Selection', IEEE Transactions on Multimedia, vol. 26, pp. 8758-8770.
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Huang, J, Gong, Y, Zhang, L, Zhang, J, Nie, L & Yin, Y 2024, 'Modeling Multiple Aesthetic Views for Series Photo Selection', IEEE Transactions on Multimedia, vol. 26, pp. 1983-1995.
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Huang, J, Zheng, M, Li, Z, He, X & Wen, S 2024, 'ISRnet: Compressed Image Inpainting Based on Generative Adversarial Network', IEEE Transactions on Emerging Topics in Computational Intelligence, pp. 1-11.
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Huang, K-C, Tseng, C-Y & Lin, C-T 2024, 'EEG Information Transfer Changes in Different Daily Fatigue Levels During Drowsy Driving', IEEE Open Journal of Engineering in Medicine and Biology, vol. 5, pp. 180-190.
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Huang, L, Fan, G, Li, J & Hao, H 2024, 'Deep learning for automated multiclass surface damage detection in bridge inspections', Automation in Construction, vol. 166, pp. 105601-105601.
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Huang, S, Hauser, K & Shell, DA 2024, 'Selected papers from RSS2022', The International Journal of Robotics Research, vol. 43, no. 4, pp. 387-388.
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Huang, S, Tsang, IW, Xu, Z & Lv, J 2024, 'CGDD: Multiview Graph Clustering via Cross-Graph Diversity Detection', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 3, pp. 4206-4219.
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Huang, S, Zheng, J, Qin, P, Zhan, Q & Chen, X 2024, 'Improved planar near-field measurement based on data assimilation', Measurement, vol. 227, pp. 114265-114265.
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Huang, T, Ben, X, Gong, C, Xu, W, Wu, Q & Zhou, H 2024, 'GaitDAN: Cross-View Gait Recognition via Adversarial Domain Adaptation', IEEE Transactions on Circuits and Systems for Video Technology, vol. 34, no. 9, pp. 8026-8040.
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Huang, W, Liao, X, Chen, H, Hu, Y, Jia, W & Wang, Q 2024, 'Deep local-to-global feature learning for medical image super-resolution', Computerized Medical Imaging and Graphics, vol. 115, pp. 102374-102374.
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Huang, X, Francis, I, Saha, G, Rahman, MM & Saha, SC 2024, 'Large eddy simulation-based modeling of cold-air inhalation from nasal cavities to the distal lung: Insights for athlete health and performance', Results in Engineering, vol. 23, pp. 102475-102475.
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Huang, X, Le, AT, Zhang, H, Zhang, JA & Guo, YJ 2024, 'Orthogonal Expanded Memory Polynomial Model for Circular Complex Gaussian Processes', IEEE Transactions on Signal Processing, vol. 72, pp. 3287-3302.
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Huang, X, Saha, SC, Saha, G, Francis, I & Luo, Z 2024, 'Transport and deposition of microplastics and nanoplastics in the human respiratory tract', Environmental Advances, vol. 16, pp. 100525-100525.
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Huang, X, Zhang, H, Le, AT, Andrew Zhang, J & Jay Guo, Y 2024, 'Digital Post-Cancellation of Nonlinear Interference for Millimeter Wave and Terahertz Systems', IEEE Transactions on Wireless Communications, vol. 23, no. 11, pp. 16033-16047.
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Huang, X, Zhang, H, Tuyen Le, A, Andrew Zhang, J & Jay Guo, Y 2024, 'Received Signal Modeling for Millimeter Wave and Terahertz Systems With Practical Impairments', IEEE Transactions on Communications, vol. 72, no. 10, pp. 6538-6552.
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Huang, Y, Feng, B, Tian, A, Dong, P, Yu, S & Zhang, H 2024, 'An Efficient Differentiated Routing Scheme for MEO/LEO-Based Multi-Layer Satellite Networks', IEEE Transactions on Network Science and Engineering, vol. 11, no. 1, pp. 1026-1041.
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Huang, Y, Huang, Y, Zhang, Z, Wu, Q, Zhong, Y & Wang, L 2024, 'Enhancing Person Re-Identification Performance Through In Vivo Learning', IEEE Transactions on Image Processing, vol. 33, pp. 639-654.
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Huang, Y, Kang, D, Chen, L, Jia, W, He, X, Duan, L, Zhe, X & Bao, L 2024, 'CARD: Semantic Segmentation With Efficient Class-Aware Regularized Decoder', IEEE Transactions on Circuits and Systems for Video Technology, vol. 34, no. 10, pp. 9024-9038.
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Huang, Y, Li, L, Li, R, Li, B, Wang, Q & Song, K 2024, 'Nitrogen cycling and resource recovery from aquaculture wastewater treatment systems: a review', Environmental Chemistry Letters, vol. 22, no. 5, pp. 2467-2482.
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Huang, Y, Wang, HB, Mak, HMW, Chu, M, Ning, Z, Organ, B, Chan, EFC, Liu, C-H, Mok, W-C, Gromke, C, Shon, HK, Lei, C & Zhou, JL 2024, 'Suitability of using carbon dioxide as a tracer gas for studying vehicle emission dispersion in a real street canyon', Journal of Environmental Sciences.
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Huang, Y, Wu, Q, Zhang, Z, Shan, C, Huang, Y, Zhong, Y & Wang, L 2024, 'Meta Clothing Status Calibration for Long-Term Person Re-Identification', IEEE Transactions on Image Processing, vol. 33, pp. 2334-2346.
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Huang, Y, Xiao, F, Cao, Z & Lin, C-T 2024, 'Fractal Belief Rényi Divergence With its Applications in Pattern Classification', IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 12, pp. 8297-8312.
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Huang, Y, Yang, D, Feng, B, Tian, A, Dong, P, Yu, S & Zhang, H 2024, 'A GNN-Enabled Multipath Routing Algorithm for Spatial-Temporal Varying LEO Satellite Networks', IEEE Transactions on Vehicular Technology, vol. 73, no. 4, pp. 5454-5468.
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Huang, Y, Yang, G, Zhou, H, Dai, H, Yuan, D & Yu, S 2024, 'VPPFL: A verifiable privacy-preserving federated learning scheme against poisoning attacks', Computers & Security, vol. 136, pp. 103562-103562.
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Huang, Y, Zhang, Z, Huang, Y, Wu, Q, Huang, H, Zhong, Y & Wang, L 2024, 'Customized meta-dataset for automatic classifier accuracy evaluation', Pattern Recognition, vol. 146, pp. 110026-110026.
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Huang, Y, Zhao, S & Lu, J 2024, 'Acoustic contrast control with a sound intensity constraint for personal sound systems', The Journal of the Acoustical Society of America, vol. 155, no. 2, pp. 879-890.
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Personal sound systems have received significant research interest in the past two decades due to their promising applications in a variety of scenarios. Various methods have been proposed to generate personal sound zones, most of which are based on sound pressure manipulation in both the acoustically bright and dark zones. Since sound intensity is closely related to human perception of sound localization, this paper proposes an acoustic contrast control method with a sound intensity constraint to increase the spatial planarity in the bright zone. In the proposed method, the sound intensity in the bright zone is projected to a specific direction while the sound pressure level in the dark zone is minimized. Simulations and experiments are carried out to compare the proposed method with the existing planarity control and pressure matching methods in terms of acoustic contrast, array effort, and planarity. The results demonstrate that the proposed method improves the planarity in the bright zone compared to existing planarity control method, while exhibitig a higher acoustic contrast and a lower array effort than the pressure matching method.
Huang, Z, Su, Q, Huang, J, He, X, Pei, Y & Yang, C 2024, 'Field assessment of a subgrade-culvert transition zone constructed with foamed concrete in the ballasted railway', International Journal of Rail Transportation, vol. 12, no. 3, pp. 391-413.
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Huang, Z, Zhao, R, Leung, FHF, Banerjee, S, Lam, K-M, Zheng, Y-P & Ling, SH 2024, 'Landmark Localization From Medical Images With Generative Distribution Prior', IEEE Transactions on Medical Imaging, vol. 43, no. 7, pp. 2679-2692.
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Hunt, L, Ingleman, J, Brennen, K, Armstrong, K, Hazell, M, Keith, N, Bickford, B, Sanchez, D, Khalil, S, Geering, S, Sigdel, SA, Skaria, S, Prabhakaran, S, Lynch, J, Alexandrou, E, Drury, P, Tran, T & Frost, SA 2024, 'A randomised controlled phase II trial to examine the feasibility of using hyper‐oxygenated fatty acids (HOFA) to prevent facial pressure injuries from medical devices among adults admitted to intensive care—A research protocol', International Wound Journal, vol. 21, no. 10.
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AbstractOne in three patients admitted to intensive care will sustain a pressure injury (PI) from a medical device. These injuries are painful and when on the face, head or neck they can result in permanent disfigurement. Preliminary evidence of the efficacy of hyper‐oxygenated fatty acids (HOFAs) to prevent facial pressure injuries from medical devices is promising; however, the feasibility of incorporating HOFAs into current standard care to prevent PI from a medical device of the face, head and neck has not been extensively explored. It is intended that the findings from this phase II feasibility study will inform the design of a larger phase III trial, by addressing two primary aims: (1) to assess the feasibility of incorporating HOFAs into standard care to prevent device‐related pressure ulcers of the skin associated with the face, head and neck assess the feasibility and (2) efficacy preliminary effectiveness of HOFA. This feasibility study is an investigator‐initiated mixed method study incorporating a multi‐centre randomised controlled trial of using HOFAs as an adjunct to standard pressure injury prevention and care, compared with standard care alone to prevent facial, head or neck from medical devices among adults admitted to intensive care. The primary outcome of interest is the incidence of facial, head or neck pressure injuries during the first 14 days in intensive care. Secondary outcomes include PI staging, medical device exposure and intensive care and hospital outcomes. The primary analysis will be undertaken using Cox's Proportional Hazards model, and due to the exploratory nature of this phase II trial, efficacy will be based on a one‐sided p‐value for superiority set at 0.10. Type I and Type II error rates are set at 20%; therefore, a total sample size of 196 study participants is planned. To explore the feasibility of incorporating HOFA into usual care and to design a large...
Huo, L, Xia, J, Zhang, L, Zhang, H & Xu, M 2024, 'Center-bridged Interaction Fusion for hyperspectral and LiDAR classification', Neurocomputing, vol. 590, pp. 127757-127757.
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Huo, X, Jiang, Z, Gu, X, Luo, Q, Li, Q & Sun, G 2024, 'On thermal and strain-rate dependences of polymethacrylimide (PMI) foam materials', Thin-Walled Structures, vol. 202, pp. 111986-111986.
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Huseien, GF, Tang, W, Yu, Y, Wong, LS, Mirza, J, Dong, K & Gu, X 2024, 'Evaluation of high-volume fly-ash cementitious binders incorporating nanosilica as eco-friendly sustainable concrete repair materials', Construction and Building Materials, vol. 447, pp. 138022-138022.
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Hussain Shah, SI, Seehar, TH, Raashid, M, Nawaz, R, Masood, Z, Mukhtar, S, Al Johani, TA, Doyle, A, Bashir, MN, Ali, MM & Kalam, MA 2024, 'Biocrude from hydrothermal liquefaction of indigenous municipal solid waste for green energy generation and contribution towards circular economy: A case study of urban Pakistan', Heliyon, vol. 10, no. 17, pp. e36758-e36758.
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Hussain, F, Khan, A & Mirdad, A 2024, 'Smart Contracts and Marketplace for Just-in-Time Management of Pharmaceutical Drugs', International Journal of Web and Grid Services, vol. 20, no. 1.
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Hussain, I, Yaqub, M, Mortazavi, M, Ehsan, MA & Hussain, M 2024, 'Study of fire-damaged circular RC columns repaired using composite confinement techniques', Proceedings of the Institution of Civil Engineers - Structures and Buildings, vol. 177, no. 3, pp. 222-234.
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Numerical and regression modelling of 21 undamaged, fire-damaged and repaired fire-damaged circular reinforced concrete (RC) columns was undertaken. The columns were exposed to temperatures of 300°C, 500°C and 900°C and tested for axial residual capacity. It was found that the concrete lost strength after exposure to a temperature of 300°C or above. Fire-damaged columns were then repaired using various composite confinement techniques. Strength was regained when carbon-fibre-reinforced polymer (CFRP) confinement was applied to the fire-damaged columns but it also increased the deformation and thus reduced the stiffness, which is undesirable. As an alternative, steel wire mesh, filled with cement–sand mortar and wrapped with CFRP was employed. A numerical model to predict the residual capacity of these columns was developed. The development of numerical techniques, including material properties, geometry, elements, loading, boundary conditions and contact algorithms for undamaged, fire-damaged and repaired fire-damaged columns is reviewed and summarised. Analytical equations were developed using linear, multiple and quadratic regression modelling. The results obtained using the proposed model and regression equations showed that these models offer a better alternative to experimental testing for the prediction of the post-fire performance of damaged and repaired RC columns.
Hussain, W, Gao, H, Karim, R & El Saddik, A 2024, 'Seventeen Years of the ACM Transactions on Multimedia Computing, Communications and Applications: A Bibliometric Overview', ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 20, no. 10, pp. 1-22.
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ACM Transactions on Multimedia Computing, Communications, and Applications has been dedicated to advancing multimedia research, fostering discoveries, innovations, and practical applications since 2005. The journal consistently publishes top-notch, original research in emerging fields through open submissions, calls for articles, special issues, rigorous review processes, and diverse research topics. This study aims to delve into an extensive bibliometric analysis of the journal, utilising various bibliometric indicators. The article seeks to unveil the latent implications within the journal’s scholarly landscape from 2005 to 2022. The data primarily draws from the Web of Science Core Collection database. The analysis encompasses diverse viewpoints, including yearly publication rates and citations, identifying highly cited articles, and assessing the most prolific authors, institutions, and countries. The article employs VOSviewer-generated graphical maps, effectively illustrating networks of co-citations, keyword co-occurrences, and institutional and national bibliographic couplings. Furthermore, the study conducts a comprehensive global and temporal examination of co-occurrences of the author’s keywords. This investigation reveals the emergence of numerous novel keywords over the past decades.
Hussain, W, Mabrok, M, Gao, H, Rabhi, FA & Rashed, EA 2024, 'Revolutionising healthcare with artificial intelligence: A bibliometric analysis of 40 years of progress in health systems', DIGITAL HEALTH, vol. 10.
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The development of artificial intelligence (AI) has revolutionised the medical system, empowering healthcare professionals to analyse complex nonlinear big data and identify hidden patterns, facilitating well-informed decisions. Over the last decade, there has been a notable trend of research in AI, machine learning (ML), and their associated algorithms in health and medical systems. These approaches have transformed the healthcare system, enhancing efficiency, accuracy, personalised treatment, and decision-making. Recognising the importance and growing trend of research in the topic area, this paper presents a bibliometric analysis of AI in health and medical systems. The paper utilises the Web of Science (WoS) Core Collection database, considering documents published in the topic area for the last four decades. A total of 64,063 papers were identified from 1983 to 2022. The paper evaluates the bibliometric data from various perspectives, such as annual papers published, annual citations, highly cited papers, and most productive institutions, and countries. The paper visualises the relationship among various scientific actors by presenting bibliographic coupling and co-occurrences of the author's keywords. The analysis indicates that the field began its significant growth in the late 1970s and early 1980s, with significant growth since 2019. The most influential institutions are in the USA and China. The study also reveals that the scientific community's top keywords include ‘ML’, ‘Deep Learning’, and ‘Artificial Intelligence’.
Hwang, Y-S, Um, J-S, Pradhan, B, Choudhury, T & Schlueter, S 2024, 'How does ChatGPT evaluate the value of spatial information in the 4th industrial revolution?', Spatial Information Research, vol. 32, no. 2, pp. 187-194.
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AbstractChat Generative Pre-trained Transformer (ChatGPT), developed by OpenAI, is a prominent AI model capable of understanding and generating human-like text based on input. Since terms and concepts of spatial information are contextual, the applications of ChatGPT on spatial information disciplines can be biased by the perceptions and perspectives of ChatGPT towards spatial information. Therefore, a thorough understanding of the real magnitude and level of comprehension of spatial information by ChatGPT is essential before exploring its potential applications in spatial information disciplines. This article aims to investigate how ChatGPT evaluates spatial information and its potential contributions to 4th Industrial Revolution (Industry 4.0). ChatGPT has summarized a notable perspective on evaluating and utilizing spatial information in the context of the Industry 4.0. The result of this study shows that ChatGPT has a good understanding on contextual concepts related to spatial information. However, it exhibits potential biases and challenges, as its responses lean towards the technological and analytical aspects. The results provide a crucial understanding on how to leverage ChatGPT’s benefits to the fullest while recognizing its constraints, with the aim to enhance the efficacy from the perspective of applications linked to spatial information.
IACOPI, F 2024, 'Editorial An Era of Surfaces', IEEE Transactions on Materials for Electron Devices, vol. 1, pp. iii-iv.
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Iacopi, F & Ferrari, AC 2024, 'Tailoring graphene for electronics beyond silicon', Nature, vol. 625, no. 7993, pp. 34-35.
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Ibanez-Hidalgo, I, Cuzmar, RH, Sanchez-Ruiz, A, Perez-Basante, A, Zubizarreta, A, Ceballos, S & Aguilera, RP 2024, 'Enhanced PI Control Based SHC-PWM Strategy for Active Power Filters', IEEE Open Journal of the Industrial Electronics Society, vol. 5, pp. 1174-1189.
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Ibanez-Hidalgo, I, Sanchez-Ruiz, A, Perez-Basante, A, Zubizarreta, A, Ceballos, S, Gil-Lopez, S & Aguilera, RP 2024, 'Real Time Selective Harmonic Control—PWM Based on Artificial Neural Networks', IEEE Transactions on Power Electronics, vol. 39, no. 1, pp. 768-783.
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Ibrahim, IA, Choudhury, T, Sargeant, J, Shah, R, Hossain, MJ & Islam, S 2024, 'CEREI: An open-source tool for Cost-Effective Renewable Energy Investments', SoftwareX, vol. 26, pp. 101708-101708.
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Ijaz Malik, MA, Mujtaba, MA, Kalam, MA, Silitonga, AS & Ikram, A 2024, 'Recent advances in hydrogen supplementation to promote biomass fuels for reducing greenhouse gases', International Journal of Hydrogen Energy, vol. 49, pp. 463-487.
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Energy security is the foremost concern for a sustainable environment. To make a sustainable environment, biomass waste products like biomass oil and biofuels must be efficiently burned. As millions of tons of waste biomass are dumped daily in major cities worldwide, it must be brought into energy products utilization. The quest for a sustainable ecosystem has pushed scientists to explore alternative fuels that are not only compatible with the engine but also eco-friendly. Hydrogen exhibits excellent combustion characteristics during dual fuel mode in a compression ignition (CI) engine. Carbon dioxide and NOx emissions are the two significant pollutants alternative fuels produce. This review study has tried to mitigate these two pollutants by combining biodiesel and hydrogen. It has been investigated that hydrogen possesses zero carbon content and can reduce CO2 emission, and biodiesel made from algae resources can help reduce NOx emission. Therefore, it is highlighted through the current review study to use the blend of hydrogen and algae-based biodiesel fuels to achieve benefits from their combined physicochemical properties and mitigate greenhouse gas emissions. The carbon-free nature of hydrogen and the oxygenated nature of biodiesel can be an excellent combination for combustion in diesel engines. Adopting third-generation fuels such as algae appears to be a viable solution to meet future energy demands. Biodiesel has a lower calorific value and viscous nature, negatively impacting fuel spray characteristics and creating abrupt fuel consumption. The purpose of this study is to promote biomass oil burning using hydrogen as a promoter supplement blend. Hydrogen has a higher heating value that can help overcome the less heating value of biodiesel fuels. Therefore, hydrogen as a blend with biodiesel makes the mixture lean and positively impacts engine performance, emissions, and combustion parameters.
Ikram, MM, Saha, G & Saha, SC 2024, 'Second law analysis of a transient hexagonal cavity with a rotating modulator', International Journal of Heat and Mass Transfer, vol. 221, pp. 125039-125039.
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Im, K, Park, M, Kabir, MM, Sohn, W, Choo, Y, Shon, HK & Nam, SY 2024, 'Human urine electrolysis for simultaneous green hydrogen and liquid fertilizer production for a circular economy: A proof of concept', Desalination, vol. 570, pp. 117059-117059.
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This study explores a novel process for hydrogen production and urine concentration using water electrolysis, employing a hydrophobic membrane and hydrogel electrolyte. The process utilizes a hydrophobic membrane to provide pure water from human urine, while simultaneously producing hydrogen through electrolysis, and concentrating urine for liquid fertilizer production. A suitable hydrogel electrolyte was developed, with polyvinyl alcohol (PVA)-based hydrogels and varying potassium hydroxide (KOH) concentration, showing efficient ion conductivity. The PVA-KOH 30 wt % hydrogel incorporating melamine exhibited promising performance in cell testing, achieving a current density of 204.35 mA/cm2 at 2 V. Long-term electrolysis tests indicated sustained efficiency, although a decline in current density during 96 h was attributed to hydrophobic membrane fouling. Nonetheless, the hydrogel electrolyte demonstrated minimal fouling, successfully concentrating the urine about 5 times. This concentrated urine serves as liquid fertilizer, while the produced hydrogen acts as an energy source, and the oxygen can be recycled for use in a membrane bioreactor (MBR), establishing a sustainable energy cycle system.
Indraratna, B, Arachchige, CMK, Rujikiatkamjorn, C, Heitor, A & Qi, Y 2024, 'Utilization of Granular Wastes in Transportation Infrastructure', Geotechnical Testing Journal, vol. 47, no. 1, pp. 20220233-20220233.
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Abstract Attributed to environmental preservation in urban infrastructure development, the recycling of waste materials produced in the coal and steel industry as well as the reusing of waste tires is a high priority in Australia. In this article, the practical applications of (i) coal wash (CW) and steel furnace slag mixtures, (ii) CW and fly ash mixtures, and (iii) rubber elements derived from recycled tires are discussed. In this regard, some examples of real-life applications are elucidated in relation to coastal reclamation as well as road and rail construction (e.g., Port Kembla, Kangaroo Valley highway, and Chullora Rail Precinct). The article outlines various aspects of site investigation, construction techniques, and the installation of instrumentation to evaluate the field performance of these waste materials in contrast to traditional (natural) quarried materials. The results from these case studies demonstrate that properly engineered granular waste mixtures can exhibit promising characteristics even to exceed the current technical standards, implying reduced intensity of maintenance. The research outcomes strongly support sustainable solutions to be embraced in the future development of transportation infrastructure, capable of withstanding increased freight loading and enhanced longevity.
Indraratna, B, Atapattu, S, Rujikiatkamjorn, C, Arivalagan, J & Jing, N 2024, 'Soft Soil Improvement by Geosynthetics for Enhanced Performance of Transport Infrastructure', Geotechnical Engineering, vol. 55, no. 1, pp. 1-10.
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Increasing demand for transportation has forced new infrastructure to be built on weak subgrade soils such as estuarine or marine clays. The application of heavy and high-frequency cyclic loads due to vehicular movement during the operational (post-construction) stage of tracks can cause (i) cyclic undrained failure, (ii) mud pumping or subgrade fluidisation, and (iii) differential and excessive settlement. This keynote paper presents the use of prefabricated vertical drains (PVDs) to enhance the performance of tracks. A series of laboratory experiments were carried out to investigate the cyclic response of remoulded soil specimens collected from a railway site near Wollongong, NSW, Australia. The results of the laboratory tests showed that beyond the critical cyclic stress ratio (CSRc), there is an internal redistribution of moisture within the specimen which causes the top portion of the specimen to soften and fluidise. The role that geosynthetics play in controlling and preventing mud pumping was analysed by assessing the development of excess pore water pressure (EPWP), the change in particle size distribution, and the water content of subgrade soil. The experimental data showed that PVDs can prevent the EPWP from building up to critical levels. PVDs provide shorter-radial drainage for EPWP to dissipate during cyclic loading, resulting in less accumulation of EPWP. Moreover, PVDs cause soil to behave in a partially drained rather than an undrained condition, while geotextiles can provide adequate surficial drainage and effective confinement at the ballast/subgrade interface. Partially drained cyclic models were developed by adopting the modified Cam clay theory to predict the behaviour of soil under cyclic loadings. The Sandgate Rail Grade Separation project case study presents a design of short PVDs to minimise the settlement and associated lateral displacement due to heavy-haul train loadings.
Indraratna, B, Malisetty, RS, Arachchige, C, Qi, Y & Rujikiatkamjorn, C 2024, 'Sustainable Performance of Recycled Rubber and Mining Waste Utilized for Efficient Rail Infrastructure', Indian Geotechnical Journal, vol. 54, no. 5, pp. 1738-1750.
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AbstractUtilizing waste byproducts from mining industries and recycled rubber as alternate materials in railway tracks promotes sustainability of transportation infrastructure, while also increasing track longevity by reducing ballast degradation. This paper provides an overview of two such applications including (i) rubber-intermixed ballast stratum (RIBS) by replacing 10% ballast aggregates with granulated rubber particles with the particle sizes carefully selected according to Australian Standards, (ii) synthetic energy absorbing layer using a mixture of steel furnace slag, coal wash and rubber crumbs to replace traditional capping layer. These materials when tested using large-scale triaxial apparatus and field trials proved that tracks with waste materials performed better than the conventional ballasted tracks by reducing ballast breakage and exploiting the higher damping potential of these materials. Though the vertical deformations of the track slightly increased by using these materials albeit within the specified standards, the overall stability improved by reduced dilation and track vibrations. Increasing the life of ballast layer can lead to long-term cost benefits by saving millions of dollars in track maintenance and provide environment benefits through minimizing quarrying of natural rock aggregates and reducing the carbon footprint of mining industries.
Indraratna, B, Malisetty, RS, Nair, L & Rujikiatkamjorn, C 2024, 'Instrumentation and Data Interpretation in Transportation Geotechnics', Indian Geotechnical Journal, vol. 54, no. 1, pp. 40-62.
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AbstractTransportation networks on the eastern coast of Australia are often built on or traverse coastal flood plains and marine clays with unfavourable soil conditions. In the past two decades, a significant number of laboratory investigations were carried out in soft soil improvement using Prefabricated Vertical Drains (PVDs) combined with vacuum-assisted surcharge preloading. In addition, significant research efforts were made to reduce the maintenance costs of railway tracks and increase their longevity by using synthetic inclusions such as geocomposites, geogrids and shock mats. These research outcomes were implemented and validated in practice through several field investigations along the eastern coast of Australia. This paper demonstrates the significance of instrumentation and data interpretation in geotechnical field investigations through 6 case histories. Field trials including Port of Brisbane Land Reclamation, Ballina Bypass Upgrade and Sandgate Rail Separation Projects were presented highlighting innovative ways of monitoring the performance of PVDs with vacuum and non-vacuum surcharge preloading. Also, railway track design improvements using geosynthetic and shock mats were discussed through Bulli and Singleton trial track case studies. Further, the heavy haul track testing facility at Russell Vale, New South Wales, was discussed as an alternative for expensive and time-consuming field trials.
Indraratna, B, Nguyen, TT, Atapattu, S, Ngo, T & Rujikiatkamjorn, C 2024, 'Subgrade soil response to rail loading: Instability mechanisms, causative factors, and preventive measures', Transportation Geotechnics, vol. 46, pp. 101267-101267.
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Iqbal, H, Zheng, J, Chai, R & Chandrasekaran, S 2024, 'Electric powered wheelchair control using user-independent classification methods based on surface electromyography signals', Medical & Biological Engineering & Computing, vol. 62, no. 1, pp. 167-182.
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AbstractWheelchairs are one of the most popular assistive technology (AT) among individuals with motor impairments due to their comfort and mobility. People with finger problems may find it difficult to operate wheelchairs using the conventional joystick control method. Therefore, in this research study, a hand gesture-based control method is developed for operating an electric-powered wheelchair (EPW). This study selected a comfort-based hand position to determine the stop maneuver. An additional exploration was undertaken to investigate four gesture recognition methods: linear regression (LR), regularized linear regression (RLR), decision tree (DT), and multi-class support vector machine (MC-SVM). The first two methods, LR and RLR, have promising accuracy values of 94.85% and 95.88%, respectively, but each new user must be trained. To overcome this limitation, this study explored two user-independent classification methods: MC-SVM and DT. These methods effectively addressed the finger dependency issue and demonstrated remarkable success in recognizing gestures across different users. MC-SVM has about 99.05% of both precision and accuracy, and the DT has about 97.77% accuracy and precision. All six participants were successful in controlling the EPW without any collisions. According to the experimental results, the proposed approach has high accuracy and can address finger dependency issues.
Irham, A, Roslan, MF, Jern, KP, Hannan, MA & Mahlia, TMI 2024, 'Hydrogen energy storage integrated grid: A bibliometric analysis for sustainable energy production', International Journal of Hydrogen Energy, vol. 63, pp. 1044-1087.
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Islam Rony, Z, Rasul, MG, Jahirul, MI & Mofijur, M 2024, 'Harnessing marine biomass for sustainable fuel production through pyrolysis to support United Nations' Sustainable Development Goals', Fuel, vol. 358, pp. 130099-130099.
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Islam, MR, Akter, S, Islam, L, Razzak, I, Wang, X & Xu, G 2024, 'Strategies for evaluating visual analytics systems: A systematic review and new perspectives', Information Visualization, vol. 23, no. 1, pp. 84-101.
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In recent times, visual analytics systems (VAS) have been used to solve various complex issues in diverse application domains. Nonetheless, an inherent drawback arises from the insufficient evaluation of VAS, resulting in occasional inaccuracies when it comes to analytical reasoning, information synthesis, and deriving insights from vast, ever-changing, ambiguous, and frequently contradictory data. Hence, the significance of implementing an appropriate evaluation methodology cannot be overstated, as it plays a pivotal role in enhancing the design and development of visualization systems. This paper assesses visualization systems by providing a systematic exploration of various evaluation strategies (ES). While several existing studies have examined some ES, the extent of comprehensive and systematic review for visualization research remains limited. In this work, we introduce seven state-of-the-art and widely recognized ES namely (1) dashboard comparison; (2) insight-based evaluation; (3) log data analysis; (4) Likert scales; (5) qualitative and quantitative analysis; (6) Nielsen’s heuristics; and (7) eye trackers. Moreover, it delves into their historical context and explores numerous applications where these ES have been employed, shedding light on the associated evaluation practices. Through our comprehensive review, we overview and analyze the predominant evaluation goals within the visualization community, elucidating their evolution and the inherent contrasts. Additionally, we identify the open challenges that arise with the emergence of new ES, while also highlighting the key themes gleaned from the existing literature that hold potential for further exploration in future studies.
Islam, MR, Razzak, I, Wang, X, Tilocca, P & Xu, G 2024, 'Natural language interactions enhanced by data visualization to explore insurance claims and manage risk', Annals of Operations Research, vol. 339, no. 3, pp. 1569-1587.
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Islam, MS, Larpruenrudee, P, Rahman, MM, Li, G, Husain, S, Munir, A, Zhao, M, Sauret, E & Gu, Y 2024, 'Pharmaceutical aerosol transport in airways: A combined machine learning (ML) and discrete element model (DEM) approach', Powder Technology, vol. 448, pp. 120271-120271.
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Islam, MS, Shahrear, P, Saha, G, Ataullha, M & Rahman, MS 2024, 'Mathematical analysis and prediction of future outbreak of dengue on time-varying contact rate using machine learning approach', Computers in Biology and Medicine, vol. 178, pp. 108707-108707.
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Islam, S, Deo, RC, Barua, PD, Soar, J, Yu, P & Rajendra Acharya, U 2024, 'Retinal health screening using artificial intelligence with digital fundus images: A review of the last decade (2012-2023)', IEEE Access, pp. 1-1.
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Ismaeel, HK, Albayati, TM, Al-Sudani, FT, Salih, IK, Dhahad, HA, Saady, NMC, Zendehboudi, S & Fattah, IMR 2024, 'The role of catalysts in biodiesel production as green energy applications: A review of developments and prospects', Chemical Engineering Research and Design, vol. 204, pp. 636-653.
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Jahangoshai Rezaee, M, Abbaspour Onari, M & Saberi, M 2024, 'A data-driven decision support framework for DEA target setting: an explainable AI approach', Engineering Applications of Artificial Intelligence, vol. 127, pp. 107222-107222.
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Jaiswal, AK, Srivastava, R, Jayakumar, A, Ahmad, A, Naidu, G & Swaminathan, J 2024, 'Evaporative cooling and sensible heat recovery enable practical waste-heat driven water purification', Desalination, vol. 586, pp. 117839-117839.
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Jakubowski, K, Vohradsky, J, Biasi, G, Chacon, A, Franklin, D, Tran, L, Guatelli, S, Naeini, MS & Rosenfeld, A 2024, 'QUAD-MOSFET DEVICE: ADVANCING QC IN ACCELERATOR-BASED BORON NEUTRON CAPTURE THERAPY', International Journal of Particle Therapy, vol. 12, pp. 100471-100471.
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Jakubowski, K, Vohradsky, J, Chacon, A, Franklin, DR, Tran, LT, Guatelli, S, Safavi-Naeini, M & Rosenfeld, A 2024, 'Computational design and evaluation of a quad-MOSFET device for quality control of therapeutic accelerator-based neutron beams', Radiation Measurements, vol. 177, pp. 107253-107253.
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Jalali, H, Yeganeh Khaksar, R, Mohammadzadeh S., D, Karballaeezadeh, N & Gandomi, AH 2024, 'Prediction of vertical displacement for a buried pipeline subjected to normal fault using a hybrid FEM-ANN approach', Frontiers of Structural and Civil Engineering, vol. 18, no. 3, pp. 428-443.
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Jamali, A, Roy, SK & Pradhan, B 2024, 'e-TransUNet: TransUNet provides a strong spatial transformation for precise deforestation mapping', Remote Sensing Applications: Society and Environment, vol. 35, pp. 101221-101221.
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Jamali, A, Roy, SK, Hashemi Beni, L, Pradhan, B, Li, J & Ghamisi, P 2024, 'Residual wave vision U-Net for flood mapping using dual polarization Sentinel-1 SAR imagery', International Journal of Applied Earth Observation and Geoinformation, vol. 127, pp. 103662-103662.
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Jamshidi, MB, Hoang, DT & Nguyen, DN 2024, 'CNN-FL for Biotechnology Industry Empowered by Internet-of-BioNano Things and Digital Twins', IEEE Internet of Things Magazine, vol. 7, no. 5, pp. 54-63.
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Jamshir M, M, Vijayakumar, S, Das, S, Thiyagarajan, K & Kodagoda, S 2024, 'A Novel Direct Digitizer for Leaky Differential Capacitive Sensors Using Phase Sensitive Integration', IEEE Sensors Letters, vol. 8, no. 2, pp. 1-4.
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Jansing, S, Jung, L, Hoffmann, F & Deuse, J 2024, 'Regressionsbasierte Ermittlung von Ausführungszeiten in Mensch-Roboter-Kollaboration', Zeitschrift für wirtschaftlichen Fabrikbetrieb, vol. 119, no. 6, pp. 440-444.
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Abstract The intensifying competitive pressures in manufacturing companies, combined with the expanding array of product variants, are propelling the integration of human-robot collaboration in operational frameworks. Effectively planning assembly processes demands particularly streamlined methods for assessing execution times, especially in the initial planning phases. While predetermined motion time systems are designed for human tasks, this contribution puts forth an approach for the time-economic analysis of robot movements without resorting to simulation.
Jansing, S, Schallow, J, Schulte-Uebbing, N, Danhausen, P, Gudergan, G & Deuse, J 2024, 'Prognosebasierte Kompetenzbewertung für einen nachhaltigen Aufbau von Schlüsselkompetenzen', Zeitschrift für wirtschaftlichen Fabrikbetrieb, vol. 119, no. 6, pp. 412-417.
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Abstract Today‘s economy is undergoing fundamental change with regard to digitalization and automation. Constantly growing market complexity and dynamics as well as the demographic change are shifting the skills required of employees and demanding a high level of flexibility by companies. In addition to the appropriate deployment of employees, there is a need to identify the skills required in the future at an early stage.
Jathar, LD, Nikam, K, Awasarmol, UV, Gurav, R, Patil, JD, Shahapurkar, K, Soudagar, MEM, Khan, TMY, Kalam, MA, Hnydiuk-Stefan, A, Gürel, AE, Hoang, AT & Ağbulut, Ü 2024, 'A comprehensive analysis of the emerging modern trends in research on photovoltaic systems and desalination in the era of artificial intelligence and machine learning', Heliyon, vol. 10, no. 3, pp. e25407-e25407.
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Javan, K, Altaee, A, BaniHashemi, S, Darestani, M, Zhou, J & Pignatta, G 2024, 'A review of interconnected challenges in the water–energy–food nexus: Urban pollution perspective towards sustainable development', Science of The Total Environment, vol. 912, pp. 169319-169319.
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Jayasinghe, H, Gunawardane, K & Zamora, R 2024, 'Multi‐objective optimisation framework for standalone DC‐microgrids with direct load control in demand‐side management', Electronics Letters, vol. 60, no. 15.
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AbstractRenewable energy‐powered DC microgrids have emerged as a sustainable alternative for standalone power systems in remote locations, which were traditionally reliant on diesel generators (DIG) only. To ensure power quality and reliability, energy storage systems (ESS) and demand‐side management (DSM) techniques are employed, addressing the intermittent nature of renewable energy sources (RES). This manuscript presents a novel multi‐objective optimisation framework to determine the equipment sizing, depth of discharge (DoD) of ESS, and share of controllable loads contributing to DSM in a standalone DC microgrid incorporated with RES as a primary energy source and a backup DIG. The proposed optimisation strategy utilises genetic algorithm with the objectives of minimizing lifecycle cost and carbon footprint. A novel battery energy storage system (BESS) management criterion is introduced, which accounts for battery degradation in the lifecycle cost calculation. The minimum allowable DoD of the BESS is considered a decision variable in the optimisation problem to assess the impact of higher DoD on lifecycle cost improvement. MATLAB simulation results demonstrate that the proposed optimisation model significantly reduces the levelized cost of electricity and per unit carbon footprint compared to previous models. Additionally, it identifies an optimal range of DoD for the BESS to enhance the lifecycle cost of a standalone DC microgrid.
Jazebi, M, Indraratna, B, Malisetty, RS & Rujikiatkamjorn, C 2024, 'Stability assessment of jointed rock capturing roughness degradation under cyclic loading with special reference to railway formation', Transportation Geotechnics, vol. 48, pp. 101310-101310.
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Ji, S, Tan, Y, Saravirta, T, Yang, Z, Liu, Y, Vasankari, L, Pan, S, Long, G & Walid, A 2024, 'Emerging trends in federated learning: from model fusion to federated X learning', International Journal of Machine Learning and Cybernetics, vol. 15, no. 9, pp. 3769-3790.
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AbstractFederated learning is a new learning paradigm that decouples data collection and model training via multi-party computation and model aggregation. As a flexible learning setting, federated learning has the potential to integrate with other learning frameworks. We conduct a focused survey of federated learning in conjunction with other learning algorithms. Specifically, we explore various learning algorithms to improve the vanilla federated averaging algorithm and review model fusion methods such as adaptive aggregation, regularization, clustered methods, and Bayesian methods. Following the emerging trends, we also discuss federated learning in the intersection with other learning paradigms, termed federated X learning, where X includes multitask learning, meta-learning, transfer learning, unsupervised learning, and reinforcement learning. In addition to reviewing state-of-the-art studies, this paper also identifies key challenges and applications in this field, while also highlighting promising future directions.
Ji, X, Li, Y, Wen, P, Barua, P & Acharya, UR 2024, 'MixSleepNet: A Multi-Type Convolution Combined Sleep Stage Classification Model', Computer Methods and Programs in Biomedicine, vol. 244, pp. 107992-107992.
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Jia, X, Luo, J, Luo, Q, Li, Q & Pang, T 2024, 'Experimental study on the effects of temperature on mechanical properties of 3D printed continuous carbon fiber reinforced polymer (C CFRP) composites', Thin-Walled Structures, vol. 205, pp. 112465-112465.
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Jiang, J, Dorji, P, Badeti, U, Sohn, W, Freguia, S, Phuntsho, S, El Saliby, I & Shon, HK 2024, 'Corrigendum to “Potential nutrient recovery from source-separated urine through hybrid membrane bioreactor and membrane capacitive deionisation” [Desalination 566 (2023) 116924]', Desalination, vol. 573, pp. 117236-117236.
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Jiang, J, Sohn, W, Almuntashiri, A, Phuntsho, S, Wang, Q, Freguia, S, El-Saliby, I & Shon, HK 2024, 'Feasibility study of powdered activated carbon membrane bioreactor (PAC-MBR) for source-separated urine treatment: A comparison with MBR', Desalination, vol. 580, pp. 117544-117544.
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Jiang, M, Sui, Y, Lei, Y, Xie, X, Li, C, Liu, Y & Tsang, IW 2024, 'Adversarial Learning for Coordinate Regression Through k-Layer Penetrating Representation', IEEE Transactions on Dependable and Secure Computing, vol. 21, no. 6, pp. 5538-5552.
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Jiang, T, Li, L, Samali, B, Yu, Y, Huang, K, Yan, W & Wang, L 2024, 'Lightweight object detection network for multi‐damage recognition of concrete bridges in complex environments', Computer-Aided Civil and Infrastructure Engineering, vol. 39, no. 23, pp. 3646-3665.
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AbstractTo solve the challenges of low recognition accuracy, slow speed, and weak generalization ability inherent in traditional methods for multi‐damage recognition of concrete bridges, this paper proposed an efficient lightweight damage recognition model, constructed by improving the you only look once v4 (YOLOv4) with MobileNetv3 and fused inverted residual blocks, named YOLOMF. First, a novel lightweight network named MobileNetv3 with fused inverted residual (MobileNetv3‐FusedIR) is constructed as the backbone network for YOLOMF. This is achieved by integrating the fused mobile inverted bottleneck convolution (Fused‐MBConv) into the shallow layers of MobileNetv3. Second, the standard convolution in YOLOv4 is replaced with the depthwise separable convolution, resulting in a reduction in the number of parameters and complexity of the model. Third, the effects of different activation functions on the damage recognition performance of YOLOMF are thoroughly investigated. Finally, to verify the effectiveness of the proposed method in complex environments, a data enhancement library named Imgaug is used to simulate concrete bridge damage images under challenging conditions such as motion blur, fog, rain, snow, noise, and color variations. The results indicate that the YOLOMF shows excellent multi‐damage recognition proficiency for concrete bridges across varying field‐of‐view sizes as well as complex environmental conditions. The detection speed of YOLOMF reaches 85f/s, facilitating effective real‐time multi‐damage detection for concrete bridges under complex environments.
Jiang, Y, Ke, Y, Yang, F, Ji, J & Peng, W 2024, 'State of Health Estimation for Second-Life Lithium-Ion Batteries in Energy Storage System With Partial Charging-Discharging Workloads', IEEE Transactions on Industrial Electronics, vol. 71, no. 10, pp. 13178-13188.
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Jiang, Y, Li, S-T, He, N, Xu, B & Fan, W 2024, 'Centrifuge Modeling Investigation of Geosynthetic-Reinforced and Pile-Supported Embankments', International Journal of Geomechanics, vol. 24, no. 8.
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Jiang, Y, Ma, B, Wang, X, Yu, G, Sun, C, Ni, W & Liu, RP 2024, 'Preventing harm to the rare in combating the malicious: A filtering-and-voting framework with adaptive aggregation in federated learning', Neurocomputing, vol. 604, pp. 128317-128317.
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Jiang, Y, Ma, B, Wang, X, Yu, G, Yu, P, Wang, Z, Ni, W & Liu, RP 2024, 'Blockchained Federated Learning for Internet of Things: A Comprehensive Survey', ACM Computing Surveys, vol. 56, no. 10, pp. 1-37.
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The demand for intelligent industries and smart services based on big data is rising rapidly with the increasing digitization and intelligence of the modern world. This survey comprehensively reviews Blockchained Federated Learning (BlockFL) that joins the benefits of both Blockchain and Federated Learning to provide a secure and efficient solution for the demand. We compare the existing BlockFL models in four Internet-of-Things (IoT) application scenarios: Personal IoT (PIoT), Industrial IoT (IIoT), Internet of Vehicles (IoV), and Internet of Health Things (IoHT), with a focus on security and privacy, trust and reliability, efficiency, and data diversity. Our analysis shows that the features of decentralization and transparency make BlockFL a secure and effective solution for distributed model training, while the overhead and compatibility still need further study. It also reveals the unique challenges of each domain presents unique challenges, e.g., the requirement of accommodating dynamic environments in IoV and the high demands of identity and permission management in IoHT, in addition to some common challenges identified, such as privacy, resource constraints, and data heterogeneity. Furthermore, we examine the existing technologies that can benefit BlockFL, thereby helping researchers and practitioners to make informed decisions about the selection and development of BlockFL for various IoT application scenarios.
Jiang, Y, Pais‐Roldán, P, Pohmann, R & Yu, X 2024, 'High Spatiotemporal Resolution Radial Encoding Single‐Vessel fMRI', Advanced Science, vol. 11, no. 26.
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AbstractHigh‐field preclinical functional MRI (fMRI) is enabled the high spatial resolution mapping of vessel‐specific hemodynamic responses, that is single‐vessel fMRI. In contrast to investigating the neuronal sources of the fMRI signal, single‐vessel fMRI focuses on elucidating its vascular origin, which can be readily implemented to identify vascular changes relevant to vascular dementia or cognitive impairment. However, the limited spatial and temporal resolution of fMRI is hindered hemodynamic mapping of intracortical microvessels. Here, the radial encoding MRI scheme is implemented to measure BOLD signals of individual vessels penetrating the rat somatosensory cortex. Radial encoding MRI is employed to map cortical activation with a focal field of view (FOV), allowing vessel‐specific functional mapping with 50 × 50 µm2 in‐plane resolution at a 1 to 2 Hz sampling rate. Besides detecting refined hemodynamic responses of intracortical micro‐venules, the radial encoding‐based single‐vessel fMRI enables the distinction of fMRI signals from vessel and peri‐vessel voxels due to the different contribution of intravascular and extravascular effects.
Jihan, JI, Saha, BK, Nag, P, Moon, NJ, Saha, G & Saha, SC 2024, 'Advancing thermal efficiency and entropy management inside decagonal enclosure with and without hot cylindrical insertions', International Journal of Thermofluids, vol. 23, pp. 100785-100785.
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Jin, P, Chang, L, Liu, Y, Guo, Y, Lei, G & Zhu, J 2024, 'Design and Implementation of Novel Rotor Side Brushless Controller With Bidirectional Wireless Power Transmission for Doubly-Fed Machine', IEEE Transactions on Industrial Electronics, vol. 71, no. 1, pp. 183-193.
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Jin, P, Hu, Y, Lei, G, Guo, Y & Zhu, J 2024, 'A Novel SVM Strategy to Reduce Current Stress of High-Frequency Link Matrix Converter', IEEE Transactions on Industrial Electronics, vol. 71, no. 5, pp. 4652-4662.
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Jin, P, Wang, H, Xu, H, Cao, C, Guo, Y, Lei, G & Zhu, J 2024, 'Stability Analysis for Three-Stage Serial PMSM Drive System Based on Bidirectional WPT', IEEE Transactions on Industrial Electronics, pp. 1-9.
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Jin, Q, Chen, H, Zhang, Y, Wang, X & Zhu, D 2024, 'Unraveling Scientific Evolutionary Paths: An Embedding-Based Topic Analysis', IEEE Transactions on Engineering Management, vol. 71, pp. 8964-8978.
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Jing, H, Ge, H, Tang, H, Farnoud, A, Saidul Islam, M, Wang, L, Wang, C & Cui, X 2024, 'Assessing airflow unsteadiness in the human respiratory tract under different expiration conditions', Journal of Biomechanics, vol. 162, pp. 111910-111910.
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John, AR, Singh, AK, Gramann, K, Liu, D & Lin, C-T 2024, 'Prediction of cognitive conflict during unexpected robot behavior under different mental workload conditions in a physical human–robot collaboration', Journal of Neural Engineering, vol. 21, no. 2, pp. 026010-026010.
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Abstract Objective. Brain–computer interface (BCI) technology is poised to play a prominent role in modern work environments, especially a collaborative environment where humans and machines work in close proximity, often with physical contact. In a physical human robot collaboration (pHRC), the robot performs complex motion sequences. Any unexpected robot behavior or faulty interaction might raise safety concerns. Error-related potentials, naturally generated by the brain when a human partner perceives an error, have been extensively employed in BCI as implicit human feedback to adapt robot behavior to facilitate a safe and intuitive interaction. However, the integration of BCI technology with error-related potential for robot control demands failure-free integration of highly uncertain electroencephalography (EEG) signals, particularly influenced by the physical and cognitive state of the user. As a higher workload on the user compromises their access to cognitive resources needed for error awareness, it is crucial to study how mental workload variations impact the error awareness as it might raise safety concerns in pHRC. In this study, we aim to study how cognitive workload affects the error awareness of a human user engaged in a pHRC. Approach. We designed a blasting task with an abrasive industrial robot and manipulated the mental workload with a secondary arithmetic task of varying difficulty. EEG data, perceived workload, task and physical performance were recorded from 24 participants moving the robot arm. The error condition was achieved by the unexpected stopping of the robot in 33% of trials. Main results. We observed a diminished amplitude for the prediction error negativity (PEN) and error positivity (Pe), indicating reduced error awareness with increasing mental workload. We further observed a...
Jorquera, E, Saco, PM, Verdon-Kidd, D, Rodríguez, JF, Timmermans, H & Nelson, F 2024, 'Effects of tropical cyclones on catchment sediment delivery to coastal ecosystems', CATENA, vol. 238, pp. 107805-107805.
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Joysoyal, R, Uddin, SS, Islam, T, Sarker, SK, Li, L, Ahsan, F, Bhatti, UA & Zafir, EI 2024, 'Blockchain for sustainable city transformation: A review on Bangladesh', Engineering Reports, vol. 6, no. 9.
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AbstractBlockchain (BCn) revolution across the world leads the global transformation by altering the existing structure and introducing the accessible and decentralized paradigm. This technology facilitates access to resources and redefines conventional frameworks, empowering people and communities globally. Motivated by these attention‐seeking potentiality of BCn features, in this article, we examine the challenges, advancements, and emerging prospects of the BCn revolution in Bangladesh. With a specific focus on BCn implications for sustainability and smart development, this study addresses the urgent need to understand how BCn can facilitate sustainable development in rapidly evolving countries like Bangladesh. The goal of this research is to draw attention to the successful revolution of BCn in the world and use this knowledge to extract best practices and cutting‐edge strategies for sustainable transformation of Bangladesh. The contribution of this study covers the underlying concepts of the BCn technology, showing the revolutionary state of BCn across the world, applications of BCn for sustainability, assessment of its current usage, and highlighting the BCn implementation problems. Furthermore, this study serves as a gateway to a transparent, decentralized future that fosters inclusive national progress and aligns with sustainable development goals. This study also strives to inspire individuals to use this transformational technology for the collective benefit of society by highlighting BCn's potential to empower communities while driving positive change.
Jozmaleki, M, Jahangoshai Rezaee, M & Saberi, M 2024, 'A Hybrid Semi-Supervised Approach for Estimating the Efficient and Optimal Level of Hospitals Outputs', Cybernetics and Systems, vol. 55, no. 2, pp. 585-613.
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Ju, M, He, C, Ding, C, Ren, W, Zhang, L & Ma, K-K 2024, 'All-Inclusive Image Enhancement for Degraded Images Exhibiting Low-Frequency Corruption', IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1.
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Ju, X, Li, S, Zhang, Y, Wu, P & Li, Y 2024, 'Design of multi-stable metamaterial cell with improved and programmable energy trapping ability based on frame reinforced curved beams', Thin-Walled Structures, vol. 202, pp. 112120-112120.
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Junaid, M, Saha, G, Shahrear, P & Saha, SC 2024, 'Phase change material performance in chamfered dual enclosures: Exploring the roles of geometry, inclination angles and heat flux', International Journal of Thermofluids, vol. 24, pp. 100919-100919.
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Jupp, JR & Parkes, M 2024, 'Integrated Construction Enterprise Systems: A Strategic Approach to Model-based Data and Process Management', Automation in Construction, vol. (to appear).
Kabir, MM, Akter, T, Sabur, GM, Sultana, N, Fazlul Karim Mamun, M, Kabir, N, Didar-ul-Alam, M, Islam, MM, Chaity, FS, Tijing, L & Shon, HK 2024, 'Characterization of Cr(VI)-reducing indigenous bacteria from a long-term tannery waste-contaminated soil', Desalination and Water Treatment, vol. 320, pp. 100861-100861.
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Kabir, MM, Maleha, SM, Hossain, MS, Sultana, N, Islam, R, Islam, S, Ahmed, F, Bahadur, NM, Choudhury, TR, Didar-ul-Alam, M, Kabir, N, Tijing, L & Shon, HK 2024, 'Molecular characterization and human health risk assessment of multi-drug and heavy metals tolerant bacteria from urban river water', Desalination and Water Treatment, vol. 317, pp. 100298-100298.
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Kabir, MM, Sabur, GM, Akter, MM, Nam, SY, Im, KS, Tijing, L & Shon, HK 2024, 'Electrodialysis desalination, resource and energy recovery from water industries for a circular economy', Desalination, vol. 569, pp. 117041-117041.
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The water industries (WIN) are now approaching towards sustainability of resource use, recovery process, and water and energy management based on the circular economy (CRE) framework. Thus, the integration of electrodialysis (ED) technology in the WIN with a CRE paradigm should be recommended for ensuring the sustainability of ED desalination, resource, and energy recovery (EDDRER). According to the literature review, and to the best of our knowledge, there is no systematic study devoted to the sustainable EDDRER; hence a comprehensive and critical knowledge generation of EDDRER is essential for further technological advancements of ED. Consequently, this review paper investigated the plausible incorporation of ED in the WIN for a CRE of EDDRER. The recent progress of EDDRER has been described comprehensively and critically. Moreover, an all-inclusive techno-economics and environmental sustainability analysis of EDDRER from WIN for a CRE has been carried out. This paper marks the first instance in which energy recovery techniques employing ED have been reported and critically discussed. In addition, the latest case studies of EDDRER in the WIN have been discussed critically, and the significant scaling-up issues of EDDRER have been assessed based on the state-of-the-art recent scientific findings. Furthermore, the potential mitigation measures for the scaling-up issues have also been addressed. This study is the first comprehensive assessment of EDDRER from WIN for a closed-loop economy. The novel insights of this study could be essential for the development of a sustainable CRE-based EDDRER process for WIN to attain sustainable development goals (SDGs).
Kaliyannan, P, Seikh, AH, Kalam, MA & Venkatesh, R 2024, 'Fabrication and Characteristics Study of Aluminium Alloy Hybrid Nanocomposite Synthesized with SiC and Waste Metal Powder', Silicon, vol. 16, no. 2, pp. 843-851.
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Kan, S, Gao, Y, Zhong, Z & Sui, Y 2024, 'Cross-Language Taint Analysis: Generating Caller-Sensitive Native Code Specification for Java', IEEE Transactions on Software Engineering, vol. 50, no. 6, pp. 1518-1533.
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Kang, J, Jia, W, He, X & Lam, KM 2024, 'Point Clouds are Specialized Images: A Knowledge Transfer Approach for 3D Understanding', IEEE Transactions on Multimedia, vol. 26, pp. 10755-10765.
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Karabey Aksalli, I, Baygin, N, Hagiwara, Y, Paul, JK, Iype, T, Barua, PD, Koh, JEW, Baygin, M, Dogan, S, Tuncer, T & Acharya, UR 2024, 'Automated characterization and detection of fibromyalgia using slow wave sleep EEG signals with glucose pattern and D’hondt pooling technique', Cognitive Neurodynamics, vol. 18, no. 2, pp. 383-404.
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Karabulut, E, Pileggi, SF, Groth, P & Degeler, V 2024, 'Ontologies in digital twins: A systematic literature review', Future Generation Computer Systems, vol. 153, pp. 442-456.
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Karkera, T, Adak, C, Chattopadhyay, S & Saqib, M 2024, 'Detecting severity of Diabetic Retinopathy from fundus images: A transformer network-based review', Neurocomputing, vol. 597, pp. 127991-127991.
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Karki, D, Far, H & Nejadi, S 2024, 'Structural Behavior of Prefabricated Composite Cold-Formed Steel and Timber Flooring Systems', Journal of Structural Engineering (United States), vol. 150, no. 7.
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In this study, the structural performance of a new type of lightweight composite cold-formed steel and timber (CFST) flooring system has been investigated by conducting four-point bending tests on 13 specimens. A bare cold-formed steel system without timber sheathing was also tested to provide a benchmark response to which the strength and stiffness of the composite system were compared. This paper presents key findings on the flooring system's structural behavior and performance parameters, such as ultimate bending capacity, load-deflection response, load-slip response, and failure modes, by categorizing 13 specimens into four subgroups based on shear connector types and spacings. In the proposed composite CFST flooring system, 45-mm thick structural plywood panels were connected to the 2.4-mm thick cold-formed steel C-section joist using self-drilling screws, coach screws, and nuts and bolts. The performance of different types of shear connectors on the composite action is experimentally investigated and compared with the theoretical plastic section. Furthermore, the load-carrying capacity, effective bending stiffness, and deflection of composite CFST beams were computed theoretically using elastic theory and compared to experimental results, which showed good agreement.
Karsa, M, Xiao, L, Ronca, E, Bongers, A, Spurling, D, Karsa, A, Cantilena, S, Mariana, A, Failes, TW, Arndt, GM, Cheung, LC, Kotecha, RS, Sutton, R, Lock, RB, Williams, O, de Boer, J, Haber, M, Norris, MD, Henderson, MJ & Somers, K 2024, 'FDA-approved disulfiram as a novel treatment for aggressive leukemia', Journal of Molecular Medicine, vol. 102, no. 4, pp. 507-519.
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Abstract Acute leukemia continues to be a major cause of death from disease worldwide and current chemotherapeutic agents are associated with significant morbidity in survivors. While better and safer treatments for acute leukemia are urgently needed, standard drug development pipelines are lengthy and drug repurposing therefore provides a promising approach. Our previous evaluation of FDA-approved drugs for their antileukemic activity identified disulfiram, used for the treatment of alcoholism, as a candidate hit compound. This study assessed the biological effects of disulfiram on leukemia cells and evaluated its potential as a treatment strategy. We found that disulfiram inhibits the viability of a diverse panel of acute lymphoblastic and myeloid leukemia cell lines (n = 16) and patient-derived xenograft cells from patients with poor outcome and treatment-resistant disease (n = 15). The drug induced oxidative stress and apoptosis in leukemia cells within hours of treatment and was able to potentiate the effects of daunorubicin, etoposide, topotecan, cytarabine, and mitoxantrone chemotherapy. Upon combining disulfiram with auranofin, a drug approved for the treatment of rheumatoid arthritis that was previously shown to exert antileukemic effects, strong and consistent synergy was observed across a diverse panel of acute leukemia cell lines, the mechanism of which was based on enhanced ROS induction. Acute leukemia cells were more sensitive to the cytotoxic activity of disulfiram than solid cancer cell lines and non-malignant cells. While disulfiram is currently under investigation in clinical trials for solid cancers, this study provides evidence for the potential of disulfiram for acute leukemia treatment. Key messages
KARTHIK, K, RAJAMANIKKAM, RK, VENKATESAN, EP, BISHWAKARMA, S, KRISHNAIAH, R, SALEEL, CA, SOUDAGAR, MEM, KALAM, MA, ALI, MM & BASHIR, MN 2024, 'State of the Art: Natural fibre-reinforced composites in advanced development and their physical/chemical/mechanical properties', Chinese Journal of Analytical Chemistry, vol. 52, no. 7, pp. 100415-100415.
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Kashyap, A, Behera, MD & Pradhan, B 2024, 'Differential surface uplift and knickpoint evolution along the transient Teesta river in the eastern Himalayas', Journal of Asian Earth Sciences, vol. 260, pp. 105974-105974.
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Katubi, KM, Shiong, NS, Pakhuruddin, MZ, Alkhalayfeh, MA, Abubaker, SA & Al-Soeidat, MR 2024, 'Over 35% efficiency of three absorber layers of perovskite solar cells using SCAPS 1-D', Optik, vol. 297, pp. 171579-171579.
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Kazemi Shariat Panahi, H, Hosseinzadeh-Bandbafha, H, Dehhaghi, M, Orooji, Y, Mahian, O, Shahbeik, H, Kiehbadroudinezhad, M, Kalam, MA, Karimi-Maleh, H, Salehi Jouzani, G, Mei, C, Guillemin, GG, Nizami, A-S, Wang, Y, Gupta, VK, Lam, SS, Pan, J, Kim, K-H, Peng, W, Aghbashlo, M & Tabatabaei, M 2024, 'Nanotechnology applications in biodiesel processing and production: A comprehensive review', Renewable and Sustainable Energy Reviews, vol. 192, pp. 114219-114219.
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Ke, L, Xiao, P, Chen, X, Yu, S, Chen, X & Wang, H 2024, 'A novel cross-domain adaptation framework for unsupervised criminal jargon detection via pre-trained contextual embedding of darknet corpus', Expert Systems with Applications, vol. 242, pp. 122715-122715.
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Ke, W, Li, Y, Chen, Q & Nimbalkar, S 2024, 'Kinematic response of a pile within a soil slope to SH wave excitation', Soil Dynamics and Earthquake Engineering, vol. 183, pp. 108730-108730.
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Kedziora, DJ, Musial, K & Gabrys, B 2024, 'AutonoML: Towards an Integrated Framework for Autonomous Machine Learning', Foundations and Trends® in Machine Learning, vol. 17, no. 4, pp. 590-766.
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Kedziora, DJ, Nguyen, T-D, Musial, K & Gabrys, B 2024, 'On taking advantage of opportunistic meta-knowledge to reduce configuration spaces for automated machine learning', Expert Systems with Applications, vol. 239, pp. 122359-122359.
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Keshavarz, R, Majidi, E, Raza, A & Shariati, N 2024, 'Ultra-Fast and Efficient Design Method Using Deep Learning for Capacitive Coupling WPT System', IEEE Transactions on Power Electronics, vol. 39, no. 1, pp. 1738-1748.
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Keshavarz, R, Nikkhah, N & Shariati, N 2024, 'In Situ Wide-Range Permittivity Measurement: Compact, Cost-Effective, and Highly Sensitive Sensor Using Reconfigurable Phase Shifter', IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 71, no. 11, pp. 5296-5305.
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Keshavarz, R, Sounas, DL, Keshavarz, S & Shariati, N 2024, 'Enabling Wireless Communications, Energy Harvesting, and Energy Saving by Using a Multimode Smart Nonlinear Circuit (MSNC)', IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1-11.
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Keshmiry, A, Hassani, S, Dackermann, U & Li, J 2024, 'Assessment, repair, and retrofitting of masonry structures: A comprehensive review', Construction and Building Materials, vol. 442, pp. 137380-137380.
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Kha, J, Croaker, P, Karimi, M & Skvortsov, A 2024, 'Uncertainty Analysis in Airfoil–Turbulence Interaction Noise Using Polynomial Chaos Expansion', AIAA Journal, vol. 62, no. 2, pp. 657-667.
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Airfoil–turbulence interaction noise is a known source of environmental disturbance and acoustic performance loss in aeroacoustics and hydroacoustics. This noise can be predicted using semi-analytical models that require input measurements of the incoming turbulent flow parameters. However, the turbulence parameters are inherently difficult to measure accurately. These parameters, which include the turbulence kinetic energy and its dissipation rate, have a stochastic nature. This study aims to investigate how small variations in the measurements of turbulence parameters affect the uncertainty of the predicted airfoil–turbulence interaction noise. This is achieved by applying polynomial chaos expansion (PCE) to the semi-analytical model of Amiet’s theory for airfoil-interaction noise. The validity of the deterministic and stochastic simulations is ensured by comparisons against available experimental data from the literature, and Monte Carlo simulations, respectively. Uncertainty quantification is then performed using a stochastic collocation technique, where the aerodynamic noise is evaluated at specific collocation points to estimate the coefficients required for PCE. Both the individual and combined effects of varying the uncertain input turbulence parameters are simulated to quantify the uncertainty of the output aerodynamic noise. The insights gained from the results suggest it is important to incorporate the stochastic behavior of the incoming turbulent flow in operational models for airfoil–turbulence interaction noise predictions.
Kha, J, Karimi, M, Maxit, L & Kirby, R 2024, 'Near- and far-field radiated acoustic pressures from a vibrating three-dimensional cylindrical shell in an underwater acoustic waveguide', Journal of Sound and Vibration, vol. 590, pp. 118534-118534.
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Khademi, P, Mousavi, M, Dackermann, U & Gandomi, AH 2024, 'Enhancing load prediction for structures with concrete overlay using transfer learning of time–frequency feature-based deep models', Engineering Structures, vol. 305, pp. 117734-117734.
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Khairnar, S, Gite, S, Mahajan, K, Pradhan, B, Alamri, A & Thepade, SD 2024, 'Advanced Techniques for Biometric Authentication: Leveraging Deep Learning and Explainable AI', IEEE Access, vol. 12, pp. 153580-153595.
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Khan, M, Wu, W & Li, L 2024, 'Grid‐forming control for inverter‐based resources in power systems: A review on its operation, system stability, and prospective', IET Renewable Power Generation, vol. 18, no. 6, pp. 887-907.
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AbstractThe increasing integration of inverter based resources (IBR) in the power system has a significant multi‐faceted impact on the power system operation and stability. Various control approaches are proposed for IBRs, broadly categorized into grid‐following and grid‐forming (GFM) control strategies. While the GFL has been in operation for some time, the relatively new GFMs are rarely deployed in the IBRs. This article aims to provide an understanding of the working principles and distinguish between these two control strategies. A survey of the recent GFM control approaches is also delivered here, expanding the existing classification. It also explores the role of GFM control and its types in power system dynamics and stability like voltage, frequency etc. Practical insight into these stabilities is provided using case studies, making this review article unique in its comprehensive approach. Lacking elsewhere, the GFMs' real‐world demonstrations and their applications in several IBRs like wind farms, photovoltaic power generation stations etc., are also analyzed. Finally, the research gaps are identified, and the prospect of GFM is presented based on the system needs, informed by GFM real‐world projects. This work is a potential road map for the GFM large‐scale deployment in the decarbonized IBR‐based bulk power system.
Khanafer, D, Altaee, A, Hawari, AH, Zhou, J & Alsaka, L 2024, 'Innovative stimuli-responsive membrane MSF brine rejection dilution by tertiary treated sewage effluent', Journal of Environmental Management, vol. 365, pp. 121517-121517.
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Khoa, TV, Son, DH, Hoang, DT, Trung, NL, Quynh, TTT, Nguyen, DN, Ha, NV & Dutkiewicz, E 2024, 'Collaborative Learning for Cyberattack Detection in Blockchain Networks', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 54, no. 7, pp. 3920-3933.
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Khodabandeh-Yalabadi, A, Sheikhalishahi, M, Torabi, SA, Naderpour, M & Radmankian, A 2024, 'Enhancing supply chain resilience under disruption: analysis of the farmed data by Monte Carlo simulation', International Journal of Systems Science: Operations & Logistics, vol. 11, no. 1.
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Khodadadi, N, Talatahari, S & Gandomi, AH 2024, 'ANNA: advanced neural network algorithm for optimisation of structures', Proceedings of the Institution of Civil Engineers - Structures and Buildings, vol. 177, no. 6, pp. 529-551.
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The purpose of this study is to develop an advanced neural network algorithm as a new optimisation for the optimal design of truss structures. The central concept of the algorithm is based on biological nerve structures and artificial neural networks. The performance of the proposed method is explored in engineering design problems. Two efficient methods for improving the standard neural network algorithm are considered here. The first is an enhanced initialisation mechanism based on opposite-based learning. The second relies on using a few tunable parameters to provide proper exploration and exploitation abilities for the algorithm, enabling better solutions to be found while the required structural analyses are reduced. The new algorithm's performance is investigated by using five well-known restricted benchmarks to assess its efficiency in relation to the latest optimisation techniques. The outcome of the examples demonstrates that the upgraded version of the algorithm has increased efficacy and robustness in comparison with the original version of the algorithm and some other methods.
Khorshidi, MS, Izady, A, Nikoo, MR, Al-Maktoumi, A, Chen, M & Gandomi, AH 2024, 'An Agent-based Framework for Transition from Traditional to Advanced Water Supply Systems in Arid Regions', Water Resources Management, vol. 38, no. 7, pp. 2565-2579.
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Khounani, Z, Abdul Razak, NN, Hosseinzadeh-Bandbafha, H, Madadi, M, Sun, F, Mohammadi, P, Mahlia, TMI, Aghbashlo, M & Tabatabaei, M 2024, 'Biphasic pretreatment excels over conventional sulfuric acid in pinewood biorefinery: An environmental analysis', Environmental Research, vol. 248, pp. 118286-118286.
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Khuat, TT, Bassett, R, Otte, E, Grevis-James, A & Gabrys, B 2024, 'Applications of machine learning in antibody discovery, process development, manufacturing and formulation: Current trends, challenges, and opportunities', Computers & Chemical Engineering, vol. 182, pp. 108585-108585.
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Kilic, M, Barua, PD, Keles, T, Yildiz, AM, Tuncer, I, Dogan, S, Baygin, M, Tuncer, T, Kuluozturk, M, Tan, R-S & Acharya, UR 2024, 'GCLP: An automated asthma detection model based on global chaotic logistic pattern using cough sounds', Engineering Applications of Artificial Intelligence, vol. 127, pp. 107184-107184.
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Kim, HT, Philip, L, McDonagh, A, Johir, M, Ren, J, Shon, HK & Tijing, LD 2024, 'Recent Advances in High‐Rate Solar‐Driven Interfacial Evaporation', Advanced Science, vol. 11, no. 26.
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AbstractRecent advances in solar‐driven interfacial evaporation (SDIE) have led to high evaporation rates that open promising avenues for practical utilization in freshwater production and industrial application for pollutant and nutrient concentration, and resource recovery. Breakthroughs in overcoming the theoretical limitation of 2D interfacial evaporation have allowed for developing systems with high evaporation rates. This study presents a comprehensive review of various evaporator designs that have achieved pure evaporation rates beyond 4 kg m−2 h−1, including structural and material designs allowing for rapid evaporation, passive 3D designs, and systems coupled with alternative energy sources of wind and joule heating. The operational mechanisms for each design are outlined together with discussion on the current benefits and areas for improvement. The overarching challenges encountered by SDIE concerning the feasibility of direct integration into contemporary practical settings are assessed, and issues relating to sustaining elevated evaporation rates under diverse environmental conditions are addressed.
Kim, J, Dong, L, Shon, HK & Park, K 2024, 'Current progress in semi-batch reverse osmosis for brackish water desalination', Desalination, vol. 578, pp. 117434-117434.
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Kim, J, Lee, J, Lee, S, Tijing, L, Shon, HK & Hong, S 2024, 'Electrically conductive membrane for fouling control: Its mechanisms and applications', Desalination, vol. 578, pp. 117445-117445.
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Kim, S, Corah, M, Keller, J, Best, G & Scherer, S 2024, 'Multi-Robot Multi-Room Exploration With Geometric Cue Extraction and Circular Decomposition', IEEE Robotics and Automation Letters, vol. 9, no. 2, pp. 1190-1197.
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Kirik, S, Tasci, I, Barua, PD, Yildiz, AM, Keles, T, Baygin, M, Tuncer, I, Dogan, S, Tuncer, T, Devi, A, Tan, R-S & Acharya, UR 2024, 'DSWIN: Automated hunger detection model based on hand-crafted decomposed shifted windows architecture using EEG signals', Knowledge-Based Systems, vol. 300, pp. 112150-112150.
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Knight, S, Bowdler, I, Ford, H & Zhou, J 2024, 'A visual scoping review of how knowledge graphs and search engine results page designs represent uncertainty and disagreement', Information and Learning Sciences, vol. 125, no. 11/12, pp. 1030-1053.
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PurposeInformational conflict and uncertainty are common features across a range of sources, topics and tasks. Search engines and their presentation of results via search engine results pages (SERPs) often underpinned by knowledge graphs (KGs) are commonly used across tasks. Yet, it is not clear how search does, or could, represent the informational conflict that exists across and within returned results. The purpose of this paper is to review KG and SERP designs for representation of uncertainty or disagreement.Design/methodology/approachThe authors address the aim through a systematic analysis of material regarding uncertainty and disagreement in KG and SERP contexts. Specifically, the authors focus on the material representation – user interface design features – that have been developed in the context of uncertainty and disagreement representation for KGs and SERPs.FindingsSearches identified n = 136 items as relevant, with n = 4 sets of visual materials identified from these for analysis of their design features. Design elements were extracted against sets of design principles, highlighting tensions in the design of such features.Originality/valueThe authors conclude by highlighting two key challenges for interface design and recommending six design principles in representing uncertainty and conflict in SERPs. Given the important role technologies play in mediating information access and learning, addressing the representation of uncertainty and disagreement in the representation of information is crucial.
Koh, Y, Ssu-Han Chen, D, Sarafianou, M, Sharma, J, Jian Goh, D, Sze Wai Choong, D, Jiaqiang Ng, E & En-Yuan Lee, J 2024, 'Close Range and High-Resolution Detection of Vibration by Ultrasonic Wave Using Silicon-on-Nothing PMUTs', IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 71, no. 10, pp. 1345-1355.
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Kolekar, S, Gite, S, Pradhan, B & Alamri, A 2024, 'Predicting Vehicle Pose in Six Degrees of Freedom from Single Image in Real-World Traffic Environments Using Deep Pretrained Convolutional Networks and Modified Centernet', International Journal on Smart Sensing and Intelligent Systems, vol. 17, no. 1.
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Abstract The study focuses on intelligent driving, emphasizing the importance of recognizing nearby vehicles and estimating their positions using visual input from a single image. It employs transfer learning techniques, integrating deep convolutional networks’ features into a modified CenterNet model for six-degrees-of-freedom (6DoF) vehicle position estimation. To address the vanishing gradient problem, the model incorporates simultaneous double convolutional blocks with skip connections. Utilizing the ApolloCar3D dataset, which surpasses KITTI in comprehensiveness, the study evaluates pretrained models’ performance using mean average precision (mAP). The recommended model, Center-DenseNet201, achieves a mAP of 11.82% for relative translation thresholds (A3DP-Rel) and 39.92% for absolute translation thresholds (A3DP-Abs). These findings highlight the effectiveness of pretrained models in the modified architecture, enhancing vehicle posture prediction accuracy from single images. The research contributes to autonomous vehicle development, fostering safer and more efficient navigation systems in real-world traffic scenarios.
Kong, M, Hou, M, Zhao, S, Liu, F, Su, R & Chen, Y 2024, 'DADIN: Domain Adversarial Deep Interest Network for cross domain recommender systems', Expert Systems with Applications, vol. 243, pp. 122880-122880.
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Kong, X, Lu, Z, Guo, X, Zhang, J & Li, H 2024, 'Resilience Evaluation of Cyber-Physical Power System Considering Cyber Attacks', IEEE Transactions on Reliability, vol. 73, no. 1, pp. 245-256.
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Kong, X, Sun, G, Luo, Q, Brykin, V & Qian, J 2024, 'Experimental and theoretical studies on 3D printed short and continuous carbon fiber hybrid reinforced composites', Thin-Walled Structures, vol. 205, pp. 112406-112406.
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Kong, ZY, Sun, S, Ang, T, Yang, A, Ong, HC & Sunarso, J 2024, 'A new strategy to design the purification process of ethyl tertiary butyl ether gasoline additive via hybrid extractive-reactive distillation', Process Safety and Environmental Protection, vol. 192, pp. 643-648.
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Kotha, M, Pradhan, S & Cetindamar, D 2024, 'Relevance of Engineering Management Courses to Managerial Skills in the Industry', IEEE Transactions on Engineering Management, vol. 71, pp. 7849-7862.
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Digital technologies have radically influenced the modern workplace, which is exacerbated by the Covid-19 pandemic making it possible to work from home or remotely. Furthermore, these changes have been assimilated, so it is now necessary and difficult to separate ourselves from the flexible working environment.This study examines the various engineering management courses offered by Australian institutions to compare the skillsets taught and those deemed significant by engineering managers. The goal is to determine this connection in the current digital age, where re-skilling, digital intelligence, and empathy are some of the themes identified in the literature review.The study is based on a review of 20 relevant research articles, interviews with ten Australian engineering and technical managers transitioning from engineering to managerial and team leadership roles, and an analysis of six Australian university curriculums. The findings highlight the importance of digital and emotional intelligence for managers. The study show that most of the skillsets offered by the Australian university curricula did not include key skills.
Krishankumar, R, Ecer, F, Mishra, AR, Ravichandran, KS, Gandomi, AH & Kar, S 2024, 'A SWOT-Based Framework for Personalized Ranking of IoT Service Providers With Generalized Fuzzy Data for Sustainable Transport in Urban Regions', IEEE Transactions on Engineering Management, vol. 71, no. 99, pp. 2937-2950.
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Sustainable transport in cities has been gaining a lot of attraction recently and a core focus of engineering management. Internet of Things (IoT) is seen as a widely accepted technology that promotes sustainability through the interconnection of diverse computing sources for solving environmental problems. Previous studies on IoT have discussed interesting factors toward its adoption, but selecting a suitable IoT service provider (IoTSP) is an open challenge due to a diverse set of factors in practice. Driven by the challenge, in this article, a generalized fuzzy-based decision model is put forward for IoTSP selection, which is the prime objective of the study. Initially, a strength, weakness, opportunity, threat (SWOT) analysis is adopted to identify the crucial challenges in IoT adoption. Later, the relative significance of these challenges is calculated by adopting the regret/rejoice approach. Due to uncertainty, certain rating information of IoTSPs is missing that are rationally imputed by proposing an analytical approach. Rating matrices from experts are transformed into opinion vectors, and a prioritization algorithm is developed with query vector for rational personalized ordering of IoTSPs. Data for the study are acquired via questionnaire, which is filled by experts. The efficacy of the developed model is exemplified by using a real case study of IoTSP selection for pollution management in Chennai. Concerning the findings, mobility, security, and connectivity are the most vital factors for IoTSP selection. Results show that the proposed model is a viable tool for IoTSP selection and it is robust, unique, and stable compared to its counterparts.
Krishankumar, R, Ramanujam, N, Zavadskas, EK, Ravichandran, KS & Gandomi, AH 2024, 'Ranking Barriers Impeding Sustainability Adoption in Clean Energy Supply Chains: A Hybrid Framework With Fermatean Fuzzy Data', IEEE Transactions on Engineering Management, vol. 71, pp. 5506-5522.
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Krishnan, A, Thiyagarajan, K, Kodagoda, S & Bhattacharjee, M 2024, 'Wearable Flexible Temperature Sensor Suite for Thermal Tactile Perception', IEEE Sensors Journal, pp. 1-1.
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Kumar, A, Huang, Y, Lin, J, Hui, D & Fohrer, N 2024, 'Heavily modified freshwater: Potential ecological indicators', Ecological Indicators, vol. 159, pp. 111620-111620.
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Kumar, A, Singh, UK & Pradhan, B 2024, 'Enhancing Interpretability in Deep Learning-Based Inversion of 2-D Ground Penetrating Radar Data: An Explainable AI (XAI) Strategy', IEEE Geoscience and Remote Sensing Letters, vol. 21, pp. 1-5.
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Kumar, A, Singh, UK & Pradhan, B 2024, 'Enhancing subsurface contamination assessment via ensemble prediction of ground electrical property: A Colorado AMD-impacted wetland case study', Journal of Environmental Management, vol. 351, pp. 119943-119943.
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Kumar, R, Kanwal, A, Asim, M, Pervez, M, Mujtaba, MA, Fouad, Y & Kalam, MA 2024, 'Transforming the transportation sector: Mitigating greenhouse gas emissions through electric vehicles (EVs) and exploring sustainable pathways', AIP Advances, vol. 14, no. 3.
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Transportation-related emissions in Pakistan have been rapidly increasing in recent years. This study aims to determine how important it is to electrify road transportation in Pakistan to reduce greenhouse gas (GHG) emissions from the transportation sector. Motivated by the need to tackle the growing environmental issues related to conventional fuel-powered automobiles, this research explores the application of electrification techniques in the context of Pakistan’s transportation system. During the 2019 fiscal year, the transportation industry in Pakistan consumed 23 × 106 tonnes of energy from the burning of fossil fuels and produced 52.9 × 106 metric tons of CO2, which made up 31% of the country’s total carbon emissions. In this research, different scenarios, such as business as usual, low carbon, strengthen low carbon, and Pakistan National Electric Vehicle Policy 2040, are evaluated for the transportation sector of the country. Using the LEAP model, this study projects the effects of electrification on Pakistan road transportation over 30 years. When estimating how electrification will affect road transportation in Pakistan over the next 30 years, several factors were taken into account, including policy frameworks, changing consumer behavior, technology advancements, and infrastructure improvements. The analysis covered the emission levels, adoption hurdles, and possible advantages of transitioning to electric vehicles (EVs). The outcomes illustrate that adopting EVs can produce substantial drops in fuel consumption and environmental emissions, providing a sustainable solution to mitigate global warming. This work is directly associated with various Sustainable Development Goals, including SDG3 (good health and well-being), SDG7 (affordable and clean energy), and SDG13 (climate action). The results of this study highlight the considerable potential for GHG reduction associated with the widespread adoption of EVs, offering crucial insights ...
Kumar, S, Chattopadhyay, S & Adak, C 2024, 'TPMCF: Temporal QoS Prediction Using Multi-Source Collaborative Features', IEEE Transactions on Network and Service Management, vol. 21, no. 4, pp. 3945-3955.
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Kumari, A, Tanveer, M & Lin, CT 2024, 'Class Probability and Generalized Bell Fuzzy Twin SVM for Imbalanced Data', IEEE Transactions on Fuzzy Systems, vol. 32, no. 5, pp. 3037-3048.
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Kumari, A, Tanveer, M & Lin, C-T 2024, 'Bell-Shaped Fuzzy Least Square Twin SVM With Biomedical Applications', IEEE Transactions on Fuzzy Systems, vol. 32, no. 9, pp. 5348-5358.
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Kurunathan, H, Huang, H, Li, K, Ni, W & Hossain, E 2024, 'Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey', IEEE Communications Surveys & Tutorials, vol. 26, no. 1, pp. 496-533.
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Kusmayadi, A, Ong, HC, Amir, F, Riayatsyah, TMI, Leong, YK & Chang, J-S 2024, 'Hydrothermal liquefaction of swine wastewater-cultivated Chlorella sorokiniana SU-1 biomass for sustainable biofuel production', Biochemical Engineering Journal, vol. 209, pp. 109383-109383.
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Kute, DV, Pradhan, B, Shukla, N & Alamri, A 2024, 'Explainable deep learning model for predicting money laundering transactions', International Journal on Smart Sensing and Intelligent Systems, vol. 17, no. 1.
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Abstract Money laundering has been a global issue for decades. The ever-changing technology landscape, digital channels, and regulations make it increasingly difficult. Financial institutions use rule-based systems to detect suspicious money laundering transactions. However, it suffers from large false positives (FPs) that lead to operational efforts or misses on true positives (TPs) that increase the compliance risk. This paper presents a study of convolutional neural network (CNN) to predict money laundering and employs SHapley Additive exPlanations (SHAP) explainable artificial intelligence (AI) method to explain the CNN predictions. The results highlight the role of CNN in detecting suspicious transactions with high accuracy and SHAP’s role in bringing out the rationale of deep learning predictions.
Laccone, F, Pietroni, N, Cignoni, P & Malomo, L 2024, 'Bending-Reinforced Grid Shells for Free-form Architectural Surfaces.', Comput. Aided Des., vol. 168, pp. 103670-103670.
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Laghari, SUA, Li, W, Manickam, S, Nanda, P, Al-Ani, AK & Karuppayah, S 2024, 'Securing MQTT Ecosystem: Exploring Vulnerabilities, Mitigations, and Future Trajectories', IEEE Access, vol. 12, pp. 139273-139289.
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Lai, J, Lv, X & Yang, Y 2024, 'A 3-D Printed 140 GHz Multifocal Dielectric Transmitarray Antenna for 2-D Mechanical Beam Scanning', IEEE Antennas and Wireless Propagation Letters, vol. 23, no. 4, pp. 1366-1370.
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Lai, N, Chang, G, Yang, Y, He, M, Tang, W, Huang, Q, Zhang, Q, Su, QP, Liao, J, Yang, Y, Wang, C & Wang, R 2024, 'CsPbX3 quantum Dots@ZIF-8 composites with enhanced luminescence emission and stability', Journal of Luminescence, vol. 266, pp. 120280-120280.
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Lameesa, A, Hoque, M, Alam, MSB, Ahmed, SF & Gandomi, AH 2024, 'Role of metaheuristic algorithms in healthcare: a comprehensive investigation across clinical diagnosis, medical imaging, operations management, and public health', Journal of Computational Design and Engineering, vol. 11, no. 3, pp. 223-247.
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Abstract Metaheuristic algorithms have emerged in recent years as effective computational tools for addressing complex optimization problems in many areas, including healthcare. These algorithms can efficiently search through large solution spaces and locate optimal or near-optimal responses to complex issues. Although metaheuristic algorithms are crucial, previous review studies have not thoroughly investigated their applications in key healthcare areas such as clinical diagnosis and monitoring, medical imaging and processing, healthcare operations and management, as well as public health and emergency response. Numerous studies also failed to highlight the common challenges faced by metaheuristics in these areas. This review thus offers a comprehensive understanding of metaheuristic algorithms in these domains, along with their challenges and future development. It focuses on specific challenges associated with data quality and quantity, privacy and security, the complexity of high-dimensional spaces, and interpretability. We also investigate the capacity of metaheuristics to tackle and mitigate these challenges efficiently. Metaheuristic algorithms have significantly contributed to clinical decision-making by optimizing treatment plans and resource allocation and improving patient outcomes, as demonstrated in the literature. Nevertheless, the improper utilization of metaheuristic algorithms may give rise to various complications within medicine and healthcare despite their numerous benefits. Primary concerns comprise the complexity of the algorithms employed, the challenge in understanding the outcomes, and ethical considerations concerning data confidentiality and the well-being of patients. Advanced metaheuristic algorithms can optimize the scheduling of maintenance for medical equipment, minimizing operational downtime and ensuring continuous access to critical resources.
Larpruenrudee, P, Bennett, NS, Fitch, R, Sauret, E, Gu, Y & Islam, MS 2024, 'Investigation of metal hydride hydrogen storage performance using phase change materials', International Journal of Hydrogen Energy, vol. 60, pp. 996-1019.
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Laskar, JI, Sen, MK, Dutta, S, Gandomi, AH & Tewari, S 2024, 'An approach for data-driven time-varying flood resilience quantification of housing infrastructure system', Sustainable and Resilient Infrastructure, vol. 9, no. 2, pp. 124-144.
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Le Gentil, C, Ouabi, O-L, Wu, L, Pradalier, C & Vidal-Calleja, T 2024, 'Accurate Gaussian-Process-Based Distance Fields With Applications to Echolocation and Mapping', IEEE Robotics and Automation Letters, vol. 9, no. 2, pp. 1365-1372.
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Le, M, Thai Hoang, D, Nguyen, DN, Pham, Q-V & Hwang, W-J 2024, 'Wirelessly Powered Federated Learning Networks: Joint Power Transfer, Data Sensing, Model Training, and Resource Allocation', IEEE Internet of Things Journal, vol. 11, no. 21, pp. 34093-34107.
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Le, Q-H, Nguyen, D-H, Sang-To, T, Khatir, S, Le-Minh, H, Gandomi, AH & Cuong-Le, T 2024, 'Machine learning based models for predicting compressive strength of geopolymer concrete', Frontiers of Structural and Civil Engineering, vol. 18, no. 7, pp. 1028-1049.
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Ledic Neto, J, Andrade, DF, Lu, H-YH, Petrassi, ACMA & Moro, ARP 2024, 'The development and evaluation of a scale to assess job satisfaction in public universities with item response theory: a Brazilian study', International Journal of Public Sector Management, vol. 37, no. 4, pp. 486-503.
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PurposeThis study aimed to develop a psychometrically reliable job satisfaction (JS) measure for university employees, guiding administrative decisions and monitoring satisfaction over time in public universities.Design/methodology/approachA JS survey developed by a Brazilian federal university’s sustainability committee containing 58 items across physical, cognitive and organizational domains was longitudinally tested with 1,214 responses collected. The data were analyzed using Item Response Theory (IRT) analysis, employing the Graded Response Model, with tools such as frequency analysis, item characteristic curve, and full-information factor analysis in RStudio. The scale’s criterion validity was also established via expert qualitative interpretation.FindingsThe instrument’s internal consistency was confirmed as the results demonstrated its high reliability with a marginal reliability coefficient of 0.95. Significant findings revealed that recognition and supervisor relationships were key discriminators of JS and that workers began to perceive satisfaction when basic environmental conditions were met.Research limitations/implicationsIt is important to mention that the application of this scale is specifically limited to higher education institutions and may not be directly applicable to other educational settings or industry sectors without modifications.Originality/valueAlthough numerous measures and scales have been developed to assess JS, one elaborated by using IRT in a public universit...
Lee, K-T, Gabriela, S, Chen, W-H, Ong, HC, Rajendran, S & Tran, K-Q 2024, 'Co-torrefaction and synergistic effect of spent coffee grounds and tea waste for sustainable waste remediation and renewable energy', Renewable Energy, vol. 233, pp. 121181-121181.
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Lee, SS, Ho, A-V, Barzegarkhoo, R, Grigoletto, FB, Siwakoti, YP & Cao, S 2024, 'Single-Phase Boost Inverters Designed Using Half-Bridges', IEEE Transactions on Industrial Electronics, vol. 71, no. 9, pp. 11690-11695.
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Lei, B, Kong, L, Guo, Y, Sun, B, Li, X, Wu, K, Tam, VWY & Li, W 2024, 'Optimizing decarbonation and sustainability of concrete pavement: A case study', Case Studies in Construction Materials, vol. 21, pp. e03574-e03574.
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Lei, B, Yang, W, Guo, Y, Wang, X, Xiong, Q, Wang, K & Li, W 2024, 'Interfacial adhesion between recycled aggregate and asphalt mastic filled with recycled concrete powder', Case Studies in Construction Materials, vol. 20, pp. e02721-e02721.
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Recycled aggregate (RA) and recycled concrete powder (RCP) hold significant potential as environmentally sustainable raw materials for asphalt mixtures. In this study, a comprehensive investigation was conducted on the bonding properties between RA and RCP-filled asphalt mastic (RCPAM). This investigation utilized an image processing-assisted modified water boiling test, binder bond strength (BBS) tests, and the surface free energy (SFE) method. The results indicate that the boiling water test method, even with the assistance of 2D image processing analysis, cannot adequately evaluate the adhesive characteristics of the RA-RCPAM interface. This limitation could be attributed to the relatively small number of samples tested and the significant variation in surface properties of RA. Increasing both the filler-to-asphalt (F/A) ratio and RCP replacement ratio adversely affected the interfacial bond strength of the RA-RCPAM interface. On the other hand, an increase in RA surface roughness contributed to a higher bond strength. Based on the experimental results, a best-fit multivariate mixed model was proposed to predict the interfacial bond strength between RCP-filled asphalt mastic and recycled aggregate within a given range of RCP replacement ratio, surface roughness, and filler-to-asphalt (F/A) ratios. The analysis of SFE suggested that moisture damage to RCPAM was caused by both cohesive and adhesive failure. Additionally, the minimal impact of adhesion work in wet condition with increasing RCP content suggested that adhesion failure energy was only marginally affected by the inclusion of RCP, even in the presence of moisture. These findings are expected to enhance the understanding of interfacial adhesion characteristics and moisture susceptibility of the RA-RCPAM interface.
Lei, B, Yu, L, Guo, Y, Xue, H, Wang, X, Zhang, Y, Dong, W, Dehn, F & Li, W 2024, 'Triaxial mechanical behaviours and life cycle assessment of sustainable multi-recycled aggregate concrete', Science of The Total Environment, vol. 923, pp. 171381-171381.
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Lei, Y, Bossut, C, Sui, Y & Zhang, Q 2024, 'Context-Free Language Reachability via Skewed Tabulation', Proceedings of the ACM on Programming Languages, vol. 8, no. PLDI, pp. 1830-1853.
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Context-free language reachability (CFL-reachability) is a prominent model for formulating program analysis problems. Almost all CFL-reachability algorithms are based on the Reps-Horwitz-Sagiv (RHS) tabulation. In essence, the RHS tabulation, based on normalized context-free grammars, is similar to the CYK algorithm for CFL-parsing. Consider a normalized rule S ::= A B and a CFL-reachability problem instance of computing S-edges in the input graph. The RHS tabulation obtains all summary edges (i.e., S-, A-, and B-edges) based on the grammar rules. However, many A- and B-edges are wasted because only a subset of those edges eventually contributes to generating S-edges in the input graph. This paper proposes a new tabulation strategy for speeding up CFL-reachability by eliminating wasted and unnecessary summary edges. We particularly focus on recursive nonterminals. Our key technical insight is that the wasted edge generations and insertions caused by recursive nonterminals can be avoided by modifying the parse trees either statically (by transforming the grammar) or dynamically (using a specialized online CFL-reachability solver). For example, if a recursive nonterminal B, generated by a rule B ::= B X, appears on the right-hand side of a rule S ::= A B, we can make S recursive (by introducing a new rule S ::= S X) and eliminate the original recursive rule (B ::= B X). Due to the rule S ::= S X, the shapes of the parse trees associated with the left-hand-side nonterminal S become more 'skewed'. Thus, we name our approach skewed tabulation for CFL-reachability. Skewed tabulation can significantly improve the scalability of CFL-reachability by reducing wasted and unnecessary summary edges. We have implemented skewed tabulation and applied the corresponding CFL-reachability algorithm to an alias analysis, a value-flow analysis, and a taint analysis. Our extensive evaluation based on SPEC 2017 benchmarks yields promising results. For th...
Lei, Y, Li, J & Hao, H 2024, 'Physics-guided deep learning based on modal sensitivity for structural damage identification with unseen damage patterns', Engineering Structures, vol. 316, pp. 118510-118510.
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Lei, Y, Ye, D, Zhu, C, Shen, S, Zhou, W & Zhu, T 2024, 'A GNN-based teacher–student framework with multi-advice', Expert Systems with Applications, vol. 250, pp. 123887-123887.
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Leng, D, Lv, P, Zhu, Z, Li, Y & Liu, G 2024, 'Experimental study on semi-active magnetorheological elastomer based isolation system for offshore platform using wave tank', Ocean Engineering, vol. 292, pp. 116467-116467.
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Leng, D, You, S, Wang, R, Li, Y & Liu, G 2024, 'Fatigue damage mitigation of monopile offshore wind turbines utilizing a rotational inertia double tuned mass damper (RIDTMD) under realistic wind-wave conditions', Ocean Engineering, vol. 305, pp. 117756-117756.
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Leng, J, Wang, J, Shi, K, Cheng, J, Wen, S & Tang, Y 2024, 'Enhanced cubic function negative-determination Lemma on stability analysis for delayed neural networks via new analytical techniques', Journal of the Franklin Institute, vol. 361, no. 3, pp. 1155-1166.
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Leon-Castro, E, Blanco-Mesa, F, Hussain, W, Flores-Sosa, M & Perez-Arellano, LA 2024, 'Tax Revenue Measurement Using OWA Operators', Cybernetics and Systems, vol. 55, no. 1, pp. 230-244.
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Leong, D, Do, T & Lin, C-T 2024, 'The distinction between object recognition and object identification in brain connectivity for brain-computer interface applications', IEEE Transactions on Cognitive and Developmental Systems, pp. 1-13.
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Li Ziqi, 李, Zhong Xiaolan, 钟, Chen Chaohao, 陈 & Wang Fan, 王 2024, '超分辨光学显微成像的新武器——镧系离子掺杂上转换纳米荧光探针(特邀)', Laser & Optoelectronics Progress, vol. 61, no. 6, pp. 0618018-0618018.
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Li, C, Fang, J, Qiu, N, Wu, C, Steven, G & Li, Q 2024, 'On fracture mechanism of additively manufactured triply periodic minimal surface structures using an explicit phase field model', Additive Manufacturing, vol. 86, pp. 104192-104192.
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Li, C, Fang, J, Qiu, N, Wu, C, Steven, G & Li, Q 2024, 'Phase field fracture in elasto-plastic solids: Considering complex loading history for crushing simulations', International Journal of Mechanical Sciences, vol. 268, pp. 108994-108994.
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Li, C, Li, J, Zhu, X & Dong, P 2024, 'An experimental and numerical study for local behaviour of steel-concrete composite structures with novel long-nut shear connectors', Structures, vol. 67, pp. 106922-106922.
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Li, C, Li, P, Chen, Z, Yang, L, Li, F, Wan, F, Cao, Z, Yao, D, Lu, B-L & Xu, P 2024, 'Brain Network Manifold Learned by Cognition-Inspired Graph Embedding Model for Emotion Recognition', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 54, no. 12, pp. 7794-7808.
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Li, C, Li, P, Zhang, Y, Li, N, Si, Y, Li, F, Cao, Z, Chen, H, Chen, B, Yao, D & Xu, P 2024, 'Effective Emotion Recognition by Learning Discriminative Graph Topologies in EEG Brain Networks', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 8, pp. 10258-10272.
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Li, C, Ni, W, Ding, M, Qu, Y, Chen, J, Smith, D, Zhang, W & Rakotoarivelo, T 2024, 'Decentralized Privacy Preservation for Critical Connections in Graphs', IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 11, pp. 5911-5925.
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Li, C, Tang, T, Pan, Y, Yang, L, Zhang, S, Chen, Z, Li, P, Gao, D, Chen, H, Li, F, Yao, D, Cao, Z & Xu, P 2024, 'An Efficient Graph Learning System for Emotion Recognition Inspired by the Cognitive Prior Graph of EEG Brain Network', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15.
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Li, C, Zhang, Y, Wu, J, Luo, Y & Yu, S 2024, 'Smart Contract-Based Decentralized Data Sharing and Content Delivery for Intelligent Connected Vehicles in Edge Computing', IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 10, pp. 14535-14545.
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Li, D, Jiang, W, Ye, Y, Luo, J, Zhou, X, Yang, L, Guo, G, Wang, S, Liu, Z, Guo, W & Ngo, HH 2024, 'A change in substance and microbial community structure during the co-composting of kitchen waste anaerobic digestion effluent, sewage sludge and Chinese medicine residue', Science of The Total Environment, vol. 907, pp. 167679-167679.
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Li, D, Jiu, J, Liu, H, Yan, X, Li, X, Yan, L, Zhang, J, Fan, Z, Li, S, Du, G, Li, JJ, Du, Y, Liu, W & Wang, B 2024, 'Tissue-engineered mesenchymal stem cell constructs alleviate tendinopathy by suppressing vascularization', Bioactive Materials, vol. 36, pp. 474-489.
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Li, F, Wang, B, Zhu, L, Li, J, Zhang, Z & Chang, X 2024, 'Cross-Domain Transfer Hashing for Efficient Cross-Modal Retrieval', IEEE Transactions on Circuits and Systems for Video Technology, vol. 34, no. 10, pp. 9664-9677.
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Li, F, Wang, H, Yu, T, Duan, S, Wen, S & Huang, T 2024, 'Observer-Based Quasi-Projective Functional Synchronization of Parameters Mismatch Dynamical Networks With Mixed Time-Varying Delays Under Impulsive Controllers', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 54, no. 12, pp. 7647-7656.
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Li, F, Zuo, W, Zhou, K, Li, Q & Huang, Y 2024, 'State of charge estimation of lithium-ion batteries based on PSO-TCN-Attention neural network', Journal of Energy Storage, vol. 84, pp. 110806-110806.
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Li, F, Zuo, W, Zhou, K, Li, Q, Huang, Y & Zhang, G 2024, 'State-of-charge estimation of lithium-ion battery based on second order resistor-capacitance circuit-PSO-TCN model', Energy, vol. 289, pp. 130025-130025.
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Li, G, Peng, F, Wu, Z, Wang, S & Xu, RYD 2024, 'ODCL: An Object Disentanglement and Contrastive Learning Model for Few-Shot Industrial Defect Detection', IEEE Sensors Journal, vol. 24, no. 11, pp. 18568-18577.
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Li, G, Xu, P, Peng, S, Wang, C, Cai, Y & Yu, S 2024, 'TTSR: Tensor-Train Subspace Representation Method for Visual Domain Adaptation', IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 11, pp. 7229-7241.
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Li, H, Bi, K, Hao, H, Yu, Y & Xu, L 2024, 'Experimental study of a novel quasi-active negative stiffness damper system for achieving optimal active control performance', Engineering Structures, vol. 299, pp. 117082-117082.
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Li, H, Cai, Z, Wang, J, Tang, J, Ding, W, Lin, C-T & Shi, Y 2024, 'FedTP: Federated Learning by Transformer Personalization', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 10, pp. 13426-13440.
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Li, H, Zhao, J, Chen, J, Fang, S, Li, Z & Huo, H 2024, 'Convolutional Tensor Ring Decomposition for Context-Aware Recommendation'.
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Li, H, Zhao, J, Huo, H, Fang, S, Chen, J, Yao, L & Hua, Y 2024, 'T3srs: Tensor Train Transformer for Compressing Sequential Recommender Systems', Expert Systems with Applications, vol. 238, pp. 122260-122260.
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Li, H, Zheng, T, Qin, W, Tian, R, Ding, H, Ji, JC & Chen, L 2024, 'Theoretical and experimental study of a bi-stable piezoelectric energy harvester under hybrid galloping and band-limited random excitations', Applied Mathematics and Mechanics, vol. 45, no. 3, pp. 461-478.
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AbstractIn the practical environment, it is very common for the simultaneous occurrence of base excitation and crosswind. Scavenging the combined energy of vibration and wind with a single energy harvesting structure is fascinating. For this purpose, the effects of the wind speed and random excitation level are investigated with the stochastic averaging method (SAM) based on the energy envelope. The results of the analytical prediction are verified with the Monte-Carlo method (MCM). The numerical simulation shows that the introduction of wind can reduce the critical excitation level for triggering an inter-well jump and make a bi-stable energy harvester (BEH) realize the performance enhancement for a weak base excitation. However, as the strength of the wind increases to a particular level, the influence of the random base excitation on the dynamic responses is weakened, and the system exhibits a periodic galloping response. A comparison between a BEH and a linear energy harvester (LEH) indicates that the BEH demonstrates inferior performance for high-speed wind. Relevant experiments are conducted to investigate the validity of the theoretical prediction and numerical simulation. The experimental findings also show that strong random excitation is favorable for the BEH in the range of low wind speeds. However, as the speed of the incoming wind is up to a particular level, the disadvantage of the BEH becomes clear and evident.
Li, J, Chen, Z, Hu, H, Liu, X & Guo, Y 2024, 'Dynamic Damping Control of High-Precision Servo System Based on Friction Model for Finite-Time Position Tracking With Deteriorating Friction', IEEE Transactions on Industry Applications, vol. 60, no. 6, pp. 8563-8574.
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Li, J, Pan, Y & Tsang, IW 2024, 'Taming Overconfident Prediction on Unlabeled Data From Hindsight', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 10, pp. 14151-14163.
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Li, J, Zhu, X, Chen, S & Ruan, W 2024, 'Contact-point response reconstruction for indirect bridge monitoring via Bayesian expectation-maximization based augmented Kalman filter', Engineering Structures, vol. 309, pp. 118066-118066.
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The drive-by bridge dynamic measurement using an instrumented vehicle during its passage over the bridge can be an indirect bridge structural health monitoring system. The identification of the contact-point (CP) responses between vehicle wheels and bridge structure from vehicle responses offers an efficient and economical way for the modal identification and condition assessment of short- to mid-span road bridges. This paper proposes a novel method for the contact-point displacement response reconstruction of the vehicle-bridge interaction (VBI) system in a joint input-state estimation manner based on Bayesian Expectation-Maximization (BEM). An analytical model of VBI including the road approach in front of a simply-supported bridge is presented firstly. Then the augmented state-space model of the vehicle with the contact-point responses included as variables along with vehicle states is established. A new method by integrating the augmented Kalman filter (AKF) with a BEM strategy is proposed to solve the state estimation problem without knowing the vehicle axle responses. The vehicle states and CP displacement responses are identified simultaneously. The effects of the measurement noise, road surface roughness and vehicle speed on the identified results are investigated using numerical simulations. The experimental tests are used to further verify the feasibility and accuracy of the proposed method. The results show that the proposed method has potential for drive-by bridge condition assessment using on-board vehicle response measurements for a large population of short- to mid-span bridges.
Li, K, Liu, Y, Sun, X, Tian, X & Lei, G 2024, 'Applications of Wireless Power Transfer System in Motors: A Review', IEEE Access, vol. 12, pp. 80590-80606.
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Li, K, Lu, J, Zuo, H & Zhang, G 2024, 'Federated Fuzzy Transfer Learning With Domain and Category Shifts', IEEE Transactions on Fuzzy Systems, pp. 1-12.
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Li, K, Lu, J, Zuo, H & Zhang, G 2024, 'Multidomain Adaptation With Sample and Source Distillation', IEEE Transactions on Cybernetics, vol. 54, no. 4, pp. 2193-2205.
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Li, K, Lu, J, Zuo, H & Zhang, G 2024, 'Multi-source domain adaptation handling inaccurate label spaces', Neurocomputing, vol. 594, pp. 127824-127824.
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Li, K, Zha, X, Sun, X & Lei, G 2024, 'Model Predictive Control with Series Structure for Five-phase PMSHM Based on Discrete Space Virtual Voltage Modulation', IEEE Transactions on Transportation Electrification, pp. 1-1.
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Li, K, Zheng, J, Yuan, X, Ni, W, Akan, OB & Poor, HV 2024, 'Data-Agnostic Model Poisoning Against Federated Learning: A Graph Autoencoder Approach', IEEE Transactions on Information Forensics and Security, vol. 19, pp. 3465-3480.
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Li, L 2024, 'Impact of the propensity to cross-cultural adaptation on online viewers' attitudes toward ethnic minority group cultural streaming: An extension of the theory of planned behaviour', Computers in Human Behavior, vol. 160, pp. 108365-108365.
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Li, L 2024, 'Online consumers build trust with online merchants through real-time interaction function', Journal of Information Economics, vol. 1, no. 4, pp. 37-48.
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<p class='MsoNormal' style='text-align: justify; text-justify: inter-ideograph; line-height: normal; mso-pagination: none; margin: 12.0pt 0cm 0cm 0cm;'><span lang='EN-US' style='font-size: 14pt; font-family: 'times new roman', times, serif;'>Given the rapid development of live streaming commerce in China, this study focuses on the interactivity and sociability of live streaming shopping activities and explores online consumers’ real-time interaction intentions and trust-building behaviours with online merchants. To discover the real-time interaction between online consumers and online merchants, this study builds a research model based on the Theory of Planned Behaviour (TPB). Through the data analysis based on the partial least squares path modelling and variance-based structural equation modelling (PLS-SEM), the key findings state that, three factors, including attitude, subject norm, and perceived control, positively affect online consumers’ real-time interaction intentions and lead them to build trust with online merchants. Meanwhile, control variables, including gender, age, and educational background, demonstrate insignificant effects across the model. Unlike existing literature, the current study pays much attention to the interactive characteristics of live streaming shopping activities and can provide some valuable suggestions both for online consumers and online merchants, which can promote the co-development of the commercial and social aspects of live streaming platforms.</span></p>
Li, L & Kang, K 2024, 'Effect of the Social and Cultural Control on Young Eastern Ethnic Minority Groups’ Online-Startup Motivation', Entrepreneurship Research Journal, vol. 14, no. 2, pp. 491-514.
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Abstract This study is developed based on particular social and cultural backgrounds and discovers young Eastern ethnic minority groups’ (EMGs) online-startup motivation on live streaming platforms. Drawing on the Hofstede cultural dimensions, this paper explores various influencing factors, including peers’ support, conservative thinking and family support. It analyses young Eastern EMGs’ entrepreneurial motivation and behaviour based on the Stimulus, Organism and Response (S-O-R) model. Compared with traditional research models, the combination of the Hofstede cultural theory and the S-O-R model could be conducive to make the research model reflect influencing factors and present their specific relationships. By analysing 531 valid online questionnaires based on the partial least squares path modelling and variance-based structural equation modelling (PLS-SEM), the paper proves that peers’ support and family support can reduce young EMGs’ conservative thinking and positively affect young people EMGs’ online-startup motivation. Based on the analysis results, some suggestions are provided for related departments, aiming to enhance young EMGs’ online-startup confidence.
Li, L & Kang, K 2024, 'Ethnic minority group college students’ liberal and conservative attitudes to online start-ups: regional difference perspective', Journal of Entrepreneurship in Emerging Economies, vol. 16, no. 6, pp. 1533-1554.
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PurposeThis study aims to analyse what factors influence ethnic minority group (EMG) college students’ attitudes towards promoting online start-ups and how their different attitudes impact their final online start-up behaviours on the live streaming platform. Based on the COM-B behaviour changing model and the theory of liberal and conservative attitudes, the research model has been established in this study, and it divides influencing factors into the environmental opportunity unit and personal capability unit.Design/methodology/approachTo test relationships among the environmental opportunity, personal capability and personal attitude units, the partial least squares path modelling and variance-based structural equation modelling have been applied on the SmartPLS. Meanwhile, this study considers the regional difference between China’s developed and less-developed regions and promotes multi-group analysis based on it.FindingsResearch results show that the online start-up opportunity and capability positively affect EMG college students’ liberal attitudes but reduce EMGs’ conservative attitudes. Meanwhile, this study finds four significant differences, such as the path between conservative attitude and EMG students’ online start-up behaviour and the path between online start-up capability and conservative attitude.Originality/valueThis paper analyses the relationship between influencing factors and EMG students’ online start-up attitudes based on the COM-B behaviour changing model, contributing to the theoretical implications. Meanwhile, considering the impact of regional di...
Li, L & Kang, K 2024, 'Impact of opportunity and capability on e-entrepreneurial motivation: a comparison of urban and rural perspectives', Journal of Entrepreneurship in Emerging Economies, vol. 16, no. 4, pp. 932-953.
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Purpose: E-entrepreneurship is developed based on digital platforms, having specific technical opportunities, such as the interactive ecosystem, fast payment method and online store function, without strict requirements for online entrepreneurs. Considering China’s e-entrepreneurship environment and cultural background, this paper aims to analyse individuals’ e-entrepreneurship motivation based on the capability–opportunity–motivation–behaviour (COM-B) behaviour changing theory. Design/methodology/approach: Through testing 602 samples based on the partial least squares path modelling and variance-based structural equation modelling, the factors from the opportunity and capability units positively affect individuals’ e-entrepreneurship motivation. Meanwhile, because of the economic and social environmental differences between China’s urban and rural regions, this study promotes the multi-group analysis based on individuals’ regional backgrounds. Findings: First, as opportunity factors, technical and policy opportunities have significantly positive relationships with individuals’ e-entrepreneurship motivation. Second, entrepreneurial and cultural capabilities are essential for Chinese entrepreneurs while making an entrepreneurial decision. Third, because of the e-entrepreneurial environment difference and educational system gap, entrepreneurial capability exerts a greater influence on the e-entrepreneurship motivation for Chinese individuals from urban regions, and cultural capability exerts a higher impact on the e-entrepreneurship motivation for Chinese individuals from rural regions. Originality/value: Whilst the phenomenon of e-entrepreneurship is emerging as a popular entrepreneurship area of study, little research has systematically explored individuals’ e-entrepreneurial motivation and analysed influencing factors from macro and minor aspects. According to the COM-B behaviour changing theory, this paper discovers influencing factors from environmen...
Li, L & Kang, K 2024, 'Impact of opportunity and capability on e-entrepreneurial motivation: a comparison of urban and rural perspectives', Journal of Entrepreneurship in Emerging Economies, vol. 16, no. 4, pp. 932-953.
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PurposeE-entrepreneurship is developed based on digital platforms, having specific technical opportunities, such as the interactive ecosystem, fast payment method and online store function, without strict requirements for online entrepreneurs. Considering China’s e-entrepreneurship environment and cultural background, this paper aims to analyse individuals’ e-entrepreneurship motivation based on the capability–opportunity–motivation–behaviour (COM-B) behaviour changing theory.Design/methodology/approachThrough testing 602 samples based on the partial least squares path modelling and variance-based structural equation modelling, the factors from the opportunity and capability units positively affect individuals’ e-entrepreneurship motivation. Meanwhile, because of the economic and social environmental differences between China’s urban and rural regions, this study promotes the multi-group analysis based on individuals’ regional backgrounds.FindingsFirst, as opportunity factors, technical and policy opportunities have significantly positive relationships with individuals’ e-entrepreneurship motivation. Second, entrepreneurial and cultural capabilities are essential for Chinese entrepreneurs while making an entrepreneurial decision. Third, because of the e-entrepreneurial environment difference and educational system gap, entrepreneurial capability exerts a greater influence on the e-entrepreneurship motivation for Chinese individuals from urban regions, and cultural capability exerts a higher impact on the e-entrepreneurship motivation for Chinese individuals from rural regions.Originality/value
Li, L & Kang, K 2024, 'Understanding the real-time interaction between middle-aged consumers and online experts based on the COM-B model', Journal of Marketing Analytics, vol. 12, no. 3, pp. 570-582.
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AbstractThis paper presents a study of middle-aged online consumers’ specific shopping behaviour on live streaming platforms and analyses the distinct marketing strategy provided by online experts. Influenced by unique social and cultural backgrounds, middle-aged online consumers lack related shopping experience and keep counterfeiting concerns to live streaming shopping, making them prefer to interact with online experts before making final decisions. Based on the COM-B Behaviour Changing theory and the Emotional attachment theory, the research model has been established in this study, and it divides influencing factors into the Emotion unit, Opportunity unit and Capability unit. To test the relationships between influencing factors and middle-aged online consumers’ interactive motivation, the partial least-squares path modelling and variance-based structural equation modelling (PLS-SEM) have been applied on the SmartPLS. By analysing 450 samples, the study shows that the counterfeiting concern and ease of use factors positively impact online consumers’ motivation to interact with online experts, and self-efficacy plays a negative role.
Li, L, Kang, K, Feng, Y & Zhao, A 2024, 'Factors affecting online consumers’ cultural presence and cultural immersion experiences in live streaming shopping', Journal of Marketing Analytics, vol. 12, no. 2, pp. 250-263.
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Li, L, Mortazavi, M, Far, H, El-Sherbeeny, AM & Ahmadian Fard Fini, A 2024, 'Simulation and modeling of polymer concrete panels using deep neural networks', Case Studies in Construction Materials, vol. 20, pp. e02912-e02912.
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Li, L, Wang, W, Zhou, T, Quan, R & Yang, Y 2024, 'Semantic Hierarchy-Aware Segmentation', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 4, pp. 2123-2138.
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Li, M, Cao, Z & Li, Z 2024, 'Augmented Mixed Vehicular Platoon Control With Dense Communication Reinforcement Learning for Traffic Oscillation Alleviation', IEEE Internet of Things Journal, vol. 11, no. 22, pp. 35989-36001.
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Li, M, Chen, S, Shen, Y, Liu, G, Tsang, IW & Zhang, Y 2024, 'Online Multi-Agent Forecasting With Interpretable Collaborative Graph Neural Networks', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 4, pp. 4768-4782.
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Li, M, Li, Z & Cao, Z 2024, 'Enhancing Car-Following Performance in Traffic Oscillations Using Expert Demonstration Reinforcement Learning', IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 7, pp. 7751-7766.
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Li, M, Wang, H, Chen, Q & Zong, Z 2024, 'Event-Based Weak Oil Impurity Detection', IEEE Access, vol. 12, pp. 18868-18876.
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Li, M, Xu, Y & Fang, J 2024, 'Orthotropic mechanical properties of PLA materials fabricated by fused deposition modeling', Thin-Walled Structures, vol. 199, pp. 111800-111800.
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Li, M, Yang, Y, Zhang, Y & Iacopi, F 2024, '3-D Printed Vertically Integrated Composite Right/Left-Handed Transmission Line and Its Applications to Microwave Circuits', IEEE Transactions on Microwave Theory and Techniques, vol. 72, no. 6, pp. 3311-3321.
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Li, P, Gao, X, Li, C, Yi, C, Huang, W, Si, Y, Li, F, Cao, Z, Tian, Y & Xu, P 2024, 'Granger Causal Inference Based on Dual Laplacian Distribution and Its Application to MI-BCI Classification', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 11, pp. 16181-16195.
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Li, S, Chau, KT, Liu, W, Liu, C & Lee, C-K 2024, 'Design and Control of Wireless Hybrid Stepper Motor System', IEEE Transactions on Power Electronics, vol. 39, no. 8, pp. 10518-10531.
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Li, S, Feng, K, Xu, Y, Li, Y, Ni, Q, Zhang, K, Wang, Y & Ding, W 2024, 'Cross-modal zero-sample diagnosis framework utilizing non-contact sensing data fusion', Information Fusion, vol. 110, pp. 102453-102453.
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Li, S, Ji, J, Feng, K, Zhang, K, Ni, Q & Xu, Y 2024, 'Composite Neuro-Fuzzy System-Guided Cross-Modal Zero-Sample Diagnostic Framework Using Multi-Source Heterogeneous Non-Contact Sensing Data', IEEE Transactions on Fuzzy Systems, pp. 1-12.
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Li, S, Ji, JC, Xu, Y, Feng, K, Zhang, K, Feng, J, Beer, M, Ni, Q & Wang, Y 2024, 'Dconformer: A denoising convolutional transformer with joint learning strategy for intelligent diagnosis of bearing faults', Mechanical Systems and Signal Processing, vol. 210, pp. 111142-111142.
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Li, T, Sun, X, Yang, Z & Lei, G 2024, 'Simplified Two-Step Model Predictive Control With Fast Voltage Vector Search', IEEE Transactions on Industrial Electronics, pp. 1-10.
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Li, T, Walker, P, Khonasty, R, van de Graaf, VA, Yelf, E, Zhao, L & Huang, S 2024, 'Robotic‐assisted burring in total hip replacement: A new surgical technique to optimise acetabular preparation', The International Journal of Medical Robotics and Computer Assisted Surgery, vol. 20, no. 1.
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AbstractBackgroundIn Total Hip replacement (THR) surgery, a critical step is to cut an accurate hemisphere into the acetabulum so that the component can be fitted accurately and obtain early stability. This study aims to determine whether burring rather than reaming the acetabulum can achieve greater accuracy in the creation of this hemisphere.MethodsA preliminary robotic system was developed to demonstrate the feasibility of burring the acetabulum using the Universal Robot (UR10). The study will describe mechanical design, robot trajectory optimisation, control algorithm development, and results from phantom experiments compared with both robotic reaming and conventional reaming. The system was also tested in a cadaver experiment.ResultsThe proposed robotic burring system can produce a surface in 2 min with an average error of 0.1 and 0.18 mm, when cutting polyurethane bone block #15 and #30, respectively. The performance was better than robotic reaming and conventional hand reaming.ConclusionThe proposed robotic burring system outperformed robotic and conventional reaming methods to produce an accurate acetabular cavity. The findings show the potential usage of a robotic‐assisted burring in THR for acetabular preparation.
Li, T, Zhao, S, Rao, L, Zou, H, Chen, K, Lu, J & Burnett, IS 2024, 'Experimental study of a distributed active noise control system with multi-device nodes based on augmented diffusion strategy', The Journal of the Acoustical Society of America, vol. 156, no. 5, pp. 3246-3259.
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Recently, distributed active noise control (DANC) algorithms have been explored as a way to reduce computational complexity while ensuring system stability, thereby outperforming conventional centralized and decentralized algorithms. Most existing DANC algorithms assume that each node has only one pair of loudspeaker and microphone, limiting their flexibility in practical applications. In contrast, this paper proposes a DANC algorithm with general multi-device nodes based on the recently developed augmented diffusion strategy, allowing flexible and scalable ANC applications. A real-time distributed ANC system based on a multi-core digital signal processor platform is developed in order to compare the control performance of the proposed extended augmented diffusion algorithm with that of existing centralized, decentralized and augmented diffusion algorithms. Real-time experiments demonstrate that the proposed algorithm exhibits noise reduction performance consistent with that of the centralized algorithm while achieving lower global computational complexity and avoiding the system instability risk of the decentralized algorithm. Further, the new algorithm improves convergence speed and reduces the global communication cost compared to the previous augmented diffusion algorithm. Experimental results indicate the application potential of the proposed DANC algorithm for a generalized system configuration.
Li, W, Bazaz, SR, Mayoh, C & Salomon, R 2024, 'Analytical Workflows for Single‐Cell Multiomic Data Using the BD Rhapsody Platform', Current Protocols, vol. 4, no. 2.
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AbstractThe conversion of raw sequencing reads to biologically relevant data in high‐throughput single‐cell RNA sequencing experiments is a complex and involved process. Drawing meaning from thousands of individual cells to provide biological insight requires ensuring not only that the data are of the highest quality but also that the signal can be separated from noise. In this article, we describe a detailed analytical workflow, including six pipelines, that allows high‐quality data analysis in single‐cell multiomics. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC.Basic Protocol 1: Image analysisBasic Protocol 2: Sequencing quality control and generation of a gene expression matrixBasic Protocol 3: Gene expression matrix data pre‐processing and analysisBasic Protocol 4: Advanced analysisBasic Protocol 5: Conversion to flow cytometry standard (FCS) formatBasic Protocol 6: Visualization using graphical interfaces
Li, W, Zhao, B, Zhu, L, Wang, Y, Zhong, Q & Yu, S 2024, 'TCEC: Integrity Protection for Containers by Trusted Chip on IoT Edge Computing Nodes', IEEE Sensors Journal, pp. 1-1.
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Li, X, Gong, Y, Liu, W, Yin, Y, Zheng, Y & Nie, L 2024, 'Dual-track spatio-temporal learning for urban flow prediction with adaptive normalization', Artificial Intelligence, vol. 328, pp. 104065-104065.
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Li, X, Li, L, Li, X, Cai, B, Jia, J, Gao, Y & Yu, S 2024, 'GMFITD: Graph Meta-Learning for Effective Few-Shot Insider Threat Detection', IEEE Transactions on Information Forensics and Security, vol. 19, pp. 7161-7175.
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Li, X, Wang, Y, Shi, J, Zhao, Z, Wang, D, Chen, Z, Cheng, L, Lu, G-H, Liang, Y, Dong, H, Shan, X, Liu, B, Chen, C, Liu, Y, Liu, F, Sun, L-D, Zhong, X & Wang, F 2024, 'Large-Area Near-Infrared Emission Enhancement on Single Upconversion Nanoparticles by Metal Nanohole Array', Nano Letters, vol. 24, no. 19, pp. 5831-5837.
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Li, X, Wang, Z, Hu, Y, Huang, Y, Xiang, L & Cheng, X 2024, 'Experimental and numerical studies of the evaporation and combustion characteristics of large-angle impinging sprays', Applied Thermal Engineering, vol. 246, pp. 122918-122918.
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Li, Y, Guo, Z, Yang, Z, Sun, Y, Zhao, L & Tombari, F 2024, 'Open-Structure: Structural Benchmark Dataset for SLAM Algorithms', IEEE Robotics and Automation Letters, vol. 9, no. 12, pp. 11457-11464.
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Li, Y, Ma, C, Gao, R, Wu, Y, Li, J, Wang, W & Wu, X 2024, 'OPF-Miner: Order-Preserving Pattern Mining With Forgetting Mechanism for Time Series', IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 12, pp. 8981-8995.
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Li, Y, Meng, X, Wang, Z, Lin, X, Xu, Y, Mou, J, Zhou, R, Tang, Y, Sze Ki Lin, C & Li, X 2024, 'Utilization of tofu wastewater and Nannochloropsis oceanica for eutrophication mitigation and eicosapentaenoic acid valorization: Advancing carbon neutrality and resource recycling', Chemical Engineering Journal, vol. 493, pp. 152706-152706.
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Li, Y, Sun, X, Chen, H, Zhang, S, Yang, Y & Xu, G 2024, 'Attention Is Not the Only Choice: Counterfactual Reasoning for Path-Based Explainable Recommendation', IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 9, pp. 4458-4471.
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Li, Y, Xiao, F, Li, H, Li, Q & Yu, S 2024, 'Meta label associated loss for fine-grained visual recognition', Science China Information Sciences, vol. 67, no. 6.
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Li, Y, Zhang, J, Cheng, D, Guo, W, Liu, H, Guo, A, Chen, X, Wang, Y & Ngo, HH 2024, 'Magnetic biochar serves as adsorbents and catalyst supports for the removal of antibiotics from wastewater: A review', Journal of Environmental Management, vol. 366, pp. 121872-121872.
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Li, Y, Zhao, H, Hu, Y, Qu, F, Zhu, D, Wang, K & Li, W 2024, 'Effect of pore water pressure on mechanical performance of recycled aggregate concrete under triaxial compression', Cement and Concrete Composites, vol. 146, pp. 105402-105402.
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Li, Z, Chen, Y, Wang, X, Yao, L & Xu, G 2024, 'Multi-view GCN for loan default risk prediction', Neural Computing and Applications, vol. 36, no. 20, pp. 12149-12162.
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AbstractAs a significant application of machine learning in financial scenarios, loan default risk prediction aims to evaluate the client’s default probability. However, most existing deep learning solutions treat each application as an independent individual, neglecting the explicit connections among different application records. Besides, these attempts suffer from the problem of missing data and imbalanced distribution (i.e., the default records are small samples against all the applications). We believe similar records could provide some auxiliary signals, which are of critical importance to alleviate the data missing issue and facilitate data argumentation. To this end, we propose multi-view loan application graphs, dubbed MLAGs. By evaluating the similarity between the records, a loan application graph can be constructed. Furthermore, we arrange different similarity thresholds to organize various graph structures for multi-graph constructions; thus, a variety of representations can be generated via information propagation and aggregation for small sample argumentation. Consequently, the imbalanced data distribution and missing values issues can be alleviated effectively. We conduct experiments on three public datasets from real-world home credit and P2P lending platforms, which show that MGCN outperforms both conventional and deep learning models. Ablation studies also illustrated the validity of each module design.
Li, Z, Lee, T-U, Pietroni, N, Snooks, R & Xie, YM 2024, 'Design and construction of catenary-ruled surfaces', Structures, vol. 59, pp. 105755-105755.
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Li, Z-R, Lv, T-R, Yang, Z, Zhang, W-H, Yin, M-J, Yong, K-T & An, Q-F 2024, '3D microprinting of QR-code integrated hydrogel tactile sensor for real-time E-healthcare', Chemical Engineering Journal, vol. 484, pp. 149375-149375.
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Lian, M, Guo, Z, Wen, S & Huang, T 2024, 'Distributed Adaptive Algorithm for Resource Allocation Problem Over Weight-Unbalanced Graphs', IEEE Transactions on Network Science and Engineering, vol. 11, no. 1, pp. 416-426.
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Lian, M, Guo, Z, Wen, S & Huang, T 2024, 'Distributed Predefined-Time Algorithm for System of Linear Equations Over Directed Networks', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 71, no. 4, pp. 2139-2143.
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Liang, H, Xu, W, Chiclana, F, Yu, S, Dong, Y & Herrera-Viedma, EE 2024, 'Evolution of Credit Scores of Enterprises in a Social Network: A Perspective Based on Opinion Dynamics', IEEE Transactions on Computational Social Systems, vol. 11, no. 3, pp. 3637-3651.
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Liang, M, Huang, S & Liu, W 2024, 'Dynamic semantic structure distillation for low-resolution fine-grained recognition', Pattern Recognition, vol. 148, pp. 110216-110216.
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Liang, R, Zhang, Q, Wang, J & Lu, J 2024, 'A Hierarchical Attention Network for Cross-Domain Group Recommendation', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 3, pp. 3859-3873.
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Many online services allow users to participate in various group activities such as online meeting or group buying, and thus need to provide user groups with services that they are interested. The group recommender systems (GRSs) emerge as required and provide personalized services for various online user groups. Data sparsity is an important issue in GRSs, since even fewer group-item interactions are observed. Moreover, the group and the group members have complex and mutual relationships with each other, which exacerbates the difficulty in modeling the preferences of both a group and its members for recommendation. The cross-domain recommender system (CDRS) is a solution to alleviate data sparsity and assist preference modeling by transferring knowledge from a source domain which has relatively dense data to another. The existing CDRSs are usually developed for individual users and cannot be directly applied for group recommendation. To alleviate the data sparsity issue in GRSs, we first study the cross-domain group recommendation problem and propose a hierarchical attention network-based cross-domain group recommendation method, called HAN-CDGR. HAN-CDGR takes the advantage of data from a source domain to benefit recommendation generation for both the individual users and groups in the target domain which has data sparsity and cannot generate accurate recommendation. In HAN-CDGR, a hierarchical attention network is constructed to learn and model individual and group preferences, with consideration of both group members' interactions and dynamic weights and the complex relationships between individuals and groups. Adversarial learning is used to effectively transfer knowledge from a source domain to the target domain. Extensive experiments, which demonstrate the effectiveness and superiority of our proposal, providing accurate recommendation for both individual users and groups, are conducted on three tasks.
Liang, X, Chu, L, Hua, B, Shi, Q, Shi, J, Meng, C & Braun, R 2024, 'Road target recognition based on radar range-Doppler spectrum with GS – ResNet', International Journal of Remote Sensing, vol. 45, no. 22, pp. 8290-8312.
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Liang, Y, Wu, W, Li, H, Chang, X, Chen, X, Peng, J & Xu, P 2024, 'DCS-Gait: A Class-Level Domain Adaptation Approach for Cross-Scene and Cross-State Gait Recognition Using Wi-Fi CSI', IEEE Transactions on Information Forensics and Security, vol. 19, pp. 2997-3007.
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Liang, Y, Zhang, C, An, S, Wang, Z, Shi, K, Peng, T, Ma, Y, Xie, X, He, J & Zheng, K 2024, 'FetchEEG: a hybrid approach combining feature extraction and temporal-channel joint attention for EEG-based emotion classification', Journal of Neural Engineering, vol. 21, no. 3, pp. 036011-036011.
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Abstract Objective. Electroencephalogram (EEG) analysis has always been an important tool in neural engineering, and the recognition and classification of human emotions are one of the important tasks in neural engineering. EEG data, obtained from electrodes placed on the scalp, represent a valuable resource of information for brain activity analysis and emotion recognition. Feature extraction methods have shown promising results, but recent trends have shifted toward end-to-end methods based on deep learning. However, these approaches often overlook channel representations, and their complex structures pose certain challenges to model fitting. Approach. To address these challenges, this paper proposes a hybrid approach named FetchEEG that combines feature extraction and temporal-channel joint attention. Leveraging the advantages of both traditional feature extraction and deep learning, the FetchEEG adopts a multi-head self-attention mechanism to extract representations between different time moments and channels simultaneously. The joint representations are then concatenated and classified using fully-connected layers for emotion recognition. The performance of the FetchEEG is verified by comparison experiments on a self-developed dataset and two public datasets. Main results. In both subject-dependent and subject-independent experiments, the FetchEEG demonstrates better performance and stronger generalization ability than the state-of-the-art methods on all datasets. Moreover, the performance of the FetchEEG is analyzed for different sliding window sizes and overlap rates in the feature extraction module. The sensitivity of emotion recognition is investigated for three- and five-frequency-band scenarios. Significance. FetchEEG is a novel hybrid method based on EEG for emotion cla...
Liang, Y, Zhu, L, Wang, X & Yang, Y 2024, 'IcoCap: Improving Video Captioning by Compounding Images', IEEE Transactions on Multimedia, vol. 26, pp. 4389-4400.
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Liang, Y, Zhu, L, Wang, X & Yang, Y 2024, 'Penalizing the Hard Example But Not Too Much: A Strong Baseline for Fine-Grained Visual Classification', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 5, pp. 7048-7059.
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Though significant progress has been achieved on fine-grained visual classification (FGVC), severe overfitting still hinders model generalization. A recent study shows that hard samples in the training set can be easily fit, but most existing FGVC methods fail to classify some hard examples in the test set. The reason is that the model overfits those hard examples in the training set, but does not learn to generalize to unseen examples in the test set. In this article, we propose a moderate hard example modulation (MHEM) strategy to properly modulate the hard examples. MHEM encourages the model to not overfit hard examples and offers better generalization and discrimination. First, we introduce three conditions and formulate a general form of a modulated loss function. Second, we instantiate the loss function and provide a strong baseline for FGVC, where the performance of a naive backbone can be boosted and be comparable with recent methods. Moreover, we demonstrate that our baseline can be readily incorporated into the existing methods and empower these methods to be more discriminative. Equipped with our strong baseline, we achieve consistent improvements on three typical FGVC datasets, i.e., CUB-200-2011, Stanford Cars, and FGVC-Aircraft. We hope the idea of moderate hard example modulation will inspire future research work toward more effective fine-grained visual recognition.
Liao, Y, Hsieh, M-H & Ferrie, C 2024, 'Quantum optimization for training quantum neural networks', Quantum Machine Intelligence, vol. 6, no. 1.
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AbstractTraining quantum neural networks (QNNs) using gradient-based or gradient-free classical optimization approaches is severely impacted by the presence of barren plateaus in the cost landscapes. In this paper, we devise a framework for leveraging quantum optimization algorithms to find optimal parameters of QNNs for certain tasks. To cast the optimization problem of training QNN into the context of quantum optimization, the parameters in QNN are quantized—moved from being classical to being stored in quantum registers which are in addition to those upon which the QNN is performing its computation. We then coherently encode the cost function of QNNs onto relative phases of a superposition state in the Hilbert space of the QNN parameters. The parameters are tuned with an iterative quantum optimization structure using adaptively selected Hamiltonians. The quantum mechanism of this framework exploits hidden structure in the QNN optimization problem and hence is expected to provide beyond-Grover speed up, mitigating the barren plateau issue.
Lin, A, Li, J, Xiang, Y, Bian, W & Prasad, M 2024, 'Normal Transformer: Extracting Surface Geometry from LiDAR Points Enhanced by Visual Semantics', IEEE Transactions on Intelligent Vehicles, pp. 1-11.
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Lin, C, Tang, J, Wang, S, Gao, Q, Liu, Y, Wu, W, Wang, X, Huang, Z & Yang, L 2024, 'Fabrication of FeP2/C/CNTs@3D interconnected graphene aerogel composite for lithium-ion battery anodes and the electrochemical performance evaluation using machine learning', Journal of Alloys and Compounds, vol. 996, pp. 174800-174800.
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Lin, C-T 2024, '2024 IEEE CIS Awards [Society Briefs]', IEEE Computational Intelligence Magazine, vol. 19, no. 1, pp. 9-12.
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Lin, C-T, Zhang, H, Ou, L, Chang, Y-C & Wang, Y-K 2024, 'Adaptive Trust Model for Multi-Agent Teaming Based on Reinforcement-Learning-Based Fusion', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 8, no. 1, pp. 229-239.
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Lin, J-Y, Wong, S-W, Qian, L, Wang, Y, Yang, Y & Liu, Q-H 2024, 'Single and Multiple-Band Bandpass Filters Using Bandstop Resonator Sections', IEEE Journal of Microwaves, vol. 4, no. 2, pp. 293-302.
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Lin, S, Liu, A, Wang, J & Kong, X 2024, 'An improved fault-tolerant cultural-PSO with probability for multi-AGV path planning', Expert Systems with Applications, vol. 237, pp. 121510-121510.
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This paper presents a hybrid evolutionary algorithm, cultural-particle swarm optimization (C-PSO), which is inspired by the cultural algorithm and the particle swarm optimization algorithm. It is aimed to balance the performance of exploration and exploitation and avoid trapping in the local optima. It introduces a probabilistic approach to update the inertia weight based on the improved metropolis rule. Generating the optimal path without collisions is challenging to ensure vehicles operate safely in real-time implementation. The contributions of C-PSO are to solve the path planning problem of multiple vehicles in modern industrial warehouses, achieving task allocation, fault tolerance and collision avoidance by a dual-layer framework. It was compared with the other algorithms, including PSO, PSO-GA, CA, HS, ABC, HPSGWO, TS and MA, by CEC benchmark functions and statistical tests to demonstrate its great performance with fewer iterations and runtime and the best solutions. It is validated through computational experiments, which involve 15 AGVs and 20 tasks for demonstration.
Lin, X, Castel, A, Deng, Z, Dong, B, Zhang, X, Zhang, S & Li, W 2024, 'Effect of crystalline admixtures on shrinkage and alkali-silica reaction of biochar-cementitious composites', Developments in the Built Environment, vol. 18, pp. 100456-100456.
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Lin, X, Ma, B, Wang, X, Yu, G, He, Y, Liu, RP & Ni, W 2024, 'ByCAN: Reverse Engineering Controller Area Network (CAN) Messages From Bit to Byte Level', IEEE Internet of Things Journal, vol. 11, no. 21, pp. 35477-35491.
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Lin, Y, Li, L, Zhang, J & Wang, J 2024, 'A scenario-based stochastic model predictive control approach for microgrid operation at an Australian cotton farm under uncertainties', International Journal of Electrical Power & Energy Systems, vol. 159, pp. 110025-110025.
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Liu Chung Ming, C, Wang, X & Gentile, C 2024, 'Protective role of acetylcholine and the cholinergic system in the injured heart', iScience, vol. 27, no. 9, pp. 110726-110726.
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Liu, B, Liu, B, Ding, M & Zhu, T 2024, 'MeST-Former: Motion-enhanced Spatiotemporal Transformer for generalizable Deepfake detection', Neurocomputing, vol. 610, pp. 128588-128588.
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Liu, B, Ni, W, Liu, RP, Guo, YJ & Zhu, H 2024, 'Optimal Electric Vehicle Charging Strategies for Long-Distance Driving', IEEE Transactions on Vehicular Technology, vol. 73, no. 4, pp. 4949-4960.
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Electric vehicles (EVs) provide sustainable and eco-friendly transportation. However, long-range driving is challenging due to limited battery capacity. While charging stations can replenish the battery, they are not as widely deployed as gas stations, causing the battery depletion of EVs. This paper presents a novel optimal charging strategy for EV long-range driving. The strategy offers EV drivers the optimal charging instructions for a given trip. A new finite-horizon Markov decision process (FH-MDP) problem is formulated to minimize the travel time of an EV while preventing its battery depletion, by optimally selecting the charging stations and charging times along the trip. By confirming the monotonicity and subadditivity of the FH-MDP, we prove the existence of a monotone deterministic Markovian policy for the optimal charging decision and reveal the optimal charging time is monotone regarding the remaining battery level and driven distance. We also reveal that the optimal charging time only changes when either of two thresholds regarding the remaining battery level or driving distance is met. By comparing its state with the thresholds, the EV can make optimal decisions with linear complexity. Simulations corroborate that our algorithm can save travel time by at least 12.6% under our simulation settings, compared to alternative methods.
Liu, B, Ni, W, Liu, RP, Guo, YJ & Zhu, H 2024, 'Privacy-Preserving Routing and Charging Scheduling for Cellular-Connected Unmanned Aerial Vehicles', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 54, no. 8, pp. 4929-4941.
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Liu, C, Du, H, Lei, G, Wang, Y & Zhu, J 2024, 'Design and Analysis of Modular Permanent Magnet Claw Pole Machines With Hybrid Cores for Electric Vehicles', IEEE Transactions on Energy Conversion, pp. 1-15.
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Liu, C, Li, P, Zhang, H, Li, L, Huang, Z, Wang, D & Yu, X 2024, 'BAVS: Bootstrapping Audio-Visual Segmentation by Integrating Foundation Knowledge', IEEE Transactions on Multimedia, vol. 26, pp. 10015-10028.
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Liu, C, Zhang, H, Wang, S, Wang, Y, Lei, G & Zhu, J 2024, 'Multiphysical Design and Optimization of High-Speed Permanent Magnet Synchronous Motor with Sinusoidal Segmented Permanent Magnet Structure', Journal of Electrical Engineering & Technology, vol. 19, no. 3, pp. 1459-1473.
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Liu, D, Balaguer, C, Dissanayake, G & Kovac, M 2024, 'Preface', Infrastructure Robotics: Methodologies, Robotic Systems and Applications, p. xix.
Liu, D, Tsang, IW & Yang, G 2024, 'A Convergence Path to Deep Learning on Noisy Labels', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 4, pp. 5170-5182.
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Liu, G, Zhang, W, Wang, X, King, S & Yu, S 2024, 'A Membership Inference and Adversarial Attack Defense Framework for Network Traffic Classifiers', IEEE Transactions on Artificial Intelligence, pp. 1-16.
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Liu, G-Y, Li, J-P, Indraratna, B & Zhou, P 2024, 'A hydraulic-mechanical (HM) coupling constitutive model for unsaturated soil-continuum interfaces considering bonding effect', Computers and Geotechnics, vol. 166, pp. 105989-105989.
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Liu, H, Li, X, Zhang, Z, Li, J, Zhou, T, Wang, Z & Wang, Q 2024, 'Urine pretreatment enhances energy recovery by boosting medium-chain fatty acids production from waste activate sludge through anaerobic fermentation', Chemical Engineering Journal, vol. 482, pp. 148842-148842.
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Liu, H, Merenda, A, Guo, M, Kim, J, Phuntsho, S, Shon, H & Sun, P 2024, 'Novel Approach for Fresh Urine Stabilization during Collection and Storage', Environmental Science & Technology Letters, vol. 11, no. 8, pp. 895-900.
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Liu, H, Yang, C, Dong, C, Wang, J, Zhang, X, Lyalin, A, Taketsugu, T, Chen, Z, Guan, D, Xu, X, Shao, Z & Huang, Z 2024, 'Electrocatalytic Ammonia Oxidation to Nitrite and Nitrate with NiOOH‐Ni', Advanced Energy Materials, vol. 14, no. 42.
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AbstractAmmonia electrooxidation in aqueous solutions can be a highly energy‐efficient process in producing nitrate and nitrite while generating hydrogen under ambient conditions. However, the kinetics of this reaction are slow and the role of catalyst in facilitating ammonia electrooxidation is not well understood. In this study, a high‐performance NiOOH‐Ni catalyst is introduced for converting ammonia into nitrite with Faraday efficiency of up to 90.4% and nitrate production rate of 1 mg h−1 cm−2. By employing Operando techniques, the role of NiOOH catalyst is elucidated in the dynamic electrooxidation of ammonia. Density functional theory (DFT) calculations support experimental observations and reveal the mechanism of the electrochemical oxidation of ammonia to nitrite and nitrate. Overall, this research contributes to the development of a cost‐effective and highly efficient catalyst for large‐scale ammonia electrolysis, while shedding light on the underlying mechanism of the NiOOH catalyst in ammonia electrooxidation.
Liu, H, Zhang, Z, Li, X, Zhou, T, Wang, Z, Li, J, Li, Y & Wang, Q 2024, 'Temperature-phased anaerobic sludge digestion effectively removes antibiotic resistance genes in a full-scale wastewater treatment plant', Science of The Total Environment, vol. 924, pp. 171555-171555.
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Liu, J, Chen, G, Wen, S & Zhu, S 2024, 'Finite-time piecewise control for discrete-time stochastic nonlinear time-varying systems with time-varying delays', Chaos, Solitons & Fractals, vol. 184, pp. 114982-114982.
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Liu, J, Chen, H & Peng, X 2024, 'Efficacy and Safety of Thermocoagulation vs. Cryotherapy for Cervical Precancerous Lesions: A Systematic Review and Meta-Analysis', Clinical and Experimental Obstetrics & Gynecology, vol. 51, no. 3, pp. 72-72.
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Liu, J, Dong, L, Li, C, Fang, J, Chen, Y & Cui, J 2024, 'Quasi-static and dynamic tensile behaviour of 316L stainless steels: Rolled versus laser-powder bed fusion (LPBF) fabricated samples', International Journal of Impact Engineering, vol. 190, pp. 104972-104972.
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Liu, J, Huang, X-L & Yu, S 2024, 'Constant Wideband Compressive Spectrum Sensing With Cascade Forward-Backward Propagating and Prior Knowledge Refining', IEEE Transactions on Wireless Communications, vol. 23, no. 3, pp. 1855-1870.
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Liu, J, Lee, C-K & Pong, PWT 2024, 'A Guided Wireless Electric Vehicle Charging Strategy Based on In-Plane Magnetic Field', IEEE Sensors Journal, vol. 24, no. 14, pp. 22916-22925.
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Liu, J, Lee, C-K & Pong, PWT 2024, 'Anti-Interference Current Sensing With Enhanced Sensitivity Based on Magnetoresistive Sensors', IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1-10.
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Liu, J, Wei, J, Li, J, Su, Y & Wu, C 2024, 'A comprehensive review of ultra-high performance concrete (UHPC) behaviour under blast loads', Cement and Concrete Composites, vol. 148, pp. 105449-105449.
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Liu, J, Wu, K, Su, T & Chen, S-L 2024, 'A Versatile Digital Antenna Array Transceiving System With Online Self-Calibration', IEEE Antennas and Wireless Propagation Letters, pp. 1-5.
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Liu, J, Wu, K, Su, T & Zhang, JA 2024, 'Practical frequency-hopping MIMO joint radar communications: design and experiment', Digital Communications and Networks.
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Liu, J, Wu, K, Su, T, Pang, G & Chen, S-L 2024, 'Near-Field Calibration of Millimeter-Wave Massive MIMO Antenna Array Using Sphere Reflectors', IEEE Antennas and Wireless Propagation Letters, pp. 1-5.
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Liu, L, Guo, Y, Lei, G & Zhu, J 2024, 'Efficient Iron Loss Estimation of Interior PMSMs in Electric Vehicles: Analytical Modelling and Experimental Validation', IEEE Transactions on Applied Superconductivity, vol. 34, no. 8, pp. 1-5.
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Liu, L, Guo, Y, Yin, W, Lei, G, Sun, X & Zhu, J 2024, 'Efficient Design Optimization of PMSM Drive Systems Using Improved Equivalent-Circuit-Based Loss Minimization Control', IEEE Transactions on Industrial Electronics, pp. 1-12.
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Liu, L, He, Y, Li, Q, Cao, C, Huang, M, Ma, D, Wu, X & Zhu, Q 2024, 'Self‐supported bimetallic array superstructures for high‐performance coupling electrosynthesis of formate and adipate', Exploration, vol. 4, no. 3.
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AbstractThe coupling electrosynthesis involving CO2 upgrade conversion is of great significance for the sustainable development of the environment and energy but is challenging. Herein, we exquisitely constructed the self‐supported bimetallic array superstructures from the Cu(OH)2 array architecture precursor, which can enable high‐performance coupling electrosynthesis of formate and adipate at the anode and the cathode, respectively. Concretely, the faradaic efficiencies (FEs) of CO2‐to‐formate and cyclohexanone‐to‐adipate conversion simultaneously exceed 90% at both electrodes with excellent stabilities. Such high‐performance coupling electrosynthesis is highly correlated with the porous nanosheet array superstructure of CuBi alloy as the cathode and the nanosheet‐on‐nanowire array superstructure of CuNi hydroxide as the anode. Moreover, compared to the conventional electrolysis process, the cell voltage is substantially reduced while maintaining the electrocatalytic performance for coupling electrosynthesis in the two‐electrode electrolyzer with the maximal FEformate and FEadipate up to 94.2% and 93.1%, respectively. The experimental results further demonstrate that the bimetal composition modulates the local electronic structures, promoting the reactions toward the target products. Prospectively, our work proposes an instructive strategy for constructing adaptive self‐supported superstructures to achieve efficient coupling electrosynthesis.
Liu, M & Chang, X 2024, 'Normalized ground state solutions for nonlinear Schrödinger equations with general Sobolev critical nonlinearities', Discrete and Continuous Dynamical Systems - S, vol. 0, no. 0, pp. 0-0.
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Liu, M, Tu, Z, Su, T, Wang, X, Xu, X & Wang, Z 2024, 'BehaviorNet: A Fine-grained Behavior-aware Network for Dynamic Link Prediction', ACM Transactions on the Web, vol. 18, no. 2, pp. 1-26.
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Dynamic link prediction has become a trending research subject because of its wide applications in the web, sociology, transportation, and bioinformatics. Currently, the prevailing approach for dynamic link prediction is based on graph neural networks, in which graph representation learning is the key to perform dynamic link prediction tasks. However, there are still great challenges because the structure of graphs evolves over time. A common approach is to represent a dynamic graph as a collection of discrete snapshots, in which information over a period is aggregated through summation or averaging. This way results in some fine-grained time-related information loss, which further leads to a certain degree of performance degradation. We conjecture that such fine-grained information is vital because it implies specific behavior patterns of nodes and edges in a snapshot. To verify this conjecture, we propose a novel fine-grained behavior-aware network (BehaviorNet) for dynamic network link prediction. Specifically, BehaviorNet adapts a transformer-based graph convolution network to capture the latent structural representations of nodes by adding edge behaviors as an additional attribute of edges. GRU is applied to learn the temporal features of given snapshots of a dynamic network by utilizing node behaviors as auxiliary information. Extensive experiments are conducted on several real-world dynamic graph datasets, and the results show significant performance gains for BehaviorNet over several state-of-the-art (SOTA) discrete dynamic link prediction baselines. Ablation study validates the effectiveness of modeling fine-grained edge and node behaviors.
Liu, Q, Li, X, Yuan, D, Yang, C, Chang, X & He, Z 2024, 'LSOTB-TIR: A Large-Scale High-Diversity Thermal Infrared Single Object Tracking Benchmark', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 7, pp. 9844-9857.
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Unlike visual object tracking, thermal infrared (TIR) object tracking methods can track the target of interest in poor visibility such as rain, snow, and fog, or even in total darkness. This feature brings a wide range of application prospects for TIR object-tracking methods. However, this field lacks a unified and large-scale training and evaluation benchmark, which has severely hindered its development. To this end, we present a large-scale and high-diversity unified TIR single object tracking benchmark, called LSOTB-TIR, which consists of a tracking evaluation dataset and a general training dataset with a total of 1416 TIR sequences and more than 643 K frames. We annotate the bounding box of objects in every frame of all sequences and generate over 770 K bounding boxes in total. To the best of our knowledge, LSOTB-TIR is the largest and most diverse TIR object tracking benchmark to date. We spilt the evaluation dataset into a short-term tracking subset and a long-term tracking subset to evaluate trackers using different paradigms. What’s more, to evaluate a tracker on different attributes, we also define four scenario attributes and 12 challenge attributes in the short-term tracking evaluation subset. By releasing LSOTB-TIR, we encourage the community to develop deep learning-based TIR trackers and evaluate them fairly and comprehensively. We evaluate and analyze 40 trackers on LSOTB-TIR to provide a series of baselines and give some insights and future research directions in TIR object tracking. Furthermore, we retrain several representative deep trackers on LSOTB-TIR, and their results demonstrate that the proposed training dataset significantly improves the performance of deep TIR trackers. Codes and dataset are available at https://github.com/QiaoLiuHit/LSOTB-TIR.
Liu, S, Huang, X, Mu, H, Zheng, M, Kuang, S, Chen, H, Xu, Y, Wang, D, Liu, H & Li, X 2024, 'Biogeography and diversity patterns of functional genes associated with C, N, P, S cycling processes across China classical sea sediments', Science of The Total Environment, vol. 906, pp. 167678-167678.
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Liu, T, Lu, J, Yan, Z & Zhang, G 2024, 'Robust Gaussian Process Regression With Input Uncertainty: A PAC-Bayes Perspective', IEEE Transactions on Cybernetics, vol. 54, no. 2, pp. 962-973.
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The Gaussian process (GP) algorithm is considered as a powerful nonparametric-learning approach, which can provide uncertainty measurements on the predictions. The standard GP requires clearly observed data, unexpected perturbations in the input may lead to learned regression model mismatching. Besides, GP also suffers from the lack of good generalization performance guarantees. To deal with data uncertainty and provide a numerical generalization performance guarantee on the unknown data distribution, this article proposes a novel robust noisy input GP (NIGP) algorithm based on the probably approximately correct (PAC) Bayes theory. Furthermore, to reduce the computational complexity, we develop a sparse NIGP algorithm, and then develop a sparse PAC-Bayes NIGP approach. Compared with NIGP algorithms, instead of maximizing the marginal log likelihood, one can optimize the PAC-Bayes bound to pursue a tighter generalization error upper bound. Experiments verify that the NIGP algorithms can attain greater accuracy. Besides, the PAC-NIGP algorithms proposed herein can achieve both robust performance and improved generalization error upper bound in the face of both uncertain input and output data.
Liu, T, Zhang, W, Wang, L, Ueland, M, Forbes, SL, Zheng, WX & Su, SW 2024, 'Numerical Differentiation From Noisy Signals: A Kernel Regularization Method to Improve Transient-State Features for the Electronic Nose', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 54, no. 6, pp. 3497-3511.
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Liu, W, Xia, R, Lin, X, Wang, Z, Ansari, AJ, Li, G & Luo, W 2024, 'Tracking fouling layer formation in membrane distillation of landfill leachate concentrate: Insights from periodic membrane autopsies', Journal of Membrane Science, vol. 693, pp. 122331-122331.
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Liu, X, Chen, Z, Lu, S, Shi, X, Qu, F, Cheng, D, Wei, W, Shon, HK & Ni, B-J 2024, 'Persistent free radicals on biochar for its catalytic capability: A review', Water Research, vol. 250, pp. 120999-120999.
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Liu, X, Jiao, Q, Qiao, S, Yan, Z, Wen, S & Wang, P 2024, 'A Hybrid Monotonic Neural Network Approach for Multi-Area Power System Load Frequency Control Against FGSM Attack', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 71, no. 8, pp. 3780-3784.
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Liu, X, Shen, H, Yu, J, Luo, F, Li, T, Li, Q, Yuan, X, Sun, Y & Zhou, Z 2024, 'Resolving the heterogeneous tumour microenvironment in cardiac myxoma through single‐cell and spatial transcriptomics', Clinical and Translational Medicine, vol. 14, no. 2.
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AbstractBackgroundCardiac myxoma (CM) is the most common (58%–80%) type of primary cardiac tumours. Currently, there is a need to develop medical therapies, especially for patients not physically suitable for surgeries. However, the mechanisms that shape the tumour microenvironment (TME) in CM remain largely unknown, which impedes the development of targeted therapies. Here, we aimed to dissect the TME in CM at single‐cell and spatial resolution.MethodsWe performed single‐cell transcriptomic sequencing and Visium CytAssist spatial transcriptomic (ST) assays on tumour samples from patients with CM. A comprehensive analysis was performed, including unsupervised clustering, RNA velocity, clonal substructure inference of tumour cells and cell–cell communication.ResultsUnsupervised clustering of 34 759 cells identified 12 clusters, which were assigned to endothelial cells (ECs), mesenchymal stroma cells (MSCs), and tumour‐infiltrating immune cells. Myxoma tumour cells were found to encompass two closely related phenotypic states, namely, EC‐like tumour cells (ETCs) and MSC‐like tumour cells (MTCs). According to RNA velocity, our findings suggest that ETCs may be directly differentiated from MTCs. The immune microenvironment of CM was found to contain multiple factors that promote immune suppression and evasion, underscoring the potential of using immunotherapies as a treatment option. Hyperactive signals sent primarily by tumour cells were identified, such as MDK, HGF, chemerin, and GDF15 signalling. Finally, the ST assay uncovered spatial features of the subclusters, proximal cell–cell communication, and clonal evolution of myxoma tumour cells.ConclusionsOur study presents the first comprehensive characte...
Liu, X, Zhao, Y, Wen, S, Chen, B & Ge, SS 2024, 'Motion segmentation with event camera: N-patches optical flow estimation and Pairwise Markov Random Fields', Expert Systems with Applications, vol. 254, pp. 124342-124342.
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Liu, Y, Cui, G, Luo, J, Chang, X & Yao, L 2024, 'Two-stream Multi-level Dynamic Point Transformer for Two-person Interaction Recognition', ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 20, no. 5, pp. 1-22.
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As a fundamental aspect of human life, two-person interactions contain meaningful information about people’s activities, relationships, and social settings. Human action recognition serves as the foundation for many smart applications, with a strong focus on personal privacy. However, recognizing two-person interactions poses more challenges due to increased body occlusion and overlap compared to single-person actions. In this article, we propose a point cloud-based network named Two-stream Multi-level Dynamic Point Transformer for two-person interaction recognition. Our model addresses the challenge of recognizing two-person interactions by incorporating local-region spatial information, appearance information, and motion information. To achieve this, we introduce a designed frame selection method named Interval Frame Sampling (IFS), which efficiently samples frames from videos, capturing more discriminative information in a relatively short processing time. Subsequently, a frame features learning module and a two-stream multi-level feature aggregation module extract global and partial features from the sampled frames, effectively representing the local-region spatial information, appearance information, and motion information related to the interactions. Finally, we apply a transformer to perform self-attention on the learned features for the final classification. Extensive experiments are conducted on two large-scale datasets, the interaction subsets of NTU RGB+D 60 and NTU RGB+D 120. The results show that our network outperforms state-of-the-art approaches in most standard evaluation settings.
Liu, Y, Cui, X, Zhang, X, Ren, J, Li, H, Wang, Z, Guo, W & Ngo, HH 2024, 'Recent advances and trends of carbon-based biocarriers for performance enhancement of anaerobic membrane bioreactor system', Journal of Water Process Engineering, vol. 59, pp. 104949-104949.
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Liu, Y, Feng, Y, Wu, Z, Alamdari, MM, Wu, D, Luo, Z, Chen, X & Gao, W 2024, 'Dynamic crack propagation in elasto-plastic materials using phase-field virtual modelling method', Computer Methods in Applied Mechanics and Engineering, vol. 429, pp. 117160-117160.
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Liu, Y, Lee, T-U, Javan, AR, Pietroni, N & Xie, YM 2024, 'Reducing the Number of Different Faces in Free-Form Surface Approximations Through Clustering and Optimization.', Comput. Aided Des., vol. 166, pp. 103633-103633.
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Liu, Y, Li, B, Wang, X, Sammut, C & Yao, L 2024, 'Attention-Aware Social Graph Transformer Networks for Stochastic Trajectory Prediction', IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 11, pp. 5633-5646.
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Liu, Y, Qi, M, Zhang, Y, Wu, Q, Wu, J & Zhuang, S 2024, 'Improving Consistency of Proxy-Level Contrastive Learning for Unsupervised Person Re-Identification', IEEE Transactions on Information Forensics and Security, vol. 19, pp. 6910-6922.
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Liu, Y, Wang, S, Huo, J, Zhang, X, Wen, H, Zhang, D, Zhao, Y, Kang, D, Guo, W & Ngo, HH 2024, 'Adsorption recovery of phosphorus in contaminated water by calcium modified biochar derived from spent coffee grounds', Science of The Total Environment, vol. 909, pp. 168426-168426.
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Liu, Y, Yang, Y, He, Y, Xin, C, Ren, F & Yu, Y 2024, 'Experimental investigation on effects of ultrasonic process parameters on the degree of impregnation of BF/PP composites', Materials Research Express, vol. 11, no. 4, pp. 045303-045303.
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Abstract The properties of basalt fiber reinforced polypropylene composites (BF/PP) were improved by ultrasonic treatment of resin building pressure to assist melt impregnation. Combined with the study of ultrasonic pressure building theory, the mechanical properties of the modified composites were analyzed using the characterization of tensile, flexural and impact strengths in response to porosity and fracture rate. The effects of ultrasonic power, frequency and distance of action on resin building pressure and composite properties were investigated. The results showed that the best effect was achieved when the ultrasonic frequency was 25 kHz, the ultrasonic power was 300 W, and the action distance was 4 mm, at which time the porosity of the prepreg was reduced to 2.99%, the fracture rate was 3.36%, and the tensile, flexural, and impact strengths were 108.73 MPa, 116.81 MPa, and 51.59 KJ.m−2.
Liu, Y, Yuan, Y, Wang, Y, Ngo, HH & Wang, J 2024, 'Research and application of active species based on high-valent iron for the degradation of pollutants: A critical review', Science of The Total Environment, vol. 924, pp. 171430-171430.
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Liu, Y, Zhang, J, Cheng, D, Guo, W, Liu, X, Chen, Z, Zhang, Z & Ngo, HH 2024, 'Fate and mitigation of antibiotics and antibiotic resistance genes in microbial fuel cell and coupled systems', Science of The Total Environment, vol. 938, pp. 173530-173530.
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Liu, Y, Zhang, X, Zeng, Z & Yu, S 2024, 'FedEco: Achieving Energy-Efficient Federated Learning by Hyperparameter Adaptive Tuning', IEEE Transactions on Cognitive Communications and Networking, pp. 1-1.
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Liu, Y, Zhu, L, Wang, X, Yamada, M & Yang, Y 2024, 'Bilaterally Normalized Scale-Consistent Sinkhorn Distance for Few-Shot Image Classification', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 8, pp. 11475-11485.
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Liu, Z, Li, Y, Yao, L, Chang, X, Fang, W, Wu, X & Saddik, AE 2024, 'Simple Primitives With Feasibility- and Contextuality-Dependence for Open-World Compositional Zero-Shot Learning', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 1, pp. 543-560.
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Liu, Z, Lu, J, Xuan, J & Zhang, G 2024, 'Deep Reinforcement Learning in Nonstationary Environments With Unknown Change Points', IEEE Transactions on Cybernetics, vol. 54, no. 9, pp. 5191-5204.
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Liu, Z, Wang, Q, Fatahi, B, Khabbaz, H, Sheng, D & Wu, D 2024, 'Hybrid uncertain buckling analysis for engineering structures through machine learning method', Engineering Structures, vol. 310, pp. 118083-118083.
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Liu, Z, Xiao, F, Lin, C-T & Cao, Z 2024, 'A Robust Evidential Multisource Data Fusion Approach Based on Cooperative Game Theory and Its Application in EEG', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 54, no. 2, pp. 729-740.
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Liu, Z, Zhang, G & Lu, J 2024, 'Semi-supervised heterogeneous domain adaptation for few-sample credit risk classification', Neurocomputing, vol. 596, pp. 127948-127948.
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Loh, HW, Ooi, CP, Oh, SL, Barua, PD, Tan, YR, Acharya, UR & Fung, DSS 2024, 'ADHD/CD-NET: automated EEG-based characterization of ADHD and CD using explainable deep neural network technique', Cognitive Neurodynamics, vol. 18, no. 4, pp. 1609-1625.
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AbstractIn this study, attention deficit hyperactivity disorder (ADHD), a childhood neurodevelopmental disorder, is being studied alongside its comorbidity, conduct disorder (CD), a behavioral disorder. Because ADHD and CD share commonalities, distinguishing them is difficult, thus increasing the risk of misdiagnosis. It is crucial that these two conditions are not mistakenly identified as the same because the treatment plan varies depending on whether the patient has CD or ADHD. Hence, this study proposes an electroencephalogram (EEG)-based deep learning system known as ADHD/CD-NET that is capable of objectively distinguishing ADHD, ADHD + CD, and CD. The 12-channel EEG signals were first segmented and converted into channel-wise continuous wavelet transform (CWT) correlation matrices. The resulting matrices were then used to train the convolutional neural network (CNN) model, and the model’s performance was evaluated using 10-fold cross-validation. Gradient-weighted class activation mapping (Grad-CAM) was also used to provide explanations for the prediction result made by the ‘black box’ CNN model. Internal private dataset (45 ADHD, 62 ADHD + CD and 16 CD) and external public dataset (61 ADHD and 60 healthy controls) were used to evaluate ADHD/CD-NET. As a result, ADHD/CD-NET achieved classification accuracy, sensitivity, specificity, and precision of 93.70%, 90.83%, 95.35% and 91.85% for the internal evaluation, and 98.19%, 98.36%, 98.03% and 98.06% for the external evaluation. Grad-CAM also identified significant channels that contributed to the diagnosis outcome. Therefore, ADHD/CD-NET can perform temporal localization and choose significant EEG channels for diagnosis, thus providing objective analysis for mental health professionals and clinicians to consider when making a diagnosis.
Long, Y, Chen, Z, Wu, L, Liu, X, Hou, Y, Vernuccio, S, Wei, W, Wong, W & Ni, B 2024, 'Electrocatalytic CO2 Reduction to Alcohols: Progress and Perspectives', Small Science, vol. 4, no. 8.
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Utilizing renewable electricity for the electrocatalytic conversion of CO2 into alcohols represents a promising avenue for generating value‐added fuels and achieving carbon neutrality. Recently, there has been growing scientific interest in achieving high‐efficiency conversion of CO2 to alcohols, with significant advancements made in mechanism understanding, reactor design, catalyst development, and more. Herein, a thorough examination of the latest advances in electrocatalytic CO2 reduction reaction (CO2RR) to alcohols is provided. General mechanisms and pathways of electrocatalytic conversion of CO2‐to‐alcohols are systematically illustrated. Subsequently, electrolyzer configurations, electrolytes, and electrocatalysts employed in CO2RR are summarized. After that, critical operating parameters (e.g., reaction pressure, temperature, and pH) that would significantly influence the CO2RR process are also analyzed. Finally, the review addresses challenges and offers perspectives in this field to guide future studies aimed at advancing CO2‐to‐alcohols conversion technologies.
Lu, D, Qu, F, Punetha, P, Zeng, X, Luo, Z & Li, W 2024, 'Graphene oxide nano-engineered recycled aggregate concrete for sustainable construction: A critical review', Developments in the Built Environment, vol. 18, pp. 100444-100444.
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Lu, D, Qu, F, Zhang, C, Guo, Y, Luo, Z, Xu, L & Li, W 2024, 'Innovative approaches, challenges, and future directions for utilizing carbon dioxide in sustainable concrete production', Journal of Building Engineering, vol. 97, pp. 110904-110904.
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Lu, J, Ma, G & Zhang, G 2024, 'Fuzzy Machine Learning: A Comprehensive Framework and Systematic Review', IEEE Transactions on Fuzzy Systems, vol. 32, no. 7, pp. 3861-3878.
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Lu, K, Zhang, Q, Hughes, D, Zhang, G & Lu, J 2024, 'AMT-CDR: A Deep Adversarial Multi-Channel Transfer Network for Cross-Domain Recommendation', ACM Transactions on Intelligent Systems and Technology, vol. 15, no. 4, pp. 1-26.
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Recommender systems are one of the most successful applications of using AI for providing personalized e-services to customers. However, data sparsity is presenting enormous challenges that are hindering the further development of advanced recommender systems. Although cross-domain recommendation partly overcomes data sparsity by transferring knowledge from a source domain with relatively dense data to augment data in the target domain, the current methods do not handle heterogeneous data very well. For example, using today’s cross-domain transfer learning schemes with data comprising clicks, ratings, user reviews, item metadata, and knowledge graphs will likely result in a poorly performing model. User preferences will not be comprehensively profiled, and accurate recommendations will not be generated. To solve these three challenges—handling heterogeneous data, avoiding negative transfer, and dealing with data sparsity—we designed a new end-to-end deep A dversarial M ulti-channel T ransfer network for C ross- D omain R ecommendation named AMT-CDR . Heterogeneous data is handled by constructing a cross-domain graph based on real-world knowledge graphs—we used Freebase and YAGO. Negative transfer is prevented through an adversarial learning strategy that maintains consistency across the different data channels. Data sparsity is addressed with an end-to-end neural network that considers data across multiple channels and generates accurate recommendations by leveraging knowledge from both the source and target domains. Extensive experiments on three dual-target cross-domain recommenda...
Lu, Q, Zhu, L, Xu, X, Whittle, J, Zowghi, D & Jacquet, A 2024, 'Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering', ACM Computing Surveys, vol. 56, no. 7, pp. 1-35.
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Responsible Artificial Intelligence (RAI) is widely considered as one of the greatest scientific challenges of our time and is key to increase the adoption of Artificial Intelligence (AI). Recently, a number of AI ethics principles frameworks have been published. However, without further guidance on best practices, practitioners are left with nothing much beyond truisms. In addition, significant efforts have been placed at algorithm level rather than system level, mainly focusing on a subset of mathematics-amenable ethical principles, such as fairness. Nevertheless, ethical issues can arise at any step of the development lifecycle, cutting across many AI and non-AI components of systems beyond AI algorithms and models. To operationalize RAI from a system perspective, in this article, we present an RAI Pattern Catalogue based on the results of a multivocal literature review. Rather than staying at the principle or algorithm level, we focus on patterns that AI system stakeholders can undertake in practice to ensure that the developed AI systems are responsible throughout the entire governance and engineering lifecycle. The RAI Pattern Catalogue classifies the patterns into three groups: multi-level governance patterns, trustworthy process patterns, and RAI-by-design product patterns. These patterns provide systematic and actionable guidance for stakeholders to implement RAI.
Lu, W, Wei, S, Peng, X, Wang, Y-F, Naseem, U & Wang, S 2024, 'Medical Question Summarization with Entity-driven Contrastive Learning', ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 23, no. 4, pp. 1-19.
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By summarizing longer consumer health questions into shorter and essential ones, medical question-answering systems can more accurately understand consumer intentions and retrieve suitable answers. However, medical question summarization is very challenging due to obvious distinctions in health trouble descriptions from patients and doctors. Although deep learning has been applied to successfully address the medical question summarization (MQS) task, two challenges remain: how to correctly capture question focus to model its semantic intention, and how to obtain reliable datasets to fairly evaluate performance. To address these challenges, this article proposes a novel medical question summarization framework based on e ntity-driven c ontrastive l earning (ECL). ECL employs medical entities present in frequently asked questions (FAQs) as focuses and devises an effective mechanism to generate hard negative samples. This approach compels models to focus on essential information and consequently generate more accurate question summaries. Furthermore, we have discovered that some MQS datasets, such as the iCliniq dataset with a 33% duplicate rate, have significant data leakage issues. To ensure an impartial evaluation of the related methods, this article carefully examines leaked samples to reorganize more reasonable datasets. Extensive experiments demonstrate that our ECL method outperforms the existing methods and achieves new state-of-the-art performance, i.e., 52.85, 43.16, 41.31, 43.52 in terms of ROUGE-1 metric on MeQSum, CHQ-Summ, iCliniq, HealthCareMagic dataset, respectively. The code and datasets are available at https://git...
Lu, X, Qiu, J, Lei, G & Zhu, J 2024, 'An Interval Prediction Method for Day-Ahead Electricity Price in Wholesale Market Considering Weather Factors', IEEE Transactions on Power Systems, vol. 39, no. 2, pp. 2558-2569.
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Lu, X, Qiu, J, Zhang, C, Lei, G & Zhu, J 2024, 'Assembly and Competition for Virtual Power Plants With Multiple ESPs Through a “Recruitment–Participation” Approach', IEEE Transactions on Power Systems, vol. 39, no. 2, pp. 4382-4396.
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Lu, X, Qiu, J, Zhang, C, Lei, G & Zhu, J 2024, 'Seizing unconventional arbitrage opportunities in virtual power plants: A profitable and flexible recruitment approach', Applied Energy, vol. 358, pp. 122628-122628.
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Lu, Y, Cai, B, Tang, X, Liu, L, Du, J, Yu, S, Atiquzzaman, M & Dustdar, S 2024, 'Tree-ORAP: A Tree-Based Oblivious Random-Access Protocol for Privacy-Protected Blockchain', IEEE Transactions on Services Computing, vol. 17, no. 3, pp. 1252-1264.
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Lu, Y, Ni, F, Wang, H, Guo, X, Zhu, L, Yang, Z, Song, R, Cheng, L & Yang, Y 2024, 'Show Me a Video: A Large-Scale Narrated Video Dataset for Coherent Story Illustration', IEEE Transactions on Multimedia, vol. 26, pp. 2456-2466.
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Lu, Y, Xu, Y, Meng, L, Ouyang, F, Cheng, J, Duan, P, Zhu, Y, Li, W, Zhang, Z, Chen, M & Huang, W 2024, 'Role of modified calcium montmorillonite and 5 A zeolite in microstructure and efflorescence formation of metakaolin-based geopolymer', Construction and Building Materials, vol. 448, pp. 138258-138258.
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Lu, Y, Zhao, G, Xu, C & Yu, S 2024, 'An Efficient Hypergraph-Based Routing Algorithm in Time-Sensitive Networks', IEEE Signal Processing Letters, vol. 31, pp. 835-839.
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Luo, H, Tao, M, Yang, Z, Zhao, R & Wu, C 2024, 'Transient response of semi-elliptical hill with an elliptical tunnel under blast and seismic loading', Soil Dynamics and Earthquake Engineering, vol. 184, pp. 108864-108864.
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Luo, J, Zou, K, Luo, Q, Li, Q & Sun, G 2024, 'On asymmetric failure in additively manufactured continuous carbon fiber reinforced composites', Composites Part B: Engineering, vol. 284, pp. 111605-111605.
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Luo, Y, Li, M, Wen, G, Tan, Y & Shi, C 2024, 'SHIP-YOLO: A Lightweight Synthetic Aperture Radar Ship Detection Model Based on YOLOv8n Algorithm', IEEE Access, vol. 12, pp. 37030-37041.
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Luo, Z, Jia, W & Perry, S 2024, 'Compressed point cloud classification with point-based edge sampling', EURASIP Journal on Image and Video Processing, vol. 2024, no. 1.
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3D point cloud data, as an immersive detailed data source, has been increasingly used in numerous applications. To deal with the computational and storage challenges of this data, it needs to be compressed before transmission, storage, and processing, especially in real-time systems. Instead of decoding the compressed data stream and subsequently conducting downstream tasks on the decompressed data, analyzing point clouds directly in their compressed domain has attracted great interest. In this paper, we dive into the realm of compressed point cloud classification (CPCC), aiming to achieve high point cloud classification accuracy in a bitrate-saving way by ensuring the bit stream contains a high degree of representative information of the point cloud. Edge information is one of the most important and representative attributes of the point cloud because it can display the outlines or main shapes. However, extracting edge points or information from point cloud models is challenging due to their irregularity and sparsity. To address this challenge, we adopt an advanced edge-sampling method that enhances existing state-of-the-art (SOTA) point cloud edge-sampling techniques based on attention mechanisms and consequently develop a novel CPCC method “CPCC-PES” that focuses on point cloud’s edge information. The result obtained on the benchmark ModelNet40 dataset shows that our model has superior rate-accuracy trade-off performance than SOTA works. Specifically, our method achieves over 90% Top-1 Accuracy with a mere 0.08 bits-per-point (bpp), marking a remarkable over 96% reduction in BD-bitrate compared with specialized codecs. This means that our method only consumes 20% of the bitrate of other SOTA works while maintaining comparable accuracy. Furthermore, we propose a new evaluation metric named BD-Top-1 Accuracy to evaluate the trade-off performance between bitrate and Top-1 Accuracy for future CPCC research.
Luo, Z, Yang, L, Su, T, Zhu, X & Gómez-García, R 2024, 'Single-Ended and Balanced Flat-Group-Delay RF Low-Pass Filters With Input-Quasi-Reflectionless Behavior for Digital-Communication Systems', IEEE Transactions on Microwave Theory and Techniques, pp. 1-14.
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Luong, NT, Nguyen, AQ & Hoang, D 2024, 'FAPDRP: a flooding attacks prevention and detection routing protocol in vehicular ad hoc network using behavior history and nonlinear median filter transformation', Wireless Networks, vol. 30, no. 6, pp. 4875-4902.
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Lv, L, Wei, Z, Li, W, Chen, J, Tian, Y, Gao, W, Wang, P, Sun, L, Ren, Z, Zhang, G, Liu, X & Ngo, HH 2024, 'Regulation of extracellular polymers based on quorum sensing in wastewater biological treatment from mechanisms to applications: A critical review', Water Research, vol. 250, pp. 121057-121057.
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Lv, Y, Liu, Z, Li, G & Chang, X 2024, 'Noise-Aware Intermediary Fusion Network for Off-Road Freespace Detection', IEEE Transactions on Intelligent Vehicles, pp. 1-11.
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Lyu, B, Wang, S, Wen, S, Shi, K, Yang, Y, Zeng, L & Huang, T 2024, 'AutoGMap: Learning to Map Large-Scale Sparse Graphs on Memristive Crossbars', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 9, pp. 12888-12898.
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Lyu, B, Wen, S, Yang, Y, Chang, X, Sun, J, Chen, Y & Huang, T 2024, 'Designing Efficient Bit-Level Sparsity-Tolerant Memristive Networks', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 9, pp. 11979-11988.
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Lyu, B, Yang, Y, Cao, Y, Shi, T, Chen, Y, Huang, T & Wen, S 2024, 'A memristive all-inclusive hypernetwork for parallel analog deployment of full search space architectures', Neural Networks, vol. 175, pp. 106312-106312.
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Lyu, B, Yang, Y, Cao, Y, Wang, P, Zhu, J, Chang, J & Wen, S 2024, 'Efficient multi-objective neural architecture search framework via policy gradient algorithm', Information Sciences, vol. 661, pp. 120186-120186.
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Lyu, B, Zhou, C, Gong, S, Wu, W, Hoang, DT & Niyato, D 2024, 'Energy-Efficiency Maximization for STAR-RIS Enabled Cell-Free Symbiotic Radio Communications', IEEE Transactions on Cognitive Communications and Networking, pp. 1-1.
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Lyu, X, Li, Y, He, Y, Ren, C, Ni, W, Liu, RP, Zhu, P & Cui, Q 2024, 'Objective-driven Differentiable Optimization of Traffic Prediction and Resource Allocation for Split AI Inference Edge Networks', IEEE Transactions on Machine Learning in Communications and Networking, pp. 1-1.
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Lyu, X, Wang, W, Li, H, Li, J & Yu, Y 2024, 'Numerical and experimental analysis on the axial compression performance of T-shaped concrete-filled thin-walled steel', Steel and Composite Structures, vol. 50, no. 4, pp. 383-401.
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The research comprehensively studies the axial compression performance of T-shaped concrete-filled thin-walled steel tubular (CTST) long columns after fire exposure. Initially, a series of tests investigate the effects of heating time, load eccentricity, and stiffeners on the column's performance. Furthermore, Finite Element (FE) analysis is employed to establish temperature and mechanical field models for the T-shaped CTST long column with stiffeners after fire exposure, using carefully determined key parameters such as thermal parameters, constitutive relations, and contact models. In addition, a parametric analysis based on the numerical models is conducted to explore the effects of heating time, section diameter, material strength, and steel ratio on the axial compressive bearing capacity, bending bearing capacity under normal temperature, as well as residual bearing capacity after fire exposure. The results reveal that the maximum lateral deformation occurs near the middle of the span, with bending increasing as heating time and eccentricity rise. Despite a decrease in axial compressive load and bending capacity after fire exposure, the columns still exhibit desirable bearing capacity and deformability. Moreover, the obtained FE results align closely with experimental findings, validating the reliability of the developed numerical models. Additionally, this study proposes a simplified design method to calculate these mechanical property parameters, satisfying the ISO-834 standard. The relative errors between the proposed simplified formulas and FE models remain within 10%, indicating their capability to provide a theoretical reference for practical engineering applications.
M.S., S, Elmakki, T, Schipper, K, Ihm, S, Yoo, Y, Park, B, Park, H, Shon, HK & Han, DS 2024, 'Integrated seawater hub: A nexus of sustainable water, energy, and resource generation', Desalination, vol. 571, pp. 117065-117065.
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Ma, B, Li, Y, Zheng, J, Zhang, J, Huang, S, Zhu, J & Lei, G 2024, 'Multiphysics Topology Optimization of SynRMs Considering Control Performance and Machinability', IEEE Transactions on Transportation Electrification, pp. 1-1.
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Ma, C, Shi, Q, Hua, B, Zhang, Y, Xu, Z, Chu, L, Braun, R & Shi, J 2024, 'Noncontact Heartbeat and Respiratory Signal Separation Using a Sub 6 GHz SDR Micro-Doppler Radar', IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology, vol. 8, no. 2, pp. 122-134.
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Ma, F, Zhao, S & Burnett, IS 2024, 'Sound field reconstruction using a compact acoustics-informed neural network', The Journal of the Acoustical Society of America, vol. 156, no. 3, pp. 2009-2021.
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Sound field reconstruction (SFR) augments the information of a sound field captured by a microphone array. Using basis function decomposition, conventional SFR methods are straightforward and computationally efficient but may require more microphones than needed to measure the sound field. Recent studies show that pure data-driven and learning-based methods are promising in some SFR tasks, but they are usually computationally heavy and may fail to reconstruct a physically valid sound field. This paper proposes a compact acoustics-informed neural network (AINN) method for SFR, whereby the Helmholtz equation is exploited to regularize the neural network. As opposed to pure data-driven approaches that solely rely on measured sound pressures, the integration of the Helmholtz equation improves robustness of the neural network against variations during the measurement processes and prompts the generation of physically valid reconstructions. The AINN is designed to be compact and able to predict not only the sound pressures but also sound pressure gradients within a spatial region of interest based on measured sound pressures along the boundary. Experiments with acoustic transfer functions measured in different environments demonstrate the superiority of the AINN method over the traditional cylindrical harmonics and singular value decomposition methods.
Ma, G, Lu, J & Zhang, G 2024, 'Multisource Domain Adaptation With Interval-Valued Target Data via Fuzzy Neural Networks', IEEE Transactions on Fuzzy Systems, vol. 32, no. 5, pp. 3094-3106.
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Ma, G, Lu, J, Fang, Z, Liu, F & Zhang, G 2024, 'Multiview Classification Through Learning From Interval-Valued Data', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15.
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Ma, G, Lu, J, Liu, F, Fang, Z & Zhang, G 2024, 'Domain Adaptation With Interval-Valued Observations: Theory and Algorithms', IEEE Transactions on Fuzzy Systems, vol. 32, no. 5, pp. 3107-3120.
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Ma, G, Lu, J, Liu, F, Fang, Z & Zhang, G 2024, 'Multiclass Classification With Fuzzy-Feature Observations: Theory and Algorithms', IEEE Transactions on Cybernetics, vol. 54, no. 2, pp. 1048-1061.
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The theoretical analysis of multiclass classification has proved that the existing multiclass classification methods can train a classifier with high classification accuracy on the test set, when the instances are precise in the training and test sets with same distribution and enough instances can be collected in the training set. However, one limitation with multiclass classification has not been solved: how to improve the classification accuracy of multiclass classification problems when only imprecise observations are available. Hence, in this article, we propose a novel framework to address a new realistic problem called multiclass classification with imprecise observations (MCIMO), where we need to train a classifier with fuzzy-feature observations. First, we give the theoretical analysis of the MCIMO problem based on fuzzy Rademacher complexity. Then, two practical algorithms based on support vector machine and neural networks are constructed to solve the proposed new problem. The experiments on both synthetic and real-world datasets verify the rationality of our theoretical analysis and the efficacy of the proposed algorithms.
Ma, H, Li, L, Fan, K, Guo, Y, Jin, Z & Luo, J 2024, 'A novel programmed hybrid modulation for electromagnetic interference mitigation and current ripple control in miniaturised synchronous machine drives', IET Electric Power Applications, vol. 18, no. 5, pp. 527-542.
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AbstractA novel programmed hybrid spreading spectrum modulation method to mitigate electromagnetic interference (EMI) emission and bearing current in the context of miniaturised and lightweight synchronous machine drives is proposed. The proposed method adopts the combination of conventional pulsewidth modulation (PWM) and low common mode voltage PWM. The offline look‐up table for switching frequency is acquired by analysing the root mean square voltage values at different modulation indices and angles. By adjusting the switching frequency according to this table, the spectrum can be spread into the sidebands of the switching frequency, and the harmonic spike can be suppressed. To address the challenge of increased current ripple and harmonic distortion in high torque density motors under overload conditions, a motor model that considers the cross‐saturation and slotting effect is presented. This motor model is used to derive the switching frequency correction factors, aiming to mitigate the influence of saturation and slotting effect on harmonic amplification. The proposed method is well‐suited for controlling synchronous motors using three‐phase two‐level voltage source inverters. The simulation and experimental results validate the effectiveness of the proposed method, showcasing its advantages in terms of computational complexity reduction and significant attenuation of peak EMI currents.
Ma, R, Chang, L, Nghiem, LD, Liu, Y, Wang, Q, Zhao, Q, Hao, Q, Gao, Y, Liu, H & Zheng, L 2024, 'Ligand-to-Metal Charge Transfer Quenching of Carbon Dots for Highly Selective Hg2+ Detection in Microfluidic Devices', ACS Applied Nano Materials, vol. 7, no. 18, pp. 22276-22284.
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Ma, R, Pang, G & Chen, L 2024, 'Harnessing collective structure knowledge in data augmentation for graph neural networks', Neural Networks, vol. 180, pp. 106651-106651.
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Ma, W, Huang, S & Sun, Y 2024, 'Triplet-Graph: Global Metric Localization Based on Semantic Triplet Graph for Autonomous Vehicles', IEEE Robotics and Automation Letters, vol. 9, no. 4, pp. 3155-3162.
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Ma, W, Song, Y, Xue, M, Wen, S & Xiang, Y 2024, 'The “Code” of Ethics: A Holistic Audit of AI Code Generators', IEEE Transactions on Dependable and Secure Computing, vol. 21, no. 5, pp. 4997-5013.
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Ma, X, Huang, M, Ni, W, Yin, M, Min, J & Jamalipour, A 2024, 'Balancing Time and Energy Efficiency by Sizing Clusters: A New Data Collection Scheme in UAV-Aided Large-Scale Internet of Things', IEEE Internet of Things Journal, vol. 11, no. 6, pp. 9355-9367.
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Ma, X, Li, Y, Yang, Z, Li, S & Li, Y 2024, 'Lightweight network for millimeter-level concrete crack detection with dense feature connection and dual attention', Journal of Building Engineering, vol. 94, pp. 109821-109821.
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Ma, Y, Shi, K, Peng, X, He, H, Zhang, P, Liu, J, Lei, Z & Niu, Z 2024, 'Deep Graph Clustering With Triple Fusion Mechanism for Community Detection', IEEE Transactions on Computational Social Systems, pp. 1-16.
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Mahafujur Rahaman, M, Bhowmick, S, Pada Ghosh, B, Xu, F, Nath Mondal, R & Saha, SC 2024, 'Transient natural convection flows and heat transfer in a thermally stratified air-filled trapezoidal cavity', Thermal Science and Engineering Progress, vol. 47, pp. 102377-102377.
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Mahmud, T, Nag, P, Molla, MM & Saha, SC 2024, 'Convective heat transfer efficacy of an experimentally observed non-Newtonian MWCNT-Fe3O4-EG hybrid nanofluid in a driven enclosure with a heated cylinder', International Journal of Thermal Sciences, vol. 204, pp. 109203-109203.
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Mai, TNA, Ali, S, Hossain, MS, Chen, C, Ding, L, Chen, Y, Solntsev, AS, Mou, H, Xu, X, Medhekar, N & Tran, TT 2024, 'Cryogenic Thermal Shock Effects on Optical Properties of Quantum Emitters in Hexagonal Boron Nitride', ACS Applied Materials & Interfaces, vol. 16, no. 15, pp. 19340-19349.
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Solid-state quantum emitters are vital building blocks for quantum information science and quantum technology. Among various types of solid-state emitters discovered to date, color centers in hexagonal boron nitride have garnered tremendous traction in recent years, thanks to their environmental robustness, high brightness, and room-temperature operation. Most recently, these quantum emitters have been employed for satellite-based quantum key distribution. One of the most important requirements to qualify these emitters for space-based applications is their optical stability against cryogenic thermal shock. Such an understanding has, however, remained elusive to date. Here, we report on the effects caused by such thermal shock that induces random, irreversible changes in the spectral characteristics of the quantum emitters. By employing a combination of structural characterizations and density functional calculations, we attribute the observed changes to lattice strain caused by cryogenic temperature shock. Our study sheds light on the stability of the quantum emitters under extreme conditions─similar to those countered in outer space.
Maidi, AM, Kalam, MA & Begum, F 2024, 'Detection of different drinkable milk using photonic crystal fibre biosensor in IR regime', Physica Scripta, vol. 99, no. 3, pp. 035516-035516.
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Abstract A simplified PCF sensor has been designed to detect the different drinkable milk that includes camel, cow and buffalo milk, and can also assess its quality. The sensor features a singular circular core design and two layers octagonal cladding air holes that was analysed using the Finite Element Method technique in COMSOL Multiphysics software and determine the sensing and optical performance parameters: power fraction, relative sensitivity, confinement loss, effective area, numerical aperture, V-Parameter, spot size, and beam divergence. At the optimum wavelength of 6.0 μm, the relative sensitivities are 96.58%, 96.78%, and 96.84%, and confinement losses of 3.51 × 10−8 dB/m, 1.47 × 10−8 dB m−1, and 8.59 × 10−9 dB/m, for camel, cow, and buffalo milk, respectively. The efficacy of the proposed PCF structure for sensing applications in the dairy industry in distinguishing between different types of milk is evidenced by these findings. Moreover, the results of confinement loss and chromatic dispersion suggest potential applications of this design in optical communication.
Maidi, AM, Salam, R, Kalam, MA & Begum, F 2024, 'Design and simulation of photonic crystal fibre sensor for harmful chemicals detection in polycarbonate plastics', Optical and Quantum Electronics, vol. 56, no. 1.
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Majidi Nezhad, M, Neshat, M, Sylaios, G & Astiaso Garcia, D 2024, 'Marine energy digitalization digital twin's approaches', Renewable and Sustainable Energy Reviews, vol. 191, pp. 114065-114065.
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Majnooni, S, Fooladi, M, Nikoo, MR, Al-Rawas, G, Haghighi, AT, Nazari, R, Al-Wardy, M & Gandomi, AH 2024, 'Smarter water quality monitoring in reservoirs using interpretable deep learning models and feature importance analysis', Journal of Water Process Engineering, vol. 60, pp. 105187-105187.
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Makepeace, RW, Tabandeh, A, Hossain, MJ & Asaduz-Zaman, M 2024, 'Techno-economic analysis of green hydrogen export', International Journal of Hydrogen Energy, vol. 56, pp. 1183-1192.
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Makhdoom, I, Abolhasan, M, Lipman, J, Piccardi, M & Franklin, D 2024, 'PrivySeC: A secure and privacy-compliant distributed framework for personal data sharing in IoT ecosystems', Blockchain: Research and Applications, vol. 5, no. 4, pp. 100220-100220.
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Makhdoom, I, Abolhasan, M, Lipman, J, Shariati, N, Franklin, D & Piccardi, M 2024, 'Securing Personally Identifiable Information: A Survey of SOTA Techniques, and a Way Forward', IEEE Access, vol. 12, pp. 116740-116770.
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Malayali, AB, R, V, Alharbi, SA & Kalam, MA 2024, 'Hybridization of concrete by the inclusions of kaolin, alumina and silica fume: Performance evaluation', Heliyon, vol. 10, no. 9, pp. e30674-e30674.
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Malisetty, RS & Indraratna, B 2024, 'Critical speed of ballasted railway tracks: Influence of ballast and subgrade degradation', Transportation Geotechnics, vol. 46, pp. 101246-101246.
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Manandhar, B, Paudel, KR, Clarence, DD, De Rubis, G, Madheswaran, T, Panneerselvam, J, Zacconi, FC, Williams, KA, Pont, LG, Warkiani, ME, MacLoughlin, R, Oliver, BG, Gupta, G, Singh, SK, Chellappan, DK, Hansbro, PM & Dua, K 2024, 'Zerumbone-incorporated liquid crystalline nanoparticles inhibit proliferation and migration of non-small-cell lung cancer in vitro', Naunyn-Schmiedeberg's Archives of Pharmacology, vol. 397, no. 1, pp. 343-356.
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AbstractLung cancer is the second most prevalent type of cancer and is responsible for the highest number of cancer-related deaths worldwide. Non-small-cell lung cancer (NSCLC) makes up the majority of lung cancer cases. Zerumbone (ZER) is natural compound commonly found in the roots ofZingiber zerumbetwhich has recently demonstrated anti-cancer activity in both in vitro and in vivo studies. Despite their medical benefits, ZER has low aqueous solubility, poor GI absorption and oral bioavailability that hinders its effectiveness. Liquid crystalline nanoparticles (LCNs) are novel drug delivery carrier that have tuneable characteristics to enhance and ease the delivery of bioactive compounds. This study aimed to formulate ZER-loaded LCNs and investigate their effectiveness against NSCLC in vitro using A549 lung cancer cells. ZER-LCNs, prepared in the study, inhibited the proliferation and migration of A549 cells. These inhibitory effects were superior to the effects of ZER alone at a concentration 10 times lower than that of free ZER, demonstrating a potent anti-cancer activity of ZER-LCNs. The underlying mechanisms of the anti-cancer effects by ZER-LCNs were associated with the transcriptional regulation of tumor suppressor genesP53andPTEN, and metastasis-associated geneKRT18. The protein array data showed downregulation of several proliferation associated proteins such as AXL, HER1, PGRN, and BIRC5 and metastasis-associated proteins such as DKK1, CAPG, CTSS, CTSB, CTSD, and PLAU. This study provides evidence of potential for increasing the potency and effectiveness of ZER with LCN formulation and developing ZER-LCNs as a treatment strategy for mitigation and treatment of NSCLC.
Manasa, P, Ananth, P, Natarajan, P, Somasundaram, K, Rajkumar, ER, Ravichandran, KS, Balasubramanian, V & Gandomi, AH 2024, 'An analysis of causative factors for road accidents using partition around medoids and hierarchical clustering techniques', Engineering Reports, vol. 6, no. 6.
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AbstractInsufficient progress in the development of national highways and state highways, coupled with a lack of public awareness regarding road safety, has resulted in prevalent traffic congestion and a high rate of accidents. Understanding the dominant and contributing factors that may influence road traffic accident severity is essential. This study identified the primary causes and the most significant target‐specific causative factors for road accident severity. A modified partitioning around medoids model determined the dominant road accident features. These clustering algorithms will extract hidden information from the road accident data and generate new features for our implementation. Then, the proposed method is compared with the other state‐of‐the‐art clustering techniques with three performance metrics: the silhouette coefficient, the Davies–Bouldin index, and the Calinski–Harabasz index. This article's main contribution is analyzing six different scenarios (different angles of the problem) concerning grievous and non‐injury accidents. This analysis provides deeper insights into the problem and can assist transport authorities in Tamil Nadu, India, in deriving new rules for road traffic. The output of different scenarios is compared with hierarchical clustering, and the overall clustering of the proposed method is compared with other clustering algorithms. Finally, it is proven that the proposed method outperforms other recently developed techniques.
Mane, D, Kulkarni, A, Pradhan, B, Gite, S, Vasant Bidwe, R, Lee, C-W & Alamri, A 2024, 'A Novel Fuzzy Hypersphere Neural Network Classifier Using Class Specific Clustering for Robust Pattern Classification', IEEE Access, vol. 12, pp. 124209-124219.
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Mangiavillano, B, Ramai, D, Kahaleh, M, Tyberg, A, Shahid, H, Sarkar, A, Samanta, J, Dhar, J, Bronswijk, M, Van der Merwe, S, Kouanda, A, Ji, H, Dai, S-C, Deprez, P, Vargas-Madrigal, J, Vanella, G, Leone, R, Arcidiacono, PG, Robles-Medranda, C, Alcivar Vasquez, J, Arevalo-Mora, M, Fugazza, A, Ko, C, Morris, J, Lisotti, A, Fusaroli, P, Dhaliwal, A, Mutignani, M, Forti, E, Cottone, I, Larghi, A, Rizzatti, G, Galasso, D, Barbera, C, Di Matteo, FM, Stigliano, S, Binda, C, Fabbri, C, Pham, KD-C, Di Mitri, R, Amata, M, Crinó, SF, Ofosu, A, De Luca, L, Al-Lehibi, A, Auriemma, F, Paduano, D, Calabrese, F, Gentile, C, Hassan, C, Repici, A & Facciorusso, A 2024, 'Correction: Outcomes of lumen apposing metal stent placement in patients with surgically altered anatomy: Multicenter international experience', Endoscopy International Open, vol. 12, no. 10, pp. C8-C8.
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Mangiavillano, B, Ramai, D, Kahaleh, M, Tyberg, A, Shahid, H, Sarkar, A, Samanta, J, Dhar, J, Bronswijk, M, Van der Merwe, S, Kouanda, A, Ji, H, Dai, S-C, Deprez, P, Vargas-Madrigal, J, Vanella, G, Leone, R, Arcidiacono, PG, Robles-Medranda, C, Alcivar Vasquez, J, Arevalo-Mora, M, Fugazza, A, Ko, C, Morris, J, Lisotti, A, Fusaroli, P, Dhaliwal, A, Mutignani, M, Forti, E, Cottone, I, Larghi, A, Rizzatti, G, Galasso, D, Barbera, C, Di Matteo, FM, Stigliano, S, Binda, C, Fabbri, C, Pham, KD-C, Di Mitri, R, Amata, M, Crinó, SF, Ofosu, A, De Luca, L, Al-Lehibi, A, Auriemma, F, Paduano, D, Calabrese, F, Gentile, C, Hassan, C, Repici, A & Facciorusso, A 2024, 'Outcomes of lumen apposing metal stent placement in patients with surgically altered anatomy: Multicenter international experience', Endoscopy International Open, vol. 12, no. 10, pp. E1143-E1149.
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Abstract Background and study aims Although outcomes of lumen-apposing metal stents (LAMS) placement in native anatomy have been reported, data on LAMS placement in surgically altered anatomy (SAA) are sparse. We aimed to assess outcomes of LAMS placement in patients with SAA for different indications. Patients and methods This was an international, multicenter, retrospective, observational study at 25 tertiary care centers through November 2023. Consecutive patients with SAA who underwent LAMS placement were included. The primary outcome was technical success defined as correct placement of LAMS. Secondary outcomes were clinical success and safety. Results Two hundred and seventy patients (125 males; average age 61 ± 15 years) underwent LAMS placement with SAA. Procedures included EUS-directed transgastric ERCP (EDGE) and EUS-directed transenteric ERCP (EDEE) (n = 82), EUS-guided entero-enterostomy (n = 81), EUS-guided biliary drainage (n = 57), EUS-guided drainage of peri-pancreatic fluid collections (n = 48), and EUS-guided pancreaticogastrostomy (n = 2). Most cases utilized AXIOS stents (n = 255) compared with SPAXUS stents (n = 15). Overall, technical success was 98%, clinical success was 97%, and the adverse event (AE) rate was 12%. Using AGREE classification, five events were rated as Grade II, 21 events as Grade IIIa, and six events as IIIb. No difference in AEs were noted among stent types (P = 0.52). Conclusions This study shows that placement of LAMS is associated with high technical and clinical success rates in patients with SAA. However, the rate of AEs is noteworthy, and thus, these procedures should be performed by expert endoscopists at tertiary centers.
Manh Doan, Q, Dinh, TH, Kumar Singh, A, Lin, C-T & Linh Trung, N 2024, 'Cascaded Thinning in Upscale and Downscale Representation for EEG Signal Processing', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 32, pp. 3677-3688.
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Manikandan, G, Pragadeesh, B, Manojkumar, V, Karthikeyan, AL, Manikandan, R & Gandomi, AH 2024, 'Classification models combined with Boruta feature selection for heart disease prediction', Informatics in Medicine Unlocked, vol. 44, pp. 101442-101442.
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Marghade, D, Shelare, S, Prakash, C, Soudagar, MEM, Yunus Khan, TM & Kalam, MA 2024, 'Innovations in metal-organic frameworks (MOFs): Pioneering adsorption approaches for persistent organic pollutant (POP) removal', Environmental Research, vol. 258, pp. 119404-119404.
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Martin, A, Hill, AJ, Seiler, KM & Balamurali, M 2024, 'Automatic excavator action recognition and localisation for untrimmed video using hybrid LSTM-Transformer networks', International Journal of Mining, Reclamation and Environment, vol. 38, no. 5, pp. 353-372.
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Martins, D, Karimi, M & Maxit, L 2024, 'Semi-analytical formulation to predict the vibroacoustic response of a fluid-loaded plate with ABH stiffeners', Thin-Walled Structures, vol. 205, pp. 112539-112539.
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Martins, GC, Choo, Y, Park, MJ, Shon, HK & Naidu, G 2024, 'Rare earth europium recovery using selective metal-organic framework incorporated mixed-matrix membrane', Chemosphere, vol. 364, pp. 143272-143272.
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Mastio, EA, Clegg, SR, Pina e Cunha, M & Dovey, K 2024, 'Leadership Ignoring Paradox to Maintain Inertial Order', Journal of Change Management, vol. 24, no. 2, pp. 83-101.
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Matin, A, Islam, MR, Zhu, Y, Wang, X, Huo, H & Xu, G 2024, 'Hybrid Deep Learning for Assembly Action Recognition in Smart Manufacturing', International Journal of Computer Vision & Signal Processing, vol. 14, no. 1, pp. 9-17.
Matos, PH, Muggleton, JM, Brennan, MJ, Almeida, FCL, Campos, BC, Paupitz, PJ, Iwanaga, MK, Karimi, M & Rustighi, E 2024, 'Enhancing Leak Location in Buried Water Pipes using Array Signal Processing Techniques: the Effect of Wave Velocity Variation', Journal of Physics: Conference Series, vol. 2647, no. 8, pp. 082011-082011.
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Abstract Leakage in buried pipelines is a significant cause of water wastage in distribution systems, resulting in water losses ranging from 30% to 50% in many countries. To address this issue, techniques have been developed to detect leaks in buried pipes over the last few decades. The leak detection procedure typically involves three steps: (1) leak detection, which involves analysis of water pressure/flow measurements along the pipelines; (2) estimation of the approximate region where the leak occurred through local pressure variations; and (3) pinpointing the estimated location of the leak to perform maintenance procedures. Acoustic pinpointing techniques are among the most effective ones to deal with the latter step. These techniques exploit the delays in time of arrival of acoustic waves, caused by the leak, between different sensors placed around the suspected leak. By calculating the cross-spectral densities (CSDs) between sensors and analysing their phase difference over frequency, it is possible to infer the estimated location of the radiating source. Existing methods rely on access points to the pipeline through correlators. However, the buried pipe acts as a radiating source, and its location could also be estimated through ground vibration signals. Although array signal processing techniques applied to source localization are well-established in the acoustics field, their adaptation to the vibroacoustic field is less well developed. Among the many challenges, the identification of wave velocity is one of the most troublesome. In this paper, the effect of the wave velocity variation on the leak pinpointing is investigated and tested against numerical data. Results show that the estimation of the leak position is sensitive to the wave velocity variabilities. The pinpointing error is found to be more significant in terms of the depth of the pipe, compared to the error on the ground surface.
Mayer, JU, Hilligan, KL, Eccles, DA, Old, SI, Domingues, RG, Brombacher, F, Mackay, CR, Gallego-Ortega, D, Gros, GL, Hepworth, MR, Lamiable, O & Ronchese, F 2024, 'Low levels of IL-13 are constitutively produced in healthy skin and locally imprint a pro-type 2 immune environment', ALLERGOLOGIE, vol. 47, no. 2.
McClements, L, Liu Chung Ming, C, Pienaar, D, Ghorbanpour, S, Margaret Roberts, L, Henry, A, Kristine McGrath, D, Ortega, DG & Gentile, C 2024, 'New cardiac platform for the management of cardiovascular risk following preeclampsia using in vitro bioengineered cardiac spheroids and patient-derived stem cells', Pregnancy Hypertension, vol. 36, pp. 35-36.
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McClements, L, Pienaar, D, Chen, H, Gentile, C, Padula, M, McGrath, KC, Henry, A, Ghorbanpour, S, Margaret Roberts, L & Liu Chung Ming, C 2024, 'New 3D cardiac in vitro models for assessing the maternal cardiovascular health five years post hypertensive disorders of pregnancy', Pregnancy Hypertension, vol. 36, pp. 15-16.
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McCourt, LR, Routley, BS, Ruppert, MG & Fleming, AJ 2024, 'Feasibility of gold nanocones for collocated tip‐enhanced Raman spectroscopy and atomic force microscope imaging', Journal of Raman Spectroscopy, vol. 55, no. 3, pp. 336-346.
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AbstractMicrocantilever probes for tip‐enhanced Raman spectroscopy (TERS) have a grainy metal coating that may exhibit multiple plasmon hotspots near the tip apex, which may compromise spatial resolution and introduce imaging artefacts. It is also possible that the optical hotspot may not occur at the mechanical apex, which introduces an offset between TERS and atomic force microscope maps. In this article, a gold nanocone TERS probe is designed and fabricated for 638 nm excitation. The imaging performance is compared to grainy probes by analysing high‐resolution TERS cross‐sections of single‐walled carbon nanotubes. Compared to the tested conventional TERS probes, the nanocone probe exhibited a narrow spot diameter, comparable optical contrast, artefact‐free images, and collocation of TERS and atomic force microscope topographic maps. The 1/ spot diameter was 12.5 nm and 19 nm with 638 nm and 785 nm excitation, respectively. These results were acquired using a single gold nanocone probe to experimentally confirm feasibility. Future work will include automating the fabrication process and statistical analysis of many probes.
Medawela, S & Indraratna, B 2024, 'PRB technology incorporating acidic ground conditions and bio-geochemical clogging – A critical review', Australian Geomechanics Journal, vol. 59, no. 2, pp. 33-56.
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Groundwater acidity resulting from pyrite oxidation in acid sulphate soil terrain presents a severe threat to the environment. The exposure of low-lying acidic coastal belts to the atmospheric oxygen, exacerbated by phreatic surface lowering in dry seasons and activities like infrastructure development and agriculture, leads to pyrite oxidation and sulfuric acid production in soil. This paper reviews the challenges posed by acid sulphate soils by emphasising the environmental and infrastructure damage caused by acidic soil leaching into water bodies. Permeable reactive barriers (PRBs) have emerged as a promising method of passive treatment for mitigating groundwater acidity in pyritic terrain. This review mainly focuses on the effectiveness of PRBs in low-lying floodplains by addressing the bio-geochemical clogging that diminishes the reactivity and porosity of PRBs over time. This paper also summarises the numerical methods needed to design PRBs in acidic terrains by identifying gaps in current research that could enhance the accuracy of future PRB designs. This comprehensive review contains valuable insights into the ongoing efforts of addressing the challenges associated with groundwater contamination in regions containing acid sulphate soil.
Mehdipour, H, Amini, E, Naeeni, STO, Neshat, M & Gandomi, AH 2024, 'Optimization of power take-off system settings and regional site selection procedure for a wave energy converter', Energy Conversion and Management: X, vol. 22, pp. 100559-100559.
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Mehta, M, Bui, TA, Care, A & Deng, W 2024, 'Targeted polymer lipid hybrid nanoparticles for in-vitro siRNA therapy in triple-negative breast cancer', Journal of Drug Delivery Science and Technology, vol. 98, pp. 105911-105911.
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Mei, G, Saltori, C, Ricci, E, Sebe, N, Wu, Q, Zhang, J & Poiesi, F 2024, 'Unsupervised Point Cloud Representation Learning by Clustering and Neural Rendering', International Journal of Computer Vision, vol. 132, no. 8, pp. 3251-3269.
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AbstractData augmentation has contributed to the rapid advancement of unsupervised learning on 3D point clouds. However, we argue that data augmentation is not ideal, as it requires a careful application-dependent selection of the types of augmentations to be performed, thus potentially biasing the information learned by the network during self-training. Moreover, several unsupervised methods only focus on uni-modal information, thus potentially introducing challenges in the case of sparse and textureless point clouds. To address these issues, we propose an augmentation-free unsupervised approach for point clouds, named CluRender, to learn transferable point-level features by leveraging uni-modal information for soft clustering and cross-modal information for neural rendering. Soft clustering enables self-training through a pseudo-label prediction task, where the affiliation of points to their clusters is used as a proxy under the constraint that these pseudo-labels divide the point cloud into approximate equal partitions. This allows us to formulate a clustering loss to minimize the standard cross-entropy between pseudo and predicted labels. Neural rendering generates photorealistic renderings from various viewpoints to transfer photometric cues from 2D images to the features. The consistency between rendered and real images is then measured to form a fitting loss, combined with the cross-entropy loss to self-train networks. Experiments on downstream applications, including 3D object detection, semantic segmentation, classification, part segmentation, and few-shot learning, demonstrate the effectiveness of our framework in outperforming state-of-the-art techniques.
Mekala, MS, Srivastava, G, Gandomi, AH, Park, JH & Jung, H-Y 2024, 'A Quantum-Inspired Sensor Consolidation Measurement Approach for Cyber-Physical Systems', IEEE Transactions on Network Science and Engineering, vol. 11, no. 1, pp. 511-524.
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Meng, L, Liang, K, Xiao, B, Zhou, S, Liu, Y, Liu, M, Yang, X, Liu, X & Li, J 2024, 'SARF: Aliasing Relation–Assisted Self-Supervised Learning for Few-Shot Relation Reasoning', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-11.
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Meng, X, Zhou, Y, Du, K, Ma, J, Meng, J, Kumar, A, Lv, J, Kim, J & Wang, S 2024, 'EFNet: enhancing feature information for 3D object detection in LiDAR point clouds', Journal of the Optical Society of America A, vol. 41, no. 4, pp. 739-739.
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With the development of autonomous driving, there has been considerable attention on 3D object detection using LiDAR. Pillar-based LiDAR point cloud detection algorithms are extensively employed in the industry due to their simple structure and high real-time performance. Nevertheless, the pillar-based detection network suffers from significant loss of 3D coordinate information during the feature degradation and extraction process. In the paper, we introduce a novel framework with high performance, termed EFNet. The EFNet uses the Enhancing Pillar Feature Module (EPFM) to provide more accurate representations of features from two directions: pillar internal space and pillar external space. Additionally, the Head Up Module (HUM) is utilized in the detection head to integrate multi-scale information and enhance the network’s information perception ability. The EFNet achieves impressive results on the nuScenes datasets, namely, 53.3% NDS and 42.4% mAP. Compared to the baseline PointPillars, EFNet improves 8% NDS and 11.9% mAP. The results demonstrate that the proposed framework can effectively improve the network’s accuracy while ensuring deployability.
Meng, Y, Qiu, J, Zhang, C, Lei, G & Zhu, J 2024, 'A Holistic P2P market for active and reactive energy trading in VPPs considering both financial benefits and network constraints', Applied Energy, vol. 356, pp. 122396-122396.
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Meraj, T, Sharif, MI, Raza, M, Alabrah, A, Kadry, S & Gandomi, AH 2024, 'Computer vision-based plants phenotyping: A comprehensive survey', iScience, vol. 27, no. 1, pp. 108709-108709.
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Merigó, JM, Gil-Lafuente, AM, Kydland, F, Amiguet, L, Vivoda, V, Campbell, G, Lei, Y & Fleming-Muñoz, D 2024, '50 years of Resources Policy: A bibliometric analysis', Resources Policy, vol. 96, pp. 105229-105229.
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Mi, Y, Wang, W & Yu, Y 2024, 'Residual Load-Carrying Capacity of Hybrid FRP-UHPC-Steel Double-Skin Tubular Column after Lateral Impact', Journal of Composites for Construction, vol. 28, no. 5.
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Mikolajczyk, K, Ferguson, S, Candy, L, Dias Pereira dos Santos, A & Bown, O 2024, 'Space shaping in the design process for creative coding: a case study in media multiplicities', Digital Creativity, vol. 35, no. 1, pp. 31-51.
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Milano, J, Ong, HC, Ong, ZC, Ghadyani, G, Ismail, ZB, Veza, I, Masudi, A, Tiong, SK & Silitonga, AS 2024, 'Strategies in the application of nanoadditives to achieve high-performance diesel, biodiesels, and their blends', Fuel Communications, vol. 19, pp. 100111-100111.
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Milano, J, Tiong, SK, Silitonga, AS, Chia, SR, Ong, MY, Kusumo, F, Sebayang, AH, Yusof, T & Kalam, MA 2024, 'Synthesis of Ceiba pentandra biodiesel using ultrasound and infrared radiation: Comparison and fuel characterisation.', IOP Conference Series: Earth and Environmental Science, vol. 1372, no. 1, pp. 012046-012046.
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Abstract The continuous expending of the economy and population in modern society has caused an increase in energy usage. Currently, fossil fuels and renewable energy are used to generate energy, contributing to greenhouse gas emissions. A significant effort has been made globally to address the issue of rising emissions by boosting the usage of renewable energy. In comparison to fossil fuels, biodiesel has many benefits, including the ability to be produced from a wide range of feedstocks, the ability to be renewable, and the reduction of atmospheric pollution emissions. Besides, advanced technologies can help the biodiesel sector meet the energy demand while producing high-quality biodiesel. The Ceiba pentandra was used for biodiesel production using ultrasound-infrared applications in the present research work. The study aims to produce biodiesel for a better conversion rate and improve fuel properties. Comparisons were conducted using a combination of infrared ultrasound versus ultrasound irradiation. The results show that ultrasound produced the highest yield of 98.76% when the conditions were as follows: methanol/oil ratio: 60%, KOH: 1%, reaction time: 50 minutes. Yet, the addition of infrared on ultrasound has also produced a high conversion yield in a shorter time than ultrasound. A 98.42% biodiesel yield option when using infrared-ultrasound irradiation with conditions as follows: methanol/oil ratio: 60%, KOH: 1%, reaction time: 30 minutes. As both applications were examined, the ultrasound-infrared application was preferable in saving time and energy constraints for biodiesel production. The fuel properties were found to be equivalent to ASTM D6751 and EN 14214 biodiesel standards.
Min, C, Akther, N, Lee, T, Choo, Y, Naidu, G, Han, D-S, Kim, S-H & Shon, HK 2024, 'Atmospheric water harvesting by osmotic distillation and direct contact membrane distillation using hydrophobic hollow fiber membranes', Process Safety and Environmental Protection, vol. 182, pp. 527-534.
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Mirdad, AR, Khan, AM & Hussain, FK 2024, 'Smart contracts and marketplace for just-in-time management of pharmaceutical drugs', International Journal of Web and Grid Services, vol. 20, no. 1, pp. 25-53.
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Mishra, DK, Eskandari, M, Abbasi, MH, Sanjeevikumar, P, Zhang, J & Li, L 2024, 'A detailed review of power system resilience enhancement pillars', Electric Power Systems Research, vol. 230, pp. 110223-110223.
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Mishra, DK, Mohanty, A & Ray, PK 2024, 'An optimal frequency regulation in interconnected power system through differential evolution and firefly algorithm', Soft Computing, vol. 28, no. 1, pp. 593-606.
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Mishra, DK, Wang, J, Li, L, Zhang, J & Hossain, MJ 2024, 'Resilience-Driven Scheme in Multiple Microgrids With Secure Transactive Energy System Framework', IEEE Transactions on Industry Applications, vol. 60, no. 2, pp. 2277-2289.
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Mishra, R, Shu, C-M, Ong, HC, Gollakota, ARK & Kumar, S 2024, 'Progress and development of biochar as a catalyst for hydrogen production', Journal of Cleaner Production, vol. 477, pp. 143853-143853.
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Mishra, S, Indraratna, B, Rujikiatkamjorn, C & Ngo, T 2024, 'Use of recycled tyre segments to enhance the stability of ballasted track by increased confinement', Canadian Geotechnical Journal, vol. 61, no. 7, pp. 1385-1398.
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The most common railway ballast is produced by quarrying, and its mechanical characteristics are crucial for both stability and drainage for safer and faster rail operations. Ballasted tracks have certain drawbacks, primarily because ballast starts to degrade over time. In this regard, reducing the rate of ballast degradation is vital to enhance track longevity and minimise maintenance costs. This study demonstrates how segments of huge waste rubber tyres (e.g., 3 m in diameter) from the mining industry can be used to improve the stability of tracks, while contributing to reduced ballast deformation and degradation. By placing arched segments cut from these tyres along the track shoulders beyond the edge of sleepers (i.e., the plan view gives a schematic impression of a caterpillar), the in-situ lateral confining pressure can be increased from 20–25 (standard track) to 40–50 kPa. This novel idea of confined-caterpillar track (CCT) was tested at a prototype physical model (1:1 scale) at the National Facility for the Heavy-haul Railroad Testing, and the experimental outcomes were compared with the performance of a conventional track. Apart from constributing to at least 25% saving of quarried aggregates, the test results prove that the CCT concept can curtail the lateral displacement and settlement of the ballast layer, while reducing particle breakage and affecting significant stress reduction in the underlying substructure layers.
Moazzen, F, Alikhani, M, Aghaei, J & Hossain, MJ 2024, 'Social welfare evaluation during demand response programs execution considering machine learning-based load profile clustering', Applied Energy, vol. 357, pp. 122518-122518.
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Mofijur, M, Ahmed, SF, Ahmed, B, Mehnaz, T, Mehejabin, F, Shome, S, Almomani, F, Chowdhury, AA, Kalam, MA, Badruddin, IA & Kamangar, S 2024, 'Impact of nanoparticle-based fuel additives on biodiesel combustion: An analysis of fuel properties, engine performance, emissions, and combustion characteristics', Energy Conversion and Management: X, vol. 21, pp. 100515-100515.
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Mofijur, M, Hasan, MM, Ahmed, SF, Djavanroodi, F, Fattah, IMR, Silitonga, AS, Kalam, MA, Zhou, JL & Khan, TMY 2024, 'Advances in identifying and managing emerging contaminants in aquatic ecosystems: Analytical approaches, toxicity assessment, transformation pathways, environmental fate, and remediation strategies', Environmental Pollution, vol. 341, pp. 122889-122889.
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Mohamad Aziz, NA, Mohamed, H, Kania, D, Ong, HC, Zainal, BS, Junoh, H, Ker, PJ & Silitonga, AS 2024, 'Bioenergy production by integrated microwave-assisted torrefaction and pyrolysis', Renewable and Sustainable Energy Reviews, vol. 191, pp. 114097-114097.
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Mohammadi, M, Oberst, S & Halkon, BJ 2024, 'Application of Time Synchronous Averaging in Mitigating UAV Noise and Signal Loss for Continuous Scanning Laser Doppler Vibrometry', Journal of Physics: Conference Series, vol. 2698, no. 1, pp. 012005-012005.
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Abstract The laser Doppler vibrometer (LDV) has been shown to be effective for a wide application of vibration assessments that are well accepted. One of the new avenues for exploring alternative measurement scenarios, mounting LDVs on unmanned aerial vehicles (UAVs) is emerging as a potential avenue for remote and harsh environment measurements. Such configurations grapple with the challenge of the LDV sensor head being sensitive to UAV vibration during flight and signal loss due to tracking error. This study investigates the effectiveness of several Time Synchronous Averaging (TSA) techniques to circumvent these obstacles. Through comprehensive evaluations, all three TSA techniques under investigation demonstrated significant potential in suppressing UAV-induced noise and minimising the effects of signal dropout. Traditional TSA showcased a remarkable sixfold enhancement in signal quality when analysed via the mean square error. However, the study also highlighted that while TSA and Multi-Cycle Time Synchronous Average (MCTSA) elevated signal clarity, there is a trade-off between noise suppression and signal duration. Additionally, the findings emphasise the importance of synchronisation between scanning and target vibration. To achieve optimal results in Continuous Scanning Laser Doppler Vibrometer measurements, there is a need for advanced algorithms capable of estimating target vibration and synchronising scanning in real-time. As the study was rooted in steady-state vibrations, future research should explore transient vibration scenarios, thereby broadening the application scope of TSA techniques in UAV-mounted LDV systems.
Mohanty, N, Behera, BK & Ferrie, C 2024, 'Solving the vehicle routing problem via quantum support vector machines', Quantum Machine Intelligence, vol. 6, no. 1.
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AbstractThe vehicle routing problem (VRP) is an example of a combinatorial optimization problem that has attracted academic attention due to its potential use in various contexts. VRP aims to arrange vehicle deliveries to several sites in the most efficient and economical manner possible. Quantum machine learning offers a new way to obtain solutions by harnessing the natural speedups of quantum effects, although many solutions and methodologies are modified using classical tools to provide excellent approximations of the VRP. In this paper, we employ 6 and 12 qubit circuits, respectively, to build and evaluate a hybrid quantum machine learning approach for solving VRP of 3- and 4-city scenarios. The approach employs quantum support vector machines (QSVMs) trained using a variational quantum eigensolver on a static or dynamic ansatz. Different encoding strategies are used in the experiment to transform the VRP formulation into a QSVM and solve it. Multiple optimizers from the IBM Qiskit framework are also evaluated and compared
Mojiri, A, Vishkaei, MN, Zhou, JL, Trzcinski, AP, Lou, Z, Kasmuri, N, Rezania, S, Gholami, A, Vakili, M & Kazeroon, RA 2024, 'Impact of polystyrene microplastics on the growth and photosynthetic efficiency of diatom Chaetoceros neogracile', Marine Environmental Research, vol. 194, pp. 106343-106343.
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Mondal, MIH, Chandra Chakraborty, S, Rahman, MS, Marjuban, SMH, Ahmed, F, Zhou, JL, Ahmed, MB & Zargar, M 2024, 'Adsorbents from rice husk and shrimp shell for effective removal of heavy metals and reactive dyes in water', Environmental Pollution, vol. 346, pp. 123637-123637.
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Mora, A, Leiva, F, Cardenas, R, Rojas, F, Pereda, J, Aguilera, RP & Travieso-Torres, JC 2024, 'Optimal Switching Sequence MPC for Four-Leg Two-Level Grid-Connected Converters', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 12, no. 2, pp. 1271-1281.
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Morshed, N, Rennie, C, Deng, W, Collins-Praino, L & Care, A 2024, 'Serum-derived protein coronas affect nanoparticle interactions with brain cells', Nanotechnology, vol. 35, no. 49, pp. 495101-495101.
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Abstract Neuronanomedicine is an emerging field bridging the gap between neuromedicine and novel nanotherapeutics. Despite promise, clinical translation of neuronanomedicine remains elusive, possibly due to a dearth of information regarding the effect of the protein corona on these neuronanomedicines. The protein corona, a layer of proteins adsorbed to nanoparticles following exposure to biological fluids, ultimately determines the fate of nanoparticles in biological systems, dictating nanoparticle–cell interactions. To date, few studies have investigated the effect of the protein corona on interactions with brain-derived cells, an important consideration for the development of neuronanomedicines. Here, two polymeric nanoparticles, poly(lactic-co-glycolic acid) (PLGA) and PLGA-polyethylene glycol (PLGA-PEG), were used to obtain serum-derived protein coronas. Protein corona characterization and liquid chromatography mass spectrometry analysis revealed distinct differences in biophysical properties and protein composition. PLGA protein coronas contained high abundance of globins (60%) and apolipoproteins (21%), while PLGA-PEG protein coronas contained fewer globins (42%) and high abundance of protease inhibitors (28%). Corona coated PLGA nanoparticles were readily internalized into microglia and neuronal cells, but not into astrocytes. Internalization of nanoparticles was associated with pro-inflammatory cytokine release and decreased neuronal cell viability, however, viability was rescued in cells treated with corona coated nanoparticles. These results showcase the importance of the protein corona in mediating nanoparticle–cell interactions.
Morshedi Rad, D, Hansen, WP, Zhand, S, Cranfield, C & Ebrahimi Warkiani, M 2024, 'A hybridized mechano-electroporation technique for efficient immune cell engineering', Journal of Advanced Research, vol. 64, pp. 31-43.
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Mortazavi, H, Beni, HM, Nadooshan, AA, Islam, MS & Ghalambaz, M 2024, '4E analysis and triple objective NSGA-II optimization of a novel solar-driven combined ejector-enhanced power and two-stage cooling (EORC-TCRC) system', Energy, vol. 294, pp. 130803-130803.
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Motalib Hossain, MA, Ker, PJ, Tiong, SK, Indra Mahlia, TM & Hannan, MA 2024, 'Recent progress in nanomaterials of battery energy storage: A patent landscape analysis, technology updates, and future prospects', Nanotechnology Reviews, vol. 13, no. 1.
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Abstract The world’s energy demand has significantly increased as a result of the growing population and accompanying rise in energy usage. Fortunately, the innovation of nanomaterials (NMs) and their corresponding processing into devices and electrodes could enhance the functionality and/or advancement of the current battery energy storage systems (BESSs). Patent landscape analysis (PLA) can offer a comprehensive overview of technological development trends and enable discussion in interdisciplinary areas that facilitate more rational technology planning in the future. In this study, PLA of recent advancements in the NM-based BESS was critically analyzed, future technologies forecasted, and potential challenges outlined. A search was performed in the Lens database using “energy storage system,” “battery,” and “nanomaterial,” and related patents under the simple family were extracted. Finally, after excluding duplicates and irrelevant patents, a total of 89 patents were selected for analysis using various parameters. The article provides a current technical overview along with an extensive bibliographic review of the patent family, trends of patent growth, key inventors and owners, patent legal status, patent jurisdiction, top cited patents, etc., as well as technological updates. Overall, nanotechnology has great potential for the future; however, further research and studies are necessary to accelerate the widespread usage of NMs in energy storage systems using cost-effective and environmentally friendly technologies.
Muhammad, G, Butler, TO, Chen, B, Lv, Y, Xiong, W, Zhao, X, Solovchenko, AE, Zhao, A, Mofijur, M, Xu, J & Alam, MA 2024, 'Sustainable production of lutein—an underexplored commercially relevant pigment from microalgae', Biomass Conversion and Biorefinery, vol. 14, no. 6, pp. 7255-7276.
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Currently, microalgae-derived lutein is gaining attention for its potential applications in cosmeceutical, nutraceutical, pharmaceutical, and food industries. Lutein is of commercial interest for a broad variety of health benefits: antioxidant activity, skin health improvement, reducing age-related macular degeneration, and the treatment of cancer. Microalgae are the fastest-growing lutein source, have the highest content in nature, and are a promising sustainable alternative to the current commercial source, marigold flowers. Microalgal cultivation has added environmental benefits over plants with higher carbon sequestration, reduced water footprint, and no pesticide use. To date, no industrial facility exists for the production of lutein from microalgae. This review outlines the existing technologies for bioprocessing of lutein at pilot scale (cultivation, harvesting, extraction, and purification). In addition, lutein encapsulation, a seldom discussed area, is explored in depth. In view of this knowledge, lutein could be anticipated as the next successful sustainable product from microalgae obtained at industrial scale for the circular economy. Graphical abstract: [Figure not available: see fulltext.]
Munot, S, Bray, J, Redfern, J, Bauman, A, Angell, B, Coggins, A, Denniss, A, Kancijanik, D, Jennings, G, Lai, K, Kumar, S, Khanlari, S, Kovoor, P, Marschner, S, Middleton, P, Oppermann, I, Rock, Z, Semsarian, C, Vukasovic, M & Chow, C 2024, 'Engaging Culturally Diverse Groups in Basic Life Support (BLS): Learning from FirstCPR - A Community Organisation-Based Intervention to Increase Training and Willingness to Respond to Out-Of-Hospital Cardiac Arrest (OHCA)', Heart, Lung and Circulation, vol. 33, pp. S142-S142.
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Musale, A, Hunashyal, AM, Patil, AY, Kumar, R, Ahamad, T, Kalam, MA & Patel, M 2024, 'Study on Nanomaterials Coated Natural Coir Fibers as Crack Arrestor in Cement Composite', Advances in Civil Engineering, vol. 2024, pp. 1-16.
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The process of inclusion of carbon nanotubes as fibers in cement paste has been proved to have optimistic effect as it enhances the tensile property of cement paste composite. Coir fibers have exceptional mechanical qualities and are thus employed as reinforcement in cement composites. Epoxy resin, which has a high Young’s modulus, is an ideal component for bonding carbon nanotubes (CNTs) to coir fiber. This paper describes a novel kind of nanocomposite made of L-12 epoxy resin and CNTs at the nanolevel, along with coir fibers at the microlevel which operate as crack arrestors. To remove surface contaminants, coir fibers are first treated with sodium hydroxide (NaOH). Epoxy/CNTs polymer coatings were developed at varying CNTs fractions (0.05, 0.1, 0.15, and 0.2 wt.% of cement). Multiwalled CNTs were combined in distilled water, followed by epoxy resin and hardener (9 : 1 v/v) polymer in an ultrasonic sonicator for 90 min to ensure full dispersion of CNTs within the epoxy polymer. This blend is now coated on the treated clustered coir fiber (length 10 cm, 10 strands) and reinforced along the length of a cement composite beam 20 mm × 20 mm × 80 mm in size. Tensile and three-point tests were performed to evaluate the mechanical characteristics of the hybrid composite. The linear elastic finite element analysis is employed to distinguish their failure phenomena via fatigue or fracture behavior. The microstructure behavior and the effect of coating material on the coir fibers were investigated using scanning electron microscope and EDX analysis. The reinforcing impact of nanopolymer coated coir fiber in cement composite diminished the tensile and flexural strength after 0.1 wt.% of CNT fraction but increased the composite’s durability and Young’s modulus. Fourier transform infrared spectroscopy analysis was carried out to assess the chemical interaction between the epoxy/CNTs and the coir fibers. The simulation was performed using ANSYS workbench, an...
Musial, J, Stebel, K, Czeczot, J, Nowak, P & Gabrys, B 2024, 'Application of self-improving Q-learning controller for a class of dynamical processes: Implementation aspects', Applied Soft Computing, vol. 152, pp. 111250-111250.
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Mustafizur Rahman, M, Al-Amin, M & Hossain, J 2024, 'Machine learning models for chronic kidney disease diagnosis and prediction', Biomedical Signal Processing and Control, vol. 87, pp. 105368-105368.
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Mysore, THM, Patil, AY, Hegde, C, Sudeept, MA, Kumar, R, Soudagar, MEM & Fattah, IMR 2024, 'Apatite insights: From synthesis to biomedical applications', European Polymer Journal, vol. 209, pp. 112842-112842.
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Nabeel, MI, Afzal, MU, Singh, K, Thalakotuna, DN & Esselle, KP 2024, 'Dual-Band Printed Near-Field Metasurface With Independent Phase Transformation for Enhanced Antenna Gain', IEEE Antennas and Wireless Propagation Letters, vol. 23, no. 8, pp. 2401-2405.
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Nagaprasad, KS, Banapurmath, NR, Madhu, D, Soudagar, MEM, Mujtaba, MA, Kalam, MA, Vadlamudi, C & Krishnappa, S 2024, 'Emission characteristics of diesel engines fuelled with B20 with different DPF, DOC and EGR arrangements', International Journal of Ambient Energy, vol. 45, no. 1.
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Namadchian, Z, Shoeibi, A, Zare, A, Gorriz, JM, Lam, H-K & Ling, SH 2024, 'Stability Analysis of Dynamic General Type-2 Fuzzy Control System With Uncertainty', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 54, no. 3, pp. 1755-1767.
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Nanda, SJ, Yadav, RP, Gandomi, AH & Saraswat, M 2024, 'Preface', Lecture Notes in Networks and Systems, vol. 819, p. v.
Nanda, SJ, Yadav, RP, Gandomi, AH & Saraswat, M 2024, 'Preface', Lecture Notes in Networks and Systems, vol. 821, p. v.
Nanda, SJ, Yadav, RP, Gandomi, AH & Saraswat, M 2024, 'Preface', Lecture Notes in Networks and Systems, vol. 818, p. v.
Nanda, SJ, Yadav, RP, Gandomi, AH & Saraswat, M 2024, 'Preface', Lecture Notes in Networks and Systems, vol. 820, p. v.
Nandanwar, L, Shivakumara, P, Jalab, HA, Ibrahim, RW, Raghavendra, R, Pal, U, Lu, T & Blumenstein, M 2024, 'A Conformable Moments-Based Deep Learning System for Forged Handwriting Detection', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 4, pp. 5407-5420.
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Naseri, H, Golroo, A, Shokoohi, M & Gandomi, AH 2024, 'Sustainable pavement maintenance and rehabilitation planning using the marine predator optimization algorithm', Structure and Infrastructure Engineering, vol. 20, no. 3, pp. 340-352.
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The sustainability of pavement, especially in Maintenance and Rehabilitation (M&R) scheduling, has become an immense concern and has received limited attention in previous studies. Therefore, this study aimed to develop the M&R scheduling optimization based on sustainability. To this end, a novel sustainability index was introduced, in which all the sustainable development aspects were considered, including highway agency cost, environmental impacts, and social effects. A conventional model was used to assess the sustainable model’s effectiveness. Two new constraints are introduced to reduce the budget fluctuation and not to apply the M&R treatments for two consecutive years to make the model practical. On the other hand, highway agencies face large-scale networks, in which the optimization of M&R scheduling has computational complexities. Thus, the novel and powerful metaheuristic algorithm, named Marine Predator Algorithm (MPA), was applied to solve the pavement M&R scheduling problem. A large-scale pavement network, including 110 sections, was analyzed over a 5-year plan as the case study. The results indicated that using the sustainable model rather than the conventional one leads to a 6.5% reduction in CO2 emission. Besides, utilizing the sustainable approach enhances the equity and safety indices by 40.7% and 2.5% compared to the conventional treatment. However, the highway agency cost is increased by 1.1% using the sustainable model.
Nasir, M, Adesina, A, Bahraq, AA, Aziz, MA, Mahmood, AH, Ibrahim, M & Yusuf, MO 2024, 'Strength, Microstructure, and Life Cycle Assessment of Silicomanganese Fume, Silica Fume, and Portland Cement Composites Designed Using Taguchi Method', Journal of Materials in Civil Engineering, vol. 36, no. 7.
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Nasir, M, Mahmood, AH & Bahraq, AA 2024, 'History, recent progress, and future challenges of alkali-activated binders – An overview', Construction and Building Materials, vol. 426, pp. 136141-136141.
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Navidpour, AH, Hao, D, Li, X, Li, D, Huang, Z & Zhou, JL 2024, 'Key factors in improving the synthesis and properties of visible-light activated g-C 3 N 4 for photocatalytic hydrogen production and organic pollutant decomposition', Catalysis Reviews, vol. 66, no. 5, pp. 1665-1736.
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Navidpour, AH, Hosseinzadeh, A, Huang, Z, Li, D & Zhou, JL 2024, 'Application of machine learning algorithms in predicting the photocatalytic degradation of perfluorooctanoic acid', Catalysis Reviews, vol. 66, no. 2, pp. 687-712.
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Perfluorooctanoic acid (PFOA) is used in a variety of industries and is highly persistent in the environment, with potential human health risks. Photocatalysis has been extensively used for the decomposition of various organic pollutants, yet its simulation and modeling are challenging. This research aimed to establish different machine learning (ML) algorithms which can simulate and predict the photocatalytic degradation of PFOA. The published results were used to estimate and predict the photocatalytic degradation of PFOA. Statistical criteria including the coefficient of determination (R2), mean absolute error (MAE), and mean squared error (MSE) were considered in assessing the best method of modeling. Among the seven ML algorithms pre-screened, Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), and Random Forest (RF) showed the best performance and were chosen for deep modeling and analysis. Grid search was used to optimize the models developed by AdaBoost, GBM, and RF; and permutation variable importance (PVI) was used to analyze the relative importance of different variables. Based on the modeling results, GBM model (R2 = 0.878, MSE = 106.660, MAE = 6.009) and RF model (R2 = 0.867, MSE = 107.500, MAE = 6.796) showed superior performances compared with AdaBoost model (R2 = 0.574, MSE = 388.369, MAE = 16.480). Furthermore, the PVI results suggested that the GBM model provided the best outcome, with the light irradiation time, type of catalyst, dosage of catalyst, solution pH, irradiation intensity, initial PFOA concentration, oxidizing agents (peroxymonosulfate, ammonium persulfate, and sodium persulfate), irradiation wavelength, and solution temperature as the most important process variables in decreasing order.
Navidpour, AH, Safaei, J, Johir, MAH, Ni, B-J, Dashti, A, Li, X & Zhou, JL 2024, 'Zinc oxide@citric acid-modified graphitic carbon nitride nanocomposites for adsorption and photocatalytic degradation of perfluorooctanoic acid', Advanced Composites and Hybrid Materials, vol. 7, no. 2.
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AbstractPerfluorooctanoic acid (PFOA) is a highly persistent organic pollutant of global concern. A novel nanocomposite composed of ZnO nanoparticles and citric acid-modified g-C3N4 was synthesized by ball milling process. The synthesized nanocomposite was more efficient than pure ball-milled ZnO nanoparticles for PFOA elimination under visible light irradiation. The optimal hybrid photocatalyst, produced by the addition of 5 wt% of citric acid-modified g-C3N4, demonstrated significantly better performance for PFOA removal than pure ZnO nanoparticles under UV irradiation, with the apparent rate constants of 0.468 h−1 and 0.097 h−1, respectively. The addition of peroxymonosulfate (0.53 g L−1) significantly increased PFOA removal, clarifying the crucial effect of sulfate radicals on PFOA photodegradation. In comparison, citric acid-modified g-C3N4 was not effective for PFOA elimination under visible light irradiation, even with the addition of peroxymonosulfate. Further experiments under dark conditions identified surface adsorption on hybrid photocatalyst as a key process in total PFOA removal. In summary, PFOA removal by ZnO@citric acid-modified graphitic carbon nitride nanocomposites is due to the combined action from adsorption and photodegradation, with adsorption as the dominating mechanism.
Navidpour, AH, Xu, B, Ahmed, MB & Zhou, JL 2024, 'Immobilization of TiO2 and ZnO by facile surface engineering methods to improve semiconductor performance in photocatalytic wastewater treatment: A review', Materials Science in Semiconductor Processing, vol. 179, pp. 108518-108518.
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Nericua, R, Wang, K, Zhu, H, Gómez-García, R & Zhu, X 2024, 'Low-Loss and Compact Millimeter-Wave Silicon-Based Filters: Overview, New Developments in Silicon-on-Insulator Technology, and Future Trends', IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 14, no. 1, pp. 30-40.
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Neshat, M, Ahmed, M, Askari, H, Thilakaratne, M & Mirjalili, S 2024, 'Hybrid Inception Architecture with Residual Connection: Fine-tuned Inception-ResNet Deep Learning Model for Lung Inflammation Diagnosis from Chest Radiographs', Procedia Computer Science, vol. 235, pp. 1841-1850.
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Neshat, M, Sergiienko, NY, Nezhad, MM, da Silva, LSP, Amini, E, Marsooli, R, Astiaso Garcia, D & Mirjalili, S 2024, 'Enhancing the performance of hybrid wave-wind energy systems through a fast and adaptive chaotic multi-objective swarm optimisation method', Applied Energy, vol. 362, pp. 122955-122955.
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Neshat, M, Sergiienko, NY, Rafiee, A, Mirjalili, S, Gandomi, AH & Boland, J 2024, 'Meta Wave Learner: Predicting wave farms power output using effective meta-learner deep gradient boosting model: A case study from Australian coasts', Energy, vol. 304, pp. 132122-132122.
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Neves, J, Hsieh, C, Nobre, IB, Sousa, SC, Ouyang, C, Maciel, A, Duchowski, A, Jorge, J & Moreira, C 2024, 'Shedding light on ai in radiology: A systematic review and taxonomy of eye gaze-driven interpretability in deep learning', European Journal of Radiology, vol. 172, pp. 111341-111341.
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Ngo, QT, Jayawickrama, B, He, Y & Dutkiewicz, E 2024, 'A Novel Satellite-Based REM Construction in Cognitive GEO-LEO Satellite IoT Networks', IEEE Internet of Things Journal, pp. 1-1.
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Ngo, QT, Phan, KT, Mahmood, A & Xiang, W 2024, 'Hybrid IRS-Assisted Secure Satellite Downlink Communications: A Fast Deep Reinforcement Learning Approach', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 8, no. 4, pp. 2858-2869.
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Ngo, QT, Tang, Z, Jayawickrama, B, He, Y, Dutkiewicz, E & Senanayake, B 2024, 'Timeliness of Information in 5G Nonterrestrial Networks: A Survey', IEEE Internet of Things Journal, vol. 11, no. 21, pp. 34652-34675.
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Ngo, T & Indraratna, B 2024, 'Use of Geogrid for Improved Performance of Ballasted Tracks: Experimental and DEM Approaches', International Journal of Geosynthetics and Ground Engineering, vol. 10, no. 3.
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AbstractThis paper presents a study on the enhanced performance of ballasted tracks through the implementation of geogrids. A series of large-scale direct shear tests and impact tests was conducted with three distinct types of geogrids. The behavior of ballast was evaluated in terms of shear stress–strain responses and stress concentration using stress sensing sheets. Additionally, a micromechanical analysis utilizing the discrete element method was simulated on ballast assemblies with different geogrid reinforcements. The shear stress–strain responses of ballast simulated from DEM are comparable with those measured from large-scale direct shear tests, indicating that the inclusion of geogrid can enhance the performance of ballast by increasing its shear strength, as well as reducing the vertical displacement and the load distribution with depth. Micromechanical analysis was performed to investigate the influences of geogrids on contact force distribution, coordination number and orientation of contact which could not be captured in a laboratory environment. The use of geogrids in ballasted tracks certainly shows promise for sustainable and efficient railway infrastructure, as evidenced by the experimental and DEM-based findings, offering valuable insights into optimizing track stability and longevity.
Nguyen, BM, Nguyen, T, Vu, Q-H, Tran, HH, Vo, H, Bao Son, D, Thanh Binh, HT, Yu, S & Wu, Z 2024, 'A Novel Nature-Inspired Algorithm for Optimal Task Scheduling in Fog–Cloud Blockchain System', IEEE Internet of Things Journal, vol. 11, no. 2, pp. 2043-2057.
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Nguyen, C-H, Saputra, YM, Hoang, DT, Nguyen, DN, Nguyen, V-D, Xiao, Y & Dutkiewicz, E 2024, 'Encrypted Data Caching and Learning Framework for Robust Federated Learning-Based Mobile Edge Computing', IEEE/ACM Transactions on Networking, vol. 32, no. 3, pp. 2705-2720.
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Nguyen, CT, Hoang, DT, Nguyen, DN, Xiao, Y, Niyato, D & Dutkiewicz, E 2024, 'MetaShard: A Novel Sharding Blockchain Platform for Metaverse Applications', IEEE Transactions on Mobile Computing, vol. 23, no. 5, pp. 4348-4361.
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Due to its security, transparency, and flexibility in verifying virtual assets, blockchain has been identified as one of the key technologies for Metaverse. Unfortunately, blockchain-based Metaverse faces serious challenges such as massive resource demands, scalability, and security/privacy concerns. To address these issues, this paper proposes a novel sharding-based blockchain framework, namely MetaShard, for Metaverse applications. Particularly, we first develop an effective consensus mechanism, namely Proof-of-Engagement, that can incentivize MUs' data and computing resource contribution. Moreover, to improve the scalability of MetaShard, we propose an innovative sharding management scheme to maximize the network's throughput while protecting the shards from 51% attacks. Since the optimization problem is NP-complete, we develop a hybrid approach that decomposes the problem (using the binary search method) into sub-problems that can be solved effectively by the Lagrangian method. As a result, the proposed approach can obtain solutions in polynomial time, thereby enabling flexible shard reconfiguration and reducing the risk of corruption from the adversary. Extensive numerical experiments show that, compared to the state-of-the-art commercial solvers, our proposed approach can achieve up to 66.6% higher throughput in less than 1/30 running time. Moreover, the proposed approach can achieve global optimal solutions in most experiments.
Nguyen, CT, Liu, Y, Du, H, Hoang, DT, Niyato, D, Nguyen, DN & Mao, S 2024, 'Generative AI-enabled Blockchain Networks: Fundamentals, Applications, and Case Study', IEEE Network, pp. 1-1.
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Nguyen, CT, Saputra, YM, Huynh, NV, Nguyen, TN, Hoang, DT, Nguyen, DN, Pham, V-Q, Voznak, M, Chatzinotas, S & Tran, D-H 2024, 'Emerging Technologies for 6G Non-Terrestrial-Networks: From Academia to Industrial Applications', IEEE Open Journal of the Communications Society, vol. 5, pp. 3852-3885.
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Nguyen, DDN, Sood, K, Xiang, Y, Gao, L, Chi, L, Singh, G & Yu, S 2024, 'Design and Robust Evaluation of Next Generation Node Authentication Approach', IEEE Transactions on Dependable and Secure Computing, vol. 21, no. 6, pp. 5311-5323.
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Nguyen, DT, Johir, MAH, Mahlia, TMI, Silitonga, AS, Zhang, X, Liu, Q & Nghiem, LD 2024, 'Microalgae-derived biolubricants: Challenges and opportunities', Science of The Total Environment, vol. 954, pp. 176759-176759.
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Nguyen, HAD, Le, HT, Barthelemy, X, Azzi, M, Duc, H, Jiang, N, Riley, M & Ha, QP 2024, 'Deep-learning based visualization tool for air pollution forecast', IEEE Software, pp. 1-8.
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Nguyen, HAD, Le, TH, Azzi, M & Ha, QP 2024, 'Monitoring and estimation of urban emissions with low-cost sensor networks and deep learning', Ecological Informatics, vol. 82, pp. 102750-102750.
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Nguyen, HAD, Le, TH, Ha, QP, Duc, H & Azzi, M 2024, 'Particulate Matter Monitoring and Forecast with Integrated Low-cost Sensor Networks and Air-quality Monitoring Stations', E3S Web of Conferences, vol. 496, pp. 04001-04001.
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The fusion of low-cost sensor networks with air quality stations has become prominent, offering a cost-effective approach to gathering fine-scaled spatial data. However, effective integration of diverse data sources while maintaining reliable information remains challenging. This paper presents an extended clustering method based on the Girvan-Newman algorithm to identify spatially correlated clusters of sensors and nearby observatories. The proposed approach enables localized monitoring within each cluster by partitioning the network into communities, optimizing resource allocation and reducing redundancy. Through our simulations with real-world data collected from the state-run air quality monitoring stations and the low-cost sensor network in Sydney’s suburbs, we demonstrate the effectiveness of this approach in enhancing localized monitoring compared to other clustering methods, namely K-Means Clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Agglomerative Clustering. Experimental results illustrate the potential for this method to facilitate comprehensive and high-resolution air quality monitoring systems, advocating the advantages of integrating low-cost sensor networks with conventional monitoring infrastructure.
Nguyen, HD, Leys, J, Riley, M, White, S, Azzi, M, Trieu, T, Salter, D, Ji, F, Nguyen, H, Chang, LT-C, Monk, K, Firth, J, Fuchs, D & Barthelemy, X 2024, 'Effects of dust storm and wildfire events on phytoplankton growth in the Southern Ocean and Tasman Sea, southeast Australia', E3S Web of Conferences, vol. 496, pp. 04003-04003.
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Dust storms and wildfires occur frequently in southeastern Australia. Their effects on ecology, environment and population exposure have been the focus of many studies recently. Dust storms do not emit ground-sequestered carbon but wildfires emit significant quantities of carbon into the atmosphere. However, both natural events promote phytoplankton growth in water bodies because carbon, and other trace elements such as iron, deposit on the surface water of oceans and promote phytoplankton growth. Carbon di-oxide is reabsorbed by phytoplankton via photosynthesis. The carbon balance of dust storms and wildfires are not well known.This study focusses on the association of dust storms and wildfires in southeastern Australia with phytoplankton growth in the Southern Ocean and Tasman Sea due to the February 2019 dust storm event and the 2019-2020 black summer wildfires. The results show the similarities and differences in phytoplankton growth patterns and carbon reabsorption amount from these events.
Nguyen, HD, Nurhayati, M, Pham, TTT, Lee, BJ, Park, J, Shon, HK & Lee, S 2024, 'Updated measurement method for transparent exopolymer particles (TEPs) and their precursors with insights into efficient monitoring', Desalination, vol. 591, pp. 117975-117975.
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Nguyen, HG, Nguyen, HT, Nguyen, LTT, Tran, TS, Ho-Pham, LT, Ling, SH & Nguyen, TV 2024, 'Development of a shape-based algorithm for identification of asymptomatic vertebral compression fractures: A proof-of-principle study', Osteoporosis and Sarcopenia, vol. 10, no. 1, pp. 22-27.
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Nguyen, KH, Tran, Y, Craig, A, Nguyen, H & Chai, R 2024, 'Clustering for mitigating subject variability in driving fatigue classification using electroencephalography source-space functional connectivity features', Journal of Neural Engineering, vol. 21, no. 6, pp. 066002-066002.
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Abstract Objective. While Electroencephalography (EEG)-based driver fatigue state classification models have demonstrated effectiveness, their real-world application remains uncertain. The substantial variability in EEG signals among individuals poses a challenge in developing a universal model, often necessitating retraining with the introduction of new subjects. However, obtaining sufficient data for retraining, especially fatigue data for new subjects, is impractical in real-world settings. Approach. In response to these challenges, this paper introduces a hybrid solution for fatigue detection that combines clustering with classification. Unsupervised clustering groups subjects based on their EEG functional connectivity (FC) in an alert state, and classification models are subsequently applied to each cluster for predicting alert and fatigue states. Main results. Results indicate that classification on clusters achieves higher accuracy than scenarios without clustering, suggesting successful grouping of subjects with similar FC characteristics through clustering, thereby enhancing the classification process. Significance. Furthermore, the proposed hybrid method ensures a practical and realistic retraining process, improving the adaptability and effectiveness of the fatigue detection system in real-world applications.
Nguyen, LV, Kwok, NM & Ha, QP 2024, 'Fermat-Weber location particle swarm optimization for cooperative path planning of unmanned aerial vehicles', Applied Soft Computing, vol. 167, pp. 112269-112269.
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Nguyen, LV, Le, TH, Nguyen, TD, Kwok, NM & Ha, QP 2024, 'Monorail bridge inspection using digitally-twinned UAVs', E3S Web of Conferences, vol. 496, pp. 04004-04004.
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This paper introduces a comprehensive approach to monorail bridge inspection utilizing unmanned aerial vehicles (UAVs) and digital twin technology. The autonomous UAV-based inspection design encompasses UAV dynamics, tracking control, path planning, and task execution. A dedicated digital twin platform is developed to facilitate rigorous testing and verification of UAV control, mitigating the necessity for extensive physical testing. Methodology validation is achieved through a combination of simulations and real-world experiments, affirming its efficacy in authentic scenarios and demonstrating the potential for advancing infrastructure inspection practices.
Nguyen, LV, Le, TH, Torres Herrera, I, Kwok, NM & Ha, QP 2024, 'Intelligent path planning for civil infrastructure inspection with multi-rotor aerial vehicles', Construction Robotics, vol. 8, no. 2.
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AbstractThis paper presents the development of algorithms for high-level control and intelligent path planning of multi-rotor aerial vehicles (MAVs) in the tasks of inspecting civil infrastructure. After revisiting the multicopter modeling, we describe the hierarchy of high-level control for MAVs and develop optimization algorithms for generating optimal paths and enabling automatic flight during inspection tasks, making use of the digital twin technology. A co-simulation framework is then established to simulate and evaluate inspection mission scenarios, integrating these essential components. Real-world examples from built infrastructure illustrate this concept. An advantage of this approach is its ability to rigorously test, validate, verify, and evaluate MAV operations under abnormal conditions without requiring physical implementation or field tests. This significantly reduces testing efforts throughout the development cycle, ensuring optimal cooperation, safety, smoothness, fault tolerance, and energy efficiency. The methodology is validated through simulations and real-world inspection of a monorail bridge.
Nguyen, MD, Yin, Z, Rey, RD, Iacopi, F & Yang, Y 2024, 'Additive Manufacturing Materials and Processes for Passive Electronics in Wireless Communication', IEEE Transactions on Materials for Electron Devices, vol. 1, pp. 97-105.
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Nguyen, NH, Lu, Z, Elbourne, A, Vasilev, K, Roohani, I, Zreiqat, H & Truong, VK 2024, 'Engineering antibacterial bioceramics: Design principles and mechanisms of action', Materials Today Bio, vol. 26, pp. 101069-101069.
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Nguyen, NHT & Nguyen, TT 2024, 'Numerical investigation of the instability of dry granular bed induced by water leakage', Acta Geotechnica, vol. 19, no. 5, pp. 3229-3239.
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AbstractUnderground pipe defects or cracks under transport infrastructure can cause water leakage to upper soil layers (e.g. subgrade and capping), inducing local cavities or even failure of overlying road/railway formation. Although numerous studies on the instability of granular beds induced by injected water have been conducted, most of them focused on the behaviour of saturated granular beds, while research on dry granular beds is still limited. This paper aims to address this gap using a numerical model coupling volume of fluid method with discrete element method. We observed that dry granular beds go through three distinct regimes as water jet velocity increases including stationary, stable deformation with heave and fluidisation. However, the flow velocities required to deform and fluidise dry granular beds are significantly higher than those required for saturated beds. Increasing granular bed thickness can alter its failure mechanism from full depth to localised erosion, leading to cavity formation around pipe cracks prior to the bed fluidisation. The gravitational and frictional components of granular mass are identified as two main resisting forces of dry granular beds against water jet force, evidenced by the increase of critical jet velocities as particle density and friction coefficient increase. Nevertheless, the moblised zone of granular mass is practically independent of both the buried depth of dry granular beds.
Nguyen, N-K-Q, Bui, X-T, Dao, T-S, Pham, M-D-T, Ngo, HH, Lin, C, Lin, K-YA, Nguyen, P-D, Huynh, K-P-H, Vo, T-K-Q, Tra, V-T & Le, T-S 2024, 'Influence of hydrodynamic shear stress on activated algae granulation process for wastewater treatment', Environmental Technology & Innovation, vol. 33, pp. 103494-103494.
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Nguyen, TN, Zhang, D & Singhatanadgid, P 2024, 'Fast analysis approach for instability problems of thin shells utilizing ANNs and a Bayesian regularization back-propagation algorithm', Nonlinear Engineering, vol. 13, no. 1.
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AbstractThis research develops a data-driven methodology for structural instability problems with highly nonlinear, difficult, noisy, and small data. A fast analysis and prediction (FAP) approach for instability problems of thin shells is first proposed. This approach contains two phases: the fast numerical analysis and the pure prediction utilizing artificial neural networks (ANNs) incorporated with the Bayesian regularization (B-R) algorithm as follows: (1) in Phase 1 (the fast numerical analysis), post-buckling analysis is conducted utilizing a minor amount of load steps. The load–displacement relation achieved from Phase 1 is not exact because of the small number of load steps utilized; (2) in Phase 2 (the prediction), the loads and deflections achieved from Phase 1 were employed as the data for training ANNs. The trained networks, including the load and displacement networks, were employed to fast predict loads and deflections at any step of the post-buckling analysis. After utilizing Phase 2, a smooth, complete and exact load–displacement curve was achieved. In Phase 1, the available formulation for post-buckling analysis of thin shells in the literature was utilized. Five popular types of instabilities chosen to confirm the effectiveness and exactness of the FAP were snap-through, snap-back, softening–hardening, kink instabilities, and delamination buckling and post-buckling of composites. The high exactness and effectiveness of the FAP were confirmed in the numerical verification section. The present approach saves a huge computation compared to the other ones. It was found that ANNs incorporated with the B-R algorithm have notable advantages compared to numerous neural networks. The proposed approach is applicable to simulations or experiments where data are “expensive”, highly nonlinear, difficult, and limited. Utilizing the proposed approach for these fields can dramatically save time and money.
Nguyen, V-D, Vu, TX, Nguyen, NT, Nguyen, DC, Juntti, M, Luong, NC, Hoang, DT, Nguyen, DN & Chatzinotas, S 2024, 'Network-Aided Intelligent Traffic Steering in 6G O-RAN: A Multi-Layer Optimization Framework', IEEE Journal on Selected Areas in Communications, vol. 42, no. 2, pp. 389-405.
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Ni, M, Sun, Z & Liu, W 2024, 'Reversible jump attack to textual classifiers with modification reduction', Machine Learning, vol. 113, no. 9, pp. 5907-5937.
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AbstractRecent studies on adversarial examples expose vulnerabilities of natural language processing models. Existing techniques for generating adversarial examples are typically driven by deterministic hierarchical rules that are agnostic to the optimal adversarial examples, a strategy that often results in adversarial samples with a suboptimal balance between magnitudes of changes and attack successes. To this end, in this research we propose two algorithms, Reversible Jump Attack (RJA) and Metropolis–Hasting Modification Reduction (MMR), to generate highly effective adversarial examples and to improve the imperceptibility of the examples, respectively. RJA utilizes a novel randomization mechanism to enlarge the search space and efficiently adapts to a number of perturbed words for adversarial examples. With these generated adversarial examples, MMR applies the Metropolis–Hasting sampler to enhance the imperceptibility of adversarial examples. Extensive experiments demonstrate that RJA-MMR outperforms current state-of-the-art methods in attack performance, imperceptibility, fluency and grammar correctness.
Ni, Q, Ji, JC, Feng, K, Zhang, Y, Lin, D & Zheng, J 2024, 'Data-driven bearing health management using a novel multi-scale fused feature and gated recurrent unit', Reliability Engineering & System Safety, vol. 242, pp. 109753-109753.
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Ni, Z, Zhang, JA, Huang, X & Liu, RP 2024, 'Frequency-Time Resource Allocation for Multiuser Uplink ISAC Systems', IEEE Transactions on Vehicular Technology, pp. 1-14.
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Nikdad, P, Mohammadi Ghaleni, M, Moghaddasi, M & Pradhan, B 2024, 'Enhancing a machine learning model for predicting agricultural drought through feature selection techniques', Applied Water Science, vol. 14, no. 6.
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AbstractThis study aims to determine the crucial variables for predicting agricultural drought in various climates of Iran by employing feature selection methods. To achieve this, two databases were used, one consisting of ground-based measurements and the other containing six reanalysis products for temperature (T), root zone soil moisture (SM), potential evapotranspiration (PET), and precipitation (P) variables during the 1987–2019 period. The accuracy of the global database data was assessed using statistical criteria in both single- and multi-product approaches for the aforementioned four variables. In addition, five different feature selection methods were employed to select the best single condition indices (SCIs) as input for the support vector regression (SVR) model. The superior multi-products based on time series (SMT) showed increased accuracy for P, T, PET, and SM variables, with an average 47%, 41%, 42%, and 52% reduction in mean absolute error compared to SSP. In hyperarid climate regions, PET condition index was found to have high relative importance with 40% and 36% contributions to SPEI-3 and SPEI-6, respectively. This suggests that PET plays a key role in agricultural drought in hyperarid regions because of very low precipitation. Additionally, the accuracy results of different feature selection methods show that ReliefF outperformed other feature selection methods in agricultural drought modeling. The characteristics of agricultural drought indicate the occurrence of drought in 2017 and 2018 in various climates in Iran, particularly arid and semi-arid climates, with five instances and an average duration of 12 months of drought in humid climates.
Nikkhah, N, Keshavarz, R, Abolhasan, M, Lipman, J & Shariati, N 2024, 'Highly Sensitive Differential Microwave Sensor Using Enhanced Spiral Resonators for Precision Permittivity Measurement', IEEE Sensors Journal, vol. 24, no. 9, pp. 14177-14188.
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Nikolic, S, Suesse, TF, Grundy, S, Haque, R, Lyden, S, Hassan, GM, Daniel, S, Belkina, M & Lal, S 2024, 'Laboratory learning objectives: ranking objectives across the cognitive, psychomotor and affective domains within engineering', European Journal of Engineering Education, vol. 49, no. 3, pp. 454-473.
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Nikoo, MR, Izady, A, Bakhtiari, PH, Al-Maktoumi, A, Chen, M & Gandomi, AH 2024, 'A Water Resources Management Simulation–Optimization Model: Application of Graph-Based Hypergame Model in Water Supply Conflicts Resolution', Group Decision and Negotiation, vol. 33, no. 2, pp. 291-326.
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Nimbalkar, S & Basack, S 2024, 'Pile group in clay subjected to cyclic lateral load: Numerical modelling and design recommendation', Marine Georesources & Geotechnology, vol. 42, no. 1, pp. 67-87.
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Nirbhav, NA, Malik, A, Maheshwar, NA & Prasad, M 2024, 'Landslide susceptibility assessment along the major transport corridor using decision tree model: a case study of Kullu-Rohtang Pass', International Journal of Business Intelligence and Data Mining, vol. 24, no. 1, pp. 1-24.
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Nirmala, M, Gandomi, AH, Babu, MR, Babu, LDD & Patan, R 2024, 'An Emoticon-Based Novel Sarcasm Pattern Detection Strategy to Identify Sarcasm in Microblogging Social Networks', IEEE Transactions on Computational Social Systems, vol. 11, no. 4, pp. 5319-5326.
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Nitai, AS, Chowdhury, T, Inam, MN, Rahman, MS, Mondal, MIH, Johir, MAH, Hessel, V, Fattah, IMR, Kalam, MA, Suwaileh, WA, Zhou, JL, Zargar, M & Ahmed, MB 2024, 'Carbon fiber and carbon fiber composites—creating defects for superior material properties', Advanced Composites and Hybrid Materials, vol. 7, no. 5.
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AbstractRecent years have seen a rise in the use of carbon fiber (CF) and its composite applications in several high-tech industries, such as the design of biomedical sensor components, 3D virtual process networks in automotive and aerospace parts, and artificial materials or electrodes for energy storage batteries. Since pristine CF have limited properties, their properties are often modified through a range of technologies, such as laser surface treatment, electron-beam irradiation grafting, plasma or chemical treatments, electrophoretic deposition, carbonization, spinning-solution or melt, electrospinning, and sol–gel, to greatly improve their properties and performance. These procedures cause faulty structures to emerge in CF. The characteristics and performances of CF (thermo-electric conductivity, resistivity, stress tolerance, stiffness and elasticity, chemical resistivity, functionality, electrochemical properties, etc.) vary greatly depending on the modification technique used. Thus, the purpose of this review is to demonstrate how the insertion of faults can result in the production of superior CF. The characteristics of CF defects were examined using a variety of analytical techniques, such as defect-forming chemistry, molecular organization, and ground-level chemistries like their crystallinities. Finally, some future work is also included. Graphical abstract
Niu, C, Pang, G & Chen, L 2024, 'Affinity Uncertainty-Based Hard Negative Mining in Graph Contrastive Learning', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 9, pp. 11681-11691.
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Niu, G, He, X, Xu, H & Dai, S 2024, 'Tunnelling-induced ground surface settlement: A comprehensive review with particular attention to artificial intelligence technologies', Natural Hazards Research, vol. 4, no. 1, pp. 148-168.
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Niu, S, Yin, Q, Ma, J, Song, Y, Xu, Y, Bai, L, Pan, W & Yang, X 2024, 'Enhancing healthcare decision support through explainable AI models for risk prediction', Decision Support Systems, vol. 181, pp. 114228-114228.
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Nouri, Y, Jouneghani, HG, Haghollahi, A, Hemati, E, Hemati, SA & Mortazavi, M 2024, 'Experimental and numerical investigation of a steel yielding arc and ring damper', Structures, vol. 68, pp. 107140-107140.
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Nuñez, A, Wakulicz, J, Kong, FH, González-Cantos, A & Fitch, R 2024, 'Risk-aware stochastic ship routing using constrained continuous belief tree search', Ocean Engineering, vol. 314, pp. 119581-119581.
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Nurulita, B, Nur, TB, Silitonga, AS, Riayatsyah, TMI, Deswita, Kalam, MA, Zulkifli, NWM, Sebayang, AH, Siahaan, S & Alfansury, M 2024, 'Novel Biolubricant Synthesis: Enhancing the Composition of Used Cooking Oil and Callophyllum Inophyllum Oil by Utilizing Infrared Heating Method', Results in Engineering, pp. 103343-103343.
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Oberst, S & Martin, R 2024, 'Feature-preserving synthesis of termite-mimetic spinodal nest morphology', iScience, vol. 27, no. 1, pp. 108674-108674.
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Oh, K-S, Qin, P-Y & Seo, D-W 2024, 'Near-Field Radar Equation Based on Fresnel Diffraction Formula to Detect Short-Ranged Targets for Automotive Radar', Journal of Electromagnetic Engineering and Science, vol. 24, no. 2, pp. 161-169.
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Although the classic radar equation has been widely used to analyze the link budget of an automotive radar, it is valid only when the targets are located in the far-field region of the transmitted field and the receiving antenna is located in the far-field region of the scattered field. This paper confirms this hypothesis using measured and simulated data for short-range radars. Furthermore, a novel radar equation based on the Fresnel diffraction formula is proposed for application in situations where the receiving antenna is located in the radiative near-field region (or Fresnel region) of a target, but the target is situated in the far-field region of the transmitting antenna. In addition, the proposed radar equation is assessed by comparing the measured and simulated data.
Oladigbolu, J, Mujeeb, A & Li, L 2024, 'Optimization and energy management strategies, challenges, advances, and prospects in electric vehicles and their charging infrastructures: A comprehensive review', Computers and Electrical Engineering, vol. 120, pp. 109842-109842.
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Oloyede, CT, Itabiyi, OE, Popoola, OA, Jekayinfa, SO, Olaniyan, MA, Adebayo, AO, Ogunkunle, O, Zamri, MFMA & Fattah, IMR 2024, 'Navigating prospects and challenges for green fuels for achieving economical, environmental and ecological resilience: a scientific review', Biofuels, vol. 15, no. 7, pp. 929-941.
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Oloyede, CT, Jekayinfa, SO, Adebonojo, SA, Uduaghan, AA, Adebayo, JM & Islam Md Rizwanul, F 2024, 'Moisture‐modulated thermo‐physical analysis of sweetsop seed (Annona squamosa L.): A potential biofuel feedstock plant', Journal of Food Process Engineering, vol. 47, no. 7.
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AbstractSweetsop seed holds significant economic value as an oil seed, with ca. 25% oil content that finds applications as a feedstock for energy generation. The moisture‐modulated thermophysical properties of the seed were determined at varying moisture contents (8.0%–32.5%). Physical properties (length [L], width [W], thickness [T], arithmetic [Amd], and geometric mean diameters [Gmd], sphericity [Sty], surface area [SA], and bulk density [ρd]) were determined using standard methods while the thermal properties (specific heat capacity [SHC], thermal conductivity [Tcd], and thermal diffusivity [Tdf]) were analyzed using a TEMPOS thermal analyzer. The results showed that the seed L, W, T, Amd, and Gmd, Sty, SA, and ρd ranged from 13.22–14.95 mm, 7.32–7.95 mm, 5.25–5.35 mm, 8.60–8.75 mm, and 7.96–8.11 mm, 0.61–0.60, 196.72–205.28 mm2, and 210.00–270.00 kg m−3, respectively. The SHC, Tcd, and Tdf ranged from 0.14–0.52 J kg−1 K−1, 0.17–0.35 W m−1
Onari, MA, Rezaee, MJ, Saberi, M & Nobile, MS 2024, 'An explainable data-driven decision support framework for strategic customer development', Knowledge-Based Systems, vol. 295, pp. 111761-111761.
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Ong Tang, RC, Jaiswal, M, Wang, C-T, Ong, ZC & Ong, HC 2024, 'Effect of bluff body embedded in flow channel on power performance of microbial fuel cell', Fuel, vol. 359, pp. 130370-130370.
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Ong, MY, Nomanbhay, S, Rosman, CUAAC, Yusaf, T & Silitonga, AS 2024, 'Hydrochar production through co-hydrothermal carbonization of water hyacinth and plastic waste', IOP Conference Series: Earth and Environmental Science, vol. 1372, no. 1, pp. 012034-012034.
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Abstract The global expansion of the economy and concerns about greenhouse gas emissions and climate change necessitate the exploration of sustainable alternatives to fossil fuels. Water hyacinth (WH) is globally recognized as one of the most problematic aquatic weeds, posing significant challenges to urban management by clogging waterways, polluting water sources, and causing harm to ecosystems. However, water hyacinth is enriched with hemicellulose, cellulose, and lignin, making it a noteworthy and superior biomass resource. Hence, this study focuses on the hydrothermal carbonization of water hyacinth into a renewable fuel source, the hydrochar. Hydrothermal treatment was implemented in this work as it can treat wet biomass, in this case, the water hyacinth, without the need of energy-extensive drying process. Plastic waste (PW), or more specifically low-density polyethylene (LDPE), was added as the co-feedstock during the HTC process with the purpose to boost the higher heating value (HHV) of the end product. The co-hydrothermal carbonization (co-HTC) process of the mixture of WH and PW at various ratios and temperatures were conducted to investigate the optimal HTC condition for high hydrochar yields. As the result, the highest hydrochar yield of 29.23 wt% was obtained with 12.5% LDPE substitution percentage, at 200 °C after a holding time of 90 min. However, in term of energy recovery efficiency (ER), the highest efficiency (27.28%) was achieved with 12.5% LDPE substitution percentage at 260 °C. The HHV value of the hydrochar produced in this work is in the range of 17.71-24.69 MJ/kg. In summary, the co-HTC of WH and LDPE could definitely be a promising alternative to bridge the gap from solid waste to renewable fuels.
Osman, SH, Chyuan, OH, Kamarudin, SK, Shaari, N, Hanapi, IH, Zakaria, Z, Ahmad Zaidi, NH, Adnan, SA & Bahru, R 2024, 'Nanocatalysts in direct liquid fuel cells: Advancements for superior performance and energy sustainability', International Journal of Green Energy, vol. 21, no. 16, pp. 3654-3674.
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Ostermeier, FF & Deuse, J 2024, 'A review and classification of scheduling objectives in unpaced flow shops for discrete manufacturing', Journal of Scheduling, vol. 27, no. 1, pp. 29-49.
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Ostermeier, FF & Deuse, J 2024, 'Modelling forgetting due to intermittent production in mixed-model line scheduling', Flexible Services and Manufacturing Journal, vol. 36, no. 2, pp. 503-532.
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Ou, L, Chang, Y-C, Wang, Y-K & Lin, C-T 2024, 'Fuzzy Centered Explainable Network for Reinforcement Learning', IEEE Transactions on Fuzzy Systems, vol. 32, no. 1, pp. 203-213.
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Ou, S, Guo, Z, Wen, S & Huang, T 2024, 'Multistability and fixed-time multisynchronization of switched neural networks with state-dependent switching rules', Neural Networks, vol. 180, pp. 106713-106713.
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Ozkaya, SG, Baygin, M, Barua, PD, Tuncer, T, Dogan, S, Chakraborty, S & Acharya, UR 2024, 'An automated earthquake classification model based on a new butterfly pattern using seismic signals', Expert Systems with Applications, vol. 238, pp. 122079-122079.
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Paeedeh, N, Pratama, M, Ma’sum, MA, Mayer, W, Cao, Z & Kowlczyk, R 2024, 'Cross-domain few-shot learning via adaptive transformer networks', Knowledge-Based Systems, vol. 288, pp. 111458-111458.
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Paeedeh, N, Pratama, M, Wibirama, S, Mayer, W, Cao, Z & Kowalczyk, R 2024, 'Few-shot class incremental learning via robust transformer approach', Information Sciences, vol. 675, pp. 120751-120751.
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Pal, PK, Jana, KC, Siwakoti, YP, Ali, JSM & Blaabjerg, F 2024, 'A Switched-Capacitor Multilevel Inverter With Modified Pulsewidth Modulation and Active DC-Link Capacitor Voltage Balancing', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 12, no. 2, pp. 1215-1229.
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Palsberg, J & Yu, N 2024, 'Optimal implementation of quantum gates with two controls', Linear Algebra and its Applications, vol. 694, pp. 206-261.
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Pan, Y, Zong, Z, Li, J, Qian, H & Wu, C 2024, 'Investigating the dynamic response of a double-box utility tunnel buried in calcareous sand against ground surface explosion', Tunnelling and Underground Space Technology, vol. 146, pp. 105636-105636.
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Pan, Y, Zong, Z, Li, J, Qian, H, Huang, J & Wu, C 2024, 'Dynamic response of calcareous sands shallow-buried reinforced concrete slab under surface explosion', Structures, vol. 67, pp. 107012-107012.
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PAN, Z, LI, H & HUANG, X 2024, 'Optimal Design of Multiuser mmWave LOS MIMO Systems Using Hybrid Arrays of Subarrays', IEICE Transactions on Communications, vol. E107.B, no. 1, pp. 262-271.
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Pang, C, Zhu, S, Shen, M, Liu, X & Wen, S 2024, 'Novel Exponential Stability Criteria for Switched Neutral-Type Neural Networks With Mixed Delays', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 71, no. 3, pp. 1306-1310.
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Papanikolaou, S, Armaghani, DJ, Mohammed, AS, Tsoukalas, MZ, Gandomi, AH & Asteris, PG 2024, 'Optimizing high-entropy alloys using deep neural networks', Materialia, vol. 36, pp. 102162-102162.
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Parkar, E, Gite, S, Mishra, S, Pradhan, B & Alamri, A 2024, 'Comparative study of deep learning explainability and causal ai for fraud detection', International Journal on Smart Sensing and Intelligent Systems, vol. 17, no. 1.
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Abstract This study aims to compare deep learning explainability (DLE) with explainable artificial intelligence and causal artificial intelligence (Causal AI) for fraud detection, emphasizing their distinct methodologies and potential to address critical challenges, particularly in finance. An empirical evaluation was conducted using the Bank Account Fraud datasets from NeurIPS 2022. DLE models, including deep learning architectures enhanced with interpretability techniques, were compared against Causal AI models that elucidate causal relationships in the data. DLE models demonstrated high accuracy (95% for Model A and 96% for Model B) and precision (97% for Model A and 95% for Model B) but exhibited reduced recall (98% for Model A and 97% for Model B) due to opaque decision-making processes. By contrast, Causal AI models showed balanced but lower performance with accuracy, precision, and recall, all at 60%. These findings underscore the need for transparent and reliable fraud detection systems, highlighting the trade-offs between model performance and interpretability. This study addresses a significant research gap by providing a comparative analysis of DLE and Causal AI in the context of fraud detection. The insights gained offer practical recommendations for enhancing model interpretability and reliability, contributing to advancements in AI-driven fraud detection systems in the financial sector.
Parsa, K, Hassall, M, Naderpour, M, Pourreza, M & Ramezani, F 2024, 'A New Alarm System Developing Approach Through Graph Modeling', IEEE Access, vol. 12, pp. 1608-1620.
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Paul, A & Saha, SC 2024, 'A Systematic Literature Review on Flexible Strategies and Performance Indicators for Supply Chain Resilience', Global Journal of Flexible Systems Management.
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AbstractSupply chain resilience is a widely useful concept for managing risk and disruption. Designing strategies for preparedness, response, and recovery can help businesses to mitigate risks and disruptions. Among them, flexible strategies can effectively improve supply chain resilience. In the literature, several studies have considered different types of flexible strategies and investigated their impacts on supply chain resilience. However, a systematic literature review (SLR) paper on this topic can further help to understand the scientific progress, research gaps, and avenues for future research. Hence, this study aims to explore how the literature has contributed to the area of flexible strategies and the impact on supply chain resilience performance. To achieve our objective, we apply an SLR methodology to identify themes such as research areas and key findings, contexts and industry sectors, methodologies, and key strategies and performance indicators in the connection between flexible strategies and supply chain resilience. The findings show that many studies connect flexible strategies to supply chain resilience. However, research gaps exist in analysing relationships between flexible strategies and performance, conducting comparative studies, developing dynamic resilience plans, applying flexible strategies, conducting theoretically grounded empirical studies, and applying multiple analytical tools to develop decision-making models for supply chain resilience. Finally, this study suggests several future research opportunities to advance the research on the topic. The findings can be a benchmark for researchers who are interested in conducting research in the area of flexible strategies and supply chain resilience.
Pavan Kumar, TVV, Taranath, NL, Rahul, R, Chandra Shekara, G, Sapra, P, Thandaiah Prabu, R, Metwally, ASM & Kalam, MA 2024, 'Photovoltaic fuzzy based modelling on defining energy efficient solar devices in industry 4.0', Optical and Quantum Electronics, vol. 56, no. 1.
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Peng, R, Guo, R, Liu, L, Ji, J, Miao, Z & Zhou, J 2024, 'Practical consensus tracking control for networked Euler–Lagrange systems based on UDE integrated with RBF neural network', Neurocomputing, vol. 583, pp. 127554-127554.
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Peng, T, Zhong, W, Wang, G, Luo, E, Yu, S, Liu, Y, Yang, Y & Zhang, X 2024, 'Privacy-Preserving Truth Discovery Based on Secure Multi-Party Computation in Vehicle-Based Mobile Crowdsensing', IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 7, pp. 7767-7779.
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Pfeffer, MA, Ling, SSH & Wong, JKW 2024, 'Exploring the frontier: Transformer-based models in EEG signal analysis for brain-computer interfaces', Computers in Biology and Medicine, vol. 178, pp. 108705-108705.
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Pineda, J, Nguyen, HV, Romero, E, Sheng, D & Gens, A 2024, 'Air permeability measurements in low porosity clayey rocks', E3S Web of Conferences, vol. 544, pp. 01027-01027.
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The paper describes the development of a high-pressure isotropic cell for studying the environmental degradation of low porosity clayey rocks. Air permeability measurements are used in this device as a tool to evaluate rock degradation in unsaturated rock specimens caused by mechanical, hydraulic and chemical paths. A modified equation, based on the air pressure decay method proposed by Yoshimi and Osterberg (1963), is presented. The proposed method is applied to an Australian clayey shale. Estimated values of air permeability are compared against those calculated using the original method which, in the case of low porosity rocks, seems to provide unrealistic values when the air pressure in the vessel decays beyond 50%.
Ping, J, Zhu, S, Mu, C, Liu, X & Wen, S 2024, 'Generalized Halanay-Type Inequalities for Finite-Time Stabilization of Delayed Systems', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 71, no. 4, pp. 2194-2198.
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Pira, L & Ferrie, C 2024, 'On the interpretability of quantum neural networks', Quantum Machine Intelligence, vol. 6, no. 2.
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AbstractInterpretability of artificial intelligence (AI) methods, particularly deep neural networks, is of great interest. This heightened focus stems from the widespread use of AI-backed systems. These systems, often relying on intricate neural architectures, can exhibit behavior that is challenging to explain and comprehend. The interpretability of such models is a crucial component of building trusted systems. Many methods exist to approach this problem, but they do not apply straightforwardly to the quantum setting. Here, we explore the interpretability of quantum neural networks using local model-agnostic interpretability measures commonly utilized for classical neural networks. Following this analysis, we generalize a classical technique called LIME, introducing Q-LIME, which produces explanations of quantum neural networks. A feature of our explanations is the delineation of the region in which data samples have been given a random label, likely subjects of inherently random quantum measurements. We view this as a step toward understanding how to build responsible and accountable quantum AI models.
Piyathilaka, L, Kodagoda, S, Thiyagarajan, K, Piccardi, M, Preethichandra, DMG & Izhar, U 2024, 'Learning Spatial Affordances From 3D Point Clouds for Mapping Unseen Human Actions in Indoor Environments', IEEE Access, vol. 12, pp. 868-877.
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Plagwitz, L, Choi, S, Yu, X, Segelcke, D, Lambers, H, Pogatzki-Zahn, E, Varghese, J, Faber, C & Pradier, B 2024, 'Data-driven time series analysis of sensory cortical processing using high-resolution fMRI across different studies', Biomedical Signal Processing and Control, vol. 93, pp. 106136-106136.
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Poblete, P, Gajardo, J, Cuzmar, RH, Aguilera, RP, Pereda, J, Lu, D & Alcaide, AM 2024, 'Predictive Optimal Variable-Angle PS-PWM Strategy for Cascaded H-Bridge Converters', IEEE Transactions on Industrial Electronics, vol. 71, no. 11, pp. 13556-13566.
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Pradeepkumar, A, Cortie, D, Smyth, E, Le Brun, AP & Iacopi, F 2024, 'A High Temperature Operando Study of Epitaxial Graphene Growth on Cubic Silicon Carbide Using Neutron Reflectometry', Neutron News, vol. 35, no. 2, pp. 9-11.
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Pradhan, B, Abdulkareem, A, Aldulaimi, A, Gite, S, Alamri, A & Mukhopadhyay, SC 2024, 'Machine Learning-based GIS Model for 2D and 3D Vehicular Noise Modelling in a Data-scarce Environment', International Journal on Smart Sensing and Intelligent Systems, vol. 17, no. 1.
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Abstract Vehicular traffic significantly contributes to economic growth but generates frictional noise that impacts urban environments negatively. Road traffic is a primary noise source, causing annoyance and interference. Traditional regression models predict two-dimensional (2D) noise maps, but this study explores the impact and visualization of noise using 2D and three-dimensional (3D) GIS (Geospatial Information Systems) functionalities. Two models were assessed: (i) a 2D noise model for roads and (ii) a 3D noise model for buildings, utilizing limited noise samples. Combining these models produced a comprehensive 3D noise map. Machine learning (ML) models—artificial neural network (ANN), random forest (RF), and support vector machine (SVM)—were evaluated using performance measures: correlation (R), correlation coefficient (R2), and root mean square error (RMSE). ANN outperformed others, with RF showing better results than SVM. GIS was applied to enhance the visualization of noise maps, reflecting average traffic noise levels during weekday mornings and afternoons in the study area.
Pradhan, B, Elias, PJ & Almazroui, M 2024, 'Evaluating and Predicting Meteorological Drought Using Different Climate Reanalysis Datasets over New South Wales, Australia', Earth Systems and Environment, vol. 8, no. 4, pp. 1657-1672.
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AbstractDroughts are one of the most disastrous natural hazards, primarily due to their persistence and spatial distribution. Drought prediction is one of the key challenges for effective drought management and to do so, studies often involve the use of station-based data which are effective only in regions with high-gauge density. Therefore, there is growing interest in the use of interpolated climatic grids to predict droughts. In recent decades, drought conditions have been aggravated by climate change and for that reason the use of climatic variables is important to accurately predict droughts. The analysis of any aspect of drought can be affected by the choice of data and drought index. Therefore, this study aims to identify the most suitable dataset and drought index for the New South Wales (NSW) region of Australia. The present study evaluates various precipitation datasets (Climate Research Unit (CRU), ERA-5, and Scientific Information for Land Owners (SILO)) and their corresponding variations on the Standardised Precipitation Index (SPI) at different time scales. Based on the findings, CRU was used to predict meteorological drought using machine learning techniques. The different machine learning models are Support Vector Regression, Random Forest and Artificial Neural Networks. The results suggest SVM to be the best performing model among these models for predicting SPI at short time scales (1 month and 3 month) and ANN to be the best performing model for long-term scales (6 months and 12 months). Such findings depict the capabilities of different models in examining drought characteristics and confirming the use of interpolated climatic grids thereby assisting in regional drought management.
Pradhan, S, Yee, T, Da Jose, T & Amatya, E 2024, 'Evaluation of volunteering program for international students in Australia: The AusLEAP program', Transform - The Journal of Engaged Scholarship, vol. 8, pp. 59-75.
Prahmana, RA, Darmanto, PS, Juangsa, FB, Reksowardojo, IK, Prakoso, T, Hendrarsakti, J, Yuwazama, Z, Ahmad, AH, Riayatsyah, TMI, Fahmi, AG, Silitonga, AS & Nurcholik, SD 2024, 'Experimental investigation on the effects of zinc oxide and goethite as additives in a diesel engine fueled by pure palm oil', Case Studies in Thermal Engineering, vol. 61, pp. 104993-104993.
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Prasad, K, Rifai, A, Recek, N, Schuessler, D, Levchenko, I, Murdock, A, Mozetič, M, Fox, K & Alexander, K 2024, 'Nanocarbon-Polymer Composites for Next-Generation Breast Implant Materials', ACS Applied Materials & Interfaces, vol. 16, no. 38, pp. 50251-50266.
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Prasad, K, Senthil, TS, Premkumar, P, Sathyamurthy, R, Hossain, I, Al, O, Kalam, M, Senthil, KT & Priya, CB 2024, 'Influence of substrate surface roughness on the thermal emissivity of titanium carbide coatings on graphite', Thermal Science, vol. 28, no. 1 Part B, pp. 755-763.
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This study focused on the impact of substrates shape on the heat radiationcharacteristics of a coating made of titanium carbide, TiC, deposited over a graphite basis. The TiC coating emissivity increase by 29.65% at 1050?C and by 37.45% at 1650?C when graphite, substrate surface roughness, was decreased from 3.01 ?m to 0.73 ?m. Simultaneously, the TiC coating?s spectrum emissivity on the graphite substrate indicated the material?s clear characteristic heat radiation. These findings demonstrated that the coating and substrate interacted to determine the coating?s heat radiation properties. A simplified coating model created to consider how the shape of the substrate affects the coating?s ability to conduct heat. Ultimately, the rough form of the substrate led to a decrease in the coating?s heat radiation characteristics and an enhancement in energy loss at the interface.
Pu, X, Che, H, Pan, B, Leung, M-F & Wen, S 2024, 'Robust Weighted Low-Rank Tensor Approximation for Multiview Clustering With Mixed Noise', IEEE Transactions on Computational Social Systems, vol. 11, no. 3, pp. 3268-3285.
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Puthal, D, Yeun, CY, Damiani, E, Mishra, AK, Yelamarthi, K & Pradhan, B 2024, 'Blockchain Data Structures and Integrated Adaptive Learning: Features and Futures', IEEE Consumer Electronics Magazine, vol. 13, no. 2, pp. 72-80.
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Qashlan, A, Nanda, P & Mohanty, M 2024, 'Differential privacy model for blockchain based smart home architecture', Future Generation Computer Systems, vol. 150, pp. 49-63.
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Qi, G, Liu, K, Xie, M, Li, Y & Ni, Q 2024, 'An adaptive Gaussian-guided feature alignment network for cross-condition and cross-machine fault diagnosis of rolling bearings', IEEE Sensors Journal, pp. 1-1.
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Qi, L, Wu, B, Chen, X, Zhou, L, Ni, W & Jamalipour, A 2024, 'Joint Optimization of Internet of Things and Smart Grid for Energy Generation, Battery (Dis)charging, and Information Delivery', IEEE Internet of Things Journal, vol. 11, no. 12, pp. 21647-21658.
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Qi, X, Liu, C, Li, L, Hou, J, Xin, H & Yu, X 2024, 'EmotionGesture: Audio-Driven Diverse Emotional Co-Speech 3D Gesture Generation', IEEE Transactions on Multimedia, vol. 26, pp. 10420-10430.
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Qi, Y, Indraratna, B, Ngo, T, Arachchige, CMK & Hettiyahandi, S 2024, 'Sustainable solutions for railway using recycled rubber', Transportation Geotechnics, vol. 46, pp. 101256-101256.
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Qian, J, Zhang, J, Li, X, Huang, L, Hu, X, Lee, JE-Y & Zhang, W 2024, 'Dual-Function Multichannel Acoustofluidic Particle Manipulation Enabled by a PMUT Array', IEEE Transactions on Electron Devices, vol. 71, no. 8, pp. 4932-4938.
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Qian, L, Lu, J, Li, W, Huan, Y, Sun, Y, Zheng, L & Zou, Z 2024, 'MCU-Enabled Epileptic Seizure Detection System With Compressed Learning', IEEE Internet of Things Journal, vol. 11, no. 5, pp. 8771-8782.
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Qin, F, Zheng, Z, Sui, Y, Gong, S, Shi, Z & Trivedi, KS 2024, 'Cross-project concurrency bug prediction using domain-adversarial neural network', Journal of Systems and Software, vol. 214, pp. 112077-112077.
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Qin, H & Stewart, MG 2024, 'Mitigating casualty risks from primary fragmentation hazards', International Journal of Protective Structures, vol. 15, no. 4, pp. 703-721.
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Primary fragmentation from detonation of high-explosive metal-cased munitions imposes significant risks to the safety of related personnel and the public. Barricades or other protective structures are commonly used to stop fragments and reduce casualty risks caused by detonated munitions when a sufficient safety distance cannot be guaranteed. This study aims to provide decision support for the positioning of barricades that can reasonably mitigate primary fragmentation hazards from the detonation of large calibre munitions using a probabilistic risk assessment approach. This approach enables a stochastic characterization of fragment ejections, stacking effects, fragment trajectories, human vulnerability and fragment hazard reduction by barricade. In a case study, the assessments of casualty risks and effectiveness of barricades were conducted for a single and a pallet of 155 mm projectiles. It was found that barricades with heights exceeding the height of munitions can significantly reduce the hazardous fragment densities and casualty risks beyond the barricade. The benefit of increasing the barricade height becomes marginal when it exceeds the height of munitions.
Qin, H, Mason, MS & Stewart, MG 2024, 'Physics-based flood vulnerability assessment for steel portal framed industrial buildings', Engineering Structures, vol. 316, pp. 118600-118600.
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Qiu, N, Wan, Y, Shen, Y & Fang, J 2024, 'Experimental and numerical studies on mechanical properties of TPMS structures', International Journal of Mechanical Sciences, vol. 261, pp. 108657-108657.
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Qiu, N, Yu, Z, Wang, D, Xiao, M, Zhang, Y, Kim, NH & Fang, J 2024, 'Bayesian optimization of origami multi-cell tubes for energy absorption considering mixed categorical-continuous variables', Thin-Walled Structures, vol. 199, pp. 111799-111799.
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Qiu, Y, Zhou, J, He, B, Armaghani, DJ, Huang, S & He, X 2024, 'Evaluation and Interpretation of Blasting-Induced Tunnel Overbreak: Using Heuristic-Based Ensemble Learning and Gene Expression Programming Techniques', Rock Mechanics and Rock Engineering, vol. 57, no. 9, pp. 7535-7563.
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Overbreak is a prevalent and detrimental phenomenon in hard rock tunnel excavation that escalates construction costs and compromises tunnel structural stability. Therefore, accurate prediction of overbreak during excavation is significant for cost reduction and risk mitigation. This study introduces an efficient metaheuristic method, namely the honey badger algorithm (HBA), for optimizing the light gradient boosting machine (LGBM) model, and proposes an explicit equation for the prediction of overbreak based on gene expression programming (GEP) technology. Utilizing a dataset comprising 523 overbreak cases collected from the Huxitai (HXT) tunnel in China, this study conducts the modeling of overbreak prediction and assesses the model's performance through various metrics and non-parametric statistical tests. The results indicate that the HBA-LGBM hybrid model developed herein achieves the highest coefficient of determination (R2) of 0.9472 among the tested models, while the GEP model reaches a R2 of 0.9275. Clearly, the overbreak prediction model constructed in this paper shows superior overall performance, and the comparative analysis of multiple models also highlights HBA's significant advantage in mitigating overfitting. Lastly, various interpretive techniques were applied to analyze the impact of input variables on overbreak, providing insights into the decision-making principles of the predictive model from both global and individual case levels. The analysis revealed that the total charge (TC) and powder factor (PF) are the most influential blasting parameters on overbreak occurrence. Furthermore, a graphical user interface for testing purposes was developed and showcased. In summary, the overbreak models established in this study effectively predict tunnel overbreak caused by blasting, demonstrating superior predictive performance and interpretability compared to previous efforts.
Qu, K, Liu, J, Zhang, J & Wu, C 2024, 'Three-dimensional meso-scale modelling of geopolymer-based ultra-high performance concrete (G-UHPC) with ceramic ball coarse aggregates under projectile impact', Structures, vol. 61, pp. 105935-105935.
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Qu, Y, Yu, S, Gao, L, Sood, K & Xiang, Y 2024, 'Blockchained Dual-Asynchronous Federated Learning Services for Digital Twin Empowered Edge-Cloud Continuum', IEEE Transactions on Services Computing, vol. 17, no. 3, pp. 836-849.
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Qu, Y, Yuan, X, Ding, M, Ni, W, Rakotoarivelo, T & Smith, D 2024, 'Learn to Unlearn: Insights Into Machine Unlearning', Computer, vol. 57, no. 3, pp. 79-90.
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Qureshi, M, Li, J, Wu, C & Sheng, D 2024, 'Mechanical strength of rubberized concrete: Effects of rubber particle size, content, and waste fibre reinforcement', Construction and Building Materials, vol. 444, pp. 137868-137868.
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Rafa, N, Ahmed, B, Zohora, F, Bakya, J, Ahmed, S, Ahmed, SF, Mofijur, M, Chowdhury, AA & Almomani, F 2024, 'Microplastics as carriers of toxic pollutants: Source, transport, and toxicological effects', Environmental Pollution, vol. 343, pp. 123190-123190.
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Rahaman, MM, Bhowmick, S, Saha, G, Xu, F & Saha, SC 2024, 'Transition to chaotic flow, bifurcation, and entropy generation analysis inside a stratified trapezoidal enclosure for varying aspect ratio', Chinese Journal of Physics, vol. 91, pp. 867-882.
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Rahimi, I, Gandomi, AH, Nikoo, MR, Mousavi, M & Chen, F 2024, 'Efficient implicit constraint handling approaches for constrained optimization problems', SCIENTIFIC REPORTS, vol. 14, no. 1.
Rahma, ON, Ain, K, Putra, AP, Rulaningtyas, R, Lutfiyah, N, Zalda, K, Alami, NRL & Chai, R 2024, 'Classification of Imagery Hand Movement Based on Electroencephalogram Signal Using Long-Short Term Memory Network Method', Mathematical Modelling of Engineering Problems, vol. 11, no. 5, pp. 1151-1159.
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Rahman, MM, Zhao, M, Islam, MS, Dong, K & Saha, SC 2024, 'A numerical study on sedimentation effect of dust, smoke and traffic particle deposition in a realistic human lung', International Journal of Multiphase Flow, vol. 171, pp. 104685-104685.
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Raman, R, Gor, M, Meenakshi, R, Jayaseelan, GM, Chaturvedi, A, Taqui, SN, Ganeshan, P, Ouladsmane, M & Kalam, MA 2024, 'Solar Energy Measurement and Monitoring Model by Using Internet of Things', Electric Power Components and Systems, vol. 52, no. 10, pp. 1796-1807.
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Ramana, M, Santra, SB, Chatterjee, D & Siwakoti, YP 2024, 'Sector Wise Modified Droop Control to Improve Voltage Regulation and Current Sharing in Parallel Boost Converter Interfaced DC Microgrid', IEEE Journal of Emerging and Selected Topics in Power Electronics, pp. 1-1.
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Ramia, G, Mitchell, E, Morris, A, Wilson, S, Hastings, C & Davies, J 2024, 'Explaining Government Policy Inaction on International Student Housing in Australia: The Perspectives of Stakeholders', Higher Education Policy, vol. 37, no. 1, pp. 21-39.
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Housing is a major concern for many international students. This is especially so in those countries where students are mostly dependent on the private market for their accommodation. Australia is one such country, and is one of the world's major destinations for international students. This article analyses governmental failure to address problems relating to international student housing affordability and conditions. Using theory on 'policy inaction' to frame the analysis, we draw on 20 interviews with policy stakeholders to explain the Australian government's reliance on: (1) market-based housing provision for international students, and (2) a longstanding policy preference not to provide support. Interviewees were widely critical of the lack of action to address international student housing problems and understood inaction in relation, rather than in opposition, to the dominance of market-based action in housing and higher education. However, analysis of stakeholder perspectives also illuminates how policy-making action benefiting some emerges as inaction for others left behind or overlooked by the status quo. The interview data points to the need for government to overhaul its policy framework, and in doing so, to collaborate with higher education providers in revising the market-based regulatory approach. The main implications for theory and policy are discussed.
Ranaweera, S, Jayawickrama, BA, He, Y, Tu Ngo, Q & Ping Liu, R 2024, 'On the Joint Optimization of Signal Constellations and Bit Mappings', IEEE Communications Letters, vol. 28, no. 11, pp. 2513-2517.
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Rao, P, Feng, W, Ouyang, P, Cui, J, Nimbalkar, S & Chen, Q 2024, 'Formation of plasma channel under high-voltage electric pulse and simulation of rock-breaking process', Physica Scripta, vol. 99, no. 1, pp. 015604-015604.
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Abstract In the context of rock fragmentation, the application of high voltage electric pulses results in the transfer of electrical energy onto the surface of the rock material, leading to a rapid electrical breakdown and the formation of a plasma channel. The ionized plasma expands at a fast velocity, generating a shock wave that causes significant damage to the rock’s integrity. In this study, we develope a numerical model that couples electrical, thermal, and mechanical forces to simulate the formation of plasma channels within rocks due to high-voltage electric pulses. The model’s accuracy is verified through field tests, and the results indicate that the configuration of the high-voltage pulse waveform, electrode spacing, and conductor particles within the rock impact the pathway of plasma channel formation. Prior to the formation of the plasma channel, minimal changes are observed in temperature and stress levels, with the majority of electric pulse energy dedicated to the creation of the plasma channel. Following the establishment of the plasma channel, the application of the electric pulse continues, resulting in notable alterations in temperature and stress levels. When the duration of the action reaches 105 ns, the temperature and stress levels surpass 104 K and 50 MPa, respectively, leading to fracture and extensive damage to the rock. The outcomes derived from the numerical model’s calculations can help to facilitate the cross-integration between physics and civil engineering and contribute to a deeper understanding of the rock fragmentation process under high voltage electric pulses.
Rao, P, Meng, J, Cui, J & Nimbalkar, S 2024, 'Field Study on Rectangular Inclined Deep Foundation Excavation in Soft Soils', Geotechnical and Geological Engineering, vol. 42, no. 3, pp. 2151-2168.
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Rao, P, Meng, J, Cui, J, Feng, W, Nimbalkar, S & Liu, Z 2024, 'Stability Analysis of Unsaturated Soil Pit Under Vehicle Load', Geotechnical and Geological Engineering, vol. 42, no. 6, pp. 4987-5001.
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Rashid, MI, Yaqoob, Z, Mujtaba, MA, Kalam, MA, Fayaz, H & Qazi, A 2024, 'Carbon capture, utilization and storage opportunities to mitigate greenhouse gases', Heliyon, vol. 10, no. 3, pp. e25419-e25419.
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Rathnayake, D, Radhakrishnan, M, Hwang, I & Misra, A 2024, 'LILOC: Leveraging LiDARs for Accurate 3D Localization in Dynamic Indoor Environments', ACM Transactions on Internet of Things, vol. 5, no. 4, pp. 1-33.
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We present LiLoc , a system for precise 3D localization and tracking of mobile IoT devices (e.g., robots) in indoor environments using multi-perspective LiDAR sensing. LiLoc stands out with two key differentiators. First, unlike traditional localization approaches, our method remains robust in dynamically changing environments, adeptly handling varying crowd levels and object layout changes. Second, LiLoc is independent of pre-built static maps, employing dynamically updated point clouds from infrastructural-mounted LiDARs and LiDARs on individual IoT devices. For fine-grained, near real-time tracking, LiLoc intermittently utilizes complex 3D “global” registration between point clouds for robust spot location estimates. It further complements this with simpler “local” registrations, continuously updating IoT device trajectories. We demonstrate that LiLoc can (a) support accurate location tracking with location and pose estimation error being ≦7.4 cm and ≦3.2°, respectively, for 84% of the time and the median error increasing only marginally (8%), for correctly estimated trajectories, when the ambient environment is dynamic; (b) achieve a 36% reduction in median location estimation error compared to an approach that uses only quasi-static global point cloud; and (c) obtain spot location estimates with a latency of only 973 msec. We also demonstrate how LiLoc efficiently integrates low-power inertial sensing, using a novel integration of inertial-based displacement to accelerate the local registration process, to enhance localization energy efficiency and latency.
Ravindran, MXY, Asikin-Mijan, N, AbdulKareem-Alsultan, G, Ong, HC, M.M, N, Lee, HV, Kurniawan, TA, Derawi, D, Yusoff, SFM, Lokman, IM & Taufiq-Yap, YH 2024, 'A review of carbon-based catalyst for production of renewable hydrocarbon rich fuel', Journal of Environmental Chemical Engineering, vol. 12, no. 2, pp. 112330-112330.
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Raza, A, Keshavarz, R & Shariati, N 2024, 'Precision Agriculture: Ultra-Compact Sensor and Reconfigurable Antenna for Joint Sensing and Communication', IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1-13.
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Raza, MA, Abolhasan, M, Lipman, J, Shariati, N, Ni, W & Jamalipour, A 2024, 'Multi-Agent Multi-Armed Bandit Learning for Grant-Free Access in Ultra-Dense IoT Networks', IEEE Transactions on Cognitive Communications and Networking, vol. 10, no. 4, pp. 1356-1370.
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Razavi Bazaz, S, Sayyah, A, Hazeri, AH, Salomon, R, Abouei Mehrizi, A & Ebrahimi Warkiani, M 2024, 'Micromixer research trend of active and passive designs', Chemical Engineering Science, vol. 293, pp. 120028-120028.
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Razavi, SE, Adibi, T, Ahmed, SF & Saha, SC 2024, 'Semi-analytical solution of nanofluid flow with convective and radiative heat transfer', International Journal of Modern Physics B, vol. 38, no. 25.
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The application of nanofluids has exploded in recent decades to improve the local number, mean Nusselt number, and rate of heat transfer. However, boundary layer equations of nanofluid across a flat plate with radiation have not been studied, and therefore this paper studies them mathematically for the first time. For water-based copper and aluminum oxide nanofluids, a similarity solution is presented in this study, and the subsequent system of the ordinary differential equation (ODE) is numerically solved by the Runge–Kutta method in MATLAB. Two different hydraulic boundary conditions are used in the simulations. In the first, the flow across a moving plate and the direction of the flow are analyzed, while in the second, the flow over a nonlinearly moving plate in a still fluid is investigated. The nanoparticle’s boundary layer thickness is found less than the thermal and hydraulics boundary layers. The local Nusselt number and friction factor of both the nanofluids are calculated and compared with the base fluid. The results demonstrate that the friction coefficient is high and the Nusselt number is low for nanoparticles with a high volume fraction. It also revealed that the friction factor for water–aluminum oxide is 16% greater than that for the water–CuO whereas the local Nusselt number for water–aluminum oxide is only 5% more than that for the water–CuO.
Rehman, UU, Munir, A, Bahadur Khan, N, Zhao, M, Kashif, M, Islam, MS, Saeed, Z & Ali, MA 2024, 'Numerical investigation of vortex-induced vibrations (VIV) of a rotating cylinder in in-line and cross-flow directions subjected to oscillatory flow', Ocean Engineering, vol. 304, pp. 117917-117917.
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Reja, VK, Goyal, S, Varghese, K, Ravindran, B & Ha, QP 2024, 'Hybrid self-supervised learning-based architecture for construction progress monitoring', Automation in Construction, vol. 158, pp. 105225-105225.
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Ren, J, Sui, Y, Cheng, X, Feng, Y & Zhao, J 2024, 'Dynamic Transitive Closure-based Static Analysis through the Lens of Quantum Search', ACM Transactions on Software Engineering and Methodology, vol. 33, no. 5, pp. 1-29.
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Many existing static analysis algorithms suffer from cubic bottlenecks because of the need to compute a dynamic transitive closure (DTC). For the first time, this article studies the quantum speedups on searching subtasks in DTC-based static analysis algorithms using quantum search (e.g., Grover’s algorithm). We first introduce our oracle implementation in Grover’s algorithm for DTC-based static analysis and illustrate our quantum search subroutine. Then, we take two typical DTC-based analysis algorithms: context-free-language reachability and set constraint-based analysis, and show that our quantum approach can reduce the time complexity of these two algorithms to truly subcubic ( \(O(N^2\sqrt {N}{\it polylog}(N))\) ), yielding better results than the upper bound ( O ( N 3 /log N )) of existing classical algorithms. Finally, we conducted a classical simulation of Grover’s search to validate our theoretical approach, due to the current quantum hardware limitation of lacking a practical, large-scale, noise-free quantum machine. We evaluated the correctness and efficiency of our approach using IBM Qiskit on nine open-source projects and randomly generated edge-labeled graphs/constraints. The results demonstrate the effectiveness of our approach and shed light on the promising direction of applying quantum algorithms to address the general challenges in static analysis.
Ren, J, Xu, J, Tian, S, Shi, K, Gu, T, Zhao, J, Li, X, Zhou, Z, Tijing, L & Shon, HK 2024, 'Hydrodynamic solar-driven interfacial evaporation - Gone with the flow', Water Research, vol. 266, pp. 122432-122432.
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Ren, L, Wang, X, Zhou, JL, Jia, Y, Hu, H, Li, C, Lin, Z, Liang, M & Wang, Y 2024, 'Biodegradation of phthalic acid esters by a novel marine bacterial strain RL-BY03: Characterization, metabolic pathway, bioaugmentation and genome analysis', Chemosphere, vol. 366, pp. 143530-143530.
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Ren, L, Zhang, Y, Zhou, JL, Wang, G, Mo, Y, Ling, Y, Huang, Y, Zhang, Y, Hu, H & Wang, Y 2024, 'RL-WG26 mediated salt stress tolerance in rice seedlings: A new insight into molecular mechanisms', Plant Stress, vol. 11, pp. 100306-100306.
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Ren, Y, Zhang, H, Du, L, Zhang, Z, Zhang, J & Li, H 2024, 'Stealthy Black-Box Attack With Dynamic Threshold Against MARL-Based Traffic Signal Control System', IEEE Transactions on Industrial Informatics, vol. 20, no. 10, pp. 12021-12031.
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Ren, Z, Ji, J, Zhu, Y & Feng, K 2024, 'An Investigation Into the Behavior of Intelligent Fault Diagnostic Models Under Imbalanced Data', IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1-20.
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Ren, Z-H, Zhang, X, Zhang, W-X, Huang, Z-G, Yang, L-M, Yang, Y-X, Li, Z-L, Li, J, Sun, W-P, Gao, M-X, Pan, H-G & Liu, Y-F 2024, 'Single Ti atoms coupled with Ti–O clusters enable low temperature hydrogen cycling by sodium alanate', Rare Metals, vol. 43, no. 6, pp. 2671-2681.
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Reza, MS, Hannan, MA, Mansor, M, Ker, PJ, Rahman, SA, Jang, G & Mahlia, TMI 2024, 'Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm', Journal of Energy Storage, vol. 98, pp. 113056-113056.
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Reza, MS, Hannan, MA, Mansor, MB, Ker, PJ, Tiong, SK & Hossain, MJ 2024, 'Gravitational Search Algorithm Based LSTM Deep Neural Network for Battery Capacity and Remaining Useful Life Prediction With Uncertainty', IEEE Transactions on Industry Applications, vol. 60, no. 6, pp. 9171-9183.
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Reza, MS, Mannan, M, Mansor, M, Ker, PJ, Mahlia, TMI & Hannan, MA 2024, 'Recent advancement of remaining useful life prediction of lithium-ion battery in electric vehicle applications: A review of modelling mechanisms, network configurations, factors, and outstanding issues', Energy Reports, vol. 11, pp. 4824-4848.
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Rezaee Jordehi, A, Mansouri, SA, Tostado-Véliz, M, Carrión, M, Hossain, MJ & Jurado, F 2024, 'A risk-averse two-stage stochastic model for optimal participation of hydrogen fuel stations in electricity markets', International Journal of Hydrogen Energy, vol. 49, pp. 188-201.
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Rezaee Jordehi, A, Mansouri, SA, Tostado-Véliz, M, Hossain, MJ, Nasir, M & Jurado, F 2024, 'Optimal placement of hydrogen fuel stations in power systems with high photovoltaic penetration and responsive electric demands in presence of local hydrogen markets', International Journal of Hydrogen Energy, vol. 50, pp. 62-76.
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Rhakho, N, Jena, S, Saxena, M, Altaee, A, Jadhav, AH & Samal, AK 2024, 'Anisotropic Cu2O nanostructures: A promising remediation for per- and polyfluoroalkyl substances', Journal of Water Process Engineering, vol. 62, pp. 105390-105390.
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Rhakho, N, Saxena, M, Pradhan, NR, H. Jadhav, A, Altaee, A & Samal, AK 2024, 'Transformative Dynamics: Self-Assembly of Iron Oxide Hydroxide Nanorods into Iron Oxide Microcubes for Enhanced Perfluoroalkyl Substance Remediation', Langmuir, vol. 40, no. 19, pp. 10184-10194.
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Rhakho, N, Yadav, S, Jinagi, M, Altaee, A, Saxena, M, Jadhav, AH & Samal, AK 2024, 'Mitigating PFAS contaminants in water: A comprehensive survey of remediation strategies', Journal of Environmental Chemical Engineering, vol. 12, no. 5, pp. 113425-113425.
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Riaz, HH, Lodhi, AH, Munir, A, Zhao, M, Farooq, U, Qadri, MNM & Islam, MS 2024, 'Breathing in danger: Mapping microplastic migration in the human respiratory system', Physics of Fluids, vol. 36, no. 4.
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The abundance of air pollutants over the last few years, including the concentration of microplastics, has become an alarming concern across the world. Initially discovered in marine life, these toxic and inflammatory particles have recently been found in human lung tissues. When inhaled, these harmful particles settle down in the lung airways and, over time, lead to respiratory failures. A recent study analyzed the microplastic transport behavior in the mouth–throat airways. However, the knowledge of the microplastic migration in bifurcating tracheobronchial airways is missing in the literature. Therefore, this first-ever study analyzes in detail the transport behavior and settling patterns of microplastic particles of different sizes and shapes at different respiratory intensities in the tracheobronchial lung airways. A numerical technique based on discrete phase modeling is employed to simulate the flow of microplastic particles in a three-dimensional realistic lung geometry. The numerical model results indicate low velocity and turbulence intensity magnitudes with smooth flow in the trachea compared to the airways of left and right lobes, which experience higher velocities and generate secondary vortices. Lower lung lobes are the deposition hotspots for the harmful microplastic particles at a lower flow rate. These hotspots shift to upper lung lobes at a higher flow rate for the same particle size. Moreover, microplastic particle size and shape influence the overall deposition rate in the tracheobronchial lung airways. The results of the current study, including microplastic accumulation regions at different breathing intensities, will contribute to the updated knowledge of pollutant inhalation and facilitate relevant treatment measures.
Riaz, HH, Munir, A, Farooq, U, Arshad, A, Chan, T-C, Zhao, M, Khan, NB & Islam, MS 2024, 'Optimal Treatment of Tumor in Upper Human Respiratory Tract Using Microaerosols', ACS Omega, vol. 9, no. 23, pp. 25106-25123.
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Lung cancer is a frequently diagnosed respiratory disease caused by particulate matter in the environment, especially among older individuals. For its effective treatment, a promising approach involves administering drug particles through the inhalation route. Multiple studies have investigated the flow behavior of inhaled particles in the respiratory airways of healthy patients. However, the existing literature lacks studies on the precise understanding of the transportation and deposition (TD) of inhaled particles through age-specific, unhealthy respiratory tracts containing a tumor, which can potentially optimize lung cancer treatment. This study aims to investigate the TD of inhaled drug particles within a tumorous, age-specific human respiratory tract. The computational model reports that drug particles within the size range of 5-10 μm are inclined to deposit more on the tumor located in the upper airways of a 70-year-old lung. Conversely, for individuals aged 50 and 60 years, an optimal particle size range for achieving the highest degree of particle deposition onto upper airway tumor falls within the 11-20 μm range. Flow disturbances are found to be at a maximum in the airway downstream of the tumor. Additionally, the impact of varying inhalation flow rates on particle TD is examined. The obtained patterns of airflow distribution and deposition efficiency on the tumor wall for different ages and tumor locations in the upper tracheobronchial airways would be beneficial for developing an efficient and targeted drug delivery system.
Rokoss, A, Syberg, M, Tomidei, L, Hülsing, C, Deuse, J & Schmidt, M 2024, 'Case study on delivery time determination using a machine learning approach in small batch production companies', Journal of Intelligent Manufacturing, vol. 35, no. 8, pp. 3937-3958.
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AbstractDelivery times represent a key factor influencing the competitive advantage, as manufacturing companies strive for timely and reliable deliveries. As companies face multiple challenges involved with meeting established delivery dates, research on the accurate estimation of delivery dates has been source of interest for decades. In recent years, the use of machine learning techniques in the field of production planning and control has unlocked new opportunities, in both academia and industry practice. In fact, with the increased availability of data across various levels of manufacturing companies, machine learning techniques offer the opportunity to gain valuable and accurate insights about production processes. However, machine learning-based approaches for the prediction of delivery dates have not received sufficient attention. Thus, this study aims to investigate the ability of machine learning to predict delivery dates early in the ordering process, and what type of information is required to obtain accurate predictions. Based on the data provided by two separate manufacturing companies, this paper presents a machine learning-based approach for predicting delivery times as soon as a request for an offer is received considering the desired customer delivery date as a feature.
Roobavannan, S, Choo, Y, Truong, DQ, Shon, HK & Naidu, G 2024, 'Selective lithium extraction using capacitive deionization with fabricated zeolitic imidazolate framework encapsulated manganese oxide carbon electrode', Chemical Engineering Journal, vol. 483, pp. 149242-149242.
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Rooholahi, B, Siwakoti, YP, Eckel, H-G, Blaabjerg, F & Bahman, AS 2024, 'Enhanced Single-Inductor Single-Input Dual-Output DC–DC Converter With Voltage Balancing Capability', IEEE Transactions on Industrial Electronics, vol. 71, no. 7, pp. 7241-7251.
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Rouzbehi, K, Mohammadi, F, Escaño, JM, Sood, VK, Bordons, C, Guerrero, JM, Sanjari, MJ, Gharehpetian, GB, Hatanaka, T, Muñoz Aguilar, RS, Hossain, J & Maestre, JM 2024, 'Integration and control of grid‐scale battery energy storage systems: challenges and opportunities', IET Renewable Power Generation, vol. 18, no. 15, pp. 2835-2837.
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Roy, SS, Roy, A, Samui, P, Gandomi, M & Gandomi, AH 2024, 'Hateful Sentiment Detection in Real-Time Tweets: An LSTM-Based Comparative Approach', IEEE Transactions on Computational Social Systems, vol. 11, no. 4, pp. 5028-5037.
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Rujikiatkamjorn, C, Ishikawa, T, Prezzi, M & Winter, M 2024, 'Editorial for the Special Issue on the 5th International Conference on Transportation Geotechnics 2024', Transportation Geotechnics, pp. 101372-101372.
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Rush, A, Catchpoole, DR, Watson, PH & Byrne, JA 2024, 'An Approach to Evaluate the Costs and Outputs of Academic Biobanks', Biopreservation and Biobanking, vol. 22, no. 5, pp. 463-474.
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Saboj, JH, Nag, P, Saha, G & Saha, SC 2024, 'Heat transfer assessment incorporated with entropy generation within a curved corner structure enclosing a cold domain', Heat Transfer, vol. 53, no. 5, pp. 2460-2479.
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AbstractIn cold climates or winter countries, maintaining optimal room temperatures is essential for comfort and energy efficiency. Conventional square or rectangle‐shaped rooms often face challenges in achieving efficient heat transfer (HT) and uniform temperature distribution. To address these limitations, this study has been explored using curved corner cavities, varying their aspect ratio (AR = 1.0 and 0.5), and incorporating a circular shape cooler to enhance HT within the room. The curved corners promote better airflow circulation, creating a more efficient HT environment. The dimensionless governing equations and corresponding boundary conditions are solved numerically using the finite element method. This research aims to assess the optimized HT and entropy production within a curved corner cavity, varying their AR and enclosing a circular shape cooler to determine the most effective configuration for maximizing HT and energy efficiency in winter conditions. This study reveals that in the case without a cooler (WOC), the average Nusselt number () is higher in the curved rectangle cavity compared with the curved square cavity for all values. Using the curved square (AR = 1.0), increases by 191.86%, while with the curved rectangle (AR = 0.5), increases by 302.63% at . Additionally, in the case with a cooler (WC), is higher than the case WOC and , and an average total entropy increases for both the WOC and WC cases for all values. Transitioning from a square to a curved rectangle (AR = 0.5) WC, increases by 329.34% at . Furthermore, in the WOC case, the curved square cavity and, in the WC case, the curved rectangle show better energy efficiency and reduce environmental impact.
Sadeghirad, H, Yaghoubi Naei, V, O’Byrne, K, Warkiani, ME & Kulasinghe, A 2024, 'In situ characterization of the tumor microenvironment', Current Opinion in Biotechnology, vol. 86, pp. 103083-103083.
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Safarkhani, M, Ahmadi, S, Ipakchi, H, Saeb, MR, Makvandi, P, Ebrahimi Warkiani, M, Rabiee, N & Huh, Y 2024, 'Advancements in Aptamer‐Driven DNA Nanostructures for Precision Drug Delivery', Advanced Science, vol. 11, no. 26.
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AbstractDNA nanostructures exhibit versatile geometries and possess sophisticated capabilities not found in other nanomaterials. They serve as customizable nanoplatforms for orchestrating the spatial arrangement of molecular components, such as biomolecules, antibodies, or synthetic nanomaterials. This is achieved by incorporating oligonucleotides into the design of the nanostructure. In the realm of drug delivery to cancer cells, there is a growing interest in active targeting assays to enhance efficacy and selectivity. The active targeting approach involves a “key‐lock” mechanism where the carrier, through its ligand, recognizes specific receptors on tumor cells, facilitating the release of drugs. Various DNA nanostructures, including DNA origami, Tetrahedral, nanoflower, cruciform, nanostar, nanocentipede, and nanococklebur, can traverse the lipid layer of the cell membrane, allowing cargo delivery to the nucleus. Aptamers, easily formed in vitro, are recognized for their targeted delivery capabilities due to their high selectivity for specific targets and low immunogenicity. This review provides a comprehensive overview of recent advancements in the formation and modification of aptamer‐modified DNA nanostructures within drug delivery systems.
Safarkhani, M, Farasati Far, B, Lima, EC, Jafarzadeh, S, Makvandi, P, Varma, RS, Huh, Y, Ebrahimi Warkiani, M & Rabiee, N 2024, 'Integration of MXene and Microfluidics: A Perspective', ACS Biomaterials Science & Engineering, vol. 10, no. 2, pp. 657-676.
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Saha, BK, Jihan, JI, Ahammad, MZ, Saha, G & Saha, SC 2024, 'Enhanced thermal performance and entropy generation analysis in a novel cavity design with circular cylinder', Heat Transfer, vol. 53, no. 3, pp. 1446-1473.
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AbstractAnalyzing fluid dynamics and heat transfer holds significant importance in the design and enhancement of engineering systems. The current investigation utilizes the finite element method to explore natural convection and heat transfer intricacies within a novel cavity containing an inner circular cylinder under steady and laminar flow conditions. The principal aim of this study is to assess the impact of Rayleigh number (Ra), Bejan number (Be), and the presence of adiabatic, hot, and cold cylinders on heat transfer, entropy generation, and fluid flow. The range of Ra considered in this investigation spans from 103 to 106, while the Prandtl number for the air is fixed at 0.71. The findings illustrate that the presence of a cylinder leads to higher Be as Ra increase, compared to scenarios where no cylinder is present. This observation suggests that buoyancy forces dominate in the absence of a cylinder, resulting in significantly enhanced convective heat transfer efficiency. However, the presence of a heated cylinder within the tooth‐shaped cavity exerts a substantial influence on the overall thermal performance of the system. Notably, the average Nusselt Number (Nu) experiences a remarkable increase of 41.97% under the influence of a heated cylinder, when compared to situations where a cold cylinder is present. This elevated average Nu signifies improved heat transfer characteristics, ultimately resulting in an overall improvement in the thermal system's efficiency.
Saha, G, Al-Waaly, AAY, Ikram, MM, Bihani, R & Saha, SC 2024, 'Unveiling the Dynamics of Entropy Generation in Enclosures: A Systematic Review', International Journal of Thermofluids, vol. 21, pp. 100568-100568.
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Saha, G, Saboj, JH, Nag, P & Saha, SC 2024, 'Synergistic Heat Transfer in Enclosures: A Hybrid Nanofluids Review', Journal of Nanofluids, vol. 13, no. 2, pp. 524-535.
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This review aims to comprehensively explore the concepts of heat transfer (HT) and entropy generation (Egen) within cavities containing hybrid nanofluids (HN). Additionally, the review encompasses various enclosure shapes, such as triangle, square, rectangle, wave, trapezoid, hexagon, octagon, semicircle, circle, cube, C-shaped, L-shaped, M-shaped, T-shaped, W-shaped, irregular shaped, and other types of cavity designs. Also, different types of hybrid nanoparticles such as silver-magnesium oxide, copper-aluminum oxide, multi-walled carbon nanotubes-iron oxide, copper-titanium dioxide, silver-copper, aluminum oxide-titanium dioxide, carbon nanotubes-aluminum oxide, multi-walled carbon nanotubes-magnesium oxide, carbon nanotubes-iron oxide, carbon nanotubes-copper, aluminum oxide-silicon dioxide, aluminum oxide-silver, nanodiamond-cobalt oxide, etc., and base fluids such as water, ethylene glycol, carboxymethyl cellulose, etc are presented in this research. In addition, a thorough analysis of the extensive literature underscores the significant influence of elements like blocks, obstacles, fins, or cylinders within cavities on both HT and Egen. These findings carry substantial practical implications for the study of thermofluid systems.
Saha, M, Islam, S, Akhi, AA & Saha, G 2024, 'Factors affecting success and failure in higher education mathematics: Students' and teachers’ perspectives', Heliyon, vol. 10, no. 7, pp. e29173-e29173.
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Saha, SC & Saha, G 2024, 'Effect of microplastics deposition on human lung airways: A review with computational benefits and challenges', Heliyon, vol. 10, no. 2, pp. e24355-e24355.
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Saha, SC, Huang, X, Francis, I & Saha, G 2024, 'Airway stability in sleep apnea: Assessing continuous positive airway pressure efficiency', Respiratory Physiology & Neurobiology, vol. 325, pp. 104265-104265.
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Saha, T, Saha, G, Parveen, N & Islam, T 2024, 'Unsteady magneto-hydrodynamic behavior of TiO2-kerosene nanofluid flow in wavy octagonal cavity', International Journal of Thermofluids, vol. 21, pp. 100530-100530.
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Sahoo, S, Sahoo, KS, Sahoo, B & Gandomi, AH 2024, 'A learning automata based edge resource allocation approach for IoT-enabled smart cities', Digital Communications and Networks, vol. 10, no. 5, pp. 1258-1266.
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Sais, D, Chowdhury, S, Dalton, JP, Tran, N & Donnelly, S 2024, 'Both host and parasite non-coding RNAs co-ordinate the regulation of macrophage gene expression to reduce pro-inflammatory immune responses and promote tissue repair pathways during infection with fasciola hepatica', RNA Biology, vol. 21, no. 1, pp. 62-77.
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Sais, D, Hill, M, Deutsch, F, Nguyen, PT, Gay, V & Tran, N 2024, 'The lncRNA and miRNA regulatory axis in HPV16-positive oropharyngeal cancers', Virology, vol. 600, pp. 110220-110220.
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Salahshoori, I, Golriz, M, Nobre, MAL, Mahdavi, S, Eshaghi Malekshah, R, Javdani-Mallak, A, Namayandeh Jorabchi, M, Ali Khonakdar, H, Wang, Q, Mohammadi, AH, Masoomeh Sadat Mirnezami, S & Kargaran, F 2024, 'Simulation-based approaches for drug delivery systems: Navigating advancements, opportunities, and challenges', Journal of Molecular Liquids, vol. 395, pp. 123888-123888.
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Salehmin, MNI, Tiong, SK, Mohamed, H, Umar, DA, Yu, KL, Ong, HC, Nomanbhay, S & Lim, SS 2024, 'Navigating challenges and opportunities of machine learning in hydrogen catalysis and production processes: Beyond algorithm development', Journal of Energy Chemistry, vol. 99, pp. 223-252.
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Salgotra, R, Sharma, P, Raju, S & gandomi, AH 2024, 'A Contemporary Systematic Review on Meta-heuristic Optimization Algorithms with Their MATLAB and Python Code Reference', Archives of Computational Methods in Engineering, vol. 31, no. 3, pp. 1749-1822.
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AbstractOptimization is a method which is used in every field, such as engineering, space, finance, fashion market, mass communication, travelling, and also in our daily activities. In every field, everyone always wants to minimize or maximize something called the objective function. Traditional and modern optimization techniques or Meta-Heuristic (MH) optimization techniques are used to solve the objective functions. But the traditional optimization techniques fail to solve the complex and real-world optimization problem consisting of non-linear objective funct