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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Abbasi, V, Kashani, MM, Rezaie, M & Lu, DD-C 2024, 'Two-Switch Ultrahigh Step-Up DC–DC Converterer With Low Input Current Ripple and Low Switch Voltage Stress', IEEE Open Journal of Power Electronics, vol. 5, pp. 1255-1266.
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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...
Abeg, AI, Islam, MR, Hossain, MA, Ishraque, MF, Islam, MR & Hossain, MJ 2024, 'Capacity and operation optimization of hybrid microgrid for economic zone using a novel meta-heuristic algorithm', Journal of Energy Storage, vol. 94, pp. 112314-112314.
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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, '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...
Adam, R, Catchpoole, DR, Simoff, SS, Kennedy, PJ & Nguyen, QV 2024, 'Novel Hybrid Edge-Cloud Framework for Efficient and Sustainable Omics Data Management', Innovations in Digital Health, Diagnostics, and Biomarkers, vol. 4, no. 2024, pp. 81-88.
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Introduction The healthcare landscape is rapidly evolving through the integration of diverse data sources such as electronic health records, omics, and genomic data into patient profiles, enhancing personalized medicine and system interoperability. However, this transformation faces challenges in data integration and analysis, compounded by technologic advancements and the increasing volume of health data. Methods This study introduces a novel hybrid edge-cloud framework designed to manage the surge of multidimensional genomic and omics data in the healthcare sector. It combines the localized processing capabilities of edge computing with the scalable resources of cloud computing. Evaluations involved using simulated cytometry datasets to demonstrate the architecture’s effectiveness. Results The implementation of the hybrid edge-cloud framework demonstrated improvements in key performance metrics. Network efficiency was enhanced by reducing data transfer latency through localized edge processing. Operational costs were minimized using advanced compression techniques, with the Zstandard (ZSTD) codec significantly reducing data size and improving upload times. The framework also ensured enhanced data privacy by leveraging edge-based anonymization techniques, which process sensitive information locally before transfer to the cloud. These findings highlight the framework’s ability to optimize large-scale omics data management through innovative approaches, achieving significant gains in scalability and security. Conclusion
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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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...
Agarwal, M, Rani, G, Kumar, A, K, PK, Manikandan, R & Gandomi, AH 2024, 'Deep learning for enhanced brain Tumor Detection and classification', Results in Engineering, vol. 22, pp. 102117-102117.
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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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Ahmad, A, Xiao, X, Mo, H & Dong, D 2024, 'Tuning data preprocessing techniques for improved wind speed prediction', Energy Reports, vol. 11, pp. 287-303.
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Ahmad, F, Rawat, S, Yang, RC, Zhang, L, Guo, Y, Fanna, DJ & Zhang, YX 2024, 'Effect of hybrid fibres on mechanical behaviour of magnesium oxychloride cement-based composites', Construction and Building Materials, vol. 424, pp. 135937-135937.
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Ahmadi, S, Cheraghian, A, Chowdhury, TF, Saberi, M & Rahman, S 2024, '3D scene generation for zero-shot learning using ChatGPT guided language prompts', Computer Vision and Image Understanding, vol. 249, pp. 104211-104211.
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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, 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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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 ...
Akinola, S, Leelakrishna, R & Varadarajan, V 2024, 'Enhancing cardiovascular disease prediction: A hybrid machine learning approach integrating oversampling and adaptive boosting techniques', AIMS Medical Science, vol. 11, no. 2, pp. 58-71.
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<abstract><p>This study presents a novel approach to enhance cardiovascular disease prediction using a hybrid machine learning (ML) model. Leveraging on Synthetic Minority oversampling techniques (SMOTE) and adaptive boosting (AdaBoost), we integrate these methods with prominent classifiers, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Extra Tree (ET). Focused on heart rate data as stress level indicators, our objective is to jointly predict cardiovascular disease, thereby addressing the global health challenge of early detection and accurate risk assessments. In response to class imbalance issues in cardiology databases, our hybrid model, which combines SMOTE and AdaBoost, demonstrates promising results. The inclusion of diverse classifiers, such as RF, XGBoost, and ET, enables the model to capture both linear and nonlinear relationships within the heart rate data, significantly enhancing the prediction accuracy. This powerful predictive tool empowers healthcare providers to identify individuals at a high risk for heart disease, thus facilitating timely interventions. This article underscores the pivotal role of ML and hybrid methodologies in advancing health research, particularly in cardiovascular disease prediction. By addressing the class imbalance and incorporating robust algorithms, our research contributes to the ongoing efforts to improve predictive modeling in healthcare. The findings presented here hold significance for medical practitioners and researchers striving for the early detection and prevention of cardiovascular diseases.</p></abstract>
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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Alam, N, Rahman, MA, Islam, MR & Hossain, MJ 2024, 'Machine learning‐based multivariate forecasting of electric vehicle charging station demand', Electronics Letters, vol. 60, no. 23.
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AbstractThe exponential rise of electric vehicles (EVs) is transforming the global automobile industry, driving a shift towards greater cleanliness and environmental sustainability. EV charging stations (EVCSs) play a pivotal role in this massive transition towards EVs, where accurate forecasting of EVCS demand is crucial for seamlessly integrating EVs into existing power grids. Most of the existing research mainly concentrates on univariate forecasting, neglecting the multiple factors influencing EVCS demand. Hence, this study offers a comparative analysis of different algorithms for univariate forecasting and multivariate forecasting, where the multivariate scheme incorporates metadata such as charging time, greenhouse gas savings, and gasoline savings. The experimental results indicate the superiority of the multivariate scheme over the univariate forecasting. For multivariate forecasting, the gated recurrent unit (GRU) has outperformed other models such as categorical boosting (Catboost), recurrent neural network (RNN), long short‐term memory (LSTM), extreme gradient boosting (XGBoost), random forest, convolutional neural network (CNN), CNN + LSTM, and LSTM + LSTM. The results of this study emphasize the significance of using the GRU model for multivariate forecasting with metadata during normal and noisy scenarios to yield more reliable and accurate predictions. This approach enhances decision‐making, policy development, and efficient grid integration in the growing EV sector.
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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Alasmari, A, Aljibori, HSS, Alimi, F, Bouzidi, M, Islam, MS, Yazdani, S & Ghalambaz, M 2024, 'A shell-tube latent heat thermal energy storage: Influence of metal foam inserts in both shell and tube sides', International Communications in Heat and Mass Transfer, vol. 159, pp. 107992-107992.
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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.
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...
Alvarado-Alvarado, AA, Smets, W, Irga, P & Denys, S 2024, 'Engineering green wall botanical biofiltration to abate indoor volatile organic compounds: A review on mechanisms, phyllosphere bioaugmentation, and modeling', Journal of Hazardous Materials, vol. 465, pp. 133491-133491.
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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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Alzubaidi, L, AL-Dulaimi, K, Salhi, A, Alammar, Z, Fadhel, MA, Albahri, AS, Alamoodi, AH, Albahri, OS, Hasan, AF, Bai, J, Gilliland, L, Peng, J, Branni, M, Shuker, T, Cutbush, K, Santamaría, J, Moreira, C, Ouyang, C, Duan, Y, Manoufali, M, Jomaa, M, Gupta, A, Abbosh, A & Gu, Y 2024, 'Comprehensive review of deep learning in orthopaedics: Applications, challenges, trustworthiness, and fusion', Artificial Intelligence in Medicine, vol. 155, pp. 102935-102935.
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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, D, Zhang, YX & Yang, RC 2024, 'Incorporating coarse aggregates into 3D concrete printing from mixture design and process control to structural behaviours and practical applications: a review', Virtual and Physical Prototyping, vol. 19, no. 1.
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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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Andrzejewski, A, Krajewska, M, Zheng, L, Nghiem, LD, Oleskowicz-Popiel, P, Prochaska, K & Szczygiełda, M 2024, 'Pectin recovery from apple pomace by forward osmosis – Assisted technology', Journal of Membrane Science, vol. 706, pp. 122956-122956.
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Ang, K, Sankaran, S, Liu, D & Scales, J 2024, 'Embracing Levin’s Legacy: Advancing Socio-Technical Learning and Development in Human-Robot Team Design Through STS Approaches', Systemic Practice and Action Research, vol. 37, no. 6, pp. 661-678.
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AbstractThis paper investigates the synergy between Levin’s theories on technology transfer as a socio-technical learning and developmental process (TLD process), and what we learnt about socio-technical systems (STS) theories in a case study developing human robot solutions for the construction sector. Levin’s extensive work highlights the significance of technology transfer as a means for organizational development. His TLD process emphasizes the intricate interplay between technology, organizational change, and learning and highlights the importance of incorporating cultural knowledge and skills into the technological transfer process. Contemporary STS views developed through our own work are introduced to complement and extend Levin’s theories by providing a systemic lens to understand the broader socio-technical context in which technology transfer occurs. To illustrate the synergies and potential challenges from Levin’s theories of technology transfer with contemporary STS concepts, we use a qualitative study of a unique case about the design and development of human-robot teams (HRTs) for construction tasks. Our findings reveal that while Levin’s theories provide a valuable foundation for understanding technology transfer and organizational change, contemporary socio-technical systems face unique challenges in the context of AI-driven human-robot teams, where intelligent robots also contribute to the socio-technical learning process. Moreover, the rapidly evolving nature of technology and innovations could exponentially impact on multidisciplinary design teams, stakeholder participation and inter-organizational dynamics. The discussions suggest an extension of co-generative learning to incorporate ‘collaborative intelligence’ between human-robot teams enabled by artificial intelligence (AI). Consequently, we suggest that Levin’s theories of technology transfer, developed before the rapid application of AI, may not...
Ang, KCS, Sankaran, S & Liu, D 2024, 'Sociotechnical considerations on developing human robot teaming solutions for construction: a case study', Construction Robotics, vol. 8, no. 2.
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Abstract This research advocates for a paradigm shift in the exploration of human–robot teaming solutions for construction automation, by focusing on an integrated view of sociotechnical systems (STS) that recognize the inter-dependencies among actors at various levels when tracing how innovative ideas about intelligent robotic technologies translate into practice in the construction sector. Through a qualitative case study, the paper examines industry and organizational considerations for developing and adopting robotic technologies, leadership vision, mediation, and change management to propose integrative strategies to enhance expectations, acceptance, and deployment of intelligent technologies in human–robot teams (HRTs). This study contributes to research in construction robotics at three organizational levels—macro, meso, and micro. The Integrated Human–Robot Teaming Framework and associated workplan schema offer guidance for navigating human–robot teaming complexities. The study recommends adopting STS principles in planning and deploying robotics applications for construction, emphasizing the integration of multiple elements across the lifecycle. Active leadership and mediation emerge as critical elements in navigating complex networks, ensuring successful outcomes in the dynamic construction environment.
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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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, Liu, Z, Khabbaz, H, Fattahi, H, Li, D & Afrazi, M 2024, 'Tree-Based Solution Frameworks for Predicting Tunnel Boring Machine Performance Using Rock Mass and Material Properties', Computer Modeling in Engineering & Sciences, vol. 141, no. 3, pp. 2421-2451.
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Armaghani, DJ, Mohammed, AS, Bhatawdekar, RM, Fakharian, P, Kainthola, A & Mahmood, WI 2024, 'Introduction to the Special Issue on Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications', Computer Modeling in Engineering & Sciences, vol. 138, no. 3, pp. 2023-2027.
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Armaghani, DJ, Rasekh, H & Asteris, PG 2024, 'An advanced machine learning technique to predict compressive strength of green concrete incorporating waste foundry sand', COMPUTERS AND CONCRETE, vol. 33, no. 1, pp. 77-90.
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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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Arman, A, Sohaib, O, Begum, V & Alkharman, AA 2024, 'Impact of Cultural Diversity on Employee Performance', International Journal of Service Science, Management, Engineering, and Technology, vol. 15, no. 1, pp. 1-14.
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The purpose of this study was to investigate the influence of cultural diversity on job satisfaction and its effect on expatriate work performance. Structured interviews were conducted to collect qualitative data from a sample group of expatriates five participants from mixed nationalities in a private sector organization based in the United Arab Emirates. Thematic analysis identified three emergent themes of culture that negatively impacted expatriates' job satisfaction, causing reduced employee performance, and two themes were identified that increased expatriate performance at work (enhanced creativity and innovation). This research contributes to the body of academic knowledge about the effect of cultural diversity on expatriate employee performance. The value for praxis from this study are recommendations for managerial action to improve expatriate work performance.
Arman, A, Sohaib, O, Kemp, L & Alzaabi, F 2024, 'Organizational Change Impact on Remote Work: Enhancing Engagement and Performance', Change Management: An International Journal, vol. 24, no. 2, pp. 23-43.
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Arnett, S, Chew, SH, Leitner, U, Hor, JY, Paul, F, Yeaman, MR, Levy, M, Weinshenker, BG, Banwell, BL, Fujihara, K, Abboud, H, Dujmovic Basuroski, I, Arrambide, G, Neubrand, VE, Quan, C, Melamed, E, Palace, J, Sun, J, Asgari, N, Broadley, SA, Abboud, H, Aktas, O, Alroughani, R, Altintas, A, Apiwattannakul, M, Arrambide, G, Avasarala, J, Banwell, B, Blaschke, TF, Bowen, J, Contentti, EC, Chitnis, T, de Seze, J, Delgado-Garcia, G, Dujmovic Basuroski, I, Flores, J, Fujihara, K, Galleguillos, L, Greenberg, BM, Han, M, Havla, J, Hellwig, K, Hor, JY, Jarius, S, Jimenez, JA, Kissani, N, Kleiter, I, Lana-Peixoto, M, Leite, MI, Levy, M, Mariotto, S, Mealy, MA, Neubrand, VE, Oreja-Guevara, C, Pandit, L, Planchon, SM, Pröbstel, A-K, Qian, P, Quan, C, Repovic, P, Riley, C, Ringelstein, M, I.Rojas, J, Rotstein, D, Ruprecht, K, Sá, MJ, Saiz, A, Salama, S, Siritho, S, Siva, A, Smith, TJ, Sotirchos, ES, de Castillo, IS, Tenembaum, S, Villoslada, P, Willekens, B, Wingerchuk, D, Yamout, BI & Yeaman, M 2024, 'Sex ratio and age of onset in AQP4 antibody-associated NMOSD: a review and meta-analysis', Journal of Neurology, vol. 271, no. 8, pp. 4794-4812.
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Abstract Background Aquaporin-4 (AQP4) antibody-associated neuromyelitis optica spectrum disorder (NMOSD) is an antibody-mediated inflammatory disease of the central nervous system. We have undertaken a systematic review and meta-analysis to ascertain the sex ratio and mean age of onset for AQP4 antibody associated NMOSD. We have also explored factors that impact on these demographic data. Methods A systematic search of databases was conducted according to the PRISMA guidelines. Articles reporting sex distribution and age of onset for AQP4 antibody-associated NMSOD were reviewed. An initially inclusive approach involving exploration with regression meta-analysis was followed by an analysis of just AQP4 antibody positive cases. Results A total of 528 articles were screened to yield 89 articles covering 19,415 individuals from 88 population samples. The female:male sex ratio was significantly influenced by the proportion of AQP4 antibody positive cases in the samples studied (p < 0.001). For AQP4 antibody-positive cases the overall estimate of the sex ratio was 8.89 (95% CI 7.78–10.15). For paediatric populations the estimate was 5.68 (95% CI 4.01–8.03) and for late-onset cases, it was 5.48 (95% CI 4.10–7.33). The mean age of onset was significantly associated with the mean life expectancy of the population sampled (p < 0.001). The mean age of onset for AQP4 antibody-positive cases in long-lived populations was 41.7 years versus 33.3 years in the remainder. Conclusions The f...
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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Asheghi Mehmandari, T, Shokouhian, M, Zakeri Josheghan, M, Mirjafari, SA, Fahimifar, A, Jahed Armaghani, D & Tee, KF 2024, 'Flexural properties of fiber-reinforced concrete using hybrid recycled steel fibers and manufactured steel fibers', Journal of Building Engineering, vol. 98, pp. 111069-111069.
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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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Asteris, PG, Karoglou, M, Skentou, AD, Vasconcelos, G, He, M, Bakolas, A, Zhou, J & Armaghani, DJ 2024, 'Predicting uniaxial compressive strength of rocks using ANN models: Incorporating porosity, compressional wave velocity, and schmidt hammer data', Ultrasonics, vol. 141, pp. 107347-107347.
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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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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, M & 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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Bahmani, MH, Esmaeili Shayan, M & Kumar Mishra, D 2024, 'Quantifying the impact of electricity pricing on electric vehicle user behavior: a V2G perspective for smart grid development', Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, vol. 46, no. 1, pp. 4524-4542.
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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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Baniya, S, Paudel, SR, Angove, MJ, Acharya, G, Wagle, A, Khatri, M, Hao Ngo, H, Guo, W & Mainali, B 2024, 'Theoretical and earthquake-induced groundwater chemistry changes: A perspective', Journal of Hydrology, vol. 643, pp. 131917-131917.
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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, L, Qi, B & Dong, D 2024, 'Stabilizing preparation of quantum Gaussian states via continuous measurement', Automatica, vol. 164, pp. 111622-111622.
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Bao, T, Damtie, MM, Wang, CY, Chen, Z, Tao, Q, Wei, W, Cho, K, Yuan, P, Frost, RL & Ni, B-J 2024, 'Comprehensive review of modified clay minerals for phosphate management and future prospects', Journal of Cleaner Production, vol. 447, pp. 141425-141425.
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Bao, T, Damtie, MM, Wang, CY, Li, CL, Chen, Z, CHO, K, Wei, W, Yuan, P, Frost, RL & Ni, B-J 2024, 'Iron-containing nanominerals for sustainable phosphate management: A comprehensive review and future perspectives', Science of The Total Environment, vol. 926, pp. 172025-172025.
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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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Barcellos-Paula, L, Merigó, JM & Gil-Lafuente, AM 2024, '100 volumes of Mathematical Methods of Operations Research: a bibliometric overview', Mathematical Methods of Operations Research, vol. 100, no. 3, pp. 753-796.
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Barkhordari, MS, Barkhordari, MM, Armaghani, DJ, Mohamad, ET & Gordan, B 2024, 'GUI-based platform for slope stability prediction under seismic conditions using machine learning algorithms', Architecture, Structures and Construction, vol. 4, no. 2-4, pp. 145-156.
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Barkhordari, MS, Fattahi, H, Armaghani, DJ, Khan, NM, Afrazi, M & Asteris, PG 2024, 'Failure mode identification in reinforced concrete flat slabs using advanced ensemble neural networks', Multiscale and Multidisciplinary Modeling, Experiments and Design, vol. 7, no. 6, pp. 5759-5773.
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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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Beck, M & Hossain, MJ 2024, 'Coordination of solar battery hybrid power plants and synchronous generators for improving black start capability', Sustainable Energy, Grids and Networks, vol. 39, pp. 101489-101489.
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Behera, MD, Krishna, JSR, Paramanik, S, Kumar, S, Behera, SK, Anto, S, Singh, SN, Verma, AK, Barik, SK, Mohanta, MR, Sahu, SC, Jeganathan, C, Srivastava, PK & Pradhan, B 2024, 'Digital hemispherical photographs and Sentinel-2 multi-spectral imagery for mapping leaf area index at regional scale over a tropical deciduous forest', Tropical Ecology, vol. 65, no. 2, pp. 258-270.
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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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Bhattacharjee, NV, Schumacher, AE, Aali, A, Abate, YH, Abbasgholizadeh, R, Abbasian, M, Abbasi-Kangevari, M, Abbastabar, H, Abd ElHafeez, S, Abd-Elsalam, S, Abdollahi, M, Abdollahifar, M-A, Abdoun, M, Abdullahi, A, Abebe, M, Abebe, SS, Abiodun, O, Abolhassani, H, Abolmaali, M, Abouzid, M, Aboye, GB, Abreu, LG, Abrha, WA, Abrigo, MRM, Abtahi, D, Abualruz, H, Abubakar, B, Abu-Gharbieh, E, Abu-Rmeileh, NME, Adal, TGG, Adane, MM, Adeagbo, OAA, Adedoyin, RA, Adekanmbi, V, Aden, B, Adepoju, AV, Adetokunboh, OO, Adetunji, JB, Adeyinka, DA, Adeyomoye, OI, Adnani, QES, Adra, S, Afolabi, RF, Afyouni, S, Afzal, MS, Afzal, S, Aghamiri, S, Agodi, A, Agyemang-Duah, W, Ahinkorah, BO, Ahlstrom, AJ, Ahmad, A, Ahmad, D, Ahmad, F, Ahmad, MM, Ahmad, S, Ahmad, T, Ahmed, A, Ahmed, A, Ahmed, H, Ahmed, LA, Ahmed, MS, Ahmed, SA, Ajami, M, Aji, B, Akalu, GT, Akbarialiabad, H, Akinyemi, RO, Akkaif, MA, Akkala, S, Al Hamad, H, Al Hasan, SM, Al Qadire, M, AL-Ahdal, TMA, Alalalmeh, SO, Alalwan, TA, Al-Aly, Z, Alam, K, Al-amer, RM, Alanezi, FM, Alanzi, TM, Albakri, A, Albashtawy, M, AlBataineh, MT, Alemi, H, Alemi, S, Alemu, YM, Al-Eyadhy, A, Al-Gheethi, AAS, Alhabib, KF, Alhajri, N, Alhalaiqa, FAN, Alhassan, RK, Ali, A, Ali, BA, Ali, L, Ali, MU, Ali, R, Ali, SSS, Alif, SM, Aligol, M, Alijanzadeh, M, Aljasir, MAM, Aljunid, SM, Al-Marwani, S, Almazan, JU, Al-Mekhlafi, HM, Almidani, O, Alomari, MA, Al-Omari, B, Alqahtani, JS, Alqutaibi, AY, Al-Raddadi, RM, Al-Sabah, SK, Altaf, A, Al-Tawfiq, JA, Altirkawi, KA, Aluh, DO, Alvi, FJ, Alvis-Guzman, N, Alwafi, H, Al-Worafi, YM, Aly, H, Aly, S, Alzoubi, KH, Ameyaw, EK, Amin, TT, Amindarolzarbi, A, Amini-Rarani, M, Amiri, S, Ampomah, IG, Amugsi, DA, Amusa, GA, Ancuceanu, R, Anderlini, D, Andrade, PP, Andrei, CL, Andrei, T, Anil, A, Anil, S, Ansar, A, Ansari-Moghaddam, A, Antony, CM, Antriyandarti, E, Anvari, S, ANWAR, S, Anwer, R, Anyasodor, AE, Arabloo, J, Arabzadeh Bahri, R, Arafa, EA, Arafat, M, Araújo, AM, Aravkin, AY, Aremu, A, Aripov, T, Arkew, M, Armocida, B, Ärnlöv, J, Arooj, M, Artamonov, AA, Arulappan, J, Aruleba, RT, Arumugam, A, Asadi-Lari, M, Asemi, Z, Asgary, S, Asghariahmadabad, M, Asghari-Jafarabadi, M, Ashemo, MY, Ashraf, M, Ashraf, T, Asika, MO, Athari, SS, Atout, MMW, Atreya, A, Aujayeb, A, Ausloos, M, Avan, A, Aweke, AM, Ayele, GM, Ayyoubzadeh, SM, Azadnajafabad, S, Azevedo, RMS, Azzam, AY & et al. 2024, 'Global fertility in 204 countries and territories, 1950–2021, with forecasts to 2100: a comprehensive demographic analysis for the Global Burden of Disease Study 2021', The Lancet, vol. 403, no. 10440, pp. 2057-2099.
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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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Billiris, G, Gill, A, Oppermann, I & Niazi, M 2024, 'Towards the Development of a Copyright Risk Checker Tool for Generative Artificial Intelligence Systems', Digital Government: Research and Practice, vol. 5, no. 4, pp. 1-21.
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Generative Artificial Intelligence (GAI) is fundamentally changing the ways of working and blurring the boundaries between human and machine-generated contents. While there is an increasing interest in the adoption of GAI systems, such as ChatGPT and DALL-E, there are also serious concerns about the copyright of the contents—the inputs or generated as outputs by the GAI systems. Such concerns need to be identified and assessed to ensure the ethical and responsible use of GAI systems. Thus, this article aims to address the key research challenge: “how to identify and assess GAI system's copyright concerns”? In response, we propose the development of a Copyright Risk Checker (CRC) Tool. This tool has been formulated and evaluated using a recognised design science research methodology, drawing on an analysis of 10 legal cases across Australia, the United Kingdom, the United States, and Europe. The CRC Tool has undergone evaluation through an experimental scenario, and the results suggest that it is suitable for conducting an indicative copyright risk check of GAI systems. The outcomes of this preliminary assessment can be further examined by expert legal advisors for an in-depth analysis. The development of the CRC Tool provides a foundation for continued research and advancement in this significant area of study.
Boland, JTJ, Yang, Z, Yin, Q, Liu, X, Xu, Z, Kong, K, Vigolo, D & Yong, K 2024, 'Computational Models for Optimizing Particle Separation in Spiral Inertial Microfluidics: A Step Toward Enhanced Biosensing and Cell Sorting', Advanced Theory and Simulations, vol. 7, no. 10.
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AbstractInertial microfluidics is essential for separating particles and cells, enabling numerous biomedical applications. Despite the simplicity of spiral microchannels, the lack of predictive models hampers real‐world applications, highlighting the need for cost‐effective computational tools. In this study, four novel data fitting models are developed using linear and power regression analyses to investigate how flow conditions influence particle behaviors within spiral microchannels. These models are rigorously tested under two different flow rates, focusing on a smaller particle representing Salmonella Typhimurium and a larger particle representing bacterial aggregates, aiming for effective separation and detection. A critical parameter, the sheath‐to‐sample flow rate ratio, is either interpolated or extrapolated using the microchannel's aspect ratios to predict particle separation. The models show strong agreement with experimental data, underscoring their predictability and efficiency. These insights suggest that further refinement of these models can significantly reduce research and development costs for advanced inertial microfluidic devices in biomedical applications. This work represents a crucial step towards establishing a robust computational framework, advancing inertial microfluidics towards practical biomedical applications.
Bone, EK, Huber, E, Gribble, L, Lys, I, Dickson-Deane, C, Campbell, C, Yu, P, Markauskaite, L, Carvalho, L & Brown, C 2024, 'A community-based practice for the co-development of women academic leaders', International Journal for Academic Development, vol. 29, no. 2, pp. 238-254.
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Booth, E 2024, 'Mystery Fiction for Misinformation Resilience: Exploring Connections between Teens’ Leisure Reading and Online Misinformation Practices', The Journal of Research on Libraries and Young Adults, vol. 13, no. 1.
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.
Borhani, A, Borhani, A, Dossick, CS & Jupp, J 2024, 'An Ontological Analysis for Comparison of the Concepts of Sustainable Building and Intelligent Building', Journal of Construction Engineering and Management, vol. 150, no. 4.
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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...
Brown, C, Huber, E, Bone, E, Gribble, L, Lys, I, Dickson-Deane, C, Yu, P, Markauskaite, L & Campbell, C 2024, 'Academic Women Co-designing Education Futures in a Postdigital World', Postdigital Science and Education, vol. 6, no. 1, pp. 300-320.
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AbstractThis paper draws on the collective knowledge-building of nine women from diverse disciplines, roles, cultures, and institutions in Australasian women in leadership programme. Brought together during Covid-19 through a shared interest and purpose concerning current and future developments in digital education, we offer knowledge and insight from our perspective as women leaders in academia, on co-designing futures in a postdigital world. Drawing on a duoethnographic research design, we reflected on our experiences as academic leaders and practitioners to systematically explore people, situations, and contexts through co-construction and dialogue. Our joint exploration uncovered themes of visibility, gravitas, and relationships. We provide evidence of the role co-design plays in our own practices, in our classrooms, and how our research design was strengthened through co-design. Finally, we offer an evolving model of co-design for leadership in higher education with communities of practice at its core.
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...
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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Cálem, J, Moreira, C & Jorge, J 2024, 'Intelligent systems in healthcare: A systematic survey of explainable user interfaces', Computers in Biology and Medicine, vol. 180, pp. 108908-108908.
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Calvert, L, Martin, JH, Anderson, AL, Bernstein, IR, Burke, ND, De Iuliis, GN, Eamens, AL, Dun, MD, Turner, BD, Roman, SD, Green, MP & Nixon, B 2024, 'Assessment of the impact of direct in vitro PFAS treatment on mouse spermatozoa', Reproduction and Fertility, vol. 5, no. 1.
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Graphical abstractAbstractPoly- and per-fluoroalkyl substances (PFAS) are synthetic environmentally persistent chemicals. Despite the phaseout of specific PFAS, their inherent stability has resulted in ubiquitous and enduring environmental contamination. PFAS bioaccumulation has been reported globally with omnipresence in most populations wherein they have been associated with a range of negative health effects, including strong associations with increased instances of testicular cancer and reductions in overall semen quality. To elucidate the biological basis of such effects, we employed an acute in vitro exposure model in which the spermatozoa of adult male mice were exposed to a cocktail of PFAS chemicals at environmentally relevant concentrations. We hypothesized that direct PFAS treatment of spermatozoa would induce reactive oxygen species generation and compromise the functional profile and DNA integrity of exposed cells. Despite this, post-exposure functional testing revealed that short-term PFAS exposure (3 h) did not elicit a cytotoxic effect, nor did it overtly influence the functional profile, capacitation rate, or the in vitro fertilization ability of spermatozoa. PFAS treatment of spermatozoa did, however, result in a significant delay in the developmental progression of the day 4 pre-implantation embryos produced in vitro. This developmental delay could not be attributed to a loss of sperm DNA integrity, DNA damage, or elevated levels of intracellular reactive oxygen species. When considered together, the results presented here raise the intriguing prospect that spermatozoa exposed to a short-term PFAS exposure period potentially harbor an altern...
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 & Chen, W 2024, 'Accurate recognition of light beams carrying orbital angular momentum through scattering media using ghost diffraction', Applied Physics Letters, vol. 125, no. 5.
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We report a ghost diffraction-based approach to realize accurate recognition of light beams carrying orbital angular momentum (OAM) through dynamic and complex scattering media. A bit sequence is first encoded into an OAM beam, which is sequentially modulated by a series of Hadamard patterns, and then an optical wave propagates through dynamic and complex scattering media. The collected single-pixel light intensities are temporally corrected, and ghost images can be reconstructed by using the principle of ghost diffraction. The reconstructed ghost images are further processed by using block-matching and 3D filtering with image registration, which are then utilized for OAM recognition assisted by the featured normalized cross correlation. Optical experiments are conducted to demonstrate that light beams carrying OAM can be accurately recognized in dynamic and complex scattering environments, and the proposed approach is feasible and effective. The developed ghost diffraction-based approach could open an avenue for various OAM-encoded applications in dynamic and complex scattering environments.
Cao, Y, Qing, L, Yao, J, Wang, Y, Gu, N, Fu, Q & Sun, Y 2024, 'Facile synthesis of integrated electrode-separator-electrolyte hydrogel for solid-state supercapacitor', Journal of Solid State Electrochemistry, vol. 28, no. 11, pp. 4067-4075.
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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, vol. 10, no. 12, pp. 1095-1105.
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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, 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, 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, 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, vol. 73, no. 12, pp. 19762-19767.
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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, He, X, Liang, F & Sheng, D 2024, 'Strength and dilatancy of an unsaturated expansive soil at high suction levels', Journal of Rock Mechanics and Geotechnical Engineering.
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Chen, K, Liu, Q, Chen, B, Zhang, S, Ferrara, L & Li, W 2024, 'Effect of raw materials on the performance of 3D printing geopolymer: A review', Journal of Building Engineering, vol. 84, pp. 108501-108501.
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Chen, K, Qu, F, Huang, Y, Cai, J, Wu, F & Li, W 2024, 'Advancing photocatalytic concrete technologies for design, performance and sustainable futures', 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, Chen, Z, Li, H & Ni, B-J 2024, 'Advanced lithium ion-sieves for sustainable lithium recovery from brines', Sustainable Horizons, vol. 9, pp. 100093-100093.
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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, R-F, Wei, C-H, Zhong, H-T, Ye, X-F, Ye, J-J, Liu, K, Zhao, Q-B & Ngo, HH 2024, 'Evaluating a hybrid process of anaerobic digestion, aerobic degradation, and electrochemical separation for swine wastewater treatment with methane and nutrient recovery', Biosystems Engineering, vol. 248, pp. 47-57.
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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, Wu, G-B, Algaba-Brazález, A, Rajo-Iglesias, E, Guo, YJ & Chan, CH 2024, 'Guest Editorial: Special Cluster on Intelligent and Highly Efficient Antennas/Metasurfaces for 6G', IEEE Antennas and Wireless Propagation Letters, vol. 23, no. 11, pp. 3521-3528.
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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, vol. 18, no. 5, pp. 752-765.
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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, Han, N, Wei, W, Chu, D & Ni, B 2024, 'Dual doping: An emerging strategy to construct efficient metal catalysts for water electrolysis', EcoEnergy, vol. 2, no. 1, pp. 114-140.
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AbstractDeveloping efficient electrocatalysts for water electrolysis is critical for sustainable hydrogen energy development. For enhancing the catalytic performance of metal catalysts, dual doping has attracted enormous interest for its high effectiveness and facile realization. Dual doping is effective for tuning the electronic properties, enhancing the electrical conductivity, populating active sites, and improving the stability of metal catalysts. In this review, recent developments in cation–cation, cation–anion, and anion–anion dual‐doped catalysts for water splitting are comprehensively summarized and discussed. An emphasis is put on illustrating how dual doping regulates the external and internal properties and boosts the catalytic performance of catalysts. Additionally, perspectives are pointed out to guide future research on engineering high‐performance heteroatom‐doped electrocatalysts.
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, Ma, T, Wei, W, Wong, W, Zhao, C & Ni, B 2024, 'Work Function‐Guided Electrocatalyst Design', Advanced Materials, vol. 36, no. 29.
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AbstractThe development of high‐performance electrocatalysts for energy conversion reactions is crucial for advancing global energy sustainability. The design of catalysts based on their electronic properties (e.g., work function) has gained significant attention recently. Although numerous reviews on electrocatalysis have been provided, no such reports on work function‐guided electrocatalyst design are available. Herein, a comprehensive summary of the latest advancements in work function‐guided electrocatalyst design for diverse electrochemical energy applications is provided. This includes the development of work function‐based catalytic activity descriptors, and the design of both monolithic and heterostructural catalysts. The measurement of work function is first discussed and the applications of work function‐based catalytic activity descriptors for various reactions are fully analyzed. Subsequently, the work function‐regulated material‐electrolyte interfacial electron transfer (IET) is employed for monolithic catalyst design, and methods for regulating the work function and optimizing the catalytic performance of catalysts are discussed. In addition, key strategies for tuning the work function‐governed material‐material IET in heterostructural catalyst design are examined. Finally, perspectives on work function determination, work function‐based activity descriptors, and catalyst design are put forward to guide future research. This work paves the way to the work function‐guided rational design of efficient electrocatalysts for sustainable energy applications.
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, Wei, W, Xu, X, Gu, X, Huang, C, Wei, W, Shao, Z, Ni, B-J & Chen, H 2024, 'Reconstructed anti-corrosive and active surface on hierarchically porous carbonized wood for efficient overall seawater electrolysis', Science Bulletin, vol. 69, no. 15, pp. 2337-2341.
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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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Chenrayan, V, Shahapurkar, K, Manivannan, C, Soudagar, MEM, Fouad, Y, Kalam, MA, Ali, MM & Bashir, MN 2024, 'Frictional stability of pumice-reinforced lightweight magnesium composite in ambient and elevated temperature environments', Journal of Materials Research and Technology, vol. 32, pp. 3465-3475.
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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, 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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Chi, K, Li, J, Shao, R, Liu, J, Liu, Z & Wu, C 2024, 'Experimental exploration on impact characteristics of ultra-high performance concrete at low and cryogenic temperature', Journal of Building Engineering, vol. 98, pp. 111478-111478.
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Cho, B, Xiao, Y, Hui, P & Dong, D 2024, 'Quantum Bandit With Amplitude Amplification Exploration in an Adversarial Environment', IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 1, pp. 311-317.
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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.
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, 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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Comfort, H, McHugh, TA, Schumacher, AE, Harris, A, May, EA, Paulson, KR, Gardner, WM, Fuller, JE, Frisch, ME, Taylor, HJ, Leever, AT, Teply, C, Verghese, NA, Alam, T, Abate, YH, Abbastabar, H, Abd ElHafeez, S, Abdelmasseh, M, Abd-Elsalam, S, Abdissa, D, Abdoun, M, Abdulkader, RS, Abebe, M, Abedi, A, Abidi, H, Abiodun, O, Aboagye, RG, Abolhassani, H, Abrigo, MRM, Abu-Gharbieh, E, Abu-Rmeileh, NME, Adane, MM, Addo, IY, Adema, BG, Adesina, MA, Adetunji, COO, Adeyinka, DA, Adnani, QES, Afzal, S, Agampodi, SB, Agodi, A, Agyemang-Duah, W, Ahinkorah, BO, Ahmad, A, Ahmad, D, Ahmadi, A, Ahmed, A, Ahmed, H, Ahmed, LA, Ajami, M, Akinosoglou, K, Al Hasan, SM, Al-Aly, Z, Alam, K, Alanezi, FM, Alanzi, TM, Albashtawy, M, Alemi, S, Algammal, AM, Al-Gheethi, AAS, Ali, A, Ali, L, Ali, MU, Alif, SM, Aljunid, SM, Almazan, JU, Al-Mekhlafi, HM, Almidani, L, Almustanyir, S, Altirkawi, KA, Aly, H, Aly, S, Amani, R, Ameyaw, EK, Amhare, AF, Amin, TT, Amiri, S, Andrei, CL, Andrei, T, Anoushiravani, A, Ansar, A, Anvari, D, Anwer, R, Appiah, F, Arab-Zozani, M, Aravkin, AY, Areda, D, Aregawi, BB, Artamonov, AA, Aryal, UR, Asemi, Z, Asemu, MT, Asgedom, AA, Ashraf, T, Asresie, MB, Atlaw, D, Atout, MMW, Atreya, A, Atteraya, MS, Aujayeb, A, Ayala Quintanilla, BP, Ayatollahi, H, Ayyoubzadeh, SM, Azadnajafabad, S, Azevedo, RMS, Azzam, AY, B, DB, Babaei, M, Badar, M, Badiye, AD, Baghcheghi, N, Baghdadi, S, Bagheri, N, Bagherieh, S, Bahrami Asl, F, Bai, R, Bakshi, RK, Bam, K, Banach, M, Banke-Thomas, A, Bansal, H, Bantie, BB, Barchitta, M, Bardhan, M, Bashiri, A, Basiru, A, Baskaran, P, Batra, K, Bayani, M, Bayleyegn, NS, Bedi, N, Begum, T, Behnoush, AH, Belgaumi, UI, Bermudez, ANC, Beyene, KA, Bhandari, BB, Bhandari, D, Bhardwaj, N, Bhardwaj, P, Bhaskar, S, Bhattarai, S, Bodolica, V, Braithwaite, D, Brenner, H, Bustanji, Y, Butt, NS, Butt, ZA, Cadri, A, Campos-Nonato, I, Cattaruzza, MS, Cembranel, F, Cerin, E, Chacón-Uscamaita, PR, Charan, J, Chattu, VK, Chauhan, D, Chavula, MP, Chen, S, Chi, G, Chitheer, A, Cho, WCS, Choudhari, SG, Chu, D-T, Cruz-Martins, N, Dadras, O, Dagnew, GW, Dalaba, MA, Dandona, L, Darwesh, AM, Das, JK, Das, S, Dash, NR, Dávila-Cervantes, CA, Davletov, K, Debela, BG, Debele, AT, Derese, M, Deribe, K, Dervišević, E, Dessie, AM, Dhali, A, Dhulipala, VR, Dirac, MA, Dong, W, Dora, BT, Dsouza, HL, Duraes, AR, Dutta, S, Dziedzic, AM, Ed-Dra, A & et al. 2024, 'Global, regional, and national stillbirths at 20 weeks' gestation or longer in 204 countries and territories, 1990–2021: findings from the Global Burden of Disease Study 2021', The Lancet, vol. 404, no. 10466, pp. 1955-1988.
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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, Ding, A, Ma, W, Zhang, R, Lin, W, Desmond, P, Ngo, HH, Li, G & Liang, H 2024, 'Reconsidering the Rationality of Ultrafiltration as the Ultimate Safeguard for Reclaimed Water Biosafety: Unexpected Risks Arise under Different Storage Conditions', ACS ES&T Water, vol. 4, no. 5, pp. 2235-2246.
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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, vol. 24, no. 24, pp. 41410-41423.
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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, 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, C, Zuo, W, Li, Q, Zhou, K, Huang, Y, Zhang, G & E, J 2024, 'Energy conversion efficiency improvement studies on the hydrogen-fueled micro planar combustor with multi-baffles for thermophotovoltaic applications', Energy, vol. 313, pp. 134099-134099.
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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, vol. 34, no. 12, pp. 13297-13310.
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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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Davidson, AL, Michailidou, K, Parsons, MT, Fortuno, C, Bolla, MK, Wang, Q, Dennis, J, Naven, M, Abubakar, M, Ahearn, TU, Alonso, MR, Andrulis, IL, Antoniou, AC, Auvinen, P, Behrens, S, Bermisheva, MA, Bogdanova, NV, Bojesen, SE, Brüning, T, Byers, HJ, Camp, NJ, Campbell, A, Castelao, JE, Cessna, MH, Chang-Claude, J, Chanock, SJ, Chenevix-Trench, G, Sahlberg, KK, Børresen-Dale, A-L, Gram, IT, Olsen, KS, Engebråten, O, Naume, B, Geisler, J, OSBREAC, Grenaker Alnæs, GI, Collée, JM, Czene, K, Dörk, T, Eriksson, M, Evans, DG, Fasching, PA, Figueroa, JD, Flyger, H, Gago-Dominguez, M, García-Closas, M, Glendon, G, González-Neira, A, Grassmann, F, Gronwald, J, Guénel, P, Hadjisavvas, A, Haeberle, L, Hall, P, Hamann, U, Hartman, M, Ho, PJ, Hooning, MJ, Hoppe, R, Howell, A, Amor, D, Andrews, L, Antill, Y, Balleine, R, Beesley, J, Bennett, I, Bogwitz, M, Bodek, S, Botes, L, Brennan, M, Brown, M, Buckley, M, Burke, J, Butow, P, Caldon, L, Campbell, I, Cao, M, Chakrabarti, A, Chauhan, D, Chauhan, M, Christian, A, Cohen, P, Colley, A, Crook, A, Cui, J, Courtney, E, Cummings, M, Dawson, S-J, deFazio, A, Delatycki, M, Dickson, R, Dixon, J, Edwards, S, Farshid, G, Fellows, A, Fenton, G, Field, M, Flanagan, J, Fong, P, Forrest, L, Fox, S, French, J, Friedlander, M, Gaff, C, Gattas, M, George, P, Greening, S, Harris, M, Hart, S, Harraka, P, Hayward, N, Hopper, J, Hoskins, C, Hunt, C, Jenkins, M, Kidd, A, Kirk, J, Koehler, J, Kollias, J, Lakhani, S, Lawrence, M, Lee, J, Li, S, Lindeman, G, Lippey, J, Lipton, L, Lobb, L, Loi, S, Mann, G, Marsh, D, McLachlan, SA, Meiser, B, Nightingale, S, O'Connell, S, O'Sullivan, S, Ortega, DG, Pachter, N, Pang, J-M, Pathak, G, Patterson, B, Pearn, A, Phillips, K, Pieper, E, Ramus, S, Rickard, E, Ragunathan, A, Robinson, B, Saleh, M, Skandarajah, A, Salisbury, E, Saunders, C, Saunus, J, Savas, P, Scott, R, Scott, C, Sexton, A, Shaw, J, Shelling, A, Srinivasa, S, Simpson, P, Taylor, J, Taylor, R, Thorne, H, Trainer, A, Tucker, K, Visvader, J, Walker, L, Williams, R, Winship, I, Young, MA, Zaheed, M, Jakubowska, A, Khusnutdinova, EK, Kristensen, VN, Li, J, Lim, J, Lindblom, A, Liu, J, Lophatananon, A, Mannermaa, A, Mavroudis, DA, Mensenkamp, AR, Milne, RL, Muir, KR, Newman, WG, Obi, N, Panayiotidis, MI, Park, SK, Park-Simon, T-W, Peterlongo, P, Radice, P, Rashid, MU, Rhenius, V, Saloustros, E, Sawyer, EJ, Schmidt, MK, Seibold, P & et al. 2024, 'Co-observation of germline pathogenic variants in breast cancer predisposition genes: Results from analysis of the BRIDGES sequencing dataset', The American Journal of Human Genetics, vol. 111, no. 9, pp. 2059-2069.
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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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Debnath, S, Debbarma, S, Nama, S, Saha, AK, Dhar, R, Yildiz, AR & Gandomi, AH 2024, 'Centroid opposition-based backtracking search algorithm for global optimization and engineering problems', Advances in Engineering Software, vol. 198, pp. 103784-103784.
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Degórska, O, Szada, D, Fu, Q, Nghiem, LD, Biadasz, A, Jesionowski, T & Zdarta, J 2024, 'Ionic liquid supported hydrogel–lipase biocatalytic systems in asymmetric synthesis of enantiomerically pure S-ibuprofen', International Journal of Biological Macromolecules, vol. 281, pp. 136221-136221.
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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, Zhang, X, Xi, W, Zheng, W & Xu, X 2024, 'A low fouling and high biocompatibility electrochemical sensor based on the electrospun gelatin‐PLGA‐CNTs nanofibers for dopamine detection in blood', Journal of Applied Polymer Science, vol. 141, no. 38.
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AbstractThe ability to fabricate resisting nonspecific protein adsorption electrochemical sensor capable of high biocompatibility in vivo will undoubtedly underpin key future developments in life sciences. Herein, a gelatin‐poly(lactide‐co‐glycolide)‐carbon nanotubes nanofibers‐membrane in three‐dimensional porous structure without any chemical crosslinking is constructed on the carbon fiber microelectrode (e‐Gelatin‐PLGA‐CNTs/CFME) using a one‐step electrospinning technology. The nanofibers‐membrane still presents good three‐dimensional porous structure and excellent hydrophily after implantation in BSA solution. In addition, the dopamine hydrochloride (DA) sensitivity at e‐Gelatin‐PLGA‐CNTs/CFME after implantation in human blood samples exhibits almost the same as preimplantation (91% ± 9%, n = 3). Importantly, the nanofibers‐membrane possesses fast cell proliferation and a low hemolysis rate (2.27% ± 0.76%), satisfying the required biocompatibility as a constructed material for the detection in vivo. The constructed micro‐electrochemical sensor realizes the detection of DA in human blood samples. Consequently, this strategy offers a new and facile platform for the development of implanted electrochemical sensor.
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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Denholm, IK, Hassan, W, Negnevitsky, M & Lu, DD-C 2024, 'Optimized Interleaved Ultra-High Gain DC-DC Power Converter With Low Ripple Input Current and Voltage Stress for Fuel Cell Systems', IEEE Access, vol. 12, pp. 121052-121063.
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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...
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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Dhull, P, Schreurs, D, Pollin, S, Abolhasan, M & Shariati, N 2024, 'Multitone ASK Waveform Design for Simultaneous Wireless Information and Power Transfer', IEEE Access, vol. 12, pp. 192813-192826.
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Dhull, P, Shariati, N, Pollin, S, Abolhasan, M & Schreurs, D 2024, 'Multitone QAM Modulation Design for Simultaneous Wireless Information and Power Transfer', IEEE Access, vol. 12, pp. 193782-193795.
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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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Dickson-Deane, C, Grant, P & McNeil, D 2024, 'Thoughts On the Ideal LMS: Dreaming of Agency, Connectivity, and Empathy.', Distance Learning Journal, vol. 21, no. 3, pp. 35-40.
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, 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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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.
Dissabandara, T, Lin, K, Forwood, M & Sun, J 2024, 'Validating real-time three-dimensional echocardiography against cardiac magnetic resonance, for the determination of ventricular mass, volume and ejection fraction: a meta-analysis', Clinical Research in Cardiology, vol. 113, no. 3, pp. 367-392.
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Abstract Introduction Real-time three-dimensional echocardiography (RT3DE) is currently being developed to overcome the challenges of two-dimensional echocardiography, as it is a much cheaper alternative to the gold standard imaging method, cardiac magnetic resonance (CMR). The aim of this meta-analysis is to validate RT3DE by comparing it to CMR, to ascertain whether it is a practical imaging method for routine clinical use. Methods A systematic review and meta-analysis method was used to synthesise the evidence and studies published between 2000 and 2021 were searched using a PRISMA approach. Study outcomes included left ventricular end-systolic volume (LVESV), left ventricular end-diastolic volume (LVEDV), left ventricular ejection fraction (LVEF), left ventricular mass (LVM), right ventricular end-systolic volume (RVESV), right ventricular end-diastolic volume (RVEDV) and right ventricular ejection fraction (RVEF). Subgroup analysis included study quality (high, moderate), disease outcomes (disease, healthy and disease), age group (50 years old and under, over 50 years), imaging plane (biplane, multiplane) and publication year (2010 and earlier, after 2010) to determine whether they explained the heterogeneity and significant difference results generated on RT3DE compared to CMR. Results The pooled mean differences for were − 5.064 (95% CI − 10.132, 0.004, p > 0.05), 4.654 (95% CI − 4.947, 14.255, p > 0.05), − 0.783 (95% CI − 5.630, 4.065, p > 0.05, − 0.200 (95% CI − 1.215, 0.815, p > 0.05) for LVEF, LVM, RVESV and RV...
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, 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, 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, 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, X, Yu, X, Liu, J, Dai, B & Xu, F 2024, 'Ethics-aware face recognition aided by synthetic face images', Neurocomputing, vol. 600, pp. 128129-128129.
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Duan, C, Liu, Z, Xia, J, Zhang, M, Liao, J & Cao, L 2024, 'Enhancing Cross-Dataset Performance of Distracted Driving Detection With Score Softmax Classifier and Dynamic Gaussian Smoothing Supervision', IEEE Transactions on Intelligent Vehicles, pp. 1-14.
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Duan, W, Lu, J & Xuan, J 2024, 'Inferring Latent Temporal Sparse Coordination Graph for Multiagent Reinforcement Learning', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-13.
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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. 35, no. 12, pp. 18160-18171.
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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.
Duarte, A, Fernandes, F, Pereira, JM, Moreira, C, Nascimento, JC & Jorge, J 2024, 'Selfredepth', Journal of Real-Time Image Processing, vol. 21, no. 4.
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AbstractDepth maps produced by consumer-grade sensors suffer from inaccurate measurements and missing data from either system or scene-specific sources. Data-driven denoising algorithms can mitigate such problems; however, they require vast amounts of ground truth depth data. Recent research has tackled this limitation using self-supervised learning techniques, but it requires multiple RGB-D sensors. Moreover, most existing approaches focus on denoising single isolated depth maps or specific subjects of interest highlighting a need for methods that can effectively denoise depth maps in real-time dynamic environments. This paper extends state-of-the-art approaches for depth-denoising commodity depth devices, proposing SelfReDepth, a self-supervised deep learning technique for depth restoration, via denoising and hole-filling by inpainting of full-depth maps captured with RGB-D sensors. The algorithm targets depth data in video streams, utilizing multiple sequential depth frames coupled with color data to achieve high-quality depth videos with temporal coherence. Finally, SelfReDepth is designed to be compatible with various RGB-D sensors and usable in real-time scenarios as a pre-processing step before applying other depth-dependent algorithms. Our results demonstrate our approach’s real-time performance on real-world datasets shows that it outperforms state-of-the-art methods in denoising and restoration performance at over 30 fps on Commercial Depth Cameras, with potential benefits for augmented and mixed-reality applications.
Duc La, D, Khong, HM, Nguyen, XQ, Dang, T-D, Bui, XT, Nguyen, MK, Ngo, HH & Nguyen, DD 2024, 'A review on advances in graphene and porphyrin-based electrochemical sensors for pollutant detection', Sustainable Chemistry One World, vol. 3, pp. 100017-100017.
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Duong, TD, Li, Q & Xu, G 2024, 'Achieving counterfactual fairness with imperfect structural causal model', Expert Systems with Applications, vol. 240, pp. 122411-122411.
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Duong, TD, Li, Q & Xu, G 2024, 'Causality-based counterfactual explanation for classification models', Knowledge-Based Systems, vol. 300, pp. 112200-112200.
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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, 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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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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Fattahi, H, Ghaedi, H & Armaghani, DJ 2024, 'Enhancing blasting efficiency: A smart predictive model for cost optimization and risk reduction', Resources Policy, vol. 97, pp. 105261-105261.
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Fattahi, H, Ghaedi, H & Armaghani, DJ 2024, 'Increasing accuracy in predicting mode I fracture toughness of rock structures: a comparative analysis of the rock engineering system method', Bulletin of Engineering Geology and the Environment, vol. 83, no. 12.
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AbstractThe investigation of crack initiation and expansion is vital for the stability of structures. The Mode I fracture toughness (KIc) of rocks is a key property used to predict crack propagation in tension and hydraulic fracturing. Various methods have been introduced to determine KIc, but results differ due to factors like sample dimensions, crack geometry, groove type, and loading conditions. The cracked chevron notched Brazilian disc (CCNBD) sample is commonly used in laboratory tests for its easy preparation. This study employs the rock engineering system (RES) technique to overcome the challenges of time-consuming and costly laboratory tests and the uncertainty in traditional methods (analytical, numerical, experimental, laboratory, regression). Using 88 CCNBD rock samples proposed by ISRM, input parameters include thickness of the disc specimen (B), uniaxial tensile strength (σt), initial crack length (α0), radius of the disc specimen (R), crack length (αB), and the length of the final crack (α1). The RES-based model used 70 data points (80% of the dataset) for development, and 18 data points (20%) for evaluation. Regression analysis compared the performance of the RES method, using statistical indicators such as squared correlation coefficient (R2), mean square error (MSE), and root mean square error (RMSE) to measure accuracy. The RES-based method outperformed other regression techniques, demonstrating significantly enha...
Fattahi, H, Ghaedi, H & Armaghani, DJ 2024, 'Optimizing fracture toughness estimation for rock structures: A soft computing approach with GWO and IWO algorithms', Measurement, vol. 238, pp. 115306-115306.
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Fattahi, H, Ghaedi, H & Armaghani, DJ 2024, 'Optimizing Underground Coal Mine Safety: Leveraging Advanced Computational Algorithms for Roof Fall Rate Prediction and Risk Mitigation', Mining, Metallurgy & Exploration, vol. 41, no. 6, pp. 2849-2867.
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AbstractThe utilization and consumption of coal in various nations have emphasized the pivotal role played by coal mines. However, aside from the substantial contribution of coal mines, miners, engineers, and craftsmen in this industry have long been exposed to numerous risks and financial losses resulting from roof collapses in underground coal mines. Hence, due to the heightened sensitivity surrounding this issue, the accurate and low-error forecasting and assessment of the roof fall rate (RFR) are deemed crucial and of utmost importance. Nonetheless, due to the intricate and uncertain inherent characteristics of the rock formations, assessing the RFR has encountered multiple challenges that cannot be precisely approximated through traditional methods. In this paper, algorithms such as the harmony search algorithm (HS) and the invasive weed Optimization algorithm (IWO) are harnessed to address the aforementioned challenges. To model the RFR, a total of 109 data points were used, incorporating input parameters such as primary roof support (PRSUP), depth of cover (D), coal mine roof rating (CMRR), mine height (MH), and intersection diagonal span (IS). For effective data analysis and model development, the dataset was split into two separate groups: one for training and the other for testing. Specifically, 80% of the data was used to build the model, while the remaining 20% was allocated for model evaluation and validation. Based on the outcomes of three statistical metrics R2, MSE, and RMSE, it is evident that the deployment of HS and IWO algorithms demonstrates high performance, with predicted values closely aligning with actual ones. Consequently, the utilization of intelligent algorithms in the field of rock engineering is positioned as a potent tool for researchers and engineers. In conclusion, a sensitivity analysis is carried out with the help of the @RISK software as...
Fattahi, H, Ghaedi, H, Malekmahmoodi, F & Armaghani, DJ 2024, 'Accurate estimation of bearing capacity of stone columns reinforced: An investigation of different optimization algorithms', Structures, vol. 64, pp. 106519-106519.
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Fattahi, H, Ghaedi, H, Malekmahmoodi, F & Armaghani, DJ 2024, 'Optimizing pile bearing capacity prediction: Insights from dynamic testing and smart algorithms in geotechnical engineering', Measurement, vol. 230, pp. 114563-114563.
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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...
Fawwaz, AA, Rahma, ON, Ain, K, Ittaqillah, SI & Chai, R 2024, 'Measurement of Mental Workload Using Heart Rate Variability and Electrodermal Activity', IEEE Access, vol. 12, pp. 197589-197601.
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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, Q, Zhang, J, Zhang, W, Qin, L, Zhang, Y & Lin, X 2024, 'Efficient k NN Search in Public Transportation Networks', Proceedings of the VLDB Endowment, vol. 17, no. 11, pp. 3402-3414.
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Public transportation plays a vital role in mitigating traffic congestion and reducing carbon emissions. The Top-k Nearest Neighbor ( k NN) search in public transportation networks is a fundamental problem in location-based services, which aims to find k nearest objects from a given query point. The traditional method, Dijkstra's algorithm has been employed to tackle the k NN problem, however, it is notably inefficient in processing queries. While other works precompute an index to speed up query processing. However, they are still slow in processing queries. Furthermore, they cannot scale to large graphs due to their reliance on resource-intensive path indexes. To address these limitations, we introduce a novel index-based approach that utilizes a simple yet effective index structure to handle k NN queries with a near-optimal time complexity. The index does not rely on a path index, making it efficient to construct and scalable to large graphs. Extensive experiments are conducted on real-world datasets to demonstrate the efficiency and scalability of our approach. The results show that our approach outperforms existing solutions by up to four orders of magnitude in query processing and two orders of magnitude in index construction.
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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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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Figuerola‐Wischke, A, Merigó, JM, Gil‐Lafuente, AM, Kydland, FE & Amiguet, L 2024, 'The Scandinavian Journal of Economics at 125: a bibliometric overview', The Scandinavian Journal of Economics, vol. 126, no. 4, pp. 643-697.
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AbstractEstablished in 1899 by David Davidson, The Scandinavian Journal of Economics is one of the most respected journals in the field of economics. In 2024, the journal celebrates its 125th anniversary. To commemorate this exceptional event, this study presents a comprehensive bibliometric analysis of the publications of the journal. The objective is to identify the leading trends that have occurred in the journal, especially during the last decades. The bibliographic data are retrieved from the Web of Science Core Collection and Scopus databases. The work also uses the VOSviewer and Biblioshiny software tools to construct and visualize bibliometric maps. The results reveal that authors affiliated with Scandinavian institutions are the most productive in the journal, along with those from the United States, the United Kingdom, and Germany. The keyword and topical analysis shows that The Scandinavian Journal of Economics covers a wide range of topics within economics, publishing frequently on labour, monetary, public, and international economics.
Forruque Ahmed, S, Alam, MSB, Afrin, S, Jannat Rafa, S, Binte Taher, S, Kabir, M, Muyeen, SM & Gandomi, AH 2024, 'Corrections to “Toward a Secure 5G-Enabled Internet of Things: A Survey on Requirements, Privacy, Security, Challenges, and Opportunities”', IEEE Access, vol. 12, pp. 162420-162420.
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Fortuno, C, Feng, B-J, Carroll, C, Innella, G, Kohlmann, W, Lázaro, C, Brunet, J, Feliubadaló, L, Iglesias, S, Menéndez, M, Teulé, A, Ballinger, ML, Thomas, DM, Campbell, A, Field, M, Harris, M, Kirk, J, Pachter, N, Poplawski, N, Susman, R, Tucker, K, Wallis, M, Williams, R, Cops, E, Goldgar, D, James, PA, Spurdle, AB, Amor, D, Andrews, L, Antill, Y, Balleine, R, Beesley, J, Bennett, I, Bogwitz, M, Bodek, S, Botes, L, Brennan, M, Brown, M, Buckley, M, Burke, J, Butow, P, Caldon, L, Campbell, I, Cao, M, Chakrabarti, A, Chauhan, D, Chauhan, M, Chenevix-Trench, G, Christian, A, Cohen, P, Colley, A, Crook, A, Cui, J, Courtney, E, Cummings, M, Dawson, S-J, deFazio, A, Delatycki, M, Dickson, R, Dixon, J, Edkins, T, Edwards, S, Farshid, G, Fellows, A, Fenton, G, Field, M, Flanagan, J, Fong, P, Forrest, L, Fox, S, French, J, Friedlander, M, Gaff, C, Gattas, M, George, P, Greening, S, Harris, M, Hart, S, Hayward, N, Hopper, J, Hoskins, C, Hunt, C, James, P, Jenkins, M, Kidd, A, Kirk, J, Koehler, J, Kollias, J, Lakhani, S, Lawrence, M, Lee, J, Li, S, Lindeman, G, Lippey, J, Lipton, L, Lobb, L, Loi, S, Mann, G, Marsh, D, McLachlan, SA, Meiser, B, Milne, R, Nightingale, S, O'Connell, S, O'Sullivan, S, Ortega, DG, Pachter, N, Pang, J-M, Pathak, G, Patterson, B, Pearn, A, Phillips, K, Pieper, E, Ramus, S, Rickard, E, Robinson, B, Saleh, M, Skandarajah, A, Salisbury, E, Saunders, C, Saunus, J, Savas, P, Scott, R, Scott, C, Sexton, A, Shaw, J, Shelling, A, Srinivasa, S, Simpson, P, Southey, M, Spurdle, A, Taylor, J, Taylor, R, Thorne, H, Trainer, A, Tucker, K, Visvader, J, Walker, L, Williams, R, Winship, I, Young, MA & Zaheed, M 2024, 'Cancer Risks Associated With TP53 Pathogenic Variants: Maximum Likelihood Analysis of Extended Pedigrees for Diagnosis of First Cancers Beyond the Li-Fraumeni Syndrome Spectrum', JCO Precision Oncology, no. 8.
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PURPOSE Establishing accurate age-related penetrance figures for the broad range of cancer types that occur in individuals harboring a pathogenic germline variant in the TP53 gene is essential to determine the most effective clinical management strategies. These figures also permit optimal use of cosegregation data for classification of TP53 variants of unknown significance. Penetrance estimation can easily be affected by bias from ascertainment criteria, an issue not commonly addressed by previous studies. MATERIALS AND METHODS We performed a maximum likelihood penetrance estimation using full pedigree data from a multicenter study of 146 TP53-positive families, incorporating adjustment for the effect of ascertainment and population-specific background cancer risks. The analysis included pedigrees from Australia, Spain, and United States, with phenotypic information for 4,028 individuals. RESULTS Core Li-Fraumeni syndrome (LFS) cancers (breast cancer, adrenocortical carcinoma, brain cancer, osteosarcoma, and soft tissue sarcoma) had the highest hazard ratios of all cancers analyzed in this study. The analysis also detected a significantly increased lifetime risk for a range of cancers not previously formally associated with TP53 pathogenic variant status, including colorectal, gastric, lung, pancreatic, and ovarian cancers. The cumulative risk of any cancer type by age 50 years was 92.4% (95% CI, 82.2 to 98.3) for females and 59.7% (95% CI, 39.9 to 81.3) for males. Females had a 63.3% (95% CI, 35.6 to 90.1) cumulative risk of developing breast cancer by age 50 years. CONCLUSION The results from maximum likelihood analysis confirm the known high lifetime risk for the core LFS-associated cancer types providing new risk estimat...
Fricke, L, Legg, R & Kabisch, N 2024, 'Impact of blue spaces on the urban microclimate in different climate zones, daytimes and seasons – A systematic review', Urban Forestry & Urban Greening, vol. 101, pp. 128528-128528.
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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, J, Sarfarazi, V, Haeri, H, Tolaminejad, B, Abharian, S, Rasekh, H, Khandelwal, M & Fatehi Marji, M 2024, 'Computational Simulation and Experimental Analysis on Wearing Mechanisms of Gypsum and Concrete Samples in Pin‐on‐Disk ASTM Abrasion Testing', International Journal for Numerical and Analytical Methods in Geomechanics, vol. 48, no. 18, pp. 4383-4398.
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ABSTRACTMechanical excavation machines, like continuous miners and road headers, have been broadly used in tunneling and underground and surface mines. The disc cutters are seated on the different cutter heads’ to cut different parts of the tunnel face. With the increase in the cutters’ size and power, the cutting disc cutters’ capacity has been extended to cut moderate and tough rock types. This experimental and numerical research includes the application of, “Pin‐on‐Disk” ASTM abrasion testing, in which the failure mechanism of an interface between both the rock‐like samples and (WC–Co) tungsten carbide has been investigated under different confining pressures. The research aims to investigate the wear mechanism of gypsum and concrete samples. The Particle Flow Code in three dimensions (PFC3D) was used for test simulations concurrently with the experimental setup. A drilling pin with a diameter of 0.4 m was positioned above the model. The pin was inserted into the model at speeds of 0.01 mm/s at depths of 1, 3, and 5 m. A total of nine lab tests were conducted. The tensile strength of the material was 2.5 MPa. The results show that the values of volume lost for the gypsum and concrete discs were detected as a function of sliding length, fitting to non‐linear behavior. The wearing depth increased by increasing the loading force. Under constant loading force, the gypsum sample wears more than the concrete sample because gypsum is less strong than concrete. The PFC generates useful findings that experimental tests cannot provide.
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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Fuentes, PMJ, Khalilpour, K & Voinov, A 2024, 'Solar energy surge: The socio-economic determinants of the photovoltaic systems growth in Australia', Energy Research & Social Science, vol. 116, pp. 103695-103695.
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Fumanal-Idocin, J, Vidaurre, C, Fernandez, J, Gómez, M, Andreu-Perez, J, Prasad, M & Bustince, H 2024, 'Supervised penalty-based aggregation applied to motor-imagery based brain-computer-interface', Pattern Recognition, vol. 145, pp. 109924-109924.
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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, 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, K, Zhu, T, Ye, D & Zhou, W 2024, 'Defending against gradient inversion attacks in federated learning via statistical machine unlearning', Knowledge-Based Systems, vol. 299, pp. 111983-111983.
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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, T, Sun, D, Sun, G, Xue, S, Chen, Y, Zhou, Y, Wong, JWC, Yang, G, Zhang, G & Ngo, HH 2024, 'Inhibition of norfloxacin on fermentative hydrogen production: Performance evaluation and metagenomic analysis', Chemical Engineering Journal, vol. 486, pp. 150167-150167.
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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, X, Xu, Z, Zhang, L, Li, G, Nghiem, LD & Luo, W 2024, 'Assimilatory sulphate reduction by acidogenesis: The key to prevent H2S formation during food and green waste composting for sustainable urbanization', Chemical Engineering Journal, vol. 499, pp. 156149-156149.
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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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Gaur, VK, Gautam, K, Vishvakarma, R, Sharma, P, Pandey, U, Srivastava, JK, Varjani, S, Chang, J-S, Ngo, HH & Wong, JWC 2024, 'Integrating advanced techniques and machine learning for landfill leachate treatment: Addressing limitations and environmental concerns', Environmental Pollution, vol. 354, pp. 124134-124134.
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Ge, H, Beydoun, G & Qu, H 2024, 'Preface', ACM International Conference Proceeding Series, p. viii.
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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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, Abdulla, S, Joseph, LP, Prasad, S, Murphy, A, Devi, A, Barua, PD, Deo, RC, Acharya, R & Yaseen, ZM 2024, 'Explainable artificial intelligence-machine learning models to estimate overall scores in tertiary preparatory general science course', Computers and Education: Artificial Intelligence, vol. 7, pp. 100331-100331.
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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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Gill, RL, Fleck, R, Chau, K, Westerhausen, MT, Lockwood, TE, Violi, JP, Irga, PJ, Doblin, MA & Torpy, FR 2024, 'Fine particle pollution during megafires contains potentially toxic elements', Environmental Pollution, vol. 344, pp. 123306-123306.
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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, Chen, P, Desmond, P, He, X, Ngo, HH & Ding, A 2024, 'Improving the phosphorus bioavailability of sludge: Comparison of oxidation treatments based on Mn(VII)-Fe(III) catalysis', Journal of Water Process Engineering, vol. 59, pp. 104986-104986.
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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, 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, Mihaita, A-S & Chen, F 2024, 'Traffic Incident Duration Prediction: A Systematic Review of Techniques', Journal of Advanced Transportation, vol. 2024, no. 1.
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This systematic literature review investigates the application of machine learning (ML) techniques for predicting traffic incident durations, a crucial component of intelligent transportation systems (ITSs) aimed at mitigating congestion and enhancing environmental sustainability. Utilizing the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) methodology, we systematically analyze literature that overviews models for incident duration prediction. Our review identifies that while traditional ML models like XGBoost and Random Forest are prevalent, significant potential exists for advanced methodologies such as bilevel and hybrid frameworks. Key challenges identified include the following: data quality issues, model interpretability, and the complexities associated with high‐dimensional datasets. Future research directions proposed include the following: (1) development of data fusion models that integrate heterogeneous datasets of incident reports for enhanced predictive modeling; (2) utilization of natural language processing (NLP) to extract contextual information from textual incident reports; and (3) implementation of advanced ML pipelines that incorporate anomaly detection, hyperparameter optimization, and sophisticated feature selection techniques. The findings underscore the transformative potential of advanced ML methodologies in traffic incident management, contributing to the development of safer, more efficient, and environmentally sustainable transportation systems.
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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Grover, H, Sharma, S, Verma, A, Hossain, MJ & Kamwa, I 2024, 'Adaptive parameter tuning strategy of VSG-based islanded microgrid under uncertainties', Electric Power Systems Research, vol. 235, pp. 110854-110854.
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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, Merigó, JM, Löfstedt, RE & Wardman, JK 2024, 'Twenty-five years of the Journal of Risk Research: a bibliometric overview', Journal of Risk Research, vol. 27, no. 8, pp. 857-900.
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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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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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Gunawardane, K, Padmawansa, N & Jayasinghe, H 2024, 'A review: compatibility of fuel cells as promising technology for DC-microgrids', Renewable Energy and Environmental Sustainability, vol. 9, pp. 7-7.
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Due to a well-established infrastructure developed over the years, fossil fuel-based energy remains the predominant global energy source. Nevertheless, with heightened global attention towards addressing climate change concerns, there has been an increased focus on green energy technologies across various sectors. The advancement of distributed renewable power generation technologies such as solar photovoltaics (PV), wind, wave, tidal, etc., has contributed to a growing independence of power consumers from centralized grids, leading to a pronounced shift towards distributed microgrids. Notably, numerous electrical devices operate on DC power, aligning with the DC power output of many distributed renewable sources. Consequently, the concept of DC microgrids is gaining traction. Amid this context, fuel cells have resurged in prominence on a global scale, alongside the development of hydrogen economies. Given fuel cells DC-based nature, they are well-suited to explore new frontiers within DC microgrids. However, the seamless integration of fuel cells into DC microgrids requires effective power electronic interfacing. Thus, a comprehensive examination of the integration of fuel cells into DC microgrids becomes imperative. This article aims to address this gap by offering an extensive review of fuel cell technologies, the landscape of DC microgrids, and the prevailing context of control architectures. Notably, this review article fills an existing void in the literature by consolidating the key elements into a unified discussion.
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, D, Li, K, Hu, B, Zhang, Y & Wang, M 2024, 'Benchmarking Micro-Action Recognition: Dataset, Methods, and Applications', IEEE Transactions on Circuits and Systems for Video Technology, vol. 34, no. 7, pp. 6238-6252.
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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, 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, 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, S, Wang, Y, Zhang, N, Su, Z, Luan, TH, Tian, Z & Shen, X 2024, 'A Survey on Semantic Communication Networks: Architecture, Security, and Privacy', IEEE Communications Surveys & Tutorials, pp. 1-1.
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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.
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 2024, 'Advanced feature engineering in microgrid PV forecasting: A fast computing and data-driven hybrid modeling framework', Renewable Energy, vol. 235, pp. 121258-121258.
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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.
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, D, Zamee, MA, Choi, G, Kim, T & Won, D 2024, 'Optimal Electrical Vehicle Charging Planning and Routing Using Real-Time Trained Energy Prediction With Physics-Based Powertrain Model', IEEE Access, vol. 12, pp. 123250-123266.
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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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Hangargi, S, Swamy, A, Raj, RG, Aruna, M, Venkatesh, R, Madhu, S, Al Obaid, S, Alharbi, SA & Kalam, MA 2024, 'Enhancement of Kevlar fiber-polypropylene composite by the inclusions of cotton stalk and granite particle: characteristics study', Biomass Conversion and Biorefinery, vol. 14, no. 23, pp. 30305-30314.
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Hani, U, Sohaib, O, Khan, K, Aleidi, A & Islam, N 2024, 'Psychological profiling of hackers via machine learning toward sustainable cybersecurity', Frontiers in Computer Science, vol. 6.
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This research addresses a challenge of the hacker classification framework based on the “big five personality traits” model (OCEAN) and explores associations between personality traits and hacker types. The method's application prediction performance was evaluated in two groups: Students with hacking experience who intend to pursue information security and ethical hacking and industry professionals who work as White Hat hackers. These professionals were further categorized based on their behavioral tendencies, incorporating Gray Hat traits. The k-means algorithm analyzed intra-cluster dependencies, elucidating variations within different clusters and their correlation with Hat types. The study achieved an 88% accuracy in mapping clusters with Hat types, effectively identifying cyber-criminal behaviors. Ethical considerations regarding privacy and bias in personality profiling methodologies within cybersecurity are discussed, emphasizing the importance of informed consent, transparency, and accountability in data management practices. Furthermore, the research underscores the need for sustainable cybersecurity practices, integrating environmental and societal impacts into security frameworks. This study aims to advance responsible cybersecurity practices by promoting awareness and ethical considerations and prioritizing privacy, equity, and sustainability principles.
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...
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, 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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Hassan, N, Yi, H, Malik, B, Gaspard-Boulinc, L, Samaraweera, SE, Casolari, DA, Seneviratne, J, Balachandran, A, Chew, T, Duly, A, Carter, DR, Cheung, BB, Norris, M, Haber, M, Kavallaris, M, Marshall, GM, Zhang, XD, Liu, T, Wang, J, Liebermann, DA, D’Andrea, RJ & Wang, JY 2024, 'Loss of the stress sensor GADD45A promotes stem cell activity and ferroptosis resistance in LGR4/HOXA9-dependent AML', Blood, vol. 144, no. 1, pp. 84-98.
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Abstract The overall prognosis of acute myeloid leukemia (AML) remains dismal, largely because of the inability of current therapies to kill leukemia stem cells (LSCs) with intrinsic resistance. Loss of the stress sensor growth arrest and DNA damage-inducible 45 alpha (GADD45A) is implicated in poor clinical outcomes, but its role in LSCs and AML pathogenesis is unknown. Here, we define GADD45A as a key downstream target of G protein-coupled receptor (LGR)4 pathway and discover a regulatory role for GADD45A loss in promoting leukemia-initiating activity and oxidative resistance in LGR4/HOXA9-dependent AML, a poor prognosis subset of leukemia. Knockout of GADD45A enhances AML progression in murine and patient-derived xenograft (PDX) mouse models. Deletion of GADD45A induces substantial mutations, increases LSC self-renewal and stemness in vivo, and reduces levels of reactive oxygen species (ROS), accompanied by a decreased response to ROS-associated genotoxic agents (eg, ferroptosis inducer RSL3) and acquisition of an increasingly aggressive phenotype on serial transplantation in mice. Our single-cell cellular indexing of transcriptomes and epitopes by sequencing analysis on patient-derived LSCs in PDX mice and subsequent functional studies in murine LSCs and primary AML patient cells show that loss of GADD45A is associated with resistance to ferroptosis (an iron-dependent oxidative cell death caused by ROS accumulation) through aberrant activation of antioxidant pathways related to iron and ROS detoxification, such as FTH1 and PRDX1, upregulation of which correlates with unfavorable outcomes in patients with AML. These results reveal a therapy resistance mechanism contributing to poor prognosis and support a role for GADD45A loss as a critical step for leukemia-initiating activity and as a target to overcome resistance in aggressive leukemia.
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, Armaghani, DJ, Tsoukalas, MZ, Qi, C, Bhatawdekar, RM & Asteris, PG 2024, 'A case study of resilient modulus prediction leveraging an explainable metaheuristic-based XGBoost', Transportation Geotechnics, vol. 45, pp. 101216-101216.
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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, 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, 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, Wang, K, Zhang, W, Lin, X & Zhang, Y 2024, 'Common Neighborhood Estimation over Bipartite Graphs under Local Differential Privacy', Proceedings of the ACM on Management of Data, vol. 2, no. 6, pp. 1-26.
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Bipartite graphs, formed by two vertex layers, arise as a natural fit for modeling the relationships between two groups of entities. In bipartite graphs, common neighborhood computation between two vertices on the same vertex layer is a basic operator, which is easily solvable in general settings. However, it inevitably involves releasing the neighborhood information of vertices, posing a significant privacy risk for users in real-world applications. To protect edge privacy in bipartite graphs, in this paper, we study the problem of estimating the number of common neighbors of two vertices on the same layer under edge local differential privacy (edge LDP). The problem is challenging in the context of edge LDP since each vertex on the opposite layer of the query vertices can potentially be a common neighbor. To obtain efficient and accurate estimates, we propose a multiple-round framework that significantly reduces the candidate pool of common neighbors and enables the query vertices to construct unbiased estimators locally. Furthermore, we improve data utility by incorporating the estimators built from the neighbors of both query vertices and devise privacy budget allocation optimizations. These improve the estimator's robustness and consistency, particularly against query vertices with imbalanced degrees. Extensive experiments on 15 datasets validate the effectiveness and efficiency of our proposed techniques.
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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Heath, L, Novis, E, Rabindran, J, van Laar Veth, A, Yang, T, Barnet, MB & Gett, R 2024, 'Oligometastatic colorectal adenocarcinoma to the spleen and ovaries', Journal of Surgical Case Reports, vol. 2024, no. 4.
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Abstract In the context of colorectal cancer, splenic and ovarian metastases are rare outside of widely disseminated disease. Growing evidence suggests that ‘oligometastatic’ or limited metastatic disease can be treated surgically with good oncological outcomes. Splenic and ovarian metastases are not well represented in studies of oligometastatic colorectal cancer, resulting in uncertainty in the best management for these patients. We present the case of a 78-year-old woman diagnosed with oligometastatic colorectal cancer to bilateral ovaries and spleen, 5 years after resection of a primary colon cancer. The patient was treated with a bilateral salpingo-oopherectomy and subsequent open splenectomy. We discuss the role of surgery and peri-operative chemotherapy in the management of oligometastatic colorectal cancer involving atypical sites.
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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Herlina Sari, N, Sujita, Suteja, Anshari, B, Syafri, E, El Achaby, M & Silitonga, AS 2024, 'Exploring the impact of water soaking on the mechanical, thermal, and physical properties of Paederia foetida fiber stem biocomposites: A study in sustainable material innovation', Case Studies in Chemical and Environmental Engineering, vol. 10, pp. 100977-100977.
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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.
Hesam-Shariati, N, Alexander, L, Chen, KY, Craig, A, Glare, PA, Jensen, MP, Lin, C-T, McAuley, JH, Middleton, JW, Moseley, GL, Newton-John, T, Restrepo, S, Skinner, IW, Zahara, P & Gustin, SM 2024, 'A home-based self-directed EEG neurofeedback intervention for people with chronic neuropathic pain following spinal cord injury (the StoPain Trial): description of the intervention', Spinal Cord, vol. 62, no. 11, pp. 658-666.
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Abstract Study design Randomised controlled trial. Objectives The objective is to describe an electroencephalography (EEG) neurofeedback intervention that will be provided in a randomised controlled trial for people with neuropathic pain following spinal cord injury (SCI): the StoPain Trial. In this trial, participants in the treatment group will implement an EEG neurofeedback system as an analgesic intervention at home, while participants in the control group will continue with the treatments available to them in the community. Setting University-based study in Sydney, Australia. Methods/results This manuscript describes the rationale and components of the EEG neurofeedback intervention designed for individuals with SCI neuropathic pain and intended for home-based implementation. Our report is based on the criteria of the Template for Intervention Description and Replication (TIDieR) checklist, and includes why the efficacy of EEG neurofeedback will be investigated, what will be provided, who will administer it, and how, where, when, and how much the EEG neurofeedback intervention will be administered. Conclusions This manuscript provides a detailed description of a complex intervention used in a randomised controlled trial. This description will facilitate the subsequent interpretation of the trial results and allow for the replication of the intervention in clinical practice and future trials.
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, vol. 23, no. 12, pp. 18532-18548.
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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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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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Ho, DJ, Agaram, NP, Frankel, AO, Lathara, M, Catchpoole, D, Keller, C & Hameed, MR 2024, 'Toward Deploying a Deep Learning Model for Diagnosis of Rhabdomyosarcoma', Modern Pathology, vol. 37, no. 3, pp. 100421-100421.
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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, T-D, Huang, X & Qin, P 2024, 'Low-Complexity Direction-of-Arrival Estimation with Orthogonal Matching Pursuit for Large-Scale Lens Antenna Array', IEEE Transactions on Communications, pp. 1-1.
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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.
Hong, S, Nerse, C, Oberst, S & Saadatfar, M 2024, 'Topological mechanical states in geometry-driven hyperuniform materials', PNAS Nexus, vol. 3, no. 12.
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Abstract Disordered hyperuniform materials are increasingly drawing attention due to their unique physical properties, associated with global isotropy and locally broken orientational symmetry, that set them apart from traditional crystalline materials. Using a dynamic space-partitioning process, we generate disordered hyperuniform cellular structures where distinct patterns of pentagonal and heptagonal topological defects emerge within hexagonal domains. The microscopic defect dynamics are guided by local topological transitions, commonly observed in viscoelastic systems. This leads to a reduction in the system’s structural entropy as hyperuniformity is attained, marked by the rise and fall of certain locally favored motifs. Further, we introduce an elastic hyperuniform material that exhibits evolving topological mechanical states in the continuum. Through vibration experiments and numerical analysis, we show energy localization around these defects, which is tied to the topological band gaps inherent to our geometry-driven material. We suggest that this robust dynamic mechanism influences a broad spectrum of disordered systems, from synthetic materials to biological structures guided by stigmergic interactions.
Hoseini Karani, MM, Nikoo, MR, Dolatshahi Pirooz, H, Shadmani, A, Al-Saadi, S & Gandomi, AH 2024, 'Multi-objective evolutionary framework for layout and operational optimization of a multi-body wave energy converter', Energy, vol. 313, pp. 134045-134045.
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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, Hannan, MA, Tiong, SK, Ker, PJ, Abu, SM, Wong, RTK & Mahlia, TMI 2024, 'Exploring nanoporous carbon architectures for enhanced solid-state hydrogen storage: Recent progress and future prospects', International Journal of Hydrogen Energy, vol. 110, pp. 271-299.
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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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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, vol. 11, no. 24, pp. 40166-40175.
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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, 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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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, H, Zhang, X, Du, Q, Gao, F, Wang, Z, Wu, G, Guo, W & Hao Ngo, H 2024, 'Assessing the Long-Term performance of an integrated microbial fuel Cell-Anaerobic membrane bioreactor for swine wastewater treatment', Chemical Engineering Journal, vol. 493, pp. 152772-152772.
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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, M, Zhou, S-H, Yang, C-J, Dong, C-L, He, Y, Wei, W, Li, X, Zhu, Q-L & Huang, Z 2024, 'Selenic Acid Etching Assisted Atomic Engineering for Designing Metal-Semimetal Dual Single-Atom Catalysts for Enhanced CO2 Electroreduction', ACS Nano, vol. 18, no. 48, pp. 33210-33219.
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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, 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, Li, T, Zhao, S, Niu, F & Lu, J 2024, 'Selection of loudspeakers based on condition number of transfer function matrix in sound zone control system', Shengxue Xuebao/Acta Acustica, vol. 49, no. 6, pp. 1162-1171.
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In order to address the difficulties in balancing robustness and performance of conventional loudspeaker selection methods in sound zone control, a loudspeaker selection method based on the principle of minimizing the condition number of the transfer function matrix is proposed. The method is further improved by combining this strategy with the minimum mean squared error method, which not only maintains algorithm robustness but also achieves better reproduction accuracy compared to conventional iterative methods. By adjusting the threshold parameters of the improved model, a balance between robustness, computational complexity, and system performance can be achieved. Free field, reverberant field, and actual room measurement simulations, along with simulated subjective evaluation results, demonstrate that the improved condition number model has greater universality and flexibility, meeting the needs of various application scenarios.
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, 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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Huat, CY, Armaghani, DJ, Lai, SH, Motaghedi, H, Asteris, PG & Fakharian, P 2024, 'Analyzing surface settlement factors in single and twin tunnels: A review study', Journal of Engineering Research.
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Hui, T, Zhai, S, Zhang, Z, Liu, C, Gong, X, Ni, Z & Yang, K 2024, 'A Fast and High-Precision Satellite-Ground Synchronization Technology in Satellite Beam Hopping Communication', Space: Science & Technology, vol. 4.
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A key requirement for future multibeam broadband satellite communication systems is the ability to flexibly adjust beam capacity according to changes in business distribution, in order to meet time-varying business requirements. Beam hopping technology provides an efficient solution to achieve the efficient use of frequency resources and power resources. At the same time, the use of beam hopping makes the beam hopping of satellite payload need to match the business signals of ground signal stations, bringing about the need for synchronization of beam hopping between satellite and ground. On the basis of analyzing the basic principles and key technologies of hopping beam, the paper analyzes the synchronization problem of satellite to ground hopping beam, proposes a signaling-assisted fast synchronization method of hopping beam for satellite to ground synchronization, and conducts simulation analysis of synchronization performance.
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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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.
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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Ibraheem, MI, Edrisi, M, Alhelou, HH & Gholipour, M 2024, 'Fractional order slide mode droop control for simultaneous voltage and frequency regulation of AC microgrid', IET Renewable Power Generation, vol. 18, no. 14, pp. 2629-2640.
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AbstractThis research proposes the application of fractional‐order sliding mode control (FOSMC) at the primary controller level to improve the stability of an islanded microgrid by adjusting its voltage and frequency. The control strategies used in the microgrid are performed in two levels (primary and secondary) in the islanded mode. Practically, most previous studies have worked to improve the primary controller. Droop control is one of the most commonly used methods at the primary level and is adopted in this study as well. The sliding mode control (SMC) strategy is normally used to control linear equations. Thus, the non‐linear microgrid equations were transformed into some linear ones using the input‐output feedback linearization technique. Further, a fractional sliding surface was acquainted. The sliding surface and FOSMC were designed to reject system uncertainties and organize the voltage and frequency. Design parameters were chosen using the Lyapunov stability theorem. The validation of the proposed method using Simulink‐MATLAB confirms its effectiveness in enhancing level power sharing, regulating frequency, and maintaining voltage stability across the system.
Ibraheem, MI, Edrisi, M, Alhelou, HH, Gholipour, M & Al-Hinai, A 2024, 'A Sophisticated Slide Mode Controller of Microgrid System Load Frequency Control Under False Data Injection Attack and Actuator Time Delay', IEEE Transactions on Industry Applications, vol. 60, no. 2, pp. 2117-2126.
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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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Ibrahim, IA, Pham, TN, Shah, R, Hossain, MJ & Islam, S 2024, 'Techno-economic assessment of an industrial prosumer with biomass investment and time varying tariffs: An Australian case study', Journal of Cleaner Production, vol. 481, pp. 143957-143957.
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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. 409-424.
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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, 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, MZ, Saha, G, Chin, YS & Saha, SC 2024, 'HEAT TRANSFER INTENSIFICATION IN A HORIZONTAL TUBE UTILIZING HEXAGONAL PERFORATED TUBE INSERTS', Journal of Naval Architecture and Marine Engineering, vol. 21, no. 2, pp. 155-168.
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In response to the century-long demand for heat energy, efficient use, conservation, and recovery of heat have become critical issues. Heat exchanger manufacturing, with its substantial capital and operational costs, now necessitates an efficient and energy-saving approach. Various methods have been developed over the years to enhance heat transfer within these systems to improve performance and reduce fuel consumption. One such approach is passive heat transfer enhancement, which involves introducing geometric alterations in the flow medium, such as using inserts or modifying the tube surface. This study aims to examine flow and heat transfer behavior within a horizontal tube, employing hexagonal perforated tube (HPT) inserts with varying diameter ratios (DR). To achieve this, a 3D model of the HPT inserts was developed and analyzed using finite volume based ANSYS Fluent software. The investigation considered Reynolds numbers (Re) within laminar flow regions, ranging from 1118 to 1676 while exploring HPT inserts with DR of 0.167, 0.238, 0.357, and 0.476, respectively. Results reveal that the Nusselt number (Nu) is notably influenced by both Re and DR of the HPT inserts. The enhancement in Nu achieved through the use of HPT inserts can reach up to an impressive 60.4% compared to the plain pipe's performance, offering substantial benefits in terms of energy conservation and system performance.
Islam, S, Deo, RC, Datta Barua, P, 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, vol. 12, pp. 176630-176685.
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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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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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Jeon, G, Cheng, X, Chehri, A, Fortino, G, Albertini, M & Wen, S 2024, 'Guest Editorial: Special Issue on Dark Side of the Socio-Cyber World: Media Manipulation, Fake News, and Misinformation', IEEE Transactions on Computational Social Systems, vol. 11, no. 4, pp. 4766-4774.
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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, C, Pan, Y, Yang, Y & Dong, D 2024, 'Interpolation of positive matrices by quantum‐inspired optimal control', IET Control Theory & Applications, vol. 18, no. 7, pp. 877-886.
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AbstractInterpolation of probability distributions can be formulated as an optimal transport problem. Positive matrix, which can be viewed as the generalization of probability distribution to higher dimension, is used in quantum theory to describe the state of a quantum system. Here, a quantum‐inspired method for the interpolation of positive matrices is proposed. Particularly, this method employs the quantum state purification of the positive matrices in an extended space. Since pure state controllability can be easily achieved using open‐loop coherent control, the continuous interpolation of positive matrices is given as a completely positive map induced by simulating the optimal control for pure state transfer. The quantum‐inspired interpolation is shape‐preserving with applications to tensor field processing.
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, Hao Ngo, H, Sun, M, Zhang, C, Zhang, S, Shi, Z & Luo, G 2024, 'Metagenomic insights into the enhanced methane production by hydrochar at varied propionate concentrations', Chemical Engineering Journal, vol. 498, pp. 155013-155013.
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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.
Jiang, Y, Zhu, S, Shen, M, Wen, S & Mu, C 2024, 'Aperiodically Intermittent Control Approach to Finite-Time Synchronization of Delayed Inertial Memristive Neural Networks', IEEE Transactions on Artificial Intelligence, pp. 1-10.
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Jiang, YC, Lai, K, Muirhead, RP, Chung, LH, Huang, Y, James, E, Liu, XT, Wu, J, Atkinson, FS, Yan, S, Fogelholm, M, Raben, A, Don, AS, Sun, J, Brand-Miller, JC & Qi, Y 2024, 'Deep serum lipidomics identifies evaluative and predictive biomarkers for individualized glycemic responses following low-energy diet-induced weight loss: a PREVention of diabetes through lifestyle Intervention and population studies in Europe and around the World (PREVIEW) substudy', The American Journal of Clinical Nutrition, vol. 120, no. 4, pp. 864-878.
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Jiao, G, Cao, Y, Liu, X, Fu, Q, Gu, N, Ren, Y & Sun, Y 2024, 'Novel polyurethane foam with low released heat for application in bone binder', Journal of Applied Polymer Science, vol. 141, no. 21.
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AbstractBecause of the significant advantages of bone adhesives compared with traditional mechanical fixation, many scholars have studied polyurethane bone adhesives. However, polyurethane will release a lot of heat in the reaction process, producing high temperature. The high temperature does not only easily cause a human discomfort but also will destroy the tissue structure of human. In this paper, a new polyurethane composite based on phase transition materials and hydroxyapatite is prepared to reduce released heat for application in bone binder. The mechanical properties, adhesive strength, and released heat‐induced temperature of polyurethane composite are investigated in detail. It exhibits good viscoelastic performance with a large elastic deformation (>30%) and good adhesive strength (>0.2 MPa). Furthermore, the released heat of polyurethane composite is effectively reduced, in which the temperature drastically decreases from 110.4 to 41.8°C. The work does not only confirm formation of high‐performance bone binder but also provides a new method to reduce released heat of polyurethane for various applications.
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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Jilagam, NK, Vaghela, G, Chakrabarty, T, Guo, J, Farid, MU, Jeong, S, Shon, HK, An, AK & Deka, BJ 2024, 'Frontier of metal-organic framework nanofillers for pre-eminent membrane distillation applications', Desalination, vol. 592, pp. 118127-118127.
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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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Jiu, J, Liu, H, Li, D, Li, J, Liu, L, Yang, W, Yan, L, Li, S, Zhang, J, Li, X, Li, JJ & Wang, B 2024, '3D bioprinting approaches for spinal cord injury repair', Biofabrication, vol. 16, no. 3, pp. 032003-032003.
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Abstract Regenerative healing of spinal cord injury (SCI) poses an ongoing medical challenge by causing persistent neurological impairment and a significant socioeconomic burden. The complexity of spinal cord tissue presents hurdles to successful regeneration following injury, due to the difficulty of forming a biomimetic structure that faithfully replicates native tissue using conventional tissue engineering scaffolds. 3D bioprinting is a rapidly evolving technology with unmatched potential to create 3D biological tissues with complicated and hierarchical structure and composition. With the addition of biological additives such as cells and biomolecules, 3D bioprinting can fabricate preclinical implants, tissue or organ-like constructs, and in vitro models through precise control over the deposition of biomaterials and other building blocks. This review highlights the characteristics and advantages of 3D bioprinting for scaffold fabrication to enable SCI repair, including bottom–up manufacturing, mechanical customization, and spatial heterogeneity. This review also critically discusses the impact of various fabrication parameters on the efficacy of spinal cord repair using 3D bioprinted scaffolds, including the choice of printing method, scaffold shape, biomaterials, and biological supplements such as cells and growth factors. High-quality preclinical studies are required to accelerate the translation of 3D bioprinting into clinical practice for spinal cord repair. Meanwhile, other technological advances will continue to improve the regenerative capability of bioprinted scaffolds, such as the incorporation of nanoscale biological particles and the development of 4D printing.
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, 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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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).
Kabisch, N, Hornick, T, Bumberger, J, Krämer, R, Legg, R, Masztalerz, O, Bastl, M, Simon, JC, Treudler, R & Dunker, S 2024, 'Monitoring and perception of allergenic pollen in urban park environments', Landscape and Urban Planning, vol. 250, pp. 105133-105133.
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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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Kanavaris, F, Vieira, M, Bishnoi, S, Zhao, Z, Wilson, W, Tagnit Hamou, A, Avet, F, Castel, A, Zunino, F, Visalakshi, T, Martirena, F, Bernal, SA, Juenger, MCG & Riding, K 2024, 'Correction: Standardisation of low clinker cements containing calcined clay and limestone: a review by RILEM TC-282 CCL', Materials and Structures, vol. 57, no. 10.
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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, vol. 150, no. 7.
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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, 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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Kazemi, MMK, Nabavi, Z & Armaghani, DJ 2024, 'A novel Hybrid XGBoost Methodology in Predicting Penetration Rate of Rotary Based on Rock-Mass and Material Properties', Arabian Journal for Science and Engineering, vol. 49, no. 4, pp. 5225-5241.
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AbstractPredicting the drill penetration rate is a fundamental requirement in mining operations, profoundly impacting both the cost-effectiveness of mining activities and strategic mine planning. Given the intricate web of factors influencing rotary drilling performance, the necessity for advanced modeling techniques becomes evident. To this end, the hybrid extreme gradient boosting (XGBoost) was utilized to gauge the penetration rate of rotary drilling machines, utilizing random search, grid search, Harris Hawk optimization (HHO), and the dragonfly algorithm (DA) as metaheuristic algorithms. Our research draws from extensive data collected in copper mine case studies, encompassing both field and investigational data. This dataset incorporates critical material properties, such as tensile strength (TS), uniaxial compressive strength (UCS), as well as vital rock-mass characteristics including joint direction (JD), joint spacing (JS), and bit diameter (D). Our investigation evaluates the reliability of these prediction methods using various performance indicators, including mean absolute error (MAE), root mean square error (RMSE), average absolute relative error (AARE), and coefficient of determination (R2). The multivariate analysis reveals that the HHO-XGB model stands out, demonstrating superior prediction accuracy (MAE: 0.457; RMSE: 2.19; AARE: 2.29; R2: 0.993) compared to alternative models. Furthermore, our sensitivity analysis emphasizes the substantial impact of uniaxial compressive strength and tensile strength on the drill penetration rate. This underlines the importance of considering these material properties in mining operations. In conclusion, our research offers robust models for forecasting the penetration rate of similar rock formations, providing invaluable insights that can significantly enhance mining operations and planning processes.
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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Ke, Y, Zhang, SS, Jedrzejko, MJ, Lin, G, Li, WG & Nie, XF 2024, 'Strength models of near-surface mounted (NSM) fibre-reinforced polymer (FRP) shear-strengthened RC beams based on machine learning approaches', Composite Structures, vol. 337, pp. 118045-118045.
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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, Al-Maktoumi, A, Chen, M, Nikoo, MR & Gandomi, AH 2024, 'Groundwater model diagnostic calibration and uncertainty analysis using information theory', Hydrological Sciences Journal, vol. 69, no. 7, pp. 878-890.
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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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Khorshidi, MS, Nikoo, MR, Al-Rawas, G, Bahrami, N, Al-Wardy, M, Talebbeydokhti, N & Gandomi, AH 2024, 'Integrating agent-based modeling and game theory for optimal water resource allocation within complex hierarchical systems', Journal of Cleaner Production, vol. 482, pp. 144164-144164.
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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, K-H, Migliozzi, S, Koo, H, Hong, J-H, Park, SM, Kim, S, Kwon, HJ, Ha, S, Garofano, L, Oh, YT, D'Angelo, F, Kim, CI, Kim, S, Lee, JY, Kim, J, Hong, J, Jang, E-H, Mathon, B, Di Stefano, A-L, Bielle, F, Laurenge, A, Nesvizhskii, AI, Hur, E-M, Yin, J, Shi, B, Kim, Y, Moon, K-S, Kwon, JT, Lee, SH, Lee, SH, Gwak, HS, Lasorella, A, Yoo, H, Sanson, M, Sa, JK, Park, C-K, Nam, D-H, Iavarone, A & Park, JB 2024, 'Integrated proteogenomic characterization of glioblastoma evolution', Cancer Cell, vol. 42, no. 3, pp. 358-377.e8.
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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, 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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Kordani, M, Nikoo, MR, Fooladi, M, Ahmadianfar, I, Nazari, R & Gandomi, AH 2024, 'Improving Long-Term Flood Forecasting Accuracy Using Ensemble Deep Learning Models and an Attention Mechanism', Journal of Hydrologic Engineering, vol. 29, no. 6.
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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, vol. 24, no. 23, pp. 39736-39743.
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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, P, Samui, P, Armaghani, DJ & Roy, SS 2024, 'Second-order reliability analysis of an energy pile with CPT data', Journal of Building Engineering, vol. 95, pp. 110165-110165.
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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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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.
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 & Yang, Y 2024, '3D Terahertz Antenna Measurement: A comparison between solid-state electronics and photomixing approaches', IEEE Antennas and Propagation Magazine, vol. 66, no. 4, pp. 37-50.
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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, DT, Sutjipto, S, Nguyen, K & Paul, G 2024, 'Design, integration, and field testing of a Digital Twin-Based Teleoperated Rock Scaling Robot', IEEE Transactions on Field Robotics (T-FR).
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This paper presents the design, integration, and field testing of a digital twin-based teleoperated rock scaling robot aimed at improving safety in mining operations. Traditional rock scaling, which involves the removal loose rocks to prevent rockfall, poses significant risks to mine site workers. The proposed solution is a teleoperated custom mobile manipulator capable of rope-based abseiling locomotion, equipped with an air chipper end-effector. Teleoperation is facilitated by live digital twins of the robot and environment, with a virtual reality (VR) interface that allows operators to perform rock scaling tasks within an immersive virtual reconstruction of the remote scene. The robot's hardware design and sensing capabilities are detailed, along with the system's teleoperation architecture. Key components include the integration of an optimised, hardware accelerated, image-based point cloud streaming implementation; a markerless depth-camera extrinsic calibration process suitable for field settings; and the system’s teleoperation interfaces featuring a cyber-physical VR interface with affordance feedback. Field tests at a sandstone quarry and an open-pit mine demonstrate significant improvements in operator safety, and highlight the system’s ability to withstand harsh mining environments while performing teleoperated rock scaling at its current scaled-down size and power. We collected and analysed user data from rope access technicians with no prior experience in robot teleoperation or VR. The results suggest the system's intuitiveness with learning effects over time. Lessons from these site trials, including hardware and software limitations, are discussed, providing directions for further robot design improvements and enhancements to the digital twin teleoperation architecture.
Le, HQ, Duong, CC, Chang, H-M, Nguyen, NC, Chien, I-C, Ngo, HH & Chen, S-S 2024, 'Innovative hyper-thermophilic aerobic submerged membrane distillation bioreactor for wastewater reclamation', Chemosphere, vol. 362, pp. 142743-142743.
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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, QA, Pham, XL, van Walsum, T, Dao, VH, Le, TL, Franklin, D, Moelker, A, Le, VH, Trung, NL & Luu, MH 2024, 'Precise ablation zone segmentation on CT images after liver cancer ablation using semi‐automatic CNN‐based segmentation', Medical Physics, vol. 51, no. 12, pp. 8882-8899.
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AbstractBackgroundAblation zone segmentation in contrast‐enhanced computed tomography (CECT) images enables the quantitative assessment of treatment success in the ablation of liver lesions. However, fully automatic liver ablation zone segmentation in CT images still remains challenging, such as low accuracy and time‐consuming manual refinement of the incorrect regions.PurposeTherefore, in this study, we developed a semi‐automatic technique to address the remaining drawbacks and improve the accuracy of the liver ablation zone segmentation in the CT images.MethodsOur approach uses a combination of a CNN‐based automatic segmentation method and an interactive CNN‐based segmentation method. First, automatic segmentation is applied for coarse ablation zone segmentation in the whole CT image. Human experts then visually validate the segmentation results. If there are errors in the coarse segmentation, local corrections can be performed on each slice via an interactive CNN‐based segmentation method. The models were trained and the proposed method was evaluated using two internal datasets of post‐interventional CECT images ( = 22, = 145; 62 patients in total) and then further tested using an external benchmark dataset ( = 12; 10 patients).ResultsTo evaluate the accuracy of the proposed approach, we used Dice similarity coefficient (DSC), average symmetric surface distance (ASSD), Hausdorff distance (HD), and volume difference (VD). The quantitative evaluation results show that the proposed approach obtained mean DSC, ASSD, HD
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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Le, T-S, Bui, X-T, Nguyen, P-D, Hao Ngo, H, Dang, B-T, Le Quang, D-T, Thi Pham, T, Visvanathan, C & Diels, L 2024, 'Bacterial community composition in a two-stage anaerobic membrane bioreactor for co-digestion of food waste and food court wastewater', Bioresource Technology, vol. 391, pp. 129925-129925.
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Le, V-G, Nguyen, M-K, Ngo, HH, Barceló, D, Nguyen, H-L, Um, MJ & Nguyen, DD 2024, 'Microplastics in aquaculture environments: Current occurrence, adverse effects, ecological risk, and nature-based mitigation solutions', Marine Pollution Bulletin, vol. 209, pp. 117168-117168.
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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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Legg, R & Kabisch, N 2024, 'The effects of allergenic pollen in green space on mental health, behaviour and perceptions: A systematic review', Urban Forestry & Urban Greening, vol. 92, pp. 128204-128204.
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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, Li, X, Guo, Y, Qu, F, Zhao, C, Tam, VWY, Wu, V & Li, W 2024, 'Recycling of copper tailing as filler material in asphalt paving mastic: A sustainable solution for mining waste recovery', Case Studies in Construction Materials, vol. 20, pp. e03237-e03237.
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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, 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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Lei, Y, Ye, D, Zhu, T & Zhou, W 2024, 'A federated advisory teacher–student framework with simultaneous learning agents', Knowledge-Based Systems, vol. 305, pp. 112637-112637.
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Leitner, U, Brits, A, Xu, D, Patil, S & Sun, J 2024, 'Efficacy of probiotics on improvement of health outcomes in cirrhotic liver disease patients: A systematic review and meta-analysis of randomised controlled trials', European Journal of Pharmacology, vol. 981, pp. 176874-176874.
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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, KY, Ho, JSY, Tehseen, S, Yafi, E & Cham, T-H 2024, 'The intangible values of live streaming and their effect on audience engagement', Journal of Marketing Analytics, vol. 12, no. 4, pp. 990-1005.
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Li, B, Cao, Y, Yang, Y, Zhu, S, Guo, Z, Huang, T & Wen, S 2024, 'Quadratic Programming Consensus Tracking Control of Uncertain Multiagent Systems via Event-Triggered Mechanism', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 54, no. 12, pp. 7861-7870.
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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, 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, Qi, X, Chen, B, Huang, S, Miro, JV, Huang, H, Ni, W & Ma, H 2024, 'Marden-Based Homotopic Enclosed Safe Motion Corridor Generation for UAV Navigation in Complex Environments', IEEE Transactions on Automation Science and Engineering, pp. 1-15.
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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, 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, 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, Xie, Q, Chang, X, Xu, J & Liu, Y 2024, 'Mutually-Guided Hierarchical Multi-Modal Feature Learning for Referring Image Segmentation', ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 20, no. 12, pp. 1-18.
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Referring image segmentation aims to locate and segment the target region based on a given textual expression query. The primary challenge is to understand semantics from visual and textual modalities and achieve alignment and matching. Prior works have attempted to address this challenge by leveraging separately pretrained unimodal models to extract global visual and textual features and perform straightforward fusion to establish cross-modal semantic associations. However, these methods often concentrate solely on the global semantics, disregarding the hierarchical semantics of expression and image and struggling with complex and open real scenarios, thus failing to capture critical cross-modal information. To address these limitations, this article introduces an innovative mutually-guided hierarchical multi-modal feature learning scheme. By leveraging the guidance of global visual features, the model mines hierarchical text features from different stages of the text encoder. Simultaneously, the guidance of global textual features is leveraged to aggregate multi-scale visual features. This mutually guided hierarchical feature learning effectively addresses the semantically inaccurate cause by free-form text and naturally occurring scale variations. Furthermore, a Segment Detail Refinement (SDR) module is designed to enhance the model’s spatial detail awareness through attention mapping of low-level visual features and cross-modal features. To evaluate the effectiveness of the proposed approach, extensive experiments are conducted on three widely used referring image object segmentation datasets. The results demonstrate the superiority of the presented method in accurately locating and segmenting objects in images.
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, vol. 32, no. 12, pp. 6708-6719.
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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, 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 & 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 & Pugalia, S 2024, 'How Wanghong Live Streamers Affect Online Users’ Purchasing Behavior on Live Streaming Platforms: A PLS-SEM Approach', Journal of Global Information Technology Management, vol. 27, no. 4, pp. 277-292.
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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, 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, 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, Q, Xu, Y, Chen, S, Liang, C, Guo, W, Ngo, HH & Peng, L 2024, 'Inorganic carbon limitation decreases ammonium removal and N2O production in the algae-nitrifying bacteria symbiosis system', Science of The Total Environment, vol. 928, pp. 172440-172440.
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Li, R, Hou, Y-N, Li, H, Han, Y, Zhang, D, Song, Y, Huang, C, Guo, J, Liu, Z, Wei, W & Ni, B-J 2024, 'Salinity responsive mechanisms of sulfur-based mixotrophic denitrification and ectoine induced tolerance enhancement', Chemical Engineering Journal, vol. 496, pp. 154266-154266.
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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, Zhao, H, Ding, H, Huang, Y, Wang, C, Wei, J & Wang, Z 2024, 'Study on cold-start macro- and micro-performance of a proton exchange membrane fuel cell using a novel quasi-two-dimensional dynamic model', International Journal of Hydrogen Energy, vol. 110, pp. 727-737.
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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, Guo, Y, Zhang, X, Dong, W, Li, X, Yu, T & Wang, K 2024, 'Development of self-sensing ultra-high-performance concrete using hybrid carbon black and carbon nanofibers', Cement and Concrete Composites, vol. 148, pp. 105466-105466.
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Li, W, Manickam, S, Chong, Y-W, Leng, W & Nanda, P 2024, 'A State-of-the-Art Review on Phishing Website Detection Techniques', IEEE Access, vol. 12, pp. 187976-188012.
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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, 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, Luo, Y, Zhou, R, Zuo, X, Zhang, Y, Wang, Z, Li, X, Zhang, X, Qin, Z & Lin, CSK 2024, 'Coculture of Chlorella protothecoides and Coccomyxa subellipsoidea enhances cell growth and lipid accumulation: An effective strategy for biodiesel production', Chemical Engineering Journal, vol. 486, pp. 150302-150302.
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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, 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, 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, X, Li, Y, Zhao, Z, Ding, R, Sun, J & Chi, C 2024, 'Safety and efficacy of adding postbiotics in infant formula: a systematic review and meta-analysis', Pediatric Research, vol. 95, no. 1, pp. 43-51.
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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, '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, K, Ramos, S & Sun, J 2024, 'Urbanization, self-harm, and suicidal ideation in left-behind children and adolescents in China: a systematic review and meta-analysis', Current Opinion in Psychiatry, vol. 37, no. 3, pp. 225-236.
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Purpose of review Economic development and urbanisation have prompted many Chinese parents to move from rural to urban regions for better job opportunities. Their children, who remain behind in rural regions, become left-behind children (LBC). With absent parents, children and adolescents are unable to maintain the secure attachment required for healthy social and emotional development, increasing the risk of mental illness. This study aimed to compare risk of self-harm and suicidal ideation in LBC and non-LBC in China. Recent findings Greater risks for poor mental health outcomes including worse depression, loneliness and anxiety have been identified in LBC in cross-sectional studies. Previous studies have also identified higher prevalence of bullying victimization, poorer school performance and worse school attendance amongst LBC. Summary Findings indicate that prolonged separation from parents put LBC at greater risks of poor mental health. Policy changes to allow children to migrate with their parents and policies to reduce inequalities in job opportunities between urban and rural regions are needed.
Lin, K, Zhou, Y-M, Ma, H-P, He, F, Huang, X-N, Tian, X-B, Zheng, Y & Sun, J 2024, 'Quality of childcare and delayed child development in left-behind children in China', Pediatric Research, vol. 95, no. 3, pp. 809-818.
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Abstract Background Inequalities in job opportunities and income prompts many Chinese parents to leave rural regions to work in urban regions. Their children are left behind in rural regions, subjected to worse quality of childcare that jeopardizes their development. This study aimed to examine the association between quality of childcare and delayed child development in under-three years children left behind in China. Methods Cross-sectional national survey was conducted in children left behind in rural China in 2017. Exploratory and confirmatory factor analysis was used to develop a quality of childcare index. Mutlilevel analyses determined factors associated with quality of childcare and child development on a province and individual level. Result The largest population of at-risk children left behind were found in higher-GDP provinces. Children left behind had the lowest mean quality of childcare score. Multilevel analysis found that province level accounted for a great proportion of variance observed. Conclusions While migration to urban regions for work may improve household income, a trade-off in worse quality of childcare and developmental delays exists. With improving household income often being the greatest contributing factor for parental migration, policies to reduce inequalities in job opportunities and wealth between rural and urban regions are required. Impact ...
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, W, Chen, R, Gong, C, Desmond, P, He, X, Nan, J, Li, G, Ma, J, Ding, A & Ngo, HH 2024, 'Sustained oxidation of Tea-Fe(III)/H2O2 simultaneously achieves sludge reduction and carbamazepine removal: The crucial role of EPS regulation', Journal of Hazardous Materials, vol. 470, pp. 134182-134182.
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Lin, W, Ding, A, Gong, C, Ding, X, Desmond, P, Oleskowicz-Popiel, P, He, X, Ma, J & Hao Ngo, H 2024, 'A three-stage oxidation method improves sludge dewaterability by achieving sustained oxidation and phased enhanced coagulation: Ingeniously using H2O2 as a “converter”', Chemical Engineering Journal, vol. 500, pp. 157539-157539.
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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, 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, 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, F, Li, K, Zhong, Z, Jia, W, Hu, B, Yang, X, Wang, M & Guo, D 2024, 'Depth Matters: Spatial Proximity-Based Gaze Cone Generation for Gaze Following in Wild', ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 20, no. 11, pp. 1-24.
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Gaze following aims to predict where a person is looking in a scene. Existing methods tend to prioritize traditional 2D RGB visual cues or require burdensome prior knowledge and extra expensive datasets annotated in 3D coordinate systems to train specialized modules to enhance scene modeling. In this work, we introduce a novel framework deployed on a simple ResNet backbone, which exclusively uses image and depth maps to mimic human visual preferences and realize 3D-like depth perception. We first leverage depth maps to formulate spatial-based proximity information regarding the objects with the target person. This process sharpens the focus of the gaze cone on the specific region of interest pertaining to the target while diminishing the impact of surrounding distractions. To capture the diverse dependence of scene context on the saliency gaze cone, we then introduce a learnable grid-level regularized attention that anticipates coarse-grained regions of interest, thereby refining the mapping of the saliency feature to pixel-level heatmaps. This allows our model to better account for individual differences when predicting others’ gaze locations. Finally, we employ the KL-divergence loss to super the grid-level regularized attention, which combines the gaze direction, heatmap regression, and in/out classification losses, providing comprehensive supervision for model optimization. Experimental results on two publicly available datasets demonstrate the comparable performance of our model with less help of modal information. Quantitative visualization results further validate the interpretability of our method. The source code will be available at https://github.com/VUT-HFUT/DepthMatters .
Liu, G-B, Wei, C-H, Liu, T, Luo, H-Y, Zhou, H, Rong, H-W, Chen, D & Ngo, HH 2024, 'Electroplating wastewater treatment and resource recovery via a hybrid process of stepwise alkalization, Fenton, and chlorination', Separation and Purification Technology, vol. 339, pp. 126658-126658.
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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, 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, 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, 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, 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, Wang, Z, Chen, C & Dong, D 2024, 'Efficient Bayesian Policy Reuse With a Scalable Observation Model in Deep Reinforcement Learning', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 10, pp. 14797-14809.
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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, vol. 23, no. 11, pp. 3624-3628.
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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, vol. 10, no. 6, pp. 1904-1914.
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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, vol. 23, no. 11, pp. 3619-3623.
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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, L, Yin, W & Guo, Y 2024, 'Hybrid mechanism‐data‐driven iron loss modelling for permanent magnet synchronous motors considering multiphysics coupling effects', IET Electric Power Applications, vol. 18, no. 12, pp. 1833-1843.
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AbstractThe precise calculation of iron losses in permanent magnet synchronous motors (PMSMs) remains challenging due to the interplay between various disciplines such as electromagnetism, magnetism, and thermal/mechanical dynamics. Purely mechanistic models require detailed theoretical knowledge and exact parameters, often struggling to accurately describe complex systems, while purely data‐driven methods lack interpretability, which are susceptible to data noise and outliers in feature extraction and complicated pattern recognition. Consequently, this paper aims to present a hybrid mechanism‐data‐driven model for accurately estimating the iron loss for PMSMs, considering the multiphysics coupling effects. Specifically, based on the well‐defined physical principles, an advanced iron loss analytical model that simultaneously considers mechanical stress, temperature rise, harmonics, load currents, and changing frequency is developed and then utilised to calculate numerous loss data under different operating conditions, providing a certain level of stability and reliability for prediction accuracy. Subsequently, a convolutional neural network (CNN) algorithm is employed to perform deep learning to extract features and patterns from the data. By defining a suitable loss function, the pre‐trained model was fine‐tuned and optimised using a small amount of actual data. To validate its superiority, extensive numerical and experimental analyses are conducted on the prototype. The results demonstrate that the iron losses computed using this hybrid model overcome the limitations of singular methods by effectively leveraging both theoretical knowledge and real‐world data, thus accurately accommodating various application scenarios. This integrated approach enhances the accuracy, stability, and interpretability of the model, laying a solid foundation for more specialised applications in the future.
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, Cheng, S, Sun, C, Chen, K, Li, W & Tam, VWY 2024, 'Steel cable bonding in fresh mortar and 3D printed beam flexural behavior', Automation in Construction, vol. 158, pp. 105165-105165.
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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, Shi, K, Zhou, K, Wen, S, Tang, Y & Tang, L 2024, 'Erratum to “Event-triggering-based H∞ load frequency control for multi-area cyber–physical power system under DoS attacks” [Franklin Open Volume 3, June 2023, 100012]', Franklin Open, vol. 9, pp. 100124-100124.
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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, Bao, H, Chen, C, Cao, W, Zhang, X, Xu, Y, Ngo, HH & Liu, Q 2024, 'Recovery of biochar particles laden with lead in saturated porous media by DC electric field', Chemosphere, vol. 355, pp. 141890-141890.
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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, Xia, X, Zheng, M & Shi, B 2024, 'Bio–Nano Toolbox for Precision Alzheimer's Disease Gene Therapy', Advanced Materials, vol. 36, no. 29.
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AbstractAlzheimer's disease (AD) is the most burdensome aging‐associated neurodegenerative disorder, and its treatment encounters numerous failures during drug development. Although there are newly approved in‐market β‐amyloid targeting antibody solutions, pathological heterogeneity among patient populations still challenges the treatment outcome. Emerging advances in gene therapies offer opportunities for more precise personalized medicine; while, major obstacles including the pathological heterogeneity among patient populations, the puzzled mechanism for druggable target development, and the precision delivery of functional therapeutic elements across the blood–brain barrier remain and limit the use of gene therapy for central neuronal diseases. Aiming for “precision delivery” challenges, nanomedicine provides versatile platforms that may overcome the targeted delivery challenges for AD gene therapy. In this perspective, to picture a toolbox for AD gene therapy strategy development, the most recent advances from benchtop to clinics are highlighted, possibly available gene therapy targets, tools, and delivery platforms are outlined, their challenges as well as rational design elements are addressed, and perspectives in this promising research field are discussed.
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, 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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Lochot, V, Khalilpour, K, Hoadley, AFA & Sánchez, DR 2024, 'French economy and clean energy transition: A macroeconomic multi-objective extended input-output analysis', Sustainable Futures, vol. 8, pp. 100285-100285.
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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, A, Zhang, Z, Huang, Y, Zhang, Y, Li, C, Tang, J & Wang, L 2024, 'Illumination Distillation Framework for Nighttime Person Re-Identification and a New Benchmark', IEEE Transactions on Multimedia, vol. 26, pp. 406-419.
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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, 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, 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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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, L, Jiang, X, Du, Y, Dzakpasu, M, Yang, C, Guo, W, Ngo, HH & Wang, XC 2024, 'Impact of organic matter molecular weight on hexavalent chromium enrichment in green microalgae', Journal of Hazardous Materials, vol. 470, pp. 134304-134304.
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Luo, L, Su, D, Wang, T & Guo, W 2024, 'Integrated assessment of available water volume for sustainable sponge city construction – A case study in Xi'an, China', Water Science & Technology, vol. 89, no. 5, pp. 1282-1296.
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Abstract To address the lack of theoretical guidance for sponge city construction (SCC) in China, this study introduces a method to evaluate the available water volume (AWV) in urban watersheds. This evaluation is based on the water balance relationship, water volume, and ecological water demand (EWD). The Xi'an urban area was selected as a case study due to its water shortage and flooding issues. Results show monthly surface and subsurface AWV ranging between 53.06 and 53.98 million m3 and between 8,701.89 and 8,898.14 million m3, respectively. By maximizing the potential for surface AWV, an annual water supply of 527.75 million m3 could be provided, surpassing the annual artificial water consumption of 394.20 million m3, effectively addressing water scarcity. During the rainy season, implementing measures such as employing permeable paving materials, establishing wetlands and rainwater gardens, and constructing lakes and reservoirs can mitigate flooding caused by rainfall exceeding 32.8 mm. While the subsurface space in Xi'an holds significant potential for subsurface AWV utilization, revitalizing the ecological environment of subsurface water is crucial. Overall, the AWV theoretical framework offers a comprehensive solution to water shortage and flooding issues in the Xi'an urban area, serving as a vital theory for SCC.
Luo, L, Tan, J, Dzakpasu, M, Lou, C, Guo, W, Ngo, HH & Wang, XC 2024, 'Impact of recharge water source quality on Chlorella vulgaris growth and biomass: Strategies for eutrophication control in urban landscape lakes', Science of The Total Environment, vol. 957, pp. 177740-177740.
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Luo, L, Yang, T, Dzakpasu, M, Jiang, X, Guo, W, Ngo, HH & Wang, XC 2024, 'Interplay of humic acid and Cr(VI) on green microalgae: Metabolic responses and chromium enrichment', Journal of Hazardous Materials, vol. 480, pp. 135885-135885.
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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.
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, vol. 10, no. 6, pp. 2209-2223.
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Lyu, L, Matheson, S, Fleck, R, Torpy, FR & Irga, PJ 2024, 'Modulating phytoremediation: How drip irrigation system affect performance of active green wall and microbial community changes', Journal of Environmental Management, vol. 370, pp. 122646-122646.
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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, vol. 2, pp. 1178-1192.
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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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M.S., S, Elmakki, T, Zavahir, S, Tariq, H, Abdulhameed, A, Park, H, Shon, HK & Han, DS 2024, 'Enhanced lithium separation from brines using nanofiltration (NF) technology: A review', Desalination, vol. 592, pp. 118148-118148.
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Ma, B, Xing, J, Huang, S, Wang, K, Zhang, J, Lei, G & Zhu, J 2024, 'Cooling System Design for High-Power Density Permanent Magnet Synchronous Motor Based on Micro Heat Pipe Array', 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, 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, S, Wu, S, Zeng, Y, Shi, K & Xu, G 2024, 'Multi-Modal Dual Attention Graph Contrastive Learning for Recommendation'.
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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, MA, Zheng, J, Chang, J, Wang, G, Liao, C, Rahman, MH, Tarique, WB, Tang, S, Bing, J, Bailey, CG, Li, Z, Yang, L, Novikova, N, Leung, TL, Chen, H, Yi, J, Tao, R, Jankovec, M, Bremner, SP, Cairney, J, Uddin, A, Nguyen, HT, Smith, T, Chueh, C & Ho‐Baillie, AWY 2024, 'Halogenated Polycyclic Aromatic Hydrocarbon for Hole Selective Layer/Perovskite Interface Modification and Passivation for Efficient Perovskite‐Organic Tandem Solar Cells with Record Fill Factor', Advanced Energy Materials, vol. 14, no. 45.
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AbstractPerovskite whentandemed with organic photovoltaics (OPV) for double‐junctions have efficiencypotentials over 40%. However, there is still room for improvement suchas better current matching, higher fill factor, as well as lower voltage and fill factor losses in the top perovskite cell. Here weaddress the issue associated with the top perovskite cell by utilising anovel halogenated polycyclic aromatic hydrocarbon compound, 1‐naphthylammoniumchloride (NA─Cl) playing dual roles of surface modification for the hole selectivelayer (HSL) and passivation of HSL/perovskiteinterface. Results of X‐ray photoelectron spectroscopy and density functionaltheory calculations reveal that NA─Cl retains self‐assembly property for the HSLwhile demonstrating high dipole moment and polarizability. This induces asurface dipole at the HSL/perovskite interface reducing the energetic barrierfor hole extraction by 210 meV thereby enhancing voltage output and fill factorof the device. Such scheme when implemented in a high bandgap (1.78 eV)perovskite solar cell, results in a respectable efficiency of 19.7% and thehighest fill factor of 85.4% amongst those of 1.78 eV perovskite cells reported.We have also achieved 23% cell efficient monolithic perovskite‐OPV tandem withan impressive fill factor of 84%, which is the highest for perovskite‐OPVtandem cells reported to‐date.
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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Mahmud, T, Saboj, JH, Nag, P, Saha, G & Saha, BK 2024, 'Artificial neural network (ANN) approach in predicting the thermo-solutal transport rate from multiple heated chips within an enclosure filled with hybrid nanocoolant', International Journal of Thermofluids, vol. 24, pp. 100923-100923.
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Mai, C, Wang, H, Wang, C, Zhang, B, Kodagoda, S & Wang, S 2024, 'LMIINet: long-range and multi-scale information interaction network for 3D object detection', Journal of Electronic Imaging, vol. 33, no. 06.
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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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Malekpour, M-R, Rezaei, N, Azadnajafabad, S, Khanali, J, Azangou-Khyavy, M, Moghaddam, SS, Heidari-Foroozan, M, Rezazadeh-Khadem, S, Ghamari, S-H, Abbasi-Kangevari, M, Abady, GG, Abdulkader, RS, Abebe, AM, Abu-Gharbieh, E, Acharya, D, Addo, IY, Adeagbo, OA, Adegboye, OA, Adeyinka, DA, Sakilah Adnani, QE, Afolabi, AA, Afzal, S, Afzal, MS, Ahmad, S, Ahmad, A, Ahmadi, A, Ahmadieh, H, Ahmed, H, Ahmed, MS, Ajami, M, Akbarialiabad, H, Akunna, CJ, Alahdab, F, Alanezi, FM, Alanzi, TM, Alhassan, RK, Ali, L, Samakkhah, SA, Alimohamadi, Y, Aljunid, SM, Almustanyir, S, Al-Sabah, SK, Altirkawi, KA, Amare, H, Ameyaw, EK, Amin, TT, Amiri, S, Andrei, T, Andrei, CL, Anvari, D, Anwar, SL, Aqeel, M, Arab-Zozani, M, Arumugam, A, Aryal, UR, Asaad, M, Asgary, S, Ashraf, T, Astell-Burt, T, Athari, SS, Atreya, A, Aujayeb, A, Awedew, AFF, Quintanilla, BPA, Aychiluhm, SB, Ayele, AD, Azizi, H, Azzam, AY, Bakkannavar, SM, Bardhan, M, Barker-Collo, SL, Barqawi, HJ, Barrow, A, Bashiri, A, Baskaran, P, Basu, S, Bedi, N, Bekele, A, Belo, L, Bennett, DA, Bensenor, IM, Berhie, AY, Bhagavathula, AS, Bhaumik, S, Bhutta, ZA, Bitaraf, S, Boloor, A, Borges, G, Borschmann, R, Boufous, S, Brauer, M, Briggs, AM, Brown, J, Bryazka, D, Cámera, LA, Cárdenas, R, Carvalho, M, Catalá-López, F, Cerin, E, Charan, J, Chattu, VK, Chien, WT, Chitheer, A, Cho, DY, McPhee Christensen, SW, Christopher, DJ, Chu, D-T, Chukwu, IS, Cislaghi, B, Clark, SR, Cruz-Martins, N, Cullen, P, Dadras, O, Dai, X, Damiani, G, Dandona, R, Darmstadt, GL, Soltani, RDC, Darwesh, AM, Dávila-Cervantes, CA, De Leo, D, de Luca, K, Demetriades, AK, Demisse, B, Demisse, FW, Demissie, S, Desye, B, Dharmaratne, SD, Diress, M, Djalalinia, S, Dodangeh, M, Dongarwar, D, Edinur, HA, Eini, E, Ekholuenetale, M, Elgar, FJ, Elgendy, IY, Elhabashy, HR, Elhadi, M, El-Huneidi, W, Emamian, MH, Bain, LE, Enyew, DB, Eshetu, HB, Eskandarieh, S, Etaee, F, Fagbamigbe, AF, Faro, A, Fasanmi, AO, Fatehizadeh, A, Feng, X, Fereshtehnejad, S-M, Ferrara, P, Fetensa, G, Fischer, F, Franklin, RC, Fukumoto, T, Galali, Y, Galehdar, N, Gankpe, FG, Gebrehiwot, M, Gebremeskel, TG, Geleta, LA, Getachew, ME, Ghafourifard, M, Nour, MG, Ghashghaee, A, Gholamrezanezhad, A, Gill, TK, Ginindza, TG, Glasbey, JC, Göbölös, L, Gohari, K, Golechha, M, Goleij, P, Grivna, M, Gunawardane, DA, Gupta, B, Hall, BJ, Hamadeh, RR, Hamal, PK & et al. 2024, 'Global, regional, and national burden of injuries, and burden attributable to injuries risk factors, 1990 to 2019: results from the Global Burden of Disease study 2019', Public Health, vol. 237, pp. 212-231.
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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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Manh, BD, Nguyen, C-H, Hoang, DT & Nguyen, DN 2024, 'Homomorphic Encryption-Enabled Federated Learning for Privacy-Preserving Intrusion Detection in Resource-Constrained IoV Networks', 2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall), pp. 1-6.
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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, D, Karimi, M & Maxit, L 2024, 'Vibroacoustic response of a heavy fluid loaded plate with ABH stiffeners', Journal of Physics: Conference Series, vol. 2909, no. 1, pp. 012035-012035.
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Abstract Stiffened structures are essential for a wide range of engineering applications including aeronautics, marine and rail systems. Adding stiffeners to a host structure leads to generation of Bloch-Floquet waves, which are induced by the interaction between the flexural waves in the host structure and the flexural/torsional waves in the stiffeners. These waves could generate unwanted noise and vibrations which should be mitigated. Acoustic black holes (ABHs) are widely used as a passive-control approach to mitigate vibrations of elastic structures. This study aims to use ABHs integrated as stiffeners for a fluid-loaded plate. Hence, the host structure is not modified, and its structural integrity is maintained. A heavy fluid-loaded infinite plate with periodic ABH stiffeners under a line force excitation is considered (i.e., a two-dimensional model). To obtain the vibrocoustic response, the system is modelled using the COMSOL Multiphysics®. The effectiveness of the ABH stiffeners in mitigating the vibration and noise from the fluid loaded plate is examined.
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.
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.
Meena, NK, Sakhare, A, Nimbalkar, S & Dodagoudar, GR 2024, 'Numerical investigation of soil arching and integral abutment bridge in a pile-supported railway embankment', International Journal of Geotechnical Engineering, vol. 18, no. 7-10, pp. 743-752.
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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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Mehmood, A, Raja, MAZ, Jalili, M & Ling, SH 2024, 'Identification of fractional Hammerstein model for electrical stimulated muscle: An application of fuzzy-weighted differential evolution.', Biomed. Signal Process. Control., vol. 87, pp. 105545-105545.
Mehmood, MF, Munir, A, Farooq, U, Riaz, HH, Zhao, M & Islam, MS 2024, 'Breath of impact: Unveiling the dynamics of exhalation-driven deposition of polydisperse particles in lung across varied physical activities', Powder Technology, vol. 448, pp. 120283-120283.
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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.
Meng, H, Chen, Z, Zhu, J, You, B, Ma, T, Wei, W, Vernuccio, S, Xu, J & Ni, B 2024, 'In Situ Amorphization of Electrocatalysts', Advanced Functional Materials, vol. 34, no. 39.
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AbstractElectrocatalysis represents an efficient and eco‐friendly approach to energy conversion, enabling the sustainable synthesis of valuable chemicals and fuels. The deliberate engineering of electrocatalysts is crucial to improving the efficacy and scalability of electrocatalysis. Notably, the occurrence of in situ amorphization within electrocatalysts has been observed during various electrochemical processes, influencing the energy conversion efficiency and catalytic mechanism understanding. Of note, the dynamic transformation of catalysts into amorphous structures is complex, often leading to various amorphous configurations. Therefore, revealing this amorphization process and understanding the function of amorphous species are pivotal for elucidating the structure‐activity relationship of electrocatalysts, which will direct the creation of highly efficient catalysts. This review examines the mechanisms behind amorphous structure formation, summarizes characterization methods for detecting amorphous species, and discusses strategies for controlling (pre)catalyst properties and electrochemical conditions that influence amorphization. It also emphasizes the importance of spontaneously formed amorphous species in electrochemical oxidation and reduction reactions. Finally, it addresses challenges in the in situ amorphization of electrocatalysts. aiming to guide the synthesis of electrocatalysts for efficient, selective, and stable electrochemical reactions, and to inspire future advancements in the field.
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, X, Michailidis, AA, Shabani, S, Miao, KC, Klimov, PV, Lloyd, J, Rosenberg, E, Acharya, R, Aleiner, I, Andersen, TI, Ansmann, M, Arute, F, Arya, K, Asfaw, A, Atalaya, J, Bardin, JC, Bengtsson, A, Bortoli, G, Bourassa, A, Bovaird, J, Brill, L, Broughton, M, Buckley, BB, Buell, DA, Burger, T, Burkett, B, Bushnell, N, Chen, Z, Chiaro, B, Chik, D, Chou, C, Cogan, J, Collins, R, Conner, P, Courtney, W, Crook, AL, Curtin, B, Dau, AG, Debroy, DM, Del Toro Barba, A, Demura, S, Di Paolo, A, Drozdov, IK, Dunsworth, A, Erickson, C, Faoro, L, Farhi, E, Fatemi, R, Ferreira, VS, Burgos, LF, Forati, E, Fowler, AG, Foxen, B, Genois, É, Giang, W, Gidney, C, Gilboa, D, Giustina, M, Gosula, R, Gross, JA, Habegger, S, Hamilton, MC, Hansen, M, Harrigan, MP, Harrington, SD, Heu, P, Hoffmann, MR, Hong, S, Huang, T, Huff, A, Huggins, WJ, Ioffe, LB, Isakov, SV, Iveland, J, Jeffrey, E, Jiang, Z, Jones, C, Juhas, P, Kafri, D, Kechedzhi, K, Khattar, T, Khezri, M, Kieferová, M, Kim, S, Kitaev, A, Klots, AR, Korotkov, AN, Kostritsa, F, Kreikebaum, JM, Landhuis, D, Laptev, P, Lau, K-M, Laws, L, Lee, J, Lee, KW, Lensky, YD, Lester, BJ, Lill, AT, Liu, W, Locharla, A, Malone, FD, Martin, O, McClean, JR, McEwen, M, Mieszala, A, Montazeri, S, Morvan, A, Movassagh, R, Mruczkiewicz, W, Neeley, M, Neill, C, Nersisyan, A, Newman, M, Ng, JH, Nguyen, A, Nguyen, M, Niu, MY, O’Brien, TE, Opremcak, A, Petukhov, A, Potter, R, Pryadko, LP, Quintana, C, Rocque, C, Rubin, NC, Saei, N, Sank, D, Sankaragomathi, K, Satzinger, KJ, Schurkus, HF, Schuster, C, Shearn, MJ, Shorter, A, Shutty, N, Shvarts, V, Skruzny, J, Smith, WC, Somma, R, Sterling, G, Strain, D, Szalay, M, Torres, A, Vidal, G, Villalonga, B, Heidweiller, CV, White, T, Woo, BWK, Xing, C, Yao, ZJ, Yeh, P, Yoo, J, Young, G, Zalcman, A, Zhang, Y, Zhu, N, Zobrist, N, Neven, H, Babbush, R, Bacon, D, Boixo, S, Hilton, J, Lucero, E, Megrant, A, Kelly, J, Chen, Y, Roushan, P, Smelyanskiy, V & Abanin, DA 2024, 'Stable quantum-correlated many-body states through engineered dissipation', Science, vol. 383, no. 6689, pp. 1332-1337.
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Engineered dissipative reservoirs have the potential to steer many-body quantum systems toward correlated steady states useful for quantum simulation of high-temperature superconductivity or quantum magnetism. Using up to 49 superconducting qubits, we prepared low-energy states of the transverse-field Ising model through coupling to dissipative auxiliary qubits. In one dimension, we observed long-range quantum correlations and a ground-state fidelity of 0.86 for 18 qubits at the critical point. In two dimensions, we found mutual information that extends beyond nearest neighbors. Lastly, by coupling the system to auxiliaries emulating reservoirs with different chemical potentials, we explored transport in the quantum Heisenberg model. Our results establish engineered dissipation as a scalable alternative to unitary evolution for preparing entangled many-body states on noisy quantum processors.
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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Miao, G, Ranaraja, I, Grundy, S, Brown, N, Belkina, M & Goldfinch, T 2024, 'Project-based learning in Australian & New Zealand universities: current practice and challenges', Australasian Journal of Engineering Education, vol. 29, no. 2, pp. 102-114.
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Miao, G, Ranaraja, I, Grundy, S, Brown, N, Belkina, M & Goldfinch, T 2024, 'Project-based learning in Australian & New Zealand universities: sustainability and scalability', Australasian Journal of Engineering Education, vol. 29, no. 2, pp. 115-125.
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Miao, K, Xia, X, Zou, Y & Shi, B 2024, 'Small Scale, Big Impact: Nanotechnology-Enhanced Drug Delivery for Brain Diseases', Molecular Pharmaceutics, vol. 21, no. 8, pp. 3777-3799.
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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, Chia, SR, Ong, MY, Sebayang, AH & Kalam, MA 2024, 'Production of biodiesel from non-edible waste palm oil and sterculia foetida using microwave irradiation', IOP Conference Series: Earth and Environmental Science, vol. 1372, no. 1, pp. 012047-012047.
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Abstract The environmental damage stemming from traditional diesel begins during crude oil extraction and persists throughout its usage. The burning of fossil fuels has further deteriorate the environmental effect and added to global warming by emitting harmful substances. Moreover, the reduction of finite fossil fuel reserves due to widespread extraction has made the adoption of renewable resources essential. Given these considerations, biodiesel emerges as a highly promising alternative to conventional diesel due to its environmentally beneficial nature, renewable source, and economic feasibility. In this study, biodiesel was prepared by a microwave reactor in the presence of potassium methoxide using blended waste palm oil and sterculia foetida. The effects of raw materials characteristics on transesterification products were studied. The studied process parameters were methanol/oil ratio, microwave temperature, catalyst concentration, reaction time, and stirring speed. The optimal yield with 98.5% FAME content was obtained at a methanol/oil ratio of 60 vol. %, microwave temperature of 120 °C, catalyst concentration of 0.3 wt.%, and 3 min reaction time, and stirring speed of 500 rpm. The potassium methoxide was used to catalyse the transesterification process. The physicochemical properties and the fatty acid methyl ester composition were discussed thoroughly. The flash point of biodiesel, at 157.5°C, exceeds that of diesel fuel by more than two times. The cetane index is 59.5 which is higher than diesel (49.6). The biodiesel’s fuel properties conformed to the requirements of both ASTM D6751 and EN 14214. High biodiesel conversion and low sulphur content show that waste palm oil and sterculia foetida are sustainable and economical feedstocks that produce clean fuel to aid the feasibility of the energy transition of the global energy sector. In additi...
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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Min, S, Lee, H, Deng, L, Guo, W, Xu, B, Yong Ng, H, Mehmood, CT, Zhong, Z, Zamora, R, Khan, E, Ranjan Dash, S, Kim, J, Pishnamazi, M, Park, P-K & Chae, SR 2024, 'Advanced strategies for mitigation of membrane fouling in anaerobic membrane bioreactors for sustainable wastewater treatment', Chemical Engineering Journal, vol. 485, pp. 149996-149996.
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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, K, Majhi, SK, Sahoo, KS, Bhoi, SK, Bhuyan, M & Gandomi, AH 2024, 'Collaborative Cloud Resource Management and Task Consolidation Using JAYA Variants', IEEE Transactions on Network and Service Management, vol. 21, no. 6, pp. 6248-6259.
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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.
Mo, H, Xiao, X, Sansavini, G & Dong, D 2024, 'Optimal defense resource allocation against cyber-attacks in distributed generation systems', Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, vol. 238, no. 6, pp. 1302-1329.
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The deployment of advanced information and communication technologies necessitates considering new security threats, such as distributed denial of service attacks and malware, which can fault power generators and feeders and exacerbate power outages in distributed generation systems (DGS). Existing cyber-security studies fail to validate the attacker–defender game model between operators and hackers or provide a DGS model that accounts for realistic characteristics and operations. Furthermore, current game models may be infeasible for large-scale systems and are not robust against uncertainties owing to the use of metaheuristic algorithms. To overcome these gaps, this study quantified the result of a game using the contest success function and estimated the parameters of this function based on real-world evidence: the dataset of cyber crime incidents from Advisen, US. The DGS management was optimized using the power flow model considering the scenario-based uncertainty stemming from cyber-attacks. A three-stage attack+defend–defend–attack framework is proposed to optimize attack–defense resource allocation using the cooperative game and [Formula: see text]-subgradient method. The results for IEEE 4, 13, 34, 123 and 342 test node feeders show that the proposed framework is applicable to large-scale systems and robust to various types of cyber-attacks. The proposed model and algorithms further enhance the DGS performance under uncertainties by protecting the entire grid or only critical nodes according to the defenders’ objectives.
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
Mohanty, SK, Nayak, PK, Bera, PK & Alhelou, HH 2024, 'An Enhanced Protective Relaying Scheme for TCSC Compensated Line Connecting DFIG-Based Wind Farm', IEEE Transactions on Industrial Informatics, vol. 20, no. 3, pp. 3425-3435.
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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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Mokdad, AH, Bisignano, C, Hsu, JM, Ababneh, HS, Abbasgholizadeh, R, Abdelkader, A, PubMed, M, Abiodun, OO, Aboagye, RG, Abu-Zaid, A, Abukhadijah, HJ, Addo, IY, Adeagbo, OA, Adegboye, OA, Adekanmbi, V, Adeyeoluwa, TE, Adzigbli, LA, Afolabi, AA, Agyemang-Duah, W, Ahmad, S, Ahmad, D, Ahmed, A, Ahmed, SA, Akkaif, MA, Akrami, AE, Akter, E, Al Hasan, SM, Al Ta'ani, O, Al-Ajlouni, Y, Al-Aly, Z, Al-Rifai, RH, Al-Tawfiq, JA, Al-Wardat, M, Al-Zyoud, WA, Alam, M, Albakri, A, Aldhaleei, WA, Aldridge, RW, Ali, MU, Ali, A, Ali, R, Ali, W, Almustanyir, S, Alqutaibi, AY, Alrawashdeh, A, Alsabri, MA, Aly, H, Amani, R, Amegbor, PM, Amindarolzarbi, A, Amiri, S, Anil, A, Appiah, F, Arabloo, J, Arafa, EA, Arafat, M, Aravkin, AY, Ardekani, A, Areda, D, Ashina, S, Atreya, A, Ayalew, FB, Azzam, AY, Babu, GR, Baghdadi, S, Bagherieh, S, Bahramian, S, Bahreini, R, Bako, AT, Bansal, K, Bärnighausen, TW, Barrow, A, Bastan, M-M, Basu, S, Batra, R, Batra, K, Bayati, M, Beiranvand, M, Bell, ML, Beloukas, A, Bemanalizadeh, M, Bennitt, FB, Benzian, H, Beran, A, Bermudez, ANC, Bernstein, RS, Beyene, HBB, Beyene, KA, Bhagavathula, AS, Bhala, N, Bhargava, A, Bhaskar, S, Bhat, V, Bodunrin, AO, Boppana, SH, Borhany, H, Bosoka, SA, Boxe, C, Boyko, EJ, Braithwaite, D, Brauer, M, Bryazka, D, Bugiardini, R, Bustanji, Y, Butt, ZA, Caetano dos Santos, FL, Cagney, J, Cao, C, Capodici, A, Castaldelli-Maia, JM, Cembranel, F, Cenko, E, Chandrasekar, EK, Chaudhary, AA, Chen, A-T, Chen, MX, Chi, G, Chong, B, Choudhari, SG, Chowdhury, R, Chung, S-C, Cogen, RM, Conde, J, Cooper, LT, Cortese, S, Criqui, MH, Cruz-Martins, N, Culbreth, GT, D'Oria, M, Dabo, B, Dai, Z, Dai, X, Damiani, G, Daoud, F, Darcho, SDD, Darwesh, AM, Das, S, Dash, NR, Dashti, M, Degenhardt, L, Des Jarlais, DC, Devanbu, VGC, Dewan, SMR, Dhama, K, Diaz, D, Diaz, LA, Diaz, MJ, Ding, DD, Do, THP, Do, TC, Doan, KD, Dongarwar, D, Dorsey, ER, Doshi, OP, Doshi, RP, Douiri, A, Dowou, RK, Dube, J, Dutta, S, Dwyer-Lindgren, L, Dziedzic, AM, E'mar, AR, Ebrahimi, A, Ehrlich, JRR, Ekundayo, TC, El Arab, RA, El Bayoumy, IF, Elhadi, M, Elmoselhi, AB, ELNahas, G, Elshaer, M, Eltaha, C, Emamverdi, M, Esposito, F, Etaee, F, Ezenwankwo, EF, Fahim, A, Fakhri-Demeshghieh, A, Fasanmi, AO, Fazylov, T, Feigin, VL, Fekadu, G, Feroze, AH, Ferreira, N, Filip, I, Fischer, F, Flor, LS, Fu, W, Fukumoto, T, Gadanya, MA, Gajjar, AA, Ganesan, B & et al. 2024, 'The burden of diseases, injuries, and risk factors by state in the USA, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021', The Lancet, vol. 404, no. 10469, pp. 2314-2340.
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Mokdad, AH, Bisignano, C, Hsu, JM, Bryazka, D, Cao, S, Bhattacharjee, NV, Dalton, BE, Lindstedt, PA, Smith, AE, Ababneh, HS, Abbasgholizadeh, R, Abdelkader, A, Abdi, P, Abiodun, OO, Aboagye, RG, Abukhadijah, HJ, Abu-Zaid, A, Acuna, JM, Addo, IY, Adekanmbi, V, Adeyeoluwa, TE, Adzigbli, LA, Afolabi, AA, Afrashteh, F, Agyemang-Duah, W, Ahmad, S, Ahmadzade, M, Ahmed, A, Ahmed, A, Ahmed, SA, Akkaif, MA, Akkala, S, Akrami, AE, Al Awaidy, S, Al Hasan, SM, Al Ta'ani, O, Al Zaabi, OAM, Alahdab, F, Al-Ajlouni, Y, Al-Aly, Z, Alam, M, Aldhaleei, WA, Algammal, AM, Alhassan, RK, Ali, MU, Ali, R, Ali, W, Al-Ibraheem, A, Almustanyir, S, Alqahatni, SA, Alrawashdeh, A, Al-Rifai, RH, Alsabri, MA, Alshahrani, NZ, Al-Tawfiq, JA, Al-Wardat, M, Aly, H, Amindarolzarbi, A, Amiri, S, Anil, A, Anyasodor, AE, Arabloo, J, Arafat, M, Aravkin, AY, Ardekani, A, Areda, D, Asghariahmadabad, M, Ayanore, MA, Ayyoubzadeh, SM, Azadnajafabad, S, Azhar, GS, Aziz, S, Azzam, AY, Babu, GR, Baghdadi, S, Bahreini, R, Bako, AT, Bärnighausen, TW, Bastan, M-M, Basu, S, Batra, K, Batra, R, Behnoush, AH, Bemanalizadeh, M, Benzian, H, Bermudez, ANC, Bernstein, RS, Beyene, KA, Bhagavathula, AS, Bhala, N, Bharadwaj, R, Bhargava, A, Bhaskar, S, Bhat, V, Bhuyan, SS, Bodunrin, AO, Boxe, C, Boyko, EJ, Braithwaite, D, Brauer, M, Bugiardini, R, Bustanji, Y, Butt, ZA, Caetano dos Santos, FL, Capodici, A, Castaldelli-Maia, JM, Cembranel, F, Cenko, E, Cerin, E, Chan, JSK, Chattu, VK, Chaudhary, AA, Chen, A-T, Chen, G, Chi, G, Ching, PR, Cho, DY, Chong, B, Choudhari, SG, Chukwu, IS, Chung, E, Chung, S-C, Coker, DC, Columbus, A, Conde, J, Cortese, S, Criqui, MH, Cruz-Martins, N, Dai, X, Dai, Z, Damiani, G, D'Anna, L, Daoud, F, Darcho, SD, Das, S, Dash, NR, Dashtkoohi, M, Degenhardt, L, Des Jarlais, DC, Desai, HD, Devanbu, VGC, Dewan, SMR, Dhama, K, Dhulipala, VR, Diaz, LAA, Ding, DD, Do, TC, Do, THP, Dongarwar, D, D'Oria, M, Dorsey, ER, Doshi, OP, Douiri, A, Dowou, RK, Dube, J, Dziedzic, AM, E'mar, AR, Ebrahimi, A, Ehrlich, JRR, Ekundayo, TC, El Bayoumy, IF, Elhadi, M, Elhadi, YAM, Eltaha, C, Etaee, F, Ezenwankwo, EF, Fadaka, AO, Fagbule, OF, Fahim, A, Fallahpour, M, Fazylov, T, Feigin, VL, Feizkhah, A, Fekadu, G, Ferreira, N, Fischer, F, Gadanya, MA, Ganesan, B, Ganiyani, MA, Gao, X, Gebregergis, MW, Gebrehiwot, M, Gholami, E, Gholamrezanezhad, A, Ghotbi, E, Ghozy, S, Gillum, RF & et al. 2024, 'Burden of disease scenarios by state in the USA, 2022–50: a forecasting analysis for the Global Burden of Disease Study 2021', The Lancet, vol. 404, no. 10469, pp. 2341-2370.
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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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Morvan, A, Villalonga, B, Mi, X, Mandrà, S, Bengtsson, A, Klimov, PV, Chen, Z, Hong, S, Erickson, C, Drozdov, IK, Chau, J, Laun, G, Movassagh, R, Asfaw, A, Brandão, LTAN, Peralta, R, Abanin, D, Acharya, R, Allen, R, Andersen, TI, Anderson, K, Ansmann, M, Arute, F, Arya, K, Atalaya, J, Bardin, JC, Bilmes, A, Bortoli, G, Bourassa, A, Bovaird, J, Brill, L, Broughton, M, Buckley, BB, Buell, DA, Burger, T, Burkett, B, Bushnell, N, Campero, J, Chang, H-S, Chiaro, B, Chik, D, Chou, C, Cogan, J, Collins, R, Conner, P, Courtney, W, Crook, AL, Curtin, B, Debroy, DM, Barba, ADT, Demura, S, Paolo, AD, Dunsworth, A, Faoro, L, Farhi, E, Fatemi, R, Ferreira, VS, Burgos, LF, Forati, E, Fowler, AG, Foxen, B, Garcia, G, Genois, É, Giang, W, Gidney, C, Gilboa, D, Giustina, M, Gosula, R, Dau, AG, Gross, JA, Habegger, S, Hamilton, MC, Hansen, M, Harrigan, MP, Harrington, SD, Heu, P, Hoffmann, MR, Huang, T, Huff, A, Huggins, WJ, Ioffe, LB, Isakov, SV, Iveland, J, Jeffrey, E, Jiang, Z, Jones, C, Juhas, P, Kafri, D, Khattar, T, Khezri, M, Kieferová, M, Kim, S, Kitaev, A, Klots, AR, Korotkov, AN, Kostritsa, F, Kreikebaum, JM, Landhuis, D, Laptev, P, Lau, K-M, Laws, L, Lee, J, Lee, KW, Lensky, YD, Lester, BJ, Lill, AT, Liu, W, Livingston, WP, Locharla, A, Malone, FD, Martin, O, Martin, S, McClean, JR, McEwen, M, Miao, KC, Mieszala, A, Montazeri, S, Mruczkiewicz, W, Naaman, O, Neeley, M, Neill, C, Nersisyan, A, Newman, M, Ng, JH, Nguyen, A, Nguyen, M, Niu, MY, O’Brien, TE, Omonije, S, Opremcak, A, Petukhov, A, Potter, R, Pryadko, LP, Quintana, C, Rhodes, DM, Rocque, C, Rosenberg, E, Rubin, NC, Saei, N, Sank, D, Sankaragomathi, K, Satzinger, KJ, Schurkus, HF, Schuster, C, Shearn, MJ, Shorter, A, Shutty, N, Shvarts, V, Sivak, V, Skruzny, J, Smith, WC, Somma, RD, Sterling, G, Strain, D, Szalay, M, Thor, D, Torres, A, Vidal, G, Heidweiller, CV, White, T, Woo, BWK, Xing, C, Yao, ZJ, Yeh, P, Yoo, J, Young, G, Zalcman, A, Zhang, Y, Zhu, N, Zobrist, N, Rieffel, EG, Biswas, R, Babbush, R, Bacon, D, Hilton, J, Lucero, E, Neven, H, Megrant, A, Kelly, J, Roushan, P, Aleiner, I, Smelyanskiy, V, Kechedzhi, K, Chen, Y & Boixo, S 2024, 'Phase transitions in random circuit sampling', Nature, vol. 634, no. 8033, pp. 328-333.
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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.
Mottaghi T, H, Masoodi, AR & Gandomi, AH 2024, 'Multiscale analysis of carbon nanotube-reinforced curved beams: A finite element approach coupled with multilayer perceptron neural network', Results in Engineering, vol. 23, pp. 102585-102585.
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Mukherjee, A, Vohnout, R & Gandomi, AH 2024, 'Guest Editorial Split Learning in Consumer Electronics for Smart Cities: Theories, Tools, Applications and Challenges', IEEE Transactions on Consumer Electronics, vol. 70, no. 3, pp. 5814-5817.
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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, no. 1, 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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Mustafa, R, Samui, P, Kumari, S & Armaghani, DJ 2024, 'Appraisal of numerous machine learning techniques for the prediction of bearing capacity of strip footings subjected to inclined loading', Modeling Earth Systems and Environment, vol. 10, no. 3, pp. 4067-4088.
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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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Naghavi, M, Ong, KL, Aali, A, Ababneh, HS, Abate, YH, Abbafati, C, Abbasgholizadeh, R, Abbasian, M, Abbasi-Kangevari, M, Abbastabar, H, Abd ElHafeez, S, Abdelmasseh, M, Abd-Elsalam, S, Abdelwahab, A, Abdollahi, M, Abdollahifar, M-A, Abdoun, M, Abdulah, DM, Abdullahi, A, Abebe, M, Abebe, SS, Abedi, A, Abegaz, KH, Abhilash, ES, Abidi, H, Abiodun, O, Aboagye, RG, Abolhassani, H, Abolmaali, M, Abouzid, M, Aboye, GB, Abreu, LG, Abrha, WA, Abtahi, D, Abu Rumeileh, S, Abualruz, H, Abubakar, B, Abu-Gharbieh, E, Abu-Rmeileh, NME, Aburuz, S, Abu-Zaid, A, Accrombessi, MMK, Adal, TG, Adamu, AA, Addo, IY, Addolorato, G, Adebiyi, AO, Adekanmbi, V, Adepoju, AV, Adetunji, CO, Adetunji, JB, Adeyeoluwa, TE, Adeyinka, DA, Adeyomoye, OI, Admass, BAA, Adnani, QES, Adra, S, Afolabi, AA, Afzal, MS, Afzal, S, Agampodi, SB, Agasthi, P, Aggarwal, M, Aghamiri, S, Agide, FD, Agodi, A, Agrawal, A, Agyemang-Duah, W, Ahinkorah, BO, Ahmad, A, Ahmad, D, Ahmad, F, Ahmad, MM, Ahmad, S, Ahmad, S, Ahmad, T, Ahmadi, K, Ahmadzade, AM, Ahmed, A, Ahmed, A, Ahmed, H, Ahmed, LA, Ahmed, MS, Ahmed, MS, Ahmed, MB, Ahmed, SA, Ajami, M, Aji, B, Akara, EM, Akbarialiabad, H, Akinosoglou, K, Akinyemiju, T, Akkaif, MA, Akyirem, S, Al Hamad, H, Al Hasan, SM, Alahdab, F, Alalalmeh, SO, Alalwan, TA, Al-Aly, Z, Alam, K, Alam, M, Alam, N, Al-amer, RM, Alanezi, FM, Alanzi, TM, Al-Azzam, S, Albakri, A, Albashtawy, M, AlBataineh, MT, Alcalde-Rabanal, JE, Aldawsari, KA, Aldhaleei, WA, Aldridge, RW, Alema, HB, Alemayohu, MA, Alemi, S, Alemu, YM, Al-Gheethi, AAS, Alhabib, KF, Alhalaiqa, FAN, Al-Hanawi, MK, Ali, A, Ali, A, Ali, L, Ali, MU, Ali, R, Ali, S, Ali, SSS, Alicandro, G, Alif, SM, Alikhani, R, Alimohamadi, Y, Aliyi, AA, Aljasir, MAM, Aljunid, SM, Alla, F, Allebeck, P, Al-Marwani, S, Al-Maweri, SAA, Almazan, JU, Al-Mekhlafi, HM, Almidani, L, Almidani, O, Alomari, MA, Al-Omari, B, Alonso, J, Alqahtani, JS, Alqalyoobi, S, Alqutaibi, AY, Al-Sabah, SK, Altaany, Z, Altaf, A, Al-Tawfiq, JA, Altirkawi, KA, Aluh, DO, Alvis-Guzman, N, Alwafi, H, Al-Worafi, YM, Aly, H, Aly, S, Alzoubi, KH, Amani, R, Amare, AT, Amegbor, PM, Ameyaw, EK, Amin, TT, Amindarolzarbi, A, Amiri, S, Amirzade-Iranaq, MH, Amu, H, Amugsi, DA, Amusa, GA, Ancuceanu, R, Anderlini, D, Anderson, DB, Andrade, PP, Andrei, CL, Andrei, T, Angus, C, Anil, A, Anil, S, Anoushiravani, A, Ansari, H, Ansariadi, A & et al. 2024, 'Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021', The Lancet, vol. 403, no. 10440, pp. 2100-2132.
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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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Nareti, UK, Adak, C & Chattopadhyay, S 2024, 'Demystifying Visual Features of Movie Posters for Multilabel Genre Identification', IEEE Transactions on Computational Social Systems, pp. 1-10.
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Naseer, A, Rani, M, Naz, S, Razzak, MI, Imran, M & Xu, G 2024, 'Retraction Note: Refining Parkinson’s neurological disorder identification through deep transfer learning', Neural Computing and Applications, vol. 36, no. 16, pp. 9611-9611.
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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, 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 & Hasan, M 2024, 'Finite Element Modelling of Geogrids Reinforced Ballasted Tracks', Transportation Infrastructure Geotechnology, vol. 11, no. 4, pp. 2425-2447.
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AbstractThis paper presents results obtained from three-dimension finite element modelling (FEM) to study the effects of geogrids on the deformation responses of ballasted tracks. In this study, a series of numerical simulations are carried out on track sections with and without the inclusion of geogrids. Sensitivity analysis was carried on parameters affecting the performance of geogrid, including the axial stiffness, interface property and the location of geogrid placement in the track substructure. The tracks are subjected to moving train loading under 150 kN wheel load travelling at a given speed of 72 km/h. Based on simulation results, it is found that geogrid provides a reinforcing function to rail track primarily in the form of confinement which resulted in reduced lateral displacement in a reinforced track compared to a traditional track. A significant reduction in vertical and lateral displacement is found from the inclusion of a geogrid layer at the ballast and capping interface while the effect of geogrid reinforcement is more pronounced with increased loading cycles. The effects of geogrid stiffness, interface conditions and geogrid placement are studied and it is found that the axial stiffness of geogrid is found to impact overall track deformation while the optimum placement of geogrid is found to directly at the ballast and capping interface.
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, 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, 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, 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, HD, Lee, H, Lee, BJ, Park, J, Shon, HK, Kim, S & Lee, S 2024, 'Fluorescence spectrometric analysis for diagnosing compositional variations in effluent organic matter by chlorination and ozonation', Chemosphere, vol. 369, pp. 143846-143846.
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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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Abstract This 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, TN, Zhang, D, Mirrashid, M, Nguyen, DK & Singhatanadgid, P 2024, 'Fast analysis and prediction approach for geometrically nonlinear bending analysis of plates and shells using artificial neural networks', Mechanics of Advanced Materials and Structures, vol. 31, no. 28, pp. 10221-10239.
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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, 'Fraud's Bargain Attack: Generating Adversarial Text Samples via Word Manipulation Process', IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 7, pp. 3062-3075.
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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, vol. 73, no. 12, pp. 18893-18906.
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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, Sandison, C, Haque, R, Daniel, S, Grundy, S, Belkina, M, Lyden, S, Hassan, GM & Neal, P 2024, 'ChatGPT, Copilot, Gemini, SciSpace and Wolfram versus higher education assessments: an updated multi-institutional study of the academic integrity impacts of Generative Artificial Intelligence (GenAI) on assessment, teaching and learning in engineering', Australasian Journal of Engineering Education, vol. 29, no. 2, pp. 126-153.
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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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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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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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Nivedhitha, KS, Banapurmath, NR, Yaliwal, VS, Umarfarooq, MA, Sajjan, AM, Venkatesh, R, Hosmath, RS, Beena, T, Yunus Khan, TM, Kalam, MA, Soudagar, MEM & Ağbulut, Ü 2024, 'From nickel–metal hydride batteries to advanced engines: A comprehensive review of hydrogen's role in the future energy landscape', International Journal of Hydrogen Energy, vol. 82, pp. 1015-1038.
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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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Nurhayati, M, Jeong, K, Lee, H, Park, J, Hong, BU, Kang, HG, Shon, HK & Lee, S 2024, 'Predicting and optimizing forward osmosis membrane operation using machine learning', Desalination, vol. 592, pp. 118154-118154.
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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, vol. 24, 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.
Onggowarsito, C, Shao, Z, Mao, S, Zhang, S, Feng, A, Li, X, Wong, EHH & Fu, Q 2024, 'Versatile cationic dual-layer hydrogel filtration system for sustainable solar steam generator', Materials Today Sustainability, vol. 26, pp. 100753-100753.
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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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Otavio Mendes, J, Merenda, A, Wilson, K, Fraser Lee, A, Della Gaspera, E & van Embden, J 2024, 'Substrate Morphology Directs (001) Sb2Se3 Thin Film Growth by Crystallographic Orientation Filtering', Small, vol. 20, no. 39, p. e2302721.
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AbstractAntimony chalcogenide, Sb2X3 (X = S, Se), applications greatly benefit from efficient charge transport along covalently bonded (001) oriented (Sb4X6)n ribbons, making thin film orientation control highly desirable – although particularly hard to achieve experimentally. Here, it is shown for the first time that substrate nanostructure plays a key role in driving the growth of (001) oriented antimony chalcogenide thin films. Vapor Transport Deposition of Sb2Se3 thin films is conducted on ZnO substrates whose morphology is tuned between highly nanostructured and flat. The extent of Sb2Se3 (001) orientation is directly correlated to the degree of substrate nanostructure. These data showcase that nanostructuring a substrate is an effective tool to control the orientation and morphology of Sb2Se3 films. The optimized samples demonstrate high (001) crystallographic orientation. A growth mechanism for these films is proposed, wherein the substrate physically restricts the development of undesirable crystallographic orientations. It is shown that the surface chemistry of the nanostructured substrates can be altered and still drive the growth of (001) Sb2Se3 thin films – not limiting this phenomenon to a particular substrate type. Insights from this work are expected to guide the rational design of Sb2X3 thin film devices and other low‐dimensional crystal‐structured materials wherein performance is intrinsically linked to morphology and orientation.
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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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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Palanikumar, P, Seikh, AH, Kalam, MA & Venkatesh, R 2024, 'Influences of black nickel coating thickness on thermal behaviour of flat plate solar collector: performance evaluation', Journal of Thermal Analysis and Calorimetry, vol. 149, no. 12, pp. 6687-6697.
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Palmer, TA & Booth, E 2024, 'The Effectiveness of Song and Music as Pedagogical Tools in Elementary School Science Lessons: A Systematic Review of Literature', International Journal of Education and the Arts, vol. 25, no. 7.
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This literature review offers compelling evidence for the significant role music and song can play in cultivating student engagement in elementary school science lessons. With students disengaging from science education, there is concern about the lack of necessary scientific literacy skills required in today's world. To explore whether music and song could be used engage students in science we conducted a thorough search of composite education databases for relevant scholarly articles published 1993-2021. Synthesis of the resulting 26 articles revealed four themes: the common goal of engagement, evidence of learning improvement, broad utility of music and songs as pedagogical tools, and limited long-term studies. While acknowledging the limited evidence presented in these articles, we emphasize that incorporating music and song into science lessons not only enriches the educational environment but also contributes to an arts-infused education known to enhance student performance across a wide range of curriculum domains. We recommend further research with a particular focus on investigating the impact of music and song on science engagement and learning over the long-term in elementary school science classrooms.
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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Panta, J, Rider, AN, Wang, J, Yang, RC, Stone, RH, Taylor, AC, Cheevers, S, Farnsworth, AL & Zhang, YX 2024, 'Influence of amino-functionalized carbon nanotubes and acrylic triblock copolymer on lap shear and butt joint strength of high viscosity epoxy at room and elevated temperatures', International Journal of Adhesion and Adhesives, vol. 134, pp. 103770-103770.
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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, 'RETRACTED ARTICLE: 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, S, Deng, S-H, Xu, L, Ngo, HH, Jin, P, Guo, W, Chen, Z & Wu, D 2024, 'Stacked dual-interface bi-hydrophilic structuration boosting solar vapor-to-water conversion', Chemical Engineering Journal, vol. 496, pp. 154040-154040.
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Peng, Y, Chen, S-L, Zhang, W, Ruan, X, Liu, H & Liu, Y 2024, 'Realization of Low-Profile and Reconfigurable Multilinear Polarization States for Cavity-Backed Magneto-Electric-Dipole Antenna', IEEE Antennas and Wireless Propagation Letters, vol. 23, no. 10, pp. 2840-2844.
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A low profile multiple linear-polarization ( multi-LP) reconfigurable cavity-backed magneto-electric (ME) dipole antenna is developed in this letter. The magnetic-dipole (M-dipole) is realized by a TM$_{020}$-mode slotted substrate-integrated cavity, and the electric-dipole (E-dipole) is the printed microstrip dipole coupled above the slotted cavity. By introducing a rotationally symmetrical configuration of ME dipoles with switchable feeding networks, we have realized the reconfigurable multi-LP states for the cavity-based ME-dipole antenna. As a benefit of the dipole-loaded slotted cavity configuration, the antenna profile is only $0.09\lambda _{0}$, which is much lower than conventional reconfigurable ME dipole antennas. The developed antenna is capable of switching eight LP states at a $22.5^{\circ }$ interval in real time by electronically controlling PIN diodes loaded on the slotted cavity. Measured results agree well with the simulated ones. At the center frequency of 5.04 GHz, a measured maximum gain of 4.46 dB is obtained for all polarization states. It is the first time to achieve simultaneous multi-LPs and low profile for ME-dipole antenna.
Peng, Y, Liu, Y, Chen, S-L, Chen, L & Liu, H 2024, 'Grating Lobe Suppression in Ultrawideband Circularly Polarized Planar Array Based on ${\text {2}}\times {\text {2}}$ Irregular Sequential Rotated Subarray (ISRS)', IEEE Antennas and Wireless Propagation Letters, vol. 23, no. 6, pp. 1764-1768.
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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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Phuong, J, Manon, S, Moles, R, Mason, D, Vleeskens, C, Rezae, F, White, C, Center, J & Carter, S 2024, 'The evaluation of an osteoporosis medication management service in community pharmacy, a cohort study', Exploratory Research in Clinical and Social Pharmacy, vol. 15, pp. 100488-100488.
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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, Luo, W, Zhang, Z, Wen, S & Mu, C 2024, 'Hybrid-Dependent Event-Triggered Schemes for T-S Fuzzy Memristive NNs With Non-Differentiable Delay', IEEE Transactions on Fuzzy Systems, pp. 1-10.
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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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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, C, Feng, Y, Jia, S, Li, Y, Nghiem, LD, Li, G & Luo, W 2024, 'Operating conditions and microbial dynamics transition to sustain integrated anaerobic and aerobic treatment of organic solid wastes for energy and nutrient recovery', Chemical Engineering Journal, vol. 496, pp. 153682-153682.
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Qi, C, Wu, M, Li, K, Hu, T, Armaghani, DJ, Chen, Q & Yilmaz, E 2024, 'Identifying mining-induced chromium contamination in soil through visible-near infrared spectroscopy and machine learning', Green and Smart Mining Engineering, vol. 1, no. 2, pp. 132-139.
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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, 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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Qian, W, Guo, D, Li, K, Zhang, X, Tian, X, Yang, X & Wang, M 2024, 'Dual-Path TokenLearner for Remote Photoplethysmography-Based Physiological Measurement With Facial Videos', IEEE Transactions on Computational Social Systems, vol. 11, no. 3, pp. 4465-4477.
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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, Huang, S, Armaghani, DJ, Pradhan, B, Zhou, A & Zhou, J 2024, 'An Optimized System of Random Forest Model by Global Harmony Search with Generalized Opposition-Based Learning for Forecasting TBM Advance Rate', Computer Modeling in Engineering & Sciences, vol. 138, no. 3, pp. 2873-2897.
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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, F, Su, Y, Lu, D, Li, N, Zeng, X & Li, W 2024, 'Anti-cracking and shrinkage performance of sustainable concrete incorporating high-volume natural pozzolans: A case design for high-speed railway concrete slab tracks', Case Studies in Construction Materials, vol. 20, pp. e03207-e03207.
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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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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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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, M, Rawat, S, Yang, RC, Mahil, A & Zhang, YX 2024, 'A comprehensive review on fresh and rheological properties of 3D printable cementitious composites', Journal of Building Engineering, vol. 91, pp. 109719-109719.
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Rahman, MA, Islam, MR, Hossain, MA, Rana, MS, Hossain, MJ & Gray, EM 2024, 'Resiliency of forecasting methods in different application areas of smart grids: A review and future prospects', Engineering Applications of Artificial Intelligence, vol. 135, pp. 108785-108785.
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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.
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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Rao, Q, Yu, X, Li, G & Zhu, L 2024, 'CMGNet: Collaborative multi-modal graph network for video captioning', Computer Vision and Image Understanding, vol. 238, pp. 103864-103864.
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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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Rawat, S, Lee, CK & Zhang, YX 2024, 'Green engineered cementitious composites with enhanced tensile and flexural properties at elevated temperatures', Cleaner Materials, vol. 12, pp. 100240-100240.
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Rawat, S, Lee, CK & Zhang, YX 2024, 'In‐situ compressive and tensile performances of high strength engineered cementitious composite at elevated temperatures', Structural Concrete, vol. 25, no. 4, pp. 3010-3019.
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AbstractThe study provides novel insights on the compressive and tensile strength of high strength engineered cementitious composite (HSECC) at elevated temperatures under in‐situ testing condition. An optimized mix design was employed and cylinder and dogbone specimens were tested to study the compressive and tensile strength of HSECC at elevated temperature. The tested results under in‐situ test condition were compared with the residual state by exposing the specimens to temperature up to 600°C. It was found that specimens under in‐situ condition showed a drastic decrease in compressive strength (26.8%–34.5%) at 200°C and the performance was much inferior to that observed for residual state which showed only 12.2% decrease. This trend was consistent for both tensile and compression test results. After this, the in‐situ specimens underwent slight increase in the compressive strength with only around 25% decrease at 400°C and 14%–16% decrease at 600°C. However, the residual state specimens underwent continuous decrease in strength. Therefore, this study confirmed that for high strength cementitious composites exposed to elevated temperatures, the residual test results should not be considered as the lower limit and in‐situ testing results are essential for accurately predicting the behavior of cementitious composites. Thus, this research underscores the importance of conducting both in‐situ and residual state tests in the design of HSECC structural members.
Rawat, S, Vongsvivut, J, Zhang, L & Zhang, YX 2024, 'Mechanical performance and microstructure evolution of MgO-doped high volume GGBFS-based engineered cementitious composites at room and elevated temperatures', Journal of Building Engineering, vol. 98, pp. 111437-111437.
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Rawat, S, Zhang, YX & Lee, CK 2024, 'Effect of specimen size and shape on the compressive performance of high strength engineered cementitious composites at elevated temperatures', Innovative Infrastructure Solutions, vol. 9, no. 8.
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AbstractThis study provides detailed insights into the effect of specimen size on the residual compressive strength of hybrid polyethylene-steel fibre reinforced high strength engineered cementitious composite after exposure to elevated temperatures. A mix design with high residual performance was selected and a total of 120 specimens with different cross-section shape (square and circular), aspect ratio (1 and 2) and sizes (cylinders of 40 mm, 75 mm, 100 mm, 150 mm diameter with height to diameter ratio of 2:1, cubes of 50 mm, 75 mm, 100 mm side and prism of size 75 × 75 × 150 mm) were cast. These specimens were subjected to temperatures ranging from 200 to 800 °C and the residual compressive strength and change in microstructure was then analysed after air cooling. Experimental results indicated that cubic specimens experienced less strength loss compared to prism specimens with the same cross-sectional area and the damage was found to decrease with increase in the volume to surface area ratio of the specimens. Furthermore, no spalling occurred in any of the specimens despite the change in specimen size or cross-section. Unlike previous studies that did not present any clear influence of specimen size, the present work established that the residual strength is dependent on aspect ratio and volume to surface area ratio of the specimen. As a result, these findings are valuable for selecting appropriate specimen size in elevated temperature studies and for the development of suitable guidelines to facilitate meaningful comparisons with the existing data.
Rawat, S, Zhang, YX, Fanna, DJ & Lee, CK 2024, 'Development of sustainable engineered cementitious composite with enhanced compressive performance at elevated temperatures using high volume GGBFS', Journal of Cleaner Production, vol. 451, pp. 142011-142011.
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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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Razzak, I, Bouadjenek, R, Cheema, A, Hameed, IA, Xu, G & Beheshti, A 2024, 'Guest Editorial: Special Issue on Generating Human Readable Explanations in NLP', IEEE Transactions on Computational Social Systems, vol. 11, no. 4, pp. 4552-4555.
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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, Q, Liu, Y, Guo, P & Chen, S-L 2024, 'A Wideband Switchable L-Probe Patch Antenna With Reconfigurable Twelve Linear Polarizations and Dual Circular Polarizations', IEEE Antennas and Wireless Propagation Letters, vol. 23, no. 11, pp. 3506-3510.
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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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Rezaei, M, Ahmadi, SR, Nguyen, H & Armaghani, DJ 2024, 'Improved determination of the S-wave velocity of rocks in dry and saturated conditions: Application of machine-learning algorithms', Transportation Geotechnics, vol. 49, pp. 101371-101371.
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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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Rosenberg, E, Andersen, TI, Samajdar, R, Petukhov, A, Hoke, JC, Abanin, D, Bengtsson, A, Drozdov, IK, Erickson, C, Klimov, PV, Mi, X, Morvan, A, Neeley, M, Neill, C, Acharya, R, Allen, R, Anderson, K, Ansmann, M, Arute, F, Arya, K, Asfaw, A, Atalaya, J, Bardin, JC, Bilmes, A, Bortoli, G, Bourassa, A, Bovaird, J, Brill, L, Broughton, M, Buckley, BB, Buell, DA, Burger, T, Burkett, B, Bushnell, N, Campero, J, Chang, H-S, Chen, Z, Chiaro, B, Chik, D, Cogan, J, Collins, R, Conner, P, Courtney, W, Crook, AL, Curtin, B, Debroy, DM, Barba, ADT, Demura, S, Di Paolo, A, Dunsworth, A, Earle, C, Faoro, L, Farhi, E, Fatemi, R, Ferreira, VS, Burgos, LF, Forati, E, Fowler, AG, Foxen, B, Garcia, G, Genois, É, Giang, W, Gidney, C, Gilboa, D, Giustina, M, Gosula, R, Dau, AG, Gross, JA, Habegger, S, Hamilton, MC, Hansen, M, Harrigan, MP, Harrington, SD, Heu, P, Hill, G, Hoffmann, MR, Hong, S, Huang, T, Huff, A, Huggins, WJ, Ioffe, LB, Isakov, SV, Iveland, J, Jeffrey, E, Jiang, Z, Jones, C, Juhas, P, Kafri, D, Khattar, T, Khezri, M, Kieferová, M, Kim, S, Kitaev, A, Klots, AR, Korotkov, AN, Kostritsa, F, Kreikebaum, JM, Landhuis, D, Laptev, P, Lau, K-M, Laws, L, Lee, J, Lee, KW, Lensky, YD, Lester, BJ, Lill, AT, Liu, W, Locharla, A, Mandrà, S, Martin, O, Martin, S, McClean, JR, McEwen, M, Meeks, S, Miao, KC, Mieszala, A, Montazeri, S, Movassagh, R, Mruczkiewicz, W, Nersisyan, A, Newman, M, Ng, JH, Nguyen, A, Nguyen, M, Niu, MY, O’Brien, TE, Omonije, S, Opremcak, A, Potter, R, Pryadko, LP, Quintana, C, Rhodes, DM, Rocque, C, Rubin, NC, Saei, N, Sank, D, Sankaragomathi, K, Satzinger, KJ, Schurkus, HF, Schuster, C, Shearn, MJ, Shorter, A, Shutty, N, Shvarts, V, Sivak, V, Skruzny, J, Smith, WC, Somma, RD, Sterling, G, Strain, D, Szalay, M, Thor, D, Torres, A, Vidal, G, Villalonga, B, Heidweiller, CV, White, T, Woo, BWK, Xing, C, Yao, ZJ, Yeh, P, Yoo, J, Young, G, Zalcman, A, Zhang, Y, Zhu, N, Zobrist, N, Neven, H, Babbush, R, Bacon, D, Boixo, S, Hilton, J, Lucero, E, Megrant, A, Kelly, J, Chen, Y, Smelyanskiy, V, Khemani, V, Gopalakrishnan, S, Prosen, T & Roushan, P 2024, 'Dynamics of magnetization at infinite temperature in a Heisenberg spin chain', Science, vol. 384, no. 6691, pp. 48-53.
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Understanding universal aspects of quantum dynamics is an unresolved problem in statistical mechanics. In particular, the spin dynamics of the one-dimensional Heisenberg model were conjectured as to belong to the Kardar-Parisi-Zhang (KPZ) universality class based on the scaling of the infinite-temperature spin-spin correlation function. In a chain of 46 superconducting qubits, we studied the probability distribution of the magnetization transferred across the chain’s center, P M . The first two moments of P M show superdiffusive behavior, a hallmark of KPZ universality. However, the third and fourth moments ruled out the KPZ conjecture and allow for evaluating other theories. Our results highlight the importance of studying higher moments in determining dynamic universality classes and provide insights into universal behavior in quantum systems.
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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Ruan, W, Xu, S, An, Y, Cui, Y, Liu, Y, Wang, Y, Ismail, M, Liu, Y & Zheng, M 2024, 'Brain‐Targeted Cas12a Ribonucleoprotein Nanocapsules Enable Synergetic Gene Co‐Editing Leading to Potent Inhibition of Orthotopic Glioblastoma', Advanced Science, vol. 11, no. 33.
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AbstractGene‐editing technology shows great potential in glioblastoma (GBM) therapy. Due to the complexity of GBM pathogenesis, a single gene‐editing‐based therapy is unlikely to be successful; therefore, a multi‐gene knockout strategy is preferred for effective GBM inhibition. Here, a non‐invasive, biodegradable brain‐targeted CRISPR/Cas12a nanocapsule is used that simultaneously targeted dual oncogenes, EGFR and PLK1, for effective GBM therapy. This cargo nanoencapsulation technology enables the CRISPR/Cas12a system to achieve extended blood half‐life, efficient blood‐brain barrier (BBB) penetration, active tumor targeting, and selective release. In U87MG cells, the combinatorial gene editing system resulted in 61% and 33% knockout of EGFR and PLK1, respectively. Following systemic administration, the CRISPR/Cas12a system demonstrated promising brain tumor accumulation that led to extensive EGFR and PLK1 gene editing in both U87MG and patient‐derived GSC xenograft mouse models with negligible off‐target gene editing detected through NGS. Additionally, CRISPR/Cas12a nanocapsules that concurrently targeted the EGFR and PLK1 oncogenes showed superior tumor growth suppression and significantly improved the median survival time relative to nanocapsules containing single oncogene knockouts, signifying the potency of the multi‐oncogene targeting strategy. The findings indicate that utilization of the CRISPR/Cas12a combinatorial gene editing technique presents a practical option for gene therapy in GBM.
Rubboli, R & Tomamichel, M 2024, 'New Additivity Properties of the Relative Entropy of Entanglement and Its Generalizations', Communications in Mathematical Physics, vol. 405, no. 7.
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Rung, AC, Sun, J & George, R 2024, 'Dental students' ability to judge the quality of composite restorations' exemplars depicted in photographs and their impact on preclinical skills', European Journal of Dental Education, vol. 28, no. 2, pp. 471-480.
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AbstractIntroductionAssessing exemplars as a formative activity is thought to promote students' learning. This study aimed to investigate dental students' ability to judge the quality of composite restorations' exemplars depicted in photographs and their impact on students' preclinical skills.Materials and MethodsIn a non‐randomised controlled crossover trial with two intervention arms, 92 undergraduates in their first preclinical course self‐enrolled in into the intervention group (A1‐INT) or control group (B1‐CT). The intervention group assessed photographic images of composite restorations before restoring an ivorine premolar with composite while the control group restored the same tooth without assessing the photographic exemplars. Intervention and control groups were swapped 3 days later in a second iteration (B2‐INT, A2‐CT). Data were analysed in SPSS® version 27 using nonparametric tests.ResultsStudents who did not complete all activities in the study were excluded. Therefore, 57 out of the 92 student participants were included in the study analysis. No significant differences were observed between intervention and control groups' ability to assess quality of photographic exemplars or restoring a tooth in both iterations.ConclusionStudents were able to identify the quality of composite restorations in photographic exemplars. It appears that assessing photographic exemplars did not have an immediate impact on students' ability to restore a tooth with composite.
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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Sabahi, N, Farajzadeh, E, Roohani, I, Wang, CH & Li, X 2024, 'Material extrusion 3D printing of polyether-ether-ketone scaffolds based on triply periodic minimal surface designs: A numerical and experimental investigation', Applied Materials Today, vol. 39, pp. 102262-102262.
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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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Saghir, A, Ahmad, M, Nabeel, MI, Hameed, H & Khan, M 2024, 'What’s the Hesitation? [Women in Microwaves]', IEEE Microwave Magazine, vol. 25, no. 11, pp. 89-91.
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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, S, Barman, A, Saha, A, Hembram, TK, Pradhan, B & Alamri, A 2024, 'Deep learning algorithms based landslide vulnerability modeling in highly landslide prone areas of Tamil Nadu, India', Geosciences Journal, vol. 28, no. 6, pp. 1013-1038.
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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. 1007-1022.
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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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Salahshoori, I, Mahdavi, S, Moradi, Z, Otadi, M, Zare Kazemabadi, F, Nobre, MAL, Ali Khonakdar, H, Baghban, A, Wang, Q & Mohammadi, AH 2024, 'Advancements in molecular simulation for understanding pharmaceutical pollutant Adsorption: A State-of-the-Art review', Journal of Molecular Liquids, vol. 410, pp. 125513-125513.
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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 functions. So many modern optimization techniques have been proposed exponentially over the last few decades to overcome these challenges. This paper discusses a brief review of the different benchmark test functions (BTFs) related to existing MH optimization algorithms (OA). It discusses the classification of MH algorithms reported in the literature regarding swarm-based, human-based, physics-based, and evolutionary-based methods. Based on the last half-century literature, MH-OAs are tabulated in terms of the proposed year, author, and inspiration agent. Furthermore, this paper presents the MATLAB and python code web-link of MH-OA. After reading this review article, readers will be able to use MH-OA to solve challenges in their field.
Salib, A, Jayatilleke, N, Seneviratne, JA, Mayoh, C, De Preter, K, Speleman, F, Cheung, BB, Carter, DR & Marshall, GM 2024, 'MYCN and SNRPD3 cooperate to maintain a balance of alternative splicing events that drives neuroblastoma progression', Oncogene, vol. 43, no. 5, pp. 363-377.
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AbstractMany of the pro-tumorigenic functions of the oncogene MYCN are attributed to its regulation of global gene expression programs. Alternative splicing is another important regulator of gene expression and has been implicated in neuroblastoma development, however, the molecular mechanisms remain unknown. We found that MYCN up-regulated the expression of the core spliceosomal protein, SNRPD3, in models of neuroblastoma initiation and progression. High mRNA expression of SNRPD3 in human neuroblastoma tissues was a strong, independent prognostic factor for poor patient outcome. Repression of SNRPD3 expression correlated with loss of colony formation in vitro and reduced tumorigenicity in vivo. The effect of SNRPD3 on cell viability was in part dependent on MYCN as an oncogenic co-factor. RNA-sequencing revealed a global increase in the number of genes being differentially spliced when MYCN was overexpressed. Surprisingly, depletion of SNRPD3 in the presence of overexpressed MYCN further increased differential splicing, particularly of cell cycle regulators, such as BIRC5 and CDK10. MYCN directly bound SNRPD3, and the protein arginine methyltransferase, PRMT5, consequently increasing SNRPD3 methylation. Indeed, the PRMT5 inhibitor, JNJ-64619178, reduced cell viability and SNRPD3 methylation in neuroblastoma cells with high SNRPD3 and MYCN expression. Our findings demonstrate a functional relationship between MYCN and SNRPD3, which maintains the fidelity of MYCN-driven alternative splicing in the narrow range required for neuroblastoma cell growth. SNRPD3 methylation and its protein-protein interface with MYCN represent novel therapeutic targets.
Salvi, M, Acharya, MR, Seoni, S, Faust, O, Tan, R, Barua, PD, García, S, Molinari, F & Acharya, UR 2024, 'Artificial intelligence for atrial fibrillation detection, prediction, and treatment: A systematic review of the last decade (2013–2023)', WIREs Data Mining and Knowledge Discovery, vol. 14, no. 3.
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AbstractAtrial fibrillation (AF) affects more than 30 million individuals worldwide, making it the most prevalent cardiac arrhythmia on a global scale. This systematic review summarizes recent advancements in applying artificial intelligence (AI) techniques for AF detection, prediction, and guiding treatment selection and risk stratification. In adherence with the PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta‐Analyses), a total of 171 pertinent studies conducted between 2013 and 2023 were examined. Studies applying machine learning (ML) and deep learning (DL) to electrocardiogram (ECG), photoplethysmography (PPG), wearable data, and other sources were evaluated. For AF detection, most works employed DL (48 studies) and ML (28 studies) on ECG data. DL methods directly analyzed ECG waveforms and outperformed approaches relying on hand‐crafted features. For prediction and risk stratification, 22 studies used ML while 7 leveraged DL on ECG. An emerging trend showed the growing potential of PPG for AF screening. Overall, AI demonstrated promising capabilities across various AF‐related tasks. However, real‐world implementation faces challenges including a lack of interpretability, the need for multimodal data integration, prospective performance validation, and regulatory compliance. Future research directions involve quantifying model uncertainty, enhancing transparency, and conducting population‐based clinical trials to facilitate translation into practice.This article is categorized under:Application Areas > Health CareApplication Areas > Science and TechnologyTechnologies > Artificial Intelligence
Salvi, M, Loh, HW, Seoni, S, Barua, PD, García, S, Molinari, F & Acharya, UR 2024, 'Multi-modality approaches for medical support systems: A systematic review of the last decade', Information Fusion, vol. 103, pp. 102134-102134.
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Samadi, A, Kong, L, Guo, W, Sillanpää, M, Boztepe, I, Song, C, Zeng, Q & Zhao, S 2024, 'Standardized methodology for performance evaluation in using polyaniline-based adsorbents to remove aqueous contaminants', Journal of Environmental Chemical Engineering, vol. 12, no. 3, pp. 112650-112650.
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Samal, PB, Chen, SJ & Fumeaux, C 2024, '3-D-Corrugated Ground Structure: A Microstrip Antenna Miniaturization Technique', IEEE Transactions on Antennas and Propagation, vol. 72, no. 5, pp. 4010-4022.
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Samal, PB, Chen, SJ & Fumeaux, C 2024, 'Flexible Hybrid-Substrate Dual-Band Dual-Mode Wearable Antenna', IEEE Transactions on Antennas and Propagation, vol. 72, no. 2, pp. 1286-1296.
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Sanni, MT, Pota, HR, Dong, D & Mo, H 2024, 'Pilot point selection for secondary voltage control in active distribution networks with applications to an Australian feeder', Ain Shams Engineering Journal, vol. 15, no. 10, pp. 102972-102972.
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Sardar, A, Umer, S, Rout, RK, Sahoo, KS & Gandomi, AH 2024, 'Enhanced Biometric Template Protection Schemes for Securing Face Recognition in IoT Environment', IEEE Internet of Things Journal, vol. 11, no. 13, pp. 23196-23206.
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Sarfarazi, V, Fu, J, Haeri, H, Abharian, S, Rasekh, H, Behzadinasab, M & Fatehi Marji, M 2024, 'Mechanical characteristics and crack propagation mechanism in rectangular and trapezoidal specimens of excavated pillars with various cavities: experimental and numerical investigations', Computational Particle Mechanics, vol. 11, no. 5, pp. 2069-2087.
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Sarifudin, A, Yaningsih, I, Kristiawan, B, Thu, K, Miyazaki, T, Susan Silitonga, A, Chyuan Ong, H & Syirat Zainal, B 2024, 'A comprehensive review of granular structures as photothermal absorber materials', Thermal Science and Engineering Progress, vol. 53, pp. 102689-102689.
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Sarifudin, A, Yaningsih, I, Kristiawan, B, Wibawa, A, Miyazaki, T, Thu, K, Silitonga, A & Ong, H 2024, 'Investigation of granular natural stone materials as photothermal absorbers for sustainable and environmentally friendly energy harvesting', Journal of Applied Engineering Science, vol. 22, no. 2, pp. 147-162.
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The development of cost-effective and environmentally friendly solar thermal technologies that deliver high performance poses several challenges, where the collector and absorber components play a pivotal role. This research addresses these issues by investigating enhanced temperature generation using a 30 cm × 30 cm Fresnel lens collector under solar illumination from a xenon lamp. Natural stone materials (andesite, coal, and pumice), characterized by granular structures with an average diameter of 1.68-2.00 mm, were selected because of their abundance and eco-friendliness. This research is focused on evaluating the effect of Fresnel lens on temperature generation performance. Two types of temperature generation tests were carried out: wet tests (where the natural stone materials were immersed in distilled water) and dry tests (where the natural stone materials were used in dry conditions). The morphologies of the natural stone materials were examined using an optical microscope and scanning electron microscope. Furthermore, the optical properties of the natural stone materials were analyzed using an ultraviolet-visible (UV-VIS) spectrophotometer. The findings revealed that there were significant improvements in the photothermal absorber performance with the use of a Fresnel lens in dry tests, where the highest temperature was achieved for coal (103.25 °C), followed by andesite (89.00 °C) and pumice (73.00 °C). The impurities varied between the materials, where the impurities were most dominant for pumice while coal was more uniform. Further examination using scanning electron microscope showed that all materials had light-trapping structures in the form of rough surfaces, pores, and crack gaps. Andesite was dominated by rough surfaces, while coal and pumice were dominated by crack gaps and pores, respectively. However, based on the UV-VIS spectrophotometric results, there were no correlations between the optical properties (absorbance, reflect...
Sarker, IH, Janicke, H, Mohsin, A, Gill, A & Maglaras, L 2024, 'Explainable AI for cybersecurity automation, intelligence and trustworthiness in digital twin: Methods, taxonomy, challenges and prospects', ICT Express, vol. 10, no. 4, pp. 935-958.
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Satapathy, AS, Mohanty, S, Mohanty, A, Rajamony, RK, M Soudagar, ME, Khan, TMY, Kalam, MA, Ali, MM & Bashir, MN 2024, 'Emerging technologies, opportunities and challenges for microgrid stability and control', Energy Reports, vol. 11, pp. 3562-3580.
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Sateesh, KA, Yaliwal, VS, Murugande, BK, Banapurmath, NR, Elumalai, PV, Balasubramanian, D, Lawrence, KR, Fouad, Y, Soudagar, MEM, Le, HC, Le, TT, Kalam, MA, Kit, CC & Balram, Y 2024, 'Effect of nano-particles on the combustion and emission characteristics of a dual fuel engine operated on biodiesel-producer gas combination', Case Studies in Thermal Engineering, vol. 64, pp. 105560-105560.
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Schlittler, M, Loban, R & Apperley, T 2024, 'Valores eurocêntricos em jogo: Modificando o colonial através de uma perspectiva indígena', Zanzalá-Revista Brasileira de Estudos sobre Gêneros Cinematográficos e Audiovisuais, vol. 12, no. 1.
Schumacher, AE, Kyu, HH, Aali, A, Abbafati, C, Abbas, J, Abbasgholizadeh, R, Abbasi, MA, Abbasian, M, Abd ElHafeez, S, Abdelmasseh, M, Abd-Elsalam, S, Abdelwahab, A, Abdollahi, M, Abdoun, M, Abdullahi, A, Abdurehman, AM, Abebe, M, Abedi, A, Abedi, A, Abegaz, TM, Abeldaño Zuñiga, RA, Abhilash, ES, Abiodun, OO, Aboagye, RG, Abolhassani, H, Abouzid, M, Abreu, LG, Abrha, WA, Abrigo, MRM, Abtahi, D, Abu Rumeileh, S, Abu-Rmeileh, NME, Aburuz, S, Abu-Zaid, A, Acuna, JM, Adair, T, Addo, IY, Adebayo, OM, Adegboye, OA, Adekanmbi, V, Aden, B, Adepoju, AV, Adetunji, CO, Adeyeoluwa, TE, Adeyomoye, OI, Adha, R, Adibi, A, Adikusuma, W, Adnani, QES, Adra, S, Afework, A, Afolabi, AA, Afraz, A, Afyouni, S, Afzal, S, Agasthi, P, Aghamiri, S, Agodi, A, Agyemang-Duah, W, Ahinkorah, BO, Ahmad, A, Ahmad, D, Ahmad, F, Ahmad, MM, Ahmad, T, Ahmadi, K, Ahmadzade, AM, Ahmadzade, M, Ahmed, A, Ahmed, H, Ahmed, LA, Ahmed, MB, Ahmed, SA, Ajami, M, Aji, B, Ajumobi, O, Akalu, GT, Akara, EM, Akinosoglou, K, Akkala, S, Akyirem, S, Al Hamad, H, Al Hasan, SM, Al Homsi, A, Al Qadire, M, Ala, M, Aladelusi, TO, AL-Ahdal, TMA, Alalalmeh, SO, Al-Aly, Z, Alam, K, Alam, M, Alam, Z, Al-amer, RM, Alanezi, FM, Alanzi, TM, Albashtawy, M, AlBataineh, MT, Aldridge, RW, Alemi, S, Al-Eyadhy, A, Al-Gheethi, AAS, Alhabib, KF, Alhalaiqa, FAN, Al-Hanawi, MK, Ali, A, Ali, A, Ali, BA, Ali, H, Ali, MU, Ali, R, Ali, SSS, Ali, Z, Alian Samakkhah, S, Alicandro, G, Alif, SM, Aligol, M, Alimi, R, Aliyi, AA, Al-Jumaily, A, Aljunid, SM, Almahmeed, W, Al-Marwani, S, Al-Maweri, SAA, Almazan, JU, Al-Mekhlafi, HM, Almidani, O, Alomari, MA, Alonso, N, Alqahtani, JS, Alqutaibi, AY, Al-Sabah, SK, Altaf, A, Al-Tawfiq, JA, Altirkawi, KA, Alvi, FJ, Alwafi, H, Al-Worafi, YM, Aly, H, Alzoubi, KH, Amare, AT, Ameyaw, EK, Amhare, AF, Amin, TT, Amindarolzarbi, A, Aminian Dehkordi, J, Amiri, S, Amu, H, Amugsi, DA, Amzat, J, Ancuceanu, R, Anderlini, D, Andrade, PP, Andrei, CL, Andrei, T, Angappan, D, Anil, A, Anjum, A, Antony, CM, Antriyandarti, E, Anuoluwa, IA, Anwar, SL, Anyasodor, AE, Appiah, SCY, Aqeel, M, Arabloo, J, Arabzadeh Bahri, R, Arab-Zozani, M, Arafat, M, Araújo, AM, Aravkin, AY, Aremu, A, Ariffin, H, Aripov, T, Armocida, B, Arooj, M, Artamonov, AA, Artanti, KD, Arulappan, J, Aruleba, IT, Aruleba, RT, Arumugam, A, Asaad, M, Asgary, S, Ashemo, MY, Ashraf, M, Asika, MO, Athari, SS, Atout, MMW & et al. 2024, 'Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021', The Lancet, vol. 403, no. 10440, pp. 1989-2056.
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Scussel, O, Muggleton, JM, Karimi, M, Williams, P, Kalkowski, MK, Joseph, PF & White, P 2024, 'On the Significance of Parameter Uncertainties for Prediction of Leak Noise Wave Speed in Buried Pipes', Journal of Physics: Conference Series, vol. 2909, no. 1, pp. 012009-012009.
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Abstract The modern world is facing the challenging issue of water wastage due to leaks, which is causing severe economic, environmental and social impacts. Consequently, the inspection and maintenance of buried water pipes is crucial and there is still a lack of investigations towards the uncertain parameters affecting the wave speed associated with the predominantly fluid-borne wave s=1, the main carrier of leak noise. This study investigates the effects of uncertainties present in the pipe and soil parameters which are affecting the speed of propagation of the leak noise wave. To achieve this, a sensitivity analysis is performed using Monte Carlo simulations and Sobol’ indices. Uncertainties are commonly associated with the material and geometrical properties of the pipe along with the surrounding soil characteristics. However, the significance of these parameters varies depending on the type of soil in which the water pipe is buried. In clay soil, the soil-related parameter plays a crucial role compared to sandy soil and this is verified through some experimental work carried out in two water pipe systems with very different properties, one in the UK and the other one in Brazil. This research is of fundamental importance for determining the most critical parameters affecting the leak noise wave, allowing to evaluate and integrate uncertainty information into decision-making of current technologies, such as loggers and leak noise correlators, aiming enhanced detection and location of water leakage in buried plastic water pipes.
Sedehi, O, Kosikova, AM, Papadimitriou, C & Katafygiotis, LS 2024, 'On the integration of Physics-Based Machine Learning with hierarchical Bayesian modeling techniques', Mechanical Systems and Signal Processing, vol. 208, pp. 111021-111021.
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Seiler, KM, Kong, FH & Fitch, R 2024, 'Multi-Horizon Multi-Agent Planning Using Decentralised Monte Carlo Tree Search', IEEE Robotics and Automation Letters, vol. 9, no. 9, pp. 7715-7722.
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Selvam, L, Aruna, M, Hossain, I, Venkatesh, R, Karthigairajan, M, Prabagaran, S, Mohanavel, V, Seikh, AH & Kalam, MA 2024, 'Impact of hybrid nanofluid on thermal behavior of flat-plate solar collector: performance study', Journal of Thermal Analysis and Calorimetry, vol. 149, no. 10, pp. 5047-5057.
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Senanayake, S, Pradhan, B, Wedathanthirige, H, Alamri, A & Park, H-J 2024, 'Monitoring soil erosion in support of achieving SDGs: A special focus on rainfall variation and farming systems vulnerability', CATENA, vol. 234, pp. 107537-107537.
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Senapati, A, Tripathy, HK, Sharma, V & Gandomi, AH 2024, 'Artificial intelligence for diabetic retinopathy detection: A systematic review', Informatics in Medicine Unlocked, vol. 45, pp. 101445-101445.
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Seneviratne, A & Veitch, D 2024, 'Message from the General Chairs', ACM SIGCOMM 2024 - Proceedings of the 2024 ACM SIGCOMM 2024 Conference, pp. I-II.
Seneviratne, JA, Ravindrarajah, D, Carter, DR, Zhai, V, Lalwani, A, Krishan, S, Balachandran, A, Ng, E, Pandher, R, Wong, M, Nero, TL, Wang, S, Norris, MD, Haber, M, Liu, T, Parker, MW, Cheung, BB & Marshall, GM 2024, 'Combined inhibition of histone methyltransferases EZH2 and DOT1L is an effective therapy for neuroblastoma', Cancer Medicine, vol. 13, no. 21.
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AbstractBackgroundThe child cancer, neuroblastoma (NB), is characterised by a low incidence of mutations and strong oncogenic embryonal driver signals. Many new targeted epigenetic modifier drugs have failed in human trials as monotherapy.MethodsWe performed a high‐throughput, combination chromatin‐modifier drug screen against NB cells. We screened 13 drug candidates in 78 unique combinations.ResultsWe found that the combination of two histone methyltransferase (HMT) inhibitors: GSK343, targeting EZH2, and SGC0946, targeting DOT1L, demonstrated the strongest synergy across 8 NB cell lines, with low normal fibroblast toxicity. High mRNA expression of both EZH2 and DOT1L in NB tumour samples correlated with the poorest patient survival. Combination HMT inhibitor treatment caused activation of ATF4‐mediated endoplasmic reticulum (ER) stress responses. In addition, glutathione and several amino acids were depleted by HMT inhibitor combination on mass spectrometry analysis. The combination of SGC0946 and GSK343 reduced tumour growth in comparison to single agents.ConclusionOur results support further investigation of HMT inhibitor combinations as a therapeutic approach in NB.
Seoni, S, Molinari, F, Rajendra Acharya, U, Lih, OS, Barua, PD, García, S & Salvi, M 2024, 'Application of spatial uncertainty predictor in CNN-BiLSTM model using coronary artery disease ECG signals', Information Sciences, vol. 665, pp. 120383-120383.
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Shadmani, A, Nikoo, MR & Gandomi, AH 2024, 'Adaptive systematic optimization of a multi-axis ocean wave energy converter', Renewable and Sustainable Energy Reviews, vol. 189, pp. 113920-113920.
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Shadmani, A, Nikoo, MR, Gandomi, AH & Chen, M 2024, 'An optimization approach for geometry design of multi-axis wave energy converter', Energy, vol. 301, pp. 131714-131714.
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Shadmani, A, Nikoo, MR, Gandomi, AH, Chen, M & Nazari, R 2024, 'Advancements in optimizing wave energy converter geometry utilizing metaheuristic algorithms', Renewable and Sustainable Energy Reviews, vol. 197, pp. 114398-114398.
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Shahani, NM, Zheng, X, Siarry, P, Armaghani, DJ & Liu, C 2024, 'Evaluating blast-induced backbreak in open pit mines using the LSSVM optimized by the GWO algorithm', Geomechanics and Engineering, vol. 39, no. 6, pp. 547-561.
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Backbreak, a recurring issue in blasting operations, causes mine wall instability, equipment failure, inappropriate disintegration, lower drilling efficiency, and increased cost of mining operations. This study aims to address these issues by developing a hybrid LSSVM-GWO model for predicting blast-induced backbreak in open pit mines. To evaluate the effectiveness of the proposed model, its predictive performance was compared with three convolutional models, such as the support vector machine, K-nearest neighbor, and the least square support vector machine. Results demonstrated that the LSSVM-GWO model outperformed the other three models, achieving coefficient of determination values of 0.998 and 0.997, mean absolute error values of 0.0068 and 0.1209, root mean squared error values of 0.0825 and 0.1936, and a20-index values of 0.99 and 1.01 for training and testing datasets, respectively. Furthermore, the SHAP machine learning technique was applied to evaluate the feature importance, revealing that the powder factor had the highest influence, while the burden exhibited the least impact on backbreak. Sensitivity analysis confirmed these findings, highlighting the robustness of the hybrid model. The study concludes that the LSSVM-GWO model significantly enhances the prediction and evaluation of backbreak in open pit mines, providing critical insights to improve blasting operations, reduce costs, and ensure mine safety.
Shahapurkar, K, Gebremaryam, G, Kanaginahal, G, Ramesh, S, Nik-Ghazali, N-N, Chenrayan, V, Soudagar, MEM, Fouad, Y & Kalam, MA 2024, 'An Experimental Study on the Hardness, Inter Laminar Shear Strength, and Water Absorption Behavior of Habeshian Banana Fiber Reinforced Composites', Journal of Natural Fibers, vol. 21, no. 1.
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Shahapurkar, K, Ramesh, S, Nik-Ghazali, N-N, Gebremaryam, G, Kanaginahal, G, Venkatesh, C, Soudagar, MEM, Fouad, Y & Kalam, MA 2024, 'Tensile, compressive, and fracture behavior of Habeshian chopped banana/epoxy core sandwich woven banana composite', Biomass Conversion and Biorefinery, vol. 14, no. 17, pp. 21553-21564.
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Shahapurkar, K, Zelalem, YM, Chenrayan, V, Soudagar, MEM, Fouad, Y, Kalam, MA & Kiran, MC 2024, 'Investigation on the mechanical and fracture properties of lightweight pumice epoxy composites', Polymer Engineering & Science, vol. 64, no. 3, pp. 1071-1082.
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AbstractPumice, which is prevalent in Ethiopia, is formed naturally during the quick cooling and solidifying of molten lava. Pumice is a naturally occurring mineral that, due to its high thermal resistance and lightweightness, can be an excellent candidate for reinforcing material for polymers. The present study investigates epoxy‐based composites reinforced with pumice particles by varying the pumice content (0, 10, 20, and 30 vol%). The densities of all composites reduce in comparison with neat epoxy as the volume proportion of pumice increases credited to the low density pumice particles. Tensile stress–strain curves depict neat epoxy with higher deformation than other pumice particulate‐filled composites in the linear elastic area followed by rapid brittle failure. Tensile modulus of all the composites increases in the range of 13%–67% in comparison with neat epoxy. The compressive characteristics of composites are greatly improved by the addition of pumice. Compressive moduli and specific compressive moduli of all composites increase with increasing volume fraction of pumice by 54%–58% and 65%–93%, respectively, in comparison with neat epoxy. The fracture toughness of P‐10, P‐20, and P‐30 composites improved by 18%, 54%, and 59%, respectively, as compared with neat epoxy mainly attributed to the foam‐like structure of pumice particles. SEM micrographs are used to analyze the morphology of compression‐tested specimens. Property mapping highlights the advantages of utilizing composites from present work over numerous syntactic foams.
Shahsavari, M, Hussain, OK, Saberi, M & Sharma, P 2024, 'Event Identification for Supply Chain Risk Management Through News Analysis by Using Large Language Models', The Review of Socionetwork Strategies, vol. 18, no. 2, pp. 255-278.
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AbstractEvent identification is important in many areas of the business world. In the supply chain risk management domain, the timely identification of risk events is vital to ensure the success of supply chain operations. One of the important sources of real-time information from across the world is news sources. However, the analysis of large amounts of daily news cannot be done manually by humans. On the other hand, extracting related news depends on the query or the keyword used in the search engine and the news content. Recent advancements in artificial intelligence have opened up opportunities to leverage intelligent techniques to automate this analysis. This paper introduces the LUEI framework, a lightweight framework that, with only the event’s name as input, can autonomously learn all the related phrases associated with that event. It then employs these phrases to search for relevant news and presents the search engine results with a label indicating their relevance. Hence, by conducting this analysis, the LUEI framework is able to identify the occurrence of the event in the real world. The framework’s novel contribution lies in its ability to identify those events (termed as the Contributing Events (CEs)) that contribute to the occurrence of a risk event, offering a proactive approach to risk management in supply chains. Pinpointing CEs from vast news data gives supply chain managers actionable insights to mitigate risks before they escalate.
Shams, KA, Reaz, MR, Rafi, MRU, Islam, S, Rahman, MS, Rahman, R, Reza, MT, Parvez, MZ, Chakraborty, S, Pradhan, B & Alamri, A 2024, 'MultiModal Ensemble Approach Leveraging Spatial, Skeletal, and Edge Features for Enhanced Bangla Sign Language Recognition', IEEE Access, vol. 12, pp. 83638-83657.
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Shan, F, He, X, Armaghani, DJ & Sheng, D 2024, 'Effects of data smoothing and recurrent neural network (RNN) algorithms for real-time forecasting of tunnel boring machine (TBM) performance', Journal of Rock Mechanics and Geotechnical Engineering, vol. 16, no. 5, pp. 1538-1551.
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Shan, F, He, X, Jahed Armaghani, D, Xu, H, Liu, X & Sheng, D 2024, 'Real-time forecasting of TBM cutterhead torque and thrust force using aware-context recurrent neural networks', Tunnelling and Underground Space Technology, vol. 152, pp. 105906-105906.
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Shan, X, Ding, L, Wang, D, Wen, S, Shi, J, Chen, C, Wang, Y, Zhu, H, Huang, Z, Wang, SSJ, Zhong, X, Liu, B, Reece, PJ, Ren, W, Hao, W, Lu, X, Lu, J, Su, QP, Chang, L, Sun, L, Jin, D, Jiang, L & Wang, F 2024, 'Author Correction: Sub-femtonewton force sensing in solution by super-resolved photonic force microscopy', Nature Photonics, vol. 18, no. 9, pp. 998-998.
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Shan, X, Ding, L, Wang, D, Wen, S, Shi, J, Chen, C, Wang, Y, Zhu, H, Huang, Z, Wang, SSJ, Zhong, X, Liu, B, Reece, PJ, Ren, W, Hao, W, Lu, X, Lu, J, Su, QP, Chang, L, Sun, L, Jin, D, Jiang, L & Wang, F 2024, 'Sub-femtonewton force sensing in solution by super-resolved photonic force microscopy', Nature Photonics, vol. 18, no. 9, pp. 913-921.
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Shang, C, Thai Hoang, D, Hao, M, Niyato, D & Yu, J 2024, 'Energy-Efficient Decentralized Federated Learning for UAV Swarm With Spiking Neural Networks and Leader Election Mechanism', IEEE Wireless Communications Letters, vol. 13, no. 10, pp. 2742-2746.
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Shao, R, Wu, C & Li, J 2024, 'Innovative development of geopolymer-based lunar high strength concrete for sustainable extra-terrestrial construction using large-scale regolith simulants', Construction and Building Materials, vol. 450, pp. 138707-138707.
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Sharen, H, Jani Anbarasi, L, Rukmani, P, Gandomi, AH, Neeraja, R & Narendra, M 2024, 'WISNet: A deep neural network based human activity recognition system', Expert Systems with Applications, vol. 258, pp. 124999-124999.
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Sharma, P, Gaur, P, Dwivedi, S, Kumari, K, Srivastava, JK, Dhakar, K, Gaur, VK, Varjani, S, Chang, J-S, Ngo, HH, Ng, HY, Dong, C-D & Sim, SJ 2024, 'Harnessing microbial potentials by advancing bioremediation of PAHs through molecular insights and genetics', International Biodeterioration & Biodegradation, vol. 194, pp. 105861-105861.
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Sharma, R, Saqib, M, Lin, CT & Blumenstein, M 2024, 'MASSNet: Multiscale Attention for Single-Stage Ship Instance Segmentation', Neurocomputing, vol. 594, pp. 127830-127830.
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Shen, D, Zhang, P, Wu, S-L, Long, Y, Wei, W & Ni, B-J 2024, 'Enhanced biomethane production from waste activated sludge anaerobic digestion by ceramsite and amended Fe2O3 ceramsite', Journal of Environmental Management, vol. 351, pp. 119973-119973.
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Shen, H, Wang, H, Ma, Y, Li, L, Duan, S & Wen, S 2024, 'Multi-LRA: Multi logical residual architecture for spiking neural networks', Information Sciences, vol. 660, pp. 120136-120136.
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Shen, J, Li, H, Wang, L, Chen, G & Wen, S 2024, 'Fixed-Time Synergetic Control Based on SEAIQR Model for COVID-19 Epidemic', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 71, no. 2, pp. 767-771.
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Shen, S, Xue, S, Zhang, H, Cai, W, Huang, C, Guo, J, Wang, A-J, Ren, N, Wei, W, Ni, B-J & Hou, Y-N 2024, 'Vulnerable Methanogenic Community in Microbial Electrolysis Cells Alters Electron Allocation in Response to Community Coalescence', ACS ES&T Engineering, vol. 4, no. 6, pp. 1378-1390.
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Shen, S, Ye, D, Zhu, T & Zhou, W 2024, 'Privacy preservation in deep reinforcement learning: A training perspective', Knowledge-Based Systems, vol. 304, pp. 112558-112558.
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Sheng, Z, Nie, L, Zhang, M, Chang, X & Yan, Y 2024, 'Stochastic Latent Talking Face Generation Toward Emotional Expressions and Head Poses', IEEE Transactions on Circuits and Systems for Video Technology, vol. 34, no. 4, pp. 2734-2748.
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Shermadurai, P & Thiyagarajan, K 2024, 'Classification of Human Mental Stress Levels Using a Deep Learning Approach on the K-EmoCon Multimodal Dataset', Traitement du Signal, vol. 41, no. 5, pp. 2559-2571.
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Sheu, A, White, CP & Center, JR 2024, 'Bone metabolism in diabetes: a clinician’s guide to understanding the bone–glucose interplay', Diabetologia, vol. 67, no. 8, pp. 1493-1506.
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AbstractSkeletal fragility is an increasingly recognised, but poorly understood, complication of both type 1 and type 2 diabetes. Fracture risk varies according to skeletal site and diabetes-related characteristics. Post-fracture outcomes, including mortality risk, are worse in those with diabetes, placing these people at significant risk. Each fracture therefore represents a sentinel event that warrants targeted management. However, diabetes is a very heterogeneous condition with complex interactions between multiple co-existing, and highly correlated, factors that preclude a clear assessment of the independent clinical markers and pathophysiological drivers for diabetic osteopathy. Additionally, fracture risk calculators and routinely used clinical bone measurements generally underestimate fracture risk in people with diabetes. In the absence of dedicated prospective studies including detailed bone and metabolic characteristics, optimal management centres around selecting treatments that minimise skeletal and metabolic harm. This review summarises the clinical landscape of diabetic osteopathy and outlines the interplay between metabolic and skeletal health. The underlying pathophysiology of skeletal fragility in diabetes and a rationale for considering a diabetes-based paradigm in assessing and managing diabetic bone disease will be discussed. Graphical Abstract
Shi, C, Li, H, Sui, Y, Lu, J, Li, L & Xue, J 2024, 'Pearl: A Multi-Derivation Approach to Efficient CFL-Reachability Solving', IEEE Transactions on Software Engineering, vol. 50, no. 9, pp. 2379-2397.
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Shi, K, Lu, J, Fang, Z & Zhang, G 2024, 'Unsupervised Domain Adaptation Enhanced by Fuzzy Prompt Learning', IEEE Transactions on Fuzzy Systems, vol. 32, no. 7, pp. 4038-4048.
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Shi, K, Peng, X, Lu, H, Zhu, Y & Niu, Z 2024, 'Multiple Knowledge-Enhanced Meteorological Social Briefing Generation', IEEE Transactions on Computational Social Systems, vol. 11, no. 2, pp. 2002-2013.
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Shi, T, Wang, F, Zhang, S, Liu, H & Li, L 2024, 'Cost‐effective secondary voltage control in dc shipboard integrated power system', IET Power Electronics, vol. 17, no. 16, pp. 2642-2655.
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AbstractFast and dramatic voltage disturbances caused by the electric propulsion and dynamic positioning process are severe issues in the DC shipboard integrated power system (DC‐SIPS). The secondary voltage control (SVC) equipped in the diesel genset (DG) and hybrid energy storage system (HESS) becomes an effective solution. However, the conventional SVC cannot distinguish the different voltage regulation characteristics between DG and HESS in a cost‐effective way when allocating the voltage regulation responsibility (VRR). The cost optimization achieved by the conventional tertiary control cannot be used for the DC‐SIPS due to frequent and unpredictable load fluctuations. In this paper, a cost‐effective secondary voltage control is proposed to realize voltage restoration and cost minimization simultaneously. First, the voltage regulation cost model is developed considering the different voltage regulation characteristics of DG and HESS. Then, the optimization problem of VRR distribution is addressed by minimizing the total voltage regulation cost function using a quadratic programming algorithm. Moreover, the voltage regulation ability of each energy storage system is fully used with the state of charge balance. Thus, the conflict among voltage restoration, cost minimization, and SoC balance is effectively addressed. Finally, promising hardware‐in‐loop test results illustrate the effectiveness of the proposed method.
Shi, T, Xiang, X, Lei, J, Liu, B, Wang, F, Li, L, Aguilera, RP & Li, W 2024, 'Parameter Boundary Characterization for DC Microgrid Islanding Detection Based on Time-Domain Voltage Oscillation Trajectory Analysis', IEEE Transactions on Smart Grid, vol. 15, no. 6, pp. 5388-5401.
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Shi, T, Xiang, X, Lei, J, Liu, B, Wang, F, Yang, H, Li, L & Li, W 2024, 'Communication-Less Active Damping Method With VSC for Stability Improvement of Grid-Connected DC Microgrid With Selected Frequency Islanding Detection', IEEE Transactions on Industrial Electronics, vol. 71, no. 10, pp. 12290-12300.
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Shi, X, Chen, Z, Wei, W & Ni, B-J 2024, 'Perspectives on sustainable plastic treatment: A shift from linear to circular economy', TrAC Trends in Analytical Chemistry, vol. 173, pp. 117631-117631.
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Shi, X, Ju, F, Wei, W, Wu, L, Chen, X & Ni, B-J 2024, 'Bioaugmentation of microalgae fermentation with yeast for enhancing microbial chain elongation: In-situ ethanol production and metabolic potential', Chemical Engineering Journal, vol. 498, pp. 155742-155742.
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Shi, X, Wei, W, Wu, L, Huang, Y & Ni, B-J 2024, 'Biosynthesis mechanisms of medium-chain carboxylic acids and alcohols in anaerobic microalgae fermentation regulated by pH conditions', Applied and Environmental Microbiology, vol. 90, no. 1.
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ABSTRACT Valorization of microalgae into high-value products and drop-in chemicals can reduce our dependence on non-renewable fossil fuels in an environmentally sustainable way. Among the valuable products, medium-chain carboxylic acids (MCCAs) and alcohols are attractive building blocks as fuel precursors. However, the biosynthetic mechanisms of MCCAs and alcohols in anaerobic microalgae fermentation and the regulating role of pH on the microbial structure and metabolism interaction among different functional groups have never been documented. In this work, we systematically investigated the roles of pH (5, 7, and 10) on the production of MCCAs and alcohols in anaerobic microalgae fermentation. The gene-centric and genome-centric metagenomes were employed to uncover the dynamics and metabolic network of the key players in the microbial communities. The results indicated that the pH significantly changed the product spectrum. The maximum production rate of alcohol was obtained at pH 5, while pH 7 was more beneficial for MCCA production. Metagenomic analysis reveals that this differential performance under different pH is attributed to the transformation of microbial guild and metabolism regulated by pH. The composition of various functional groups for MCCA and alcohol production also varies at different pH levels. Finally, a metabolic network was proposed to reveal the microbial interactions at different pH levels and thus provide insights into bioconversion of microalgae to high-value biofuels. IMPORTANCE Carboxylate platforms encompass a biosynthesis process involving a mixed and undefined culture, enabling the conversion of microalgae, rich in carbohydrates and protein, into valuable fuels and mitigating the risks associated with algae blooms. However, there is little known about the effects of pH on the metabolic pathways...
Shi, Y, Feng, A, Mao, S, Onggowarsito, C, Stella Zhang, X, Guo, W & Fu, Q 2024, 'Hydrogels in solar-driven water and energy production: Recent advances and future perspectives', Chemical Engineering Journal, vol. 492, pp. 152303-152303.
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Shi, Z, Feng, Y, Stewart, MG & Gao, W 2024, 'Virtual modelling based fragility assessment of structures under bushfire propagation', Reliability Engineering & System Safety, vol. 245, pp. 110000-110000.
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Shi, Z, Sun, X, Yang, Z, Cai, Y, Lei, G, Zhu, J & Lee, CHT 2024, 'Design Optimization of a Spoke-Type Axial-Flux PM Machine for In-Wheel Drive Operation', IEEE Transactions on Transportation Electrification, vol. 10, no. 2, pp. 3770-3781.
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Shindi, O, Yu, Q, Girdhar, P & Dong, D 2024, 'Model-Free Quantum Gate Design and Calibration Using Deep Reinforcement Learning', IEEE Transactions on Artificial Intelligence, vol. 5, no. 1, pp. 346-357.
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Shoeibi, S, Jamil, F, Parsa, SM, Mehdi, S, Kargarsharifabad, H, Mirjalily, SAA, Guo, W, Ngo, HH, Ni, B-J & Khiadani, M 2024, 'Recent advancements in applications of encapsulated phase change materials for solar energy systems: A state of the art review', Journal of Energy Storage, vol. 94, pp. 112401-112401.
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Shojaeezadeh, SA, Al-Wardy, M, Nikoo, MR, Mooselu, MG, Alizadeh, MR, Adamowski, JF, Moradkhani, H, Alamdari, N & Gandomi, AH 2024, 'Soil erosion in the United States: Present and future (2020–2050)', CATENA, vol. 242, pp. 108074-108074.
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Shrivastava, A & Sui, Y 2024, 'Message from the Chairs', Proceedings of the ACM SIGPLAN Conference on Languages, Compilers, and Tools for Embedded Systems (LCTES), p. III.
Shu, X, Yang, Y, Liu, J, Chang, X & Wu, B 2024, 'BDAL: Balanced Distribution Active Learning for MRI Cardiac Multistructures Segmentation', IEEE Transactions on Industrial Informatics, vol. 20, no. 4, pp. 6099-6108.
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Shu, Y, Li, Q, Liu, L & Xu, G 2024, 'Semi-Supervised Adversarial Learning for Attribute-Aware Photo Aesthetic Assessment', IEEE Transactions on Multimedia, vol. 26, no. 99, pp. 4086-4096.
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Aesthetic attributes are crucial for aesthetics because they explicitly present some photo quality cues that a human expert might use to evaluate a photo's aesthetic quality. However, annotating aesthetic attributes is a time-consuming, costly, and error-prone task, which leads to the issue that photos available are partially annotated with attributes. To alleviate this issue, we propose a novel semi-supervised adversarial learning method for photo aesthetic assessment from partially attribute-annotated photos, which can greatly reduce the reliance on manual attribute annotation. Specifically, the proposed method consists of a score-attributes generator R, a photo generator G, and a discriminator D. The score-attributes generator learns the aesthetic score and attributes simultaneously to capture their dependencies and construct better feature representations. The photo generator reconstructs the photo by feeding aesthetic attributes, score, and informative feature representation. A discriminator is used to force the convergence of the features-attributes-score tuples generated from the score-attributes generator, the photo generator, and the ground-truth distribution in labeled data for training data. The proposed method significantly outperforms the state of the art, increasing the Spearman rank-order correlation coefficient (SRCC) from the existing best reported of 0.726 to 0.761 on Aesthetic and attributes database and 0.756 to 0.774 on Aesthetic visual analysis database, respectively.
Siami, M, Naderpour, M, Ramezani, F & Lu, J 2024, 'Risk Assessment Through Big Data: An Autonomous Fuzzy Decision Support System', IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 8, pp. 9016-9027.
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Silva, IN, Indraratna, B, Nguyen, TT & Rujikiatkamjorn, C 2024, 'The influence of soil fabric on the monotonic and cyclic shear behaviour of consolidated and compacted specimens', Canadian Geotechnical Journal, vol. 61, no. 6, pp. 1159-1176.
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While the fabric of soil can significantly influence its behaviour, the effect of varying fabric parameters on the subgrade shear response is still not well understood. This study creates soil specimens with different fabrics which are then captured through X-ray microscopic-computed tomography scanning and quantified by image processing techniques. A comprehensive laboratory investigation is conducted to understand how the soil fabric affects its monotonic and cyclic shear behaviour. The results indicate that the consolidation method creates a more homogeneous fabric with mainly small-to-medium interconnected pores, whereas the compaction technique creates significantly large and mostly inter-aggregate pores with lower connectivity. In this regard, the consolidated specimens exhibit an elastic-perfectly plastic behaviour, while the compacted specimens show strain-hardening transformation during isotropic monotonic shearing. Under anisotropic conditions, the compacted specimens exhibit a greater strain softening response and excess pore pressure than the consolidated specimens because they have a weaker fabric. Furthermore, the compacted specimens show a smaller threshold strain at a lower critical number of cycles due to the collapse of large pores. These current findings prove the decisive role that soil fabric plays in determining the shear response and failure of subgrade soils.
Singh, P, Balasubramanian, D, Venugopal, IP, Tyagi, VV, Goel, V, Wae-Hayee, M, Kalam, MA & Varuvel, EG 2024, 'A comprehensive review on the applicability of hydrogen and natural gas as gaseous fuel for dual fuel engine operation', Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, vol. 46, no. 1, pp. 1559-1587.
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Singh, R, Gupta, A, Paul, AR, Paul, B & Saha, SC 2024, 'Improving Heat Transfer in Parabolic Trough Solar Collectors by Magnetic Nanofluids', Energy Engineering, vol. 121, no. 4, pp. 835-848.
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Singh, RB, Patra, KC, Pradhan, B & Samantra, A 2024, 'HDTO-DeepAR: A novel hybrid approach to forecast surface water quality indicators', Journal of Environmental Management, vol. 352, pp. 120091-120091.
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Singh, S, S, S, Varma, P, Sreelekha, G, Adak, C, Shukla, RP & Kamble, VB 2024, 'Metal oxide-based gas sensor array for VOCs determination in complex mixtures using machine learning', Microchimica Acta, vol. 191, no. 4.
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AbstractDetection of volatile organic compounds (VOCs) from the breath is becoming a viable route for the early detection of diseases non-invasively. This paper presents a sensor array of 3 component metal oxides that give maximal cross-sensitivity and can successfully use machine learning methods to identify four distinct VOCs in a mixture. The metal oxide sensor array comprises NiO-Au (ohmic), CuO-Au (Schottky), and ZnO–Au (Schottky) sensors made by the DC reactive sputtering method and having a film thickness of 80–100 nm. The NiO and CuO films have ultrafine particle sizes of < 50 nm and rough surface texture, while ZnO films consist of nanoscale platelets. This array was subjected to various VOC concentrations, including ethanol, acetone, toluene, and chloroform, one by one and in a pair/mix of gases. Thus, the response values show severe interference and departure from commonly observed power law behavior. The dataset obtained from individual gases and their mixtures were analyzed using multiple machine learning algorithms, such as Random Forest (RF), K-Nearest Neighbor (KNN), Decision Tree, Linear Regression, Logistic Regression, Naive Bayes, Linear Discriminant Analysis, Artificial Neural Network, and Support Vector Machine. KNN and RF have shown more than 99% accuracy in classifying different varying chemicals in the gas mixtures. In regression analysis, KNN has delivered the best results with an R2 value of more than 0.99 and LOD of 0.012 ppm, 0.015 ppm, 0.014 ppm, and 0.025 ppm for predicting the concentrations of acetone, toluene, ethanol, and chloroform, respectively, in complex mixtures. Therefore, it is demonstrated that the array utilizing the provided algorithms can classify and predict the concentrations of the four gases simultaneously for disease diagnosis and treatment monitoring. Graphical Abstract
Singha, C, Sahoo, S, Govind, A, Pradhan, B, Alrawashdeh, S, Hamdi Aljohani, T, Almohamad, H, Md Towfiqul Islam, AR & Abdo, HG 2024, 'Impacts of hydroclimate change on climate-resilient agriculture at the river basin management', Journal of Water and Climate Change, vol. 15, no. 1, pp. 209-232.
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Abstract This paper focuses on exploring the potential of Climate resilient agriculture (CRA) for river basin-scale management. Our analysis is based on long-term historical and future climate and hydrological datasets within a GIS environment, focusing on the Ajoy River basin in West Bengal, Eastern India. The standardized anomaly index (SAI) and slope of the linear regression (SLR) methods were employed to analyse the spatial pattern of the climate variables (precipitation, Tmax and Tmin) and hydrological variables (actual evapotranspiration (AET), runoff (Q), vapor pressure deficit (VPD), potential evapotranspiration (PET), and climate water deficit (DEF)) using the TerraClimate dataset spanning from 1958 to 2020. Future climate trend analysis spanning 2021 to 2050 was conducted using the CMIP6 based GCMs (MIROC6 and EC-Earth3) dataset under shared socio-economic pathway (SSP2-4.5, SSP5-8.5 and historical). For spatiotemporal water storage analysis, we relied on Gravity Recovery and Climate Experiment (GRACE) from the Center for Space Research (CSR) and the Jet Propulsion Laboratory (JPL) data, covering the period from 2002 to 2021. Validation was performed using regional groundwater level data, employing various machine learning classification models. Our findings revealed a negative precipitation trend (approximately −0.04 mm/year) in the southern part, whereas the northern part exhibited a positive trend (approximately 0.10 mm/year).
Singha, C, Swain, KC, Pradhan, B & Alamri, A 2024, 'Integration of FuzzyAHP and machine learning algorithms for climate-driven gully erosion susceptibility mapping: predicting future trends in the eastern lateritic region, West Bengal, India', Geosciences Journal, vol. 28, no. 6, pp. 981-1011.
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Singha, C, Swain, KC, Pradhan, B, Rusia, DK, Moghimi, A & Ranjgar, B 2024, 'Mapping groundwater potential zone in the subarnarekha basin, India, using a novel hybrid multi-criteria approach in Google earth Engine', Heliyon, vol. 10, no. 2, pp. e24308-e24308.
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Siva, S, Bressel, M, Sidhom, M, Sridharan, S, Vanneste, BGL, Davey, R, Montgomery, R, Ruben, J, Foroudi, F, Higgs, B, Lin, C, Raman, A, Hardcastle, N, Hofman, MS, De Abreu Lourenco, R, Shaw, M, Mancuso, P, Moon, D, Wong, L-M, Lawrentschuk, N, Wood, S, Brook, NR, Kron, T, Martin, J, Pryor, D, Chesson, B, Ali, M, Chander, S, Moore, A, Cook, O, Eade, T, Sharma, H, Ramanathan, M, Howe, K & Frewen, H 2024, 'Stereotactic ablative body radiotherapy for primary kidney cancer (TROG 15.03 FASTRACK II): a non-randomised phase 2 trial', The Lancet Oncology, vol. 25, no. 3, pp. 308-316.
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Skarding, J, Gabrys, B & Musial, K 2024, 'On the Effectiveness of Heterogeneous Ensembles Combining Graph Neural Networks and Heuristics for Dynamic Link Prediction', IEEE Transactions on Network Science and Engineering, vol. 11, no. 4, pp. 3250-3259.
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Skarding, J, Hellmich, M, Gabrys, B & Musial, K 2024, 'Corrections to “A Robust Comparative Analysis of Graph Neural Networks on Dynamic Link Prediction”', IEEE Access, vol. 12, pp. 6912-6913.
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Sohn, W, El Saliby, I, Merenda, A, Phuntsho, S, Freguia, S, Guan, J, Gao, L, Lee, S & Shon, HK 2024, 'Anthroponics: Application and effects on growth of parsley, rhipsalis, coriander, and basil fed with urine fertiliser', Desalination and Water Treatment, vol. 320, pp. 100682-100682.
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Sohn, W, Jiang, J, Phuntsho, S & Shon, HK 2024, 'Membrane bioreactor incorporated with biofilm carriers and activated carbon for enhanced biological nitrification of urine', Desalination, vol. 570, pp. 117061-117061.
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Long hydraulic retention time (HRT) of a membrane bioreactor (MBR) for the nitrification of source-separated urine remains one of the major challenges of which reduces energy efficiency and increases footprint. In this study, a powdered activated carbon (PAC) incorporated MBR with biofilm carrier addition was operated to investigate their effects on the enhancement of nitrification rates and their HRT. The presence of biofilm carrier in the MBR reduced restabilisation time and the start-up period by 26 % and 18 %, respectively. The combination of biofilm carriers and PAC showed a significantly higher nitrification rate of 304 ± 73 mgN/L·d compared to 194 ± 60 mgN/L·d for the conventional MBR (control), which significantly helped reduced HRT during urine treatment. This study therefore shows that PAC and biofilm carrier incorporated MBR is more compact and high-performing contributing to commercial applications and helping achieve circular economy of nutrients.
Sohn, W, Jiang, J, Su, Z, Zheng, M, Wang, Q, Phuntsho, S & Kyong Shon, H 2024, 'Microbial community analysis of membrane bioreactor incorporated with biofilm carriers and activated carbon for nitrification of urine', Bioresource Technology, vol. 397, pp. 130462-130462.
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Song, F, Wang, L, Hu, X, Zong, X & Wen, S 2024, 'Novel distributed event/self-triggered sliding-mode control: Application to practical fixed-time consensus of second-order multi-agent systems', Information Sciences, vol. 677, pp. 120808-120808.
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Song, H, Kim, T, Hajimohammadi, A, Oh, JE & Castel, A 2024, 'Detailed characterisation of hemp and hempcrete pore structures: Effects on thermal and acoustic properties', Cement and Concrete Research, vol. 186, pp. 107702-107702.
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Song, J, Gao, J, Gharehghani, A, Gao, J, Huang, Y, Wang, X, Wang, Y, Fu, Z, Qi, M & Tian, G 2024, 'Research on the in-cylinder combustion and emissions of opposed rotary piston engines over various altitudes', Fuel, vol. 376, pp. 132644-132644.
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Song, J, Wen, D, Xu, L, Qin, L, Zhang, W & Lin, X 2024, 'On Querying Historical Connectivity in Temporal Graphs', Proceedings of the ACM on Management of Data, vol. 2, no. 3, pp. 1-25.
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We study the historical connectivity query in temporal graphs where edges continuously arrive. Given an arbitrary time window, and two query vertices, the problem aims to identify if two vertices are connected by a path in the snapshot of the window. The state-of-the-art method designs an index based on the two-hop cover, and updating the index is costly when new edges arrive. In this paper, we propose a new framework and design a novel forest-based index for historical connectivity queries. The index enables us to answer queries by searching if two vertices are connected in the forest. We update the index by modifying a forest structure. Our techniques also work for connectivity query processing in a sliding window of temporal graphs. Extensive experiments have been conducted to show the considerable advantages of our approach compared with the state-of-the-art methods in both historical connectivity queries and sliding-window connectivity queries.
Song, L, Zhang, T, Qin, P-Y, Du, J & Guo, YJ 2024, 'Sub-THz Broadband Transmitting Metasurfaces With Enhanced Frequency-Scanning Capability', IEEE Transactions on Terahertz Science and Technology, vol. 14, no. 1, pp. 82-90.
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In this manuscript, broadband transmitting metasurfaces are developed to enhance frequency-dependent beam scanning. A triple-gold-layer unit cell is designed for wideband transmissions with low losses and quasi-linear phase variations. Comprehensive analyses of phase-gradient metasurfaces are provided to enable high-efficiency and wide-angle frequency scanning. For verifications, two metasurfaces with different phase gradients are simulated, manufactured, and measured. Continuous beam scanning performance has been demonstrated successfully from 80 GHz to 220 GHz, showing beam scanning ranges of 25° and 31.5° from two prototypes, respectively. Peak transmission efficiencies of 84% and 75% have been obtained from experiments. The results from simulation and measurement agree very well. The developed metasurfaces have many potential applications such as frequency-scanning terahertz (THz) imaging.
Song, L-Z, Squires, A, van der Laan, T & Du, J 2024, 'THz graphene-integrated metasurface for electrically reconfigurable polarization conversion', Nanophotonics, vol. 13, no. 13, pp. 2349-2359.
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Abstract Terahertz (THz) waves have been widely hailed as a key enabling technology for future sixth generation (6G) wireless networks. Dynamic modulation of their polarization states is of great attraction for high-capacity communications and anisotropic sensing. The development of such technology is, however, still in very early stage owing to the difficulties of realizing electrical reconfigurability for THz devices. Artificially constructed metasurfaces and new nanomaterials, such as graphene, have been shown to provide revolutionary platforms for manipulating and controlling the wave properties, especially at THz frequencies. This work leverages the light–matter interaction in a graphene-integrated metasurface functioning as an electrically reconfigurable THz polarization converter. A novel graphene-gold bilayer topology is applied to construct such a metasurface which enables wide-range electrical tunability of the polarization conversion. Under a y-polarized illumination, the reflected components of x- and y-polarizations are tuned dynamically through an external bias voltage across the metasurface, thereby producing an elliptically polarized wave with tuneable ellipticity and angle. By changing the voltage from 0 V to 12 V, the reflected polarization ellipticity has been tuned from −0.94 to −0.5 at around 240 GHz, featuring linear-to-circular and linear-to-elliptical polarization conversions. Meanwhile, the polarization angle has been modulated from 12° to −23° at around 236 GHz. This work provides an experimentally validated THz graphene-integrated metasurface with wide polarization modulation depths, low biasing voltages and simple configuration. It promises great potential for applications in future THz communications and sensing.
Song, L-Z, Zhang, T, Lai, J-X, Yang, Y & Du, J 2024, 'A 180-GHz to 220-GHz Wideband Transmitarray With Wide-Angle Beam Steering for Intersatellite Communications', IEEE Transactions on Antennas and Propagation, vol. 72, no. 1, pp. 950-955.
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Song, N, Lu, M, Liu, J, Lin, M, Shangguan, P, Wang, J, Shi, B & Zhao, J 2024, 'A Giant Heterometallic Polyoxometalate Nanocluster for Enhanced Brain‐Targeted Glioma Therapy', Angewandte Chemie, vol. 136, no. 10.
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AbstractGiant heterometallic polyoxometalate (POM) clusters with precise atom structures, flexibly adjustable and abundant active sites are promising for constructing functional nanodrugs. However, current POM drugs are almost vacant in orthotopic brain tumor therapy due to the inability to effectively penetrate the blood–brain barrier (BBB) and low drug activity. Here, we designed the largest (3.0 nm × 6.0 nm) transition‐metal–lanthanide co‐encapsulated POM cluster {[Ce10Ag6(DMEA)(H2O)27W22O70][B‐α‐TeW9O33]9}288− featuring 238 metal centers via synergistic coordination between two geometry‐unrestricted Ce3+ and Ag+ linkers with tungsten‐oxo cluster fragments. This POM was combined with brain‐targeted peptide to prepare a brain‐targeted nanodrug that could efficiently traverse BBB and target glioma cells. The Ag+ active centers in the nanodrug specifically activate reactive oxygen species to regulate the apoptosis pathway of glioma cells with a low half‐maximal inhibitory concentration (5.66 μM). As the first brain‐targeted POM drug, it efficiently prolongs the survival of orthotopic glioma‐bearing mice.
Song, N, Lu, M, Liu, J, Lin, M, Shangguan, P, Wang, J, Shi, B & Zhao, J 2024, 'A Giant Heterometallic Polyoxometalate Nanocluster for Enhanced Brain‐Targeted Glioma Therapy', Angewandte Chemie International Edition, vol. 63, no. 10.
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AbstractGiant heterometallic polyoxometalate (POM) clusters with precise atom structures, flexibly adjustable and abundant active sites are promising for constructing functional nanodrugs. However, current POM drugs are almost vacant in orthotopic brain tumor therapy due to the inability to effectively penetrate the blood–brain barrier (BBB) and low drug activity. Here, we designed the largest (3.0 nm × 6.0 nm) transition‐metal–lanthanide co‐encapsulated POM cluster {[Ce10Ag6(DMEA)(H2O)27W22O70][B‐α‐TeW9O33]9}288− featuring 238 metal centers via synergistic coordination between two geometry‐unrestricted Ce3+ and Ag+ linkers with tungsten‐oxo cluster fragments. This POM was combined with brain‐targeted peptide to prepare a brain‐targeted nanodrug that could efficiently traverse BBB and target glioma cells. The Ag+ active centers in the nanodrug specifically activate reactive oxygen species to regulate the apoptosis pathway of glioma cells with a low half‐maximal inhibitory concentration (5.66 μM). As the first brain‐targeted POM drug, it efficiently prolongs the survival of orthotopic glioma‐bearing mice.
Song, R, Liu, K, Liu, C, Yang, J & Li, J 2024, 'The Dynamic Compressive Behavior of Waved Fiber-Reinforced Ultrahigh-Performance Cementitious Composites Containing Fly Ash and Ground Granulated Blast-Furnace Slag', Journal of Materials in Civil Engineering, vol. 36, no. 1.
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Song, X, Hou, S, Huang, Y, Cao, C, Liu, X, Huang, Y & Shan, C 2024, 'Gait Attribute Recognition: A New Benchmark for Learning Richer Attributes From Human Gait Patterns', IEEE Transactions on Information Forensics and Security, vol. 19, pp. 1-14.
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Song, X, Liu, C, Zheng, Y, Feng, Z, Li, L, Zhou, K & Yu, X 2024, 'HairStyle Editing via Parametric Controllable Strokes', IEEE Transactions on Visualization and Computer Graphics, vol. 30, no. 7, pp. 3857-3870.
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Song, Y, Liu, Z, Li, G, Xie, J, Wu, Q, Zeng, D, Xu, L, Zhang, T & Wang, J 2024, 'EMS: A Large-Scale Eye Movement Dataset, Benchmark, and New Model for Schizophrenia Recognition', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-12.
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Song, Y, Sun, X, Nghiem, LD, Duan, J, Liu, W, Liu, Y & Cai, Z 2024, 'Insight into Fe-O-Bi electron migration channel in MIL-53(Fe)/Bi4O5I2 Z-scheme heterojunction for efficient photocatalytic decontamination', Journal of Colloid and Interface Science, vol. 667, pp. 321-337.
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Soni, V, Shah, D, Joshi, J, Gite, S, Pradhan, B & Alamri, A 2024, 'Introducing AOD 4: A dataset for air borne object detection', Data in Brief, vol. 56, pp. 110801-110801.
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Soo, A & Shon, HK 2024, 'A nutrient circular economy framework for wastewater treatment plants', Desalination, vol. 592, pp. 118090-118090.
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Soo, A, Gao, L & Shon, HK 2024, 'Machine learning framework for wastewater circular economy — Towards smarter nutrient recoveries', Desalination, vol. 592, pp. 118092-118092.
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Soo, A, Kim, J & Shon, HK 2024, 'Technologies for the wastewater circular economy – A review', Desalination and Water Treatment, vol. 317, pp. 100205-100205.
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Soudagar, MEM, Kiong, TS, Jathar, L, Nik Ghazali, NN, Ramesh, S, Awasarmol, U & Ong, HC 2024, 'Perspectives on cultivation and harvesting technologies of microalgae, towards environmental sustainability and life cycle analysis', Chemosphere, vol. 353, pp. 141540-141540.
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Soudagar, MEM, Kiong, TS, Ramesh, S, Ghazali, NNN, Kalam, MA, Mujtaba, MA, Venu, H, Nur-E-Alam, M & Ali, HM 2024, 'Correction: Utilization of non-edible bio-feedstock Pongamia Pinnata-diethyl ether ternary fuel blend supplemented with graphene oxide nanoparticles on CRDi engine characteristics', Journal of Thermal Analysis and Calorimetry, vol. 149, no. 17, pp. 10165-10165.
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Soudagar, MEM, Kiong, TS, Ramesh, S, Ghazali, NNN, Kalam, MA, Mujtaba, MA, Venu, H, Nur-E-Alam, M & Ali, HM 2024, 'Utilization of non-edible bio-feedstock Pongamia Pinnata-diethyl ether ternary fuel blend supplemented with graphene oxide nanoparticles on CRDi engine characteristics', Journal of Thermal Analysis and Calorimetry, vol. 149, no. 11, pp. 5687-5712.
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Soudagar, MEM, Shelare, S, Marghade, D, Belkhode, P, Nur-E-Alam, M, Kiong, TS, Ramesh, S, Rajabi, A, Venu, H, Yunus Khan, TM, Mujtaba, MA, Shahapurkar, K, Kalam, MA & Fattah, IMR 2024, 'Optimizing IC engine efficiency: A comprehensive review on biodiesel, nanofluid, and the role of artificial intelligence and machine learning', Energy Conversion and Management, vol. 307, pp. 118337-118337.
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Srivastava, A, Yetemen, O, Rodriguez, JF, Kumari, N & Saco, PM 2024, 'The imprint of coevolving semi-arid landscapes, soil, and vegetation on soil moisture and vegetation variability', CATENA, vol. 242, pp. 108125-108125.
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Staplevan, MJ, Ansari, AJ, Ahmed, A & Hai, FI 2024, 'Effect of embedding a sieving phase into the current plastic recycling process to capture microplastics', Journal of Water Process Engineering, vol. 66, pp. 106075-106075.
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Staplevan, MJ, Ansari, AJ, Ahmed, A & Hai, FI 2024, 'Impact of bioplastic contamination on the mechanical recycling of conventional plastics', Waste Management, vol. 185, pp. 1-9.
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Steinmetz, JD, Seeher, KM, Schiess, N, Nichols, E, Cao, B, Servili, C, Cavallera, V, Cousin, E, Hagins, H, Moberg, ME, Mehlman, ML, Abate, YH, Abbas, J, Abbasi, MA, Abbasian, M, Abbastabar, H, Abdelmasseh, M, Abdollahi, M, Abdollahi, M, Abdollahifar, M-A, Abd-Rabu, R, Abdulah, DM, Abdullahi, A, Abedi, A, Abedi, V, Abeldańo Zuńiga, RA, Abidi, H, Abiodun, O, Aboagye, RG, Abolhassani, H, Aboyans, V, Abrha, WA, Abualhasan, A, Abu-Gharbieh, E, Aburuz, S, Adamu, LH, Addo, IY, Adebayo, OM, Adekanmbi, V, Adekiya, TA, Adikusuma, W, Adnani, QES, Adra, S, Afework, T, Afolabi, AA, Afraz, A, Afzal, S, Aghamiri, S, Agodi, A, Agyemang-Duah, W, Ahinkorah, BO, Ahmad, A, Ahmad, D, Ahmad, S, Ahmadzade, AM, Ahmed, A, Ahmed, A, Ahmed, H, Ahmed, JQ, Ahmed, LA, Ahmed, MB, Ahmed, SA, Ajami, M, Aji, B, Ajumobi, O, Akade, SE, Akbari, M, Akbarialiabad, H, Akhlaghi, S, Akinosoglou, K, Akinyemi, RO, Akonde, M, Al Hasan, SM, Alahdab, F, AL-Ahdal, TMA, Al-amer, RM, Albashtawy, M, AlBataineh, MT, Aldawsari, KA, Alemi, H, Alemi, S, Algammal, AM, Al-Gheethi, AAS, Alhalaiqa, FAN, Alhassan, RK, Ali, A, Ali, EA, Ali, L, Ali, MU, Ali, MM, Ali, R, Ali, S, Ali, SSS, Ali, Z, Alif, SM, Alimohamadi, Y, Aliyi, AA, Aljofan, M, Aljunid, SM, Alladi, S, Almazan, JU, Almustanyir, S, Al-Omari, B, Alqahtani, JS, Alqasmi, I, Alqutaibi, AY, Al-Shahi Salman, R, Altaany, Z, Al-Tawfiq, JA, Altirkawi, KA, Alvis-Guzman, N, Al-Worafi, YM, Aly, H, Aly, S, Alzoubi, KH, Amani, R, Amindarolzarbi, A, Amiri, S, Amirzade-Iranaq, MH, Amu, H, Amugsi, DA, Amusa, GA, Amzat, J, Ancuceanu, R, Anderlini, D, Anderson, DB, Andrei, CL, Androudi, S, Angappan, D, Angesom, TW, Anil, A, Ansari-Moghaddam, A, Anwer, R, Arafat, M, Aravkin, AY, Areda, D, Ariffin, H, Arifin, H, Arkew, M, Ärnlöv, J, Arooj, M, Artamonov, AA, Artanti, KD, Aruleba, RT, Asadi-Pooya, AA, Asena, TF, Asghari-Jafarabadi, M, Ashraf, M, Ashraf, T, Atalell, KA, Athari, SS, Atinafu, BTT, Atorkey, P, Atout, MMW, Atreya, A, Aujayeb, A, Avan, A, Ayala Quintanilla, BP, Ayatollahi, H, Ayinde, OO, Ayyoubzadeh, SM, Azadnajafabad, S, Azizi, Z, Azizian, K, Azzam, AY, Babaei, M, Badar, M, Badiye, AD, Baghdadi, S, Bagherieh, S, Bai, R, Baig, AA, Balakrishnan, S, Balalla, S, Baltatu, OC, Banach, M, Bandyopadhyay, S, Banerjee, I, Baran, MF, Barboza, MA, Barchitta, M, Bardhan, M, Barker-Collo, SL, Bärnighausen, TW, Barrow, A, Bashash, D, Bashiri, H & et al. 2024, 'Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021', The Lancet Neurology, vol. 23, no. 4, pp. 344-381.
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Stenfors, A, Dilshani, K, Guo, A & Mere, P 2024, 'Detecting the risk of cross-product manipulation in the EUREX fixed income futures market', Journal of International Financial Markets, Institutions and Money, vol. 92, pp. 101984-101984.
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Stewart, A, Zhu, Y, Liu, Y, Simpson, DA & Reece, PJ 2024, 'Optical Tweezers Assembled Nanodiamond Quantum Sensors', Nano Letters, vol. 24, no. 39, pp. 12188-12195.
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Stewart, MG, Netherton, MD, Qin, H & Li, J 2024, 'Explosive field trial for repetitive testing of VBIEDs to probabilistically measure blast and fragmentation hazards', International Journal of Protective Structures, vol. 15, no. 4, pp. 937-963.
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This paper describes results from an explosive field trial of the detonation of Vehicle-Borne Improvised Explosive Devices (VBIEDs). The purpose of the trials is to replicate tests with identical car type and explosive mass to help probabilistically characterise the uncertainty and variability of blast pressures and fragment hazards. These variabilities may be considerable, and it is important to recognise that the world is not deterministic. The paper describes the spatial variability (directionality) of incident pressure, impulse and time of positive phase duration, and compares these to results from a bare charge, and the hemispherical surface burst Kingery and Bulmash polynomials often used for predicting blast loads from IEDs, such as ConWep. This also allows directional airblast factors to be quantified. The spatial distribution of over 26,000 fragments on the ground is also presented over the 250 m × 300 m test arena. The fragment densities and velocities obtained from the witness panels are also described, and preliminary fatality risks were estimated. These data may help develop or validate airblast and fragment hazard numerical or other models. Ultimately, probabilistic approaches will provide decision support for the determination of safety distance and risk reduction measures to prevent fatality and injury from blast pressure and fragmentation hazards.
Stuart, B, Guan, J, Collins, S, Thomas, P & Ueland, M 2024, 'A preliminary study of non-woven fabrics for forensic identification purposes', Australian Journal of Forensic Sciences, vol. 56, no. 2, pp. 144-153.
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While traditional woven textiles have been the subject of many forensic investigations, non-woven fabrics have received minimal attention thus far. Given the expansion of commercial applications of non-woven fabrics, a preliminary investigation of household wipes has been carried out to characterize the compositions of these widely available non-woven fabrics. Infrared spectroscopy and thermogravimetric analysis were employed to identify the fibre type and additives of three types of commercial wipes. Polyester and/or viscose fibres were found to be the main components and, along with the identification of binders, enable source types to be differentiated. The predicted different sensitivities of the fibre types to biodeterioration highlights the importance of future environmental studies for the correct characterization of non-woven fabrics in evidence.
Sturnieks, DL, Hicks, C, Smith, N, Ratanapongleka, M, Menant, J, Turner, J, Lo, J, Chaplin, C, Garcia, J, Valenzuela, MJ, Delbaere, K, Herbert, RD, Sherrington, C, Toson, B & Lord, SR 2024, 'Exergame and cognitive training for preventing falls in community-dwelling older people: a randomized controlled trial', Nature Medicine, vol. 30, no. 1, pp. 98-105.
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Stylianou, N, Sebina, I, Matigian, N, Monkman, J, Doehler, H, Röhl, J, Allenby, M, Nam, A, Pan, L, Rockstroh, A, Sadeghirad, H, Chung, K, Sobanski, T, O'Byrne, K, Almeida, ACSF, Rebutini, PZ, Machado‐Souza, C, Stonoga, ETS, Warkiani, ME, Salomon, C, Short, K, McClements, L, de Noronha, L, Huang, R, Belz, GT, Souza‐Fonseca‐Guimaraes, F, Clifton, V & Kulasinghe, A 2024, 'Whole transcriptome profiling of placental pathobiology in SARS‐CoV‐2 pregnancies identifies placental dysfunction signatures', Clinical & Translational Immunology, vol. 13, no. 2.
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AbstractObjectivesSevere Acute Respiratory Syndrome Coronavirus 2 (SARS‐CoV‐2) virus infection in pregnancy is associated with higher incidence of placental dysfunction, referred to by a few studies as a ‘preeclampsia‐like syndrome’. However, the mechanisms underpinning SARS‐CoV‐2‐induced placental malfunction are still unclear. Here, we investigated whether the transcriptional architecture of the placenta is altered in response to SARS‐CoV‐2 infection.MethodsWe utilised whole‐transcriptome, digital spatial profiling, to examine gene expression patterns in placental tissues from participants who contracted SARS‐CoV‐2 in the third trimester of their pregnancy (n = 7) and those collected prior to the start of the coronavirus disease 2019 (COVID‐19) pandemic (n = 9).ResultsThrough comprehensive spatial transcriptomic analyses of the trophoblast and villous core stromal cell subpopulations in the placenta, we identified SARS‐CoV‐2 to promote signatures associated with hypoxia and placental dysfunction. Notably, genes associated with vasodilation (NOS3), oxidative stress (GDF15, CRH) and preeclampsia (FLT1, EGFR, KISS1, PAPPA2) were enriched with SARS‐CoV‐2. Pathways related to increased nutrient uptake, vascular tension, hypertension and inflammation were also enriched in SARS‐CoV‐2 samples compared to uninfected controls.ConclusionsOur findings demonstrate the utility of spatially resolved transcriptomic analysis in defining the underlying pathogenic mechanisms of S...
Su, G, Jiang, P, Zhou, H, Zulkifli, NWM, Ong, HC & Ibrahim, S 2024, 'Integrated production of methanol and biochar from bagasse and plastic waste: A three-in-one solution for carbon sequestration, bioenergy production, and waste valorization', Energy Conversion and Management, vol. 307, pp. 118344-118344.
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Su, Y, Qian, J, Wang, J, Mi, X, Huang, Q, Zhang, Y, Jiang, Q & Wang, Q 2024, 'Unraveling the mechanism of norfloxacin removal and fate of antibiotics resistance genes (ARGs) in the sulfur-mediated autotrophic denitrification via metagenomic and metatranscriptomic analyses', Science of The Total Environment, vol. 922, pp. 171328-171328.
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Su, Z, Sun, X, Lei, G & Yao, M 2024, 'Improved Model-Free Predictive Current Control for SPMSM Drives With Adaptive Prediction Horizon Strategy', IEEE Transactions on Industrial Electronics, pp. 1-11.
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Su, Z, Sun, X, Lei, G & Yao, M 2024, 'Model-Free Predictive Current Control for Dual Three-Phase PMSM Drives With an Optimal Modulation Pattern', IEEE Transactions on Industrial Electronics, vol. 71, no. 9, pp. 10140-10149.
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Sui, S, Han, Q, Lu, D, Wu, S & Xu, G 2024, 'A novel complex network prediction method based on multi-granularity contrastive learning', CCF Transactions on Pervasive Computing and Interaction, vol. 6, no. 4, pp. 394-405.
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Sultan, HS, Ali, MH, Shafi, J, Fteiti, M, Baro, M, Almutairi, K, Islam, MS, Harb, K, Alharbi, FS & Ghalambaz, M 2024, 'Design improvement of latent heat thermal energy storage in wavy channel enclosures using neural networks', Journal of Energy Storage, vol. 79, pp. 110061-110061.
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Sultan, HS, Ali, MH, Shafi, J, Fteiti, M, Baro, M, Alresheedi, F, Islam, MS, Yusaf, T & Ghalambaz, M 2024, 'Improving phase change heat transfer in an enclosure filled by uniform and heterogenous metal foam layers: A neural network design approach', Journal of Energy Storage, vol. 85, pp. 110954-110954.
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Sultana, S, Alam, MM, Su’ud, MM, Mustapha, JC & Prasad, M 2024, 'A Deep Dive into Robot Vision - An Integrative Systematic Literature Review Methodologies and Research Endeavor Practices', ACM Computing Surveys, vol. 56, no. 9, pp. 1-33.
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Novel technological swarm and industry 4.0 mold the recent Robot vision research into innovative discovery. To enhance technological paradigm Deep Learning offers remarkable pace to move towards diversified advancement. This research considers the most topical, recent, related and state-of-the-art research reviews that revolve around Robot vision, and shapes the research into Systematic Literature Survey SLR. The SLR considers a combination of more than 100 reviews and empirical studies to perform a critical categorical study and shapes findings against research questions. The research study contribution spans over multiple categories of Robot vision and is tinted along with technical limitations and future research endeavors. Previously multiple research studies have been observed to leverage Robotic vision techniques. Yet, there is none like SLR summarizing recent vision techniques for all targeted Robotic fields. This research SLR could be a precious milestone in Robot vision for each glimpse of Robotics.
Sun, C, Li, J, Liu, Q, Chen, K, Li, W & Pan, F 2024, 'Compressive performance and damage mechanism of concrete short columns confined by steel wires reinforced 3DPM', Case Studies in Construction Materials, vol. 21, pp. e03457-e03457.
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Sun, C, Ren, X, Dong, X, Qiu, P, Wu, X, Zhao, L, Guo, Y, Gong, Y & Zhang, C 2024, 'Mining actionable repetitive positive and negative sequential patterns', Knowledge-Based Systems, vol. 302, pp. 112398-112398.
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Sun, J, Chen, B, Liu, P, Wen, S & Wang, Y 2024, 'A Memcapacitor Biomimetic Circuit Realizing Classical Conditioning and Fear Learning', IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 71, no. 12, pp. 5694-5706.
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Sun, J, Gao, P, Wen, S, Liu, P & Wang, Y 2024, 'Memristor-Based Conditioned Inhibition Neural Network Circuit With Blocking Generalization and Differentiation', IEEE Internet of Things Journal, vol. 11, no. 7, pp. 11259-11270.
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Sun, J, Liu, Y, Luo, Q, Ren, Y & Guo, YJ 2024, 'Efficient and Accurate Pattern Synthesis of Circular Antenna Array Employing Iterative Fast Segmented Cyclic Convolution', IEEE Antennas and Wireless Propagation Letters, vol. 23, no. 1, pp. 269-273.
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Sun, J, Wang, J, Wen, S, Wang, Y & Wang, Y 2024, 'Neural Network Circuits for Bionic Associative Memory and Temporal Order Memory Based on DNA Strand Displacement', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-12.
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Sun, L, Liu, S, Zhao, H, Muhammad, U, Chen, D & Li, W 2024, 'Dynamic performance of fiber-reinforced ultra-high toughness cementitious composites: A comprehensive review from materials to structural applications', Engineering Structures, vol. 317, pp. 118647-118647.
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Sun, M, Xu, K, Yang, Y, Chen, S, Wang, T, Yu, D, Yu, X & Wang, G 2024, 'Folded Transmitarray Antenna via Independent Amplitude/Phase Control With Low Side-Lobe for Millimeter-Wave Communication', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 71, no. 4, pp. 2004-2008.
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Sun, T, He, B-G, Chen, J, Lu, H, Fang, B & Zhou, Y 2024, 'Optimization of electric vehicle charging and scheduling based on VANETs', Vehicular Communications, vol. 50, pp. 100857-100857.
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Sun, W, Guo, W, Li, B, Wen, S, Cao, J & Abdel-Aty, M 2024, 'Finite/Fixed-Time Controls of Neural Networks in a Signed Graph', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 54, no. 2, pp. 1049-1058.
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Sun, X, Chen, Z, Pan, M, Cai, Y, Jin, Z, Lei, G & Tian, X 2024, 'Robust Energy Management Optimization for PHEB Considering Driving Uncertainties by Using Sequential Taguchi Method', IEEE Transactions on Transportation Electrification, pp. 1-1.
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Sun, X, Dong, Z, Jin, Z, Lei, G & Tian, X 2024, 'System-Level Energy Management Optimization of Power-Split Hybrid Electric Vehicle Based on Nested Design', IEEE Transactions on Industrial Electronics, vol. 71, no. 9, pp. 10987-10997.
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Sun, X, Lin, X, Guo, D, Lei, G & Yao, M 2024, 'Improved Deadbeat Predictive Current Control With Extended State Observer for Dual Three-Phase PMSMs', IEEE Transactions on Power Electronics, vol. 39, no. 6, pp. 6769-6782.
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Sun, X, Su, Z, Lei, G & Yao, M 2024, 'Robust Predictive Cascaded Speed and Current Control for PMSM Drives Considering Parameter Variations', IEEE Transactions on Industrial Electronics, vol. 71, no. 9, pp. 10235-10245.
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Sun, X, Su, Z, Lei, G, Cai, Y & Yao, M 2024, 'Adaptive Model-Free Predictive Current Control for SPMSM Drives With Optimal Virtual Vector Modulation', IEEE/ASME Transactions on Mechatronics, vol. 29, no. 4, pp. 2569-2578.
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Sun, X, Wang, N, Yao, M & Lei, G 2024, 'Position Sensorless Control of Switched Reluctance Motors Based on Angle Adjustment Using Nonlinear Inductance and Flux Model', IEEE Transactions on Industrial Electronics, vol. 71, no. 12, pp. 15467-15477.
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Sun, X, Wen, Y, Zhu, Y, Zhang, L, Yao, M & Lei, G 2024, 'FCS-MPC With Improved Prediction for Suppressing Torque and Current Pulsations of Switched Reluctance Motors', IEEE Transactions on Industrial Electronics, pp. 1-9.
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Sun, X, Zhu, Y, Cai, Y, Yao, M, Sun, Y & Lei, G 2024, 'Optimized-Sector-Based Model Predictive Torque Control With Sliding Mode Controller for Switched Reluctance Motor', IEEE Transactions on Energy Conversion, vol. 39, no. 1, pp. 379-388.
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Sun, Y, Li, M, Zheng, M, Zou, Y & Shi, B 2024, 'Blood-brain barrier penetrating nanosystems enable synergistic therapy of glioblastoma', Nano Today, vol. 56, pp. 102310-102310.
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Sun, Y-L, Wang, H-L, Ngo, HH, Guo, W, Ni, B-J, Zhang, X-N & Wei, W 2024, 'Adapting to seasonal temperature variations: A dynamic multi-subunit strategy for sulfur autotrophic denitrification bioreactors', Environmental Research, vol. 240, pp. 117493-117493.
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Sun, Y-L, Wang, J-Y, Ngo, HH, Wei, W, Guo, W, Zhang, X-N, Cheng, H-Y, Yang, J-X & Wang, A-J 2024, 'Inducement mechanism and control of self-acidification in elemental sulfur fluidizing bioreactor', Bioresource Technology, vol. 393, pp. 130081-130081.
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Sun, Z, Lv, X & Yang, Y 2024, 'Wideband Sequential Rotation Dual-Circularly Polarized Magnetoelectric Dipole Array with Polarization Independent Control for Intelligent Vehicle Communications', IEEE Transactions on Antennas and Propagation, pp. 1-1.
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Sun, Z, Lv, X, Zhu, X, Liang, Z & Yang, Y 2024, 'A Dual-Band Circularly Polarized Antenna with Shared Aperture for Satellite-Assisted Internet of Things Communications', IEEE Internet of Things Journal, pp. 1-1.
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Suparmanto, EK, Mohamad, ET, Jahed Armaghani, D, Ahmad Legiman, MK, Zainal, Z, Zainuddin, NE & Abdul Razak, MH 2024, 'An overview of excavatability classification for bedded and non bedded rock in a tropical region', Geomechanics and Geophysics for Geo-Energy and Geo-Resources, vol. 10, no. 1.
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Suparmanto, EK, Mohamad, ET, Rathinasamy, V, Ahmad Legiman, MK, Zainal, Z, Zainuddin, NE, Slamat, F, Md Dan Azlan, MF & Armaghani, DJ 2024, 'A series of regression models to predict the weathering index of tropical granite rock mass', Environmental Earth Sciences, vol. 83, no. 17.
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AbstractIn the recent past, several weathering indicators have been developed to describe its state of weathering. The state of rock weathering is a useful indicator to estimate the integrity of tropically weathered rock material and mass which weatherability plays an important role in a tropical region. Through a ground assessment tool, the strength and durability of the rock mass could be estimated and complex or adopted to simplify the early prediction of the complex engineering parameter. This paper presents several models of the Weathering Index (WI) using selected significant parameters using statistical analysis. For this purpose, several sites have been chosen to represent granitic rock mass. Forty (40) numbers of samples were collected and tested comprising from four (4) sites in Malaysia. Several laboratory tests have been conducted such as Point Load Index (Is(50)), dry density, Slake Durability 1 (SD1), Slake Durability 2 (SD2) and moisture content. The field and laboratory data sets are used to determine the WI by using simple regression and MLR analysis Significant parameters found to be useful in determining the WI are selected namely SD1, dry density, Is(50), and block volume. These parameters were selected based on stepwise analysis using Statistical Package for the Social Sciences (SPSS). Following the models’ implementation, the models were evaluated and the best prediction model was selected after considering statistical coefficients, such as coefficient of determination (R2), variance account for (VAF), and root mean squared error (RMSE), as well as utilizing a straightforward ranking approach. The findings of this study could contribute to the more accurate prediction of WI using a more simplistic field and laboratory parameters. Therefore, the WI is useful during the initial stages and planning of rock excavation work and provides a good description of weat...
Surawski, NC, Awadallah, M, Zhao, E, Zhou, S, Dunn, T, Hall, C & Walker, PD 2024, 'Reducing real driving fuel consumption and emissions with a hydraulic hybrid vehicle', Science of The Total Environment, vol. 954, pp. 176549-176549.
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Swain, KC, Singha, C & Pradhan, B 2024, 'Estimating Total Rice Biomass and Crop Yield at Field Scale Using PlanetScope Imagery Through Hybrid Machine Learning Models', Earth Systems and Environment, vol. 8, no. 4, pp. 1713-1731.
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AbstractNear real-time crop monitoring has been a challenging due to the lack of high-resolution remote sensing images suitable for agricultural applications. The PlanetScope constellation, comprising approximately 130 Dove satellites, collects images of the entire Earth daily, with a resolution of 3.7 m. The high-resolution images from the PlanetScope satellite, along with vegetation indices, geo-environmental data, and soil and crop parameters, were utilized and analysed using machine learning models to enhance the accuracy of predicting total biomass and rice crop yield at the field scale. The study area, covering nearly 214 sample rice plots, was located in the Tarekswar block of Hooghly, West Bengal, India. Alongside ten vegetation indices and three Principal Component Analysis (PCA) soil nutrient levels, approximately thirty-six factors were analyzed to predict rice total biomass and crop yield using ten machine learning (ML) models, namely Random forest (RF), Extreme Gradient Boosting (XGB), Support Vector Machine (SVM), Bagging Tree (Treebag), Generalized Additive Models (gamSpline), Elastic Net (enet), Ordinary regression with LASSO penalty (rqlasso), Tree Models from Genetic Algorithm (evtree), Bayesian Regularized Neutral Networks (brnn), cubist models, and there hybrid of ensembles. Boruta and multi-collinearity analysis were also conducted for the selected factors to explore their influence levels. The study area exhibited robust rice yields ranging from 5 to 10 t/ha, accompanied by healthy biomass growth. Four ML models ─cubist, random forest, enet, and the hybrid model—showed promising predictions with R2 > 0.88. Most models classified less than 20 ha of the study area as falling into the “very-low suitable class”, showing the region’s suitability for rice cultivation due to its highly fertile alluvial soil. Boruta sensitive analysis revealed that nearly 2...
Swain, S, Mishra, PK, Nandi, S, Pradhan, B, Sahoo, S & Al-Ansari, N 2024, 'A simplistic approach for monitoring meteorological drought over arid regions: a case study of Rajasthan, India', Applied Water Science, vol. 14, no. 2.
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AbstractThe commonly used precipitation-based drought indices typically rely on probability distribution functions that can be suitable when the data exhibit minimal discrepancies. However, in arid and semi-arid regions, the precipitation data often display significant discrepancies due to highly irregular rainfall patterns. Consequently, imposing any probability distributions on the data for drought analysis in such regions may not be effective. To address this issue, this study employs a novel drought index called the Discrepancy Precipitation Index (DPI), specifically designed for arid regions. Unlike traditional methods, the DPI does not impose a probability distribution on the precipitation data; instead, it relies on the discrepancy between the data and the mean value. Drought severity classifications (i.e., Drought-I, Drought-II, and Drought-III) are proposed based on the DPI values. The DPI is used to characterize and assess the meteorological drought years based on annual and monsoonal precipitation over nineteen districts in Western Rajasthan, India, during 1901–2019. Additionally, a novel statistic called Discrepancy Measure (DM) is employed to assess the degree of discrepancy in the precipitation climatology of the districts for annual and monsoon precipitation time series. Based on annual precipitation, Jaisalmer district exhibited the highest number of historical drought years (35), whereas three districts, i.e., Jhunjhunu, Dausa, and Bhilwara exhibited the lowest number of drought years (11). Similarly, based on monsoon precipitation, Jaisalmer and Bhilwara encountered the highest (34) and the lowest (11) number of drought years, respectively. The return period of Drought-II is lower for monsoon precipitation-based DPI as compared to that of the annual precipitation-based DPI for all the districts. The DM and DPI-based total number of droughts are found to be strongly correlated for both annual and monsoon...
Syrmakesis, AD, Alhelou, HH & Hatziargyriou, ND 2024, 'A Novel Cyber Resilience Method for Frequency Control in Power Systems Considering Nonlinearities and Practical Challenges', IEEE Transactions on Industry Applications, vol. 60, no. 2, pp. 2176-2190.
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Syrmakesis, AD, Alhelou, HH & Hatziargyriou, ND 2024, 'A Novel Cyberattack-Resilient Frequency Control Method for Interconnected Power Systems Using SMO-Based Attack Estimation', IEEE Transactions on Power Systems, vol. 39, no. 4, pp. 5672-5686.
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Syrmakesis, AD, Alhelou, HH & Hatziargyriou, ND 2024, 'Novel SMO-Based Detection and Isolation of False Data Injection Attacks Against Frequency Control Systems', IEEE Transactions on Power Systems, vol. 39, no. 1, pp. 1434-1446.
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T., P, kumar, DR, Kumar, M, Samui, P & Armaghani, DJ 2024, 'A novel approach to estimate rock deformation under uniaxial compression using a machine learning technique', Bulletin of Engineering Geology and the Environment, vol. 83, no. 7.
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Tabandeh, A, Hossain, MJ, Khalilpour, K & Huang, Z 2024, 'Planning framework for establishment of hydrogen hubs incorporating the path towards net-zero-driven policies', International Journal of Hydrogen Energy, vol. 82, pp. 162-180.
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Tabassum, F, Islam, MR, Azim, MI, Rahman, MA, Faruque, MO, Shezan, SA & Hossain, MJ 2024, 'Secured energy data transaction for prosumers under diverse cyberattack scenarios', Sustainable Energy, Grids and Networks, vol. 40, pp. 101555-101555.
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Taghikhah, FR, Taghikhah, M, Marshall, JP & Voinov, A 2024, 'Navigating the community renewable energy landscape: An analytics-driven policy formulation', Applied Energy, vol. 362, pp. 123007-123007.
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Tahir, ZUR, Mukhtar, MF, Shad, MR, Asghar, F, Shahzad, M, Asim, M, Hassan, M, Mujtaba, MA, Siddiqi, SH, Ali, T, Fouad, Y & Kalam, MA 2024, 'Techno-economic analysis and optimization of 50 MWe linear fresnel reflector solar thermal power plant for different climatic conditions', Case Studies in Thermal Engineering, vol. 61, pp. 104909-104909.
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Tai, P, Indraratna, B, Rujikiatkamjorn, C, Chen, R & Li, Z 2024, 'Cyclic behaviour of stone column reinforced subgrade under partially drained condition', Transportation Geotechnics, vol. 47, pp. 101281-101281.
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Tai, T-W, Chen, H-Y, Shih, C-A, Huang, C-F, McCloskey, E, Lee, J-K, Yeap, SS, Cheung, C-L, Charatcharoenwitthaya, N, Jaisamrarn, U, Kuptniratsaikul, V, Yang, R-S, Lin, S-Y, Taguchi, A, Mori, S, Li-Yu, J, Ang, SB, Chan, D-C, Chan, WS, Ng, H, Chen, J-F, Tu, S-T, Chuang, H-H, Chang, Y-F, Chen, F-P, Tsai, K-S, Ebeling, PR, Marin, F, Nistal Rodríguez, FJ, Shi, H, Hwang, KR, Kim, K-K, Chung, Y-S, Reid, IR, Chandran, M, Ferrari, S, Lewiecki, EM, Hew, FL, Ho-Pham, LT, Nguyen, TV, Nguyen, VH, Lekamwasam, S, Pandey, D, Bhadada, S, Chen, C-H, Hwang, J-S & Wu, C-H 2024, 'Asia-Pacific consensus on long-term and sequential therapy for osteoporosis', Osteoporosis and Sarcopenia, vol. 10, no. 1, pp. 3-10.
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Talhami, M, Wakjira, T, Alomar, T, Fouladi, S, Fezouni, F, Ebead, U, Altaee, A, AL-Ejji, M, Das, P & Hawari, AH 2024, 'Single and ensemble explainable machine learning-based prediction of membrane flux in the reverse osmosis process', Journal of Water Process Engineering, vol. 57, pp. 104633-104633.
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Tang, A, Wang, X & Zhang, JA 2024, 'Interference Management for Full-Duplex ISAC in B5G/6G Networks: Architectures, Challenges, and Solutions', IEEE Communications Magazine, vol. 62, no. 9, pp. 20-26.
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Tang, H, Zhao, Y, Du, C, Xu, M & Wu, Q 2024, 'CAA: Class-Aware Affinity calculation add-on for semantic segmentation', Knowledge-Based Systems, vol. 299, pp. 112097-112097.
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Tang, Y, Chong, CT, Ng, J-H, Herraiz, L, Li, J, Ong, HC, Lam, SS, Tabatabaei, M & Chong, WWF 2024, 'Thermoexergetic analysis and response optimisation of selective exhaust gas recirculation with solvent-based CO2 capture in a natural gas-fired combined cycle power plant', Clean Technologies and Environmental Policy, vol. 26, no. 5, pp. 1643-1667.
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Tang, Y, Liu, J, Zhou, Z, Yu, X & Huo, Y 2024, 'Efficient 3D Representation Learning for Medical Image Analysis', World Scientific Annual Review of Artificial Intelligence, vol. 02.
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Machine learning approaches have significantly advanced the 3D medical images analysis, such as the CT and MRI scans, which enables improved diagnosis and treatment evaluation. These image volumes provide rich spatial context for understanding the internal brain and body anatomies. Typical medical image analysis tasks, such as segmentation, reconstruction and registration, are essential for characterizing this context. Related to 3D data formats, meshes, point clouds and others are used to represent the anatomical structures, each with unique applications. To better capture the spatial information and address data scarcity, self- and semi-supervised learning methods have emerged. However, efficient 3D representation learning remains challenging. Recently, Transformers have shown promise, leveraging the self-attention mechanisms that perform well on transfer learning and self-supervised methods. These techniques are applied for medical domains without extensive manual labeling. This work explores data-efficient models, scalable deep learning, semantic context utilization and transferability in 3D medical image analysis. We also evaluated the foundational models, self-supervised pre- training, transfer learning and prompt tuning, thus advancing this critical field.
Tangirala, A, Rawat, S & Lahoti, M 2024, 'A year-long study of eco-friendly fibre reinforced cementitious composites with high volume fly ash and industrial waste aggregates', Innovative Infrastructure Solutions, vol. 9, no. 5.
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Tangirala, A, Rawat, S & Lahoti, M 2024, 'Enhancing the resistance of cementitious composites to environmental thermal fatigue using high-volume fly ash and steel slag', Journal of Building Engineering, vol. 94, pp. 109905-109905.
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Tanveer, M, Sajid, M, Akhtar, M, Quadir, A, Goel, T, Aimen, A, Mitra, S, Zhang, Y-D, Lin, CT & Ser, JD 2024, 'Fuzzy Deep Learning for the Diagnosis of Alzheimer's Disease: Approaches and Challenges', IEEE Transactions on Fuzzy Systems, vol. 32, no. 10, pp. 5477-5492.
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Tao, M, Xu, Y, Zhao, R, Liu, Y & Wu, C 2024, 'Energy control and block performance optimization of bench blasting', International Journal of Rock Mechanics and Mining Sciences, vol. 180, pp. 105830-105830.
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Tao, M, Yang, Z, Zhao, Y, Wu, X & Wu, C 2024, 'Failure characteristics of microwave heat-treated stressed sandstone: Implications for deep rock breakage using TBM cutting', Energy, vol. 292, pp. 130489-130489.
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Tao, X, Yan, S, Gong, X & Adak, C 2024, 'Learning Multiresolution Features for Unsupervised Anomaly Localization on Industrial Textured Surfaces', IEEE Transactions on Artificial Intelligence, vol. 5, no. 1, pp. 127-139.
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Tapas, MJ, Thomas, P & Sirivivatnanon, V 2024, 'Effect of carbonation on high-slag binders', Concrete in Australia, vol. 50, no. 2, pp. 52-58.
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Cement production results in the significant release of CO2 into the environment. Therefore, the partial substitution of cement with supplementary cementitious materials (SCMs) is a primary strategy to reduce the upfront embodied carbon of concrete. Amongst the commercially available SCMs, ground granulated blast furnace slag (GGBFS) which has a composition closest to that of cement, allows the highest level of substitution in binary and ternary blends at ≥50%. GGBFS, other than helping lower the upfront embodied carbon, also improves most durability properties of the concrete including resistance to chlorides, sulfate and alkali-silica reaction. However, despite the known benefits, the use of high substitution levels of GGBFS has also been documented to reduce the carbonation resistance of the concrete. Carbonation is a natural process that has both detrimental and beneficial effects. Whilst carbonation results in the drop of the concrete pH increasing the susceptibility of the steel to reinforcement corrosion, it also facilitates the reabsorption of CO2 into the concrete. Owing to the increasing use of GGBFS and interest in carbonation, this paper investigates the effect of carbonation on the phase development and microstructure of high GGBFS binders. This study shows that: 1) the main driver for the drop in the pH of the pore solution during carbonation is the consumption of portlandite, 2) carbonation not only reduces the pH but also results in the decalcification of other phases and modification of the microstructure of the binder and, 3) binders with lower CaO content carbonate faster but have lower capacity to absorb CO2 per gram.
Tarte, JV, Johir, MAH, Tra, V-T, Cai, Z, Wang, Q & Nghiem, LD 2024, 'Optimising microplastics analysis for quantifying and identifying microplastic fibres in laundry wastewater', Science of The Total Environment, vol. 952, pp. 175907-175907.
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Tasci, I, Baygin, M, Barua, PD, Hafeez-Baig, A, Dogan, S, Tuncer, T, Tan, R-S & Acharya, UR 2024, 'Black-white hole pattern: an investigation on the automated chronic neuropathic pain detection using EEG signals', Cognitive Neurodynamics, vol. 18, no. 5, pp. 2193-2210.
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AbstractElectroencephalography (EEG) signals provide information about the brain activities, this study bridges neuroscience and machine learning by introducing an astronomy-inspired feature extraction model. In this work, we developed a novel feature extraction function, black-white hole pattern (BWHPat) which dynamically selects the most suitable pattern from 14 options. We developed BWHPat in a four-phase feature engineering model, involving multileveled feature extraction, feature selection, classification, and cortex map generation. Textural and statistical features are extracted in the first phase, while tunable q-factor wavelet transform (TQWT) aids in multileveled feature extraction. The second phase employs iterative neighborhood component analysis (INCA) for feature selection, and the k-nearest neighbors (kNN) classifier is applied for classification, yielding channel-specific results. A new cortex map generation model highlights the most active channels using median and intersection functions. Our BWHPat-driven model consistently achieved over 99% classification accuracy across three scenarios using the publicly available EEG pain dataset. Furthermore, a semantic cortex map precisely identifies pain-affected brain regions. This study signifies the contribution to EEG signal classification and neuroscience. The BWHPat pattern establishes a unique link between astronomy and feature extraction, enhancing the understanding of brain activities.
Tatli, S, Macin, G, Tasci, I, Tasci, B, Barua, PD, Baygin, M, Tuncer, T, Dogan, S, Ciaccio, EJ & Acharya, UR 2024, 'Transfer-transfer model with MSNet: An automated accurate multiple sclerosis and myelitis detection system', Expert Systems with Applications, vol. 236, pp. 121314-121314.
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Tavakoli, J, Hu, Q, Tipper, JL & Tang, Y 2024, 'Aggregation‐induced emission biomarkers for early detection of orthopaedic implant failure', Aggregate, vol. 5, no. 6.
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AbstractIn recent years, the substantial increase in total joint replacements for treating degenerative joint disease has heightened concerns regarding implant loosening and failure. This is especially critical as more young patients are undergoing both initial and subsequent joint replacement procedures. These complications often necessitate additional revision surgeries. Unfortunately, current clinical practices lack effective methods for the early detection of implant failure, and there is a noticeable absence of strategies utilizing molecular markers to identify post‐surgery implant issues. This article critically assesses the potential of aggregation‐induced emission (AIE) biomarkers in detecting molecular markers relevant to implant failure. It begins by outlining the pathogenesis of implant loosening and identifying pertinent molecular markers. The study then delves into how AIE luminogens (AIEgens) can play a crucial role in detecting processes such as osteogenesis and osteoclastogenesis. Notably, it discusses the utilization of AIEgens in detecting key molecular markers, including TNF‐α, osteocalcin, and urinary N‐terminal telopeptide. The prospect of AIE biomarkers for the early detection of bone loss and implant failure presents a promising avenue for enhancing our understanding of skeletal health and improving clinical outcomes through timely intervention and personalized treatment approaches. Ongoing research and development in this area are crucial for translating AIE‐based technologies into practical tools for optimizing bone health management.
Tavares, JMRS, Karri, C, Machado, JJM, Jain, DK, Dannana, S, Gottapu, SK & Gandomi, AH 2024, 'Recent Technology Advancements in Smart City Management: A Review', Computers, Materials & Continua, vol. 81, no. 3, pp. 3617-3663.
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Teng, J, Dong, A, Zhang, S, Zhang, X & Sheng, D 2024, 'Freezing‐Thawing Hysteretic Behavior of Soils', Water Resources Research, vol. 60, no. 7.
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AbstractThe soil freezing characteristic curve (SFCC) plays a crucial role in investigating the soil freezing‐thawing process. Due to the challenges associated with measuring the SFCC, there is a shortage of high‐quality or rigorous test results with sufficient metadata to be effectively used for applications. Current researchers typically conduct freezing tests to measure the SFCC and assume a singular SFCC when studying the freezing‐thawing process of soils, although limited studies indicated that there is a hysteresis during the freezing and thawing process. In this paper, a series of freezing‐thawing tests were performed to assess the SFCC, utilizing a precise nuclear magnetic resonance apparatus. The test results reveal a hysteresis between the SFCC obtained from the freezing process and that from the thawing process. Through analyzing the test results, the hysteresis mechanism of the SFCC is attributed to supercooling. Supercooling inhibits initial pore ice formation during freezing, causing a drastic liquid water‐ice phase change once supercooling ends. Despite being considered closely related, the hysteresis of the SFCC differs from the soil water characteristic curve (SWCC), and the models used to simulate the hysteresis of SWCC cannot directly be used. To address the impact of supercooling on soil freezing‐thawing hysteresis, a novel theoretical model is proposed. Comparisons between the measured and predicted results affirm the validity of the proposed model.
Teymouri, D, Sedehi, O, Song, M, Moaveni, B, Papadimitriou, C & Katafygiotis, LS 2024, 'Hierarchical Bayesian finite element model updating: Optimal weighting of modal residuals with application to FINO3 offshore platform', Mechanical Systems and Signal Processing, vol. 211, pp. 111150-111150.
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Thacharodi, A, Hassan, S, Meenatchi, R, Bhat, MA, Hussain, N, Arockiaraj, J, Ngo, HH, Sharma, A, Nguyen, HT & Pugazhendhi, A 2024, 'Mitigating microplastic pollution: A critical review on the effects, remediation, and utilization strategies of microplastics', Journal of Environmental Management, vol. 351, pp. 119988-119988.
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Thacharodi, A, Meenatchi, R, Hassan, S, Hussain, N, Bhat, MA, Arockiaraj, J, Ngo, HH, Le, QH & Pugazhendhi, A 2024, 'Microplastics in the environment: A critical overview on its fate, toxicity, implications, management, and bioremediation strategies', Journal of Environmental Management, vol. 349, pp. 119433-119433.
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Thakkar, J, Kolekar, S, Gite, S, Pradhan, B & Alamri, A 2024, 'Evaluating the Adaptability of Large Language Models for Knowledge-aware Question and Answering', International Journal on Smart Sensing and Intelligent Systems, vol. 17, no. 1.
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Abstract Large language models (LLMs) have transformed open-domain abstractive summarization, delivering coherent and precise summaries. However, their adaptability to user knowledge levels is largely unexplored. This study investigates LLMs’ efficacy in tailoring summaries to user familiarity. We assess various LLM architectures across different familiarity settings using metrics like linguistic complexity and reading grade levels. Findings expose current capabilities and constraints in knowledge-aware summarization, paving the way for personalized systems. We analyze LLM performance across three familiarity levels: none, basic awareness, and complete familiarity. Utilizing established readability metrics, we gauge summary complexity. Results indicate LLMs can adjust summaries to some extent based on user familiarity. Yet, challenges persist in accurately assessing user knowledge and crafting informative, comprehensible summaries. We highlight areas for enhancement, including improved user knowledge modeling and domain-specific integration. This research informs the advancement of adaptive summarization systems, offering insights for future development.
Thirunavukkarasu, M, Selvaraj, K, Chiranjeevi, C, Rathinavelu, V, Maguluri, L, Obaid, S, Alharbi, S, Kalam, A & Yokeswaran, R 2024, 'Enhancement and experimental study on thermal behaviour of heat pipe based solar absorber by using CuO nanofluid', Thermal Science, vol. 28, no. 1 Part A, pp. 241-247.
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Technological growth in thermal science found that the awareness of solar thermal energy improved widely in various applications and spotted issues on conventional flat plate solar collectors operating with water fluid: lower thermal efficiency, limited thermal performance during low sunlight, and unavoidable heat loss for extended plate surface. This research attempts to enhance the thermal performance of solar collectors modified with heat pipe solar absorber (HPSA) evaluated by 0.010, 0.015, and 0.02 volume fractions of CuO nanofluid at 18 Lpm. The effect of CuO on varied flow rate on temperature gain, heat transfer coefficient, and thermal efficiency of HPSA is experimentally studied, and its findings are compared with water fluid. The HPSA operates with 0.015 volume CuO nanofluid with a higher rate of flow, proving better thermal performance and offering a maximum temperature gain of 68?C with a better heat transfer coefficient of 81.5W/m2K results enhanced thermal efficiency of 85.2%, which are higher than the water fluid operated HPSA system. An optimum operating parameter of HPSA is suggested for heat exchanger applications.
Thiviyanathan, VA, Ker, PJ, Hoon Tang, SG, Amin, EPP, Yee, W, Hannan, MA, Jamaludin, Z, Nghiem, LD & Indra Mahlia, TM 2024, 'Microalgae biomass and biomolecule quantification: Optical techniques, challenges and prospects', Renewable and Sustainable Energy Reviews, vol. 189, pp. 113926-113926.
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Thomas, P & Sirivivatnanon, V 2024, 'Highlights From 17th ICAAR', Concrete in Australia, vol. 50, no. 3, pp. 36-40.
Tian, J, Lei, J, Zhang, J, Xie, W & Li, Y 2024, 'SwiMDiff: Scene-Wide Matching Contrastive Learning With Diffusion Constraint for Remote Sensing Image', IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-13.
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Tian, S, Li, X, Ren, J, Zhou, Z, Wang, F, Shi, K, Xu, J, Gu, T & Shon, H 2024, 'Emerging heat-localized solar distillation systems: Solar interfacial distillation VS photothermal membrane distillation', Desalination, vol. 572, pp. 117147-117147.
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Heat-localized solar distillation (HLSD) is an emerging environmentally- friendly high-efficiency distillation technology for clean water production. Solar interfacial distillation (SID) and photothermal membrane distillation (PMD) are two featured HLSD processes that have attracted a lot of attentions from researchers recently. Both SID and PMD systems produce water vapor from thin film surfaces where sunlight can be absorbed and converted as localized-heat to minimize heat loss and improve heat conversion efficiency. This article offers an overview of SID and PMD, including their respective classifications and characteristics. Subsequently, the water production capacities and purification efficiencies of the two systems are compared and the influencing factors are analyzed. Moreover, this paper compares the crystallization processes between the two systems and elucidates the methods employed to prevent salt fouling and achieve salt recovery. Finally, this study highlights the current challenges and future prospects of both systems to guide future research.
Tian, S, Zhou, Z, Li, X, Wang, F, Zhao, Y, Tijing, L, Shon, HK, Xu, B & Ren, J 2024, 'Applications of solar-driven interfacial evaporation-coupled photocatalysis in water treatment: A mini review', Desalination, vol. 592, pp. 118159-118159.
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Tian, W, Li, Q, Wang, Q, Chen, D & Gao, W 2024, 'Additive manufacturing error quantification on stability of composite sandwich plates with lattice-cores through machine learning technique', Composite Structures, vol. 327, pp. 117645-117645.
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Tian, Y, Feng, Y, Ruan, D, Luo, Z, Yang, C, Wu, D & Gao, W 2024, 'Full-field experiment-aided virtual modelling framework for inverse-based stochastic prediction of structures with elastoplasticity', Computer Methods in Applied Mechanics and Engineering, vol. 431, pp. 117284-117284.
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Tian, Y, Li, Q, Feng, Y, Luo, Z, Ruan, D & Gao, W 2024, 'Nonlinear dynamic analysis of the graphene platelets reinforced porous plate with magneto-electro-elastic sheets subjected to impact load', Nonlinear Dynamics, vol. 112, no. 3, pp. 1661-1690.
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Tian, Z, Cui, L, Zhang, C, Tan, S, Yu, S & Tian, Y 2024, 'The Role of Class Information in Model Inversion Attacks Against Image Deep Learning Classifiers', IEEE Transactions on Dependable and Secure Computing, vol. 21, no. 4, pp. 2407-2420.
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Tian, Z, Vo, H, Zhang, C, Min, G & Yu, S 2024, 'An Asynchronous Multi-Task Semantic Communication Method', IEEE Network, vol. 38, no. 4, pp. 275-283.
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Tian, Z, Vo, H, Zhang, C, Min, G & Yu, S 2024, 'An Asynchronous Multi-Task Semantic Communication Method', IEEE Network.
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Semantic communication has sparked great interest, due to the rising demands of emerging applications on high communication capacity and low latency. The majority of existing semantic communication methods are task-oriented, which transmit task-related semantic information via synchronous trained deep learning-based (DL-based) encoders and decoders. However, these methods have limitations in handling multi-task communications. Moreover, the synchronous training paradigm also leads to significant communication overhead in the establishing phase. In this article, we propose an asynchronous multi-task semantic communication method. In the proposed method, the DL-based encoder is trained independently using a contrastive learning method to extract task-independent semantic knowledge. Then, the receiver trains different DL-based decoders to perform various communication tasks based on the pre-trained encoder. Our method enables the accomplishment of multiple communication tasks in a single transmission. Moreover, the asynchronous training paradigm can reduce the communication overhead during the training phase of our system. The experimental results demonstrate that the proposed method achieves state-of-the-art performance in image classification and reconstruction tasks while requiring less than 10% of the training communication time compared to existing semantic communication systems.
Tian, Z, Xu, J & Tang, J 2024, 'Clustering High-Dimensional Noisy Categorical Data', Journal of the American Statistical Association, vol. 119, no. 548, pp. 3008-3019.
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Tiburcio, PDB, Desai, K, Kim, J, Zhou, Q, Guo, L, Xiao, X, Zhou, L, Yuksel, A, Catchpoole, DR, Amatruda, JF, Xu, L & Chen, KS 2024, 'DROSHA Regulates Mesenchymal Gene Expression in Wilms Tumor', Molecular Cancer Research, vol. 22, no. 8, pp. 711-720.
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Abstract Wilms tumor, the most common pediatric kidney cancer, resembles embryonic renal progenitors. Currently, there are no ways to therapeutically target Wilms tumor driver mutations, such as in the microRNA processing gene DROSHA. In this study, we used a “multiomics” approach to define the effects of DROSHA mutation in Wilms tumor. We categorized Wilms tumor mutations into four mutational subclasses with unique transcriptional effects: microRNA processing, MYCN activation, chromatin remodeling, and kidney developmental factors. In particular, we find that DROSHA mutations are correlated with de-repressing microRNA target genes that regulate differentiation and proliferation and a self-renewing, mesenchymal state. We model these findings by inhibiting DROSHA expression in a Wilms tumor cell line, which led to upregulation of the cell cycle regulator cyclin D2 (CCND2). Furthermore, we observed that DROSHA mutations in Wilms tumor and DROSHA silencing in vitro were associated with a mesenchymal state with aberrations in redox metabolism. Accordingly, we demonstrate that Wilms tumor cells lacking microRNAs are sensitized to ferroptotic cell death through inhibition of glutathione peroxidase 4, the enzyme that detoxifies lipid peroxides. Implications: This study reveals genotype–transcriptome relationships in Wilms tumor and points to ferroptosis as a potentially therapeutic vulnerability in one subset of Wilms tumor.
Tomidei, L, Guertler, M, Sick, N, Paul, G & Carmichael, M 2024, 'Design Principles for Safe Human Robot Collaboration.', Interaction Design and Architecture(s), vol. 61, no. 61, pp. 66-97.
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With the development of collaborative robots (cobots), a paradigm shift in human-robot collaboration (HRC) is emerging in the workplace. When introducing cobots, a new range of hazards and harms needs to be considered. While physical hazards have been extensively studied and were paramount in the development of cobots, lesser-known hazards are related to mental and ethical wellbeing. Accordingly, most existing safety measures are designed to address exclusively physical hazards including ergonomics. To this end, this study sets out to develop holistic design principles for safe HRC by adopting a human-centred approach. A systematic review of the relevant literature combined with real-world insights gathered through interviews with industry and academic experts leads to design principles for safe HRC that can contribute to the future development of collaborative robot systems. This also highlights challenges which future research around safety guidelines and standards needs to address.
Tran, D-T, To, T-D, Le, T-H, Dao, Q-T & Nghiem, LD 2024, 'Synthesis of highly effective and easily recoverable MIL-100(Fe)/MgFe2O4 adsorbent for enhanced antibiotic removal from water', Journal of Industrial and Engineering Chemistry.
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Tran, E, Sun, J & Gundara, J 2024, 'Systematic review of robotic ventral hernia repair with meta‐analysis', ANZ Journal of Surgery, vol. 94, no. 1-2, pp. 37-46.
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AbstractBackgroundDespite being one of the most common operations performed by general surgeons, there is a lack of consensus regarding the recommended approach for ventral hernia repair (VHR). Recent times have seen the rapid development of new techniques, such as robotic ventral hernia repair (RVHR). This systematic review and meta‐analysis aims to evaluate the currently available evidence relating to RVHR, in comparison to open VHR (OVHR) and laparoscopic VHR (LVHR).MethodsA systematic search of the following databases was conducted: PubMed, Embase, Scopus and Web of Science. A meta‐analysis was performed for the outcomes of length of stay (LOS), recurrence, operative time, intraoperative complications, wound complications, 30‐day readmission, 30‐day reoperation, mortality and costs.ResultsA total of 39 studies met inclusion criteria. Overall, RVHR reduced LOS, intra‐operative complications, wound complications and readmission compared to OVHR. Compared to LVHR, RVHR was associated with increased operative time and costs, with comparable clinical outcomes.ConclusionThere is currently a lack of robust evidence to support the robotic approach in VHR. It does not demonstrate major benefits in comparison to LVHR, which is more affordable and accessible. Strong quality, long‐term data is required to help with establishing a gold standard approach in VHR.
Tran, NH, Sais, D & Tran, N 2024, 'Advances in human papillomavirus detection and molecular understanding in head and neck cancers: Implications for clinical management', Journal of Medical Virology, vol. 96, no. 6.
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AbstractHead and neck cancers (HNCs), primarily head and neck squamous cell carcinoma (HNSCC), are associated with high‐risk human papillomavirus (HR HPV), notably HPV16 and HPV18. HPV status guides treatment and predicts outcomes, with distinct molecular pathways in HPV‐driven HNSCC influencing survival rates. HNC incidence is rising globally, with regional variations reflecting diverse risk factors, including tobacco, alcohol, and HPV infection. Oropharyngeal cancers attributed to HPV have significantly increased, particularly in regions like the United States. The HPV16 genome, characterized by oncoproteins E6 and E7, disrupts crucial cell cycle regulators, including tumor protein p53 (TP53) and retinoblastoma (Rb), contributing to HNSCC pathogenesis. P16 immunohistochemistry (IHC) is a reliable surrogate marker for HPV16 positivity, while in situ hybridization and polymerase chain reaction (PCR) techniques, notably reverse transcription‐quantitative PCR (RT‐qPCR), offer sensitive HPV detection. Liquid‐based RT‐qPCR, especially in saliva, shows promise for noninvasive HPV detection, offering simplicity, cost‐effectiveness, and patient compliance. These molecular advancements enhance diagnostic accuracy, guide treatment decisions, and improve patient outcomes in HNC management. In conclusion, advances in HPV detection and molecular understanding have significant clinical management implications. Integrating these advancements into routine practice could ultimately improve patient outcomes.
Tran, T, Bliuc, D, Abrahamsen, B, Chen, W, Eisman, JA, Hansen, L, Vestergaard, P, Nguyen, TV, Blank, RD & Center, JR 2024, 'Multimorbidity clusters potentially superior to individual diseases for stratifying fracture risk in older people: a nationwide cohort study', Age and Ageing, vol. 53, no. 7.
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Abstract Rationale Comorbidities are common in fracture patients, but the interaction between fracture and comorbidities remains unclear. This study aimed to define specific multimorbidity clusters in older adults and quantify the association between the multimorbidity clusters and fracture risk. Methods This nationwide cohort study includes 1.7 million adults in Denmark aged ≥50 years who were followed from 2001 through 2014 for an incident low-trauma fracture. Chronic diseases and fractures were identified from the Danish National Hospital Discharge Register. Latent class analysis and Cox’s regression were conducted to define the clusters and quantify fracture risk, respectively. Results The study included 793 815 men (age: 64 ± 10) and 873 524 women (65.5 ± 11), with a third having ≥1 chronic disease. The pre-existent chronic diseases grouped individuals into low-multimorbidity (80.3% in men, 83.6% in women), cardiovascular (12.5%, 10.6%), malignant (4.1%, 3.8%), diabetic (2.4%, 2.0%) and hepatic clusters (0.7%, men only). These clusters distinguished individuals with advanced, complex, or late-stage disease from those having earlier-stage disease. During a median follow-up of 14 years (IQR: 6.5, 14), 95 372 men and 212 498 women sustained an incident fracture. The presence of multimorbidity was associated with a significantly greater risk of fracture, independent of age and sex. Importantly, the multimorbidity clusters had the highest discriminative performance in assessing fracture risk, whereas the strength of their association with fracture risk equalled or exceeded that ...
Tran, TS, Bliuc, D, Blank, RD, Center, JR & Nguyen, TV 2024, 'Fracture risk assessment in the presence of competing risk of death', Osteoporosis International, vol. 35, no. 11, pp. 1989-1998.
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Trzciński, M, Łukasik, S & Gandomi, AH 2024, 'Optimizing the Structures of Transformer Neural Networks Using Parallel Simulated Annealing', Journal of Artificial Intelligence and Soft Computing Research, vol. 14, no. 3, pp. 267-282.
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Abstract The Transformer is an important addition to the rapidly increasing list of different Artificial Neural Networks (ANNs) suited for extremely complex automation tasks. It has already gained the position of the tool of choice in automatic translation in many business solutions. In this paper, we present an automated approach to optimizing the Transformer structure based upon Simulated Annealing, an algorithm widely recognized for both its simplicity and usability in optimization tasks where the search space may be highly complex. The proposed method allows for the use of parallel computing and time-efficient optimization, thanks to modifying the structure while training the network rather than performing the two one after another. The algorithm presented does not reset the weights after changes in the transformer structure. Instead, it continues the training process to allow the results to be adapted without randomizing all the training parameters. The algorithm has shown a promising performance during experiments compared to traditional training methods without structural modifications. The solution has been released as open-source to facilitate further development and use by the machine learning community.
Tuan, HD, Nasir, AA, Dutkiewicz, E, Poor, HV & Hanzo, L 2024, 'Active-RIS Enhances the Multi-User Rate of Multi-Carrier Communications', IEEE Transactions on Vehicular Technology, vol. 73, no. 11, pp. 16948-16963.
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Tuan, HD, Nasir, AA, Dutkiewicz, E, Poor, HV & Hanzo, L 2024, 'RIS-Aided Multiple-Input Multiple-Output Broadcast Channel Capacity', IEEE Transactions on Communications, vol. 72, no. 1, pp. 117-132.
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Tuan, LA & Ha, QP 2024, 'Adaptive fractional-order integral fast terminal sliding mode and fault-tolerant control of dual-arm robots', Robotica, vol. 42, no. 5, pp. 1476-1499.
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AbstractClosed-loop kinematics of a dual-arm robot (DAR) often induces motion conflict. Control formulation is increasingly difficult in face of actuator failures. This article presents a new approach for fault-tolerant control of DARs based on advanced sliding mode control. A comprehensive fractional-order model is proposed taking nonlinear viscous and viscoelastic friction at the joints into account. Using integral fast terminal sliding mode control and fractional calculus, we develop two robust controllers for robots subject to motor faults, parametric uncertainties, and disturbances. Their merits rest with their strong robustness, speedy finite-time convergence, shortened reaching phase, and flexible selection of derivative orders. To avoid the need for full knowledge of faults, robot parameters, and disturbances, two versions of the proposed approach, namely adaptive integral fractional-order fast terminal sliding mode control, are developed. Here, an adaptation mechanism is equipped for estimating a common representative of individual uncertainties. Simulation and experiment are provided along with an extensive comparison with existing approaches. The results demonstrate the superiority of the proposed control technique. The robot performs well the tasks with better responses (e.g., with settling time reduced by at least 16%).
Tuncer, T, Barua, PD, Tuncer, I, Dogan, S & Acharya, UR 2024, 'A lightweight deep convolutional neural network model for skin cancer image classification', Applied Soft Computing, vol. 162, pp. 111794-111794.
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Tuncer, T, Dogan, S, Baygin, M, Barua, PD, Palmer, EE, March, S, Ciaccio, EJ, Tan, R-S & Acharya, UR 2024, 'FLP: Factor lattice pattern-based automated detection of Parkinson's disease and specific language impairment using recorded speech', Computers in Biology and Medicine, vol. 173, pp. 108280-108280.
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Tuncer, T, Dogan, S, Baygin, M, Tasci, I, Mungen, B, Tasci, B, Barua, PD & Acharya, UR 2024, 'Directed Lobish-based explainable feature engineering model with TTPat and CWINCA for EEG artifact classification', Knowledge-Based Systems, vol. 305, pp. 112555-112555.
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Tung, NX, Chien, TV, Hoang, DT & Hwang, WJ 2024, 'Jointly Optimizing Power Allocation and Device Association for Robust IoT Networks under Infeasible Circumstances', IEEE Transactions on Network and Service Management, pp. 1-1.
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Tusher, AS, Rahman, MA, Islam, MR & Hossain, MJ 2024, 'A comparative analysis of different transmission line fault detectors and classifiers during normal conditions and cyber‐attacks', The Journal of Engineering, vol. 2024, no. 7.
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AbstractTransmission lines, the core part of the transmission and distribution system in the smart grid, require effective, efficient, and reliable protective measures against faults to avoid severe damage to physical infrastructure and financial losses. Due to their growing popularity, machine learning models are used in fault detection and classification, whose performances can be severely affected by cyber‐attacks due to their data dependency, posing a critical concern. Hence, this paper introduces false data injection attacks to address the vulnerability of machine learning‐based fault detectors and classifiers. A comparative study of 9 detection models and 6 classification models under normal conditions and during a combination of two models of false data injection attacks is presented to evaluate the severity of cyber‐attacks. Experimental results show that highly accurate models in normal conditions are more susceptible to cyber‐attacks, with up to 69% and 28% degradations in accuracy for fault detectors and classifiers, respectively. Furthermore, the detection models are found to be more vulnerable to cyber‐attacks than the classification models. With no robust detectors and classifiers being found, this work addresses the importance of developing attack‐resilient fault detection and classification schemes considering their academic and industrial significance.
Tusher, AS, Rahman, MA, Islam, MR & Hossain, MJ 2024, 'Adversarial training-based robust lifetime prediction system for power transformers', Electric Power Systems Research, vol. 231, pp. 110351-110351.
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Urbański, P, Huang, Y, Gallas, D, Zhou, JL & Merkisz, J 2024, 'Real-world assessment of the energy consumption and emissions performance of a novel diesel-electric dual-drive locomotive', Sustainable Energy Technologies and Assessments, vol. 71, pp. 104017-104017.
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Usayiwevu, M, Sukkar, F, Yoo, C, Fitch, R & Vidal-Calleja, T 2024, 'Continuous planning for inertial-aided systems', Autonomous Robots, vol. 48, no. 8.
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AbstractInertial-aided systems require continuous motion excitation among other reasons to characterize the measurement biases that will enable accurate integration required for localization frameworks. This paper proposes the use of informative path planning to find the best trajectory for minimizing the uncertainty of IMU biases and an adaptive traces method to guide the planner towards trajectories that aid convergence. The key contribution is a novel regression method based on Gaussian Process (GP) to enforce continuity and differentiability between waypoints from a variant of the $$\hbox {RRT}^*$$ RRT ∗ planning algorithm. We employ linear operators applied to the GP kernel function to infer not only continuous position trajectories, but also velocities and accelerations. The use of linear functionals enable velocity and acceleration constraints given by the IMU measurements to be imposed on the position GP model. The results from both simulation and real-world experiments show that planning for IMU bias convergence helps minimize localization errors in state estimation frameworks.
Üstün, G, Morello, A & Devitt, S 2024, 'Single-step parity check gate set for quantum error correction', Quantum Science and Technology, vol. 9, no. 3, pp. 035037-035037.
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Abstract A key requirement for an effective quantum error correction (QEC) scheme is that the physical qubits have error rates below a certain threshold. The value of this threshold depends on the details of the specific QEC scheme, and its hardware-level implementation. This is especially important with parity-check circuits, which are the fundamental building blocks of QEC codes. The standard way of constructing the parity check circuit is using a universal set of gates, namely sequential CNOT gates, single-qubit rotations and measurements. We exploit the insight that a QEC code does not require universal logic gates, but can be simplified to perform the sole task of error detection and correction. By building gates that are fundamental to QEC, we can boost the threshold and ease the experimental demands on the physical hardware. We present a rigorous formalism for constructing and verifying the error behavior of these gates, linking the physical measurement of a process matrix to the abstract error models commonly used in QEC analysis. This allows experimentalists to directly map the gates used in their systems to thresholds derived for a broad-class of QEC codes. We give an example of these new constructions using the model system of two nuclear spins, coupled to an electron spin, showing the potential benefits of redesigning fundamental gate sets using QEC primitives, rather than traditional gate sets reliant on simple single and two-qubit gates.
Üstündağlı Erten, E, Güzeloğlu, EB, Ifaei, P, Khalilpour, K, Ifaei, P & Yoo, C 2024, 'Decoding intersectionality: A systematic review of gender and energy dynamics under the structural and situational effects of contexts', Energy Research & Social Science, vol. 110, pp. 103432-103432.
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Van Alboom, M, Baert, F, Bernardes, SF, Verhofstadt, L, Bracke, P, Jia, M, Musial, K, Gabrys, B & Goubert, L 2024, 'Examining the Role of Structural and Functional Social Network Characteristics in the Context of Chronic Pain: An Ego-centered Network Design', The Journal of Pain, vol. 25, no. 9, pp. 104525-104525.
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Van Huynh, N, Wang, J, Du, H, Hoang, DT, Niyato, D, Nguyen, DN, Kim, DI & Letaief, KB 2024, 'Generative AI for Physical Layer Communications: A Survey', IEEE Transactions on Cognitive Communications and Networking, vol. 10, no. 3, pp. 706-728.
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Vandenput, L, Johansson, H, McCloskey, EV, Liu, E, Schini, M, Åkesson, KE, Anderson, FA, Azagra, R, Bager, CL, Beaudart, C, Bischoff-Ferrari, HA, Biver, E, Bruyère, O, Cauley, JA, Center, JR, Chapurlat, R, Christiansen, C, Cooper, C, Crandall, CJ, Cummings, SR, da Silva, JAP, Dawson-Hughes, B, Diez-Perez, A, Dufour, AB, Eisman, JA, Elders, PJM, Ferrari, S, Fujita, Y, Fujiwara, S, Glüer, C-C, Goldshtein, I, Goltzman, D, Gudnason, V, Hall, J, Hans, D, Hoff, M, Hollick, RJ, Huisman, M, Iki, M, Ish-Shalom, S, Jones, G, Karlsson, MK, Khosla, S, Kiel, DP, Koh, W-P, Koromani, F, Kotowicz, MA, Kröger, H, Kwok, T, Lamy, O, Langhammer, A, Larijani, B, Lippuner, K, McGuigan, FEA, Mellström, D, Merlijn, T, Nguyen, TV, Nordström, A, Nordström, P, O’Neill, TW, Obermayer-Pietsch, B, Ohlsson, C, Orwoll, ES, Pasco, JA, Rivadeneira, F, Schott, A-M, Shiroma, EJ, Siggeirsdottir, K, Simonsick, EM, Sornay-Rendu, E, Sund, R, Swart, KMA, Szulc, P, Tamaki, J, Torgerson, DJ, van Schoor, NM, van Staa, TP, Vila, J, Wareham, NJ, Wright, NC, Yoshimura, N, Zillikens, M, Zwart, M, Harvey, NC, Lorentzon, M, Leslie, WD & Kanis, JA 2024, 'A meta-analysis of previous falls and subsequent fracture risk in cohort studies', Osteoporosis International, vol. 35, no. 3, pp. 469-494.
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Venkatesh, R, Vignesh Kumar, M, Kantharaj, I, David, R, De Poures, MV, Hossain, I, Seikh, AH, Kalam, MA & P, M 2024, 'Improvement of mechanical performance on zirconium dioxide nanoparticle synthesized magnesium alloy nano composite', Heliyon, vol. 10, no. 9, pp. e29892-e29892.
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Venu, H, Kiong, TS, Soudagar, MEM, Razali, NM, Ramesh, S, Fouad, Y, Rajabi, A, Appavu, P, Raju, VD, Veza, I, Subrmani, L, Kalam, MA & Ağbulut, Ü 2024, 'A comprehensive combustion, performance, and environmental analyses of algae biofuel, hydrogen gas, and nano-sized particles (liquid-gas-solid mix) in agricultural CRDI engines', International Journal of Hydrogen Energy, vol. 73, pp. 839-855.
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Vermeulen, J, Caldwell, G, Teixeira, M, Burden, A & Guertler, M 2024, 'To Safety and Beyond! A Scoping Review of Human Factors Enriching the Design of Human-Robot Collaboration', Interaction Design and Architecture(s), vol. 61, no. 61, pp. 42-65.
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This scoping review explores human factors that enrich the design of Human-Robot Collaboration (HRC) beyond the traditional focus on ergonomics and safety. As Industry 5.0 shifts towards a human-centric perspective, understanding the multifaceted interactions within socio-technical systems becomes crucial. The review investigates diverse fields, including design, psychology, and engineering, to identify human factors influencing the successful integration of Collaborative Robotics. The research findings confirm the need and potentiality of using the holistic lens of human factors to illuminate human-centric needs in HRC designs. Moving beyond quantitative measures, the study advocates for qualitative insights to inform the design of HRC and enhance worker conditions through individualised and contextualised experiences of collaborating with cobots. The findings contribute to advancing the understanding of HRC’s complexity and underscore the significance of user-driven perspectives in future research and design efforts.
Vettori, L, Tran, HA, Mahmodi, H, Filipe, EC, Wyllie, K, Liu Chung Ming, C, Cox, TR, Tipper, J, Kabakova, IV, Rnjak-Kovacina, J & Gentile, C 2024, 'Silk fibroin increases the elasticity of alginate-gelatin hydrogels and regulates cardiac cell contractile function in cardiac bioinks', Biofabrication, vol. 16, no. 3, pp. 035025-035025.
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Abstract Silk fibroin (SF) is a natural protein extracted from Bombyx mori silkworm thread. From its common use in the textile industry, it emerged as a biomaterial with promising biochemical and mechanical properties for applications in the field of tissue engineering and regenerative medicine. In this study, we evaluate for the first time the effects of SF on cardiac bioink formulations containing cardiac spheroids (CSs). First, we evaluate if the SF addition plays a role in the structural and elastic properties of hydrogels containing alginate (Alg) and gelatin (Gel). Then, we test the printability and durability of bioprinted SF-containing hydrogels. Finally, we evaluate whether the addition of SF controls cell viability and function of CSs in Alg–Gel hydrogels. Our findings show that the addition of 1% (w/v) SF to Alg–Gel hydrogels makes them more elastic without affecting cell viability. However, fractional shortening (FS%) of CSs in SF–Alg–Gel hydrogels increases without affecting their contraction frequency, suggesting an improvement in contractile function in the 3D cultures. Altogether, our findings support a promising pathway to bioengineer bioinks containing SF for cardiac applications, with the ability to control mechanical and cellular features in cardiac bioinks.
Vieira, L, Mordechai, HS, Sharabi, M, Tipper, JL & Tavakoli, J 2024, 'Stress relaxation behavior of the transition zone in the intervertebral disc', Acta Biomaterialia, vol. 189, pp. 366-376.
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Vijayan, MK, Paler, A, Gavriel, J, Casey, M, Rohde, PP & Devitt, S 2024, 'Compilation of algorithm-specific graph states for quantum circuits', Quantum Science and Technology, vol. 9, pp. 1-22.
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We present a quantum circuit compiler that prepares an algorithm-specific graph state from quantum circuits described in high level languages, such as Cirq and Q#. The computation can then be implemented using a series of non-Pauli measurements on this graph state. By compiling the graph state directly instead of starting with a standard lattice cluster state and preparing it over the course of the computation, we are able to better understand the resource costs involved and eliminate wasteful Pauli measurements on the actual quantum device. Access to this algorithm-specific graph state also allows for optimisation over locally equivalent graph states to implement the same quantum circuit. The compiler presented here finds ready application in measurement based quantum computing, NISQ devices and logical level compilation for fault tolerant implementations.
Viswanathan, S, Arockiaraj, G, Obaid, S & Kalam, M 2024, 'Enhancement of quick charging and discharging of tes system by PCM mixed with Al2O3 nano particles for EV', Thermal Science, vol. 28, no. 1 Part A, pp. 189-196.
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Technology for storing heat energy with small amount of area has been proposed a challenge to the researchers over the past few decades. This would render highly useful for the thermal management system of electric vehicles. The PCM was used as an energy storage system in this work. It offers the chief advantage of higher storage density which is very much expected for both industrial and domestic needs, especially electric vehicles. In this work, the enhancement of specific heat capacity for the provided PCM was improved by embedding alumina nanoparticles into the storage medium. The addition of nanoparticles in the PCM resulted in the increase of heat absorption capacity, a 50% increase in charging time and a 25% reduction in discharging time of the PCM for the volume concentration of 0.833%. The increase of efficiency by 6% during charging and 4% during the discharging processes were observed as the effect of addition of alumina nanoparticle in the system.
Vital, MLN 2024, 'A Constant Common Mode Voltage Single-Phase Five-Level Transformerless PV Inverter Considering the Effect of Switch Device Junction Capacitance', CPSS Transactions on Power Electronics and Applications, vol. 9, no. 3.
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Vo, PHN, Danaee, S, Hai, HTN, Huy, LN, Nguyen, TAH, Nguyen, HTM, Kuzhiumparambil, U, Kim, M, Nghiem, LD & Ralph, PJ 2024, 'Biomining for sustainable recovery of rare earth elements from mining waste: A comprehensive review', Science of The Total Environment, vol. 908, pp. 168210-168210.
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Vo, PHN, Kuzhiumparambil, U, Kim, M, Hinkley, C, Pernice, M, Nghiem, LD & Ralph, PJ 2024, 'Biomining using microalgae to recover rare earth elements (REEs) from bauxite', Bioresource Technology, vol. 406, pp. 131077-131077.
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Vo, TP, Danaee, S, Chaiwong, C, Pham, BT, Poddar, N, Kim, M, Kuzhiumparambil, U, Songsomboon, C, Pernice, M, Ngo, HH, Ralph, PJ & Vo, PHN 2024, 'Microalgae-bacteria consortia for organic pollutants remediation from wastewater: A critical review', Journal of Environmental Chemical Engineering, vol. 12, no. 6, pp. 114213-114213.
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Vollset, SE, Ababneh, HS, Abate, YH, Abbafati, C, Abbasgholizadeh, R, Abbasian, M, Abbastabar, H, Abd Al Magied, AHA, Abd ElHafeez, S, Abdelkader, A, Abdelmasseh, M, Abd-Elsalam, S, Abdi, P, Abdollahi, M, Abdoun, M, Abdullahi, A, Abebe, M, Abiodun, O, Aboagye, RG, Abolhassani, H, Abouzid, M, Aboye, GB, Abreu, LG, Absalan, A, Abualruz, H, Abubakar, B, Abukhadijah, HJJ, Addolorato, G, Adekanmbi, V, Adetunji, CO, Adetunji, JB, Adeyeoluwa, TE, Adha, R, Adhikary, RK, Adnani, QES, Adzigbli, LA, Afrashteh, F, Afzal, MS, Afzal, S, Agbozo, F, Agodi, A, Agrawal, A, Agyemang-Duah, W, Ahinkorah, BO, Ahlstrom, AJ, Ahmad, A, Ahmad, F, Ahmad, MM, Ahmad, S, Ahmad, S, Ahmed, A, Ahmed, A, Ahmed, H, Ahmed, S, Ahmed, SA, Akinosoglou, K, Akkaif, MA, Akrami, AE, Akter, E, Al Awaidy, S, Al Hasan, SM, Al Mosa, AS, Al Ta'ani, O, Al Zaabi, OAM, Alahdab, F, Alajlani, MM, Al-Ajlouni, Y, Alalalmeh, SO, Al-Aly, Z, Alam, K, Alam, N, Alam, T, Alam, Z, Al-amer, RM, Alanezi, FM, Alanzi, TM, Albakri, A, Aldhaleei, WA, Aldridge, RW, Alemohammad, SY, Alemu, YM, Al-Gheethi, AAS, Al-Hanawi, MK, Ali, A, Ali, A, Ali, I, Ali, MU, Ali, R, Ali, SSS, Ali, VE, Ali, W, Al-Ibraheem, A, Alicandro, G, Alif, SM, Aljunid, SM, Alla, F, Almazan, JU, Al-Mekhlafi, HM, Alqutaibi, AY, Alrawashdeh, A, Alrousan, SM, Al-Sabah, SK, Alsabri, MA, Altaany, Z, Al-Tammemi, AB, Al-Tawfiq, JA, Altirkawi, KA, Aluh, DO, Alvis-Guzman, N, Al-Wardat, MS, Al-Worafi, YM, Aly, H, Alyahya, MS, Alzoubi, KH, Al-Zyoud, W, Amani, R, Ameyaw, EK, Amin, TT, Amindarolzarbi, A, Amiri, S, Amirzade-Iranaq, MH, Amu, H, Amugsi, DA, Ancuceanu, R, Anderlini, D, Anderson, DB, Andrade, PP, Andrei, CL, Andrei, T, Andrews, EA, Anil, A, Anil, S, Anoushiravani, A, Antony, CM, Antriyandarti, E, Anuoluwa, BS, Anvari, S, Anyasodor, AE, Appiah, F, Aquilano, M, Arab, JP, Arabloo, J, Arafa, EA, Arafat, M, Aravkin, AY, Ardekani, A, Areda, D, Aregawi, BB, Aremu, A, Ariffin, H, Arkew, M, Armani, K, Artamonov, AA, Arumugam, A, Asghari-Jafarabadi, M, Ashbaugh, C, Astell-Burt, T, Athari, SS, Atorkey, P, Atout, MMW, Aujayeb, A, Ausloos, M, Awad, H, Awotidebe, AW, Ayatollahi, H, Ayuso-Mateos, JL, Azadnajafabad, S, Azeez, FK, Azevedo, RMS, Badar, M, Baghdadi, S, Bagheri, M, Bagheri, N, Bai, R, Baker, JL, Bako, AT, Balakrishnan, S, Balcha, WF, Baltatu, OC, Barchitta, M, Bardideh, E, Barker-Collo, SL, Bärnighausen, TW, Barqawi, HJ & et al. 2024, 'Burden of disease scenarios for 204 countries and territories, 2022–2050: a forecasting analysis for the Global Burden of Disease Study 2021', The Lancet, vol. 403, no. 10440, pp. 2204-2256.
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Vu, H & Catchpoole, DR 2024, 'Data Data Everywhere: Harnessing Digital Health: Reflections from the Australasian Institute of Digital Health’s Healthcare Innovations Community Conference, Brisbane, August 5–7, 2024', Innovations in Digital Health, Diagnostics, and Biomarkers, vol. 4, no. 2024, pp. 94-95.
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Vu, HP, Kuzhiumparambil, U, Cai, Z, Wang, Q, Ralph, PJ & Nghiem, LD 2024, 'Enhanced biomethane production from Scenedesmus sp. using polymer harvesting and expired COVID-19 disinfectant for pretreatment', Chemosphere, vol. 356, pp. 141869-141869.
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Vu, TT, Chu, NH, Phan, KT, Hoang, DT, Nguyen, DN & Dutkiewicz, E 2024, 'Energy-Based Proportional Fairness in Cooperative Edge Computing', IEEE Transactions on Mobile Computing, vol. 23, no. 12, pp. 12229-12246.
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Waheed, S, Ahmed, A, Abid, M, Mufti, RA, Ferreira, F, Bashir, MN, Shah, AUR, Jafry, AT, Zulkifli, NW & Fattah, IMR 2024, 'Ionic liquids as lubricants: An overview of recent developments', Journal of Molecular Structure, vol. 1301, pp. 137307-137307.
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Wali, SB, Hannan, MA, Ker, PJ, Rahman, SA, Le, KN, Begum, RA, Tiong, SK & Mahlia, TMI 2024, 'Grid-connected lithium-ion battery energy storage system towards sustainable energy: A patent landscape analysis and technology updates', Journal of Energy Storage, vol. 77, pp. 109986-109986.
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Wambsganss, A, Tomidei, L, Sick, N, Salomo, S & Miled, EB 2024, 'Machine learning-based method to cluster a converging technology system: The case of printed electronics', World Patent Information, vol. 78, pp. 102301-102301.
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Wan, S, Jin, Y, Xu, G & Nappi, M 2024, 'Editorial to Special Issue on Multimedia Cognitive Computing for Intelligent Transportation System', ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 20, no. 2, pp. 1-2.
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Wan, Y, Bi, Z, He, Y, Zhang, J, Zhang, H, Sui, Y, Xu, G, Jin, H & Yu, P 2024, 'Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit', ACM Computing Surveys, vol. 56, no. 12, pp. 1-41.
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Code intelligence leverages machine learning techniques to extract knowledge from extensive code corpora, with the aim of developing intelligent tools to improve the quality and productivity of computer programming. Currently, there is already a thriving research community focusing on code intelligence, with efforts ranging from software engineering, machine learning, data mining, natural language processing, and programming languages. In this paper, we conduct a comprehensive literature review on deep learning for code intelligence, from the aspects of code representation learning, deep learning techniques, and application tasks. We also benchmark several state-of-the-art neural models for code intelligence, and provide an open-source toolkit tailored for the rapid prototyping of deep-learning-based code intelligence models. In particular, we inspect the existing code intelligence models under the basis of code representation learning, and provide a comprehensive overview to enhance comprehension of the present state of code intelligence. Furthermore, we publicly release the source code and data resources to provide the community with a ready-to-use benchmark, which can facilitate the evaluation and comparison of existing and future code intelligence models (https://xcodemind.github.io). At last, we also point out several challenging and promising directions for future research.
Wan, Y, Qu, Y, Ni, W, Xiang, Y, Gao, L & Hossain, E 2024, 'Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Survey', IEEE Communications Surveys & Tutorials, vol. 26, no. 3, pp. 1861-1897.
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Wan, Z, Liu, X, Wang, B, Qiu, J, Li, B, Guo, T, Chen, G & Wang, Y 2024, 'Spatio-temporal Contrastive Learning-enhanced GNNs for Session-based Recommendation', ACM Transactions on Information Systems, vol. 42, no. 2, pp. 1-26.
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Session-based recommendation (SBR) systems aim to utilize the user’s short-term behavior sequence to predict the next item without the detailed user profile.Most recent works try to model the user preference by treating the sessions as between-item transition graphs and utilize various graph neural networks (GNNs) to encode the representations of pair-wise relations among items and their neighbors. Some of the existing GNN-based models mainly focus on aggregating information from the view of spatial graph structure, which ignores the temporal relations within neighbors of an item during message passing and the information loss results in a sub-optimal problem. Other works embrace this challenge by incorporating additional temporal information but lack sufficient interaction between the spatial and temporal patterns. To address this issue, inspired by the uniformity and alignment properties of contrastive learning techniques, we propose a novel framework called Session-based Recommendation with Spatio-temporal Contrastive Learning-enhanced GNNs (RESTC). The idea is to supplement the GNN-based main supervised recommendation task with the temporal representation via an auxiliary cross-view contrastive learning mechanism. Furthermore, a novel global collaborative filtering graph embedding is leveraged to enhance the spatial view in the main task.Extensive experiments demonstrate the significant performance of RESTC compared with the state-of-the-art baselines. We release our source code athttps://github.com/SUSTechBruce/RESTC-Source-code.
Wang, C, Wang, L, Yu, H, Soo, A, Wang, Z, Rajabzadeh, S, Ni, B-J & Shon, HK 2024, 'Machine learning for layer-by-layer nanofiltration membrane performance prediction and polymer candidate exploration', Chemosphere, vol. 350, pp. 140999-140999.
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Wang, C, Wei, W, Wu, L, Liu, X, Duan, H, Chen, Z, Hou, Y-N, Chen, X & Ni, B-J 2024, 'Electron Donor-Driven Microalgae Upgrading into High-Value Fatty Acids via a Microbial Platform', ACS ES&T Engineering, vol. 4, no. 12, pp. 3080-3091.
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Wang, C, Wei, W, Wu, L, Wang, Y, Dai, X & Ni, B-J 2024, 'A Novel Sustainable and Self-Sufficient Biotechnological Strategy for Directly Transforming Sewage Sludge into High-Value Liquid Biochemicals', Environmental Science & Technology, vol. 58, no. 28, pp. 12520-12531.
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Sewage sludge, as a carbon-rich byproduct of wastewater treatment, holds significant untapped potential as a renewable resource. Upcycling this troublesome waste stream represents great promise in addressing global escalating energy demands through its wide practice of biochemical recovery concurrently. Here, we propose a biotechnological concept to gain value-added liquid bioproducts from sewage sludge in a self-sufficient manner by directly transforming sludge into medium-chain fatty acids (MCFAs). Our findings suggest that yeast, a cheap and readily available commercial powder, would involve ethanol-type fermentation in chain elongation to achieve abundant MCFA production from sewage sludge using electron donors (i.e., ethanol) and acceptors (i.e., short-chain fatty acids) produced in situ. The enhanced abundance and transcriptional activity of genes related to key enzymes, such as butyryl-CoA dehydrogenase and alcohol dehydrogenase, affirm the robust capacity for the self-sustained production of MCFAs. This is indicative of an effective metabolic network established between yeast and anaerobic microorganisms within this innovative sludge fermentation framework. Furthermore, life cycle assessment and techno-economic analysis evidence the sustainability and economic competitiveness of this biotechnological strategy. Overall, this work provides insights into sewage sludge upgrading independent of additional carbon input, which can be applied in existing anaerobic sludge fermentation infrastructure as well as to develop new applications in a diverse range of industries.
Wang, C, Young, AS, Raj, C, Bradshaw, AP, Nham, B, Rosengren, SM, Calic, Z, Burke, D, Halmagyi, GM, Bharathy, GK, Prasad, M & Welgampola, MS 2024, 'Machine learning models help differentiate between causes of recurrent spontaneous vertigo', Journal of Neurology, vol. 271, no. 6, pp. 3426-3438.
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Wang, D, Du, R, Yang, Q, Yu, D, Wan, F, Gong, X, Xu, G & Deng, S 2024, 'Category-aware self-supervised graph neural network for session-based recommendation', World Wide Web, vol. 27, no. 5.
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Wang, D, Zhang, X, Yin, Y, Yu, D, Xu, G & Deng, S 2024, 'Multi-View Enhanced Graph Attention Network for Session-Based Music Recommendation', ACM Transactions on Information Systems, vol. 42, no. 1, pp. 1-30.
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Traditional music recommender systems are mainly based on users’ interactions, which limit their performance. Particularly, various kinds of content information, such as metadata and description can be used to improve music recommendation. However, it remains to be addressed how to fully incorporate the rich auxiliary/side information and effectively deal with heterogeneity in it. In this paper, we propose a M ulti-view E nhanced G raph A ttention N etwork (named MEGAN ) for session-based music recommendation. MEGAN can learn informative representations (embeddings) of music pieces and users from heterogeneous information based on graph neural network and attention mechanism. Specifically, the proposed approach MEGAN firstly models users’ listening behaviors and the textual content of music pieces with a Heterogeneous Music Graph (HMG). Then, a devised Graph Attention Network is used to learn the low-dimensional embedding of music pieces and users and by integrating various kinds of information, which is enhanced by multi-view from HMG in an adaptive and unified way. Finally, users’ hybrid preferences are learned from users’ listening behaviors and music pieces that satisfy users real-time requirements are recommended. Comprehensive experiments are conducted on two real-world datasets, and the results show that MEGAN achieves better performance than baselines, including several state-of-the-art recommendation methods.
Wang, F, Long, G & Zhou, JL 2024, 'Enhanced green remediation and refinement disposal of electrolytic manganese residue using air-jet milling and horizontal-shaking leaching', Journal of Hazardous Materials, vol. 465, pp. 133419-133419.
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Wang, F, Nghiem, LD, Sarkar, D, Rene, E, Zou, L, Hu, Y, Bui, TX, Fujioka, T, Bolzonella, D, Dong, Z, Du, K & Gin, K 2024, 'Environmental Technology & Innovation Holiday Season Message Dec 2023', Environmental Technology & Innovation, vol. 33, pp. 103507-103507.
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Wang, F, Yang, Y, Gao, J, Li, X, Tian, S, Lu, Z, Zhou, Z, Sohn, W, Shon, HK & Ren, J 2024, 'Biological nitrification-based nutrient recovery technologies for source-separated urine treatment: A critical review', Desalination, vol. 591, pp. 118027-118027.
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Wang, G, Chen, Z, Wei, W & Ni, B 2024, 'Electrocatalysis‐driven sustainable plastic waste upcycling', Electron, vol. 2, no. 2.
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AbstractWith large quantities and natural resistance to degradation, plastic waste raises growing environmental concerns in the world. To achieve the upcycling of plastic waste into value‐added products, the electrocatalytic‐driven process is emerging as an attractive option due to the mild operation conditions, high reaction selectivity, and low carbon emission. Herein, this review provides a comprehensive overview of the upgrading of plastic waste via electrocatalysis. Specifically, key electrooxidation processes including the target products, intermediates and reaction pathways in the plastic electro‐reforming process are discussed. Subsequently, advanced electrochemical systems, including the integration of anodic plastic monomer oxidation and value‐added cathodic reduction and photo‐involved electrolysis processes, are summarized. The design strategies of electrocatalysts with enhanced activity are highlighted and catalytic mechanisms in the electrocatalytic oxidation of plastic waste are elucidated. To promote the electrochemistry‐driven sustainable upcycling of plastic waste, challenges and opportunities are further put forward.
Wang, H, Li, S, Zhou, Y, Zhang, X, Wang, Z, Wen, H, Liu, Y, Guo, W & Ngo, HH 2024, 'Enhancing efficacy and microbial community dynamics in forward osmosis membrane bioreactors for treating micro-polluted surface water', Journal of Water Process Engineering, vol. 60, pp. 105040-105040.
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Wang, H, Xue, S, Qian, K, Li, Y & Wang, J 2024, 'Magnetic field-dependent dynamic behavior of magnetorheological grease composite in a wide temperature range: Experiment and modeling', Colloids and Surfaces A: Physicochemical and Engineering Aspects, vol. 686, pp. 133468-133468.
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Wang, H, Yu, J, Wang, X, Chen, C, Zhang, W & Lin, X 2024, 'Neural Similarity Search on Supergraph Containment', IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 1, pp. 281-295.
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Wang, H, Zhang, Q-W, Chen, G, Li, X, Wang, Q-L, Gao, L, Wang, J, He, D & Li, M 2024, 'The loss of dissolved organic matter from biological soil crust at various successional stages under rainfall of different intensities: Insights into the changes of molecular components at different rainfall stages', Water Research, vol. 257, pp. 121719-121719.
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Wang, H, Zhao, X, Huang, S, Li, Q & Liu, Y 2024, 'A branch-and-bound based globally optimal solution to 2D multi-robot relative pose estimation problems', Automatica, vol. 164, pp. 111654-111654.
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Wang, J, Cao, M, Han, L, Shangguan, P, Liu, Y, Zhong, Y, Chen, C, Wang, G, Chen, X, Lin, M, Lu, M, Luo, Z, He, M, Sung, HHY, Niu, G, Lam, JWY, Shi, B & Tang, BZ 2024, 'Blood–Brain Barrier-Penetrative Fluorescent Anticancer Agents Triggering Paraptosis and Ferroptosis for Glioblastoma Therapy', Journal of the American Chemical Society, vol. 146, no. 42, pp. 28783-28794.
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Wang, J, Duan, Y, Lyu, X, Yu, Y & Xiao, J 2024, 'Axial compression behavior of coal gangue coarse aggregate concrete-filled steel tube stub columns', Journal of Constructional Steel Research, vol. 215, pp. 108534-108534.
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Wang, J, He, Y, Su, D, Itoyama, K, Nakadai, K, Wu, J, Huang, S, Li, Y & Kong, H 2024, 'SLAM-Based Joint Calibration of Multiple Asynchronous Microphone Arrays and Sound Source Localization', IEEE Transactions on Robotics, vol. 40, pp. 4024-4044.
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Wang, J, O’Brien, E, Holloway, P, Nolan, P, Stewart, MG & Ryan, PC 2024, 'Climate change impact and adaptation assessment for road drainage systems', Journal of Environmental Management, vol. 364, pp. 121209-121209.
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Wang, J, Wang, K, Li, Z, Lu, H, Jiang, H & Xing, Q 2024, 'A Multitask Integrated Deep-Learning Probabilistic Prediction for Load Forecasting', IEEE Transactions on Power Systems, vol. 39, no. 1, pp. 1240-1250.
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Wang, J, Wu, Y, Wang, X, Zhang, Y, Qin, L, Zhang, W & Lin, X 2024, 'Efficient Influence Minimization via Node Blocking', Proceedings of the VLDB Endowment, vol. 17, no. 10, pp. 2501-2513.
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Given a graph G , a budget k and a misinformation seed set S, Influence Minimization (IMIN) via node blocking aims to find a set of k nodes to be blocked such that the expected spread of S is minimized. This problem finds important applications in suppressing the spread of misinformation and has been extensively studied in the literature. However, existing solutions for IMIN still incur significant computation overhead, especially when k becomes large. In addition, there is still no approximation solution with non-trivial theoretical guarantee for IMIN via node blocking prior to our work. In this paper, we conduct the first attempt to propose algorithms that yield data-dependent approximation guarantees. Based on the Sandwich framework, we first develop submodular and monotonic lower and upper bounds for our non-submodular objective function and prove the computation of proposed bounds is #P-hard. In addition, two advanced sampling methods are proposed to estimate the value of bounding functions. Moreover, we develop two novel martingale-based concentration bounds to reduce the sample complexity and design two non-trivial algorithms that provide (1 - 1/ e - ϵ )-approximate solutions to our bounding functions. Comprehensive experiments on 9 real-world datasets are conducted to validate the efficiency and effectiveness of the proposed techniques. Compared with the state-of-the-art methods, our solutions can achieve up to two orders of magnitude speedup and provide theoretical guarantees for the quality of returned results.
Wang, J, Yu, Y, Zeng, B & Lu, H 2024, 'Hybrid ultra-short-term PV power forecasting system for deterministic forecasting and uncertainty analysis', Energy, vol. 288, pp. 129898-129898.
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Wang, J, Zhu, S, Ma, Q, Mu, C, Liu, X & Wen, S 2024, 'Multistability of recurrent neural networks with general periodic activation functions and unbounded time-varying delays', Journal of the Franklin Institute, vol. 361, no. 18, pp. 107236-107236.
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Wang, J, Zhu, S, Mu, C, Liu, X & Wen, S 2024, 'Unified analysis on multistablity of fraction-order multidimensional-valued memristive neural networks', Neural Networks, vol. 179, pp. 106498-106498.
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Wang, K, Cai, M, Chen, X, Lin, X, Zhang, W, Qin, L & Zhang, Y 2024, 'Efficient algorithms for reachability and path queries on temporal bipartite graphs', The VLDB Journal, vol. 33, no. 5, pp. 1399-1426.
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Wang, K, Lu, J, Liu, A & Zhang, G 2024, 'TS-DM: A Time Segmentation-Based Data Stream Learning Method for Concept Drift Adaptation', IEEE Transactions on Cybernetics, vol. 54, no. 10, pp. 6000-6011.
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Wang, K, Shi, L, Zou, H, Zhao, S, Shen, C & Lu, J 2024, 'A broadband active sound absorber with adjustable absorption coefficient and bandwidth', The Journal of the Acoustical Society of America, vol. 156, no. 2, pp. 1048-1057.
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Broadband adjustable sound absorbers are desired for controlling the acoustic conditions within enclosed spaces. Existing studies on acoustic absorbers, either passive or active, aim to maximize the sound absorption coefficients over an extended frequency band. By contrast, this paper introduces a tunable acoustic absorber, whose working frequency band and sound absorption characteristics can be defined by users for different applications. The approach leverages an error signal that can be synthesized using a standing wave separation technique. The error signal encodes different target reflection coefficients, leading to arbitrary absorption coefficients between 0 and 1. Experimental validation is conducted in a one-dimensional standing wave tube, demonstrating that the proposed active absorber achieves near-perfect absorption within the 150–1600 Hz frequency range, boasting an average absorption coefficient of 0.98. Adjustable absorption is demonstrated across three octave bands, aligning closely with theoretical predictions. Furthermore, when coupled with a shaping filter, the absorber exhibits spectrally tunable broadband absorption capabilities, selectively reflecting specific frequency bands while effectively absorbing others. These outcomes underscore the versatile tunability of the proposed active acoustic absorber, which is expected to pave the way for personalized regulating of the indoor acoustic environment.
Wang, K, Xiong, L & Xue, R 2024, 'Real-time data stream learning for emergency decision-making under uncertainty', Physica A: Statistical Mechanics and its Applications, vol. 633, pp. 129429-129429.
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Wang, K, Xiong, L, Liu, A, Zhang, G & Lu, J 2024, 'A self-adaptive ensemble for user interest drift learning', Neurocomputing, vol. 577, pp. 127308-127308.
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Wang, K, Zhao, S, Shen, C, Shi, L, Zou, H, Lu, J & Alù, A 2024, 'Breaking the causality limit for broadband acoustic absorption using a noncausal active absorber', Device, vol. 2, no. 10, pp. 100502-100502.
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Wang, L, Hu, Y, Hu, C, Zhou, Y & Wen, S 2024, 'Finite-time synchronization of delayed fuzzy inertial neural networks via intermittent control', Neurocomputing, vol. 574, pp. 127288-127288.
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Wang, L, Sang, L, Zhang, Q, Wu, Q & Xu, M 2024, 'A privacy-preserving framework with multi-modal data for cross-domain recommendation', Knowledge-Based Systems, vol. 304, pp. 112529-112529.
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Wang, L, Yi, S, Yu, Y, Gao, C & Samali, B 2024, 'Automated ultrasonic-based diagnosis of concrete compressive damage amidst temperature variations utilizing deep learning', Mechanical Systems and Signal Processing, vol. 221, pp. 111719-111719.
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Wang, L, Zhang, J, Cheng, D, Guo, W, Cao, X, Xue, J, Haris, M, Ye, Y & Ngo, HH 2024, 'Biochar-based functional materials for the abatement of emerging pollutants from aquatic matrices', Environmental Research, vol. 252, pp. 119052-119052.
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Wang, M, Hu, N, Li, X, Mo, Y, Xie, W, Chen, Z & Tian, Z 2024, 'A 2-Bit Electronically Planar Reconfigurable Array Antenna With 2-D Beam-Scanning Capacity Using Hybrid Phase Control Method', IEEE Antennas and Wireless Propagation Letters, vol. 23, no. 7, pp. 1966-1970.
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Wang, M, Zhu, S, Shen, M, Liu, X & Wen, S 2024, 'Fault-Tolerant Synchronization for Memristive Neural Networks With Multiple Actuator Failures', IEEE Transactions on Cybernetics, vol. 54, no. 9, pp. 5092-5101.
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Wang, M, Zhu, T, Zuo, X, Ye, D, Yu, S & Zhou, W 2024, 'Blockchain-Based Gradient Inversion and Poisoning Defense for Federated Learning', IEEE Internet of Things Journal, vol. 11, no. 9, pp. 15667-15681.
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Wang, M, Zhu, T, Zuo, X, Ye, D, Yu, S & Zhou, W 2024, 'Blockchain-Empowered Multiagent Systems: Advancing IoT Security and Transaction Efficiency', IEEE Internet of Things Journal, vol. 11, no. 7, pp. 11217-11231.
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Wang, M, Zhu, T, Zuo, X, Ye, D, Yu, S & Zhou, W 2024, 'Public and Private Blockchain Infusion: A Novel Approach to Federated Learning', IEEE Internet of Things Journal, vol. 11, no. 10, pp. 17525-17537.
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Wang, Q & Chang, X 2024, 'Normalized Solutions of $$L^2$$-Supercritical Kirchhoff Equations in Bounded Domains', The Journal of Geometric Analysis, vol. 34, no. 12.
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Wang, Q & Ying, M 2024, 'Quantum Algorithm for Lexicographically Minimal String Rotation', Theory of Computing Systems, vol. 68, no. 1, pp. 29-74.
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AbstractLexicographically minimal string rotation (LMSR) is a problem to find the minimal one among all rotations of a string in the lexicographical order, which is widely used in equality checking of graphs, polygons, automata and chemical structures. In this paper, we propose an$$O(n^{3/4})$$O(n3/4)quantum query algorithm for LMSR. In particular, the algorithm has average-case query complexity$$O(\sqrt{n} \log n)$$O(nlogn), which is shown to be asymptotically optimal up to a polylogarithmic factor, compared to its$$\Omega \left( \sqrt{n/\log n}\right) $$Ωn/lognlower bound. Furthermore, we show that our quantum algorithm outperforms any (classical) randomized algorithms in both worst and average cases. As an application, it is used in benzenoid identification and disjoint-cycle automata minimization.
Wang, Q & Ying, M 2024, 'Quantum Algorithm for Lexicographically Minimal String Rotation.', Theory Comput. Syst., vol. 68, pp. 29-74.
Wang, Q & Ying, M 2024, 'Quantum Büchi automata', Theoretical Computer Science, vol. 1012, pp. 114740-114740.
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Wang, Q, Jin, W, Qin, Y, Zhou, X, Han, W, Gao, S, Li, X, Naushad, M, Jiang, G & Liu, H 2024, 'Development of an alternative low-cost culture medium for a new isolated high-production DHA strain using kitchen wastewater', Process Safety and Environmental Protection, vol. 183, pp. 698-707.
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Wang, Q, Jin, W, Zhou, X, Chen, C, Han, W, Mahlia, TMI, Li, X, Jiang, G, Liu, H & Wang, Q 2024, 'Enhancing docosahexaenoic acid production in Aurantiochytrium species using atmospheric and room temperature plasma mutagenesis and comprehensive multi-omics analysis', Science of The Total Environment, vol. 912, pp. 169217-169217.
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Wang, Q, Wu, D, Li, G, Liu, Z, Tong, J, Chen, X & Gao, W 2024, 'Machine learning aided uncertainty quantification for engineering structures involving material-geometric randomness and data imperfection', Computer Methods in Applied Mechanics and Engineering, vol. 423, pp. 116868-116868.
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Wang, Q, Yu, G & Chen, S 2024, 'Cryptocurrency in the Aftermath: Unveiling the Impact of the SVB Collapse', IEEE Transactions on Computational Social Systems, vol. 11, no. 5, pp. 5839-5857.
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Wang, S, Ko, RKL, Bai, G, Dong, N, Choi, T & Zhang, Y 2024, 'Evasion Attack and Defense on Machine Learning Models in Cyber-Physical Systems: A Survey', IEEE Communications Surveys & Tutorials, vol. 26, no. 2, pp. 930-966.
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Wang, S, Ma, Y, Ding, Y, Hu, Z, Fan, C, Lv, T, Deng, Z & Yu, X 2024, 'StyleTalk++: A Unified Framework for Controlling the Speaking Styles of Talking Heads', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 6, pp. 4331-4347.
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Wang, S, Shi, K, Cao, J & Wen, S 2024, 'Fuzzy adaptive event-triggered synchronization control mechanism for T–S fuzzy RDNNs under deception attacks', Communications in Nonlinear Science and Numerical Simulation, vol. 134, pp. 107985-107985.
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Wang, S, Shi, K, Wang, J, Yu, Y, Wen, S, Yang, J & Han, S 2024, 'Synchronization sampled-data control of uncertain neural networks under an asymmetric Lyapunov–Krasovskii functional method', Expert Systems with Applications, vol. 239, pp. 122475-122475.
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Wang, W & Cao, L 2024, 'Explicit and Implicit Pattern Relation Analysis for Discovering Actionable Negative Sequences', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 4, pp. 5183-5197.
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Wang, W, Lyu, X, Liu, X, Zheng, J, Gao, H & Yu, Y 2024, 'Behavior of cross-shaped stiffened concrete-filled steel tubular stub columns after fire exposure', Structures, vol. 68, pp. 107266-107266.
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Wang, W, Xiong, Z, Yu, Y, Chen, D & Wu, C 2024, 'Residual behaviour and damage assessment of UHPC-filled double-skin steel tubular columns after lateral impact', Thin-Walled Structures, vol. 205, pp. 112602-112602.
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Wang, W, Zhang, C, Tian, Z & Yu, S 2024, 'FedU: Federated Unlearning via User-Side Influence Approximation Forgetting', IEEE Transactions on Dependable and Secure Computing, pp. 1-14.
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Wang, W, Zhang, C, Tian, Z & Yu, S 2024, 'Machine Unlearning via Representation Forgetting With Parameter Self-Sharing', IEEE Transactions on Information Forensics and Security, vol. 19, pp. 1099-1111.
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Wang, X, Ding, C, Zhao, G, Li, S, Chen, Y & Sun, H 2024, 'Differential and Symmetrical Decoupling Network for Differentially Fed Antennas', IEEE Antennas and Wireless Propagation Letters, vol. 23, no. 3, pp. 1129-1133.
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Wang, X, Fei, Z, Liu, P, Zhang, JA, Wu, Q & Wu, N 2024, 'Sensing-Aided Covert Communications: Turning Interference Into Allies', IEEE Transactions on Wireless Communications, vol. 23, no. 9, pp. 10726-10739.
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Wang, X, Guo, Y, Tao, Z, Shi, L & Li, W 2024, 'Self-heating performance of phase change cementitious mortar with hybrid carbon-based nanomaterials', Journal of Energy Storage, vol. 104, pp. 114495-114495.
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Wang, X, Huang, Y, Shi, L, Zhang, S & Li, W 2024, 'Enhanced thermal performance of phase change mortar using multi-scale carbon-based materials', Journal of Building Engineering, vol. 98, pp. 111259-111259.
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Wang, X, Li, Q, Yu, D, Huang, W, Li, Q & Xu, G 2024, 'Neural Causal Graph collaborative filtering', Information Sciences, vol. 677, pp. 120872-120872.
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Wang, X, Li, Q, Yu, D, Li, Q & Xu, G 2024, 'Constrained Off-policy Learning over Heterogeneous Information for Fairness-aware Recommendation', ACM Transactions on Recommender Systems, vol. 2, no. 4, pp. 1-27.
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Fairness-aware recommendation eliminates discrimination issues to build trustworthy recommendation systems. Existing fairness-aware approaches ignore accounting for rich user and item attributes and thus cannot capture the impact of attributes on affecting recommendation fairness. These real-world attributes severely cause unfair recommendations by favoring items with popular attributes, leading to item exposure unfairness in recommendations. Moreover, existing approaches mostly mitigate unfairness for static recommendation models, e.g., collaborative filtering. Static models can not handle dynamic user interactions with the system that reflect users’ preferences shift through time. Thus, static models are limited in their ability to adapt to user behavior shifts to gain long-run user satisfaction. As user and item attributes are largely involved in modern recommenders and user interactions are naturally dynamic, it is essential to develop a novel method that eliminates unfairness caused by attributes meanwhile embrace the dynamic modeling of user behavior shifts. In this article, we propose Constrained Off-policy Learning over Heterogeneous Information for Fairness-aware Recommendation (Fair-HINpolicy) , which uses recent advances in context-aware off-policy learning to produce fairness-aware recommendations with rich attributes from a Heterogeneous Information Network. In particular, we formulate the off-policy learning as a Constrained Markov Decision Process (CMDP) by dynamically constraining the fairness of item exposure at each iteration. We also design an attentive action sampling to reduce the search space for off-policy learning. Our solution adaptively receives HIN-augmented corrections for counterfactual risk minimization, and ultimately yields an effective policy that maximizes long-term user satisfaction. We extensively evaluate our method through simulations on large-s...
Wang, X, Li, Q, Yu, D, Li, Q & Xu, G 2024, 'Counterfactual Explanation for Fairness in Recommendation', ACM Transactions on Information Systems, vol. 42, no. 4, pp. 1-30.
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Fairness-aware recommendation alleviates discrimination issues to build trustworthy recommendation systems. Explaining the causes of unfair recommendations is critical, as it promotes fairness diagnostics, and thus secures users’ trust in recommendation models. Existing fairness explanation methods suffer high computation burdens due to the large-scale search space and the greedy nature of the explanation search process. Besides, they perform feature-level optimizations with continuous values, which are not applicable to discrete attributes such as gender and age. In this work, we adopt counterfactual explanations from causal inference and propose to generate attribute-level counterfactual explanations, adapting to discrete attributes in recommendation models. We use real-world attributes from Heterogeneous Information Networks (HINs) to empower counterfactual reasoning on discrete attributes. We propose a Counterfactual Explanation for Fairness (CFairER) that generates attribute-level counterfactual explanations from HINs for item exposure fairness. Our CFairER conducts off-policy reinforcement learning to seek high-quality counterfactual explanations, with attentive action pruning reducing the search space of candidate counterfactuals. The counterfactual explanations help to provide rational and proximate explanations for model fairness, while the attentive action pruning narrows the search space of attributes. Extensive experiments demonstrate our proposed model can generate faithful explanations while maintaining favorable recommendation performance.
Wang, X, Li, Q, Yu, D, Li, Q & Xu, G 2024, 'Reinforced Path Reasoning for Counterfactual Explainable Recommendation', IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 7, pp. 3443-3459.
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Wang, X, Li, W, Guo, Y, Kashani, A, Wang, K, Ferrara, L & Agudelo, I 2024, 'Concrete 3D printing technology for sustainable construction: A review on raw material, concrete type and performance', Developments in the Built Environment, vol. 17, pp. 100378-100378.
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Wang, X, Li, W, Guo, Y, Kashani, A, Wang, K, Ferrara, L & Agudelo, I 2024, 'Corrigendum to “Concrete 3D printing technology for sustainable construction: A review on raw material, concrete type and performance” [Dev. Built. Environ. 17 (2024) 100378]', Developments in the Built Environment, vol. 19, pp. 100442-100442.
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Wang, X, Thiyagarajan, K, Kodagoda, S & Sharma, C 2024, 'PIPE-CovNet+: A Hyper-Dense CNN for Improved Pipe Abnormality Detection', IEEE Sensors Letters, vol. 8, no. 4, pp. 1-4.
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Wang, X, Yang, S, Guo, Z, Ge, Q, Wen, S & Huang, T 2024, 'A Distributed k-Winners-Take-All Model With Binary Consensus Protocols', IEEE Transactions on Cybernetics, vol. 54, no. 5, pp. 3327-3337.
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This article concentrates on solving the k -winners-take-all (k WTA) problem with large-scale inputs in a distributed setting. We propose a multiagent system with a relatively simple structure, in which each agent is equipped with a 1-D system and interacts with others via binary consensus protocols. That is, only the signs of the relative state information between neighbors are required. By virtue of differential inclusion theory, we prove that the system converges from arbitrary initial states. In addition, we derive the convergence rate as O(1/t) . Furthermore, in comparison to the existing models, we introduce a novel comparison filter to eliminate the resolution ratio requirement on the input signal, that is, the difference between the k th and (k+1) th largest inputs must be larger than a positive threshold. As a result, the proposed distributed k WTA model is capable of solving the k WTA problem, even when more than two elements of the input signal share the same value. Finally, we validate the effectiveness of the theoretical results through two simulation examples.
Wang, Y, Chen, Q, Luo, Q, Li, Q & Sun, G 2024, 'Characterizing damage evolution in fiber reinforced composites using in-situ X-ray computed tomography, deep machine learning and digital volume correlation (DVC)', Composites Science and Technology, vol. 254, pp. 110650-110650.
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Wang, Y, Hu, L, Cao, X, Chang, Y & Tsang, IW 2024, 'Enhancing Locally Adaptive Smoothing of Graph Neural Networks Via Laplacian Node Disagreement', IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 3, pp. 1099-1112.
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Wang, Y, Li, T, Li, S, Yuan, X & Ni, W 2024, 'New Adversarial Image Detection Based on Sentiment Analysis', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 10, pp. 14060-14074.
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Wang, Y, Wang, Z, Zhang, JA, Zhang, H & Xu, M 2024, 'Vital Sign Monitoring in Dynamic Environment via mmWave Radar and Camera Fusion', IEEE Transactions on Mobile Computing, vol. 23, no. 5, pp. 4163-4180.
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Wang, Y, Wei, W, Dai, X, Wu, L, Chen, X & Ni, B-J 2024, 'Different Electron Donors Drive the Variation in the Performance of Medium-Chain Fatty Acid Production from Waste-Activated Sludge', ACS ES&T Engineering, vol. 4, no. 3, pp. 650-659.
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Wang, Y, Xiao, S, Yu, Q & Dong, D 2024, '量子系统辨识与参数估计', SCIENTIA SINICA Mathematica.
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Wang, Y, Xu, J, Li, W & Dong, W 2024, 'Improved conductive and self-sensing properties of cement concrete by PDMS/NCB-impregnated recycled fine aggregate', Construction and Building Materials, vol. 426, pp. 136229-136229.
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Wang, Y, Zhang, Q, Andrew Zhang, J, Wei, Z, Feng, Z & Peng, J 2024, 'Interference Characterization and Mitigation for Multi-Beam ISAC Systems in Vehicular Networks', IEEE Transactions on Wireless Communications, vol. 23, no. 10, pp. 14729-14742.
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Wang, Z, Huang, Z, Huang, Y, Wittram, C, Zhuang, Y, Wang, S & Nie, B 2024, 'Synergy of carbon capture, waste heat recovery and hydrogen production for industrial decarbonisation', Energy Conversion and Management, vol. 312, pp. 118568-118568.
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Wang, Z, Li, X, Li, Y, Liu, H, Ki Lin, CS, Sun, J & Wang, Q 2024, 'Unveiling the occurrence and ecological risks of triclosan in surface water through meta-analysis', Environmental Pollution, vol. 361, pp. 124901-124901.
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Wang, Z, Li, X, Liu, H, Li, J, Vodnar, DC, Lin, CSK & Wang, Q 2024, 'Life cycle assessment of traditional and innovative sludge management scenarios in Australia: Focusing on environmental impacts, energy balance, and economic benefits', Resources, Conservation and Recycling, vol. 204, pp. 107496-107496.
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Wang, Z, Li, X, Liu, H, Mou, J, Khan, SJ, Lin, CSK & Wang, Q 2024, 'Evaluating energy balance and environmental footprint of sludge management in BRICS countries', Water Research X, vol. 25, pp. 100255-100255.
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Wang, Z, Li, X, Liu, H, Zhou, T, Li, J, Li, Y, Lin, CSK & Wang, Q 2024, 'Triclosan in sludge: Exploring its journey from the sewage treatment plants to land application and potential impacts on the environment', CRITICAL REVIEWS IN ENVIRONMENTAL SCIENCE AND TECHNOLOGY, vol. 54, no. 18, pp. 1340-1363.
Wang, Z, Li, X, Liu, H, Zhou, T, Li, J, Li, Y, Lin, CSK & Wang, Q 2024, 'Triclosan in sludge: Exploring its journey from the sewage treatment plants to land application and potential impacts on the environment', Critical Reviews in Environmental Science and Technology, vol. 54, no. 18, pp. 1340-1363.
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Wang, Z, Li, X, Liu, H, Zhou, T, Li, J, Siddiqui, MA, Lin, CSK, Huang, S, Cairney, JM & Wang, Q 2024, 'Enhanced short-chain fatty acids production from anaerobic fermentation of secondary sludge by lignosulfonate addition: Towards circular economy', Journal of Cleaner Production, vol. 434, pp. 140252-140252.
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Wang, Z, Lu, J, Deng, P, Li, S, Wang, K, Zhang, C, Cheng, X, Zhang, J & Huang, Y 2024, 'Mechanisms of enhanced heat transfer of piston oscillating cooling in a heavy-duty diesel engine', Applied Thermal Engineering, vol. 245, pp. 122875-122875.
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Warmbier, E, Altaee, A, Różański, J, Kazwini, T, Różańska, S, Ibrar, I, Wagner, P, Al-Ejji, M & Hawari, AH 2024, 'Stability of Viscoelastic Solutions: BrijL4 and Sodium Cholate Mixtures with Metal Ions Across a Broad pH and Temperature Range', Langmuir, vol. 40, no. 3, pp. 1707-1716.
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Wei, C-H, Wang, Z-W, Dai, J-H, Xiao, K, Yu, H-R, Qu, F-S, Rong, H-W, He, J-G & Ngo, HH 2024, 'Enhanced anaerobic digestion performance and sludge filterability by membrane microaeration for anaerobic membrane bioreactor application', Bioresource Technology, vol. 402, pp. 130787-130787.
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Wei, C-H, Zhai, X-Y, Jiang, Y-D, Rong, H-W, Zhao, L-G, Liang, P, Huang, X & Ngo, HH 2024, 'Simultaneous carbon, nitrogen and phosphorus removal in sequencing batch membrane aerated biofilm reactor with biofilm thickness control via air scouring aided by computational fluid dynamics', Bioresource Technology, vol. 409, pp. 131267-131267.
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Wei, G, Wang, Y, He, Y, Ziolkowski, RW & Guo, YJ 2024, 'Ultra-Wideband Vertically Polarized Long-Slot Circular Phased Array', IEEE Transactions on Antennas and Propagation, vol. 72, no. 5, pp. 4161-4172.
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Wei, J, Li, W, Liu, J, Li, J & Wu, C 2024, 'Effect of stirrup ratio on response of ultra-high performance concrete beams subjected to low-velocity impact loadings', Journal of Building Engineering, vol. 92, pp. 109799-109799.
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Wei, Y, Jin, X, Luo, Q, Li, Q & Sun, G 2024, 'Adhesively bonded joints – A review on design, manufacturing, experiments, modeling and challenges', Composites Part B: Engineering, vol. 276, pp. 111225-111225.
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Wei, Z, Piao, J, Yuan, X, Wu, H, Zhang, JA, Feng, Z, Wang, L & Zhang, P 2024, 'Waveform Design for MIMO-OFDM Integrated Sensing and Communication System: An Information Theoretical Approach', IEEE Transactions on Communications, vol. 72, no. 1, pp. 496-509.
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WeiKoh, JE, Rajinikanth, V, Vicnesh, J, Pham, T, Oh, SL, Yeong, CH, Sankaranarayanan, M, Kamath, A, Bairy, GM, Barua, PD & Cheong, KH 2024, 'Application of local configuration pattern for automated detection of schizophrenia with electroencephalogram signals', Expert Systems, vol. 41, no. 5.
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AbstractRecently, a mix of traditional and modern approaches have been proposed to detect brain abnormalities using bio‐signal/bio‐image‐assisted methods. In hospitals, most of the initial/scheduled assessments consider the bio‐signal‐based appraisal, due to its non‐invasive nature and low cost. Further, brain bio‐signal scans can be recorded using a single/multi‐channel electrode setup, which is further evaluated by an experienced doctor, as well as computer software, to identify the nature and severity of abnormality. In this paper, we describe the development of a system for computer supported detection (CSD) of schizophrenia using the electroencephalogram (EEG) signal collected with a 19‐channel electrode array. Schizophrenia is a mental illness that interferes with the way an individual thinks and behaves. It is characterised by psychotic symptoms such as hallucinations or delusions, negative symptoms such as decreased motivation or a lack of interest in daily activities and cognitive symptoms such challenges in processing information to make informed decisions or staying focused. This research has utilized 1142 EEGs (516 normal and 626 schizophrenia) with a frame length of 25 s (6250 samples) for investigation. The work initially converts the EEG signals to images using a spectrogram. Local configuration pattern features were extracted from the images thereafter, and 10‐fold validation technique was used wherein Student's t‐test and z‐score standardization were computed per fold. The highest accuracy of 97.20% was achieved with the K‐nearest neighbour (
Welsh, JA, Goberdhan, DCI, O'Driscoll, L, Buzas, EI, Blenkiron, C, Bussolati, B, Cai, H, Di Vizio, D, Driedonks, TAP, Erdbrügger, U, Falcon‐Perez, JM, Fu, Q, Hill, AF, Lenassi, M, Lim, SK, Mahoney, MG, Mohanty, S, Möller, A, Nieuwland, R, Ochiya, T, Sahoo, S, Torrecilhas, AC, Zheng, L, Zijlstra, A, Abuelreich, S, Bagabas, R, Bergese, P, Bridges, EM, Brucale, M, Burger, D, Carney, RP, Cocucci, E, Crescitelli, R, Hanser, E, Harris, AL, Haughey, NJ, Hendrix, A, Ivanov, AR, Jovanovic‐Talisman, T, Kruh‐Garcia, NA, Ku'ulei‐Lyn Faustino, V, Kyburz, D, Lässer, C, Lennon, KM, Lötvall, J, Maddox, AL, Martens‐Uzunova, ES, Mizenko, RR, Newman, LA, Ridolfi, A, Rohde, E, Rojalin, T, Rowland, A, Saftics, A, Sandau, US, Saugstad, JA, Shekari, F, Swift, S, Ter‐Ovanesyan, D, Tosar, JP, Useckaite, Z, Valle, F, Varga, Z, van der Pol, E, van Herwijnen, MJC, Wauben, MHM, Wehman, AM, Williams, S, Zendrini, A, Zimmerman, AJ, Théry, C & Witwer, KW 2024, 'Minimal information for studies of extracellular vesicles (MISEV2023): From basic to advanced approaches', Journal of Extracellular Vesicles, vol. 13, no. 2.
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AbstractExtracellular vesicles (EVs), through their complex cargo, can reflect the state of their cell of origin and change the functions and phenotypes of other cells. These features indicate strong biomarker and therapeutic potential and have generated broad interest, as evidenced by the steady year‐on‐year increase in the numbers of scientific publications about EVs. Important advances have been made in EV metrology and in understanding and applying EV biology. However, hurdles remain to realising the potential of EVs in domains ranging from basic biology to clinical applications due to challenges in EV nomenclature, separation from non‐vesicular extracellular particles, characterisation and functional studies. To address the challenges and opportunities in this rapidly evolving field, the International Society for Extracellular Vesicles (ISEV) updates its ‘Minimal Information for Studies of Extracellular Vesicles’, which was first published in 2014 and then in 2018 as MISEV2014 and MISEV2018, respectively. The goal of the current document, MISEV2023, is to provide researchers with an updated snapshot of available approaches and their advantages and limitations for production, separation and characterisation of EVs from multiple sources, including cell culture, body fluids and solid tissues. In addition to presenting the latest state of the art in basic principles of EV research, this document also covers advanced techniques and approaches that are currently expanding the boundaries of the field. MISEV2023 also includes new sections on EV release and uptake and a brief discussion of in vivo approaches to study EVs. Compiling feedback from ISEV expert task forces and more than 1000 researchers, this document conveys the current state of EV research to facilitate robust scientific discoveries and move the field forward even more rapidly.
Wen, DJ, Tavakoli, J & Tipper, JL 2024, 'Lumbar Total Disc Replacements for Degenerative Disc Disease: A Systematic Review of Outcomes With a Minimum of 5 years Follow-Up', Global Spine Journal, vol. 14, no. 6, pp. 1827-1837.
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Study Design Systematic Review. Objectives To systematically review the clinical outcomes, re-operation, and complication rates of lumbar TDR devices at mid-to long-term follow-up studies for the treatment of lumbar degenerative disc disease (DDD). Methods A systematic search was conducted on PubMed, SCOPUS, and Google Scholar to identify follow-up studies that evaluated clinical outcomes of lumbar TDR in patients with DDD. The included studies met the following criteria: prospective or retrospective studies published from 2012 to 2022; a minimum of 5 years post-operative follow-up; a study sample size >10 patients; patients >18 years of age; containing clinical outcomes with Oswestry Disability Index (ODI), Visual Analog Scale (VAS), complication or reoperation rates. Results Twenty-two studies were included with data on 2284 patients. The mean follow-up time was 8.30 years, with a mean follow-up rate of 86.91%. The study population was 54.97% female, with a mean age of 42.34 years. The mean VAS and ODI pain score improvements were 50.71 ± 6.91 and 30.39 ± 5.32 respectively. The mean clinical success and patient satisfaction rates were 74.79% ± 7.55% and 86.34% ± 5.64%, respectively. The mean complication and reoperation rates were 18.53% ± 6.33% and 13.6% ± 3.83%, respectively. There was no significant difference when comparing mid-term and long-term follow-up studies for all clinical outcomes. Conclusions There were significant improvements in pain reduction at last follow-up in patients with TDRs. Mid-term follow-up data on clinical outcomes, complication and reoperation rates of lumbar TDRs were maintained longer term.
Wen, J, Gabrys, B & Musial, K 2024, 'DTCNS: A python toolbox for digital twin-oriented complex networked systems', SoftwareX, vol. 27, pp. 101818-101818.
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Weththasinghe, K, Jayawickrama, B & He, Y 2024, 'Machine Learning-Based Channel Estimation for 5G New Radio', IEEE Wireless Communications Letters, vol. 13, no. 4, pp. 1133-1137.
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Weththasinghe, K, Ngo, QT, He, Y & Jayawickrama, B 2024, 'Optimising Beam Size in Multibeam LEO Satellite Networks: Addressing Interbeam Interference, Doppler Shift, and Frequency Reuse', IEEE Transactions on Aerospace and Electronic Systems, pp. 1-15.
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Weygant, J, Entezari, A, Koch, F, Galaviz, RA, Garciamendez, CE, Hernández, P, Ortiz, V, Ruiz, DSR, Aguilar, F, Andolfi, A, Cai, L, Maharjan, S, Osorio, A & Zhang, YS 2024, 'Droplet 3D cryobioprinting for fabrication of free‐standing and volumetric structures', Aggregate, vol. 5, no. 5.
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AbstractDroplet‐based bioprinting has shown remarkable potential in tissue engineering and regenerative medicine. However, it requires bioinks with low viscosities, which makes it challenging to create complex 3D structures and spatially pattern them with different materials. This study introduces a novel approach to bioprinting sophisticated volumetric objects by merging droplet‐based bioprinting and cryobioprinting techniques. By leveraging the benefits of cryopreservation, we fabricated, for the first time, intricate, self‐supporting cell‐free or cell‐laden structures with single or multiple materials in a simple droplet‐based bioprinting process that is facilitated by depositing the droplets onto a cryoplate followed by crosslinking during revival. The feasibility of this approach is demonstrated by bioprinting several cell types, with cell viability increasing to 80%–90% after up to 2 or 3 weeks of culture. Furthermore, the applicational capabilities of this approach are showcased by bioprinting an endothelialized breast cancer model. The results indicate that merging droplet and cryogenic bioprinting complements current droplet‐based bioprinting techniques and opens new avenues for the fabrication of volumetric objects with enhanced complexity and functionality, presenting exciting potential for biomedical applications.
Wiegmans, AP, Ivanova, E, Naei, VY, Monkman, J, Fletcher, J, Mullally, W, Warkiani, ME, O’Byrne, K & Kulasinghe, A 2024, 'Poor patient outcome correlates with active engulfment of cytokeratin positive CTCs within cancer-associated monocyte population in lung cancer', Clinical & Experimental Metastasis, vol. 41, no. 3, pp. 219-228.
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AbstractHigh rates of mortality in non-small cell lung cancer lung cancer is due to inherent and acquired resistance to systemic therapies and subsequent metastatic burden. Metastasis is supported by suppression of the immune system at secondary organs and within the circulation. Modulation of the immune system is now being exploited as a therapeutic target with immune checkpoint inhibitors. The tracking of therapeutic efficacy in a real-time can be achieved with liquid biopsy, and evaluation of circulating tumour cells and the associated immune cells. A stable liquid biopsy biomarker for non-small cell lung cancer lung cancer has yet to be approved for clinical use. We performed a cross-sectional single-site study, and collected liquid biopsies from patients diagnosed with early, locally advanced, or metastatic lung cancer, undergoing surgery, or systemic therapy (chemotherapy/checkpoint inhibitors). Evaluation of overall circulating tumour cell counts, or cluster counts did not correlate with patient outcome. Interestingly, the numbers of Pan cytokeratin positive circulating tumour cells engulfed by tumour associated monocytes correlated strongly with patient outcome independent of circulating tumour cell counts and the use of checkpoint inhibitors. We suggest that Pan cytokeratin staining within monocytes is an important indicator of tumour-associated inflammation post-therapy and an effective biomarker with strong prognostic capability for patient outcome.
Williams M., RJ, Saberi, M & Meschke, G 2024, 'Numerical investigation of artificial ground freezing–thawing processes in tunnel construction', Computers and Geotechnics, vol. 173, pp. 106477-106477.
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Williams, N, Hemsworth, L, Chaplin, S, Shephard, R & Fisher, A 2024, 'Analysis of substantiated welfare investigations in extensive farming systems in Victoria, Australia', Australian Veterinary Journal, vol. 102, no. 9, pp. 440-452.
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Substantiated incidents of poor welfare affecting cattle, sheep and goats (livestock) in non‐dairy extensive farming systems continue to occur. This study sought to describe the common causes of poor welfare of livestock and the associated circumstances, by analysing 39 years of de‐identified, livestock welfare investigation records. There were a total of 2179 alleged offenders (AOff), defined as individual/s that had an incident of poor welfare affecting livestock on at least one occasion. Approximately 27% of AOff were found to have poor welfare on more than one occasion. The majority of livestock welfare incidents were associated with neglect, more specifically, inadequate nutrition (56%), treatment (65%) and management/husbandry (83%). Records of malicious acts were rare (1%). In the analysis, cases were allocated to 10 animal welfare severity categories (AWSC) based on the number of incidents and visits, whether the AOff reoffended, or if the incident was ongoing and whether the welfare issue was likely to affect the whole herd. A significantly higher proportion of cases in the most severe AWSC had a failure to shear, mark, dip/drench, draft and wean/cull, were overstocked or were not providing proper and sufficient feed, compared to the least severe AWSC (P ≤ 0.05). Reoffending was significantly more likely when animals were found to be injured/unwell, recumbent, stuck in mud/yard/pen or in poor body condition, or when there was a failure to wean/cull, mark, dip/drench and draft. Some of the issues identified here may be risk factors more commonly identified on farms with poor livestock welfare.
Withana, H, Rawat, S & Zhang, YX 2024, 'Effect of nanocellulose on mechanical properties of cementitious composites – A review', Advanced Nanocomposites, vol. 1, no. 1, pp. 201-216.
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Withana, H, Rawat, S, Fanna, DJ & Zhang, YX 2024, 'Engineered cementitious composite with nanocellulose and high-volume fly ash', Construction and Building Materials, vol. 451, pp. 138849-138849.
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Wocker, MM, Ostermeier, FF, Wanninger, T, Zwinkau, R & Deuse, J 2024, 'Flexible job shop scheduling with preventive maintenance consideration', Journal of Intelligent Manufacturing, vol. 35, no. 4, pp. 1517-1539.
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Wolff, JO, Ashley, LJ, Schmitt, C, Heu, C, Denkova, D, Jani, M, Řezáčová, V, Blamires, SJ, Gorb, SN, Garb, J, Goodacre, SL & Řezáč, M 2024, 'From fibres to adhesives: evolution of spider capture threads from web anchors by radical changes in silk gland function', Journal of The Royal Society Interface, vol. 21, no. 216.
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Spider webs that serve as snares are one of the most fascinating and abundant type of animal architectures. In many cases they include an adhesive coating of silk lines—so-called viscid silk—for prey capture. The evolutionary switch from silk secretions forming solid fibres to soft aqueous adhesives remains an open question in the understanding of spider silk evolution. Here we functionally and chemically characterized the secretions of two types of silk glands and their behavioural use in the cellar spider, Pholcus phalangioides. Both being derived from the same ancestral gland type that produces fibres with a solidifying glue coat, the two types produce respectively a quickly solidifying glue applied in thread anchorages and prey wraps, or a permanently tacky glue deployed in snares. We found that the latter is characterized by a high concentration of organic salts and reduced spidroin content, showing up a possible pathway for the evolution of viscid properties by hygroscopic-salt-mediated hydration of solidifying adhesives. Understanding the underlying molecular basis for such radical switches in material properties not only helps to better understand the evolutionary origins and versatility of ecologically impactful spider web architectures, but also informs the bioengineering of spider silk-based products with tailored properties.
Wong, L, Imran Kabir, MD, Wang, J, Zhang, YX & Yang, RC 2024, 'Numerical Slow Growth Damage Assessment of an Adhesively Bonded Composite Joint Under Compression Through Four-Point Bending', International Journal of Computational Methods, vol. 21, no. 08.
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In this study, an extended finite element method (XFEM)-based numerical analysis procedure is developed as part of a framework for assessing damage slow growth behaviors of an adhesively bonded composite joint. This CFRP-CFRP single strap joint is stabilized with an aluminium honeycomb subjected to static compression through four-point bending. The adhesively bonded patch has a 140[Formula: see text]mm overlap length centered on its 440[Formula: see text]mm parent structure. The residual strengths are determined using an adhesive element failure criterion and a failure index based on energy release rate for disbonded and delaminated joints. XFEM is utilized to introduce pre-initiated cracks of various lengths and to calculate the energy release rates for each of four failure scenarios. Mesh convergence studies are conducted to acquire appropriate element sizes at crack tip. Additionally, multiple numerical benchmark models to experimental and numerical literatures are devised to validate individual components of the proposed finite element model. Results of the energy release rates for joints with cracks starting at the gap region suggest that a mixed-mode fracture occurs. At small crack lengths, mode-I is relatively low, and mode-II is high. As the crack length increases, mode-I increases, and mode-II gradually decreases. The energy release rates for joints with cracks starting in the taper end show that only a mode-II fracture exists. Finally, slow growth damage could be identified in each of the four damage scenarios. Joints with cracks initiated from the taper end exhibit substantially longer periods of slow growth damage when compared to joints with cracks initiated from the gap region.
Wu, C, Wan, B, Entezari, A, Fang, J, Xu, Y & Li, Q 2024, 'Machine learning-based design for additive manufacturing in biomedical engineering', International Journal of Mechanical Sciences, vol. 266, pp. 108828-108828.
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Wu, C, Wan, B, Xu, Y, Al Maruf, DSA, Cheng, K, Lewin, WT, Fang, J, Xin, H, Crook, JM, Clark, JR, Steven, GP & Li, Q 2024, 'Dynamic optimisation for graded tissue scaffolds using machine learning techniques', Computer Methods in Applied Mechanics and Engineering, vol. 425, pp. 116911-116911.
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Wu, C, Yu, Z, Shao, R & Li, J 2024, 'A comprehensive review of extraterrestrial construction, from space concrete materials to habitat structures', Engineering Structures, vol. 318, pp. 118723-118723.
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Wu, J, Huang, Y, Gao, M, Gao, Z, Zhao, J, Zhang, H & Zhang, A 2024, 'A Two-Stream Hybrid Convolution-Transformer Network Architecture for Clothing-Change Person Re-Identification', IEEE Transactions on Multimedia, vol. 26, pp. 5326-5339.
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Wu, K, Pegoraro, J, Meneghello, F, Zhang, JA, Lacruz, JO, Widmer, J, Restuccia, F, Rossi, M, Huang, X, Zhang, D, Caire, G & Guo, YJ 2024, 'Sensing in Bistatic ISAC Systems With Clock Asynchronism: A signal processing perspective', IEEE Signal Processing Magazine, vol. 41, no. 5, pp. 31-43.
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Wu, K, Zhang, JA, Ni, Z, Huang, X, Guo, YJ & Chen, S 2024, 'Joint Communications and Sensing Employing Optimized MIMO-OFDM Signals', IEEE Internet of Things Journal, vol. 11, no. 6, pp. 10368-10383.
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Wu, L 2024, 'A Study on the Battle of Changping', Journal of Chinese Military History, pp. 1-27.
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AbstractThis article conducts a comprehensive study on the battle of Changping 長平 (260 BCE) between Qin 秦 and Zhao 趙 and challenges some traditional views on it. This article estimates the sizes and losses of the opposing sides and argues that although the number of losses of the Zhao army in this battle looks unreasonably large, it is too subjective to say that the ancient sources exaggerate the numbers because the definition of “soldiers” at that time was different from today. This article concludes that the reasons for Zhao’s defeat are not because of replacing an experienced chief commander with an inexperienced one or shortage in supply but because it was the relatively weaker side and more importantly, its long-term strategic planning and diplomacy were inferior to Qin’s. This article also argues that the influence of this battle is not as significant as claimed by some scholars.
Wu, L, Ngo, HH, Wang, C, Hou, Y, Chen, X, Guo, W, Duan, H, Ni, B-J & Wei, W 2024, 'Lactobacillus inoculation mediated carboxylates and alcohols production from waste activated sludge fermentation system: Insight into process outcomes and metabolic network', Bioresource Technology, vol. 409, pp. 131191-131191.
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Wu, L, Wei, W, Chen, Z, Shi, X, Qian, J & Ni, B-J 2024, 'Novel anaerobic fermentation paradigm of producing medium-chain fatty acids from food wastes with self-produced ethanol as electron donor', Chemical Engineering Journal, vol. 483, pp. 149236-149236.
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Wu, L吳 2024, 'A Choice between Su 速 and Jiu 久', Monumenta Serica, vol. 72, no. 1, pp. 1-18.
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Wu, M, Sivertsen, G, Zhang, L, Qi, F & Zhang, Y 2024, 'Scientific Progress or Societal Progress? A Language Modelbased Classification of the Aims of the Research in Scientific Publications'.
Wu, Q, Huang, Y, Irga, P, Kumar, P, Li, W, Wei, W, Shon, HK, Lei, C & Zhou, JL 2024, 'Synergistic control of urban heat island and urban pollution island effects using green infrastructure', Journal of Environmental Management, vol. 370, pp. 122985-122985.
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Wu, Q, Liu, Q, He, Y & Wu, Z 2024, 'UAV-Enabled Energy-Efficient Aerial Computing: A Federated Deep Reinforcement Learning Approach', IEEE Transactions on Reliability, pp. 1-14.
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Wu, R, Wang, M, Li, Z, Zhou, J, Chen, F, Wang, X & Sun, C 2024, 'Few-Shot Stereo Matching with High Domain Adaptability Based on Adaptive Recursive Network', International Journal of Computer Vision, vol. 132, no. 5, pp. 1484-1501.
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Wu, S-L, Wu, L, Wei, W, Shentu, J, Long, Y, Shen, D & Ni, B-J 2024, 'Challenges and enhancement technologies of medium chain carboxylates production in open culture anaerobic fermentation', Chemical Engineering Journal, vol. 494, pp. 153224-153224.
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Wu, X, Lu, J, Yan, Z & Zhang, G 2024, 'Disentangling Stochastic PDE Dynamics for Unsupervised Video Prediction', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 11, pp. 15427-15441.
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Wu, X, Zhong, Y, Ling, Z, Yang, J, Li, L, Sheng, W & Jiang, B 2024, 'Nonlinear learning method for local causal structures', Information Sciences, vol. 654, pp. 119789-119789.
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Wu, Y, Cao, J & Xu, G 2024, 'FASTER: A Dynamic Fairness-assurance Strategy for Session-based Recommender Systems', ACM Transactions on Information Systems, vol. 42, no. 1, pp. 1-26.
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When only users’ preferences and interests are considered by a recommendation algorithm, it will lead to the severe long-tail problem over items. Therefore, the unfair exposure phenomenon of recommended items caused by this problem has attracted widespread attention in recent years. For the first time, we reveal the fact that there is a more serious unfair exposure problem in session-based recommender systems (SRSs), which learn the short-term and dynamic preferences of users from anonymous sessions. Considering the fact that in SRSs, recommendations are provided multiple times and item exposures are accumulated over interactions in a session, we define new metrics both for the fairness of item exposure and recommendation quality among sessions. Moreover, we design a dynamic F airness- A ssurance ST rategy for s E ssion-based R ecommender systems ( FASTER ). FASTER is a post-processing strategy that tries to keep a balance between item exposure fairness and recommendation quality. It can also maintain the fairness of recommendation quality among sessions. The effectiveness of FASTER is verified on three real-world datasets and five original algorithms. The experiment results show that FASTER can generally reduce the unfair exposure of different session-based recommendation algorithms while still ensuring a high level of recommendation quality.
Wu, Y, Li, S, Zhang, J, Li, Y, Li, Y & Zhang, Y 2024, 'Dual attention transformer network for pixel-level concrete crack segmentation considering camera placement', Automation in Construction, vol. 157, pp. 105166-105166.
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Pixel-level crack segmentation remains a challenging task due to the trade-off between computational cost and accuracy, as well as the small size of real-world cracks, typically submillimeter in width, resulting in limited pixels for analysis. To address these challenges, this paper proposes a Pixel Crack Transformer Network (PCTNet) to investigate the impact of different camera placements on network performance. PCTNet adopts a hierarchical structure with Cross-Scale PatchEmbedding Layer and Dual Attention Transformer Block, enabling the generation of multi-scale feature maps and the fusion of global and local features. PCTNet achieves a reduction of up to 64% in computational cost compared to transformer networks while outperforming both convolutional and transformer networks, achieving 95.89% precision, 93.77% recall, 94.8% F1-score, and 90.53% mIoU. Furthermore, this work introduces Crack-R dataset, which encompasses crack images captured at varying distances, facilitating the evaluation of segmentation accuracy in real-world scenarios with different crack-to-pixel ratios.
Wu, Y, Sun, R, Wang, X, Wen, D, Zhang, Y, Qin, L & Lin, X 2024, 'Efficient Maximal Frequent Group Enumeration in Temporal Bipartite Graphs', Proceedings of the VLDB Endowment, vol. 17, no. 11, pp. 3243-3255.
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Cohesive subgraph mining is a fundamental problem in bipartite graph analysis. In reality, relationships between two types of entities often occur at some specific timestamps, which can be modeled as a temporal bipartite graph. However, the temporal information is widely neglected by previous studies. Moreover, directly extending the existing models may fail to find some critical groups in temporal bipartite graphs, which appear in a unilateral (i.e., one-layer) form. To fill the gap, in this paper, we propose a novel model, called maximalλ-frequency group (MFG). Given a temporal bipartite graph𝒢=(U, V, ℰ), a vertex setVS⊆Vis an MFG ifi) there are no less thanλtimestamps, at each of whichVScan form a (τU, τV)-biclique with some vertices inUat the corresponding snapshot, andii) it is maximal. To solve the problem, a filter-and-verification (FilterV) method is proposed based on the Bron-Kerbosch framework, incorporating novel filtering techniques to reduce the search space and array-based strategy to accelerate the frequency and maximality verification. Nevertheless, the cost of frequency verification in each valid candidate set computation and maximality check could limit the scalability of FilterV to larger graphs. Therefore, we further develop a novel verification-free (VFree) approach by leveraging the advanced dynamic counting structure proposed. Theoretically, we prove that VFree can reduce the cost of each valid candidate set computation in FilterV by a factor ofO(|V|). Furthermore, VFr...
Wu, Y, Wan, Y, Zhang, H, Sui, Y, Wei, W, Zhao, W, Xu, G & Jin, H 2024, 'Automated Data Visualization from Natural Language via Large Language Models: An Exploratory Study', Proceedings of the ACM on Management of Data, vol. 2, no. 3, pp. 1-28.
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The Natural Language to Visualization (NL2Vis) task aims to transform natural-language descriptions into visual representations for a grounded table, enabling users to gain insights from vast amounts of data. Recently, many deep learning-based approaches have been developed for NL2Vis. Despite the considerable efforts made by these approaches, challenges persist in visualizing data sourced from unseen databases or spanning multiple tables. Taking inspiration from the remarkable generation capabilities of Large Language Models (LLMs), this paper conducts an empirical study to evaluate their potential in generating visualizations, and explore the effectiveness of in-context learning prompts for enhancing this task. In particular, we first explore the ways of transforming structured tabular data into sequential text prompts, as to feed them into LLMs and analyze which table content contributes most to the NL2Vis. Our findings suggest that transforming structured tabular data into programs is effective, and it is essential to consider the table schema when formulating prompts. Furthermore, we evaluate two types of LLMs: finetuned models (e.g., T5-Small) and inference-only models (e.g., GPT-3.5), against state-of-the-art methods, using the NL2Vis benchmarks (i.e., nvBench). The experimental results reveal that LLMs outperform baselines, with inference-only models consistently exhibiting performance improvements, at times even surpassing fine-tuned models when provided with certain few-shot demonstrations through in-context learning. Finally, we analyze when the LLMs fail in NL2Vis, and propose to iteratively update the results using strategies such as chain-of-thought, role-playing, and code-interpreter. The experimental results confirm the efficacy of iterative updates and hold great potential for future study.
Wu, Z, Guo, K, Luo, E, Wang, T, Wang, S, Yang, Y, Zhu, X & Ding, R 2024, 'Medical long-tailed learning for imbalanced data: Bibliometric analysis', Computer Methods and Programs in Biomedicine, vol. 247, pp. 108106-108106.
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Wu, Z, Rao, P, Cui, J, Chen, Q & Nimbalkar, S 2024, 'Lateral Response Evaluation of Existing Pile by Adjacent Pile Driving in Claye Slope', Geotechnical and Geological Engineering, vol. 42, no. 2, pp. 1313-1337.
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Wu, Z, Zhang, Q, Miao, D, Zhao, X & Shi, K 2024, 'Adapting GNNs for Document Understanding: A Flexible Framework With Multiview Global Graphs', IEEE Transactions on Computational Social Systems, pp. 1-14.
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Wu, Z, Zheng, D, Pan, S, Gan, Q, Long, G & Karypis, G 2024, 'TraverseNet: Unifying Space and Time in Message Passing for Traffic Forecasting', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 2, pp. 2003-2013.
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Xi, W, Kong, Z, Deng, Z, Chen, Y, Mou, H, Zhang, Y, Zhang, Z, Li, Z, Xu, X & Zheng, W 2024, 'A novel electrochemical sensor based on Fe3+-curcumin/multi-walled carbon nanotubes complex enabling electrochemical sensing of hydroxylamine', Diamond and Related Materials, vol. 146, pp. 111133-111133.
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Xia, J, Huang, W, Xu, M, Zhang, J, Zhang, H, Sheng, Z & Xu, D 2024, 'Unsupervised Part Discovery via Dual Representation Alignment', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 12, pp. 10597-10613.
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Xia, R, Liu, W, Nghiem, LD, Cao, D, Li, Y, Li, G & Luo, W 2024, 'A novel chitosan and polyferric sulfate composite coagulant for biogas slurry pretreatment by simultaneous flocculation and floatation: Performance and underlying mechanisms', Water Research, vol. 258, pp. 121781-121781.
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Xiang, G, Tao, M, Zhao, R, Zhao, H, Memon, MB & Wu, C 2024, 'Dynamic response of water-rich tunnel subjected to plane P wave considering excavation induced damage zone', Underground Space, vol. 15, pp. 113-130.
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Xiao, F, Zhu, Q, Guan, J, Liu, X, Liu, H, Zhang, K, He, G & Wang, W 2024, 'Enhancing automated audio captioning with synthetic supervision', Shengxue Xuebao/Acta Acustica, vol. 49, no. 6, pp. 1315-1323.
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The data-driven automated audio captioning methods are limited by the quantity and quality of available audio-text pairs, resulting in insufficient cross-modal representation, which undermines the captioning performance. To address this, this paper proposes an audio captioning framework enhanced with synthetic supervision, termed SynthAC. This framework leverages commonly available high-quality image captioning text corpus and a text-to-audio generative model to create synthetic audio signals. Therefore, the proposed SynthAC framework can effectively expand audio-text pairs and enhance the cross-modal text-audio representation by learning relations within synthetic audio-text pairs. Experiments demonstrate that the proposed SynthAC framework can significantly improve audio captioning performance by incorporating high-quality text corpus from image captioning, providing an effective solution to the challenge of data scarcity. Additionally, SynthAC can be easily adapted to various state-of-the-art methods, significantly enhancing audio captioning performance without modifying the existing model structures.
Xiao, P, Du, S, Wei, Z, Hong, Q & Wen, S 2024, 'Design and Application of Programmable Analog Computing Circuit for Kalman Filter Algorithm Based on Memristive Array', IEEE Transactions on Circuits and Systems for Artificial Intelligence, vol. 1, no. 2, pp. 272-284.
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Xiao, S, Wang, Y, Yu, Q, Zhang, J, Dong, D & Petersen, IR 2024, 'Quantum state tomography from observable time traces in closed quantum systems', Control Theory and Technology, vol. 22, no. 2, pp. 222-234.
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Xiao, T, Halkon, B, Wang, S, Oberst, S & Qiu, X 2024, 'Refracto-vibrometry for active control of sound radiation through an opening', Journal of Sound and Vibration, vol. 577, pp. 118242-118242.
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Xiao, Y, Xia, R, Li, Y, Shi, G, Nguyen, DN, Hoang, DT, Niyato, D & Krunz, M 2024, 'Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-Supervised Learning Approach', IEEE Transactions on Mobile Computing, vol. 23, no. 2, pp. 1815-1829.
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With the rising demand for wireless services and increased awareness of the need for data protection, existing network traffic analysis and management architectures are facing unprecedented challenges in classifying and synthesizing the increasingly diverse services and applications. This paper proposes FS-GAN, a federated self-supervised learning framework to support automatic traffic analysis and synthesis over a large number of heterogeneous datasets. FS-GAN is composed of multiple distributed Generative Adversarial Networks (GANs), with a set of generators, each being designed to generate synthesized data samples following the distribution of an individual service traffic, and each discriminator being trained to differentiate the synthesized data samples and the real data samples of a local dataset. A federated learning-based framework is adopted to coordinate local model training processes of different GANs across different datasets. FS-GAN can classify data of unknown types of service and create synthetic samples that capture the traffic distribution of the unknown types. We prove that FS-GAN can minimize the Jensen-Shannon Divergence (JSD) between the distribution of real data across all the datasets and that of the synthesized data samples. FS-GAN also maximizes the JSD among the distributions of data samples created by different generators, resulting in each generator producing synthetic data samples that follow the same distribution as one particular service type. Extensive simulation results show that the classification accuracy of FS-GAN achieves over $20\%$ improvement in average compared to the state-of-the-art clustering-based traffic analysis algorithms. FS-GAN also has the capability to synthesize highly complex mixtures of traffic types without requiring any human-labeled data samples.
Xiao, Y, Zhang, X, Li, Y, Shi, G, Krunz, M, Nguyen, DN & Hoang, DT 2024, 'Time-Sensitive Learning for Heterogeneous Federated Edge Intelligence', IEEE Transactions on Mobile Computing, vol. 23, no. 2, pp. 1382-1400.
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Real-time machine learning (ML) has recently attracted significant interest due to its potential to support instantaneous learning, adaptation, and decision making in a wide range of application domains, including self-driving vehicles, intelligent transportation, and industry automation. In this paper, we investigate real-time ML in a federated edge intelligence (FEI) system, an edge computing system that implements federated learning (FL) solutions based on data samples collected and uploaded from decentralized data networks, e.g., Internet-of-Things (IoT) and/or wireless sensor networks. FEI systems often exhibit heterogenous communication and computational resource distribution, as well as non-i.i.d. data samples arrived at different edge servers, resulting in long model training time and inefficient resource utilization. Motivated by this fact, we propose a time-sensitive federated learning (TS-FL) framework to minimize the overall run-time for collaboratively training a shared ML model with desirable accuracy. Training acceleration solutions for both TS-FL with synchronous coordination (TS-FL-SC) and asynchronous coordination (TS-FL-ASC) are investigated. To address the straggler effect in TS-FL-SC, we develop an analytical solution to characterize the impact of selecting different subsets of edge servers on the overall model training time. A server dropping-based solution is proposed to allow some slow-performance edge servers to be removed from participating in the model training if their impact on the resulting model accuracy is limited. A joint optimization algorithm is proposed to minimize the overall time consumption of model training by selecting participating edge servers, the local epoch number (the number of model training iterations per coordination), and the data batch size (the number of data samples for each model training iteration). Motivated by the fact that data samples at the slowest edge server may exhibit special characteristi...
Xiao, Y, Zhao, J, Yu, Y, Ding, X, Liu, S, Bao, W, Wen, S & Zhou, X 2024, 'SimpleCNN-UNet: An optic disc image segmentation network based on efficient small-kernel convolutions', Expert Systems with Applications, vol. 256, pp. 124935-124935.
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Xiao, Z, Liu, X, Zeng, Y, Zhang, JA, Jin, S & Zhang, R 2024, 'Rethinking Waveform for 6G: Harnessing Delay-Doppler Alignment Modulation', IEEE Communications Magazine, pp. 1-8.
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Xie, D, Wang, Z, Chen, C & Dong, D 2024, 'Depthwise Convolution for Multi-Agent Communication With Enhanced Mean-Field Approximation', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 6, pp. 8557-8569.
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Xie, F, Gao, Y, Meng, D, Xu, Y, Wu, C, Fang, J & Li, Q 2024, 'Topology optimization for fiber-reinforced plastic (FRP) composite for frequency responses', Computer Methods in Applied Mechanics and Engineering, vol. 428, pp. 117114-117114.
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Xie, K, Fu, Q, Chen, F, Zhu, H, Wang, X, Huang, G, Zhan, H, Liang, Q, Doherty, CM, Wang, D, Qiao, GG & Li, D 2024, 'Controlling the Supramolecular Architecture Enables High Lithium Cationic Conductivity and High Electrochemical Stability for Solid Polymer Electrolytes', Advanced Functional Materials, vol. 34, no. 17.
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AbstractSolid polymer electrolytes (SPEs) are long sought after for versatile applications due to their low cost, light weight, flexibility, ease of scale‐up, and low interfacial impedance. However, obtaining SPEs with high Li+ conductivity (σ+) and high voltage stability to avoid concentrated polarization and premature capacity loss has proven challenging. Here a stretchable dry‐SPE is reported with a semi‐interpenetrating, supermolecular architecture consisting of a cross‐linked polyethylene oxide (PEO) tetra‐network and an alternating copolymer poly(ethylene oxide‐alt‐butylene terephthalate). Such a unique supermolecular architecture suppresses the formation of Li+/PEO intermolecular complex and enhances the oxidation stability of PEO‐based electrolyte, thus maintaining high chain segmental motion even with high salt loading (up to 50 wt%) and achieving a wide electrochemical stability window of 5.3 V. These merits enable the simultaneous accomplishment of high ionic conductivity and high Li+ transference number (t+) to enhance the energy efficiency of energy storage device, and electrochemical stability.
Xie, Y, Far, H, Mortazavi, M & El-Sherbeeny, AM 2024, 'Improving the Mechanical Properties of Concrete Mixtures by Shape Memory Alloy Fibers and Silica Fume', Buildings, vol. 14, no. 6, pp. 1709-1709.
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Concrete, as one of the most widely applied materials in buildings, has high environmental impacts. Researchers are continually seeking solutions to mitigate these environmental issues while enhancing the mechanical strength and durability of concrete. However, there is a lack of studies on the effect of combining silica fume (SF) as pozzolanic materials and shape memory alloy (SMA) fibers on the mechanical properties of concrete. Moreover, there is very limited research on the influence of these materials on concrete mixtures after primary failure cracks using the secondary compressive strength test. In this research, 0.1, 0.2, and 0.3% SMA and 5, 7.5, and 10% SF were applied and then subjected to compressive strength, splitting tensile strength, flexural strength, secondary compressive strength, and ultrasonic pulse velocity tests. According to the results, 10% SF is more economical, which increases the compressive, splitting tensile, and flexural strength by 14%, 7%, and 10%, respectively. Also, using 0.3% SMA improves the compressive, splitting tensile, and flexural strength by 2%, 5%, and 8%, respectively. Furthermore, SMA has the ability to reduce the secondary compressive strength compared to other samples, indicating the quality of this material in controlling stress after cracking. Finally, it was indicated that the combined use of these two materials increases the strength parameters.
Xing, B & Tsang, IW 2024, 'Co-Guiding for Multi-Intent Spoken Language Understanding', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 5, pp. 2965-2980.
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Xiong, K, Yu, J, Hu, C, Wen, S & Kong, F 2024, 'Nonseparation analysis-based finite/fixed-time synchronization of fully complex-valued impulsive dynamical networks', Applied Mathematics and Computation, vol. 467, pp. 128500-128500.
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Xu, B, Fu, Y, Lu, X, Li, Z, He, M, Teo, WJ, Song, W, Nghiem, LD, Bae, S & Ng, HY 2024, 'Integration of CaO2/Fe2+ and ultrafiltration acts as multiple-workfunctions pretreatment for seawater desalination during harmful algal blooms', Desalination, vol. 587, pp. 117908-117908.
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Xu, D, Li, Z, Leitner, U & Sun, J 2024, 'Efficacy of Remote Cognitive Behavioural Therapy for Insomnia in Improving Health Status of Patients with Insomnia Symptoms: A Meta-analysis', Cognitive Therapy and Research, vol. 48, no. 2, pp. 177-211.
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Abstract Background Insomnia is highly prevalent and cognitive behavioural therapy is the first-line treatment for it. This study aimed to assess the efficacy of remote cognitive behavioural therapy for insomnia, specifically, treatment fully delivered through the internet, mobile phones and telephones for sleep and other health outcomes in adults diagnosed with insomnia or reporting insomnia symptoms. This study also aimed to evaluate the effect of various intervention components as subgroup variables to explain the efficacy of remote cognitive behavioural therapy on health outcomes. Methods Randomised controlled trial studies were obtained from five electronic databases. The PEDro scale was used to assess the quality of the studies. A random effect model was used to assess the mean difference, standardised mean difference and standard deviation of the outcome variables. Heterogeneity among the study articles was assessed using I2 and Q tests. Egger regression analysis was used to assess publication bias. Results Remote cognitive behavioural therapy for insomnia had significant and positive effects on improving sleep outcomes, depression, anxiety, fatigue and mental health compared with the control conditions. Its effect on physical health was not significant. The effect of the therapy was enhanced when the total length of intervention was shorter than 6 weeks, delivered via the internet and did not include therapist support. Conclusion Remote cognitive beh...
Xu, G, Jia, W, Wu, T, Chen, L & Gao, G 2024, 'HAFormer: Unleashing the Power of Hierarchy-Aware Features for Lightweight Semantic Segmentation', IEEE Transactions on Image Processing, vol. 33, pp. 4202-4214.
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Xu, H, Cai, J, Sawhney, R, Jiang, S, Buys, N & Sun, J 2024, 'The Effectiveness of Cognitive-Behavioral Therapy in Helping People on Sick Leave to Return to Work: A Systematic Review and Meta-analysis', Journal of Occupational Rehabilitation, vol. 34, no. 1, pp. 4-36.
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Abstract Purpose Previous research has systematically studied the effectiveness of Cognitive Behavioral Therapy (CBT)-based interventions in managing both mental and physical symptoms of chronic disease including depression, stress-related mental disorders (SMD), and chronic pain that are common causes of sick leave. However, a systematic review focusing on the effectiveness of CBT in facilitating RTW is lacking. This study compiles research on utilizing CBT-based interventions for helping employees on sick leave return to work. Methods Randomized controlled trials (RCT) published between 1 January 1990 and 27 June 2022 were searched in MEDLINE, EMBASE, The Cochrane Library, Scopus, PsycINFO, Web of Science, and PubMed. The primary outcome variables included a return to work (RTW) measure and sickness absences. The secondary outcomes include psychological conditions (mental illness, stress, anxiety, and depression) and physical condition (working ability, fatigue, and physical function). Results Thirty-four RCTs were included in the analysis. Fifteen RCTs with 1727 participants reported on sick leave. Results showed that participants who completed CBT intervention had reduced sick leave in days (mean reduction − 3.654; 95%CI − 5.253, − 2.046; p < 0.001) compared to the control group. Sixteen papers with 2298 participants reported that the intervention group RTW 1.5 days earlier (95%CI 1.019, 1.722; p < 0.05). CBT-based interventions were effective in managing fatigue, mental illness, and depression, and improving physical function while it showed no effects in managing stress, anxiety and working ability.
Xu, H, Liu, H, Huang, S & Sun, Y 2024, 'C2L-PR: Cross-modal Camera-to-LiDAR Place Recognition via Modality Alignment and Orientation Voting', IEEE Transactions on Intelligent Vehicles, pp. 1-17.
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Xu, H, Liu, M, Bu, Y, Sun, S, Zhang, Y, Zhang, C, Acuna, DE, Gray, S, Meyer, E & Ding, Y 2024, 'The impact of heterogeneous shared leadership in scientific teams', Information Processing & Management, vol. 61, no. 1, pp. 103542-103542.
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Xu, H, Nanda, P & Liang, J 2024, 'Reciprocal Federated Learning Framework: Balancing incentives for model and data owners', Future Generation Computer Systems, vol. 161, pp. 146-161.
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Xu, H, Xuan, J, Zhang, G & Lu, J 2024, 'Trust region policy optimization via entropy regularization for Kullback–Leibler divergence constraint', Neurocomputing, vol. 589, pp. 127716-127716.
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Xu, J & Cao, L 2024, 'Copula Variational LSTM for High-Dimensional Cross-Market Multivariate Dependence Modeling', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 11, pp. 16233-16247.
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Xu, J, Han, P, Jin, Y, Lu, H, Sun, B, Gao, B, He, T, Xu, X, Pinna, N & Wang, G 2024, 'Hybrid Molecular Sieve-Based Interfacial Layer with Physical Confinement and Desolvation Effect for Dendrite-free Zinc Metal Anodes', ACS Nano, vol. 18, no. 28, pp. 18592-18603.
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Xu, K, Le, AT, Huang, X & Ryu, H-G 2024, 'Generalized Analog Least Mean Square Loop for Self-Interference Cancellation in In-Band Full-Duplex Communications', IEEE Transactions on Wireless Communications, pp. 1-1.
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Xu, L, Wen, D, Qin, L, Li, R, Zhang, Y & Lin, X 2024, 'Constant-time Connectivity Querying in Dynamic Graphs', Proceedings of the ACM on Management of Data, vol. 2, no. 6, pp. 1-23.
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Connectivity query processing is a fundamental problem in graph processing. Given an undirected graph and two query vertices, the problem aims to identify whether they are connected via a path. Given frequent edge updates in real graph applications, in this paper, we study connectivity query processing in fully dynamic graphs, where edges are frequently inserted or deleted. A recent solution, called D-tree, maintains a spanning tree for each connected component and applies several heuristics to reduce the depth of the tree. To improve the efficiency, we propose a new spanning-tree-based solution by maintaining a disjoint-set tree simultaneously. By combining the advantages of two trees, we achieve the constant query time complexity and also significantly improve the theoretical running time in both edge insertion and edge deletion. Our performance studies on real large datasets show considerable improvement of our algorithms.
Xu, M, Peng, C, Hou, Y, Xiao, Y & Sohaib, O 2024, 'Consumer Sentiment Analysis and Product Improvement Strategy Based on Improved GCN Model', Journal of Organizational and End User Computing, vol. 36, no. 1, pp. 1-38.
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In recent years, the amount of online shopping review data has increased dramatically. Obtaining information that helps business decision-making from such complex and massive reviews has become a difficult and important task for merchants. This paper uses sentiment analysis technology to innovatively introduce the attention mechanism on the LSTM infrastructure of the baseline model, and proposes a word vector structure and a BiGRU structure to build an online user sentiment analysis system based on deep learning. The system includes a user review sentiment classification and sentiment analysis model based on the improved GCN model. The experimental results also show the superiority of our method, which brings 4.73%, 7.84% and 5.72% F1-score improvements to the algorithm respectively. It proves that the two algorithms proposed in this paper can effectively achieve their goals and achieve high performance.
Xu, P, Lei, Y, Sui, Y & Xue, J 2024, 'Iterative-Epoch Online Cycle Elimination for Context-Free Language Reachability', Proceedings of the ACM on Programming Languages, vol. 8, no. OOPSLA1, pp. 1437-1462.
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Context-free language reachability (CFL-reachability) is a fundamental framework for implementing various static analyses. CFL-reachability utilizes context-free grammar (CFG) to extend the expressiveness of ordinary graph reachability from an unlabeled graph to an edge-labeled graph. Solving CFL-reachability requires a (sub)cubic time complexity with respect to the graph size, which limits its scalability in practice. Thus, an approach that can effectively reduce the graph size while maintaining the reachability result is highly desirable. Most of the existing graph simplification techniques for CFL-reachability work during the preprocessing stage, i.e., before the dynamic CFL-reachability solving process. However, in real-world CFL-reachability analyses, there is a large number of reducible nodes and edges that can only be discovered during dynamic solving, leaving significant room for on-the-fly improvements. This paper aims to reduce the graph size of CFL-reachability dynamically via online cycle elimination. We propose a simple yet effective approach to detect collapsible cycles in the graph based on the input context-free grammar. Our key insight is that symbols with particular forms of production rules in the grammar are the essence of transitivity of reachability relations in the graph. Specifically, in the graph, a reachability relation to a node v_i can be 'transited' to another node v_j if there is a transitive relation from v_i to v_j, and cycles formed by transitive relations are collapsible. In this paper, we present an approach to identify the transitive symbols in a context-free grammar and propose an iterative-epoch framework for online cycle elimination. From the perspective of non-parallelized CFL-reachability solving, our iterative-epoch framework is well compatible with both the standard (unordered) solver and the recent ordered solver, and can significantly improve their performance. Our experimen...
Xu, Q, Chen, H, Du, H, Zhang, H, Łukasik, S, Zhu, T & Yu, X 2024, 'M3A: A multimodal misinformation dataset for media authenticity analysis', Computer Vision and Image Understanding, vol. 249, pp. 104205-104205.
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Xu, S, Zeng, H, Yuan, P, Liu, J, Yang, T, Shao, R, Su, Y & Wu, C 2024, 'Experimental and numerical study of G-HPC slabs rapidly repaired by G-HPC canvas and G-UHPC under contact detonations', Engineering Structures, vol. 306, pp. 117877-117877.
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Xu, T, Xu, Z, Liu, Z, Zhang, Y, Castel, A & Yang, G 2024, 'Linear and nonlinear tensile creep of steam-cured UHPC', Cement and Concrete Composites, vol. 145, pp. 105323-105323.
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Xu, Y, Feng, Z, Gao, Y, Wu, C, Fang, J, Sun, G, Qiu, N, Steven, GP & Li, Q 2024, 'Topology optimization for additive manufacturing of CFRP structures', International Journal of Mechanical Sciences, vol. 269, pp. 108967-108967.
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Xu, Y, Li, Y, Zhang, JA, Renzo, MD & Quek, TQS 2024, 'Joint Beamforming for RIS-Assisted Integrated Sensing and Communication Systems', IEEE Transactions on Communications, vol. 72, no. 4, pp. 2232-2246.
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Xu, Y, Liu, Y, Liang, C, Guo, W, Ngo, HH & Peng, L 2024, 'Favipiravir biotransformation by a side-stream partial nitritation sludge: Transformation mechanisms, pathways and toxicity evaluation', Chemosphere, vol. 353, pp. 141580-141580.
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Xu, Y, Lu, Y, Meng, L, Cheng, J, Ouyang, F, Duan, P, Li, W, Zhang, H, Zhu, Y & Zhang, Z 2024, 'Performance and heavy metal leaching of porous geopolymer based on solid wastes', Construction and Building Materials, vol. 427, pp. 136186-136186.
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Xu, Y, Tao, M, Liu, Y, Hong, Z & Wu, C 2024, 'Cracking behavior of brittle materials under eccentric decoupled charge blasting', Engineering Failure Analysis, vol. 163, pp. 108536-108536.
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Xu, Y, Wang, X, Gu, Y, Liang, C, Guo, W, Ngo, HH & Peng, L 2024, 'Optimizing ciprofloxacin removal through regulations of trophic modes and FNA levels in a moving bed biofilm reactor performing sidestream partial nitritation', Water Research X, vol. 22, pp. 100216-100216.
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Xu, Y, Zhang, W, Xu, X, Li, B & Zhang, Y 2024, 'Scalable and Effective Temporal Graph Representation Learning With Hyperbolic Geometry', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15.
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Xu, Y, Zhu, H & Guo, YJ 2024, 'Compact Multi-Beamforming Networks Based on Generalized Joined Coupler Matrix With Flexible Beam Angles and Low Sidelobe Levels', IEEE Open Journal of Antennas and Propagation, vol. 5, no. 4, pp. 810-822.
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Xu, Y, Zhu, H & Guo, YJ 2024, 'Compact Wideband 3 × 3 Nolen Matrix With Couplers Integrated With Phase Shifters', IEEE Microwave and Wireless Technology Letters, vol. 34, no. 2, pp. 159-162.
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Xu, Z, Gao, X, Li, G, Nghiem, LD, Luo, W & Zhang, F 2024, 'Microbial Sources and Sinks of Nitrous Oxide during Organic Waste Composting', Environmental Science & Technology, vol. 58, no. 17, pp. 7367-7379.
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Xu, Z, Zhu, S, Luo, W, Liu, X & Wen, S 2024, 'Fixed‐time synchronization and energy cost estimation of delayed coupled neural networks', International Journal of Robust and Nonlinear Control, vol. 34, no. 6, pp. 4064-4078.
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AbstractThis article concentrates on the fixed‐time synchronization (FTS) and energy cost (EC) problems of delayed coupled neural networks (NNs). A switching controller is proposed and some sufficient conditions are advanced to ensure FTS of delayed coupled NNs based on the comparison theorem. Following that, the required EC is estimated during the control process. The main results of FTS and EC for considered NNs are obtained based on the Lyapunov functions in the sense of vector 1‐norm, 2‐norm, and ‐norm, respectively. Under the same controller parameters, this article shows that the minimum estimation of settling time and EC are sometimes not in one norm sense. Finally, numerical examples are given to show the effectiveness of theoretical results.
Xue, H, Deng, L, Kang, D, Zhao, Y, Zhang, X, Liu, Y, Chen, H, Ngo, HH & Guo, W 2024, 'Advanced biochar-based materials for specific antibiotics removal from hospital wastewater via adsorption and oxidative degradation', Journal of Environmental Chemical Engineering, vol. 12, no. 6, pp. 114275-114275.
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Xue, Y, Song, K, Wang, Z, Xia, Z, Li, R, Wang, Q & Li, L 2024, 'Nanoplastics occurrence, detection methods, and impact on the nitrogen cycle: a review', Environmental Chemistry Letters, vol. 22, no. 5, pp. 2241-2255.
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Yaghoubi Naei, V, Mullally, W, Warkiani, M, O'Byrne, K & Kulasinghe, A 2024, 'P2.11A.24 Characterization of Circulating Tumor-Associated and Immune Cells in Advanced-Stage NSCLC', Journal of Thoracic Oncology, vol. 19, no. 10, pp. S263-S263.
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YAMADA, K & JI, J 2024, 'Substructure elimination and binding method for vibration systems governed by a one-dimensional wave equation', Mechanical Engineering Journal, vol. 11, no. 2.
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Yan, B, Zhao, Q, Zhang, J, Zhang, JA & Yao, X 2024, 'Gridless Evolutionary Approach for Line Spectral Estimation With Unknown Model Order', IEEE Transactions on Cybernetics, vol. 54, no. 2, pp. 935-947.
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Gridless methods show great superiority in line spectral estimation. These methods need to solve an atomic l0 norm (i.e., the continuous analog of l0 norm) minimization problem to estimate frequencies and model order. Since this problem is NP-hard to compute, relaxations of the atomic l0 norm, such as the nuclear norm and reweighted atomic norm, have been employed for promoting sparsity. However, the relaxations give rise to a resolution limit, subsequently leading to biased model order and convergence error. To overcome the above shortcomings of relaxation, we propose a novel idea of simultaneously estimating the frequencies and model order using the atomic l0 norm. To accomplish this idea, we build a multiobjective optimization model. The measurement error and the atomic l0 norm are taken as the two optimization objectives. The proposed model directly exploits the model order via the atomic l0 norm, thus breaking the resolution limit. We further design a variable-length evolutionary algorithm to solve the proposed model, which includes two innovations. One is a variable-length coding and search strategy. It flexibly codes and interactively searches diverse solutions with different model orders. These solutions act as steppingstones that helpfully exploring the variable and open-ended frequency search space and provide extensive potentials toward the optima. Another innovation is a model-order pruning mechanism, which heuristically prunes less contributive frequencies within the solutions, thus significantly enhancing convergence and diversity. Simulation results confirm the superiority of our approach in both frequency estimation and model-order selection.
Yan, L, Li, D, Li, S, Jiao Li, J, Du, G, Liu, H, Zhang, J, Li, X, Fan, Z, Jiu, J, Li, R, Kong, N, Liu, W, Du, Y & Wang, B 2024, 'Exosomes derived from 3D-cultured MSCs alleviate knee osteoarthritis by promoting M2 macrophage polarization through miR-365a-5p and inhibiting TLR2/Myd88/NF-κB pathway', Chemical Engineering Journal, vol. 497, pp. 154432-154432.
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Yan, T, Xu, S, Huang, H, Li, H, Tan, L, Chang, X & Lau, RWH 2024, 'NRGlassNet: Glass surface detection from visible and near-infrared image pairs', Knowledge-Based Systems, vol. 294, pp. 111722-111722.
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Yan, T, Zhu, X, Chen, X, He, W, Wang, C, Yang, Y, Wang, Y & Chang, X 2024, 'GLGFN: Global-Local Grafting Fusion Network for High-Resolution Image Deraining', IEEE Transactions on Circuits and Systems for Video Technology, vol. 34, no. 11, pp. 10860-10873.
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Yan, X, Li, Y & Li, S 2024, 'A Fast Algorithm for Computing the Deficiency Number of a Mahjong Hand', Chinese Journal of Electronics, vol. 33, no. 6, pp. 1383-1398.
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Yan, Z, Sun, W, Guo, W, Li, B, Wen, S & Cao, J 2024, 'Complete Stability of Delayed Recurrent Neural Networks With New Wave-Type Activation Functions', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-13.
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Yang, C, Wang, X, Yao, L, Long, G & Xu, G 2024, 'Dyformer: A dynamic transformer-based architecture for multivariate time series classification', Information Sciences, vol. 656, pp. 119881-119881.
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Yang, F-A, Hou, Y-N, Cao, C, Huang, C, Shen, S, Ren, N, Wang, A-J, Guo, J, Wei, W & Ni, B-J 2024, 'Electroactive properties of EABs in response to long-term exposure to polystyrene microplastics/nanoplastics and the underlying adaptive mechanisms', Journal of Hazardous Materials, vol. 465, pp. 133438-133438.
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Yang, G, Lei, J, Fang, Z, Li, Y, Zhang, J & Xie, W 2024, 'HyBNN: Quantifying and Optimizing Hardware Efficiency of Binary Neural Networks', ACM Transactions on Reconfigurable Technology and Systems, vol. 17, no. 2, pp. 1-24.
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Binary neural network (BNN), where both the weight and the activation values are represented with one bit, provides an attractive alternative to deploy highly efficient deep learning inference on resource-constrained edge devices. However, our investigation reveals that, to achieve satisfactory accuracy gains, state-of-the-art (SOTA) BNNs, such as FracBNN and ReActNet, usually have to incorporate various auxiliary floating-point components and increase the model size, which in turn degrades the hardware performance efficiency. In this article, we aim to quantify such hardware inefficiency in SOTA BNNs and further mitigate it with negligible accuracy loss. First, we observe that the auxiliary floating-point (AFP) components consume an average of 93% DSPs, 46% LUTs, and 62% FFs, among the entire BNN accelerator resource utilization. To mitigate such overhead, we propose a novel algorithm-hardware co-design, called FuseBNN , to fuse those AFP operators without hurting the accuracy. On average, FuseBNN reduces AFP resource utilization to 59% DSPs, 13% LUTs, and 16% FFs. Second, SOTA BNNs often use the compact MobileNetV1 as the backbone network but have to replace the lightweight 3 × 3 depth-wise convolution (DWC) with the 3 × 3 standard convolution (SC, e.g., in ReActNet and our ReActNet-adapted BaseBNN) or even more complex fractional 3 × 3 SC (e.g., in FracBNN) to bridge the accuracy gap. As a result, the model parameter size is significantly increased and becomes 2.25× larger than that of the 4-bit direct quantization with the original DWC (4-Bit-Net); the number of multiply-accumulate operations is also significantly increased so that the overall LUT resource usage of BaseBNN is almost the same as that of 4-Bit-Net. To address this issue, we propose HyBNN , where we binarize depth-wise separation convolution (DSC) blocks for the fi...
Yang, H, Feng, Z, Wei, Z, Zhang, Q, Yuan, X, Quek, TQS & Zhang, P 2024, 'Intelligent Computation Offloading for Joint Communication and Sensing-Based Vehicular Networks', IEEE Transactions on Wireless Communications, vol. 23, no. 4, pp. 3600-3616.
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Yang, H, Wang, Y, Zhao, X, Chen, H, Yin, H, Li, Q & Xu, G 2024, 'Multi-Level Graph Knowledge Contrastive Learning', IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 12, pp. 8829-8841.
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Yang, J & Lin, C-T 2024, 'Enhanced Adjacency-Constrained Hierarchical Clustering Using Fine-Grained Pseudo Labels', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 8, no. 3, pp. 2481-2492.
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Yang, J, Huang, J, He, X, Wen, S & Wang, H 2024, 'Bipartite Synchronization of Signed Networks With Time-Vary Delays Based on T-S Fuzzy System', IEEE Transactions on Fuzzy Systems, vol. 32, no. 6, pp. 3521-3528.
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Yang, K, Tang, Z, Li, W, Long, Z, He, J, Ma, G, Li, Y, Xiang, Y, Xie, Y & Long, G 2024, 'A comprehensive review on the toughening technologies of cement-based materials: From multiscale materials to advanced processes', Construction and Building Materials, vol. 456, pp. 139274-139274.
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Yang, K, Tang, Z, Li, W, Wu, H, Ma, G, Xiang, Y, Xie, Y & Long, G 2024, 'A systematic review on the evaluation methods for the flexural toughness of cement-based materials: From classification analysis to case study', Journal of Building Engineering, vol. 93, pp. 109855-109855.
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Yang, L, Bao, G, Yao, C, Diao, T, Su, Z, Liu, T, Li, G, Wang, G, Chen, X, Xu, X, Sun, B, Xu, X, He, B & Zheng, Y 2024, 'Mitigating adverse effects of Cu-containing intrauterine devices using a highly biocompatible Cu 5Fe alloy', Acta Biomaterialia, vol. 189, pp. 651-667.
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Yang, L, Malki, M, Muñoz-Ferreras, J-M, Zhu, X & Gómez-García, R 2024, 'Multilayer Reflectionless RF Bandpass Filters With Wideband Quasi-Constant Group-Delay Responses', IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 71, no. 12, pp. 6433-6446.
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Yang, T, Xu, S, Yuan, P, Wang, D, Liao, R, Yang, Y, Shao, R & Wu, C 2024, 'Bond performance and constitutive model of steel bar in G-UHPC under cyclic loading', Engineering Structures, vol. 317, pp. 118671-118671.
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Yang, W, Huang, J, He, X & Wen, S 2024, 'Fixed-time synchronization of complex-valued neural networks for image protection and 3D point cloud information protection', Neural Networks, vol. 172, pp. 106089-106089.
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Yang, W, Huang, J, He, X, Wen, S & Huang, T 2024, 'Finite-Time Synchronization of Neural Networks With Proportional Delays for RGB-D Image Protection', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 6, pp. 8149-8160.
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Since the depth information of images facilitates the analysis of the spatial distance of objects in computer vision applications, it is necessary to protect the image depth information. Thus this article proposes a novel red-green-blue-depth (RGB-D) image protection algorithm, which is implemented with the finite-time synchronization (FTS) of neural networks (NNs) with proportional delays via the quantized intermittent control to derive the system synchronization criterion based on Lyapunov stability theory. The performance of RGB-D image protection depends on the synchronization error of the system by driving the system sequence to encrypt the RGB-D image and responding to the system sequence to decrypt the encrypted image. Subsequently, the validity of the proposed criteria is verified by simulation examples, and the practical application of RGB-D image protection is verified.
Yang, X, Bui, TA, Mei, H, Aksoy, YA, Deng, F, Hutvagner, G & Deng, W 2024, 'Exploring the Potential and Challenges of CRISPR Delivery and Therapeutics for Genetic Disease Treatment', Advanced Functional Materials, vol. 34, no. 38.
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AbstractHuman genetic disorders, arising from a range of genetic irregularities, can significantly affect human physiology, often with limited available treatment options. The development of the CRISPR system, facilitating precise editing of the genome, has opened new avenues for addressing a range of mutations found in various genetic disorders. However, there is currently a lack of comprehensive reviews that specifically address the application of CRISPR in genetic diseases. To bridge this gap, this review focuses on exploring the advancements in CRISPR technology and their utility in therapeutic approaches for various genetic disorders. This review introduces human genetic disorders, explains the fundamental mechanisms of CRISPR editing, and highlights the latest advancements in CRISPR technology. Additionally, it examines three CRISPR delivery techniques, including physical delivery, viral vectors, and nanocarriers. It further reviews CRISPR's applications in therapeutic approaches for genetic disorders. Finally, it identifies the primary hurdles associated with industrial development and ethics considerations that should be addressed before the application of CRISPR in a medical context.
Yang, X, Che, H, Leung, M-F & Wen, S 2024, 'Self-paced regularized adaptive multi-view unsupervised feature selection', Neural Networks, vol. 175, pp. 106295-106295.
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Yang, X, Che, H, Leung, M-F, Liu, C & Wen, S 2024, 'Auto-weighted Multi-view Deep Non-negative Matrix Factorization with Multi-kernel Learning', IEEE Transactions on Signal and Information Processing over Networks, pp. 1-13.
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Yang, X, Li, H, Guo, Q, Zhang, JA, Huang, X & Cheng, Z 2024, 'Sensing Aided Uplink Transmission in OTFS ISAC With Joint Parameter Association, Channel Estimation and Signal Detection', IEEE Transactions on Vehicular Technology, vol. 73, no. 6, pp. 9109-9114.
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Yang, X, Zhu, H, Zhao, Y, Chen, Z, Sun, F & Zhu, X 2024, 'A Second-Order Bandpass Filter With 1.6-dB Insertion Loss and 47-dB Upper-Stopband Suppression in 45-nm SOI CMOS Technology', IEEE Electron Device Letters, vol. 45, no. 10, pp. 1710-1713.
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Yang, Y & Tentzeris, MM 2024, 'Additively Manufactured Electronic Components in Multimaterial 3-D and 4-D Printing', Proceedings of the IEEE, vol. 112, no. 8, pp. 950-953.
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Yang, Y, Guo, W, Hao Ngo, H, Zhang, X, Ye, Y, Peng, L, Wei, C & Zhang, H 2024, 'Mini critical review: Membrane fouling control in membrane bioreactors by microalgae', Bioresource Technology, vol. 406, pp. 131022-131022.
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Yang, Y, Guo, W, Ngo, HH, Zhang, X, Liang, S, Deng, L, Cheng, D & Zhang, H 2024, 'Bioflocculants in anaerobic membrane bioreactors: A review on membrane fouling mitigation strategies', Chemical Engineering Journal, vol. 486, pp. 150260-150260.
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Yang, Y, Guo, W, Zhang, J, Liang, S, Liu, Q, Liu, J, Ngo, HH & Zhang, H 2024, 'Applicability analysis of algae biochar for anaerobic membrane bioreactors in wastewater treatment: A review from a sustainability assessment perspective', Science of The Total Environment, vol. 957, pp. 177609-177609.
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Yang, Y, Lu, P, Liu, Z, Dong, L, Lin, J, Yang, T, Ren, Q & Wu, C 2024, 'Effect of steel fibre with different orientations on mechanical properties of 3D-printed steel-fibre reinforced concrete: Mesoscale finite element analysis', Cement and Concrete Composites, vol. 150, pp. 105545-105545.
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Yang, Y, Lu, P, Shao, R, Zhao, Q, Yang, T & Wu, C 2024, 'A comprehensive review of multisource solid wastes in sustainable concrete: From material properties to engineering application', Construction and Building Materials, vol. 435, pp. 136775-136775.
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Yang, Y, Qi, J, Hu, J, Zhou, Y, Zheng, J, Deng, W, Inam, M, Guo, J, Xie, Y, Li, Y, Xu, C, Deng, W & Chen, W 2024, 'Lovastatin/SN38 co-loaded liposomes amplified ICB therapeutic effect via remodeling the immunologically-cold colon tumor and synergized stimulation of cGAS-STING pathway', Cancer Letters, vol. 588, pp. 216765-216765.
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Yang, Y, Yin, Z, Zhu, X, Jamal, HA, Lv, X, Hu, K, Joshi, M, Wille, N, Li, M, Zhang, B, Luo, Z, Magdassi, S & Tentzeris, M 2024, 'A Review of Multimaterial Additively Manufactured Electronics and 4-D Printing/Origami Shape-Memory Devices: Design, Fabrication, and Implementation', Proceedings of the IEEE, vol. 112, no. 8, pp. 954-999.
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Yang, Y, Zhang, C, Liu, Z, Dong, L, Yang, T, Zhao, Q & Wu, C 2024, 'Effect of hydration process on the interlayer bond tensile mechanical properties of ultra-high performance concrete for 3D printing', Construction and Building Materials, vol. 451, pp. 138902-138902.
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Yang, Z, Yin, Q, He, M, Chong, S, Xu, Z, Liu, X, Vega‐Sánchez, C, Jaiswal, A, Vigolo, D & Yong, K 2024, 'Synthesis of Anisotropic Gold Microparticles via L‐Glutathione‐Mediated Pathways in Droplet Microfluidics', Particle & Particle Systems Characterization, vol. 41, no. 10.
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AbstractMicrofluidic‐assisted synthesis of nanoparticles has generated significant interest for its precise control and high throughput capabilities. Among various nanomaterials, gold nanoparticles (AuNPs) have shown remarkable potential in numerous applications, such as disease detection, photothermotherapy, drug delivery, and even defense applications. Recent synthesis strategy of peptide‐mediated method has sparked greater interest by offering unique chiroptical properties and their applications in biomedical applications. In this study, the use of droplet microfluidics is explored for the synthesis of peptide‐mediated AuNPs, aiming to accelerate automated production via flow chemistry. This method leads to the formation of anisotropic gold particles, with sizes ranging from hundreds of nanometers to the micron scale. The interfacial energy is identified at the water/oil interface as a critical factor influencing this outcome, with L‐glutathione (L‐GSH) playing a significant role in the development of hyper‐branched structures. These results demonstrate the capability of droplet microfluidics in producing anisotropic gold particles at micron scales, presenting new possibilities for the advancement of nanoparticle synthesis techniques.
Yao, K, Chang, L & Qin, L 2024, 'Identifying Large Structural Balanced Cliques in Signed Graphs', IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 3, pp. 1145-1160.
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Yao, L, McAuley, J, Wang, X & Jannach, D 2024, 'Special Issue on Responsible Recommender Systems Part 1', ACM Transactions on Intelligent Systems and Technology, vol. 15, no. 4, pp. 1-3.
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Yao, Y, Pan, Y, Li, J, Tsang, IW & Yao, X 2024, 'Sanitized clustering against confounding bias', Machine Learning, vol. 113, no. 6, pp. 3711-3730.
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AbstractReal-world datasets inevitably contain biases that arise from different sources or conditions during data collection. Consequently, such inconsistency itself acts as a confounding factor that disturbs the cluster analysis. Existing methods eliminate the biases by projecting data onto the orthogonal complement of the subspace expanded by the confounding factor before clustering. Therein, the interested clustering factor and the confounding factor are coarsely considered in the raw feature space, where the correlation between the data and the confounding factor is ideally assumed to be linear for convenient solutions. These approaches are thus limited in scope as the data in real applications is usually complex and non-linearly correlated with the confounding factor. This paper presents a new clustering framework named Sanitized Clustering Against confounding Bias, which removes the confounding factor in the semantic latent space of complex data through a non-linear dependence measure. To be specific, we eliminate the bias information in the latent space by minimizing the mutual information between the confounding factor and the latent representation delivered by variational auto-encoder. Meanwhile, a clustering module is introduced to cluster over the purified latent representations. Extensive experiments on complex datasets demonstrate that our SCAB achieves a significant gain in clustering performance by removing the confounding bias.
Yao, Y, Pan, Y, Tsang, IW & Yao, X 2024, 'Differential-Critic GAN: Generating What You Want by a Cue of Preferences', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 3, pp. 3754-3768.
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This article proposes differential-critic generative adversarial network (DiCGAN) to learn the distribution of user-desired data when only partial instead of the entire dataset possesses the desired property. DiCGAN generates desired data that meet the user's expectations and can assist in designing biological products with desired properties. Existing approaches select the desired samples first and train regular GANs on the selected samples to derive the user-desired data distribution. However, the selection of the desired data relies on global knowledge and supervision over the entire dataset. DiCGAN introduces a differential critic that learns from pairwise preferences, which are local knowledge and can be defined on a part of training data. The critic is built by defining an additional ranking loss over the Wasserstein GAN's critic. It endows the difference of critic values between each pair of samples with the user preference and guides the generation of the desired data instead of the whole data. For a more efficient solution to ensure data quality, we further reformulate DiCGAN as a constrained optimization problem, based on which we theoretically prove the convergence of our DiCGAN. Extensive experiments on a diverse set of datasets with various applications demonstrate that our DiCGAN achieves state-of-the-art performance in learning the user-desired data distributions, especially in the cases of insufficient desired data and limited supervision.
Yari, M, Khandelwal, M, Abbasi, P, Koutras, EI, Armaghani, DJ & Asteris, PG 2024, 'Applications of Soft Computing Methods in Backbreak Assessment in Surface Mines: A Comprehensive Review', Computer Modeling in Engineering & Sciences, vol. 140, no. 3, pp. 2207-2238.
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Yazdanparast, R, Rafiee, R, Kalhori, H & Li, B 2024, 'Dynamic mechanical behavior of CNT-reinforced epoxy under medium-strain rate: A comparative study', Composite Structures, vol. 344, pp. 118343-118343.
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Ye, D, Zhu, T, Gao, K & Zhou, W 2024, 'Defending Against Label-Only Attacks via Meta-Reinforcement Learning', IEEE Transactions on Information Forensics and Security, vol. 19, pp. 3295-3308.
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Ye, Y, Guo, W, Ngo, HH, Wei, W, Cheng, D, Bui, XT, Hoang, NB & Zhang, H 2024, 'Biofuel production for circular bioeconomy: Present scenario and future scope', Science of The Total Environment, vol. 935, pp. 172863-172863.
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Ye, Y, Yan, X, Luo, H, Kang, J, Liu, D, Ren, Y, Ngo, HH, Guo, W, Cheng, D & Jiang, W 2024, 'Comparative study of the removal of sulfate by UASB in light and dark environment', Bioprocess and Biosystems Engineering, vol. 47, no. 6, pp. 943-955.
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Yen, P, Li, Y & Liu, H 2024, 'Enhancing the profitability of pay what you want: A study of suggested prices', Psychology & Marketing, vol. 41, no. 7, pp. 1502-1513.
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AbstractA suggested price is oftentimes utilized in practice to enhance the profitability of pay what you want (PWYW). In this paper, we focus on this suggested price strategy. We conduct five experiments to explore factors that impact buyers' payments after seeing the suggested price. In Study 1, we show a relationship between the suggested price and payments. Intuitively, a higher suggested price results in increased payments. Studies 2A and 2B demonstrate that the relationship between the suggested price and payments is mediated by cost estimation, while Studies 3A and 3B display that the relationship is moderated by the presence of a charitable element. Interestingly, in the case with a charitable element, increasing the suggested price does not stimulate an increment in payments on a par with the case without a charitable element. This paper proposes that sellers who use PWYW could employ a suggested price to furnish buyers with pertinent cost information, but utilizing PWYW with a suggested price may not be suitable for products with a charitable component.
Yi, B, Fan, Y, Liu, D & Romero, JG 2024, 'Simultaneous Position-and-Stiffness Control of Underactuated Antagonistic Tendon-Driven Continuum Robots', IEEE Transactions on Automation Science and Engineering, pp. 1-17.
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Yi, K, Zhang, Q, He, H, Shi, K, Hu, L, An, N & Niu, Z 2024, 'Deep Coupling Network for Multivariate Time Series Forecasting', ACM Transactions on Information Systems, vol. 42, no. 5, pp. 1-28.
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Multivariate time series (MTS) forecasting is crucial in many real-world applications. To achieve accurate MTS forecasting, it is essential to simultaneously consider both intra- and inter-series relationships among time series data. However, previous work has typically modeled intra- and inter-series relationships separately and has disregarded multi-order interactions present within and between time series data, which can seriously degrade forecasting accuracy. In this article, we reexamine intra- and inter-series relationships from the perspective of mutual information and accordingly construct a comprehensive relationship learning mechanism tailored to simultaneously capture the intricate multi-order intra- and inter-series couplings. Based on the mechanism, we propose a novel deep coupling network for MTS forecasting, named DeepCN, which consists of a coupling mechanism dedicated to explicitly exploring the multi-order intra- and inter-series relationships among time series data concurrently, a coupled variable representation module aimed at encoding diverse variable patterns, and an inference module facilitating predictions through one forward step. Extensive experiments conducted on seven real-world datasets demonstrate that our proposed DeepCN achieves superior performance compared with the state-of-the-art baselines.
Yi, S, Yang, C, Sun, X, Li, J, Wang, L, Gao, C & Yu, Y 2024, 'Evaluation of compressive damage in concrete using ultrasonic nonlinear coda wave interferometry', Ultrasonics, vol. 144, pp. 107438-107438.
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Yin, S, Gao, L, Fan, X, Gao, S, Zhou, X, Jin, W, He, Z & Wang, Q 2024, 'Performance of sewage sludge treatment for the removal of antibiotic resistance genes: Status and prospects', Science of The Total Environment, vol. 907, pp. 167862-167862.
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Yin, S, Jin, W, Xi, T, Zhou, X, He, Z, Meng, X, Naushad, M, Jiang, G & Li, X 2024, 'Factors affect the oxygen production of Chlorella pyrenoidosa in a bacterial-algal symbiotic system: Light intensity, temperature, pH and static magnetic field', Process Safety and Environmental Protection, vol. 184, pp. 492-501.
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Yin, Y, Cheng, X, Shi, F, Liu, X, Huo, H & Chen, S 2024, 'High-Order Spatial Interactions Enhanced Lightweight Model for Optical Remote Sensing Image-Based Small Ship Detection', IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-16.
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Yin, Y, Ochieng, ND, Sun, J, Bao, X & Wang, Z 2024, 'PeNet: A feature excitation learning approach to advertisement click-through rate prediction', Neural Networks, vol. 172, pp. 106127-106127.
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Yin, Y, Wang, L, Thai Hoang, D, Wang, W & Niyato, D 2024, 'Sparse Attention-Driven Quality Prediction for Production Process Optimization in Digital Twins', IEEE Internet of Things Journal, vol. 11, no. 23, pp. 38569-38584.
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Young, MW, Webster, C, Tanis, D, Schurr, AF, Hanna, CS, Lynch, SK, Ratkiewicz, AS, Dickinson, E, Kong, FH & Granatosky, MC 2024, 'What does climbing mean exactly? Assessing spatiotemporal gait characteristics of inclined locomotion in parrots', Journal of Comparative Physiology A, vol. 210, no. 1, pp. 19-33.
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Yu, D, Guo, G, Wang, D, OuYang, T, Wan, F, Liu, J, Xu, G & Deng, S 2024, 'Dynamic Spatial-Temporal Graph Convolution Network for E-Bike Traffic Flow Forecasting', IEEE Transactions on Vehicular Technology, pp. 1-14.
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Yu, D, Guo, G, Wang, D, Zhang, H, Li, B, Xu, G & Deng, S 2024, 'Modeling dynamic spatio-temporal correlations and transitions with time window partitioning for traffic flow prediction', Expert Systems with Applications, vol. 252, pp. 124187-124187.
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Yu, D, Li, Q, Wang, X, Li, Q & Xu, G 2024, 'Counterfactual Explainable Conversational Recommendation', IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 6, pp. 2388-2400.
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Yu, D, Wang, X, Xiong, Y, Shen, X, Wu, R, Wang, D, Zou, Z & Xu, G 2024, 'MHANER: A Multi-source Heterogeneous Graph Attention Network for Explainable Recommendation in Online Games', ACM Transactions on Intelligent Systems and Technology, vol. 15, no. 4, pp. 1-23.
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Recommender system helps address information overload problem and satisfy consumers’ personalized requirement in many applications such as e-commerce, social networks, and in-game store. However, existing approaches mainly focus on improving the accuracy of recommendation tasks but usually ignore how to improve the interpretability of recommendation, which is still a challenging and crucial task, especially for some complicated scenarios such as large-scale online games. A few previous attempts on explainable recommendation mostly depend on a large amount of a priori knowledge or user-provided review corpus, which is labor consuming as well as often suffers from data deficiency. To relieve this issue, we propose a Multi-source Heterogeneous Graph Attention Network for Explainable Recommendation (MHANER) for the case without enough a priori knowledge or corpus of user comments. Specifically, MHANER employs the attention mechanism to model players’ preference to in-game store items as the support for the explanation of recommendation. Then a graph neural network–based method is designed to model players’ multi-source heterogeneous information, including the players’ historical behavior data, historical purchase data, and attributes of the player-controlled character, which is leveraged to recommend possible items for players to buy. Finally, the multi-level subgraph pattern mining is adopted to combine the characteristics of a recommendation list to generate corresponding explanations of items. Extensive experiments on three real-world datasets, two collected from JD and one from NetEase game, demonstrate that the proposed model MHANER outperforms state-of-the-art baselines. Moreover, the generated explanations are verified by human encoding comprised of hard-core game players and endorsed by experts from game developers.
Yu, E, Lu, J & Zhang, G 2024, 'Fuzzy Shared Representation Learning for Multistream Classification', IEEE Transactions on Fuzzy Systems, vol. 32, no. 10, pp. 5625-5637.
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Yu, G, Wang, X, Wang, Q, Bi, T, Dong, Y, Liu, RP, Georgalas, N & Reeves, A 2024, 'Toward Web3 Applications: Easing the Access and Transition', IEEE Transactions on Computational Social Systems, vol. 11, no. 5, pp. 6098-6111.
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Yu, H, Guo, Y, Ye, L, Han, T & Su, SW 2024, 'Experimental Design for the Inclination-Based Linear Magnetometers Calibration Method', IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1-9.
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Yu, H, Li, J, Lu, J, Song, Y, Xie, S & Zhang, G 2024, 'Type-LDD: A Type-Driven Lite Concept Drift Detector for Data Streams', IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 12, pp. 9476-9489.
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Yu, H, Tuan, HD, Nasir, AA, Dutkiewicz, E & Hanzo, L 2024, 'Rate-Fairness-Aware Low Resolution RIS-Aided Multi-User OFDM Beamforming', IEEE Transactions on Vehicular Technology, vol. 73, no. 2, pp. 2401-2415.
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Yu, H, Zhao, X, Dong, D & Chen, C 2024, 'Hamiltonian Identification via Quantum Ensemble Classification', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 8, pp. 11261-11275.
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Yu, J, Wu, M, Ji, J & Yang, W 2024, 'Neural Network-Based Region Tracking Control for a Flexible-Joint Robot Manipulator', Journal of Computational and Nonlinear Dynamics, vol. 19, no. 2.
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Abstract The present paper proposes a neural network-based adaptive region-tracking control strategy for a flexible-joint robot manipulator subjected to region constraints. The developed neural network-based control strategy can globally stabilize the robot manipulator and cope with model uncertainties and the external unknown bounded disturbances. Different from the existing literature, by using the sliding mode technology and the singular perturbation theory, the developed control strategy does not require the high-order derivatives of the link states such as jerk and acceleration since the high-order derivative information is not always available in practical applications. By using Lyapunov stability theory, it is proved that the proposed neural network-based control strategy can guarantee that all the parameter variables in the closed-loop system are bounded, and the flexible-joint robot manipulator with unknown dynamics can reach inside the dynamic region and also maintain the velocity matching with the desired moving region. Since the assumption of linearization of the unknown dynamic parameters is removed, the proposed control strategy does not require the calculation of the complex regression matrix. Therefore, the proposed method has great robustness and the ability of model generalization. Simulations are given to demonstrate the validity of the proposed control strategy.
Yu, J, Wu, M, Yang, W & Ji, J 2024, 'A system decomposition method for region tracking control of a non‐holonomic mobile robot with dynamic parameter uncertainties', Asian Journal of Control, vol. 26, no. 3, pp. 1459-1471.
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AbstractThe tracking control problem of non‐holonomic mobile robot systems has been extensively investigated in the past decades, however, most of the existing control strategies were developed specifically for the fixed‐point tracking. This technical note focuses on the region tracking control for a non‐holonomic mobile robot system with parameter uncertainties in the robot dynamics. With the system decomposition and adaptive control method, some restrictions imposed on the angular and linear velocities of the non‐holonomic mobile robot in recent literature are removed, enabling to track dynamic trajectories with any values of the angular and line velocities. The proposed adaptive control scheme can simultaneously solve both the regulation and region tracking problems of a non‐holonomic mobile robot with one passive wheel and two actuated wheels. By utilizing the designed control laws, the mobile robot system is able to globally reach inside a moving region specified by potential functions whose path can be a circular curve, a straight line, or sinusoidal curve, by using a single adaptive controller. Since the dynamic region can be specified arbitrarily small, the fixed‐point tracking can be regarded as a special case of region tracking studied in this paper. Compared with the traditional fixed‐point tracking, region tracking has more flexibility and better robustness. Numerical results are presented to show the effectiveness of the designed strategy.
Yu, KL, Ong, HC & Zaman, HB 2024, 'Integrated energy informatics technology on microalgae-based wastewater treatment to bioenergy production: A review', Journal of Environmental Management, vol. 368, pp. 122085-122085.
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Yu, L, Wang, Y & Pradhan, B 2024, 'Enhancing landslide susceptibility mapping incorporating landslide typology via stacking ensemble machine learning in Three Gorges Reservoir, China', Geoscience Frontiers, vol. 15, no. 4, pp. 101802-101802.
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Yu, N 2024, 'Quantum temporal logic and reachability problems of matrix semigroups', Information and Computation, vol. 300, pp. 105197-105197.
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Yu, X, Hoggenmüller, M, Tran, TTM, Wang, Y & Tomitsch, M 2024, 'Understanding the Interaction between Delivery Robots and Other Road and Sidewalk Users: A Study of User-generated Online Videos', ACM Transactions on Human-Robot Interaction, vol. 13, no. 4, pp. 1-32.
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The deployment of autonomous delivery robots in urban environments presents unique challenges in navigating complex traffic conditions and interacting with diverse road and sidewalk users. Effective communication between robots and road and sidewalk users is crucial to address these challenges. This study investigates real-world encounter scenarios where delivery robots and road and sidewalk users interact, seeking to understand the essential role of communication in ensuring seamless encounters. Following an online ethnography approach, we collected 117 user-generated videos from TikTok and their associated 2,067 comments. Our systematic analysis revealed several design opportunities to augment communication between delivery robots and road and sidewalk users, which include facilitating multi-party path negotiation, managing unexpected robot behaviour via transparency information, and expressing robot limitations to request human assistance. Moreover, the triangulation of video and comments analysis provides a set of design considerations to realise these opportunities. The findings contribute to understanding the operational context of delivery robots and offer insights for designing interactions with road and sidewalk users, facilitating their integration into urban spaces.
Yu, X, Li, J, Yu, Y & Song, A 2024, 'Advancing service life estimation of reinforced concrete considering the coupling effects of multiple factors: Hybridized physical testing and machine learning approach', Journal of Building Engineering, vol. 84, pp. 108476-108476.
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Yu, X, Yang, Q, Tang, Y, Gao, R, Bao, S, Cai, LY, Lee, HH, Huo, Y, Moore, AZ, Ferrucci, L & Landman, BA 2024, 'Deep conditional generative model for longitudinal single-slice abdominal computed tomography harmonization', Journal of Medical Imaging, vol. 11, no. 02.
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Yu, Z, Shao, R, Li, J & Wu, C 2024, 'An in-depth review of phase change materials in concrete for enhancing building energy-efficient temperature control systems', Journal of Energy Storage, vol. 104, pp. 114533-114533.
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Yuan, D, Chang, X, Liu, Q, Yang, Y, Wang, D, Shu, M, He, Z & Shi, G 2024, 'Active Learning for Deep Visual Tracking', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 10, pp. 13284-13296.
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Yuan, D, Zhang, H, Shu, X, Liu, Q, Chang, X, He, Z & Shi, G 2024, 'An Attention Mechanism Based AVOD Network for 3D Vehicle Detection', IEEE Transactions on Intelligent Vehicles, pp. 1-13.
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Yuan, D, Zhang, H, Shu, X, Liu, Q, Chang, X, He, Z & Shi, G 2024, 'Thermal Infrared Target Tracking: A Comprehensive Review', IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1-19.
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Yuan, P, Xu, S, Yang, T, Zhou, Y, Chong, C & Wu, C 2024, 'Impact-resistant performance of DVST sandwich panel under low-velocity impact and numerical cases study of protective effect on RC column', Thin-Walled Structures, vol. 205, pp. 112536-112536.
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Yuan, X, Hu, S, Ni, W, Wang, X & Jamalipour, A 2024, 'Empowering Reconfigurable Intelligent Surfaces with Artificial Intelligence to Secure Air-To-Ground Internet-of-Things', IEEE Internet of Things Magazine, vol. 7, no. 2, pp. 14-21.
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Yusuf, AA, Veza, I, Ukundimana, Z, Nippae, A, Asumana, C, Jebboe, EK, Mujtaba, MA, Rizwanul Fattah, IM & Soudagar, MEM 2024, 'A quantitative comparison of economic viability, volatile organic compounds, and particle-bound carbon emissions from a diesel engine fueled with biodiesel blends', Measurement: Energy, vol. 3, pp. 100017-100017.
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Zafar, AM, Mohamed, BA, Wang, Q & Aly Hassan, A 2024, 'Life cycle analysis of seawater biodesalination using algae', Desalination, vol. 578, pp. 117433-117433.
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Zafra, E, Vazquez, S, Geyer, T, Aguilera, RP, Freire, E & Franquelo, LG 2024, 'Computational Analysis of the Long Horizon FCS-MPC Problem for Power Converters', IEEE Transactions on Power Electronics, vol. 39, no. 10, pp. 12762-12773.
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Zahedi, A, Liyanapathirana, R & Thiyagarajan, K 2024, 'Biodegradable and Renewable Antennas for Green IoT Sensors: A Review', IEEE Access, pp. 1-1.
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Zainal, BS, Jern, KP, Mohamed, H, Ong, HC, Fattah, IMR, Rahman, SMA, Nghiem, LD & Mahlia, TMI 2024, 'Corrigendum to Recent advancement and assessment of green hydrogen production technologies [Renew. Sustain. Energy Rev. 189 (2024) 5–5/113941]', Renewable and Sustainable Energy Reviews, vol. 202, pp. 114734-114734.
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Zainal, BS, Ker, PJ, Mohamed, H, Ong, HC, Fattah, IMR, Rahman, SMA, Nghiem, LD & Mahlia, TMI 2024, 'Recent advancement and assessment of green hydrogen production technologies', Renewable and Sustainable Energy Reviews, vol. 189, pp. 113941-113941.
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Zainal, BS, Yu, KL, Mohamed, H, Ong, HC & Mahlia, TMI 2024, 'Hydrothermal pretreatment: A sustainable approach to biohydrogen production from palm oil mill effluent', Sustainable Energy Technologies and Assessments, vol. 72, pp. 104062-104062.
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Zainal, BS, Yu, KL, Ong, HC, Mohamed, H, Ker, PJ, Abdulkreem-Alsultan, G, Taufiq-Yap, YH & Mahlia, TMI 2024, 'Synergising hydrothermal pre-treatment and biological processes for enhancing biohydrogen production from palm oil mill effluent', Process Safety and Environmental Protection, vol. 192, pp. 424-436.
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Zamani, MG, Nikoo, MR, Al-Rawas, G, Nazari, R, Rastad, D & Gandomi, AH 2024, 'Hybrid WT–CNN–GRU-based model for the estimation of reservoir water quality variables considering spatio-temporal features', Journal of Environmental Management, vol. 358, pp. 120756-120756.
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Zamee, MA, Lee, Y & Won, D 2024, 'Self-Supervised Adaptive Learning Algorithm for Multi-Horizon Electricity Price Forecasting', IEEE Access, vol. 12, pp. 54913-54933.
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Zang, Q, Zhang, J, Bo, L, Xiao, Y, Gao, G, Zhang, H, Li, H, Zhong, Z & Ren, Y 2024, 'A fully automatic adjacent key-points localization framework for minimal repeated pattern detection in printed fabric images', Knowledge-Based Systems, vol. 300, pp. 112157-112157.
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Zare, MS, Nikoo, MR, Al-Rawas, G, Nazari, R, Al-Wardy, M, Etri, T & Gandomi, AH 2024, 'Integrated ensemble learning approach for multi-depth water quality estimation in reservoir environments', Journal of Water Process Engineering, vol. 66, pp. 105840-105840.
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Zavahir, S, Riyaz, NS, Elmakki, T, Tariq, H, Ahmad, Z, Chen, Y, Park, H, Ho, Y-C, Shon, HK & Han, DS 2024, 'Ion-imprinted membranes for lithium recovery: A review', Chemosphere, vol. 354, pp. 141674-141674.
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Zeng, F, Ding, C & Guo, YJ 2024, 'A Reconfigurable Millimeter-Wave Antenna Array With Wide-Range Continuous Beamwidth Control (WCBC) Based on Polarization-Mixing', IEEE Transactions on Antennas and Propagation, vol. 72, no. 4, pp. 3092-3103.
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Zeng, H, Yuan, P, Yang, T, Xu, S & Wu, C 2024, 'Experimental and numerical study of G-UHPC composite slab against contact blast', Baozha Yu Chongji/Explosion and Shock Waves, vol. 44, no. 6.
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In order to improve the blast resistance performance of engineering structures to ensure the safety of important targets and reduce the adverse effects of high cement content on the environment of cement-based ultra-high performance concrete, a new type of composite slab based on geopolymer ultra-high performance concrete (G-UHPC) is proposed. Three G-UHPC composite slabs were prepared with G-UHPC, steel wire mesh, and energy-absorbing foam materials, and an ordinary concrete slab was prepared with C40 concrete. Explosion tests were carried out in the field to verify the blast resistance performance of the new G-UHPC composite slab. The crater diameter, depth, and spalling of each specimen under a 0.4 kg TNT contact explosion were obtained, and the blast resistance performance and failure mode were analyzed. The effects of G-UHPC, steel wire mesh, and energy-absorbing foam materials on the blast resistance performance of concrete slabs were discussed. Based on the explosion test results, a refined finite element model was established using LS-DYNA finite element analysis software and numerical simulation analysis was conducted. The effectiveness of the numerical model was verified by comparing the experimental results with the simulation analysis results. On this basis, the model was used to further analyze the impact of G-UHPC and steel wire mesh on the blast resistance performance of concrete slabs. The failure process of composite slabs was analyzed by simulating the propagation of explosive waves in energy-absorbing foam-reinforced G-UHPC composite slabs, and the failure mechanism of G-UHPC composite slabs was revealed. A parameter analysis was carried out to further study the blast resistance performance of the G-UHPC composite slab. Based on the damage morphology of the G-UHPC composite plate, the mid-span displacement of the plate bottom and the energy absorption of the energy-absorbing layer, the influence of the energy-absorbing foam material l...
Zeng, J, Desmond, P, Ngo, HH, Lin, W, Liu, X, Liu, B, Li, G & Ding, A 2024, 'Membrane modification in enhancement of virus removal: A critical review', Journal of Environmental Sciences, vol. 146, pp. 198-216.
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Zeng, Y, Guo, X, Ma, B, Liu, Z & Ma, J 2024, 'Fine-grained defense methods in federated encrypted traffic classification', Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University, vol. 51, no. 1, pp. 157-164.
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In recent years,various robust algorithms and defense schemes have been presented to prevent the harm caused by abnormal traffic to the federal encrypted traffic classification model.The existing defense methods,which improve the robustness of the global model by removing the traffic of abnormal models,are coarse-grained.Nevertheless,the coarse-grained methods can lead to issues of excessive defense and normal traffic loss.To solve the above problems,we propose a fine-grained defense method to avoid abnormal traffic according to the collaborative federated encrypted traffic classification framework.The proposed method narrows the range of the abnormal traffic by dividing the local data set of abnormal nodes,achieving fine-grained localization of abnormal nodes.According to the localization results of abnormal traffic,the method realizes the fine-grained defense by eliminating abnormal traffic during model aggregation,which avoids the excessive defense and normal traffic loss.Experimental results show that the proposed method can significantly improve the efficiency of model detection without affecting accuracy.Compared with the existing coarse-grained methods,the accuracy of the fine-grained defense method can reach 91.4%,and the detection efficiency is improved by 32.3%.
Zhai, Z, Lin, F, Yang, Y & Sun, H 2024, 'Additively Manufactured Wideband Low-Profile Bidirectional 2-D Beam-Scanning Antenna Using Double Folded Transmitarrays With Curved Polarizers', IEEE Transactions on Antennas and Propagation, vol. 72, no. 1, pp. 476-486.
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Zhan, P, Wang, J, Zhao, H, Li, W, Shah, SP & Xu, J 2024, 'Impact of synthetic C-S-H seeds on early hydration and pore structure evolution of cement pastes: A study by 1H low-field NMR and path analysis', Cement and Concrete Research, vol. 175, pp. 107376-107376.
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Zhang, B, Huynh, NV, Hoang, DT, Nguyen, DN & Pham, Q-V 2024, 'DDPG-E2E: A Novel Policy Gradient Approach for End-to-End Communication Systems', IEEE Transactions on Cognitive Communications and Networking, pp. 1-1.
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Zhang, B, Lin, X, Zhang, YX & Zhang, L 2024, 'Deterioration of fire resistance of ultra-high-performance polypropylene-fibre-reinforced concrete (UHPPFRC) under long-term acid rain corrosion action', Journal of Building Engineering, vol. 98, pp. 111064-111064.
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Zhang, C & Wang, Z 2024, 'Data-driven distributionally robust optimization under combined ambiguity for cracking production scheduling', Computers & Chemical Engineering, vol. 181, pp. 108538-108538.
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Zhang, C, Mayr, P, Lu, W & Zhang, Y 2024, 'An editorial note on extraction and evaluation of knowledge entities from scientific documents', Scientometrics, vol. 129, no. 11, pp. 7169-7174.
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Zhang, C, Sheng, Z, Zhang, C & Wen, S 2024, 'Multi-lead-time short-term runoff forecasting based on Ensemble Attention Temporal Convolutional Network', Expert Systems with Applications, vol. 243, pp. 122935-122935.
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Zhang, C, Tian, Z, Wang, W & Yu, S 2024, 'Semantic Communications Toward Graph Data', IEEE Wireless Communications, vol. 31, no. 6, pp. 178-185.
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Zhang, C, Wang, S, Cao, Y, Zhu, S, Guo, Z & Wen, S 2024, 'Robust model predictive control for continuous nonlinear systems with the quasi-infinite adaptive horizon algorithm', Journal of the Franklin Institute, vol. 361, no. 2, pp. 748-763.
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Zhang, C, Wang, W, Tian, Z & Yu, S 2024, 'Forgetting and Remembering Are Both You Need: Balanced Graph Structure Unlearning', IEEE Transactions on Information Forensics and Security, vol. 19, pp. 6751-6763.
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Zhang, C, Yu, S, Tian, Z & Yu, JJQ 2024, 'Generative Adversarial Networks: A Survey on Attack and Defense Perspective', ACM Computing Surveys, vol. 56, no. 4, pp. 1-35.
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Generative Adversarial Networks (GANs) are a remarkable creation with regard to deep generative models. Thanks to their ability to learn from complex data distributions, GANs have been credited with the capacity to generate plausible data examples, which have been widely applied to various data generation tasks over image, text, and audio. However, as with any powerful technology, GANs have a flip side: their capability to generate realistic data can be exploited for malicious purposes. Many recent studies have demonstrated the security and privacy (S&P) threats brought by GANs, especially the attacks on machine learning (ML) systems. Nevertheless, so far as we know, there is no existing survey that has systematically categorized and discussed the threats and strategies of these GAN-based attack methods. In this article, we provide a comprehensive survey of GAN-based attacks and countermeasures. We summarize and articulate: (1) what S&P threats of GANs expose to ML systems; (2) why GANs are useful for certain attacks; (3) what strategies can be used for GAN-based attacks; and (4) what countermeasures can be effective to GAN-based attacks. Finally, we provide several promising research directions combining the existing limitations of GAN-based studies and the prevailing trend in the associated research fields.
Zhang, G & Wen, S 2024, 'New Approximate Results of Fixed-Time Stabilization for Delayed Inertial Memristive Neural Networks', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 71, no. 7, pp. 3428-3432.
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Zhang, G, Liu, B, Zhu, T, Ding, M & Zhou, W 2024, 'PPFed: A Privacy-Preserving and Personalized Federated Learning Framework', IEEE Internet of Things Journal, vol. 11, no. 11, pp. 19380-19393.
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Zhang, H & Zhu, X 2024, 'Predicting the seismic performance of large-scale dome structures with hybrid uncertainties based on Bayesian inference', Engineering Applications of Artificial Intelligence, vol. 136, pp. 109031-109031.
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Zhang, H, Chen, S, Karimi, M, Li, B, Saydam, S & Hassan, M 2024, 'Numerical and experimental investigation of an auxetic piezoelectric energy harvester with frequency self-tuning capability', Smart Materials and Structures, vol. 33, no. 5, pp. 055022-055022.
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Abstract To deal with the limited availability of long-lasting power sources for sensor nodes in industrial environments, a novel piezoelectric energy harvester with high efficiency and a wide working bandwidth was designed to harvest broadband and random vibrations from the ambient environment. The developed energy harvester adopts a doubly clamped piezoelectric beam with a peanut-shaped auxetic structure to improve the power output. It also incorporates a sliding proof mass for frequency self-tuning, enabling a wider working bandwidth. As the doubly clamped beam exhibits geometry nonlinearity under large vibration amplitudes, the power output of the energy harvester can be further enhanced in the frequency self-tuning process. Finite element simulations are conducted to evaluate the impact of the auxetic structure and the position of the proof mass on the performance of the energy harvester. Experiments are performed to examine the energy harvesting performance of the proposed energy harvester. Under an excitation acceleration of 0.3 g, the use of the sliding proof mass widens the working bandwidth of the auxetic energy harvester (AEH) by 9 Hz, with the maximum root mean square output power of AEH reaching 18.78 μW, which is much higher than that of the plain energy harvester (PEH) or the AEH with a fixed proof mass. The developed energy harvester can successfully power a wireless temperature and humidity sensor node based on the vibration produced by a centrifuge, which demonstrates the practical feasibility of the proposed energy harvester for industrial applications.
Zhang, H, Huang, X & Zhang, JA 2024, 'Low-Overhead OTFS Transmission With Frequency or Time Domain Channel Estimation', IEEE Transactions on Vehicular Technology, vol. 73, no. 1, pp. 799-811.
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Zhang, H, Xia, J, Zhang, G & Xu, M 2024, 'Learning Graph Representations Through Learning and Propagating Edge Features', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 6, pp. 8429-8440.
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Zhang, H, Xu, M, Zhang, G & Niwa, K 2024, 'SSFG: Stochastically Scaling Features and Gradients for Regularizing Graph Convolutional Networks', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 2, pp. 2223-2234.
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Zhang, H, Yin, W, Liao, G, Liu, J, Dong, G, Wang, J, Guo, W & Ngo, HH 2024, 'The identification of a correlation between lipid content in the model diatom Phaeodactylum tricornutum and pH treatment strategies', Science of The Total Environment, vol. 915, pp. 169897-169897.
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Zhang, H, Yuan, D, Shu, X, Li, Z, Liu, Q, Chang, X, He, Z & Shi, G 2024, 'A Comprehensive Review of RGBT Tracking', IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1-23.
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Zhang, J, Chen, Z, Liu, Y, Wei, W & Ni, B-J 2024, 'Removal of emerging contaminants (ECs) from aqueous solutions by modified biochar: A review', Chemical Engineering Journal, vol. 479, pp. 147615-147615.
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Zhang, J, Lei, J, Xie, W, Jiang, K, Zhang, X, Cao, M & Li, Y 2024, 'Distribution-Aware Interactive Attention Network and Large-Scale Cloud Recognition Benchmark on FY-4A Satellite Image', IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-15.
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Zhang, J, Lei, J, Xie, W, Yang, G, Li, D & Li, Y 2024, 'Multimodal Informative ViT: Information Aggregation and Distribution for Hyperspectral and LiDAR Classification', IEEE Transactions on Circuits and Systems for Video Technology, vol. 34, no. 8, pp. 7643-7656.
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Zhang, J, Yuan, L, Li, W, Qin, L, Zhang, Y & Zhang, W 2024, 'Label-constrained shortest path query processing on road networks', The VLDB Journal, vol. 33, no. 3, pp. 569-593.
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Zhang, J, Zhang, Q, Gong, Y, Zhang, J, Chen, L & Zeng, D 2024, 'Weakly Supervised Semantic Segmentation With Consistency-Constrained Multiclass Attention for Remote Sensing Scenes', IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-18.
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Zhang, J, Zhu, S, Liu, X, Wen, S & Mu, C 2024, 'Finite-Time Stabilization of Inertial Memristive Neural Networks via Nonreduced Order Method', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-11.
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Zhang, J, Zhu, S, Wu, K-N, Shen, M & Wen, S 2024, 'Finite-Time Stabilization of Semi-Markov Reaction-Diffusion Memristive NNs With Unbounded Time-Varying Delays', IEEE Transactions on Circuits and Systems I: Regular Papers, pp. 1-11.
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Zhang, L, Guo, Y, Zhang, Y & Su, J 2024, 'Compete or not Evidence from global biopharmaceutical innovation and market', International Journal of Technology Management, vol. 94, no. 2, pp. 213-241.
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Zhang, L, Lv, M, Zhang, Z-Y, Wang, Y, Zeng, F, Ding, C & Dai, C 2024, 'A Single-Antenna Full-Duplex Subsystem With High Isolation and High Gain', IEEE Open Journal of Antennas and Propagation, vol. 5, no. 3, pp. 620-625.
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Zhang, L, Shi, Y, Chang, Y-C & Lin, C-T 2024, 'Robust Fuzzy Neural Network With an Adaptive Inference Engine', IEEE Transactions on Cybernetics, vol. 54, no. 5, pp. 3275-3285.
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Zhang, M, Wang, Q, Luo, Z & Gao, W 2024, 'Stochastic bandgap optimization for multiscale elastic metamaterials with manufacturing imperfections', International Journal of Mechanical Sciences, vol. 268, pp. 109035-109035.
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Zhang, M, Wang, Q, Luo, Z & Gao, W 2024, 'Virtual model-aided reliability analysis considering material and geometrical uncertainties for elastic metamaterials', Mechanical Systems and Signal Processing, vol. 211, pp. 111199-111199.
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Zhang, M, Zheng, S, Xiao, Y & Qin, Q-H 2024, 'Mode conversion approach for wave attenuation enhancement of 3D rainbow metamaterials', Engineering Structures, vol. 321, pp. 118999-118999.
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Zhang, N, Lu, J, Li, K, Fang, Z & Zhang, G 2024, 'Source-Free Unsupervised Domain Adaptation: Current research and future directions', Neurocomputing, vol. 564, pp. 126921-126921.
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Zhang, P, Lu, X, Kuang, S & Dong, D 2024, 'Optimal tripartite quantum teleportation protocols via noisy channels by feed-forward control and environment-assisted measurement', Results in Physics, vol. 60, pp. 107632-107632.
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Zhang, P, Shen, D, Shao, J, He, X, Zeng, J, Wu, S-L, Long, Y, Wei, W & Ni, B-J 2024, 'Green synthesis of Fe3O4@ceramsite from sludge improving anaerobic digestion performance of waste activated sludge', Journal of Environmental Management, vol. 359, pp. 121085-121085.
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Zhang, P, Zhang, L, Hao, Y, Xu, M, Pang, M, Wang, C, Yang, A & Voinov, A 2024, 'Food–energy–water nexus optimization brings substantial reduction of urban resource consumption and greenhouse gas emissions', PNAS Nexus, vol. 3, no. 2.
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Abstract Urban sustainability is a key to achieving the UN sustainable development goals (SDGs). Secure and efficient provision of food, energy, and water (FEW) resources is a critical strategy for urban sustainability. While there has been extensive discussion on the positive effects of the FEW nexus on resource efficiency and climate impacts, measuring the extent to which such synergy can benefit urban sustainability remains challenging. Here, we have developed a systematic and integrated optimization framework to explore the potential of the FEW nexus in reducing urban resource demand and greenhouse gas (GHG) emissions. Demonstrated using the Metropolis Beijing, we have identified that the optimized FEW nexus can reduce resource consumption and GHG emissions by 21.0 and 29.1%, respectively. These reductions come with increased costs compared to the siloed FEW management, but it still achieved a 16.8% reduction in economic cost compared to the business-as-usual scenario. These findings underscore the significant potential of FEW nexus management in enhancing urban resource efficiency and addressing climate impacts, while also identifying strategies to address trade-offs and increase synergies.
Zhang, Q & Sohaib, O 2024, 'Enhancing Physical Education: A Dynamic Fuzzy Neural Network-Based Information Processing System Design', IEEE Access, vol. 12, pp. 80976-80985.
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Zhang, Q, Hu, J, Guo, H, Yang, C, Li, J, Liu, N, Guo, W, Dai, C, Wang, L, Tian, Y & Ngo, HH 2024, 'Preparation of C=C polymerization-oriented magnetic protein molecularly imprinted polymer and the application for membrane flux improvement', Desalination, vol. 573, pp. 117206-117206.
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Zhang, Q, Lai, N, He, M, Yang, Y, Huang, Q, Quan, Y, Hou, S, Gao, X, Song, Y, Liao, J & Wang, R 2024, 'Tunable broadband luminescence of Bi‐ion‐doped glasses via Gd2O3 co‐doping', Journal of the American Ceramic Society, vol. 107, no. 6, pp. 3837-3844.
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AbstractThe doping of bismuth (Bi) ions in borosilicate glasses has gained attention for its potential applications in LED light sources and imaging displays. Here, gadolinium oxide (Gd2O3) was introduced into the glass matrix in varying concentrations using a high‐temperature melting method to investigate its impact on the luminescence properties of Bi ions. The resulting glass exhibited bimodal emission peaks at 465 and 750 nm when excited with 325 nm light. The luminescence intensity and fluorescence half width at half height initially increased, followed by a subsequent decrease as the Gd2O3 content in the glass increased from 10 to 43 mol%. Additionally, the color of the luminescence transformed from purple–red to green under white light irradiation. The composition and excitation wavelength of the glass can be adjusted to achieve selective tuning of the luminescence.
Zhang, Q, Yang, Y, Shi, C, Lao, A, Hu, L, Wang, S & Naseem, U 2024, 'Rumor Detection With Hierarchical Representation on Bipartite Ad Hoc Event Trees', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 10, pp. 14112-14124.
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The rapid growth of social media has caused tremendous effects on information propagation, raising extreme challenges in detecting rumors. Existing rumor detection methods typically exploit the reposting propagation of a rumor candidate for detection by regarding all reposts to a rumor candidate as a temporal sequence and learning semantics representations of the repost sequence. However, extracting informative support from the topological structure of propagation and the influence of reposting authors for debunking rumors is crucial, which generally has not been well addressed by existing methods. In this article, we organize a claim post in circulation as an ad hoc event tree, extract event elements, and convert it into bipartite ad hoc event trees in terms of both posts and authors, i.e., author tree and post tree. Accordingly, we propose a novel rumor detection model with hierarchical representation on the bipartite ad hoc event trees called BAET. Specifically, we introduce word embedding and feature encoder for the author and post tree, respectively, and design a root-aware attention module to perform node representation. Then we adopt the tree-like RNN model to capture the structural correlations and propose a tree-aware attention module to learn tree representation for the author tree and post tree, respectively. Extensive experimental results on two public Twitter datasets demonstrate the effectiveness of BAET in exploring and exploiting the rumor propagation structure and the superior detection performance of BAET over state-of-the-art baseline methods.
Zhang, R, Li, Y, Gui, Y, Armaghani, DJ & Yari, M 2024, 'A stacked deep multi-kernel learning framework for blast induced flyrock prediction', International Journal of Rock Mechanics and Mining Sciences, vol. 177, pp. 105741-105741.
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Zhang, R, Li, Y, Gui, Y, Armaghani, DJ & Yari, M 2024, 'A stacked multiple kernel support vector machine for blast induced flyrock prediction', Geohazard Mechanics, vol. 2, no. 1, pp. 37-48.
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Zhang, S, Ding, L, Xie, M, He, X, Yang, R & Tong, C 2024, 'Reliability analysis of slope stability by neural network, principal component analysis, and transfer learning techniques', Journal of Rock Mechanics and Geotechnical Engineering, vol. 16, no. 10, pp. 4034-4045.
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Zhang, S, Liu, C, Wang, Y, Niu, F, Lei, G & Zhu, J 2024, 'Shape Design Optimization and Comparative Analysis of a Novel Synchronous Reluctance Machine With Grain-Oriented Silicon Steel', IEEE Transactions on Magnetics, vol. 60, no. 9, pp. 1-5.
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Zhang, S, Zhao, L, Huang, S, Wang, H, Luo, Q, Hao, Q & Stoyanov, D 2024, 'SLAM-TKA: Simultaneously Localizing X-Ray Device and Mapping Pins in Conventional Total Knee Arthroplasty', IEEE Transactions on Medical Robotics and Bionics, vol. 6, no. 4, pp. 1526-1541.
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Zhang, SY 2024, 'Preface: Numerical Modelling of Fibre-Reinforced Materials and Structures', International Journal of Computational Methods, vol. 21, no. 08.
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Zhang, T, Zhang, S & Jia, W 2024, 'Person Reidentification Based on Adaptive Relation Attention Network in Intelligent Monitoring System for the IoB', IEEE Transactions on Engineering Management, vol. 71, pp. 12648-12657.
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Zhang, W, Chen, J, Wen, S & Huang, T 2024, 'Event-Triggered Random Delayed Impulsive Consensus of Multi-Agent Systems With Time-Varying Delay', IEEE Transactions on Emerging Topics in Computational Intelligence, pp. 1-6.
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Zhang, W, Li, L, Ding, Y, Chen, W, Deng, Z & Yu, X 2024, 'Detecting Facial Action Units From Global-Local Fine-Grained Expressions', IEEE Transactions on Circuits and Systems for Video Technology, vol. 34, no. 2, pp. 983-994.
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Zhang, W, Peng, Y & Li, M 2024, 'Efficient Circular Flat-Top Pattern Synthesis With Circular Planar Array via Dimensionality Reduction', IEEE Antennas and Wireless Propagation Letters, vol. 23, no. 1, pp. 64-68.
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Zhang, W, Yin, W, Zhang, Z, Liu, L & Peng, K 2024, 'Blade Pitch Control of Wind Turbines with Speed Regulating Differential Mechanism Considering Impeller Imbalance', Zhongguo Jixie Gongcheng/China Mechanical Engineering, vol. 35, no. 10, pp. 1890-1899.
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To cnsurc the energy efficiency and stability of the SRDM-bascd WTs across the entire wind speed ranges, a control method was proposed based on radial basis function(RBF) neural net-works and sliding mode variable strueture control(SMVSC), which enabled precise and rapid pitch angle adjustment for SRDM-bascd WTs, while considering the effects of wind wheel imbalance, wind shear, and tower shadow. This approach incorporated the sliding mode error into the adaptive law, al-lowing for the effective suppression of chatting effects by dynamically adjusting the weights and center values of the RBF neural network in real-time. A Simulation model of 1.5 MW SRDM-bascd WTs was establishcd, and then verified using the built cxperimental platform, through which the control Performance of the proposed RBF-SMVSC method was compared and validated. The results indicate that, compared to independent pitch methods with traditional proportional-intcgral(Pl) and SMC, the proposed control method may adjust WT s speed and power Output morc rapidly and accuratcly under va-rious wind speed conditions, and significantly enhance energy capture and reduce unbalanccd loads.
Zhang, W, Zhou, L, Zhang, X, Huang, Z, Fang, F, Hong, Z, Li, J, Gao, M, Sun, W, Pan, H & Liu, Y 2024, 'Lithium Borohydride Nanorods: Self‐Assembling Growth and Remarkable Hydrogen Cycling Properties', Small, vol. 20, no. 32.
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AbstractNanostructured metal hydrides with unique morphology and improved hydrogen storage properties have attracted intense interests. However, the study of the growth process of highly active borohydrides remains challenging. Herein, for the first time the synthesis of LiBH4 nanorods through a hydrogen‐assisted one‐pot solvothermal reaction is reported. Reaction of n‐butyl lithium with triethylamine borane in n‐hexane under 50 bar of H2 at 40–100 °C gives rise to the formation of the [100]‐oriented LiBH4 nanorods with 500–800 nm in diameter, whose growth is driven by orientated attachment and ligand adsorption. The unique morphology enables the LiBH4 nanorods to release hydrogen from ≈184 °C, 94 °C lower than the commercial sample (≈278 °C). Hydrogen release amounts to 13 wt% within 40 min at 450 °C with a stable cyclability, remarkably superior to the commercial LiBH4 (≈9.1 wt%). More importantly, up to 180 °C reduction in the onset temperature of hydrogenation is successfully attained by the nanorod sample with respect to the commercial counterpart. The LiBH4 nanorods show no foaming during dehydrogenation, which improves the hydrogen cycling performance. The new approach will shed light on the preparation of nanostructured metal borohydrides as advanced functional materials.
Zhang, W, Zhu, H, Wen, S & Huang, T 2024, 'Finite-Time Bipartite Tracking Consensus of Fractional-Order Multi-Layer Signed Networks by Aperiodically Intermittent Control', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 71, no. 6, pp. 3061-3065.
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Zhang, X & Far, H 2024, 'Beneficial and detrimental impacts of soil-structure interaction on seismic response of high-rise buildings', Advances in Structural Engineering, vol. 27, no. 11, pp. 1862-1886.
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In the traditional design method, structures are usually assumed as rigid base structures without considering soil-structure interaction (SSI). However, whether the effect of SSI on the seismic performance of structures is beneficial or detrimental is far from consensus among researchers. Moreover, previous literature mostly concentrated on the seismic behaviour of mid-rise buildings and moment-resisting frames. Therefore, it is in real need to comprehensively investigate the seismic response of tall buildings considering SSI. In this study, a soil-foundation-structure model developed in finite element software and verified by shaking table tests is used to critically explore the effects of SSI on high-rise buildings with a series of superstructure and substructure parameters. The beneficial and detrimental impacts of SSI are identified and discussed. Numerical simulation results indicate the rise in the stiffness of subsoil can dramatically amplify the base shear of structures. As the foundation rotation increases, inter-storey drifts are increased, and base shears are reduced. In general, SSI amplifies the inter-storey drifts showing detrimental effects of SSI. However, as for the base shear, SSI exerts detrimental effects on most piled foundation cases as well as classical compensated foundation structures resting on Ce soil, whereas, for compensated foundation structures resting on soil types De and Ee, effects of SSI are beneficial since the base shear is reduced. Moreover, regarding buildings with different structural systems and foundation types, minimum base shear ratios considering the SSI reduction effect are presented.
Zhang, X, Fang, Y, Liu, Q & Yazdani, D 2024, 'Multi-objective Robust Optimization Over Time for Dynamic Disassembly Sequence Planning', International Journal of Precision Engineering and Manufacturing, vol. 25, no. 1, pp. 111-130.
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Zhang, X, Fang, Y, Liu, Q & Yazdani, D 2024, 'Multi-objective Robust Optimization Over Time for Dynamic Disassembly Sequence Planning', International Journal of Precision Engineering and Manufacturing, vol. 25, no. 1, pp. 111-130.
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Disassembly sequence planning aims to optimize disassembly sequences of end-of-life (EOL) products in order to minimize the cost and environmental pollutant emission. Various unpredictable factors in the disassembly environment (e.g., EOL product status and capabilities of operators) lead to significant uncertainty making the optimal disassembly sequence change over time. Considering existing multiple objectives and dynamic environment, this problem is indeed dynamic multi-objective optimization. As deploying a new solution (i.e., disassembly sequence) is costly in this problem, it is undesirable to change the deployed solution after each environmental change. In this paper, we first propose a model for disassembly sequence planning problem in which several factors including the environmental changes, deployed solution switching cost, constraints, and multiple objectives are taken into account. To solve this problem where frequently changing the deployed solution must be avoided, we propose a new multi-objective robust optimization over time (ROOT) framework to find robust solutions based on two new robustness definitions: average performance and stability. The proposed framework benefits from a novel accurate online predictor and the knee-oriented dominance which is applied to select the naturally preferred tradeoff solution to meet the application requirements of ROOT. Computational experiments demonstrate the effectiveness of the proposed ROOT framework.
Zhang, X, Jiang, S, Zhao, S, Zan, R, Chen, X, Shu, M, Lu, X, Suo, T, Guo, J & Xin, Z 2024, 'Performance Study of Biodegradable Polyurethanes from Different Biodicarboxylic Acids and Their Potential Applications', ACS Applied Polymer Materials, vol. 6, no. 17, pp. 10488-10498.
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Zhang, X, Liu, C, Yuan, W, Zhang, JA & Ng, DWK 2024, 'Sparse Prior-Guided Deep Learning for OTFS Channel Estimation', IEEE Transactions on Vehicular Technology, vol. 73, no. 12, pp. 19913-19918.
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Zhang, X, Liu, F, Cheng, X, Yan, S, Liao, Z & Ge, Z 2024, 'Autonomous novel class discovery for vision-based recognition in non-interactive environments', Cognitive Robotics, vol. 4, pp. 191-203.
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Zhang, X, Liu, J, Li, Y, Cui, Q, Tao, X, Liu, RP & Li, W 2024, 'Vehicle-oriented ridesharing package delivery in blockchain system', Digital Communications and Networks, vol. 10, no. 4, pp. 1014-1023.
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Zhang, X, Peng, H, Tang, T, Liu, Y, Wang, Y & Zhang, J 2024, 'Knowledge-based Dual External Attention Network for peptide detectability prediction', Knowledge-Based Systems, vol. 286, pp. 111378-111378.
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Zhang, X, Wang, X, So, HC, Zoubir, AM, Zhang, JA & Guo, YJ 2024, 'Transmit Waveform Design for Integrated Wideband MIMO Radar and Bi-Directional Communications', IEEE Transactions on Vehicular Technology, vol. 73, no. 9, pp. 13482-13497.
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Zhang, X, Xie, W, Li, Y, Lei, J, Jiang, K, Fang, L & Du, Q 2024, 'Block-Wise Partner Learning for Model Compression', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 12, pp. 17582-17595.
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Zhang, X, Zheng, H, Wang, Z, Su, Y, Chen, H, Liu, Q, Yao, P, Mameda, N, Ngo, HH & Nghiem, LD 2024, 'Pretreatment by a novel photo-electro reactor to control organic and biofouling during reverse osmosis filtration of reclaimed water', Chemical Engineering Journal, vol. 482, pp. 148893-148893.
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Zhang, Y, Huang, Z, Chen, J & Guo, Y 2024, 'Electromagnetic Distribution of Stacked HTS Tapes Subjected to a Rotating Magnetic Field', IEEE Transactions on Applied Superconductivity, vol. 34, no. 8, pp. 1-3.
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Zhang, Y, Jin, J, Chen, J, Huang, Z, Guo, Y & Zhu, J 2024, 'Numerical analysis of stacked HTS tapes in rotating magnetic fields using H and T-A formulations', Physica C: Superconductivity and its Applications, vol. 624, pp. 1354553-1354553.
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Zhang, Y, Li, R-H, Zhang, Q, Qin, H, Qin, L & Wang, G 2024, 'Efficient Algorithms for Pseudoarboricity Computation in Large Static and Dynamic Graphs', Proceedings of the VLDB Endowment, vol. 17, no. 11, pp. 2722-2734.
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The arboricity a ( G ) of a graph G is defined as the minimum number of edge-disjoint forests that the edge set of G can be partitioned into. It is a fundamental metric and has been widely used in many graph analysis applications. However, computing a ( G ) is typically a challenging task. To address this, an easier-to-compute alternative called pseudoarboricity was proposed. Pseudoarboricity has been shown to be closely connected to many important measures in graphs, including the arboricity and the densest subgraph density ρ ( G ). Computing the exact pseudoarboricity can be achieved by employing a parametric max-flow algorithm, but it becomes computationally expensive for large graphs. Existing 2-approximation algorithms, while more efficient, often lack satisfactory approximation accuracy. To overcome these limitations, we propose two new approximation algorithms with theoretical guarantees to approximate the pseudoarboricity. We show that our approximation algorithms can significantly reduce the number of times the max-flow algorithm is invoked, greatly improving its efficiency for exact pseudoarboricity computation. In addition, we also study the pseudoarboricity maintenance problem in dynamic graphs. We propose two novel and efficient algorithms for maintaining the pseudoarboricity when the graph is updated by edge insertions or deletions. Furthermore, we develop two incremental pseudoarboricity maintenance algorithms specifically designed for insertion-only scenarios. We conduct extensive experiments on 195 real-world graphs...
Zhang, Y, Liu, Y, Hu, R, Wu, Q & Zhang, J 2024, 'Mutual Dual-Task Generator With Adaptive Attention Fusion for Image Inpainting', IEEE Transactions on Multimedia, vol. 26, pp. 1539-1550.
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Zhang, Y, Mishra, PN, Tiwari, S, Scheuermann, A & Li, L 2024, 'Suppressive effects of geotextiles on soil water evaporation', Acta Geotechnica, vol. 19, no. 4, pp. 2163-2174.
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AbstractGeotextiles find wide applications in the field for filtration and drainage. When applied on the soil surface they influence soil evaporation. The objective of this work is twofold: (a) to assess the effectiveness of four different geotextiles as cover materials on soil evaporation, (b) to study the combined effect of geotextile and perforated mechanical barriers on soil evaporation. The first set of experimental programs consisted of three soil samples i.e. kaolin, dredged mud from the port of Brisbane and a locally obtained red mud sample from Queensland, Australia tested with four types of non-woven geotextiles under four controlled climatic conditions. All the 4 geotextiles had suppression effects on soil evaporation to degrees that varied with the type of soil, ratio of pore size to thickness of geotextiles (M*), product of pore size to thickness of the geotextiles (N*) and climatic conditions. Geotextiles with a higher pore size (O95) and M* allowed water vapor to move through relatively easily leading to higher evaporation rates. Geotextile with a higher thickness and N* value provided a higher suppression effect on soil evaporation. In a recently introduced dewatering method involving perforated ventilated well method, evaporation from soil take place through geotextiles and the perforated well. Mimicking this, impacts on soil evaporation with geotextiles sandwiched between soil sample and perforated sections were also studied. Maintaining similar number and arrangement of the perforations, soil evaporation was noted to be higher with rectangular shaped perforation compared to circular shaped perforations.
Zhang, Y, Wang, Z, Lu, Y, Sanchez, DJ, Li, J, Wang, L, Meng, X, Chen, J, Kien, TT, Zhong, M, Gao, W & Ding, X 2024, 'Region‐Specific CD16+ Neutrophils Promote Colorectal Cancer Progression by Inhibiting Natural Killer Cells', Advanced Science, vol. 11, no. 29.
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AbstractThe colon is the largest compartment of the immune system, with innate immune cells exposed to antigens in the environment. However, the mechanisms by which the innate immune system is instigated are poorly defined in colorectal cancer (CRC). Here, a population of CD16+ neutrophils that specifically accumulate in CRC tumor tissues by imaging mass cytometry (IMC), immune fluorescence, and flow cytometry, which demonstrated pro‐tumor activity by disturbing natural killer (NK) cells are identified. It is found that these CD16+ neutrophils possess abnormal cholesterol accumulation due to activation of the CD16/TAK1/NF‐κB axis, which upregulates scavenger receptors for cholesterol intake including CD36 and LRP1. Consequently, these region‐specific CD16+ neutrophils not only competitively inhibit cholesterol intake of NK cells, which interrupts NK lipid raft formation and blocks their antitumor signaling but also release neutrophil extracellular traps (NETs) to induce the death of NK cells. Furthermore, CD16‐knockout reverses the pro‐tumor activity of neutrophils and restored NK cell cytotoxicity. Collectively, the findings suggest that CRC region‐specific CD16+ neutrophils can be a diagnostic marker and potential therapeutic target for CRC.
Zhang, Y, Yu, JX, Zhang, Y & Qin, L 2024, 'Maintaining Top-$t$ Cores in Dynamic Graphs', IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 9, pp. 4766-4780.
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Zhang, Y, Zhang, C, Mayr, P, Suominen, A & Ding, Y 2024, 'An editorial of “AI + informetrics”: Robust models for large-scale analytics', Information Processing & Management, vol. 61, no. 1, pp. 103495-103495.
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Zhang, Y-L, Qiu, Y-G, Armaghani, DJ, Monjezi, M & Zhou, J 2024, 'Enhancing rock fragmentation prediction in mining operations: A Hybrid GWO-RF model with SHAP interpretability', Journal of Central South University, vol. 31, no. 8, pp. 2916-2929.
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Zhang, Z, Debbah, M, Eldar, YC, Hoang, DT, Tong, W & Wong, K-K 2024, 'Guest Editorial: Sustainable Big AI Model for Wireless Networks', IEEE Wireless Communications, vol. 31, no. 3, pp. 18-19.
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Zhang, Z, Li, X, Liu, H, Zhou, T, Wang, Z, Nghiem, LD & Wang, Q 2024, 'Biofouling control of reverse osmosis membrane using free ammonia as a cleaning agent', Journal of Membrane Science, vol. 694, pp. 122414-122414.
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Zhang, Z, Liu, Y, Chen, S-L, Chen, D & Ban, Y-L 2024, 'A Wideband High-Gain Multilinear Polarization-Reconfigurable Antenna Integrated With Nonuniform Partially Reflective Surface', IEEE Transactions on Antennas and Propagation, vol. 72, no. 11, pp. 8870-8875.
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Zhang, Z, Liu, Y, Peng, Y, Ruan, X & Chen, S-L 2024, 'A Wide-Beamwidth and Multilinear Polarized Dielectric Resonator Antenna', IEEE Antennas and Wireless Propagation Letters, vol. 23, no. 11, pp. 3584-3588.
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Zhang, Z, Veedu, PNP, Halkon, B & Chou, SM 2024, 'Application of Support Vector Machine in the Evaluation of Table Tennis Motion Profiles', Computing Open, vol. 02.
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Racket sports such as table tennis involve a wide range of three-dimensional complex spatial movements of the human body and the racket. Novice players might benefit from the evaluation of the motion profile of the racket to facilitate better adoption of more expert movement. Computer-based evaluation of such novice vs. expert play behavior characteristics includes reducing the required multiple human interactions and easy applicability for subsequent automation to accurately differentiate the motion profile of a novice player from that of an expert. This study has, for the first time, applied the widely used support vector machine (SVM) classification technique for the development of a table tennis player movement evaluation model. The model was trained using an existing dataset of displacements and velocities from various important anatomical landmarks across the body and points on the racket. These were obtained and evaluated for table tennis forehand strokes for two subgroups of expert and novice ability levels, respectively. Different combinations of variables were selected for model input from the same dataset with the outcomes being noted for each. The resulting SVM classification model exhibited good/noteworthy performance ([Formula: see text]% accuracy) in distinguishing racket motion between expert and novice players.
Zhang, Z, Yu, G, Sun, C, Wang, X, Wang, Y, Zhang, M, Ni, W, Liu, RP, Reeves, A & Georgalas, N 2024, 'TbDd: A new trust-based, DRL-driven framework for blockchain sharding in IoT', Computer Networks, vol. 244, pp. 110343-110343.
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Zhang, Z, Zhou, Y, Shi, K, Chen, H, Wen, S & Yan, H 2024, 'VSG-LFC on multi-area power system under unsecured communication channel using hybrid trigger mechanism', Expert Systems with Applications, vol. 255, pp. 124571-124571.
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Zhao, D, Mihăiţă, A-S, Ou, Y, Grzybowska, H & Li, M 2024, 'Origin–destination matrix estimation for public transport: A multi-modal weighted graph approach', Transportation Research Part C: Emerging Technologies, vol. 165, pp. 104694-104694.
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Zhao, E, Wang, Q, Alamdari, MM, Luo, Z & Gao, W 2024, 'Reliability and sustainability integrated design optimization for engineering structures with active machine learning technique', Journal of Building Engineering, vol. 98, pp. 111480-111480.
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Zhao, F, Ji, JC, Cao, S, Ye, K & Luo, Q 2024, 'QZS isolators with multi-pairs of oblique bars for isolating ultralow frequency vibrations', Nonlinear Dynamics, vol. 112, no. 3, pp. 1815-1842.
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Zhao, H, Hu, Y, Li, Y, Wang, K, Dehn, F & Li, W 2024, 'Triaxial compressive performance of recycled aggregate/glass sand concrete: Experimental study and mechanism analysis', Journal of Cleaner Production, vol. 442, pp. 141006-141006.
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Zhao, H, Li, W, Gan, Y, Mahmood, AH, Zhao, X & Wang, K 2024, 'Nano- and Microscopic Investigation on the Strengthening Mechanism of ITZs Using Waste Glass Powder in Modeled Aggregate Concrete', Journal of Materials in Civil Engineering, vol. 36, no. 4.
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Zhao, J, Lu, Z, Andrew Zhang, J, Li, W, Xiong, Y, Han, Z, Wen, X & Gu, T 2024, 'Performance Bounds for Passive Sensing in Asynchronous ISAC Systems', IEEE Transactions on Wireless Communications, vol. 23, no. 11, pp. 15872-15887.
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Zhao, J, Lu, Z, Zhang, JA, Dong, S & Zhou, S 2024, 'Multiple-Target Doppler Frequency Estimation in ISAC With Clock Asynchronism', IEEE Transactions on Vehicular Technology, vol. 73, no. 1, pp. 1382-1387.
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Zhao, L, Zhu, H, Ding, C, Liu, G, Zhao, H, Mou, J & Guo, YJ 2024, 'An Ultrawideband Dual-Polarized Tightly Coupled Dipole Array (TCDA) With Wide Scanning Range', IEEE Antennas and Wireless Propagation Letters, vol. 23, no. 7, pp. 1961-1965.
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Zhao, M, Qi, X, Hu, Z, Li, L, Zhang, Y, Huang, Z & Yu, X 2024, 'Calligraphy Font Generation via Explicitly Modeling Location-Aware Glyph Component Deformations', IEEE Transactions on Multimedia, vol. 26, pp. 5939-5950.
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Zhao, P, Pan, Y, Li, X, Chen, X, Tsang, IW & Liao, L 2024, 'Coarse-to-Fine Contrastive Learning on Graphs', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 4, pp. 4622-4634.
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Zhao, Q, Zhu, S, Mu, C, Liu, X & Wen, S 2024, 'Multistability of Complex-Valued NNs With General Periodic-Type Activation Functions and Its Application to Associative Memories', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 54, no. 6, pp. 3749-3761.
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Zhao, Q, Zhu, S, Zhang, Z, Luo, W & Wen, S 2024, 'Multistability of Almost Periodic Solutions for Fuzzy Competitive NNs With Time-Varying Delays', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-12.
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Zhao, R, Tao, M, Wang, S, Tao, T & Wu, C 2024, 'Mechanical response and failure mechanism of circular inclusion embedded in brittle materials under dynamic impact', International Journal of Impact Engineering, vol. 194, pp. 105088-105088.
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Zhao, S & Ma, F 2024, 'A circular microphone array with virtual microphones based on acoustics-informed neural networks', The Journal of the Acoustical Society of America, vol. 156, no. 1, pp. 405-415.
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Acoustic beamforming aims to focus acoustic signals to a specific direction and suppress undesirable interferences from other directions. Despite its flexibility and steerability, beamforming with circular microphone arrays suffers from significant performance degradation at frequencies corresponding to zeros of the Bessel functions. To conquer this constraint, baffled or concentric circular microphone arrays have been studied; however, the former need a bulky baffle that interferes with the original sound field, whereas the latter require more microphones that increase the complexity and cost, both of which are undesirable in practical applications. To tackle this challenge, this paper proposes a circular microphone array equipped with virtual microphones, which resolves the performance degradation commonly associated with circular microphone arrays without resorting to physical modifications. The sound pressures at the virtual microphones are predicted from those measured by the physical microphones based on an acoustics-informed neural network, and then the sound pressures measured by the physical microphones and those predicted at the virtual microphones are integrated to design the beamformer. Experimental results demonstrate that the proposed approach not only eliminates the performance degradation but also suppresses spatial aliasing at high frequencies, thereby underscoring its promising potential.
Zhao, S, Zhang, H, Xiong, L, Wen, S, Cao, J & Zhang, Y 2024, 'Resilient adaptive event‐triggered synchronization control of piecewise‐homogeneous Markov jump delayed neural networks under aperiodic DoS attacks', International Journal of Robust and Nonlinear Control, vol. 34, no. 3, pp. 1493-1521.
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SummaryUnder the aperiodic Denial‐of‐service (DoS) attacks, this paper studies the resilient adaptive event‐triggered synchronization control problem for a class of Piecewise‐Homogeneous Uncertain Markov Jump Neural Networks (PHUMJNNs) with time‐varying delays. First of all, a new way of carving DoS attacks is given from the defenders' perspective, that is, aperiodic DoS attacks based on fixed detection periods. Then, under such attacks, a new Resilient Adaptive Event‐triggered Communication (RAETC) is designed between sensor and controller, which has a threshold function based on the net change rate and is real‐time updated depending on the present sampling state. Next, a single functional is used in the construction of the Lyapunov‐Krasovskii functional, while a new looped functional is introduced that makes full use of the state information of the current instant , the trigger instant and the next trigger instant . Based on the constructed single functional, under the framework of the input delay method and the linear matrix inequality technique, the exponential mean square stabilization criterion of the error system is obtained, which makes the master system and the slave system synchronized. In the end, three simulation examples are used to illustrate the validity of the obtained results.
Zhao, S, Zhou, C, Zan, R, Shu, M, Suo, T & Xin, Z 2024, 'Preparation of effective antibacterial composites of low-density polyethylene modified with quaternary ammonium functionalized zinc oxide nanoparticles', Journal of Polymer Research, vol. 31, no. 9.
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Zhao, S, Zhou, C, Zan, R, Suo, T & Xin, Z 2024, 'Fabrication of a Universal Composite Coating for Biliary Stents with Protein Antifouling and Antibacterial Synergies', Industrial & Engineering Chemistry Research, vol. 63, no. 22, pp. 9833-9844.
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Zhao, Y, Liu, B, Zhu, T, Ding, M, Yu, X & Zhou, W 2024, 'Proactive image manipulation detection via deep semi-fragile watermark', Neurocomputing, vol. 585, pp. 127593-127593.
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Zhao, Y, Zhang, X, Xue, H, Gong, B, Li, Q, Guo, W & Meng, X 2024, 'Effective immobilization and biosafety assessment of antimony in soil with zeolite-supported nanoscale zero-valent iron', Environmental Pollution, vol. 352, pp. 124082-124082.
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Zhao, Z, Cao, L & Lin, K-Y 2024, 'Out-of-Distribution Detection by Cross-Class Vicinity Distribution of In-Distribution Data', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 10, pp. 13777-13788.
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Zhao, Z, Lu, W, Peng, X, Xing, L, Zhang, W & Zheng, C 2024, 'Automated ICD Coding via Contrastive Learning With Back-Reference and Synonym Knowledge for Smart Self-Diagnosis Applications', IEEE Transactions on Consumer Electronics, vol. 70, no. 3, pp. 6042-6053.
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Zhen, J, Zheng, M, Wei, W, Ni, S-Q & Ni, B-J 2024, 'Extracellular electron transfer (EET) enhanced anammox process for progressive nitrogen removal: A review', Chemical Engineering Journal, vol. 482, pp. 148886-148886.
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Zheng, B, Verma, S, Zhou, J, Tsang, IW & Chen, F 2024, 'Imitation Learning: Progress, Taxonomies and Challenges', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 5, pp. 6322-6337.
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Imitation learning (IL) aims to extract knowledge from human experts' demonstrations or artificially created agents to replicate their behaviors. It promotes interdisciplinary communication and real-world automation applications. However, the process of replicating behaviors still exhibits various problems, such as the performance is highly dependent on the demonstration quality, and most trained agents are limited to perform well in task-specific environments. In this survey, we provide an insightful review on IL. We first introduce the background knowledge from development history and preliminaries, followed by presenting different taxonomies within IL and key milestones of the field. We then detail challenges in learning strategies and present research opportunities with learning policy from suboptimal demonstration, voice instructions, and other associated optimization schemes.
Zheng, B, Zhou, J, Liu, C, Li, Y & Chen, F 2024, 'Explaining Imitation Learning Through Frames', IEEE Intelligent Systems, vol. 39, no. 6, pp. 18-27.
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Zheng, C, Hu, C, Yu, J & Wen, S 2024, 'Saturation function-based continuous control on fixed-time synchronization of competitive neural networks', Neural Networks, vol. 169, pp. 32-43.
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Zheng, J, Gao, Q, Dong, D, Lü, J & Deng, Y 2024, 'A Quantum Multimodal Neural Network Model for Sentiment Analysis on Quantum Circuits', IEEE Transactions on Artificial Intelligence, pp. 1-15.
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Zheng, J, Guo, Y, Ji, J, Tong, V-C, Zhang, X, Dong, H, Hu, S & Xu, L 2024, 'Contact force and pressure analysis of the three-row roller pitch bearing in a large-scale wind turbine', Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, vol. 238, no. 19, pp. 9475-9490.
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Pitch bearings are the key components of wind turbines (WTs). The reliability of pitch bearings affects the electric energy output efficiency of WTs. Due to its large loading ability, constant contact angle and high stiffness, the three-row roller slewing bearing (TRSB) is becoming a more attractive choice as a pitch bearing, especially in large-scale WTs. The contact force and pressure distributions significantly affect the fatigue life of a three-row roller pitch bearing (TRPB). Therefore, it is essential to propose a precise calculation method for the contact force and pressure. The present work establishes a quasi-static five degree-of-freedom (DOF) mathematical model for TRPBs, in which the bearing under general loading conditions is considered. The influences of combined external forces, overturning moments and inner ring misalignment angle on the contact force and pressure distributions of TRPBs are comprehensively investigated. According to the analysis results, the overturning moment and axial force can significantly affect the number of load-carrying rollers in each row. Furthermore, a slight variation of the inner ring angular misalignment has a significant effect on the contact force and pressure of TRPBs. Finally, it is more prone to pressure concentration in the non-crowned roller than that in the crowned roller, especially in the case of the inner ring with a misalignment angle. Since the high calculation efficiency and accuracy, the proposed method has remarkable potential applications in the fatigue life prediction and design of TRPBs.
Zheng, J, Hu, S, Ji, J, Zhang, X, Tong, V-C, Yin, S, Feng, K, Dong, H & Xu, L 2024, 'A review of fatigue failure and structural design of main bearings in tunnel boring machines based on engineering practical examples', Engineering Failure Analysis, vol. 163, pp. 108611-108611.
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Zheng, Y, Ngo, HH, Luo, H, Wang, R, Li, C, Zhang, C & Wang, X 2024, 'Production of cost-competitive bioethanol and value-added co-products from distillers' grains: Techno-economic evaluation and environmental impact analysis', Bioresource Technology, vol. 397, pp. 130470-130470.
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Zheng, Y, Zhao, X, Yao, L & Cao, L 2024, 'Deep Multidilation Temporal and Spatial Dependence Modeling in Stereoscopic 3-D EEG for Visual Discomfort Assessment', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 54, no. 4, pp. 2125-2136.
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Zhong, JP, Li, YS, Xie, WY, Lei, J & Paolo, G 2024, 'Region-Guided and Dual Attention Discriminative Learning Network for Hyperspectral Target Detection', Tien Tzu Hsueh Pao/Acta Electronica Sinica, vol. 52, no. 5, pp. 1716-1729.
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Hyperspectral images (HSIs) have high spectral resolution and rich spectral information, which can obtain the physical and chemical information of the target of interest by using a large number of narrow-band waves. HSIs can effectively distinguish different substances by corresponding spectral features, and complete the task of target detection. However, the problem of target and background confusion caused by limited samples, a small amount of prior information, high dimensional similar background, and differences between different classes make hyperspectral target detection (HTD) still face challenges. To this end, we propose a region-guided and dual-attention discriminative learning network (RADN) for HTD to solve the problem of intra-class differences and inter-class similarities under a few samples. It can reduce the computational complexity caused by high-dimensional redundant features and improve detection accuracy. In this paper, we introduce the empirical region-guided network for training. We employ the spectrally constrained unsupervised clustering network to determine the network input. To selectively focus on salient features and regions of interest, we add a dual-channel attention mechanism in the generator and discriminator to assist in the estimation of complex background distributions; We introduce an inter-class spectral prior loss function in the network and further reduce the interference of high-dimensional complex background and spectral changes to the target. Experimental results and analysis show that RADN outperforms existing state-of-the-art algorithms on different datasets.
Zhou, C, Lyu, B, You, C & Hoang, DT 2024, 'Cooperative Commensal and Parasitic Symbiotic Radio Communication Systems', IEEE Wireless Communications Letters, vol. 13, no. 3, pp. 676-680.
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Zhou, H, Dai, H-N, Cheng, X, Nguyen, DN & Tabassum, H 2024, 'Guest Editorial: Mobile AI-Generated Content (AIGC) in 6G Era', IEEE Wireless Communications, vol. 31, no. 4, pp. 12-13.
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Zhou, I, Tofigh, F, Piccardi, M, Abolhasan, M, Franklin, D & Lipman, J 2024, 'Secure Multi-Party Computation for Machine Learning: A Survey', IEEE Access, vol. 12, pp. 53881-53899.
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Zhou, J, Duan, Y, Chang, Y-C, Wang, Y-K & Lin, C-T 2024, 'BELT: Bootstrapped EEG-to-Language Training by Natural Language Supervision', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 32, pp. 3278-3288.
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Zhou, J, Li, D, Chen, G & Wen, S 2024, 'Projective synchronization for distinct fractional-order neural networks consist of inconsistent orders via sliding mode control', Communications in Nonlinear Science and Numerical Simulation, vol. 133, pp. 107986-107986.
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Zhou, M, Lu, J, Lu, P & Zhang, G 2024, 'Dynamic Graph Regularization for Multi-Stream Concept Drift Self-Adaptation', IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 11, pp. 6016-6028.
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Zhou, S, Halkon, B, Guo, Z & Eager, D 2024, 'Transient waveform replication for flexible structures in shaker testing using time-domain convolution techniques'.
Zhou, S, Lin, W, Jin, X, Niu, R, Yuan, Z, Chai, T, Zhang, Q, Guo, M, Kim, SS, Liu, M, Deng, Y, Park, JB, Choi, SI, Shi, B & Yin, J 2024, 'CD97 maintains tumorigenicity of glioblastoma stem cells via mTORC2 signaling and is targeted by CAR Th9 cells', Cell Reports Medicine, vol. 5, no. 12, pp. 101844-101844.
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Zhou, S, Zhu, T, Ye, D, Yu, X & Zhou, W 2024, 'Boosting Model Inversion Attacks With Adversarial Examples', IEEE Transactions on Dependable and Secure Computing, vol. 21, no. 3, pp. 1451-1468.
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Zhou, S, Zhu, T, Ye, D, Zhou, W & Zhao, W 2024, 'Inversion-Guided Defense: Detecting Model Stealing Attacks by Output Inverting', IEEE Transactions on Information Forensics and Security, vol. 19, pp. 4130-4145.
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Zhou, T, Li, X, Liu, H, Dong, S, Zhang, Z, Wang, Z, Li, J, Nghiem, LD, Khan, SJ & Wang, Q 2024, 'Occurrence, fate, and remediation for per-and polyfluoroalkyl substances (PFAS) in sewage sludge: A comprehensive review', Journal of Hazardous Materials, vol. 466, pp. 133637-133637.
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Zhou, W, Zhu, T, Ye, D, Ren, W & Choo, K-KR 2024, 'A Concurrent Federated Reinforcement Learning for IoT Resources Allocation With Local Differential Privacy', IEEE Internet of Things Journal, vol. 11, no. 4, pp. 6537-6550.
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Zhou, X, Lin, Z, Gu, R, Ni, W & Jamalipour, A 2024, 'A New Meta-Learning Framework for Estimating Atmospheric Turbulence and Phase Noise in Optical Satellite Internet of Things Systems', IEEE Internet of Things Journal, vol. 11, no. 7, pp. 11190-11201.
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Zhu, C, Ye, D, Huo, H, Zhou, W & Zhu, T 2024, 'A location-based advising method in teacher–student frameworks', Knowledge-Based Systems, vol. 285, pp. 111333-111333.
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Zhu, C, Ye, D, Zhu, T & Zhou, W 2024, 'Location-Based Real-Time Updated Advising Method for Traffic Signal Control', IEEE Internet of Things Journal, vol. 11, no. 8, pp. 14551-14562.
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Zhu, H, Wang, G, Wang, K, Liu, G, Zhou, Y, Xie, S, Di, Y, Xu, J, Zhou, H, Mou, J & Ding, C 2024, 'Grid composite meta-surface absorber with thermal isolation structure for terahertz detection', Optics Express, vol. 32, no. 1, pp. 205-205.
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This paper specifically focuses on the absorber, the critical component responsible for the detector's response performance. The meta-surface absorber combines two resonant structures and achieves over 80% absorptance around 210 GHz, resulting in a broad operating frequency range. FR-4 is selected as the dielectric layer to be compatible with standard printed circuit board (PCB) technology, which reduces the overall fabrication time and cost. The absorbing unit and array layout are symmetrically designed, providing stable absorptance performance even under incident waves of different polarization angles. The polarization-insensitive absorptance characteristic further enhances the compatibility between the absorber and the detector in the application scenario. Furthermore, the thermal insulation performance of the absorber is ensured by introducing thermal insulation gaps. After completing fabrication through PCB technology, testing revealed that the absorber maintained excellent absorptance performance within its primary operating frequency range. This performance consistency closely matched the simulation results.
Zhu, HY, Hieu, NQ, Hoang, DT, Nguyen, DN & Lin, C-T 2024, 'A Human-Centric Metaverse Enabled by Brain-Computer Interface: A Survey', IEEE Communications Surveys & Tutorials, vol. 26, no. 3, pp. 2120-2145.
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Zhu, J & Yang, Y 2024, '3-D Printed Transmission-Reflection-Integrated Metasurface for Spin-Decoupled Full-Space Quadruplex Channels Independent Phase Modulation', IEEE Transactions on Microwave Theory and Techniques, vol. 72, no. 8, pp. 4790-4800.
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Zhu, J, El-Zein, A, Airey, DW & Miao, G 2024, 'An experimental study on root-reinforced soil strength via a steel root analogue in unsaturated silty soil', Acta Geotechnica, vol. 19, no. 1, pp. 255-272.
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AbstractLandslides due to catastrophic weather events, especially heavy rainfall, have risen significantly over the last several decades, causing significant damage and affecting the health and livelihoods of millions of people. Using tree roots to bio-engineer shallow slopes has been proven to be a cost-effective, sustainable measure and thus has gained increasing popularity. As slope failure often occurs under heavy precipitation, it is important to understand the mechanical interactions in the soil matrix surrounding a root to better estimate the reinforcement capacity of a root system, especially as the soil undergoes wetting from drier conditions. However, very few studies of root reinforcements have considered the effects of degree of saturation on behaviour. In this study, steel wires are used as a root analogue to explore the impact of root geometry, soil dilation and soil saturation on the pull-out behaviour of a root and three commonly used unsaturated soil strength models have been used to interpret the pull-out results. It was found that roots with larger diameter did not contribute to additional resistance. Also, a linear relationship between degree of saturation and pull-out strength was identified over a large range of suctions and one of the unsaturated soil strength models seemed to provide a more reasonable interpretation. The results will help future bioengineering slope design by improving the understanding of soil-root interface behaviour, including the effect of root diameter in slippage failure and greater emphasis on the importance of taking degree of saturation into account in unsaturated soil strength models.
Zhu, J, Liao, S, Zhu, X, Yang, Y & Xue, Q 2024, 'C-/Ka-Band Aperture-Shared Dual Circularly Polarized Heterogeneous Reflectarray for Vehicular Communications', IEEE Transactions on Vehicular Technology, vol. 73, no. 6, pp. 8671-8680.
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Zhu, J, Xiao, G, Zheng, Z & Sui, Y 2024, 'Deep semi-supervised learning for recovering traceability links between issues and commits', Journal of Systems and Software, vol. 216, pp. 112109-112109.
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Zhu, J, Yang, Y, Lai, J & Li, M 2024, '3-D Printed Noninterleaved Reflective Metasurfaces Supporting Dual-Band Spin-Decoupled Quadruplex Channel Independent Beam-Shaping With Controllable Energy Distribution', IEEE Transactions on Microwave Theory and Techniques, vol. 72, no. 2, pp. 1196-1205.
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Zhu, S, Guo, M, Mu, C, Liu, X & Wen, S 2024, 'Reachable Set Estimation for Delayed Memristive Neural Networks With Bounded Disturbances', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 54, no. 9, pp. 5523-5528.
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Zhu, S, Shen, Y, Mu, C, Liu, X & Wen, S 2024, 'Generalized-Type Multistability of Almost Periodic Solutions for Memristive Cohen–Grossberg Neural Networks', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 7, pp. 9483-9494.
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This article investigates a generalized type of multistability about almost periodic solutions for memristive Cohen–Grossberg neural networks (MCGNNs). As the inevitable disturbances in biological neurons, almost periodic solutions are more common in nature than equilibrium points (EPs). They are also generalizations of EPs in mathematics. According to the concepts of almost periodic solutions and $\Psi$ -type stability, this article presents a generalized-type multistability definition of almost periodic solutions. The results show that $(K+1)^n$ generalized stable almost periodic solutions can coexist in a MCGNN with $n$ neurons, where $K$ is a parameter of the activation functions. The enlarged attraction basins are also estimated based on the original state space partition method. Some comparisons and convincing simulations are given to verify the theoretical results at the end of this article.
Zhu, S, Zhang, J, Liu, X, Shen, M, Wen, S & Mu, C 2024, 'Multistability and Robustness of Competitive Neural Networks With Time-Varying Delays', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 12, pp. 18746-18757.
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Zhu, S, Zhang, YX & Lee, CK 2024, 'Modelling of flexural fatigue behaviours of hybrid engineered cementitious composite link slabs using cycle-driven analysis', Structures, vol. 63, pp. 106476-106476.
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Zhu, W, Tuan, HD, Dutkiewicz, E, Fang, Y, Poor, HV & Hanzo, L 2024, 'Long-Term Rate-Fairness-Aware Beamforming Based Massive MIMO Systems', IEEE Transactions on Communications, vol. 72, no. 4, pp. 2386-2398.
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Zhu, W, Tuan, HD, Dutkiewicz, E, Poor, HV & Hanzo, L 2024, 'A New Class of Analog Precoding for Multi-Antenna Multi-User Communications Over High-Frequency Bands', IEEE Transactions on Wireless Communications, vol. 23, no. 9, pp. 11493-11507.
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A network relying on a large antenna-array-aided base station is designed for delivering multiple information streams to multi-antenna users over high-frequency bands such as the millimeter-wave and sub-Terahertz bands. The state-of-the-art analog precoder (AP) dissipates excessive circuit power due to its reliance on a large number of phase shifters. To mitigate the power consumption, we propose a novel AP relying on a controlled number of phase shifters. Within this new AP framework, we design a hybrid precoder (HP) for maximizing the users’ minimum throughput, which poses a computationally challenging problem of large-scale, nonsmooth mixed discrete-continuous log-determinant optimization. To tackle this challenge, we develop an algorithm which iterates through solving convex problems to generate a sequence of HPs that converges to the max-min solution. We also introduce a new framework of smooth optimization termed soft max-min throughput optimization. Additionally, we develop another algorithm, which iterates by evaluating closed-form expressions to generate a sequence of HPs that converges to the soft max-min solution. Simulation results reveal that the HP soft max-min solution approaches the Pareto-optimal solution constructed for simultaneously optimizing both the minimum throughput and sum-throughput. Explicitly, it achieves a minimum throughput similar to directly maximizing the users’ minimum throughput and it also attains a sum-throughput similar to directly maximizing the sum-throughput.
Zhu, W, Tuan, HD, Dutkiewicz, E, Poor, HV & Hanzo, L 2024, 'Max-Min Rate Optimization of Low-Complexity Hybrid Multi-User Beamforming Maintaining Rate-Fairness', IEEE Transactions on Wireless Communications, vol. 23, no. 6, pp. 5648-5662.
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Zhu, X & Chen, L 2024, 'Recent Advances in Integrated mm-Wave Power Amplifiers for 5G and Beyond', IEEE Microwave Magazine, vol. 25, no. 12, pp. 112-127.
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Zhu, X, Gómez-García, R, Li, C-H & Schwitter, B 2024, 'Guest Editorial: Integrated Devices, Circuits, and Systems for the 6G Era', IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 14, no. 1, pp. 3-6.
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Zhuang, Y, Li, Y, Zhai, R, Huang, Y, Wang, X, Tang, L, Wang, K, Tang, S & Lin, Z 2024, 'Research on the impact of nitromethane on the combustion mechanism of ammonia/methanol blends', Journal of the Energy Institute, vol. 117, pp. 101867-101867.
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Zhuang, Z, Wang, Y-K, Chang, Y-C, Liu, J & Lin, C-T 2024, 'A Connectivity-Aware Graph Neural Network for Real-Time Drowsiness Classification', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 32, pp. 83-93.
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Drowsy driving is one of the primary causes of driving fatalities. Electroencephalography (EEG), a method for detecting drowsiness directly from brain activity, has been widely used for detecting driver drowsiness in real-time. Recent studies have revealed the great potential of using brain connectivity graphs constructed based on EEG data for drowsy state predictions. However, traditional brain connectivity networks are irrelevant to the downstream prediction tasks. This article proposes a connectivity-aware graph neural network (CAGNN) using a self-attention mechanism that can generate task-relevant connectivity networks via end-to-end training. Our method achieved an accuracy of 72.6% and outperformed other convolutional neural networks (CNNs) and graph generation methods based on a drowsy driving dataset. In addition, we introduced a squeeze-and-excitation (SE) block to capture important features and demonstrated that the SE attention score can reveal the most important feature band. We compared our generated connectivity graphs in the drowsy and alert states and found drowsiness connectivity patterns, including significantly reduced occipital connectivity and interregional connectivity. Additionally, we performed a post hoc interpretability analysis and found that our method could identify drowsiness features such as alpha spindles. Our code is available online1.
Zhuo, M, Chen, Z, Liu, X, Wei, W, Shen, Y & Ni, B-J 2024, 'A broad horizon for sustainable catalytic oxidation of microplastics', Environmental Pollution, vol. 340, pp. 122835-122835.
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Zogan, H, Razzak, I, Jameel, S & Xu, G 2024, 'Hierarchical Convolutional Attention Network for Depression Detection on Social Media and Its Impact During Pandemic', IEEE Journal of Biomedical and Health Informatics, vol. 28, no. 4, pp. 1815-1823.
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Zou, X, Xin, C, Wang, C, Li, Y, Wang, S, Zhang, W, Li, J, Su, S & Xiao, M 2024, 'Non-destructive detection of chicken freshness based on multiple features image fusion and support vector machine', International Journal of Agricultural and Biological Engineering, vol. 17, no. 6, pp. 264-272.
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Zuo, S, Wang, D, Zhang, Y & Luo, Q 2024, 'An innovative design of parabolic cam-roller quasi-zero-stiffness isolators for ultralow frequency vibration isolation', Nonlinear Dynamics, vol. 112, no. 21, pp. 18717-18744.
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Zuo, W, Li, D, Li, Q, Cheng, Q & Huang, Y 2024, 'Effects of intermittent pulsating flow on the performance of multi-channel cold plate in electric vehicle lithium-ion battery pack', Energy, vol. 294, pp. 130832-130832.
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Zuo, W, Li, F, Li, Q, Chen, Z, Huang, Y & Chu, H 2024, 'Multi-objective optimization of micro planar combustor with tube outlet by RSM and NSGA-II for thermophotovoltaic applications', Energy, vol. 291, pp. 130396-130396.
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Zuo, W, Wang, Z, Li, Q, Zhou, K & Huang, Y 2024, 'Numerical investigations on the performance enhancement of a hydrogen-fueled micro planar combustor with finned bluff body for thermophotovoltaic applications', Energy, vol. 293, pp. 130752-130752.
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Zuo, Y, Yao, W, Zeng, Y, Xie, J, Fang, Y, Huang, Y & Jiang, W 2024, 'CFNet: Conditional filter learning with dynamic noise estimation for real image denoising', Knowledge-Based Systems, vol. 284, pp. 111320-111320.
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Zuo, Z, Zhang, T, Huang, X, Cen, X, Lu, X, Liu, T, Shon, HK & Zheng, M 2024, 'A hybrid oxidation approach for converting high-strength urine ammonia into ammonium nitrate', Water Research X, vol. 25, pp. 100277-100277.
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Zurnadzhy, V, Chabak, Y, Petryshynets, I, Efremenko, A, Sili, I, Sagirov, R & Efremenko, V 2024, 'Advancing the ductile behaviour of heavy-wall API X70 pipeline steel by a 'Slab/Sheet' thickness ratio increase', Manufacturing Technology, vol. 24, no. 5, pp. 843-854.
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Abbasi, M, Kurdkandi, NV, Abbasi, E, Li, L, Aguilera, RP, Lu, D & Wang, F 1970, 'A New Dual-Purpose Flyback-based DC–DC/AC Converter with Dynamic Voltage Gain', 2024 IEEE Applied Power Electronics Conference and Exposition (APEC), 2024 IEEE Applied Power Electronics Conference and Exposition (APEC), IEEE, pp. 366-369.
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ABTAHI, H, KARIMI, M & MAXIT, L 1970, '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, Institute of Noise Control Engineering (INCE), 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.
Ahamed, M, Guertler, M & Sick, N 1970, 'Bridging the Gap: Barriers to and Requirements for Human-Robot Knowledge Transfer', The 51st International Conference on Computers and Industrial Engineering (CIE51), Sydney, Australia.
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This article focuses on the need for efficient information exchange between humans and collaborative robots (cobots) in advanced manufacturing by investigating the barriers to and requirements for effective knowledge transfer. Industry 4.0-based manufacturing systems heavily rely on the collaboration between humans and robots to improve safety, productivity, and adaptability. On the other hand, insufficient communication within the existing HRC systems results in a lack of trust and decreased effectiveness. The study combines a literature search, qualitative analysis of interviews and a design research methodology (DRM) to synthesize findings. The study integrates human expertise with cobot capabilities and examines the primary obstacles to knowledge transfer within Industry 4.0 frameworks. The literature gap is thoroughly examined by incorporating real industry settings and expert opinions while considering HRC's technical and interpersonal aspects. Focusing on the list of barriers like technological incompatibility, proper communication, and lack of human-centred design and requirements seeks to improve the smooth exchange of knowledge and skills between individuals and cobots, ensuring efficient collaboration. Therefore, it is essential to integrate a socio-technical system theory and resource-based view to handle the complex interaction between humans and robots in a collaborative environment. The study's findings highlight the significance of considering both technological and human-centered factors to promote seamless interactions and knowledge sharing, which require ongoing monitoring and feedback to improve teamwork. In conclusion, the study highlights the significance of efficient knowledge transfer in improving the manufacturing industry's efficiency, competitiveness, and innovation. This study builds the foundation for developing targeted interventions to overcome collaborative barriers in Industry 4.0 settings, thus advancing both a methodic...
Ahuja, T, Gardner, A & Machet, T 1970, 'Professional Skills – A Cross Cultural Perspective', 2024 World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC), 2024 World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC), IEEE, pp. 1-8.
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Ahuja, T, Gardner, A & Machet, T 1970, 'Professional skills – the balancing act!', Australasian Association for Engineering Education Annual Conference, New Zealand.
Alabsi, M, Gill, A & Bandara, M 1970, 'Integrated Interaction Journey and Privacy Risk Assessment: A Graph Model', Procedia Computer Science, Elsevier BV, pp. 1594-1603.
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Alalmaie, A, Waheed, N, Alalyan, M, Nanda, P, Jia, W & He, X 1970, 'Zero Trust for Intrusion Detection System: A Systematic Literature Review', Proceedings of the 16th International Conference on Agents and Artificial Intelligence, 16th International Conference on Agents and Artificial Intelligence, SCITEPRESS - Science and Technology Publications, pp. 170-177.
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Alam, MM, Hossain, J & Mofidul, RB 1970, 'Data Driven Approach for User-owned Renewable Energy Sources Allocation in Community Microgrid', 2024 IEEE 34th Australasian Universities Power Engineering Conference (AUPEC), 2024 IEEE 34th Australasian Universities Power Engineering Conference (AUPEC), IEEE, pp. 1-6.
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Alanazi, I, AL-Doghman, F, Alsubhi, A & Hussain, F 1970, 'Carbon Credits Price Prediction Model (CCPPM)', Springer Nature Switzerland, pp. 143-150.
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Alexis, S, Kaveesha, D, Guo, T & Peter, M 1970, 'A Model to Quantify the Risk of Cross-Product Manipulation: Evidence from the European Government Bond Futures Market', 11th Annual Conference on Financial Market Regulation, 36 University Dr # 333, Bethlehem, PA 18015, United States.
Alghanmi, NA, Bukhari, AA, Alsulaimani, S, Bawazeer, B, Alawfi, D & Khadeer Hussain, F 1970, 'Social Carbon Credits: A New Approach to Assessing the Impact of Social Development Projects Against the Sustainable Development Goals', 2024 IEEE International Conference on e-Business Engineering (ICEBE), 2024 IEEE International Conference on e-Business Engineering (ICEBE), IEEE, pp. 191-199.
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Alharbi, A, Sharma, N & Hussain, F 1970, 'Arabic Sentiment Analysis with Social Network Data: A Comparative Study', 2024 IEEE International Conference on e-Business Engineering (ICEBE), 2024 IEEE International Conference on e-Business Engineering (ICEBE), IEEE, pp. 87-94.
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Ali, MY, Lalbakhsh, A, Ahmed, F, Taheri, SH & Asadnia, M 1970, 'A Perforated Dielectric Metasurface Using 3-D Printing for Aperture Antenna Performance Improvement', 2024 6th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT), 2024 6th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT), IEEE, pp. 1038-1041.
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Alkhalaf, A & Hussain, FK 1970, 'Towards Priority VM Placement in Fog Networks', Springer Nature Switzerland, pp. 404-414.
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Almalki, MA, Abualhamayl, AJ, Alyoubi, AA, Al-Doghman, F & Hussain, FK 1970, 'Blockchain-Based Joint Ownership Group Formation', 2024 IEEE International Conference on e-Business Engineering (ICEBE), 2024 IEEE International Conference on e-Business Engineering (ICEBE), IEEE, pp. 206-211.
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Alnefaie, A & Kang, K 1970, 'Cultural Transformations of E-commerce Consumer Behavior and Intention Toward Using Artificial Intelligence (AI) Assistants', Proceedings of the 57th Hawaii International Conference on System Sciences, The 57th Hawaii International Conference on System Sciences, the 57th Hawaii International Conference on System Sciences, Hawaii, US.
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The growth of Artificial Intelligence (AI) applications has been explosive in recent years. More AI assistants are emerging, causing cultural transformation to change consumer habits and behaviors. Culture influences how consumers communicate with online businesses; therefore, consumers' cultures could strongly influence AI assistant adoption in e-commerce. This paper presents a framework for examining the effects of cultural dimensions, AI characteristics, and consumer experience on behavior and intention toward AI assistants. The study collected data from two different cultures, Western and Eastern, and analyzed survey responses. The framework demonstrates how COVID19 influences consumer behavior toward AI assistant usage. The findings reveal the significant impact of cultural beliefs on attitudes and how these contribute to consumer intention and behavior toward using AI assistants. The results provide insights for future research on related behavior and help e-commerce practitioners determine the cultural influences and develop effective marketing strategies.
Alorini, A, Sawad, AB, Alharbi, S, Ijaz, K, Prasad, M & Kocaballi, AB 1970, 'Understanding Privacy in Smart Speakers: A Narrative Review', Springer Nature Singapore, pp. 143-157.
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Alotaibi, NN, Saberi, M, Bandara, M & Chang, E 1970, 'At Scaled Research Opportunity Identification: The Marriage of Explicit and Parametric Knowledge', 2024 IEEE International Conference on e-Business Engineering (ICEBE), 2024 IEEE International Conference on e-Business Engineering (ICEBE), IEEE, pp. 200-205.
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Alqahtani, S & Nanda, P 1970, 'Effects of Personal Characteristics on Phishing Awareness, Anti-Phishing Tool Usage, and Phishing Avoidance Behavior: A Structural Equation Modeling Approach', 2024 17th International Conference on Security of Information and Networks (SIN), 2024 17th International Conference on Security of Information and Networks (SIN), IEEE, University of Technology Sydney, pp. 1-9.
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Alqahtani, S & Nanda, P 1970, 'Enhancing Phishing Resilience in Academia: The Mediating Role of Anti-Phishing Tools on Student Awareness and Behavior', 2024 17th International Conference on Security of Information and Networks (SIN), 2024 17th International Conference on Security of Information and Networks (SIN), IEEE, University of Technology Sydney, pp. 1-8.
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Alshatri, N, Saberi, M & Hussain, FK 1970, 'A Comprehensive Analysis for Determining Key Influencing Factors on Carbon Credit Prices', 2024 IEEE International Conference on e-Business Engineering (ICEBE), 2024 IEEE International Conference on e-Business Engineering (ICEBE), IEEE, pp. 152-159.
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Alsolmy, M & Hussain, FK 1970, 'Advancing Blockchain Integration in Manufacturing: A Roadmap Aligned with Saudi Vision 2030', 2024 IEEE International Conference on e-Business Engineering (ICEBE), 2024 IEEE International Conference on e-Business Engineering (ICEBE), IEEE, pp. 160-167.
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Alsuhaibani, A, Zogan, H, Razzak, I, Jameel, S & Xu, G 1970, 'IDoFew: Intermediate Training Using Dual-Clustering in Language Models for Few Labels Text Classification', Proceedings of the 17th ACM International Conference on Web Search and Data Mining, WSDM '24: The 17th ACM International Conference on Web Search and Data Mining, ACM, pp. 18-27.
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Altaee, A, Hamdi, F, Alsaka, L, Ibrar, I, AL-Ejji, M, Zhou, J, Samal, A & Hawari, A 1970, 'proceedings of the 5th International Conference on Environmental Science and Applications (ICESA 2024)', 5th International Conference on Environmental Science and Applications (ICESA 2024), Lisbon, Portugal.
Alyassine, W, Raju, AS, Braytee, A, Anaissi, A & Naji, M 1970, 'An Efficient and Reliable scRNA-seq Data Imputation Method Using Variational Autoencoders', Springer Nature Switzerland, pp. 84-97.
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Amiri, M, Abolhasan, M, Shariati, N & Lipman, J 1970, 'Highly Efficient Polarization-Insensitive EM Energy Harvester', 2024 18th European Conference on Antennas and Propagation (EuCAP), 2024 18th European Conference on Antennas and Propagation (EuCAP), IEEE, pp. 1-5.
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Anaissi, A, Jia, Y, Braytee, A, Naji, M & Alyassine, W 1970, 'Damage GAN: A Generative Model for Imbalanced Data', Springer Nature Singapore, pp. 48-61.
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Ang, JD, Yang, L, Gómez-García, R & Zhu, X 1970, 'A Millimeter-Wave Input-Reflectionless Amplifier in 45-nm SOI CMOS Technology', 2024 IEEE International Symposium on Circuits and Systems (ISCAS), 2024 IEEE International Symposium on Circuits and Systems (ISCAS), IEEE, pp. 1-5.
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Ansari, M, Song, L, Qin, P, Smith, SL & Guo, YJ 1970, '3D Printed Multi-Beam Flat Lens Antenna System', 2024 International Symposium on Antennas and Propagation (ISAP), 2024 International Symposium on Antennas and Propagation (ISAP), IEEE, pp. 1-2.
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Arachchige, SM & Pradhan, B 1970, 'Hurricane risk assessment in Texas using machine learning and remote sensing data', 2024 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS), 2024 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS), IEEE, pp. 1-3.
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Araya, B, Garcia, C, Acuna, P, Aguilera, R, Castillo, C, Sanchez, D & Rodriguez, J 1970, 'Virtual Vector Optimal Switching Sequence Model Predictive Control for Computational Burden Reduction', 2024 IEEE 9th Southern Power Electronics Conference (SPEC), 2024 IEEE 9th Southern Power Electronics Conference (SPEC), IEEE, pp. 1-6.
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Armstrong, T, Leong, TW, Buckingham Shum, S & van den Hoven, E 1970, ''This is the kind of experience I want to have': Supporting the experiences of queer young men on social platforms through design', Designing Interactive Systems Conference, DIS '24: Designing Interactive Systems Conference, ACM, pp. 1681-1700.
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Arnaz, A, Lipman, J & Abolhasan, M 1970, 'Adversarial Attack Vectors Against Near-Real-Time AI xApps in the Open RAN', 2024 17th International Conference on Security of Information and Networks (SIN), 2024 17th International Conference on Security of Information and Networks (SIN), IEEE, pp. 1-9.
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Arnett, S, Jimenez, S, Osborne, C, Leitner, U, Ward, K, Zhang, P, Sun, J & Broadley, S 1970, 'Treating MS does make a difference long-term', MULTIPLE SCLEROSIS JOURNAL, 40th Congress of the European-Committee-for-Treatment-and-Research-in-Multiple-Sclerosis (ECTRIMS), SAGE PUBLICATIONS LTD, DENMARK, Copenhagen, pp. 176-176.
Arnett, S, Jimenez, S, Osborne, C, Leitner, U, Ward, K, Zhang, P, Sun, J & Broadley, S 1970, 'Vitamin D may be both good and bad for multiple sclerosis', MULTIPLE SCLEROSIS JOURNAL, 40th Congress of the European-Committee-for-Treatment-and-Research-in-Multiple-Sclerosis (ECTRIMS), SAGE PUBLICATIONS LTD, DENMARK, Copenhagen, pp. 779-779.
Asiri, A, Wu, F, Tian, Z & Yu, S 1970, 'From the Perspective of AI Safety: Analyzing the Impact of XAI Performance on Adversarial Attack', GLOBECOM 2024 - 2024 IEEE Global Communications Conference, GLOBECOM 2024 - 2024 IEEE Global Communications Conference, IEEE, pp. 4982-4987.
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Atkins, N, Johnston, A & Kocaballi, B 1970, 'Message Bank', Proceedings of the 9th International Conference on Movement and Computing, MOCO '24: 9th International Conference on Movement and Computing, ACM, pp. 1-2.
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Atputhanathan, BK, Vu, TH, Kandasamy, J, Li, Y & Sirivivatnanon, V 1970, 'Optimizing Triple BlendCompositions of Fly Ash,Slag, and OPC forLow-Carbon ConcretePerformance', The Decarbonising Building Industry (DBI) 2024 Conference, Melbourne, Australia.
Ayamga, D, Nanda, P & Mohanty, M 1970, 'The Bell-LaPadula (BLP) Enterprise Security Architecture Model vs Inference Attacks', 2024 17th International Conference on Security of Information and Networks (SIN), 2024 17th International Conference on Security of Information and Networks (SIN), IEEE, pp. 1-8.
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Bae, HY, Saberi, M, Shariflou, S, Kalloniatis, M, Phu, J, Agar, A, Cheraghian, A & Golzan, SM 1970, 'Enhancing Glaucoma Diagnosis through Vision-Language Models and Large Language Model Descriptions', 2024 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2024 International Conference on Digital Image Computing: Techniques and Applications (DICTA), IEEE, pp. 198-205.
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Bano, M, Zowghi, D & Gervasi, V 1970, 'A Vision for Operationalising Diversity and Inclusion in AI', Proceedings of the 2nd International Workshop on Responsible AI Engineering, RAIE '24: 2nd International Workshop on Responsible AI Engineering, ACM, pp. 36-45.
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Bao, S, Guo, J, Lee, HH, Deng, R, Cui, C, Remedios, LW, Liu, Q, Yang, Q, Xu, K, Yu, X, Li, J, Li, Y, Roland, JT, Liu, Q, Lau, KS, Wilson, KT, Coburn, LA, Landman, BA & Huo, Y 1970, 'Mitigating Over-Saturated Fluorescence Images Through a Semi-Supervised Generative Adversarial Network', 2024 IEEE International Symposium on Biomedical Imaging (ISBI), 2024 IEEE International Symposium on Biomedical Imaging (ISBI), IEEE, pp. 1-5.
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Basheer, A, Feng, Y, Ferrie, C & Li, S 1970, 'Ansatz-Agnostic Exponential Resource Saving in Variational Quantum Algorithms Using Shallow Shadows', Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence, Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}, International Joint Conferences on Artificial Intelligence Organization, pp. 3706-3714.
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Variational Quantum Algorithms (VQA) have been identified as a promising candidate for the demonstration of near-term quantum advantage in solving optimization tasks in chemical simulation, quantum information, and machine learning. The standard model of training requires a significant amount of quantum resources, which led researchers to use classical shadows to devise an alternative that consumes exponentially fewer quantum resources. However, the approach only works when the observables are local and the ansatz is the shallow Alternating Layered Ansatz (ALA), thus severely limiting its potential in solving problems such as quantum state preparation, where the ideal state might not be approximable with an ALA. In this work, we present a protocol based on shallow shadows that achieves similar levels of savings for almost any shallow ansatz studied in the literature, when combined with observables of low Frobenius norm. We show that two important applications in quantum information for which VQAs can be a powerful option, namely variational quantum state preparation and variational quantum circuit synthesis, are compatible with our protocol. We also experimentally demonstrate orders of magnitude improvement in comparison to the standard VQA model.
Beyhan, B, Akcomak, IS & Cetindamar, D 1970, 'How do technology-based accelerators build their legitimacy as new organizations in an emerging entrepreneurship ecosystem?', Journal of Entrepreneurship in Emerging Economies, European Academy of Management, Dublin, Ireland, pp. 954-976.
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Purpose: This paper aims to understand technology-based accelerators’ legitimation efforts in an emerging entrepreneurship ecosystem. Design/methodology/approach: This research is based on qualitative inductive methodology using ten Turkish technology-based accelerators. Findings: The 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/value: This 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.
Bharathy, G, Xiaofeng, W, Crawley, FP, Lijin, Y, Ekmekci, PE, Chen, C, CAO, J, Scaplehorn, N, Yadav, G, Bednarczyk, D, Li, Z, Hu, L, Zhu, C, Zhang, L, Li, J & He, N 1970, 'AI Risk: A Systems Perspective', International Seminar on BioData and AI, 2023 ISBA Organization Committee, Shenzhen, China, pp. 17-20.
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While these principles have been establishedas generic guidelines based on learning fromother domains, there is a lack of studies applyingthese principles to actual AI development. Inpractice, managing AI risks from a systemsthinking perspective involves a comprehensiveapproach, starting from problem formulation to alifecycle approach, understanding and identifyinginteractions, hyper-stakeholder management,including participatory modelling, and anticipatorythinking. In this study, we consider the risks and challengesassociated with AI by examining four compellingcase studies that the author had carried out in thepast. These case studies shed light on the potentialpitfalls and complexities that arise in the realm ofAI, allowing us to gain a deeper understanding ofthe risks involved.
Bi, Z, Wan, Y, Wang, Z, Zhang, H, Guan, B, Lu, F, Zhang, Z, Sui, Y, Jin, H & Shi, X 1970, 'Iterative Refinement of Project-Level Code Context for Precise Code Generation with Compiler Feedback', Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp. 2336-2353.
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Large Language Models (LLMs) have shown remarkable progress in automated code generation. Yet, LLM-generated code may contain errors in API usage, class, data structure, or missing project-specific information. As much of this project-specific context cannot fit into the prompts of LLMs, we must find ways to allow the model to explore the project-level code context. We present COCOGEN, a new code generation approach that uses compiler feedback to improve the LLM-generated code. COCOGEN first leverages static analysis to identify mismatches between the generated code and the project's context. It then iteratively aligns and fixes the identified errors using information extracted from the code repository. We integrate COCOGEN with two representative LLMs, i.e., GPT-3.5-Turbo and Code Llama (13B), and apply it to Python code generation. Experimental results show that COCOGEN significantly improves the vanilla LLMs by over 80% in generating code dependent on the project context and consistently outperforms the existing retrieval-based code generation baselines.
Bläser, M, Chen, Z, Duong, DH, Joux, A, Nguyen, T, Plantard, T, Qiao, Y, Susilo, W & Tang, G 1970, 'On Digital Signatures Based on Group Actions: QROM Security and Ring Signatures', Springer Nature Switzerland, pp. 227-261.
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Bourahmoune, K, Ishac, K & Carmichael, M 1970, 'Fitness Activity Recognition Using a Novel Pressure Sensing Mat and Machine Learning for the Future of Accessible Training', Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence, Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}, International Joint Conferences on Artificial Intelligence Organization, pp. 7197-7205.
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Physical inactivity is still a major problem contributing to a growing public health crisis despite a fast-expanding body of technological solutions and wellness research around fitness training. The inaccessibility of professional fitness training remains a leading cause of this gap for reasons encompassing socioeconomic factors, cultural and demographic barriers, and more recently the threat of global pandemics that disrupt traditional modes of staying physically active. Previous lines of work have explored using AI for fitness activity recognition from various sensing modalities such as computer vision, wearable sensors, and force and pressure sensors. However, these works are limited by their feasibility, deployability, and accessibility in real-world scenarios, in addition to the technical challenges faced by each modality for accurate and reliable activity recognition. In this paper, we propose an accessible system for gym activity recognition and correction focusing on foundational fitness activities using ML and a novel pressure sensing mat, and validate its deployability in a real-world use case in a natural gym setting. We present the detailed and previously under-investigated Centre of Pressure (COP) profile of four main gym activities in terms of several COP-related metrics specifically as targets for ML-based recognition tasks. Based on this, we identify COP displacement and COP balance measures as important features for ML-based recognition of these fitness activities for future research in this area. Furthermore, we compare the performance of several ML models in the activity recognition task, achieving 98.5% recognition accuracy using ML models suitable for real-time deployment. Finally, we demonstrate the feasibility of our system in a live real-world with use case in a natural gym environment.
Boyd-Weetman, B, Thomas, P, De Silva, P, Tapas, MJ & Sirivivatnanon, V 1970, 'Assessing the Role of Standard Test Methods in Alkali Content Threshold Determination for ASR Reactive Aggregates', Springer Nature Switzerland, pp. 409-417.
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Boye, T, Lindeck, J, Machet, T, Miao, G, Daniel, S, Goldsmith, R & Cheng, E 1970, 'Collaborative teaching approaches in Engineering and IT', Proceedings of the 35th Australasian Association for Engineering Education Annual Conference, Australasian Association for Engineering Education Annual Conference, Australasian Association for Engineering Education, Christchurch, New Zealand.
Braytee, A, Yang, AS-C, Anaissi, A, Chaturvedi, K & Prasad, M 1970, 'A Novel Dual-Pipeline based Attention Mechanism for Multimodal Social Sentiment Analysis', Companion Proceedings of the ACM Web Conference 2024, WWW '24: The ACM Web Conference 2024, ACM, pp. 1816-1822.
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Büscher, J, Zajackowski, J, Rademacher, H-G, Tillmann, W & Deuse, J 1970, 'Trustworthiness of Artificial Intelligence Applications for Quality Optimisation in Metal Additive Manufacturing', Procedia CIRP, Elsevier BV, pp. 497-502.
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Cai, J, Deng, L, Yang, Y & Li, S 1970, 'Inverse Design of High-Order Bessel Vortex Wave Generator Based on Deep Learning', 2024 International Conference on Microwave and Millimeter Wave Technology (ICMMT), 2024 International Conference on Microwave and Millimeter Wave Technology (ICMMT), IEEE, pp. 1-3.
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Calderon, P & Rizoiu, M-A 1970, 'What Drives Online Popularity: Author, Content or Sharers? Estimating Spread Dynamics with Bayesian Mixture Hawkes', Springer Nature Switzerland, pp. 142-160.
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Candra, H, Yuniati, U & Chai, R 1970, 'The Application of Virtual Reality Using Kinect Sensor in Biomedical and Healthcare Environment: A Review', Springer Nature Singapore, pp. 15-38.
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Cañeda, JD, Hora, JA & Zhu, X 1970, '32.1 dB Gain D-Band Line-Based Power Amplifier for 5G-Advanced Applications in 22nm FDSOI Technology', 2024 23rd International Symposium on Communications and Information Technologies (ISCIT), 2024 23rd International Symposium on Communications and Information Technologies (ISCIT), IEEE, pp. 198-202.
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Capel, T, Ploderer, B, Bircanin, F, Hanmer, S, Yates, JP, Wang, J, Khor, KL, Leong, TW, Wadley, G & Newcomb, M 1970, 'Studying Self-Care with Generative AI Tools: Lessons for Design', Designing Interactive Systems Conference, DIS '24: Designing Interactive Systems Conference, ACM, pp. 1620-1637.
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Castillo, C, Cabezas, V, García, C, Acuna, P, Aguilera, R & Maure, S 1970, 'Selective Harmonic Elimination Model Predictive Control for a 3 level Flying Capacitor Converter', 2024 IEEE Energy Conversion Congress and Exposition (ECCE), 2024 IEEE Energy Conversion Congress and Exposition (ECCE), IEEE, pp. 4598-4603.
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Castillo, C, Cabezas, V, Garcia, C, Acuna, P, Aguilera, R, Araya, B & Sanchez, D 1970, 'Selective Harmonic Elimination Model Predictive Control for a Five-Level Active NPC Converter', 2024 IEEE 9th Southern Power Electronics Conference (SPEC), 2024 IEEE 9th Southern Power Electronics Conference (SPEC), IEEE, pp. 1-6.
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Cetindamar Kozanoglu, D 1970, 'Tools for digital entrepreneurs', Australian Centre for Entrepreneurship Research Exchange, Sydney, Australia, pp. 1-25.
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This paper is a conceptual paper drawing from the literature to shed light on lean startup (LS)practices for digital startups to highlight its value as a tool for entrepreneurs starting digitalventures. Digital entrepreneurs could benefit from various tools developed for startups, such aslean startup (LS) practices that have been promoted to help entrepreneurs fight uncertainties. Ontop of what an ordinary startup could face, digital startups find additional uncertainty challengesarising from the particular characteristics of digital technologies. Most studies investigating theadoption of LS practices focus on software startups in mature entrepreneurial ecosystems,disregarding their applicability for opportunity exploitation in other technological backgrounds.This paper will summarize some tools that might be instrumental for digital startups. By doingso, we draw the attention of entrepreneurs, researchers, and practitioners to tackle the challengesof managing digital startups and equip entrepreneurs to cope with them better by improving theirtoolsets.
Cetindamar, D, Sheriff, R, Sharma, A & Sreekumar, A 1970, 'Setting an Agenda for Technology Professionals to Overcome the Challenges of Earthquakes', 2024 Portland International Conference on Management of Engineering and Technology (PICMET), 2024 Portland International Conference on Management of Engineering and Technology (PICMET), IEEE, pp. 1-6.
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Chamani, S, Lv, X, Bird, TS & Yang, Y 1970, 'Exploring a Dual-Port Open-ended Coaxial Cable and a Multi-Layered Tissue Model for Skin Cancer Detection', 2024 49th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz), 2024 49th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz), IEEE, pp. 1-2.
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Chandanshree, NS, Anitha Kumari, SD & Nimbalkar, S 1970, 'Physical and Mechanical Characterization of Recycled Concrete Aggregates (RCA) as Compacted Fill in Road Pavement', Springer Nature Singapore, pp. 233-244.
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Chauhan, R, Mehta, K, Eiad, Y & Zuhairi, MF 1970, 'Prediction of Autism Spectrum Disorder Using AI and Machine Learning', 2024 18th International Conference on Ubiquitous Information Management and Communication (IMCOM), 2024 18th International Conference on Ubiquitous Information Management and Communication (IMCOM), IEEE, pp. 1-7.
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Chauhan, R, Satyam, A, Yafi, E & Zuhairi, MF 1970, 'Predict the Elderly Fall Using IoT and AI Technology', Springer Nature Singapore, pp. 101-113.
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Chen, C, Liu, Y, Chen, L & Zhang, C 1970, 'Multivariate Traffic Demand Prediction via 2D Spectral Learning and Global Spatial Optimization', Springer Nature Switzerland, pp. 72-88.
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Chen, C, Liu, Y, Chen, L & Zhang, C 1970, 'Test-Time Training for Spatial-Temporal Forecasting', Proceedings of the 2024 SIAM International Conference on Data Mining, SDM 2024, Society for Industrial and Applied Mathematics, pp. 463-471.
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Despite the recent success of deep neural networks in spatial-temporal forecasting, existing methods suffer from distribution shifts between the training and test data, failing to address the non-stationary and abrupt changes at test time. To solve this problem, we propose a novel test-time training framework for spatial-temporal forecasting. Instead of employing a fixed trained model, we adapt the trained model with only one or a mini-batch of test examples to address the test data shifts. The unique spatial structure with hundreds of geographical locations offers an effective batch size to explore the test-time distribution and avoid overfitting. To implement test-time training on spatial-temporal data, we devise a bidirectional cycle-consistent architecture consisting of a forward and a backward cyclic network. Each network has a shared encoder and two direction-aware decoders. At the test time, two self-supervised auxiliary tasks (forward→backward and backward→forward reconstruction) are proposed to adapt the trained model without accessing the target labels. Besides, the bi-cyclic structure of our model can also improve the forecasting task at training time, and ensure consistency between the training and test time. Comprehensive experiments are performed on various spatial-temporal forecasting datasets, demonstrating the effectiveness of the test-time training framework and the bidirectional-cyclic structure.
Chen, G, Wang, F, Li, K, Wu, Z, Fan, H, Yang, Y, Wang, M & Guo, D 1970, 'Prototype Learning for Micro-gesture Classification', CEUR Workshop Proceedings.
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In this paper, we briefly introduce the solution developed by our team, HFUT-VUT, for the track of Micro-gesture Classification in the MiGA challenge at IJCAI 2024. The task of micro-gesture classification task involves recognizing the category of a given video clip, which focuses on more fine-grained and subtle body movements compared to typical action recognition tasks. Given the inherent complexity of micro-gesture recognition, which includes large intra-class variability and minimal inter-class differences, we utilize two innovative modules, i.e., the cross-modal fusion module and prototypical refinement module, to improve the discriminative ability of MG features, thereby improving the classification accuracy. Our solution achieved significant success, ranking 1st in the track of Micro-gesture Classification. We surpassed the performance of last year’s leading team by a substantial margin, improving Top-1 accuracy by 6.13%.
Chen, K, Wen, D, Li, W, Yang, Z & Zhang, W 1970, 'On Compressing Historical Cliques in Temporal Graphs', Springer Nature Singapore, pp. 37-53.
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Chen, K, Wen, D, Zhang, W, Zhang, Y, Wang, X & Lin, X 1970, 'Querying Structural Diversity in Streaming Graphs', Proceedings of the VLDB Endowment, Association for Computing Machinery (ACM), pp. 1034-1046.
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Structural diversity of a vertex refers to the diversity of connections within its neighborhood and has been applied in various fields such as viral marketing and user engagement. The paper studies querying the structural diversity of a vertex for any query time windows in streaming graphs. Existing studies are limited to static graphs which fail to capture vertices' structural diversities in snapshots evolving over time. We design an elegant index structure to significantly reduce the index size compared to the basic approach. We propose an optimized incremental algorithm to update the index for continuous edge arrivals. Extensive experiments on real-world streaming graphs demonstrate the effectiveness of our framework.
Chen, Q, Geng, X, Rosset, C, Buractaon, C, Lu, J, Shen, T, Zhou, K, Xiong, C, Gong, Y, Bennett, P, Craswell, N, Xie, X, Yang, F, Tower, B, Rao, N, Dong, A, Jiang, W, Liu, Z, Li, M, Liu, C, Li, Z, Majumder, R, Neville, J, Oakley, A, Risvik, KM, Simhadri, HV, Varma, M, Wang, Y, Yang, L, Yang, M & Zhang, C 1970, 'MS MARCO Web Search: A Large-scale Information-rich Web Dataset with Millions of Real Click Labels', Companion Proceedings of the ACM Web Conference 2024, WWW '24: The ACM Web Conference 2024, ACM, pp. 292-301.
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Chen, S, Long, G, Jiang, J & Zhang, C 1970, 'Personalized Adapter for Large Meteorology Model on Devices: Towards Weather Foundation Models', Advances in Neural Information Processing Systems.
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This paper demonstrates that pre-trained language models (PLMs) are strong foundation models for on-device meteorological variables modeling. We present LM-WEATHER, a generic approach to taming PLMs, that have learned massive sequential knowledge from the universe of natural language databases, to acquire an immediate capability to obtain highly customized models for heterogeneous meteorological data on devices while keeping high efficiency. Concretely, we introduce a lightweight personalized adapter into PLMs and endows it with weather pattern awareness. During communication between clients and the server, low-rank-based transmission is performed to effectively fuse the global knowledge among devices while maintaining high communication efficiency and ensuring privacy. Experiments on real-wold dataset show that LM-WEATHER outperforms the state-of-the-art results by a large margin across various tasks (e.g., forecasting and imputation at different scales). We provide extensive and in-depth analyses experiments, which verify that LM-WEATHER can (1) indeed leverage sequential knowledge from natural language to accurately handle meteorological sequence, (2) allows each devices obtain highly customized models under significant heterogeneity, and (3) generalize under data-limited and out-of-distribution (OOD) scenarios. Code available on https://github.com/shengchaochen82/LM-Weather.
Chen, S, Long, G, Shen, T, Jiang, J & Zhang, C 1970, 'Federated Prompt Learning for Weather Foundation Models on Devices', Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence, Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}, International Joint Conferences on Artificial Intelligence Organization, pp. 5772-5780.
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On-device intelligence for weather forecasting uses local deep learning models to analyze weather patterns without centralized cloud computing, holds significance for supporting human activates. Federated Learning is a promising solution for such forecasting by enabling collaborative model training without sharing raw data. However, it faces three main challenges that hinder its reliability: (1) data heterogeneity among devices due to geographic differences; (2) data homogeneity within individual devices and (3) communication overload from sending large model parameters for collaboration. To address these challenges, this paper propose Federated Prompt learning for Weather Foundation Models on Devices (FedPoD), which enables devices to obtain highly customized models while maintaining communication efficiency. Concretely, our Adaptive Prompt Tuning leverages lightweight prompts guide frozen foundation model to generate more precise predictions, also conducts prompt-based multi-level communication to encourage multi-source knowledge fusion and regulate optimization. Additionally, Dynamic Graph Modeling constructs graphs from prompts, prioritizing collaborative training among devices with similar data distributions to against heterogeneity. Extensive experiments demonstrates FedPoD leads the performance among state-of-the-art baselines across various setting in real-world on-device weather forecasting datasets.
Chen, Y, Ding, C, Zhu, H, Liu, Y & Guo, YJ 1970, 'A Four-Channel In-Band Full-Duplex (IBFD) Antenna System with Shared Radiation Aperture', 2024 18th European Conference on Antennas and Propagation (EuCAP), 2024 18th European Conference on Antennas and Propagation (EuCAP), IEEE, pp. 1-4.
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Chen, Y, Tuan, HD & Yu, H 1970, 'QoD aware Energy Efficient Active RIS-assisted Multi-User Multi-Stream Precoding by Scalable Complex Iterations', 2024 Tenth International Conference on Communications and Electronics (ICCE), 2024 Tenth International Conference on Communications and Electronics (ICCE), IEEE, pp. 1-6.
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Chen, Y-N, Ding, C, Liu, Y & Guo, YJ 1970, 'A Four-Port Shared-Aperture In-Band Full-Duplex Antenna Array Based on Modes Combination Method', 2024 IEEE International Conference on Computational Electromagnetics (ICCEM), 2024 IEEE International Conference on Computational Electromagnetics (ICCEM), IEEE, pp. 1-3.
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Chen, Z, Grochow, JA, Qiao, Y, Tang, G & Zhang, C 1970, 'On the complexity of isomorphism problems for tensors, groups, and polynomials iii: Actions by classical groups', Leibniz International Proceedings in Informatics, LIPIcs.
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We study the complexity of isomorphism problems for d-way arrays, or tensors, under natural actions by classical groups such as orthogonal, unitary, and symplectic groups. These problems arise naturally in statistical data analysis and quantum information. We study two types of complexitytheoretic questions. First, for a fixed action type (isomorphism, conjugacy, etc.), we relate the complexity of the isomorphism problem over a classical group to that over the general linear group. Second, for a fixed group type (orthogonal, unitary, or symplectic), we compare the complexity of the isomorphism problems for different actions. Our main results are as follows. First, for orthogonal and symplectic groups acting on 3-way arrays, the isomorphism problems reduce to the corresponding problems over the general linear group. Second, for orthogonal and unitary groups, the isomorphism problems of five natural actions on 3-way arrays are polynomial-Time equivalent, and the d-Tensor isomorphism problem reduces to the 3-Tensor isomorphism problem for any fixed d 3. For unitary groups, the preceding result implies that LOCC classification of tripartite quantum states is at least as difficult as LOCC classification of d-partite quantum states for any d. Lastly, we also show that the graph isomorphism problem reduces to the tensor isomorphism problem over orthogonal and unitary groups.
Chen, Z, Yu, W, Ni, Q & Ji, J 1970, 'Learnable Topology Enhanced Heterogeneous Network for Unbalanced Cross-Domain Fault Diagnosis', 2024 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC), 2024 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC), IEEE, pp. 1-7.
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Cheng, X, Wang, J & Sui, Y 1970, 'Precise Sparse Abstract Execution via Cross-Domain Interaction', Proceedings of the IEEE/ACM 46th International Conference on Software Engineering, ICSE '24: IEEE/ACM 46th International Conference on Software Engineering, ACM, pp. 1-12.
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Cheng, X-Y, Ding, C & Ziolkowski, RW 1970, 'Cross-Band De-Scattering and Decoupling for Dual-Band Shared-Aperture Dielectric Resonator Antennas', 2024 IEEE International Symposium on Antennas and Propagation and INC/USNC‐URSI Radio Science Meeting (AP-S/INC-USNC-URSI), 2024 IEEE International Symposium on Antennas and Propagation and INC/USNC‐URSI Radio Science Meeting (AP-S/INC-USNC-URSI), IEEE, pp. 1929-1930.
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Cheng, X-Y, Ding, C & Ziolkowski, RW 1970, 'Cross-Band Interference Mitigation Design for Highly-Integrated Dielectric Resonator Antennas', 2024 IEEE International Conference on Computational Electromagnetics (ICCEM), 2024 IEEE International Conference on Computational Electromagnetics (ICCEM), IEEE, pp. 1-3.
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Chu, Z, Wan, Y, Li, Q, Wu, Y, Zhang, H, Sui, Y, Xu, G & Jin, H 1970, 'Graph Neural Networks for Vulnerability Detection: A Counterfactual Explanation', Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis, ISSTA '24: 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis, ACM, pp. 389-401.
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Clemon, L, Edwards, R & Rizvi, D 1970, 'Material Extrusion Infill Strength Under Different Toolpaths', Volume 1: Additive Manufacturing; Advanced Materials Manufacturing; Biomanufacturing; Life Cycle Engineering, ASME 2024 19th International Manufacturing Science and Engineering Conference, American Society of Mechanical Engineers.
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Abstract Additive manufacturing continues to change many aspects of the manufacturing industry. Although widely implemented in industry for the design and prototype stages of product development, printing speed is the critical factor that prevents the technology from being implemented in medium and large-scale production environments. Recent work developed a novel algorithm using greedy search to reduce extrusionless travel and thus reduces the total print time. The greedy algorithm reorders contour deposition between the slicing and printing processes. The impacts of this reordering of printed line segments is expected to impact mechanical properties in the fabricated parts by affecting the cooling time between layers in different regions differentially. This work evaluates the effect of these toolpath changes on the mechanical properties of 3D printed parts. The new toolpath is compared to a standard toolpath generated from Cura. Of keen interest in additive manufacturing is that of lattice structures for lightweight and strength optimized components. We test three lattice structures under default and improved deposition plans using the same hardware and material. In a lattice structure, defects in individual lattices has a more pronouced impact on macrostructural properties, thus the mechanical properties of lattices fabricated using this local search ordering is tested. We find the local search algorithm, in addition to generating up to 21% quicker prints with 66% less wasted motion, also increases absorbed energy in compression for some structures and has little or no impact on peak force.
Collier-Reed, B, Daniel, S, Dickson-Deane, C, Donald, C, Kloot, B, Machet, T, Mankelow, C & Hu, Y 1970, 'Reflections on the Competencies of the ‘Global Engineer’', Australia Association of Engineering Education Conference 2024, Christchurch.
Cross, Z, Watsford, M & Eager, D 1970, 'A Preliminary Study of Ground Reaction Forces for Summersault Landings: implications for performance and injury risk', 11th Australasian Congress on Applied Mechanics, ACAM 2024, pp. 88-100.
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This study provided an exploratory investigation of the ground reaction forces (GRF) of landing summersaults under repetitive conditions. The peak GRF and rating of perceived exertion (RPE) were assessed in one experienced female gymnast (21 yr, 1.59 m, 62 kg) landing 10 repeated summersaults on three consecutive days. The summersaults were interspersed by 2-minute rest periods which aimed to simulate the repetitions completed in a gymnastics training session to describe the peak and repeated GRF. Peak landing forces of 7107 ± 1214 N were recorded on Day 1. Results from Days 2 (8015 ± 1637 N) and Day 3 (7322 ± 2030 N) demonstrated that fatigue may influence landing performance. Individual trials reached a peak force of 9625 N, which corresponds to >16 times the gymnast’s bodyweight. This value is higher than values reported in the literature. A tendency for a relationship between fatigue and variable landing forces was evident and by Day 3 the gymnast recorded RPE measurements of 7/10 and the landing force ranged between 9 to 14 times the gymnast’s bodyweight. This research utilizes the GRF data to analyze the jerk force of the gymnast’s landing. The jerk forces averaged at 21 k.m/s3 with a peak of 29 k.m/s3, indicating the possibility for large jerk forces to be generated upon landing. This research provided objective measures of the forces associated with landing summersaults. Further, the assessment of repetitive landings provided preliminary insights into the effects of fatigue on landing forces. Fatigue may increase the likelihood of high or variable forces, which might subsequently elevate the injury risk to the lower extremities. Further research with larger sample sizes and other landing conditions are required to further understand fatigue and variability in landings.
Cuzmar, RH, Aguilera, RP, Pereda, J, Poblete, P, Mora, A & Dah-Chuan Lu, D 1970, 'MPC Strategy Applied to Modular Multilevel Matrix Converters for Low-Frequency AC Transmission Systems', 2024 IEEE 9th Southern Power Electronics Conference (SPEC), 2024 IEEE 9th Southern Power Electronics Conference (SPEC), IEEE, pp. 1-6.
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Cuzmar, RH, Aguilera, RP, Pereda, J, Poblete, P, Mora, A & Lu, DD-C 1970, 'MPC-Based Current and Local Cluster Balancing Controls for Low-Frequency AC Transmission Systems', 2024 IEEE 34th Australasian Universities Power Engineering Conference (AUPEC), 2024 IEEE 34th Australasian Universities Power Engineering Conference (AUPEC), IEEE, pp. 1-6.
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Daghriri, I, Liang, CJ & Huang, W 1970, 'Spatial Immersive Learning Environments in Primary Education: A Review of Impacts and Implementation Challenges', Proceedings of the 2024 International Conference on Information Technology, Data Science, and Optimization, I-DO '24: International Conference on Information Technology, Data Science, and Optimization, ACM, pp. 99-105.
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Dai, A & Ying, M 1970, 'QReach: A Reachability Analysis Tool for Quantum Markov Chains', Springer Nature Switzerland, pp. 520-532.
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AbstractWe present QReach, the first reachability analysis tool for quantum Markov chains based on decision diagrams CFLOBDD (presented at CAV 2023). QReach provides a novel framework for finding reachable subspaces, as well as a series of model-checking subprocedures like image computation. Experiments indicate its practicality in verification of quantum circuits and algorithms. QReach is expected to play a central role in future quantum model checkers.
Dang, A & Beydoun, G 1970, 'Capable – A Framework for Assessing Software Architecture Development Capability for Public Health Information Systems Projects', Australasian Conference on Information Systems, AIS, Canberra, Australia, pp. 1-16.
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Context: In the public health domain, the prevalence of unsuccessful Information Systems projects is notable. Technical issues persist beyond the pervasive problems of inflated budgets and extended deadlines. These include poor usability, system instability, suboptimal performance, and data inconsistencies. These undesirable outcomes are linked to the Software Engineering process and the Software Architecture underpinning the system. To mitigate these issues, a project’s capability to achieve Software Architecture quality must be assessed. The socio-technical nature of Information Systems projects in the Public Health Domain necessitates a holistic approach. Aim: To address the need to assess Software Architecture development capability within the context of the Public Health Information Systems. Method: The framework was synthesised and evaluated using Design Science Research. The synthesis incorporated Australian and American Public Health Information Systems failure exemplars and drew upon the existing Software Engineering literature. The framework’s theoretical constructs were evaluated using an unstructured data source (government audit reports of Public Health Information Systems failures). Results: The conceptual aspects of the framework were evaluated. The framework was capable of detecting failure scenarios. Furthermore, the framework provided an indicative capability score and capability grade while providing possible actions for improving the project’s Software Architecture development capability.
Daniel, S, Nikolic, S, Sandison, C, Haque, R, Grundy, S, Belkina, M, Lyden, S, Hassan, GM & Neal, P 1970, 'Engineering Assessment in the Age of Generative Artificial Intelligence: A Critical Analysis', 2024 World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC), 2024 World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC), IEEE, pp. 1-6.
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Dassanayake, C, Kularatna, N, Steyn-Ross, A, Gurusinghe, N & Gunawardane, K 1970, 'Preliminary experiments quantifying the arcing process in a DC circuit breaker development project', 2024 IEEE Applied Power Electronics Conference and Exposition (APEC), 2024 IEEE Applied Power Electronics Conference and Exposition (APEC), IEEE, pp. 2986-2993.
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Dausch, V, Langner, C, Roth, D, Kreimeyer, M & Guertler, MR 1970, 'Investigating low data consistency in work planning processes – causes, measures, and opportunities', Proceedings of the Design Society, Cambridge University Press (CUP), pp. 225-234.
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AbstractDigital transformation increases the need for interdisciplinary collaboration along the product lifecycle. It is currently hindered by a low data consistency resulting from the use of heterogeneous systems and data models. Especially in work planning, where several data models are combined, this decreases efficiency. Systems Lifecycle management (SysLM) offers a solution to this remedy. However, a sudden switch to SysLM is not possible in brownfields. Thus, it is necessary to examine the challenges and opportunities to derive case-specific measures that enable its adoption in work planning.
Davis, BR, Bray, E & Best, G 1970, 'Multi-Goal Path Planning in Cluttered Environments with PRM-Guided Self-Organising Maps', 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, Abu Dhabi, UAE, pp. 10746-10753.
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Deng, J, Shi, K, Huo, H, Wang, D & Xu, G 1970, 'Homogeneous-listing-augmented Self-supervised Multimodal Product Title Refinement', Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2024: The 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, pp. 2870-2874.
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Deng, Z, Jiang, J, Long, G & Zhang, C 1970, 'What Hides behind Unfairness? Exploring Dynamics Fairness in Reinforcement Learning', Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence, Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}, International Joint Conferences on Artificial Intelligence Organization, pp. 3908-3916.
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In sequential decision-making problems involving sensitive attributes like race and gender, reinforcement learning (RL) agents must carefully consider long-term fairness while maximizing returns. Recent works have proposed many different types of fairness notions, but how unfairness arises in RL problems remains unclear. In this paper, we address this gap in the literature by investigating the sources of inequality through a causal lens. We first analyse the causal relationships governing the data generation process and decompose the effect of sensitive attributes on long-term well-being into distinct components. We then introduce a novel notion called dynamics fairness, which explicitly captures the inequality stemming from environmental dynamics, distinguishing it from those induced by decision-making or inherited from the past. This notion requires evaluating the expected changes in the next state and the reward induced by changing the value of the sensitive attribute while holding everything else constant. To quantitatively evaluate this counterfactual concept, we derive identification formulas that allow us to obtain reliable estimations from data. Extensive experiments demonstrate the effectiveness of the proposed techniques in explaining, detecting, and reducing inequality in reinforcement learning. We publicly release code at https://github.com/familyld/InsightFair.
Deuse, J, Wöstmann, R, Syberg, M, West, N, Wagstyl, D & Moreno, VH 1970, 'Establishing a Machine Learning and Internet of Things Learning Infrastructure by Operating Transnational Cyber-Physical Brewing Labs', Springer Nature Switzerland, pp. 171-178.
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Diffen, J, Johnston, A & Sazdov, R 1970, 'Aural Architects: Exploring Professional Practice in Videogame Audio', Audio Mostly 2024 - Explorations in Sonic Cultures, AM '24: Audio Mostly 2024 - Explorations in Sonic Cultures, ACM, pp. 174-180.
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Dinh, PV, Hoang, DT, Uy, NQ, Nguyen, DN, Bao, SP & Dutkiewicz, E 1970, 'Multiple-Input Auto-Encoder for IoT Intrusion Detection Systems with Heterogeneous Data', ICC 2024 - IEEE International Conference on Communications, ICC 2024 - IEEE International Conference on Communications, IEEE, pp. 2707-2712.
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Dinh, TH, Le, CH & Ha, Q 1970, 'UAV Imaging: Correlation Between Contrast and F1-Score for Vision-Based Crack Detection', 2024 IEEE/SICE International Symposium on System Integration (SII), 2024 IEEE/SICE International Symposium on System Integration (SII), IEEE, pp. 657-662.
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Dong, J, Gowda, N, Wang, Y, Choe, M, Alsaid, A, Alvarez, I, Krome, S & Jeon, M 1970, 'Inside Out: Emotion GaRage Vol. V', Adjunct Proceedings of the 16th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI '24: 16th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, ACM, pp. 260-263.
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Dong, LP, Mizuno, R, Trihadi, W & Nguyen, TT 1970, 'Design and Lesson-Learnt of the Eco-Friendly Bamboo Pile Foundation in Soft Soil—A Case Study in Patimban Deep Seaport', Springer Nature Singapore, pp. 1413-1429.
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Dong, S, Zhao, J, Lu, Z, Zhang, JA, Yang, T & Deng, J 1970, 'Signal Subspace Tracking for AoA Estimation in ISAC Systems', 2024 IEEE Wireless Communications and Networking Conference (WCNC), 2024 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, pp. 1-6.
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Du, X, Fang, Z, Diakonikolas, I & Li, Y 1970, 'HOW DOES UNLABELED DATA PROVABLY HELP OUT-OF-DISTRIBUTION DETECTION?', 12th International Conference on Learning Representations, ICLR 2024.
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Using unlabeled data to regularize the machine learning models has demonstrated promise for improving safety and reliability in detecting out-of-distribution (OOD) data. Harnessing the power of unlabeled in-the-wild data is non-trivial due to the heterogeneity of both in-distribution (ID) and OOD data. This lack of a clean set of OOD samples poses significant challenges in learning an optimal OOD classifier. Currently, there is a lack of research on formally understanding how unlabeled data helps OOD detection. This paper bridges the gap by introducing a new learning framework SAL (Separate And Learn) that offers both strong theoretical guarantees and empirical effectiveness. The framework separates candidate outliers from the unlabeled data and then trains an OOD classifier using the candidate outliers and the labeled ID data. Theoretically, we provide rigorous error bounds from the lens of separability and learnability, formally justifying the two components in our algorithm. Our theory shows that SAL can separate the candidate outliers with small error rates, which leads to a generalization guarantee for the learned OOD classifier. Empirically, SAL achieves state-of-the-art performance on common benchmarks, reinforcing our theoretical insights. Code is publicly available at https://github.com/deeplearning-wisc/sal.
Duan, W, Lu, J & Xuan, J 1970, 'Group-Aware Coordination Graph for Multi-Agent Reinforcement Learning', Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence, Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}, International Joint Conferences on Artificial Intelligence Organization, pp. 3926-3934.
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Cooperative Multi-Agent Reinforcement Learning (MARL) necessitates seamless collaboration among agents, often represented by an underlying relation graph. Existing methods for learning this graph primarily focus on agent-pair relations, neglecting higher-order relationships. While several approaches attempt to extend cooperation modelling to encompass behaviour similarities within groups, they commonly fall short in concurrently learning the latent graph, thereby constraining the information exchange among partially observed agents. To overcome these limitations, we present a novel approach to infer the Group-Aware Coordination Graph (GACG), which is designed to capture both the cooperation between agent pairs based on current observations and group-level dependencies from behaviour patterns observed across trajectories. This graph is further used in graph convolution for information exchange between agents during decision-making. To further ensure behavioural consistency among agents within the same group, we introduce a group distance loss, which promotes group cohesion and encourages specialization between groups. Our evaluations, conducted on StarCraft II micromanagement tasks, demonstrate GACG's superior performance. An ablation study further provides experimental evidence of the effectiveness of each component of our method.
Duan, Y, Zhuang, Z, Zhou, J, Chang, Y-C, Wang, Y-K & Lin, C-T 1970, 'Enhancing End-to-End Autonomous Driving Systems Through Synchronized Human Behavior Data', Proceedings of the 1st International Workshop on Brain-Computer Interfaces (BCI) for Multimedia Understanding, MM '24: The 32nd ACM International Conference on Multimedia, ACM, pp. 1-8.
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Duani, G, Torpy, F, Irga, P, Fares, R & Castel, A 1970, 'A state-of-the-art review of hempcrete performance: a critical evaluation of the physical, structural and functional properties', Australian Industrial Hemp Conference, Hunter Valley.
Edwards, R & Clemon, L 1970, 'Improving the strength of Fused Filament Fabrication parts by non-planar alignment of material extrusion with stress vectors', 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE), 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE), IEEE, pp. 2249-2255.
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Elvitigala, DS, Karahanoğlu, A, Matviienko, A, Turmo Vidal, L, Postma, D, Jones, MD, Montoya, MF, Harrison, D, Elbæk, L, Daiber, F, Burr, LA, Patibanda, R, Buono, P, Hämäläinen, P, Van Delden, R, Bernhaupt, R, Ren, X, Van Rheden, V, Zambetta, F, Van Den Hoven, E, Lallemand, C, Reidsma, D & Mueller, FF 1970, 'Grand Challenges in SportsHCI', Proceedings of the CHI Conference on Human Factors in Computing Systems, CHI '24: CHI Conference on Human Factors in Computing Systems, ACM, pp. 1-20.
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Estevez, MP, Gay, V, Leong, TW & Garcia, J 1970, 'Can Asynchronous Exergames Increase Physical Activity in the Elderly?', 2024 IEEE 12th International Conference on Serious Games and Applications for Health (SeGAH), 2024 IEEE 12th International Conference on Serious Games and Applications for Health (SeGAH), IEEE, pp. 1-6.
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Fan, S, Wang, W, Xiao, F, Zhang, S, Zhu, Q & Guan, J 1970, 'Independent Feature Enhanced Crossmodal Fusion for Match-Mismatch Classification of Speech Stimulus and EEG Response', 2024 IEEE 14th International Symposium on Chinese Spoken Language Processing (ISCSLP), 2024 IEEE 14th International Symposium on Chinese Spoken Language Processing (ISCSLP), IEEE, pp. 209-213.
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Fang, J, Fang, N, Huang, F, Zhou, J, Qiao, M & Gao, F 1970, 'Learning Discriminative Style Representations for Unsupervised and Few-Shot Artistic Portrait Drawing Generation', ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp. 3675-3679.
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Fang, S, Yu, X, Wang, Z, Li, S, Kirby, RM & Zhe, S 1970, 'FUNCTIONAL BAYESIAN TUCKER DECOMPOSITION FOR CONTINUOUS-INDEXED TENSOR DATA', 12th International Conference on Learning Representations, ICLR 2024.
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Tucker decomposition is a powerful tensor model to handle multi-aspect data. It demonstrates the low-rank property by decomposing the grid-structured data as interactions between a core tensor and a set of object representations (factors). A fundamental assumption of such decomposition is that there are finite objects in each aspect or mode, corresponding to discrete indexes of data entries. However, real-world data is often not naturally posed in this setting. For example, geographic data is represented as continuous indexes of latitude and longitude coordinates, and cannot fit tensor models directly. To generalize Tucker decomposition to such scenarios, we propose Functional Bayesian Tucker Decomposition (FunBaT). We treat the continuous-indexed data as the interaction between the Tucker core and a group of latent functions. We use Gaussian processes (GP) as functional priors to model the latent functions. Then, we convert each GP into a state-space prior by constructing an equivalent stochastic differential equation (SDE) to reduce computational cost. An efficient inference algorithm is developed for scalable posterior approximation based on advanced message-passing techniques. The advantage of our method is shown in both synthetic data and several real-world applications. We release the code of FunBaT at https://github.com/xuangu-fang/Functional-Bayesian-Tucker-Decomposition.
Fang, W & Ying, M 1970, 'SymPhase: Phase Symbolization for Fast Simulation of Stabilizer Circuits', Proceedings of the 61st ACM/IEEE Design Automation Conference, DAC '24: 61st ACM/IEEE Design Automation Conference, ACM, pp. 1-6.
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Fares, R, Duani, G, Irga, P, Torpy, F, Wilkinson, S, Nair, SG, Georgakopoulos, F, Marosszeky, K & Castel, A 1970, 'Decarbonising Built Environments using Hempcrete and Green Wall Technology', Australian Industrial Hemp Conference, Hunter Valley.
Farhood, H, Najafi, M & Saberi, M 1970, 'Improving Deep Learning Transparency: Leveraging the Power of LIME Heatmap', Springer Nature Singapore, pp. 72-83.
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Fataliyev, K & Liu, W 1970, 'MStoCast: Multimodal Deep Network for Stock Market Forecast', Springer Nature Singapore, pp. 121-136.
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Feng, H, Jia, Y, Xu, R, Prasad, M, Anaissi, A & Braytee, A 1970, 'Integration of Self-supervised BYOL in Semi-supervised Medical Image Recognition', Springer Nature Switzerland, pp. 163-170.
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Feng, J, Tao, C, Geng, X, Shen, T, Xu, C, Long, G, Zhao, D & Jiang, D 1970, 'Synergistic Interplay between Search and Large Language Models for Information Retrieval', Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp. 9571-9583.
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Information retrieval (IR) plays a crucial role in locating relevant resources from vast amounts of data, and its applications have evolved from traditional knowledge bases to modern retrieval models (RMs). The emergence of large language models (LLMs) has further revolutionized the IR field by enabling users to interact with search systems in natural languages. In this paper, we explore the advantages and disadvantages of LLMs and RMs, highlighting their respective strengths in understanding user-issued queries and retrieving up-to-date information. To leverage the benefits of both paradigms while circumventing their limitations, we propose InteR, a novel framework that facilitates information refinement through synergy between RMs and LLMs. InteR allows RMs to expand knowledge in queries using LLM-generated knowledge collections and enables LLMs to enhance prompt formulation using retrieved documents. This iterative refinement process augments the inputs of RMs and LLMs, leading to more accurate retrieval. Experiments on large-scale retrieval benchmarks involving web search and low-resource retrieval tasks show that InteR achieves overall superior zero-shot retrieval performance compared to state-of-the-art methods, even those using relevance judgment. Source code is available at https://github.com/Cyril-JZ/InteR.
Feng, S, Lu, DD-C, Siwakoti, YP, Alam, MM, Hassan, W & Aljarajreh, H 1970, 'Non-Isolated Three-Port Boost H-Bridge Inverter with Hybrid Modulation for Single-Phase Renewable Power Systems', 2024 IEEE 9th Southern Power Electronics Conference (SPEC), 2024 IEEE 9th Southern Power Electronics Conference (SPEC), IEEE, Brisbane, pp. 1-5.
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Feng, X, Zheng, Z, Yu, Y, Wang, X, Wang, J, Han, X, Zhang, Z, Cai, J & Wen, S 1970, 'Optimization for Deep Takagi-Sugeno-Kang Fuzzy Classifier By Self-Adaptive Hybrid Search Evolutionary Algorithm with Competitive Behavior', 2024 International Joint Conference on Neural Networks (IJCNN), 2024 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1-9.
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Francis, B & Litvinov, A 1970, 'Students as Partners: closing the gap between academic studies in engineering education and the first career', 2024 World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC), 2024 World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC), IEEE, pp. 1-6.
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Gao, F, Dai, L, Zhu, J, Du, M, Zhang, Y, Qiao, M, Xia, C, Wang, N & Li, P 1970, 'Human-Robot Interactive Creation of Artistic Portrait Drawings', 2024 IEEE International Conference on Robotics and Automation (ICRA), 2024 IEEE International Conference on Robotics and Automation (ICRA), IEEE, pp. 11297-11304.
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Gao, F, Lin, Y, Shi, J, Qiao, M & Wang, N 1970, 'AesMamba: Universal Image Aesthetic Assessment with State Space Models', Proceedings of the 32nd ACM International Conference on Multimedia, MM '24: The 32nd ACM International Conference on Multimedia, ACM, pp. 7444-7453.
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Garces, K, Xuan, J & Zuo, H 1970, 'Transformed Successor Features for Transfer Reinforcement Learning', Springer Nature Singapore, pp. 298-309.
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Gay, VC, Brookes, W, Branchet, B, Ouvrieu, A & Josserand, E 1970, 'Adapting Studio-Based Learning in Engineering to Incorporate International Perspectives', 2024 World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC), 2024 World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC), IEEE, pp. 1-9.
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Ghantous, GB 1970, 'Enabling Digital Transformation Using DRA', Springer Nature Switzerland, pp. 173-188.
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Ghantous, GB 1970, 'Enhancing devops using ai', CEUR Workshop Proceedings, Ukraine, pp. 133-145.
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This literature review delves into the amalgamation of Artificial Intelligence (AI) technologies with DevOps methodologies to augment software development and deployment processes. The paper explores into the multifaceted contributions of AI across various facets of DevOps, encompassing source code management, continuous integration/continuous deployment (CI/CD) pipelines, deployment infrastructure, software testing frameworks, logging mechanisms, data analysis tools, and comprehensive reporting systems. Furthermore, the research investigates the impact of AI on team communication, collaboration, and workflow orchestration within DevOps environments. Through a meticulous analysis of AI-driven advancements, this review aims to shed light on the symbiotic relationship between AI and DevOps, showcasing their collective potential in fostering efficient, high-quality software delivery pipelines. The insights gleaned from this exploration offer valuable perspectives and opinions for researchers and practitioners seeking to leverage cutting-edge technologies for optimizing their software development lifecycle.
Gharoun, H, Khorshidi, MS, Yazdanjue, N, Chen, F & Gandomi, AH 1970, 'Shadow Gene Guidance: A Novel Approach for Elevating Genetic Programming Classifications and Boosting Predictive Confidence', Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO '24 Companion: Genetic and Evolutionary Computation Conference Companion, ACM, pp. 2095-2098.
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Ghasemi, Z, Neshat, M, Aldrich, C, Karageorgos, J, Zanin, M, Neumann, F & Chen, L 1970, 'Enhanced Genetic Programming Models with Multiple Equations for Accurate Semi-Autogenous Grinding Mill Throughput Prediction', 2024 IEEE Congress on Evolutionary Computation (CEC), 2024 IEEE Congress on Evolutionary Computation (CEC), IEEE, pp. 1-9.
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Ghosh, S, Ramegowda, PC, Goh, DJ, Sharma, J, Koh, Y & Lee, J 1970, 'Temperature Coefficients of Transverse Elastic Properties of Scandium-Doped Aluminum Nitride (ScAlN) Thin Film Grown on Preformed Cavities', 2024 IEEE Ultrasonics, Ferroelectrics, and Frequency Control Joint Symposium (UFFC-JS), 2024 IEEE Ultrasonics, Ferroelectrics, and Frequency Control Joint Symposium (UFFC-JS), IEEE, pp. 1-4.
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Gill, AQ & Bandara, M 1970, 'Using Knowledge Graphs for Architecting and Implementing Air Quality Data Exchange: Australian Context', Proceedings of the 25th Annual International Conference on Digital Government Research, dg.o 2024: 25th Annual International Conference on Digital Government Research, ACM, pp. 534-541.
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Gill, AQ & Hansnata, M 1970, 'Digital Government Ecosystem: Adaptive Architecture for Digital and ICT Investment Decision Making', Proceedings of the 25th Annual International Conference on Digital Government Research, dg.o 2024: 25th Annual International Conference on Digital Government Research, ACM, pp. 555-564.
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Girard, A, Zowghi, D, Bano, M & Riziou, MA 1970, 'Inclusive and Explainable AI Systems: A Systematic Literature Review', Proceedings of the Annual Hawaii International Conference on System Sciences, pp. 1297-1306.
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Explainable AI (XAI) plays a crucial role in enhancing transparency and providing rational explanations to support users of AI systems. Inclusive AI actively seeks to engage and represent individuals with diverse attributes who are affected by and contribute to the AI ecosystem. Both inclusion and XAI advocate for the active involvement of the users and stakeholders during the entire AI system lifecycle. However, the relationship between XAI and Inclusive AI has not been explored. In this paper, we present the results of a systematic literature review with the objective to explore this relationship in the recent AI research literature. We were able to identify 18 research articles on the topic. Our analysis focused on exploring approaches to (1) the human attributes and perspectives, (2) preferred explanation methods, and (3) human-AI interaction. Based on our findings, we identified potential future XAI research directions and proposed strategies for practitioners involved in the design and development of inclusive AI systems.
Goldsmith, R, Boye, T, Daniel, S, Lindeck, J, Machet, T & Miao, G 1970, 'Building collaborative teaching teams across units of study', Proceedings of the 35th Australasian Association for Engineering Education Annual Conference, Australasian Association for Engineering Education Annual Conference, Australasian Association for Engineering Education, Christchurch, New Zealand.
Gong, X, Bisht, N & Xu, G 1970, 'Does Label Smoothing Help Deep Partial Label Learning?', Proceedings of Machine Learning Research, pp. 15823-15838.
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Although deep partial label learning (deep PLL) classifiers have shown their competitive performance, they are heavily influenced by the noisy false-positive labels leading to poorer performance as the training progresses. Meanwhile, existing deep PLL research lacks theoretical guarantee on the analysis of correlation between label noise (or ambiguity degree) and classification performance. This paper addresses the above limitations with label smoothing (LS) from both theoretical and empirical aspects. In theory, we prove lower and upper bounds of the expected risk to show that label smoothing can help deep PLL. We further derive the optimal smoothing rate to investigate the conditions, i.e., when label smoothing benefits deep PLL. In practice, we design a benchmark solution and a novel optimization algorithm called Label Smoothing-based Partial Label Learning (LS-PLL). Extensive experimental results on benchmark PLL datasets and various deep architectures validate that label smoothing does help deep PLL in improving classification performance and learning distinguishable representations, and the best results can be achieved when the empirical smoothing rate approximately approaches the optimal smoothing rate in theoretical findings. Code is publicly available at https://github.com/kalpiree/LS-PLL.
Gong, X, McCarthy, PX, Rizoiu, M-A & Boldi, P 1970, 'Harmony in the Australian Domain Space', ACM Web Science Conference, Websci '24: 16th ACM Web Science Conference, ACM, pp. 92-102.
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In this paper we use for the first time a systematic approach in the study ofharmonic centrality at a Web domain level, and gather a number of significantnew findings about the Australian web. In particular, we explore therelationship between economic diversity at the firm level and the structure ofthe Web within the Australian domain space, using harmonic centrality as themain structural feature. The distribution of harmonic centrality values isanalyzed over time, and we find that the distributions exhibit a consistentpattern across the different years. The observed distribution is well capturedby a partition of the domain space into six clusters; the temporal movement ofdomain names across these six positions yields insights into the AustralianDomain Space and exhibits correlations with other non-structuralcharacteristics. From a more global perspective, we find a significantcorrelation between the median harmonic centrality of all domains in each OECDcountry and one measure of global trust, the WJP Rule of Law Index. Furtherinvestigation demonstrates that 35 countries in OECD share similar harmoniccentrality distributions. The observed homogeneity in distribution presents acompelling avenue for exploration, potentially unveiling critical corporate,regional, or national insights.
Gooch, LJ, Masia, MJ, Stewart, MG & Collard, C 1970, 'Spatial Correlation of Flexural Tensile Bond Strength in Unreinforced Masonry Walls', Springer Nature Singapore, pp. 3-11.
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Grady, SD, Hutt, S, Badillo-Urquiola, K, Osardu, GO-B, Stewart, AEB & Yafi, E 1970, 'Creating an equitable CHI - What does it mean to be an ally?', Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, CHI '24: CHI Conference on Human Factors in Computing Systems, ACM, pp. 1-3.
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Grant, D, Garcia, J & Raffe, W 1970, 'Leaving the NavMesh: An Ablative Analysis of Deep Reinforcement Learning for Complex Navigation in 3D Virtual Environments', Springer Nature Singapore, pp. 286-297.
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Grigoletto, FB, Cedieu, S, Lee, SS & Siwakoti, YP 1970, 'An Eight-Switch Nine-Level Common-Ground Inverter with Four-fold Voltage Gain', 2024 16th Seminar on Power Electronics and Control (SEPOC), 2024 16th Seminar on Power Electronics and Control (SEPOC), IEEE, pp. 1-6.
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Guan, J, Feng, Y, Turrini, A & Ying, M 1970, 'Measurement-Based Verification of Quantum Markov Chains', Springer Nature Switzerland, pp. 533-554.
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AbstractModel-checking techniques have been extended to analyze quantum programs and communication protocols represented as quantum Markov chains, an extension of classical Markov chains. To specify qualitative temporal properties, a subspace-based quantum temporal logic is used, which is built on Birkhoff-von Neumann atomic propositions. These propositions determine whether a quantum state is within a subspace of the entire state space. In this paper, we propose the measurement-based linear-time temporal logic MLTL to check quantitative properties. MLTL builds upon classical linear-time temporal logic (LTL) but introduces quantum atomic propositions that reason about the probability distribution after measuring a quantum state. To facilitate verification, we extend the symbolic dynamics-based techniques for stochastic matrices described by Agrawal et al. (JACM 2015) to handle more general quantum linear operators (super-operators) through eigenvalue analysis. This extension enables the development of an efficient algorithm for approximately model checking a quantum Markov chain against an MLTL formula. To demonstrate the utility of our model-checking algorithm, we use it to simultaneously verify linear-time properties of both quantum and classical random walks. Through this verification, we confirm the previously established advantages discovered by Ambainis et al. (STOC 2001) of quantum walks over classical random walks and discover new phenomena unique to quantum walks.
Guertler, MR, Bauer, P & Burden, A 1970, 'A matrix-based approach to step-wise assess the safety of collaborative robots in manufacturing', Proceedings of the Design Society, Cambridge University Press (CUP), pp. 2555-2564.
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AbstractCollaborative robots (cobots) allow for flexible manufacturing, supporting more customised product designs. Although safety is key for socio-technical human-cobot workplaces, existing safety assessment support like standards and guidelines require extensive experience and can be experienced as overwhelming. To make cobot risk assessments more accessible, especially for novices, and increase traceability from hazard to risk to mitigation, this paper presents a matrix-based approach that decomposes this daunting activity into smaller better manageable steps.
Gunawan, R, Tran, Y, Zheng, J, Nguyen, H, Carrigan, A, Mills, MK & Chai, R 1970, 'Comparing Inclusion Methods on Juxta-pleural into Lung Parenchyma', 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, pp. 1-4.
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Guo, D, Li, X, Li, K, Chen, H, Hu, J, Zhao, G, Yang, Y & Wang, M 1970, 'MAC 2024: Micro-Action Analysis Grand Challenge', Proceedings of the 32nd ACM International Conference on Multimedia, MM '24: The 32nd ACM International Conference on Multimedia, ACM, pp. 11304-11305.
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Guo, K, Lin, H, Grosser, M, Zhang, G & Lu, J 1970, 'Geno-GCN: A Genome-specific Graph Convolutional Network for Diabetes Prediction', 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, pp. 1-4.
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Guo, T, Alexis, S, Boyu, L, Kaveesha, D, Peter, M & Fang, C 1970, 'Identification of Shadow Trading Risks in US Equity Markets via a Spatio-Temporal Graph Attention Network', 5th Annual Boca-ECGI Corporate Finance and Governance Conference, Universidad CEU San Pablo, Madrid, Spain.
Guo, Y, Lei, G & Zhu, J 1970, 'Characterization of Advanced Magnetic Materials for Developing High-Power-Density High-Efficiency Electric Motors for Driving Electric Vehicles', 2024 IEEE Transportation Electrification Conference and Expo, Asia-Pacific (ITEC Asia-Pacific), 2024 IEEE Transportation Electrification Conference and Expo, Asia-Pacific (ITEC Asia-Pacific), IEEE, pp. 863-868.
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HADDAS, M & HUSSAIN, F 1970, 'Technology Factors Affecting Australian Manufacturing SMEs', Communications of International Proceedings, 43th, IBIMA Publishing, Madrid, Spain.
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Within the context of rapidly evolving technologies, collaborative robots (cobots) are revolutionizing the manufacturing processes of small and medium-sized enterprises (SMEs) and transforming the entire work structure. Several studies have highlighted cobots from an individual level; however, little attention has been given to empirical research that focuses on adopting cobots from an organisational perspective in SMEs. This paper aims to focus extensively on understanding technology factors that may potentially affect Cobot’s adoption process within the firm. Based on the Diffusion of Innovation theory (DOI), the paper utilised the five technology factors and followed a semi-structured interview method with decision-makers in Australian manufacturing SMEs. Results discovered the relative advantage revealed is the most significant factor. The other three factors, compatibility, trialability and complexity, were noted to have a lower impact, and the final factor, observability, had an unclear influence. This paper also discussed the mechanisms underlying these impacts and the potential implications.
Hai, NT, Vinh, NT, Vigneswaran, S, Ha, NTH, Hoai, TT & Tuan, NQ 1970, 'Groundwater quality and people’s awareness – A case study in Hoang Tay commune, Kim Bang district, Ha Nam province', IOP Conference Series: Earth and Environmental Science, IOP Publishing, pp. 012011-012011.
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Abstract In this study, groundwater quality in Hoang Tay commune, Kim Bang districts, Ha Nam province was investigated. The results show that the groundwater in this area was highly polluted by arsenic (As), iron (Fe), ammonium (NH4 +), and coliform. Total As concentrations in the groundwater ranged from 0.06 to 0.178 mg/L. Although total As concentrations remarkably decreased after sand filtration (0.013-0.109 mg/L), As in the sand-filtered groundwater was still up to 10 times higher than the As safety limit (0.01 mg/L). In addition, the high concentration of Fe and the Fe/As ratio in Hoang Tay’s groundwater play a critical function in effectively removing As from groundwater. NH4 + level in groundwater was also remarkably high (8.62–58.8 mg/L), which is roundly 28 to 196 times higher than the NH4 + safety standard in Vietnam’s technical guideline on domestic water quality (QCVN 01:2018/BYT). The structured interview showed that most of interviewed people (69%) are aware of groundwater quality issues. However, due to the issues of tap water supply in the area, the majority of the households (78.3%) were still using groundwater for their drinking purposes and other daily activities. There is a high demand for safe water in this commune.
Haider, W & Ha, QP 1970, 'A Lossless Passive Snubber for Soft-Switching of Flyback Converters', 2024 IEEE 19th Conference on Industrial Electronics and Applications (ICIEA), 2024 IEEE 19th Conference on Industrial Electronics and Applications (ICIEA), IEEE, pp. 1-6.
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Hakami, MHM, Chirayath Kurian, J & Beydoun, G 1970, 'Extended Reality in Information Systems Education - Analyzing Technology Readiness Levels', 2024 World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC), 2024 World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC), IEEE, Sydney, pp. 1-8.
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Halder, A, Palaiahnakote, S, Pal, U, Blumenstein, M & Liu, C-L 1970, 'A New Unsupervised Approach for Text Localization in Shaky and Non-shaky Scene Video', Springer Nature Switzerland, pp. 162-179.
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Halkon, B, Darroch, M, Cooper-Woolley, B, Zhao, S, Miller, A, Hanson, D, Marrinan, M, Hendy, A, Parnell, J & Mifsud, S 1970, 'Advancing AI-based Acoustic Classifiers for (Rail) Construction Noise - A Pilot Project', Proceedings of Acoustics 2024: Acoustics in the Sun.
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Recent advances in AI technology have lowered the barrier to entry and cost of ownership of Internet-of-Things (IoT) related sensing. This may present opportunities to supplement or replace the current approaches for construction noise management with dynamic, real-time systems which can provide direct feedback to site managers. Improvements in noise predictions and better real-time tools will also convey strong benefits to surrounding impacted communities. In this paper, a recent pilot project, which aimed to establish that AI is suited to airborne noise data analysis and predictive capability and can be improved with increasing training data in the context of construction noise, will be described. Existing technology developed by SiteHive, which can predict noise sources from audio recordings, was deployed on Sydney Metro construction sites with audio gathered from a number of typical activities. Using accepted measurements of AI model accuracy, it is demonstrated that the Audio Classifier (AC) was able to increase its predictive accuracy from 29% to 81%, within 14 shortlisted classes comprising 10 construction and four non-construction categories. A strong predictive capability and rapid learning with increased body of training data demonstrate the potential of the AC in this and other applications in environmental acoustics.
Halkon, B, Kalhori, H, Lidfors Lindqvist, A & Francis, B 1970, 'Threshold Exams, Viva-Voces and Major Projects for Grade Agency and Assessment Authenticity in Higher Education', Christchurch, New Zealand.
Hanna, B, Xu, G, Wang, X & Hossain, J 1970, 'Leveraging Artificial Intelligence for Affordable and Clean Energy: Advancing UN Sustainable Development Goal 7', 2024 11th International Conference on Behavioural and Social Computing (BESC), 2024 11th International Conference on Behavioural and Social Computing (BESC), IEEE, pp. 1-9.
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Hanna, P, Carmichael, M & Clemon, L 1970, 'Biorobotic Actuator Selection Space Mapping', Volume 1: Acoustics, Vibration, and Phononics; Advanced Design and Information Technologies, ASME 2024 International Mechanical Engineering Congress and Exposition, American Society of Mechanical Engineers.
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Abstract Actuator selection is critical in the design of human compatible robotics, such as prosthetics, exoskeletons, and humanoids. Each has its own set of parameters, from output specifications to package sizing and applicable environmental conditions. A multitude of design factors must be considered in selection, some are dictated by performance relations, while other engineering decisions are latent and unobserved. In biorobotic design, weight is often a key trade-off parameter with actuator performance. We analyze a database of over 1900 motors that are of relevant size for biorobotic designs to identify underlying trends that affect selection options. Selected motors range from 0.000013Nm to 3.66Nm in torque and 0.0016kg to 5.67kg in weight. We then generate Ashby-style charts to evaluate trends across motor selection dimensions. We find a wide disparity between manufacturers and where their actuators are specialized. The results provide a means for rapidly narrowing the selection space for designers, which is shown through an example application and reduces design time and improves the actuator selection.
Haque, AKMN, Sukkar, F, Tanz, L, Carmichael, MG & Vidal-Calleja, T 1970, 'Constrained Bootstrapped Learning for Few-Shot Robot Skill Adaptation', 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, pp. 5189-5194.
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Havaei, Z, Saberi, M & Hussain, OK 1970, 'Evaluation of Large Language Model Responses for 32 Diverse Personality Types Using the Best Worst Method (BWM)', 2024 IEEE International Conference on e-Business Engineering (ICEBE), 2024 IEEE International Conference on e-Business Engineering (ICEBE), IEEE, pp. 262-271.
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He, Y, Ding, C, Wei, G & Guo, YJ 1970, 'A Bowl-Shaped Base Station Antenna with Wideband Cross-Band De-Scattering Capability', 2024 IEEE International Symposium on Antennas and Propagation and INC/USNC‐URSI Radio Science Meeting (AP-S/INC-USNC-URSI), 2024 IEEE International Symposium on Antennas and Propagation and INC/USNC‐URSI Radio Science Meeting (AP-S/INC-USNC-URSI), IEEE, pp. 2197-2198.
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He, Y, Wang, K, Zhang, W, Lin, X, Ni, W & Zhang, Y 1970, 'Butterfly Counting over Bipartite Graphs with Local Differential Privacy', 2024 IEEE 40th International Conference on Data Engineering (ICDE), 2024 IEEE 40th International Conference on Data Engineering (ICDE), IEEE, pp. 2351-2364.
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Heon Lee, JJ, Yoo, C, Anstee, S & Fitch, R 1970, 'Multi-query TDSP for Path Planning in Time-varying Flow Fields', 2024 IEEE International Conference on Robotics and Automation (ICRA), 2024 IEEE International Conference on Robotics and Automation (ICRA), IEEE, pp. 7005-7011.
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Hoang, T-D, Huang, X & Qin, P 1970, 'Low-Complexity Compressed Sensing-Aided Coherent Direction-of-Arrival Estimation for Large-Scale Lens Antenna Array', ICC 2024 - IEEE International Conference on Communications, ICC 2024 - IEEE International Conference on Communications, IEEE, pp. 1-6.
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Horton, B & Wei, D 1970, 'Virtual Labs for Mechanical dynamics – a distraction OR a catalyst for better learning', 2024 World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC), 2024 World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC), IEEE, pp. 1-6.
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Hossain Sakib, MK, Islam, MR, Akter Prome, S, Nguyen, TTL, Asirvatham, D, Ari Ragavan, N, Wang, X & Sanin, C 1970, 'MVis4LD: Multimodal Visual Interactive System for Lie Detection', Springer Nature Singapore, pp. 28-43.
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Hossain, MI & Eager, D 1970, 'Rapid quadrupedal locomotion: a study of greyhound galloping mechanics', The 11th Australasian Congress on Applied Mechanics, Brisbane, Australia.
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Greyhounds can run very fast with their galloping gait mechanics. The galloping gait of the greyhound is remarkable for gait efficiency among quadrupedal as it is believed that the biomechanical structure supporting the mass is decoupled from the locomotive structure. With the help of rotatory galloping, a greyhound achieves the double suspension gait which is the fastest gait but also exhausting one. In this study, using a high frame rates camera with 250 - 500 frames per second motion images of the greyhound rotatory gallop mechanism were captured in slow motion in the straight run chase for studying the rotatory galloping mechanism and features. By carefully setting up a video image tracking software and a frame of reference, the greyhound galloping gait paws motion was analysed for relative displacement, speed and acceleration to infer kinetic energy in the gait. Likewise, using the manual and auto-tracking functions of the software, the galloping-related relative position of the hard points was analysed for pose and gait study. The motion tracking study of the greyhound galloping mechanism allowed a better understanding of the greyhound galloping mechanism and also the underlying functions and characteristics of greyhound rotatory galloping. Through the study, it is also shown that the contribution of different elements of rotatory galloping for greyhound locomotion. This study could give future directions to efficient robot locomotion and injury prevention mechanisms for fast runners.
Hu, J, Guo, D, Li, K, Si, Z, Yang, X & Wang, M 1970, 'Maskable Retentive Network for Video Moment Retrieval', Proceedings of the 32nd ACM International Conference on Multimedia, MM '24: The 32nd ACM International Conference on Multimedia, ACM, pp. 1476-1485.
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Hu, K, Li, L, Xie, Q, Tao, X & Xu, G 1970, 'CrimeAlarm: Towards Intensive Intent Dynamics in Fine-Grained Crime Prediction', Springer Nature Singapore, pp. 104-120.
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Hu, Y, McFarlane, A & Hussain, F 1970, 'CarbonApp: Blockchain Enabled Carbon Offset Project Management', Springer Nature Switzerland, pp. 13-25.
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Hu, Y, Wu, K, Zhang, JA, Deng, W & Guo, YJ 1970, 'Performance Bounds for CSI-Ratio Based Bi-Static Doppler Sensing in ISAC Systems', 2024 IEEE International Conference on Communications Workshops (ICC Workshops), 2024 IEEE International Conference on Communications Workshops (ICC Workshops), IEEE, pp. 1535-1540.
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Huang, H, Chang, X, Hu, W & Yao, L 1970, 'MatchNAS: Optimizing Edge AI in Sparse-Label Data Contexts via Automating Deep Neural Network Porting for Mobile Deployment', Proceedings of the ACM Web Conference 2024, WWW '24: The ACM Web Conference 2024, ACM, pp. 2965-2975.
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Huang, Y, Zhang, Z, Wu, Q, Zhong, Y & Wang, L 1970, 'Attribute-Guided Pedestrian Retrieval: Bridging Person Re-ID with Internal Attribute Variability', 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, pp. 17689-17699.
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Hui, K-P, Phillips, D, Kekirigoda, A, Allwright, A, Zhang, JA, Zhang, H, Le, AT & Jayawickrama, BA 1970, 'Unveiling MIMO Potential: A Prototype for Enhanced Tactical Communications with Interference Suppression', MILCOM 2024 - 2024 IEEE Military Communications Conference (MILCOM), MILCOM 2024 - 2024 IEEE Military Communications Conference (MILCOM), IEEE, pp. 1-6.
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Hull, R, Moratuwage, D, Scheide, E, Fitch, R & Best, G 1970, 'Communicating Intent as Behaviour Trees for Decentralised Multi-Robot Coordination', 2024 IEEE International Conference on Robotics and Automation (ICRA), 2024 IEEE International Conference on Robotics and Automation (ICRA), IEEE, pp. 7215-7221.
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Huo, H, Ding, X, Guo, Z, Shen, S, Ye, D, Pham, O, Milne, D, Mathieson, L & Gardner, A 1970, 'Accelerating Learning with AI: Improving Students’ Capability to Receive and Build Automated Feedback for Programming Courses', 2024 World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC), 2024 World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC), IEEE, pp. 1-9.
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Hussain, A, Hossain, MJ, Aguilera, RP & Leiva, RC 1970, 'A Bumpless Transition Strategy for Efficient Partial Shading Detection in PV Systems', 2024 IEEE 15th International Symposium on Power Electronics for Distributed Generation Systems (PEDG), 2024 IEEE 15th International Symposium on Power Electronics for Distributed Generation Systems (PEDG), IEEE, pp. 1-6.
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Indrajith, B, Gunawardane, K, Li, L, Zamora, R, Hossain, A & Nicholson, R 1970, 'Operation Comparison of Hydrogen-DC Microgrid in Grid-Connected and Islanded Scenarios', 2024 18th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), 2024 18th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), IEEE, pp. 1-6.
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Ivanyos, G, Mendoza, EJ, Qiao, Y, Sun, X & Zhang, C 1970, 'Faster Isomorphism Testing of $p$-Groups of Frattini Class 2', 2024 IEEE 65th Annual Symposium on Foundations of Computer Science (FOCS), 2024 IEEE 65th Annual Symposium on Foundations of Computer Science (FOCS), IEEE, pp. 1408-1424.
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Jaiswal, A, Roy, P, Bugalia, N, Varghese, K & Ha, QP 1970, 'Framework to Identify Directions for Future Construction and Demolition Waste Management Technologies', E3S Web of Conferences, EDP Sciences, pp. 04005-04005.
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Effective waste management is essential for sustainable urban development, and Construction and Demolition (C&D) waste poses a significant challenge due to its volume and composition in urban regions. In recent years, technological advancements have offered innovative solutions to improve the management of C&D waste. This exploratory study primarily uses secondary data and draws on case studies from 5 Indian cities and global literature on technological innovations in C&D waste management to propose a preliminary framework for identifying how different technologies can play a vital role and where they can be incorporated into the reverse supply chain of C&D waste in the Indian urban context. This paper paves the road for future research that will use this initial framework to identify more practical technological solutions based on a thorough understanding of ground reality, improving chances of technology adoption.
Jakubowski, K, Vohradsky, J, Filipev, I, Tran, L, Garbe, U, Olsen, S, Franklin, D, Stopic, A, Bevitt, J, Rosenfeld, A, Guatelli, S & Safavi-Naeini, M 1970, 'Monte Carlo Simulation Model of the Dingo Thermal Neutron Imaging Beamline at ANSTO', 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD), 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD), IEEE, pp. 1-2.
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Jayasuriya, M, Hu, G, Le, DDK, Ang, K, Sankaran, S & Liu, D 1970, 'A 3D Vector Field and Gaze Data Fusion Framework for Hand Motion Intention Prediction in Human-Robot Collaboration', 2024 IEEE International Conference on Robotics and Automation (ICRA), 2024 IEEE International Conference on Robotics and Automation (ICRA), IEEE, pp. 5637-5643.
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Ji, C, Wu, J, Leung Lee, AT & Lee, C-K 1970, 'Isolated Single-Input Multiple-Output DC-AC Inverter for Multi-Coil Wireless Power Transfer', 2024 IEEE Wireless Power Technology Conference and Expo (WPTCE), 2024 IEEE Wireless Power Technology Conference and Expo (WPTCE), IEEE, pp. 774-777.
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Jia, C, Luo, M, Chang, X, Dang, Z, Han, M, Wang, M, Dai, G, Dang, S & Wang, J 1970, 'Generating Action-conditioned Prompts for Open-vocabulary Video Action Recognition', Proceedings of the 32nd ACM International Conference on Multimedia, MM '24: The 32nd ACM International Conference on Multimedia, ACM, pp. 4640-4649.
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Jiang, W, Gao, X, Xu, G, Chen, T & Yin, H 1970, 'Challenging Low Homophily in Social Recommendation', Proceedings of the ACM Web Conference 2024, WWW '24: The ACM Web Conference 2024, ACM, pp. 3476-3484.
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Jiang, X, Liu, F, Fang, Z, Chen, H, Liu, T, Zheng, F & Han, B 1970, 'NEGATIVE LABEL GUIDED OOD DETECTION WITH PRETRAINED VISION-LANGUAGE MODELS', 12th International Conference on Learning Representations, ICLR 2024.
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Out-of-distribution (OOD) detection aims at identifying samples from unknown classes, playing a crucial role in trustworthy models against errors on unexpected inputs. Extensive research has been dedicated to exploring OOD detection in the vision modality. Vision-language models (VLMs) can leverage both textual and visual information for various multi-modal applications, whereas few OOD detection methods take into account information from the text modality. In this paper, we propose a novel post hoc OOD detection method, called NegLabel, which takes a vast number of negative labels from extensive corpus databases. We design a novel scheme for the OOD score collaborated with negative labels. Theoretical analysis helps to understand the mechanism of negative labels. Extensive experiments demonstrate that our method NegLabel achieves state-of-the-art performance on various OOD detection benchmarks and generalizes well on multiple VLM architectures. Furthermore, our method NegLabel exhibits remarkable robustness against diverse domain shifts. The codes are available at https://github.com/tmlr-group/NegLabel.
Jiang, X, Ma, W, Duan, Y, Do, T & Lin, C-T 1970, 'Contrastive Masked Autoencoders for Character-Level Open-Set Writer Identification', 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE, pp. 4581-4587.
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Jiang, Y, Xiong, J, Wu, J & Liu, B 1970, 'Random Matrix Theory Based Radio Frequency Fingerprinting Identification of WiFi Signal', 2024 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), 2024 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), IEEE, pp. 1-5.
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Kabir, ZS, Kang, K & Sohaib, O 1970, 'Perceived Subjective Norms Influence Continuance Intention in Augmented Reality Platforms: A User Experience Study', 30th Americas Conference on Information Systems, AMCIS 2024, Americas Conference on Information Systems (AMCIS) 2024, Association of Information (AIS), Salt Lake City, Utah, USA.
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Augmented reality (AR) changes sources of information to provide user comfort in e-commerce mobile platforms. These sources of information enhance user experience (UX) in the new interaction paradigm through AR. It demands understanding the impact of different sources of information in perceiving UX that influences continuance intention. This study investigates a nuanced understanding of how sources of information affect users' subjective norms, influencing continuance intention in Australia. A quantitative method was used to validate a conceptual model followed by the stimulus-organism-response (SOR) framework. Based on our online survey with 886 responses, we assessed the influences of interactivity, insight experience, and online reviews in perceiving subjective norms. We investigated their impact on continuance intention in an augmented reality environment. Our findings confirm that sources of information, especially online reviews, positively affect in perceiving subjective norms. Also, trust has a more significant influence on the continuance intention to use AR mobile platforms.
Karmokar, DK, Thalakotuna, DN & Esselle, KP 1970, 'Continuous Scanning Leaky-Wave Antenna Using Partially Radiating Substrate Integrated Waveguide', 2024 IEEE International Symposium on Antennas and Propagation and INC/USNC‐URSI Radio Science Meeting (AP-S/INC-USNC-URSI), 2024 IEEE International Symposium on Antennas and Propagation and INC/USNC‐URSI Radio Science Meeting (AP-S/INC-USNC-URSI), IEEE, pp. 1013-1014.
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Keshavarz, R, Abdullah, A & Shariati, N 1970, 'Autonomous Nonlinear Passive Transmit-Receive Switch for Compact IoT Devices: A Three-Port Agile Network', 2024 54th European Microwave Conference (EuMC), 2024 54th European Microwave Conference (EuMC), IEEE, pp. 76-79.
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Khatkar, J, Sukkar, F, Clemon, L & Mettu, R 1970, 'Coordinated Multi-arm 3D Printing using Reeb Decomposition', 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, pp. 14212-14218.
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Khoa, TV, Son, DH, Hoang, DT, Trung, NL, Thuy Quynh, TT, Nguyen, DN, Ha, NV & Dutkiewicz, E 1970, 'Real-time Cyberattack Detection with Collaborative Learning for Blockchain Networks', 2024 IEEE Wireless Communications and Networking Conference (WCNC), 2024 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, pp. 1-6.
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Khorshidi, MS, Yazdanjue, N, Gharoun, H, Yazdani, D, Nikoo, MR, Chen, F & Gandomi, AH 1970, 'Enhancing Classification Through Multi-view Synthesis in Multi-Population Ensemble Genetic Programming', Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO '24 Companion: Genetic and Evolutionary Computation Conference Companion, ACM, pp. 2099-2102.
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Kim, J, Xuan, J, Liang, C & Hussain, F 1970, 'Decoupling Exploration and Exploitation for Unsupervised Pre-training with Successor Features', 2024 International Joint Conference on Neural Networks (IJCNN), 2024 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1-8.
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King, I, Long, G, Xu, Z & Yu, H 1970, 'FL@FM-TheWebConf'24: International Workshop on Federated Foundation Models for the Web', Companion Proceedings of the ACM Web Conference 2024, WWW '24: The ACM Web Conference 2024, ACM, pp. 1546-1547.
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Kiyani, A, Abbas, SM, Matekovits, L, Mahmoud, A & Esselle, KP 1970, 'Enhancing Radiation Characteristics of Antenna Arrays Over a Sparse Area', 2024 IEEE International Symposium on Antennas and Propagation and INC/USNC‐URSI Radio Science Meeting (AP-S/INC-USNC-URSI), 2024 IEEE International Symposium on Antennas and Propagation and INC/USNC‐URSI Radio Science Meeting (AP-S/INC-USNC-URSI), IEEE, pp. 311-312.
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Krikke, T, Brown, G & Parnell, J 1970, 'Designing and Complying with the Acoustic Objectives for Underground Railway Stations', Proceedings of Acoustics 2024: Acoustics in the Sun.
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The four largest capital cities in Australia are currently constructing and commissioning what tend to be their biggest CBD public rail transportation infrastructure projects in a generation. These projects all come with significant acoustic challenges, some of which are well planned for, and some which emerge during later stages of construction and commissioning. With a focus on the diverse and acoustically complex architecture associated with rail stations, the aim of this paper is to examine how some of the challenges posed by fixed station facilities have been identified and practical solutions were developed by the various acoustic teams charged with testing and commissioning of these operational stations. The aim of this paper is to discuss lessons learnt from the commissioning across multiple station sites and working with multiple contractors. These lessons will potentially inform the design and setting of acoustic objectives for future underground railway (station) projects in Australia.
Krishnan, A, Thiyagarajan, K, Bhattacharjee, M & An, Y 1970, 'Performance Evaluation of a Skin Conformable Polymer-based Flexible Temperature Sensor', 2024 IEEE SENSORS, 2024 IEEE SENSORS, IEEE, pp. 1-4.
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Kulbacki, M, Chaczko, Z, Barton, S, Wajs-Chaczko, P, Nikodem, J, Rozenblit, JW, Klempous, R, Ito, A & Kulbacki, M 1970, 'A Review of the Weaponization of IoT: Security Threats and Countermeasures', 2024 IEEE 18th International Symposium on Applied Computational Intelligence and Informatics (SACI), 2024 IEEE 18th International Symposium on Applied Computational Intelligence and Informatics (SACI), IEEE, pp. 000279-000284.
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Kumari, G, Adak, C & Ekbal, A 1970, 'Mu2STS: A Multitask Multimodal Sarcasm-Humor-Differential Teacher-Student Model for Sarcastic Meme Detection', Springer Nature Switzerland, pp. 19-37.
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Kurian, JC, John, BM & Beydoun, G 1970, 'Digital Workplace Components and their Value in Emergency Services', 30th Americas Conference on Information Systems, AMCIS 2024, The America's Conference on Information Systems, Association for Information Systems, Salt Lake City, Utah.
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The digital workplace, which is characterized by its components - physical space, technology, and people, plays a significant role in the digital transformation of organizations. The purpose of this study is to identify the components of a digital workplace in a community-based emergency service organization and to understand its value to various stakeholders. Most existing studies in emergency services discuss general benefits without relating them to specific components of the digital workplace or their specific value for stakeholders. This study is based on data collected from the New South Wales - Rural Fire Services (NSW-RFS) and uses the case study methodology. As a result of our analysis, we identify three values (social, emotional, and functional) eventuating from the digital workplace components in a community-based emergency service organization. The physical space component of the digital workplace generates social value, the technology component generates functional, social, and emotional values, and the people component generates mainly functional value for stakeholders. It was evident that technology provides all three types of values for stakeholders when compared with the other two components of digital workplace. Theoretical and practical implications are discussed.
Laccone, F, Pietroni, N, Froli, M, Cignoni, P & Malomo, L 1970, 'Statics and Stability of Bending-Optimized Double-Layer Grid Shell', Springer Nature Switzerland, pp. 569-578.
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Lai, J & Yang, Y 1970, '3D Printed Multifocal Lens Antenna for Terahertz Communication', 2024 49th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz), 2024 49th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz), IEEE, pp. 1-2.
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Lama, S & Pradhan, S 1970, 'Use of Affordance in Design Science Research: A Systematic Literature Review and Research Agenda', Australasian Conference on Information Systems, Canberra.
Lama, S & Pradhan, S 1970, 'Use of Affordance in Design Science Research: A Systematic Literature Review and Research Agenda', Australasian Conference on Information Systems, Canberra.
Lan, H, Zhu, Q, Guan, J, Wei, Y & Wang, W 1970, 'Hierarchical Metadata Information Constrained Self-Supervised Learning for Anomalous Sound Detection under Domain Shift', ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE.
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Langner, C, Paliyenko, Y, Müller, B, Roth, D, Guertler, MR & Kreimeyer, M 1970, 'Challenges for capturing data within data-driven design processes', Proceedings of the Design Society, Cambridge University Press (CUP), pp. 2099-2108.
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AbstractCyber-Physical-Systems provide extensive data gathering opportunities along the lifecycle, enabling data-driven design to improve the design process. However, its implementation faces challenges, particularly in the initial data capturing stage. To identify those, a comprehensive approach combining a systematic literature review and an industry survey was applied. Four groups of interrelated challenges were identified as most relevant to practitioners: data selection, data availability in systems, knowledge about data science processes and tools, and guiding users in targeted data capturing.
Le Gentil, C, Falque, R & Vidal-Calleja, T 1970, 'Real-Time Truly-Coupled Lidar-Inertial Motion Correction and Spatiotemporal Dynamic Object Detection', 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, pp. 12565-12572.
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Le, L, Zhao, G, Zhang, X, Zuccon, G & Demartini, G 1970, 'CoLAL: Co-learning Active Learning for Text Classification', Proceedings of the AAAI Conference on Artificial Intelligence, AAAI, Association for the Advancement of Artificial Intelligence (AAAI), Vancouver, pp. 13337-13345.
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In the machine learning field, the challenge of effectively learning with limited data has become increasingly crucial. Active Learning (AL) algorithms play a significant role in this by enhancing model performance. We introduce a novel AL algorithm, termed Co-learning (CoLAL), designed to select the most diverse and representative samples within a training dataset. This approach utilizes noisy labels and predictions made by the primary model on unlabeled data. By leveraging a probabilistic graphical model, we combine two multi-class classifiers into a binary one. This classifier determines if both the main and the peer models agree on a prediction. If they do, the unlabeled sample is assumed to be easy to classify and is thus not beneficial to increase the target model's performance. We prioritize data that represents the unlabeled set without overlapping decision boundaries. The discrepancies between these boundaries can be estimated by the probability that two models result in the same prediction. Through theoretical analysis and experimental validation, we reveal that the integration of noisy labels into the peer model effectively identifies target model's potential inaccuracies. We evaluated the CoLAL method across seven benchmark datasets: four text datasets (AGNews, DBPedia, PubMed, SST-2) and text-based state-of-the-art (SOTA) baselines, and three image datasets (CIFAR100, MNIST, OpenML-155) and computer vision SOTA baselines. The results show that our CoLAL method significantly outperforms existing SOTA in text-based AL, and is competitive with SOTA image-based AL techniques.
Le, TH, Nguyen, HAD, Barthelemy, X, Nguyen, TT, Ha, QP, Jiang, N, Duc, H, Azzi, M & Riley, M 1970, 'Visualization Platform for Multi-Scale Air Pollution Monitoring and Forecast', 2024 IEEE/SICE International Symposium on System Integration (SII), 2024 IEEE/SICE International Symposium on System Integration (SII), IEEE, pp. 01-06.
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Lei, CL & Dickson-Deane, C 1970, 'Interface Design for Educational Chatbot to Increase Engagement for Online Learning: A Conceptual Design', Springer Nature Switzerland, pp. 38-52.
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Lei, Y, Cao, Y, Zhou, T, Shen, T & Yates, A 1970, 'Corpus-Steered Query Expansion with Large Language Models', EACL 2024 - 18th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference, pp. 393-401.
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Recent studies demonstrate that query expansions generated by large language models (LLMs) can considerably enhance information retrieval systems by generating hypothetical documents that answer the queries as expansions. However, challenges arise from misalignments between the expansions and the retrieval corpus, resulting in issues like hallucinations and outdated information due to the limited intrinsic knowledge of LLMs. Inspired by Pseudo Relevance Feedback (PRF), we introduce Corpus-Steered Query Expansion (CSQE) to promote the incorporation of knowledge embedded within the corpus. CSQE utilizes the relevance assessing capability of LLMs to systematically identify pivotal sentences in the initially-retrieved documents. These corpus-originated texts are subsequently used to expand the query together with LLM-knowledge empowered expansions, improving the relevance prediction between the query and the target documents. Extensive experiments reveal that CSQE exhibits strong performance without necessitating any training, especially with queries for which LLMs lack knowledge.
Lei, Y, Wu, D, Zhou, T, Shen, T, Cao, Y, Tao, C & Yates, A 1970, 'Meta-Task Prompting Elicits Embeddings from Large Language Models', Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, pp. 10141-10157.
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We introduce a new unsupervised text embedding method, Meta-Task Prompting with Explicit One-Word Limitation (MetaEOL), for generating high-quality sentence embeddings from Large Language Models (LLMs) without the need for model fine-tuning. Leveraging meta-task prompting, MetaEOL guides LLMs to produce embeddings through a series of carefully designed prompts that address multiple representational aspects. Our comprehensive experiments demonstrate that embeddings averaged from various meta-tasks are versatile embeddings that yield competitive performance on Semantic Textual Similarity (STS) benchmarks and excel in downstream tasks, surpassing contrastive-trained models. Our findings suggest a new scaling law, offering a versatile and resource-efficient approach for embedding generation across diverse scenarios.
Li, A, Yang, B, Huo, H, Hussain, FK & Xu, G 1970, 'Structure- and Logic-Aware Heterogeneous Graph Learning for Recommendation', 2024 IEEE 40th International Conference on Data Engineering (ICDE), 2024 IEEE 40th International Conference on Data Engineering (ICDE), IEEE, pp. 544-556.
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Li, E, Ouyang, J, Xiang, S, Qin, L & Chen, L 1970, 'Relation-Aware Heterogeneous Graph Neural Network for Fraud Detection', Springer Nature Singapore, pp. 240-255.
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Li, F, Wang, X, Cheng, D, Zhang, W, Zhang, Y & Lin, X 1970, 'Hypergraph Self-supervised Learning with Sampling-efficient Signals', IJCAI International Joint Conference on Artificial Intelligence, pp. 4398-4406.
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Self-supervised learning (SSL) provides a promising alternative for representation learning on hypergraphs without costly labels. However, existing hypergraph SSL models are mostly based on contrastive methods with the instance-level discrimination strategy, suffering from two significant limitations: (1) They select negative samples arbitrarily, which is unreliable in deciding similar and dissimilar pairs, causing training bias. (2) They often require a large number of negative samples, resulting in expensive computational costs. To address the above issues, we propose SE-HSSL, a hypergraph SSL framework with three sampling-efficient self-supervised signals. Specifically, we introduce two sampling-free objectives leveraging the canonical correlation analysis as the node-level and group-level self-supervised signals. Additionally, we develop a novel hierarchical membership-level contrast objective motivated by the cascading overlap relationship in hypergraphs, which can further reduce membership sampling bias and improve the efficiency of sample utilization. Through comprehensive experiments on 7 real-world hypergraphs, we demonstrate the superiority of our approach over the state-of-the-art method in terms of both effectiveness and efficiency.
Li, H, Wang, X, Yu, G, Ni, W, Liu, RP, Georgalas, N & Reeves, A 1970, 'Smart Contract Vulnerability Detection Based on Generative Adversarial Networks and Graph Matching Networks', Springer Nature Singapore, pp. 269-283.
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Li, K, Gong, X, Wu, J & Hu, W 1970, 'Contrastive Learning Drug Response Models from Natural Language Supervision', Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence, Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}, International Joint Conferences on Artificial Intelligence Organization, pp. 2126-2134.
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Deep learning-based drug response prediction (DRP) methods can accelerate the drug discovery process and reduce research and development costs. Despite their high accuracy, generating regression-aware representations remains challenging for mainstream approaches. For instance, the representations are often disordered, aggregated, and overlapping, and they fail to characterize distinct samples effectively. This results in poor representation during the DRP task, diminishing generalizability and potentially leading to substantial costs during the drug discovery. In this paper, we propose CLDR, a contrastive learning framework with natural language supervision for the DRP. The CLDR converts regression labels into text, which is merged with the drug response caption as a second sample modality instead of the traditional modes, i.e., graphs and sequences. Simultaneously, a common-sense numerical knowledge graph is introduced to improve the continuous text representation. Our framework is validated using the genomics of drug sensitivity in cancer dataset with average performance increases ranging from 7.8% to 31.4%. Furthermore, experiments demonstrate that the proposed CLDR effectively maps samples with distinct label values into a high-dimensional space. In this space, the sample representations are scattered, significantly alleviating feature overlap. The code is available at: https://github.com/DrugD/CLDR.
Li, M, Chen, S-L & Guo, YJ 1970, 'Frequency-Controlled Polarization Reconfigurable Antenna: Concept and Design', 2024 IEEE International Symposium on Antennas and Propagation and INC/USNC‐URSI Radio Science Meeting (AP-S/INC-USNC-URSI), 2024 IEEE International Symposium on Antennas and Propagation and INC/USNC‐URSI Radio Science Meeting (AP-S/INC-USNC-URSI), IEEE, pp. 395-396.
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Li, S, Ma, Y, Ma, K, Liu, W, Li, N, Liu, X, Peng, L, Zhao, W, Huang, S & Yu, X 1970, 'Recent update on the Tsinghua tabletop Kibble balance', 2024 Conference on Precision Electromagnetic Measurements (CPEM), 2024 Conference on Precision Electromagnetic Measurements (CPEM), IEEE, pp. 1-2.
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Li, S, Unanue, IJ & Piccardi, M 1970, 'LayerGLAT: A Flexible Non-autoregressive Transformer for Single-Pass and Multi-pass Prediction', Springer Nature Switzerland, pp. 233-249.
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Li, S, Yu, X, Xing, W, Kirby, RM, Narayan, A & Zhe, S 1970, 'Multi-Resolution Active Learning of Fourier Neural Operators', Proceedings of Machine Learning Research, pp. 2440-2448.
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Fourier Neural Operator (FNO) is a popular operator learning framework. It not only achieves the state-of-the-art performance in many tasks, but also is efficient in training and prediction. However, collecting training data for the FNO can be a costly bottleneck in practice, because it often demands expensive physical simulations. To overcome this problem, we propose Multi-Resolution Active learning of FNO (MRA-FNO), which can dynamically select the input functions and resolutions to lower the data cost as much as possible while optimizing the learning efficiency. Specifically, we propose a probabilistic multi-resolution FNO and use ensemble Monte-Carlo to develop an effective posterior inference algorithm. To conduct active learning, we maximize a utility-cost ratio as the acquisition function to acquire new examples and resolutions at each step. We use moment matching and the matrix determinant lemma to enable tractable, efficient utility computation. Furthermore, we develop a cost annealing framework to avoid over-penalizing high-resolution queries at the early stage. The over-penalization is severe when the cost difference is significant between the resolutions, which renders active learning often stuck at low-resolution queries and inferior performance. Our method overcomes this problem and applies to general multi-fidelity active learning and optimization problems. We have shown the advantage of our method in several benchmark operator learning tasks. The code is available at https://github.com/shib0li/MRA-FNO.
Li, Y, Huang, Z, Yu, G, Chen, L, Wei, Y & Jiao, J 1970, 'Disentangled Pre-training for Image Matting', 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), IEEE, pp. 168-177.
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Li, Y, Shao, Z, Chen, W, Wang, S, Du, Y & Lu, W 1970, 'Significance-aware Medication Recommendation with Medication Representation Learning', 2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD), 2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD), IEEE, pp. 1633-1638.
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Li, Y, Xu, C, Long, G, Shen, T, Tao, C & Jiang, J 1970, 'CCPrefix: Counterfactual Contrastive Prefix-Tuning for Many-Class Classification', EACL 2024 - 18th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference, pp. 2977-2988.
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Recently, prefix-tuning was proposed to efficiently adapt pre-trained language models to a broad spectrum of natural language classification tasks. It leverages soft prefix as task-specific indicators and language verbalizers as categorical-label mentions to narrow the formulation gap from pre-training language models. However, when the label space increases considerably (i.e., many-class classification), such a tuning technique suffers from a verbalizer ambiguity problem since the many-class labels are represented by semantic-similar verbalizers in short language phrases. To overcome this, inspired by the human-decision process that the most ambiguous classes would be mulled over for each instance, we propose a brand-new prefix-tuning method, Counterfactual Contrastive Prefix-tuning (CCPrefix), for many-class classification. Basically, an instance-dependent soft prefix, derived from fact-counterfactual pairs in the label space, is leveraged to complement the language verbalizers in many-class classification. We conduct experiments on many-class benchmark datasets in both the fully supervised setting and the few-shot setting, which indicates that our model outperforms former baselines.
Li, Y, Yang, Y, Cao, J, Liu, S, Tang, H & Xu, G 1970, 'Toward Structure Fairness in Dynamic Graph Embedding: A Trend-aware Dual Debiasing Approach', Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ACM, pp. 1701-1712.
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Li, Y, Zhang, C, Yu, G, Yang, W, Wang, Z, Fu, B, Lin, G, Shen, C, Chen, L & Wei, Y 1970, 'Enhanced Visual Instruction Tuning with Synthesized Image-Dialogue Data', Findings of the Association for Computational Linguistics ACL 2024, Findings of the Association for Computational Linguistics ACL 2024, Association for Computational Linguistics, pp. 14512-14531.
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The remarkable multimodal capabilities demonstrated by OpenAI's GPT-4 have sparked significant interest in the development of multimodal Large Language Models (LLMs). A primary research objective of such models is to align visual and textual modalities effectively while comprehending human instructions. Current methodologies often rely on annotations derived from benchmark datasets to construct image-dialogue datasets for training purposes, akin to instruction tuning in LLMs. However, these datasets often exhibit domain bias, potentially constraining the generative capabilities of the models. In an effort to mitigate these limitations, we propose a novel methodology for data collection, which synchronously synthesizes images and dialogues for visual instruction tuning. This approach leverages the combined capabilities of generative text-to-image models and large language models, facilitating the creation of a dataset that is both diverse and scalable, and more importantly, customized to enhance the models' performance across a broad spectrum of tasks. Our research includes comprehensive experiments conducted on various datasets. The results emphasize substantial enhancements in more than ten commonly assessed capabilities. Additionally, our model achieves state-of-the-art results across multiple widely recognized multimodal benchmarks.
Li, Z, Long, G & Zhou, T 1970, 'FEDERATED RECOMMENDATION WITH ADDITIVE PERSONALIZATION', 12th International Conference on Learning Representations, ICLR 2024.
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Building recommendation systems via federated learning (FL) is a new emerging challenge for next-generation Internet service. Existing FL models share item embedding across clients while keeping the user embedding private and local on the client side. However, identical item embedding cannot capture users' individual differences in perceiving the same item and may lead to poor personalization. Moreover, dense item embedding in FL results in expensive communication costs and latency. To address these challenges, we propose Federated Recommendation with Additive Personalization (FedRAP), which learns a global view of items via FL and a personalized view locally on each user. FedRAP encourages a sparse global view to save FL's communication cost and enforces the two views to be complementary via two regularizers. We propose an effective curriculum to learn the local and global views progressively with increasing regularization weights. To produce recommendations for a user, FedRAP adds the two views together to obtain a personalized item embedding. FedRAP achieves the best performance in FL setting on multiple benchmarks. It outperforms recent federated recommendation methods and several ablation study baselines. Our code is available at https://github.com/mtics/FedRAP.
Li, Z, Wu, J, Xiong, J & Liu, B 1970, 'Research on Automatic Path Planning of Wind Turbines Inspection Based on Combined UAV', 2024 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), 2024 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), IEEE, pp. 1-6.
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Lidfors Lindqvist, A, Willey, K, Lidfors, L & Francis, B 1970, 'Advancing Fidelity and Feedback in Practical Activities with Formative Sprints', Christchurch, New Zealand.
Lin, S, Lyu, P, Liu, D, Tang, T, Liang, X, Song, A & Chang, X 1970, 'MLP Can Be a Good Transformer Learner', 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, pp. 19489-19498.
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Lin, X, Ai, D, Ma, B, Wang, X, Yu, G, He, Y, Ni, W & Liu, RP 1970, 'Federated Learning-Based Intrusion Detection System for In-Vehicle Network Using Statistics of Controller Area Network Messages', Springer Nature Singapore, pp. 237-251.
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Liu, B, Liu, B, Ding, M & Zhu, T 1970, 'Detection of Diffusion Model-Generated Faces by Assessing Smoothness and Noise Tolerance', 2024 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), 2024 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), IEEE, pp. 1-6.
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Liu, J, Jian, GD, Ssu-Han Chen, D, Sze Wai, DC, Shyam, T, Ramegowda, PC, Srinivas, M, Huamao, L, Xin, ZQ, Chang Hyun Kee, P, Das, A, Sciarrone, A, Leotti, A, Giusti, D, Lee, JE-Y & Koh, Y 1970, 'Towards Unparallelled CMOS-compatible Air-coupled pMUT Performance with 30% Sc-doped AlN through an Analysis of Residual Stress Effects', 2024 IEEE Ultrasonics, Ferroelectrics, and Frequency Control Joint Symposium (UFFC-JS), 2024 IEEE Ultrasonics, Ferroelectrics, and Frequency Control Joint Symposium (UFFC-JS), IEEE, pp. 1-4.
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Liu, J, Nerse, C & Oberst, S 1970, 'INFLUENCE ON CLASSIFICATION ACCURACY OF PARTIALLY ANNOTATED UNDERWATER ANIMAL SOUNDS BY COMBINING MEL-SPECTRA AND RECURRENCE PLOTS', Proceedings of the International Congress on Sound and Vibration.
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Rapidly advancing technology including an increasing number of sensors and large data collections of different sources and quality can lead to inaccurate and inconsistent labelling of information. We employ FastAI as an off-the-shelf convolutional neural network model to classify scattered white noise data from a controlled environment (sound transmission loss suite) using different loudspeaker locations. Specifically, Mel-spectrograms and recurrence plots (RP), both effective analysis techniques are investigated alone, then in combination to optimise acoustic signal classification. We then apply the method on more tonal, underwater acoustic animal signals (field recordings), downloaded as open-source data of varying quality and origin, from various websites. While RP-based classification performed better for the controlled acoustic experiment (90.3% accuracy), Mel-spectrograms excelled in underwater animal sounds (91.2%). RP outperformed Mel-spectrograms in subtle signal changes within controlled environments where background nuances are non-existent, possible due to their sensitivity to data set noise. On the other hand, recurrence plots may have picked up labelling issues (due to varying directionality, changing path source, shadowing effects, different recording settings) which led to inaccurate predictions. However, both systems received the highest prediction accuracy if a cropped Mel-spectrogram was combined with a properly embedded RP (94.3%, 91.3%) which indicated the potential of adding RP as aid to the classification process. The results suggest a potential advantage of RP in controlled settings and when it comes to distinguishing finer differences as the primary signal effects. The underwater animal acoustics did not offer such a distinct environment and the main signals were different (strong) enough to be segregated, irrespective of their different recording quality. For better annotated data, RP in their combination with Mel spectra likel...
Liu, J, Zhong, Y, Hu, S, Fu, H, Fu, Q, Chang, X & Yang, Y 1970, 'MAXIMUM ENTROPY HETEROGENEOUS-AGENT REINFORCEMENT LEARNING', 12th International Conference on Learning Representations, ICLR 2024.
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Multi-agent reinforcement learning (MARL) has been shown effective for cooperative games in recent years. However, existing state-of-the-art methods face challenges related to sample complexity, training instability, and the risk of converging to a suboptimal Nash Equilibrium. In this paper, we propose a unified framework for learning stochastic policies to resolve these issues. We embed cooperative MARL problems into probabilistic graphical models, from which we derive the maximum entropy (MaxEnt) objective for MARL. Based on the MaxEnt framework, we propose Heterogeneous-Agent Soft Actor-Critic (HASAC) algorithm. Theoretically, we prove the monotonic improvement and convergence to quantal response equilibrium (QRE) properties of HASAC. Furthermore, we generalize a unified template for MaxEnt algorithmic design named Maximum Entropy Heterogeneous-Agent Mirror Learning (MEHAML), which provides any induced method with the same guarantees as HASAC. We evaluate HASAC on six benchmarks: Bi-DexHands, Multi-Agent MuJoCo, StarCraft Multi-Agent Challenge, Google Research Football, Multi-Agent Particle Environment, and Light Aircraft Game. Results show that HASAC consistently outperforms strong baselines, exhibiting better sample efficiency, robustness, and sufficient exploration.
Liu, K, Zhao, F, Yang, Y & Xu, G 1970, 'DySarl: Dynamic Structure-Aware Representation Learning for Multimodal Knowledge Graph Reasoning', Proceedings of the 32nd ACM International Conference on Multimedia, MM '24: The 32nd ACM International Conference on Multimedia, ACM, Melbourne, pp. 8247-8256.
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Liu, P, Wang, F, Li, K, Chen, G, Wei, Y, Tang, S, Wu, Z & Guo, D 1970, 'Micro-gesture Online Recognition using Learnable Query Points', CEUR Workshop Proceedings.
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In this paper, we briefly introduce the solution developed by our team, HFUT-VUT, for the Micro-gesture Online Recognition track in the MiGA challenge at IJCAI 2024. The Micro-gesture Online Recognition task involves identifying the category and locating the start and end times of micro-gestures in video clips. Compared to the typical Temporal Action Detection task, the Micro-gesture Online Recognition task focuses more on distinguishing between micro-gestures and pinpointing the start and end times of actions. Our solution ranks 2nd in the Micro-gesture Online Recognition track.
Liu, R, Li, M, Zhao, S, Chen, L, Chang, X & Yao, L 1970, 'In-Context Learning for Zero-shot Medical Report Generation', Proceedings of the 32nd ACM International Conference on Multimedia, MM '24: The 32nd ACM International Conference on Multimedia, ACM, pp. 8721-8730.
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Liu, R, Song, Y & Huang, S 1970, 'First Estimate Jacobian EKF for Multi-robot SLAM', Australasian Conference on Robotics and Automation, ACRA.
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Addressing inconsistency issues, i.e. underestimation of uncertainty, is crucial for the performance of Extended Kalman Filter (EKF) based Simultaneous Localization and Mapping (SLAM). By using the first estimate Jacobians, this paper designs a consistent EKF for the point feature-based multi-robot SLAM. First, the standard EKF (Std-EKF) for the considered problems is presented. Then, through the observability analysis, we prove that Std-EKF has an observable subspace of dimension higher than the underlying system, leading to the inconsistency issue. Accordingly, we propose the first estimate Jacobian EKF (FEJ-EKF), which shares the same dimension of observable subspace with the underlying system, alleviating the inconsistency issue. Finally, the effectiveness of the proposed method is validated by simulations and a practical dataset. By making the MATLAB code for this research available online1, we hope to facilitate collaboration and allow others to build upon and improve the methodology.
Liu, Y, Zhang, W, Si, J, Li, Z, Peng, X & Lu, W 1970, 'Generating Personalized Imputations for Patient Health Status Prediction in Electronic Health Records', 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp. 1048-1053.
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Liu, Z, Braytee, A, Anaissi, A, Zhang, G, Qin, L & Akram, J 1970, 'Ensemble Pretrained Models for Multimodal Sentiment Analysis using Textual and Video Data Fusion', Companion Proceedings of the ACM Web Conference 2024, WWW '24: The ACM Web Conference 2024, ACM, pp. 1841-1848.
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Liu, Z, Lu, J, Zhang, G & Xuan, J 1970, 'A Behavior-Aware Approach for Deep Reinforcement Learning in Non-stationary Environments without Known Change Points', Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence, Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}, International Joint Conferences on Artificial Intelligence Organization, pp. 4634-4642.
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Deep reinforcement learning is used in various domains, but usually under the assumption that the environment has stationary conditions like transitions and state distributions. When this assumption is not met, performance suffers. For this reason, tracking continuous environmental changes and adapting to unpredictable conditions is challenging yet crucial because it ensures that systems remain reliable and flexible in practical scenarios. Our research introduces Behavior-Aware Detection and Adaptation (BADA), an innovative framework that merges environmental change detection with behavior adaptation. The key inspiration behind our method is that policies exhibit different global behaviors in changing environments. Specifically, environmental changes are identified by analyzing variations between behaviors using Wasserstein distances without manually set thresholds. The model adapts to the new environment through behavior regularization based on the extent of changes. The results of a series of experiments demonstrate better performance relative to several current algorithms. This research also indicates significant potential for tackling this long-standing challenge.
Long, G 1970, 'The Rise of Federated Intelligence: From Federated Foundation Models Toward Collective Intelligence', Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence, Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}, International Joint Conferences on Artificial Intelligence Organization, pp. 8547-8552.
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The success of foundation models advances the development of various intelligent and personalized agents to handle intricate tasks in their daily lives, however finite resources and privacy concerns from end users limit the potential of customizing the large intelligent agents for personal use. This paper explores the preliminary design of federated intelligence that paves the way toward personalized intelligent agents in large-scale collaboration scenarios. In Federated Intelligence, agents can collaboratively augment their intelligence quotient (IQ) by learning complementary knowledge and fine-grained adaptations. These personalized intelligent agents can also co-work together to jointly address complex tasks in the form of collective intelligence. The paper will highlight federated intelligence as a new pathway for tackling complex intelligent tasks by refining and extending centralized foundation models to an open and collaborative paradigm.
Lu, D, Chen, X, Chen, R, Wu, S & Xu, G 1970, 'Fairness-Aware Mutual Information for Multimodal Recommendation', 2024 11th International Conference on Behavioural and Social Computing (BESC), 2024 11th International Conference on Behavioural and Social Computing (BESC), IEEE, pp. 1-9.
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Lu, D, Zhang, H, Li, L, Wu, S & Xu, G 1970, 'Cascading Hypergraph Convolution Networks for Multi-Behavior Sequential Recommendation*', 2024 11th International Conference on Behavioural and Social Computing (BESC), 2024 11th International Conference on Behavioural and Social Computing (BESC), IEEE, pp. 1-7.
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Lu, DD-C 1970, 'Reliability and Efficiency Evaluation of Cascaded Two-stage Boost Converters', 2024 IEEE 34th Australasian Universities Power Engineering Conference (AUPEC), 2024 IEEE 34th Australasian Universities Power Engineering Conference (AUPEC), IEEE, pp. 1-5.
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Lu, G, Niu, K, Peng, X, Zhou, Y, Zhang, K & Tai, W 1970, 'Self-KT: Self-attentive Knowledge Tracing with Feature Fusion Pre-training in Online Education', 2024 International Joint Conference on Neural Networks (IJCNN), 2024 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1-8.
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Lu, W, Zhang, G, Peng, X, Guan, H & Wang, S 1970, 'Medical Entity Disambiguation with Medical Mention Relation and Fine-grained Entity Knowledge', 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings, pp. 11148-11158.
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Medical entity disambiguation (MED) plays a crucial role in natural language processing and biomedical domains, which is the task of mapping ambiguous medical mentions to structured candidate medical entities from knowledge bases (KBs). However, existing methods for MED often fail to fully utilize the knowledge within medical KBs and overlook essential interactions between medical mentions and candidate entities, resulting in knowledge- and interaction-inefficient modeling and suboptimal disambiguation performance. To address these limitations, this paper proposes a novel approach, MED with Medical Mention Relation and Fine-grained Entity Knowledge (MMR-FEK). Specifically, MMR-FEK incorporates a mention relation fusion module and an entity knowledge fusion module, followed by an interaction module. The former employs a relation graph convolutional network to fuse mention relation information between medical mentions to enhance mention representations, while the latter leverages an attention mechanism to fuse synonym and type information of candidate entities to enhance entity representations. Afterwards, an interaction module is designed to employ a bidirectional attention mechanism to capture interactions between medical mentions and entities to generate the matching representation. Extensive experiments on two publicly available real-world datasets demonstrate MMR-FEK's superiority over state-of-the-art(SOTA) MED baselines across all metrics. Our source code is publicly available.
Luo, Q, Zhang, W, Yang, Z, Wen, D, Wang, X, Yu, D & Lin, X 1970, 'Hierarchical Structure Construction on Hypergraphs', Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, CIKM '24: The 33rd ACM International Conference on Information and Knowledge Management, ACM, pp. 1597-1606.
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Luo, S, Herrera, A, Quirk, P, Chase, M, Ranasinghe, DC & Kanhere, SS 1970, 'Make out like a (Multi-Armed) Bandit: Improving the Odds of Fuzzer Seed Scheduling with T-Scheduler', Proceedings of the 19th ACM Asia Conference on Computer and Communications Security, ASIA CCS '24: 19th ACM Asia Conference on Computer and Communications Security, ACM, pp. 1463-1479.
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Lv, X, Luo, Z & Yang, Y 1970, '3-D Centrally-Loaded FSS Leveraging Conductive and Dielectric Multimaterial Additive Manufacturing for Broadband Performance', 2024 IEEE/MTT-S International Microwave Symposium - IMS 2024, 2024 IEEE/MTT-S International Microwave Symposium - IMS 2024, IEEE, pp. 18-21.
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Lv, X, Luo, Z & Yang, Y 1970, 'Detachable Impedance-Matching Boosters for FSS-Based Broadband Designs', 2024 IEEE International Symposium on Antennas and Propagation and INC/USNC‐URSI Radio Science Meeting (AP-S/INC-USNC-URSI), 2024 IEEE International Symposium on Antennas and Propagation and INC/USNC‐URSI Radio Science Meeting (AP-S/INC-USNC-URSI), IEEE, pp. 1-2.
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Lyu, J, Meng, J, Zhang, Y, Ling, SH & Sun, L 1970, 'Joint Semantic Feature and Optical Flow Learning for Automatic Echocardiography Segmentation', Springer Nature Singapore, pp. 160-171.
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Ma, B, Wang, X, Jiang, Y & Lin, X 1970, 'Adaptive Cloaking Region Obfuscation in Road Networks', 2024 3rd Conference on Fully Actuated System Theory and Applications (FASTA), 2024 3rd Conference on Fully Actuated System Theory and Applications (FASTA), IEEE, pp. 331-336.
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Ma, B, Yu, J, Zheng, J, Zhu, J & Lei, G 1970, 'Topology Optimization of an IPMSM Rotor Considering the Torque Profile Enhancement', 2024 IEEE 21st Biennial Conference on Electromagnetic Field Computation (CEFC), 2024 IEEE 21st Biennial Conference on Electromagnetic Field Computation (CEFC), IEEE, pp. 01-02.
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Ma, B, Zhao, Y, Wang, X, Jiang, Y, Li, J, Ni, W & Liu, RP 1970, 'Differential Privacy-Based Location Privacy Protection with Hilbert Curve in Vehicular Networks', Springer Nature Singapore, pp. 252-268.
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Ma, LE, Dickson-Deane, C, Raffe, W, Murphy, AR & Garcia, J 1970, 'Gaming for Equity: The Power of Diversity within Gender and Race in Gamers', 2024 IEEE Conference on Games (CoG), 2024 IEEE Conference on Games (CoG), IEEE, pp. 1-8.
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Ma, R, Pang, G & Chen, L 1970, 'Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural Networks', 2024 International Joint Conference on Neural Networks (IJCNN), 2024 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1-8.
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MacLeod, S, Bevitt, J, Zahra, D, Chacon, A, Franklin, D & Safavi-Naeini, M 1970, 'Post-Neutron-Tomography SPECT for 3D Isotopic Reconstruction in Bulk Samples', 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD), 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD), IEEE, pp. 1-1.
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Madani, P & McGregor, C 1970, 'Cybersecurity Issues in Space Optical Communication Networks and Future of Secure Space Health Systems', 2024 IEEE Aerospace Conference, 2024 IEEE Aerospace Conference, IEEE, pp. 1-8.
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Malisetty, RS, Indraratna, B & Vinod, JS 1970, 'Application of Multi-laminate Model to Analyze the Influence of Principal Stress Rotation Stress Paths on Permanent Deformation of Railway Ballast', Indian Geotechnical Conference, Springer Nature Singapore, Kochi, pp. 485-495.
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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, Arcidiacono, P, Leone, R, 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, K, Di Mitri, R, Amata, M, Crino, SF, Ofosu, A, Auriemma, F, Paduano, D, Calabrese, F, Gentile, C, Hassan, C, Repici, A & Facciorusso, A 1970, 'OUTCOMES OF LUMEN APPOSING METAL STENT PLACEMENT IN PATIENTS WITH SURGICALLY ALTERED ANATOMY: A MULTICENTER INTERNATIONAL EXPERIENCE', GASTROINTESTINAL ENDOSCOPY, Digestive Disease Week (DDW), MOSBY-ELSEVIER, DC, Washington, pp. AB757-AB757.
Mannen, T, Seo, B, Isobe, T & Pham, HN 1970, 'Buck-Type Current Unfolding Converter With Discontinuous Conduction Mode in Ultra-Low Power-Factor Operation', PCIM Europe Conference Proceedings, pp. 831-836.
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This paper proposes a control method for a buck-type current unfolding converter, especially operating under non-unity power factor. The proposed method utilizes discontinuous conduction mode (DCM) to control the inductor current. DCM enables the converter to drastically reduce its inductors and allows the current controller to handle step changes in current due to non-unity power factor operations. Furthermore, this method enables the application of a feedforward-based control strategy by utilizing DCM, thereby eliminating the need for current sensors in the converter. Experimental results demonstrate sinusoidal output current waveforms at both unity and zero power factors, achieved without any current sensors in the converter prototype. These results confirm the effectiveness of the proposed method, particularly its robust current control capabilities. The proposed method is expected to increase the switching frequency and reduce the size of the passive components.
Martin, L, Thomas, P, de Silva, P & Sirivivatnanon, V 1970, 'Assessing the Role of ASR Reactive Aggregates in Concrete Mixes Susceptible to DEF', Springer Nature Switzerland, pp. 168-175.
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Marturano, F, Gomez-Cid, L, Straney, D, Chen, IY-C, Marie Cécile Albrecht, A, Yu, X, Ay, I & Bonmassar, G 1970, 'High-Frequency Trans-Spinal Magnetic Stimulation for Chronic Neuropathic Pain Treatment', 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, pp. 1-4.
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Matin, A, Islam, MR, Wang, X & Huo, H 1970, 'Robust Multimodal Approach for Assembly Action Recognition', Procedia Computer Science, International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, Elsevier BV, Seville, Spain, pp. 4916-4925.
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Mavrovouniotis, M, Li, C, Yazdani, D & Handjimitsis, D 1970, 'Exchange Strategies for Multi-Colony Ant Algorithms in Dynamic Environments', 2024 IEEE Congress on Evolutionary Computation (CEC), 2024 IEEE Congress on Evolutionary Computation (CEC), IEEE, pp. 01-08.
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Meng, Z, Li, B, Fan, X, Li, Z, Wang, Y, Chen, F & Zhou, F 1970, 'TransFeat-TPP: An Interpretable Deep Covariate Temporal Point Processes', IOS Press.
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The classical temporal point process (TPP) constructs an intensity function by taking the occurrence times into account. Nevertheless, occurrence time may not be the only relevant factor, other contextual data, termed covariates, may also impact the event evolution. Incorporating such covariates into the model is beneficial, while distinguishing their relevance to the event dynamics is of great practical significance. In this work, we propose a Transformer-based covariate temporal point process (TransFeat-TPP) model to improve the interpretability of deep covariate-TPPs while maintaining powerful expressiveness. TransFeat-TPP can effectively model complex relationships between events and covariates, and provide enhanced interpretability by discerning the importance of various covariates. Experimental results on synthetic and real datasets demonstrate improved prediction accuracy and consistently interpretable feature importance when compared to existing deep covariate-TPPs. Our code is available at https://github.com/waystogetthere/TransFeat.git.
Meng, Z, Wan, K, Huang, Y, Li, Z, Wang, Y & Zhou, F 1970, 'Interpretable Transformer Hawkes Processes: Unveiling Complex Interactions in Social Networks', Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ACM, pp. 2200-2211.
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Menon, VA, Miller, A, Hanson, D & Parnell, J 1970, 'Advanced Analysis of Ground-Borne Vibration Noise Impacts for Underground Railway Tunnel Construction', Proceedings of Acoustics 2024: Acoustics in the Sun.
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This paper analyses ground-borne noise and vibration generated from tunnel boring machines (TBMs) and cross-passage excavation activities associated with tunnelling in the Sydney region. While a body of literature for TBMs and excavation activities is available from overseas sources with respect to peak particle velocity (PPV), this is usually limited to the relatively short setback distances applicable to structural damage concerns. This study presents PPVs, A-weighted ground-borne RMS vibration levels and one-third octave band spectra from TBMs and cross-passage excavation activities at various offset distances in the ground, out to distances of 300 m, that may be applicable to ground-borne noise or even sensitive equipment concerns. This data should inform future predictions of vibration and ground-borne noise impacts on other tunnelling and excavation projects in the Sydney region, and may be applicable to other regions. It also highlights the importance of monitoring to verify predictions for any Project given uncertainties in predictions, ground conditions and the practicalities of tunnelling processes.
Mizuno, R, Dong, LP, Karya, A & Nguyen, TT 1970, 'Design Challenges of Large Diameter and Long Steel Pipe Pile in High Plasticity Clay at Patimban Port Development Project', Springer Nature Singapore, pp. 203-220.
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Mohapatra, AR, Nerse, C, Oberst, S, Navarro-Payá, D, Etxeberria, J, Matus, JT, Bianco, L, Tucci, MR, Casacci, LP & Barbero, F 1970, 'A STUDY TO CLASSIFY WILD BEES’ SIGNAL USING TIME SERIES ANALYSIS', Proceedings of the International Congress on Sound and Vibration.
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Bees (Anthophila) are among the most effective pollinators in nature being responsible for approximately one-third of the total crop pollination for human dietary supply. The interaction between plants and bees plays here an essential role and may also include vibro-acoustic signals as an important medium of information transmission. Plants have been shown to respond to airborne acoustic signals of flying pollinators by increasing the sugar concentration in the nectar. Yet very little is known about the pollinators’ vibro-acoustic signatures and plant-relevant effective traits of the signal. Here we present an analysis framework of acoustic signals for three different bee species, namely Rhodanthidium sticticum, Amegilla quadrifasciata, and Apis mellifera, recorded in the rural areas (Chera, Chulilla, and Macastre) of the Province of Valencia, Spain, visiting Antirrhinum (snapdragon) plants. First, from audio-visual recordings, audio signals for different bee behaviours during visits were identified. We showed that periodogram and recurrence-based spectrograms could be used to classify real-life bio-acoustic data recorded outdoors. This approach can also be used to predict future data sets for which a traditional approach like spectral analysis is unsuitable, especially for noisy, more nonlinear, and complex data.
Moreno, VH, Fernandez, L, Carmichael, MG & Deuse, J 1970, 'Multi-volume Potential Fields for Effective Learning from Demonstration', 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE), 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE), IEEE, pp. 2342-2347.
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Nabeel, MI, Afzal, MU, Thalakotuna, DN & Esselle, KP 1970, 'Beam Steering 2D Leaky Wave Resonant Cavity Antenna for Ka-Band Satellite Communication', 2024 18th European Conference on Antennas and Propagation (EuCAP), 2024 18th European Conference on Antennas and Propagation (EuCAP), IEEE, pp. 1-5.
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Nabeel, MI, Singh, K, Afzal, MU, Thalakotuna, DN & Esselle, KP 1970, 'Dual-Band Printed Coded Metasurface for Split Beam Generation in Arbitrary Direction', 2024 International Symposium on Antennas and Propagation (ISAP), 2024 International Symposium on Antennas and Propagation (ISAP), IEEE, pp. 1-2.
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Naligama, CA, Kularatna, N, Steyn-Ross, A & Gunawardane, K 1970, 'Supercapacitor Assisted Multilevel Inverter Topology for Off-Grid Renewable Energy Systems', 2024 13th International Conference on Renewable Energy Research and Applications (ICRERA), 2024 13th International Conference on Renewable Energy Research and Applications (ICRERA), IEEE, pp. 1800-1805.
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Narayanan, AK, Qiao, Y & Tang, G 1970, 'Algorithms for Matrix Code and Alternating Trilinear Form Equivalences via New Isomorphism Invariants', Springer Nature Switzerland, pp. 160-187.
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Nassar, R-U-D & Sohaib, O 1970, 'Prediction of the Compressive Strength of Sustainable Concrete Produced with Powder Glass Using Standalone and Stack Machine Learning Methods', Springer Nature Singapore, pp. 147-158.
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Nduwamungu, A, Lie, TT, Lestas, I, Nair, N-KC & Gunawardane, K 1970, 'Decentralized Voltage Regulation and Accurate Current Sharing in a Parallel Buck Converters With Optimized Piecewise Linear Droop Control', 2024 18th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), 2024 18th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), IEEE, pp. 1-6.
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Ngo, QT, He, Y & Dutkiewicz, E 1970, 'Performance Analysis of RIS-Aided Cognitive GEO-LEO Satellite Networks', 2024 23rd International Symposium on Communications and Information Technologies (ISCIT), 2024 23rd International Symposium on Communications and Information Technologies (ISCIT), IEEE, pp. 12-16.
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Ngo, T & Indraratna, B 1970, 'Geocell-Reinforced Capping Layer in Rail Tracks Subjected to Cyclic Loading: Laboratory and Numerical Modeling Study', Geo-Congress 2024, Geo-Congress 2024, American Society of Civil Engineers, pp. 397-406.
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This research investigates the behavior of geocell-reinforced capping layers in ballasted tracks subjected to cyclic loading. Large-scale laboratory testing and numerical modeling techniques were employed. The cyclic tests applied a 25-t axle load under frequencies ranging from 10 to 30 Hz. The geocell-reinforced capping layers were modeled using the discrete element method. The geocell structure was simulated by bonding small balls to build a realistic geometry and shape. Model parameters were calibrated based on tensile and bending tests performed on the geocell material. The DEM simulations accurately represented the irregular shape of the capping aggregates using bonded circular particles. The findings indicated that the geocell effectively reduced both vertical and lateral displacements of the capping layer. The DEM analysis provided valuable insights into the contact force chain distributions within the capping assembly.
Ngo, TS, Phuong Nguyen, TT, Nguyen, VQ, Cao, TD, Ha, DT, Dinh, TH & Ha, Q 1970, 'Vision-Based Household Waste Classification and Smart Bin Development', 2024 13th International Conference on Control, Automation and Information Sciences (ICCAIS), 2024 13th International Conference on Control, Automation and Information Sciences (ICCAIS), IEEE, pp. 1-6.
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Nguyen, C-H, Manh, BD, Thai Hoang, D, Nguyen, DN & Dutkiewicz, E 1970, 'Towards Secure AI-empowered Vehicular Networks: A Federated Learning Approach using Homomorphic Encryption', 2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall), 2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall), IEEE, pp. 1-6.
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Nguyen, DDK, Paul, G & Alempijevic, A 1970, 'Decentralized multi-phase formation control for cattle herding', 2024 IEEE International Conference on Robotics and Automation (ICRA), 2024 IEEE International Conference on Robotics and Automation (ICRA), IEEE, Yokohama, Japan, pp. 17948-17953.
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Nguyen, LV, Le, TH & Ha, QP 1970, 'Grey Wolf Optimization-Based Path Planning for Unmanned Aerial Vehicles in Bridge Inspection', 2024 IEEE/SICE International Symposium on System Integration (SII), 2024 IEEE/SICE International Symposium on System Integration (SII), IEEE.
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Nguyen, M, Zhu, H, Sun, H, Nguyen, V, Singh, A, Jin, C & Lin, C-T 1970, 'A Case Study on the Effects of Auditory Cues in Influencing Spatial Memory and Representation for a Person Who is Blind', Proceedings of the 1st International Workshop on Brain-Computer Interfaces (BCI) for Multimedia Understanding, MM '24: The 32nd ACM International Conference on Multimedia, ACM, pp. 49-53.
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Nguyen, Q-T, Le, L, Tran, X-T, Do, T & Lin, C-T 1970, 'FairAD-XAI: Evaluation Framework for Explainable AI Methods in Alzheimer's Disease Detection with Fairness-in-the-loop', Companion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp '24: The 2024 ACM International Joint Conference on Pervasive and Ubiquitous Computing, ACM, pp. 870-876.
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Nguyen, TT, Huynh, TQ, Khabbaz, H & Le Nguyen, K 1970, 'Machine Learning-Aided Prediction of Pile Behaviour: The Role of Data Quality', Springer Nature Singapore, pp. 515-526.
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Nie, J, Zhang, Y, Fang, Z, Liu, T, Han, B & Tian, X 1970, 'OUT-OF-DISTRIBUTION DETECTION WITH NEGATIVE PROMPTS', 12th International Conference on Learning Representations, ICLR 2024.
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Out-of-distribution (OOD) detection is indispensable for open-world machine learning models. Inspired by recent success in large pre-trained language-vision models, e.g., CLIP, advanced works have achieved impressive OOD detection results by matching the similarity between image features and features of learned prompts, i.e., positive prompts. However, existing works typically struggle with OOD samples having features similar to those of known classes. One straightforward approach is to introduce negative prompts to achieve a dissimilarity matching, which further assesses the anomaly level of image features by introducing the absence of specific features. Unfortunately, our experimental observations show that employing a prompt like 'not a photo of a' or learning a shared prompt for all classes fails to capture the dissimilarity for identifying OOD samples. The failure may be attributed to the diversity of negative features, i.e., tons of features could indicate features not belonging to a known class. To this end, we propose to learn a set of negative prompts for each class. The learned positive prompt (for all classes) and negative prompts (for each class) are leveraged to measure the similarity and dissimilarity in the feature space simultaneously, enabling more accurate detection of OOD samples. Extensive experiments are conducted on diverse OOD detection benchmarks, showing the effectiveness of our proposed method.
Nikolic, S, Heath, A, Vu, BA, Daniel, S, Alimardani, A, Sandison, C, Lu, X, Stappenbelt, B & Hastie, D 1970, 'Prompt Potential: A Pilot Assessment of Using Generative Artificial Intelligence (ChatGPT-4) as a Tutor for Engineering and Maths', SEFI 2024 - 52nd Annual Conference of the European Society for Engineering, Proceedings: Educating Responsible Engineers, pp. 769-781.
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The meteoric rise of GenAI has caused many educators to be consternated by its potential to undermine assessment. However, there is a more optimistic view to instead focus on the pedagogical affordances that GenAI can bring, for example, in tailoring personalised learning experiences for students. In this pilot study, we investigate ChatGPT-4's potential to act as a one-on-one tutor for engineering and mathematical concepts. We use three research-informed prompt strategies and simulate interactions with high-, mid-, and low-performing students. We find that the learning experience is best tailored to high-performing students. However, to gain comfort in using it, the experience must be error-free. We discovered performance varied by topic, but there indeed are topics that ChatGPT-4 can engage with error-free or with a slight chance of errors.
Niu, C, Pang, G & Chen, L 1970, 'Graph Continual Learning with Debiased Lossless Memory Replay', IOS Press.
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Real-life graph data often expands continually, rendering the learning of graph neural networks (GNNs) on static graph data impractical. Graph continual learning (GCL) tackles this problem by continually adapting GNNs to the expanded graph of the current task while maintaining the performance over the graph of previous tasks. Memory replay-based methods, which aim to replay data of previous tasks when learning new tasks, have been explored as one principled approach to mitigate the forgetting of the knowledge learned from the previous tasks. In this paper we extend this methodology with a novel framework, called Debiased Lossless Memory replay (DeLoMe). Unlike existing methods that sample nodes/edges of previous graphs to construct the memory, DeLoMe learns lossless prototypical node representations as the memory. The learned memory can not only preserve the graph data privacy but also capture the holistic graph information, both of which the sampling-based methods fail to achieve. Further, prior methods suffer from bias toward the current task due to the data imbalance between the classes in the memory data and the current data. A debiased GCL loss function is devised in DeLoMe to effectively alleviate this bias. Extensive experiments on four graph datasets show the effectiveness of DeLoMe under both class- and task-incremental learning settings. Code is available at https://github.com/mala-lab/DeLoMe.
Okour, M, Falque, R & Alempijevic, A 1970, 'Sim2real Cattle Joint Estimation in 3D point clouds', 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, pp. 1818-1823.
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Olszak, CM, Zurada, JM, Kozanoglu, D & Weichbroth, P 1970, 'Introduction to the Minitrack on Business Intelligence and Big Data for Innovative, Collaborative and Sustainable Development of Organizations in Digital Era.', HICSS, ScholarSpace, pp. 277-277.
Omar, A, Beydoun, G, Win, KT & Jelinek, H 1970, 'Strategic Data Manipulation in the Development of a Knowledge-Based System for Type 2 Diabetes Prediction', Australasian Conference on Information Systems, Canberra.
Ormiston, J, Bliemel, M, Gonzalez Lago, M, Cetindamar Kozanoglu, D, Schweitzer, J & Renando, C 1970, 'Understanding the impact of entrepreneurial support organisations (ESOs): A multi-level framework', Australian Centre For Entrepreneurship Research Exchange, Sydney, Australia.
Ou, L, Do, T, Tran, X-T, Leong, D, Chang, Y-C, Wang, Y-K & Lin, C-T 1970, 'Improving CCA Algorithms on SSVEP Classification with Reinforcement Learning Based Temporal Filtering', Springer Nature Singapore, pp. 376-386.
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Overdevest, N, Patibanda, R, Saini, A, Van Den Hoven, E & Mueller, FF 1970, 'GazeAway: Designing for Gaze Aversion Experiences', Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, CHI '24: CHI Conference on Human Factors in Computing Systems, ACM, pp. 1-6.
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Padmawansa, N, Gunawardane, K & Subasinghage, K 1970, 'Fuel Cell and Supercapacitor Hybrid Energy Storage for Regulating DC Link Voltage in DC Microgrids', 2024 IEEE 9th Southern Power Electronics Conference (SPEC), 2024 IEEE 9th Southern Power Electronics Conference (SPEC), IEEE, pp. 1-6.
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Pähler, S, Syberg, M & Deuse, J 1970, 'Modular Data Analytics as a Tool for Citizen Data Scientists in Quality Management', 2024 International Conference on Electrical, Computer and Energy Technologies (ICECET, 2024 International Conference on Electrical, Computer and Energy Technologies (ICECET), IEEE, pp. 1-8.
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Palanisamy, A, Hassan, J, Lu, D, Aguilera, RP & Siwakoti, YP 1970, 'A High Step-Down Flying Capacitor Resonant Converter with Quadruple Frequency at the Resonant Tank', 2024 IEEE 9th Southern Power Electronics Conference (SPEC), 2024 IEEE 9th Southern Power Electronics Conference (SPEC), IEEE, pp. 1-4.
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Pandey, S, Beydoun, G, Bandara, M, McCusker, B & Devalence, B 1970, 'Ontology-based Framework for Carbon Reduction Assessment', Australasian Conference of Information Systems, Canberra.
Parnell, J, Jauregi Unanue, I & Piccardi, M 1970, 'SumTra: A Differentiable Pipeline for Few-Shot Cross-Lingual Summarization', Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), Association for Computational Linguistics, pp. 2399-2415.
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Cross-lingual summarization (XLS) generates summaries in a language different from that of the input documents (e.g., English to Spanish), allowing speakers of the target language to gain a concise view of their content. In the present day, the predominant approach to this task is to take a performing, pretrained multilingual language model (LM) and fine-tune it for XLS on the language pairs of interest. However, the scarcity of fine-tuning samples makes this approach challenging in some cases. For this reason, in this paper we propose revisiting the summarize-and-translate pipeline, where the summarization and translation tasks are performed in a sequence. This approach allows reusing the many, publicly-available resources for monolingual summarization and translation, obtaining a very competitive zero-shot performance. In addition, the proposed pipeline is completely differentiable end-to-end, allowing it to take advantage of few-shot fine-tuning, where available. Experiments over two contemporary and widely adopted XLS datasets (CrossSum and WikiLingua) have shown the remarkable zero-shot performance of the proposed approach, and also its strong few-shot performance compared to an equivalent multilingual LM baseline, that the proposed approach has been able to outperform in many languages with only 10% of the fine-tuning samples.
Pasumarthy, N, Nisal, S, Danaher, J, van den Hoven, E & Khot, RA 1970, 'Go-Go Biome: Evaluation of a Casual Game for Gut Health Engagement and Reflection', Proceedings of the CHI Conference on Human Factors in Computing Systems, CHI '24: CHI Conference on Human Factors in Computing Systems, ACM, pp. 1-20.
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Patibanda, R, Overdevest, N, Nisal, S, Saini, A, Elvitigala, DS, Knibbe, J, Van Den Hoven, E & Mueller, FF 1970, 'Shared Bodily Fusion: Leveraging Inter-Body Electrical Muscle Stimulation for Social Play', Designing Interactive Systems Conference, DIS '24: Designing Interactive Systems Conference, ACM, pp. 2088-2106.
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Patibanda, R, Overdevest, N, Saini, A, Li, Z, Andres, J, Knibbe, J, van den Hoven, E & Mueller, FF 1970, 'Exploring Shared Bodily Control: Designing Augmented Human Systems for Intra- and Inter-Corporeality', Proceedings of the Augmented Humans International Conference 2024, AHs 2024: The Augmented Humans International Conference, ACM, pp. 318-323.
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Peng, B, Fang, Z, Zhang, G & Lu, J 1970, 'Knowledge Distillation with Auxiliary Variable', Proceedings of Machine Learning Research, pp. 40185-40199.
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Knowledge distillation (KD) provides an efficient framework for transferring knowledge from a teacher model to a student model by aligning their predictive distributions. The existing KD methods adopt the same strategy as the teacher to formulate the student's predictive distribution. However, employing the same distribution-modeling strategy typically causes sub-optimal knowledge transfer due to the discrepancy in model capacity between teacher and student models. Designing student-friendly teachers contributes to alleviating the capacity discrepancy, while it requires either complicated or student-specific training schemes. To cast off this dilemma, we propose to introduce an auxiliary variable to promote the ability of the student to model predictive distribution. The auxiliary variable is defined to be related to target variables, which will boost the model prediction. Specifically, we reformulate the predictive distribution with the auxiliary variable, deriving a novel objective function of KD. Theoretically, we provide insights to explain why the proposed objective function can outperform the existing KD methods. Experimentally, we demonstrate that the proposed objective function can considerably and consistently outperform existing KD methods.
Peng, B, Luo, Y, Zhang, Y, Li, Y & Fang, Z 1970, 'CONJNORM: TRACTABLE DENSITY ESTIMATION FOR OUT-OF-DISTRIBUTION DETECTION', 12th International Conference on Learning Representations, ICLR 2024.
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Post-hoc out-of-distribution (OOD) detection has garnered intensive attention in reliable machine learning. Many efforts have been dedicated to deriving score functions based on logits, distances, or rigorous data distribution assumptions to identify low-scoring OOD samples. Nevertheless, these estimate scores may fail to accurately reflect the true data density or impose impractical constraints. To provide a unified perspective on density-based score design, we propose a novel theoretical framework grounded in Bregman divergence, which extends distribution considerations to encompass an exponential family of distributions. Leveraging the conjugation constraint revealed in our theorem, we introduce a CONJNORM method, reframing density function design as a search for the optimal norm coefficient p against the given dataset. In light of the computational challenges of normalization, we devise an unbiased and analytically tractable estimator of the partition function using the Monte Carlo-based importance sampling technique. Extensive experiments across OOD detection benchmarks empirically demonstrate that our proposed CONJNORM has established a new state-of-the-art in a variety of OOD detection setups, outperforming the current best method by up to 13.25% and 28.19% (FPR95) on CIFAR-100 and ImageNet-1K, respectively.
Peng, Y, Song, A, Fayek, HM, Ciesielski, V & Chang, X 1970, 'SWAP-NAS: SAMPLE-WISE ACTIVATION PATTERNS FOR ULTRA-FAST NAS', 12th International Conference on Learning Representations, ICLR 2024.
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Training-free metrics (a.k.a. zero-cost proxies) are widely used to avoid resource-intensive neural network training, especially in Neural Architecture Search (NAS). Recent studies show that existing training-free metrics have several limitations, such as limited correlation and poor generalisation across different search spaces and tasks. Hence, we propose Sample-Wise Activation Patterns and its derivative, SWAP-Score, a novel high-performance training-free metric. It measures the expressivity of networks over a batch of input samples. The SWAP-Score is strongly correlated with ground-truth performance across various search spaces and tasks, outperforming 15 existing training-free metrics on NAS-Bench-101/201/301 and TransNAS-Bench-101. The SWAP-Score can be further enhanced by regularisation, which leads to even higher correlations in cell-based search space and enables model size control during the search. For example, Spearman's rank correlation coefficient between regularised SWAP-Score and CIFAR-100 validation accuracies on NAS-Bench-201 networks is 0.90, significantly higher than 0.80 from the second-best metric, NWOT. When integrated with an evolutionary algorithm for NAS, our SWAP-NAS achieves competitive performance on CIFAR-10 and ImageNet in approximately 6 minutes and 9 minutes of GPU time respectively.
Pham, TT, Nguyen, VBH & Leong, PHW 1970, 'Body Area Network Design for Spacewalk Nonverbal Communication in Extreme Conditions', 2024 IEEE International Conference on Wireless for Space and Extreme Environments (WiSEE), 2024 IEEE International Conference on Wireless for Space and Extreme Environments (WiSEE), IEEE, pp. 154-159.
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Pileggi, SF 1970, 'A Cross-Domain Perspective to Clustering with Uncertainty', Springer Nature Switzerland, pp. 295-308.
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Poblete, P, Aguilera, RP, Alcaide, AM, Cuzmar, RH, Siwakoti, YP & Lu, DD-C 1970, 'LQG Current Control Strategy for a CHB Converter-Based Second-Life Battery Energy Storage System without Steady-State Error', 2024 IEEE 34th Australasian Universities Power Engineering Conference (AUPEC), 2024 IEEE 34th Australasian Universities Power Engineering Conference (AUPEC), IEEE, pp. 1-6.
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Poblete, P, Aguilera, RP, Pereda, J, Cuzmar, RH, Lu, D & Siwakoti, YP 1970, 'Instantaneous Circulating Current Reference Design Strategy for Inter-arm Power Imbalance Control in Delta-connected CHB Converters', 2024 IEEE 9th Southern Power Electronics Conference (SPEC), 2024 IEEE 9th Southern Power Electronics Conference (SPEC), IEEE, pp. 1-6.
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Pratten, DR & Mathieson, L 1970, 'Relational Expressions for Data Transformation and Computation', Springer Nature Switzerland, pp. 241-255.
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Pubill-Font, M, Mesa, F, Algaba-Brazález, A, Johansson, M, Manholm, L, Castillo-Tapia, P, Clendinning, S, Ding, C, Guo, YJ & Quevedo-Teruel, O 1970, 'Efficient Ray- Tracing Model for Generalized 2D Dielectric Lenses Combined with Arrays', 2024 18th European Conference on Antennas and Propagation (EuCAP), 2024 18th European Conference on Antennas and Propagation (EuCAP), IEEE, pp. 1-5.
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Puchalski, R, Giernacki, W & Ha, Q 1970, 'Real-Time UAV Fault Detection and Classification Using Measurement Data from the PADRE Database', 2024 IEEE/SICE International Symposium on System Integration (SII), 2024 IEEE/SICE International Symposium on System Integration (SII), IEEE, pp. 663-668.
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Punetha, P & Nimbalkar, S 1970, 'Three-dimensional Numerical Modeling of the Behavior of Unbound Granular Pavements under Moving Tire Loads', Indian Geotechnical Conference 2024, Chhatrapati Sambhajinagar, Maharashtra, India.
Qaiser, G, Chandrasekaran, S, Chai, R & Zheng, J 1970, 'Deep Learning-Based Hybrid Algorithm for Detecting Cyber-Attacks', 2024 International Conference on Emerging Trends in Networks and Computer Communications (ETNCC), 2024 International Conference on Emerging Trends in Networks and Computer Communications (ETNCC), IEEE, pp. 1-6.
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Qian, W, Li, K, Guo, D, Hu, B & Wang, M 1970, 'Cluster-Phys: Facial Clues Clustering Towards Efficient Remote Physiological Measurement', Proceedings of the 32nd ACM International Conference on Multimedia, MM '24: The 32nd ACM International Conference on Multimedia, ACM, pp. 330-339.
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Qian, W, Li, Q, Li, K, Wang, X, Sun, X, Wang, M & Guo, D 1970, 'Joint Spatial-Temporal Modeling and Contrastive Learning for Self-supervised Heart Rate Measurement', CEUR Workshop Proceedings, pp. 27-39.
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This paper briefly introduces the solutions developed by our team, HFUT-VUT, for Track 1 of selfsupervised heart rate measurement in the 3rd Vision-based Remote Physiological Signal Sensing (RePSS) Challenge hosted at IJCAI 2024. The goal is to develop a self-supervised learning algorithm for heart rate (HR) estimation using unlabeled facial videos. To tackle this task, we present two self-supervised HR estimation solutions that integrate spatial-temporal modeling and contrastive learning, respectively. Specifically, we first propose a non-end-to-end self-supervised HR measurement framework based on spatial-temporal modeling, which can effectively capture subtle rPPG clues and leverage the inherent bandwidth and periodicity characteristics of rPPG to constrain the model. Meanwhile, we employ an excellent end-to-end solution based on contrastive learning, aiming to generalize across different scenarios from complementary perspectives. Finally, we combine the strengths of the above solutions through an ensemble strategy to generate the final predictions, leading to a more accurate HR estimation. As a result, our solutions achieved a remarkable RMSE score of 8.85277 on the test dataset, securing 2nd place in Track 1 of the challenge.
Qiao, Y & Sun, X 1970, 'Canonical Forms for Matrix Tuples in Polynomial Time', 2024 IEEE 65th Annual Symposium on Foundations of Computer Science (FOCS), 2024 IEEE 65th Annual Symposium on Foundations of Computer Science (FOCS), IEEE, pp. 780-789.
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Qin, P-Y & Guo, YJ 1970, 'Advances on Huygens Metasurface Based Transmitarrays at University of Technology Sydney', 2024 International Conference on Electromagnetics in Advanced Applications (ICEAA), 2024 International Conference on Electromagnetics in Advanced Applications (ICEAA), IEEE, pp. 085-085.
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Qin, P-Y & Guo, YJ 1970, 'Reconfigurable Yagi-Uda Antenna', 2024 IEEE International Workshop on Antenna Technology (iWAT), 2024 IEEE International Workshop on Antenna Technology (iWAT), IEEE, pp. 320-322.
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Qiu, H, Sun, R, Chen, C, Wang, X & Zhang, Y 1970, 'Critical Nodes Detection: Node Merging Approach', Companion Proceedings of the ACM Web Conference 2024, WWW '24: The ACM Web Conference 2024, ACM, pp. 863-866.
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Qu, YN, Ji, Z, Cui, L, Zhang, C, Liu, L & Tian, Z 1970, 'Continuous Verification of Catastrophic Recalling in Machine Unlearning via Adversarial Testing', 2024 IEEE 9th International Conference on Data Science in Cyberspace (DSC), 2024 IEEE 9th International Conference on Data Science in Cyberspace (DSC), IEEE, pp. 370-377.
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Rahman, I, Mathieson, L & Ahamed, F 1970, 'Comparative Analysis of Genetic Algorithm, Ant Colony Optimisation and Particle Swarm Optimisation on the Travelling Salesman Problem and the 0/1 Knapsack Problem', 2024 IEEE International Conference on Future Machine Learning and Data Science (FMLDS), 2024 IEEE International Conference on Future Machine Learning and Data Science (FMLDS), IEEE, pp. 429-434.
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Rao, Y, Sun, L, Zhang, J, Jiang, H, Zhang, J & Zeng, D 1970, 'Densely Connected Transformer with Frequency Awareness and Sam Guidance for Semi-Supervised Hyperspectral Image Classification', 2024 IEEE International Conference on Multimedia and Expo (ICME), 2024 IEEE International Conference on Multimedia and Expo (ICME), IEEE, pp. 1-6.
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Rathnayake, D, Sabbella, H, Radhakrishnan, M & Misra, A 1970, 'D2SR: Decentralized Detection, De-Synchronization, and Recovery of LiDAR Interference', 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, pp. 4994-5001.
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Raza, A, Keshavarz, R & Shariati Moghadam, N 1970, 'Miniaturized Frequency Reconfigurable Patch Antenna for Wireless Power Transfer', Australia.
Rees, N, Thiyagarajan, K & Kodagoda, S 1970, 'Robotic Guide Dog for Real-time Indoor Object Detection and Classification with Localization', 2024 IEEE Applied Sensing Conference (APSCON), 2024 IEEE Applied Sensing Conference (APSCON), IEEE, pp. 1-4.
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Ren, Q, Guo, P, Chen, S-L & Liu, Y 1970, 'Wideband Reconfigurable Proximity-coupled Patch Antenna with Highly Flexible Polarization', 2024 International Symposium on Antennas and Propagation (ISAP), 2024 International Symposium on Antennas and Propagation (ISAP), IEEE, pp. 1-2.
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Reconfigurable antennas that combine wide bandwidth with extensive polarization switching capabilities are challenging to develop, yet they are highly beneficial for future smart wireless systems.This paper presents the development of a novel proximity-coupled patch antenna designed to achieve high polarization switching flexibility over a wide operating bandwidth.Specifically, the antenna can switch among twelve linear polarizations (LPs) and dual circular polarizations (CPs) across an over 20% overlapping bandwidth.Full-wave simulations and thorough measurements of the fabricated prototype verify the antenna performance.Notably, it is the first time to facilitate more than 20% impedance bandwidth with such extensive LP and CP switching flexibility within a single antenna element.
Rizvi, D, Le, DT, Ahamed, M, Sutjipto, S & Paul, G 1970, 'Multi-modal Feedback for Enhanced Hydraulic Maintenance Operations', International Conference on Computers and Industrial Engineering, Sydney, Australia.
Rosetta, R, Blooma, J, Marcus, J, Simon, T & Jayan Chirayath Kurian 1970, 'StandFram – A Method to Design and Build a Standards and Frameworks Knowledge Graph', Association for Information Systems, canberra.
Roth, T, Unanue, IJ, Abuadbba, A & Piccardi, M 1970, 'XVD: Cross-Vocabulary Differentiable Training for Generative Adversarial Attacks', 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings, pp. 17753-17763.
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An adversarial attack to a text classifier consists of an input that induces the classifier into an incorrect class prediction, while retaining all the linguistic properties of correctly-classified examples. A popular class of adversarial attacks exploits the gradients of the victim classifier to train a dedicated generative model to produce effective adversarial examples. However, this training signal alone is not sufficient to ensure other desirable properties of the adversarial attacks, such as similarity to non-adversarial examples, linguistic fluency, grammaticality, and so forth. For this reason, in this paper we propose a novel training objective which leverages a set of pretrained language models to promote such properties in the adversarial generation. A core component of our approach is a set of vocabulary-mapping matrices which allow cascading the generative model to any victim or component model of choice, while retaining differentiability end-to-end. The proposed approach has been tested in an ample set of experiments covering six text classification datasets, two victim models, and four baselines. The results show that it has been able to produce effective adversarial attacks, outperforming the compared generative approaches in a majority of cases and proving highly competitive against established token-replacement approaches.
Rudd, DH, Gao, X, Islam, MR, Huo, H & Xu, G 1970, 'Speech Emotion Recognition Using Mel Spectrogram HPCA and Variational Mode Decomposition', 2024 11th International Conference on Behavioural and Social Computing (BESC), 2024 11th International Conference on Behavioural and Social Computing (BESC), IEEE, pp. 1-7.
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Saini, A, Patibanda, R, Overdevest, N, Van Den Hoven, E & Mueller, FF 1970, 'PneuMa: Designing Pneumatic Bodily Extensions for Supporting Movement in Everyday Life', Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, CHI '24: CHI Conference on Human Factors in Computing Systems, ACM, pp. 1-4.
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Saini, A, Patibanda, R, Overdevest, N, Van Den Hoven, E & Mueller, FF 1970, 'PneuMa: Designing Pneumatic Bodily Extensions for Supporting Movement in Everyday Life', Proceedings of the Eighteenth International Conference on Tangible, Embedded, and Embodied Interaction, TEI '24: Eighteenth International Conference on Tangible, Embedded, and Embodied Interaction, ACM, pp. 1-16.
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Saini, A, Van Den Hoven, E & Mueller, FF 1970, 'PneuExtensio: Designing Pneumatic-based Bodily Extensions to Facilitate Embodiment across Everyday Life Experiences', Designing Interactive Systems Conference, DIS '24: Designing Interactive Systems Conference, ACM, pp. 19-23.
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Salah, AA, Shalby, MM & Al-Soeidat, MR 1970, 'Design and Development of a Hybrid Electric Vehicle Charging Station in Jordan', 2024 4th International Conference on Smart Grid and Renewable Energy (SGRE), 2024 4th International Conference on Smart Grid and Renewable Energy (SGRE), IEEE, pp. 1-6.
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Saleh, K, Mihaita, A-S & Chalup, S 1970, 'Agent Trajectory Prediction in Urban Traffic Environments via Deep Reward Learning', 27th IEEE International COnference on intelligent Transportation Systems, Edmonton, Canada.
Samal, PB, Thalakotuna, DN, Esselle, KP, Kodithuwakkuge, J & Cetin, E 1970, 'Subarray Antenna Design for Inter-Satellite Communication Link at 60 GHz with Digital Beamforming', MILCOM 2024 - 2024 IEEE Military Communications Conference (MILCOM), MILCOM 2024 - 2024 IEEE Military Communications Conference (MILCOM), IEEE, pp. 894-899.
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Sapkota, S, Huang, H, Hu, Y & Hussain, F 1970, 'A Deep Neural Network (DNN) Based Contract Policy on Hyperledger Fabric for Secure Internet of Things (IoTs)', Springer Nature Switzerland, pp. 313-325.
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Saribatir, E, Zurstraßen, N, Hildenbrand, D, Stock, F, Piña, AM, Wegner, FV, Yan, Z, Wen, S & Arnold, M 1970, 'Game Physics Engine Using Optimised Geometric Algebra RISC-V Vector Extensions Code Using Fourier Series Data', Springer Nature Switzerland, pp. 250-261.
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Sarker, P, Atmakuru, A, Chakraborty, S, Paul, M, Barua, PD & Pradhan, B 1970, 'Leveraging Convolutional Neural Networks for Precise Diagnosis of Autism Through Transfer Learning and Ensemble Model', 2024 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2024 International Conference on Digital Image Computing: Techniques and Applications (DICTA), IEEE, pp. 470-476.
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Sarker, SK, Shafei, H, Shi, T, Li, L, Hossain, MJ & Aguilera, RP 1970, 'A Data-driven Multivariable Adaptive Cybersecurity Framework for Isolated Microgrids', 2024 IEEE 34th Australasian Universities Power Engineering Conference (AUPEC), 2024 IEEE 34th Australasian Universities Power Engineering Conference (AUPEC), IEEE, pp. 1-6.
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Savery, A, Ferguson, S & Johnston, A 1970, 'Gesture and Narrative: Blending Human Performance with Visual Storytelling', Proceedings of the International Conference on New Interfaces for Musical Expression.
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Kind Regards-for a friend is a narrative-driven audiovisual composition that examines the interplay between a human performer and a visual agent. This project was integrated with the development of a new musical interface for the violin bow, and encompassed various strategies for gesture mapping solutions and narrative development. An online audience response survey examined audience experiences of the piece, garnering insights into the effectiveness of our creative and technical processes. Reflections on the project underscored its narrative and interactive strengths, while also identifying music and visual elements that could be further refined to augment the immersive quality of the experience.
Selim, A, Mo, H, Pota, H, Wu, D & Dong, D 1970, 'State of Health Prediction for Battery Energy Storage Systems under Random Walk Operations', 2024 6th International Conference on System Reliability and Safety Engineering (SRSE), 2024 6th International Conference on System Reliability and Safety Engineering (SRSE), IEEE, pp. 8-14.
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Shafei, H, Sarker, SK, Li, L, Aguilera, RP & Alhelou, HH 1970, 'Innovative Observer-Based Framework for Attack Reconstruction and Mitigation in AC Microgrids', 2024 IEEE 34th Australasian Universities Power Engineering Conference (AUPEC), 2024 IEEE 34th Australasian Universities Power Engineering Conference (AUPEC), IEEE, pp. 1-6.
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Shahsavari, M, Hussain, OK, Saberi, M & Sharma, P 1970, 'Empowering Supply chains Resilience: LLMs-Powered BN for Proactive Supply Chain Risk Identification', CEUR Workshop Proceedings.
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The dynamic and unpredictable nature of today's global risk landscape renders supply chains (SCs) susceptible to vulnerabilities, potentially leading to significant business disruptions if left unaddressed. This paper endeavors to construct a proactive risk identification model aimed at enhancing SC resilience. Our approach incorporates agent models, capable of continuous monitoring and early warning recommendations. To imbue these agents with intelligence, we harness the capabilities of Large Language Models (LLMs) to facilitate text comprehension. Specifically, we employ a Bayesian network (BN) as an agent, utilizing news feeds as its primary information source. We introduce a novel methodology, leveraging the expertise of risk managers and LLMs, to determine the relevance of detected events to the targeted SC risks. This research not only strives to equip businesses with the foresight to anticipate potential risk events but also emphasizes the identification and analysis of contributing events. These contributing events are systematically evaluated to understand their potential to precipitate primary risk events, thereby providing a more nuanced insight into the causative chains that lead to SC disruptions. Our methodology enables the proactive quantification of risk likelihood, enhancing predictive capabilities in SC management.
Shan, J, Zhang, Q, Shi, C, Gui, M, Wang, S & Naseem, U 1970, 'Structural Representation Learning and Disentanglement for Evidential Chinese Patent Approval Prediction', Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, CIKM '24: The 33rd ACM International Conference on Information and Knowledge Management, ACM, pp. 2014-2023.
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Shan, Y, Liang, CJ & Xu, M 1970, '3D Reconstruction and Estimation from Single-view 2D Image by Deep Learning – A Survey', 2024 IEEE Conference on Artificial Intelligence (CAI), 2024 IEEE Conference on Artificial Intelligence (CAI), IEEE, pp. 1-7.
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Sheina, A, Zamora, R, Oo, AMT & Gunawardane, K 1970, 'Adaptive Overcurrent Relay Protection Strategy for Demand-side in Offshore DC Microgrids', 2024 IEEE 34th Australasian Universities Power Engineering Conference (AUPEC), 2024 IEEE 34th Australasian Universities Power Engineering Conference (AUPEC), IEEE, pp. 1-6.
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Shen, J, Liang, CJ & Xuan, J 1970, 'Improving the Factuality of Abstractive Text Summarization with Syntactic Structure-Aware Latent Semantic Space', 2024 International Joint Conference on Neural Networks (IJCNN), 2024 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1-8.
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Shen, T, Long, G, Geng, X, Tao, C, Lei, Y, Zhou, T, Blumenstein, M & Jiang, D 1970, 'Retrieval-Augmented Retrieval: Large Language Models are Strong Zero-Shot Retriever', Findings of the Association for Computational Linguistics ACL 2024, Findings of the Association for Computational Linguistics ACL 2024, Association for Computational Linguistics, pp. 15933-15946.
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We propose a simple method that applies a large language model (LLM) to large-scale retrieval in zero-shot scenarios. Our method, the Large language model as Retriever (LameR), is built upon no other neural models but an LLM in a retrieval-augmented retrieval fashion, while breaking brute-force combinations of retrievers with LLMs and lifting the performance of zero-shot retrieval to be very competitive on benchmark datasets. Essentially, we propose to augment a query with its potential answers by prompting LLMs with a composition of the query and the query's in-domain candidates. The candidates, regardless of correct or wrong, are obtained by a vanilla retrieval procedure on the target collection. As a part of the prompts, they are likely to help LLM generate more precise answers by pattern imitation or candidate summarization. Even if all the candidates are wrong, the prompts at least make LLM aware of in-collection patterns and genres. Moreover, due to the low performance of a self-supervised retriever, the LLM-based query augmentation becomes less effective as the retriever bottlenecks the whole pipeline. Therefore, we propose to leverage a non-parametric lexicon-based method (e.g., BM25) as the retrieval module to capture query-document overlap in a literal fashion. As such, LameR makes the retrieval procedure transparent to the LLM, thus circumventing the bottleneck.
Shen, X, Cai, H, Gong, X & Zheng, Y 1970, 'Contrastive Transformer Masked Image Hashing for Degraded Image Retrieval', Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence, Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}, International Joint Conferences on Artificial Intelligence Organization, pp. 1218-1226.
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Hashing utilizes hash code as a compact image representation, offering excellent performance in large-scale image retrieval due to its computational and storage advantages. However, the prevalence of degraded images on social media platforms, resulting from imperfections in the image capture process, poses new challenges for conventional image retrieval methods. To address this issue, we propose Contrastive Transformer Masked Image Hashing (CTMIH), a novel deep unsupervised hashing method specifically designed for degraded image retrieval, which is challenging yet relatively less studied. CTMIH addresses the problem by training on transformed and masked images, aiming to learn transform-invariant hash code in an unsupervised manner to mitigate performance degradation caused by image deterioration. CTMIH utilizes Vision Transformer (ViT) architecture applied to image patches to capture distant semantic relevance. CTMIH introduces cross-view debiased contrastive loss to align hash tokens from augmented views of the same image and presents semantic mask reconstruction loss at the patch level to recover masked patch tokens. Extensive empirical studies conducted on three benchmark datasets demonstrate the superiority of the proposed CTMIH over the state-of-the-art in both degraded and normal image retrieval.
Shen, X, Shi, L, Gong, X & Pan, S 1970, 'Unsupervised Deep Graph Structure and Embedding Learning', Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence, Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}, International Joint Conferences on Artificial Intelligence Organization, pp. 2342-2350.
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Graph Neural Network (GNN) is powerful in graph embedding learning, but its performance has been shown to be heavily degraded under adversarial attacks. Deep graph structure learning (GSL) is proposed to defend attack by jointly learning graph structure and graph embedding, typically in node classification task. Label supervision is expensive in real-world applications, and thus unsupervised GSL is more challenging and still remains less studied. To fulfill this gap, this paper proposes a new unsupervised GSL method, i.e., unsupervised property GNN (UPGNN). UPGNN first refines graph structure by exploring properties of low rank, sparsity, feature smoothness. UPGNN employs graph mutual information loss to learn graph embedding by maximizing its correlation with refined graph. The proposed UPGNN learns graph structure and embedding without label supervision, and thus can be applied various downstream tasks. We further propose Accelerated UPGNN (AUPGNN) to reduce computational complexity, providing a efficient alternative to UPGNN. Our extensive experiments on node classification and clustering demonstrate the effectiveness of the proposed method over the state-of-the-arts especially under heavy perturbation.
Shen, Y, Sun, Y, Li, X, Cao, Z, Eberhard, A & Zhang, G 1970, 'Adaptive Stabilization Based on Machine Learning for Column Generation', Proceedings of Machine Learning Research, pp. 44741-44758.
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Column generation (CG) is a well-established method for solving large-scale linear programs. It involves iteratively optimizing a subproblem containing a subset of columns and using its dual solution to generate new columns with negative reduced costs. This process continues until the dual values converge to the optimal dual solution to the original problem. A natural phenomenon in CG is the heavy oscillation of the dual values during iterations, which can lead to a substantial slowdown in the convergence rate. Stabilization techniques are devised to accelerate the convergence of dual values by using information beyond the state of the current subproblem. However, there remains a significant gap in obtaining more accurate dual values at an earlier stage. To further narrow this gap, this paper introduces a novel approach consisting of 1) a machine learning approach for accurate prediction of optimal dual solutions and 2) an adaptive stabilization technique that effectively capitalizes on accurate predictions. On the graph coloring problem, we show that our method achieves a significantly improved convergence rate compared to traditional methods.
Shi, K, Lu, J, Fang, Z & Zhang, G 1970, 'CLIP-Enhanced Unsupervised Domain Adaptation with Consistency Regularization', 2024 International Joint Conference on Neural Networks (IJCNN), 2024 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1-8.
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Shi, K, Lu, J, Fang, Z & Zhang, G 1970, 'Enhancing Vision-Language Models Incorporating TSK Fuzzy System for Domain Adaptation', 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, pp. 1-8.
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Shi, Y, Xu, M, Zhang, H, Zi, X & Wu, Q 1970, 'A Learnable Agent Collaboration Network Framework for Personalized Multimodal AI Search Engine', Proceedings of the 2nd International Workshop on Deep Multimodal Generation and Retrieval, MM '24: The 32nd ACM International Conference on Multimedia, ACM, pp. 12-20.
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Shi, Y, Zi, X, Shi, Z, Zhang, H, Wu, Q & Xu, M 1970, 'Enhancing Retrieval and Managing Retrieval: A Four-Module Synergy for Improved Quality and Efficiency in RAG Systems', IOS Press.
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Retrieval-augmented generation (RAG) techniques leverage the in-context learning capabilities of large language models (LLMs) to produce more accurate and relevant responses. Originating from the simple ‘retrieve-then-read’ approach, the RAG framework has evolved into a highly flexible and modular paradigm. A critical component, the Query Rewriter module, enhances knowledge retrieval by generating a search-friendly query. This method aligns input questions more closely with the knowledge base. Our research identifies opportunities to enhance the Query Rewriter module to Query Rewriter+ by generating multiple queries to overcome the Information Plateaus associated with a single query and by rewriting questions to eliminate Ambiguity, thereby clarifying the underlying intent. We also find that current RAG systems exhibit issues with Irrelevant Knowledge; to overcome this, we propose the Knowledge Filter. These two modules are both based on the instruction-tuned Gemma-2B model, which together enhance response quality. The final identified issue is Redundant Retrieval; we introduce the Memory Knowledge Reservoir and the Retriever Trigger to solve this. The former supports the dynamic expansion of the RAG system’s knowledge base in a parameter-free manner, while the latter optimizes the cost for accessing external knowledge, thereby improving resource utilization and response efficiency. These four RAG modules synergistically improve the response quality and efficiency of the RAG system. The effectiveness of these modules has been validated through experiments and ablation studies across six common QA datasets. The source code can be accessed at https://github.com/Ancientshi/ERM4.
Shibani, A, Mattins, F, Selvaraj, S, Rajalakshmi, R & Bharathy, G 1970, 'Tamil Co-Writer: Towards inclusive use of generative AI for writing support', CEUR Workshop Proceedings, pp. 240-248.
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The increasing use of generative AI in education highlights its potential for enriching learning experiences. One application for utilising the capabilities of Large Language Models (LLMs) such as the Generative Pre-trained Transformer (GPT) is the creation of writing support tools. In particular, tools that can work in partnership with humans to co-write with AI hold great promise and have been tested out for English language writing. However, the adaptation of such tools to languages other than English is limited, presenting a disadvantage for learners from linguistically diverse backgrounds. In the current study, we extend previous works in English to develop a writing aid prototype for the low-resource Indian regional language Tamil for co-writing with AI called Tamil Co-Writer. The tool additionally provides a visual summary of user interaction and co-authorship metrics for each writing session for users to reflect on their usage of AI in their own writing. We posit that such interactive tools using the latest generative AI technologies can help writers improve their writing skills and productivity in their own regional languages supporting inclusive AI for education.
Singh, A, Zhou, J, Lin, C-T, Lal, S, Eidels, A, Jiang, X & Brown, S 1970, 'Enhancing Marine Navigation Performance Using the Head-Up Interface', 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE, pp. 496-502.
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Singh, K, Attygalle, M, Thalakotuna, D & Esselle, K 1970, 'Multi-Feed Resonant Cavity Antenna with In-Antenna Power Combination for mm-Wave Communication', 2024 18th European Conference on Antennas and Propagation (EuCAP), 2024 18th European Conference on Antennas and Propagation (EuCAP), IEEE, pp. 1-5.
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Singh, K, Mulinde, R, Attygalle, M, Thalakotuna, DN & Esselle, KP 1970, 'Experimental Validation of Multi-feed Resonant Cavity Antenna for mm-Wave Communication', 2024 17th International Conference on Signal Processing and Communication System (ICSPCS), 2024 17th International Conference on Signal Processing and Communication System (ICSPCS), IEEE, pp. 1-7.
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Singh, K, Nabeel, MI, Thalakotuna, D & Esselle, K 1970, 'Steering the Beam of an End-Fire Antenna Using Near-Field Meta-Steering Method', 2024 International Conference on Electromagnetics in Advanced Applications (ICEAA), 2024 International Conference on Electromagnetics in Advanced Applications (ICEAA), IEEE, pp. 361-366.
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Slomma, D, Huang, S & Zhao, L 1970, 'Efficient and Accurate Template-based Reconstruction of Deformable Surfaces', 2024 18th International Conference on Control, Automation, Robotics and Vision (ICARCV), 2024 18th International Conference on Control, Automation, Robotics and Vision (ICARCV), IEEE, pp. 672-678.
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Smith, J, Daniel, S, Simmons, J, Lindeck, J, Berenjforoush Azar, B, Pearson, A & Machet, T 1970, 'Creating A Collaborative Benchmarking Community', Australasian Association of Engineering Education 2024, Christchurch.
Son, DH, Manh, BD, Khoa, TV, Trung, NL, Hoang, DT, Minh, HT, Alem, Y & Minh, LQ 1970, 'Semi-Supervised Learning for Anomaly Detection in Blockchain-Based Supply Chains', 2024 23rd International Symposium on Communications and Information Technologies (ISCIT), 2024 23rd International Symposium on Communications and Information Technologies (ISCIT), IEEE, pp. 140-145.
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Song, H, Castel, A, Hajimohammadi, A & Kim, T 1970, 'Classification and Quantification of Pore Structure of Hempcrete', fib Symposium, pp. 922-929.
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Hemp (i.e., Cannabis sativa L.) was used to produce paper or rope, but its usage declined due to the discovery of cotton and negative views on the drug. However, it starts to gain attention in agriculture and construction, mainly in France and Belgium. Hemp particle (i.e., hemp shiv) is currently one of the promising bio-based aggregates along with cereal straws, wood aggregates, bast fibres, and palm tree fibres). In addition, it is also well known that hempcrete can be used as sound-proof or thermal insulated cementitious materials due to the large porosity of hemp. However, the correlation between the sound and thermal performances of hempcrete and the porous structure of hempcrete has not been well understood. This is mainly due to the complex structure of pores in hempcrete, which consists of three types of porosities, including (1) inter-porosity in hempcrete (i.e., air void), (2) intra-porosity in the binder, and (3) intra-porosity in raw hemp. In this study, three types of porosities were quantified using X-ray micro-computed tomography to understand the pore structures of hempcrete comprehensively.
Song, L, Qin, P & Guo, YJ 1970, 'A 2-D Multi-Beam GRIN Lens Using Multilayer Metasurfaces', 2024 IEEE International Workshop on Radio Frequency and Antenna Technologies (iWRF&AT), 2024 IEEE International Workshop on Radio Frequency and Antenna Technologies (iWRF&AT), IEEE, pp. 99-100.
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Su, E, Raffe, W, Mathieson, L & Wang, Y 1970, 'Better Understanding of Humans for Cooperative AI through Clustering', 2024 IEEE Conference on Games (CoG), 2024 IEEE Conference on Games (CoG), IEEE, pp. 1-8.
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Su, SW 1970, 'Decentralized multi-variable successive loop closure', 2024 36th Chinese Control and Decision Conference (CCDC), 2024 36th Chinese Control and Decision Conference (CCDC), IEEE, pp. 756-760.
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Sun, H, Zhu, HY, Nguyen, MTD, Nguyen, V, Lin, C-T & Jin, CT 1970, 'From RIR to BRIR: A Sparse Recovery Beamforming Approach for Virtual Binaural Sound Rendering', ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp. 1231-1235.
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Sun, S-Y, Ding, C, Sun, H-H & Guo, YJ 1970, 'Characteristic-Mode-Guided Suppression of Cross-Band Scattering and Coupling in Antenna Array', 2024 IEEE International Symposium on Antennas and Propagation and INC/USNC‐URSI Radio Science Meeting (AP-S/INC-USNC-URSI), 2024 IEEE International Symposium on Antennas and Propagation and INC/USNC‐URSI Radio Science Meeting (AP-S/INC-USNC-URSI), IEEE, pp. 577-578.
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Sun, Z, Wang, J, Tan, Z, Dong, D, Ma, H, Li, H & Gong, D 1970, 'EGGen: Image Generation with Multi-entity Prior Learning through Entity Guidance', Proceedings of the 32nd ACM International Conference on Multimedia, MM '24: The 32nd ACM International Conference on Multimedia, ACM, pp. 6637-6645.
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Surawski, N, Awadallah, M, Smit, R, Bagheri, S, Zhao, E & Walker, P 1970, 'Real driving solid particle number emissions from a hydraulic hybrid heavy commercial vehicle and diesel sports utilities vehicles in Australia', ETH NANOPARTICLES CONFERENCE (NPC), ETH Zurich, Switzeland.
Tabandeh, A, Hossain, MJ & Khalilpour, K 1970, 'A Planning Framework for On-Grid Microgrids Incorporating Green Hydrogen and Renewables', 2024 IEEE International Conference on Environment and Electrical Engineering and 2024 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), 2024 IEEE International Conference on Environment and Electrical Engineering and 2024 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), IEEE, pp. 1-6.
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Tang, J, Li, L, Qi, X, Chen, Y, Fan, C & Yu, X 1970, 'AS-NeRF: Learning Auxiliary Sampling for Generalizable Novel View Synthesis from Sparse Views', 2024 IEEE International Conference on Multimedia and Expo (ICME), 2024 IEEE International Conference on Multimedia and Expo (ICME), IEEE, pp. 1-6.
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Tang, W & Long, G 1970, 'WeightRelay: Efficient Heterogeneous Federated Learning on Time Series', Springer Nature Singapore, pp. 129-140.
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Tapas, MJ, Sofia, L, Thomas, P, Vessalas, K, Sirivivatnanon, V & Scrivener, K 1970, 'Long Term Efficacy of Fly Ash in Mitigating Alkali-Silica Reaction Assessed by Pore Solution Method', Springer Nature Switzerland, pp. 403-408.
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Thalakotuna, DN, Esselle, KP, Singh, K & Boers, M 1970, 'Enhancing Multiband Antenna Performance with an Electromagnetically Transparent S-Band Dipole', 2024 IEEE International Symposium on Antennas and Propagation and INC/USNC‐URSI Radio Science Meeting (AP-S/INC-USNC-URSI), 2024 IEEE International Symposium on Antennas and Propagation and INC/USNC‐URSI Radio Science Meeting (AP-S/INC-USNC-URSI), IEEE, pp. 559-560.
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Thiyagarajan, K & Kodagoda, S 1970, 'Sensing Acid Permeation Conditions of Wastewater Pipe Coatings for Long-term Quality Assurance', 2024 IEEE Applied Sensing Conference (APSCON), 2024 IEEE Applied Sensing Conference (APSCON), IEEE, pp. 1-4.
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Thomas, P, Martin, L, De Silva, P, Sirivivatnanon, V & Šimon, P 1970, 'Modelling the Kinetic Behaviour of Delayed Ettringite Formation in Concrete Prisms', Springer Nature Switzerland, pp. 201-208.
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Tian, H, Zhang, G, Liu, B, Zhu, T, Ding, M & Zhou, W 1970, 'When Fairness Meets Privacy: Exploring Privacy Threats in Fair Binary Classifiers via Membership Inference Attacks', Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence, Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}, International Joint Conferences on Artificial Intelligence Organization, pp. 512-520.
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While in-processing fairness approaches show promise in mitigating bias predictions, their potential impact on privacy leakage remains under-explored. We aim to address this gap by assessing the privacy risks of fairness-enhanced binary classifiers with membership inference attacks (MIAs). Surprisingly, our results reveal that these fairness interventions exhibit increased resilience against existing attacks, indicating that enhancing fairness does not necessarily lead to privacy compromises. However, we find current attack methods are ineffective as they typically degrade into simple threshold models with limited attack effectiveness. Following this observation, we discover a novel threat dubbed Fairness Discrepancy Membership Inference Attacks (FD-MIA) that exploits prediction discrepancies between fair and biased models. This attack reveals more potent vulnerabilities and poses significant privacy risks to model privacy. Extensive experiments across multiple datasets, attack methods, and representative fairness approaches confirm our findings and demonstrate the efficacy of the proposed attack method. Our study exposes the overlooked privacy threats in fairness studies, advocating for thorough evaluations of potential security vulnerabilities before model deployments.
Tian, X & Liu, F 1970, 'Take a Close Look at the Optimization of Deep Kernels for Non-parametric Two-Sample Tests', Springer Nature Switzerland, pp. 17-29.
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Tjong, D, Mihaita, A-S, Mao, T, Saleh, K & Herrán, LCF 1970, 'E-scooter driving behaviour analysis using BEAM data: a case study from Brisbane, Australia', 2024 International Symposium on Electromobility (ISEM), 2024 International Symposium on Electromobility (ISEM), IEEE, GUadalajara, Mexico, pp. 1-6.
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Tong, M, Huang, X & Zhang, JA 1970, 'Joint Interference Cancellation for FTN Signaling in I/Q Imbalanced Environment', 2024 23rd International Symposium on Communications and Information Technologies (ISCIT), 2024 23rd International Symposium on Communications and Information Technologies (ISCIT), IEEE, pp. 223-228.
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Torres, I, Ha, Q & Aguilera, R 1970, 'Implementation Guidelines of a UAV Fixed-Wing for Advanced Real-Time Control Algorithms', 2024 IEEE/SICE International Symposium on System Integration (SII), 2024 IEEE/SICE International Symposium on System Integration (SII), IEEE, pp. 804-809.
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Torres, IJ, Aguilera, RP & Ha, QP 1970, 'On the Stability of Nonlinear Model Predictive Control for 3D Target Tracking', IFAC-PapersOnLine, Elsevier BV, pp. 194-199.
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Tran, HV, McGregor, C & Kennedy, PJ 1970, 'Detecting Stress from Multivariate Time Series Data Using Topological Data Analysis', Springer Nature Singapore, pp. 341-353.
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Tran, TTM, Parker, C, Hoggenmüller, M, Wang, Y & Tomitsch, M 1970, 'Exploring the Impact of Interconnected External Interfaces in Autonomous Vehicles on Pedestrian Safety and Experience', Proceedings of the CHI Conference on Human Factors in Computing Systems, CHI '24: CHI Conference on Human Factors in Computing Systems, ACM, pp. 1-17.
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Tran, TTM, Yu, X, Wang, Y, Parker, C & Tomitsch, M 1970, 'Mapping Pedestrian-to-Driver Gestures: Implications for Autonomous Vehicle Bidirectional Interaction', Adjunct Proceedings of the 16th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI '24: 16th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, ACM, pp. 1-7.
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Tran, X-T, Nguyen, Q-T, Le, L, Do, T & Lin, C-T 1970, 'EEG-Based Contrastive Learning Models For Object Perception Using Multisensory Image-Audio Stimuli', Proceedings of the 1st International Workshop on Brain-Computer Interfaces (BCI) for Multimedia Understanding, MM '24: The 32nd ACM International Conference on Multimedia, ACM, pp. 39-47.
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Trianni, A, Bennett, N, Cantley-Smith, R, Cheng, C-T, Dunstall, S, Hasan, ASMM, Katic, M, Leak, J, Lindsay, D, Pears, A, Wheatland, FT, White, S & Zeichner, F 1970, 'Industry 4.0 for Energy Productivity: Insights and Future Perspective for Australia', Springer Nature Switzerland, pp. 547-554.
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Tripathy, K, Kumar, D, Thiyagarajan, K & Bhattacharjee, M 1970, 'Flexible Tactile Sensing Using PDMS/TiO2 Based Capacitor with MOSFET Structure', 2024 IEEE SENSORS, 2024 IEEE SENSORS, IEEE, pp. 1-4.
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Trivedi, S, Goh, DJ, Chen, W, Toh, WD, Zhang, J, Ghosh, S, Giusti, D, Leotti, A, Chan, HC, Koppisetti, G, Zhang, Y, Gao, Y, Lee, JE-Y & Koh, Y 1970, 'Long-Range Ultrasound Wake-Up Receiver Using Pzt-Scaln Hybrid Pmut Link With Exponential Horn', 2024 IEEE 37th International Conference on Micro Electro Mechanical Systems (MEMS), 2024 IEEE 37th International Conference on Micro Electro Mechanical Systems (MEMS), IEEE, pp. 501-504.
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Triwiyanto, Yulianto, E, Rahmawati, T & Chai, R 1970, 'A Deep CNN-Based Approach for 10-Class with Two-Channel EMG Signal Classification', Springer Nature Singapore, pp. 685-699.
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Tsekouras, E, Aguilera, RP & Geyer, T 1970, 'Real-time Computation of Optimized Pulse Patterns for Compensation of Estimated Grid Voltage Harmonics', 2024 IEEE Energy Conversion Congress and Exposition (ECCE), 2024 IEEE Energy Conversion Congress and Exposition (ECCE), IEEE, pp. 4590-4597.
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Ubaid, A, Lin, X & Hussain, FK 1970, 'SW Forecaster: An Intelligent Data-Driven Approach for Water Usage Demand Forecasting', 2024 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), 2024 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), IEEE, Abu Dhabi, pp. 17-24.
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Uddin, M, Mo, H & Dong, D 1970, 'Storage-based Energy Management for Multi-energy Community Microgrid', 2024 IEEE Kansas Power and Energy Conference (KPEC), 2024 IEEE Kansas Power and Energy Conference (KPEC), IEEE, pp. 1-5.
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Vali, M, Levente, K & Gandomi, AH 1970, 'A New Mutation Operator for Tabu Search Algorithm for Continuous Optimization', 2024 IEEE 18th International Symposium on Applied Computational Intelligence and Informatics (SACI), 2024 IEEE 18th International Symposium on Applied Computational Intelligence and Informatics (SACI), IEEE, pp. 000509-000514.
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Varanasi, VS, Li, L & Khalilpour, K 1970, 'Leveraging Hydrogen Integration to Optimise Wind Energy Utilisation and Minimise Fuel Costs in Remote Mines', 2024 IEEE 34th Australasian Universities Power Engineering Conference (AUPEC), 2024 IEEE 34th Australasian Universities Power Engineering Conference (AUPEC), IEEE, pp. 1-6.
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Vaseghi, S, Sheng, D, Kodikara, J & Khabbaz, H 1970, 'Deformation of Unbound Granular Materials in Three-Dimensional Stress State', Geo-Congress 2024, Geo-Congress 2024, American Society of Civil Engineers, pp. 256-265.
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Vo, N, Nguyen, DQ, Le, DD, Piccardi, M & Buntine, W 1970, 'Improving Vietnamese-English Medical Machine Translation', 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings, pp. 8955-8962.
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Machine translation for Vietnamese-English in the medical domain is still an under-explored research area. In this paper, we introduce MedEV-a high-quality Vietnamese-English parallel dataset constructed specifically for the medical domain, comprising approximately 360K sentence pairs. We conduct extensive experiments comparing Google Translate, ChatGPT (gpt-3.5-turbo), state-of-the-art Vietnamese-English neural machine translation models and pre-trained bilingual/multilingual sequence-to-sequence models on our new MedEV dataset. Experimental results show that the best performance is achieved by fine-tuning vinai-translate (Nguyen et al., 2022b) for each translation direction. We publicly release our dataset to promote further research.
Wagstyl, D, Syberg, M, Büscher, JN, Schlunder, P, Kimberger, J, Wöstmann, R, Wolf, N, Schulte, L & Deuse, J 1970, 'Towards Carbon Neutrality Using Green Digital Twins for Industrial Energy Systems', 2024 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia), 2024 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia), IEEE, pp. 1-9.
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Wai Choong, DS, Ssu-Han Chen, D, Sarafianou, M, Goh, DJ, Liu, J, Merugu, S, Ghosh, S, Ramegowda, P, Zhang, QX, Chang, P, Lee, JE-Y, Jia, J, Leotti, A, Baretta, L, Giusti, D, Savoia, A & Koh, Y 1970, 'A Study on PVD PZT and Sc0.2Al0.8N PMUTS in Series and Parallel Connection for Optimizing Acoustic Performance', 2024 IEEE Ultrasonics, Ferroelectrics, and Frequency Control Joint Symposium (UFFC-JS), 2024 IEEE Ultrasonics, Ferroelectrics, and Frequency Control Joint Symposium (UFFC-JS), IEEE, pp. 1-4.
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Wang, B, Zhou, J, Li, Y & Chen, F 1970, 'Impact of Fidelity and Robustness of Machine Learning Explanations on User Trust', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Nature Singapore, pp. 209-220.
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EXplainable machine learning (XML) has recently emerged as a promising approach to address the inherent opacity of machine learning (ML) systems by providing insights into their reasoning processes. This paper explores the relationships among user trust, fidelity, and robustness within the context of ML explanations. To investigate these relationships, a user study is implemented within the context of predicting students’ performance. The study is designed to focus on two scenarios: (1) fidelity-based scenario—exploring dynamics of user trust across different explanations at varying fidelity levels and (2) robustness-based scenario—examining dynamics of in user trust concerning robustness. For each scenario, we conduct experiments based on two different metrics, including self-reported trust and behaviour-based trust metrics. For the fidelity-based scenario, we find that users trust both high and low-fidelity explanations compared to without-fidelity explanations (no explanations) based on the behaviour-based trust results, rather than relying on the self-reported trust results. We also obtain consistent findings based on different metrics, indicating no significant differences in user trust when comparing different explanations across fidelity levels. Additionally, for the robustness-based scenario, we get contrasting results from the two metrics. The self-reported trust metric does not demonstrate any variations in user trust concerning robustness levels, whereas the behaviour-based trust metric suggests that user trust tends to be higher when robustness levels are higher.
Wang, D, Li, S, Xiao, G, Liu, Y, Sui, Y, He, P & Lyu, MR 1970, 'An Exploratory Investigation of Log Anomalies in Unmanned Aerial Vehicles', Proceedings of the IEEE/ACM 46th International Conference on Software Engineering, ICSE '24: IEEE/ACM 46th International Conference on Software Engineering, ACM, pp. 1-13.
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Wang, K, Lu, J, Liu, A & Zhang, G 1970, 'An Adaptive Stacking Method for Multiple Data Streams Learning under Concept Drift', Intelligent Management of Data and Information in Decision Making, 16th FLINS Conference on Computational Intelligence in Decision and Control & the 19th ISKE Conference on Intelligence Systems and Knowledge Engineering (FLINS-ISKE 2024), WORLD SCIENTIFIC, pp. 267-274.
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Wang, K, Lu, J, Liu, A & Zhang, G 1970, 'An Augmented Learning Approach for Multiple Data Streams Under Concept Drift', Springer Nature Singapore, pp. 391-402.
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Multiple data streams learning attracts more and more attention recently. Different from learning a single data stream, the uncertain and complex occurrence of concept drift in multiple data streams, bring challenges in real-time learning task. To address this issue, this paper proposed a method called time-warping-based concept drift learning method (TW-CDM) for dealing with multiple data streams. First, a time-warping-based drift identification process is given to recognize the drift region. Second, an augmented learning process is developed by crossly using the located region data. Finally, a selectively augmented learning process is given to reduce the influence of different drift severity. The proposed method is evaluated on both synthetic and real-world datasets, and compared with benchmark methods. The experiment results show the efficiency of the proposed method.
Wang, L-C, Liu, W & Liao, C-S 1970, 'Incremental Random Forest for Unsupervised Learning', 2024 IEEE Conference on Artificial Intelligence (CAI), 2024 IEEE Conference on Artificial Intelligence (CAI), IEEE, pp. 704-705.
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Wang, M, Li, X, Luo, W, Chen, H & Chen, S-L 1970, 'A Wideband Circularly Polarized Vivaldi Array Antenna with Axial Ratio Enhancement', 2024 Photonics & Electromagnetics Research Symposium (PIERS), 2024 Photonics & Electromagnetics Research Symposium (PIERS), IEEE, pp. 1-5.
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Wang, M, Zhang, H, Hu, N, Xie, W, Chen, S & Chen, Z 1970, 'A Wideband Circularly Polarized Filtering Array Antenna Using Dual-Layer Circular Cross Slotted Patch', 2024 18th European Conference on Antennas and Propagation (EuCAP), 2024 18th European Conference on Antennas and Propagation (EuCAP), IEEE, pp. 1-3.
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Wang, Q, Wang, Z, Carmichael, MG, Liu, D & Lin, C-T 1970, 'Comparison of Rating Scale and Pairwise Comparison Methods for Measuring Human Co-worker Subjective Impression of Robot during Physical Human-Robot Collaboration', 2024 IEEE International Conference on Robotics and Automation (ICRA), 2024 IEEE International Conference on Robotics and Automation (ICRA), IEEE, pp. 907-913.
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Wang, R, Zuo, H, Fang, Z & Lu, J 1970, 'Multiple Teacher Model for Continual Test-Time Domain Adaptation', Springer Nature Singapore, pp. 304-314.
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Wang, R, Zuo, H, Fang, Z & Lu, J 1970, 'Prompt-Based Memory Bank for Continual Test-Time Domain Adaptation in Vision-Language Models', 2024 International Joint Conference on Neural Networks (IJCNN), 2024 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1-8.
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Wang, S, Wang, W, Zhang, X, Wang, Y, Liu, H & Chen, F 1970, 'A Hierarchical and Disentangling Interest Learning Framework for Unbiased and True News Recommendation', Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ACM, pp. 3200-3211.
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Wang, W, Huang, B, Liu, F, You, X, Liu, T, Zhang, K & Gong, M 1970, 'Optimal Kernel Choice for Score Function-based Causal Discovery', Proceedings of Machine Learning Research, pp. 50650-50668.
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Score-based methods have demonstrated their effectiveness in discovering causal relationships by scoring different causal structures based on their goodness of fit to the data. Recently, Huang et al. (2018) proposed a generalized score function that can handle general data distributions and causal relationships by modeling the relations in reproducing kernel Hilbert space (RKHS). The selection of an appropriate kernel within this score function is crucial for accurately characterizing causal relationships and ensuring precise causal discovery. However, the current method involves manual heuristic selection of kernel parameters, making the process tedious and less likely to ensure optimality. In this paper, we propose a kernel selection method within the generalized score function that automatically selects the optimal kernel that best fits the data. Specifically, we model the generative process of the variables involved in each step of the causal graph search procedure as a mixture of independent noise variables. Based on this model, we derive an automatic kernel selection method by maximizing the marginal likelihood of the variables involved in each search step. We conduct experiments on both synthetic data and real-world benchmarks, and the results demonstrate that our proposed method outperforms heuristic kernel selection methods.
Wang, X, Ding, C, Zhao, G, Li, S & Chen, Y 1970, 'A Symmetrical Decoupling Method for Differentially-Fed Antennas (DFA)', 2024 IEEE International Workshop on Radio Frequency and Antenna Technologies (iWRF&AT), 2024 IEEE International Workshop on Radio Frequency and Antenna Technologies (iWRF&AT), IEEE, pp. 17-21.
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Wang, X, Ding, C, Zhao, G, Li, S & Sun, H 1970, 'Decoupling of A 1 × 4 Differentially-Fed Antenna (DFA) Array with Symmetrical Feeding Network', 2024 IEEE International Symposium on Antennas and Propagation and INC/USNC‐URSI Radio Science Meeting (AP-S/INC-USNC-URSI), 2024 IEEE International Symposium on Antennas and Propagation and INC/USNC‐URSI Radio Science Meeting (AP-S/INC-USNC-URSI), IEEE, pp. 1749-1750.
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Wang, X, Li, M, Xu, P, Liu, W, Zhang, LY, Hu, S & Zhang, Y 1970, 'PointAPA: Towards Availability Poisoning Attacks in 3D Point Clouds', Springer Nature Switzerland, pp. 125-145.
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Wang, Y, Hoggenmüller, M, Zhang, G, Tran, TTM & Tomitsch, M 1970, 'Immersive In-Situ Prototyping: Influence of Real-World Context on Evaluating Future Pedestrian Interfaces in Virtual Reality', Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, CHI '24: CHI Conference on Human Factors in Computing Systems, ACM, pp. 1-8.
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Wang, Y, Tran, TTM & Tomitsch, M 1970, 'Physiological Measurements in Automated Vehicle-Pedestrian Research: Review and Future Opportunities', Adjunct Proceedings of the 16th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI '24: 16th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, ACM, pp. 172-177.
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Wang, Y, Zhao, L & Huang, S 1970, 'Grid-based Submap Joining: An Efficient Algorithm for Simultaneously Optimizing Global Occupancy Map and Local Submap Frames', 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, pp. 10121-10128.
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Wang, Z & Carmichael, MG 1970, 'Exploring the Effect of Base Compliance on Physical Human-Robot Collaboration', 2024 IEEE International Conference on Robotics and Automation (ICRA), 2024 IEEE International Conference on Robotics and Automation (ICRA), IEEE, pp. 3198-3204.
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Wei, S, Peng, X, Guan, H, Geng, L, Jian, P, Wu, H & Lu, W 1970, 'Multi-view Contrastive Learning for Medical Question Summarization', 2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD), 2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD), IEEE, pp. 1826-1831.
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Wei, T, Chen, Z & Yu, X 1970, 'Snap and Diagnose: An Advanced Multimodal Retrieval System for Identifying Plant Diseases in the Wild', Proceedings of the 6th ACM International Conference on Multimedia in Asia, MMAsia '24: ACM Multimedia Asia, ACM, pp. 1-3.
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Wei, T, Chen, Z, Huang, Z & Yu, X 1970, 'Benchmarking In-the-Wild Multimodal Disease Recognition and A Versatile Baseline', Proceedings of the 32nd ACM International Conference on Multimedia, MM '24: The 32nd ACM International Conference on Multimedia, ACM, pp. 1593-1601.
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Wen, J, Gabrys, B & Musial, K 1970, 'Fuzzy Feature Representation for Digital Twin-Oriented Social Network Simulators', 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, pp. 1-8.
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Weng, H, Liu, Y & Chen, L 1970, 'Spatial Bottleneck Transformer for Cellular Traffic Prediction in the Urban City', Springer Nature Singapore, pp. 265-276.
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West, N & Deuse, J 1970, 'A Comparative Study of Machine Learning Approaches for Anomaly Detection in Industrial Screw Driving Data', Proceedings of the Annual Hawaii International Conference on System Sciences, pp. 1050-1059.
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This paper investigates the application of Machine Learning (ML) approaches for anomaly detection in time series data from screw driving operations, a pivotal process in manufacturing. Leveraging a novel, open-access real-world dataset, we explore the efficacy of several unsupervised and supervised ML models. Among unsupervised models, DBSCAN demonstrates the best performance with an accuracy of 96.68% and a Macro F1 score of 90.70%. Within the supervised models, the Random Forest classifier excels, achieving an accuracy of 99.02% and a Macro F1 score of 98.36%. These results not only underscore the potential of ML in boosting manufacturing quality and efficiency, but also highlight the challenges in their practical deployment. This research encourages further investigation and refinement of ML techniques for industrial anomaly detection, thereby contributing to the advancement of resilient, efficient, and sustainable manufacturing processes. The entire analysis, comprising the complete dataset as well as the Python-based scripts are made publicly available via a dedicated repository. This commitment to open science aims to support the practical application and future adaptation of our work to support business decisions in quality management and the manufacturing industry.
Weththasinghe, K, Tu Ngo, Q, He, Y, Jayawickrama, B & Dutkiewicz, E 1970, 'Cognitive GEO-LEO Dual Satellite Networks: Multibeam Sensing', 2024 23rd International Symposium on Communications and Information Technologies (ISCIT), 2024 23rd International Symposium on Communications and Information Technologies (ISCIT), IEEE, pp. 105-109.
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Wijesena, S & Pradhan, B 1970, 'Weather index insurance parameter optimisation using machine learning and remote sensing data', 2024 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS), 2024 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS), IEEE, pp. 1-3.
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Wijesooriya, K, Zhu, S, Rawat, S, Lee, C-K & Mohotti, D 1970, 'Engineered Cementitious Composite Beams in Impact Damage Mitigation for Bridge Piers', Springer Nature Singapore, pp. 595-605.
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Williams, P, Karimi, M, Sepehrirahnama, S, Garcia, JR, Croft, B, Hanson, D, Miller, A & Parnell, J 1970, 'Investigation into noise attenuation strategies within train tunnels', Proceedings of Acoustics 2024: Acoustics in the Sun.
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Across Australia, cities are extending their public transportation systems through additional tunnels and metro systems. The sound energy generated by metro trains operating in tunnels is mainly confined within the tunnel by the tunnel walls, causing higher noise levels within the carriages than comparable above-ground networks. This work investigates in-tunnel noise using a computational model of the metro tunnel, with the objective of developing a tool that can be used to investigate a range of novel mitigation measures. A numerical model is developed that uses the semi-analytical finite element (SAFE) method to provide a novel modal-based approach towards tunnel noise modelling. The first stage of this method involves performing a two-dimensional finite element eigenvalue decomposition over the cross-section of the tunnel to determine the characteristics of each tunnel mode. Propagation of sound pressure along the length of the tunnel is then implemented analytically, avoiding the computational expense of three-dimensional standard finite element methods. For simplicity, the carriage can be included as a rigid body, and the variations of the tunnel cross-section are neglected. Predictions are compared to the measured in-tunnel noise generated using a known input source. Numerical and experimental studies will allow for the investigation of cost-effective noise reduction solutions that may be applied to current and future rail projects.
Win, Z, Zhu, H, Chan, B, Bahr, R & Yang, Y 1970, 'A Compact Broadband Quadrature Coupler using 3D-Printing Package Technology', 2024 54th European Microwave Conference (EuMC), 2024 54th European Microwave Conference (EuMC), IEEE, pp. 361-364.
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Wöstmann, R, Möhle, R, Krappe, H & Deuse, J 1970, 'Comprehensive equipment behaviour descriptionin production lifecycle using digital twin concepts and ISO standards of Equipment Behaviour Catalogues', IFAC-PapersOnLine, Elsevier BV, pp. 223-228.
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Wu, C, Wang, H, Zhang, X, Fang, Z & Bu, J 1970, 'Spatio-temporal Heterogeneous Federated Learning for Time Series Classification with Multi-view Orthogonal Training', Proceedings of the 32nd ACM International Conference on Multimedia, MM '24: The 32nd ACM International Conference on Multimedia, ACM, pp. 2613-2622.
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Wu, J, Huang, Y, Gao, M, Niu, Y, Yang, M, Gao, Z & Zhao, J 1970, 'Selective and Orthogonal Feature Activation for Pedestrian Attribute Recognition', Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), pp. 6039-6047.
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Pedestrian Attribute Recognition (PAR) involves identifying the attributes of individuals in person images. Existing PAR methods typically rely on CNNs as the backbone network to extract pedestrian features. However, CNNs process only one adjacent region at a time, leading to the loss of long-range inter-relations between different attribute-specific regions. To address this limitation, we leverage the Vision Transformer (ViT) instead of CNNs as the backbone for PAR, aiming to model long-range relations and extract more robust features. However, PAR suffers from an inherent attribute imbalance issue, causing ViT to naturally focus more on attributes that appear frequently in the training set and ignore some pedestrian attributes that appear less. The native features extracted by ViT are not able to tolerate the imbalance attribute distribution issue. To tackle this issue, we propose two novel components: the Selective Feature Activation Method (SFAM) and the Orthogonal Feature Activation Loss. SFAM smartly suppresses the more informative attribute-specific features, compelling the PAR model to capture discriminative features from regions that are easily overlooked. The proposed loss enforces an orthogonal constraint on the original feature extracted by ViT and the suppressed features from SFAM, promoting the complementarity of features in space. We conduct experiments on several benchmark PAR datasets, including PETA, PA100K, RAPv1, and RAPv2, demonstrating the effectiveness of our method. Specifically, our method outperforms existing state-of-the-art approaches by GRL, IAA-Caps, ALM, and SSC in terms of mA on the four datasets, respectively.
Wu, M, Xuan, J & Lu, J 1970, 'Functional Wasserstein Bridge Inference for Bayesian Deep Learning', Proceedings of Machine Learning Research, pp. 3791-3815.
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Bayesian deep learning (BDL) is an emerging field that combines the strong function approximation power of deep learning with the uncertainty modeling capabilities of Bayesian methods. In addition to those virtues, however, there are accompanying issues brought by such a combination to the classical parameter-space variational inference, such as the nonmeaningful priors, intricate posteriors, and possible pathologies. In this paper, we propose a new function-space variational inference solution called Functional Wasserstein Bridge Inference (FWBI), which can assign meaningful functional priors and obtain well-behaved posterior. Specifically, we develop a Wasserstein distance-based bridge to avoid the potential pathological behaviors of Kullback-Leibler (KL) divergence between stochastic processes that arise in most existing functional variational inference approaches. The derived functional variational objective is well-defined and proved to be a lower bound of the model evidence. We demonstrate the improved predictive performance and better uncertainty quantification of our FWBI on several tasks compared with various parameter-space and function-space variational methods.
Wu, M, Yu, C, Xu, J, Ding, Y & Zhang, Y 1970, 'Biomedical association inference on pandemic knowledge graphs: A comparative study', CEUR Workshop Proceedings, pp. 124-127.
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Acquiring insights and understanding from historical pandemics is crucial for reducing the likelihood of their recurrence. The utilization of knowledge graphs stands as an essential tool for researchers, with knowledge inference emerging as a prominent task within these graphs to deduce previously unidentified connections between entities. This study endeavors to construct a knowledge graph centered on pandemic research and to evaluate the efficacy of various mainstream methodologies in the context of biomedical association inference. Our findings indicate that techniques for graph representation hold significant promise in executing these tasks and heterogeneous graph representation techniques demonstrate high predicting accuracy. Nonetheless, the advancement in this area of research necessitates more refined experimental designs and the adoption of more adaptive learning strategies.
Wu, Q, Huang, Y & Irga, P 1970, 'Urban heat island mitigation by a modular parklet during a heat wave event in Sydney', 24th Australasian Fluid Mechanics Conference, Canberra, Australia.
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The research presented here numerically investigated the impact of a modular parklet on the urban heat island (UHI) effect, during a heat wave event in Sydney, Australia. The computational domain was meshed based on an ideal symmetric street canyon with a simplified parklet geometry. The parklet was modelled as a porous media with appropriate resistance, porosity, energy, and turbulence equations. The simulation also utilized ANSYS Fluent’s solar calculator and discrete ordinate radiation model to determine the distribution of radiative heat fluxes on surfaces. Field experiments were conducted to collect ambient air temperature and air temperature near the parklet, which were used to validate the model. The simulation results indicated that the air temperature on the parklet is lower by 0.4 °C, and the parklet can cool the surrounding air at pedestrian height of over 0.1 °C. The wind velocity magnitude is reduced near the parklet, while the average wind velocity at pedestrian height is slightly higher (0.075 m/s) than the canyon without the parklet.
Wu, S, Zhou, J, Dong, Y & Chen, F 1970, 'Enhancing Explainability of Deep Learning-Based ECG Diagnosis Using Large Language Models', Proceedings of the 2024 8th International Conference on Advances in Artificial Intelligence, ICAAI 2024: 2024 The 8th International Conference on Advances in Artificial Intelligence, ACM, pp. 61-65.
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Wu, T & Shi, K 1970, 'Enhancing Academic Title Drafting Through Abstractive Summarization', 2024 11th International Conference on Behavioural and Social Computing (BESC), 2024 11th International Conference on Behavioural and Social Computing (BESC), IEEE, pp. 1-7.
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Wu, Y, Sun, R, Wang, X, Zhang, Y, Qin, L, Zhang, W & Lin, X 1970, 'Efficient Maximal Temporal Plex Enumeration', 2024 IEEE 40th International Conference on Data Engineering (ICDE), 2024 IEEE 40th International Conference on Data Engineering (ICDE), IEEE, pp. 3098-3110.
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Xiao, F, Guan, J, Zhu, Q, Liu, X, Wang, W, Qi, S, Zhang, K, Sun, J & Wang, W 1970, 'A REFERENCE-FREE METRIC FOR LANGUAGE-QUERIED AUDIO SOURCE SEPARATION USING CONTRASTIVE LANGUAGE-AUDIO PRETRAINING', Workshop on Detection and Classification of Acoustic Scenes and Events, Tokyo, Japan.
Xie, F, Yan, C, Meng, MH, Teng, S, Zhang, Y & Bai, G 1970, 'Are Your Requests Your True Needs? Checking Excessive Data Collection in VPA App', Proceedings of the IEEE/ACM 46th International Conference on Software Engineering, ICSE '24: IEEE/ACM 46th International Conference on Software Engineering, ACM, pp. 1-12.
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Xie, W, Li, H, Ma, J, Li, Y, Lei, J, Liu, D & Fang, L 1970, 'JointSQ: Joint Sparsification-Quantization for Distributed Learning', 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, pp. 5778-5787.
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Xu, B, Indraratna, B, Cholachat, R & Thanh, N 1970, 'Radial consolidation analysis of layered soil under various load and drainage boundary conditions: spectral-based solutions', Proceedings of the 2024 Sydney Symposium, Sydney.
Xu, C, Fu, H, Ma, L, Jia, W, Zhang, C, Xia, F, Ai, X, Li, B & Zhang, W 1970, 'Seeing Text in the Dark: Algorithm and Benchmark', Proceedings of the 32nd ACM International Conference on Multimedia, MM '24: The 32nd ACM International Conference on Multimedia, ACM, pp. 2870-2878.
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Xu, G, Jia, W, Wu, T, Chen, L & Gao, G 1970, 'MFPNet: A Multi-scale Feature Propagation Network for Lightweight Semantic Segmentation', Springer Nature Switzerland, pp. 76-86.
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Xu, H, Xuan, J, Zhang, G & Lu, J 1970, 'Reciprocal Trust Region Policy Optimization', Intelligent Management of Data and Information in Decision Making, 16th FLINS Conference on Computational Intelligence in Decision and Control & the 19th ISKE Conference on Intelligence Systems and Knowledge Engineering (FLINS-ISKE 2024), WORLD SCIENTIFIC, pp. 187-194.
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Xu, Q, Du, H, Chen, H, Liu, B & Yu, X 1970, 'MMOOC: A Multimodal Misinformation Dataset for Out-of-Context News Analysis', Springer Nature Singapore, pp. 444-459.
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Xu, X, Li, C, Chen, Y, Chang, X, Liu, J & Wang, S 1970, 'No Token Left Behind: Efficient Vision Transformer via Dynamic Token Idling', Springer Nature Singapore, pp. 28-41.
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Xu, X, Tao, C, Shen, T, Xu, C, Xu, H, Long, G, Lou, J-G & Ma, S 1970, 'Re-Reading Improves Reasoning in Large Language Models', Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, pp. 15549-15575.
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To enhance the reasoning capabilities of off-the-shelf Large Language Models (LLMs), we introduce a simple, yet general and effective prompting method, RE2, i.e., Re-Reading the question as input. Unlike most thought-eliciting prompting methods, such as Chain-of-Thought (CoT), which aim to elicit the reasoning process in the output, RE2 shifts the focus to the input by processing questions twice, thereby enhancing the understanding process. Consequently, RE2 demonstrates strong generality and compatibility with most thought-eliciting prompting methods, including CoT. Crucially, RE2 facilitates a 'bidirectional' encoding in unidirectional decoder-only LLMs because the first pass could provide global information for the second pass. We begin with a preliminary empirical study as the foundation of RE2, illustrating its potential to enable 'bidirectional' attention mechanisms. We then evaluate RE2 on extensive reasoning benchmarks across 14 datasets, spanning 112 experiments, to validate its effectiveness and generality. Our findings indicate that, with the exception of a few scenarios on vanilla ChatGPT, RE2 consistently enhances the reasoning performance of LLMs through a simple re-reading strategy. Further analyses reveal RE2's adaptability, showing how it can be effectively integrated with different LLMs, thought-eliciting prompting, and ensemble strategies.
Xu, Y, Zhu, H & Guo, YJ 1970, 'A Multibeam Antenna Array Fed by a New Design of RF Beamforming Network', 2024 International Conference on Electromagnetics in Advanced Applications (ICEAA), 2024 International Conference on Electromagnetics in Advanced Applications (ICEAA), IEEE, pp. 1-1.
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Xuan, J, Wu, M, Liu, Z & Lu, J 1970, 'Functional Wasserstein Variational Policy Optimization', Proceedings of Machine Learning Research, pp. 3893-3911.
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Variational policy optimization has become increasingly attractive to the reinforcement learning community because of its strong capability in uncertainty modeling and environment generalization. However, almost all existing studies in this area rely on Kullback-Leibler (KL) divergence which is unfortunately ill-defined in several situations. In addition, the policy is parameterized and optimized in weight space, which may not only bring additional unnecessary bias but also make the policy learning harder due to the complicatedly dependent weight posterior. In the paper, we design a novel functional Wasserstein variational policy optimization (FWVPO) based on the Wasserstein distance between function distributions. Specifically, we firstly parameterize policy as a Bayesian neural network but from a function-space view rather than a weight-space view and then propose FWVPO to optimize and explore the functional policy posterior. We prove that our FWVPO is a valid variational Bayesian objective and also guarantees the monotonic expected reward improvement under certain conditions. Experimental results on multiple reinforcement learning tasks demonstrate the efficiency of our new algorithm in terms of both cumulative rewards and uncertainty modeling capability.
Yafi, E, Chuahan, R, Sharma, A & Zuhairi, MF 1970, 'Integrated Empowered AI and IoT Approach for Heart Prediction', 2024 18th International Conference on Ubiquitous Information Management and Communication (IMCOM), 2024 18th International Conference on Ubiquitous Information Management and Communication (IMCOM), IEEE, pp. 1-7.
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Yan, C, Chang, X, Li, Z, Yao, L, Luo, M & Zheng, Q 1970, 'MASKED DISTILLATION ADVANCES SELF-SUPERVISED TRANSFORMER ARCHITECTURE SEARCH', 12th International Conference on Learning Representations, ICLR 2024.
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Transformer architecture search (TAS) has achieved remarkable progress in automating the neural architecture design process of vision transformers. Recent TAS advancements have discovered outstanding transformer architectures while saving tremendous labor from human experts. However, it is still cumbersome to deploy these methods in real-world applications due to the expensive costs of data labeling under the supervised learning paradigm. To this end, this paper proposes a masked image modelling (MIM) based self-supervised neural architecture search method specifically designed for vision transformers, termed as MaskTAS, which completely avoids the expensive costs of data labeling inherited from supervised learning. Based on the one-shot NAS framework, MaskTAS requires to train various weight-sharing subnets, which can easily diverged without strong supervision in MIM-based self-supervised learning. For this issue, we design the search space of MaskTAS as a siamesed teacher-student architecture to distill knowledge from pre-trained networks, allowing for efficient training of the transformer supernet. To achieve self-supervised transformer architecture search, we further design a novel unsupervised evaluation metric for the evolutionary search algorithm, where each candidate of the student branch is rated by measuring its consistency with the larger teacher network. Extensive experiments demonstrate that the searched architectures can achieve state-of-the-art accuracy on CIFAR-10, CIFAR-100, and ImageNet datasets even without using manual labels. Moreover, the proposed MaskTAS can generalize well to various data domains and tasks by searching specialized transformer architectures in self-supervised manner.
Yan, P & Long, G 1970, 'Client-Supervised Federated Learning: Towards One-Model-for-All Personalization', 2024 IEEE International Conference on Multimedia and Expo (ICME), 2024 IEEE International Conference on Multimedia and Expo (ICME), IEEE, pp. 1-6.
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Yang, G, Lei, J, Fang, Z, Zhang, J, Zhang, J, Xie, W & Li, Y 1970, 'E4SA: An Ultra-Efficient Systolic Array Architecture for 4-Bit Convolutional Neural Networks', Proceedings of the 2024 ACM/SIGDA International Symposium on Field Programmable Gate Arrays, FPGA '24: The 2024 ACM/SIGDA International Symposium on Field Programmable Gate Arrays, ACM, pp. 183-183.
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Yang, G, Lei, J, Fang, Z, Zhang, J, Zhang, J, Xie, W & Li, Y 1970, 'SA4: A Comprehensive Analysis and Optimization of Systolic Array Architecture for 4-bit Convolutions', 2024 34th International Conference on Field-Programmable Logic and Applications (FPL), 2024 34th International Conference on Field-Programmable Logic and Applications (FPL), IEEE, pp. 204-212.
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Yang, G, Xie, Y, Xue, ZJ, Chang, S-E, Li, Y, Dong, P, Lei, J, Xie, W, Wang, Y, Lin, X & Fang, Z 1970, 'SDA: Low-Bit Stable Diffusion Acceleration on Edge FPGAs', 2024 34th International Conference on Field-Programmable Logic and Applications (FPL), 2024 34th International Conference on Field-Programmable Logic and Applications (FPL), IEEE, pp. 264-273.
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Yang, J & Lin, S-K 1970, 'Shedding New Light on Traditional Image Clustering: A Non-Deep Approach With Competitive Performance and Interpretability', 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE, pp. 2289-2296.
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Yang, J & Long, G 1970, 'Concept-Guided Interpretable Federated Learning', Springer Nature Singapore, pp. 160-172.
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Yang, L, Malki, M, Muñoz-Ferreras, J-M, Zhu, X & Gómez-García, R 1970, 'High-Order Multilayer Input-Absorptive RF Filter With Wideband Quasi-Flat Group Delay and Multiple Stopband Transmission Zeros', 2024 IEEE International Symposium on Circuits and Systems (ISCAS), 2024 IEEE International Symposium on Circuits and Systems (ISCAS), IEEE, pp. 1-5.
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Yang, M, Li, W, Wang, W, Wen, D & Qin, L 1970, 'Querying Numeric-Constrained Shortest Distances on Road Networks', 2024 IEEE 40th International Conference on Data Engineering (ICDE), 2024 IEEE 40th International Conference on Data Engineering (ICDE), IEEE, pp. 2463-2475.
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Yang, W, Li, Y, Fang, M & Chen, L 1970, 'Enhancing Temporal Sensitivity and Reasoning for Time-Sensitive Question Answering', Findings of the Association for Computational Linguistics: EMNLP 2024, Findings of the Association for Computational Linguistics: EMNLP 2024, Association for Computational Linguistics, pp. 14495-14508.
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Yang, W, Xu, Y, Li, Y, Wang, K, Huang, B & Chen, L 1970, 'Continual Learning for Temporal-Sensitive Question Answering', 2024 International Joint Conference on Neural Networks (IJCNN), 2024 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1-9.
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Yang, X, Wang, Y, Zhang, X, Wang, S, Wang, H & Lam, K-Y 1970, 'UPDATE: Mining User-News Engagement Patterns for Dual-Target Cross-Domain Fake News Detection', 2024 IEEE 11th International Conference on Data Science and Advanced Analytics (DSAA), 2024 IEEE 11th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp. 1-10.
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Yang, Y, Long, G, Shen, T, Jiang, J & Blumenstein, M 1970, 'Dual-Personalizing Adapter for Federated Foundation Models', Advances in Neural Information Processing Systems.
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Recently, foundation models, particularly large language models (LLMs), have demonstrated an impressive ability to adapt to various tasks by fine-tuning diverse instruction data. Notably, federated foundation models (FedFM) emerge as a privacy preservation method to fine-tune models collaboratively under federated learning (FL) settings by leveraging many distributed datasets with non-IID data. To alleviate communication and computation overhead, parameter-efficient methods are introduced for efficiency, and some research adapted personalization methods to FedFM for better user preferences alignment. However, a critical gap in existing research is the neglect of test-time distribution shifts in real-world applications, and conventional methods for test-time distribution shifts in personalized FL are less effective for FedFM due to their failure to adapt to complex distribution shift scenarios and the requirement to train all parameters. To bridge this gap, we refine the setting in FedFM, termed test-time personalization, which aims to learn personalized federated foundation models on clients while effectively handling test-time distribution shifts simultaneously. To address challenges in this setting, we explore a simple yet effective solution, a Federated Dual-Personalizing Adapter (FedDPA) architecture. By co-working with a foundation model, a global adapter and a local adapter jointly tackle the test-time distribution shifts and client-specific personalization. Additionally, we introduce an instance-wise dynamic weighting mechanism that dynamically integrates the global and local adapters for each test instance during inference, facilitating effective test-time personalization. The effectiveness of the proposed method has been evaluated on benchmark datasets across different NLP tasks with released code.
Yang, Y, Vu, TH, Khatri, R, Kerley, M & Thomas, P 1970, 'Enhancing Chloride Resistance and Reducing Embodied Carbon in Concrete Using Fly Ash and Ground Granulated Blast Furnace Slag', The Decarbonising Building Industry (DBI), Melbourne, Australia.
Yao, Q, Kollmeyer, PJ, Dah-Chuan Lu, D & Emadi, A 1970, 'A Comparison Study of Unidirectional and Bidirectional Recurrent Neural Network for Battery State of Charge Estimation', 2024 IEEE Transportation Electrification Conference and Expo (ITEC), 2024 IEEE Transportation Electrification Conference and Expo (ITEC), IEEE, pp. 1-6.
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Yazdani, D & Yao, X 1970, 'A Deep Dive into Robust Optimization Over Time: Problems, Algorithms, and Beyond', Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO '24 Companion: Genetic and Evolutionary Computation Conference Companion, ACM, pp. 1187-1196.
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Yazdani, D, Branke, J, Khorshidi, MS, Omidvar, MN, Li, X, Gandomi, AH & Yao, X 1970, 'Clustering in Dynamic Environments: A Framework for Benchmark Dataset Generation With Heterogeneous Changes', Proceedings of the Genetic and Evolutionary Computation Conference, GECCO '24: Genetic and Evolutionary Computation Conference, ACM, pp. 50-58.
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Ye, F, Lyu, Y, Wang, X, Zhang, Y & Tsang, IW 1970, 'ADAPTIVE STOCHASTIC GRADIENT ALGORITHM FOR BLACK-BOX MULTI-OBJECTIVE LEARNING', 12th International Conference on Learning Representations, ICLR 2024.
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Multi-objective optimization (MOO) has become an influential framework for various machine learning problems, including reinforcement learning and multitask learning. In this paper, we study the black-box multi-objective optimization problem, where we aim to optimize multiple potentially conflicting objectives with function queries only. To address this challenging problem and find a Pareto optimal solution or the Pareto stationary solution, we propose a novel adaptive stochastic gradient algorithm for black-box MOO, called ASMG. Specifically, we use the stochastic gradient approximation method to obtain the gradient for the distribution parameters of the Gaussian smoothed MOO with function queries only. Subsequently, an adaptive weight is employed to aggregate all stochastic gradients to optimize all objective functions effectively. Theoretically, we explicitly provide the connection between the original MOO problem and the corresponding Gaussian smoothed MOO problem and prove the convergence rate for the proposed ASMG algorithm in both convex and non-convex scenarios. Empirically, the proposed ASMG method achieves competitive performance on multiple numerical benchmark problems. Additionally, the state-of-the-art performance on the black-box multi-task learning problem demonstrates the effectiveness of the proposed ASMG method.
Ye, K, Ji, JC, Li, J & Yamada, K 1970, 'Origami-Inspired Vibration Isolation with Inherent Nonlinear Inerter', Springer Nature Singapore, pp. 876-884.
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Ye, S & Lu, J 1970, 'Sequence Unlearning for Sequential Recommender Systems', Springer Nature Singapore, pp. 403-415.
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Yi, B, Fan, Y & Liu, D 1970, 'A Novel Model for Layer Jamming-based Continuum Robots', 2024 IEEE International Conference on Robotics and Automation (ICRA), 2024 IEEE International Conference on Robotics and Automation (ICRA), IEEE, pp. 12727-12733.
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Yin, H, Wang, K, Zhang, W, Wen, D, Wang, X & Zhang, Y 1970, 'Discovering Densest Subgraph over Heterogeneous Information Networks', Springer Nature Switzerland, pp. 341-355.
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Yiyuan, Y, Long, G, Blumenstein, M, Geng, X, Tao, C, Shen, T & Jiang, D 1970, 'Pre-training Cross-Modal Retrieval by Expansive Lexicon-Patch Alignment', 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings, pp. 12977-12987.
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Recent large-scale vision-language pre-training depends on image-text global alignment by contrastive learning and is further boosted by fine-grained alignment in a weakly contrastive manner for cross-modal retrieval. Nonetheless, besides semantic matching learned by contrastive learning, cross-modal retrieval also largely relies on object matching between modalities. This necessitates fine-grained categorical discriminative learning, which however suffers from scarce data in full-supervised scenarios and information asymmetry in weakly-supervised scenarios when applied to cross-modal retrieval. To address these issues, we propose expansive lexicon-patch alignment (ELA) to align image patches with a vocabulary rather than only the words explicitly in the text for annotation-free alignment and information augmentation, thus enabling more effective fine-grained categorical discriminative learning for cross-modal retrieval. Experimental results show that ELA could effectively learn representative fine-grained information and outperform state-of-the-art methods on cross-modal retrieval.
Yong, F & Yang, Y 1970, 'Electric Field Measurement Method Based on Rydberg Atom', 2024 49th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz), 2024 49th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz), IEEE, pp. 1-2.
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Yu, Q, Du, H, Liu, C & Yu, X 1970, 'When 3D Bounding-Box Meets SAM: Point Cloud Instance Segmentation with Weak-and-Noisy Supervision', 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), IEEE, pp. 3707-3716.
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Yu, R, Gong, Y, Wang, S, Si, J, Peng, X, Xu, B & Lu, W 1970, 'Time-Series Representation Learning via Dual Reference Contrasting', Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, CIKM '24: The 33rd ACM International Conference on Information and Knowledge Management, ACM, pp. 3042-3051.
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Yu, X, Tran, TTM, Wang, Y, Mah, K, Cao, Y, Johansen, SS, Johal, W, Lupetti, ML, Rose, M, Rittenbruch, M, Zsolczay, RG & Hoggenmüller, M 1970, 'Out of Place Robot in the Wild: Envisioning Urban Robot Contextual Adaptability Challenges Through a Design Probe', Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, CHI '24: CHI Conference on Human Factors in Computing Systems, ACM, pp. 1-7.
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Zamee, MA, Sakib, S, Alam, MM, Habib, MA & Hossain, MJ 1970, 'Stability-Assured ARMA-Kalman Filter Based Adaptive Short-Term Solar PV System Forecasting', 2024 IEEE 34th Australasian Universities Power Engineering Conference (AUPEC), 2024 IEEE 34th Australasian Universities Power Engineering Conference (AUPEC), IEEE, pp. 1-6.
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Zeng, FC, Ding, C & Guo, YJ 1970, 'A Polarization-Mixed Antenna Array with Wide-Range Continuous Beamwidth Control', 2024 IEEE International Symposium on Antennas and Propagation and INC/USNC‐URSI Radio Science Meeting (AP-S/INC-USNC-URSI), 2024 IEEE International Symposium on Antennas and Propagation and INC/USNC‐URSI Radio Science Meeting (AP-S/INC-USNC-URSI), IEEE, pp. 1865-1866.
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Zhang, B, Lu, J, Wang, K & Zhang, G 1970, 'ML4MDS: A Machine Leaning Platform for Multiple Data Streams', Intelligent Management of Data and Information in Decision Making, 16th FLINS Conference on Computational Intelligence in Decision and Control & the 19th ISKE Conference on Intelligence Systems and Knowledge Engineering (FLINS-ISKE 2024), WORLD SCIENTIFIC, pp. 323-330.
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Zhang, C, Fang, Y, Liang, X, Zhang, H, Zhou, P, Wu, X, Yang, J, Jiang, B & Sheng, W 1970, 'Efficient Multi-view Unsupervised Feature Selection with Adaptive Structure Learning and Inference', Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence, Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}, International Joint Conferences on Artificial Intelligence Organization, pp. 5443-5452.
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As data with diverse representations become high-dimensional, multi-view unsupervised feature selection has been an important learning paradigm. Generally, existing methods encounter the following challenges: (i) traditional solutions either concatenate different views or introduce extra parameters to weight them, affecting the performance and applicability; (ii) emphasis is typically placed on graph construction, yet disregarding the clustering information of data; (iii) exploring the similarity structure of all samples from the original features is suboptimal and extremely time-consuming. To solve this dilemma, we propose an efficient multi-view unsupervised feature selection (EMUFS) to construct bipartite graphs between samples and anchors. Specifically, a parameter-free manner is devised to collaboratively fuse the membership matrices and graphs to learn the compatible structure information across all views, naturally balancing different views. Moreover, EMUFS leverages the similarity relations of data in the feature subspace induced by l2,0-norm to dynamically update the graph. Accordingly, the cluster information of anchors can be accurately propagated to samples via the graph structure and further guide feature selection, enhancing the quality of selected features and the computational costs in solution processes. A convergent optimization is developed to solve the formulated problem, and experiments demonstrate the effectiveness and efficiency of EMUFS.
Zhang, C, Long, G, Guo, H, Fang, X, Song, Y, Liu, Z, Zhou, G, Zhang, Z, Liu, Y & Yang, B 1970, 'Federated Adaptation for Foundation Model-based Recommendations', Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence, Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}, International Joint Conferences on Artificial Intelligence Organization, pp. 5453-5461.
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With the recent success of large language models, particularly foundation models with generalization abilities, applying foundation models for recommendations becomes a new paradigm to improve existing recommendation systems. It becomes a new open challenge to enable the foundation model to capture user preference changes in a timely manner with reasonable communication and computation costs while preserving privacy. This paper proposes a novel federated adaptation mechanism to enhance the foundation model-based recommendation system in a privacy-preserving manner. Specifically, each client will learn a lightweight personalized adapter using its private data. The adapter then collaborates with pre-trained foundation models to provide recommendation service efficiently with fine-grained manners. Importantly, users' private behavioral data remains secure as it is not shared with the server. This data localization-based privacy preservation is embodied via the federated learning framework. The model can ensure that shared knowledge is incorporated into all adapters while simultaneously preserving each user's personal preferences. Experimental results on four benchmark datasets demonstrate our method's superior performance. The code is available.
Zhang, C, Long, G, Zhou, T, Zhang, Z, Yan, P & Yang, B 1970, 'GPFedRec: Graph-Guided Personalization for Federated Recommendation', Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ACM, pp. 4131-4142.
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Zhang, C, Long, G, Zhou, T, Zhang, Z, Yan, P & Yang, B 1970, 'When Federated Recommendation Meets Cold-Start Problem: Separating Item Attributes and User Interactions', Proceedings of the ACM Web Conference 2024, WWW '24: The ACM Web Conference 2024, ACM, pp. 3632-3642.
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Zhang, C, Zhang, Y, Mayr, P, Lu, W, Suominen, A, Chen, H & Ding, Y 1970, 'Preface to the Joint Workshop of the 5th Extraction and Evaluation of Knowledge Entities from Scientific Documents (EEKE2024) and the 4th AI + Informetrics (AII2024)', CEUR Workshop Proceedings, pp. 1-8.
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The Joint Workshop of the 5th Extraction and Evaluation of Knowledge Entities from Scientific Documents (EEKE2024; https://eeke-workshop.github.io/) and the 4th AI + Informetrics (AII2024; https://ai-informetrics.github.io/) was held in Changchun, China and online, co-located with the iConference2024. The two workshop series are designed to actively engage diverse communities in addressing open challenges related to the extraction and evaluation of knowledge entities from scientific documents and the modeling and applications of AI-empowered informetrics for broad interests in science of science, science, technology, & innovation, etc. The joint workshop features a comprehensive agenda, including keynotes from leading experts, oral presentations showcasing cutting-edge research, and poster sessions for in-depth discussions. The primary topics covered in the proceedings encompass the methodologies and applications of entity extraction, as well as the convergence of AI and informetrics, to drive advancements in these fields.
Zhang, G, Liu, B, Tian, H, Zhu, T, Ding, M & Zhou, W 1970, 'How Does a Deep Learning Model Architecture Impact Its Privacy? A Comprehensive Study of Privacy Attacks on CNNs and Transformers', Proceedings of the 33rd USENIX Security Symposium, pp. 6795-6812.
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As a booming research area in the past decade, deep learning technologies have been driven by big data collected and processed on an unprecedented scale. However, privacy concerns arise due to the potential leakage of sensitive information from the training data. Recent research has revealed that deep learning models are vulnerable to various privacy attacks, including membership inference attacks, attribute inference attacks, and gradient inversion attacks. Notably, the efficacy of these attacks varies from model to model. In this paper, we answer a fundamental question: Does model architecture affect model privacy? By investigating representative model architectures from convolutional neural networks (CNNs) to Transformers, we demonstrate that Transformers generally exhibit higher vulnerability to privacy attacks than CNNs. Additionally, we identify the micro design of activation layers, stem layers, and LN layers, as major factors contributing to the resilience of CNNs against privacy attacks, while the presence of attention modules is another main factor that exacerbates the privacy vulnerability of Transformers. Our discovery reveals valuable insights for deep learning models to defend against privacy attacks and inspires the research community to develop privacy-friendly model architectures.
Zhang, G, Peng, X, Shen, T, Long, G, Si, J, Qin, L & Lu, W 1970, 'Extractive Medical Entity Disambiguation with Memory Mechanism and Memorized Entity Information', Findings of the Association for Computational Linguistics: EMNLP 2024, Findings of the Association for Computational Linguistics: EMNLP 2024, Association for Computational Linguistics, pp. 13811-13822.
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Medical entity disambiguation (MED) aims to ground medical mentions in text with ontological entities in knowledge bases (KBs). A notable challenge of MED is the long medical text usually contains multiple entities' mentions with intricate correlations. However, limited by computation overhead, many existing methods consider only a single candidate entity mention during the disambiguation process. As such, they focus only on local MED optimal while ignoring the sole-mention disambiguation possibly boosted by richer context from other mentions' disambiguating processes - missing global optimal on entity combination in the text. Motivated by this, we propose a new approach called Extractive Medical Entity Disambiguation with Memory Mechanism and Memorized Entity Information (M3E). Specifically, we reformulate MED as a text extraction task, which simultaneously accepts the context of medical mentions, all possible candidate entities, and entity definitions, and it is then trained to extract the text span corresponding to the correct entity. Upon our new formulation, 1) to alleviate the computation overhead from the enriched context, we devise a memory mechanism module that performs memory caching, retrieval, fusion and cross-network residual; and 2) to utilize the disambiguation clues from other mentions, we design an auxiliary disambiguation module that employs a gating mechanism to assist the disambiguation of remaining mentions. Extensive experiments on two benchmark datasets demonstrate the superiority of M3E over the state-of-the-art MED methods on all metrics.
Zhang, H, Hu, S, Wang, Y, Zhang, LY, Zhou, Z, Wang, X, Zhang, Y & Chen, C 1970, 'Detector Collapse: Backdooring Object Detection to Catastrophic Overload or Blindness in the Physical World', Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence, Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}, International Joint Conferences on Artificial Intelligence Organization, pp. 1670-1678.
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Object detection tasks, crucial in safety-critical systems like autonomous driving, focus on pinpointing object locations. These detectors are known to be susceptible to backdoor attacks. However, existing backdoor techniques have primarily been adapted from classification tasks, overlooking deeper vulnerabilities specific to object detection. This paper is dedicated to bridging this gap by introducing Detector Collapse (DC), a brand-new backdoor attack paradigm tailored for object detection. DC is designed to instantly incapacitate detectors (i.e., severely impairing detector's performance and culminating in a denial-of-service). To this end, we develop two innovative attack schemes: Sponge for triggering widespread misidentifications and Blinding for rendering objects invisible. Remarkably, we introduce a novel poisoning strategy exploiting natural objects, enabling DC to act as a practical backdoor in real-world environments. Our experiments on different detectors across several benchmarks show a significant improvement (~10%-60% absolute and ~2-7x relative) in attack efficacy over state-of-the-art attacks.
Zhang, H, Wang, Y & Tran, TTM 1970, 'External Speech Interface: Effects of Gendered and Aged Voices on Pedestrians' Acceptance of Autonomous Vehicles in Shared Spaces', Adjunct Proceedings of the 16th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI '24: 16th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, ACM, pp. 190-196.
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Zhang, H, Zhu, Q, Guan, J, Liu, H, Xiao, F, Tian, J, Mei, X, Liu, X & Wang, W 1970, 'First-Shot Unsupervised Anomalous Sound Detection with Unknown Anomalies Estimated by Metadata-Assisted Audio Generation', ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp. 1271-1275.
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Zhang, J, Li, K, Wei, Y, Wang, F, Qian, W, Zhou, J & Guo, D 1970, 'Repetitive Action Counting with Feature Interaction Enhancement and Adaptive Gate Fusion', Proceedings of the 6th ACM International Conference on Multimedia in Asia, MMAsia '24: ACM Multimedia Asia, ACM, pp. 1-7.
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Zhang, J, Zhu, C, Wu, D, Sun, X, Yong, J & Long, G 1970, 'BADFSS: Backdoor Attacks on Federated Self-Supervised Learning', Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence, Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}, International Joint Conferences on Artificial Intelligence Organization, pp. 548-558.
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Self-supervised learning (SSL) is capable of learning remarkable representations from centrally available data. Recent works further implement federated learning with SSL to learn from rapidly growing decentralized unlabeled images (e.g., from cameras and phones), often resulting from privacy constraints. Extensive attention has been paid to designing new frameworks or methods that achieve better performance for the SSL-based FL. However, such an effort has not yet taken the security of SSL-based FL into consideration. We aim to explore backdoor attacks in the context of SSL-based FL via an in-depth empirical study. In this paper, we propose a novel backdoor attack BADFSS against SSL-based FL. First, BADFSS learns a backdoored encoder via supervised contrastive learning on poison datasets constructed based on local datasets. Then, BADFSS employs attention alignment to enhance the backdoor effect and maintain the consistency between backdoored and global encoders. Moreover, we perform empirical evaluations of the proposed backdoor attacks on four datasets and compared BADFSS with three existing backdoor attacks that are transferred into federated self-supervised learning. The experiments demonstrate that BADFSS outperforms baseline methods and is effective under various settings.
Zhang, T, Zhang, H, Zhu, H, Huang, X & Du, J 1970, 'A 50 Gbps Real-time Wireless Communication Link at 245 GHz', 2024 49th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz), 2024 49th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz), IEEE, pp. 1-1.
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Zhang, X, Peng, X, Chen, W, Zhang, W, Ren, X & Luv, W 1970, 'Multi-Channel Hypergraph Network for Sequential Diagnosis Prediction in Healthcare', 2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD), 2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD), IEEE, pp. 2937-2942.
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Zhang, Y, Huang, S, Ma, B, Zhu, J & Lei, G 1970, 'Variable Frequency Transformer Design for New Energy Microgrids and Grid Interconnections', 2024 IEEE 21st Biennial Conference on Electromagnetic Field Computation (CEFC), 2024 IEEE 21st Biennial Conference on Electromagnetic Field Computation (CEFC), IEEE, pp. 1-2.
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Zhang, Y, Shi, Y, Wang, S, Vora, A, Perincherry, A, Chen, Y & Li, H 1970, 'Increasing SLAM Pose Accuracy by Ground-to-Satellite Image Registration', 2024 IEEE International Conference on Robotics and Automation (ICRA), 2024 IEEE International Conference on Robotics and Automation (ICRA), IEEE, pp. 8522-8528.
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Zhang, Y, Sun, R, Shen, L, Bai, G, Xue, M, Meng, MH, Li, X, Ko, R & Nepal, S 1970, 'Privacy-Preserving and Fairness-Aware Federated Learning for Critical Infrastructure Protection and Resilience', Proceedings of the ACM Web Conference 2024, WWW '24: The ACM Web Conference 2024, ACM, pp. 2986-2997.
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Zhang, Z & Su, SW 1970, 'The extension of decentralized integral controllability to non-square processes', 2024 36th Chinese Control and Decision Conference (CCDC), 2024 36th Chinese Control and Decision Conference (CCDC), IEEE, pp. 2034-2040.
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Zhang, Z, Fang, M & Chen, L 1970, 'RetrievalQA: Assessing Adaptive Retrieval-Augmented Generation for Short-form Open-Domain Question Answering', Findings of the Association for Computational Linguistics ACL 2024, Findings of the Association for Computational Linguistics ACL 2024, Association for Computational Linguistics, pp. 6963-6975.
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Adaptive retrieval-augmented generation (ARAG) aims to dynamically determine the necessity of retrieval for queries instead of retrieving indiscriminately to enhance the efficiency and relevance of the sourced information. However, previous works largely overlook the evaluation of ARAG approaches, leading to their effectiveness being understudied. This work presents a benchmark, RetrievalQA, comprising 1,271 short-form questions covering new world and long-tail knowledge. The knowledge necessary to answer the questions is absent from LLMs; therefore, external information must be retrieved to answer correctly. This makes RetrievalQA a suitable testbed to evaluate existing ARAG methods. We observe that calibration-based methods heavily rely on threshold tuning, while vanilla prompting is inadequate for guiding LLMs to make reliable retrieval decisions. Based on our findings, we propose Time-Aware Adaptive REtrieval (TA-ARE), a simple yet effective method that helps LLMs assess the necessity of retrieval without calibration or additional training.
Zhang, Z, Liu, Y, Bian, J, Yepes, AJ, Shen, J, Li, F, Long, G & Salim, FD 1970, 'Boosting Patient Representation Learning via Graph Contrastive Learning', Springer Nature Switzerland, pp. 335-350.
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Zhao, H, Wu, H, Bian, X, Liu, S, Cheng, G, Hu, X & Tian, Z 1970, 'Unveiling the Unseen: Video Recognition Attacks on Social Software', Springer Nature Singapore, pp. 412-432.
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Zhao, J, Shi, Z, Li, Y, Pei, Y, Chen, L, Fang, M & Pechenizkiy, M 1970, 'More than Minorities and Majorities: Understanding Multilateral Bias in Language Generation', Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp. 9987-10001.
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Pretrained models learned from real corpora can often capture undesirable features, leading to bias issues against different demographic groups. Most existing studies on bias dataset construction or bias mitigation methods only focus on one demographic group pair to study a certain bias, e.g. black vs. white for racial bias. However, in real-world applications, there are more than two demographic groups that are at risk of the same bias. In this paper, we propose to analyze and reduce biases across multiple demographic groups. We collect and build a multi-demographic bias dataset including five commonly discussed bias dimensions. To mitigate multi-demographic bias, we adopt several novel debiasing methods, including regularisation-based and augmentation-based methods, as well as appropriate evaluation metrics for multi-demographic bias measurement. Experimental results on the proposed multi-demographic dataset show that a fairer model can be achieved using a multi-demographic debiasing approach. Also, the model debiased using the proposed multi-demographic debiasing methods can better transfer to unseen demographics without sacrificing the performance of the pretrained model.
Zhao, M, Hussain, O, Zhang, Y & Saberi, M 1970, 'Optimizing Supply Chain Risk Management: An Integrated Framework Leveraging Large Language Models', 2024 IEEE Conference on Artificial Intelligence (CAI), 2024 IEEE Conference on Artificial Intelligence (CAI), IEEE, pp. 1057-1062.
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Zhao, M, Hussain, O, Zhang, Y, Saberi, M & Leshob, A 1970, 'Enhancing Supply Chain Risk Management with Large Language Models: Software Prototyping and Interactive Visualization', 2024 IEEE International Conference on e-Business Engineering (ICEBE), 2024 IEEE International Conference on e-Business Engineering (ICEBE), IEEE, pp. 284-291.
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Zhao, R, Hao, J, Feng, Z, Wang, X, Prasad, M & Huo, H 1970, 'Optimizing Algorithms for Human Behavior Recognition: Single-Modality Spatiotemporal Convolutional Networks', 2024 11th International Conference on Behavioural and Social Computing (BESC), 2024 11th International Conference on Behavioural and Social Computing (BESC), IEEE, pp. 1-6.
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Zhao, R, Zhao, F, Wang, L, Wang, X & Xu, G 1970, 'KG-CoT: Chain-of-Thought Prompting of Large Language Models over Knowledge Graphs for Knowledge-Aware Question Answering', Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence, Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}, International Joint Conferences on Artificial Intelligence Organization, pp. 6642-6650.
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Large language models (LLMs) encounter challenges such as hallucination and factual errors in knowledge-intensive tasks. One the one hand, LLMs sometimes struggle to generate reliable answers based on the black-box parametric knowledge, due to the lack of responsible knowledge. Moreover, fragmented knowledge facts extracted by knowledge retrievers fail to provide explicit and coherent reasoning paths for improving LLM reasoning. To address these challenges, we propose KG-CoT, a novel knowledge-augmented paradigm that leverages a small-scale step-by-step graph reasoning model to reason over knowledge graphs (KGs) and utilizes a reasoning path generation method to generate chains of reasoning with high confidence for large-scale LLMs. Extensive experiments demonstrate that our KG-CoT significantly improves the performance of LLMs on knowledge-intensive question answering tasks, such as multi-hop, single-hop, and open-domain question answering benchmarks, without fine-tuning LLMs. KG-CoT outperforms the CoT prompting as well as prior retrieval-augmented and knowledge base question answering baselines. Moreover, KG-CoT can reduce the number of API calls and cost and generalize to various LLM backbones in a lightweight plug-and-play manner.
Zhao, S, Zhang, G, Cheng, E & Burnett, IS 1970, 'A DISTRIBUTED ALGORITHM FOR PERSONAL SOUND ZONES SYSTEMS', Proceedings of the International Congress on Sound and Vibration.
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A Personal Sound Zones (PSZ) system aims to generate two or more independent listening zones that allow multiple users to listen to different music/audio content in a shared space without the need for wearing headphones. Most existing studies assume that the acoustic paths between loudspeakers and microphones are measured beforehand in a stationary environment. Recently, adaptive PSZ systems have been explored to adapt the system in a time-varying acoustic environment. However, because a PSZ system usually requires multiple loudspeakers, the multichannel adaptive algorithms impose a high computational load on a single processor. To overcome that problem, this paper proposes an efficient distributed algorithm for PSZ systems, which not only spreads the computational burden over multiple PSZ subsystems but also reduces the overall computational complexity, at the expense of a slight decrease in performance. Simulation results with true room impulse responses measured in a Hemi-Anechoic chamber are performed to verify the effectiveness of the proposed distributed PSZ system. The computational complexity of the proposed distributed algorithm is analyzed and compared with that of the centralized algorithm.
Zhao, W, Hou, Z, Wu, S, Gao, Y, Dong, H, Wan, Y, Zhang, H, Sui, Y & Zhang, H 1970, 'NL2FORMULA: Generating Spreadsheet Formulas from Natural Language Queries', EACL 2024 - 18th Conference of the European Chapter of the Association for Computational Linguistics, Findings of EACL 2024, pp. 2377-2388.
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Writing formulas on spreadsheets, such as Microsoft Excel and Google Sheets, is a widespread practice among users performing data analysis. However, crafting formulas on spreadsheets remains a tedious and error-prone task for many end-users, particularly when dealing with complex operations. To alleviate the burden associated with writing spreadsheet formulas, this paper introduces a novel benchmark task called NL2FORMULA, with the aim to generate executable formulas that are grounded on a spreadsheet table, given a Natural Language (NL) query as input. To accomplish this, we construct a comprehensive dataset consisting of 70,799 paired NL queries and corresponding spreadsheet formulas, covering 21,670 tables and 37 types of formula functions. We realize the NL2FORMULA task by providing a sequence-to-sequence baseline implementation called fCODER. Experimental results validate the effectiveness of fCODER, demonstrating its superior performance compared to the baseline models. Furthermore, we also compare fCODER with an initial GPT-3.5 model (i.e., text-davinci-003). Lastly, through in-depth error analysis, we identify potential challenges in the NL2FORMULA task and advocate for further investigation.
Zhao, Z, Peng, X, Li, Y, Wu, H, Zhang, W & Lu, W 1970, 'Thinking the Importance of Patient’s Chief Complaint in TCM Syndrome Differentiation', 2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD), 2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD), IEEE, pp. 1534-1539.
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Zheng, B, Zhou, J, Ma, J & Chen, F 1970, 'Genetic Imitation Learning by Reward Extrapolation', 2024 International Joint Conference on Neural Networks (IJCNN), 2024 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1-8.
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Zheng, P, Zhang, Y, Fang, Z, Liu, T, Lian, D & Han, B 1970, 'NOISEDIFFUSION: CORRECTING NOISE FOR IMAGE INTERPOLATION WITH DIFFUSION MODELS BEYOND SPHERICAL LINEAR INTERPOLATION', 12th International Conference on Learning Representations, ICLR 2024.
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Image interpolation based on diffusion models is promising in creating fresh and interesting images. Advanced interpolation methods mainly focus on spherical linear interpolation, where images are encoded into the noise space and then interpolated for denoising to images. However, existing methods face challenges in effectively interpolating natural images (not generated by diffusion models), thereby restricting their practical applicability. Our experimental investigations reveal that these challenges stem from the invalidity of the encoding noise, which may no longer obey the expected noise distribution, e.g., a normal distribution. To address these challenges, we propose a novel approach to correct noise for image interpolation, NoiseDiffusion. Specifically, NoiseDiffusion approaches the invalid noise to the expected distribution by introducing subtle Gaussian noise and introduces a constraint to suppress noise with extreme values. In this context, promoting noise validity contributes to mitigating image artifacts, but the constraint and introduced exogenous noise typically lead to a reduction in signal-to-noise ratio, i.e., loss of original image information. Hence, NoiseDiffusion performs interpolation within the noisy image space and injects raw images into these noisy counterparts to address the challenge of information loss. Consequently, NoiseDiffusion enables us to interpolate natural images without causing artifacts or information loss, thus achieving the best interpolation results. Our code is available at https://github.com/tmlr-group/NoiseDiffusion.
Zhou, J, Duan, Y, Zhao, Z, Chang, Y-C, Wang, Y-K, Do, T & Lin, C-T 1970, 'Towards Linguistic Neural Representation Learning and Sentence Retrieval from Electroencephalogram Recordings', Proceedings of the 1st International Workshop on Brain-Computer Interfaces (BCI) for Multimedia Understanding, MM '24: The 32nd ACM International Conference on Multimedia, ACM, pp. 19-28.
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Zhou, J, Sia, J, Duan, Y, Chang, Y-C, Wang, Y-K & Lin, C-T 1970, 'Masked EEG Modeling for Driving Intention Prediction', 2024 International Joint Conference on Neural Networks (IJCNN), 2024 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1-9.
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Zhu, J, Yang, Y & Xue, Q 1970, 'Aperture-Shared Reflectarray Antenna for Sub-6 GHz and Mm-Wave Dual-Band Dual Circular Polarization Independent Beam-Shaping', 2024 IEEE International Workshop on Antenna Technology (iWAT), 2024 IEEE International Workshop on Antenna Technology (iWAT), IEEE, pp. 213-215.
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Zhu, Y, Cao, J, Liu, B, Chen, T, Xie, R & Song, L 1970, 'Identity-Consistent Video De-identification via Diffusion Autoencoders', 2024 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), 2024 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), IEEE, pp. 1-6.
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Zhu, Y, Lin, Q, Lu, H, Shi, K, Liu, D, Chambua, J, Wan, S & Niu, Z 1970, 'Recommending Learning Objects through Attentive Heterogeneous Graph Convolution and Operation- Aware Neural Network (Extended Abstract)', 2024 IEEE 40th International Conference on Data Engineering (ICDE), 2024 IEEE 40th International Conference on Data Engineering (ICDE), IEEE, pp. 5747-5748.
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