Abbasnejad, B, Nasirian, A, Duan, S, Diro, A, Prasad Nepal, M & Song, Y 2024, 'Measuring BIM Implementation: A Mathematical Modeling and Artificial Neural Network Approach', Journal of Construction Engineering and Management, vol. 150, no. 5.
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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, '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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Adak, C, Chattopadhyay, S & Saqib, M 2024, 'Deep Analysis of Visual Product Reviews', IEEE Transactions on Emerging Topics in Computational Intelligence, pp. 1-6.
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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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Aditya, L, Vu, HP, Johir, MAH, Mao, S, Ansari, A, Fu, Q & Nghiem, LD 2024, 'Synthesizing cationic polymers and tuning their properties for microalgae harvesting', Science of The Total Environment, vol. 917, pp. 170423-170423.
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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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Ahmed, F, Afzal, MU, Esselle, KP & Thalakotuna, DN 2024, 'Novel Dual-Band Phase-Gradient Metascreen and Dual-Band Near-Field Meta-Steering Antennas', IEEE Transactions on Antennas and Propagation, vol. 72, no. 3, pp. 2202-2216.
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Ahmed, SF, Alam, MSB, Afrin, S, Rafa, SJ, Rafa, N & Gandomi, AH 2024, 'Insights into Internet of Medical Things (IoMT): Data fusion, security issues and potential solutions', Information Fusion, vol. 102, pp. 102060-102060.
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Ahmed, SF, Alam, MSB, Afrin, S, Rafa, SJ, Taher, SB, Kabir, M, Muyeen, SM & Gandomi, AH 2024, 'Toward a Secure 5G-Enabled Internet of Things: A Survey on Requirements, Privacy, Security, Challenges, and Opportunities', IEEE Access, vol. 12, pp. 13125-13145.
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Ahmed, SF, Islam, N, Tasannum, N, Mehjabin, A, Momtahin, A, Chowdhury, AA, Almomani, F & Mofijur, M 2024, 'Microplastic removal and management strategies for wastewater treatment plants', Chemosphere, vol. 347, pp. 140648-140648.
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Discharging microplastics into the environment with treated wastewater is becoming a major concern around the world. Wastewater treatment plants (WWTPs) release microplastics into terrestrial and aquatic habitats, mostly from textile, laundry, and cosmetic industries. Despite extensive research on microplastics in the environment, their removal, and WWTP management strategies, highlighting their environmental effects, little is known about microplastics' fate and behaviour during various treatment processes. Microplastics interact with treatment technologies differently due to their diverse physical and chemical characteristics, resulting in varying removal efficiency. Microplastics removed from WWTPs may accumulate in soil and harm terrestrial ecosystems. Few studies have examined the cost, energy use, and trade-offs of large-scale implementation of modern treatment methods for the removal of microplastics. To safeguard aquatic and terrestrial habitats from microplastics' contamination, focused and efficient management techniques must bridge these knowledge gaps. This review summarizes microplastic detection, collection, removal and management strategies. A compilation of treatment process studies on microplastics' removal efficiency and their destiny and transit paths shows recent improvement. Bioremediation, membrane bioreactor (MBR), electrocoagulation, sol-gel technique, flotation, enhanced filtering, and AOPs are evaluated for microplastic removal. The fate and behaviour of microplastics in WWTPs suggest they may be secondary suppliers of microplastics to receiving ecosystems. Innovative microplastic removal strategies and technologies such as nanoparticles, microorganism-based remediation, and tertiary treatment raise issues. These new WWTP technologies are examined for feasibility, limitations, and implementation issues. Pretreatment modifies microplastic size, adsorption potential, and surface morphology to remove microplastics from WWTPs. Memb...
Ahmed, SF, Kumar, PS, Ahmed, B, Mehnaz, T, Shafiullah, GM, Nguyen, VN, Duong, XQ, Mofijur, M, Badruddin, IA & Kamangar, S 2024, 'Carbon-based nanomaterials: Characteristics, dimensions, advances and challenges in enhancing photocatalytic hydrogen production', International Journal of Hydrogen Energy, vol. 52, pp. 424-442.
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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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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, pp. 1-9.
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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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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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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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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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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.
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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Arsad, SR, Arsad, AZ, Ker, PJ, Hannan, MA, Tang, SGH, Goh, SM & Mahlia, TMI 2024, 'Recent advancement in water electrolysis for hydrogen production: A comprehensive bibliometric analysis and technology updates', International Journal of Hydrogen Energy, vol. 60, pp. 780-801.
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Arsad, SR, Ker, PJ, Hannan, MA, Tang, SGH, R S, N, Chau, CF & Mahlia, TMI 2024, 'Patent landscape review of hydrogen production methods: Assessing technological updates and innovations', International Journal of Hydrogen Energy, vol. 50, pp. 447-472.
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Asheralieva, A, Niyato, D & Miyanaga, Y 2024, 'Efficient Dynamic Distributed Resource Slicing in 6G Multi-Access Edge Computing Networks With Online ADMM and Message Passing Graph Neural Networks', IEEE Transactions on Mobile Computing, vol. 23, no. 4, pp. 2614-2638.
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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.
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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 of COVID‐19 patient outcomes using DERGA, 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.
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.
Azizivahed, A, Gholami, K, Arefi, A, Li, L, Arif, MT & Haque, ME 2024, 'Stochastic scheduling of energy sharing in reconfigurable multi-microgrid systems in the presence of vehicle-to-grid technology', Electric Power Systems Research, vol. 231, pp. 110285-110285.
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Babakian, A, Huston, G, Braun, R & Lipman, J 2024, 'Internet Identifiers: A Survey of History, Challenges, and Future Perspectives', IEEE Access, vol. 12, pp. 51919-51941.
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Bai, J, Hu, B, Huo, S & Li, M 2024, 'Uncertainty Analysis Method for Electromagnetic Compatibility Simulation Based on Random Variable Black Box Model', Progress In Electromagnetics Research M, vol. 123, pp. 23-33.
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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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Banerjee, S, Huang, Z, Lyu, J, Leung, FHF, Lee, T, Yang, D, Zheng, Y, McAviney, J & Ling, SH 2024, 'Automatic Assessment of Ultrasound Curvature Angle for Scoliosis Detection Using 3-D Ultrasound Volume Projection Imaging', Ultrasound in Medicine & Biology, vol. 50, no. 5, pp. 647-660.
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Bano, M, Hoda, R, Zowghi, D & Treude, C 2024, 'Large language models for qualitative research in software engineering: exploring opportunities and challenges', Automated Software Engineering, vol. 31, no. 1.
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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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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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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, 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.
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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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.
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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.
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.
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...
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, pp. 1-1.
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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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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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Cao, X & Tsang, IW 2024, 'Distribution Matching for Machine Teaching', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-14.
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Cao, Y, Yao, L, Pan, L, Sheng, QZ & Chang, X 2024, 'Guided Image-to-Image Translation by Discriminator-Generator Communication', IEEE Transactions on Multimedia, vol. 26, pp. 1528-1538.
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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, pp. 1-26.
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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.
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, pp. 1-12.
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Even though digital technologies such as cloud technologies are prevalent in transforming businesses, the role of employees and their digital skills in the process is, to a large extent, neglected. This study brings forward the novel concept of digital literacy to explore the role of employees in understanding the wide variety of opportunities of digital technologies and their actualization. By treating digital literacy as the antecedent of cognitive behavior of employees in utilizing cloud technology at companies, we apply the Theory of Planned Behavior (TPB) for analyzing preliminary empirical data collected from 124 Australian employees’ technology use intentionality and behavior. The quantitative analysis shows that the TPB holds for the utilization of cloud technology and there is a positive relationship between employees' digital literacy and the utilization of cloud technology at companies. Overall, the study contributes to the technology management literature by offering a workable construct to measure the digital skills of employees in the form of digital literacy. Further, it expands the TPB framework by introducing digital literacy as a perceived behavior control variable that helps to examine the role of employees in digital transformation. The paper ends with implications and limitations of our preliminary study, followed with suggestions for future studies.
Cetindamar, D, 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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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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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, F & Long, G 2024, 'FedGE: Break the scalability limitation of Graph Neural Network with Federated Graph Embedding', IEEE Transactions on Big Data, vol. PP, no. 99, pp. 1-11.
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Neighborhood aggregation algorithms, represented as graph convolutional networks, have attained non-negligible success in numerous topological structure-based scenarios with the assumption that the topological structure of the given graph is pre-defined and relatively small. However, a real-world graph generally is a super large graph consisting of many small graphs that are interconnected and overlapping. Which makes graph embedding in real-life industries, by nature, fall into the federated learning scheme. While current graph-based algorithms are only able to capture the individual topology of each natural graph, learning the complete structural information of the merged large graph remains challenging due to the unsustainable computational cost of graph convolutional operations. We propose a tailored federated graph embedding framework to learn the intact structural information of the numerous inherently linked small-scale graphs and the embedding of each node. We leverage graphs with around two and a half million nodes to validate the effectiveness and the correctness of the proposed framework.
Chen, H, Wang, H, Chen, H, Zhang, Y, Zhang, W & Lin, X 2024, 'Denoising Variational Graph of Graphs Auto-Encoder for Predicting Structured Entity Interactions', IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 3, pp. 1016-1029.
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Chen, H, Zhu, T, Liu, C, Yu, S & Zhou, W 2024, 'High-Frequency Matters: Attack and Defense for Image-Processing Model Watermarking', IEEE Transactions on Services Computing, pp. 1-15.
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Chen, H, Zhu, T, Zhang, T, Zhou, W & Yu, PS 2024, 'Privacy and Fairness in Federated Learning: On the Perspective of Tradeoff', ACM Computing Surveys, vol. 56, no. 2, pp. 1-37.
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Federated learning (FL) has been a hot topic in recent years. Ever since it was introduced, researchers have endeavored to devise FL systems that protect privacy or ensure fair results, with most research focusing on one or the other. As two crucial ethical notions, the interactions between privacy and fairness are comparatively less studied. However, since privacy and fairness compete, considering each in isolation will inevitably come at the cost of the other. To provide a broad view of these two critical topics, we presented a detailed literature review of privacy and fairness issues, highlighting unique challenges posed by FL and solutions in federated settings. We further systematically surveyed different interactions between privacy and fairness, trying to reveal how privacy and fairness could affect each other and point out new research directions in fair and private FL.
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, 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, 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 & Jay Guo, Y 2024, 'Synthesis of Large-Scale Planar Isophoric Sparse Arrays Utilizing Iterative Least Squares With Non-Redundant Constraints (ILS-NRC)', IEEE Transactions on Antennas and Propagation, pp. 1-1.
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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, W, Gong, X, Wu, J, Wei, Y, Shi, H, Yan, Z, Yang, Y & Wang, Z 2024, 'Understanding and Accelerating Neural Architecture Search With Training-Free and Theory-Grounded Metrics', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 2, pp. 749-763.
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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, 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, pp. 1-15.
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Chen, X, Liang, Q, Chen, Y, Wang, P, Yu, H & Luo, X 2024, 'Cognitive-based knowledge learning framework for recommendation', Knowledge-Based Systems, vol. 287, pp. 111446-111446.
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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, 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, 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, pp. 1-17.
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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, pp. 1-1.
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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, 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, 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, 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.
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.
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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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, 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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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, 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.
Crowther, CA, Ashwood, P, Middleton, PF, McPhee, A, Tran, T & Harding, JE 2024, 'Prenatal Intravenous Magnesium at 30–34 Weeks' Gestation and Neurodevelopmental Outcomes in Offspring: The MAGENTA Randomized Clinical Trial', Obstetrical & Gynecological Survey, vol. 79, no. 2, pp. 78-80.
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(Abstracted from JAMA 2023;330(7):603–614) Preterm birth is a focus of many studies and interventions but remains the leading cause of global neonatal morbidity and mortality. One particular risk that is elevated in preterm infants is cerebral palsy, which affects movement and/or posture, causing health problems and high health care costs for children and their families.
Cui, H, Han, M, Xu, F, Liu, Q & Saha, SC 2024, 'Scaling analysis of intrusion flow and thermal plume for Pr > 1 in the triangular cavity', International Journal of Thermal Sciences, vol. 195, pp. 108616-108616.
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Cui, 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, pp. 1-11.
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Dai, H, Nguyen, PL & Kutay, C 2024, 'Offline collaborative learning approach for remote Northern territory students', Interactive Technology and Smart Education, vol. 21, no. 1, pp. 67-82.
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PurposeDigital learning systems are crucial for education and data collected can analyse students learning performances to improve support. The purpose of this study is to design and build an asynchronous hardware and software system that can store data on a local device until able to share. It was developed for staff and students at university who are using the limited internet access in areas such as remote Northern Territory. This system can asynchronously link the users’ devices and the central server at the university using unstable internet.Design/methodology/approachA Learning Box has been build based on minicomputer and a web learning management system (LMS). This study presents different options to create such a system and discusses various approaches for data syncing. The structure of the final setup is a Moodle (Modular Object Oriented Developmental Learning Environment) LMS on a Raspberry Pi which provides a Wi-Fi hotspot. The authors worked with lecturers from X University who work in remote Northern Territory regions to test this and provide feedback. This study also considered suitable data collection and techniques that can be used to analyse the available data to support learning analysis by the staff. This research focuses on building an asynchronous hardware and software system that can store data on a local device until able to share. It was developed for staff and students at university who are using the limited internet access in areas such as remote Northern Territory. This system can asynchronously link the users’ devices and the central server at the university using unstable internet. Digital learning systems are crucial for education, and data collected can analyse students learning performances to improve support.<...
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.
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.
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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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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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-H, Jiang, J, Long, G & Zhang, C 2024, 'Causal Reinforcement Learning: A Survey', Transactions on Machine Learning Research.
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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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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Dhruva, S, Krishankumar, R, Zavadskas, EK, Ravichandran, KS & Gandomi, AH 2024, 'Selection of Suitable Cloud Vendors for Health Centre: A Personalized Decision Framework with Fermatean Fuzzy Set, LOPCOW, and CoCoSo', Informatica, pp. 65-98.
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Cloud computing has emerged as a transformative technology in the healthcare industry, but selecting the most suitable CV (“cloud vendor”) remains a complex task. This research presents a decision framework for CV selection in the healthcare industry, addressing the challenges of uncertainty, expert hesitation, and conflicting criteria. The proposed framework incorporates FFS (“Fermatean fuzzy set”) to handle uncertainty and data representation effectively. The importance of experts is attained via the variance approach, which considers hesitation and variability. Furthermore, the framework addresses the issue of extreme value hesitancy in criteria through the LOPCOW (“logarithmic percentage change-driven objective weighting”) method, which ensures a balanced and accurate assessment of criterion importance. Personalized grading of CVs is done via the ranking algorithm that considers the formulation of CoCoSo (“combined compromise solution”) with rank fusion, providing a compromise solution that balances conflicting criteria. By integrating these techniques, the proposed framework aims to enhance the rationale and reduce human intervention in CV selection for the healthcare industry. Also, valuable insights are gained from the framework for making informed decisions when selecting CVs for efficient data management and process implementation. A case example from Tamil Nadu is presented to testify to the applicability, while sensitivity and comparison analyses reveal the pros and cons of the framework.
Dhull, P, Schreurs, D, Paolini, G, Costanzo, A, Abolhasan, M & Shariati, N 2024, 'Multitone PSK Modulation Design for Simultaneous Wireless Information and Power Transfer', IEEE Transactions on Microwave Theory and Techniques, vol. 72, no. 1, pp. 446-460.
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Ding, L, Chen, C, Shan, X, Liu, B, Wang, D, Du, Z, Zhao, G, Su, QP, Yang, Y, Halkon, B, Tran, TT, Liao, J, Aharonovich, I, Zhang, M, Cheng, F, Fu, L, Xu, X & Wang, F 2024, 'Optical Nonlinearity Enabled Super‐Resolved Multiplexing Microscopy', Advanced Materials, vol. 36, no. 2.
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AbstractOptical multiplexing for nanoscale object recognition is of great significance within the intricate domains of biology, medicine, anti‐counterfeiting, and microscopic imaging. Traditionally, the multiplexing dimensions of nanoscopy are limited to emission intensity, color, lifetime, and polarization. Here, a novel dimension, optical nonlinearity, is proposed for super‐resolved multiplexing microscopy. This optical nonlinearity is attributable to the energy transitions between multiple energy levels of the doped lanthanide ions in upconversion nanoparticles (UCNPs), resulting in unique optical fingerprints for UCNPs with different compositions. A vortex beam is applied to transport the optical nonlinearity onto the imaging point‐spread function (PSF), creating a robust super‐resolved multiplexing imaging strategy for differentiating UCNPs with distinctive optical nonlinearities. The composition information of the nanoparticles can be retrieved with variations of the corresponding PSF in the obtained image. Four channels multiplexing super‐resolved imaging with a single scanning, applying emission color and nonlinearity of two orthogonal imaging dimensions with a spatial resolution higher than 150 nm (1/6.5λ), are demonstrated. This work provides a new and orthogonal dimension – optical nonlinearity – to existing multiplexing dimensions, which shows great potential in bioimaging, anti‐counterfeiting, microarray assays, deep tissue multiplexing detection, and high‐density data storage.
Ding, L, Chen, C, Shan, X, Liu, B, Wang, D, Du, Z, Zhao, G, Su, QP, Yang, Y, Halkon, B, Tran, TT, Liao, J, Aharonovich, I, Zhang, M, Cheng, F, Fu, L, Xu, X & Wang, F 2024, 'Optical Nonlinearity Enabled Super‐Resolved Multiplexing Microscopy (Adv. Mater. 2/2024)', Advanced Materials, vol. 36, no. 2.
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Ding, W, Fan, X, Zhou, X, Liu, R, Chen, C, Jin, W, Sun, J, Li, X, Jiang, G & Liu, H 2024, 'Performance and mechanisms of zero valent iron enhancing short-chain fatty acids production during thermophilic anaerobic fermentation of waste activated sludge', Science of The Total Environment, vol. 912, pp. 169025-169025.
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Ding, W, Geng, Y, Huang, J, Ju, H, Wang, H & Lin, C-T 2024, 'MGRW-Transformer: Multigranularity Random Walk Transformer Model for Interpretable Learning', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15.
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Ding, W, Sun, Y, 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.
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.
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, 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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Dong, C, Weng, J, Li, M, Liu, J-N, Liu, Z, Cheng, Y & Yu, S 2024, 'Privacy-Preserving and Byzantine-Robust Federated Learning', IEEE Transactions on Dependable and Secure Computing, vol. 21, no. 2, pp. 889-904.
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Dong, J, Cong, Y, Sun, G, Fang, Z & Ding, Z 2024, 'Where and How to Transfer: Knowledge Aggregation-Induced Transferability Perception for Unsupervised Domain Adaptation', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 3, pp. 1664-1681.
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Dong, L, Yang, Y, Liu, Z, Yang, T, Xue, C, Shao, R & Wu, C 2024, 'Effect of chloride ion migration behaviour on the microstructure and mechanical properties of ultra-high performance concrete: A review', Journal of Building Engineering, vol. 82, pp. 108233-108233.
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Dong, 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...
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, pp. 1-12.
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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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Duan, W, Xuan, J, Qiao, M & Lu, J 2024, 'Graph Convolutional Neural Networks With Diverse Negative Samples via Decomposed Determinant Point Processes', IEEE Transactions on Neural Networks and Learning Systems, vol. PP, no. 99, pp. 1-12.
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Graph convolutional neural networks (GCNs) have achieved great success in graph representation learning by extracting high-level features from nodes and their topology. Since GCNs generally follow a message-passing mechanism, each node aggregates information from its first-order neighbor to update its representation. As a result, the representations of nodes with edges between them should be positively correlated and thus can be considered positive samples. However, there are more non-neighbor nodes in the whole graph, which provide diverse and useful information for the representation update. Two non-adjacent nodes usually have different representations, which can be seen as negative samples. Besides the node representations, the structural information of the graph is also crucial for learning. In this article, we used quality-diversity decomposition in determinant point processes (DPPs) to obtain diverse negative samples. When defining a distribution on diverse subsets of all non-neighboring nodes, we incorporate both graph structure information and node representations. Since the DPP sampling process requires matrix eigenvalue decomposition, we propose a new shortest-path-base method to improve computational efficiency. Finally, we incorporate the obtained negative samples into the graph convolution operation. The ideas are evaluated empirically in experiments on node classification tasks. These experiments show that the newly proposed methods not only improve the overall performance of standard representation learning but also significantly alleviate over-smoothing problems.
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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Erten, M, Aydemir, E, Barua, PD, Baygin, M, Dogan, S, Tuncer, T, Tan, R-S, Hafeez-Baig, A & Rajendra Acharya, U 2024, 'Novel tiny textural motif pattern-based RNA virus protein sequence classification model', Expert Systems with Applications, vol. 242, pp. 122781-122781.
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Etaati, B, Neshat, M, Dehkordi, AA, Pargoo, NS, El-Abd, M, Sadollah, A & Gandomi, AH 2024, 'Shape and sizing optimisation of space truss structures using a new cooperative coevolutionary-based algorithm', Results in Engineering, vol. 21, pp. 101859-101859.
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Fallahpoor, M, Chakraborty, S, Pradhan, B, Faust, O, Barua, PD, Chegeni, H & Acharya, R 2024, 'Deep learning techniques in PET/CT imaging: A comprehensive review from sinogram to image space', Computer Methods and Programs in Biomedicine, vol. 243, pp. 107880-107880.
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Fan, L, Zhang, X, Zhao, Y, Sood, K & Yu, S 2024, 'Online Training Flow Scheduling for Geo-Distributed Machine Learning Jobs Over Heterogeneous and Dynamic Networks', IEEE Transactions on Cognitive Communications and Networking, vol. 10, no. 1, pp. 277-291.
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Fan, Y, Xu, B, Zhang, L, Tan, G, Yu, S, Li, K-C & Zomaya, A 2024, 'psvCNN: A Zero-Knowledge CNN Prediction Integrity Verification Strategy', IEEE Transactions on Cloud Computing, pp. 1-11.
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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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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, pp. 1-15.
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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, pp. 1-1.
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Fatahi, B 2024, 'Uncertainty, modeling, and decision making in geotechnics Uncertainty, modeling, and decision making in geotechnics , edited by Kok-Kwang Phoon, Takayuki Shuku, and Jianye Ching, Boca Raton, CRC Press, 2024, 502 pp., ISBN: 978-1-032-36749-1 (hbk), ISBN: 978-1-032-36750-7 (pbk), ISBN: 978-1-003-33358-6 (ebk), $315 hardback, doi:10.1201/9781003333586', Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, vol. 18, no. 1, pp. 314-316.
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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, Y, Alamdari, MM, Wu, D, Luo, Z, Ruan, D, Egbelakin, T, Chen, X & Gao, W 2024, 'Virtual modelling aided safety assessment for ductile structures against high-velocity impact', Engineering Structures, vol. 301, pp. 117373-117373.
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Feng, Y, Li, L & Zhao, A 2024, 'A Cognitive-Emotional Model From Mobile Short-Form Video Addiction to Intermittent Discontinuance: The Moderating Role of Neutralization', International Journal of Human–Computer Interaction, vol. 40, no. 7, pp. 1505-1517.
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Discontinuance is considered very effective in the treatment of IT addiction. However, prior literature on the mechanism from IT addiction to discontinuance has reached inconsistent conclusions. Also, scholars cannot explain why some individuals do not make any changes after becoming addicted, even when they realize that their behavior is not in line with the norms. To fill the gaps, our research developed a cognitive-emotional model, with neutralization as a moderator, to understand the internal mechanism from mobile short-form video addiction to intermittent discontinuance. Based on the online survey of 493 Chinese mobile short-form video users, this study used partial least squares structural equation modeling (PLS-SEM) to empirically verify our model and hypothesis. The results show that mobile short-form video addiction positively affects cognitive dissonance and emotional fluctuation. And cognitive dissonance and emotional fluctuation are found to have a positive effect on intermittent discontinuance through attitudinal ambivalence. Furthermore, neutralization negatively moderates the relationship between cognitive dissonance and attitudinal ambivalence, emotional fluctuation and attitudinal ambivalence. Theoretical and practical implications of these findings are discussed.
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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Gao, S, Wang, X, Song, B, Liu, R, Yao, S, Zhou, W & Yu, S 2024, 'Exploiting Type I Adversarial Examples to Hide Data Information: A New Privacy-Preserving Approach', IEEE Transactions on Emerging Topics in Computational Intelligence, pp. 1-11.
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Gao, X, Xu, Z, Shi, T, Qi, C, Nghiem, LD, Li, G & Luo, W 2024, 'Role of Lignocellulosic Biomass Composition to Regulate Microbial Mutualism for Organic Mineralization and Humification during Digestate Composting', ACS ES&T Engineering, vol. 4, no. 4, pp. 771-782.
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Gao, Y, Chen, L, Han, J, Yu, S & Fang, H 2024, 'Similarity-based Secure Deduplication for IIoT Cloud Management System', IEEE Transactions on Dependable and Secure Computing, pp. 1-16.
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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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Ge, H, Dai, G, Wang, F, Yu, Y & Liu, W 2024, 'Theoretical solution for bond-slip behavior of composite structures consisting of H-shape beam and concrete based on experiment, numerical simulation, and theoretical derivation', Engineering Structures, vol. 302, pp. 117456-117456.
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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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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, pp. 103910-103910.
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Grigoletto, FB, Cedieu, S, Chaves, DB, Sing Lee, S & Siwakoti, YP 2024, 'A five‐level common‐ground inverter with step‐up/step‐down dual‐mode operation for transformerless grid‐connected PV application', International Journal of Circuit Theory and Applications, vol. 52, no. 3, pp. 1210-1230.
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AbstractStep‐up multilevel inverters with common‐ground feature are attractive for transformerless photovoltaic systems. However, their performance deteriorates at step‐down voltage range. Considering a five‐level inverter with double voltage gain, the number of output voltage levels decreases from 5 to 3 for a modulation index smaller than 0.5, declining the quality of the output currents. This paper proposes a new dual‐mode five‐level common‐grounded inverter with a reduced number of switches. The proposed topology has the ability to operate as step‐up or step‐down making it well suited for application with wide input voltage range. The proposed inverter consists of five unidirectional switches, one bidirectional switch, two diodes, and two capacitors. In addition, the description of the topology, the design guidelines, and a comparison among the main topologies are given in detail. Experimental results are obtained to attest the practicability of the proposed solution.
Grigorev, A, Mihăiţă, A-S, Saleh, K & Chen, F 2024, 'Automatic Accident Detection, Segmentation and Duration Prediction Using Machine Learning', IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 2, pp. 1547-1568.
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Grochow, JA & Qiao, Y 2024, 'On p -Group Isomorphism: Search-to-Decision, Counting-to-Decision, and Nilpotency Class Reductions via Tensors', ACM Transactions on Computation Theory, vol. 16, no. 1, pp. 1-39.
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In this article, we study some classical complexity-theoretic questions regarding Group Isomorphism ( GpI ). We focus on p -groups (groups of prime power order) with odd p , which are believed to be a bottleneckcase for GpI , and work in the model of matrix groups over finite fields. Our main results are as follows: • Although search-to-decision and counting-to-decision reductions have been known for more than four decades for Graph Isomorphism , they had remained open for GpI , explicitly asked by Arvind and Torán ( EATCS Bull. , 2005). Extending methods from Tensor Isomorphism (TI) (Grochow and Qiao, ITCS 2021), we show moderately exponential-time such reductions within p -groups of class 2 and exponent p . • Despite the widely held belief that p -groups of class 2 and exponent p are the hardest cases of GpI , there was no reduction to these groups from ...
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, 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, Y, Zou, S, Peng, H, Ni, W, Sun, Y & Gao, H 2024, 'Cooperative UAV Trajectory Design for Disaster Area Emergency Communications: A Multiagent PPO Method', IEEE Internet of Things Journal, vol. 11, no. 5, pp. 8848-8859.
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Gudigar, A, Kadri, NA, Raghavendra, U, Samanth, J, Maithri, M, Inamdar, MA, Prabhu, MA, Hegde, A, Salvi, M, Yeong, CH, Barua, PD, Molinari, F & Acharya, UR 2024, 'Automatic identification of hypertension and assessment of its secondary effects using artificial intelligence: A systematic review (2013–2023)', Computers in Biology and Medicine, vol. 172, pp. 108207-108207.
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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, 'Multi-Model Neural Network for Live Classification of Water Pipe Leaks from Vibro-Acoustic Signals', IEEE Sensors Journal, pp. 1-1.
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Guo, CA, Guo, YJ & Yuan, J 2024, 'Multibeam Receiving Antennas Employing Generalized Joined Coupler Matrix', IEEE Transactions on Antennas and Propagation, vol. 72, no. 1, pp. 424-432.
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Owing to the characteristics of directional couplers, series fed multibeam receiving antenna arrays require different treatment from their transmitting counterparts. In this paper, the theory and strategies for the feed network synthesis of multibeam receiving antennas employing the generalized joined coupler matrix (GJC) are presented. Given M incident waves in any set of directions, we first derive the output signal matrix as a function of the incident signal matrix. This serves as an important tool for both synthesizing and analysing GJC receiving matrices. Then, we present three different synthesis strategies and show how they are related to the array patterns. We also reveal how the receiving power efficiency of the GJC matrix changes with the antenna beam pattern. We further demonstrate that, despite the employment of matched loads, high receiving power efficiencies can be achieved using the GJC matrix.
Guo, 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, Y, Yu, H, Xie, S, Ma, L, Cao, X & Luo, X 2024, 'DSCA: A Dual Semantic Correlation Alignment Method for domain adaptation object detection', Pattern Recognition, vol. 150, pp. 110329-110329.
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Habaraduwa Peellage, W, Fatahi, B & Rasekh, H 2024, 'Stiffness and damping characteristics of jointed rocks under cyclic triaxial loading subjected to prolonged cyclic loading', International Journal of Fatigue, vol. 181, pp. 108121-108121.
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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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Halder, A, Shivakumara, P, Pal, U, Blumenstein, M & Ghosal, P 2024, 'A Locally Weighted Linear Regression-Based Approach for Arbitrary Moving Shaky and Nonshaky Video Classification', International Journal of Pattern Recognition and Artificial Intelligence, vol. 38, no. 01.
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Classification and identification of objects are complex and challenging in pattern recognition and artificial intelligence if a shaky and nonshaky camera captures the videos at different distances during the day and nighttime. This work presents a model for classifying a given video as a static, uniform, or arbitrarily moving videos so that the complexity of the problem can be reduced. To avoid the threat of different distances between the objects and the camera, the proposed work introduces new steps for estimating the depth of the objects in the video frames. We explore locally weighted linear regression for feature extraction from depth information based on the notion that the regression line fits almost all the points for uniformity and does not fit for arbitrary moving. The extracted features are fed to a random forest classifier to classify static, uniform, or arbitrary moving video. The results on a large dataset, which includes videos captured day and night, show that the proposed method successfully classifies static, uniform and arbitrary videos with 0.86, 1.00 and 0.67 F-measures, respectively. Overall, our method obtains 87% accuracy for classification of static, uniform and arbitrary video, which is superior to the state-of-the-art methods.
Halkon, B, Perrin, R & Guo, Z 2024, 'Extensional and inextensional modes of axisymmetric structures', Experimental Techniques: a publication for the practicing engineer.
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, pp. 172516-172516.
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Han, M, Wang, Y, Li, M, Chang, X, Yang, Y & Qiao, Y 2024, 'Progressive Frame-Proposal Mining for Weakly Supervised Video Object Detection', IEEE Transactions on Image Processing, vol. 33, pp. 1560-1573.
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Han, M, Zhu, T & Zhou, W 2024, 'Fair Federated Learning with Opposite GAN', Knowledge-Based Systems, vol. 287, pp. 111420-111420.
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Hannigan, IP, Rosengren, SM, Bharathy, GK, Prasad, M, Welgampola, MS & Watson, SRD 2024, 'Subjective and objective responses to caloric stimulation help separate vestibular migraine from other vestibular disorders', Journal of Neurology, vol. 271, no. 2, pp. 887-898.
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Abstract Background Nystagmus generated during bithermal caloric test assesses the horizontal vestibulo-ocular-reflex. Any induced symptoms are considered unwanted side effects rather than diagnostic information. Aim We hypothesized that nystagmus slow-phase-velocity (SPV) and subjective symptoms during caloric testing would be higher in vestibular migraine (VM) patients compared with peripheral disorders such as Meniere’s disease (MD) and non-vestibular dizziness (NVD). Methods Consecutive patients (n = 1373, 60% female) referred for caloric testing were recruited. During caloric irrigations, patients scored their subjective sensations. We assessed objective-measures, subjective vertigo (SVS), subjective nausea (SNS), and test completion status. Results Nystagmus SPV for VM, MD (unaffected side), and NVD were 29 ± 12.8, 30 ± 15.4, and 28 ± 14.2 for warm irrigation and 24 ± 8.9, 22 ± 10.0, and 25 ± 12.8 for cold-irrigation. The mean SVS were 2.5 ± 1.1, 1.5 ± 1.33, and 1.5 ± 1.42 for warm irrigation and 2.2 ± 1.1, 1.1 ± 1.19, and 1.1 ± 1.16 for cold-irrigation. Age was significantly correlated with SVS and SNS, (p < 0.001) for both. The SVS and SNS were significantly higher in VM compared with non-VM groups (p < 0.001), and there was no difference in nystagmus SPV. VM patients SVS was significantly different to the SVS of migraineurs in the other diagnostic groups (p < 0.001). Testing was incomplete for 34.4% of VM an...
Hasan, MM, Rasul, MG, Jahirul, MI & Mofijur, M 2024, 'Fuelling the future: Unleashing energy and exergy efficiency from municipal green waste pyrolysis', Fuel, vol. 357, pp. 129815-129815.
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Hasanpour, S, Siwakoti, YP & Blaabjerg, F 2024, 'A new soft‐switching high gain DC/DC converter with bipolar outputs', IET Power Electronics, vol. 17, no. 1, pp. 144-156.
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AbstractThis paper introduces a new single‐input multi‐output step‐up DC/DC converter with soft‐switching performance and low input current for renewable energy applications. The proposed topology uses a three‐winding coupled‐inductor (TWCI) and voltage multiplier circuits to achieve high voltage gains. The bipolar output voltages of the proposed converter can be varied independently by tuning the turns ratios of the TWCI. Due to the semi‐trans‐inverse specification of the suggested topology, high voltage gains can be obtained under a lower number of turns ratio in the magnetic device. Furthermore, a regenerative passive clamp technique mitigates the voltage stress on the single power switch. Additionally, the power dissipations are further reduced by considering a resonant tank in the circuit. In the converter, the parasitic leakage inductances of the TWCI windings help to provide the soft‐switching conditions for the switch and also to eliminate the reverse‐recovery loss for all converter diodes. The operating mode of the presented converter has been introduced and the steady state, along with the main operating equations have also been derived. Finally, the theoretical analysis is verified by a sample prototype 235 W at the input voltage 25 V and outputs of 200 V and −200 V.
Hassani, S, Dackermann, U, Mousavi, M & Li, J 2024, 'A systematic review of data fusion techniques for optimized structural health monitoring', Information Fusion, vol. 103, pp. 102136-102136.
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Hassani, S, Dackermann, U, Mousavi, M & Li, J 2024, 'Enhanced damage detection for noisy input signals using improved reptile search algorithm and data analytics techniques', Computers & Structures, vol. 296, pp. 107293-107293.
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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. PP, no. 99, pp. 1-13.
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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, pp. 1-1.
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He, W-N, Huang, X-L, Xu, Z, Hu, F & Yu, S 2024, 'Robust Localization for Mobile Targets Along a Narrow Path With LoS/NLoS Interference', IEEE Internet of Things Journal, pp. 1-1.
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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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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 ...
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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Hertrampf, T & Oberst, S 2024, 'Recurrence Rate spectrograms for the classification of nonlinear and noisy signals', Physica Scripta, vol. 99, no. 3, pp. 035223-035223.
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Abstract Time series analysis of real-world measurements is fundamental in natural sciences and engineering, and machine learning has been recently of great assistance especially for classification of signals and their understanding. Yet, the underlying system’s nonlinear response behaviour is often neglected. Recurrence Plot (RP) based Fourier-spectra constructed through τ-Recurrence Rate (RR τ ) have shown the potential to reveal nonlinear traits otherwise hidden from conventional data processing. We report a so far disregarded eligibility for signal classification of nonlinear time series by training RESnet-50 on spectrogram images, which allows recurrence-spectra to outcompete conventional Fourier analysis. To exemplify its functioning, we employ a simple nonlinear physical flow of a continuous stirred tank reactor, able to exhibit exothermic, first order, irreversible, cubic autocatalytic chemical reactions, and a plethora of fast-slow dynamics. For dynamics with noise being ten times stronger than the signal, the classification accuracy was up to ≈ 75% compared to ≈ 17% for the periodogram. We show that an increase in entropy only detected by the RR τ allows differentiation. This shows that RP power spectra, combined with off-the-shelf machine learning techniques, have the potential to significantly improve the detection of nonlinear and noise contaminated signals.
Heusdens, R & Zhang, G 2024, 'Distributed Optimisation With Linear Equality and Inequality Constraints Using PDMM', IEEE Transactions on Signal and Information Processing over Networks, vol. 10, pp. 294-306.
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Hoang, TM, Vahid, A, Tuan, HD & Hanzo, L 2024, 'Physical Layer Authentication and Security Design in the Machine Learning Era', IEEE Communications Surveys & Tutorials, pp. 1-1.
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Security at the physical layer (PHY) is a salient research topic in wireless systems, and machine learning (ML) is emerging as a powerful tool for providing new data-driven security solutions. Therefore, the application of ML techniques to the PHY security is of crucial importance in the landscape of more and more data-driven wireless services. In this context, we first summarize the family of bespoke ML algorithms that are eminently suitable for wireless security. Then, we review the recent progress in ML-aided PHY security, where the term “PHY security” is classified into two different types: i) PHY authentication and ii) secure PHY transmission. Moreover, we treat NNs as special types of ML and present how to deal with PHY security optimization problems using NNs. Finally, we identify some major challenges and opportunities in tackling PHY security challenges by applying carefully tailored ML tools.
Hossain, 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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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.
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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, pp. 1-1.
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Hu, S, Yuan, X, Ni, W, Wang, X, Hossain, E & Poor, HV 2024, 'OFDMA-F2L: Federated Learning With Flexible Aggregation Over an OFDMA Air Interface', IEEE Transactions on Wireless Communications, pp. 1-1.
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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, pp. 1-1.
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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, 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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Hua, H, Zahmatkesh, S, Osman, H, Tariq, A & Zhou, JL 2024, 'Effects of hydraulic retention time and cultivation on nutrients removal and biomass production in wastewater by membrane photobioreactor: Modeling and optimization by machine learning and response surface methodology', Chemosphere, pp. 141394-141394.
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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, pp. 1-14.
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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, 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, 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, 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, Y, Feng, B, Tian, A, Dong, P, Yu, S & Zhang, H 2024, 'An Efficient Differentiated Routing Scheme for MEO/LEO-Based Multi-Layer Satellite Networks', IEEE Transactions on Network Science and Engineering, vol. 11, no. 1, pp. 1026-1041.
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Huang, Y, Huang, Y, Zhang, Z, Wu, Q, Zhong, Y & Wang, L 2024, 'Enhancing Person Re-Identification Performance Through In Vivo Learning', IEEE Transactions on Image Processing, vol. 33, pp. 639-654.
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Huang, Y, 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, Yang, D, Feng, B, Tian, A, Dong, P, Yu, S & Zhang, H 2024, 'A GNN-Enabled Multipath Routing Algorithm for Spatial-Temporal Varying LEO Satellite Networks', IEEE Transactions on Vehicular Technology, pp. 1-15.
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Huang, Y, Yang, G, Zhou, H, Dai, H, Yuan, D & Yu, S 2024, 'VPPFL: A verifiable privacy-preserving federated learning scheme against poisoning attacks', Computers & Security, vol. 136, pp. 103562-103562.
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Huang, Y, Zhang, Z, Huang, Y, Wu, Q, Huang, H, Zhong, Y & Wang, L 2024, 'Customized meta-dataset for automatic classifier accuracy evaluation', Pattern Recognition, vol. 146, pp. 110026-110026.
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Huang, Y, Zhao, S & Lu, J 2024, 'Acoustic contrast control with a sound intensity constraint for personal sound systems', The Journal of the Acoustical Society of America, vol. 155, no. 2, pp. 879-890.
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Personal sound systems have received significant research interest in the past two decades due to their promising applications in a variety of scenarios. Various methods have been proposed to generate personal sound zones, most of which are based on sound pressure manipulation in both the acoustically bright and dark zones. Since sound intensity is closely related to human perception of sound localization, this paper proposes an acoustic contrast control method with a sound intensity constraint to increase the spatial planarity in the bright zone. In the proposed method, the sound intensity in the bright zone is projected to a specific direction while the sound pressure level in the dark zone is minimized. Simulations and experiments are carried out to compare the proposed method with the existing planarity control and pressure matching methods in terms of acoustic contrast, array effort, and planarity. The results demonstrate that the proposed method improves the planarity in the bright zone compared to existing planarity control method, while exhibitig a higher acoustic contrast and a lower array effort than the pressure matching method.
Huang, Z, Zhao, R, Leung, FHF, Banerjee, S, Lam, K-M, Zheng, Y-P & Ling, SH 2024, 'Landmark Localization from Medical Images with Generative Distribution Prior', IEEE Transactions on Medical Imaging, pp. 1-1.
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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.
Huynh, NV, 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, pp. 1-1.
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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 & Ferrari, AC 2024, 'Tailoring graphene for electronics beyond silicon', Nature, vol. 625, no. 7993, pp. 34-35.
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Ijaz Malik, MA, Mujtaba, MA, Kalam, MA, Silitonga, AS & Ikram, A 2024, 'Recent advances in hydrogen supplementation to promote biomass fuels for reducing greenhouse gases', International Journal of Hydrogen Energy, vol. 49, pp. 463-487.
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Energy security is the foremost concern for a sustainable environment. To make a sustainable environment, biomass waste products like biomass oil and biofuels must be efficiently burned. As millions of tons of waste biomass are dumped daily in major cities worldwide, it must be brought into energy products utilization. The quest for a sustainable ecosystem has pushed scientists to explore alternative fuels that are not only compatible with the engine but also eco-friendly. Hydrogen exhibits excellent combustion characteristics during dual fuel mode in a compression ignition (CI) engine. Carbon dioxide and NOx emissions are the two significant pollutants alternative fuels produce. This review study has tried to mitigate these two pollutants by combining biodiesel and hydrogen. It has been investigated that hydrogen possesses zero carbon content and can reduce CO2 emission, and biodiesel made from algae resources can help reduce NOx emission. Therefore, it is highlighted through the current review study to use the blend of hydrogen and algae-based biodiesel fuels to achieve benefits from their combined physicochemical properties and mitigate greenhouse gas emissions. The carbon-free nature of hydrogen and the oxygenated nature of biodiesel can be an excellent combination for combustion in diesel engines. Adopting third-generation fuels such as algae appears to be a viable solution to meet future energy demands. Biodiesel has a lower calorific value and viscous nature, negatively impacting fuel spray characteristics and creating abrupt fuel consumption. The purpose of this study is to promote biomass oil burning using hydrogen as a promoter supplement blend. Hydrogen has a higher heating value that can help overcome the less heating value of biodiesel fuels. Therefore, hydrogen as a blend with biodiesel makes the mixture lean and positively impacts engine performance, emissions, and combustion parameters.
Ikram, MM, Saha, G & Saha, SC 2024, 'Second law analysis of a transient hexagonal cavity with a rotating modulator', International Journal of Heat and Mass Transfer, vol. 221, pp. 125039-125039.
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Im, K, Park, M, Kabir, MM, Sohn, W, Choo, Y, Shon, HK & Nam, SY 2024, 'Human urine electrolysis for simultaneous green hydrogen and liquid fertilizer production for a circular economy: A proof of concept', Desalination, vol. 570, pp. 117059-117059.
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This study explores a novel process for hydrogen production and urine concentration using water electrolysis, employing a hydrophobic membrane and hydrogel electrolyte. The process utilizes a hydrophobic membrane to provide pure water from human urine, while simultaneously producing hydrogen through electrolysis, and concentrating urine for liquid fertilizer production. A suitable hydrogel electrolyte was developed, with polyvinyl alcohol (PVA)-based hydrogels and varying potassium hydroxide (KOH) concentration, showing efficient ion conductivity. The PVA-KOH 30 wt % hydrogel incorporating melamine exhibited promising performance in cell testing, achieving a current density of 204.35 mA/cm2 at 2 V. Long-term electrolysis tests indicated sustained efficiency, although a decline in current density during 96 h was attributed to hydrophobic membrane fouling. Nonetheless, the hydrogel electrolyte demonstrated minimal fouling, successfully concentrating the urine about 5 times. This concentrated urine serves as liquid fertilizer, while the produced hydrogen acts as an energy source, and the oxygen can be recycled for use in a membrane bioreactor (MBR), establishing a sustainable energy cycle system.
Indraratna, B, Arachchige, CMK, Rujikiatkamjorn, C, Heitor, A & Qi, Y 2024, 'Utilization of Granular Wastes in Transportation Infrastructure', Geotechnical Testing Journal, vol. 47, no. 1, pp. 20220233-20220233.
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Abstract Attributed to environmental preservation in urban infrastructure development, the recycling of waste materials produced in the coal and steel industry as well as the reusing of waste tires is a high priority in Australia. In this article, the practical applications of (i) coal wash (CW) and steel furnace slag mixtures, (ii) CW and fly ash mixtures, and (iii) rubber elements derived from recycled tires are discussed. In this regard, some examples of real-life applications are elucidated in relation to coastal reclamation as well as road and rail construction (e.g., Port Kembla, Kangaroo Valley highway, and Chullora Rail Precinct). The article outlines various aspects of site investigation, construction techniques, and the installation of instrumentation to evaluate the field performance of these waste materials in contrast to traditional (natural) quarried materials. The results from these case studies demonstrate that properly engineered granular waste mixtures can exhibit promising characteristics even to exceed the current technical standards, implying reduced intensity of maintenance. The research outcomes strongly support sustainable solutions to be embraced in the future development of transportation infrastructure, capable of withstanding increased freight loading and enhanced longevity.
Indraratna, B, 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.
Iqbal, H, Zheng, J, Chai, R & Chandrasekaran, S 2024, 'Electric powered wheelchair control using user-independent classification methods based on surface electromyography signals', Medical & Biological Engineering & Computing, vol. 62, no. 1, pp. 167-182.
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AbstractWheelchairs are one of the most popular assistive technology (AT) among individuals with motor impairments due to their comfort and mobility. People with finger problems may find it difficult to operate wheelchairs using the conventional joystick control method. Therefore, in this research study, a hand gesture-based control method is developed for operating an electric-powered wheelchair (EPW). This study selected a comfort-based hand position to determine the stop maneuver. An additional exploration was undertaken to investigate four gesture recognition methods: linear regression (LR), regularized linear regression (RLR), decision tree (DT), and multi-class support vector machine (MC-SVM). The first two methods, LR and RLR, have promising accuracy values of 94.85% and 95.88%, respectively, but each new user must be trained. To overcome this limitation, this study explored two user-independent classification methods: MC-SVM and DT. These methods effectively addressed the finger dependency issue and demonstrated remarkable success in recognizing gestures across different users. MC-SVM has about 99.05% of both precision and accuracy, and the DT has about 97.77% accuracy and precision. All six participants were successful in controlling the EPW without any collisions. According to the experimental results, the proposed approach has high accuracy and can address finger dependency issues.
Irham, A, Roslan, MF, Jern, KP, Hannan, MA & Mahlia, TMI 2024, 'Hydrogen energy storage integrated grid: A bibliometric analysis for sustainable energy production', International Journal of Hydrogen Energy, vol. 63, pp. 1044-1087.
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Islam Rony, Z, Rasul, MG, Jahirul, MI & Mofijur, M 2024, 'Harnessing marine biomass for sustainable fuel production through pyrolysis to support United Nations' Sustainable Development Goals', Fuel, vol. 358, pp. 130099-130099.
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Islam, MR, Akter, S, Islam, L, Razzak, I, Wang, X & Xu, G 2024, 'Strategies for evaluating visual analytics systems: A systematic review and new perspectives', Information Visualization, vol. 23, no. 1, pp. 84-101.
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In recent times, visual analytics systems (VAS) have been used to solve various complex issues in diverse application domains. Nonetheless, an inherent drawback arises from the insufficient evaluation of VAS, resulting in occasional inaccuracies when it comes to analytical reasoning, information synthesis, and deriving insights from vast, ever-changing, ambiguous, and frequently contradictory data. Hence, the significance of implementing an appropriate evaluation methodology cannot be overstated, as it plays a pivotal role in enhancing the design and development of visualization systems. This paper assesses visualization systems by providing a systematic exploration of various evaluation strategies (ES). While several existing studies have examined some ES, the extent of comprehensive and systematic review for visualization research remains limited. In this work, we introduce seven state-of-the-art and widely recognized ES namely (1) dashboard comparison; (2) insight-based evaluation; (3) log data analysis; (4) Likert scales; (5) qualitative and quantitative analysis; (6) Nielsen’s heuristics; and (7) eye trackers. Moreover, it delves into their historical context and explores numerous applications where these ES have been employed, shedding light on the associated evaluation practices. Through our comprehensive review, we overview and analyze the predominant evaluation goals within the visualization community, elucidating their evolution and the inherent contrasts. Additionally, we identify the open challenges that arise with the emergence of new ES, while also highlighting the key themes gleaned from the existing literature that hold potential for further exploration in future studies.
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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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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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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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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Jiang, C, Li, W, Ng, C-T & Deng, M 2024, 'Numerical and experimental investigations on quasistatic pulse generation of ultrasonic guided waves in fiber reinforced composite pipes', Journal of Sound and Vibration, vol. 574, pp. 118238-118238.
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Jiang, C, Li, W, Ng, C-T & Deng, M 2024, 'Quasistatic component generation of group velocity mismatched guided waves in tubular structures for microdamage localization', Applied Acoustics, vol. 217, pp. 109813-109813.
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Jiang, J, Dorji, P, Badeti, U, Sohn, W, Freguia, S, Phuntsho, S, El Saliby, I & Shon, HK 2024, 'Corrigendum to “Potential nutrient recovery from source-separated urine through hybrid membrane bioreactor and membrane capacitive deionisation” [Desalination 566 (2023) 116924]', Desalination, vol. 573, pp. 117236-117236.
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Jiang, J, Sohn, W, Almuntashiri, A, Phuntsho, S, Wang, Q, Freguia, S, El-Saliby, I & Shon, HK 2024, 'Feasibility study of powdered activated carbon membrane bioreactor (PAC-MBR) for source-separated urine treatment: A comparison with MBR', Desalination, vol. 580, pp. 117544-117544.
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Jiang, 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, pp. 1-11.
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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, W, Zhao, B, Zhang, Y, Huang, J & Yu, H 2024, 'WordTransABSA: Enhancing Aspect-based Sentiment Analysis with masked language modeling for affective token prediction', Expert Systems with Applications, vol. 238, pp. 122289-122289.
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Jing, H, Ge, H, Tang, H, Farnoud, A, Saidul Islam, M, Wang, L, Wang, C & Cui, X 2024, 'Assessing airflow unsteadiness in the human respiratory tract under different expiration conditions', Journal of Biomechanics, vol. 162, pp. 111910-111910.
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John, AR, Singh, AK, Gramann, K, Liu, D & Lin, C-T 2024, 'Prediction of cognitive conflict during unexpected robot behavior under different mental workload conditions in a physical human–robot collaboration', Journal of Neural Engineering, vol. 21, no. 2, pp. 026010-026010.
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Abstract Objective. Brain–computer interface (BCI) technology is poised to play a prominent role in modern work environments, especially a collaborative environment where humans and machines work in close proximity, often with physical contact. In a physical human robot collaboration (pHRC), the robot performs complex motion sequences. Any unexpected robot behavior or faulty interaction might raise safety concerns. Error-related potentials, naturally generated by the brain when a human partner perceives an error, have been extensively employed in BCI as implicit human feedback to adapt robot behavior to facilitate a safe and intuitive interaction. However, the integration of BCI technology with error-related potential for robot control demands failure-free integration of highly uncertain electroencephalography (EEG) signals, particularly influenced by the physical and cognitive state of the user. As a higher workload on the user compromises their access to cognitive resources needed for error awareness, it is crucial to study how mental workload variations impact the error awareness as it might raise safety concerns in pHRC. In this study, we aim to study how cognitive workload affects the error awareness of a human user engaged in a pHRC. Approach. We designed a blasting task with an abrasive industrial robot and manipulated the mental workload with a secondary arithmetic task of varying difficulty. EEG data, perceived workload, task and physical performance were recorded from 24 participants moving the robot arm. The error condition was achieved by the unexpected stopping of the robot in 33% of trials. Main results. We observed a diminished amplitude for the prediction error negativity (PEN) and error positivity (Pe), indicating reduced error awareness with increasing mental workload. We further observed a...
Jorquera, E, Saco, PM, Verdon-Kidd, D, Rodríguez, JF, Timmermans, H & Nelson, F 2024, 'Effects of tropical cyclones on catchment sediment delivery to coastal ecosystems', CATENA, vol. 238, pp. 107805-107805.
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Joshi, S, Sharma, M, Bartwal, S, Joshi, T & Prasad, M 2024, 'Critical challenges of integrating OPEX strategies with I4.0 technologies in manufacturing SMEs: a few pieces of evidence from developing economies', The TQM Journal, vol. 36, no. 1, pp. 108-138.
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PurposeThe study proposes to determine the impending challenges to lean integration with Industry 4.0 (I4.0) in manufacturing that aims at achieving desired operational performance. Integrating lean and Industry 4.0 as the two industrial approaches is synergetic in providing operational benefits such as increasing flexibility, improving productivity, reducing cost, reducing delivery time, improving quality and value stream mapping (VSM). There is an urgent need to understand the integrated potential of OPEX strategies like lean manufacturing and also to determine the challenges for manufacturing SMEs and further suggest a strategic roadmap for the future.Design/methodology/approachThe current work has used a combined approach on interpretative structural modeling (ISM) and fuzzy Matrice d'impacts croisés multiplication appliquée á un classment (MICMAC) approach to structure the multiple level analysis for the implementation challenges to integrate OPEX strategies with Industry 4.0.FindingsThe research has found that the indulgence of various implementation issues like lack of standardization, lack of vision and lack of trained support, all are the major challenges that inhibit the integration of OPEX strategies with I4.0 technologies in manufacturing.Research limitations/implicationsThe research has investigated the internal factors acting as a roadblock to lean and Industry 4.0 adoption. Further studies may consider external factors to lean and Industry 4.0 implementation. Also, further research may consider other operational excellence approaches and extend further to relevant sectors.<...
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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Kabir, MM, Sabur, GM, Akter, MM, Nam, SY, Im, KS, Tijing, L & Shon, HK 2024, 'Electrodialysis desalination, resource and energy recovery from water industries for a circular economy', Desalination, vol. 569, pp. 117041-117041.
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The water industries (WIN) are now approaching towards sustainability of resource use, recovery process, and water and energy management based on the circular economy (CRE) framework. Thus, the integration of electrodialysis (ED) technology in the WIN with a CRE paradigm should be recommended for ensuring the sustainability of ED desalination, resource, and energy recovery (EDDRER). According to the literature review, and to the best of our knowledge, there is no systematic study devoted to the sustainable EDDRER; hence a comprehensive and critical knowledge generation of EDDRER is essential for further technological advancements of ED. Consequently, this review paper investigated the plausible incorporation of ED in the WIN for a CRE of EDDRER. The recent progress of EDDRER has been described comprehensively and critically. Moreover, an all-inclusive techno-economics and environmental sustainability analysis of EDDRER from WIN for a CRE has been carried out. This paper marks the first instance in which energy recovery techniques employing ED have been reported and critically discussed. In addition, the latest case studies of EDDRER in the WIN have been discussed critically, and the significant scaling-up issues of EDDRER have been assessed based on the state-of-the-art recent scientific findings. Furthermore, the potential mitigation measures for the scaling-up issues have also been addressed. This study is the first comprehensive assessment of EDDRER from WIN for a closed-loop economy. The novel insights of this study could be essential for the development of a sustainable CRE-based EDDRER process for WIN to attain sustainable development goals (SDGs).
Kaliyannan, P, Seikh, AH, Kalam, MA & Venkatesh, R 2024, 'Fabrication and Characteristics Study of Aluminium Alloy Hybrid Nanocomposite Synthesized with SiC and Waste Metal Powder', Silicon, vol. 16, no. 2, pp. 843-851.
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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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Karki, D, Far, H & Nejadi, S 2024, 'Structural Behaviour 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, 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 thirteen 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 behaviour and performance parameters, such as ultimate bending capacity, load-deflection response, load-slip response and failure modes, by categorising thirteen specimens into four sub-groups based on shear connector types and spacings. In the proposed composite CFST flooring system, 45mm thick structural plywood panels were connected to the 2.4mm 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 theoretical 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
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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Ke, L, Xiao, P, Chen, X, Yu, S, Chen, X & Wang, H 2024, 'A novel cross-domain adaptation framework for unsupervised criminal jargon detection via pre-trained contextual embedding of darknet corpus', Expert Systems with Applications, vol. 242, pp. 122715-122715.
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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, pp. 1-10.
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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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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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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, pp. 1-14.
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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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Khosravi, K, Golkarian, A, Saco, PM, Booij, MJ & Melesse, AM 2024, 'Model identification and accuracy for estimation of suspended sediment load', Geocarto International, vol. 37, no. 27, pp. 18520-18545.
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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, J, Dong, L, Shon, HK & Park, K 2024, 'Current progress in semi-batch reverse osmosis for brackish water desalination', Desalination, vol. 578, pp. 117434-117434.
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Kim, J, Lee, J, Lee, S, Tijing, L, Shon, HK & Hong, S 2024, 'Electrically conductive membrane for fouling control: Its mechanisms and applications', Desalination, vol. 578, pp. 117445-117445.
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Kim, S, Corah, M, Keller, J, Best, G & Scherer, S 2024, 'Multi-Robot Multi-Room Exploration With Geometric Cue Extraction and Circular Decomposition', IEEE Robotics and Automation Letters, vol. 9, no. 2, pp. 1190-1197.
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Kong, M, Hou, M, Zhao, S, Liu, F, Su, R & Chen, Y 2024, 'DADIN: Domain Adversarial Deep Interest Network for cross domain recommender systems', Expert Systems with Applications, vol. 243, pp. 122880-122880.
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Kong, X, Lu, Z, Guo, X, Zhang, J & Li, H 2024, 'Resilience Evaluation of Cyber-Physical Power System Considering Cyber Attacks', IEEE Transactions on Reliability, vol. 73, no. 1, pp. 245-256.
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Kotha, M, Pradhan, S & Cetindamar, D 2024, 'Relevance of Engineering Management Courses to Managerial Skills in the Industry', IEEE Transactions on Engineering Management, pp. 1-14.
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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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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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Kumari, A, Tanveer, M & Lin, CT 2024, 'Class Probability and Generalized Bell Fuzzy Twin SVM for Imbalanced Data', IEEE Transactions on Fuzzy Systems, pp. 1-12.
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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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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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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, AT, Huang, X, Ding, C, Zhang, H & Guo, YJ 2024, 'An In-Band Full-Duplex Prototype With Joint Self-Interference Cancellation in Antenna, Analog, and Digital Domains', IEEE Transactions on Microwave Theory and Techniques, pp. 1-10.
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Le, M, Hoang, DT, Nguyen, DN, Hwang, W-J & Pham, Q-V 2024, 'Wirelessly Powered Federated Learning Networks: Joint Power Transfer, Data Sensing, Model Training, and Resource Allocation', IEEE Internet of Things Journal, pp. 1-1.
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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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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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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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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, P, Zhang, Y, Li, N, Si, Y, Li, F, Cao, Z, Chen, H, Chen, B, Yao, D & Xu, P 2024, 'Effective Emotion Recognition by Learning Discriminative Graph Topologies in EEG Brain Networks', IEEE Transactions on Neural Networks and Learning Systems, 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, 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, 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, pp. 1-15.
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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, J, Yu, H, Zhang, Z, Luo, X & Xie, S 2024, 'Concept Drift Adaptation by Exploiting Drift Type', ACM Transactions on Knowledge Discovery from Data, vol. 18, no. 4, pp. 1-22.
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Concept drift is a phenomenon where the distribution of data streams changes over time. When this happens, model predictions become less accurate. Hence, models built in the past need to be re-learned for the current data. Two design questions need to be addressed in designing a strategy to re-learn models: which type of concept drift has occurred, and how to utilize the drift type to improve re-learning performance. Existing drift detection methods are often good at determining when drift has occurred. However, few retrieve information about how the drift came to be present in the stream. Hence, determining the impact of the type of drift on adaptation is difficult. Filling this gap, we designed a framework based on a lazy strategy called Type-Driven Lazy Drift Adaptor (Type-LDA). Type-LDA first retrieves information about both how and when a drift has occurred, then it uses this information to re-learn the new model. To identify the type of drift, a drift type identifier is pre-trained on synthetic data of known drift types. Furthermore, a drift point locator locates the optimal point of drift via a sharing loss. Hence, Type-LDA can select the optimal point, according to the drift type, to re-learn the new model. Experiments validate Type-LDA on both synthetic data and real-world data, and the results show that accurately identifying drift type can improve adaptation accuracy.
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, 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, Mortazavi, M, Far, H, El-Sherbeeny, AM & Ahmadian Fard Fini, A 2024, 'Simulation and modeling of polymer concrete panels using deep neural networks', Case Studies in Construction Materials, vol. 20, pp. e02912-e02912.
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Li, L, Wang, W, Zhou, T, Quan, R & Yang, Y 2024, 'Semantic Hierarchy-Aware Segmentation', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 4, pp. 2123-2138.
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Li, L, Xiao, J, Shi, H, Wang, W, Shao, J, Liu, A-A, Yang, Y & Chen, L 2024, 'Label Semantic Knowledge Distillation for Unbiased Scene Graph Generation', IEEE Transactions on Circuits and Systems for Video Technology, vol. 34, no. 1, pp. 195-206.
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Li, M, Chen, S, Shen, Y, Liu, G, Tsang, IW & Zhang, Y 2024, 'Online Multi-Agent Forecasting With Interpretable Collaborative Graph Neural Networks', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 4, pp. 4768-4782.
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Li, M, Li, Z & Cao, Z 2024, 'Enhancing Car-Following Performance in Traffic Oscillations Using Expert Demonstration Reinforcement Learning', IEEE Transactions on Intelligent Transportation Systems, pp. 1-16.
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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, pp. 1-11.
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Li, P, Gao, X, Li, C, Yi, C, Huang, W, Si, Y, Li, F, Cao, Z, Tian, Y & Xu, P 2024, 'Granger Causal Inference Based on Dual Laplacian Distribution and Its Application to MI-BCI Classification', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15.
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Li, S, Ji, JC, Xu, Y, Feng, K, Zhang, K, Feng, J, Beer, M, Ni, Q & Wang, Y 2024, 'Dconformer: A denoising convolutional transformer with joint learning strategy for intelligent diagnosis of bearing faults', Mechanical Systems and Signal Processing, vol. 210, pp. 111142-111142.
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Li, T, 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, 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, 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, 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.
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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-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 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, Y, Zhu, L, Wang, X & Yang, Y 2024, 'IcoCap: Improving Video Captioning by Compounding Images', IEEE Transactions on Multimedia, vol. 26, pp. 4389-4400.
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Liang, Y, Zhu, L, Wang, X & Yang, Y 2024, 'Penalizing the Hard Example But Not Too Much: A Strong Baseline for Fine-Grained Visual Classification', IEEE Transactions on Neural Networks and Learning Systems, vol. PP, no. 99, pp. 1-12.
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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.
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-T, Zhang, H, Ou, L, Chang, Y-C & Wang, Y-K 2024, 'Adaptive Trust Model for Multi-Agent Teaming Based on Reinforcement-Learning-Based Fusion', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 8, no. 1, pp. 229-239.
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Lin, J-Y, Wong, S-W, Qian, L, Wang, Y, Yang, Y & Liu, Q-H 2024, 'Single and Multiple-Band Bandpass Filters Using Bandstop Resonator Sections', IEEE Journal of Microwaves, vol. 4, no. 2, pp. 293-302.
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Lin, S, Liu, A, Wang, J & Kong, X 2024, 'An improved fault-tolerant cultural-PSO with probability for multi-AGV path planning', Expert Systems with Applications, vol. 237, pp. 121510-121510.
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This paper presents a hybrid evolutionary algorithm, cultural-particle swarm optimization (C-PSO), which is inspired by the cultural algorithm and the particle swarm optimization algorithm. It is aimed to balance the performance of exploration and exploitation and avoid trapping in the local optima. It introduces a probabilistic approach to update the inertia weight based on the improved metropolis rule. Generating the optimal path without collisions is challenging to ensure vehicles operate safely in real-time implementation. The contributions of C-PSO are to solve the path planning problem of multiple vehicles in modern industrial warehouses, achieving task allocation, fault tolerance and collision avoidance by a dual-layer framework. It was compared with the other algorithms, including PSO, PSO-GA, CA, HS, ABC, HPSGWO, TS and MA, by CEC benchmark functions and statistical tests to demonstrate its great performance with fewer iterations and runtime and the best solutions. It is validated through computational experiments, which involve 15 AGVs and 20 tasks for demonstration.
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, C, Zhu, T, Zhao, Y, Zhang, J & Zhou, W 2024, 'Disentangling different levels of GAN fingerprints for task-specific forensics', Computer Standards & Interfaces, vol. 89, pp. 103825-103825.
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Liu, D, Tsang, IW & Yang, G 2024, 'A Convergence Path to Deep Learning on Noisy Labels', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 4, pp. 5170-5182.
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Liu, G, Zhang, W, Wang, X, King, S & Yu, S 2024, 'A Membership Inference and Adversarial Attack Defense Framework for Network Traffic Classifiers', IEEE Transactions on Artificial Intelligence, pp. 1-16.
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Liu, G-Y, Li, J-P, Indraratna, B & Zhou, P 2024, 'A hydraulic-mechanical (HM) coupling constitutive model for unsaturated soil-continuum interfaces considering bonding effect', Computers and Geotechnics, vol. 166, pp. 105989-105989.
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Liu, H, Li, X, Zhang, Z, Li, J, Zhou, T, Wang, Z & Wang, Q 2024, 'Urine pretreatment enhances energy recovery by boosting medium-chain fatty acids production from waste activate sludge through anaerobic fermentation', Chemical Engineering Journal, vol. 482, pp. 148842-148842.
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Liu, H, Zhang, Z, Li, X, Zhou, T, Wang, Z, Li, J, Li, Y & Wang, Q 2024, 'Temperature-phased anaerobic sludge digestion effectively removes antibiotic resistance genes in a full-scale wastewater treatment plant', Science of The Total Environment, vol. 924, pp. 171555-171555.
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Liu, J, Dong, L, Li, C, Fang, J, Chen, Y & Cui, J 2024, 'Quasi-static and dynamic tensile behaviour of 316L stainless steels: Rolled versus laser-powder bed fusion (LPBF) fabricated samples', International Journal of Impact Engineering, vol. 190, pp. 104972-104972.
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Liu, J, Huang, X-L & Yu, S 2024, 'Constant Wideband Compressive Spectrum Sensing With Cascade Forward-Backward Propagating and Prior Knowledge Refining', IEEE Transactions on Wireless Communications, vol. 23, no. 3, pp. 1855-1870.
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Liu, J, 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 & Zhang, JA 2024, 'Practical frequency-hopping MIMO joint radar communications: design and experiment', Digital Communications and Networks.
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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. PP, no. 99, pp. 1-14.
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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, pp. 1-15.
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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, 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, 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, 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, pp. 1-14.
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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, 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, Zhu, L, Wang, X, Yamada, M & Yang, Y 2024, 'Bilaterally Normalized Scale-Consistent Sinkhorn Distance for Few-Shot Image Classification', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-11.
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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, pp. 1-14.
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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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Lu, J, Ma, G & Zhang, G 2024, 'Fuzzy Machine Learning: A Comprehensive Framework and Systematic Review', IEEE Transactions on Fuzzy Systems, pp. 1-18.
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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, Y, Cai, B, Tang, X, Liu, L, Du, J, Yu, S, Atiquzzaman, M & Dustdar, S 2024, 'Tree-ORAP: A Tree-Based Oblivious Random-Access Protocol for Privacy-Protected Blockchain', IEEE Transactions on Services Computing, pp. 1-13.
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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, Zhao, G, Xu, C & Yu, S 2024, 'An Efficient Hypergraph-Based Routing Algorithm in Time-Sensitive Networks', IEEE Signal Processing Letters, vol. 31, pp. 835-839.
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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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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, pp. 1-11.
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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. PP, no. 99, pp. 1-10.
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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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M.S., S, Elmakki, T, Schipper, K, Ihm, S, Yoo, Y, Park, B, Park, H, Shon, HK & Han, DS 2024, 'Integrated seawater hub: A nexus of sustainable water, energy, and resource generation', Desalination, vol. 571, pp. 117065-117065.
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Ma, G, Lu, J & Zhang, G 2024, 'Multi-source Domain Adaptation with Interval-Valued Target Data via Fuzzy Neural Networks', IEEE Transactions on Fuzzy Systems, pp. 1-13.
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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, pp. 1-13.
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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, 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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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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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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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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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 of Zingiber zerumbet which 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 genes P53 and PTEN, and metastasis-associated gene KRT18. 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.
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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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.
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.
Mekala, MS, Srivastava, G, Gandomi, AH, Park, JH & Jung, H-Y 2024, 'A Quantum-Inspired Sensor Consolidation Measurement Approach for Cyber-Physical Systems', IEEE Transactions on Network Science and Engineering, vol. 11, no. 1, pp. 511-524.
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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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Min, C, Akther, N, Lee, T, Choo, Y, Naidu, G, Han, D-S, Kim, S-H & Shon, HK 2024, 'Atmospheric water harvesting by osmotic distillation and direct contact membrane distillation using hydrophobic hollow fiber membranes', Process Safety and Environmental Protection, vol. 182, pp. 527-534.
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Mirdad, AR, Khan, AM & Hussain, FK 2024, 'Smart contracts and marketplace for just-in-time management of pharmaceutical drugs', International Journal of Web and Grid Services, vol. 20, no. 1, pp. 25-53.
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Mishra, DK, Eskandari, M, Abbasi, MH, Sanjeevikumar, P, Zhang, J & Li, L 2024, 'A detailed review of power system resilience enhancement pillars', Electric Power Systems Research, vol. 230, pp. 110223-110223.
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Mishra, DK, 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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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.
Mojiri, A, Vishkaei, MN, Zhou, JL, Trzcinski, AP, Lou, Z, Kasmuri, N, Rezania, S, Gholami, A, Vakili, M & Kazeroon, RA 2024, 'Impact of polystyrene microplastics on the growth and photosynthetic efficiency of diatom Chaetoceros neogracile', Marine Environmental Research, vol. 194, pp. 106343-106343.
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Mondal, MIH, Chandra Chakraborty, S, Rahman, MS, Marjuban, SMH, Ahmed, F, Zhou, JL, Ahmed, MB & Zargar, M 2024, 'Adsorbents from rice husk and shrimp shell for effective removal of heavy metals and reactive dyes in water', Environmental Pollution, vol. 346, pp. 123637-123637.
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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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Mueller, FF, Lallemand, C, Reidsma, D, van den Hoven, E & Montoya, MF 2024, 'Report from Dagstuhl seminar 23292', Dagstuhl Reports, vol. 13, no. 7.
Musial, J, Stebel, K, Czeczot, J, Nowak, P & Gabrys, B 2024, 'Application of self-improving Q-learning controller for a class of dynamical processes: Implementation aspects', Applied Soft Computing, vol. 152, pp. 111250-111250.
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Mustafizur Rahman, M, Al-Amin, M & Hossain, J 2024, 'Machine learning models for chronic kidney disease diagnosis and prediction', Biomedical Signal Processing and Control, vol. 87, pp. 105368-105368.
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Mysore, THM, Patil, AY, Hegde, C, Sudeept, MA, Kumar, R, Soudagar, MEM & Fattah, IMR 2024, 'Apatite insights: From synthesis to biomedical applications', European Polymer Journal, vol. 209, pp. 112842-112842.
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Nagaprasad, KS, Banapurmath, NR, Madhu, D, Soudagar, MEM, Mujtaba, MA, Kalam, MA, Vadlamudi, C & Krishnappa, S 2024, 'Emission characteristics of diesel engines fuelled with B20 with different DPF, DOC and EGR arrangements', International Journal of Ambient Energy, vol. 45, no. 1.
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Namadchian, Z, Shoeibi, A, Zare, A, Gorriz, JM, Lam, H-K & Ling, SH 2024, 'Stability Analysis of Dynamic General Type-2 Fuzzy Control System With Uncertainty', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 54, no. 3, pp. 1755-1767.
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Nanda, SJ, Yadav, RP, Gandomi, AH & Saraswat, M 2024, 'Preface', Lecture Notes in Networks and Systems, vol. 819, p. v.
Nanda, SJ, Yadav, RP, Gandomi, AH & Saraswat, M 2024, 'Preface', Lecture Notes in Networks and Systems, vol. 821, p. v.
Nanda, SJ, Yadav, RP, Gandomi, AH & Saraswat, M 2024, 'Preface', Lecture Notes in Networks and Systems, vol. 818, p. v.
Nanda, SJ, Yadav, RP, Gandomi, AH & Saraswat, M 2024, 'Preface', Lecture Notes in Networks and Systems, vol. 820, p. v.
Nandanwar, L, Shivakumara, P, Jalab, HA, Ibrahim, RW, Raghavendra, R, Pal, U, Lu, T & Blumenstein, M 2024, 'A Conformable Moments-Based Deep Learning System for Forged Handwriting Detection', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 4, pp. 5407-5420.
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Naseri, H, Golroo, A, Shokoohi, M & Gandomi, AH 2024, 'Sustainable pavement maintenance and rehabilitation planning using the marine predator optimization algorithm', Structure and Infrastructure Engineering, vol. 20, no. 3, pp. 340-352.
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The sustainability of pavement, especially in Maintenance and Rehabilitation (M&R) scheduling, has become an immense concern and has received limited attention in previous studies. Therefore, this study aimed to develop the M&R scheduling optimization based on sustainability. To this end, a novel sustainability index was introduced, in which all the sustainable development aspects were considered, including highway agency cost, environmental impacts, and social effects. A conventional model was used to assess the sustainable model’s effectiveness. Two new constraints are introduced to reduce the budget fluctuation and not to apply the M&R treatments for two consecutive years to make the model practical. On the other hand, highway agencies face large-scale networks, in which the optimization of M&R scheduling has computational complexities. Thus, the novel and powerful metaheuristic algorithm, named Marine Predator Algorithm (MPA), was applied to solve the pavement M&R scheduling problem. A large-scale pavement network, including 110 sections, was analyzed over a 5-year plan as the case study. The results indicated that using the sustainable model rather than the conventional one leads to a 6.5% reduction in CO2 emission. Besides, utilizing the sustainable approach enhances the equity and safety indices by 40.7% and 2.5% compared to the conventional treatment. However, the highway agency cost is increased by 1.1% using the sustainable model.
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.
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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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, pp. 1-12.
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Nguyen, BM, Nguyen, T, Vu, Q-H, Tran, HH, Vo, H, Bao Son, D, Thanh Binh, HT, Yu, S & Wu, Z 2024, 'A Novel Nature-Inspired Algorithm for Optimal Task Scheduling in Fog–Cloud Blockchain System', IEEE Internet of Things Journal, vol. 11, no. 2, pp. 2043-2057.
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Nguyen, C-H, Saputra, YM, Hoang, DT, Nguyen, DN, Nguyen, V-D, Xiao, Y & Dutkiewicz, E 2024, 'Encrypted Data Caching and Learning Framework for Robust Federated Learning-Based Mobile Edge Computing', IEEE/ACM Transactions on Networking, pp. 1-16.
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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, DDN, Sood, K, Xiang, Y, Gao, L, Chi, L, Singh, G & Yu, S 2024, 'Design and Robust Evaluation of Next Generation Node Authentication Approach', IEEE Transactions on Dependable and Secure Computing, pp. 1-12.
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Nguyen, HAD, Le, TH, Ha, QP, Duc, H & Azzi, M 2024, 'Particulate Matter Monitoring and Forecast with Integrated Low-cost Sensor Networks and Air-quality Monitoring Stations', E3S Web of Conferences, vol. 496, pp. 04001-04001.
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The fusion of low-cost sensor networks with air quality stations has become prominent, offering a cost-effective approach to gathering fine-scaled spatial data. However, effective integration of diverse data sources while maintaining reliable information remains challenging. This paper presents an extended clustering method based on the Girvan-Newman algorithm to identify spatially correlated clusters of sensors and nearby observatories. The proposed approach enables localized monitoring within each cluster by partitioning the network into communities, optimizing resource allocation and reducing redundancy. Through our simulations with real-world data collected from the state-run air quality monitoring stations and the low-cost sensor network in Sydney’s suburbs, we demonstrate the effectiveness of this approach in enhancing localized monitoring compared to other clustering methods, namely K-Means Clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Agglomerative Clustering. Experimental results illustrate the potential for this method to facilitate comprehensive and high-resolution air quality monitoring systems, advocating the advantages of integrating low-cost sensor networks with conventional monitoring infrastructure.
Nguyen, 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, 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, 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, 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, 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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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, pp. 1-1.
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Nikoo, MR, Izady, A, Bakhtiari, PH, Al-Maktoumi, A, Chen, M & Gandomi, AH 2024, 'A Water Resources Management Simulation–Optimization Model: Application of Graph-Based Hypergame Model in Water Supply Conflicts Resolution', Group Decision and Negotiation, vol. 33, no. 2, pp. 291-326.
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Nimbalkar, S & Basack, S 2024, 'Pile group in clay subjected to cyclic lateral load: Numerical modelling and design recommendation', Marine Georesources & Geotechnology, vol. 42, no. 1, pp. 67-87.
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Nirbhav, NA, Malik, A, Maheshwar, NA & Prasad, M 2024, 'Landslide susceptibility assessment along the major transport corridor using decision tree model: a case study of Kullu-Rohtang Pass', International Journal of Business Intelligence and Data Mining, vol. 24, no. 1, pp. 1-24.
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Nirmala, M, Gandomi, AH, Babu, MR, Babu, LDD & Patan, R 2024, 'An Emoticon-Based Novel Sarcasm Pattern Detection Strategy to Identify Sarcasm in Microblogging Social Networks', IEEE Transactions on Computational Social Systems, pp. 1-8.
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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, pp. 1-11.
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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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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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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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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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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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Ozkaya, SG, Baygin, M, Barua, PD, Tuncer, T, Dogan, S, Chakraborty, S & Acharya, UR 2024, 'An automated earthquake classification model based on a new butterfly pattern using seismic signals', Expert Systems with Applications, vol. 238, pp. 122079-122079.
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Paeedeh, N, Pratama, M, Ma’sum, MA, Mayer, W, Cao, Z & Kowlczyk, R 2024, 'Cross-domain few-shot learning via adaptive transformer networks', Knowledge-Based Systems, vol. 288, pp. 111458-111458.
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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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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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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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Pavan Kumar, TVV, Taranath, NL, Rahul, R, Chandra Shekara, G, Sapra, P, Thandaiah Prabu, R, Metwally, ASM & Kalam, MA 2024, 'Photovoltaic fuzzy based modelling on defining energy efficient solar devices in industry 4.0', Optical and Quantum Electronics, vol. 56, no. 1.
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Peng, B, Chen, S, Li, Y, Zhang, L, Wu, H, Li, M, Wang, R & Huang, R 2024, 'Modeling the Parasitic Resistances and Capacitances of Advanced Vertical Gate-All-Around Transistors', IEEE Transactions on Electron Devices, vol. 71, no. 1, pp. 461-467.
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Peng, R, Guo, R, Liu, L, Ji, J, Miao, Z & Zhou, J 2024, 'Practical consensus tracking control for networked Euler–Lagrange systems based on UDE integrated with RBF neural network', Neurocomputing, vol. 583, pp. 127554-127554.
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Peng, T, Zhong, W, Wang, G, Luo, E, Yu, S, Liu, Y, Yang, Y & Zhang, X 2024, 'Privacy-Preserving Truth Discovery Based on Secure Multi-Party Computation in Vehicle-Based Mobile Crowdsensing', IEEE Transactions on Intelligent Transportation Systems, pp. 1-13.
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Perrin, R & Halkon, B 2024, 'Sacred Geometry in the English Church Bell', IMA Journal of Applied Mathematics.
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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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 & Marquez, AM 2024, 'Predictive Optimal Variable-Angle PS-PWM Strategy for Cascaded H-Bridge Converters', IEEE Transactions on Industrial Electronics, pp. 1-11.
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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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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, pp. 1-1.
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Qi, Y, Indraratna, B, Ngo, T, Arachchige, CMK & Hettiyahandi, S 2024, 'Sustainable solutions for railway using recycled rubber', Transportation Geotechnics, pp. 101256-101256.
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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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Qu, K, Liu, J, Zhang, J & Wu, C 2024, 'Three-dimensional meso-scale modelling of geopolymer-based ultra-high performance concrete (G-UHPC) with ceramic ball coarse aggregates under projectile impact', Structures, vol. 61, pp. 105935-105935.
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Qu, Y, Yuan, X, Ding, M, Ni, W, Rakotoarivelo, T & Smith, D 2024, 'Learn to Unlearn: Insights Into Machine Unlearning', Computer, vol. 57, no. 3, pp. 79-90.
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Rafa, N, Ahmed, B, Zohora, F, Bakya, J, Ahmed, S, Ahmed, SF, Mofijur, M, Chowdhury, AA & Almomani, F 2024, 'Microplastics as carriers of toxic pollutants: Source, transport, and toxicological effects', Environmental Pollution, vol. 343, pp. 123190-123190.
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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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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, 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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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, pp. 1-1.
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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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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, L, Zhang, Y, Zhou, JL, Wang, G, Mo, Y, Ling, Y, Huang, Y, Zhang, Y, Hu, H & Wang, Y 2024, 'RL-WG26 mediated salt stress tolerance in rice seedlings: A new insight into molecular mechanisms', Plant Stress, vol. 11, pp. 100306-100306.
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Ren, Y, Zhang, H, Cao, X, Yang, C, Zhang, J & Li, H 2024, 'Promoting or Hindering: Stealthy Black-Box Attacks Against DRL-Based Traffic Signal Control', IEEE Internet of Things Journal, vol. 11, no. 4, pp. 5816-5825.
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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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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, pp. 1-11.
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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, pp. 1-10.
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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, Farasati Far, B, Lima, EC, Jafarzadeh, S, Makvandi, P, Varma, RS, Huh, Y, Ebrahimi Warkiani, M & Rabiee, N 2024, 'Integration of MXene and Microfluidics: A Perspective', ACS Biomaterials Science & Engineering, vol. 10, no. 2, pp. 657-676.
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Saha, BK, Jihan, JI, Ahammad, MZ, Saha, G & Saha, SC 2024, 'Enhanced thermal performance and entropy generation analysis in a novel cavity design with circular cylinder', Heat Transfer, vol. 53, no. 3, pp. 1446-1473.
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AbstractAnalyzing fluid dynamics and heat transfer holds significant importance in the design and enhancement of engineering systems. The current investigation utilizes the finite element method to explore natural convection and heat transfer intricacies within a novel cavity containing an inner circular cylinder under steady and laminar flow conditions. The principal aim of this study is to assess the impact of Rayleigh number (Ra), Bejan number (Be), and the presence of adiabatic, hot, and cold cylinders on heat transfer, entropy generation, and fluid flow. The range of Ra considered in this investigation spans from 103 to 106, while the Prandtl number for the air is fixed at 0.71. The findings illustrate that the presence of a cylinder leads to higher Be as Ra increase, compared to scenarios where no cylinder is present. This observation suggests that buoyancy forces dominate in the absence of a cylinder, resulting in significantly enhanced convective heat transfer efficiency. However, the presence of a heated cylinder within the tooth‐shaped cavity exerts a substantial influence on the overall thermal performance of the system. Notably, the average Nusselt Number (Nu) experiences a remarkable increase of 41.97% under the influence of a heated cylinder, when compared to situations where a cold cylinder is present. This elevated average Nu signifies improved heat transfer characteristics, ultimately resulting in an overall improvement in the thermal system's efficiency.
Saha, 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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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.
Salih, AK, Aditya, L, Matar, F, Nghiem, LD & Ton-That, C 2024, 'Improved flux and anti-fouling performance of a photocatalytic ZnO membrane on porous stainless steel substrate for microalgae harvesting', Journal of Membrane Science, vol. 694, pp. 122405-122405.
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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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Samal, PB, Chen, SJ & Fumeaux, C 2024, '3D-Corrugated Ground Structure: A Microstrip Antenna Miniaturization Technique', IEEE Transactions on Antennas and Propagation, pp. 1-1.
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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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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, pp. 1-1.
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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. 1, 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...
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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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 & 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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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.
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, S, Cai, C, Shen, Y, Wu, X, Ke, W & Yu, S 2024, 'MFGD3QN: Enhancing Edge Intelligence Defense against DDoS with Mean-Field Games and Dueling Double Deep Q-network', IEEE Internet of Things Journal, pp. 1-1.
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Shen, Y, Chen, Y, Shi, X, An, Y, Yang, M & Qi, Y 2024, 'Assessing the role of global food commodity prices in achieving the 2030 agenda for SDGs', iScience, vol. 27, no. 2, pp. 108832-108832.
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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, 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, pp. 1-11.
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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, 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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Shivakumara, P, Banerjee, A, Nandanwar, L, Pal, U, Antonacopoulos, A, Lu, T & Blumenstein, M 2024, 'A new deep CNN for 3D text localization in the wild through shadow removal', Computer Vision and Image Understanding, vol. 238, pp. 103863-103863.
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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.
Singh, M, Sahoo, KS & Gandomi, AH 2024, 'An Intelligent-IoT-Based Data Analytics for Freshwater Recirculating Aquaculture System', IEEE Internet of Things Journal, vol. 11, no. 3, pp. 4206-4217.
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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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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, pp. 1-19.
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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, 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, 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, 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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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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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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Stone, B, Mitchell, S, Halkon, B, Peirce, N & Harland, A 2024, 'The Dynamic Properties of Cricket and Cricket Training Balls', Sports Engineering (SPEN).
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, 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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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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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, K, Wu, J, Pan, Q, Zheng, X, Li, J & Yu, S 2024, 'Leveraging Digital Twin and DRL for Collaborative Context Offloading in C-V2X Autonomous Driving', IEEE Transactions on Vehicular Technology, pp. 1-16.
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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, 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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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...
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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Taheri, A, RahimiZadeh, K, Beheshti, A, Baumbach, J, Rao, RV, Mirjalili, S & Gandomi, AH 2024, 'Partial reinforcement optimizer: An evolutionary optimization algorithm', Expert Systems with Applications, vol. 238, pp. 122070-122070.
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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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Tan, S, Yu, S, Liu, W, He, D & Chan, S 2024, 'You Can Glimpse but You Cannot Identify: Protect IoT Devices from Being Fingerprinted', IEEE Transactions on Dependable and Secure Computing, pp. 1-14.
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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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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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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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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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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, 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, pp. 1-14.
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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. PP, no. 99, pp. 1-1.
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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.
Tihin, GL, Mo, KH, Juan, JC, Ong, HC, Asikin-Mijan, N & Lee, HV 2024, 'Enhanced energy recovery of non-hazardous organic wastes via moderate pyrolysis with natural calcium- and potassium-based additives', Journal of Thermal Analysis and Calorimetry, vol. 149, no. 6, pp. 2521-2535.
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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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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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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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Val, DV, Andrade, C, Sykora, M, Stewart, MG, Bastidas-Arteaga, E, Mlcoch, J, Truong, QC & El Soueidy, C-P 2024, 'TEMPORARY REMOVAL: Probabilistic modelling of deterioration of reinforced concrete structures', Structural Safety, pp. 102454-102454.
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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, pp. 104525-104525.
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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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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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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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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, 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, pp. 1-1.
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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 at https://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, Yao, T, Fan, T, Peng, S, Xu, C & Yu, S 2024, 'Modeling on Resource Allocation for Age-Sensitive Mobile-Edge Computing Using Federated Multiagent Reinforcement Learning', IEEE Internet of Things Journal, vol. 11, no. 2, pp. 3121-3131.
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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, G, Li, C, Yuan, L, Peng, J, Xian, X, Liang, X, Chang, X & Lin, L 2024, 'DNA Family: Boosting Weight-Sharing NAS With Block-Wise Supervisions', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 5, pp. 2722-2740.
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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, 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, 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, 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, Wei, Y, Zhang, Z, Fan, J, Zhao, Y, Yang, Y & Wang, M 2024, 'Progressive Stereo Image Dehazing Network via Cross-view Region Interaction', IEEE Transactions on Multimedia, pp. 1-13.
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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, 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, 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, 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, pp. 1-1.
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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, 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, pp. 1-1.
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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, pp. 1-17.
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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, Luo, Y, Chen, Z, Jiang, T, Yang, Y & Xiao, J 2024, 'Taking a Closer Look At Visual Relation: Unbiased Video Scene Graph Generation With Decoupled Label Learning', IEEE Transactions on Multimedia, vol. 26, pp. 5718-5728.
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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, Fei, Z, Liu, P, Zhang, JA, Wu, Q & Wu, N 2024, 'Sensing Aided Covert Communications: Turning Interference into Allies', IEEE Transactions on Wireless Communications, pp. 1-1.
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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, 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, 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, Wang, W, Shao, J & Yang, Y 2024, 'Learning to Follow and Generate Instructions for Language-Capable Navigation', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 5, pp. 3334-3350.
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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, pp. 1-15.
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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, 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, 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, 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, pp. 1-1.
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Wei, J, Yang, Q, Yu, Y, Jiang, Q, Li, X, Liu, S, Li, K & Wang, Q 2024, 'Experimental study of multiscale hybrid fiber-reinforced ambient-cured LEGC under uniaxial compression', Construction and Building Materials, vol. 411, pp. 134386-134386.
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Wei, X, Huang, J, Zhao, R, Yu, H & Xu, Z 2024, 'Multi-Label Text Classification Model Based on Multi-Level Constraint Augmentation and Label Association Attention', ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 23, no. 1, pp. 1-20.
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In the multi-label text classification task, a text usually corresponds to multiple label categories, and the labels have correlation and hierarchical structure. However, when the label hierarchy is unknown, the number of various labels is not balanced, which makes it difficult for the model to classify low-frequency labels. In addition, labels have semantic similarities that make it difficult for the model to distinguish between them. In this article, we propose a multi-label text classification model based on multi-level constraint augmentation and label association attention. Compared with traditional methods, our method has two contributions: (1) In order to alleviate the problem of unbalanced number of different label categories and ensure the rationality of sample generation, we propose a data augmentation method based on multi-level constraints. In the process of sample generation, this method uses historical generation information, sample original text information, and sample topic to constrain the generated text. (2) In order to make the model recognize the associated labels accurately, we propose an interaction mechanism based on label association attention and filter gate. This method combines text information and label weight information. At the same time, our classification model considers the important weights of text sentences and effectively utilizes the co-occurrence relationship between labels. Experimental results on three benchmark datasets show that our model outperforms state-of-the-art methods on all main evaluation metrics, especially on low-frequency label prediction with sparse samples.
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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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, Y, Liu, X & Yu, H 2024, 'Adaptive tree-like neural network: Overcoming catastrophic forgetting to classify streaming data with concept drifts', Knowledge-Based Systems, vol. 293, pp. 111636-111636.
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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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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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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, G, Chen, X, Shen, Y, Xu, Z, Zhang, H, Shen, S & Yu, S 2024, 'Combining Lyapunov Optimization With Actor-Critic Networks for Privacy-Aware IIoT Computation Offloading', IEEE Internet of Things Journal, pp. 1-1.
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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, 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, X, Lu, J, Yan, Z & Zhang, G 2024, 'Disentangling Stochastic PDE Dynamics for Unsupervised Video Prediction', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15.
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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, 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, 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, Y, Jia, W, Miao, Q, Feng, J, Ren, J & Luo, H 2024, 'Detection-Driven Exposure-Correction Network for Nighttime Drone-View Object Detection', IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-14.
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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, 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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Xie, S, Li, Y, Wang, X, Zhang, H, Zhang, Z, Luo, X & Yu, H 2024, 'Hierarchical relationship modeling in multi-agent reinforcement learning for mixed cooperative–competitive environments', Information Fusion, vol. 108, pp. 102318-102318.
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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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Xing, J, Lv, T, Li, W, Ni, W & Jamalipour, A 2024, 'Joint Optimization of Beamforming and Noise Injection for Covert Downlink Transmissions in Cell-Free Internet of Things Networks', IEEE Internet of Things Journal, vol. 11, no. 6, pp. 10525-10536.
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Xu, H, Zhu, T, Zhang, L, Zhou, W & Yu, PS 2024, 'Machine Unlearning: A Survey', ACM Computing Surveys, vol. 56, no. 1, pp. 1-36.
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Machine learning has attracted widespread attention and evolved into an enabling technology for a wide range of highly successful applications, such as intelligent computer vision, speech recognition, medical diagnosis, and more. Yet, a special need has arisen where, due to privacy, usability, and/or the right to be forgotten , information about some specific samples needs to be removed from a model, called machine unlearning. This emerging technology has drawn significant interest from both academics and industry due to its innovation and practicality. At the same time, this ambitious problem has led to numerous research efforts aimed at confronting its challenges. To the best of our knowledge, no study has analyzed this complex topic or compared the feasibility of existing unlearning solutions in different kinds of scenarios. Accordingly, with this survey, we aim to capture the key concepts of unlearning techniques. The existing solutions are classified and summarized based on their characteristics within an up-to-date and comprehensive review of each category’s advantages and limitations. The survey concludes by highlighting some of the outstanding issues with unlearning techniques, along with some feasible directions for new research opportunities.
Xu, J & Cao, L 2024, 'Copula Variational LSTM for High-Dimensional Cross-Market Multivariate Dependence Modeling', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15.
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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, 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, pp. 1-1.
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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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Yan, C, Chang, X, Luo, M, Liu, H, Zhang, X & Zheng, Q 2024, 'Semantics-Guided Contrastive Network for Zero-Shot Object Detection', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 3, pp. 1530-1544.
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Zero-shot object detection (ZSD), the task that extends conventional detection models to detecting objects from unseen categories, has emerged as a new challenge in computer vision. Most existing approaches on ZSD are based on a strict mapping-transfer strategy that learns a mapping function from visual to semantic space over seen categories, then directly generalizes the learned mapping function to unseen object detection. However, the ZSD task still remains challenging, since those works fail to consider the two key factors that hamper the ZSD performance: (a) the domain shift problem between seen and unseen classes leads to poor transferable ability of the model; (b) the original visual feature space is suboptimal for ZSD since it lacks discriminative information.To alleviate these issues, we develop a novel Semantics-Guided Contrastive Network for ZSD (ContrastZSD), a detection framework that first brings the contrastive learning paradigm into the realm of ZSD. The pairwise contrastive tasks take advantage of class label and semantic relation as additional supervision signals. Under the guidance of those explicit semantic supervision, the model can learn more knowledge about unseen categories to avoid over-fitting to the seen concepts.
Yang, A, Lin, S, Yeh, C-H, Shu, M, Yang, Y & Chang, X 2024, 'Context Matters: Distilling Knowledge Graph for Enhanced Object Detection', IEEE Transactions on Multimedia, vol. 26, pp. 487-500.
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Yang, B, Sang, R, Li, Y, Goldys, E & Deng, W 2024, 'Abstract 5743: Liposome platform enables X-ray induced photodynamic therapy treatment against human triple negative breast cancer cells', Cancer Research, vol. 84, no. 6_Supplement, pp. 5743-5743.
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Abstract In this study, we employed X-ray induced photodynamic therapy (X-PDT) for the treatment on triple negative breast cancer (TNBC) cells. To do this, we rationally developed a liposome delivery system co-loaded with protoporphyrin IX (PPIX) and perfluorooctyl bromide (PFOB). Low-dose X-ray at 2Gy was employed to activate PPIX for reactive oxygen species (ROS) generation, and the co-loading of PFOB provided additional oxygen to augment ROS production. The highly toxic ROS triggered TNBC cell death. In vitro X-PDT effects including intracellular ROS generation, cytotoxicity, cell viability and apoptosis/necrosis assay in TNBC cells were studied. Our results indicate that the nanocarriers effectively induced X-PDT effect with very low dose radiation, which makes it possible to damage cancer cells. Our strategy may offer a paradigm-shifting treatment alternative for TNBC patients who need neoadjuvant radiotherapy but wish to avoid long term detrimental effect on functional outcome by undergoing X-PDT using only a fraction of the conventional radiotherapy. Citation Format: Biyao Yang, Rui Sang, Yi Li, Ewa Goldys, Wei Deng. Liposome platform enables X-ray induced photodynamic therapy treatment against human triple negative breast cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5743.
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, J & Lin, C-T 2024, 'Enhanced Adjacency-Constrained Hierarchical Clustering Using Fine-Grained Pseudo Labels', IEEE Transactions on Emerging Topics in Computational Intelligence, pp. 1-12.
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Yang, J & Lin, C-T 2024, 'Toward Autonomous Distributed Clustering', IEEE Transactions on Emerging Topics in Computational Intelligence, pp. 1-8.
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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, pp. 1-8.
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Yang, M, Guo, T, Zhu, T, Tjuawinata, I, Zhao, J & Lam, K-Y 2024, 'Local differential privacy and its applications: A comprehensive survey', Computer Standards & Interfaces, vol. 89, pp. 103827-103827.
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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, 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, pp. 1-6.
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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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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, 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.
Yaqoob, H, Tan, ES, Ali, HM, Ong, HC, Jamil, MA & Farooq, MU 2024, 'Sustainable energy generation from plastic waste: An in-depth review of diesel engine application', Environmental Technology & Innovation, vol. 34, pp. 103467-103467.
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Yazdani, D, Omidvar, MN, Yazdani, D, Branke, J, Nguyen, TT, Gandomi, AH, Jin, Y & Yao, X 2024, 'Robust Optimization Over Time: A Critical Review', IEEE Transactions on Evolutionary Computation, pp. 1-1.
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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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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, 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, Z, Cheng, N, Song, Y, Hui, Y, Li, Y, Luan, TH & Yu, S 2024, 'UAV-Assisted Secure Uplink Communications in Satellite-Supported IoT: Secrecy Fairness Approach', IEEE Internet of Things Journal, vol. 11, no. 4, pp. 6904-6915.
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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, G, Wang, X, Sun, C, Wang, Q, Yu, P, Ni, W & Liu, RP 2024, 'IronForge: An Open, Secure, Fair, Decentralized Federated Learning', IEEE Transactions on Neural Networks and Learning Systems, vol. PP, pp. 1-15.
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Federated learning (FL) offers an effective learning architecture to protect data privacy in a distributed manner. However, the inevitable network asynchrony, overdependence on a central coordinator, and lack of an open and fair incentive mechanism collectively hinder FL's further development. We propose IronForge, a new generation of FL framework, that features a directed acyclic graph (DAG)-based structure, where nodes represent uploaded models, and referencing relationships between models form the DAG that guides the aggregation process. This design eliminates the need for central coordinators to achieve fully decentralized operations. IronForge runs in a public and open network and launches a fair incentive mechanism by enabling state consistency in the DAG. Hence, the system fits in networks where training resources are unevenly distributed. In addition, dedicated defense strategies against prevalent FL attacks on incentive fairness and data privacy are presented to ensure the security of IronForge. Experimental results based on a newly developed test bed FLSim highlight the superiority of IronForge to the existing prevalent FL frameworks under various specifications in performance, fairness, and security. To the best of our knowledge, IronForge is the first secure and fully decentralized FL (DFL) framework that can be applied in open networks with realistic network and training settings.
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, pp. 1-14.
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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, 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, S, Zhai, R, Shen, Y, Wu, G, Zhang, H, Yu, S & Shen, S 2024, 'Deep Q-Network-Based Open-Set Intrusion Detection Solution for Industrial Internet of Things', IEEE Internet of Things Journal, vol. 11, no. 7, pp. 12536-12550.
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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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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, 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, 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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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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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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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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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, 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, pp. 1-1.
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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, pp. 1-1.
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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, pp. 1-12.
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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, Yu, H, Wang, X, Wang, M, Zhang, Z, Li, Y, Xie, S & Luo, X 2024, 'Knowledge-guided communication preference learning model for multi-agent cooperation', Information Sciences, vol. 667, pp. 120395-120395.
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Zhang, H, Zhu, L, Wang, X & Yang, Y 2024, 'Divide and Retain: A Dual-Phase Modeling for Long-Tailed Visual Recognition', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-12.
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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, Zhu, C, Sun, X, Ge, C, Chen, B, Susilo, W & Yu, S 2024, 'FLPurifier: Backdoor Defense in Federated Learning via Decoupled Contrastive Training', IEEE Transactions on Information Forensics and Security, pp. 1-1.
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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, pp. 1-11.
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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, 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, Chen, N, Shen, S, Yu, S, Kumar, N & Hsu, C-H 2024, 'AI-Enabled Space-Air-Ground Integrated Networks: Management and Optimization', IEEE Network, pp. 1-7.
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Zhang, P, Chen, N, Shen, S, Yu, S, Wu, S & Kumar, N 2024, 'Future Quantum Communications and Networking: A Review and Vision', IEEE Wireless Communications, vol. 31, no. 1, pp. 141-148.
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Based on quantum mechanics, quantum communications have clock synchronization properties and unbreakable security. Recently, related technologies of quantum communications have made breakthroughs one after another, and it has also realized functions and applications that are difficult to achieve in traditional networks. In this era of the rise of quantum networks similar to the eve of the birth of the classical Internet 'Arpanet', researchers are full of longing for the blueprint of the future quantum network. In this work, we summarize, analyze and look forward to quantum communications and networking compared with the traditional Internet. Specifically, the related fundamentals, related technological developments, and existing challenges of quantum networks are firstly analyzed. On this basis, we look ahead and propose a prototype of the future quantum network architectures. Finally, we sort out and analyze the paradigm, principle, and properties of quantum communications. To the best of our knowledge, this is the first review and vision work on quantum communications and networking.
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, 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. PP, pp. 1-13.
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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, S, Yin, M, Xiao, F, Cao, Z & Pelusi, D 2024, 'A Complex Gaussian Fuzzy Numbers-Based Multisource Information Fusion for Pattern Classification', IEEE Transactions on Fuzzy Systems, pp. 1-13.
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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, pp. 1-10.
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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, 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, pp. 1-1.
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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, 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, 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, pp. 1-16.
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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, Feng, B, Tian, A, Yu, S & Zhang, H 2024, 'Task Offloading Control and Customized Workload Scheduling in Multi-Layer Cloud Networks', IEEE Transactions on Network and Service Management, vol. 21, no. 1, pp. 714-728.
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Zhang, Y, Li, B, Liu, B & Chang, J 2024, 'Building PUF as a Service: Distributed Authentication and Recoverable Data Sharing With Multidimensional CRPs Security Protection', IEEE Internet of Things Journal, pp. 1-1.
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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, 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, 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, Zheng, H, Hong, R, Fan, J, Yang, Y & Yan, S 2024, 'FRC-Net: A Simple Yet Effective Architecture for Low-Light Image Enhancement', IEEE Transactions on Consumer Electronics, pp. 1-1.
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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, J, He, W, Fu, W, Ding, Y & Guo, Y 2024, 'Comparative Studies on Performances of Slotted and Slotless High-Speed PMBLDC Motors', IEEE Access, vol. 12, pp. 13431-13441.
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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, pp. 1-5.
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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, pp. 1-13.
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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, Zhu, Q, Cheng, E & Burnett, IS 2024, 'Erratum: A room impulse response database for multizone sound field reproduction (L) [J. Acoust. Soc. Am. 152(4), 2505–2512 (2022)]', The Journal of the Acoustical Society of America, vol. 155, no. 3, pp. 2170-2170.
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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, 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, pp. 1-12.
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Zhao, Z, Cao, L & Wang, C-D 2024, 'Gray Learning From Non-IID Data With Out-of-Distribution Samples', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-14.
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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, Ji, J, Peng, R, Miao, Z & Zhou, J 2024, 'Energy shaping-based consensus control in networked underactuated Euler–Lagrange systems with communication and input delays', Journal of the Franklin Institute, vol. 361, no. 6, pp. 106705-106705.
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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. PP, no. 99, pp. 1-16.
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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, 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, 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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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, I, Tofigh, F, Piccardi, M, Abolhasan, M, Franklin, D & Lipman, J 2024, 'Secure Multi-Party Computation for Machine Learning: A Survey', IEEE Access, pp. 1-1.
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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, Yang, Z, Yu, H & Yu, S 2024, 'VDFChain: Secure and verifiable decentralized federated learning via committee-based blockchain', Journal of Network and Computer Applications, vol. 223, pp. 103814-103814.
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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, Zhu, T, Ye, D, Yu, X & Zhou, W 2024, 'Boosting Model Inversion Attacks with Adversarial Examples', IEEE Transactions on Dependable and Secure Computing, pp. 1-18.
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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, pp. 1-1.
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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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Zhou, Y, Wang, R, Liu, J, Wu, D, Yu, S & Wen, Y 2024, 'Exploring the Practicality of Differentially Private Federated Learning: A Local Iteration Tuning Approach', IEEE Transactions on Dependable and Secure Computing, pp. 1-15.
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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, pp. 1-1.
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Zhu, R, Zhang, W, Wang, P, Chen, J, Wang, J & Yu, S 2024, 'Energy-Efficient Graph Reinforced vNFC Deployment in Elastic Optical Inter-DC Networks', IEEE Transactions on Network Science and Engineering, vol. 11, no. 2, pp. 1591-1604.
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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, pp. 1-1.
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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, pp. 1-1.
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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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Zhu, Y, Zhang, Z & Ji, J 2024, 'Bifurcations of a Filippov ecological system with an A-type discontinuity boundary', Chaos, Solitons & Fractals, vol. 182, pp. 114834-114834.
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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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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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