Abdollahi, A & Pradhan, B 2023, 'Explainable artificial intelligence (XAI) for interpreting the contributing factors feed into the wildfire susceptibility prediction model', Science of The Total Environment, vol. 879, pp. 163004-163004.
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Abdollahi, A, Pradhan, B, Alamri, A & Lee, C-W 2023, 'Google Earth Engine for Advanced Land Cover Analysis from Landsat-8 Data with Spectral and Topographic Insights', Journal of Sensors, vol. 2023, pp. 1-14.
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The primary goal of this research is to see how effective cloud-based computing services such as Google Earth Engine (GEE) platform are at classifying multitemporal satellite images and producing high-quality land cover maps for the target year of 2020, with the possibility of using it on a larger-scale area such as metropolitan Melbourne as a test site. To create high-quality land cover maps, the GEE is utilized to analyze a total of 80 Landsat-8 images. The support vector machine (SVM) approach is used to classify the images. Moreover, we use spectral bands, spectral indices, and topographic parameters to improve classification and address the limitations of existing approaches for classification with restricted input variables. Furthermore, we apply a postprocessing strategy to increase the model’s performance by removing the salt-and-pepper noise created by misclassified pixels in supervised classification results. The results demonstrate that given all parameters, the SVM approach achieves an overall accuracy (OA) and kappa accuracy of 88.47% and 85.34%, respectively. Following the implementation of the postprocessing technique, the OA and kappa improve to 92.90% and 90.99%, respectively. The results indicate that Landsat-8 multitemporal data, spectral indices, topographic components, and postprocessing techniques are all important in land cover mapping. Therefore, the use of freely accessible GEE technology and multitemporal Landsat-8 data ensures that decision makers have the resources they need to track land cover throughout the year.
Abharian, S, Sarfarazi, V, Marji, MF, Rasekh, H & Sadrekarimi, A 2023, 'Effect of geogrid reinforcement on tensile failure of high-strength self-compacted concrete', Magazine of Concrete Research, vol. 75, no. 8, pp. 379-401.
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In this study, the tensile strength, failure mechanism and ductile behaviour of geogrid-reinforced high-strength self-compacting concrete discs subjected to both the Brazilian tensile strength test and a biaxial compressive test are studied. To determine the combined effects of geogrid layer numbers and inclination angle on the ultimate tensile strength of concrete samples, 21 experiments were conducted with up to three layers of geogrids inclined at angles of 0° to 90°, at increments of 15°. In addition, discrete-element numerical simulations were conducted using two-dimensional particle flow code to examine the failure behaviour of geogrid-reinforced high-strength self-compacting concrete discs. The numerical models were first calibrated by the experimental results and then the failure behaviour of models containing geogrids was investigated. Both experimental and numerical results demonstrate that augmenting the concrete discs with geogrids increases the ductility of specimens, especially after failure. As the number of geogrid layers increased, the tensile strength of specimens also increased, whereas the tensile strength and absorbed energy were the same for specimens with different numbers of geogrid layers and inclination angles of 75° and 90°. The specimen with three horizontal geogrid layers had the highest tensile strength, biaxial compression strength and ductility of all specimens tested.
Aboughaly, M & Fattah, IMR 2023, 'Environmental Analysis, Monitoring, and Process Control Strategy for Reduction of Greenhouse Gaseous Emissions in Thermochemical Reactions', Atmosphere, vol. 14, no. 4, pp. 655-655.
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This review paper illustrates the recommended monitoring technologies for the detection of various greenhouse gaseous emissions for solid waste thermochemical reactions, including incineration, pyrolysis, and gasification. The illustrated gas analyzers are based on the absorption principle, which continuously measures the physicochemical properties of gaseous mixtures, including oxygen, carbon dioxide, carbon monoxide, hydrogen, and methane, during thermochemical reactions. This paper illustrates the recommended gas analyzers and process control tools for different thermochemical reactions and aims to recommend equipment to increase the sensitivity, linearity, and dynamics of various thermochemical reactions. The equipment achieves new levels of on-location, real-time analytical capability using FTIR analysis. The environmental assessment study includes inventory analysis, impact analysis, and sensitivity analysis to compare the mentioned solid waste chemical recycling methods in terms of greenhouse gaseous emissions, thermal efficiency, electrical efficiency, and sensitivity analysis. The environmental impact assessment compares each technology in terms of greenhouse gaseous emissions, including CO2, NOx, NH3, N2O, CO, CH4, heat, and electricity generation. The conducted environmental assessment compares the mentioned technologies through 15 different emission-related impact categories, including climate change impact, ecosystem quality, and resource depletion. The continuously monitored process streams assure the online monitoring of gaseous products of thermochemical processes that enhance the quality of the end products and reduce undesired products, such as tar and char. This state-of-the-art monitoring and process control framework provides recommended analytical equipment and monitoring tools for different thermochemical reactions to optimize process parameters and reduce greenhouse gaseous emissions and undesired products.
Abounahia, N, Ibrar, I, Kazwini, T, Altaee, A, Samal, AK, Zaidi, SJ & Hawari, AH 2023, 'Desalination by the forward osmosis: Advancement and challenges', Science of The Total Environment, vol. 886, pp. 163901-163901.
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Abraham, MT, Vaddapally, M, Satyam, N & Pradhan, B 2023, 'Spatio-temporal landslide forecasting using process-based and data-driven approaches: A case study from Western Ghats, India', CATENA, vol. 223, pp. 106948-106948.
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Abu, SM, Hannan, MA, Hossain Lipu, MS, Mannan, M, Ker, PJ, Hossain, MJ & Mahlia, TMI 2023, 'State of the art of lithium-ion battery material potentials: An analytical evaluations, issues and future research directions', Journal of Cleaner Production, vol. 394, pp. 136246-136246.
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Abu, SM, Hannan, MA, Ker, PJ, Mansor, M, Tiong, SK & Mahlia, TMI 2023, 'Recent progress in electrolyser control technologies for hydrogen energy production: A patent landscape analysis and technology updates', Journal of Energy Storage, vol. 72, pp. 108773-108773.
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Alternative low-to-zero carbon technologies must be developed to facilitate the clean energy transition rather than only concentrating on one or a few specific technology trajectories. The hydrogen electrolyser has many benefits over traditional energy storage technologies, making it a competitive alternative to the current fossil fuel combustion-based energy generation system. To better understand the impact and developments of electrolyser control technologies for hydrogen production, this study aims to shed light on current research and patent trends. The research was conducted by performing extensive keyword searches on electrolyser control methods for hydrogen generation in the Lens database and then extracting the bibliometric data from the 107 patent publications selected based on keywords, family filtering and material exclusion. An up-to-date technical overview is provided with a bibliographic study of patent growth, key players and innovators, patent distribution across jurisdictions and technological sectors, and patent categorization using the cooperative patent classification (CPC) code. Key owners, inventors, and jurisdictional hierarchies in patent publications are also identified, and the potential for further study is assessed. These selected patent documents and their landscape analysis aim to provide a systematic foundation for future developments in electrolyser technologies and materials related to hydrogen production and to propose emerging research and commercialization prospects for future researchers.
Abualigah, L, Hanandeh, ES, Zitar, RA, Thanh, C-L, Khatir, S & Gandomi, AH 2023, 'Revolutionizing sustainable supply chain management: A review of metaheuristics', Engineering Applications of Artificial Intelligence, vol. 126, pp. 106839-106839.
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Acharya, R, Aleiner, I, Allen, R, Andersen, TI, Ansmann, M, Arute, F, Arya, K, Asfaw, A, Atalaya, J, Babbush, R, Bacon, D, Bardin, JC, Basso, J, Bengtsson, A, Boixo, S, Bortoli, G, Bourassa, A, Bovaird, J, Brill, L, Broughton, M, Buckley, BB, Buell, DA, Burger, T, Burkett, B, Bushnell, N, Chen, Y, Chen, Z, Chiaro, B, Cogan, J, Collins, R, Conner, P, Courtney, W, Crook, AL, Curtin, B, Debroy, DM, Del Toro Barba, A, Demura, S, Dunsworth, A, Eppens, D, Erickson, C, Faoro, L, Farhi, E, Fatemi, R, Flores Burgos, L, Forati, E, Fowler, AG, Foxen, B, Giang, W, Gidney, C, Gilboa, D, Giustina, M, Grajales Dau, A, Gross, JA, Habegger, S, Hamilton, MC, Harrigan, MP, Harrington, SD, Higgott, O, Hilton, J, Hoffmann, M, Hong, S, Huang, T, Huff, A, Huggins, WJ, Ioffe, LB, Isakov, SV, Iveland, J, Jeffrey, E, Jiang, Z, Jones, C, Juhas, P, Kafri, D, Kechedzhi, K, Kelly, J, Khattar, T, Khezri, M, Kieferová, M, Kim, S, Kitaev, A, Klimov, PV, Klots, AR, Korotkov, AN, Kostritsa, F, Kreikebaum, JM, Landhuis, D, Laptev, P, Lau, K-M, Laws, L, Lee, J, Lee, K, Lester, BJ, Lill, A, Liu, W, Locharla, A, Lucero, E, Malone, FD, Marshall, J, Martin, O, McClean, JR, McCourt, T, McEwen, M, Megrant, A, Meurer Costa, B, Mi, X, Miao, KC, Mohseni, M, Montazeri, S, Morvan, A, Mount, E, Mruczkiewicz, W, Naaman, O, Neeley, M, Neill, C, Nersisyan, A, Neven, H, Newman, M, Ng, JH, Nguyen, A, Nguyen, M, Niu, MY, O’Brien, TE, Opremcak, A, Platt, J, Petukhov, A, Potter, R, Pryadko, LP, Quintana, C, Roushan, P, Rubin, NC, Saei, N, Sank, D, Sankaragomathi, K, Satzinger, KJ, Schurkus, HF, Schuster, C, Shearn, MJ, Shorter, A, Shvarts, V, Skruzny, J, Smelyanskiy, V, Smith, WC, Sterling, G, Strain, D, Szalay, M, Torres, A, Vidal, G, Villalonga, B, Vollgraff Heidweiller, C, White, T, Xing, C, Yao, ZJ, Yeh, P, Yoo, J, Young, G, Zalcman, A, Zhang, Y & Zhu, N 2023, 'Suppressing quantum errors by scaling a surface code logical qubit', Nature, vol. 614, no. 7949, pp. 676-681.
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AbstractPractical quantum computing will require error rates well below those achievable with physical qubits. Quantum error correction1,2 offers a path to algorithmically relevant error rates by encoding logical qubits within many physical qubits, for which increasing the number of physical qubits enhances protection against physical errors. However, introducing more qubits also increases the number of error sources, so the density of errors must be sufficiently low for logical performance to improve with increasing code size. Here we report the measurement of logical qubit performance scaling across several code sizes, and demonstrate that our system of superconducting qubits has sufficient performance to overcome the additional errors from increasing qubit number. We find that our distance-5 surface code logical qubit modestly outperforms an ensemble of distance-3 logical qubits on average, in terms of both logical error probability over 25 cycles and logical error per cycle ((2.914 ± 0.016)% compared to (3.028 ± 0.023)%). To investigate damaging, low-probability error sources, we run a distance-25 repetition code and observe a 1.7 × 10−6 logical error per cycle floor set by a single high-energy event (1.6 × 10−7 excluding this event). We accurately model our experiment, extracting error budgets that highlight the biggest challenges for future systems. These results mark an experimental demonstration in which quantum error correction begins to improve performance with increasing qubit number, illuminating the path to reaching the logical error rates required for computation.
Adak, C, Chattopadhyay, S & Saqib, M 2023, 'Deep Analysis of Visual Product Reviews', IEEE Transactions on Emerging Topics in Computational Intelligence, pp. 1-6.
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Adhikari, S, Thapa, S, Naseem, U, Lu, HY, Bharathy, G & Prasad, M 2023, 'Explainable hybrid word representations for sentiment analysis of financial news.', Neural Networks, vol. 164, pp. 115-123.
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Aditya, L, Vu, HP, Abu, HJM, Mahlia, TMI, Silitonga, AS, Zhang, X, Liu, Q, Tra, V-T, Ngo, HH & Nghiem, LD 2023, 'Role of culture solution pH in balancing CO2 input and light intensity for maximising microalgae growth rate.', Chemosphere, vol. 343, pp. 140255-140255.
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The interplay between CO2 input and light intensity is investigated to provide new insight to optimise microalgae growth rate in photobioreactors for environmental remediation, carbon capture, and biomass production. Little is known about the combined effect of carbon metabolism and light intensity on microalgae growth. In this study, carbonated water was transferred to the microalgae culture at different rates and under different light intensities for observing the carbon composition and growth rate. Results from this study reveal opposing effects from CO2 input and light intensity on the culture solution pH and ultimately microalgae growth rate. Excessive CO2 concentration can inhibit microalgae growth due to acidification caused by CO2 dissolution. While increasing light intensity can increase pH because the carboxylation process consumes photons and transfers hydrogen ions into the cell. This reaction is catalysed by the enzyme RuBisCO, which functions optimally within a specific pH range. By balancing CO2 input and light intensity, high microalgae growth rate and carbon capture could be achieved. Under the intermittent CO2 transfer mode, at the optimal condition of 850 mg/L CO2 input and 1089 μmol/m2/s light intensity, leading to the highest microalgae growth rate and carbon fixation of 4.2 g/L as observed in this study.
Aditya, L, Vu, HP, Nguyen, LN, Mahlia, TMI, Hoang, NB & Nghiem, LD 2023, 'Microalgae enrichment for biomass harvesting and water reuse by ceramic microfiltration membranes', Journal of Membrane Science, vol. 669, pp. 121287-121287.
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Harvesting and water reuse are two critical issues for large-scale microalgae cultivation. Using two representative microalgae species, namely C. vulgaris and Scenedesmus sp., this study evaluates the performance of a ceramic microfiltration membrane to extract clean water for reuse and pre-concentrate the microalgae solution for subsequent harvesting. The results show that fouling was specific to each individual microalgae species due to the difference in cell properties (e.g. size, shape, and cell membrane). Importantly, membrane fouling could be efficiently mitigated by aeration and regular backwashing without any chemical addition. Aeration reduced the transmembrane pressure when filtering C. vulgaris and Scenedesmus sp. by 56 and 38%, respectively. In long-term performance experiments, C. vulgaris showed considerable membrane fouling over time; by contrast, Scenedesmus sp. showed negligible fouling. The results reaffirmed that membrane filtration efficiency was microalgae species-specific. Permeate water reuse for growing another batch of microalgae was also demonstrated using both species. Results reported here suggest that ceramic microfiltration membrane can simultaneously enrich the microalgae solution and recycle permeated water for microalgae cultivation.
Adnan Farooq, M & Nimbalkar, S 2023, 'Novel sustainable base material for concrete slab track', Construction and Building Materials, vol. 366, pp. 130260-130260.
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Afroz, S, Kim, T & Castel, A 2023, 'Distinct Effect of Hydration of Calcined Kaolinitic Clay–Limestone Blended Cement on Microstructure and Autogenous Shrinkage', Journal of Materials in Civil Engineering, vol. 35, no. 12.
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Afroz, S, Nguyen, QD, Zhang, Y, Kim, T & Castel, A 2023, 'Cracking of limestone calcined clay blended concrete and mortar under restrained shrinkage', Construction and Building Materials, vol. 386, pp. 131599-131599.
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Afroz, S, Zhang, Y, Nguyen, QD, Kim, T & Castel, A 2023, 'Shrinkage of blended cement concrete with fly ash or limestone calcined clay', Materials and Structures, vol. 56, no. 1.
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AbstractThis study investigates the shrinkage of two sustainable aluminosilicate blends with fly ash or limestone-calcined clay (LC3). Paste and concrete were prepared using these SCMs for the highest possible replacement of binder without compromising the strength. The chemical and autogenous shrinkage were assessed for paste samples and further investigation were conducted on hydration by thermogravimetric analysis (TGA) and Fourier transform infrared spectroscopy (FTIR). Opting for an engineering approach, comparison among different segments of shrinkage i.e., autogenous, drying and total shrinkage of concrete having a specific compressive strength were considered. The initial investigation on paste samples highlighted the dissimilarities in shrinkage and hydration of fly ash and calcined clay. LC3 hydrated faster compared to fly ash leading to greater autogenous shrinkage. The high autogenous shrinkage in the LC3 blend was compensated by a low drying shrinkage for a specific compressive strength. Considering the replacement level of cement, shrinkage, and ecological impact, LC3 proved to be a more sustainable and eco-friendly concrete compared to fly ash.
Afsari, M, Jiang, J, Phuntsho, S, Shon, HK & Tijing, LD 2023, 'Ammonia recovery from source-separated hydrolyzed urine via a dual-membrane distillation in-series process', Chemical Engineering Journal, vol. 470, pp. 144215-144215.
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Afsari, M, Li, Q, Karbassiyazdi, E, Shon, HK, Razmjou, A & Tijing, LD 2023, 'Electrospun nanofiber composite membranes for geothermal brine treatment with lithium enrichment via membrane distillation', Chemosphere, vol. 318, pp. 137902-137902.
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Afsari, M, Shirazi, MMA, Ghorbani, AH, Sayar, O, Shon, HK & Tijing, LD 2023, 'Triple-layer nanofiber membrane with improved energy efficiency for treatment of hypersaline solution via membrane distillation', Journal of Environmental Chemical Engineering, vol. 11, no. 5, pp. 110638-110638.
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Ahmad, K, Abdelrazek, M, Arora, C, Baniya, AA, Bano, M & Grundy, J 2023, 'Requirements engineering framework for human-centered artificial intelligence software systems.', Appl. Soft Comput., vol. 143, pp. 110455-110455.
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Ahmad, K, Abdelrazek, M, Arora, C, Bano, M & Grundy, J 2023, 'Requirements practices and gaps when engineering human-centered Artificial Intelligence systems.', Appl. Soft Comput., vol. 143, pp. 110421-110421.
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Ahmed, F, Afzal, MU, Thalakotuna, DN & Esselle, KP 2023, 'Novel Dual-Band Metascreen for Dual-Band Near-Field Phase Correction', IEEE Transactions on Antennas and Propagation, vol. 71, no. 7, pp. 5591-5604.
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Ahmed, F, Hayat, T, Afzal, MU, Zhang, S, Esselle, KP & Whittow, WG 2023, '3-D Printable Synthetic Metasurface to Realize 2-D Beam-Steering Antenna', IEEE Open Journal of Antennas and Propagation, vol. 4, pp. 506-519.
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This article presents highly radio-frequency (RF) transparent phase gradient synthetic metasurfaces made of sub-wavelength-sized 3D printable meta-atoms with tailored permittivity that cannot be achieved with off-the-shelf, commercially available materials. The synthesized meta-atoms design uses one dielectric block of PREPERM ® ABS 1000 material with air and metallic inclusions to make low- and high-permittivity materials. The inclusions' size and height are varied to achieve a complete phase range from 0 to 360°, while maintaining transmission magnitudes greater than -3.0 dB. A two-dimensional array of meta-atoms forms a metasurface, which can be used for phase-shifting structures. Such metasurfaces can manipulate RF waves by introducing progressive phase delay into array elements. The proposed meta-atoms are employed to create highly RF transparent phase-gradient metal-dielectric composite metasurfaces (MDCMs) operating at 11 GHz. These MDCMs can be implemented through 3D printed technology using low-cost thermoplastics or polymers with composite filaments and minimal human intervention. A pair of MDCMs are combined with an array of microstrip patches to demonstrate 2D beam steering functionality numerically. The antenna system provides a peak directivity of 19.9 dBi with a maximum conical scanning angle of 114° and a directivity variation of less than 3 dB.
Ahmed, F, Singh, K & Esselle, KP 2023, 'State-of-the-Art Passive Beam-Steering Antenna Technologies: Challenges and Capabilities', IEEE Access, vol. 11, pp. 1-1.
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This article reviews the latest developments of beam steering antennas that are entirely passive to realize interference-free, power-efficient, and highly secured end-to-end wireless communication. We briefly introduce metamaterials and metasurfaces, a timely advanced topic in electromagnetics (EM) and optics. Mathematical formulas associated with the design of beam steering metasurfaces have been numerically explained. In addition, reflect and transmit array antennas are also discussed for an in-depth understanding of beam scanning principles in elevation and azimuth planes. We then provide intuitive design examples and discuss three broad classes of the latest beam scanning antenna systems, namely i) Reflectarrays, ii) Transmitarrays, and iii) Near-Field Meta-Steering antennas that are available in up-to-date literature. The third category’s unprecedented scanning performance and aesthetically compact size are elucidated compared to previous antenna systems, such as reflector dishes or large phased arrays. Alongside the working principles, the trade-offs for the scanning techniques, operation, and physical size of each antenna type are also discussed. Towards the end, an evaluative conclusion with a comparative discussion on the beam-steering antenna systems is provided. Future research directions considering mass-market demands are also indicated.
Ahmed, SF, Alam, MSB, Hassan, M, Rozbu, MR, Ishtiak, T, Rafa, N, Mofijur, M, Shawkat Ali, ABM & Gandomi, AH 2023, 'Deep learning modelling techniques: current progress, applications, advantages, and challenges', Artificial Intelligence Review, vol. 56, no. 11, pp. 13521-13617.
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AbstractDeep learning (DL) is revolutionizing evidence-based decision-making techniques that can be applied across various sectors. Specifically, it possesses the ability to utilize two or more levels of non-linear feature transformation of the given data via representation learning in order to overcome limitations posed by large datasets. As a multidisciplinary field that is still in its nascent phase, articles that survey DL architectures encompassing the full scope of the field are rather limited. Thus, this paper comprehensively reviews the state-of-art DL modelling techniques and provides insights into their advantages and challenges. It was found that many of the models exhibit a highly domain-specific efficiency and could be trained by two or more methods. However, training DL models can be very time-consuming, expensive, and requires huge samples for better accuracy. Since DL is also susceptible to deception and misclassification and tends to get stuck on local minima, improved optimization of parameters is required to create more robust models. Regardless, DL has already been leading to groundbreaking results in the healthcare, education, security, commercial, industrial, as well as government sectors. Some models, like the convolutional neural network (CNN), generative adversarial networks (GAN), recurrent neural network (RNN), recursive neural networks, and autoencoders, are frequently used, while the potential of other models remains widely unexplored. Pertinently, hybrid conventional DL architectures have the capacity to overcome the challenges experienced by conventional models. Considering that capsule architectures may dominate future DL models, this work aimed to compile information for stakeholders involved in the development and use of DL models in the contemporary world.
Ahmed, SF, Debnath, JC, Mehejabin, F, Islam, N, Tripura, R, Mofijur, M, Hoang, AT, Rasul, MG & Vo, D-VN 2023, 'Utilization of nanomaterials in accelerating the production process of sustainable biofuels', Sustainable Energy Technologies and Assessments, vol. 55, pp. 102894-102894.
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Ahmed, SF, Islam, N, Kumar, PS, Hoang, AT, Mofijur, M, Inayat, A, Shafiullah, GM, Vo, D-VN, Badruddin, IA & Kamangar, S 2023, 'Perovskite solar cells: Thermal and chemical stability improvement, and economic analysis', Materials Today Chemistry, vol. 27, pp. 101284-101284.
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Ahmed, SF, Kumar, PS, Ahmed, B, Mehnaz, T, Shafiullah, GM, Nguyen, VN, Duong, XQ, Mofijur, M, Badruddin, IA & Kamangar, S 2023, 'Carbon-based nanomaterials: Characteristics, dimensions, advances and challenges in enhancing photocatalytic hydrogen production', International Journal of Hydrogen Energy.
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Ahmed, SF, Rafa, SJ, Mehjabin, A, Tasannum, N, Ahmed, S, Mofijur, M, Lichtfouse, E, Almomani, F, Badruddin, IA & Kamangar, S 2023, 'Bio-oil from microalgae: Materials, production, technique, and future', Energy Reports, vol. 10, pp. 3297-3314.
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Ahmed, ST, Basha, SM, Ramachandran, M, Daneshmand, M & Gandomi, AH 2023, 'An Edge-AI-Enabled Autonomous Connected Ambulance-Route Resource Recommendation Protocol (ACA-R3) for eHealth in Smart Cities', IEEE Internet of Things Journal, vol. 10, no. 13, pp. 11497-11506.
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Ahmed, T, Cha, JS, Park, C-G, Shon, HK, Han, DS & Park, H 2023, 'Activated Carbon-Embedded Reduced Graphene Oxide Electrodes for Capacitive Desalination', Journal of Electrochemical Science and Technology, vol. 14, no. 3, pp. 222-230.
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Capacitive deionization of saline water is one of the most promising water purification technologies due to its high energy efficiency and cost-effectiveness. This study synthesizes porous carbon composites composed of reduced graphene oxide (rGO) and activated carbon (AC) with various rGO/AC ratios using a facile chemical method. Surface characterization of the rGO/AC composites shows a successful chemical reduction of GO to rGO and incorporation of AC into rGO. The optimized rGO/AC composite electrode exhibits a specific capacitance of ~243 F g<sup>−1</sup> in a 1 M NaCl solution. The galvanostatic charging-discharging test shows excellent reversible cycles, with a slight shortening in the cycle time from the ~260<sup>th</sup> to the 530<sup>th</sup> cycle. Various monovalent sodium salts (NaF, NaCl, NaBr, and NaI) and chloride salts (LiCl, NaCl, KCl, and CsCl) are deionized with the rGO/AC electrode pairs at a cell voltage of 1.3 V. Among them, NaI shows the highest specific adsorption capacity of ~22.2 mg g<sup>−1</sup>. Detailed surface characterization and electrochemical analyses are conducted.
Akbal, E, Barua, PD, Dogan, S, Tuncer, T & Acharya, UR 2023, 'Explainable automated anuran sound classification using improved one-dimensional local binary pattern and Tunable Q Wavelet Transform techniques', Expert Systems with Applications, vol. 225, pp. 120089-120089.
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Akbarzadeh, M, Oberst, S, Sepehrirahnama, S & Halkon, B 2023, 'Acoustic radiation force-induced push-pull particle oscillations', Journal of the Acoustical Society of America.
Alabdali, SA, Pileggi, SF & Cetindamar, D 2023, 'Influential Factors, Enablers, and Barriers to Adopting Smart Technology in Rural Regions: A Literature Review', Sustainability, vol. 15, no. 10, pp. 7908-7908.
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Smart Technology is a quickly and constantly evolving concept; it has different applications that cover a wide range of areas, such as healthcare, education, business, agriculture, and manufacturing. An effective application of these technologies increases productivity and performance within complex systems. On one side, trends show a lack of appeal for rural environments as people prefer to move to cities, looking for better opportunities and lifestyles. On the other side, recent studies and reports show that the attractiveness of rural areas as places with opportunities is increasing. Sustainable solutions are needed to enhance development in the rural context, and technological innovation is expected to lead and support the stability for people and organizations in rural regions. While Smart City is progressively becoming a reality and a successful model for integrating Smart Technology into different aspects of everyday life, its effective application in a rural context according to a Sustainable Development approach is not yet completely defined. This study adopts comparative and categorial content analysis to address the different applications and the specific characteristics of rural regions, which often present significant peculiarities depending on the country and the context. The main goal is to investigate and discuss how the Smart City model may be adopted and effectively applied within rural contexts, looking at major gaps and challenges. Additionally, because of the complexity of the topic, we provide an overview of the current adoption of Smart Technology in the different applications in rural areas, including farming, education, business, healthcare, and governance. The study highlights the huge difficulties in rural life and the potentiality of Smart Technology to enhance their Sustainable Development, which is still challenging. While the holistic analysis clearly points out a gap, there is no specific strategic roadmap to re-use or adap...
Alalyan, MS, Jaafari, NA, Hussain, FK & Gill, AQ 2023, 'A systematic review of blockchain adoption in education institutions', International Journal of Web and Grid Services, vol. 19, no. 2, pp. 156-184.
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Alalyan, MS, Jaafari, NA, Hussain, FK & Gill, AQ 2023, 'A systematic review of blockchain adoption in education institutions.', Int. J. Web Grid Serv., vol. 19, no. 2, pp. 156-184.
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Alam, MM, Farhangi, M, D.-C.LU, D, Siwakoti, YP & Aljarajreh, H 2023, 'Design, Implementation and Reliability Assessment of a Fault-Tolerant Three-Port Converter', IEEE Transactions on Industry Applications, pp. 1-12.
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Alcaide, AM, Poblete, P, Vazquez, S, Aguilera, RP, Leon, JI, Kouro, S & Franquelo, LG 2023, 'Generalized Feed-Forward Sampling Method for Multilevel Cascaded H-Bridge Converters', IEEE Transactions on Industrial Electronics, pp. 1-9.
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Al-Doghman, F, Moustafa, N, Khalil, I, Sohrabi, N, Tari, Z & Zomaya, AY 2023, 'AI-Enabled Secure Microservices in Edge Computing: Opportunities and Challenges', IEEE Transactions on Services Computing, vol. 16, no. 2, pp. 1485-1504.
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Alfouneh, M, Hoang, V-N, Luo, Z & Luo, Q 2023, 'Topology optimization for multi-layer multi-material composite structures', Engineering Optimization, vol. 55, no. 5, pp. 773-790.
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ALGHAMDI, A, Pileggi, S & Sohaib, O 2023, 'Social Media Analysis to Enhance Sustainable Knowledge Management: A Concise Literature Review', Sustainability, vol. 15, no. 13, pp. 9957-9957.
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Although knowledge management relying on data from social networks has become an integral part of common practices, there needs to be a well-defined body of knowledge that explicitly addresses the process and the value generated. Sustainable knowledge management practices, which promote responsible and ethical knowledge sharing between different stakeholders, can also be facilitated through social media. This can foster a culture of continuous learning and innovation while considering the social implications of knowledge sharing. The main goal of this study is to critically and holistically discuss the impact of social media analysis in the knowledge management process holistically and maximize its value in a given context. More concretely, we conducted a systematic literature review (2012–2022) based on the PRISMA guidelines. We first approached the ideal phases of the knowledge management process and then discussed key issues and challenges from an application perspective. Overall, the study points out the positive impact of social network analysis on knowledge sharing, creativity and productivity, knowledge formulation, building trust, and cognitive capital. Additionally, value is provided in knowledge acquisition by simplifying and massively gathering information, reducing uncertainty and ambiguity, and organizing knowledge through storage, retrieval, and classification practices. At an application level, such knowledge may improve the quality of services and encourage creativity. Finally, this study analyzed specific domains, such as healthcare, marketing, politics, tourism, and event management, focusing on the potential and added value.
Alhosaini, H, Alharbi, S, Wang, X & Xu, G 2023, 'API recommendation for mashup creation: A comprehensive survey', The Computer Journal.
Al-Hunaity, SA, Karki, D & Far, H 2023, 'Shear connection performance of cold-formed steel and plywood composite flooring systems: Experimental and numerical investigation', Structures, vol. 48, pp. 901-917.
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Ali, M, Qayoom, A, Ghulam, Z, Imran, S, Yusoff, M, Kalam, MA, Mahmoud, O & El-Shafay, AS 2023, 'Experimental Study on the Cold Flow Behaviour of Azadirachta Indica (NEEM) Biodiesel Blended with Petroleum-based Fuels and Natural Organic Solvents', Journal of Applied Science and Engineering (Taiwan), vol. 26, no. 8, pp. 1153-1167.
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Due to the poor cold flow behaviour of biodiesel in winter, it tends to form solidifying gel in many cold regions across the world, making it challenging to use as an alternative fuel in diesel engines. This research investigation was conducted to investigate the comparative impact on cold flow parameters of biodiesel produced from Azadirachta indica (Neem oil) by blending with petroleum-based fuels and natural organic solvents. The B20 blends with kerosene showed significant improvement in CP, PP, and CFPP to -10 °C, -19 °C, and -20 °C, respectively. B20* blend enhanced the CP, PP and CFPP to 8 °C, 2 °C, and 6 °C respectively, while mixed kerosene/ diesel B20** blend improved CFPP, CP and PP by -4.5 °C, -7 °C and -8 °C respectively. Blend (B20T10) with natural turpentine oil improved CP, PP, and CFPP to 7 °C, 5 °C, and -2 °C, respectively. Diethyl ether and n-butanol did not show substantial improvement in biodiesel cold flow characteristics. The ester functional group in the biodiesel from ATR-FTIR spectral peaks was found at 1740.2 cm−1 denotes C=O, i.e., carbonyl group confirmation in the presence of ester linkage. The cost analysis of B20y** and B20T10 were found to be USD 0.747 and USD 0.782 per L, respectively in comparison to that of petroleum diesel, USD 0.770 per L. Neem biodiesel production showed a positive net energy balance of 14.28%. According to the current study, it is recommended to use blended mixed kerosene/diesel (B20**) and biodiesel blend with turpentine oil (B20T10) in diesel engines with suitable physico-chemical and cold flow properties in compliance with ASTM D6571standard.
AlJaberi, FY, Ahmed, SA, Makki, HF, Naje, AS, Zwain, HM, Salman, AD, Juzsakova, T, Viktor, S, Van, B, Le, P-C, La, DD, Chang, SW, Um, M-J, Ngo, HH & Nguyen, DD 2023, 'Recent advances and applicable flexibility potential of electrochemical processes for wastewater treatment', Science of The Total Environment, vol. 867, pp. 161361-161361.
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Almadani, MS, Alotaibi, S, Alsobhi, H, Hussain, OK & Hussain, FK 2023, 'Blockchain-based multi-factor authentication: A systematic literature review', Internet of Things, vol. 23, pp. 100844-100844.
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Al-Maliki, S, Bouanani, FE, Ahmad, K, Abdallah, M, Hoang, DT, Niyato, D & Al-Fuqaha, A 2023, 'Toward Improved Reliability of Deep Learning Based Systems Through Online Relabeling of Potential Adversarial Attacks', IEEE Transactions on Reliability, pp. 1-16.
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Al-Najar, JA, Al-Humairi, ST, Lutfee, T, Balakrishnan, D, Veza, I, Soudagar, MEM & Fattah, IMR 2023, 'Cost-Effective Natural Adsorbents for Remediation of Oil-Contaminated Water', Water (Switzerland), vol. 15, no. 6, pp. 1186-1186.
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Oil-contaminated water is among the most significant environmental challenges from various industries and manufacturing processes. Oily water poses a severe environmental threat and is toxic to many forms of life. This study aims to investigate the potential of natural adsorbents, namely animal bones (ABs) and anise residues (ARs), for removing oil from water using a batch adsorption process. The effects of adsorbent dosage (0.2–2 g), oil concentration (200–1000 mg/L), and contact time (30–120 min) on the adsorption process were evaluated. This study is the first to employ ABs and ARs as adsorbents for oil removal, and their efficacy for this purpose has not been previously reported. The results indicate that ABs exhibit superior oil removal capacity compared to ARs. Specifically, ABs removed 45 mg/g of oil from water, while ARs removed only 30 mg/g of oil. Furthermore, ABs achieved a percentage removal rate of 94%, whereas ARs had a percentage removal rate of 70%. The adsorbents were characterised using Fourier transform infrared (FTIR) spectrometry, contact angle measurements before and after adsorption, and thermogravimetric analysis (TGA). In addition to the experimental analysis, several kinetic and adsorption models were employed to investigate the adsorption process. The pseudo-first-order and pseudo-second-order models were used to represent the kinetics of the reaction, while the Langmuir and Freundlich isotherm models were used to represent the adsorption isotherm. Marquardt’s percent standard deviation (MPSD) error function was used to confirm the fit of the experimental data with the isotherm model, in addition to the correlation coefficient R2. The isotherm studies indicated that the experimental data of the two adsorbents used with the Langmuir isotherm model were consistent with one another. The kinetics study demonstrated that the adsorption process using the two adsorbents adheres to a pseudo-second-order kinetics model.
Al-Najjar, HAH, Pradhan, B, Beydoun, G, Sarkar, R, Park, H-J & Alamri, A 2023, 'A novel method using explainable artificial intelligence (XAI)-based Shapley Additive Explanations for spatial landslide prediction using Time-Series SAR dataset', Gondwana Research, vol. 123, pp. 107-124.
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Alsalibi, B, Mirjalili, S, Abualigah, L, yahya, RI & Gandomi, AH 2023, 'Correction to: A Comprehensive Survey on the Recent Variants and Applications of Membrane-Inspired Evolutionary Algorithms', Archives of Computational Methods in Engineering, vol. 30, no. 5, pp. 3467-3467.
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Alsobhi, HA, Alakhtar, RA, Ubaid, A, Hussain, OK & Hussain, FK 2023, 'Blockchain-based micro-credentialing system in higher education institutions: Systematic literature review', Knowledge-Based Systems, vol. 265, pp. 110238-110238.
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Alsoibi, I, Agarwal, R, Bharathy, G, Samarawickrama, M, Unhelkar, B & Prasad, M 2023, 'A Systematic Review and Taxonomy of Data Analytics in Non-profit Organizations', Asia Pacific Journal of Information Systems (APJIS), vol. 33, no. 1, pp. 33-68.
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Nonprofit organisations (NPOs) use data analytics and corresponding visualisations to discover and interpret
patterns of donations and donor behaviours, predict future funds, and analyse time series to undertake decisions
and resolve issues. Further detailed understanding of these activities in the context of NPOs is required for
efficient and effective utilisation of data analytics. This article reports a systematic review of available literature
on data analytics applications in NPOs to answer three research questions: (1) What are the proposed approaches
and frameworks for adopting and applying data analytics in NPOs? (2) What aspects of data analytics are used
for NPO activities and missions? (3) What challenges and barriers face NPOs regarding the adoption and application
of data analytics for their missions? We answered the three research questions by collecting and examining
data and using it to develop a new taxonomy. The results show the utilisation of data analytics applications
by NPOs has not been examined in depth, indicating the need for further research. This study contributes
to the literature by providing insights on the existing use of data analytics applications in various domains,
and their benefits and drawbacks for NPOs. This study also presents future research directions.
Alsolbi, I, Agarwal, R, Bharathy, G, Samarawickrama, M, Unhelkar, B & Prasad, M 2023, 'A Systematic Review and Taxonomy of Data Analytics in Nonprofit Organisations', Asia Pacific Journal of Information Systems, vol. 33, no. 1, pp. 39-68.
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Alsolbi, I, Shavaki, FH, Agarwal, R, Bharathy, GK, Prakash, S & Prasad, M 2023, 'Big data optimisation and management in supply chain management: a systematic literature review', Artificial Intelligence Review, vol. 56, no. S1, pp. 253-284.
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AbstractThe increasing interest from technology enthusiasts and organisational practitioners in big data applications in the supply chain has encouraged us to review recent research development. This paper proposes a systematic literature review to explore the available peer-reviewed literature on how big data is widely optimised and managed within the supply chain management context. Although big data applications in supply chain management appear to be often studied and reported in the literature, different angles of big data optimisation and management technologies in the supply chain are not clearly identified. This paper adopts the explanatory literature review involving bibliometric analysis as the primary research method to answer two research questions, namely: (1) How to optimise big data in supply chain management? and (2) What tools are most used to manage big data in supply chain management? A total of thirty-seven related papers are reviewed to answer the two research questions using the content analysis method. The paper also reveals some research gaps that lead to prospective future research directions.
Alsouda, F, Bennett, NS, Saha, SC, Salehi, F & Islam, MS 2023, 'Vapor Compression Cycle: A State-of-the-Art Review on Cycle Improvements, Water and Other Natural Refrigerants', Clean Technologies, vol. 5, no. 2, pp. 584-608.
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Air conditioning and refrigeration have become necessary in modern life, accounting for more than 7.8% of greenhouse gases (GHG) emitted globally. Reducing the environmental impact of these systems is crucial for meeting the global GHG emission targets. Two principal directions must be considered to reduce the environmental impact of air conditioning systems. Firstly, reducing the direct effect by looking at less harmful refrigerants and secondly, reducing the indirect effect by searching for options to improve the system efficiency. This study presents the latest developments in the vapor compression cycle and natural refrigerants, focusing on water as a refrigerant. Natural refrigerants, and especially water, could be the ultimate solution for the environmental problems associated with the operation of vapor compression cycle (VCC) cooling systems, including ozone depletion (OD) and global warming (GW). Reducing the environmental impact of building cooling systems is essential, and the recent system improvements made to enhance the system coefficient of performance (COP) are thoroughly discussed in this paper. Though the cycle improvements discussed in this work are essential and could increase the system efficiency, they still need to solve the direct environmental impact of refrigerants. Accordingly, this paper suggests that natural refrigerants, including water, are the most suitable strategic choice to replace the current refrigerants in the refrigeration and air conditioning industry. Finally, this study reviews the latest VCC system improvements and natural refrigerants in order to guide interested researchers with solutions that may reduce the environmental impact of VCC systems and suggest future research areas.
Altaf, T, Wang, X, Ni, W, Liu, RP & Braun, R 2023, 'NE-GConv: A lightweight node edge graph convolutional network for intrusion detection', Computers & Security, vol. 130, pp. 103285-103285.
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Altaf, T, Wang, X, Ni, W, Yu, G, Liu, RP & Braun, R 2023, 'A new concatenated Multigraph Neural Network for IoT intrusion detection', Internet of Things, vol. 22, pp. 100818-100818.
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Alyami, A, Pileggi, S, Sohaib, O & Hawryszkiewycz, I 2023, 'Seamless transformation from use case tosequence diagrams', PeerJ Computer Science.
Alyami, A, Pileggi, SF & Hawryszkiewycz, I 2023, 'Knowledge development, technology and quality of experience in collaborative learning: a perspective from Saudi Arabia universities', Quality & Quantity, vol. 57, no. 4, pp. 3085-3104.
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AbstractTechnology has recently gained relevance within collaborative learning environments to provide robustness, agility and flexibility. Several recent studies have investigated the role of technology, as well as researchers have defined different metrics to assess learning outcomes and experience along the collaborative knowledge development process. More recently, technology has played a key role to face the new challenges related to COVID-19, which forced to move on remote or hybrid learning. This research focuses on the quality of learning experience in terms of academic performance and perceived satisfaction. From a methodological point of view, a conceptual framework has been proposed and a quantitative study has been conducted among undergraduate and postgraduate students that are undertaking programs related to System Design in Saudi Arabia universities. 152 responses have been collected through an online survey and analysed using SPSS and SmartPLS. Results show a positive impact of technology along the collaborative knowledge development process and a strong correlation among the different quality of learning experience parameters considered. Indeed, despite some challenges, an integrated use of technology seems to properly support the most pressing needs in terms of quality experience, while the well-known social/educational issues related to the COVID-19 pandemic are not object of this study. Those findings are expected to contribute to the Saudi Arabia’s vision 2030 and, more holistically, to the assessment of collaborative learning environments that extensively rely on technology.
Alyami, A, Pileggi, SF, Sohaib, O & Hawryszkiewycz, IT 2023, 'Seamless transformation from use case to sequence diagrams.', PeerJ Comput. Sci., vol. 9, pp. e1444-e1444.
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System design is an essential subject taught in information systems and has become a core course in its curriculum. Unified modelling language (UML) has been broadly adopted, and it is common to support the system design process using different diagrams. Each diagram serves a purpose by focusing on a specific part of a particular system. Design consistency ensures a seamless process, as the diagrams are generally interrelated. However, creating a well-designed system takes a lot of work, especially for university students with work experience. To overcome this challenge, aligning the concepts across diagrams is essential, which can help achieve better consistency and management of the design system, especially in an educational setting. This article is an extension of our previous work, as we have discussed a simple scenario of Automated teller machines to demonstrate the alignment concepts between UML diagrams. From a more technical perspective, the current contribution provides a Java program that aligns concepts by converting text-based use cases to text-based sequence diagrams. Then, the text is transformed in PlantUML to generate its graphical representation. The developed alignment tool is expected to contribute to helping students and instructors during the system design phases to be more consistent and practical. Limitations and future work are presented.
Al-Zainati, N, Ibrar, I, Altaee, A, Subbiah, S & Zhou, J 2023, 'Multiple staging of pressure retarded osmosis: Impact on the energy generation', Desalination, pp. 117199-117199.
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Amiri, M, Abolhasan, M, Shariati, N & Lipman, J 2023, 'RF-Self-Powered Sensor for Fully Autonomous Soil Moisture Sensing', IEEE Transactions on Microwave Theory and Techniques, vol. 71, no. 3, pp. 1374-1387.
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Amirkhani, F, Dashti, A, Jokar, M, Mohammadi, AH, Gholamzadeh Chofreh, A, Varbanov, PS & Zhou, JL 2023, 'Estimation of CO2 solubility in aqueous solutions of commonly used blended amines: Application to optimised greenhouse gas capture', Journal of Cleaner Production, vol. 430, pp. 139435-139435.
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An, Y, Lam, H-K & Ling, SH 2023, 'Multi-classification for EEG motor imagery signals using data evaluation-based auto-selected regularized FBCSP and convolutional neural network.', Neural Comput. Appl., vol. 35, no. 16, pp. 12001-12027.
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AbstractIn recent years, there has been a renewal of interest in brain–computer interface (BCI). One of the BCI tasks is to classify the EEG motor imagery (MI). A great deal of effort has been made on MI classification. What seems to be lacking, however, is multiple MI classification. This paper develops a single-channel-based convolutional neural network to tackle multi-classification motor imagery tasks. For multi-classification, a single-channel learning strategy can extract effective information from each independent channel, making the information between adjacent channels not affect each other. A data evaluation method and a mutual information-based regularization parameters auto-selection algorithm are also proposed to generate effective spatial filters. The proposed method can be used to tackle the problem of an inaccurate mixed covariance matrix caused by fixed regularization parameters and invalid training data. To illustrate the merits of the proposed methods, we used the tenfold cross-validation accuracy and kappa as the evaluation measures to test two data sets. BCI4-2a and BCI3a data sets have four mental classes. For the BCI4-2a data set, the average accuracy is 79.01%, and the kappa is 0.7202 using data evaluation-based auto-selected filter bank regularized common spatial pattern voting (D-ACSP-V) and single-channel series convolutional neural network (SCS-CNN). Compared to traditional FBRCSP, the proposed method improved accuracy by 7.14% for the BCI4-2a data set. By using the BCI3a data set, the proposed method improved accuracy by 9.54% compared with traditional FBRCSP, the average accuracy of the proposed method is 83.70%, and the kappa is 0.7827.
Anand, V, Singh, V, Guo, X, Sathik, MAJ, Siwakoti, YP, Mekhilef, S & Blaabjerg, F 2023, 'Seventeen Level Switched Capacitor Inverters With the Capability of High Voltage Gain and Low Inrush Current', IEEE Journal of Emerging and Selected Topics in Industrial Electronics, vol. 4, no. 4, pp. 1138-1150.
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Andersen, TI, Lensky, YD, Kechedzhi, K, Drozdov, IK, Bengtsson, A, Hong, S, Morvan, A, Mi, X, Opremcak, A, Acharya, R, Allen, R, Ansmann, M, Arute, F, Arya, K, Asfaw, A, Atalaya, J, Babbush, R, Bacon, D, Bardin, JC, Bortoli, G, Bourassa, A, Bovaird, J, Brill, L, Broughton, M, Buckley, BB, Buell, DA, Burger, T, Burkett, B, Bushnell, N, Chen, Z, Chiaro, B, Chik, D, Chou, C, Cogan, J, Collins, R, Conner, P, Courtney, W, Crook, AL, Curtin, B, Debroy, DM, Del Toro Barba, A, Demura, S, Dunsworth, A, Eppens, D, Erickson, C, Faoro, L, Farhi, E, Fatemi, R, Ferreira, VS, Burgos, LF, Forati, E, Fowler, AG, Foxen, B, Giang, W, Gidney, C, Gilboa, D, Giustina, M, Gosula, R, Dau, AG, Gross, JA, Habegger, S, Hamilton, MC, Hansen, M, Harrigan, MP, Harrington, SD, Heu, P, Hilton, J, Hoffmann, MR, Huang, T, Huff, A, Huggins, WJ, Ioffe, LB, Isakov, SV, Iveland, J, Jeffrey, E, Jiang, Z, Jones, C, Juhas, P, Kafri, D, Khattar, T, Khezri, M, Kieferová, M, Kim, S, Kitaev, A, Klimov, PV, Klots, AR, Korotkov, AN, Kostritsa, F, Kreikebaum, JM, Landhuis, D, Laptev, P, Lau, K-M, Laws, L, Lee, J, Lee, KW, Lester, BJ, Lill, AT, Liu, W, Locharla, A, Lucero, E, Malone, FD, Martin, O, McClean, JR, McCourt, T, McEwen, M, Miao, KC, Mieszala, A, Mohseni, M, Montazeri, S, Mount, E, Movassagh, R, Mruczkiewicz, W, Naaman, O, Neeley, M, Neill, C, Nersisyan, A, Newman, M, Ng, JH, Nguyen, A, Nguyen, M, Niu, MY, O’Brien, TE, Omonije, S, Petukhov, A, Potter, R, Pryadko, LP, Quintana, C, Rocque, C, Rubin, NC, Saei, N, Sank, D, Sankaragomathi, K, Satzinger, KJ, Schurkus, HF, Schuster, C, Shearn, MJ, Shorter, A, Shutty, N, Shvarts, V, Skruzny, J, Smith, WC, Somma, R, Sterling, G, Strain, D, Szalay, M, Torres, A, Vidal, G, Villalonga, B, Heidweiller, CV, White, T, Woo, BWK, Xing, C, Yao, ZJ, Yeh, P, Yoo, J, Young, G, Zalcman, A, Zhang, Y, Zhu, N, Zobrist, N, Neven, H, Boixo, S, Megrant, A, Kelly, J, Chen, Y, Smelyanskiy, V, Kim, E-A, Aleiner, I & Roushan, P 2023, 'Non-Abelian braiding of graph vertices in a superconducting processor', Nature, vol. 618, no. 7964, pp. 264-269.
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AbstractIndistinguishability of particles is a fundamental principle of quantum mechanics1. For all elementary and quasiparticles observed to date—including fermions, bosons and Abelian anyons—this principle guarantees that the braiding of identical particles leaves the system unchanged2,3. However, in two spatial dimensions, an intriguing possibility exists: braiding of non-Abelian anyons causes rotations in a space of topologically degenerate wavefunctions4–8. Hence, it can change the observables of the system without violating the principle of indistinguishability. Despite the well-developed mathematical description of non-Abelian anyons and numerous theoretical proposals9–22, the experimental observation of their exchange statistics has remained elusive for decades. Controllable many-body quantum states generated on quantum processors offer another path for exploring these fundamental phenomena. Whereas efforts on conventional solid-state platforms typically involve Hamiltonian dynamics of quasiparticles, superconducting quantum processors allow for directly manipulating the many-body wavefunction by means of unitary gates. Building on predictions that stabilizer codes can host projective non-Abelian Ising anyons9,10, we implement a generalized stabilizer code and unitary protocol23 to create and braid them. This allows us to experimentally verify the fusion rules of the anyons and braid them to realize their statistics. We then study the prospect of using the anyons for quantum computation and use braiding to create an entangled state of anyons encoding three logical qubits. Our work provides new insights about non-Abelian braiding and, through the future inclusion of error correction to achieve topological protection, could open a path towards fault-tolerant quantum computing.
Ansari, M, Zetterstrom, O, Fonseca, NJG, Quevedo-Teruel, O & Guo, YJ 2023, 'A Lightweight Spherical Generalized Luneburg Lens Antenna with Low Cross-Polarization over a Wide Range in Azimuth and Elevation', IEEE Open Journal of Antennas and Propagation, pp. 1-1.
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Anvari, AT, Babanajad, S & Gandomi, AH 2023, 'Data-Driven Prediction Models For Total Shear Strength of Reinforced Concrete Beams With Fiber Reinforced Polymers Using An Evolutionary Machine Learning Approach', Engineering Structures, vol. 276, pp. 115292-115292.
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Appiahene, P, Chaturvedi, K, Asare, JW, Donkoh, ET & Prasad, M 2023, 'CP-AnemiC: A conjunctival pallor dataset and benchmark for anemia detection in children', Medicine in Novel Technology and Devices, vol. 18, pp. 100244-100244.
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Appio, FP, Bröring, S, Sick, N, Lee, S & Mora, L 2023, 'Editorial Deciphering Convergence: Novel Insights and Future Ideas on Science, Technology, and Industry Convergence', IEEE Transactions on Engineering Management, vol. 70, no. 4, pp. 1389-1401.
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Arango, E, Nogal, M, Yang, M, Sousa, HS, Stewart, MG & Matos, JC 2023, 'Dynamic thresholds for the resilience assessment of road traffic networks to wildfires', Reliability Engineering & System Safety, vol. 238, pp. 109407-109407.
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Arsad, AZ, Hannan, MA, Al-Shetwi, AQ, Begum, RA, Hossain, MJ, Ker, PJ & Mahlia, TMI 2023, 'Hydrogen electrolyser technologies and their modelling for sustainable energy production: A comprehensive review and suggestions', International Journal of Hydrogen Energy, vol. 48, no. 72, pp. 27841-27871.
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Arsad, AZ, Hannan, MA, Al-Shetwi, AQ, Hossain, MJ, Begum, RA, Ker, PJ, Salehi, F & Muttaqi, KM 2023, 'Hydrogen electrolyser for sustainable energy production: A bibliometric analysis and future directions', International Journal of Hydrogen Energy, vol. 48, no. 13, pp. 4960-4983.
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Arsad, SR, Ker, PJ, Hannan, MA, Tang, SGH, R S, N, Chau, CF & Mahlia, TMI 2023, 'Patent landscape review of hydrogen production methods: Assessing technological updates and innovations', International Journal of Hydrogen Energy.
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Asadabadi, MR, Saberi, M, Sadghiani, NS, Zwikael, O & Chang, E 2023, 'Enhancing the analysis of online product reviews to support product improvement: integrating text mining with quality function deployment', Journal of Enterprise Information Management, vol. 36, no. 1, pp. 275-302.
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PurposeThe purpose of this paper is to develop an effective approach to support and guide production improvement processes utilising online product reviews.Design/methodology/approachThis paper combines two methods: (1) natural language processing (NLP) to support advanced text mining to increase the accuracy of information extracted from product reviews and (2) quality function deployment (QFD) to utilise the extracted information to guide the product improvement process.FindingsThe paper proposes an approach to automate the process of obtaining voice of the customer (VOC) by performing text mining on available online product reviews while considering key factors such as the time of review and review usefulness. The paper enhances quality management processes in organisations and advances the literature on customer-oriented product improvement processes.Originality/valueOnline product reviews are a valuable source of information for companies to capture the true VOC. VOC is then commonly used by companies as the main input for QFD to enhance quality management and product improvement. However, this process requires considerable time, during which VOC may change, which may negatively impact the output of QFD. This paper addresses this challenge by providing an improved approach.
Aseeri, M & Kang, K 2023, 'Organisational culture and big data socio-technical systems on strategic decision making: Case of Saudi Arabian higher education', Education and Information Technologies, vol. 28, no. 7, pp. 8999-9024.
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Asheralieva, A, Niyato, D & Miyanaga, Y 2023, '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, pp. 1-18.
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Ashfaque Ahmed, S, Elahi M. Soudagar, M, Rahamathullah, I, Sadhik Basha, J, Yunus Khan, TM, Javed, S, Elfasakhany, A & Kalam, MA 2023, 'Investigation of ternary blends of animal fat biodiesel-diethyl ether-diesel fuel on CMFIS-CI engine characteristics', Fuel, vol. 332, pp. 126200-126200.
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The experimental study aims at investigation of the ternary blends of animal fat oil biodiesel, diesel and diethyl ether in-terms of performance, emission and combustion analysis in single cylinder four stroke diesel engine. The animal (mono) fat biodiesel was prepared through transesterification process by using alcohol (6:1) and potassium hydroxide (4:1) at 50 °C –55 °C and obtained 90 % of biodiesel. The prepared biodiesel was blended with neat diesel to get the tested blend, animal fat biodiesel (AFB20). To improve the performance parameters of AFB20, the prepared biodiesel blend AFB20 was again blend with diethyl ether (DEE) in various proportions by 10 % and 20 % to obtain the ternary blend AFB20DEE10 and AFB20DEE20. Addition of DEE to AFB20 enhances the physicochemical properties of the biodiesel blends. The first stage of this study was that the neat diesel was examined in the single cylinder four stroke diesel engine to obtain the reference readings. The second and third stage of this investigation was the prepared animal fat biodiesel blend AFB20 and AFB20DEE10 and AFB20DEE20 were examined in the diesel engine. The fourth stage of this investigation was that, the obtained results of neat diesel was compared with the other tested blends. The experimental outcome reveals that, AFB20DEE20 blend perform better than that of the other tested blends. 4.8 % higher fuel is consumed, and 7.1 % lowered brake thermal efficiency and exhaust gas temperature was found in the blend AFB20DEE20 compared to neat diesel. The blend AFB20DEE20 exhibits higher cylinder pressure by 70.43 bar and lower heat release rate by 35.23 J/deg., compared to neat diesel. 0.15 % lower CO emission and 37.8 % lower UBHC emission were found in AFB20DEE20 compared with ND. Reduction of NOx emission by 4.18 % and higher smoke emissions were found in the blend AFB20DEE20 compared to neat diesel.
Aworanti, OA, Agbede, OO, Agarry, SE, Ajani, AO, Ogunkunle, O, Laseinde, OT, Rahman, SMA & Fattah, IMR 2023, 'Decoding Anaerobic Digestion: A Holistic Analysis of Biomass Waste Technology, Process Kinetics, and Operational Variables', Energies, vol. 16, no. 8, pp. 3378-3378.
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The continual generation and discharge of waste are currently considered two of the main environmental problems worldwide. There are several waste management options that can be applied, though anaerobic digestion (AD) process technology seems to be one of the best, most reliable, and feasible technological options that have attracted remarkable attention due to its benefits, including the generation of renewable energy in the form of biogas and biomethane. There is a large amount of literature available on AD; however, with the continuous, progressive, and innovative technological development and implementation, as well as the inclusion of increasingly complex systems, it is necessary to update current knowledge on AD process technologies, process variables and their role on AD performance, and the kinetic models that are most commonly used to describe the process-reaction kinetics. This paper, therefore, reviewed the AD process technologies for treating or processing organic biomass waste with regard to its classification, the mechanisms involved in the process, process variables that affect the performance, and the process kinetics. Gazing into the future, research studies on reduced MS-AD operational cost, integrated or hybrid AD-biorefinery technology, integrated or hybrid AD-thermochemical process, novel thermochemical reactor development, nutrient recovery from integrated AD-thermochemical process, and solid and liquid residual disposal techniques are more likely to receive increased attention for AD process technology of biomass wastes.
Aydemir, E, Baygin, M, Dogan, S, Tuncer, T, Barua, PD, Chakraborty, S, Faust, O, Arunkumar, N, Kaysi, F & Acharya, UR 2023, 'Mental performance classification using fused multilevel feature generation with EEG signals', International Journal of Healthcare Management, vol. 16, no. 4, pp. 574-587.
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Azizi, M, Talatahari, S & Gandomi, AH 2023, 'Fire Hawk Optimizer: a novel metaheuristic algorithm', Artificial Intelligence Review, vol. 56, no. 1, pp. 287-363.
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AbstractThis study proposes the Fire Hawk Optimizer (FHO) as a novel metaheuristic algorithm based on the foraging behavior of whistling kites, black kites and brown falcons. These birds are termed Fire Hawks considering the specific actions they perform to catch prey in nature, specifically by means of setting fire. Utilizing the proposed algorithm, a numerical investigation was conducted on 233 mathematical test functions with dimensions of 2–100, and 150,000 function evaluations were performed for optimization purposes. For comparison, a total of ten different classical and new metaheuristic algorithms were utilized as alternative approaches. The statistical measurements include the best, mean, median, and standard deviation of 100 independent optimization runs, while well-known statistical analyses, such as Kolmogorov–Smirnov, Wilcoxon, Mann–Whitney, Kruskal–Wallis, and Post-Hoc analysis, were also conducted. The obtained results prove that the FHO algorithm exhibits better performance than the compared algorithms from literature. In addition, two of the latest Competitions on Evolutionary Computation (CEC), such as CEC 2020 on bound constraint problems and CEC 2020 on real-world optimization problems including the well-known mechanical engineering design problems, were considered for performance evaluation of the FHO algorithm, which further demonstrated the superior capability of the optimizer over other metaheuristic algorithms in literature. The capability of the FHO is also evaluated in dealing with two of the real-size structural frames with 15 and 24 stories in which the new method outperforms the previously developed metaheuristics.
Azizivahed, A, Gholami, K, Li, L & Zhang, J 2023, 'Accurate optimal power flow for active distribution networks via floating tangent surface', Electric Power Systems Research, vol. 217, pp. 109167-109167.
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Ba, X, Sun, X, Gong, Z, Guo, Y, Zhang, C & Zhu, J 2023, 'A Generalized Per-Phase Equivalent Circuit Model of the PMSM With Predictable Core Loss', IEEE/ASME Transactions on Mechatronics, vol. 28, no. 3, pp. 1512-1521.
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Bagirov, A, Seifollahi, S, Piccardi, M, Zare Borzeshi, E & Kruger, B 2023, 'SMGKM: An Efficient Incremental Algorithm for Clustering Document Collections', pp. 314-328.
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Bahrami, H, Sichetti, F, Puppo, E, Vettori, L, Liu Chung Ming, C, Perry, S, Gentile, C & Pietroni, N 2023, 'Physically-based simulation of elastic-plastic fusion of 3D bioprinted spheroids', Biofabrication, vol. 15, no. 4, pp. 045021-045021.
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Abstract
Spheroids are microtissues containing cells organized in a spherical shape whose diameter is usually less than a millimetre. Depending on the properties of the environment they are placed in, some nearby spheroids spontaneously fuse and generate a tissue. Given their potential to mimic features typical of body parts and their ability to assemble by fusing in permissive hydrogels, they have been used as building blocks to 3D bioprint human tissue parts. Parameters controlling the shape and size of a bioprinted tissue using fusing spheroid cultures include cell composition, hydrogel properties, and their relative initial position. Hence, simulating, anticipating, and then controlling the spheroid fusion process is essential to control the shape and size of the bioprinted tissue. This study presents the first physically-based framework to simulate the fusion process of bioprinted spheroids. The simulation is based on elastic-plastic solid and fluid continuum mechanics models. Both models use the ‘smoothed particle hydrodynamics’ method, which is based on discretizing the continuous medium into a finite number of particles and solving the differential equations related to the physical properties (e.g. Navier–Stokes equation) using a smoothing kernel function. To further investigate the effects of such parameters on spheroid shape and geometry, we performed sensitivity and morphological analysis to validate our simulations with in-vitro spheroids. Through our in-silico simulations by changing the aforementioned parameters, we show that the proposed models appropriately simulate the range of the elastic-plastic behaviours of in-vitro fusing spheroids to generate tissues of desired shapes and sizes. Altogether, this study presented a physically-based simulation that can provide a framework for monitoring and controlling the geometrica...
Bahrami, N, Nikoo, MR, Al-Rawas, G, Al-Jabri, K & Gandomi, AH 2023, 'Optimal Treated Wastewater Allocation Among Stakeholders Based on an Agent-based Approach', Water Resources Management, vol. 37, no. 1, pp. 135-156.
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Using unconventional water resources, such as treated wastewater (TWW), is an excellent alternative to meet excess water demands. Policymakers should consider optimal and equitable allocation of TWW to relieve conflicts among stakeholders. In the current research, an agent-based model (ABM) is integrated with a multi-objective optimization method (MOM) to fairly distribute water among different beneficiaries in Tehran Province, Iran. In ABM there are two groups of agents: water users and managers. Water users seek to minimize water shortages, and water managers are responsible for allocating water to the users fairly. Managers also assess different bankruptcy scenarios (BSs) for allocating TWW to each stakeholder, and the most agreeable scenario is selected. The Conditional Value-at-Risk (CVaR)-based objective functions are used to assess the risk of uncertainties under different confidence levels. Then, to prioritize the Pareto-optimal solutions, a novel multi-criteria decision-making (MCDM) method, named R-method, is utilized. Results show that considering stakeholders’ objectives and interactions can lead to finding a more equitable solution. Interactions among beneficiaries can diminish water shortages in the study area through an investment by the industrial sector in the agricultural sector to improve the efficiency of agricultural activities.
Bai, H, Cheng, R, Yazdani, D, Tan, KC & Jin, Y 2023, 'Evolutionary Large-Scale Dynamic Optimization Using Bilevel Variable Grouping', IEEE Transactions on Cybernetics, vol. 53, no. 11, pp. 6937-6950.
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Variable grouping provides an efficient approach to large-scale optimization, and multipopulation strategies are effective for both large-scale optimization and dynamic optimization. However, variable grouping is not well studied in large-scale dynamic optimization when cooperating with multipopulation strategies. Specifically, when the numbers/sizes of the variable subcomponents are large, the performance of the algorithms will be substantially degraded. To address this issue, we propose a bilevel variable grouping (BLVG)-based framework. First, the primary grouping applies a state-of-the-art variable grouping method based on variable interaction analysis to group the variables into subcomponents. Second, the secondary grouping further groups the subcomponents into variable cells, that is, combination variable cells and decomposition variable cells. We then tailor a multipopulation strategy to process the two types of variable cells efficiently in a cooperative coevolutionary (CC) way. As indicated by the empirical study on large-scale dynamic optimization problems (DOPs) of up to 300 dimensions, the proposed framework outperforms several state-of-the-art frameworks for large-scale dynamic optimization.
Bai, S, Zhang, Q, He, H, Hu, L, Wang, S & Niu, Z 2023, 'Cluster-aware attentive convolutional recurrent network for multivariate time-series forecasting', Neurocomputing, vol. 558, pp. 126701-126701.
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Multivariate time-series (MTS) forecasting plays a crucial role in various real-world applications, but the complex dependencies between time-series variables (i.e., inter-series dependencies) make this task extremely challenging. While most existing studies focus on modeling intra-series (temporal) dependencies by capturing long- and short-term patterns, they fail to explore and exploit the inter-series dependencies to enhance MTS forecasting. In this paper, we propose a Cluster-aware Attentive Convolutional Recurrent Network (CACRN) to capture both inter-series and intra-series dependencies in MTS data. Specifically, CACRN first introduces a cluster-aware variable representation module that separates irrelevant variables and captures the interaction between relevant variables to learn cluster-aware variable representations. Then, CACRN feeds these representations into parallel convolutional recurrent neural networks (CRNNs) to capture the short- and long-term temporal dependencies in a cluster-wise manner. Next, a cluster-aware attention mechanism is introduced to attend to temporal information in each cluster and co-attend all cluster information jointly to capture intra-cluster and inter-cluster dependencies for the downstream forecasting task. Our extensive experiments on six real-world datasets demonstrate that CACRN is effective and outperforms representative and state-of-the-art baselines. Our proposed method is suitable for a wide range of real-world data collections, especially those with clear dependencies of variables.
Bailo, F, Johns, A & Rizoiu, M-A 2023, 'Riding Information Crises: The Performance of Far-Right Twitter Users in Australia During the 2019–20 Bushfires and the COVID-19 Pandemic', Information, Communication & Society, pp. 1-19.
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Bakhanova, E, Garcia, JA, Raffe, WL & Voinov, A 2023, 'Gamification Framework for Participatory Modeling: A Proposal', Group Decision and Negotiation, vol. 32, no. 5, pp. 1167-1182.
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AbstractProblem structuring methods imply the involvement of stakeholders and aim to create a shared understanding of the problem and commitment among them. The process and outcomes of such interventions entirely depend on the stakeholder’s level of engagement and willingness to contribute to the discussion. Gamification, in its turn, has been extensively used to increase engagement in an activity and nudge certain behaviors. Several gamification frameworks exist for stakeholder engagement; however, none fully considers the context of the modeling workshops with stakeholders.In this paper, we focus on a specific method for problem structuring, called Participatory Modeling (PM), and aim to explore the essential components and steps to gamify the PM process. We look at the literature on gamification, stakeholder engagement, problem structuring methods and, specifically, PM. Based on this analysis, we propose a gamification framework for PM, which includes the steps commonly mentioned in other existing frameworks and more nuanced features within each step that are specific to the PM context. Emphasis is given to analyzing the context of the gamified activity, including such aspects as participants, group interaction, and modeling. In addition, consideration of ethical points and potential risks of gamification is suggested as a necessary step to prevent undesired side effects during the gamified PM process.The gamification framework for PM leads to a variety of ways in which gamified intervention can be designed and incorporated into the process. Further research on the appropriateness of gamification use, practical applications, their evaluation, and risks associated with gamified interventions can contribute to the extension and clarification of the proposed framework.
Bala, D, Hossain, MS, Hossain, MA, Abdullah, MI, Rahman, MM, Manavalan, B, Gu, N, Islam, MS & Huang, Z 2023, 'MonkeyNet: A robust deep convolutional neural network for monkeypox disease detection and classification', Neural Networks, vol. 161, pp. 757-775.
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Bansal, P, Mirjalili, S & Wen, S 2023, 'Special issue on soft computing for high-dimensional data analytics and optimization', Soft Computing, vol. 27, no. 18, pp. 13517-13518.
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Bao, S, Cui, C, Li, J, Tang, Y, Lee, HH, Deng, R, Remedios, LW, Yu, X, Yang, Q, Chiron, S, Patterson, NH, Lau, KS, Liu, Q, Roland, JT, Coburn, LA, Wilson, KT, Landman, BA & Huo, Y 2023, 'Topological-preserving membrane skeleton segmentation in multiplex immunofluorescence imaging', Medical Imaging 2023: Digital and Computational Pathology.
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Barua, PD, Aydemir, E, Dogan, S, Erten, M, Kaysi, F, Tuncer, T, Fujita, H, Palmer, E & Acharya, UR 2023, 'Novel favipiravir pattern-based learning model for automated detection of specific language impairment disorder using vowels', Neural Computing and Applications, vol. 35, no. 8, pp. 6065-6077.
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Barua, PD, Aydemir, E, Dogan, S, Kobat, MA, Demir, FB, Baygin, M, Tuncer, T, Oh, SL, Tan, R-S & Acharya, UR 2023, 'Multilevel hybrid accurate handcrafted model for myocardial infarction classification using ECG signals', International Journal of Machine Learning and Cybernetics, vol. 14, no. 5, pp. 1651-1668.
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Barua, PD, Dogan, S, Kavuran, G, Tuncer, T, Tan, R-S & Rajendra Acharya, U 2023, 'NFSDense201: microstructure image classification based on non-fixed size patch division with pre-trained DenseNet201 layers', Neural Computing and Applications, vol. 35, no. 30, pp. 22253-22263.
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AbstractIn the field of nanoscience, the scanning electron microscope (SEM) is widely employed to visualize the surface topography and composition of materials. In this study, we present a novel SEM image classification model called NFSDense201, which incorporates several key components. Firstly, we propose a unique nested patch division approach that divides each input image into four patches of varying dimensions. Secondly, we utilize DenseNet201, a deep neural network pretrained on ImageNet1k, to extract 2920 deep features from the last fully connected and global average pooling layers. Thirdly, we introduce an iterative neighborhood component analysis function to select the most discriminative features from the merged feature vector, which is formed by concatenating the four feature vectors extracted per input image. This process results in a final feature vector of optimal length 698. Lastly, we employ a standard shallow support vector machine classifier to perform the actual classification. To evaluate the performance of NFSDense201, we conducted experiments using a large public SEM image dataset. The dataset consists of 972, 162, 326, 4590, 3820, 3925, 4755, 181, 917, and 1624.jpeg images belonging to the following microstructural categories: “biological,” “fibers,” “film-coated surfaces,” “MEMS devices and electrodes,” “nanowires,” “particles,” “pattern surfaces,” “porous sponge,” “powder,” and “tips,” respectively. For both four-class and ten-class classification tasks, we evaluated NFSDense201 using subsets of the dataset containing 5080 and 21,272 images, respectively. The results demonstrate the superior performance of NFSDense201, achieving a four-class classification accuracy rate of 99.53% and a ten-class classification accuracy rate of 97.09%. These accuracy rates compare favorably against previously published SEM image classification models. Additionally, we report the performance of NFSDense201 for each class in ...
Barua, PD, Kobayashi, M, Tanabe, M, Baygin, M, Paul, JK, Iype, T, Dogan, S, Tuncer, T, Tan, R-S & Acharya, UR 2023, 'Innovative Fibromyalgia Detection Approach Based on Quantum-Inspired 3LBP Feature Extractor Using ECG Signal', IEEE Access, vol. 11, pp. 101359-101372.
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Barua, PD, Yildiz, AM, Canpolat, N, Keles, T, Dogan, S, Baygin, M, Tuncer, I, Tuncer, T, Tan, R-S, Fujita, H & Acharya, UR 2023, 'An accurate automated speaker counting architecture based on James Webb Pattern', Engineering Applications of Artificial Intelligence, vol. 119, pp. 105821-105821.
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Barzegarkhoo, R, Farhangi, M, Lee, SS, Aguilera, RP, Blaabjerg, F & Siwakoti, YP 2023, 'A Novel Active Neutral Point-Clamped Five-Level Inverter With Single-Stage Integrated Dynamic Voltage Boosting Feature', IEEE Transactions on Power Electronics, vol. PP, no. 99, pp. 1-14.
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Barzegarkhoo, R, Farhangi, M, Lee, SS, Aguilera, RP, Liserre, M & Siwakoti, YP 2023, 'New Family of Dual-Mode Active Neutral Point-Clamped Five-Level Converters', IEEE Transactions on Power Electronics, vol. 38, no. 10, pp. 12236-12253.
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Basak, S, Agrawal, H, Jena, S, Gite, S, Bachute, M, Pradhan, B & Assiri, M 2023, 'Challenges and Limitations in Speech Recognition Technology: A Critical Review of Speech Signal Processing Algorithms, Tools and Systems', Computer Modeling in Engineering & Sciences, vol. 135, no. 2, pp. 1053-1089.
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Basavaraja, BM, Bantwal, RP, Tripathi, A, Hegde, G, John, NS, Thapa, R, Hegde, G, Balakrishna, RG, Saxena, M, Altaee, A & Samal, AK 2023, 'Functionalized Silver Nanocubes for the Detection of Hazardous Analytes through Surface-Enhanced Raman Scattering: Experimental and Computational Studies', ACS Sustainable Chemistry & Engineering, vol. 11, no. 29, pp. 10605-10619.
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Baygin, M, Barua, PD, Chakraborty, S, Tuncer, I, Dogan, S, Palmer, E, Tuncer, T, Kamath, AP, Ciaccio, EJ & Acharya, UR 2023, 'CCPNet136: automated detection of schizophrenia using carbon chain pattern and iterative TQWT technique with EEG signals', Physiological Measurement, vol. 44, no. 3, pp. 035008-035008.
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Abstract Objective. Schizophrenia (SZ) is a severe, chronic psychiatric-cognitive disorder. The primary objective of this work is to present a handcrafted model using state-of-the-art technique to detect SZ accurately with EEG signals. Approach. In our proposed work, the features are generated using a histogram-based generator and an iterative decomposition model. The graph-based molecular structure of the carbon chain is employed to generate low-level features. Hence, the developed feature generation model is called the carbon chain pattern (CCP). An iterative tunable q-factor wavelet transform (ITQWT) technique is implemented in the feature extraction phase to generate various sub-bands of the EEG signal. The CCP was applied to the generated sub-bands to obtain several feature vectors. The clinically significant features were selected using iterative neighborhood component analysis (INCA). The selected features were then classified using the k nearest neighbor (kNN) with a 10-fold cross-validation strategy. Finally, the iterative weighted majority method was used to obtain the results in multiple channels. Main results. The presented CCP-ITQWT and INCA-based automated model achieved an accuracy of 95.84% and 99.20% using a single channel and majority voting method, respectively with kNN classifier. Significance. Our results highlight the success of the proposed CCP-ITQWT and INCA-based model in the automated detection of SZ using EEG signals.
Baygin, M, Tuncer, I, Dogan, S, Barua, PD, Tuncer, T, Cheong, KH & Acharya, UR 2023, 'Automated facial expression recognition using exemplar hybrid deep feature generation technique', Soft Computing, vol. 27, no. 13, pp. 8721-8737.
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Baygin, N, Aydemir, E, Barua, PD, Baygin, M, Dogan, S, Tuncer, T, Tan, R-S & Rajendra Acharya, U 2023, 'Automated mental arithmetic performance detection using quantum pattern- and triangle pooling techniques with EEG signals', Expert Systems with Applications, vol. 227, pp. 120306-120306.
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Bazli, M, Ashrafi, H, Rajabipour, A & Kutay, C 2023, '3D printing for remote housing: Benefits and challenges', Automation in Construction, vol. 148, pp. 104772-104772.
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Beck, AT & Stewart, MG 2023, 'Risk-based cost-benefit analysis of structural strengthening to mitigate disproportionate collapse of buildings under abnormal blast loading', Structures, vol. 57, pp. 105103-105103.
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Beck, AT, Ribeiro, LDR, Costa, LGL & Stewart, MG 2023, 'Comparison of risk-based robustness indices in progressive collapse analysis of building structures', Structures, vol. 57, pp. 105295-105295.
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Beena, KS, Sandeep, MN, Indraratna, B & Malisetty, RS 2023, 'Near-field vibrations in railway track on soft subgrades for semi high-speed trains', Transportation Engineering, vol. 12, pp. 100176-100176.
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Benkhaya, S, Lgaz, H, Tang, H, Altaee, A, Haida, S, Vatanpour, V & Xiao, Y 2023, 'Investigating the effects of polypropylene-TiO2 loading on the performance of polysulfone/polyetherimide ultrafiltration membranes for azo dye removal: Experimental and molecular dynamics simulation', Journal of Water Process Engineering, vol. 56, pp. 104317-104317.
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Best, G, Garg, R, Keller, J, Hollinger, GA & Scherer, S 2023, 'Multi-robot, multi-sensor exploration of multifarious environments with full mission aerial autonomy', INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH.
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Beyhan, B, Akcomak, IS & Cetindamar, D 2023, 'How do technology-based accelerators build their legitimacy as new organizations in an emerging entrepreneurship ecosystem?', Journal of Entrepreneurship in Emerging Economies, pp. 1-37.
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Purpose
This paper aims to understand technology-based accelerators’ legitimation efforts in an emerging entrepreneurship ecosystem.
Design/methodology/approach
This research is based on qualitative inductive methodology using ten Turkish technology-based accelerators.
Findings
The analysis indicates that accelerators’ legitimation efforts are shaped around crafting a distinctive identity and mobilizing allies around this identity; and establishing new collaborations to enable collective action. Further, the authors observe two types of technology-based accelerators, namely, “deal flow makers” and “welfare stimulators” in Turkey. These variations among accelerators affect how they build their legitimacy. Different types of accelerators make alliances with different actors in the entrepreneurship ecosystem. Accelerators take collective action to build a collective identity and simultaneously imply how they are distinguished from other organizations in the same category and the ones in the old category.
Originality/value
This study presents a framework to understand how accelerators use strategies and actions to legitimize themselves as new organizations and advocate new norms, values and routines in an emerging entrepreneurship ecosystem. The framework also highlights how different accelerators support legitimacy building by managing the judgments of diverse audiences and increasing the variety of resources these audiences provide to the ecosystem.
Bhattad, A, Rao, BN, Atgur, V, Veza, I, Zamri, MFMA & Fattah, IMR 2023, 'Thermal Performance Evaluation of Plate-Type Heat Exchanger with Alumina–Titania Hybrid Suspensions', Fluids, vol. 8, no. 4, pp. 120-120.
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This paper aims to develop models for the thermal conductivity and viscosity of hybrid nanofluids of aluminium oxide and titanium dioxide (Al2O3-TiO2). The study investigates the impact of fluid temperature (283 K–298 K) on the performance of a plate heat exchanger using Al2O3-TiO2 hybrid nanofluids with different particle volume ratios (0:5, 1:4, 2:3, 3:2, 4:1, and 5:0) prepared with a 0.1% concentration in deionised water. Experimental evaluations were conducted to assess the heat transfer rate, Nusselt number, heat transfer coefficient, Prandtl number, pressure drop, and performance index. Due to the lower thermal conductivity of TiO2 nanoparticles compared to Al2O3, a rise in the TiO2 ratio decreased the heat transfer coefficient, Nusselt number, and heat transfer rate. Inlet temperature was found to decrease pressure drop and performance index. The Al2O3 (5:0) nanofluid demonstrated the maximum enhancement of around 16.9%, 16.9%, 3.44%, and 3.41% for the heat transfer coefficient, Nusselt number, heat transfer rate, and performance index, respectively. Additionally, the TiO2 (0:5) hybrid nanofluid exhibited enhancements of 0.61% and 2.3% for pressure drop and Prandtl number, respectively. The developed hybrid nanofluids enhanced the performance of the heat exchanger when used as a cold fluid.
Bhol, P, Patil, SA, Barman, N, Siddharthan, EE, Thapa, R, Saxena, M, Altaee, A & Samal, AK 2023, 'Design and fabrication of cobaltx nickel(1-x) telluride microfibers on nickel foam for battery-type supercapacitor and oxygen evolution reaction study', Materials Today Chemistry, vol. 30, pp. 101557-101557.
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Bhola, B, Kumar, R, Priyadarshini, I, So-In, C, Padhy, T, Slowik, A & Gandomi, AH 2023, 'Internet-of-Things-Based Sensor Module for Respiratory Tracking System', IEEE Sensors Journal, vol. 23, no. 16, pp. 18664-18674.
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Bi, S, Wang, C, Shen, J, Xiang, W, Ni, W, Wang, X, Wu, B & Gong, Y 2023, 'A Novel RFID Localization Approach to Smart Self-Service Borrowing and Returning System', Computer Modeling in Engineering & Sciences, vol. 135, no. 1, pp. 527-538.
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Bi, S, Wang, C, Wu, B, Hu, S, Huang, W, Ni, W, Gong, Y & Wang, X 2023, 'A comprehensive survey on applications of AI technologies to failure analysis of industrial systems', Engineering Failure Analysis, vol. 148, pp. 107172-107172.
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Bird, TS 2023, 'Platinum Jubilee of the IEEE Transactions on Antennas and Propagation', IEEE Transactions on Antennas and Propagation, vol. 71, no. 8, pp. 6276-6285.
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Bird, TS 2023, 'The Role of History in Technology Fields [Historically Speaking]', IEEE Antennas and Propagation Magazine, vol. 65, no. 4, pp. 99-100.
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Bishop, AN & Del Moral, P 2023, 'On the mathematical theory of ensemble (linear-Gaussian) Kalman–Bucy filtering', Mathematics of Control, Signals, and Systems, vol. 35, no. 4, pp. 835-903.
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AbstractThe purpose of this review is to present a comprehensive overview of the theory of ensemble Kalman–Bucy filtering for continuous-time, linear-Gaussian signal and observation models. We present a system of equations that describe the flow of individual particles and the flow of the sample covariance and the sample mean in continuous-time ensemble filtering. We consider these equations and their characteristics in a number of popular ensemble Kalman filtering variants. Given these equations, we study their asymptotic convergence to the optimal Bayesian filter. We also study in detail some non-asymptotic time-uniform fluctuation, stability, and contraction results on the sample covariance and sample mean (or sample error track). We focus on testable signal/observation model conditions, and we accommodate fully unstable (latent) signal models. We discuss the relevance and importance of these results in characterising the filter’s behaviour, e.g. it is signal tracking performance, and we contrast these results with those in classical studies of stability in Kalman–Bucy filtering. We also provide a novel (and negative) result proving that the bootstrap particle filter cannot track even the most basic unstable latent signal, in contrast with the ensemble Kalman filter (and the optimal filter). We provide intuition for how the main results extend to nonlinear signal models and comment on their consequence on some typical filter behaviours seen in practice, e.g. catastrophic divergence.
Bisui, S, Pradhan, B, Roy, S, Sengupta, D, Bhunia, GS & Shit, PK 2023, 'Estimating Forest-Based Livelihood Strategies Focused on Accessibility of Market Demand and Forest Proximity', Small-scale Forestry, vol. 22, no. 3, pp. 537-556.
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Blamires, S, Lozano-Picazo, P, Bruno, AL, Arnedo, M, Ruiz-León, Y, González-Nieto, D, Rojo, FJ, Elices, M, Guinea, GV & Pérez-Rigueiro, J 2023, 'The Spider Silk Standardization Initiative (S3I): A powerful tool to harness biological variability and to systematize the characterization of major ampullate silk fibers spun by spiders from suburban Sydney, Australia', Journal of the Mechanical Behavior of Biomedical Materials, vol. 140, pp. 105729-105729.
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Bordbar, M, Nikoo, MR, Sana, A, Nematollahi, B, Al-Rawas, G & Gandomi, AH 2023, 'Assessment of the vulnerability of hybrid coastal aquifers: application of multi-attribute decision-making and optimization models', Hydrological Sciences Journal, vol. 68, no. 8, pp. 1095-1108.
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Borra, A, Valova, V, Fleming, C, Irga, P, Torpy, F, Gunaway, C, Cole, L & McGrath, K 2023, 'Air Pollutant Particles and Their Effects on the Heart', Heart, Lung and Circulation, vol. 32, pp. S407-S407.
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Borthwick, J, Chang, X, Jeanjean, L & Soave, N 2023, 'Normalized solutions of L 2-supercritical NLS equations on noncompact metric graphs with localized nonlinearities', Nonlinearity, vol. 36, no. 7, pp. 3776-3795.
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Abstract
In this paper we are concerned with the existence of normalized solutions for nonlinear Schrödinger equations on noncompact metric graphs with localized nonlinearities. In a L
2-supercritical regime, we obtain the existence of solutions for any prescribed mass. This result is obtained through an approach which could prove successful to treat more general equations on noncompact graphs.
Boshir Ahmed, M, Alom, J, Hasan, MS, Asaduzzaman, M, Rahman, MS, Hossen, R, Abu Hasan Johir, M, Taufiq Alam, M, Zhou, JL, Zhu, Y & Zargar, M 2023, 'General Doping Chemistry of Carbon Materials', ChemNanoMat, vol. 9, no. 4.
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AbstractCarbon has an extraordinary ability to bind with itself and other elements, resulting in unique structures for a wide range of applications. Recently, intensive research has been focused on the properties of carbon‐based materials (CBMs) and on increasing their performance by doping them with metals and non‐metallic elements. While materials with excellent performance have been experimentally achieved, a fundamental knowledge of the relationship between the electronic, physical, and electrochemical properties and their structural features, particularly the chemistry of carbon‐based materials remains a top challenge. This review begins with the doping chemistries of CBMs, covering the role of electron affinity, orbital chemistry, the chemistry of band gap, conductivity, bonding type, spin redistribution, and conducting relevant comparisons. These will lead to providing an in‐depth understanding of the overall picture in the CBMs doping chemistry particularly as catalysts. The future research prospects and challenges for doped CBMs are highlighted.
Brown, A, Lamb, E, Deo, A, Pasin, D, Liu, T, Zhang, W, Su, S & Ueland, M 2023, 'The use of novel electronic nose technology to locate missing persons for criminal investigations', iScience, vol. 26, no. 4, pp. 106353-106353.
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Bui, HT, Hussain, OK, Prior, D, Hussain, FK & Saberi, M 2023, 'SIAEF/PoE: Accountability of Earnestness for encoding subjective information in Blockchain', Knowledge-Based Systems, vol. 269, pp. 110501-110501.
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Cai, J, Xu, H, Jiang, S, Sung, J, Sawhney, R, Broadley, S & Sun, J 2023, 'Effectiveness of telemonitoring intervention on glycaemic control in patients with type 2 diabetes mellitus: A systematic review and meta-analysis', Diabetes Research and Clinical Practice, vol. 201, pp. 110727-110727.
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Cai, P, Li, H, Guo, Q & Huang, X 2023, 'UAMP-Based Equalization for Dual Pulse Shaping Transmission Systems', IEEE Wireless Communications Letters, vol. 12, no. 7, pp. 1164-1168.
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Cai, X, Shi, K, She, K, Zhong, S, Wen, S & Xie, Y 2023, 'Communication security of autonomous ground vehicles based on networked control systems: The optimized LMI approach', Security and Safety, vol. 2, pp. 2023016-2023016.
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The paper presents a study of networked control systems (NCSs) that are subjected to periodic denial-of-service (DoS) attacks of varying intensity. The use of appropriate Lyapunov–Krasovskii functionals (LKFs) help to reduce the constraints of the basic conditions and lower the conservatism of the criteria. An optimization problem with constraints is formulated to select the trigger threshold, which is solved using the gradient descent algorithm (GDA) to improve resource utilization. An intelligent secure event-triggered controller (ISETC) is designed to ensure the safe operation of the system under DoS attacks. The approach is validated through experiments with an autonomous ground vehicle (AGV) system based on the Simulink platform. The proposed method offers the potential for developing effective defense mechanisms against DoS attacks in NCSs.
Cai, X, Shi, K, Sun, Y, Cao, J, Wen, S & Tian, Z 2023, '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, pp. 1-11.
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Cai, X, Shi, K, Sun, Y, Wen, S, Yan, H & Xie, Y 2023, 'Fuzzy Memory Controller Design Based-Machine Learning Algorithm and Stability Analysis for Nonlinear NCSs Under Asynchronous Cyber Attacks', IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 1-12.
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Calabrese, F, Poletti, V, Auriemma, F, Paduano, D, Gentile, C, Facciorusso, A, Franchellucci, G, De Marco, A, Brandaleone, L, Ofosu, A, Samanta, J, Ramai, D, De Luca, L, Al-Lehibi, A, Zuliani, W, Hassan, C, Repici, A & Mangiavillano, B 2023, 'New Perspectives in Endoscopic Treatment of Gastroesophageal Reflux Disease', Diagnostics, vol. 13, no. 12, pp. 2057-2057.
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Gastroesophageal reflux disease has a high incidence and prevalence in the general population. Clinical manifestations are heterogenous, and so is the response to medical treatment. Proton pump inhibitors are still the most common agents used to control reflux symptoms and for healing esophagitis, but they are not a one-size-fits-all solution for the disease. Patients with persistent troublesome symptoms despite medical therapy, those experiencing some adverse drug reaction, or those unwilling to take lifelong medications deserve valid alternatives. Anti-reflux Nissen fundoplication is an effective option, but the risk of adverse events has limited its spread. In recent years, advancements in therapeutic endoscopy have been made, and three major endoluminal alternatives are now available, including (1) the delivery of radiofrequency energy to the esophago–gastric junction, (2) transoral incisionless fundoplication (TIF), and (3) anti-reflux mucosal interventions (ARMI) based on mucosal resection (ARMS) and mucosal ablation (ARMA) techniques to remodel the cardia. Endoscopic techniques have shown interesting results, but their diffusion is still limited to expert endoscopists in tertiary centers. This review discusses the state of the art in the endoscopic approach to gastroesophageal reflux disease.
Cancino, CA, Merigó, JM, Urbano, D & Amorós, JE 2023, 'Evolution of the entrepreneurship and innovation research in Ibero-America between 1986 and 2015', Journal of Small Business Management, vol. 61, no. 2, pp. 322-352.
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Cao, J-S, Wang, S-N, Xu, R-Z, Luo, J-Y, Ni, B-J & Fang, F 2023, 'Phosphorus recovery from synthetic anaerobic fermentation supernatant via vivianite crystallization: Coupling effects of various physicochemical process parameters', Science of The Total Environment, vol. 897, pp. 165416-165416.
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Cao, K, Dong, F, Ma, L, Khan, NM, Alarifi, SS, Hussain, S & Armaghani, DJ 2023, 'Infrared radiation constitutive model of sandstone during loading fracture', Infrared Physics & Technology, vol. 133, pp. 104755-104755.
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Cao, K, Xu, Y, Khan, NM, Li, X, Cui, R, Hussain, S, Jahed Armaghani, D & Alarifi, SS 2023, 'A comprehensive model for evaluating infrared radiation and acoustic emission characteristics of sandstone fracture', Engineering Fracture Mechanics, vol. 283, pp. 109217-109217.
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Cao, L 2023, 'AI and data science for smart emergency, crisis and disaster resilience.', Int J Data Sci Anal, vol. 15, no. 3, pp. 231-246.
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The uncertain world has seen increasing emergencies, crises and disasters (ECDs), such as the COVID-19 pandemic, hurricane Ian, global financial inflation and recession, misinformation disaster, and cyberattacks. AI for smart disaster resilience (AISDR) transforms classic reactive and scripted disaster management to digital proactive and intelligent resilience across ECD ecosystems. A systematic overview of diverse ECDs, classic ECD management, ECD data complexities, and an AISDR research landscape are presented in this article. Translational disaster AI is essential to enable smart disaster resilience.
Cao, L 2023, 'Trans-AI/DS: transformative, transdisciplinary and translational artificial intelligence and data science', International Journal of Data Science and Analytics, vol. 15, no. 2, pp. 119-132.
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AbstractAfter the many ups and downs over the past 70 years of AI and 50 years of data science (DS), AI/DS have migrated into their new age. This new-generation AI/DS build on the consilience and universology of science, technology and engineering. In particular, it synergizes AI and data science, inspiring Trans-AI/DS (i.e., Trans-AI, Trans-DS and their hybridization) thinking, vision, paradigms, approaches and practices. Trans-AI/DS feature their transformative (or transformational), transdisciplinary, and translational AI/DS in terms of thinking, paradigms, methodologies, technologies, engineering, and practices. Here, we discuss these important paradigm shifts and directions. Trans-AI/DS encourage big and outside-the-box thinking beyond the classic AI, data-driven, model-based, statistical, shallow and deep learning hypotheses, methodologies and developments. They pursue foundational and original AI/DS thinking, theories and practices from the essence of intelligences and complexities inherent in humans, nature, society, and their creations.
Cao, MX & Tomamichel, M 2023, 'Comments on “Channel Coding Rate in the Finite Blocklength Regime”: On the Quadratic Decaying Property of the Information Rate Function', IEEE Transactions on Information Theory, vol. 69, no. 9, pp. 5528-5531.
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Cao, X, Liu, W & Tsang, IW 2023, 'Data-Efficient Learning via Minimizing Hyperspherical Energy', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-15.
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Cao, Y, Cheng, H, Gu, N, Ou, K, Wang, Z, Liu, Q, Guan, R, Fu, Q & Sun, Y 2023, 'Excellent mechanical durability of superhydrophobic coating by electrostatic spraying', Materials Chemistry and Physics, vol. 301, pp. 127658-127658.
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Cao, Y, Zhao, L, Zhong, Q, Wen, S, Shi, K, Xiao, J & Huang, T 2023, 'Adaptive fixed-time output synchronization for complex dynamical networks with multi-weights', Neural Networks, vol. 163, pp. 28-39.
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Cao, Y, Zhao, L, Zhong, Q, Zhu, S, Guo, Z & Wen, S 2023, 'Adaptive PI control for H∞ synchronization of multiple delayed coupled neural networks', Neurocomputing, vol. 560, pp. 126855-126855.
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Cao, Z & Lin, C-T 2023, 'Reinforcement Learning From Hierarchical Critics', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 2, pp. 1066-1073.
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Caruana, A, Bandara, M, Musial, K, Catchpoole, D & Kennedy, PJ 2023, 'Machine learning for administrative health records: A systematic review of techniques and applications', Artificial Intelligence in Medicine, vol. 144, pp. 102642-102642.
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Ceballos‐González, CF, Bolívar‐Monsalve, EJ, Quevedo‐Moreno, DA, Chávez‐Madero, C, Velásquez‐Marín, S, Lam‐Aguilar, LL, Solís‐Pérez, ÓE, Cantoral‐Sánchez, A, Neher, M, Yzar‐García, E, Zhang, YS, Gentile, C, Boccaccini, AR, Alvarez, MM & Trujillo‐de Santiago, G 2023, 'Plug‐and‐Play Multimaterial Chaotic Printing/Bioprinting to Produce Radial and Axial Micropatterns in Hydrogel Filaments', Advanced Materials Technologies, vol. 8, no. 17.
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AbstractNature abounds with micro‐architected materials containing layered multi‐material patterns that often transition within the very same monolithic piece. Fabricating these complex materials using current technologies is challenging. Multimaterial chaotic printing is presented—an extrusive printing method based on the use of chaotic advection—that can fabricate microstructured hydrogels with well‐defined multimaterial and multilayered micropatterns. Printheads containing internal Kenics static mixing (KSM) elements and top‐ and lateral‐positioned inlets are used to produce a wide repertoire of multilayered hydrogel filaments. In this plug‐and‐play system, the radial and axial micropatterns can be designed ad hoc by defining the printhead configuration (i.e., the number of KSM elements and inlets, and the inlet positions) and the flow program (i.e., activation/deactivation of the ink‐flow through each inlet). Computational fluid dynamics simulations closely predict the microstructure obtained by a given printhead configuration. The application of this platform is illustrated for easy fabrication of fibers with radial microgradients, bacterial ecosystems, structured emulsions, micro‐channeled hydrogel filaments, a pre‐vascularized tumor niche model, and skeletal muscle‐like tissues with axial and radial transitions of bioactive glass compartments. It is envisioned that multimaterial chaotic printing will be a valuable addition to the toolbox of additive manufacturing for the rational fabrication of advanced materials.
Ceballos‐González, CF, Bolívar‐Monsalve, EJ, Quevedo‐Moreno, DA, Chávez‐Madero, C, Velásquez‐Marín, S, Lam‐Aguilar, LL, Solís‐Pérez, ÓE, Cantoral‐Sánchez, A, Neher, M, Yzar‐García, E, Zhang, YS, Gentile, C, Boccaccini, AR, Alvarez, MM & Trujillo‐de Santiago, G 2023, 'Plug‐and‐Play Multimaterial Chaotic Printing/Bioprinting to Produce Radial and Axial Micropatterns in Hydrogel Filaments (Adv. Mater. Technol. 17/2023)', Advanced Materials Technologies, vol. 8, no. 17.
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Cetindamar, D & Phaal, R 2023, 'Technology Management in the Age of Digital Technologies', IEEE Transactions on Engineering Management, vol. 70, no. 7, pp. 2507-2515.
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Cetindamar, KD, Renando, C, Bliemel, M & De, KS 2023, 'The evolution of the Australian start-up and innovation ecosystem: Mapping policy developments, key actors, activities, and artefacts', Science, Technology and Society: an international journal devoted to the developing world, pp. 1-34.
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This paper maps the evolution of the Australian start-up and innovation ecosystem by exploring policy developments and mapping the key actors, activities, and artefacts. The 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 paper concludes with a discussion of policy gaps.
Chakrabortty, R, Pal, SC, Ghosh, M, Arabameri, A, Saha, A, Roy, P, Pradhan, B, Mondal, A, Ngo, PTT, Chowdhuri, I, Yunus, AP, Sahana, M, Malik, S & Das, B 2023, 'Retraction Note: Weather indicators and improving air quality in association with COVID-19 pandemic in India', Soft Computing, vol. 27, no. 15, pp. 11067-11068.
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Challis, VJ, Xu, X, Halfpenny, A, Cramer, AD, Saunders, M, Roberts, AP & Sercombe, TB 2023, 'Understanding the effect of microstructural texture on the anisotropic elastic properties of selective laser melted Ti-24Nb-4Zr-8Sn', Acta Materialia, vol. 254, pp. 119021-119021.
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Chan, KY, Abu-Salih, B, Muhammad, K, Palade, V & Chai, R 2023, 'Editorial: Deep neural networks with cloud computing', Neurocomputing, vol. 521, pp. 189-190.
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Chang, X, Ren, P, Xu, P, Li, Z, Chen, X & Hauptmann, AG 2023, 'A Comprehensive Survey of Scene Graphs: Generation and Application.', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 1, pp. 1-26.
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Scene graph is a structured representation of a scene that can clearly express the objects, attributes, and relationships between objects in the scene. As computer vision technology continues to develop, people are no longer satisfied with simply detecting and recognizing objects in images; instead, people look forward to a higher level of understanding and reasoning about visual scenes. For example, given an image, we want to not only detect and recognize objects in the image, but also know the relationship between objects (visual relationship detection), and generate a text description (image captioning) based on the image content. Alternatively, we might want the machine to tell us what the little girl in the image is doing (Visual Question Answering (VQA)), or even remove the dog from the image and find similar images (image editing and retrieval), etc. These tasks require a higher level of understanding and reasoning for image vision tasks. The scene graph is just such a powerful tool for scene understanding. Therefore, scene graphs have attracted the attention of a large number of researchers, and related research is often cross-modal, complex, and rapidly developing. However, no relatively systematic survey of scene graphs exists at present.
Chau, K, Fleck, R, Irga, PJ, Torpy, FR, Wilkinson, SJ & Castel, A 2023, 'Hempcrete as a substrate for fungal growth under high humidity and variable temperature conditions', Construction and Building Materials, vol. 398, pp. 132373-132373.
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Bio-based alternatives for existing construction materials can be used to reduce the carbon footprint of the built environment. Hempcrete is one of these materials and is both an excellent hygric/thermal regulator and is carbon negative. However, this novel material is still incompletely researched, especially its fungal growth potential specifically within warm and humid environments. The incorporation of significant biological material within hempcrete can enable it to act as a microbial growth medium, with the corresponding potential for the release of bioaerosols. The aim of this research was thus to investigate the overall practicality of hempcrete implementation in a humid climate. To achieve this, the endogenous fungal genera on a sample of hempcrete were identified, fungal propagules aerosolized from a hempcrete sample enumerated, and a range of temperatures tested to determine their effect on fungi growth determined. Trials were performed to determine whether hempcrete can be effectively decontaminated with common materials to manage microbial growth. Under high humidity, fungal propagule emissions were high with low diversity, with potentially allergenic fungi detected. Disinfection of high fungal load hempcrete samples was able to reduce ∼94% of the fungal observations and reduce aerosolized counts to average background tropical fungal counts. The range of temperatures tested were not found to effect fungal growth, contrary to the consensus of the literature. Overall, these properties make hempcrete suited to humid areas, however, further research to investigate the potential effects of fungi on the material remains lacking.
Chauhan, R & Yafi, E 2023, 'Applicability of classifier to discovery knowledge for future prediction modelling', Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 8, pp. 10347-10362.
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AbstractThe immense growth of new technological interventions has forced researchers and scientists around the globe to adopt the widely anticipated technology of Machine Learning (ML) and Artificial Intelligence (AI). ML and AI have generously prospected itself from the past decade in the discovery of knowledge from databases. Several ML and AI based adoptive technologies have emerged in varied application domains, and are thus widely opening a new era of knowledge in decision making. Moreover, ML and AI are techniques that can improve the treatment and diagnosis of diseases. In the current study, we have designed and deployed a “PROCLAVE”. The tool was designed in varied layers of structure, where each layer plays a significant role in determining the patterns. We have applied several libraries for the processing of a prototype to develop a visualization interface. The tool forecasts health vulnerability, makes a comparison among variable classifiers and visualize the results for end users. Moreover, the proposed architecture is based on the concepts of conceptualization and visualization to detect the overall dashboard. Furthermore, the current approach was synthesized and populated with a database that allows the end users to select the variable features and relatively determine the interactive patterns for the number of cases. The database was collected from the National Institute of Health Stroke (NIHS) in the United States. Data was gathered for stroke patients who were diagnosed with stroke from 1950 to 2015. The study was based on several attributes which included causes of death, sex, race, Hispanic origin and others to discover unknown patterns for future decision making.
Che, H, Pan, B, Leung, M-F, Cao, Y & Yan, Z 2023, 'Tensor Factorization With Sparse and Graph Regularization for Fake News Detection on Social Networks', IEEE Transactions on Computational Social Systems, pp. 1-11.
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Chen, C, Liu, Y, Chen, L & Zhang, C 2023, 'Bidirectional Spatial-Temporal Adaptive Transformer for Urban Traffic Flow Forecasting', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 10, pp. 6913-6925.
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Chen, D, Wu, C & Li, J 2023, 'Assessment of modeling methods for predicting load resulting from hydrogen-air detonation', Process Safety and Environmental Protection, vol. 180, pp. 752-765.
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Chen, H, Wang, H, Chen, H, Zhang, Y, Zhang, W & Lin, X 2023, 'Denoising Variational Graph of Graphs Auto-Encoder for Predicting Structured Entity Interactions', IEEE Transactions on Knowledge and Data Engineering, pp. 1-14.
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Chen, J, Hao, D, Chen, W, Liu, Y, Yin, Z, Hsu, H, Ni, B, Wang, A, Lewis, SW & Jia, G 2023, 'Engineering Colloidal Metal‐Semiconductor Nanorods Hybrid Nanostructures for Photocatalysis†', Chinese Journal of Chemistry, vol. 41, no. 22, pp. 3050-3062.
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Comprehensive SummaryEmerging engineering strategies of colloidal metal‐semiconductor nanorod hybrid nanostructures spanning from type, size, dimension, and location of both metal nanoparticles and semiconductors, co‐catalyst, band gap structure, surface ligand to hole scavenger are elaborated symmetrically to rationalize the design of this type of intriguing materials for efficient photocatalytic applications.
Chen, J, Vinod, JS, Indraratna, B, Ngo, T & Liu, Y 2023, 'DEM study on the dynamic responses of a ballasted track under moving loading', Computers and Geotechnics, vol. 153, pp. 105105-105105.
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This paper presents the discrete element modeling of the dynamic response of a ballasted track under moving loads. The DEM model, consisting of sleepers, ballast, and sub-ballast, has been calibrated using field and laboratory data. This model was further used to examine the dynamic responses of the ballasted track subjected to a series of moving traffic loading representing various train axle loads and speeds. The results show that the permanent settlement of the sleeper, the breakage of ballast, and the dynamic stresses in the track substructure increase with an increase in train axle load and speed. As the train moves, the magnitudes of dynamic stresses and the orientations of principal stress axes in the track change continuously, and a more pronounced principal stress rotation is observed at sleeper edges than those underneath sleepers. The capping layer is found to play a critical role in reducing train-induced stress and further alleviating the disturbance from the trains to the subgrade. The interparticle contacts and the vibration of ballast during the movement of the train including the influences of train axle load and speed on the dynamic responses of ballasted railway tracks are captured and analyzed from a micromechanical perspective.
Chen, K, He, X, Liang, F & Sheng, D 2023, 'Contribution of capillary pressure to effective stress for unsaturated soils: Role of wet area fraction and water retention curve', Computers and Geotechnics, vol. 154, pp. 105140-105140.
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Chen, K, Wang, Q, Long, P & Ying, M 2023, 'Unitarity Estimation for Quantum Channels.', IEEE Trans. Inf. Theory, vol. 69, no. 8, pp. 5116-5134.
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Chen, L, Chen, L, Ge, Z, Sun, Y & Zhu, X 2023, 'A 40-GHz Load Modulated Balanced Power Amplifier Using Unequal Power Splitter and Phase Compensation Network in 45-nm SOI CMOS', IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 70, no. 8, pp. 3178-3186.
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Chen, L, Chen, L, Zhu, H, Gomez-Garcia, R & Zhu, X 2023, 'A Wideband Balanced Amplifier Using Edge-Coupled Quadrature Couplers in 0.13-μm SiGe HBT Technology', IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 70, no. 2, pp. 631-641.
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Chen, L, Far, H, Mortazavi, M & Ragab, AE 2023, 'Comparative Study in Design of Fiber-Reinforced Concrete at Elevated Temperatures by Numerical Evaluation through Developed Hybrid Metaheuristic Algorithms', Buildings, vol. 13, no. 8, pp. 2045-2045.
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Fibrous concrete has good properties such as high ductility, high strength, suitable energy absorption and cracking resistance, which can be useful in many applications. This type of concrete is one of the best materials used in the construction of impact-resistant masonries, such as burial masonry structures, and explosive masonry warehouses. In this study, an artificial intelligence assessment based on the experimental test data from a laboratory has been performed on the fibrous concrete to evaluate the behavior of the samples at elevated temperatures and determine the most governing parameter on the mechanical properties of the fibrous concrete at elevated temperatures. For the first time, a hybrid intelligence algorithm has been developed based on the neural network structure using both genetic and swarm optimization algorithms. The ANFIS-PSO-GA (APG) algorithm was trained with experimental data and evaluated the flexural load and deflection of the samples. In order to detect the most prominent feature in the fire resistance of the fibrous concrete, five different subdatasets were designed. The results of the APG algorithm have been challenged with the ANFIS-PSO algorithm, which is a well-known hybrid numerical evaluation algorithm. As per the results, the newly designed APG algorithm has been successfully performed on both deflection and flexural prediction phases. Based on the numerical achievements, fiber features such as the fiber content and fiber mechanical properties are governing factors on the fibrous concrete resistance at elevated temperatures.
Chen, L, Liu, Y, Yang, S, Hu, J & Guo, YJ 2023, 'Synthesis of Wideband Frequency-Invariant Beam Patterns for Nonuniformly Spaced Arrays by Generalized Alternating Projection Approach', IEEE Transactions on Antennas and Propagation, vol. 71, no. 1, pp. 1099-1104.
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Chen, P, Ouyang, L, Lang, C, Zhong, H, Liu, J, Wang, H, Huang, Z & Zhu, M 2023, 'All-pH Hydrogen Evolution by Heterophase Molybdenum Carbides Prepared via Mechanochemical Synthesis', ACS Sustainable Chemistry & Engineering, vol. 11, no. 9, pp. 3585-3593.
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Chen, Q, Rong, H, Tao, G, Nimbalkar, S & Xie, K 2023, 'Fatigue characteristics of nano-SiO2 cemented soil under coupled effects of dry-wet cycle and seawater corrosion', Construction and Building Materials, vol. 401, pp. 132579-132579.
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Chen, Q, Xie, K, Tao, G, Nimbalkar, S, Peng, P & Rong, H 2023, 'Laboratory investigation of microstructure, strength and durability of cemented soil with Nano-SiO2 and basalt fibers in freshwater and seawater environments', Construction and Building Materials, vol. 392, pp. 132008-132008.
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Chen, Q, Yu, R, Gaoliang, T & Nimbalkar, S 2023, 'Microstructure, strength and durability of nano-cemented soils under different seawater conditions: laboratory study', Acta Geotechnica, vol. 18, no. 3, pp. 1607-1627.
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Chen, Q, Zhang, H, Ye, J, Tao, G & Nimbalkar, S 2023, 'Corrosion Resistance and Compressive Strength of Cemented Soil Mixed with Nano-Silica in Simulated Seawater Environment', KSCE Journal of Civil Engineering, vol. 27, no. 4, pp. 1535-1550.
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Chen, R, Pye, JS, Li, J, Little, CB & Li, JJ 2023, 'Multiphasic scaffolds for the repair of osteochondral defects: Outcomes of preclinical studies', Bioactive Materials, vol. 27, pp. 505-545.
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Chen, S, Zhang, P, Xie, G-S, Peng, Q, Cao, Z, Yuan, W & You, X 2023, 'Kernelized Similarity Learning and Embedding for Dynamic Texture Synthesis', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 2, pp. 824-837.
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Chen, S-L, Song, L-Z, Karmokar, DK, Jones, B & Guo, YJ 2023, 'Wideband Fixed-Beam Single-Piece Leaky Wave Antenna With Controlled Dispersion Slope', IEEE Transactions on Antennas and Propagation, vol. 71, no. 11, pp. 8429-8440.
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Chen, W-H, Biswas, PP, Ong, HC, Hoang, AT, Nguyen, T-B & Dong, C-D 2023, 'A critical and systematic review of sustainable hydrogen production from ethanol/bioethanol: Steam reforming, partial oxidation, and autothermal reforming', Fuel, vol. 333, pp. 126526-126526.
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Chen, W-H, Biswas, PP, Ubando, AT, Kwon, EE, Lin, K-YA & Ong, HC 2023, 'A review of hydrogen production optimization from the reforming of C1 and C2 alcohols via artificial neural networks', Fuel, vol. 345, pp. 128243-128243.
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Chen, X, Feng, Z, Andrew Zhang, J, Yuan, X & Zhang, P 2023, 'Kalman Filter-based Sensing in Communication Systems with Clock Asynchronism', IEEE Transactions on Communications, pp. 1-1.
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Chen, X, Feng, Z, Wei, Z, Yuan, X, Zhang, P, Zhang, JA & Yang, H 2023, 'Multiple Signal Classification Based Joint Communication and Sensing System', IEEE Transactions on Wireless Communications, vol. 22, no. 10, pp. 6504-6517.
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Chen, X, Feng, Z, Wei, Z, Zhang, JA, Yuan, X & Zhang, P 2023, 'Concurrent Downlink and Uplink Joint Communication and Sensing for 6G Networks', IEEE Transactions on Vehicular Technology, vol. 72, no. 6, pp. 8175-8180.
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Chen, X, Wang, H, Li, Q, Qi, Y, Li, F, He, W, Wang, Q, Jin, F, Guo, Y, Hei, M & Xie, Z 2023, 'A fatal case of neonatal viral sepsis caused by human parainfluenza virus type 3.', Virol J, vol. 20, no. 1, p. 248.
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BACKGROUND: Sepsis is a systemic inflammatory response syndrome caused by severe infection in children, but cases of sepsis associated with human parainfluenza virus (HPIV) have been rarely reported in newborns. CASE PRESENTATION: We report a case of HPIV-3 positive full-term newborn admitted to the Neonatal Intensive Care Unit of Beijing Children's Hospital due to hematuria, gloomy spirit, inactivity and loss of appetite for 6 h. He had septic shock when he arrived the Accident & Emergency Department requiring immediate intubation and mechanical ventilation. Intravenous antibiotics were started. He had completely negative response to all anti-shock treatments including fluid resuscitation and vasopressor supports, and died 14 h later. Viral nucleic acid detection and metagenomic next-generation sequencing (mNGS) analyses of nasopharyngeal aspirate and blood specimens verified an HPIV-3 infection, with negative bacterial culture results. The HPIV-3 strain detected in this patient was subtyped as HPIV C3a, and two unreported amino acid mutations were found in the HN protein region. CONCLUSION: The patient had a severe infection associated with HPIV-3, which was the cause of sepsis and septic shock. This study showed the diagnostic value of mNGS in etiological diagnosis, especially in severe neonatal case.
Chen, X, Yao, L, McAuley, J, Zhou, G & Wang, X 2023, 'Deep reinforcement learning in recommender systems: A survey and new perspectives', Knowledge-Based Systems, vol. 264, pp. 110335-110335.
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Chen, X, Zhang, Y, Tsang, IW, Pan, Y & Su, J 2023, 'Toward Equivalent Transformation of User Preferences in Cross Domain Recommendation', ACM Transactions on Information Systems, vol. 41, no. 1, pp. 1-31.
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Cross domain recommendation (CDR) is one popular research topic in recommender systems. This article focuses on a popular scenario for CDR where different domains share the same set of users but no overlapping items. The majority of recent methods have explored the shared-user representation to transfer knowledge across domains. However, the idea of shared-user representation resorts to learning the overlapped features of user preferences and suppresses the domain-specific features. Other works try to capture the domain-specific features by an MLP mapping but require heuristic human knowledge of choosing samples to train the mapping. In this article, we attempt to learn both features of user preferences in a more principled way. We assume that each user’s preferences in one domain can be expressed by the other one, and these preferences can be mutually converted to each other with the so-called equivalent transformation. Based on this assumption, we propose an equivalent transformation learner (ETL), which models the joint distribution of user behaviors across domains. The equivalent transformation in ETL relaxes the idea of shared-user representation and allows the learned preferences in different domains to preserve the domain-specific features as well as the overlapped features. Extensive experiments on three public benchmarks demonstrate the effectiveness of ETL compared with recent state-of-the-art methods. Codes and data are available online: https://github.com/xuChenSJTU/ETL-master.
Chen, Y, Hu, R, Li, Z, Yang, C, Wang, X & Xu, G 2023, '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, Huang, X, An, P & Wu, Q 2023, 'Enhanced Light Field Reconstruction by Combining Disparity and Texture Information in PSVs via Disparity-Guided Fusion', IEEE Transactions on Computational Imaging, vol. 9, pp. 665-677.
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Chen, Y, Li, C, Yang, T, Ekimov, EA, Bradac, C, Ha, ST, Toth, M, Aharonovich, I & Tran, TT 2023, 'Real-Time Ratiometric Optical Nanoscale Thermometry', ACS Nano, vol. 17, no. 3, pp. 2725-2736.
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Chen, Y, Li, G, An, P, Liu, Z, Huang, X & Wu, Q 2023, 'Light Field Salient Object Detection with Sparse Views via Complementary and Discriminative Interaction Network', IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1.
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Chen, Y, Lin, S, Qin, Y, Surawski, NC & Huang, X 2023, 'Carbon distribution and multi-criteria decision analysis of flexible waste biomass smouldering processing technologies', Waste Management, vol. 167, pp. 183-193.
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Chen, Y, White, S, Ekimov, EA, Bradac, C, Toth, M, Aharonovich, I & Tran, TT 2023, 'Ultralow-Power Cryogenic Thermometry Based on Optical-Transition Broadening of a Two-Level System in Diamond', ACS Photonics, vol. 10, no. 8, pp. 2481-2487.
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Cryogenic temperatures are the prerequisite for many advanced scientific applications and technologies. The accurate determination of temperature in this range and at the submicrometer scale is, however, nontrivial. This is due to the fact that temperature reading in cryogenic conditions can be inaccurate due to optically induced heating. Here, we present an ultralow-power, optical thermometry technique that operates at cryogenic temperatures. The technique exploits the temperature-dependent linewidth broadening measured by resonant photoluminescence of a two-level system: a germanium-vacancy color center in a nanodiamond host. The proposed technique achieves a relative sensitivity of ∼20% K-1, at 5 K. This is higher than any other all-optical nanothermometry method. Additionally, it achieves such sensitivities while employing excitation powers of just a few tens of nanowatts, several orders of magnitude lower than other traditional optical thermometry protocols. To showcase the performance of the method, we demonstrate its ability to accurately read out local differences in temperatures at various target locations of a custom-made microcircuit. Our work is a step toward the advancement of nanoscale optical thermometry at cryogenic temperatures.
Chen, Y, Zhou, X, Ni, W, Hossain, E & Wang, X 2023, 'Optimal Power Allocation for Multiuser Photon-Counting Underwater Optical Wireless Communications Under Poisson Shot Noise', IEEE Transactions on Communications, vol. 71, no. 4, pp. 2230-2245.
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Chen, Y, Zhou, X, Ni, W, Wang, X & Hanzo, L 2023, 'Underwater Photon-Counting Systems Under Poisson Shot Noise: Rate Analysis and Power Allocation', IEEE Transactions on Communications, vol. 71, no. 9, pp. 5152-5168.
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Photon counting is an effective detection technique for weak optical signals in underwater optical wireless communications (UOWC). This paper proposes a new approach for power allocation in an uplink M-ary pulse position modulation (PPM), photo-counting non-orthogonal multiple-access (PhC-NOMA) system. Different from existing techniques in photon-counting systems, the new approach supports consistent duty cycles across underwater devices and adjusts the transmit rates of the devices through their transmit powers, thereby avoiding the delays of duty cycle adjustments and supporting high-speed transmissions. Power allocation is non-trivial in photon-counting systems due to signal-dependent Poisson shot noises. As a key contribution, we derive the exact and asymptotic expressions for the achievable rate of the M-ary PPM PhC-NOMA system with the signal-dependent Poisson shot noise and multiuser interference considered. With the expressions, we reveal the received power at the base station (BS) is minimized when their minimum data rate requirements are delivered and can be solved using an incremental algorithm. We also asymptotically maximize the photon efficiency of the devices while preventing the saturation of the receiving photon detector, using Karush-Kuhn-Tucker (KKT) conditions. Simulations show that our approach can reduce the received power at the BS by up to 25% and double the photon efficiency, as compared to the existing techniques.
Chen, Y, Zhu, S, Mu, C, Liu, X & Wen, S 2023, 'Improved Criteria for Stability of a Class of Recurrent Neural Networks With Generalized Piecewise Constant Argument', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 11, pp. 7246-7255.
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Chen, Y, Zhu, S, Yan, H, Shen, M, Liu, X & Wen, S 2023, 'Event-Based Global Exponential Synchronization for Quaternion-Valued Fuzzy Memristor Neural Networks With Time-Varying Delays', IEEE Transactions on Fuzzy Systems, pp. 1-10.
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Chen, Y-N, Ding, C, Zhu, H & Liu, Y 2023, 'A ±45°-Polarized Antenna System with Four Isolated Channels for In-Band Full-Duplex (IBFD)', IEEE Transactions on Antennas and Propagation, vol. PP, no. 99, pp. 1-1.
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Chen, Z, Han, N, Zheng, R, Ren, Z, Wei, W & Ni, B 2023, 'Design of earth‐abundant amorphous transition metal‐based catalysts for electrooxidation of small molecules: Advances and perspectives', SusMat, vol. 3, no. 3, pp. 290-319.
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AbstractElectrochemical oxidation of small molecules (e.g., water, urea, methanol, hydrazine, and glycerol) has gained growing scientific interest in the fields of electrochemical energy conversion/storage and environmental remediation. Designing cost‐effective catalysts for the electrooxidation of small molecules (ESM) is thus crucial for improving reaction efficiency. Recently, earth‐abundant amorphous transition metal (TM)‐based nanomaterials have aroused souring interest owing to their earth‐abundance, flexible structures, and excellent electrochemical activities. Hundreds of amorphous TM‐based nanomaterials have been designed and used as promising ESM catalysts. Herein, recent advances in the design of amorphous TM‐based ESM catalysts are comprehensively reviewed. The features (e.g., large specific surface area, flexible electronic structure, and facile structure reconstruction) of amorphous TM‐based ESM catalysts are first analyzed. Afterward, the design of various TM‐based catalysts with advanced strategies (e.g., nanostructure design, component regulation, heteroatom doping, and heterostructure construction) is fully scrutinized, and the catalysts’ structure‐performance correlation is emphasized. Future perspectives in the development of cost‐effective amorphous TM‐based catalysts are then outlined. This review is expected to provide practical strategies for the design of next‐generation amorphous electrocatalysts.
Chen, Z, Wei, W, Chen, H & Ni, BJ 2023, 'Eco-designed electrocatalysts for water splitting: A path toward carbon neutrality', International Journal of Hydrogen Energy, vol. 48, no. 16, pp. 6288-6307.
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Realizing sustainable hydrogen fuel production through water electrolysis is crucial to achieving carbon neutrality. However, the development of cost-effective electrocatalysts continues to be a challenge. Eco-designed electrocatalysts derived from wastes and naturally abundant materials have recently received increasing attention. The development of eco-designed electrocatalysts is of great environmental and economic significance and makes green hydrogen more accessible to the wider community. Here, recent advances in eco-designed electrocatalysts for water splitting are summarized. Eco-design strategies such as pyrolysis, ball milling, wet-chemical methods, and electrochemical treatment are first analyzed. Recent achievements in eco-designed electrocatalysts for hydrogen evolution reaction (HER), oxygen evolution reaction (OER), and overall water splitting (OWS) are then detailed, with an emphasis on analyzing the eco-design strategy-catalyst property-catalytic performance correlation. Perspectives in this blooming field for a greener hydrogen economy are finally outlined.
Chen, Z, Zheng, R, Bao, T, Ma, T, Wei, W, Shen, Y & Ni, B-J 2023, 'Dual-Doped Nickel Sulfide for Electro-Upgrading Polyethylene Terephthalate into Valuable Chemicals and Hydrogen Fuel', Nano-Micro Letters, vol. 15, no. 1.
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Abstract
Electro-upcycling of plastic waste into value-added chemicals/fuels is an attractive and sustainable way for plastic waste management. Recently, electrocatalytically converting polyethylene terephthalate (PET) into formate and hydrogen has aroused great interest, while developing low-cost catalysts with high efficiency and selectivity for the central ethylene glycol (PET monomer) oxidation reaction (EGOR) remains a challenge. Herein, a high-performance nickel sulfide catalyst for plastic waste electro-upcycling is designed by a cobalt and chloride co-doping strategy. Benefiting from the interconnected ultrathin nanosheet architecture, dual dopants induced up-shifting d band centre and facilitated in situ structural reconstruction, the Co and Cl co-doped Ni3S2 (Co, Cl-NiS) outperforms the single-doped and undoped analogues for EGOR. The self-evolved sulfide@oxyhydroxide heterostructure catalyzes EG-to-formate conversion with high Faradic efficiency (> 92%) and selectivity (> 91%) at high current densities (> 400 mA cm−2). Besides producing formate, the bifunctional Co, Cl-NiS-assisted PET hydrolysate electrolyzer can achieve a high hydrogen production rate of 50.26 mmol h−1 in 2 M KOH, at 1.7 V. This study not only demonstrates a dual-doping strategy to engineer cost-effective bifunctional catalysts for electrochemical conversion processes, but also provides a green and sustainable way for plastic waste upcycling and simultaneous energy-saving hydrogen production.
Cheng, D, Ye, Y, Xiang, S, Ma, Z, Zhang, Y & Jiang, C 2023, 'Anti-Money Laundering by Group-Aware Deep Graph Learning', IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 12, pp. 12444-12457.
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Cheng, H-C, Winter, A & Yu, N 2023, 'Discrimination of Quantum States Under Locality Constraints in the Many-Copy Setting', Communications in Mathematical Physics, vol. 404, no. 1, pp. 151-183.
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Cheng, K, Zhang, T, Peng, K, Feng, Y, Liu, H & Medawela, S 2023, 'Zone of flow: A new finding on the characteristics of airflow within the zone of influence during air sparging in aquifers', Journal of Contaminant Hydrology, vol. 255, pp. 104165-104165.
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Cheng, W, Wan, C, Li, X, Chai, H, Yang, Z, Wei, S, Su, J, Tang, X & Wu, Y 2023, 'Waste to wealth: Oxygen-nitrogen-sulfur codoped lignin-derived carbon microspheres from hazardous black liquors for high-performance DSSCs', Journal of Energy Chemistry, vol. 83, pp. 549-563.
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Chi, K, Li, J & Wu, C 2023, 'Numerical simulation of buried steel pipelines subjected to ground surface blast loading', Thin-Walled Structures, vol. 186, pp. 110716-110716.
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Steel gas pipelines are important component in energy sector. Due to its easy accessibility and importance, shallow-buried pipelines are becoming targets of intentional attack. Therefore, it is urgent to investigate the failure mechanism of buried pipelines subjected to ground surface blast loadings and carry out quantitative damage assessment of pipelines. The present study performs numerical simulation on the resistance of buried pipelines subjected to ground surface explosion. The simulated ground shock propagation in the soil medium was validated with technical manual TM5-855-1 as well as experimental data. The effects of charge weight, stand-off distance, explosive position offset, pipe diameter, pipe wall thickness, buried depth, and steel grade as well as different soil types were investigated. It was found that for the grade X70 pipe with the same buried depth 760 mm, the cross-sectional flattening ratio under charge weight 227 kg (typical sedan bomb) was nearly 544 times greater than the case in 2.3 kg charge weight (typical pipe bomb). The flattening ratio decreased 99.9% because of the buried depth increased from 300 mm to 1800 mm. The decrease in pipe diameter from 860 mm to 350 mm caused 89.6% reduction in flattening ratio. The increase in wall thickness from 4.80 mm to 12.7 mm caused 99.7% decline in flattening ratio. Similarly, it showed the flattening ratio decreased 29.3% when the steel grade increased from X42 (yield strength 290 MPa) to X80 (yield strength 580 MPa). The blast resistance was the worst when the pipeline was buried in clay soil, in which the flattening ratio was 74.8% and 40.3% greater as compared with sandy loam and soil medium. An analytical formula was derived to predict the flattening ratio of pipelines against surface explosion.
Chinh Nguyen, H, Hagos Aregawi, B, Fu, C-C, Chyuan Ong, H, Barrow, CJ, Su, C-H, Wu, S-J, Juan, H-Y & Wang, F-M 2023, 'Biodiesel production through electrolysis in the presence of choline chloride-based deep eutectic solvent: Optimization by response surface methodology', Journal of Molecular Liquids, vol. 379, pp. 121633-121633.
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Chu, NH, Hoang, DT, Nguyen, DN, Phan, KT, Dutkiewicz, E, Niyato, D & Shu, T 2023, 'MetaSlicing: A Novel Resource Allocation Framework for Metaverse', IEEE Transactions on Mobile Computing, pp. 1-18.
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Chu, NH, Nguyen, DN, Hoang, DT, Pham, QV, Phan, KT, Hwang, WJ & Dutkiewicz, E 2023, 'AI-enabled mm-Waveform Configuration for Autonomous Vehicles with Integrated Communication and Sensing', IEEE Internet of Things Journal, vol. PP, no. 99, pp. 1-1.
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Integrated Communications and Sensing (ICS) has recently emerged as an enabling technology for ubiquitous sensing and IoT applications. For ICS application to Autonomous Vehicles (AVs), optimizing the waveform structure is one of the most challenging tasks due to strong influences between sensing and data communication functions. Specifically, the preamble of a data communication frame is typically leveraged for the sensing function. As such, the higher number of preambles in a Coherent Processing Interval (CPI) is, the greater sensing task’s performance is. In contrast, communication efficiency is inversely proportional to the number of preambles. Moreover, surrounding radio environments are usually dynamic with high uncertainties due to their high mobility, making the ICS’s waveform optimization problem even more challenging. To that end, this paper develops a novel ICS framework established on the Markov decision process and recent advanced techniques in deep reinforcement learning. By doing so, without requiring complete knowledge of the surrounding environment in advance, the ICS-AV can adaptively optimize its waveform structure (i.e., number of frames in the CPI) to maximize sensing and data communication performance under the surrounding environment’s dynamic and uncertainty. Extensive simulations show that our proposed approach can improve the joint communication and sensing performance up to 46.26% compared with other baseline methods.
Ci, Q, Wang, Y, Wu, B, Coy, E, Li, JJ, Jiang, D, Zhang, P & Wang, G 2023, 'Fe-Doped Carbon Dots as NIR-II Fluorescence Probe for In Vivo Gastric Imaging and pH Detection.', Adv Sci (Weinh), vol. 10, no. 7, pp. e2206271-2206271.
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Carbon dots (CDs) with excellent cytocompatibility, tunable optical properties, and simple synthesis routes are highly desirable for use in optical bioimaging. However, the majority of existing CDs are triggered by ultraviolet/blue light, presenting emissions in the visible/first near-infrared (NIR-I) regions, which do not allow deep tissue penetration. Emerging research into CDs with NIR-II emission in the red region has generated limited designs with poor quantum yield, restricting their in vivo imaging applications due to low penetration depth. Developing novel CDs with NIR-II emissions and high quantum yield has significant and far-reaching applications in bioimaging and photodynamic therapy. Here, it is developed for the first time Fe-doped CDs (Fe-CDs) exhibiting the excellent linear relationship between 900-1200 nm fluorescence-emission and pH values, and high quantum yield (QY-1.27%), which can be used as effective probes for in vivo NIR-II bioimaging. These findings demonstrate reliable imaging accuracy in tissue as deep as 4 mm, reflecting real-time pH changes comparable to a standard pH electrode. As an important example application, the Fe-CDs probe can non-invasively monitor in vivo gastric pH changes during the digestion process in mice, illustrating its potential applications in aiding imaging-guided diagnosis of gastric diseases or therapeutic delivery.
Consoli, NC, Tebechrani Neto, A, Khajeh, A, Salimi, M, Specht, LP, Vestena, PM & da Rocha, CG 2023, 'Dynamic Properties of Reclaimed Asphalt Pavement–Green Cement Blends for Road Base Layer', Geotechnical and Geological Engineering, vol. 41, no. 6, pp. 3495-3511.
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Crowther, CA, Ashwood, P, Middleton, PF, McPhee, A, Tran, T, Harding, JE, Alsweiler, J, Baker, E, Eaglen, D, Groom, K, Hauch, H, Mackay, L, Pacella, MJ, Preest, A, Taylor, J, Williamson, K, Tottman, A, Austin, N, Darlow, BA, Dixon, B, Ellis, N, Graham, P, Gullam, J, Leishman, D, Van Dyk, MM, Broadbent, R, Dawson, P, Devenish, C, Douglas, J, McCaffrey, F, Carey, R, Marshall, P, Morris, S, Nguyen, T, Gaerty, K, Grupp, O, Boddice, G, Green, A, Mahomed, K, Turner, L, Baldwin, M, Dennis, A, Fisher, E, Gee, K, Gee, M, Strong, D, Asadi, S, Burakevych, N, Griffth, R, Kendaragama, A, Ksionda, O, Kurkchi, K, Paine, C, Philipsen, S, Rogers, J, Samuel, D, Shah, R, Slabkevich, N, Stewart, H, Vasilenko, A, Beckman, M, Bolton, E, Chaplin, J, Cooper, C, Fox, J, Gray, P, Hawley, G, Hickey, J, Hoey, J, Hurrion, E, Jardine, L, Kan, J, Lynn, L, McHale, T, Poad, D, Poulsen, L, Warhurst, K, Bice, C, Davis, N, Duff, J, Jones, A, Kelly, EA, Magrath, E, Malcolm, D, O'Connor, K-A, Opie, G, Turner, A-M, Walker, S, Williamson, A, Woods, H, Hou, D, Kippen, M, Schroder, J, Thesing, AJ, Wadsworth, S, Camadoo, L, Dyer, C, Jones, S, Kothari, A, Markovic, V, Owens, J, Shallcross, M, Butterley, K, Davis, C, De Paoli, A, Dodson, S, Holmes, M, Kenchapla, H, Matzolic, T, McGregor, A, Patel, S, Simic, S, Andrijic, V, Biggs, V, Brandrick, S, Goldstein, S, Lainchbury, A, Lui, K, Lyons, S, Shand, A, Sutton, L, Barnes, L, Bowen, J, Harvey, L, Jacobs, C, Milligan, J, Morris, J, Nippita, T, Sau-Harvey, R, Sparks, A, Wegener, A, Burnett, A, Callanan, K, Cheong, J, De Luca, C, Doyle, L, du Plessis, J, Duff, J, Hutchinson, E, Kane, SC, Kelly, E, Kornman, L, Maxwell, D, McDonald, M, Poth, M, Arcus, JC, Cruickshank, M, Devoy, B, Fanning, MJ, Henriksen, K, Morse, F, Schiller, A, Tomlinson, PA, Davis, G, Dosen, A, Roberts, L, Rowe, C, Creen, J, Gee, K, Hurley, T, Pallett, L, Smitheram, C, Thompson, A, Weaver, E, Lynch, L-A, Pszczola, R, Said, J, Shekleton, J, Craine, K, Fergus, J, Ford, J, Harris, A, Kummer, M, Thurnell, C, Boniface, C, Davis, A, Dickinson, C, Ireland, S, Lawrence, A, Mandell, K, Menon, S, Watson, D, Bennett, M, Elder, R, Hayne, P, Massov, L, Miller, H, Sandler, ME, Schenk, V, Wilkes, N, Sibanda, T, Davis, W, Dill, N, Espinoza, N, Kunjunju, A, Wright, I, Anderson, C, Ball, V, Bhatia, V, Burford-Rice, R, Gagliardi, D, Gooding, ML, Han, S, Headley, B, Holst, C, Keir, A, Khong, TY, Kochar, A & et al. 2023, 'Prenatal Intravenous Magnesium at 30-34 Weeks’ Gestation and Neurodevelopmental Outcomes in Offspring', JAMA, vol. 330, no. 7, pp. 603-603.
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ImportanceIntravenous magnesium sulfate administered to pregnant individuals before birth at less than 30 weeks’ gestation reduces the risk of death and cerebral palsy in their children. The effects at later gestational ages are unclear.ObjectiveTo determine whether administration of magnesium sulfate at 30 to 34 weeks’ gestation reduces death or cerebral palsy at 2 years.Design, Setting, and ParticipantsThis randomized clinical trial enrolled pregnant individuals expected to deliver at 30 to 34 weeks’ gestation and was conducted at 24 Australian and New Zealand hospitals between January 2012 and April 2018.InterventionIntravenous magnesium sulfate (4 g) was compared with placebo.Main Outcomes and MeasuresThe primary outcome was death (stillbirth, death of a live-born infant before hospital discharge, or death after hospital discharge before 2 years’ corrected age) or cerebral palsy (loss of motor function and abnormalities of muscle tone and power assessed by a pediatrician) at 2 years’ corrected age. There were 36 secondary outcomes that assessed the health of the pregnant individual, infant, and child.ResultsOf the 1433 pregnant individuals enrolled (mean age, 30.6 [SD, 6.6] years; 46 [3.2%] self-identified as Aboriginal or Torres Strait Islander, 237 [16.5%] as Asian, 82 [5.7%] as Māori, 61 [4.3%] as Pacific, and 966 [67.4%] as White) and their 1679 infants, 1365 (81%) offspring (691 in the magnesium group and 674 in the placebo group) were included in the primary outcome analysis. Death or cerebral palsy at 2 years’ corrected age was not significantly different between the magnesium and placebo gr...
Crowther, CA, Samuel, D, McCowan, LME, Edlin, R, Tran, T & McKinlay, CJ 2023, 'Lower Versus Higher Glycemic Criteria for Diagnosis of Gestational Diabetes', Obstetrical & Gynecological Survey, vol. 78, no. 2, pp. 85-87.
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ABSTRACT
Gestational diabetes mellitus (GDM) in pregnancy is a global health problem. Maternal risks associated with this condition include higher rates of induced labor, cesarean delivery, and preeclampsia. Fetal exposure to GDM can increase the risk of large for gestational age (LGA), as well as operative birth, shoulder dystocia, and birth injuries. To reduce these risks, GDM is managed with nutritional therapy, blood glucose monitoring, and pharmacologic treatment. However, the criteria to diagnose GDM in pregnant individuals vary globally. Although the International Association of Diabetes and Pregnancy Study Groups (IADPSG) recommends using a lower glycemic threshold to diagnose maternal hyperglycemia, its adoption has been mixed. The aim of this study was to evaluate whether lower glycemic criteria to diagnosis GDM would improve perinatal and maternal outcomes.
This was a randomized trial conducted in 2 health boards in New Zealand between April 2015 and August 2020. Included were women with singleton pregnancies who had a 75-g oral glucose tolerance test (OGTT) at 24 to 32 weeks of gestation. Excluded were those with diabetes mellitus or a history of GDM. The women were randomly assigned to be evaluated for GDM using lower glycemic criteria for diagnosis or higher criteria in a 1:1 ratio. The lower criteria were defined as a fasting plasma glucose level ≥5.1 mmol/L, a 1-hour level ≥10.1 mmol/L, or a 2-hour level ≥8.5 mmol/L. The higher criterion was a fasting plasma glucose level ≥5.5 mmol/L or a 2-hour level ≥9 mmol/L. The primary outcome was LGA.
A total of 4050 women and their infants were randomized and included in the analysis—2019 in the lower criteria group and 2013 in the higher criteria group. There were 8.8% LGA infants born in the lower criteria group and 8.9% in the higher criteria group (unadjusted relative risk, 0.99; 95% confidence interval [CI]...
Cui, J, Rao, P, Li, J, Chen, Q & Nimbalkar, S 2023, 'Time-dependent evolution of bearing capacity of driven piles in clays', Proceedings of the Institution of Civil Engineers - Geotechnical Engineering, vol. 176, no. 4, pp. 402-418.
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Analysis of the time-dependent variation in the axial capacity of driven piles is difficult yet critical for geotechnical engineers. In this work, to investigate the short-term evolution of the bearing capacity of driven piles, a two-dimensional finite-element (FE) model was developed using the Abaqus program. Pile installation, soil consolidation and loading were incorporated in an integrated FE model. Changes in the excess pore pressure and the void ratio of the surrounding soil were investigated to evaluate the consolidation mechanism. The findings revealed that excess pore water pressure dissipation was the primary cause of the short-term evolution of the pile's bearing capacity. The dissipation of excess pore water pressure lowered the void ratio and increased the strength and stiffness of the surrounding soil. The effect of the permeability coefficient was also assessed. The permeability coefficient was found to affect the rate of evolution but not its magnitude. A centrifuge model test was used to verify the numerical results. The findings of this study may serve as a guide for improved design and construction of driven piles.
Cui, L, Ma, J, Zhou, Y & Yu, S 2023, 'Boosting Accuracy of Differentially Private Federated Learning in Industrial IoT with Sparse Responses', IEEE Transactions on Industrial Informatics, vol. 19, no. 1, pp. 910-920.
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Empowered by 5G, it has been extensively explored by existing works on the deployment of differentially private federated learning (DPFL) in the Industrial Internet of Things (IIoT). Through federated learning, decentralized IIoT devices can collaboratively train a machine learning model by merely exchanging model gradients with a parameter server (PS) for multiple global iterations. Differentially private (DP) mechanisms will be incorporated by IIoT devices (also called clients) to prevent the leakage of privacy due to the exposure of gradients because original gradients will be distorted DP noises. Yet, learning with distorted gradients can seriously deteriorate model accuracy, making DPFL unusable in reality. To address this problem, we propose a novel DPFL with sparse responses (DPFL-SR) algorithm, which applies the sparse vector technique to reduce the privacy budget consumption in each global iteration. Specifically, DPFL-SR evaluates the value of each gradient, and only distorts and uploads significant gradients to the PS because significant gradients are more essential for model training. Since insignificant gradients are not disclosed, the reserved privacy budget can be used to return significant gradients for more iterations so that DPFL-SR can achieve higher model accuracy without lowering the privacy protection level. Extensive experiments are conducted with the MNIST and Fashion-MNIST datasets to demonstrate the practicability and superiority of DPFL-SR in IIoT systems.
Cui, Y, Lv, T, Ni, W & Jamalipour, A 2023, 'Digital Twin-Aided Learning for Managing Reconfigurable Intelligent Surface-Assisted, Uplink, User-Centric Cell-Free Systems', IEEE Journal on Selected Areas in Communications, vol. 41, no. 10, pp. 3175-3190.
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Cui, Y, Miao, MZ, Wang, M, Su, QP, Qiu, K, Arbeeva, L, Chubinskaya, S, Diekman, BO & Loeser, RF 2023, 'Yes-associated protein nuclear translocation promotes anabolic activity in human articular chondrocytes.', Osteoarthritis Cartilage, vol. 31, no. 8, pp. 1078-1090.
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OBJECTIVE: Yes-associated protein (YAP) has been widely studied as a mechanotransducer in many cell types, but its function in cartilage is controversial. The aim of this study was to identify the effect of YAP phosphorylation and nuclear translocation on the chondrocyte response to stimuli relevant to osteoarthritis (OA). DESIGN: Cultured normal human articular chondrocytes from 81 donors were treated with increased osmolarity media as an in vitro model of mechanical stimulation, fibronectin fragments (FN-f) or IL-1β as catabolic stimuli, and IGF-1 as an anabolic stimulus. YAP function was assessed with gene knockdown and inhibition by verteporfin. Nuclear translocation of YAP and its transcriptional co-activator TAZ and site-specific YAP phosphorylation were determined by immunoblotting. Immunohistochemistry and immunofluorescence to detect YAP were performed on normal and OA human cartilage with different degrees of damage. RESULTS: Chondrocyte YAP/TAZ nuclear translocation increased under physiological osmolarity (400 mOsm) and IGF-1 stimulation, which was associated with YAP phosphorylation at Ser128. In contrast, catabolic stimulation decreased the levels of nuclear YAP/TAZ through YAP phosphorylation at Ser127. Following YAP inhibition, anabolic gene expression and transcriptional activity decreased. Additionally, YAP knockdown reduced proteoglycan staining and levels of type II collagen. Total YAP immunostaining was greater in OA cartilage, but YAP was sequestered in the cytosol in cartilage areas with more severe damage. CONCLUSIONS: YAP chondrocyte nuclear translocation is regulated by differential phosphorylation in response to anabolic and catabolic stimuli. Decreased nuclear YAP in OA chondrocytes may contribute to reduced anabolic activity and promotion of further cartilage loss.
Cui, Y, Tian, H, Chen, C, Ni, W, Wu, H & Nie, G 2023, 'New Geographical Routing Protocol for Three-Dimensional Flying Ad-Hoc Network Based on New Effective Transmission Range', IEEE Transactions on Vehicular Technology, pp. 1-13.
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da Rocha, CG, Wijayaratna, K, Jong, K & Haller, D 2023, 'Examining Off-Site Construction from a Flow Viewpoint', Journal of Construction Engineering and Management, vol. 149, no. 3.
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Dang, B-T, Bui, X-T, Nguyen, T-T, Ngo, HH, Nghiem, LD, Huynh, K-P-H, Vo, T-K-Q, Vo, T-D-H, Lin, C & Chen, S-S 2023, 'Effect of biomass retention time on performance and fouling of a stirred membrane photobioreactor.', Sci Total Environ, vol. 864, pp. 161047-161047.
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Co-culture of microalgae-activated sludge has the potential to purify wastewater while reduce energy demand from aeration. In this work, a mechanically stirred membrane photobioreactor (stirred-MPBR) was used to evaluate the impact of the biomass retention time (BRT) on the treatment performance and membrane fouling. Results showed that stirred-MPBR was affected by BRT during treating domestic wastewater at a flux of 16.5 L m-2 h-1. The highest productivity was attained at BRT 7d (102 mg L-1 d-1), followed by BRT 10d (86 mg L-1 d-1), BRT 5d (85 mg L-1 d-1), and BRT 3d (83 mg L-1 d-1). Statistical analysis results showed that BRT 7d had a higher COD removal rate than BRT 10d, however, there is no difference in total nitrogen removal rate. The highest TP removal occurred when the biomass operated at BRT as short as 3d. Reduced BRTs caused a change in the microalgae-activated sludge biomass fraction that encouraged nitrification activity while simultaneously contributing to a higher fouling rate. The bound protein concentrations dropped from 31.35 mg L-1 (BRT 10d) to 10.67 mg L-1 (BRT 3d), while soluble polysaccharides increased from 0.99 to 1.82 mg L-1, respectively. The concentrations of extracellular polymeric substance fractions were significantly altered, which decreased the mean floc size and contributed to the escalating fouling propensity. At the optimum BRT of 7d, the stirred-MPBR showed sufficient access to light and nutrients exchange for mutualistic interactions between the microalgae and activated sludge.
Dang, KB, Pham, HH, Nguyen, TN, Giang, TL, Pham, TPN, Nghiem, VS, Nguyen, DH, Vu, KC, Bui, QD, Pham, HN, Nguyen, TT & Ngo, HH 2023, 'Monitoring the effects of urbanization and flood hazards on sandy ecosystem services', Science of The Total Environment, vol. 880, pp. 163271-163271.
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Dang, Z, Luo, M, Jia, C, Yan, C, Chang, X & Zheng, Q 2023, 'Counterfactual Generation Framework for Few-Shot Learning', IEEE Transactions on Circuits and Systems for Video Technology, vol. 33, no. 8, pp. 3747-3758.
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Das, CM, Yang, F, Yang, Z, Liu, X, Hoang, QT, Xu, Z, Neermunda, S, Kong, KV, Ho, H, Ju, LA, Xiong, J & Yong, K 2023, 'Computational Modeling for Intelligent Surface Plasmon Resonance Sensor Design and Experimental Schemes for Real‐Time Plasmonic Biosensing: A Review', Advanced Theory and Simulations, vol. 6, no. 9.
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AbstractThe spectacular physical phenomenon of surface plasmon resonance (SPR) is the essence of present‐day plasmonic sensors. Meanwhile, the unique properties of the interaction between light and matter have been carved out into the development of modern‐day diagnostic biosensors. Plasmons, in simple terms, are oscillating free electrons in metallic nano‐structures triggered by an incoming electromagnetic (EM) wave. With the advantages of real‐time and label‐free bio‐sensing, plasmonic sensors are being utilized in multiple diverse areas of food technology, the bio‐medical diagnostic sector, and even the chemical industry. Although this review will be brief, readers can gain a comprehensive picture of the essential elements by taking a broader look into the exploration of SPR sensor design via simulated studies and representative experimental plasmonic schemes developed for bio‐sensing. In short, the various SPR sensing schemes that researchers have explored to realize enhanced SPR sensitivity are reviewed and summarized. Different experimental plasmonic sensors are also examined in which new SPR excitation schemes have been adopted. These 'unconventional' designs, specifically those involving hybrid localized surface plasmon resonance (LSPR)‐SPR excitation, may inspire those in the plasmonic field.
Das, S, Bhattacharjee, M, Thiyagarajan, K & Kodagoda, S 2023, 'Conformable packaging of a soft pressure sensor for tactile perception', Flexible and Printed Electronics, vol. 8, no. 3, pp. 035006-035006.
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Abstract
Humans can perceive surface properties of an unfamiliar object without relying solely on vision. One way to achieve it is by physically touching the object. This human-inspired tactile perception is a complementary skill for robotic tactile perception. Robot perception depends on the informational quality of the tactile sensor; thus, packaging sensors and integrating them with robots plays a crucial role. In this work, we investigate the influence of conformable packaging designs on soft polydimethylsiloxane-based flexible pressure sensors that work in a variety of surface conditions and load levels. Four different 3D printed packaging designs capable of maintaining sensor trends have been developed. The low detection limits of 0.7 kPa and 0.1 kPa in the piezoresistive and piezocapacitive sensors, respectively, remain unaffected, and a performance variation as low as 30% is observed. Coefficient of variation and sensitivity studies have also been performed. Limit tests show that the designs can handle large forces ranging from 500 N to more than a 1000 N. Lastly, a qualitative study was performed, which covered prospective use-case scenarios as well as the advantages and downsides of each sensor casing design. Overall, the findings indicate that each sensor casing is distinct and best suited for tactile perception when interacting with objects, depending on surface properties.
Das, S, Bhattacharjee, M, Thiyagarajan, K & Kodagoda, S 2023, 'Nonlinear Response Analysis of a Polymer-Based Piezoresistive Flexible Tactile Sensor at Low Pressure', IEEE Sensors Letters, vol. 7, no. 11, pp. 1-4.
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Dashti, A, Raji, M, Riasat Harami, H, Zhou, JL & Asghari, M 2023, 'Biochar performance evaluation for heavy metals removal from industrial wastewater based on machine learning: Application for environmental protection', Separation and Purification Technology, vol. 312, pp. 123399-123399.
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Davarpanah T.Q., A, Masoodi, AR & Gandomi, AH 2023, 'Unveiling the potential of an evolutionary approach for accurate compressive strength prediction of engineered cementitious composites', Case Studies in Construction Materials, vol. 19, pp. e02172-e02172.
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de Couvreur, LA, Cobo, MJ, Kennedy, PJ & Ellis, JT 2023, 'Bibliometric analysis of parasite vaccine research from 1990 to 2019', Vaccine, vol. 41, no. 44, pp. 6468-6477.
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Dehkordi, AA, Etaati, B, Neshat, M & Mirjalili, S 2023, 'Adaptive Chaotic Marine Predators Hill Climbing Algorithm for Large-Scale Design Optimizations', IEEE Access, vol. 11, pp. 39269-39294.
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Delle-Vergini, S, Ally, M, Eacersall, D, Dann, C & Chakraborty, S 2023, 'Teaching project management to primary school children: Exploring the perspectives of project practitioners', Issues in Educational Research, vol. 33, no. 1, pp. 41-70.
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Project management knowledge and skills are an important component of the Australian Curriculum. The Australian Curriculum, Assessment and Reporting Authority calls for the explicit teaching of project management in primary school. However, it is uncertain if teachers possess the knowledge required to provide explicit teaching of project management, and to what extent it is being taught in primary school. To support the efforts of educators, seventeen project management experts were recruited to provide their perspectives. The Delphi method was used for consensus-building and the identification of core project management hard and soft skills. The findings revealed ten hard skills and twenty soft skills, ranked in order of importance and difficulty level, that children require to successfully manage projects. This study has significance for the project management profession by including the perspectives of industry practitioners on the skills required to successfully manage projects. It also provides educators with an evidence-based perspective for the design and delivery of project management content.
Deng, L, Guo, W, Ngo, HH, Zhang, X, Wei, D, Wei, Q & Deng, S 2023, 'Novel catalysts in catalytic upcycling of common polymer wastes', Chemical Engineering Journal, vol. 471, pp. 144350-144350.
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Deng, R, Huo, P, Chen, X, Chen, Z, Yang, L, Liu, Y, Wei, W & Ni, B-J 2023, 'Towards efficient heterotrophic recovery of N2O via Fe(II)EDTA-NO: A modeling study', Science of The Total Environment, vol. 859, pp. 160285-160285.
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Deng, S, Wang, C, Ngo, HH, Guo, W, You, N, Tang, H, Yu, H, Tang, L & Han, J 2023, 'Comparative review on microbial electrochemical technologies for resource recovery from wastewater towards circular economy and carbon neutrality', Bioresource Technology, vol. 376, pp. 128906-128906.
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Deng, Z, Li, W, Dong, W, Sun, Z, Kodikara, J & Sheng, D 2023, 'Multifunctional asphalt concrete pavement toward smart transport infrastructure: Design, performance and perspective', Composites Part B: Engineering, vol. 265, pp. 110937-110937.
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Deveci, Ö, Shannon, AG & Mansımova, A 2023, 'The cyclic sequences in non-Abelian groups', Communications in Algebra, vol. 51, no. 7, pp. 2956-2962.
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Devitt, SJ 2023, 'How do we quantify the utility of quantum algorithms?', Research Directions: Quantum Technologies, vol. 1, pp. 1-4.
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Quantum computing advantage emerges not from brute force power, but from subtle differences in information processing that can occur for key bottleneck subroutines. In 2019, the Google Quantum AI team performed a landmark experiment demonstrating quantum computational supremacy (Arute et al., 2019) where they performed a quantum computation that, at the time, could not be done on a classical supercomputer. This was remarkable because it was achieved by a processor with only 53 qubits, an observation that emerged from theoretical work which identified that quantum computers could have a massive advantage for certain specially designed benchmarking tasks (Boixo et al., 2018; Bremner et al., 2016).
Dewi, OC, Putra, N, Yatim, A, Mahlia, TMI, Rahmasari, K, Hanjani, T, Siregar, R, Rangin, B & Izzatur, N 2023, 'Zoning and activity-based post occupancy evaluation of multipurpose auditorium in campus facility', Energy and Buildings, vol. 295, pp. 113319-113319.
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Dhull, P, Schreurs, D, Paolini, G, Costanzo, A, Abolhasan, M & Shariati, N 2023, 'Multitone PSK Modulation Design for Simultaneous Wireless Information and Power Transfer', IEEE Transactions on Microwave Theory and Techniques, pp. 1-15.
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Ding, A, Lin, W, Shi, H, Chen, R, Ngo, HH, He, X, Nan, J, Li, G & Ma, J 2023, 'Enhanced Sludge Dewaterability by Efficient Oxidation of α-Mn2O3/Peroxymonosulfate: Analysis of the Mechanism and Evaluation of Engineering Application', ACS ES&T Engineering, vol. 3, no. 2, pp. 236-247.
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Ding, H & Ji, JC 2023, 'Vibration control of fluid-conveying pipes: a state-of-the-art review', Applied Mathematics and Mechanics, vol. 44, no. 9, pp. 1423-1456.
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AbstractFluid-conveying pipes are widely used to transfer bulk fluids from one point to another in many engineering applications. They are subject to various excitations from the conveying fluids, the supporting structures, and the working environment, and thus are prone to vibrations such as flow-induced vibrations and acoustic-induced vibrations. Vibrations can generate variable dynamic stress and large deformation on fluid-conveying pipes, leading to vibration-induced fatigue and damage on the pipes, or even leading to failure of the entire piping system and catastrophic accidents. Therefore, the vibration control of fluid-conveying pipes is essential to ensure the integrity and safety of pipeline systems, and has attracted considerable attention from both researchers and engineers. The present paper aims to provide an extensive review of the state-of-the-art research on the vibration control of fluid-conveying pipes. The vibration analysis of fluid-conveying pipes is briefly discussed to show some key issues involved in the vibration analysis. Then, the research progress on the vibration control of fluid-conveying pipes is reviewed from four aspects in terms of passive control, active vibration control, semi-active vibration control, and structural optimization design for vibration reduction. Furthermore, the main results of existing research on the vibration control of fluid-conveying pipes are summarized, and future promising research directions are recommended to address the current research gaps. This paper contributes to the understanding of vibration control of fluid-conveying pipes, and will help the research work on the vibration control of fluid-conveying pipes attract more attention.
Ding, L, Ji, J, Li, Y, Wang, S, Noman, K & Feng, K 2023, 'A novel weak feature extraction method for rotating machinery: link dispersion entropy', IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-1.
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The entropy-based feature extraction is a promising tool for extracting weak features from rotating machinery. However, the existing research has paid little attention to the state transition process, which brings the problem of accuracy and comprehensiveness in complexity estimation. To address this issue, this paper proposes link dispersion entropy (LDE) based on the theory of the Markov chain for weak feature extraction. By calculating the transition probability of symbol patterns, the LDE can extract the fault information contained in the transition, enabling it to capture the early weak fault. Furthermore, LDE is extended to a multiscale analysis by combining it with the coarse-gaining process for comprehensive feature extraction, termed multiscale LDE (MLDE). Finally, three simulated signals and two different experimental data are utilized to verify the advantage of MLDE in extracting the weak fault features. Results demonstrate that MLDE has the best performance in fault diagnosis of rotating machinery compared with the existing five methods, namely sample entropy, fuzzy entropy, permutation entropy, dispersion entropy and symbolic dynamic entropy.
Ding, L, Oh, S, Shrestha, J, Lam, A, Wang, Y, Radfar, P & Warkiani, ME 2023, 'Scaling up stem cell production: harnessing the potential of microfluidic devices', Biotechnology Advances, vol. 69, pp. 108271-108271.
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Ding, W, Geng, Y, Huang, J, Ju, H, Wang, H & Lin, C-T 2023, '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, Liu, J, Lin, C & Mrozek, D 2023, 'Special issue on Recent Advances in Fuzzy Deep Learning for Uncertain Medicine Data', Information Sciences, vol. 642, pp. 118997-118997.
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Ding, W, Wang, H, Huang, J, Ju, H, Geng, Y, Lin, C-T & Pedrycz, W 2023, 'FTransCNN: Fusing Transformer and a CNN based on fuzzy logic for uncertain medical image segmentation', Information Fusion, vol. 99, pp. 101880-101880.
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Ding, Z, Chen, X, Dong, Y, Yu, S & Herrera, F 2023, 'Consensus Convergence Speed in Social Network DeGroot Model: The Effects of the Agents With High Self-Confidence Levels', IEEE Transactions on Computational Social Systems, vol. 10, no. 5, pp. 2882-2892.
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Ding, Z, Yu, Y & Xia, Y 2023, 'Nonlinear hysteretic parameter identification using an attention-based long short-term memory network and principal component analysis', Nonlinear Dynamics, vol. 111, no. 5, pp. 4559-4576.
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Ding, Z, Yu, Y, Tan, D & Yuen, K-V 2023, 'Adaptive vision feature extractions and reinforced learning-assisted evolution for structural condition assessment', Structural and Multidisciplinary Optimization, vol. 66, no. 9.
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Dinh, TH, Anh, VTT, Nguyen, T, Hieu Le, C, Trung, NL, Duc, ND & Lin, C-T 2023, 'Toward Vision-Based Concrete Crack Detection: Automatic Simulation of Real-World Cracks', IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-15.
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Doan, T, Indraratna, B, Nguyen, TT & Rujikiatkamjorn, C 2023, 'Interactive Role of Rolling Friction and Cohesion on the Angle of Repose through a Microscale Assessment', International Journal of Geomechanics, vol. 23, no. 1.
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Cohesion and rolling friction play key roles in governing the behavior of soil; however, only a limited number of studies have been able to assess the simultaneous contributions of these two microparameters on the macroproperties of soil. In this respect, the innovation of the current study includes an attempt to examine the interplay of these two primary parameters on the angle of repose (AoR) based on the discrete-element method (DEM). Lifting cylinder tests on cohesive wet sand have been carried out in DEM, while the cohesion and rolling friction are captured through proposed computational models. In this paper, macroparameters, such as the geometry and developmental stages of sand piles obtained in DEM simulation, are compared with experimental data, while their microevolution is quantified in detail. The results show that a large AoR can only be obtained when the cohesive and rotational frictional forces work in tandem. Increasing the cohesion and rolling friction results in smaller contact numbers, with increasing chain-like connections between particles and larger pore spaces to account for a larger AoR. For the first time, this study distinctly identifies three major stages that contribute to the AoR, based on the development of contact numbers and the transformation of energy. Accordingly, the linkage between macroscale AoR and the microstructural coordination number is formulated with varying levels of cohesion and rolling friction. The DEM results prove that the more cohesive the particles are, the greater the delay in the dissipation of kinetic energy.
Dogan, S, Baygin, M, Tasci, B, Loh, HW, Barua, PD, Tuncer, T, Tan, R-S & Acharya, UR 2023, 'Primate brain pattern-based automated Alzheimer's disease detection model using EEG signals', Cognitive Neurodynamics, vol. 17, no. 3, pp. 647-659.
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Dong, C, Weng, J, Li, M, Liu, J-N, Liu, Z, Cheng, Y & Yu, S 2023, 'Privacy-Preserving and Byzantine-Robust Federated Learning', IEEE Transactions on Dependable and Secure Computing, pp. 1-16.
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Dong, J, Wu, K, Liu, C, Mei, X & Wang, W 2023, 'Discriminative analysis dictionary learning with adaptively ordinal locality preserving', Neural Networks, vol. 165, pp. 298-309.
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Dong, Q, Zheng, X, Fu, A, Su, M, Zhou, L & Yu, S 2023, 'DMRA: Model Usability Detection Scheme Against Model-Reuse Attacks in the Internet of Things', IEEE Internet of Things Journal, vol. 10, no. 19, pp. 16907-16916.
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Dong, Y, Ran, Q, Chao, X, Li, C & Yu, S 2023, 'Personalized Individual Semantics Learning to Support a Large-Scale Linguistic Consensus Process', ACM Transactions on Internet Technology, vol. 23, no. 2, pp. 1-27.
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When making decisions, individuals often express their preferences linguistically. The computing with words methodology is a key basis for supporting linguistic decision making, and the words in that methodology may mean different things to different individuals. Thus, in this article, we propose a continual personalized individual semantics learning model to support a consensus-reaching process in large-scale linguistic group decision making. Specifically, we first derive personalized numerical scales from the data of linguistic preference relations. We then perform a clustering ensemble method to divide large-scale group and conduct consensus management. Finally, we present a case study of intelligent route optimization in shared mobility to illustrate the usability of our proposed model. We also demonstrate its effectiveness and feasibility through a comparative analysis.
Dou, J, Xie, G, Tian, Z, Cui, L & Yu, S 2023, 'Modeling and Analyzing the Spatial-Temporal Propagation of Malware in Mobile Wearable IoT Networks', IEEE Internet of Things Journal, pp. 1-1.
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Du, G, Pu, T, Zhou, Q, Wang, L, Lei, G & Zhu, J 2023, 'Multiphysics Comparative Study of High Speed PM Machines for Ring PM Rotor and Solid PM Rotor', IEEE Transactions on Energy Conversion, vol. 38, no. 2, pp. 1421-1432.
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Du, J, Xue, Y, Sugumaran, V, Hu, M & Dong, P 2023, 'Improved biogeography-based optimization algorithm for lean production scheduling of prefabricated components', Engineering, Construction and Architectural Management, vol. 30, no. 4, pp. 1601-1635.
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PurposeFor prefabricated building construction, improper handling of the production scheduling for prefabricated components is one of the main reasons that affect project performance, which causes overspending, schedule overdue and quality issues. Prior research on prefabricated components production schedule has shown that optimizing the flow shop scheduling problem (FSSP) is the basis for solving this issue. However, some key resources and the behavior of the participants in the context of actual prefabricated components production are not considered comprehensively.Design/methodology/approachThis paper characterizes the production scheduling of the prefabricated components problem into a permutation flow shop scheduling problem (PFSSP) with multi-optimization objectives, and limitation on mold and buffers size. The lean construction principles of value-based management (VBM) and just-in-time (JIT) are incorporated into the production process of precast components. Furthermore, this paper applies biogeography-based optimization (BBO) to the production scheduling problem of prefabricated components combined with some improvement measures.FindingsThis paper focuses on two specific scenarios: production planning and production rescheduling. In the production planning stage, based on the production factor, this study establishes a multi-constrained and multi-objective prefabricated component production scheduling mathematical model and uses the improved BBO for prefabricated component production scheduling. In the production rescheduling stage, the proposed model allows real-time production plan adjustments based on uncertain events. An actual case has been used to verify the effectiveness of the...
Du, K, Luo, X, Yang, S, Danial, JA & Zhou, J 2023, 'An insight from energy index characterization to determine the proneness of rockburst for hard rock', Geomechanics for Energy and the Environment, vol. 35, pp. 100478-100478.
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Du, S, Shen, Y, Zheng, Y, Cheng, Y, Xu, X, Chen, D & Xia, D 2023, 'Systematic in vitro and in vivo study on biodegradable binary Zn-0.2 at% Rare Earth alloys (Zn-RE: Sc, Y, La–Nd, Sm–Lu)', Bioactive Materials, vol. 24, pp. 507-523.
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Du, X, Chen, Z, Li, Q, Yang, S, Jiang, L, Yang, Y, Li, Y & Gu, Z 2023, 'Organoids revealed: morphological analysis of the profound next generation in-vitro model with artificial intelligence', Bio-Design and Manufacturing, vol. 6, no. 3, pp. 319-339.
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Du, Y, Zhang, Y, Pu, X, Fu, X, Li, X, Bai, L, Chen, Y & Qian, J 2023, 'Synthesis of bifunctional NiFe layered double hydroxides (LDH)/Mo-doped g-C3N4 electrocatalyst for efficient methanol oxidation and seawater splitting.', Chemosphere, vol. 312, no. Pt 1, pp. 137203-137203.
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To boost the oxygen evolution reaction (OER) and methanol oxidation reaction (MOR) of pristine NiFe-layered double hydroxides (LDH), the NiFe-LDH/Mo-doped graphitic carbon nitride (NiFe-LDH/MoCN) heterojunction was synthesized herein through hydrothermal method. The establishment of built-in electric field in NiFe-LDH/MoCN heterojunction enhanced the electrochemical oxidation activities towards both seawater splitting and methanol oxidation, via the improving electrocatalyst surface wettability and conductivity. Almost 10-fold enhancement of turnover frequency (TOF) and electrochemical active surface area (ECSA) than pure NiFe-LDH implied more active sites to participate in catalytic reactions via Mo doping and the formation of heterostructure. Moreover, the local charge redistribution demonstrated in the NiFe-LDH/MoCN interface region may favor the adsorption of methanol and OH- in the seawater. The present work may expound the strong coupling interaction and the establishment of built-in electric field in the interface between NiFe-LDH and semiconductor to enhance both methanol oxidation and seawater oxidation for NiFe-LDH.
Duan, J-L, Han, Y, Feng, L-J, Ma, J-Y, Sun, X-D, Liu, X-Y, Geng, F-S, Jiang, J-L, Liu, M-Y, Sun, Y-C, Peu, P, Ni, B-J & Yuan, X-Z 2023, 'Single bubble probe atomic force microscope and impinging-jet technique unravel the interfacial interactions controlled by long chain fatty acid in anaerobic digestion', Water Research, vol. 231, pp. 119657-119657.
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Duan, L, Sun, Y, Ni, W, Ding, W, Liu, J & Wang, W 2023, 'Attacks Against Cross-Chain Systems and Defense Approaches: A Contemporary Survey', IEEE/CAA Journal of Automatica Sinica, vol. 10, no. 8, pp. 1647-1667.
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Duan, L, Yang, L, Liu, C, Ni, W & Wang, W 2023, 'A New Smart Contract Anomaly Detection Method by Fusing Opcode and Source Code Features for Blockchain Services', IEEE Transactions on Network and Service Management, pp. 1-1.
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Duan, Q, Huang, J, Hu, S, Deng, R, Lu, Z & Yu, S 2023, 'Combining Federated Learning and Edge Computing Toward Ubiquitous Intelligence in 6G Network: Challenges, Recent Advances, and Future Directions', IEEE Communications Surveys & Tutorials, pp. 1-1.
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Duan, W, Xuan, J, Qiao, M & Lu, J 2023, 'Graph Convolutional Neural Networks With Diverse Negative Samples via Decomposed Determinant Point Processes.', IEEE Trans Neural Netw Learn Syst, 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.
Duan, Y, Lu, Y, Shen, S, Yu, S, Zhang, P, Zhang, W & Igorevich, KK 2023, 'NFLCS: An Service Function Chain Path Optimization Strategy Based on Network-Functional Layout Clustering', IEEE Transactions on Vehicular Technology, vol. 72, no. 8, pp. 10813-10825.
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Duan, Y, Wang, Z, Li, Y, Tang, J, Wang, Y-K & Lin, C-T 2023, 'Cross task neural architecture search for EEG signal recognition', Neurocomputing, vol. 545, pp. 126260-126260.
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Ebrahimi Farshchi, M, Madadian Bozorg, N, Ehsani, A, Aghdasinia, H, Chen, Z, Rostamnia, S & Ni, B-J 2023, 'Green valorization of PET waste into functionalized Cu-MOF tailored to catalytic reduction of 4-nitrophenol', Journal of Environmental Management, vol. 345, pp. 118842-118842.
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Ejegwa, PA, Wen, S, Feng, Y, Zhang, W & Liu, J 2023, 'A three-way Pythagorean fuzzy correlation coefficient approach and its applications in deciding some real-life problems', Applied Intelligence, vol. 53, no. 1, pp. 226-237.
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Correlation coefficient (CC) is a reliable information measure for measuring interrelationship between Pythagorean fuzzy sets (PFSs). Some approaches for calculating CC of PFSs have been considered. These hitherto approaches assess only the strength of relationship between PFSs, and are described within the interval [0,1]. This paper proposes a three-way approach for the computation of CC between PFSs by using the concepts of variance and covariance, respectively. This new approach is defined within the interval [− 1,1] akin to classical statistics, shows the strength of relationship between the considered PFSs and indicates whether the PFSs are either positively or negatively correlated. By including the three conventional parameters of PFSs in the proposed technique, the possibility of error due to information leakage is reasonably minimized. The new technique is validated with some theoretical results to show its suitability as reliable information measure. Some numerical examples are considered to show the edges of the new methods over similar methods. From the comparative analysis, the proposed methods of computing CCPFSs give more reliable and reasonable results compare to similar existing methods as presented in Table 13. Certain decision-making problems involving recognition of patterns and diagnostic medicine are resolved with the aid of the new method. The three-way technique of computing correlation coefficient between PFSs can solve decision-making problems that are multi-attributes in nature.
Eklund, M, Khalilpour, K, Voinov, A & Hossain, MJ 2023, 'Understanding the community in community microgrids: A conceptual framework for better decision-making', Energy Research & Social Science, vol. 104, pp. 103260-103260.
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A community microgrid comes with the introduction of non-conventional distributed renewable energy infrastructure, affecting the behaviour of community members and their relationship with energy. The aspects of ownership, trust, collaboration and its often-discursive structure will be reflected in the cultural and social factors, such as norms and values in a community. The success of specific community microgrids is widely dependent on the community's ability to engage in various activities connected to the microgrid installation and operation. This paper conceptualises existing literature on community microgrids, focusing on the representation and inclusion of community preferences, needs and behaviour across the development stages. From this analysis, a conceptual-theoretical framework is proposed based on social capital theory for identifying community characteristics to determine key needs and considerations for microgrid adoption. The framework is divided into four components: social capital, community capability, community type and microgrid impact. Social capital, including its dimensions such as structural, cognitive, and relational capital forms the foundation of the framework and serves to evaluate the community capability and determine its type, which in turn affects its impact on the community microgrid. Finally, we present an initial step in operationalising our conceptual framework as a practical tool to guide further research in the development of community microgrids. Ultimately, this research can benefit both academia and industry by providing a comprehensive and practical approach to understanding the importance of social factors in community microgrid success.
El Majzoub, A, Rabhi, FA & Hussain, W 2023, 'Evaluating interpretable machine learning predictions for cryptocurrencies', Intelligent Systems in Accounting, Finance and Management, vol. 30, no. 3, pp. 137-149.
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SummaryThis study explores various machine learning and deep learning applications on financial data modelling, analysis and prediction processes. The main focus is to test the prediction accuracy of cryptocurrency hourly returns and to explore, analyse and showcase the various interpretability features of the ML models. The study considers the six most dominant cryptocurrencies in the market: Bitcoin, Ethereum, Binance Coin, Cardano, Ripple and Litecoin. The experimental settings explore the formation of the corresponding datasets from technical, fundamental and statistical analysis. The paper compares various existing and enhanced algorithms and explains their results, features and limitations. The algorithms include decision trees, random forests and ensemble methods, SVM, neural networks, single and multiple features N‐BEATS, ARIMA and Google AutoML. From experimental results, we see that predicting cryptocurrency returns is possible. However, prediction algorithms may not generalise for different assets and markets over long periods. There is no clear winner that satisfies all requirements, and the main choice of algorithm will be tied to the user needs and provided resources.
Elashmawy, MA, Elamvazuthi, I, Izhar, LI, Paramasivam, S & Su, S 2023, 'Detection of Tuberculosis Based on Hybridized Pre-Processing Deep Learning Method', International Journal of Advanced Computer Science and Applications, vol. 14, no. 8, pp. 69-76.
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The disease, tuberculosis (TB) is a serious health concern as it primarily affects the lungs and can lead to fatalities. However, early detection and treatment can cure the disease. One potential method for detecting TB is using Computer Aided Diagnosis (CAD) systems, which can analyze Chest X-Ray Images (CXR) for signs of TB. This paper proposes a new approach for improving the performance of CAD systems by using a hybrid pre-processing method for Convolutional Neural Network (CNN) models. The goal of the research is to enhance the accuracy and Area Under Curve (AUC) of detection for TB in CXR images by combining two different pre-processing methods and multi-classifying different manifestations of the disease. The hypothesis is that this approach will result in more accurate detection of TB in CXR images. To achieve this, this research used augmentation and segmentation techniques to pre-process the CXR images before feeding them into a pre-trained CNN model for classification. The VGG16 model managed to achieve an AUC of 0.935, an accuracy of 90% and a 0.8975 F1-score with the proposed pre-processing method.
Elgharabawy, A, Prasad, M & Lin, C-T 2023, 'Preference Neural Network', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 7, no. 5, pp. 1362-1376.
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Elkhodr, M, Gide, E, Wu, R & Darwish, O 2023, 'ICT students' perceptions towards ChatGPT: An experimental reflective lab analysis', STEM Education, vol. 3, no. 2, pp. 70-88.
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<abstract><p>This study explores the use of the Generative artificial intelligence (GenAI) tool ChatGPT in higher education. Amidst the potential benefits and the risk of misuse, this research investigates the tool's role as a classroom aid and its impact on learning outcomes and experiences. Three case studies involving undergraduate and postgraduate ICT students were conducted. Findings revealed a positive perception of ChatGPT as a useful and enjoyable learning resource. Most students indicated a willingness to use such AI tools in the future. Additionally, the study suggested improved performance in functionality, user flow, and content comprehension among students using ChatGPT, compared to those relying solely on traditional search engines.</p></abstract>
Elsawah, S, Bakhanova, E, Hämäläinen, RP & Voinov, A 2023, 'A Competency Framework for Participatory Modeling', Group Decision and Negotiation, vol. 32, no. 3, pp. 569-601.
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Participatory modeling (PM) is a craft that is often learned by training ‘on the job’ and mastered through years of practice. There is little explicit knowledge available on identifying and documenting the skills needed to perform PM. In the modeling literature, existing attempts to identify relevant competencies have focused on the specific technical skills required for specific technical model development. The other skills required to organize and conduct the stakeholder process seem to be more vaguely and poorly defined in this context. The situation is complicated by PM being an essentially transdisciplinary craft, with no single discipline or skill set to borrow ideas and recommendations from. In this paper, we aim to set the foundation for both the practice and capacity-building efforts for PM by identifying the relevant core competencies. Our inquiry into this topic starts with reviewing and compiling literature on competencies in problem-solving research areas related to PM (e.g., systems thinking, facilitated model building, operations research, and so forth). We augment our inquiry with results from a PM practitioners’ survey to learn how they perceive the importance of different competencies and how the scope of these competencies may vary across the various roles that participatory modellers play. As a result, we identified five core competency areas essential for PM: systems thinking, modeling, group facilitation, project management and leadership, and, more recently, designing and running virtual workshops and events.
Entezari, A, Liu, N-C, Zhang, Z, Fang, J, Wu, C, Wan, B, Swain, M & Li, Q 2023, 'Nondeterministic multiobjective optimization of 3D printed ceramic tissue scaffolds', Journal of the Mechanical Behavior of Biomedical Materials, vol. 138, pp. 105580-105580.
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Erdem, K, Kobat, MA, Bilen, MN, Balik, Y, Alkan, S, Cavlak, F, Poyraz, AK, Barua, PD, Tuncer, I, Dogan, S, Baygin, M, Erten, M, Tuncer, T, Tan, R & Acharya, UR 2023, 'Hybrid‐Patch‐Alex: A new patch division and deep feature extraction‐based image classification model to detect COVID‐19, heart failure, and other lung conditions using medical images', International Journal of Imaging Systems and Technology, vol. 33, no. 4, pp. 1144-1159.
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AbstractCOVID‐19, chronic obstructive pulmonary disease (COPD), heart failure (HF), and pneumonia can lead to acute respiratory deterioration. Prompt and accurate diagnosis is crucial for effective clinical management. Chest X‐ray (CXR) and chest computed tomography (CT) are commonly used for confirming the diagnosis, but they can be time‐consuming and biased. To address this, we developed a computationally efficient deep feature engineering model called Hybrid‐Patch‐Alex for automated COVID‐19, COPD, and HF diagnosis. We utilized one CXR dataset and two CT image datasets, including a newly collected dataset with four classes: COVID‐19, COPD, HF, and normal. Our model employed a hybrid patch division method, transfer learning with pre‐trained AlexNet, iterative neighborhood component analysis for feature selection, and three standard classifiers (k‐nearest neighbor, support vector machine, and artificial neural network) for automated classification. The model achieved high accuracy rates of 99.82%, 92.90%, and 97.02% on the respective datasets, using kNN and SVM classifiers.
Erten, M, Barua, PD, Tuncer, I, Dogan, S, Baygin, M, Tuncer, T, Tan, R-S & Acharya, UR 2023, 'Swin-LBP: a competitive feature engineering model for urine sediment classification', Neural Computing and Applications, vol. 35, no. 29, pp. 21621-21632.
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AbstractAutomated urine sediment analysis has become an essential part of diagnosing, monitoring, and treating various diseases that affect the urinary tract and kidneys. However, manual analysis of urine sediment is time-consuming and prone to human bias, and hence there is a need for an automated urine sediment analysis systems using machine learning algorithms. In this work, we propose Swin-LBP, a handcrafted urine sediment classification model using the Swin transformer architecture and local binary pattern (LBP) technique to achieve high classification performance. The Swin-LBP model comprises five phases: preprocessing of input images using shifted windows-based patch division, six-layered LBP-based feature extraction, neighborhood component analysis-based feature selection, support vector machine-based calculation of six predicted vectors, and mode function-based majority voting of the six predicted vectors to generate four additional voted vectors. Our newly reconstructed urine sediment image dataset, consisting of 7 distinct classes, was utilized for training and testing our model. Our proposed model has several advantages over existing automated urinalysis systems. Firstly, we used a feature engineering model that enables high classification performance with linear complexity. This means that it can provide accurate results quickly and efficiently, making it an attractive alternative to time-consuming and biased manual urine sediment analysis. Additionally, our model outperformed existing deep learning models developed on the same source urine sediment image dataset, indicating its superiority in urine sediment classification. Our model achieved 92.60% accuracy for 7-class urine sediment classification, with an average precision of 92.05%. These results demonstrate that the proposed Swin-LBP model can provide a reliable and efficient solution for the diagnosis, surveillance, and therapeutic monitoring of various diseases...
Eskandari, M, Savkin, AV & Ni, W 2023, 'Consensus-Based Autonomous Navigation of a Team of RIS-Equipped UAVs for LoS Wireless Communication With Mobile Nodes in High-Density Areas', IEEE Transactions on Automation Science and Engineering, vol. 20, no. 2, pp. 923-935.
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Faisal, SN, Do, T-TN, Torzo, T, Leong, D, Pradeepkumar, A, Lin, C-T & Iacopi, F 2023, 'Noninvasive Sensors for Brain–Machine Interfaces Based on Micropatterned Epitaxial Graphene', ACS Applied Nano Materials, vol. 6, no. 7, pp. 5440-5447.
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Fan, C, Zhang, X, Zhao, Y, Liu, Y & Yu, S 2023, 'Self-Adaptive Gradient Quantization for Geo-Distributed Machine Learning over Heterogeneous and Dynamic Networks', IEEE Transactions on Cloud Computing, pp. 1-16.
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Fan, H, Yang, Y & Kankanhalli, M 2023, 'Point Spatio-Temporal Transformer Networks for Point Cloud Video Modeling', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 2, pp. 2181-2192.
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Fan, J, Li, J, Wang, Y, Li, Y, Hsieh, M-H & Du, J 2023, 'Partially Concatenated Calderbank-Shor-Steane Codes Achieving the Quantum Gilbert-Varshamov Bound Asymptotically', IEEE Transactions on Information Theory, vol. 69, no. 1, pp. 262-272.
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Fan, J, Yan, J, Zhou, M, Xu, Y, Lu, Y, Duan, P, Zhu, Y, Zhang, Z, Li, W, Wang, A & Sun, D 2023, 'Heavy metals immobilization of ternary geopolymer based on nickel slag, lithium slag and metakaolin', Journal of Hazardous Materials, vol. 453, pp. 131380-131380.
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Fan, L, Zhang, X, Zhao, Y, Sood, K & Yu, S 2023, 'Online Training Flow Scheduling for Geo-Distributed Machine Learning Jobs Over Heterogeneous and Dynamic Networks', IEEE Transactions on Cognitive Communications and Networking, pp. 1-1.
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Fan, W, Xiao, F, Cai, H, Chen, X & Yu, S 2023, 'Disjoint Paths Construction and Fault-Tolerant Routing in BCube of Data Center Networks', IEEE Transactions on Computers, vol. 72, no. 9, pp. 2467-2481.
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Fan, Z, Yan, Z & Wen, S 2023, 'Deep Learning and Artificial Intelligence in Sustainability: A Review of SDGs, Renewable Energy, and Environmental Health', Sustainability, vol. 15, no. 18, pp. 13493-13493.
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Artificial intelligence (AI) and deep learning (DL) have shown tremendous potential in driving sustainability across various sectors. This paper reviews recent advancements in AI and DL and explores their applications in achieving sustainable development goals (SDGs), renewable energy, environmental health, and smart building energy management. AI has the potential to contribute to 134 of the 169 targets across all SDGs, but the rapid development of these technologies necessitates comprehensive regulatory oversight to ensure transparency, safety, and ethical standards. In the renewable energy sector, AI and DL have been effectively utilized in optimizing energy management, fault detection, and power grid stability. They have also demonstrated promise in enhancing waste management and predictive analysis in photovoltaic power plants. In the field of environmental health, the integration of AI and DL has facilitated the analysis of complex spatial data, improving exposure modeling and disease prediction. However, challenges such as the explainability and transparency of AI and DL models, the scalability and high dimensionality of data, the integration with next-generation wireless networks, and ethics and privacy concerns need to be addressed. Future research should focus on enhancing the explainability and transparency of AI and DL models, developing scalable algorithms for processing large datasets, exploring the integration of AI with next-generation wireless networks, and addressing ethical and privacy considerations. Additionally, improving the energy efficiency of AI and DL models is crucial to ensure the sustainable use of these technologies. By addressing these challenges and fostering responsible and innovative use, AI and DL can significantly contribute to a more sustainable future.
Fang, C, Hu, Z, Meng, X, Tu, S, Wang, Z, Zeng, D, Ni, W, Guo, S & Han, Z 2023, 'DRL-Driven Joint Task Offloading and Resource Allocation for Energy-Efficient Content Delivery in Cloud-Edge Cooperation Networks', IEEE Transactions on Vehicular Technology, pp. 1-13.
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Fang, K, Zhao, J, Li, X, Li, Y & Duan, R 2023, 'Quantum NETwork: from theory to practice', Science China Information Sciences, vol. 66, no. 8.
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Fang, Z, Lu, J & Zhang, G 2023, 'An Extremely Simple Algorithm for Source Domain Reconstruction', IEEE Transactions on Cybernetics, pp. 1-13.
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Fang, Z, Wu, Z, Ni, W, Wang, X & Hossain, E 2023, 'Beamforming Design for Novel Relay-Assisted Multi-User Multi-Tag Symbiotic Radios', IEEE Wireless Communications Letters, pp. 1-1.
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Farhangi, M, Barzegarkhoo, R, Aguilera, RP, Lu, DD-C, Liserre, M & Siwakoti, YP 2023, 'A Single-Stage Switched-Boost Grid-Connected Five-Level Converter With Integrated Active Power Decoupling Under Polluted Grid Voltage Condition', IEEE Open Journal of the Industrial Electronics Society, vol. 4, pp. 328-345.
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Farhart, P, Beakley, D, Diwan, A, Duffield, R, Rodriguez, EP, Chamoli, U & Watsford, M 2023, 'Intrinsic variables associated with low back pain and lumbar spine injury in fast bowlers in cricket: a systematic review.', BMC Sports Sci Med Rehabil, vol. 15, no. 1, p. 114.
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BACKGROUND: Lumbar spine injuries in fast bowlers account for the greatest missed playing time in cricket. A range of extrinsic and intrinsic variables are hypothesised to be associated with low back pain and lumbar spine injury in fast bowlers, and an improved understanding of intrinsic variables is necessary as these may alter load tolerance and injury risk associated with fast bowling. This review critically evaluated studies reporting intrinsic variables associated with low back pain and lumbar spine injury in fast bowlers and identified areas for future investigation. METHODS: OVID Medline, EMBASE, SPORTDiscus, CINAHL, Web of Science and SCOPUS databases were last searched on 3 June 2022 to identify studies investigating intrinsic variables associated with low back pain and lumbar spine injury in cricket fast bowlers. Terms relevant to cricket fast bowling, and intrinsic variables associated with lumbar spine injury and low back pain in fast bowlers were searched. 1,503 abstracts were screened, and 118 full-text articles were appraised to determine whether they met inclusion criteria. Two authors independently screened search results and assessed risk of bias using a modified version of the Quality in Prognostic Studies tool. RESULTS: Twenty-five studies met the inclusion criteria. Overall, no included studies demonstrated a low risk of bias, two studies were identified as moderate risk, and twenty-three studies were identified as high risk. Conflicting results were reported amongst studies investigating associations of fast bowling kinematics and kinetics, trunk and lumbar anatomical features, anthropometric traits, age, and neuromuscular characteristics with low back pain and lumbar spine injury. CONCLUSION: Inconsistencies in results may be related to differences in study design, injury definitions, participant characteristics, measurement parameters, and statistical analyses. Low back pain and lumbar spine injury occurrence in fast bowlers rema...
Farina, E, Loglio, A, Tosetti, G, Degasperi, E, Viganò, M, Gentile, C, Monico, S, Perbellini, R, Borghi, M, Facchetti, F, Uceda Renteria, SC, Ceriotti, F, Cerini, F, Primignani, M & Lampertico, P 2023, 'Long‐term endoscopic surveillance in HBV compensated cirrhotic patients treated with Tenofovir or Entecavir for 11 years', Alimentary Pharmacology & Therapeutics, vol. 57, no. 12, pp. 1407-1416.
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SummaryBackgroundLong‐term administration of TDF/ETV in patients with HBV‐related compensated cirrhosis reduces HCC and decompensation events but the effect of this regimen on development/regression of oesophageal varices (EV) is currently unknown.AimTo assess the risk of EV development/progression in this population.MethodsA total of 186 Caucasian HBV‐monoinfected compensated cirrhotics were enrolled in a long‐term cohort study from TDF/ETV introduction. Upper GI endoscopies were performed according to Baveno recommendations. Primary endpoint was development/progression of oesophageal/gastric varices over time.ResultsAt TDF/ETV start, median age was 61 years, 80% males, 60% HBV‐DNA undetectable, 63% NUCs previously exposed, 73% normal ALT, 40% platelets <150,000/mmc and 25 (13%) with low‐risk varices (LRV). During 11 years of antiviral therapy and 666 endoscopies performed, 9 patients either developed or had a progression of oesophageal or gastric varices with an 11‐year cumulative probability of 5.1% (95% CI 3–10%); no patient bled. Out of 161 patients without EV at baseline, the 11‐year probably was 4.5% with all varices developing within the first six years of treatment. In 25 patients with LRV at baseline, the 11‐year probability of progression or regression was 9.3% and 58%, respectively. Only baseline platelet count (HR 0.96, p = 0.028) was associated with LRV development at multivariate analysis: platelet ≤90,000/mmc (AUROC 0.70) had 98.1% specificity, 42.9% sensitivity, 50% PPV for LRV onset.ConclusionsIn compensated cirrhotic patients under long‐term effective TDF/ETV treatment, the 11‐year risk of developing/...
Farooq, U, Riaz, HH, Munir, A, Zhao, M, Tariq, A & Islam, MS 2023, 'Application of heliox for optimized drug delivery through respiratory tract', Physics of Fluids, vol. 35, no. 10.
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Understanding the transportation and deposition (TD) of inhaled particles in the upper respiratory tract is crucial for predicting health risks and treating pulmonary diseases. The available literature reports highly turbulent flow in the extrathoracic (ET) region during normal breathing, which leads to higher deposition of the drug aerosol in this region. To improve the targeted deposition of inhaled drugs, in the tracheobronchial airways, it is essential to understand the flow and particle transport dynamics and reduce the turbulence behavior at the ET region. The less-dense heliox gas could reduce the turbulence behavior at the ET; however, the knowledge of heliox inhalation therapies in drug aerosol TD remains underachieved to realize the full potential for assisted breathing and drug delivery. Additionally, the impact of the inhalation of heliox mixed with other gases on particle deposition is missing in the literature. Therefore, this study aims to develop a mixture model to advance the knowledge of inhalation therapy. A heliox (78% helium and 22% oxygen) and a mixture of heliox and air are used to understand the flow behavior and particle TD in airways. The impact of different inhalation and Stokes numbers on the deposition efficiencies in the ideal and age-specific upper airways is studied. The study reports that less-dense heliox gas has lower turbulence intensity and results in lower deposition efficiency in the G3–G5 lung airways compared to air and mixture inhalations. Moreover, slightly higher deposition efficiencies during mixture inhalation as compared to air inhalation are found in the upper airways. The deposition patterns of different inhalations obtained in this study could help improve targeted drug delivery into the upper and deeper lung airways.
Farzinpour, A, Mohammadi Dehcheshmeh, E, Broujerdian, V, Nasr Esfahani, S & Gandomi, AH 2023, 'Efficient boosting-based algorithms for shear strength prediction of squat RC walls', Case Studies in Construction Materials, vol. 18, pp. e01928-e01928.
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Fathalla, A, Salah, A, Bekhit, M, Eldesouky, E, Talha, A, Zenhom, A & Ali, A 2023, 'Real-Time and Automatic System for Performance Evaluation of Karate Skills Using Motion Capture Sensors and Continuous Wavelet Transform', International Journal of Intelligent Systems, vol. 2023, pp. 1-11.
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In sports science, the automation of performance analysis and assessment is urgently required to increase the evaluation accuracy and decrease the performance analysis time of a subject. Existing methods of performance analysis and assessment are either performed manually based on human experts’ opinions or using motion analysis software, i.e., biomechanical analysis software, to assess only one side of a subject. Therefore, we propose an automated system for performance analysis and assessment that can be used for any human movement. The performance of any skill can be described by a curve depicting the joint angle over the time required to perform a skill. In this study, we focus on only 14 body joints, and each joint comprises three angles. The proposed system comprises three main stages. In the first stage, data are obtained using motion capture inertial measurement unit sensors from top professional fighters/players while they are performing a certain skill. In the second stage, the collected sensor data obtained are input to the biomechanical software to extract the player’s joint angle curve. Finally, each joint angle curve is processed using a continuous wavelet transform to extract the main curve points (i.e., peaks and valleys). Finally, after extracting the joint curves from several top players, we summarize the players’ curves based on five statistical indicators, i.e., the minimum, maximum, mean, and mean
standard deviation. These five summarized curves are regarded as standard performance curves for the joint angle. When a player’s joint curve is surrounded by the five summarized curves, the performance is considered acceptable. Otherwise, the performance is considered unsatisfactory. The proposed system is evaluated based on four differen...
FathollahZadeh Aghdam, R, Ahmad, N, Naveed, A & Berenjforoush Azar, B 2023, 'On the relationship between energy and development: A comprehensive note on causation and correlation', Energy Strategy Reviews, vol. 46, pp. 101034-101034.
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Fathoni, AM, Putra, N & Mahlia, TMI 2023, 'A systematic review of battery thermal management systems based on heat pipes', Journal of Energy Storage, vol. 73, pp. 109081-109081.
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The global economic increase is accompanied by an increase in energy use. As a result, there is an increase in emissions of greenhouse gases that can harm the environment and lead to global warming. Electric vehicles powered by lithium-ion batteries were developed to lower the transportation sector's contribution to greenhouse gas emissions. In order to work optimally, the battery must be maintained at its optimal temperature. Heat pipe-based thermal management systems of electric vehicles' batteries have been gaining interest recently due to their ability to dissipate heat to the environment quickly and work passively without any added energy. Heat pipes are anticipated to keep battery packs for electric vehicles at their ideal operating temperature, ensure temperature uniformity between battery cells, and minimize thermal runaway possibility. This paper mainly discusses the application of heat pipes in the thermal management system of the electric vehicle battery. Besides conventional heat pipes, hybrid thermal management systems for electric vehicle batteries based on heat pipes have also been reviewed and discussed. For the hybrid battery management system, heat pipes coupled with phase change materials, air cooling and liquid cooling have been analysed. Finally, this review study describes the limitations and future work opportunities in the research area of thermal management systems in electric vehicle batteries based on heat pipes.
Fatima, Z, Quinto, M, Zhou, JL & Li, D 2023, 'Active substances of fat-soluble vitamins: Advances in extraction and analysis approaches', TrAC Trends in Analytical Chemistry, vol. 167, pp. 117276-117276.
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Fattah, IMR, Farhan, ZA, Kontoleon, KJ, kianfar, E & Hadrawi, SK 2023, 'Hollow fiber membrane contactor based carbon dioxide absorption − stripping: a review', Macromolecular Research, vol. 31, no. 4, pp. 1-27.
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Energy need is predicted to increase by 47% in the next 30 years. Global warming resulting from the continuously increasing atmospheric Carbon dioxide concentration is becoming a serious and pressing issue that needs to be controlled. Carbon dioxide capture and storage/use (CCS/CCU) provide a promising route to mitigate the environmental consequences of Carbon dioxide emission from fossil fuel combustion. In recent years, hollow fiber membrane contactors are regarded as an advanced technique with several competitive advantages over conventional technologies such as easy scale-up, independent control of flow rates, more operational flexibility, absence of flooding and foaming as well as high interfacial area per unit volume. However, many factors such as the membrane material selection, proper choice of solvent, and membrane module design are critical to success. In this regard, this paper aims at covering all areas related to hollow fiber membranes, including membrane material, membrane modification, membrane surface modification, shape, solvent characterization, operating parameters and costs, hybrid process, hydrophilicity and hydrophobicity of the absorption materials in the membranes, Advantages and Disadvantages of Membrane Contact Technology, membrane lifetime, and energy consumption as well as commercially available systems. Current progress, future potential, and development of pilot-scale applications and thermal fluid of this strategy are also assessed carefully. Furthermore, pore wetting as the main technical challenge in membrane contactor industrial implementation for post- and pre-combustion Carbon dioxide capture processes is investigated in detail. Graphical abstract: [Figure not available: see fulltext.].
Faust, O, De Michele, S, Koh, JEW, Jahmunah, V, Lih, OS, Kamath, AP, Barua, PD, Ciaccio, EJ, Lewis, SK, Green, PH, Bhagat, G & Acharya, UR 2023, 'Automated analysis of small intestinal lamina propria to distinguish normal, Celiac Disease, and Non-Celiac Duodenitis biopsy images', Computer Methods and Programs in Biomedicine, vol. 230, pp. 107320-107320.
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Fei, Y, Han, N, Shi, J, Tang, S, Zhuang, H, Wang, L, Ran, J, Gao, E, Habila, MA, Chen, Z, Tao, D, Ni, B-J & Jiang, M 2023, 'Red mud-derived iron carbon catalyst for the removal of organic pollutants in wastewater', Chemosphere, vol. 337, pp. 139211-139211.
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Feng, A, Mao, S, Onggowarsito, C, Naidu, G, Li, W & Fu, Q 2023, 'Tillandsia-Inspired Composite Materials for Atmospheric Water Harvesting', ACS Sustainable Chemistry and Engineering, vol. 11, no. 15, pp. 5819-5825.
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Atmospheric water harvesting (AWH) is a potentially promising small-scale approach to alleviate the water crisis in arid or semiarid regions. Inspired by the asymmetric structure of tillandsia leaves, a plant species native to semiarid regions, we report the development of a bioinspired composite (BiC) to draw moisture for AWH applications. With the advent of the post-COVID era, the nonwoven materials in used masks are discarded, landfilled, or incinerated along with the masks as medical waste, and the negative impact on the environment is inevitable. The nonwoven sheet has porosity, softness, and certain mechanical strength. We innovatively developed BiCs, immobilizing hygroscopic salt with a nonwoven mask for fast vapor liquefaction and using a polymer network to store water. The resulting BiC material manages to achieve a high-water adsorption capacity of 1.24 g g-1 under a low-moderate humidity environment and a high-water release ratio of ca. 90% without the use of photothermal materials, while maintaining high structural integrity in cyclic testing.
Feng, K, Ji, JC & Ni, Q 2023, 'A novel adaptive bandwidth selection method for Vold–Kalman filtering and its application in wind turbine planetary gearbox diagnostics', Structural Health Monitoring, vol. 22, no. 2, pp. 1027-1048.
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The planetary gearbox transmission system in wind turbines has complex structures and generally operates under non-stationary conditions. Thus its measured responses are of high complexity and nonlinearity, which brings a great challenge in the development of reliable condition monitoring techniques for the planetary gearbox transmission system. As a prevalent and effective tool for analyzing the non-stationary vibration signal with strong nonlinearity, the Vold–Kalman filtration technique has excellent capabilities of tracking the targeted harmonic components of vibrations, which can significantly benefit planetary gearbox fault diagnostics. However, the tracking accuracy is heavily enslaved to the selection of the rational bandwidth for the Vold–Kalman filter. An inappropriate bandwidth could impair the characteristics of the targeted harmonic responses, and as a consequence, the monitoring process becomes no longer reliable. To address this issue, a novel bandwidth selection methodology for the Vold–Kalman filter is developed in this paper. Through comprehensively depicting the targeted harmonic response using features in multiple domains, the rational bandwidth can be selected for Vold–Kalman filtering, and then, a reliable monitoring process can be ensured. Additionally, a tacho-less speed estimation procedure is utilized in this paper to acquire the instantaneous rotational speed from the vibration signal directly. With the rational bandwidth and the estimated rotational speed, the desired harmonic components of vibrations can be adaptively extracted and tracked through the Vold–Kalman filter with high accuracy, and at the same time, the irrelevant or unwanted components are excluded completely. The effectiveness and superiority of the proposed adaptive Vold–Kalman filtration for wind turbine planetary gearbox diagnostics are demonstrated and validated experimentally.
Feng, K, Ji, JC & Ni, Q 2023, 'A novel gear fatigue monitoring indicator and its application to remaining useful life prediction for spur gear in intelligent manufacturing systems', International Journal of Fatigue, vol. 168, pp. 107459-107459.
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With the material degradation of gear over its service lifespan, the gearbox is prone to fatigue, especially under harsh working environments. The interaction between gear fatigue and gear dynamics often results in high complexity measurements. This poses significant challenges to developing effective vibration-based techniques to monitor the gear fatigue propagation and predict its remaining useful life (RUL). To address this issue, a novel transmission error-based indicator is proposed to assess the fatigue severity, and then it is utilized to predict the RUL of the gearbox. The effectiveness of the proposed prognostic methodology is validated using endurance tests.
Feng, K, Ji, JC, Ni, Q & Beer, M 2023, 'A review of vibration-based gear wear monitoring and prediction techniques', Mechanical Systems and Signal Processing, vol. 182, pp. 109605-109605.
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Feng, K, Ji, JC, Ni, Q, Li, Y, Mao, W & Liu, L 2023, 'A novel vibration-based prognostic scheme for gear health management in surface wear progression of the intelligent manufacturing system', Wear, vol. 522, pp. 204697-204697.
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Gearbox has a compact structure, a stable transmission capability, and high transmission efficiency. Thus, it is widely applied and used as a critical transmission system in intelligent manufacturing systems, such as machine tools and robotics. The gearbox usually operates in harsh and non-stationary working environments, making the gear surface prone to wear. The progression of gear surface wear may lead to severe gear failures, such as gear tooth breakage and root crack, potentially damaging the whole gear transmission system. Therefore, it is essential to assess the gear surface wear progression and predict its remaining useful life (RUL) in order to ensure the reliable operation of the gear transmission system. To this end, this paper developed a novel gear wear prognostic scheme based on vibration analysis for gear health management. More specifically, a novel health indicator (HI) is first developed for gear wear monitoring in the proposed prognostic scheme. The novel HI, inferred from the cyclic correntropy and Wasserstein distance (WD), can accurately reflect the wear-induced cyclic correntropy spectra distribution change over time. Therefore, the novel HI can robustly evaluate the gear wear severity with high accuracy. With the developed HI, a network, namely the optimized gated recurrent unit (GRU), is applied for predicting the gear transmission system RUL during surface wear progression. As for the optimized GRU network, the genetic algorithm (GA) is applied to find the optimal hyperparameters adaptively, which can significantly improve the practicality of the developed prognostic scheme. To conclude, the developed prognostic scheme can effectively reveal the gear wear propagation characteristics and predict the RUL accurately. A series of endurance tests are conducted to verify the effectiveness of the developed prognostic scheme for gear health management in surface wear progression.
Feng, K, Ji, JC, Ni, Q, Yun, H, Zheng, J & Liu, Z 2023, 'A novel vibration indicator to monitor gear natural fatigue pitting propagation', Structural Health Monitoring, vol. 22, no. 5, pp. 3126-3140.
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Fatigue pitting can reduce the gear surface durability and induce other severe failures, which will eventually lead to the complete loss of transmission function of the transmission system. Thus, monitoring fatigue pitting progression is vital to avoid unexpected economic losses and incidents. Thanks to the unique characteristics of the gear meshing process, there is a close relationship between the tribological features of fatigue pitting and gear vibration cyclostationarity. Based on the vibration cyclostationarity, this paper develops a novel second-order cyclostationary (CS2) fatigue pitting monitoring indicator, which can accurately assess the degradation status of the gear system and benefit subsequent health management. The advantage of the developed cyclostationary indicator in evaluating and monitoring the process of fatigue pitting propagation is demonstrated with the natural fatigue pitting progression test, through comparisons with other conventional indicators.
Feng, K, Ji, JC, Zhang, Y, Ni, Q, Liu, Z & Beer, M 2023, 'Digital twin-driven intelligent assessment of gear surface degradation', Mechanical Systems and Signal Processing, vol. 186, pp. 109896-109896.
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Feng, L, Hu, C, Zhu, Q, Kong, F & Wen, S 2023, 'Distributed dynamic event-triggered control for fixed/preassigned-time output synchronization of output-coupling complex networks', Information Sciences, vol. 649, pp. 119651-119651.
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Feng, S, Ngo, HH, Guo, W, Khan, MA, Zhang, S, Luo, G, Liu, Y, An, D & Zhang, X 2023, 'Fruit peel crude enzymes for enhancement of biohydrogen production from synthetic swine wastewater by improving biohydrogen-formation processes of dark fermentation', Bioresource Technology, vol. 372, pp. 128670-128670.
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Feng, X, Chen, Z, Wang, S, Cen, L, Ni, B-J & Liu, Q 2023, 'Insights into the weathering behavior of pyrite in alkaline soil through electrochemical characterizations: Actual hazards or potentially benefits?', J Hazard Mater, vol. 451, pp. 131145-131145.
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Pyrite is the most common metal sulfide mineral in the crust and readily weathers under natural circumstances to release H+ to acidify surrounding groundwater and soil, resulting in heavy metal ions in the surrounding environment (e.g., meadow and saline soils). Meadow and saline soils are two common, widely distributed alkaline soils and can affect pyrite weathering. Currently, the weathering behaviors of pyrite in saline and meadow soil solutions have not been systematically studied. Electrochemistry coupled with surface analysis methods were employed to study pyrite weathering behaviors in simulated saline and meadow soil solutions in this work. Experimental results suggest that saline soil and higher temperatures increase pyrite weathering rates due to the lower resistance and greater capacitance. Surface reactions and diffusion control the weathering kinetics, and the activation energies for the simulated meadow and saline soil solutions are 27.1 and 15.8 kJ mol-1, respectively. In-depth investigations reveal that pyrite is initially oxidized to Fe(OH)3 and S0, and Fe(OH)3 further transforms into goethite γ-FeOOH and hematite α-Fe2O3, while S0 ultimately converts into sulfate. When these iron compounds enter alkaline soils, the alkalinity of soil changes, and iron (hydr)oxides effectively reduce the bioavailability of heavy metals and benefit alkaline soils. Meanwhile, weathering of natural pyrite ores containing toxic elements (such as Cr, As, and Cd) makes these elements bioavailable and potentially degrades the surrounding environment.
Feng, Y & Li, S 2023, 'Abstract interpretation, Hoare logic, and incorrectness logic for quantum programs', Information and Computation, vol. 294, pp. 105077-105077.
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Feng, Y, Wang, Q, Chen, X, Wu, D & Gao, W 2023, 'Virtual modelling technique for geometric-material nonlinear dynamics of structures', Structural Safety, vol. 100, pp. 102284-102284.
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This paper presents a virtual modelling technique for dynamic safety assessment of practical structures undergoing geometric and material blended nonlinearities. The variational inputs of systematic properties are treated within the 3D dynamic geometric-elastoplastic analyses. To circumvent numerical challenges in solving the coupled nonlinear problems, a freshly developed dynamic virtual modelling (DVM) technique is employed to determine the inherent relationship between the variational input data and the nonlinear structural response by using a new clustering based extended support vector regression (C-XSVR) algorithm with a novel T-spline polynomial kernel function. The virtual modelling models can be constructed at each time step within the Newmark's time integration procedure, which then can be used to predict deflection, force, and stress of the concerned structure at different periods. The DVM is capable of visibly forecasting potential large deformation nonlinear behaviours in an efficient manner, based on the explicit relationship functions. To demonstrate the accuracy and effectiveness of the proposed framework, nonlinear behaviours of two practical applications under future forecasted working conditions are predicted and validated in the numerical investigations.
Feng, Y, Wang, Q, Yu, Y, Zhang, T, Wu, D, Chen, X, Luo, Z & Gao, W 2023, 'Experimental-numerical-virtual (ENV) modelling technique for composite structure against low velocity impacts', Engineering Structures, vol. 278, pp. 115488-115488.
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Feng, Y, Wu, D, Stewart, MG & Gao, W 2023, 'Past, current and future trends and challenges in non-deterministic fracture mechanics: A review', Computer Methods in Applied Mechanics and Engineering, vol. 412, pp. 116102-116102.
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Fonseka, C, Ryu, S, Naidu, G, Kandasamy, J, Thiruvenkatachari, R & Vigneswaran, S 2023, 'Europium adsorption by granulated Cr-MIL-PMIDA metal−organic frameworks and dynamic fixed bed column modelling', Journal of Water Process Engineering, vol. 56, pp. 104475-104475.
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Fraile Navarro, D, Kocaballi, AB, Dras, M & Berkovsky, S 2023, 'Collaboration, not Confrontation: Understanding General Practitioners’ Attitudes Towards Natural Language and Text Automation in Clinical Practice', ACM Transactions on Computer-Human Interaction, vol. 30, no. 2, pp. 1-34.
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General Practitioners are among the primary users and curators of textual electronic health records, highlighting the need for technologies supporting record access and administration. Recent advancements in natural language processing facilitate the development of clinical systems, automating some time-consuming record-keeping tasks. However, it remains unclear what automation tasks would benefit clinicians most, what features such automation should exhibit, and how clinicians will interact with the automation. We conducted semi-structured interviews with General Practitioners uncovering their views and attitudes toward text automation. The main emerging theme was doctor-AI collaboration, addressing a reciprocal clinician-technology relationship that does not threaten to substitute clinicians, but rather establishes a constructive synergistic relationship. Other themes included: (i) desired features for clinical text automation; (ii) concerns around clinical text automation; and (iii) the consultation of the future. Our findings will inform the design of future natural language processing systems, to be implemented in general practice.
Galpathage, SG, Indraratna, B, Heitor, A & Rujikiatkamjorn, C 2023, 'Pull-out behaviour of simulated tree roots embedded in compacted soil', Proceedings of the Institution of Civil Engineers - Ground Improvement, vol. 176, no. 1, pp. 54-64.
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Vegetated ground is strengthened by the suction generated during evapotranspiration as well as physically and mechanically by the reinforcement effect induced by tree roots. When suction suddenly decreases during flooding or intense rainfall, the shear strength of the soil–plant system relies mainly on the physical root reinforcement. In this paper, the pull-out behaviour of simulated roots embedded in a compacted soil is investigated to assess how the soil–plant system is mechanically strengthened. An analytical framework is developed to estimate the pull-out force and it is validated using a series of pull-out tests. The tests are carried out on simulated roots embedded in a box of compacted soil having different water contents and equivalent dry unit weights. The results show that the pull-out capacity of this root system is mainly influenced by the initial (as-compacted) suction, and the length and diameter of the roots. The model predictions agree reasonably well with the experimental observations.
Ganbat, N, Hamdi, FM, Ibrar, I, Altaee, A, Alsaka, L, Samal, AK, Zhou, J & Hawari, AH 2023, 'Iron slag permeable reactive barrier for PFOA removal by the electrokinetic process', Journal of Hazardous Materials, vol. 460, pp. 132360-132360.
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Gandomi, AH, Deb, K, Averill, RC, Rahnamayan, S & Omidvar, MN 2023, 'Variable functioning and its application to large scale steel frame design optimization', Structural and Multidisciplinary Optimization, vol. 66, no. 1.
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AbstractTo solve complex real-world problems, heuristics and concept-based approaches can be used to incorporate information into the problem. In this study, a concept-based approach called variable functioning (Fx) is introduced to reduce the optimization variables and narrow down the search space. In this method, the relationships among one or more subsets of variables are defined with functions using information prior to optimization; thus, the function variables are optimized instead of modifying the variables in the search process. By using the problem structure analysis technique and engineering expert knowledge, the Fx method is used to enhance the steel frame design optimization process as a complex real-world problem. Herein, the proposed approach was coupled with particle swarm optimization and differential evolution algorithms then applied for three case studies. The algorithms are applied to optimize the case studies by considering the relationships among column cross-section areas. The results show that Fx can significantly improve both the convergence rate and the final design of a frame structure, even if it is only used for seeding.
Gandomi, AH, Soize, C & Stewart, JR 2023, 'AI in computational mechanics and engineering sciences', Computer Methods in Applied Mechanics and Engineering, vol. 407, pp. 115935-115935.
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Gandomi, M, Pirooz, MD, Nematollahi, B, Nikoo, MR, Varjavand, I, Etri, T & Gandomi, AH 2023, 'Multi-criteria decision-making optimization model for permeable breakwaters characterization', Ocean Engineering, vol. 280, pp. 114447-114447.
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Ganguly, D, Schmidt, MO, Coleman, M, Ngo, T-VC, Sorrelle, N, Dominguez, ATA, Murimwa, GZ, Toombs, JE, Lewis, C, Fang, YV, Valdes-Mora, F, Gallego-Ortega, D, Wellstein, A & Brekken, RA 2023, 'Pleiotrophin drives a prometastatic immune niche in breast cancer', Journal of Experimental Medicine, vol. 220, no. 5.
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Metastatic cancer cells adapt to thrive in secondary organs. To investigate metastatic adaptation, we performed transcriptomic analysis of metastatic and non-metastatic murine breast cancer cells. We found that pleiotrophin (PTN), a neurotrophic cytokine, is a metastasis-associated factor that is expressed highly by aggressive breast cancers. Moreover, elevated PTN in plasma correlated significantly with metastasis and reduced survival of breast cancer patients. Mechanistically, we find that PTN activates NF-κB in cancer cells leading to altered cytokine production, subsequent neutrophil recruitment, and an immune suppressive microenvironment. Consequently, inhibition of PTN, pharmacologically or genetically, reduces the accumulation of tumor-associated neutrophils and reverts local immune suppression, resulting in increased T cell activation and attenuated metastasis. Furthermore, inhibition of PTN significantly enhanced the efficacy of immune checkpoint blockade and chemotherapy in reducing metastatic burden in mice. These findings establish PTN as a previously unrecognized driver of a prometastatic immune niche and thus represents a promising therapeutic target for the treatment of metastatic breast cancer.
Gao, H, Dai, B, Miao, H, Yang, X, Barroso, RJD & Walayat, H 2023, 'A Novel GAPG Approach to Automatic Property Generation for Formal Verification: The GAN Perspective', ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 19, no. 1, pp. 1-22.
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Formal methods have been widely used to support software testing to guarantee correctness and reliability. For example, model checking technology attempts to ensure that the verification property of a specific formal model is satisfactory for discovering bugs or abnormal behavior from the perspective of temporal logic. However, because automatic approaches are lacking, a software developer/tester must manually specify verification properties. A generative adversarial network (GAN) learns features from input training data and outputs new data with similar or coincident features. GANs have been successfully used in the image processing and text processing fields and achieved interesting and automatic results. Inspired by the power of GANs, in this article, we propose a GAN-based automatic property generation (GAPG) approach to generate verification properties supporting model checking. First, the verification properties in the form of computational tree logic (CTL) are encoded and used as input to the GAN. Second, we introduce regular expressions as grammar rules to check the correctness of the generated properties. These rules work to detect and filter meaningless properties that occur because the GAN learning process is uncontrollable and may generate unsuitable properties in real applications. Third, the learning network is further trained by using labeled information associated with the input properties. These are intended to guide the training process to generate additional new properties, particularly those that map to corresponding formal models. Finally, a series of comprehensive experiments demonstrate that the proposed GAPG method can obtain new verification properties from two aspects: (1) using only CTL formulas and (2) using CTL formulas combined with Kripke structures.
Gao, H, Fang, D, Xiao, J, Hussain, W & Kim, JY 2023, 'CAMRL: A Joint Method of Channel Attention and Multidimensional Regression Loss for 3D Object Detection in Automated Vehicles', IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 8, pp. 8831-8845.
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Gao, L, Li, X, Li, M, Zamyadi, A & Wang, Q 2023, 'Recent research advances in aqueous pollutants and treatment approaches', Process Safety and Environmental Protection, vol. 171, pp. 132-135.
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The water industry faces significant challenges under the impacts of climate change, population growth, and water resource scarcity. Great efforts and progress have been made to understand the presence and behaviors of emerging contaminants, their health and environmental impacts, and the energy-efficient and cost-effective treatment technologies. In this ‘Aqueous Emerging Pollutants and Treatment’ special issue, we collect 32 articles to demonstrate recent research progress in aqueous pollutants and treatment approaches. An overview of these 32 articles is provided. Six main trends for future research in aqueous pollutants and their treatment technologies have been provided.
Gao, S, Wang, R, Wang, X, Yu, S, Dong, Y, Yao, S & Zhou, W 2023, 'Detecting Adversarial Examples on Deep Neural Networks With Mutual Information Neural Estimation', IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 6, pp. 5168-5181.
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Gao, Y, Chen, L, Han, J, Yu, S & Fang, H 2023, 'Similarity-based Secure Deduplication for IIoT Cloud Management System', IEEE Transactions on Dependable and Secure Computing, pp. 1-16.
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Gaur, M, Chaturvedi, K, Vishwakarma, DK, Ramasamy, S & Prasad, M 2023, 'Self-supervised ensembled learning for autism spectrum classification', Research in Autism Spectrum Disorders, vol. 107, pp. 102223-102223.
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Gautam, K, Sharma, P, Dwivedi, S, Singh, A, Gaur, VK, Varjani, S, Srivastava, JK, Pandey, A, Chang, J-S & Ngo, HH 2023, 'A review on control and abatement of soil pollution by heavy metals: Emphasis on artificial intelligence in recovery of contaminated soil', Environmental Research, vol. 225, pp. 115592-115592.
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GBD, CRDC 2023, 'Global burden of chronic respiratory diseases and risk factors, 1990-2019: an update from the Global Burden of Disease Study 2019.', EClinicalMedicine, vol. 59, pp. 101936-101936.
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BACKGROUND: Updated data on chronic respiratory diseases (CRDs) are vital in their prevention, control, and treatment in the path to achieving the third UN Sustainable Development Goals (SDGs), a one-third reduction in premature mortality from non-communicable diseases by 2030. We provided global, regional, and national estimates of the burden of CRDs and their attributable risks from 1990 to 2019. METHODS: Using data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we estimated mortality, years lived with disability, years of life lost, disability-adjusted life years (DALYs), prevalence, and incidence of CRDs, i.e. chronic obstructive pulmonary disease (COPD), asthma, pneumoconiosis, interstitial lung disease and pulmonary sarcoidosis, and other CRDs, from 1990 to 2019 by sex, age, region, and Socio-demographic Index (SDI) in 204 countries and territories. Deaths and DALYs from CRDs attributable to each risk factor were estimated according to relative risks, risk exposure, and the theoretical minimum risk exposure level input. FINDINGS: In 2019, CRDs were the third leading cause of death responsible for 4.0 million deaths (95% uncertainty interval 3.6-4.3) with a prevalence of 454.6 million cases (417.4-499.1) globally. While the total deaths and prevalence of CRDs have increased by 28.5% and 39.8%, the age-standardised rates have dropped by 41.7% and 16.9% from 1990 to 2019, respectively. COPD, with 212.3 million (200.4-225.1) prevalent cases, was the primary cause of deaths from CRDs, accounting for 3.3 million (2.9-3.6) deaths. With 262.4 million (224.1-309.5) prevalent cases, asthma had the highest prevalence among CRDs. The age-standardised rates of all burden measures of COPD, asthma, and pneumoconiosis have reduced globally from 1990 to 2019. Nevertheless, the age-standardised rates of incidence and prevalence of interstitial lung disease and pulmonary sarcoidosis have increased throughout this period. Low- and lo...
Gedela, R, Indraratna, B, Medawela, S & Nguyen, TT 2023, 'EFFECTS OF FINES CONTENT ON THE STRENGTH AND STIFFNESS OF BIOPOLYMER TREATED LOW-PLASTICITY SOILS', Australian Geomechanics Journal, vol. 58, no. 1, pp. 23-41.
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The use of biopolymers to enhance the engineering properties of soil has received increasing attention in recent years, however, the interactive role that biopolymers and the fines content of the soil play in governing the geotechnical parameters still requires insightful investigation, in relation to chemical soil treatment that can be ecologically detrimental. This paper examines the combined effects of Xanthan Gum (XG) derived from specific bacterial strains and the presence of clay fines content (kaolin) on the strength and stiffness of low plasticity soils, with special reference of cyclic traffic (road and rail) loading. In this study, fine sand is mixed with different contents of kaolin, whereby laboratory compression and tensile tests were conducted on natural (untreated) and XG-treated soil specimens. The results indicate that soil strength can be enhanced significantly when XG is added, however the effectiveness is a function of the kaolin content (KC). At an optimum XG content of 2% and a fines content increasing from 5% to 30%, split tensile strength increases from 230 to 750 kPa,while the unconfined compressive strength rises from 1.4 to 7.9 MPa, respectively. For XG content between 0.5% and 2%, the small strain stiffness of treated soil increases fourfold from 206 to 854 MPa.
Gedela, R, Indraratna, B, Nguyen, TT & Medawela, S 2023, 'The effect of biopolymer treatment on the potential instability of a soft soil under cyclic loading', Transportation Geotechnics, vol. 42, pp. 101102-101102.
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Ghabussi, A, Mortazavi, M & Betha, R 2023, 'Seismic performance of a cold-formed and hot-rolled steel wall system equipped with curved steel dampers', Structures, vol. 53, pp. 296-316.
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Ghadi, MJ, Mishra, DK, Azizivahed, A, Li, L & Zhang, J 2023, 'Mobile compressed air energy storage for active distribution systems', International Journal of Electrical Power & Energy Systems, vol. 154, pp. 109434-109434.
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Ghalambaz, M, Mehryan, SAM, Ramezani, SR, Hajjar, A, El Kadri, M, Islam, MS, Younis, O & Ghodrat, M 2023, 'Phase change heat transfer in a vertical metal foam-phase change material thermal energy storage heat dissipator', Journal of Energy Storage, vol. 66, pp. 107370-107370.
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Ghannam, S & Hussain, F 2023, 'Comparison of deep learning approaches for forecasting urban short-term water demand a Greater Sydney Region case study', Knowledge-Based Systems, vol. 275, pp. 110660-110660.
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Gharekhani, M, Reza Nikoo, M, Allah Nadiri, A, Al-Rawas, G, Sana, A, Gandomi, AH, Nematollahi, B & Senapathi, V 2023, 'A new approach for assessing the assembled vulnerability of coastal aquifers based on optimization models', Journal of Hydrology, vol. 625, pp. 130084-130084.
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Ghezelbash, R, Maghsoudi, A, Shamekhi, M, Pradhan, B & Daviran, M 2023, 'Genetic algorithm to optimize the SVM and K-means algorithms for mapping of mineral prospectivity', Neural Computing and Applications, vol. 35, no. 1, pp. 719-733.
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Gholami, H, Mohammadifar, A, Golzari, S, Song, Y & Pradhan, B 2023, 'Interpretability of simple RNN and GRU deep learning models used to map land susceptibility to gully erosion', Science of The Total Environment, vol. 904, pp. 166960-166960.
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Gholami, K, Abbasi, M, Azizivahed, A & Li, L 2023, 'An efficient bi-objective approach for dynamic economic emission dispatch of renewable-integrated microgrids', Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 8, pp. 10695-10714.
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AbstractTo overcome the challenges of conventional power systems, such as increasing power demand, requirements of stability and reliability, and increasing integration of renewable energy sources, the concept of microgrids was introduced and is currently one of the most important solutions for solving the mentioned problems. Generally, microgrids have two operating modes, namely grid-connected and islanded modes. Based on the literature and its unique characteristics, the islanded mode is more challenging than the other one. In this paper, a new self-adaptive comprehensive differential evolution (SACDE) algorithm is proposed for solving economic load dispatch (ELD) and combined economic emission dispatch (CEED) problems, achieving optimal power consumption in isolated microgrids. Initially, SACDE is employed for solving the ELD problem as a single-objective function, meaning that the operational cost is just considered as the objective function, and thereby, the resources are scheduled accordingly. Then, a multi-objective platform based on SACDE is also proposed to solve the CEED problem. It means two objective functions, including operational cost and emission, are simultaneously optimized. For evaluating the performance of the proposed method, three different scenarios under various cases are considered. According to the results, when SACDE is employed to solve the single objective function (cost minimization) problem, it has better performance than other methods. In terms of the bi-objective scheme (cost and emission minimization), SACDE is significantly superior to the price penalty factor technique which is frequently used in previous studies.
Gholami, K, Azizivahed, A, Arefi, A & Li, L 2023, 'Risk-averse Volt-VAr management scheme to coordinate distributed energy resources with demand response program', International Journal of Electrical Power & Energy Systems, vol. 146, pp. 108761-108761.
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Ghorbanpour, S, Richards, C, Cole, L, McGrath, K, Warkiani, ME & McClements, L 2023, 'New 3D multicellular models of placental tissue for studying important mechanisms of preeclampsia', Placenta, vol. 140, pp. e6-e6.
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Ghorbanpour, S, Richards, C, Pienaar, D, Sesperez, K, Aboulkheyr Es, H, Nikolic, V, Karadzov-Orlic, N, Mikovic, Z, Stefanovic, M, Cakic, Z, Alqudah, A, Cole, L, Gorrie, C, Mcgrath, K, Kavurma, MM, Warkiani, ME & McClements, L 2023, 'SC3_3. FKBPL signalling in placental development and preeclampsia', Pregnancy Hypertension, vol. 33, pp. e77-e78.
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Gokhan Ozkaya, S, Baygin, N, Barua, PD, Singh, AR, Bajaj, M, Baygin, M, Dogan, S, Tuncer, T, Tan, R-S & Rajendra Acharya, U 2023, 'Most complicated lock pattern-based seismological signal framework for automated earthquake detection', International Journal of Applied Earth Observation and Geoinformation, vol. 118, pp. 103297-103297.
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Goldsmith, R, Miao, G, Daniel, S, Briozzo, P, Chai, H & Gardner, A 2023, 'Becoming an engineering education researcher through a kaleidoscope of practice theory perspectives', Australasian Journal of Engineering Education, vol. 28, no. 1, pp. 85-96.
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Gomes, SDC, Nguyen, QD, Li, W & Castel, A 2023, 'Carbonation resistance of calcined clay-ground granulated blast furnace slag alkali-activated mortar', Construction and Building Materials, vol. 393, pp. 131811-131811.
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Gong, S, Ball, J & Surawski, N 2023, 'A method of estimating imperviousness for the catchment modelling of urban environments', Journal of Hydroinformatics, vol. 25, no. 2, pp. 451-468.
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Abstract Urban impervious surfaces, a symbol of urbanisation, have permanently changed urban hydrology behaviour and play a critical role in modelling rainfall-runoff process. The distribution pattern of impervious surfaces is intrinsically connected with functional land zoning schemes. However, estimating impervious fractions for catchment modelling is becoming increasingly difficult due to intricate land zoning categories and heterogeneous land use land cover (LULC) during urbanisation. This study demonstrates an integrated approach of deep learning (DL) and grid sampling method to overcome the challenges of LULC classification, sample standardisation and statistical sample extraction. The classified impervious features were extracted within the land zoning scope and translated into polynomial functions using a probability-fitting approach to measure the occurrence likelihood distribution of samples' impervious fraction. Then, we use the information entropy (IE) to evaluate prediction stability by quantifying the condition entropy and information gain (IG) from each functional land zones to the occurrence likelihood of different impervious fraction intervals. The DL model shows robust LULC prediction, while probability-fitting study of impervious samples reflects the distribution differential of impervious fractions under the land zoning categories. The IE stability test shows a robust approach that clarifies different confident ranges of imperviousness estimation based on land zoning information.
Gong, S, Cui, L, Gu, B, Lyu, B, Hoang, DT & Niyato, D 2023, 'Hierarchical Deep Reinforcement Learning for Age-of-Information Minimization in IRS-aided and Wireless-powered Wireless Networks', IEEE Transactions on Wireless Communications, pp. 1-1.
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Gong, S, Guo, Z & Wen, S 2023, 'Finite-time synchronization of T-S fuzzy memristive neural networks with time delay', Fuzzy Sets and Systems, vol. 459, pp. 67-81.
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Gong, S, Guo, Z, Liu, M, Wen, S & Huang, T 2023, 'Aperiodic Event-Triggered Synchronization Control for Neural Networks With Stochastic Perturbations and Time Delay', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 70, no. 6, pp. 1986-1990.
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Gong, S, Guo, Z, Ou, S, Wen, S & Huang, T 2023, 'Synchronization Control for T-S Fuzzy Neural Networks With Time Delay: A Novel Event-Triggered Mechanism', IEEE Transactions on Fuzzy Systems, pp. 1-9.
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Gong, S, Wang, M, Gu, B, Zhang, W, Hoang, DT & Niyato, D 2023, 'Bayesian Optimization Enhanced Deep Reinforcement Learning for Trajectory Planning and Network Formation in Multi-UAV Networks', IEEE Transactions on Vehicular Technology, vol. 72, no. 8, pp. 10933-10948.
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Gong, Y, Li, Z, Liu, W, Lu, X, Liu, X, Tsang, IW & Yin, Y 2023, 'Missingness-Pattern-Adaptive Learning With Incomplete Data', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PP, no. 99, pp. 1-14.
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Many real-world problems deal with collections of data with missing values, e.g., RNA sequential analytics, image completion, video processing, etc. Usually, such missing data is a serious impediment to a good learning achievement. Existing methods tend to use a universal model for all incomplete data, resulting in a suboptimal model for each missingness pattern. In this paper, we present a general model for learning with incomplete data. The proposed model can be appropriately adjusted with different missingness patterns, alleviating competitions between data. Our model is based on observable features only, so it does not incur errors from data imputation. We further introduce a low-rank constraint to promote the generalization ability of our model. Analysis of the generalization error justifies our idea theoretically. In additional, a subgradient method is proposed to optimize our model with a proven convergence rate. Experiments on different types of data show that our method compares favorably with typical imputation strategies and other state-of-the-art models for incomplete data. More importantly, our method can be seamlessly incorporated into the neural networks with the best results achieved. The source code is released at https://github.com/YS-GONG/missingness-patterns.
Gooch, LJ, Masia, MJ, Stewart, MG & Lam, CY 2023, 'Statistical assessment of tensile and shear properties of unreinforced clay brick masonry', Construction and Building Materials, vol. 386, pp. 131578-131578.
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Goodswen, SJ, Kennedy, PJ & Ellis, JT 2023, 'A guide to current methodology and usage of reverse vaccinology towards in silico vaccine discovery.', FEMS Microbiol Rev, vol. 47, no. 2, p. fuad004.
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Reverse vaccinology (RV) was described at its inception in 2000 as an in silico process that starts from the genomic sequence of the pathogen and ends with a list of potential protein and/or peptide candidates to be experimentally validated for vaccine development. Twenty-two years later, this process has evolved from a few steps entailing a handful of bioinformatics tools to a multitude of steps with a plethora of tools. Other in silico related processes with overlapping workflow steps have also emerged with terms such as subtractive proteomics, computational vaccinology, and immunoinformatics. From the perspective of a new RV practitioner, determining the appropriate workflow steps and bioinformatics tools can be a time consuming and overwhelming task, given the number of choices. This review presents the current understanding of RV and its usage in the research community as determined by a comprehensive survey of scientific papers published in the last seven years. We believe the current mainstream workflow steps and tools presented here will be a valuable guideline for all researchers wanting to apply an up-to-date in silico vaccine discovery process.
Goodswen, SJ, Kennedy, PJ & Ellis, JT 2023, 'A state-of-the-art methodology for high-throughput in silico vaccine discovery against protozoan parasites and exemplified with discovered candidates for Toxoplasma gondii.', Sci Rep, vol. 13, no. 1, p. 8243.
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Vaccine discovery against eukaryotic parasites is not trivial as highlighted by the limited number of known vaccines compared to the number of protozoal diseases that need one. Only three of 17 priority diseases have commercial vaccines. Live and attenuated vaccines have proved to be more effective than subunit vaccines but adversely pose more unacceptable risks. One promising approach for subunit vaccines is in silico vaccine discovery, which predicts protein vaccine candidates given thousands of target organism protein sequences. This approach, nonetheless, is an overarching concept with no standardised guidebook on implementation. No known subunit vaccines against protozoan parasites exist as a result of this approach, and consequently none to emulate. The study goal was to combine current in silico discovery knowledge specific to protozoan parasites and develop a workflow representing a state-of-the-art approach. This approach reflectively integrates a parasite's biology, a host's immune system defences, and importantly, bioinformatics programs needed to predict vaccine candidates. To demonstrate the workflow effectiveness, every Toxoplasma gondii protein was ranked in its capacity to provide long-term protective immunity. Although testing in animal models is required to validate these predictions, most of the top ranked candidates are supported by publications reinforcing our confidence in the approach.
Goss, DM, Vasilescu, SA, Sacks, G, Gardner, DK & Warkiani, ME 2023, 'Microfluidics facilitating the use of small extracellular vesicles in innovative approaches to male infertility', Nature Reviews Urology, vol. 20, no. 2, pp. 66-95.
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Sperm are transcriptionally and translationally quiescent and, therefore, rely on the seminal plasma microenvironment for function, survival and fertilization of the oocyte in the oviduct. The male reproductive system influences sperm function via the binding and fusion of secreted epididymal (epididymosomes) and prostatic (prostasomes) small extracellular vesicles (S-EVs) that facilitate the transfer of proteins, lipids and nucleic acids to sperm. Seminal plasma S-EVs have important roles in sperm maturation, immune and oxidative stress protection, capacitation, fertilization and endometrial implantation and receptivity. Supplementing asthenozoospermic samples with normospermic-derived S-EVs can improve sperm motility and S-EV microRNAs can be used to predict non-obstructive azoospermia. Thus, S-EV influence on sperm physiology might have both therapeutic and diagnostic potential; however, the isolation of pure populations of S-EVs from bodily fluids with current conventional methods presents a substantial hurdle. Many conventional techniques lack accuracy, effectiveness, and practicality; yet microfluidic technology has the potential to simplify and improve S-EV isolation and detection.
Grelewicz, P, Khuat, TT, Czeczot, J, Nowak, P, Klopot, T & Gabrys, B 2023, 'Application of Machine Learning to Performance Assessment for a Class of PID-Based Control Systems', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 7, pp. 4226-4238.
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Grelewicz, P, Nowak, P, Khuat, TT, Czeczot, J, Klopot, T & Gabrys, B 2023, 'Practical implementation of computationally-efficient machine learning-based control performance assessment system for a class of closed loop systems', Applied Soft Computing, vol. 146, pp. 110690-110690.
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Grigorev, A, Mihăiţă, A-S, Saleh, K & Chen, F 2023, 'Automatic Accident Detection, Segmentation and Duration Prediction Using Machine Learning', IEEE Transactions on Intelligent Transportation Systems, vol. PP, no. 99, pp. 1-22.
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Grochow, J & Qiao, Y 2023, 'On the Complexity of Isomorphism Problems for Tensors, Groups, and Polynomials I: Tensor Isomorphism-Completeness', SIAM Journal on Computing, vol. 52, no. 2, pp. 568-617.
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Guan, J, Fang, W, Huang, M & Ying, M 2023, 'Detecting Violations of Differential Privacy for Quantum Algorithms.', CoRR, vol. abs/2309.04819.
Guan, J, Liu, Y, Kong, Q, Xiao, F, Zhu, Q, Tian, J & Wang, W 2023, 'Transformer-based autoencoder with ID constraint for unsupervised anomalous sound detection', EURASIP Journal on Audio, Speech, and Music Processing, vol. 2023, no. 1.
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AbstractUnsupervised anomalous sound detection (ASD) aims to detect unknown anomalous sounds of devices when only normal sound data is available. The autoencoder (AE) and self-supervised learning based methods are two mainstream methods. However, the AE-based methods could be limited as the feature learned from normal sounds can also fit with anomalous sounds, reducing the ability of the model in detecting anomalies from sound. The self-supervised methods are not always stable and perform differently, even for machines of the same type. In addition, the anomalous sound may be short-lived, making it even harder to distinguish from normal sound. This paper proposes an ID-constrained Transformer-based autoencoder (IDC-TransAE) architecture with weighted anomaly score computation for unsupervised ASD. Machine ID is employed to constrain the latent space of the Transformer-based autoencoder (TransAE) by introducing a simple ID classifier to learn the difference in the distribution for the same machine type and enhance the ability of the model in distinguishing anomalous sound. Moreover, weighted anomaly score computation is introduced to highlight the anomaly scores of anomalous events that only appear for a short time. Experiments performed on DCASE 2020 Challenge Task2 development dataset demonstrate the effectiveness and superiority of our proposed method.
Guan, J, Pan, L, Wang, C, Yu, S, Gao, L & Zheng, X 2023, 'Trustworthy Sensor Fusion Against Inaudible Command Attacks in Advanced Driver-Assistance Systems', IEEE Internet of Things Journal, vol. 10, no. 19, pp. 17254-17264.
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Guan, S, Lu, H, Zhu, L & Fang, G 2023, 'PoseGU: 3D human pose estimation with novel human pose generator and unbiased learning', Computer Vision and Image Understanding, vol. 233, pp. 103715-103715.
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Guan, W, Song, X, Wang, K, Wen, H, Ni, H, Wang, Y & Chang, X 2023, 'Egocentric Early Action Prediction via Multimodal Transformer-Based Dual Action Prediction', IEEE Transactions on Circuits and Systems for Video Technology, vol. 33, no. 9, pp. 4472-4483.
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Guan, Y, Zou, S, Peng, H, Ni, W, Sun, Y & Gao, H 2023, 'Cooperative UAV Trajectory Design for Disaster Area Emergency Communications: A Multi-Agent PPO Method', IEEE Internet of Things Journal, pp. 1-1.
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Guertler, M, Tomidei, L, Sick, N, Carmichael, M, Paul, G, Wambsganss, A, Hernandez Moreno, V & Hussain, S 2023, 'WHEN IS A ROBOT A COBOT? MOVING BEYOND MANUFACTURING AND ARM-BASED COBOT MANIPULATORS', Proceedings of the Design Society, vol. 3, pp. 3889-3898.
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AbstractCollaborative robots (“cobots”) have attracted growing attention in academia and industry over the last years. Due to in-built safety features and easy programming, they allow for close human-cobot collaboration and support e.g. flexible manufacturing. However, the lack of a common understanding what a cobot is along with its traditional focus on arm-based cobots complicates further research and industry adoption. Thus, this paper analyses the variety of definitions in literature incl. standards and practice examples to derive a consistent and holistic definition and taxonomy of what a collaborative robot is. Aside from contributing a structured overview of various forms of human-robot collaboration, this builds an important foundation for future research as it systematically differentiates different cobot types. Companies and other organisations will benefit by a better understanding of what type of cobot they need and how to ensure safe collaboration.
Guertler, MR, Brackemann, T, Burden, A & Caldwell, G 2023, 'Mapping socio-technical dependencies to enable the successful adoption of collaborative robots in industry', Procedia CIRP, vol. 119, pp. 564-569.
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Gul, M, Zulkifli, NWM, Masjuki, HH, Kalam, MA, Mujtaba, MA, Harith, MH, Syahir, AZ, Ahmed, W & Farooq, AB 2023, 'Corrigendum to “Effect of TMP-based-cottonseed oil-biolubricant blends on tribological behavior of cylinder liner-piston ring combinations” [Fuel 278 (2020) 118242]', Fuel, vol. 331, pp. 125742-125742.
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Gulied, M, Logade, K, Mutahir, H, Shaftah, S, Salauddin, S, Hameed, A, Zavahir, S, Elmakki, T, Shon, HK, Hong, S, Park, H & Han, DS 2023, 'A review of membrane-based dewatering technology for the concentration of liquid foods', Journal of Environmental Chemical Engineering, vol. 11, no. 5, pp. 110583-110583.
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Gunawan, Y, Firmansyah, AI, Supriatna, NK, al Irsyad, MI, Cendrawati, DG, Ahadi, K, Adilla, I & Silitonga, AS 2023, 'Comprehensive assessment using preheat crude palm oil on endurance test engine diesel: Technical and supply chain scheme', Industrial Crops and Products, vol. 204, pp. 117286-117286.
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Guo, B, Zhang, X, Su, M, Ma, H, Yang, Y & Siwakoti, YP 2023, 'A Single-Phase Common-Ground Five-Level Transformerless Inverter With Low Component Count for PV Applications', IEEE Transactions on Industrial Electronics, vol. 70, no. 3, pp. 2662-2674.
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Multilevel inverters (MLIs) featuring reactive power ability and common ground (CG) have become much popular in transformerless grid-tied photovoltaic applications. A switched-capacitor (SC) based five-level transformerless inverter configuration is thus proposed in this article, which requires only six switches, one diode, three capacitors, and one input dc voltage supply. Notably, only four power switches are operated in high frequency, thus, the switching losses are reduced. Moreover, the presented topology can effectively tackle the leakage current problem using a CG architecture. A multicarrier phase disposition pulsewidth modulation strategy is employed to achieve reactive power regulation and SC voltage self-balancing. To achieve the neutral point voltage balance of the presented five-level inverter, a simple closed-loop voltage-balancing control method is proposed. The operation principles with modulation strategy and voltage self-balancing of SC are discussed in depth. Comparisons between the proposed and the state-of-the-art MLIs are presented in detail. Finally, experimental tests on a single-phase 1.1-kW prototype validate the appropriate performance of the proposed inverter.
Guo, CA, Guo, YJ, Zhu, H, Ni, W & Yuan, J 2023, 'Optimization of Multibeam Antennas Employing Generalized Joined Coupler Matrix', IEEE Transactions on Antennas and Propagation, vol. 71, no. 1, pp. 215-224.
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Guo, K, Wu, M, Soo, Z, Yang, Y, Zhang, Y, Zhang, Q, Lin, H, Grosser, M, Venter, D, Zhang, G & Lu, J 2023, 'Artificial intelligence-driven biomedical genomics', Knowledge-Based Systems, vol. 279, pp. 110937-110937.
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Guo, M, Zhao, K, Sun, J, Wen, S & Dou, G 2023, 'Implementing bionic associate memory based on spiking signal', Information Sciences, vol. 649, pp. 119613-119613.
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Guo, Y, Lei, B, Yu, L, Lin, X & Li, W 2023, 'Investigation on mechanical properties and failure criterion of multi-recycled aggregate concrete under triaxial compression', Procedia Structural Integrity, vol. 45, pp. 66-73.
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Guo, Y, Liu, L, Ba, X, Lu, H, Lei, G, Yin, W & Zhu, J 2023, 'Measurement and Modeling of Magnetic Materials under 3D Vectorial Magnetization for Electrical Machine Design and Analysis', Energies, vol. 16, no. 1, pp. 417-417.
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The magnetic properties of magnetic cores are essential for the design of electrical machines, and consequently appropriate mathematical modeling is needed. Usually, the design and analysis of electrical machines consider only the one-dimensional (1D) magnetic properties of core materials, i.e., the relationship of magnetic flux density (B) versus magnetic field strength (H), and their associated power loss under 1D magnetization, in which the B and H are constrained in the same orientation. Some studies have also been performed with the two-dimensional (2D) magnetizations in which the B and H are vectorial, rotating on a plane, and they may not be in the same direction. It has been discovered that the 2D rotational property is very different from its 1D alternating counterpart. However, the magnetic fields in an electrical machine, in particular claw pole and transverse flux machines, are naturally three-dimensional (3D), and the B and H vectors are rotational and may not lie on the same plane. It can be expected that the 3D vectorial property might be different from its 2D or 1D counterpart, and hence it should be investigated for the interests of both academic research and engineering application. This paper targets at a general summary about the magnetic material characterization with 3D vectorial magnetization, and their application prospect in electrical machine design and analysis.
Guo, Y, Wang, H, Xiao, M, Guan, X, Lei, Y, Diao, T, Long, P, Zeng, R, Lai, X, Cai, H, You, Y, Wen, Y, Li, W, Wang, X, Wang, Y, Chen, Q, Yang, Y, Qiu, Y, Chen, J, Zeng, H, Ni, W, Zhao, Y, Ouyang, K, Wang, J, Wang, Q, Liu, L, Song, L, Wang, Y, Guo, H, Li, X, Wu, T & Yuan, Y 2023, 'Long-term outcomes of COVID-19 convalescents: An 18.5-month longitudinal study in Wuhan', International Journal of Infectious Diseases, vol. 127, pp. 85-92.
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Guo, Y, Yu, H, Ma, L, Zeng, L & Luo, X 2023, 'THFE: A Triple-hierarchy Feature Enhancement method for tiny boat detection', Engineering Applications of Artificial Intelligence, vol. 123, pp. 106271-106271.
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Guo, Z, Li, M & Krunz, M 2023, 'Exploiting Successive Interference Cancellation for Spectrum Sharing over Unlicensed Bands', IEEE Transactions on Mobile Computing, pp. 1-18.
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Gupta, A, Kumar, D, Verma, H, Tanveer, M, Javier, AP, Lin, C-T & Prasad, M 2023, 'Recognition of multi-cognitive tasks from EEG signals using EMD methods', Neural Computing and Applications, vol. 35, no. 31, pp. 22989-23006.
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AbstractMental task classification (MTC), based on the electroencephalography (EEG) signals is a demanding brain–computer interface (BCI). It is independent of all types of muscular activity. MTC-based BCI systems are capable to identify cognitive activity of human. The success of BCI system depends upon the efficient feature representation from raw EEG signals for classification of mental activities. This paper mainly presents on a novel feature representation (formation of most informative features) of the EEG signal for the both, binary as well as multi MTC, using a combination of some statistical, uncertainty and memory- based coefficient. In this work, the feature formation is carried out in the two stages. In the first stage, the signal is split into different oscillatory functions with the help of three well-known empirical mode decomposition (EMD) algorithms, and a new set of eight parameters (features) are calculated from the oscillatory function in the second stage of feature vector construction. Support vector machine (SVM) is used to classify the feature vectors obtained corresponding to the different mental tasks. This study consists the problem formulation of two variants of MTC; two-class and multi-class MTC. The suggested scheme outperforms the existing work for the both types of mental tasks classification.
Gupta, BB, Prajapati, V, Nedjah, N, Vijayakumar, P, El-Latif, AAA & Chang, X 2023, 'Machine learning and smart card based two-factor authentication scheme for preserving anonymity in telecare medical information system (TMIS)', Neural Computing and Applications, vol. 35, no. 7, pp. 5055-5080.
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Ha, QP, La, HM, Wang, S & Balaguer, C 2023, 'Special issue on recent advances in field and service robotics: handling harsh environments and cooperation', Robotica, vol. 41, no. 2, pp. 1-3.
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Abstract This Special Issue of the Robotica is on recent advances in field and service robotics with a focus on the use of robotic and autonomous technologies to handle tasks in harsh environments and tasks that involve the multirobot cooperation and human–robot interactions.
Hafiz, M, Alfahel, R, Altaee, A & Hawari, AH 2023, 'Techno-economic assessment of forward osmosis as a pretreatment process for mitigation of scaling in multi-stage flash seawater desalination process', Separation and Purification Technology, vol. 309, pp. 123007-123007.
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Hajikarimi, P, Ehsani, M, Moghadas Nejad, F & Gandomi, AH 2023, 'Formulation of Constitutive Viscoelastic Properties of Modified Bitumen Mastic Using Genetic Programming', Journal of Engineering Mechanics, vol. 149, no. 11.
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Hakim, WL, Fadhillah, MF, Park, S, Pradhan, B, Won, J-S & Lee, C-W 2023, 'InSAR time-series analysis and susceptibility mapping for land subsidence in Semarang, Indonesia using convolutional neural network and support vector regression', Remote Sensing of Environment, vol. 287, pp. 113453-113453.
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Hamdi, FM, Ganbat, N, Altaee, A, Samal, AK, Ibrar, I, Zhou, JL & Sharif, AO 2023, 'Hybrid and enhanced electrokinetic system for soil remediation from heavy metals and organic matter', Journal of Environmental Sciences.
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Han, C, Li, W, Li, W, Yang, L & Huang, Z 2023, 'CoFeNi based trifunctional electrocatalysts featuring in-situ formed heterostructure', Inorganic Chemistry Communications, vol. 149, pp. 110402-110402.
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All-in-one transition metal-based electrocatalysts with high activities towards different reactions in aqueous electrolytes are of critical importance as they can dramatically bring down the cost of relevant energy devices. Herein a facile and low-cost synthesis of CoFeNi nanoparticles encapsulated by an N-doped carbon layer has been developed by pyrolyzing Prussian blue (PB) precursors. The obtained catalyst features tri-catalytic activity towards OER, ORR, and HER reactions in alkaline and acidic condition, and show great potential as a catalyst for water splitting and anode material for Zinc-Air batteries. Moreover, compared with the single phase, the sample with the heterostructure composed of both fcc and bcc phases exhibited dramatic enhancement in multi-catalytic activity. The heterostructure originates from an in-situ phase separation induced by composition variation. This demonstrates the effectiveness of heterostructure engineering introduced by in-situ phase separation in boosting the multi-catalytic activity.
Hang, J, Wu, Y, Li, Y, Lai, T, Zhang, J & Li, Y 2023, 'A deep learning semantic segmentation network with attention mechanism for concrete crack detection', Structural Health Monitoring, vol. 22, no. 5, pp. 3006-3026.
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In this research, an attention-based feature fusion network (AFFNet), with a backbone residual network (ResNet101) enhanced with two attention mechanism modules, is proposed for automatic pixel-level detection of concrete crack. In particular, the inclusion of attention mechanism modules, for example, the vertical and horizontal compression attention module (VH-CAM) and the efficient channel attention upsample module (ECAUM), is to enable selective concentration on the crack feature. The VH-CAM generates a feature map integrating pixel-level information in vertical and horizontal directions. The ECAUM applied on each decoder layer combines efficient channel attention (ECA) and feature fusion, which can provide rich contextual information as guidance to help low-level features recover crack localization. The proposed model is evaluated on the test dataset and the results reach 84.49% for mean intersection over union (MIoU). Comparison with other state-of-the-art models proves high efficiency and accuracy of the proposed method.
Haq, S, Indraratna, B, Nguyen, TT & Rujikiatkamjorn, C 2023, 'Micromechanical Analysis of Internal Instability during Shearing', International Journal of Geomechanics, vol. 23, no. 6, p. 04023078.
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Internal instability means that finer particles pass through the constrictions of coarser particles at a hydraulic gradient well below that of heave or piping, rendering the soil ineffective for its intended purpose. The soil could make a transition from an internally stable state to an unstable state or vice versa due to shear-induced deformation. The discrete element method (DEM) is adopted in this study to examine and quantify soil behavior by simulating the quasi-static shear deformation of internally stable and unstable soils at the micro- and macroscales. The dense bimodal specimens were sheared under drained conditions following a constant mean stress path in order to investigate the influence of stress heterogeneity. At the macroscale, the peak deviatoric stress was found to be a function of the fines content and the initial void ratios of the specimens. The development of the average number of contacts per particle and the stress transfer to the finer fraction during shearing are discussed. The simulation results innovatively show that a dense specimen could undergo a transition from an internally stable to an unstable soil as it dilates during shear. These numerical results have significant implications on the importance of real-life situations, such as predicting mud pumping in railroad tracks.
Hasan, ASMM & Trianni, A 2023, 'Boosting the adoption of industrial energy efficiency measures through industry 4.0 technologies to improve operational performance', Journal of Cleaner Production, vol. 425, pp. 138597-138597.
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Hasanpour, S, Nouri, T, Blaabjerg, F & Siwakoti, YP 2023, 'High Step-Up SEPIC-Based Trans-Inverse DC-DC Converter With Quasi-Resonance Operation for Renewable Energy Applications.', IEEE Trans. Ind. Electron., vol. 70, no. 1, pp. 485-497.
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Hasanpour, S, Siwakoti, YP & Blaabjerg, F 2023, 'A New High Efficiency High Step-Up DC/DC Converter for Renewable Energy Applications', IEEE Transactions on Industrial Electronics, vol. 70, no. 2, pp. 1489-1500.
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This article introduces a new nonisolated soft-switching coupled-inductor (CI) step-up dc/dc converter. The presented topology utilizes a three-winding CI (TWCI) along with a voltage multiplier circuit to increase the voltage conversion ratio without needing a high duty cycle. Using this circuit, a high voltage gain can be achieved without requiring a large number of turns ratio in the CI. The input current ripple of the introduced converter is very low, which is very desirable for renewable energy sources applications. The TWCI also creates an additional design freedom to increase the voltage gain, which indicates more circuit flexibility. Additionally, the voltage stress across the single power switch is limited with the help of a regenerative clamp capacitor. The leakage inductor of the CI is used to create a resonant tank to reduce power losses further. The leakage inductances help provide the zero-current switching conditions for the single power switch and decrease the reverse-recovery issues for all diodes, leading to an efficiency improvement. The operation principle, steady-state analysis, and design considerations are discussed thoroughly. Finally, the theoretical analysis is validated through experimental results obtained from a 200 W prototype with 250 V output voltage.
Hassan, A, Makhdoom, I, Iqbal, W, Ahmad, A & Raza, A 2023, 'From trust to truth: Advancements in mitigating the Blockchain Oracle problem', Journal of Network and Computer Applications, vol. 217, pp. 103672-103672.
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Hassan, M, Kennard, M, Yoshitake, S, Ishac, K, Takahashi, S, Kim, S, Matsui, T & Hirokawa, M 2023, 'Augmenting the Sense of Social Presence in Online Video Games Through the Sharing of Biosignals'.
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Hassani, S, Mousavi, M & Dackermann, U 2023, 'Johansen cointegration of frequency response functions contaminated with nonstationary colored noise for structural damage detection', Journal of Sound and Vibration, vol. 552, pp. 117641-117641.
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Hastings, C, Overgaard, C, Wilson, S, Ramia, G, Morris, A & Mitchell, E 2023, 'Crowded house: accommodation precarity and self-reported academic performance of international students', Compare, vol. ahead-of-print, no. ahead-of-print, pp. 1-20.
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This article draws on two surveys of international students in Sydney and Melbourne, undertaken in 2019 and during the 2020 COVID-19 lockdowns. Using the concept of bounded agency, we identify how the challenges of living in one of the world’s most expensive rental housing markets impact students’ perceptions of their academic attainment. We find housing insecurity, unaffordability and condition, amplified by financial stress, contribute significantly to student anxiety about their studies. These relationships differ by student background and education. We argue students’ agency to meet their educational ambitions in Australia is constrained by the cost of housing and the housing choices they consequently make to mitigate financial stress. Our findings suggest the importance of ‘town’ or non-institutional aspects of the international student experience on their satisfaction and academic outcomes. We call for further research to explore these relationships in other global contexts.
Hastings, C, Ramia, G, Wilson, S, Mitchell, E & Morris, A 2023, 'Precarity Before and During the Pandemic: International Student Employment and Personal Finances in Australia', Journal of Studies in International Education, vol. 27, no. 1, pp. 102831532110651-102831532110651.
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There is mounting evidence of increased international student financial and work precarity over the last decade in Australia. Yet, there has been a little scholarly analysis of which students are most affected by precarity and its sources. Drawing on two surveys of international students in Australia's two largest cities, conducted before and during the pandemic, we investigate the financial and work vulnerabilities of international students. We demonstrate that vulnerability is related to characteristics which describe particular cohorts of students: being from low-income countries, working class families, seeking a low-level qualification, enrolled in a non-university institution, and being without a scholarship. The concepts of “noncitizenship” and “work precarity” are used to explain how the mechanisms of each characteristic heighten vulnerability, thereby contributing to a broader evidence-base about the causality of international student precarity.
Hazeri, AH, Abouei Mehrizi, A, Mohseni, SS, Ebrahimi Warkiani, M & Razavi Bazaz, S 2023, 'A Novel Strategy for Square-Wave Micromixers: A Survey of RBC Lysis for Further Biological Analysis', Industrial & Engineering Chemistry Research, vol. 62, no. 40, pp. 16215-16224.
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He, B, Armaghani, DJ & Lai, SH 2023, 'Assessment of tunnel blasting-induced overbreak: A novel metaheuristic-based random forest approach', Tunnelling and Underground Space Technology, vol. 133, pp. 104979-104979.
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He, H, Zhang, Q, Wang, S, Yi, K, Niu, Z & Cao, L 2023, '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, J, Li, H, Mai, J, Ke, Y, Zhai, C, Li, JJ, Jiang, L, Shen, G & Ding, X 2023, 'Profiling extracellular vesicle surface proteins with 10 µL peripheral plasma within 4 h', Journal of Extracellular Vesicles, vol. 12, no. 9.
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AbstractExtracellular vesicle (EV) surface proteins, expressed by primary tumours, are important biomarkers for early cancer diagnosis. However, the detection of these EV proteins is complicated by their low abundance and interference from non‐EV components in clinical samples. Herein, we present a MEmbrane‐Specific Separation and two‐step Cascade AmpLificatioN (MESS2CAN) strategy for direct detection of EV surface proteins within 4 h. MESS2CAN utilises novel lipid probes (long chains linked by PEG2K with biotin at one end, and DSPE at the other end) and streptavidin‐coated magnetic beads, permitting a 49.6% EV recovery rate within 1 h. A dual amplification strategy with a primer exchange reaction (PER) cascaded by the Cas12a system then allows sensitive detection of the target protein at 10 EV particles per microliter. Using 4 cell lines and 90 clinical test samples, we demonstrate MESS2CAN for analysing HER2, EpCAM and EGFR expression on EVs derived from cells and patient plasma. MESS2CAN reports the desired specificity and sensitivity of EGFR (AUC = 0.98) and of HER2 (AUC = 1) for discriminating between HER2‐positive breast cancer, triple‐negative breast cancer and healthy donors. MESS2CAN is a pioneering method for highly sensitive in vitro EV diagnostics, applicable to clinical samples with trace amounts of EVs.
He, L, Shi, K, Wang, D, Wang, X & Xu, G 2023, 'A topic-controllable keywords-to-text generator with knowledge base network', CAAI Transactions on Intelligence Technology.
He, X, Xu, H & Sheng, D 2023, 'Ready-to-use deep-learning surrogate models for problems with spatially variable inputs and outputs', Acta Geotechnica, vol. 18, no. 4, pp. 1681-1698.
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AbstractData-driven intelligent surrogate models gain popularity recently. Particularly in Monte-Carlo-style stochastic analysis, the influencing factors are considered as inputs, the quantities of interest are considered as outputs, and cheaper-to-evaluate surrogates models are built from a small amount of sample data and are used for the full Monte-Carlo analysis. This paper presents a framework with three innovations: (1) we build surrogate models for a particular problem that covers any possible material properties or boundary conditions commonly encountered in practice, so the models are ready to use, and do not require new data or training anymore. (2) The inputs and outputs to the problem are both spatially variable. Even after discretization, the input and output sizes are in the order of tens of thousands, which is challenging for traditional machine-learning algorithms. We take the footing failure mechanism as an example. Two types of neural networks are examined, fully connected networks and deep neural networks with complicated non-sequential structures (a modified U-Net). (3) This study is also the first attempt to use U-Nets as surrogate models for geotechnical problems. Results show that fully connected networks can fit well simple problems with a small input and output size, but fail for complex problems. Deep neural networks that account for the data structure give better results.
He, Z, Fan, X, Jin, W, Gao, S, Yan, B, Chen, C, Ding, W, Yin, S, Zhou, X, Liu, H, Li, X & Wang, Q 2023, 'Chlorine-resistant bacteria in drinking water: Generation, identification and inactivation using ozone-based technologies', Journal of Water Process Engineering, vol. 53, pp. 103772-103772.
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He, Z, Fan, X, Qu, L, Zhou, X, Jin, W, Hatshan, MR, Li, X, Liu, H, Jiang, G & Wang, Q 2023, 'Cultivation of Chlorella pyrenoidosa and Scenedesmus obliquus in swine wastewater: Nitrogen and phosphorus removal and microalgal growth', Process Safety and Environmental Protection, vol. 179, pp. 887-895.
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Using microalgae to treat swine wastewater can effectively reduce the increasing pollution and save the cost of cultivating microalgae. In this study, the growth and denitrification and phosphorus removal effects of Scenedesmus obliquus and Chlorella pyrenoidosa at different dilutions in swine wastewater were investigated to solve the problem that microalgae could not be cultivated in the raw swine wastewater. After diluting the swine wastewater 8 and 12 times, the growth was optimized after 11 days of cultivation of Scenedesmus obliquus and 9 days of cultivation of Chlorella pyrenoidosa. Compared to Chlorella pyrenoidosa, the biomass and chlorophyll-a content were higher in Scenedesmus obliquus, at 1.48 g/L and 18.46 mg/L, respectively. The removal of nitrogen and phosphorus indicators was almost 100 %. Subsequently, Scenedesmus obliquus was cultured in an 8-fold dilution of swine and domestic wastewater, with dry weights of 0.83 g/L and 1.44 g/L, and lipid contents of 41.26 % and 25.11 %, respectively. Compared to Chlorella pyrenoidosa, Scenedesmus obliquus was more tolerant to nitrogen and phosphorus in swine wastewater, and at the same time, it had a higher growth rate, making it more suitable for treating swine wastewater and accumulating biomass.
He, Z, Zhou, X, Fan, X, Jin, W, Chen, C, Yan, B, Yin, S, Zhou, T, Li, X & Jiang, G 2023, 'Advanced oxidation-based combined conditioning technologies to improve sludge dewaterability: A mini review', Journal of Water Process Engineering, vol. 53, pp. 103773-103773.
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Herse, S, Vitale, J & Williams, M-A 2023, 'Using Agent Features to Influence User Trust, Decision Making and Task Outcome during Human-Agent Collaboration', International Journal of Human–Computer Interaction, vol. 39, no. 9, pp. 1740-1761.
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Hieu, NQ, Hoang, DT, Niyato, D, Nguyen, DN, Kim, DI & Jamalipour, A 2023, 'Joint Power Allocation and Rate Control for Rate Splitting Multiple Access Networks With Covert Communications', IEEE Transactions on Communications, vol. 71, no. 4, pp. 2274-2287.
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Rate Splitting Multiple Access (RSMA) has recently emerged as a promising technique to enhance the transmission rate for multiple access networks. Unlike conventional multiple access schemes, RSMA requires splitting and transmitting messages at different rates. The joint optimization of the power allocation and rate control at the transmitter is challenging given the uncertainty and dynamics of the environment. Furthermore, securing transmissions in RSMA networks is a crucial problem because the messages transmitted can be easily exposed to adversaries. This work first proposes a stochastic optimization framework that allows the transmitter to adaptively adjust its power and transmission rates allocated to users, and thereby maximizing the sum-rate and fairness of the system under the presence of an adversary. We then develop a highly effective learning algorithm that can help the transmitter to find the optimal policy without requiring complete information about the environment in advance. Extensive simulations show that our proposed scheme can achieve non-saturated transmission rates at high SNR values with infinite blocklength. More significantly, our proposed scheme can achieve positive covert transmission rates in the finite blocklength regime, compared with zero-valued covert rates of a conventional multiple access scheme.
Hieu, NQ, Nguyen, DN, Hoang, DT & Dutkiewicz, E 2023, 'When Virtual Reality Meets Rate Splitting Multiple Access: A Joint Communication and Computation Approach', IEEE Journal on Selected Areas in Communications, vol. 41, no. 5, pp. 1536-1548.
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Rate Splitting Multiple Access (RSMA) has emerged as an effective interference management scheme for applications that require high data rates. Although RSMA has shown advantages in rate enhancement and spectral efficiency, it has yet not to be ready for latency-sensitive applications such as virtual reality streaming, which is an essential building block of future 6G networks. Unlike conventional High-Definition streaming applications, streaming virtual reality applications requires not only stringent latency requirements but also the computation capability of the transmitter to quickly respond to dynamic users' demands. Thus, conventional RSMA approaches usually fail to address the challenges caused by computational demands at the transmitter, let alone the dynamic nature of the virtual reality streaming applications. To overcome the aforementioned challenges, we first formulate the virtual reality streaming problem assisted by RSMA as a joint communication and computation optimization problem. A novel multicast approach is then proposed to cluster users into different groups based on a Field-of-View metric and transmit multicast streams in a hierarchical manner. After that, we propose a deep reinforcement learning approach to obtain the solution for the optimization problem. Extensive simulations show that our framework can achieve the millisecond-latency requirement, which is much lower than other baseline schemes.
Hoang, AT, Balasubramanian, D, Venugopal, IP, Rajendran, V, Nguyen, DT, Lawrence, KR, Nguyen, XP & Kalam, MA 2023, 'A feasible and promising approach for diesel engine fuelled with a blend of biodiesel and low-viscosity Cinnamon oil: A comprehensive analysis of performance, combustion, and exergy', Journal of Cleaner Production, vol. 401, pp. 136682-136682.
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Hoang, AT, Murugesan, P, PV, E, Balasubramanian, D, Parida, S, Priya Jayabal, C, Nachippan, M, Kalam, MA, Truong, TH, Cao, DN & Le, VV 2023, 'Strategic combination of waste plastic/tire pyrolysis oil with biodiesel for natural gas-enriched HCCI engine: Experimental analysis and machine learning model', Energy, vol. 280, pp. 128233-128233.
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Hoang, T-D, Huang, X & Qin, P 2023, 'Gradient Descent-Based Direction-of-Arrival Estimation for Lens Antenna Array', IEEE Signal Processing Letters, vol. 30, pp. 838-842.
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Hoang, TM, Xu, C, Vahid, A, Tuan, HD, Duong, TQ & Hanzo, L 2023, 'Secrecy-Rate Optimization of Double RIS-Aided Space–Ground Networks', IEEE Internet of Things Journal, vol. 10, no. 15, pp. 13221-13234.
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Hofer, OJ, Alsweiler, J, Tran, T & Crowther, CA 2023, 'Glycemic control in gestational diabetes and impact on biomarkers in women and infants.', Pediatr Res, vol. 94, no. 2, pp. 1-11.
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BACKGROUND: Gestational diabetes mellitus (GDM) is linked to the dysregulation of inflammatory markers in women with GDM compared to women without. It is unclear whether the intensity of glycemic control influences these biomarkers. We aimed to assess whether different glycemic targets for women with GDM and compliance influence maternal and infant biomarkers. METHODS: Maternity hospitals caring for women with GDM were randomized in the TARGET Trial to tight or less tight glycemic targets. Maternal blood was collected at study entry, 36 weeks' gestation, and 6 months postpartum, and cord plasma after birth. We assessed compliance to targets and concentrations of maternal serum and infant biomarkers. RESULTS: Eighty-two women and infants were included in the study. Concentrations of maternal and infant biomarkers did not differ between women assigned to tighter and less tight glycemic targets; however, concentrations were altered in maternal serum leptin and CRP and infant cord C-peptide, leptin, and IGF in women who complied with tighter targets. CONCLUSIONS: Use of tighter glycemic targets in women with GDM does not change the concentrations of maternal and infant biomarkers compared to less tight targets. However, when compliance is achieved to tighter targets, maternal and infant biomarkers are altered. IMPACT: The use of tighter glycemic targets in gestational diabetes does not result in changes to maternal or cord plasma biomarkers. However, for women who complied with tighter targets, maternal serum leptin and CRP and infant cord C-peptide, leptin and IGF were altered compared with women who complied with the use of the less tight targets. This article adds to the current evidence base regarding the impact of gestational diabetes on maternal and infant biomarkers. This article highlights the need for further research to assess enablers to meet the tighter target recommendations and to assess the impact on relevant biomarkers.
Holloway, R, Ho, D, Delotavo, C, Xie, WY, Rahimi, I, Nikoo, MR & Gandomi, AH 2023, 'Optimal location selection for a distributed hybrid renewable energy system in rural Western Australia: A data mining approach', Energy Strategy Reviews, vol. 50, pp. 101205-101205.
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Holmes, NP, Roohani, I, Entezari, A, Guagliardo, P, Mirkhalaf, M, Lu, Z, Chen, Y-S, Yang, L, Dunstan, CR, Zreiqat, H & Cairney, JM 2023, 'Discovering an unknown territory using atom probe tomography: Elemental exchange at the bioceramic scaffold/bone tissue interface', Acta Biomaterialia, vol. 162, pp. 199-210.
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Hong, Z, Tao, M, Cui, X, Wu, C & Zhao, M 2023, 'Experimental and numerical studies of the blast-induced overbreak and underbreak in underground roadways', Underground Space, vol. 8, pp. 61-79.
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Hong, Z, Tao, M, Liu, L, Zhao, M & Wu, C 2023, 'An intelligent approach for predicting overbreak in underground blasting operation based on an optimized XGBoost model', Engineering Applications of Artificial Intelligence, vol. 126, pp. 107097-107097.
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Hong, Z, Tao, M, Wu, C, Zhou, J & Wang, D 2023, 'The spatial distribution of excavation damaged zone around underground roadways during blasting excavation', Bulletin of Engineering Geology and the Environment, vol. 82, no. 4.
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Ho-Pham, LT, Nguyen, HG, Nguyen-Pham, SQ, Hoang, DK, Tran, TS & Nguyen, TV 2023, 'Longitudinal changes in bone mineral density during perimenopausal transition: the Vietnam Osteoporosis Study', Osteoporosis International, vol. 34, no. 8, pp. 1381-1387.
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Horry, MJ, Chakraborty, S, Pradhan, B, Shulka, N & Almazroui, M 2023, 'Two-Speed Deep-Learning Ensemble for Classification of Incremental Land-Cover Satellite Image Patches', Earth Systems and Environment, vol. 7, no. 2, pp. 1-16.
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High-velocity data streams present a challenge to deep learning-based computer vision models due to the resources needed to retrain for new incremental data. This study presents a novel staggered training approach using an ensemble model comprising the following: (i) a resource-intensive high-accuracy vision transformer; and (ii) a fast training, but less accurate, low parameter-count convolutional neural network. The vision transformer provides a scalable and accurate base model. A convolutional neural network (CNN) quickly incorporates new data into the ensemble model. Incremental data are simulated by dividing the very large So2Sat LCZ42 satellite image dataset into four intervals. The CNN is trained every interval and the vision transformer trained every half interval. We call this combination of a complementary ensemble with staggered training a “two-speed” network. The novelty of this approach is in the use of a staggered training schedule that allows the ensemble model to efficiently incorporate new data by retraining the high-speed CNN in advance of the resource-intensive vision transformer, thereby allowing for stable continuous improvement of the ensemble. Additionally, the ensemble models for each data increment out-perform each of the component models, with best accuracy of 65% against a holdout test partition of the RGB version of the So2Sat dataset.
Hossain, E, Rana, R, Higgins, N, Soar, J, Barua, PD, Pisani, AR & Turner, K 2023, 'Natural Language Processing in Electronic Health Records in relation to healthcare decision-making: A systematic review', Computers in Biology and Medicine, vol. 155, pp. 106649-106649.
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Hossain, SM, Yu, H, Choo, Y, Naidu, G, Han, DS & Shon, HK 2023, 'ZiF-8 induced carbon electrodes for selective lithium recovery from aqueous feed water by employing capacitive deionization system', Desalination, vol. 546, pp. 116201-116201.
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Hosseini, SAH, Rahmani, O, Hayati, H & Keshtkar, M 2023, 'An exact solution of dynamic response of DNS with a medium viscoelastic layer by moving load', Advances in Materials Research (South Korea), vol. 12, no. 3, pp. 193-210.
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This paper aims to analyze the dynamic response of a double nanobeam system with a medium viscoelastic layer under a moving load. The governing equations are based on the Eringen nonlocal theory. A thin viscoelastic layer has coupled two nanobeams together. An exact solution is derived for each nanobeam, and the dynamic deflection is achieved. The effect of parameters such as nonlocal parameter, velocity of moving load, spring coefficient and the viscoelastic layer damping ratio was studied. The results showed that the effect of the nonlocal parameter is significantly important and the classical theories are not suitable for nano and microstructures.
Hosseinzadeh, A, Altaee, A, Li, X & Zhou, JL 2023, 'Machine learning-based modeling and analysis of perfluoroalkyl and polyfluoroalkyl substances controlling systems in protecting water resources', Current Opinion in Chemical Engineering, vol. 42, pp. 100983-100983.
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Hosseinzadeh-Bandbafha, H, Kazemi Shariat Panahi, H, Dehhaghi, M, Orooji, Y, Shahbeik, H, Mahian, O, Karimi-Maleh, H, Kalam, MA, Salehi Jouzani, G, Mei, C, Nizami, A-S, Guillemin, GG, Gupta, VK, Lam, SS, Yang, Y, Peng, W, Pan, J, Kim, K-H, Aghbashlo, M & Tabatabaei, M 2023, 'Applications of nanotechnology in biodiesel combustion and post-combustion stages', Renewable and Sustainable Energy Reviews, vol. 182, pp. 113414-113414.
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Hou, S, Ni, W, Zhao, K, Cheng, B, Zhao, S, Wan, Z, Liu, X & Chen, S 2023, 'Fine-Grained Online Energy Management of Edge Data Centers Using Per-Core Power Gating and Dynamic Voltage and Frequency Scaling', IEEE Transactions on Sustainable Computing, vol. 8, no. 3, pp. 522-536.
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It is important to minimize the energy consumption of large-scale, geographically distributed edge data centers (EDCs). While modern processing units (PUs) have energy-saving features like Dynamic Voltage and Frequency Scaling (DVFS) and Per-Core Power Gating (PCPG), optimization is still complex and requires a holistic approach. This article presents a new decentralized, three-Timescale, online optimization approach that enables multicore micro data centers (MDCs) to optimize their per-PU power states, per-enabled-PU voltage-frequency levels and offloading schedules at three different timescales. The key idea is that we employ multi-Timescale Lyapunov optimization to decouple the energy minimization between workload scheduling and result delivery at a small timescale and PU configuration at large timescales. Another important aspect is that we apply the primal decomposition to decouple the PU configuration between a per-enabled-PU voltage-frequency level at an intermediate timescale and a per-PU power state at a large timescale. Experiments demonstrate that the proposed approach improves energy efficiency significantly by up to 4.5 times in our considered lightly loaded situations where DVFS alone does not work effectively, compared to existing benchmarks.
Hu, R, Wang, X, Chang, X, Zhang, Y, Hu, Y, Liu, X & Yu, S 2023, '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 & Jamalipour, A 2023, 'RIS-Assisted Jamming Rejection and Path Planning for UAV-Borne IoT Platform: A New Deep Reinforcement Learning Framework', IEEE Internet of Things Journal, vol. 10, no. 22, pp. 20162-20173.
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Hu, S, Yuan, X, Ni, W, Wang, X & Jamalipour, A 2023, 'Visual Camouflage and Online Trajectory Planning for Unmanned Aerial Vehicle-Based Disguised Video Surveillance: Recent Advances and a Case Study', IEEE Vehicular Technology Magazine, vol. 18, no. 3, pp. 48-57.
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Hu, Y, Deng, W, Zhang, JA & Guo, YJ 2023, 'Resource Optimization for Delay Estimation in Perceptive Mobile Networks', IEEE Wireless Communications Letters, pp. 1-1.
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Hu, Z, Wang, C, Yuan, X, Zhang, S, Chen, S, Hou, Z, Xu, B, Song, L, Ning, Y, Zhang, Y & Feng, W 2023, 'Potential of Quantitative Flow Ratio for Selecting Target Vessels for Radial Artery Grafting: A Retrospective Observational Study', Circulation, vol. 148, no. 17, pp. 1340-1342.
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Huang, C-W, Huang, W-Y, Lin, C, Li, Y-L, Huang, T-P, Bui, X-T & Ngo, HH 2023, 'Ecological risk assessment and corrective actions for dioxin-polluted sediment in a chemical plant's brine water storage pond', Science of The Total Environment, vol. 859, pp. 160239-160239.
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Huang, C-W, Li, Y-L, Lin, C, Bui, X-T, Vo, T-D-H & Ngo, HH 2023, 'Seasonal influence on pollution index and risk of multiple compositions of microplastics in an urban river', Science of The Total Environment, vol. 859, pp. 160021-160021.
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Emerging contaminant microplastics (MPs) are getting worldwide attention for their ubiquitous occurrence and potential risk to the environment. However, the seasonal influence on freshwater MP pollution remains poorly understood. To better understand and evaluate the riverine MPs in different seasons, this study conducted the risk assessment of MPs in an urban river, Houjin River, during the different seasons. The present study found that the MPs (0.1-5 mm, mostly 0.1-2 mm) were more abundant in the dry season (183.33 ± 128.95 items/m3) compared with the wet season (102.08 ± 45.80 items/m3). Similarly, the mixture of different MPs polymers was more diverse in the dry season. The related pollution indices such as the contamination factor (CF) and pollution load index (PLI) showed that average CF and PLI were 5.15 and 2.10 in the dry season, which significantly decreased to 1.58 and 1.25, respectively, in the wet season (p < 0.05). Additionally, significant difference of the average risk quotient (RQ) was observed, which was 0.037 in the dry season and 0.021 in the wet season (p < 0.05). To sum up, the results of this study indicate the seasonal effects on the pollution and risk of multiple compositions of MPs in the urban river, suggesting higher impacts of riverine MPs pollution in the dry season, as well as the potential increase of MPs, may lead to environmental risk in the future.
Huang, H, Zhao, G, Bo, Y, Yu, J, Liang, L, Yang, Y & Ou, K 2023, 'Railway intrusion detection based on refined spatial and temporal features for UAV surveillance scene', Measurement, vol. 211, pp. 112602-112602.
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Huang, J, Gong, Y, Zhang, L, Zhang, J, Nie, L & Yin, Y 2023, 'Modeling Multiple Aesthetic Views for Series Photo Selection', IEEE Transactions on Multimedia, pp. 1-14.
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Huang, J, Song, X, Xiao, F, Cao, Z & Lin, C-T 2023, 'Belief f-divergence for EEG complexity evaluation', Information Sciences, vol. 643, pp. 119189-119189.
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Huang, M, Cao, C, Liu, L, Wei, W, Zhu, QL & Huang, Z 2023, 'Controlled synthesis of MOF-derived hollow and yolk–shell nanocages for improved water oxidation and selective ethylene glycol reformation', eScience, vol. 3, no. 5, pp. 100118-100118.
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Delicately designed metal–organic framework (MOF)-derived nanostructured electrocatalysts are essential for improving the reaction kinetics of the oxygen evolution reaction and tuning the selectivity of small organic molecule oxidation reactions. Herein, novel oxalate-modified hollow CoFe-based layered double hydroxide nanocages (h-CoFe-LDH NCs) and yolk–shell ZIF@CoFe-LDH nanocages (ys-ZIF@CoFe-LDH NCs) are developed through an etching–doping reconstruction strategy from a Co-based MOF precursor (ZIF-67). The distinctive nanostructures, along with the incorporation of the secondary metal element and intercalated oxalate groups, enable h-CoFe-LDH NCs and ys-ZIF@CoFe-LDH NCs to expose more active sites with high intrinsic activity. The resultant h-CoFe-LDH NCs exhibit outstanding OER activity with an overpotential of only 278 mV to deliver a current density of 50 mA cm−2. Additionally, controlling the reconstruction degree enables the formation of ys-ZIF@CoFe-LDH NCs with a yolk–shell nanocage nanostructure, which show outstanding electrocatalytic performance for the selective ethylene glycol oxidation reaction (EGOR) toward formate, with a Faradaic efficiency of up to 91%. Consequently, a hybrid water electrolysis system integrating the EGOR and the hydrogen evolution reaction using Pt/C||ys-ZIF@CoFe-LDH NCs is explored for energy-saving hydrogen production, requiring a cell voltage 127 mV lower than water electrolysis to achieve a current density of 50 mA cm−2. This work demonstrates a feasible way to design advanced MOF-derived electrocatalysts toward enhanced electrocatalytic reactions.
Huang, M, Li, G, Liu, Z & Zhu, L 2023, 'Lightweight Distortion-Aware Network for Salient Object Detection in Omnidirectional Images', IEEE Transactions on Circuits and Systems for Video Technology, vol. 33, no. 10, pp. 6191-6197.
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Huang, M, Zhou, S, Ma, DD, Wei, W, Zhu, QL & Huang, Z 2023, 'MOF-derived MoC-Fe heterojunctions encapsulated in N-doped carbon nanotubes for water splitting', Chemical Engineering Journal, vol. 473, pp. 145170-145170.
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Engineering the synergistic interfacial structures in nanostructured electrocatalysts is an effective yet challenging pursuit. Here we report porous nitrogen-doped carbon nanotubes (NCNTs) entrapping heterojunctions between carbide and transition metal nanoparticles (NPs) as excellent bifunctional catalyst for hydrogen and oxygen evolution reactions (HER and OER). Dual-phase MoC and Fe NPs confined in NCNTs (denoted as MoC-Fe@NCNTs) was fabricated by trapping [Fe(C2O4)3]3– into Zn/Mo-HZIF framework followed by pyrolysis. The resultant catalyst exhibited commendable bifunctional activities with small overpotentials at 50 mA cm−2 for the HER of 252 and OER of 304 mV, respectively. Theoretical calculations and experimental observation prove that the combination of Fe NPs generates synergistic heterointerfaces and improves OER activity of MoC, thus endowing outstanding bifunctional electrocatalytic performances. Moreover, the NCNTs, as the electronic communication amplifier, can facilitate electron transfer and inhibit the aggregation and corrosion of the active species. The controllable fabrication of MOF-derived heterostructures reported in this work provides a prospect for developing bifunctional MOF derivatives for water electrolysis.
Huang, Q-S, Chu, C, Li, Q, Liu, Q, Liu, X, Sun, J, Ni, B-J & Mao, S 2023, 'Three-Phase Interface Construction on Hydrophobic Carbonaceous Catalysts for Highly Active and Selective Photocatalytic CO2 Conversion', ACS Catalysis, vol. 13, no. 17, pp. 11232-11243.
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Huang, S, Liu, Y, Tsang, IW, Xu, Z & Lv, J 2023, 'Multi-View Subspace Clustering by Joint Measuring of Consistency and Diversity', IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 8, pp. 8270-8281.
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Huang, S, Tegg, L, Qu, J, Yang, L, McCarroll, I, Burr, P & Cairney, JM 2023, 'Nanoscale Distribution of Alloying Elements in Optimized ZIRLO Using the Invizo 6000', Microscopy and Microanalysis, vol. 29, no. Supplement_1, pp. 614-615.
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Huang, W, Zhuo, M, Zhu, T, Zhou, S & Liao, Y 2023, 'Differential privacy: Review of improving utility through cryptography‐based technologies', Concurrency and Computation: Practice and Experience, vol. 35, no. 5.
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SummaryDue to successful applications of data analysis technologies in many fields, various institutions have accumulated a large amount of data to improve their services. As the speed of data collection has increased dramatically over the last few years, an increasing number of users are growing concerned about their personal information. Therefore, privacy preservation has become an urgent problem to be solved. Differential privacy as a strong privacy preservation tool has attracted significant attention. In this review, we focus on improving data utility of differentially private mechanisms through technologies related to cryptography. In particular, we first focus on how to improve data utility through anonymous communication. Then, we summarize how to improve data utility by combining differentially private mechanisms with homomorphic encryption schemes. Next, we summarize hardness results of what is impossible to achieve for differentially private mechanisms' data utility from the view of cryptography. Differential privacy borrowed intuitions from cryptography and still benefits from the progress of cryptography. To summarize the state‐of‐the‐art and to benefit future researches, we are motivated to provide this review.
Huang, W-Y, Huang, C-W, Li, Y-L, Huang, T-P, Lin, C, Ngo, HH & Bui, X-T 2023, 'Reduced pollution level and ecological risk of mercury-polluted sediment in a alkali-chlorine factory’s brine water storage pond after corrective actions: A case study in Southern Taiwan', Environmental Technology & Innovation, vol. 29, pp. 103003-103003.
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Huang, Y, Du, Z, Bao, G, Fang, G, Cappadona, M, McClements, L, Tuch, BE, Lu, H & Xu, X 2023, 'Smart Drug-Delivery System of Upconversion Nanoparticles Coated with Mesoporous Silica for Controlled Release.', Pharmaceutics, vol. 15, no. 1, pp. 89-89.
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Drug-delivery vehicles have garnered immense interest in recent years due to unparalleled progress made in material science and nanomedicine. However, the development of stimuli-responsive devices with controllable drug-release systems (DRSs) is still in its nascent stage. In this paper, we designed a two-way controlled drug-release system that can be promoted and prolonged, using the external stimulation of near-infrared light (NIR) and protein coating. A hierarchical nanostructure was fabricated using upconversion nanoparticles (UCNPs)-mesoporous silica as the core-shell structure with protein lysozyme coating. The mesoporous silica shell provides abundant pores for the loading of drug molecules and a specific type of photosensitive molecules. The morphology and the physical properties of the nanostructures were thoroughly characterized. The results exhibited the uniform core-shell nanostructures of ~four UCNPs encapsulated in one mesoporous silica nanoparticle. The core-shell nanoparticles were in the spherical shape with an average size of 200 nm, average surface area of 446.54 m2/g, and pore size of 4.6 nm. Using doxorubicin (DOX), a chemotherapy agent as the drug model, we demonstrated that a novel DRS with capacity of smart modulation to promote or inhibit the drug release under NIR light and protein coating, respectively. Further, we demonstrated the therapeutic effect of the designed DRSs using breast cancer cells. The reported novel controlled DRS with dual functionality could have a promising potential for chemotherapy treatment of solid cancers.
Huang, Y, Feng, B, Tian, A, Dong, P, Yu, S & Zhang, H 2023, 'An Efficient Differentiated Routing Scheme for MEO/LEO-Based Multi-Layer Satellite Networks', IEEE Transactions on Network Science and Engineering, pp. 1-16.
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Huang, Y, Xiao, F, Cao, Z & Lin, C-T 2023, 'Higher Order Fractal Belief Rényi Divergence With Its Applications in Pattern Classification', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 12, pp. 14709-14726.
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Huang, Z, Shivakumara, P, Kaljahi, MA, Kumar, A, Pal, U, Lu, T & Blumenstein, M 2023, 'Writer age estimation through handwriting', Multimedia Tools and Applications, vol. 82, no. 11, pp. 16033-16055.
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Huo, P, Deng, R, Chen, X, Liu, Y, Yang, L, Wu, L, Wei, W & Ni, B-J 2023, 'Model-Based Evaluation of N2O Recovery as an Energy Source in Sulfur-Driven NO-Based Autotrophic Denitrification', Chemical Engineering Journal, vol. 453, pp. 139732-139732.
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Huo, P, Deng, R, Yang, L, Liu, Y, Wei, W, Ni, B-J & Chen, X 2023, 'Exposure of sulfur-driven autotrophic denitrification to hydroxylamine/hydrazine: Underlying mechanisms and implications for promoting partial denitrification and N2O recovery', Chemical Engineering Journal, vol. 477, pp. 146943-146943.
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Huo, Y, Zheng, H, Jiang, Y, Chen, H, Cao, W, Mameda, N, Nghiem, LD, Zhang, X & Liu, Q 2023, 'Comparison and Characterization of Nitrogen/Sulfur-Doped Activated Carbon for Activating Peroxydisulfate to Degrade Acid Orange 7: An Experimental and Theoretical Study', Industrial & Engineering Chemistry Research, vol. 62, no. 30, pp. 11894-11904.
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Huq, T, Ong, HC, Chew, BT, Kazi, SN, Zubir, MNM, Ong, ZC & Azlan, NBBM 2023, 'Graphene nanoplatelet nanofluids stabilised by hybridisation with graphene oxide: preparation, stability, and performance in flat plate solar thermal collector', Journal of Thermal Analysis and Calorimetry, vol. 148, no. 5, pp. 2105-2118.
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Hussain, OK, Mirdad, A & Hussain, FK 2023, 'A systematic literature review on pharmaceutical supply chain: research gaps and future opportunities', International Journal of Web and Grid Services, vol. 19, no. 2, pp. 233-233.
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Hussain, W & Merigo, JM 2023, 'Onsite/offsite social commerce adoption for SMEs using fuzzy linguistic decision making in complex framework', Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 9, pp. 12875-12894.
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AbstractThere has been a growing social commerce adoption trend among SMEs for few years. However, it is often a challenging strategic task for SMEs to choose the right type of social commerce. SMEs usually have a limited budget, technical skills and resources and want to maximise productivity with those limited resources. There is much literature that discusses the social commerce adoption strategy for SMEs. However, there is no work to enable SMEs to choose social commerce—onsite/offsite or hybrid strategy. Moreover, very few studies allow the decision-makers to handle uncertain, complex nonlinear relationships of social commerce adoption factors. The paper proposes a fuzzy linguistic multi-criteria group decision-making in a complex framework for onsite, offsite social commerce adoption to address the problem. The proposed approach uses a novel hybrid approach by combining FAHP, FOWA and selection criteria of the technological–organisation–environment (TOE) framework. Unlike previous methods, the proposed approach uses the decision maker's attitudinal characteristics and recommends intelligently using the OWA operator. The approach further demonstrates the decision behaviour of the decision-makers with Fuzzy Minimum (FMin), Fuzzy Maximum (FMax), Laplace criteria, Hurwicz criteria, FWA, FOWA and FPOWA. The framework enables the SMEs to choose the right type of social commerce considering TOE factors that help them build a stronger relationship with current and potential customers. The approach's applicability is demonstrated using a case study of three SMEs seeking to adopt a social commerce type. The analysis results indicate the proposed approach's effectiveness in handling uncertain, complex nonlinear decisions in social commerce adoption.
Hussain, W, Merigó, JM, Gil-Lafuente, J & Gao, H 2023, 'Complex nonlinear neural network prediction with IOWA layer', Soft Computing, vol. 27, no. 8, pp. 4853-4863.
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AbstractNeural network methods are widely used in business problems for prediction, clustering, and risk management to improving customer satisfaction and business outcome. The ability of a neural network to learn complex nonlinear relationship is due to its architecture that uses weight parameters to transform input data within the hidden layers. Such methods perform well in many situations where the ordering of inputs is simple. However, for a complex reordering of a decision-maker, the process is not enough to get an optimal prediction result. Moreover, existing machine learning algorithms cannot reduce computational complexity by reducing data size without losing any information. This paper proposes an induced ordered weighted averaging (IOWA) operator for the artificial neural network IOWA-ANN. The operator reorders the data according to the order-inducing variable. The proposed sorting mechanism in the neural network can handle a complex nonlinear relationship of a dataset, which results in reduced computational complexities. The proposed approach deals with the complexity of the neuron, collects the data and allows a degree of customisation of the structure. The application further extended to IGOWA and Quasi-IOWA operators. We present a numerical example in a financial decision-making process to demonstrate the approach's effectiveness in handling complex situations. This paper opens a new research area for various complex nonlinear predictions where the dataset is big enough, such as cloud QoS and IoT sensors data. The approach can be used with different machine learning, neural networks or hybrid fuzzy neural methods with other extensions of the OWA operator.
Iacopi, F & Balestra, F 2023, 'Preface', More-than-Moore Devices and Integration for Semi Conductors, pp. v-x.
Ibrar, I, Alsaka, L, Yadav, S, Altaee, A, Zhou, JL & Shon, HK 2023, 'Kappa carrageenan-vanillin composite hydrogel for landfill leachate wastewater treatment', Desalination, vol. 565, pp. 116826-116826.
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Ibrar, I, Yadav, S, Altaee, A, Braytee, A, Samal, AK, Zaid, SMJ & Hawari, AH 2023, 'A machine learning approach for prediction of reverse solute flux in forward osmosis', Journal of Water Process Engineering, vol. 54, pp. 103956-103956.
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Ijaz Malik, MA, Mujtaba, MA, Kalam, MA, Silitonga, AS & Ikram, A 2023, 'Recent advances in hydrogen supplementation to promote biomass fuels for reducing greenhouse gases', International Journal of Hydrogen Energy.
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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 2023, 'Unsteady conjugate heat transfer characteristics in hexagonal cavity equipped with a multi-blade dynamic modulator', International Journal of Heat and Mass Transfer, vol. 200, pp. 123527-123527.
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Imran, S, Gul, M, Kalam, MA, Zulkifli, NWM, Mujtaba, MA, Yusoff, MNAM & Awang, MSN 2023, 'Effect of various nanoparticle biodiesel blends on thermal efficiency and exhaust pollutants', International Journal of Energy and Environmental Engineering, vol. 14, no. 4, pp. 937-948.
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Inamdar, MA, Raghavendra, U, Gudigar, A, Bhandary, S, Salvi, M, Deo, RC, Barua, PD, Ciaccio, EJ, Molinari, F & Acharya, UR 2023, 'A Novel Attention-Based Model for Semantic Segmentation of Prostate Glands Using Histopathological Images', IEEE Access, vol. 11, pp. 108982-108994.
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Inan, DI, Beydoun, G & Othman, SH 2023, 'Risk Assessment and Sustainable Disaster Management', Sustainability, vol. 15, no. 6, pp. 5254-5254.
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A risk assessment is a process of identifying potential risks and hazards, evaluating the likelihood and impact of these risks, and developing strategies to manage these risks across all disaster management (DM) phases: prevention, preparedness, response, and recovery (PPRR) [...]
Inayat, A, Jamil, F, Ahmed, SF, Ayoub, M, Abdul, PM, Aslam, M, Mofijur, M, Khan, Z & Mustafa, A 2023, 'Thermal degradation characteristics, kinetic and thermodynamic analyses of date palm surface fibers at different heating rates', Fuel, vol. 335, pp. 127076-127076.
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Inbanaathan, PV, Balasubramanian, D, Nguyen, VN, Le, VV, Wae-Hayee, M, R, R, Veza, I, Yukesh, N, Kalam, MA, Sonthalia, A & Varuvel, EG 2023, 'Comprehensive study on using hydrogen-gasoline-ethanol blends as flexible fuels in an existing variable speed SI engine', International Journal of Hydrogen Energy.
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Indraratna, B, Armaghani, DJ, Gomes Correia, A, Hunt, H & Ngo, T 2023, 'Prediction of resilient modulus of ballast under cyclic loading using machine learning techniques', Transportation Geotechnics, vol. 38, pp. 100895-100895.
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Irga, P, Fleck, R, Wooster, E, Rojahn, J, Torpy, F & Irga, P 2023, 'Biosolar green roofs – achieving biodiversity outcomes and solar power on the same roof, at the same time', Research Matters Newsletter of the Australian Flora Foundation, vol. 38, pp. 2-8.
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Urban green spaces, such as parks and vegetation along roadsides, are readily recognisable examples of ecologically significant urban green areas. However, with growing human populations and limited space in cities, there is a rising trend for the adoption of space-efficient green solutions such as green roofs and green walls. While the ecological importance of residential and roadside vegetation is acknowledged in terms of supporting biodiversity, the impact of urban green roofs on biodiversity is still not well understood. Additionally, green roofs play a role in regulating urban ambient temperatures, thereby enhancing the efficiency of solar panels by creating favourable conditions for energy production. There is a compelling correlation between the performance of photovoltaic panels (PV) and the negative impacts of rising ambient temperatures in their vicinity. What may come as a surprise to some is that as surfaces of solar panels heat up beyond 25°C, panel efficiency decreases. Green roofs have the potential to lower ambient temperatures around solar panels through evapotranspiration, thereby maximising the power output of PV systems.
Irga, P, Wilkinson, S & Georgakopoulos, FM 2023, 'EMBODIED CARBON IN CONSTRUCTION MATERIALS: THE POTENTIAL ROLE OF HEMPCRETE', Built Environment Economist - Australia and New Zealand, vol. June 2023.
Irga, P, Wooster, E, Torpy, F, Rojahn, J & Fleck, R 2023, 'A green roof or rooftop solar? You can combine them in a biosolar roof, boosting both biodiversity and power output'.
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Growing city populations and limited space are driving the adoption of green roofs and green walls covered with living plants. As well as boosting biodiversity, green roofs could play another unexpectedly valuable role by increasing the electricity output of solar panels.As solar panels heat up beyond 25℃, their efficiency decreases markedly. Green roofs moderate rooftop temperatures. So we wanted to find out: could green roofs help with the problem of heat reducing the output of solar panels?Our research compared a “biosolar” green roof – one that combines a solar system with a green roof – and a comparable conventional roof with an equivalent solar system. We measured the impacts on biodiversity and solar output, as well as how the plants coped with having panels installed above them.The green roof supported much more biodiversity, as one might expect. By reducing average maximum temperatures by about 8℃, it increased solar generation by as much as 107% during peak periods. And while some plant species outperformed others, the vegetation flourished.
Irga, PJ, Morgan, A, Fleck, R & Torpy, FR 2023, 'Phytoremediation of indoor air pollutants from construction and transport by a moveable active green wall system', Atmospheric Pollution Research, vol. 14, no. 10, pp. 101896-101896.
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Isfeld, AC, Stewart, MG & Masia, MJ 2023, 'Structural reliability and partial safety factor assessment of unreinforced masonry in vertical bending', Australian Journal of Structural Engineering, vol. 24, no. 3, pp. 191-205.
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Islam Rony, Z, Mofijur, M, Hasan, MM, Rasul, MG, Jahirul, MI, Forruque Ahmed, S, Kalam, MA, Anjum Badruddin, I, Yunus Khan, TM & Show, P-L 2023, 'Alternative fuels to reduce greenhouse gas emissions from marine transport and promote UN sustainable development goals', Fuel, vol. 338, pp. 127220-127220.
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Islam, MM, Ramezani, F, Lu, HY & Naderpour, M 2023, 'Optimal placement of applications in the fog environment: A systematic literature review', Journal of Parallel and Distributed Computing, vol. 174, pp. 46-69.
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Islam, MR, Akter, S, Islam, L, Razzak, I, Wang, X & Xu, G 2023, 'Strategies for evaluating visual analytics systems: A systematic review and new perspectives', Information Visualization.
Islam, MS, Molley, TG, Hung, T-T, Sathish, CI, Putra, VDL, Jalandhra, GK, Ireland, J, Li, Y, Yi, J, Kruzic, JJ & Kilian, KA 2023, 'Magnetic Nanofibrous Hydrogels for Dynamic Control of Stem Cell Differentiation', ACS Applied Materials & Interfaces, vol. 15, no. 44, pp. 50663-50678.
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Islam, MS, Rahman, MM, Larpruenrudee, P, Arsalanloo, A, Beni, HM, Islam, MA, Gu, Y & Sauret, E 2023, 'How microplastics are transported and deposited in realistic upper airways?', Physics of Fluids, vol. 35, no. 6.
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Microplastics are tiny plastic debris in the environment from industrial processes, various consumer items, and the breakdown of industrial waste. Recently, microplastics have been found for the first time in the airways, which increases the concern about long-term exposure and corresponding impacts on respiratory health. To date, a precise understanding of the microplastic transport to the airways is missing in the literature. Therefore, this first-ever study aims to analyze the microplastic transport and deposition within the upper lung airways. A computational fluid dynamics-discrete phase model approach is used to analyze the fluid flow and microplastic transport in airways. The sphericity concept and shape factor values are used to define the non-spherical microplastics. An accurate mesh test is performed for the computational mesh. The numerical results report that the highly asymmetric and complex morphology of the upper airway influences the flow fields and microplastic motion along with the flow rate and microplastic shape. The nasal cavity, mouth-throat, and trachea have high pressure, while a high flow velocity is observed at the area after passing the trachea. The flow rates, shape, and size of microplastics influence the overall deposition pattern. A higher flow rate leads to a lower deposition efficiency for all microplastic shapes. The nasal cavity has a high deposition rate compared to other regions. The microplastic deposition hot spot is calculated for shape and size-specific microplastic at various flow conditions. The findings of this study and more case-specific analysis will improve the knowledge of microplastic transport in airways and benefit future therapeutics development. The future study will be focused on the effect of various microplastic shapes on the human lung airways under the healthy and diseased airways conditions.
Islam, MT & Hossain, MJ 2023, 'Artificial Intelligence for Hosting Capacity Analysis: A Systematic Literature Review', Energies, vol. 16, no. 4, pp. 1864-1864.
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Distribution network operators face technical and operational challenges in integrating the increasing number of distributed energy resources (DER) with the distribution network. The hosting capacity analysis quantifies the level of power quality violation on the network due to the high penetration of the DER, such as over voltage, under voltage, transformer and feeder overloading, and protection failures. Real-time monitoring of the power quality factors such as the voltage, current, angle, frequency, harmonics and overloading that would help the distribution network operators overcome the challenges created by the high penetration of the DER. In this paper, different conventional hosting capacity analysis methods have been discussed. These methods have been compared based on the network constraints, impact factors, required input data, computational efficiency, and output accuracy. The artificial intelligence approaches of the hosting capacity analysis for the real-time monitoring of distribution network parameters have also been covered in this paper. Different artificial intelligence techniques have been analysed for sustainable integration, power system optimisation, and overcoming real-time monitoring challenges of conventional hosting capacity analysis methods. An overview of the conventional hosting capacity analysis methods, artificial intelligence techniques for overcoming the challenges of distributed energy resources integration, and different impact factors affecting the real-time hosting capacity analysis has been summarised. The distribution system operators and researchers will find the review paper as an easy reference for planning and further research. Finally, it is evident that artificial intelligence techniques could be a better alternative solution for real-time estimation and forecasting of the distribution network hosting capacity considering the intermittent nature of the DER, consumer loads, and network constraints.
Izadi, R, Assarian, D, Altaee, A & Mahinroosta, M 2023, 'Investigation of methods for fuel desulfurization wastewater treatment', Chemical Engineering Research and Design, vol. 190, pp. 198-219.
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Jacob, A, Ashok, B, Ong, HC & Le, PTK 2023, 'Scaling-up heterotrophic cultures of C. Pyrenoidosa microalgae for sustainable synthesis of low-density biodiesel mixtures and predict CI engine behavior at optimal proportions', Environment, Development and Sustainability, vol. 25, no. 1, pp. 400-422.
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Jafari, M, Shoeibi, A, Khodatars, M, Ghassemi, N, Moridian, P, Alizadehsani, R, Khosravi, A, Ling, SH, Delfan, N, Zhang, Y, Wang, S-H, Górriz, JM, Alinejad-Rokny, H & Acharya, UR 2023, 'Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: A review.', Comput. Biol. Medicine, vol. 160, pp. 106998-106998.
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Jafari, SM, Nikoo, MR, Bozorg-Haddad, O, Alamdari, N, Farmani, R & Gandomi, AH 2023, 'A robust clustering-based multi-objective model for optimal instruction of pipes replacement in urban WDN based on machine learning approaches', Urban Water Journal, vol. 20, no. 6, pp. 689-706.
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Jafarizadeh, S & Veitch, D 2023, 'Robust Weighted-Average Continuous-Time Consensus With Communication Time Delay', IEEE Transactions on Cybernetics, vol. 53, no. 4, pp. 2074-2086.
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Jahandari, S, Tao, Z, Alim, MA & Li, W 2023, 'Integral waterproof concrete: A comprehensive review', Journal of Building Engineering, vol. 78, pp. 107718-107718.
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Jahmunah, V, En Wei Koh, J, Sudarshan, VK, Raghavendra, U, Gudigar, A, Lih Oh, S, Wen Loh, H, Faust, O, Datta Barua, P, Ciaccio, EJ & Rajendra Acharya, U 2023, 'Endoscopy, video capsule endoscopy, and biopsy for automated celiac disease detection: A review', Biocybernetics and Biomedical Engineering, vol. 43, no. 1, pp. 82-108.
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Jain, K, Pradhan, B & Mishra, V 2023, 'Preface', Lecture Notes in Civil Engineering, vol. 304, pp. v-vi.
Jaradat, Y & Far, H 2023, 'Impact Stiffness of Linear Viscoelastic Model for Seismic Pounding Simulation: An Experimental Evaluation', Civil Engineering Journal, vol. 9, no. 6, pp. 1289-1311.
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Pounding between adjacent structures occurs when the separating distance within the two buildings is inadequate to contain the movement between them during an earthquake event. Seismic pounding can lead to significant harm or even the destruction of neighbouring structures. In creating a model for structural response, impact stiffness is considered as a critical factor in calculating the impact force throughout the collision within adjacent structures. It is important to derive realistic stiffness values when performing a numerical simulation of pounding forces within abutting structures to attain valid results. The objective of this study is to ascertain the impact stiffness within the linear viscoelastic contact model, using data obtained from shaking table experiments of pounding between neighboring five-storey and 15-storey single-bay model of steel-frame. The steel models were subjected to scaled ground acceleration records, two far-field and two near-field. The study’s findings indicate that there is a significant discrepancy between the theoretical impact parameters and the measured experimental value because the assumptions made to derive the theoretical formulas do not align with the actual impact conditions. The accuracy and precision of the experimental formula adopted in this study have been validated in comparison with the numerical results. Doi: 10.28991/CEJ-2023-09-06-01 Full Text: PDF
Jaradat, Y, Far, H & Mortazavi, M 2023, 'A Mathematical Approach for Predicting Sufficient Separation Gap between Adjacent Buildings to Avoid Earthquake-Induced Pounding', Civil Engineering Journal, vol. 9, no. 10, pp. 2370-2398.
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Studies on earthquake-related damage underscore that buildings are vulnerable to significant harm or even collapse during moderate to strong ground motions. Of particular concern is seismic-induced pounding, observed in numerous past and recent earthquakes, often resulting from inadequate separation gaps between neighboring structures. This study conducted an experimental and numerical investigation to develop a mathematical equation to calculate a sufficient separation gap in order to avoid the collision between adjacent mid-rise steel-frame buildings during seismic excitation. In this study, the coupled configuration of 15-storey & 10-storey, 15-storey & 5-storey, and 10-storey & 5-storey steel frame structures was considered in the investigation. The investigation concluded with a large number of data outputs. The outputs were used to predict structural behavior during earthquakes. The obtained data were categorized into three main categories according to the earthquake's Peak Ground Acceleration (PGA) levels. Also, the derived equations were divided into three different equations to estimate the required seismic gap between neighboring buildings accordingly. The derived equations are distilled to empower engineers to rigorously evaluate non-irregular mid-rise steel frame buildings. Doi: 10.28991/CEJ-2023-09-10-02 Full Text: PDF
Jaradat, Y, Far, H & Mortazavi, M 2023, 'Experimental Evaluation of Theoretical Impact Models for Seismic Pounding', Journal of Earthquake Engineering, vol. 27, no. 12, pp. 3269-3289.
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Jaradat, Y, Sobhi, P & Far, H 2023, 'An Investigation into Adequacy of Separation Gap to Preclude Earthquake-induced Pounding', Structural Engineering and Mechanics, vol. 86, no. 1, pp. 29-48.
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Pounding happens when contiguous structures with differing heights vibrate out of line caused by a seismic activity. The situation is aggravated due to the insufficient separation gap between the structures which can lead to the crashing of the buildings or total collapse of an edifice. Countries around the world have compiled building standards to address the pounding issue. One of the strategies recommended is the introduction of the separation gap between structures. AS1170.4-2007 is an Australian standard that requires 1% of the building height as a minimum separation gap between buildings to preclude pounding. This article presents experimental and numerical tests to determine the adequacy of this specification to prevent the occurrence of seismic pounding between steel frame structures under near-field and far-field earthquakes. The results indicated that the recommended minimum separation gap based on the Australian Standard is inaccurate if low-rise structure in a coupled case is utilised under both near and far field earthquakes. The standard is adequate if a tall building is involved but only when a far-field earthquake happens. The research likewise presents results derived by using the ABS and SRSS methods.
Jathar, LD, Ganesan, S, Awasarmol, U, Nikam, K, Shahapurkar, K, Soudagar, MEM, Fayaz, H, El-Shafay, AS, Kalam, MA, Bouadila, S, Baddadi, S, Tirth, V, Nizami, AS, Lam, SS & Rehan, M 2023, 'Comprehensive review of environmental factors influencing the performance of photovoltaic panels: Concern over emissions at various phases throughout the lifecycle', Environmental Pollution, vol. 326, pp. 121474-121474.
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Javaherian, C & Ferrie, C 2023, 'Energy transport and optimal design of noisy Platonic quantum networks', Physica Scripta, vol. 98, no. 3, pp. 035105-035105.
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Abstract
Optimal energy transport is one of the primary goals for designing efficient quantum networks. In this work, the maximum energy transport is investigated for three-dimensional quantum networks with Platonic geometries affected by dephasing and dissipative Markovian noise. The network and the environmental characteristics corresponding the optimal design are obtained and investigated for five Platonic networks with 4, 6, 8, 12, and 20 number of sites that one of the sites is connected to a sink site through a Markovian dissipative process. Such optimal designs could have various applications like switching and multiplexing in quantum circuits.
Jena, R, Pradhan, B, Almazroui, M, Assiri, M & Park, H-J 2023, 'Earthquake-induced liquefaction hazard mapping at national-scale in Australia using deep learning techniques', Geoscience Frontiers, vol. 14, no. 1, pp. 101460-101460.
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Jena, R, Pradhan, B, Gite, S, Alamri, A & Park, H-J 2023, 'A new method to promptly evaluate spatial earthquake probability mapping using an explainable artificial intelligence (XAI) model', Gondwana Research, vol. 123, pp. 54-67.
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Machine learning (ML) models have been extensively used in several geological applications. Owing to the increase in model complexity, interpreting the outputs becomes quite challenging. Shapley additive explanation (SHAP) measures the importance of each input attribute on the model's output. This study implemented SHAP to estimate earthquake probability using two different types of ML approaches, namely, artificial neural network (ANN) and random forest (RF). The two algorithms were first compared to evaluate the importance and effect of the factors. SHAP was then carried out to interpret the output of the models designed for the earthquake probability. This study aims not only to achieve high accuracy in probability estimation but also to rank the input parameters and select appropriate features for classification. SHAP was tested on earthquake probability assessment using eight factors for the Indian subcontinent. The models obtained an overall accuracy of 96 % for ANN and 98 % for RF. SHAP identified the high contributing factors as epicenter distance, depth density, intensity variation, and magnitude density in a sequential order for ANN. Finally, the authors argued that an explainable artificial intelligence (AI) model can help in earthquake probability estimation, which then open avenues to building a transferable AI model.
Jena, R, Shanableh, A, Al-Ruzouq, R, Pradhan, B, Gibril, MBA, Khalil, MA, Ghorbanzadeh, O & Ghamisi, P 2023, 'Earthquake spatial probability and hazard estimation using various explainable AI (XAI) models at the Arabian peninsula', Remote Sensing Applications: Society and Environment, vol. 31, pp. 101004-101004.
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Jia, C, Luo, M, Yan, C, Zhu, L, Chang, X & Zheng, Q 2023, 'Collaborative Contrastive Refining for Weakly Supervised Person Search', IEEE Transactions on Image Processing, vol. 32, pp. 4951-4963.
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Jia, M, Gabrys, B & Musial, K 2023, 'A Network Science Perspective of Graph Convolutional Networks: A Survey', IEEE Access, vol. 11, pp. 39083-39122.
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Jia, Z, Xu, X, Zhu, D & Zheng, Y 2023, 'Design, printing, and engineering of regenerative biomaterials for personalized bone healthcare', Progress in Materials Science, vol. 134, pp. 101072-101072.
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Jiang, J, Dorji, P, Badeti, U, Sohn, W, Freguia, S, Phuntsho, S, El Saliby, I & Shon, HK 2023, 'Potential nutrient recovery from source-separated urine through hybrid membrane bioreactor and membrane capacitive deionisation', Desalination, vol. 566, pp. 116924-116924.
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Jiang, L, Li, F, Chen, Z, Zhu, B, Yi, C, Li, Y, Zhang, T, Peng, Y, Si, Y, Cao, Z, Chen, A, Yao, D, Chen, X & Xu, P 2023, 'Information transmission velocity-based dynamic hierarchical brain networks', NeuroImage, vol. 270, pp. 119997-119997.
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Jiang, P, Yu, Y & Li, K 2023, 'Hydrophilic TiO 2 @MWCNT/PVDF membrane for enhanced photodegradation of methyl orange in water', Fullerenes, Nanotubes and Carbon Nanostructures, vol. 31, no. 12, pp. 1185-1191.
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Jiang, W, Tao, J, Luo, J, Xie, W, Zhou, X, Cheng, B, Guo, G, Ngo, HH, Guo, W, Cai, H, Ye, Y, Chen, Y & Pozdnyakov, IP 2023, 'Pilot-scale two-phase anaerobic digestion of deoiled food waste and waste activated sludge: Effects of mixing ratios and functional analysis', Chemosphere, vol. 329, pp. 138653-138653.
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Jiang, Y, Li, C, Wu, C, Rabczuk, T & Fang, J 2023, 'A double-phase field method for mixed mode crack modelling in 3D elasto-plastic solids with crack-direction-based strain energy decomposition', Computer Methods in Applied Mechanics and Engineering, vol. 405, pp. 115886-115886.
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Jiang, Z, Li, C, Chang, X, Chen, L, Zhu, J & Yang, Y 2023, 'Dynamic Slimmable Denoising Network', IEEE Transactions on Image Processing, vol. 32, pp. 1-1.
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Jiao, S, Goel, V, Navasardyan, S, Yang, Z, Khachatryan, L, Yang, Y, Wei, Y, Zhao, Y & Shi, H 2023, 'Collaborative Content-Dependent Modeling: A Return to the Roots of Salient Object Detection', IEEE Transactions on Image Processing, vol. 32, pp. 4237-4246.
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Jin, P, Hu, Y, Lei, G, Guo, Y & Zhu, J 2023, 'A Novel SVM Strategy to Reduce Current Stress of High-Frequency Link Matrix Converter', IEEE Transactions on Industrial Electronics, pp. 1-10.
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Jin, P, Zhang, J, Lu, Y, Guo, Y, Lei, G & Zhu, J 2023, 'Variable Frequency Isolated Bidirectional CLLC Resonant Converter With Voltage Controlled Variable Capacitors', IEEE Transactions on Industrial Electronics, vol. 70, no. 9, pp. 8907-8917.
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Jin, Q, Chen, H, Zhang, Y, Wang, X & Zhu, D 2023, 'Unraveling Scientific Evolutionary Paths: An Embedding-Based Topic Analysis', IEEE Transactions on Engineering Management, pp. 1-15.
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Jin, W, Zhao, B, Yu, H, Tao, X, Yin, R & Liu, G 2023, 'Improving embedded knowledge graph multi-hop question answering by introducing relational chain reasoning', Data Mining and Knowledge Discovery, vol. 37, no. 1, pp. 255-288.
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Jin, W, Zhao, B, Zhang, L, Liu, C & Yu, H 2023, 'Back to common sense: Oxford dictionary descriptive knowledge augmentation for aspect-based sentiment analysis', Information Processing & Management, vol. 60, no. 3, pp. 103260-103260.
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Joo, EM, Pelusi, D & Wen, S 2023, 'Guest Editorial — Introduction to the Special Issue on Smart Fuzzy Optimization for Decision-Making in Uncertain Environments', International Journal of Computational Intelligence and Applications, vol. 22, no. 01.
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Joshi, A, Pradhan, B, Chakraborty, S & Behera, MD 2023, 'Winter wheat yield prediction in the conterminous United States using solar-induced chlorophyll fluorescence data and XGBoost and random forest algorithm', Ecological Informatics, vol. 77, pp. 102194-102194.
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Jui, SJJ, Deo, RC, Barua, PD, Devi, A, Soar, J & Acharya, UR 2023, 'Application of Entropy for Automated Detection of Neurological Disorders With Electroencephalogram Signals: A Review of the Last Decade (2012–2022)', IEEE Access, vol. 11, pp. 71905-71924.
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Jupp, JR & Parkes, M 2023, 'Integrated Construction Enterprise Systems: A Strategic Approach to Model-based Data and Process Management', Automation in Construction, vol. (to appear).
Kabir, MM, Akter, MM, Huang, Z, Tijing, L & Shon, HK 2023, 'Hydrogen production from water industries for a circular economy', Desalination, vol. 554, pp. 116448-116448.
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Kabir, MM, Nahar, N, Akter, MM, Alam, F, Gilroyed, BH, Misu, MM, Didar-ul-Alam, M, Hakim, M, Tijing, L & Shon, HK 2023, 'Agro-waste-based functionalized and economic adsorbents for the effective treatment of toxic contaminants from tannery effluent', Journal of Water Process Engineering, vol. 52, pp. 103578-103578.
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Kabir, MM, Roy, SK, Alam, F, Nam, SY, Im, KS, Tijing, L & Shon, HK 2023, 'Machine learning-based prediction and optimization of green hydrogen production technologies from water industries for a circular economy', Desalination, vol. 567, pp. 116992-116992.
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Kaim, V, Singh, N, Kanaujia, BK, Matekovits, L, Esselle, KP & Rambabu, K 2023, 'Multi-Channel Implantable Cubic Rectenna MIMO System With CP Diversity in Orthogonal Space for Enhanced Wireless Power Transfer in Biotelemetry', IEEE Transactions on Antennas and Propagation, vol. 71, no. 1, pp. 200-214.
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Kalam, MA, Asif, CAA, Stormer, A, Bishop, T, Jackson‐deGraffenried, M & Talukder, A 2023, 'Use of designing for behaviour change framework in identifying and addressing barriers to and enablers of animal source feeding to children ages 8–23 months in Bandarban Hill District in Bangladesh: Implications for a nutrition‐sensitive agriculture programme', Maternal & Child Nutrition, vol. 19, no. 2.
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AbstractInadequate diet quality is a cause of undernutrition among children 6–23 months of age in Bangladesh, particularly in remote and isolated areas such as Bandarban District. Feeding animal source foods can help to combat stunting and wasting problems among children, but it may not be accessible or acceptable. A barrier analysis using the Designing for Behavior Change Framework was conducted in Bandarban district with participants from 4 ethnic groups, to explore potential barriers and key motivators by examining 12 behavioural determinants of consumption of animal‐source food in complementary feeding for children 8–23 months. Data were collected from 45 mothers of children 8–23 months, who provided animal‐source foods to their children (doers), and from 45 mothers who did not (non‐doers), for a total of 90 interviews. Nine determinants were statistically significantly different between doers and non‐doers as follows: self‐efficacy, positive consequences, negative consequences, social norms, access, reminders, perceived risk, perceived severity and perceived action efficacy. Nearby access to purchase animal‐source foods, rearing poultry or livestock at home and the support of household and community members are enablers to feeding animal‐source food. In contrast, these same factors are barriers for non‐doers. The lack of money to spend on animal‐source foods is also a barrier. An integrated nutrition‐sensitive and gender‐transformative animal‐based food production, and inclusive market programme could increase access to meat and eggs at the household level, increase opportunities to earn income and support gender‐equitable household workloads and decision‐making for optimal child feeding.
Kang, J, Jia, W & He, X 2023, 'Toward extracting and exploiting generalizable knowledge of deep 2D transformations in computer vision', Neurocomputing, vol. 562, pp. 126882-126882.
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Kang, K, Li, L & Sohaib, O 2023, 'Graduates’ intention to develop live commerce: The educa tional background perspective using multi-group analysis', Entrepreneurial Business and Economics Review, vol. 11, no. 1, pp. 113-126.
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Kapellos, TS, Baßler, K, Fujii, W, Nalkurthi, C, Schaar, AC, Bonaguro, L, Pecht, T, Galvao, I, Agrawal, S, Saglam, A, Dudkin, E, Frishberg, A, de, DE, Horne, A, Donovan, C, Kim, RY, Gallego-Ortega, D, Gillett, TE, Ansari, M, Schulte-Schrepping, J, Offermann, N, Antignano, I, Sivri, B, Lu, W, Eapen, MS, van, UM, Osei-Sarpong, C, van, DBM, Donker, HC, Groen, HJM, Sohal, SS, Klein, J, Schreiber, T, Feißt, A, Yildirim, A, Schiller, HB, Nawijn, MC, Becker, M, Händler, K, Beyer, M, Capasso, M, Ulas, T, Hasenauer, J, Pizarro, C, Theis, FJ, Hansbro, PM, Skowasch, D & Schultze, JL 2023, 'Systemic alterations in neutrophils and their precursors in early-stage chronic obstructive pulmonary disease.', Cell Rep, vol. 42, no. 6, pp. 112525-112525.
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Systemic inflammation is established as part of late-stage severe lung disease, but molecular, functional, and phenotypic changes in peripheral immune cells in early disease stages remain ill defined. Chronic obstructive pulmonary disease (COPD) is a major respiratory disease characterized by small-airway inflammation, emphysema, and severe breathing difficulties. Using single-cell analyses we demonstrate that blood neutrophils are already increased in early-stage COPD, and changes in molecular and functional neutrophil states correlate with lung function decline. Assessing neutrophils and their bone marrow precursors in a murine cigarette smoke exposure model identified similar molecular changes in blood neutrophils and precursor populations that also occur in the blood and lung. Our study shows that systemic molecular alterations in neutrophils and their precursors are part of early-stage COPD, a finding to be further explored for potential therapeutic targets and biomarkers for early diagnosis and patient stratification.
Kaplan, E, Baygin, M, Barua, PD, Dogan, S, Tuncer, T, Altunisik, E, Palmer, EE & Acharya, UR 2023, 'ExHiF: Alzheimer's disease detection using exemplar histogram-based features with CT and MR images', Medical Engineering & Physics, vol. 115, pp. 103971-103971.
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Karbassiyazdi, E, Altaee, A, Ibrar, I, Razmjou, A, Alsaka, L, Ganbat, N, Malekizadeh, A, Ghobadi, R & Khabbaz, H 2023, 'Fabrication of carbon-based hydrogel membrane for landfill leachate wastewater treatment', Desalination, vol. 564, pp. 116783-116783.
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The challenge of effectively managing the discharge of metal ions into aquatic environments, which poses a significant risk to both human health and ecosystems, persists despite the availability of various analytical tools and techniques. There are limitations of existing separation technologies and the inefficacy of hydrogel materials in removing low molecular weight contaminants, such as metal ions, in aqueous solutions. This study added carbon powder to the hydrogel membrane to reduce the low-mechanical strength and drying problems and increase its capacity for adsorbing ionic and non-ionic substances. The study introduced a novel carbon-based aluminium hydroxide hydrogel for wastewater filtration. CG was characterized using various analytical techniques, including examining surface morphology, elemental analysis, surface functional groups, and surface charge. These analytical tools provided a comprehensive understanding of the properties and performance of the CG. The effects of different carbon-based hydrogel (CG) concentrations on water flux and ion rejection were evaluated in a gravity filtration setup. Experiments investigated the influence of different ion concentrations, activated carbon (AC) concentration, centrifugation, water flux, and rejection on removing heavy metals from synthetic and natural wastewater. The pure water flux of the hydrogel membrane was 120 LMH. The results indicated that an AC concentration of 4 g/L in the aqueous solution is optimal for heavy metals removal, with 99.9 % removal for Pb2+ and Cu2+, 84 % rejection for Ca2+, and 85 % rejection for Mg2+ in 10 mg/L of synthetic water. Besides, the 4 g/L AC hydrogel membrane removed 90 % of Ni, Zn, Pb, As, and Cu ions and 53 % of the total organic carbon from leachate wastewater.
Karbassiyazdi, E, Altaee, A, Razmjou, A, Samal, AK & Khabbaz, H 2023, 'Gravity-Driven Composite Cellulose Acetate/Activated Carbon Aluminium-Based Hydrogel Membrane for Landfill Wastewater Treatment', Chemical Engineering Research and Design.
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Karbassiyazdi, E, Kasula, M, Modak, S, Pala, J, Kalantari, M, Altaee, A, Esfahani, MR & Razmjou, A 2023, 'A juxtaposed review on adsorptive removal of PFAS by metal-organic frameworks (MOFs) with carbon-based materials, ion exchange resins, and polymer adsorbents', Chemosphere, vol. 311, pp. 136933-136933.
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Katzmarek, DA, Mancini, A, Maier, SA & Iacopi, F 2023, 'Direct synthesis of nanopatterned epitaxial graphene on silicon carbide', Nanotechnology, vol. 34, no. 40, pp. 405302-405302.
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Abstract
This article introduces a straightforward approach for the direct synthesis of transfer-free, nanopatterned epitaxial graphene on silicon carbide on silicon substrates. A catalytic alloy tailored to optimal SiC graphitization is pre-patterned with common lithography and lift-off techniques to form planar graphene structures on top of an unpatterned SiC layer. This method is compatible with both electron-beam lithography and UV-lithography, and graphene gratings down to at least ∼100 nm width/space can be realized at the wafer scale. The minimum pitch is limited by the flow of the metal catalyst during the liquid-phase graphitization process. We expect that the current pitch resolution could be further improved by optimizing the metal deposition method and lift-off process.
Ke, G, Duanxiong, K, Zhang, X, Tang, Z, Yang, R & Li, W 2023, 'Synthesis of Quaternary Hydrotalcite-Carbon Nanotube Composite and Its Sulfate Adsorption Performance in Cement Paste', Journal of Materials in Civil Engineering, vol. 35, no. 11.
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Ke, Y, Shi, FL, Zhang, SS, Nie, XF & Li, WG 2023, 'Strength Model for Debonding Failure in RC Beams Flexurally Strengthened with NSM FRP and Anchored with FRP U-Jackets', Journal of Composites for Construction, vol. 27, no. 5.
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The flexural performance of reinforced concrete (RC) beams could be effectively improved by applying a near-surface mounted (NSM) fiber-reinforced polymer (FRP) at the beam soffit. However, such NSM FRP flexurally-strengthened beams frequently failed due to FRP debonding, which limited the full utilization of the FRP strength. In some experimental studies, FRP U-jackets have been used as the anchorage to mitigate or prevent debonding failures in NSM FRP flexurally-strengthened beams. These studies showed excellent anchoring performance of the FRP U-jackets. The authors recently developed a finite-element (FE) approach that could accurately predict the behavior of RC beams that had been flexurally strengthened with NSM FRP (NSM-strengthened beams), which were anchored with FRP U-jackets. Based on a parametric study that was undertaken, which used the simplified version of the FE approach, this paper proposed a strength model for the most common debonding failure mode in NSM-strengthened beams with FRP U-jackets. The proposed strength model consisted of an equation for the maximum NSM FRP strain (Ef) at debonding failure. Once the maximum FRP strain was known, the load-carrying capacity of the strengthened beam could be obtained through a section analysis. Comparing the predictions made by the proposed strength model with the test results showed that the proposed strength model could provide close predictions.
Keivanian, F, Chiong, R, Kashani, AR & Gandomi, AH 2023, 'A fuzzy adaptive metaheuristic algorithm for identifying sustainable, economical, and earthquake-resistant reinforced concrete cantilever retaining walls', Journal of Computational Science, vol. 70, pp. 101978-101978.
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Keles, T, Yildiz, AM, Barua, PD, Dogan, S, Baygin, M, Tuncer, T, Demir, CF, Ciaccio, EJ & Acharya, UR 2023, 'A new one-dimensional testosterone pattern-based EEG sentence classification method', Engineering Applications of Artificial Intelligence, vol. 119, pp. 105722-105722.
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Keshavarz, R, Majidi, E, Raza, A & Shariati, N 2023, 'Ultra-Fast and Efficient Design Method Using Deep Learning for Capacitive Coupling WPT System', IEEE Transactions on Power Electronics, pp. 1-12.
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Keshavarz, R, Shariati, N & Miri, M-A 2023, 'Real-Time Discrete Fractional Fourier Transform Using Metamaterial Coupled Lines Network', IEEE Transactions on Microwave Theory and Techniques, vol. 71, no. 8, pp. 3414-3423.
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Keshavarz-Fathi, M, Yazdanpanah, N, Kolahchi, S, Ziaei, H, Darmstadt, GL, Dorigo, T, Dochy, F, Levin, L, Thongboonkerd, V, Ogino, S, Chen, W-H, Perc, M, Tremblay, MS, Olusanya, BO, Rao, IM, Hatziargyriou, N, Moradi-Lakeh, M, Bella, F, Rosivall, L, Gandomi, AH, Sorooshian, A, Gupta, M, Gal, C, Lozano, AM, Weaver, C, Tanzer, M, Poggi, A, Sepanlou, SG, Weiskirchen, R, Jambrak, AR, Torres, PJ, Capanoglu, E, Barba, FJ, Ernest, CKJ, Sigman, M, Pluchino, S, Gharehpetian, GB, Fereshtehnejad, S-M, Yang, M-H, Thomas, S, Cai, W, Comini, E, Scolding, NJ, Myles, PS, Nieto, JJ, Perry, G, Sedikides, C & Rezaei, N 2023, 'Universal research index: An inclusive metric to quantify scientific research output', The Journal of Academic Librarianship, vol. 49, no. 3, pp. 102714-102714.
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Kha, J, Karimi, M, Maxit, L, Skvortsov, A & Kirby, R 2023, 'Forced vibroacoustic response of a cylindrical shell in an underwater acoustic waveguide', Ocean Engineering, vol. 273, pp. 113899-113899.
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Khabbaz, H, Rujikiatkamjorn, C & Parsa, A 2023, 'Preface', Lecture Notes in Civil Engineering, vol. 325 LNCE, pp. v-vi.
Khade, S, Gite, S, D. Thepade, S, Pradhan, B & Alamri, A 2023, 'Iris Liveness Detection Using Fragmental Energy of Haar Transformed Iris Images Using Ensemble of Machine Learning Classifiers', Computer Modeling in Engineering & Sciences, vol. 136, no. 1, pp. 323-345.
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Khademi, P, Mousavi, M, Dackermann, U & Gandomi, AH 2023, 'Time–frequency analysis of ultrasonic signals for quality assessment of bonded concrete', Construction and Building Materials, vol. 403, pp. 133062-133062.
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Khan, A, Ibrar, I, Mirdad, A, Al-Juboori, RA, Deka, P, Subbiah, S & Altaee, A 2023, 'Novel Approach to Landfill Wastewater Treatment Fouling Mitigation: Air Gap Membrane Distillation with Tin Sulfide-Coated PTFE Membrane', Membranes, vol. 13, no. 5, pp. 483-483.
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This study addressed the fouling issue in membrane distillation (M.D.) technology, a promising method for water purification and wastewater reclamation. To enhance the anti-fouling properties of the M.D. membrane, a tin sulfide (TS) coating onto polytetrafluoroethylene (PTFE) was proposed and evaluated with air gap membrane distillation (AGMD) using landfill leachate wastewater at high recovery rates (80% and 90%). The presence of TS on the membrane surface was confirmed using various techniques, such as Field Emission Scanning Electron Microscopy (FE-SEM), Fourier Transform Infrared Spectroscopy (FT-IR), Energy Dispersive Spectroscopy (EDS), contact angle measurement, and porosity analysis. The results indicated the TS-PTFE membrane exhibited better anti-fouling properties than the pristine PTFE membrane, and its fouling factors (FFs) were 10.4–13.1% compared to 14.4–16.5% for the PTFE membrane. The fouling was attributed to pore blockage and cake formation of carbonous and nitrogenous compounds. The study also found that physical cleaning with deionized (DI) water effectively restored the water flux, with more than 97% recovered for the TS-PTFE membrane. Additionally, the TS-PTFE membrane showed better water flux and product quality at 55 °C and excellent stability in maintaining the contact angle over time compared to the PTFE membrane.
Khan, AUH, Liu, Y, Fang, C, Naidu, R, Shon, HK, Rogers, Z & Dharmarajan, R 2023, 'A comprehensive physicochemical characterization of zinc oxide nanoparticles extracted from sunscreens and wastewaters', Environmental Advances, vol. 12, pp. 100381-100381.
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Khan, AUH, Naidu, R, Dharmarajan, R, Fang, C, Shon, H, Dong, Z & Liu, Y 2023, 'The interaction mechanisms of co-existing polybrominated diphenyl ethers and engineered nanoparticles in environmental waters: A critical review', Journal of Environmental Sciences, vol. 124, pp. 227-252.
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Khan, NA, Hussain Khoja, A, Ahmed, N, Riaz, F, Mahmood, M, Ali, M, Kalam, MA & Mujtaba, MA 2023, 'Solar-assisted hybrid oil heating system for heavy refinery products storage', Case Studies in Thermal Engineering, vol. 49, pp. 103276-103276.
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Khare, SK, March, S, Barua, PD, Gadre, VM & Acharya, UR 2023, 'Application of data fusion for automated detection of children with developmental and mental disorders: A systematic review of the last decade', Information Fusion, vol. 99, pp. 101898-101898.
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Khavari, KMR, Wang, Q, Khatebasreh, M, Li, X, Sheikh, AAM, Boczkaj, G & Ghanbari, F 2023, 'Sequential treatment of landfill leachate by electrocoagulation/aeration, PMS/ZVI/UV and electro-Fenton: Performance, biodegradability and toxicity studies.', J Environ Manage, vol. 338, pp. 117781-117781.
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This study presents a systematic study on sequential treatment of highly resistant landfill leachate by electrocoagulation (EC)/aeration, sulfate radical advanced oxidation process (SR-AOP) and electro-Fenton (EF). In case of SR-AOP, peroxymonosulfate (PMS) catalyzed by zero valent iron (ZVI) and ultraviolet irradiation (UV) system was developed. Treatment process was optimized in respect to COD removal. Analysis of results revealed that sequential application of EC/aeration, PMS/ZVI/UV, and EF processes provide an extraordinary performance and meet the environmental regulations. The source of iron for EF process was provided from previous process reducing the cost of sequential process. Separately, EC/aeration (inlet COD = 4040 mg/L), PMS/ZVI/UV (inlet COD = 1560 mg/L), and EF (inlet COD = 471 mg/L) removed 61, 69 and 82% of COD respectively. Overall, sequential processes of EC/aeration, PMS/ZVI/UV and EF could remove the COD, TOC and ammonia of the landfill leachate around 98%, 93% and 94%, respectively. The comparison of different sequences of following processes indicated that current configuration (EC/aeration-PMS/ZVI/UV-EF) could meet the discharge standards. Furthermore, humification degree was significantly improved after oxidative processes. Biodegradability study was also performed by means of BOD/COD, average oxidation state (AOS), and Zahn-Wellens test, and the best results associated with these indices were obtained 0.56, 2.37, and over 98%, respectively. Phytotoxicity of leachate was remarkably reduced and the final effluent can be considered as a non-phytotoxic wastewater.
Khodasevych, I, Wesemann, L, Roberts, A & Iacopi, F 2023, 'Tunable nonlocal metasurfaces based on graphene for analogue optical computation', Optical Materials Express, vol. 13, no. 5, pp. 1475-1475.
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Meta-optical devices have recently emerged as ultra-compact candidates for real-time computation in the spatial domain. The use of meta-optics for applications in image processing and wavefront sensing could enable an order of magnitude increase in processing speed and data throughput, while simultaneously drastically reducing the footprint of currently available solutions to enable miniaturisation. Most research to date has focused on static devices that can perform a single operation. Dynamically tunable devices, however, offer increased versatility. Here we propose graphene covered subwavelength silicon carbide gratings as electrically tunable optical computation and image processing devices at mid-infrared wavelengths.
Khoo, WH, Jackson, K, Phetsouphanh, C, Zaunders, JJ, Alquicira-Hernandez, J, Yazar, S, Ruiz-Diaz, S, Singh, M, Dhenni, R, Kyaw, W, Tea, F, Merheb, V, Lee, FXZ, Burrell, R, Howard-Jones, A, Koirala, A, Zhou, L, Yuksel, A, Catchpoole, DR, Lai, CL, Vitagliano, TL, Rouet, R, Christ, D, Tang, B, West, NP, George, S, Gerrard, J, Croucher, PI, Kelleher, AD, Goodnow, CG, Sprent, JD, Powell, JE, Brilot, F, Nanan, R, Hsu, PS, Deenick, EK, Britton, PN & Phan, TG 2023, 'Tracking the clonal dynamics of SARS-CoV-2-specific T cells in children and adults with mild/asymptomatic COVID-19', Clinical Immunology, vol. 246, pp. 109209-109209.
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Khounani, Z, Abdul Razak, NN, Hosseinzadeh-Bandbafha, H, Madadi, M, Sun, F, Fattah, IMR, Karimi, K, Gupta, VK, Aghbashlo, M & Tabatabaei, M 2023, 'Assessing the environmental impacts of furfural production in a poplar wood biorefinery: A study on the role of mannitol concentration and catalyst type', Industrial Crops and Products, vol. 203, pp. 117230-117230.
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Khuat, TT & Gabrys, B 2023, 'An online learning algorithm for a neuro-fuzzy classifier with mixed-attribute data', Applied Soft Computing, vol. 137, pp. 110152-110152.
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Khuat, TT & Gabrys, B 2023, 'hyperbox-brain: A Python toolbox for hyperbox-based machine learning algorithms', SoftwareX, vol. 23, pp. 101425-101425.
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Khuat, TT, Kedziora, DJ & Gabrys, B 2023, 'The Roles and Modes of Human Interactions with Automated Machine Learning Systems: A Critical Review and Perspectives', Foundations and Trends® in Human–Computer Interaction, vol. 17, no. 3-4, pp. 195-387.
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Kim, B-J, Shon, HK, Han, DS & Park, H 2023, 'In-situ desalination-coupled electrolysis with concurrent one-step-synthesis of value-added chemicals', Desalination, vol. 551, pp. 116431-116431.
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Kim, J, Tijing, L, Shon, HK & Hong, S 2023, 'Electrically conductive membrane distillation via an alternating current operation for zero liquid discharge', Water Research, vol. 244, pp. 120510-120510.
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Kirik, S, Dogan, S, Baygin, M, Barua, PD, Demir, CF, Keles, T, Yildiz, AM, Baygin, N, Tuncer, I, Tuncer, T, Tan, R-S & Acharya, UR 2023, 'FGPat18: Feynman graph pattern-based language detection model using EEG signals', Biomedical Signal Processing and Control, vol. 85, pp. 104927-104927.
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Kołodziejczak-Radzimska, A, Bielejewski, M, Zembrzuska, J, Ciesielczyk, F, Jesionowski, T & Nghiem, LD 2023, 'Exploring the functionality of an active ZrF-laccase biocatalyst towards tartrazine decolorization', Environmental Technology & Innovation, vol. 31, pp. 103201-103201.
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Kong, X, Lu, Z, Guo, X, Zhang, J & Li, H 2023, 'Resilience Evaluation of Cyber-Physical Power System Considering Cyber Attacks', IEEE Transactions on Reliability, pp. 1-12.
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Kong, Y, Liu, L, Qiao, M, Wang, Z & Tao, D 2023, 'Trust-Region Adaptive Frequency for Online Continual Learning', International Journal of Computer Vision, vol. 131, no. 7, pp. 1825-1839.
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AbstractIn the paradigm of online continual learning, one neural network is exposed to a sequence of tasks, where the data arrive in an online fashion and previously seen data are not accessible. Such online fashion causes insufficient learning and severe forgetting on past tasks issues, preventing a good stability-plasticity trade-off, where ideally the network is expected to have high plasticity to adapt to new tasks well and have the stability to prevent forgetting on old tasks simultaneously. To solve these issues, we propose a trust-region adaptive frequency approach, which alternates between standard-process and intra-process updates. Specifically, the standard-process replays data stored in a coreset and interleaves the data with current data, and the intra-process updates the network parameters based on the coreset. Furthermore, to improve the unsatisfactory performance stemming from online fashion, the frequency of the intra-process is adjusted based on a trust region, which is measured by the confidence score of current data. During the intra-process, we distill the dark knowledge to retain useful learned knowledge. Moreover, to store more representative data in the coreset, a confidence-based coreset selection is presented in an online manner. The experimental results on standard benchmarks show that the proposed method significantly outperforms state-of-art continual learning algorithms.
Krishankumar, R, Mishra, AR, Ravichandran, KS, Kar, S, Gandomi, AH & Bausys, R 2023, 'An integrated personalized decision approach with probabilistic linguistic context for grading restaurants in India', Applied Soft Computing, vol. 136, pp. 110089-110089.
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Krishna, DP, Ramaguru, R, Praveen, K, Sethumadhavan, M, Ravichandran, KS, Krishankumar, R & Gandomi, AH 2023, 'SSH-DAuth: secret sharing based decentralized OAuth using decentralized identifier.', Sci Rep, vol. 13, no. 1, p. 18335.
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OAuth2.0 is a Single Sign-On approach that helps to authorize users to log into multiple applications without re-entering the credentials. Here, the OAuth service provider controls the central repository where data is stored, which may lead to third-party fraud and identity theft. To circumvent this problem, we need a distributed framework to authenticate and authorize the user without third-party involvement. This paper proposes a distributed authentication and authorization framework using a secret-sharing mechanism that comprises a blockchain-based decentralized identifier and a private distributed storage via an interplanetary file system. We implemented our proposed framework in Hyperledger Fabric (permissioned blockchain) and Ethereum TestNet (permissionless blockchain). Our performance analysis indicates that secret sharing-based authentication takes negligible time for generation and a combination of shares for verification. Moreover, security analysis shows that our model is robust, end-to-end secure, and compliant with the Universal Composability Framework.
Krunz, M, Aykin, I, Sarkar, S & Akgun, B 2023, 'Online Reinforcement Learning for Beam Tracking and Rate Adaptation in Millimeter-wave Systems', IEEE Transactions on Mobile Computing, pp. 1-16.
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Kuang, J, Zhong, J, Yang, P, Bai, X, Liang, Y, Cheval, B, Herold, F, Wei, G, Taylor, A, Zhang, J, Chen, C, Sun, J, Zou, L & Arnett, JJ 2023, 'Psychometric evaluation of the inventory of dimensions of emerging adulthood (IDEA) in China', International Journal of Clinical and Health Psychology, vol. 23, no. 1, pp. 100331-100331.
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Kulandaivelu, J, Chen, Y, Choi, PM, Li, X, Rebosura, M, Song, Y, Yuan, Z, Mueller, JF & Jiang, G 2023, 'Fate of micropollutants in a lab-scale urban wastewater system: Impact of iron-rich drinking water treatment sludge', Journal of Hazardous Materials Advances, vol. 12, pp. 100360-100360.
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Kumar, A, Naidu, G, Fukuda, H, Du, F, Vigneswaran, S, Drioli, E & Lienhard, JH 2023, 'Correction to “Metals Recovery from Seawater Desalination Brines: Technologies, Opportunities, and Challenges”', ACS Sustainable Chemistry & Engineering, vol. 11, no. 1, pp. 464-465.
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Kurdkandi, NV, Marangalu, MG, Husev, O, Aghaie, A, Islam, MR, Siwakoti, YP, Muttaqi, KM & Hosseini, SH 2023, 'A New Seven-Level Transformer-Less Grid-Tied Inverter With Leakage Current Limitation and Voltage Boosting Feature', IEEE Journal of Emerging and Selected Topics in Industrial Electronics, vol. 4, no. 1, pp. 228-241.
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Kurunathan, H, Huang, H, Li, K, Ni, W & Hossain, E 2023, 'Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey', IEEE Communications Surveys & Tutorials, pp. 1-1.
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Lal Mohammadi, E, Khaksar Najafi, E, Zanganeh Ranjbar, P, Payan, M, Jamshidi Chenari, R & Fatahi, B 2023, 'Recycling industrial alkaline solutions for soil stabilization by low-concentrated fly ash-based alkali cements', Construction and Building Materials, vol. 393, pp. 132083-132083.
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Lalsangi, S, Yaliwal, VS, Banapurmath, NR, Soudagar, MEM, Ağbulut, Ü & Kalam, MA 2023, 'Analysis of CRDI diesel engine characteristics operated on dual fuel mode fueled with biodiesel-hydrogen enriched producer gas under the single and multi-injection scheme', International Journal of Hydrogen Energy, vol. 48, no. 74, pp. 28927-28944.
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Larpruenrudee, P, Bennett, NS, Luo, Z, Fitch, R, Sauret, E & Islam, MS 2023, 'A novel design for faster hydrogen storage: A combined semi-cylindrical and central return tube heat exchanger', Journal of Energy Storage, vol. 71, pp. 108018-108018.
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Lavaei, MH, Mohammadi Dehcheshmeh, E, Safari, P, Broujerdian, V & Gandomi, AH 2023, 'Reliability-based design optimization of post-tensioned self-centering rocking steel frame structures', Journal of Building Engineering, vol. 75, pp. 106955-106955.
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Laval, H, Holmes, A, Marcus, MA, Watts, B, Bonfante, G, Schmutz, M, Deniau, E, Szymanski, R, Lartigau‐Dagron, C, Xu, X, Cairney, JM, Hirakawa, K, Awai, F, Kubo, T, Wantz, G, Bousquet, A, Holmes, NP & Chambon, S 2023, 'Toward High Efficiency Water Processed Organic Photovoltaics: Controlling the Nanoparticle Morphology with Surface Energies', Advanced Energy Materials, vol. 13, no. 26.
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AbstractHere efficient organic photovoltaic devices fabricated from water‐based colloidal dispersions with donor:acceptor composite nanoparticles achieving up to 9.98% power conversion efficiency (PCE) are reported. This high efficiency for water processed organic solar cells is attributed to morphology control by surface energy matching between the donor and the acceptor materials. Indeed, due to a low interfacial energy between donor and the acceptor, no large phase separation occurs during the nanoparticle formation process as well as upon thermal annealing. Indeed, synchrotron‐based scanning transmission X‐ray microscopy reveals that the internal morphology of composite nanoparticles is intermixed as well as the active layer morphology after thermal treatment. The PCE of this system reaches 85% that of devices prepared from chlorinated solvent. The gap between water‐based inks and organic solvent‐based inks gets narrower, which is promising for the development of eco‐friendly processing and fabrication of organic photovoltaics.
Law, D, Patrisia, Y, Gunasekara, C, Castel, A, Nguyen, QD & Wardhono, A 2023, 'Durability Assessment of Alkali-Activated Concrete Exposed to a Marine Environment', Journal of Materials in Civil Engineering, vol. 35, no. 9.
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Le Gentil, C & Vidal-Calleja, T 2023, 'Continuous latent state preintegration for inertial-aided systems', The International Journal of Robotics Research, vol. 42, no. 10, pp. 874-900.
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Traditionally, the pose and velocity prediction of a system at time t2 given inertial measurements from a 6-DoF IMU depends on the knowledge of the system’s state at time t1. It involves a series of integration and double integration that can be computationally expensive if performed regularly, in particular in the context of inertial-aided optimisation-based state estimation. The concept of preintegration consists of creating pseudo-measurements that are independent of the system’s initial conditions (pose and velocity at t1) in order to predict the system’s state at t2. These pseudo-measurements, so-called preintegrated measurements, were originally computed numerically using the integration rectangle rule. This article presents a novel method to perform continuous preintegration using Gaussian processes (GPs) to model the system’s dynamics focusing on high accuracy. It represents the preintegrated measurement’s derivatives in a continuous latent state that is learnt/optimised according to asynchronous IMU gyroscope and accelerometer measurements. The GP models allow for analytical integration and double integration of the latent state to generate accurate preintegrated measurements called unified Gaussian preintegrated measurements (UGPMs). We show through extensive quantitative experiments that the proposed UGPMs outperform the standard preintegration method by an order of magnitude. Additionally, we demonstrate that the UGPMs can be integrated into off-the-shelf multi-modal estimation frameworks with ease based on lidar-inertial, RGBD-inertial, and visual-inertial real-world experiments.
Le, L-T, Nghiem, LD, Bui, X-T & Jahng, D 2023, 'Improve nitrogen removal of the biofilm single-stage PN/A process by optimizing the intermittent aeration strategy', Environmental Technology & Innovation, vol. 30, pp. 103078-103078.
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Lee, HH, Tang, Y, Yang, Q, Yu, X, Cai, LY, Remedios, LW, Bao, S, Landman, BA & Huo, Y 2023, 'Semantic-Aware Contrastive Learning for Multi-Object Medical Image Segmentation', IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 9, pp. 4444-4453.
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Lee, SS, Cao, S, Barzegarkhoo, R, Farhangi, M & Siwakoti, YP 2023, 'Single-Phase 5-Level Split-Midpoint Cross-Clamped (5L-SMCC) Inverter: An Alternative to the Two-Stage ANPC Topology', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 11, no. 2, pp. 1995-2003.
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Lee, SS, Lim, RJS, Barzegarkhoo, R, Lim, CS, Grigoletto, FB & Siwakoti, YP 2023, 'A Family of Single-Phase Single-Stage Boost Inverters', IEEE Transactions on Industrial Electronics, vol. 70, no. 8, pp. 7955-7964.
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H-bridge inverter is a common topology used for single-phase applications. Due to its limited voltage gain, a two-stage power conversion with a front-end dc-dc converter is usually adopted to accommodate the low dc source voltage. Recently, single-stage boost inverters are gaining significant interest due to their higher power efficiency and compactness. This paper presents a family of boost inverters with continuous dc source current. By the incorporation of merely a power switch and a boost inductor to the first leg of H-bridge, voltage-boosting and 3-level generation can be simultaneously achieved within a single-stage operation. All potential topologies using the same number of components are derived. An extension to generate five voltage levels with voltage gain enhancement is also proposed. The operation of the proposed boost inverters is thoroughly analyzed. Simulation and experimental results are presented for verification.
Lee, SS, Siwakoti, YP, Barzegarkhoo, R & Lee, K-B 2023, 'Five-Level Unity-Gain Active Neutral-Point-Clamped Inverters Designed Using Half-Bridges', IEEE Transactions on Industry Applications, vol. 59, no. 3, pp. 3520-3529.
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Lei, B, Xiong, Q, Zhao, H, Dong, W, Tam, VWY, Sun, Z & Li, W 2023, 'Performance of asphalt mortar with recycled concrete powder under different filler-to-asphalt weight ratios', Case Studies in Construction Materials, vol. 18, pp. e01834-e01834.
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The feasibility of using recycled concrete powder (RCP) as fillers in asphalt mortar is investigated in this study, to achieve a sustainable and economical asphalt production. The differences in physical properties between limestone powder (LSP) and RCP are analyzed and compared. Moreover, the interactions of LSP and RCP with asphalt are studied as well. The properties of asphalt mortar studied include ductility, softening point, penetration, viscous and elastic behaviors of asphalt mortar. Compared to LSP, the RCP presented a rougher surface, additional pores, more complex pore structures, larger Brunauer, Emmett and Teller (BET) surface areas, and smaller particle size. At 0.6 and 0.9 of filler-to-asphalt weight ratio (F/A), the RCP is more effective for the performance enhancement of asphalt mortar compared with the LSP. When the F/A is 0.9 and LSP is completely replaced by RCP, the 15 °C penetration index (PI) and ductility of asphalt mortar decrease by 9.3% and 29.2% respectively. The softening point increases by 5.4%. By contrast, the RCP causes a considerable decrease in PI, equivalent brittle point (T1.2) and ductility when F/A ratio is 1.2. After RCP completely replace LSP, the PI, T1.2, and ductility of asphalt mortar decrease by 47.1%, 44.0%, and 29.0%, respectively. However, at F/A of 0.6, the asphalt mortar with 100% RCP replacement ratio presented both acceptable ductility and plasticity. Under the same temperature and F/A, the complex shear modulus G* and rutting resistance factor G* /sinδ of asphalt mortar raise with the increase of RCP replacement, which indicates that the RCP can better enhance the high-temperature rutting resistance of asphalt mortar than the counterpart LSP. It also implies that the modification of LSP and RCP in asphalt mortar mainly depends on the physical interactions rather than the chemical reactions.
Lei, B, Yu, H, Guo, Y, Dong, W, Liang, R, Wang, X, Lin, X, Wang, K & Li, W 2023, 'Fracture behaviours of sustainable multi-recycled aggregate concrete under combined compression-shear loading', Journal of Building Engineering, vol. 72, pp. 106382-106382.
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Lei, B, Yu, H, Guo, Y, Zhao, H, Wang, K & Li, W 2023, 'Mechanical properties of multi-recycled aggregate concrete under combined compression-shear loading', Engineering Failure Analysis, vol. 143, pp. 106910-106910.
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The mechanical properties and strength failure criteria of multi-recycled aggregate concrete (multi-RAC) under combined compression and shear loading states are investigated in this paper. The peak shear strength, peak shear displacement, and failure patterns are compared under different regeneration cycles and normal compressive stress ratios. The results reveal that both the peak shear strength and peak shear displacement increase with the increased normal stress ratio. The shear failure pattern with higher severity corresponds to more spalling powder and debris deposited on the shear fracture surface. When the regeneration cycles of coarse aggregate increase, the peak shear strength decreases and the descending trend become more evident with the higher vertical compressive stress ratio. Under the normal compressive stress, contact friction strength is the dominant component of peak shear strength among the cohesive strength, contact friction strength, and shear dilation strength. Based on different stress expressions, three compression-shear failure criterion models considering the regeneration cycles of coarse aggregate under planar stress state were established for RAC. The stress invariance failure criterion model and octahedral stress failure criterion model in quadratic parabolic functional form can provide high prediction accuracies.
Lei, B, Yu, L, Guo, Y, Mahmood, AH, Qu, F, Wang, X & Li, W 2023, 'Failure behaviour and damage evolution of multi-recycled aggregate concrete under triaxial compression', Engineering Failure Analysis, vol. 153, pp. 107572-107572.
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Lei, Y, Sui, Y, Tan, SH & Zhang, Q 2023, 'Recursive State Machine Guided Graph Folding for Context-Free Language Reachability', Proceedings of the ACM on Programming Languages, vol. 7, no. PLDI, pp. 318-342.
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Context-free language reachability (CFL-reachability) is a fundamental framework for program analysis. A large variety of static analyses can be formulated as CFL-reachability problems, which determines whether specific source-sink pairs in an edge-labeled graph are connected by a reachable path, i.e., a path whose edge labels form a string accepted by the given CFL. Computing CFL-reachability is expensive. The fastest algorithm exhibits a slightly subcubic time complexity with respect to the input graph size. Improving the scalability of CFL-reachability is of practical interest, but reducing the time complexity is inherently difficult.
In this paper, we focus on improving the scalability of CFL-reachability from a more practical perspective---reducing the input graph size. Our idea arises from the existence of trivial edges, i.e., edges that do not affect any reachable path in CFL-reachability. We observe that two nodes joined by trivial edges can be folded---by merging the two nodes with all the edges joining them removed---without affecting the CFL-reachability result. By studying the characteristic of the recursive state machines (RSMs), an alternative form of CFLs, we propose an approach to identify foldable node pairs without the need to verify the underlying reachable paths (which is equivalent to solving the CFL-reachability problem). In particular, given a CFL-reachability problem instance with an input graph G and an RSM, based on the correspondence between paths in G and state transitions in RSM, we propose a graph folding principle, which can determine whether two adjacent nodes are foldable by examining only their incoming and outgoing edges.
On top of the graph folding principle, we propose an efficient graph folding algorithm GF. The time complexity of GF is linear with respect to the number of nodes in the input graph. Our evaluations on two clients (alias analysis and value-flow analy...
Leng, D, Wang, R, Yang, Y, Li, Y & Liu, G 2023, 'Study on a three-dimensional variable-stiffness TMD for mitigating bi-directional vibration of monopile offshore wind turbines', Ocean Engineering, vol. 281, pp. 114791-114791.
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Leon-Castro, E, Sahni, M, Blanco-Mesa, F, Alfaro-Garcia, V & Merigo, J 2023, 'Preface', Innovation and Sustainability in Governments and Companies: A Perspective to the New Realities, p. xiii.
Li, A, Yang, B, Huo, H, Chen, H, Xu, G & Wang, Z 2023, 'Hyperbolic Neural Collaborative Recommender', IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 9, pp. 9114-9127.
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Li, B, Cui, W, Zhang, L, Zhu, C, Wang, W, Tsang, IW & Zhou, JT 2023, 'DifFormer: Multi-Resolutional Differencing Transformer With Dynamic Ranging for Time Series Analysis', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 11, pp. 13586-13598.
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Time series analysis is essential to many far-reaching applications of data science and statistics including economic and financial forecasting, surveillance, and automated business processing. Though being greatly successful of Transformer in computer vision and natural language processing, the potential of employing it as the general backbone in analyzing the ubiquitous times series data has not been fully released yet. Prior Transformer variants on time series highly rely on task-dependent designs and pre-assumed 'pattern biases', revealing its insufficiency in representing nuanced seasonal, cyclic, and outlier patterns which are highly prevalent in time series. As a consequence, they can not generalize well to different time series analysis tasks. To tackle the challenges, we propose DifFormer, an effective and efficient Transformer architecture that can serve as a workhorse for a variety of time-series analysis tasks. DifFormer incorporates a novel multi-resolutional differencing mechanism, which is able to progressively and adaptively make nuanced yet meaningful changes prominent, meanwhile, the periodic or cyclic patterns can be dynamically captured with flexible lagging and dynamic ranging operations. Extensive experiments demonstrate DifFormer significantly outperforms state-of-the-art models on three essential time-series analysis tasks, including classification, regression, and forecasting. In addition to its superior performances, DifFormer also excels in efficiency - a linear time/memory complexity with empirically lower time consumption.
Li, B, Guo, T, Li, R, Wang, Y, Gandomi, AH & Chen, F 2023, 'Self-Adaptive Predictive Passenger Flow Modeling for Large-Scale Railway Systems', IEEE Internet of Things Journal, vol. 10, no. 16, pp. 14182-14196.
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Li, B, Wen, S, Yan, Z, Wen, G & Huang, T 2023, 'A Survey on the Control Lyapunov Function and Control Barrier Function for Nonlinear-Affine Control Systems', IEEE/CAA Journal of Automatica Sinica, vol. 10, no. 3, pp. 584-602.
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Li, B, Zhu, JG, Liu, CC, Lei, G & Li, YJ 2023, 'Design and optimization of dual-stator FSPMM for integrated compressor', Dianji yu Kongzhi Xuebao/Electric Machines and Control, vol. 27, no. 1, pp. 101-109.
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An integrated flux-switching permanent magnet machine (FSPMM) was proposed based on the 6 / 4 dual stator structure for the issue of sizing and cooling in axial flow compressor. Firstly, the analytical expression of cogging torque considering the rotor skew was derived and the model of flow trajectory is built. An E-core and C-core dual-stator FSPMM were designed and finite element model was built by the three-dimensional finite-element method. The electromagnetic performance was calculated and compared, including the distribution of magnetic density, no-load back-EMF, cogging torque, electromagnetic torque and torque ripple. The results show that the performance of E-core FSPMM is obviously better than that of C-core FSPMM. The rotor of E-core FSPMM is optimized considering electromagnetic and hydrodynamic performance. The results show that the torque ripple of optimized 6 / 4 dual E-core stator FSPMM reduces by 15. 25% and the outlet velocity is pushed up to 113. 27 m / s for driving multistage axial-flow compressors.
Li, C, Cheng, X & Liu, F 2023, 'Energy efficient transceiver design for SWIPT systems with non-orthogonal multiple access and power splitting', AEU - International Journal of Electronics and Communications, vol. 158, pp. 154449-154449.
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Li, C, Fang, J, Wan, Y, Qiu, N, Steven, G & Li, Q 2023, 'Phase field fracture model for additively manufactured metallic materials', International Journal of Mechanical Sciences, vol. 251, pp. 108324-108324.
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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 2023, '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, Ou, T, Fu, Q, Li, D-S, Liu, Z & Sun, Y 2023, 'A Novel Thin Film Composite Membrane for Osmotic Energy Generation', Industrial & Engineering Chemistry Research, vol. 62, no. 14, pp. 5889-5897.
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Li, D, Ou, T, Fu, Q, Li, D-S, Liu, Z & Sun, Y 2023, 'A Novel Thin Film Composite Membrane for Osmotic Energy Generation', INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, vol. 62, no. 14, pp. 5889-5897.
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Li, D, Wang, G, Li, J, Yan, L, Liu, H, Jiu, J, Li, X, Li, JJ & Wang, B 2023, 'Biomaterials for Tissue-Engineered Treatment of Tendinopathy in Animal Models: A Systematic Review', Tissue Engineering Part B: Reviews, vol. 29, no. 4, pp. 387-413.
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Li, F, Zheng, J, Zhang, Y-F, Jia, W, Wei, Q & He, X 2023, 'Cross-domain learning for underwater image enhancement', Signal Processing: Image Communication, vol. 110, pp. 116890-116890.
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Li, G, Wu, Y, Wang, C, Peng, S, Niu, J & Yu, S 2023, 'The SRVM: A Similarity-Based Relevance Vector Machine for Remaining Useful Lifetime Prediction in the Industrial Internet of Things', IEEE Intelligent Systems, vol. 38, no. 5, pp. 45-55.
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Li, H, Cai, Z, Wang, J, Tang, J, Ding, W, Lin, C-T & Shi, Y 2023, 'FedTP: Federated Learning by Transformer Personalization', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15.
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Li, H, Feng, C-M, Xu, Y, Zhou, T, Yao, L & Chang, X 2023, 'Zero-Shot Camouflaged Object Detection', IEEE Transactions on Image Processing, vol. 32, pp. 5126-5137.
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Li, H, Lv, T, Shui, Y, Zhang, J, Zhang, H, Zhao, H & Ma, S 2023, 'An Improved grey wolf optimizer with weighting functions and its application to Unmanned Aerial Vehicles path planning', Computers and Electrical Engineering, vol. 111, pp. 108893-108893.
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Li, H, Wen, S & Shi, K 2023, 'A simple and effective multi-person pose estimation model for low power embedded system', Microprocessors and Microsystems, vol. 96, pp. 104739-104739.
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Li, J, Jiang, M, Qin, Y, Zhang, R & Ling, SH 2023, 'Intelligent depression detection with asynchronous federated optimization', Complex & Intelligent Systems, vol. 9, no. 1, pp. 115-131.
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AbstractThe growth of population and the various intensive life pressures everyday deepen the competitions among people. Tens of millions of people each year suffer from depression and only a fraction receives adequate treatment. The development of social networks such as Facebook, Twitter, Weibo, and QQ provides more convenient communication and provides a new emotional vent window. People communicate with their friends, sharing their opinions, and shooting videos to reflect their feelings. It provides an opportunity to detect depression in social networks. Although depression detection using social networks has reflected the established connectivity across users, fewer researchers consider the data security and privacy-preserving schemes. Therefore, we advocate the federated learning technique as an efficient and scalable method, where it enables the handling of a massive number of edge devices in parallel. In this study, we conduct the depression analysis on the basis of an online microblog called Weibo. A novel algorithm termed as CNN Asynchronous Federated optimization (CAFed) is proposed based on federated learning to improve the communication cost and convergence rate. It is shown that our proposed method can effectively protect users' privacy under the premise of ensuring the accuracy of prediction. The proposed method converges faster than the Federated Averaging (FedAvg) for non-convex problems. Federated learning techniques can identify quality solutions of mental health problems among Weibo users.
Li, J, Li, X, Liu, H, Gao, L, Wang, W, Wang, Z, Zhou, T & Wang, Q 2023, 'Climate change impacts on wastewater infrastructure: A systematic review and typological adaptation strategy.', Water Res, vol. 242, pp. 120282-120282.
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Wastewater infrastructures play an indispensable role in society's functioning, human production activities, and sanitation safety. However, climate change has posed a serious threat to wastewater infrastructures. To date, a comprehensive summary with rigorous evidence evaluation for the impact of climate change on wastewater infrastructure is lacking. We conducted a systematic review for scientific literature, grey literature, and news. In total, 61,649 documents were retrieved, and 96 of them were deemed relevant and subjected to detailed analysis. We developed a typological adaptation strategy for city-level decision-making for cities in all-income contexts to cope with climate change for wastewater structures. 84% and 60% of present studies focused on the higher-income countries and sewer systems, respectively. Overflow, breakage, and corrosion were the primary challenge for sewer systems, while inundation and fluctuation of treatment performance were the major issues for wastewater treatment plants. In order to adapt to the climate change impact, typological adaptation strategy was developed to provide a simple guideline to rapidly select the adaptation measures for vulnerable wastewater facilities for cities with various income levels. Future studies are encouraged to focus more on the model-related improvement/prediction, the impact of climate change on other wastewater facilities besides sewers, and countries with low or lower-middle incomes. This review provided insight to comprehensively understand the climate change impact on wastewater facilities and facilitate the policymaking in coping with climate change.
Li, J, Pan, Y, Lyu, Y, Yao, Y, Sui, Y & Tsang, IW 2023, 'Earning Extra Performance from Restrictive Feedbacks', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-13.
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Li, J, Tang, S, Zhu, L, Zhang, W, Yang, Y, Chua, T-S, Wu, F & Zhuang, Y 2023, 'Variational Cross-Graph Reasoning and Adaptive Structured Semantics Learning for Compositional Temporal Grounding', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-16.
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Li, J, Yu, Y, Kim, T & Hajimohammadi, A 2023, 'Unveiling the underlying mechanisms of tensile behaviour enhancement in fibre reinforced foam concrete', Construction and Building Materials, vol. 398, pp. 132509-132509.
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Li, J, Zhu, T, Ren, W & Raymond, K-K 2023, 'Improve individual fairness in federated learning via adversarial training', Computers & Security, vol. 132, pp. 103336-103336.
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Li, K, Chen, J, Sun, X, Lei, G, Cai, Y & Chen, L 2023, 'Application of wireless energy transmission technology in electric vehicles', Renewable and Sustainable Energy Reviews, vol. 184, pp. 113569-113569.
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Li, K, Cui, Y, Li, W, Lv, T, Yuan, X, Li, S, Ni, W, Simsek, M & Dressler, F 2023, 'When Internet of Things Meets Metaverse: Convergence of Physical and Cyber Worlds', IEEE Internet of Things Journal, vol. 10, no. 5, pp. 4148-4173.
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In recent years, the Internet of Things (IoT) has been studied in the context of the Metaverse to provide users with immersive cyber-virtual experiences in mixed-reality environments. This survey introduces six typical IoT applications in the Metaverse, including collaborative healthcare, education, smart city, entertainment, real estate, and socialization. In the IoT-inspired Metaverse, we also comprehensively survey four pillar technologies that enable augmented reality (AR) and virtual reality (VR), namely, responsible artificial intelligence (AI), high-speed data communications, cost-effective mobile edge computing (MEC), and digital twins. According to the physical-world demands, we outline the current industrial efforts and seven key requirements for building the IoT-inspired Metaverse: immersion, variety, economy, civility, interactivity, authenticity, and independence. In addition, this survey describes the open issues in the IoT-inspired Metaverse, which need to be addressed to eventually achieve the convergence of physical and cyber worlds.
Li, K, Lau, BPL, Yuan, X, Ni, W, Guizani, M & Yuen, C 2023, 'Towards Ubiquitous Semantic Metaverse: Challenges, Approaches, and Opportunities', IEEE Internet of Things Journal, pp. 1-1.
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Li, K, Li, X, Chen, Q & Nimbalkar, S 2023, 'Laboratory Analyses of Noncoaxiality and Anisotropy of Spherical Granular Media under True Triaxial State', International Journal of Geomechanics, vol. 23, no. 9.
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Li, K, Lu, J, Zuo, H & Zhang, G 2023, 'Multidomain Adaptation With Sample and Source Distillation', IEEE Transactions on Cybernetics, pp. 1-13.
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Li, K, Lu, J, Zuo, H & Zhang, G 2023, 'Source-Free Multi-Domain Adaptation with Fuzzy Rule-based Deep Neural Networks', IEEE Transactions on Fuzzy Systems, pp. 1-15.
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Li, K, Niu, Z, Shi, K & Qiu, P 2023, 'Paper Recommendation Based on Academic Knowledge Graph and Subject Feature Embedding', Data Analysis and Knowledge Discovery, vol. 7, no. 5, pp. 48-59.
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[Objective] This paper proposes a new model that integrates multiple features to provide accurate paper recommendation services for researchers. [Methods] First, we designed a feature extraction framework to extract and fuse entity relation features and topic features from the knowledge graph and the content of academic papers, respectively. Then, we proposed a paper recommendation method based on the knowledge embedding-based encoding-decoding model, which improved the learning effect of high-dimensional fusion features. [Results] We examined our new model on the DBLP-v11 dataset. The proposed method improved the Recall and MRR scores by 8.9% and 2.9%, respectively, compared with the suboptimal model. [Limitations] The proposed graph feature learning method does not consider the weight of entities in the real environment. [Conclusions] The new paper recommendation method could effectively learn high-dimensional features, which provide guidance for subsequent research.
Li, L & kang, K 2023, 'Factors Affecting Chinese Students Promote Distance Learning On the Telecommunication Platform during the COVID-19 Pandemic', INTERNATIONAL JOURNAL OF SOCIAL SCIENCE HUMANITY & MANAGEMENT RESEARCH, vol. 2, no. 07, pp. 536-542.
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Influenced by the situation of COVID-19, all Chinese students have to follow a self-isolation policy and accept distance education from home. Among telecommunication platforms, the DingTalk platform is the most popular one in China and already has more than 130 million student users. Although DingTalk provides teachers and schools with comprehensive teaching functions, there are numerous negative feedbacks from Chinese students. To analyse related problems and promote distance education successfully, this paper establishes the research model based on the COM-B Behaviour Changing theory and the Hofstede cultural theory, and it analyses specific factors affecting Chinese students’ motivation to promote distance learning on telecommunication platform under the situation of COVID-19. The research results will be beneficial to improve the distance learning system, which can attract more and more young students to accept a high-quality distance education during and after the COVID-19 pandemic.
Li, L & Kang, K 2023, 'Why ethnic minority groups’ online-startups are booming in China’s tight cultural ecosystem?', Journal of Entrepreneurship in Emerging Economies, vol. 15, no. 2, pp. 278-300.
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Purpose
Although most Chinese ethnic minority groups (EMGs) hold conservative thinking to online-startups, the new entrepreneurial model is booming on live streaming platforms. In China’s tight cultural ecosystem, the tight cultural control would lead EMG entrepreneurs to keep conservative thinking and avoid challenging careers. Still, it would be helpful for Chinese Governments to issue systematical entrepreneurial policies and improve online-startup environment for EMGs. To discover the relationships among influencing factors and EMGs’ online-startup motivation, this paper aims to draw on the tight and loose cultural theory and the capability-opportunity-motivation-behaviour (COM-B) behaviour changing theory and establishes the research model based on China’s tight cultural ecosystem.
Design/methodology/approach
Through analysing 617 questionnaires from 37 EMGs based on the partial least squares path modelling and variance-based structural equation modelling method, the study proves that environmental opportunity factors and personal capability factors have positive impacts on EMGs’ online-startup motivation and EMGs’ conservative thinking negatively moderates the relationship between their online-startup motivation and entrepreneurial development behaviour. In addition to testing the hypotheses, the paper also measures the importance-performance map analysis to explore additional findings of influencing factors and provide suitable suggestions for EMG entrepreneurs and related departments.
Findings
Regarding the environmental opportunity unit, both policy support and platform support significantly impact Chinese EMGs’ motivation to promote online-startups. For the personal capability un...
Li, L, Guo, R, You, P, Bai, J, Qin, P-Y & Liu, Y 2023, 'Pattern-Reconfigurable Sparse Linear Array Synthesis Under Minimum Element Spacing Control by Alternating Sequential Quadratic Programming', IEEE Antennas and Wireless Propagation Letters, vol. 22, no. 6, pp. 1271-1275.
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Li, L, Ju, N & Sheng, D 2023, 'Seismic performance and failure mechanism of interbedded slopes with steep rock layers', Engineering Geology, vol. 326, pp. 107312-107312.
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Numerous interbedded rock (IR) slopes fail during the Wenchuan earthquake in the mountainous region of western China. Landslides are also triggered in IR slopes with a 60° layer inclination, which are generally stable in gravity-dominant environments. This study examines the effect of seismic motion on the response characteristics and failure patterns of IR slopes with steep layers to develop a landslide hazard assessment tool for earthquake-prone regions. First, we use a centrifuge shaking table test to model the failure process and acceleration responses of two IR slope models with stratigraphic dips of 60° and 80°, respectively, under different seismic intensities. Next, we adopt the Particle Flow Code to examine the crack propagation features and peak ground acceleration amplification effects for the IR slopes. We find that the seismic failure pattern of IR slopes depends largely on rock layer inclination: buckling failure is triggered when rock layers are parallel or nearly parallel to the slope surface, while toppling failure is triggered when the rock layer inclination is significantly higher than that of the slope surface. Following seismic excitation, the damage is mainly observed in the weak rock layers, creating lateral stress on adjacent strong rocks, which undergoes deformation and ultimate macroscopic failure. Further, displacement of the IR slope is negatively correlated to rock layer inclination. Rock layer thickness has a major influence on the damaged area inside the slope mass, while rock layer stiffness mainly affects the deformation distribution near the slope shoulder.
Li, L, Kang, K, Zhao, A & Feng, Y 2023, 'The impact of social presence and facilitation factors on online consumers' impulse buying in live shopping – celebrity endorsement as a moderating factor', Information Technology & People, vol. 36, no. 6, pp. 2611-2631.
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PurposeAlthough prior studies have studied the relationship between online consumers' attitudes and buying behaviour, the research focussing on online consumers' impulse buying behaviours and exploring the role of celebrity endorsement is limited. Drawing on the social presence and the social facilitation theory, this paper establishes a research model based on the stimuli–organism–response (S–O–R) model and the motivation theory. It explores how live streamers impact online consumers' impulse buying behaviours under specific social and cultural backgrounds, with celebrity endorsement as a moderating variable.Design/methodology/approachTo test the research model, the online questionnaire method has been conducted in this study. This paper utilises Chinese online consumers as samples and promotes an online survey. Using the variance-based structural equation modelling and partial least squares path modelling (SEM-PLS), 433 valid questionnaires have been analysed on SmartPLS.FindingsFirst, live streamers' attractive appearance positively correlates with online consumers' hedonic attitude and positively impacts their utilitarian attitude to live shopping. Second, live streamers' real-time interaction positively affects consumers' utilitarian attitudes because of their professional marketing and communication skills. Third, their hedonic and utilitarian attitudes positively influence online consumers' impulse buying behaviours. Finally, this paper presents that celebrity endorsement negatively moderates the relationship between online consumers' hedonic attitudes and impulse buying during live shopping.Originality/value
Li, L, Xiao, J, Shi, H, Wang, W, Shao, J, Liu, A-A, Yang, Y & Chen, L 2023, 'Label Semantic Knowledge Distillation for Unbiased Scene Graph Generation', IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1.
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Li, M & Yang, Y 2023, 'Single- and Multiple-Material Additively Manufactured Electronics: A Further Step From the Microwave-to-Terahertz Regimes', IEEE Microwave Magazine, vol. 24, no. 1, pp. 30-45.
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Li, M, Chen, S-L, Liu, Y & Guo, YJ 2023, 'Wide-Angle Beam Scanning Phased Array Antennas: A Review', IEEE Open Journal of Antennas and Propagation, vol. 4, pp. 695-712.
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Li, M, Liu, R, Wang, F, Chang, X & Liang, X 2023, 'Auxiliary signal-guided knowledge encoder-decoder for medical report generation', World Wide Web, vol. 26, no. 1, pp. 253-270.
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AbstractMedical reports have significant clinical value to radiologists and specialists, especially during a pandemic like COVID. However, beyond the common difficulties faced in the natural image captioning, medical report generation specifically requires the model to describe a medical image with a fine-grained and semantic-coherence paragraph that should satisfy both medical commonsense and logic. Previous works generally extract the global image features and attempt to generate a paragraph that is similar to referenced reports; however, this approach has two limitations. Firstly, the regions of primary interest to radiologists are usually located in a small area of the global image, meaning that the remainder parts of the image could be considered as irrelevant noise in the training procedure. Secondly, there are many similar sentences used in each medical report to describe the normal regions of the image, which causes serious data bias. This deviation is likely to teach models to generate these inessential sentences on a regular basis. To address these problems, we propose an Auxiliary Signal-Guided Knowledge Encoder-Decoder (ASGK) to mimic radiologists’ working patterns. Specifically, the auxiliary patches are explored to expand the widely used visual patch features before fed to the Transformer encoder, while the external linguistic signals help the decoder better master prior knowledge during the pre-training process. Our approach performs well on common benchmarks, including CX-CHR, IU X-Ray, and COVID-19 CT Report dataset (COV-CTR), demonstrating combining auxiliary signals with transformer architecture can bring a significant improvement in terms of medical report generation. The experimental results confirm that auxiliary signals driven Transformer-based models are with solid capabilities to outperform previous approaches on both medical terminology classification and paragraph generation metrics.
Li, M, Liu, Y, Bao, Z, Chen, L, Hu, J & Guo, YJ 2023, 'Efficient Phase-Only Dual- and Multi-Beam Pattern Synthesis With Accurate Beam Direction and Power Control Employing Partitioned Iterative FFT', IEEE Transactions on Antennas and Propagation, vol. 71, no. 4, pp. 3719-3724.
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Li, M, Ni, W, Li, S, Tong, S, Chen, R & Li, C 2023, 'Toward excellent oxidation resistance of Al2O3-SiC-C castables: new insights based on a novel pore-filling agent', Journal of the Australian Ceramic Society, vol. 59, no. 3, pp. 501-510.
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Li, P, Gao, X, Li, C, Yi, C, Huang, W, Si, Y, Li, F, Cao, Z, Tian, Y & Xu, P 2023, '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, P, Li, W, Wang, K, Zhao, H & Shah, SP 2023, 'Hydration and microstructure of cement paste mixed with seawater – An advanced investigation by SEM-EDS method', Construction and Building Materials, vol. 392, pp. 131925-131925.
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Li, P, Li, W, Wang, K, Zhou, JL, Castel, A, Zhang, S & Shah, SP 2023, 'Hydration of Portland cement with seawater toward concrete sustainability: Phase evolution and thermodynamic modelling', Cement and Concrete Composites, vol. 138, pp. 105007-105007.
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Li, P, Yu, H, Luo, X & Wu, J 2023, 'LGM-GNN: A Local and Global Aware Memory-Based Graph Neural Network for Fraud Detection', IEEE Transactions on Big Data, vol. 9, no. 4, pp. 1116-1127.
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Li, Q, Wu, D, Gao, W & Hui, D 2023, 'Nonlinear dynamic stability analysis of axial impact loaded structures via the nonlocal strain gradient theory', Applied Mathematical Modelling, vol. 115, pp. 259-278.
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In engineering applications, there has been an overwhelming tendency towards portability, miniaturization, and integration in recent years. To link the intrinsic size dependency feature of the small-scale structure with its structural stability, the nonlocal strain gradient theory, which captures the size effect in a more general size-dependent continuum-based model, is introduced to explore the nonlinear dynamic stability behaviour of nanoplates. Four types of axial impact loading configurations, namely, sinusoidal, exponential, rectangular, and damping, are considered. Some practical factors, such as Winkler-Pasternak elastic foundation and damping, are taken into account in the analysis. The equations of motion for the size-dependent initially imperfect plate are derived in the framework of the first-order shear deformation plate theory in conjunction with the Von Kármán nonlinear terms. Then the Airy stress function corresponding to simply supported nanoplate is introduced; then, by applying the Galerkin method, the obtained differential equations are addressed by the fourth-order Runge-Kutta algorithm. Subsequently, the specific value of the critical dynamic buckling load is determined by the Volmir criterion. Organic solar cells (OSCs), a type of emerging solar-to-electrical energy conversion nanodevice, are used as an illustrative example within the existing framework. The effects of size dependency in conjunction with the pulse load configuration, the initial imperfection, the elastic foundation, as well as the damping ratio on the nonlinear dynamic buckling behaviour of the OSC are thoroughly investigated.
Li, R, Millist, L, Foster, E, Yuan, X, Guvenc, U, Radfar, M, Marendy, P, Ni, W, O’Brien, TJ & Casillas-Espinosa, PM 2023, 'Spike and wave discharges detection in genetic absence epilepsy rat from Strasbourg and patients with genetic generalized epilepsy', Epilepsy Research, vol. 194, pp. 107181-107181.
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Li, R, Yuan, X, Radfar, M, Marendy, P, Ni, W, O'Brien, TJ & Casillas-Espinosa, P 2023, 'Graph Signal Processing, Graph Neural Network and Graph Learning on Biological Data: A Systematic Review', IEEE Reviews in Biomedical Engineering, vol. 16, pp. 109-135.
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Li, S, Ji, JC, Xu, Y, Sun, X, Feng, K, Sun, B, Wang, Y, Gu, F, Zhang, K & Ni, Q 2023, 'IFD-MDCN: Multibranch denoising convolutional networks with improved flow direction strategy for intelligent fault diagnosis of rolling bearings under noisy conditions', Reliability Engineering & System Safety, vol. 237, pp. 109387-109387.
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Li, T, Lian, S, Zhao, S, Lu, J & Burnett, IS 2023, 'Distributed Active Noise Control Based on an Augmented Diffusion FxLMS Algorithm', IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 31, pp. 1449-1463.
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Li, W, Hu, Y, Jiang, C, Wu, S, Bai, Q & Lai, E 2023, 'ABEM: An adaptive agent-based evolutionary approach for influence maximization in dynamic social networks', Applied Soft Computing, vol. 136, pp. 110062-110062.
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Li, W, Ji, J, Huang, L & Cai, Z 2023, 'Periodic orbit analysis for a delayed model of malicious signal transmission in wireless sensor networks with discontinuous control', Mathematical Methods in the Applied Sciences, vol. 46, no. 5, pp. 5267-5285.
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This paper employs a discontinuous temporary immunity control to obtain the periodic orbit for a class of delayed malicious signal transmission model in wireless sensor networks under the framework of differential inclusion. The positivity and boundedness of the solution for the discontinuous system is proved first. Then, by using the Kakutani's fixed point theorem of set-valued maps, the existence of a periodic orbit is obtained under some assumptions and constraints. Furthermore, the globally exponentially stable (Formula presented.) -periodic orbit is investigated using the Lyapunov functional method. The obtained results can help us better understand the dynamic characteristics of discontinuous delayed systems and have direct applications to the wireless sensor networks for guaranteeing fast response to malicious signals. Finally, the numerical simulations of three examples are given to validate the correctness of the theoretical results.
Li, W, Ji, J, Huang, L & Zhang, L 2023, 'Global dynamics and control of malicious signal transmission in wireless sensor networks', Nonlinear Analysis: Hybrid Systems, vol. 48, pp. 101324-101324.
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This paper studies the global dynamics of a discontinuous delayed model of malicious signal transmission in wireless sensor networks under the framework of differential inclusion. The local stability of two types of steady states are investigated for the discontinuous system by studying the corresponding characteristic equation. The sufficient conditions for the existence of two types of globally asymptotically stable steady states are obtained for the discontinuous system by using the comparison arguments method. Furthermore, the optimal control of the discontinuous system is investigated by using Pontryagin's maximum principle. Numerical simulations of two examples are carried out to illustrate the main theoretical results. The obtained results can help us to better control and predict the spread of malicious signal transmission in wireless sensor networks.
Li, W, Ji, J, Huang, L & Zhang, Y 2023, 'Complex dynamics and impulsive control of a chemostat model under the ratio threshold policy', Chaos, Solitons & Fractals, vol. 167, pp. 113077-113077.
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Li, W, Li, X, Han, C, Gao, L, Wu, H & Li, M 2023, 'A new view into three-dimensional excitation-emission matrix fluorescence spectroscopy for dissolved organic matter.', Sci Total Environ, vol. 855, pp. 158963-158963.
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Three-dimensional excitation-emission matrix fluorescence spectroscopy (3D EEMs) has been extensively used for dissolved organic matter (DOM) characterization. However, the application of 3D EEMs is constantly limited by issues such as contradictory component identification, confusing interpretation of spectral indicators, and inability to establish biodegradability. In this study, some improvements were proposed by investigating the 3D EEMs, spectral indicators, and degradability of the standard and representative DOM. To overcome the unclear identification of DOM components, it was recommended to partition 3D EEMs into three subareas: aromatic protein (New-I), humic-like (New-II), and soluble microbial by-product-like (New-III). Significant strong positive correlations (ρ = 0.727, P < 0.001) were observed between fluorescence index (FI) and biological index (BIX), and (R = 0.809, P < 0.001) humification index (HIX) and specific ultraviolet absorbance of 254 nm (SUVA254). Except for FI (R = -0.483, P = 0.023), no other spectral indicators (P > 0.05) were found to be significantly correlated with molecular weight. As thence results, the FI and HIX were the most suitable indicators for evaluating DOM. The half-life (20 < 21 < 26 < 29 < 46 days) revealed that the degradability of individual DOM components was in the order of tyrosine > tryptophan > fulvic acid > protein > humic acid. The degradation dynamics were governed by first-order decay kinetics (R2 = 0.91-0.99). This study clarified the fluorescence properties and degradability of DOM, as well as the reliability of spectral indicators. The degradation performance of individual DOM components engaged in the carbon cycling process was revealed, paving the path for further applications of 3D EEMs in DOM research.
Li, W, Lv, T, Cao, Y, Ni, W & Peng, M 2023, 'Multi-Carrier NOMA-Empowered Wireless Federated Learning with Optimal Power and Bandwidth Allocation', IEEE Transactions on Wireless Communications, pp. 1-1.
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Li, W, Zhang, Y, Ji, J & Huang, L 2023, 'Dynamics of a diffusion epidemic SIRI system in heterogeneous environment', Zeitschrift für angewandte Mathematik und Physik, vol. 74, no. 3.
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Li, X, Li, X, Jia, J, Li, L, Yuan, J, Gao, Y & Yu, S 2023, 'A High Accuracy and Adaptive Anomaly Detection Model With Dual-Domain Graph Convolutional Network for Insider Threat Detection', IEEE Transactions on Information Forensics and Security, vol. 18, pp. 1638-1652.
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Li, X, Liu, H, Gao, L, Sherchan, SP, Zhou, T, Khan, SJ, van, LMCM & Wang, Q 2023, 'Wastewater-based epidemiology predicts COVID-19-induced weekly new hospital admissions in over 150 USA counties.', Nat Commun, vol. 14, no. 1, p. 4548.
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Although the coronavirus disease (COVID-19) emergency status is easing, the COVID-19 pandemic continues to affect healthcare systems globally. It is crucial to have a reliable and population-wide prediction tool for estimating COVID-19-induced hospital admissions. We evaluated the feasibility of using wastewater-based epidemiology (WBE) to predict COVID-19-induced weekly new hospitalizations in 159 counties across 45 states in the United States of America (USA), covering a population of nearly 100 million. Using county-level weekly wastewater surveillance data (over 20 months), WBE-based models were established through the random forest algorithm. WBE-based models accurately predicted the county-level weekly new admissions, allowing a preparation window of 1-4 weeks. In real applications, periodically updated WBE-based models showed good accuracy and transferability, with mean absolute error within 4-6 patients/100k population for upcoming weekly new hospitalization numbers. Our study demonstrated the potential of using WBE as an effective method to provide early warnings for healthcare systems.
Li, X, Liu, H, Zhang, Z, Zhou, T & Wang, Q 2023, 'Sulfite pretreatment enhances the medium-chain fatty acids production from waste activated sludge anaerobic fermentation.', Sci Total Environ, vol. 871, pp. 162080-162080.
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Production of high-value medium chain fatty acids (MCFAs) from anaerobic fermentation of waste activated sludge (WAS) has been considered as a promising alternative for renewable energy resources. However, the low biodegradability of WAS greatly limits the anaerobic fermentation performance. This study proposed and demonstrated a novel approach, sulfite pretreatment, to efficiently produce MCFAs through anaerobic fermentation of WAS. Pretreatment of WAS at a sulfite concentration of 100-500 mg S/L for 24 h effectively improved the MCFAs production and MCFAs selectivity and the promotion effect was positively correlated with the sulfite concentration used in pretreatment (Pearson's R > 0.9). The maximum MCFAs production of 6.84 g COD/L and MCFAs selectivity of 39.1 % were both achieved under 500 mg S/L sulfite pretreatment, which accounts for 2.6 times and 2.4 times of the control, respectively (MCFAs production of 2.62 g COD/L and MCFAs selectivity of 16.4 % in the control). Sulfite pretreatment also enhanced the WAS degradation from 25 ± 2 % in the control to a maximum of 39 ± 2 % under 500 mg S/L sulfite pretreatment. The electron transfer efficiency and COD flows from the substrate to products were enhanced by up to 25 % due to the sulfite pretreatment, which supports the enhanced WAS degradation. Sulfite pretreatment also promoted the solubilization, hydrolysis, and acidification processes during the anaerobic fermentation by up to 200 %, 60 %, and 45 %, respectively, which subsequently makes more substrates available for MCFAs production. The findings from this study provide a potential solution of using industrial sulfite-laden wastes for WAS pretreatment, to enhance the MCFAs production at a minimized cost.
Li, X, Liu, Q, Wu, S, Cao, Z & Bai, Q 2023, 'Game theory based compatible incentive mechanism design for non-cryptocurrency blockchain systems', Journal of Industrial Information Integration, vol. 31, pp. 100426-100426.
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Li, X, Peng, Y & Xu, M 2023, 'Patch-shuffle-based semi-supervised segmentation of bone computed tomography via consistent learning', Biomedical Signal Processing and Control, vol. 80, pp. 104239-104239.
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Li, X, Yan, L, Li, D, Fan, Z, Liu, H, Wang, G, Jiu, J, Yang, Z, Li, JJ & Wang, B 2023, 'Failure modes after anterior cruciate ligament reconstruction: a systematic review and meta-analysis.', Int Orthop, vol. 47, no. 3, pp. 719-734.
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PURPOSE: The reason for graft failure after anterior cruciate ligament reconstruction (ACLR) is multifactorial. Controversies remain regarding the predominant factor and incidence of failure aetiology in the literature. This review aimed to provide a meta-analysis of the literature to evaluate the relative proportion of various failure modes among patients with ACLR failure. METHODS: The PubMed, Embase, Cochrane Library, Web of Science, and EBSCO databases were searched for literature on ACLR failure or revision from 1975 to 2021. Data related to causes for ACLR surgical failure were extracted, and a random effects model was used to pool the results, which incorporates potential heterogeneity. Failure modes were compared between different populations, research methods, graft types, femoral portal techniques, and fixation methods by subgroup analysis or linear regression. Funnel plots were used to identify publication bias and small-study effects. RESULTS: A total of 39 studies were analyzed, including 33 cohort studies and six registry-based studies reporting 6578 failures. The results showed that among patients with ACLR failure or revision, traumatic reinjury was the most common failure mode with a rate of 40% (95% CI: 35-44%), followed by technical error (34%, 95% CI: 28-42%) and biological failure (11%, 95% CI: 7-15%). Femoral tunnel malposition was the most common cause of the technical error (29%, 95% CI: 18-41%), with more than two times higher occurrence than tibial tunnel malposition (11%, 95% CI: 6-16%). Traumatic reinjury was the most common factor for ACLR failure in European populations and in recent studies, while technical errors were more common in Asian populations, earlier studies, and surgery performed using the transtibial (TT) portal technique. Biological factors were more likely to result in ACLR failure in hamstring (HT) autografts compared to bone-patellar tendon-bone (BPTB) autografts. CONCLUSION: Trauma is the most important fa...
Li, X, Zhang, S, Sherchan, S, Orive, G, Lertxundi, U, Haramoto, E, Honda, R, Kumar, M, Arora, S, Kitajima, M & Jiang, G 2023, 'Correlation between SARS-CoV-2 RNA concentration in wastewater and COVID-19 cases in community: A systematic review and meta-analysis', Journal of Hazardous Materials, vol. 441, pp. 129848-129848.
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Li, Y, Chen, H, Li, Y, Li, L, Yu, PS & Xu, G 2023, 'Reinforcement Learning Based Path Exploration for Sequential Explainable Recommendation', IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 11, pp. 11801-11814.
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Li, Y, Chen, Z, Sun, X, Gao, C, Liu, X & Guo, Y 2023, 'Back propagation neural network-based torque ripple reduction strategy for high frequency square-wave voltage injection-based interior permanent magnet synchronous motor sensorless control', IET Electric Power Applications, vol. 17, no. 2, pp. 195-205.
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In interior permanent magnet synchronous motor (IPMSM) position-sensorless drives, the high-frequency (HF) square-wave voltage injection method is often used to estimate the rotor position and speed in low-speed range by tracking the salient polarity of the motor. In order to reduce the torque ripple caused by HF signal injection, a strategy to update the magnitude of the injected signal online by back propagation neural network is proposed in this paper. With the proposed method, the neural network can update the magnitude of the injected signal online according to the d-axis current and the position error information. It can not only ensure the accuracy of position extraction but also effectively reduce the current harmonics caused by the injected signal, and then the torque ripple can be reduced. In addition, the proposed method is easy to implement, resulting in low computation burden. Finally, the experiments are implemented on a 1-kW IPMSM drive. The experimental results show that compared with the conventional fixed magnitude injection, the peak-to-peak value of the torque ripple is reduced by nearly half along with the decrease of the injected magnitude.
Li, Y, Liu, Z, Chang, X, McAuley, J & Yao, L 2023, 'Diversity-Boosted Generalization-Specialization Balancing for Zero-Shot Learning', IEEE Transactions on Multimedia, pp. 1-11.
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Li, Y, Ma, B, Zheng, J, Zhu, J & Lei, G 2023, 'Electromagnetic and Mechanical Topology Optimization for SynRM Rotors Considering High Dimensional Constraints', IEEE Transactions on Industrial Electronics, vol. 70, no. 12, pp. 12048-12059.
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Li, Y, Shen, J & Cetindamar, D 2023, 'Think Tank Innovation-Driven Knowledge Service Ecosystems: A Conceptual Framework and Case Study Application', Sustainability, vol. 15, no. 10, pp. 8355-8355.
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By drawing on ecosystem and innovation-driven development theories, the aim of this paper is to increase our understanding of their application to think tanks. The composition, structure, and features of the knowledge service ecosystem of think tanks are conceptualized via a literature review. The model developed from this was validated by analyzing the data collected from 25 think tanks in the United States (US). The model constructed provides a reference for the sustainable and healthy development of knowledge services in think tanks and an innovation-driven development perspective for researchers interested in their innovation ecosystem dynamics. The intake of talent forms a necessary part of think tank construction, but, more importantly, this continuous intake is a crucial driving force for their sustainable development. This paper suggests that an increasing focus on talents in knowledge service ecosystems can lead to and assist in establishing innovative think tanks in many countries.
Li, Y, Wang, X, Zheng, J, Feng, K & Ji, JC 2023, 'Bi-filter multiscale-diversity-entropy-based weak feature extraction for a rotor-bearing system', Measurement Science and Technology, vol. 34, no. 6, pp. 065011-065011.
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Abstract
Multiscale-based entropy methods have proven to be a promising tool for extracting fault information due to their high feature extraction ability and easy application. Despite multiscale analysis showing great potential in extracting fault characteristics, it has some drawbacks, such as cutting the data length and neglecting high-frequency information. This paper proposes a bi-filter multiscale diversity entropy (BMDE) to filter comprehensive fault information and address the data length problem. First, the low-frequency information is filtered out by moving average in a multi-low procedure and the high-frequency information is filtered out by an adjacent subtraction in a multi-high procedure. Second, a modified coarse-grained process is introduced to overcome the issue of data length. The validity of the BMDE method is evaluated using both simulation signals and experimental measurements. Results demonstrate that the proposed method offers optimal feature extraction capability with the highest diagnostic accuracy compared with four other traditional entropy-based diagnosis methods.
Li, Y, Yin, J & Chen, L 2023, 'Informative pseudo-labeling for graph neural networks with few labels', Data Mining and Knowledge Discovery, vol. 37, no. 1, pp. 228-254.
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AbstractGraph neural networks (GNNs) have achieved state-of-the-art results for semi-supervised node classification on graphs. Nevertheless, the challenge of how to effectively learn GNNs with very few labels is still under-explored. As one of the prevalent semi-supervised methods, pseudo-labeling has been proposed to explicitly address the label scarcity problem. It is the process of augmenting the training set with pseudo-labeled unlabeled nodes to retrain a model in a self-training cycle. However, the existing pseudo-labeling approaches often suffer from two major drawbacks. First, these methods conservatively expand the label set by selecting only high-confidence unlabeled nodes without assessing their informativeness. Second, these methods incorporate pseudo-labels to the same loss function with genuine labels, ignoring their distinct contributions to the classification task. In this paper, we propose a novel informative pseudo-labeling framework (InfoGNN) to facilitate learning of GNNs with very few labels. Our key idea is to pseudo-label the most informative nodes that can maximally represent the local neighborhoods via mutual information maximization. To mitigate the potential label noise and class-imbalance problem arising from pseudo-labeling, we also carefully devise a generalized cross entropy with a class-balanced regularization to incorporate pseudo-labels into model retraining. Extensive experiments on six real-world graph datasets validate that our proposed approach significantly outperforms state-of-the-art baselines and competitive self-supervised methods on graphs.
Li, Y, Zeng, D, Gu, L, Zhu, A, Chen, Q & Yu, S 2023, 'PASTO: Enabling Secure and Efficient Task Offloading in TrustZone-Enabled Edge Clouds', IEEE Transactions on Vehicular Technology, vol. 72, no. 6, pp. 8234-8238.
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Li, Y, Zhang, X, Ngo, HH, Guo, W, Long, T, Wen, H & Zhang, D 2023, 'Combination of magnetic biochar beads and peroxymonosulfate pretreatment process for mitigating ultrafiltration membrane fouling caused by typical natural organic matters in water', Journal of Membrane Science, vol. 670, pp. 121383-121383.
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Li, Z, Gao, W, Kessissoglou, N, Oberst, S, Wang, MY & Luo, Z 2023, 'Multifunctional mechanical metamaterials with tunable double-negative isotropic properties', Materials & Design, vol. 232, pp. 112146-112146.
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Li, Z, Gao, W, Yu, WM, Wang, CH & Luo, Z 2023, 'Three-dimensional metamaterials exhibiting extreme isotropy and negative Poisson's ratio', International Journal of Mechanical Sciences, vol. 259, pp. 108617-108617.
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Li, Z, Xu, P, Chang, X, Yang, L, Zhang, Y, Yao, L & Chen, X 2023, 'When Object Detection Meets Knowledge Distillation: A Survey', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 8, pp. 10555-10579.
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Lian, J-W, Ansari, M, Hu, P, Guo, YJ & Ding, D 2023, 'Wideband and High-Efficiency Parallel-Plate Luneburg Lens Employing All-Metal Metamaterial for Multibeam Antenna Applications', IEEE Transactions on Antennas and Propagation, vol. 71, no. 4, pp. 3193-3203.
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Lian, M, Guo, Z, Wang, X, Wen, S & Huang, T 2023, 'Adaptive Exact Penalty Design for Optimal Resource Allocation', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 3, pp. 1430-1438.
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Lian, M, Guo, Z, Wen, S & Huang, T 2023, 'Distributed Adaptive Algorithm for Resource Allocation Problem Over Weight-Unbalanced Graphs', IEEE Transactions on Network Science and Engineering, pp. 1-10.
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Liang, C, Wang, W, Zhou, T, Miao, J, Luo, Y & Yang, Y 2023, 'Local-Global Context Aware Transformer for Language-Guided Video Segmentation', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 8, pp. 10055-10069.
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Liang, Y, Zhu, L, Wang, X & Yang, Y 2023, 'IcoCap: Improving Video Captioning by Compounding Images', IEEE Transactions on Multimedia, pp. 1-12.
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Lih, OS, Jahmunah, V, Palmer, EE, Barua, PD, Dogan, S, Tuncer, T, García, S, Molinari, F & Acharya, UR 2023, 'EpilepsyNet: Novel automated detection of epilepsy using transformer model with EEG signals from 121 patient population', Computers in Biology and Medicine, vol. 164, pp. 107312-107312.
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Lim, L-A, Atif, A, Heggart, K & Nicole, S 2023, 'In Search of Alignment between Learning Analytics and Learning Design: A Multiple Case Study in a Higher Education Institution', MDPI Education Sciences, no. Using Learning Analytics for Personalised, Data-Informed Feedback and Support: Studies of Impact, Challenges, and Future Directions, pp. 1-21.
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Learning design (LD) has increasingly been recognized as a significant contextual element
for the interpretation and adoption of learning analytics (LA). Yet, few studies have explored how
instructors integrate LA feedback into their learning designs, especially within open automated feedback
(AF) systems. This research presents a multiple-case study at one higher education institution to
unveil instructors’ pilot efforts in using an open AF system to align LA and LD within their unique
contexts, with the goal of delivering personalized feedback and tailored support. A notable finding
from these cases is that instructors successfully aligned LA with LD for personalized feedback through
checkpoint analytics in highly structured courses. Moreover, they relied on checkpoint analytics as
an evaluation mechanism for evaluating impact. Importantly, students perceived a stronger sense of
instructors’ support, reinforcing previous findings on the effectiveness of personalized feedback. This
study contributes essential empirical insights to the intersection of learning analytics and learning
design, shedding light on practical ways educators align LA and LD for personalized feedback
and support.
Lin, C-T 2023, '2023 IEEE CIS Awards [Society Briefs]', IEEE Computational Intelligence Magazine, vol. 18, no. 1, pp. 5-9.
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Lin, C-T, Liu, J, Fang, C-N, Hsiao, S-Y, Chang, Y-C & Wang, Y-K 2023, 'Multistream 3-D Convolution Neural Network With Parameter Sharing for Human State Estimation', IEEE Transactions on Cognitive and Developmental Systems, vol. 15, no. 1, pp. 261-271.
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Lin, C-T, Wang, Y, Chen, S-F, Huang, K-C & Liao, L-D 2023, 'Design and verification of a wearable wireless 64-channel high-resolution EEG acquisition system with wi-fi transmission', Medical & Biological Engineering & Computing, vol. 61, no. 11, pp. 3003-3019.
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Lin, C-T, Zhang, H, Ou, L, Chang, Y-C & Wang, Y-K 2023, 'Adaptive Trust Model for Multi-Agent Teaming Based on Reinforcement-Learning-Based Fusion', IEEE Transactions on Emerging Topics in Computational Intelligence, pp. 1-11.
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Lin, D, Ji, J, Yu, C, Wang, X & Xu, N 2023, 'A non-linear model of screen panel for dynamics analysis of a flip-flow vibrating screen', Powder Technology, vol. 418, pp. 118312-118312.
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By taking advantage of periodic high-frequency flexure deformation of screen panels, flip-flow vibrating screens (FFVSs) can achieve outstanding sieving performance. As the amplitude of the relative displacement between the main frame and the floating frame of a FFVS exceeds the relaxing length of screen panel, the tensile stress generated from the deformation of screen panel can considerably affect the dynamics and the screening performance of the FFVS. However, there is a research gap in understanding the mechanical properties (especially the stiffness and damping) of screen panels. To address this research issue, the dynamic tests are first conducted to investigate the dynamic behavior of screen panels under harmonic excitations. Then the Kelvin-Voigt (KV) model is adopted to represent the hysteresis feature of the tension force. Furthermore, to characterize the mechanical properties of the screen panels under different stretching lengths, a nonlinear mechanical model is introduced and incorporated into the dynamic model of the FFVS. The effects of the stiffness, damping and relaxing length of screen panel, the shear springs and the eccentric mass moment on the vibration characteristics of the FFVS are numerically studied using a genetic algorithm and Newmark-β algorithm. The obtained results show that the panel tension force can induce the hardening nonlinearity in the relative displacement response of FFVS and the soft type of nonlinearity in the displacement response of the main screen frame in a certain frequency region. Furthermore, at the second-order resonance peak, a small change in frequency can cause a substantial increase in the vibration amplitude of the main frame and a significant decrease in the relative amplitude. This nonlinear phenomenon would induce a large alternating stress on the main frame structure and thus reduce the service life of the FFVS.
Lin, D, Ji, JC, Wang, X, Wang, Y, Xu, N, Ni, Q, Zhao, G & Feng, K 2023, 'A rigid-flexible coupled dynamic model of a flip-flow vibrating screen considering the effects of processed materials', Powder Technology, vol. 427, pp. 118753-118753.
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Flip-flow vibrating screens (FFVSs) are the critical screening equipment for classifying and dewatering wet materials in mining processing industry. During the screening process, the FFVSs can be regarded as a complex rigid-flexible coupled multi-body system where the screening operation and the dynamics of two screen frames interact. However, there exists no mechanical model that can describe the dynamics of FFVSs during the screening process. The lack of such a dynamic model causes the amplitudes of the main and the floating screen frames unpredictable after the processed materials are loaded on FFVSs, which affects the screening performance and the service life of FFVSs. To bridge this research gap, the loaded dynamic model of a FFVS is established in this paper. First, dynamic tests are performed to investigate the equivalent stiffness and the equivalent damping of the force along the screen surface which is induced by the processed materials. Then, the proposed model of the FFVS is verified qualitatively by existing experimental results, and the effects of the processed materials on the dynamics of the FFVS are explored by comparing the non-load dynamics of the FFVS. Finally, the sensitivities of the main parameters on the dynamic response are investigated based on Sobol's method of global sensitivity analysis. It is shown that the proposed rigid-flexible coupled multi-body dynamic model of the FFVS can not only effectively reveal the dynamic response of FFVS in the screening process, but can also provide a reference for modelling the dynamics of the screening process of other screening equipment.
Lin, G, Khan, JU, Zhand, S, Liu, Y & Jin, D 2023, 'Modular DNAzymes-Hydrogel Membrane Carriers for Highly Sensitive Isothermal Cross-Cascade Detection of Pathogenic Bacteria Nucleic Acids', Analytical Chemistry, vol. 95, no. 35, pp. 13353-13360.
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Lin, K, Chern, S & Sun, J 2023, 'Mapping the quality of prenatal and postnatal care and demographic differences on child mortality in 26 low to middle-income countries', World Journal of Pediatrics, vol. 19, no. 9, pp. 835-850.
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Lin, S, Liu, A, Wang, J & Kong, X 2023, 'An intelligence-based hybrid PSO-SA for mobile robot path planning in warehouse', Journal of Computational Science, vol. 67, pp. 101938-101938.
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Mobile robots play crucial roles in industry and commerce, and automatic guided vehicles (AGV) are one of the primary parts of smart manufactory and intelligent logistics. Path planning is the core task for the AGV system, and it generates the path from origin to destination. The motivation of the study is to improve the scalability, flexibility, adaptability, and performance of the robot path planning systems. We propose the hybrid PSO-SA algorithm for the optimization of AGV path planning. Compared with other heuristic algorithms by benchmark functions, including HS, FA, ABC and GA, the proposed algorithm shows excellent performance in dealing with optimization problems. It reduces the possibility of getting trapped in one local optimum and enhances the efficiency to get the best global solution with faster convergence and less time consumption. It is evaluated with multiple cost functions and path planning with simulations and experiments. The objective of the proposed algorithm is to minimize the path length and produce a smooth path without collision. The proposed PSO-SA algorithm is compared with PSO in the path planning application, and the mean runtime and iteration times are usually significantly lower than PSO.
Lin, W, Guo, J, Zeng, J, Chen, R, Ngo, HH, Nan, J, Li, G, Ma, J & Ding, A 2023, 'Enhanced sludge dewaterability by ferrate/ferric chloride: The key role of Fe(IV) on the changes of EPS properties', Science of The Total Environment, vol. 858, pp. 159562-159562.
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Lin, X, Li, W, Guo, Y, Dong, W, Castel, A & Wang, K 2023, 'Biochar-cement concrete toward decarbonisation and sustainability for construction: Characteristic, performance and perspective', Journal of Cleaner Production, vol. 419, pp. 138219-138219.
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Lin, Y, Chen, Y, Chen, J, Chen, J, Yang, L, Wei, W, Ni, B-J & Chen, X 2023, 'Efficient Chloroquine Removal by Electro-Fenton with FeS2-Modified Cathode: Performance, Influencing Factors, Pathway Contributions, and Degradation Mechanisms', ACS ES&T Water, vol. 3, no. 8, pp. 2786-2796.
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Liu, B, Ni, W, Liu, RP, Guo, YJ & Zhu, H 2023, 'Decentralized, Privacy-Preserving Routing of Cellular-Connected Unmanned Aerial Vehicles for Joint Goods Delivery and Sensing', IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 9, pp. 9627-9641.
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Liu, B, Ni, W, Liu, RP, Guo, YJ & Zhu, H 2023, 'Optimal Routing of Unmanned Aerial Vehicle for Joint Goods Delivery and in-Situ Sensing', IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 3, pp. 3594-3599.
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Liu, C & Zowghi, D 2023, 'Citizen involvement in digital transformation: a systematic review and a framework', Online Information Review, vol. 47, no. 4, pp. 644-660.
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PurposeThe purpose of this paper is to improve the understanding of the factors influencing the success of digital transformation (DT) and problems/challenges in DT as well as the communication methods used to involve citizens, based on a systematic literature review of research articles about citizen involvement in DT published between January 2010 and May 2021.Design/methodology/approachAfter establishing inclusion and exclusion criteria, a systematic review of relevant studies was conducted. Out of a total of 547 articles, 33 met the paper selection criteria.FindingsThe analysis of the included 33 empirical studies reveals that the factors influencing the success of DT can be described as the opposite side from challenges and problems in DT. These factors and challenges/problems all influence DT and they can be grouped into organisational values, management capabilities, organisational infrastructure, and workforce capabilities. The communication methods for citizen involvement in DT include: (1) communication mediated by human, (2) communication mediated by computers, and (3) mixed communication methods.Originality/valueThe study identified specific factors that influence DT supported by citizen involvement, at a more fine-grained level. The findings concerning communication methods extend related studies for citizen involvement by adding town hall meetings and communication methods mediated by computers. Furthermore, this study links the research findings to develop a framework for citizen involvement in DT, assisting in better selecting communication methods to involve citizens for addressing...
Liu, C, Chen, H, Zhu, T, Zhang, J & Zhou, W 2023, 'Making DeepFakes More Spurious: Evading Deep Face Forgery Detection via Trace Removal Attack', IEEE Transactions on Dependable and Secure Computing, pp. 1-15.
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Liu, C, Zhu, T, Zhang, J & Zhou, W 2023, 'Privacy Intelligence: A Survey on Image Privacy in Online Social Networks', ACM Computing Surveys, vol. 55, no. 8, pp. 1-35.
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Image sharing on online social networks (OSNs) has become an indispensable part of daily social activities, but it has also increased the risk of privacy invasion. An online image can reveal various types of sensitive information, prompting the public to rethink individual privacy needs in OSN image sharing critically. However, the interaction of images and OSN makes the privacy issues significantly complicated. The current real-world solutions for privacy management fail to provide adequate personalized, accurate, and flexible privacy protection. Constructing a more intelligent environment for privacy-friendly OSN image sharing is urgent in the near future. Meanwhile, given the dynamics in both users’ privacy needs and OSN context, a comprehensive understanding of OSN image privacy throughout the entire sharing process is preferable to any views from a single side, dimension, or level. To fill this gap, we contribute a survey of “privacy intelligence” that targets modern privacy issues in dynamic OSN image sharing from a user-centric perspective. Specifically, we present the important properties and a taxonomy of OSN image privacy, along with a high-level privacy analysis framework based on the lifecycle of OSN image sharing. The framework consists of three stages with different principles of privacy by design. At each stage, we identify typical user behaviors in OSN image sharing and their associated privacy issues. Then a systematic review of representative intelligent solutions to those privacy issues is conducted, also in a stage-based manner. The analysis results in an intelligent “privacy firewall” for closed-loop privacy management. Challenges and future directions in this area are also discussed.
Liu, D, Li, W, Duan, L, Tsang, IW & Yang, G 2023, 'Noisy Label Learning With Provable Consistency for a Wider Family of Losses', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 11, pp. 13536-13552.
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Deep models have achieved state-of-the-art performance on a broad range of visual recognition tasks. Nevertheless, the generalization ability of deep models is seriously affected by noisy labels. Though deep learning packages have different losses, this is not transparent for users to choose consistent losses. This paper addresses the problem of how to use abundant loss functions designed for the traditional classification problem in the presence of label noise. We present a dynamic label learning (DLL) algorithm for noisy label learning and then prove that any surrogate loss function can be used for classification with noisy labels by using our proposed algorithm, with a consistency guarantee that the label noise does not ultimately hinder the search for the optimal classifier of the noise-free sample. In addition, we provide a depth theoretical analysis of our algorithm to verify the justifies' correctness and explain the powerful robustness. Finally, experimental results on synthetic and real datasets confirm the efficiency of our algorithm and the correctness of our justifies and show that our proposed algorithm significantly outperforms or is comparable to current state-of-the-art counterparts.
Liu, H, Li, X, Zhang, Z, Nghiem, LD, Gao, L, Batstone, DJ & Wang, Q 2023, 'Achieving expanded sludge treatment capacity with additional benefits for an anaerobic digester using free ammonia pretreatment', Chemical Engineering Journal, vol. 465, pp. 142846-142846.
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Population growth rapidly increased waste activated sludge (WAS) production in wastewater treatment plants (WWTPs), making the expansion of sludge treatment capacity urgent. Free ammonia (FA) pretreatment is experimentally applied to expand the treatment capacity of an anaerobic digester through reducing sludge retention time (SRT) for the first time. Two semi-continuous flow mesophilic (37 °C) anaerobic digestion systems, control system with a uniform SRT of 12 d and the experimental systems with progressively reduced SRTs (from 12 d to 10 d and then 8 d), were operated for>7 months. The volatile solids (VS) destruction in the experimental system at a SRT of 8 d was comparable to the control system (30.0 ± 1.4 % vs 30.5 ± 1.7 %) but increased by 16.2 % (35.1 ± 1.5 % vs 30.2 ± 1.4 %) under an SRT of 10 d, which was supported by methane production and total chemical oxygen demand (COD) removal. The biomass-specific hydrolysis rate was significantly increased by up to 80 % (from 0.05 ± 0.01 g COD/g VS/d to 0.09 ± 0.01 g COD/g VS/d), which may contribute to the expanded capacity. The volatile fatty acids (VFAs)/alkalinity of systems maintained a reasonable range (0.01 – 0.06), suggesting the stability of digesters. FA pretreatment played a dominant role in the changes in the bacterial microbial community (52.80 % in PC1) and archaeal community (94.25 % in PC1). FA pretreatment improved the removal of pathogen by 1.3–2.0 log and antibiotic resistance genes by 34–86 %. This study first demonstrated that FA pretreatment expands the treatment capacity of an anaerobic digester by up to 50 % with economic and environmental benefits, promoting FA pretreatment to be a wider and pragmatic implementation for WWTPs.
Liu, H, Li, X, Zhou, T, Zhang, Z, Nghiem, LD, Gao, L & Wang, Q 2023, 'Long-term effect of free ammonia pretreatment on the semi-continuous anaerobic primary sludge digester for enhancing performance: Towards sustainable sludge treatment', Chemical Engineering Journal, vol. 465, pp. 142780-142780.
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Primary sludge (PS) is one of the major sludge sources for anaerobic digesters in wastewater treatment plants. Although the impact of free ammonia (FA) pretreatment on methane production from anaerobic PS digestion was previously investigated using batch biochemical methane potential tests, these tests could not fully represent the continuous/semi-continuous anaerobic digestion that is currently used in practice. This study comprehensively evaluated the impact of FA pretreatment on the performance of anaerobic PS digestion for the first time using semi-continuous systems that run for over 120 days. FA pretreatment (560 mg NH3-N/L, 24 h) improved the volatile solids (VS) removal of PS by 12.2 % from 60.5 % to 67.9 %, with a similar improvement in total chemical oxygen demand removal of 14.9 % and methane production of 16.1 %. FA pretreatment increased the biomass-specific hydrolysis rate of digesters by 23.5 %. Model-based analysis revealed that the enhanced anaerobic digestion performance may be due to both the increased apparent hydrolysis rate (increased by 26.7 %) and the enhanced degradability extent (increased by 9.5 %) of PS, caused by FA pretreatment. The dewaterability of digested sludge was enhanced by 14.0 % due to FA pretreatment, which is also supported by the reduced capillary suction time from 15.1 s to 10.9 s. Removals of Fecal Coliform and E. Coli were enhanced by 0.6 and 1.4 log Most Probable Number/g vS by FA pretreatment. This study firstly manifested that FA pretreatment is a favourable approach to improve the performance of anaerobic PS digestion with extra benefits in pathogen removal and dewaterability.
Liu, H, Wang, C, Sohn, W, Wang, Q, Shon, HK & Sun, P 2023, 'Source-separated urine treatment based on forward osmosis technology: Performance, applications and future prospects', Desalination, vol. 565, pp. 116872-116872.
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Liu, H, Wu, K, Ong, Y-S, Bian, C, Jiang, X & Wang, X 2023, 'Learning Multitask Gaussian Process Over Heterogeneous Input Domains', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 10, pp. 6232-6244.
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Liu, H, Yan, X, Jiu, J, Li, JJ, Zhang, Y, Wang, G, Li, D, Yan, L, Du, Y, Zhao, B & Wang, B 2023, 'Self-assembly of gelatin microcarrier-based MSC microtissues for spinal cord injury repair', Chemical Engineering Journal, vol. 451, pp. 138806-138806.
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Current approaches for treating spinal cord injury (SCI) are mainly based on cell transplantation. Mesenchymal stem cells (MSCs) can help slow the progression of SCI due to their trophic function. However, SCI creates a complex microenvironment that reduces cell activity and hence cellular function, ultimately resulting in poor therapeutic outcomes. To help maintain function in transplanted cells, we produced functional tissue constructs by self-assembly of MSC microtissues comprising of porous gelatin microcarriers (GM) and MSCs. These microtissues maintained cellular activity without incurring an excessive amount of apoptosis and delayed senescence in vitro. The paracrine function of MSCs also improved within microtissues, shown by the increased secretion of nerve regeneration-related factors. Microtissues were transplanted in a rat model of complete spinal cord transection, and therapeutic effects were evaluated through behavioral measurements, imaging, histology, and western blot analysis. RNA-seq of spinal cord tissues using Gene Ontology analysis further revealed that the microtissues may have induced repair in SCI through mechanisms related to neurotrophin-3 (NT-3) regulation of response mediator protein 2 (CRMP2) phosphorylation, and inhibition of inflammatory response through interleukin-17 (IL-17), Chemokine C-X-C motif Ligand 1 (CXCL1) axis. The gelatin microcarrier-based MSC microtissues we developed may be effective in providing a new treatment strategy for SCI.
Liu, J, Cai, P, Liu, C, Liu, P, Su, Y, Xu, S & Wu, C 2023, 'Mechanical properties of geopolymer-based ultra-high performance concrete with ceramic ball coarse aggregates', Journal of Cleaner Production, vol. 420, pp. 138318-138318.
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Liu, J, He, Z, Liu, P, Wei, J, Li, J & Wu, C 2023, 'High-velocity projectile impact resistance of reinforced concrete slabs with ultra-high performance concrete strengthening - A numerical study', Structures, vol. 52, pp. 422-436.
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Liu, J, Huang, X-L & Yu, S 2023, 'Constant Wideband Compressive Spectrum Sensing with Cascade Forward-Backward Propagating and Prior Knowledge Refining', IEEE Transactions on Wireless Communications, pp. 1-1.
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Liu, K, Liu, J, Li, J, Tao, M & Wu, C 2023, 'Experimental investigation of heating–cooling effects on the mechanical properties of geopolymer-based high performance concrete heated to elevated temperatures', Structures, vol. 47, pp. 735-747.
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Liu, K, Lyu, S, Shivakumara, P, Blumenstein, M & Lu, Y 2023, 'A New Few-Shot Learning-Based Model for Prohibited Objects Detection in Cluttered Baggage X-Ray Images Through Edge Detection and Reverse Validation', IEEE Signal Processing Letters, vol. 30, pp. 1607-1611.
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Liu, Q, Geng, X, Huang, H, Qin, T, Lu, J & Jiang, D 2023, 'MGRC: An End-to-End Multigranularity Reading Comprehension Model for Question Answering', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 5, pp. 2594-2605.
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Liu, Q-F, Cai, Y, Peng, H, Meng, Z, Mundra, S & Castel, A 2023, 'A numerical study on chloride transport in alkali-activated fly ash/slag concretes', Cement and Concrete Research, vol. 166, pp. 107094-107094.
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Liu, S, Cheng, J, You, H, Chong, W, Zheng, M, Wei, Q, Liu, W, Chen, H, Li, X & Liu, H 2023, 'Spatial distribution of ammonia oxidizers in marine sediments of the Bohai, Yellow and East China Seas', Journal of Water Process Engineering, vol. 53, pp. 103867-103867.
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Liu, S, Wang, S, Liu, X, Dai, J, Muhammad, K, Gandomi, AH, Ding, W, Hijji, M & de Albuquerque, VHC 2023, 'Human Inertial Thinking Strategy: A Novel Fuzzy Reasoning Mechanism for IoT-Assisted Visual Monitoring', IEEE Internet of Things Journal, vol. 10, no. 5, pp. 3735-3748.
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Liu, S, Xu, M, Zheng, M, Liu, H, Kuang, S, Chen, H & Li, X 2023, 'Abundance, diversity, and community structure of comammox cladeA in sediments of China's offshore continental shelf', Science of The Total Environment, vol. 889, pp. 164290-164290.
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Liu, S-H, Ke, J-R, Ong, HC & Lin, C-W 2023, 'Isopropanol and styrene removal from aqueous solutions and simultaneous power generation using microbial fuel cells with encapsulated deoxygenated anodes', Journal of Water Process Engineering, vol. 53, pp. 103729-103729.
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Liu, T, Xia, J, Ling, Z, Fu, X, Yu, S & Chen, M 2023, 'Efficient Federated Learning for AIoT Applications Using Knowledge Distillation', IEEE Internet of Things Journal, vol. 10, no. 8, pp. 7229-7243.
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Liu, W, Song, X, Ding, X, Xia, R, Lin, X, Li, G, Nghiem, LD & Luo, W 2023, 'Antibiotic removal from swine farming wastewater by anaerobic membrane bioreactor: Role of hydraulic retention time', Journal of Membrane Science, vol. 677, pp. 121629-121629.
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Liu, W, Wang, Y, Xia, R, Ding, X, Xu, Z, Li, G, Nghiem, LD & Luo, W 2023, 'Occurrence and fate of antibiotics in swine waste treatment: An industrial case', Environmental Pollution, vol. 331, pp. 121945-121945.
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Liu, X, Gong, K, Duan, X, Wei, W, Wang, T, Chen, Z, Zhang, L & Ni, B-J 2023, 'Photo-Induced Bismuth Single Atoms on TiO2 for Highly Efficient Photocatalytic Defluorination of Perfluorooctanoic Acid: Ionization of the C–F Bond', ACS ES&T Engineering, vol. 3, no. 10, pp. 1626-1636.
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Liu, X, Shi, K, Cheng, J, Wen, S & Liu, Y 2023, 'Adaptive memory-based event-triggering resilient LFC for power system under DoS attack', Applied Mathematics and Computation, vol. 451, pp. 128041-128041.
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Liu, X, Shi, K, Yan, H, Cheng, J & Wen, S 2023, 'Integral-based event-triggering switched LFC scheme for power system under deception attack', Expert Systems with Applications, vol. 234, pp. 121075-121075.
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Liu, X, Shi, K, Zhou, K, Wen, S, Tang, Y & Tang, L 2023, 'Event-triggering-based H∞ load frequency control for multi-area cyber–physical power system under DoS attacks', Franklin Open, vol. 3, pp. 100012-100012.
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Liu, X, Tian, K, Chen, Z, Wei, W, Xu, B & Ni, B-J 2023, 'Online TG-FTIR-MS analysis of the catalytic pyrolysis of polyethylene and polyvinyl chloride microplastics.', J Hazard Mater, vol. 441, pp. 129881-129881.
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Microplastics (MPs) are frequently detected in urban waters, which would pose a threat to human health through the food chain. Thus, efficient approaches to the elimination of MPs are urgently required. Pyrolysis is a powerful technique for the potential treatment of MPs. The online thermogravimetry-Fourier transform infrared reflection-Mass spectrometry (TG-FTIR-MS) is applied for tracking the pyrolysis process of representative polyethylene (PE) and polyvinyl chloride (PVC) MPs in urban waters, together with or without the FeAlOx catalyst. TG could quantitatively determine the decomposition behavior and kinetics of MPs while FTIR and MS spectra would be capable of characterizing the pyrolysis products. The results revealed that FeAlOx is an excellent carbon support, and the deposited carbon can be gasified to CO at higher pyrolysis temperatures. Moreover, more aromatic compounds were generated from the pyrolysis of PE MPs with the catalyzation of FeAlOx. Large quantities of benzene were also produced in the PVC MPs pyrolysis with or without FeAlOx. Also, FeAlOx largely decreased the concentrations of chlorine-containing compounds in the liquid products of PVC MPs pyrolysis. This study provides a efficient technique for the online observation of the MPs' catalytic pyrolysis process, which would guide future upcycling of MPs into value-added products.
Liu, X, Xu, Q, Du, M, Yang, J, Lu, Q, Pan, M, Zhong, H, Wang, D & Ni, B-J 2023, 'Calcium peroxide mediated sustainable microalgal-bacterial consortium system: Role and significance of configured anaerobic fermentation', Chemical Engineering Journal, vol. 476, pp. 146807-146807.
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Liu, Y & Piccardi, M 2023, 'Topic-Based Unsupervised and Supervised Dictionary Induction', ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 22, no. 3, pp. 1-21.
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Word translation is a natural language processing task that provides translation between the words of a source and a target language. As a task, it reduces to the induction of a bilingual dictionary, which is typically performed by aligning word embeddings of the source language to word embeddings of the target language. To date, all the existing approaches have focused on performing a single, global alignment in word embedding space. However, semantic differences between the various languages, in addition to differences in the content of the corpora used for training the word embeddings, can hinder the effectiveness of such a global alignment. For this reason, in this article we propose conducting the alignment between the source and target embedding spaces by multiple mappings at topic level. The experimental results show that our approach has been able to achieve an average accuracy improvement of +3.30 percentage points over a state-of-the-art approach in unsupervised dictionary induction from languages as diverse as German, French, Italian, Spanish, Finnish, Turkish, and Chinese to English, and +3.95 points average improvement in supervised dictionary induction.
Liu, Y, Feng, Y, Wu, D, Chen, X & Gao, W 2023, 'Virtual modelling integrated phase field method for dynamic fracture analysis', International Journal of Mechanical Sciences, vol. 252, pp. 108372-108372.
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Liu, Y, Guo, Q, Fu, L, Ke, Z, Xu, K, Feng, W, Tsang, IW & Lau, RWH 2023, 'Structure-Informed Shadow Removal Networks', IEEE Transactions on Image Processing, vol. 32, pp. 5823-5836.
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Liu, Y, Huang, Y, Wang, S, Lu, W & Wu, H 2023, 'Modality Coupling for Privacy Image Classification', IEEE Transactions on Information Forensics and Security, vol. 18, pp. 4843-4853.
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Privacy image classification (PIC) has attracted increasing attention as it can help people make appropriate privacy decisions when sharing images. Most recently, some pioneer research efforts have been made to utilize multimodal information for PIC, since multi-modality can provide richer information than single modality. Those research efforts on multimodal PIC are under the assumption of independently identically distribution. However, connections between different modalities commonly exist in real-world cases. Taking the modalities of scene and object as example, in the scene of 'library/indoor', the object 'book jacket' resides with high probabilities. To this end, in this paper, a novel PIC approach, called CoupledPIC, is proposed to bridge this gap by comprehensively capturing the coupling relations between different modalities. In CoupledPIC, two submodules are designed to capture explicit and implicit coupling relations between different modalities respectively. The explicit modality coupling is learned with a tensor fusion networks based submodule, via the direct interaction of features. For the implicit modality coupling, a graph convolutional networks based submodule is proposed to learn on both the initial graphs and attention guided graphs, via information aggregation on graphs. Extensive experiments on the public benchmark, PicAlert, demonstrate the effectiveness of the proposed CoupledPIC, yielding significant improvement by modeling inter-modality coupling information.
Liu, Y, Lee, T-U, Koronaki, A, Pietroni, N & Xie, YM 2023, 'Reducing the number of different nodes in space frame structures through clustering and optimization', Engineering Structures, vol. 284, pp. 116016-116016.
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Liu, Y, Wen, S, Wang, F, Zuo, C, Chen, C, Zhou, J & Jin, D 2023, 'Population Control of Upconversion Energy Transfer for Stimulation Emission Depletion Nanoscopy', Advanced Science, vol. 10, no. 20.
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AbstractUpconverting stimulated emission depletion microscopy (U‐STED) is emerging as an effective approach for super‐resolution imaging due to its significantly low depletion power and its ability to surpass the limitations of the square‐root law and achieve higher resolution. Though the compelling performance, a trade‐off between the spatial resolution and imaging quality in U‐STED has been recognized in restricting the usability due to the low excitation power drove high depletion efficiency. Moreover, it is a burden to search for the right power relying on trial and error as the underpinning mechanism is unknown. Here, a method is proposed that can easily predict the ideal excitation power for high depletion efficiency with the assistance of the non‐saturate excitation based on the dynamic cross‐relaxation (CR) energy transfer of upconversion nanoparticles. This allows the authors to employ the rate equation model to simulate the populations of each relevant energy state of lanthanides and predict the ideal excitation power for high depletion efficiency. The authors demonstrate that the resolution of STED with the assistance of nonsaturated confocal super‐resolution results can easily achieve the highest resolution of sub‐40 nm, 1/24th of the excitation wavelengths. The finding on the CR effect provides opportunities for population control in realizing low‐power high‐resolution nanoscopy.
Liu, Y, Yang, PF, Zhang, LJ, Wu, ZL & Feng, Y 2023, 'Survey on Robustness Verification of Feedforward Neural Networks and Recurrent Neural Networks', Ruan Jian Xue Bao/Journal of Software, vol. 34, no. 7, pp. 1-33.
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With the advent of the intelligent age, the applications of intelligent systems equipped with deep neural networks (DNNs) have penetrated into every aspect of our life. However, due to the black-box and large-scale characteristics, the predictions of the neural networks are difficult to be completely convincing. When neural networks are applied to security-critical fields such as autonomous driving, how to ensure their security is still a great challenge for the academia and industry. For this reason, the academia carried out much research on robustness—a kind of special security of neural networks, and proposed many algorithms for robustness analysis and verification. The verification algorithms for feedforward neural networks (FNNs) include precise algorithms and approximate algorithms, which have been developed relatively prosperously. The verification algorithms for other types of networks, such as recurrent neural networks (RNNs), are still in the primary stage. This study reviews the current development of DNNs and the challenges of deploying them into our life. It also exhaustively investigates the robustness verification algorithms of FNNs and RNNs, analyzes and compares the intrinsic connection among these algorithms. The security verification algorithms of RNNs in specific application scenarios are investigated, and the future research directions in the robustness verification field of neural networks are clarified.
Liu, Y, Zhang, W, Zhang, X, Yang, L, Huang, Z, Fang, F, Sun, W, Gao, M & Pan, H 2023, 'Nanostructured light metal hydride: Fabrication strategies and hydrogen storage performance', Renewable and Sustainable Energy Reviews, vol. 184, pp. 113560-113560.
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Liu, Y, Zhang, X, Xu, Y, Liu, Q, Ngo, HH & Cao, W 2023, 'Transport behaviors of biochar particles in saturated porous media under DC electric field', Science of The Total Environment, vol. 856, pp. 159084-159084.
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Liu, Y, Zhang, X, Zhao, Y, He, Y, Yu, S & Zhu, K 2023, 'Chronos: Accelerating Federated Learning With Resource Aware Training Volume Tuning at Network Edges', IEEE Transactions on Vehicular Technology, vol. 72, no. 3, pp. 3889-3903.
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Liu, Y, Zhu, L, Wang, X, Yamada, M & Yang, Y 2023, '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, Li, Y, Yao, L, Chang, X, Fang, W, Wu, X & Saddik, AE 2023, 'Simple Primitives with Feasibility- and Contextuality-Dependence for Open-World Compositional Zero-shot Learning', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-18.
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Liu, Z, Sun, G, Chen, Z, Ma, Y, Qiu, K, Li, M & Ni, B-J 2023, 'Anchoring Cu-N active sites on functionalized polyacrylonitrile fibers for highly selective H2S/CO2 separation', Journal of Hazardous Materials, vol. 450, pp. 131084-131084.
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Liu, Z, Wei, J, Li, R & Zhou, J 2023, 'Learning multi-modal brain tumor segmentation from privileged semi-paired MRI images with curriculum disentanglement learning', Computers in Biology and Medicine, vol. 159, pp. 106927-106927.
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Liu, Z, Xiao, F, Lin, C-T & Cao, Z 2023, '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, pp. 1-12.
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Liu, Z, Yin, X, Ni, B, Chen, X, Xie, F, Guo, Z, Li, D, Liu, W, Yue, X & Zhou, A 2023, 'Synchronous vivianite and hydrogen recovery from waste activated sludge fermentation liquid via electro-fermentation mediated by iron anode', Chemical Engineering Journal, vol. 474, pp. 145442-145442.
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Liyanaarachchi, H, Thambiliyagodage, C, Lokuge, H & Vigneswaran, S 2023, 'Kinetics and Thermodynamics Study of Methylene Blue Adsorption to Sucrose- and Urea-Derived Nitrogen-Enriched, Hierarchically Porous Carbon Activated by KOH and H3PO4', ACS Omega, vol. 8, no. 18, pp. 16158-16173.
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Lo, Y-C, Blamires, SJ, Liao, C-P & Tso, I-M 2023, 'Nocturnal and diurnal predator and prey interactions with crab spider color polymorphs', Behavioral Ecology and Sociobiology, vol. 77, no. 2.
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Loengbudnark, W, Khalilpour, K, Bharathy, G, Voinov, A & Thomas, L 2023, 'Impact of occupant autonomy on satisfaction and building energy efficiency', Energy and Built Environment, vol. 4, no. 4, pp. 377-385.
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Logan, J, Kennedy, PJ & Catchpoole, D 2023, 'A review of the machine learning datasets in mammography, their adherence to the FAIR principles and the outlook for the future.', Sci Data, vol. 10, no. 1, p. 595.
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The increasing rates of breast cancer, particularly in emerging economies, have led to interest in scalable deep learning-based solutions that improve the accuracy and cost-effectiveness of mammographic screening. However, such tools require large volumes of high-quality training data, which can be challenging to obtain. This paper combines the experience of an AI startup with an analysis of the FAIR principles of the eight available datasets. It demonstrates that the datasets vary considerably, particularly in their interoperability, as each dataset is skewed towards a particular clinical use-case. Additionally, the mix of digital captures and scanned film compounds the problem of variability, along with differences in licensing terms, ease of access, labelling reliability, and file formats. Improving interoperability through adherence to standards such as the BIRADS criteria for labelling and annotation, and a consistent file format, could markedly improve access and use of larger amounts of standardized data. This, in turn, could be increased further by GAN-based synthetic data generation, paving the way towards better health outcomes for breast cancer.
Loganathan, P, Vigneswaran, S, Kandasamy, J, Nguyen, TV, Katarzyna Cuprys, A & Ratnaweera, H 2023, 'Bisphenols in water: Occurrence, effects, and mitigation strategies', Chemosphere, vol. 328, pp. 138560-138560.
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Loh, HW, Ooi, CP, Oh, SL, Barua, PD, Tan, YR, Molinari, F, March, S, Acharya, UR & Fung, DSS 2023, 'Deep neural network technique for automated detection of ADHD and CD using ECG signal', Computer Methods and Programs in Biomedicine, vol. 241, pp. 107775-107775.
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Long, G, Xie, M, Shen, T, Zhou, T, Wang, X & Jiang, J 2023, 'Multi-center federated learning: clients clustering for better personalization', World Wide Web, vol. 26, no. 1, pp. 481-500.
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AbstractPersonalized decision-making can be implemented in a Federated learning (FL) framework that can collaboratively train a decision model by extracting knowledge across intelligent clients, e.g. smartphones or enterprises. FL can mitigate the data privacy risk of collaborative training since it merely collects local gradients from users without access to their data. However, FL is fragile in the presence of statistical heterogeneity that is commonly encountered in personalized decision making, e.g., non-IID data over different clients. Existing FL approaches usually update a single global model to capture the shared knowledge of all users by aggregating their gradients, regardless of the discrepancy between their data distributions. By comparison, a mixture of multiple global models could capture the heterogeneity across various clients if assigning the client to different global models (i.e., centers) in FL. To this end, we propose a novel multi-center aggregation mechanism to cluster clients using their models’ parameters. It learns multiple global models from data as the cluster centers, and simultaneously derives the optimal matching between users and centers. We then formulate it as an optimization problem that can be efficiently solved by a stochastic expectation maximization (EM) algorithm. Experiments on multiple benchmark datasets of FL show that our method outperforms several popular baseline methods. The experimental source codes are publicly available on the Github repository (GitHub repository: https://github.com/mingxuts/multi-center-fed-learning).
Long, H, Ci, J, Guo, Z, Wen, S & Huang, T 2023, 'Synchronization of coupled switched neural networks subject to hybrid stochastic disturbances', Neural Networks, vol. 166, pp. 459-470.
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Long, S, Yang, J, Hao, Z, Shi, Z, Liu, X, Xu, Q, Wang, Y, Wang, D & Ni, B-J 2023, 'Multiple roles of humic substances in anaerobic digestion systems: A review', Journal of Cleaner Production, vol. 418, pp. 138066-138066.
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Loureiro, PHCDSB, Toebe, A, Siwakoti, YP & Andrade, AMSS 2023, 'Comparison of Coupled Inductor With Three Windings and Two Windings.', IEEE Trans. Circuits Syst. II Express Briefs, vol. 70, no. 6, pp. 1996-2000.
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Lu, J, Gama, J, Yao, X & Minku, L 2023, 'Guest Editorial: Special Issue on Stream Learning', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 10, pp. 6683-6685.
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Lu, J, Guo, Z, Li, M, He, M, Zhen, J, Ni, B-J & Zhang, J 2023, 'Manganese ore enhanced polycyclic aromatic hydrocarbons removal in constructed wetlands: Insights into the key removal mechanism and main driving factor', Chemical Engineering Journal, vol. 467, pp. 143430-143430.
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Lu, Q, Zhu, L, Xu, X, Whittle, J, Zowghi, D & Jacquet, A 2023, 'Operationalizing Responsible AI at Scale: CSIRO Data61's Pattern-Oriented Responsible AI Engineering Approach', Communications of the ACM, vol. 66, no. 7, pp. 64-66.
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Lu, X, Qiu, J, Lei, G & Zhu, J 2023, 'An Interval Prediction Method for Day-Ahead Electricity Price in Wholesale Market Considering Weather Factors', IEEE Transactions on Power Systems, pp. 1-11.
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Lu, X, Qiu, J, Lei, G & Zhu, J 2023, 'Degradation Mode Knowledge Transfer Method for LFP Batteries', IEEE Transactions on Transportation Electrification, vol. 9, no. 1, pp. 1142-1152.
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Lu, X, Qiu, J, Lei, G & Zhu, J 2023, 'State of Health Estimation of Lithium Iron Phosphate Batteries Based on Degradation Knowledge Transfer Learning', IEEE Transactions on Transportation Electrification, vol. 9, no. 3, pp. 4692-4703.
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Lu, X, Qiu, J, Zhang, C, Lei, G & Zhu, J 2023, 'Assembly and Competition for Virtual Power Plants With Multiple ESPs Through a “Recruitment–Participation” Approach', IEEE Transactions on Power Systems, pp. 1-14.
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Lu, X, Xiao, L, Li, P, Ji, X, Xu, C, Yu, S & Zhuang, W 2023, 'Reinforcement Learning-Based Physical Cross-Layer Security and Privacy in 6G', IEEE Communications Surveys & Tutorials, vol. 25, no. 1, pp. 425-466.
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Lu, Y, Ni, F, Wang, H, Guo, X, Zhu, L, Yang, Z, Song, R, Cheng, L & Yang, Y 2023, 'Show Me a Video: A Large-Scale Narrated Video Dataset for Coherent Story Illustration', IEEE Transactions on Multimedia, pp. 1-12.
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Lu, Y, Xiao, W & Lu, DD-C 2023, 'Enhanced Voltage Regulation for PV Power Conversion Using Quasi-Proportional Resonant Extended State Observer', IEEE Journal of Emerging and Selected Topics in Industrial Electronics, vol. 4, no. 1, pp. 70-77.
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Lu, Y, Xiao, W & Lu, DD-C 2023, 'Improved Voltage Regulation of PV System With Current-Sensorless Active Damping Technique', IEEE Journal of Emerging and Selected Topics in Industrial Electronics, vol. 4, no. 1, pp. 60-69.
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Lu, Y, Yu, H, Ni, W & Song, L 2023, '3D real-time human reconstruction with a single RGBD camera', Applied Intelligence, vol. 53, no. 8, pp. 8735-8745.
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Luo, H, Tao, M, Wu, C & Cao, W 2023, 'Dynamic response of an elliptic cylinder inclusion with imperfect interfaces subjected to plane SH wave', Geomechanics and Geophysics for Geo-Energy and Geo-Resources, vol. 9, no. 1, p. 24.
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AbstractUnderground chambers or tunnels often contain inclusions, the interface between the inclusion and the surrounding rock is not always perfect, which influences stress wave propagation. In this study, the imperfect interface and transient seismic wave were represented using the spring model and Ricker wavelet. Based on the wave function expansion method and Fourier transform, an analytical formula for the dynamic stress concentration factor (DSCF) for an elliptical inclusion with imperfect interfaces subjected to a plane SH-wave was determined. The theoretical solution was verified via numerical simulations using the LS-DYNA software, and the results were analyzed. The effects of the wave number (k), radial coordinate (ξ), stiffness parameter (β), and differences in material properties on the dynamic response were evaluated. The numerical results revealed that the maximum DSCF always occurred at both ends of the elliptical minor axis, and the transient DSCF was generally a factor of 2–3 greater than the steady-state DSCF. Changes in k and ξ led to variations in the DSCF value and spatial distribution, changes in β resulted only in variations in the DSCF value, and lower values of ωp and β led to a greater DSCF under the same parameter conditions. In addition, the differences in material properties between the medium and inclusion significantly affected the variation characteristics of the DSCF with k and ξ.
Luo, T, Dai, X, Chen, Z, Wu, L, Wei, W, Xu, Q & Ni, B-J 2023, 'Different microplastics distinctively enriched the antibiotic resistance genes in anaerobic sludge digestion through shifting specific hosts and promoting horizontal gene flow', Water Research, vol. 228, pp. 119356-119356.
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Luu, HM, Mai, HS, Pham, XL, Le, QA, Le, QK, Walsum, TV, Le, NH, Franklin, DR, Le, HV, Moelker, A, Duc, TC & Trung, NL 2023, 'Quantification of liver-Lung shunt fraction on 3D SPECT/CT images for selective internal radiation therapy of liver cancer using CNN-based segmentations and non-rigid registration.', Comput. Methods Programs Biomed., vol. 233, pp. 107453-107453.
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Lv, M, Wang, J, Niu, X & Lu, H 2023, 'A newly combination model based on data denoising strategy and advanced optimization algorithm for short-term wind speed prediction', Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 7, pp. 8271-8290.
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Ly, QV, Cui, L, Asif, MB, Khan, W, Nghiem, LD, Hwang, Y & Zhang, Z 2023, 'Membrane-based nanoconfined heterogeneous catalysis for water purification: A critical review✰', Water Research, vol. 230, pp. 119577-119577.
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Lyu, B, Hamdi, M, Yang, Y, Cao, Y, Yan, Z, Li, K, Wen, S & Huang, T 2023, 'Efficient Spectral Graph Convolutional Network Deployment on Memristive Crossbars', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 7, no. 2, pp. 415-425.
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Lyu, B, Lu, L, Hamdi, M, Wen, S, Yang, Y & Li, K 2023, 'MTLP-JR: Multi-task learning-based prediction for joint ranking in neural architecture search', Computers and Electrical Engineering, vol. 105, pp. 108474-108474.
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Lyu, B, Wang, S, Wen, S, Shi, K, Yang, Y, Zeng, L & Huang, T 2023, '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, Shi, K & Huang, T 2023, 'Multiobjective Reinforcement Learning-Based Neural Architecture Search for Efficient Portrait Parsing', IEEE Transactions on Cybernetics, vol. 53, no. 2, pp. 1158-1169.
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Lyu, B, Wen, S, Yang, Y, Chang, X, Sun, J, Chen, Y & Huang, T 2023, 'Designing Efficient Bit-Level Sparsity-Tolerant Memristive Networks', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-10.
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Lyu, B, Yang, Y, Wen, S, Huang, T & Li, K 2023, 'Neural Architecture Search for Portrait Parsing', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 3, pp. 1112-1121.
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Lyu, B, Zhou, C, Gong, S, Hoang, DT & Liang, Y-C 2023, 'Robust Secure Transmission for Active RIS Enabled Symbiotic Radio Multicast Communications', IEEE Transactions on Wireless Communications, pp. 1-1.
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Ma, C, Li, J, Ding, M, Liu, B, Wei, K, Weng, J & Poor, HV 2023, 'RDP-GAN: A Rényi-Differential Privacy Based Generative Adversarial Network', IEEE Transactions on Dependable and Secure Computing, pp. 1-15.
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Ma, C, Li, J, Wei, K, Liu, B, Ding, M, Yuan, L, Han, Z & Vincent Poor, H 2023, 'Trusted AI in Multiagent Systems: An Overview of Privacy and Security for Distributed Learning', Proceedings of the IEEE, vol. 111, no. 9, pp. 1097-1132.
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Ma, C, Xu, Z, Hua, B, Zhang, Y, Shi, Q, Chu, L, Braun, R & Shi, J 2023, 'Random body movement interference mitigation in radar breath detection based on L1 norm', IEEE Sensors Letters, pp. 1-4.
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Ma, C-Q, Han, N, Zhang, R-Z, Lin, S-N, Chen, Z, Liu, H, Yu, S, Dong, R-Z, Wang, Y-B, Ni, B-J & Xing, L-B 2023, 'Construction of artificial light-harvesting system based on host-guest interactions of sulfobutylether-β-cyclodextrin and its application in photocatalysis', Environmental Surfaces and Interfaces, vol. 1, pp. 3-9.
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Ma, M, Tam, VW, Le, KN, Butera, A, Li, W & Wang, X 2023, 'COMPARATIVE ANALYSIS ON INTERNATIONAL CONSTRUCTION AND DEMOLITION WASTE MANAGEMENT POLICIES AND LAWS FOR POLICY MAKERS IN CHINA', JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT, vol. 29, no. 2, pp. 107-130.
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In the current age of enhanced environmental awareness, transformation to sustainable management in the construction sector is needed. China currently produces the largest amount of construction and demolition (C&D) waste around the world, but the average recovery rate of the waste was only about 5% in 2017. In order to investigate problems in current C&D waste management in China, a cross-national comparative analysis is conducted among China and seven selected countries (Japan, South Korea, Germany, Italy, Austria, the Netherlands and the United Kingdom), to compare legal texts of national policies and laws which relate to C&D waste management and are currently being used. Through the comparison, problems in management of C&D waste in China are investigated. The problems could be concluded to: (a) inadequate guidance on recycling, (b) lack of market incentives in utilising recycled materials, (c) incomplete knowledge of stakeholders’ responsibilities, (d) lack of penalty for other stakeholders, and (e) inefficient supervision system. By understanding these problems, this paper further provides recommendations to enhance the performance of C&D waste management in China.
Ma, T, Cami, B, Javankhoshdel, S, Corkum, B, Chan, N & Gandomi, AH 2023, 'Spline Search for Slip Surfaces in 3D Slopes', International Journal of Geomechanics, vol. 23, no. 8.
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Ma, X, Xu, H, Gao, H, Bian, M & Hussain, W 2023, 'Real-Time Virtual Machine Scheduling in Industry IoT Network: A Reinforcement Learning Method', IEEE Transactions on Industrial Informatics, vol. 19, no. 2, pp. 2129-2139.
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Ma, Y, Wu, N, Wu, K & Zhang, JA 2023, 'VAMP-Based Iterative Equalization for Index-Modulated Multicarrier FTN Signaling', IEEE Transactions on Communications, vol. 71, no. 4, pp. 2304-2316.
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Mahmoudi, A, Khezri, R, Bidram, A, Khooban, M, Aki, H, Khalilpour, K, Abdeltawab, H & Muyeen, SM 2023, 'Guest editorial: Application of cloud energy storage systems in power systems', IET Generation, Transmission & Distribution, vol. 17, no. 8, pp. 1687-1689.
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Maidi, AM, Kalam, MA & Begum, F 2023, 'Photonic crystal fibre for blood components sensing', Sensing and Bio-Sensing Research, vol. 41, pp. 100565-100565.
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Majnooni, S, Nikoo, MR, Nematollahi, B, Fooladi, M, Alamdari, N, Al-Rawas, G & Gandomi, AH 2023, 'Long-term precipitation prediction in different climate divisions of California using remotely sensed data and machine learning', Hydrological Sciences Journal, vol. 68, no. 14, pp. 1984-2008.
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Makhdoom, I, Abolhasan, M, Franklin, DR, Lipman, J, Zimmermann, C, Piccardi, M & Shariati, N 2023, 'Detecting compromised IoT devices: Existing techniques, challenges, and a way forward.', Comput. Secur., vol. 132, pp. 103384-103384.
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Mangiavillano, B, Brandaleone, L, Auriemma, F, Calabrese, F, Paduano, D, Gentile, CS & Repici, A 2023, 'One-step endoscopic ultrasound-guided fine-needle biopsy of pancreatic mass, gastroenterostomy, and gallbladder drainage for malignant biliary and gastric outlet obstruction', Endoscopy, vol. 55, no. S 01, pp. E936-E937.
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Mannina, G, Ni, B-J, Makinia, J, Harmand, J, Alliet, M, Brepols, C, Ruano, MV, Robles, A, Heran, M, Gulhan, H, Rodriguez-Roda, I & Comas, J 2023, 'Biological processes modelling for MBR systems: A review of the state-of-the-art focusing on SMP and EPS', Water Research, vol. 242, pp. 120275-120275.
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Mao, X, Zhou, X, Fan, X, Jin, W, Xi, J, Tu, R, Naushad, M, Li, X, Liu, H & Wang, Q 2023, 'Proteomic analysis reveals mechanisms of mixed wastewater with different N/P ratios affecting the growth and biochemical characteristics of Chlorella pyrenoidosa', Bioresource Technology, vol. 381, pp. 129141-129141.
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Mao, Z, Zhao, L, Huang, S, Jin, T, Fan, Y & Lee, AP-W 2023, 'Complete region of interest reconstruction by fusing multiview deformable three-dimensional transesophageal echocardiography images.', Med Phys, vol. 50, no. 1, pp. 61-73.
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BACKGROUND: While three-dimensional transesophageal echocardiography (3D TEE) has been increasingly used for assessing cardiac anatomy and function, it still suffers from a limited field of view (FoV) of the ultrasound transducer. Therefore, it is difficult to examine a complete region of interest without moving the transducer. Existing methods extend the FoV of 3D TEE images by mosaicing multiview static images, which requires synchronization between 3D TEE images and electrocardiogram (ECG) signal to avoid deformations in the images and can only get the widened image at a specific phase. PURPOSE: This work aims to develop a novel multiview nonrigid registration and fusion method to extend the FoV of 3D TEE images at different cardiac phases, avoiding the bias toward the specifically chosen phase. METHODS: A multiview nonrigid registration and fusion method is proposed to enlarge the FoV of 3D TEE images by fusing dynamic images captured from different viewpoints sequentially. The deformation field for registering images is defined by a collection of affine transformations organized in a graph structure and is estimated by a direct (intensity-based) method. The accuracy of the proposed method is evaluated by comparing it with two B-spline-based methods, two Demons-based methods, and one learning-based method VoxelMorph. Twenty-nine sequences of in vivo 3D TEE images captured from four patients are used for the comparative experiments. Four performance metrics including checkerboard volumes, signed distance, mean absolute distance (MAD), and Dice similarity coefficient (DSC) are used jointly to evaluate the accuracy of the results. Additionally, paired t-tests are performed to examine the significance of the results. RESULTS: The qualitative results show that the proposed method can align images more accurately and obtain the fused images with higher quality than the other five methods. Additionally, in the evaluation of the segmented left atrium (LA) w...
Marjanovic, O, Patmore, G & Balnave, N 2023, 'Visual Analytics: Transferring, Translating and Transforming Knowledge from Analytics Experts to Non-technical Domain Experts in Multidisciplinary Teams', Information Systems Frontiers, vol. 25, no. 4, pp. 1571-1588.
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Abstract
Today’s complex problems call for multidisciplinary analytics teams comprising of both analytics and non-technical domain (i.e. subject matter) experts. Recognizing the difference between data visualisaion (DV) (i.e. static visual outputs) and visual analytics (VA) (i.e. a process of interactive visual data exploration, guided by user’s domain and contextual knowledge), this paper focuses on VA for non-technical domain experts. By seeking to understand knowledge sharing from VA experts to non-technical users of VA in a multidisciplinary team, we aim to explore how these domain experts learn to use VA as a thinking tool, guided by their knowing-in-practice. The research described in this paper was conducted in the context of a long-term industry-wide research project called the ‘Visual Historical Atlas of the Australian Co-operatives’, led by a multidisciplinary VA team who faced the challenge tackled by this research. Using Action Design Research (ADR) and the combined theoretical lens of boundary objects and secondary design, the paper theorises a three-phase method for knowledge transfer, translation and transformation from VA experts to domain experts using different types of VA-related boundary objects. Together with the proposed set of design principles, the three-phase model advances the well-established stream of research on organizational use of analytics, extending it to the emerging area of visual analytics for non-technical decision makers.
Martins, D, Karimi, M, Maxit, L & Kirby, R 2023, 'Non-negative intensity for a heavy fluid-loaded stiffened plate', Journal of Sound and Vibration, vol. 566, pp. 117891-117891.
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Localisation of sound sources on vibrating structures is a critical part of the design in many engineering applications. In structures with stiffeners, so-called Bloch–Floquet waves are generated due to the interaction between the flexural waves in the host structure and the flexural/torsional waves of the stiffeners. It is known that the Bloch–Floquet waves have a significant contribution to the radiated sound. However, it is not understood which area of the vibrating stiffened structures contributes significantly to the radiation in the far-field. Non-negative intensity (NNI) is a powerful tool developed recently to locate the surface regions on structures that can contribute to the radiated sound power. Although NNI has been used for several distinct structures under different excitations, it has not been considered for analysing structures with stiffeners. In this work, NNI is evaluated for an infinite fluid-loaded stiffened plate subjected to a point force to localise the sources of sound and to shed light on the mechanism involved in the far-field radiation. An analytical model formulated in the wavenumber domain is presented to carry out fast calculations of the plate vibroacoustic responses and the NNI maps. A parametric study is then performed by comparing the vibroacoustic responses with the NNI maps to highlight the capability of NNI for sound source localisation. This is achieved by analysing the results for stiffened/unstiffened structures with excitation on/between the stiffeners, and at frequencies either in a passband or in a stopband. Moreover, the NNI maps are further explained and interpreted using identified Bloch–Floquet radiating bands.
Mas-Tur, A, Roig-Tierno, N, Sarin, S, Haon, C, Sego, T, Belkhouja, M, Porter, A & Merigó, JM 2023, 'Corrigendum to ‘Co-citation, bibliographic coupling and leading authors, institutions and countries in the 50 years of technological forecasting and social change’ [Technol. Forecast. Soc. Change 165 (2021) 120487]', Technological Forecasting and Social Change, vol. 186, pp. 122157-122157.
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Matheson, S, Fleck, R, Irga, PJ & Torpy, FR 2023, 'Phytoremediation for the indoor environment: a state-of-the-art review', Reviews in Environmental Science and Bio/Technology, vol. 22, no. 1, pp. 249-280.
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AbstractPoor indoor air quality has become of particular concern within the built environment due to the time people spend indoors, and the associated health burden. Volatile organic compounds (VOCs) off-gassing from synthetic materials, nitrogen dioxide and harmful outdoor VOCs such benzene, toluene, ethyl-benzene and xylene penetrate into the indoor environment through ventilation and are the main contributors to poor indoor air quality with health effects. A considerable body of literature over the last four decades has demonstrate the removal of gaseous contaminants through phytoremediation, a technology that relies on plant material and technologies to remediate contaminated air streams. In this review we present a state-of-the-art on indoor phytoremediation over the last decade. Here we present a review of 38 research articles on both active and passive phytoremediation, and describe the specific chemical removal efficiency of different systems. The literature clearly indicates the efficacy of these systems for the removal of gaseous contaminants in the indoor environment, however it is evident that the application of phytoremediation technologies for research purposes in-situ is currently significantly under studied. In addition, it is common for research studies to assess the removal of single chemical species under controlled conditions, with little relevancy to real-world settings easily concluded. The authors therefore recommend that future phytoremediation research be conducted both in-situ and on chemical sources of a mixed nature, such as those experienced in the urban environment like petroleum vapour, vehicle emissions, and mixed synthetic furnishings off-gassing. The assessment of these systems both in static chambers for their theoretical performance, and in-situ for these mixed chemical sources is essential for the progression of th...
Matheson, S, Fleck, R, Lockwood, T, Gill, RL, Irga, PJ & Torpy, FR 2023, 'Fuelling phytoremediation: gasoline degradation by green wall systems—a case study', Environmental Science and Pollution Research.
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AbstractThe capacity for indoor plants including green wall systems to remove specific volatile organic compounds (VOCs) is well documented in the literature; however under realistic settings, indoor occupants are exposed to a complex mixture of harmful compounds sourced from various emission sources. Gasoline vapour is one of the key sources of these emissions, with several studies demonstrating that indoor occupants in areas surrounding gasoline stations or with residentially attached garages are exposed to far higher concentrations of harmful VOCs. Here we assess the potential of a commercial small passive green wall system, commercially named the ‘LivePicture Go’ from Ambius P/L, Australia, to drawdown VOCs that comprise gasoline vapour, including total VOC (TVOC) removal and specific removal of individual speciated VOCs over time. An 8-h TVOC removal efficiency of 42.45% was achieved, along with the complete removal of eicosane, 1,2,3-trimethyl-benzene, and hexadecane. Further, the green wall also effectively reduced concentrations of a range of harmful benzene derivatives and other VOCs. These results demonstrate the potential of botanical systems to simultaneously remove a wide variety of VOCs, although future research is needed to improve upon and ensure efficiency of these systems over time and within practical applications.
Matin, A, Islam, R, Wang, X, Huo, H & Xu, G 2023, 'AIoT for sustainable manufacturing: Overview, challenges, and opportunities', Internet of Things, vol. 24, pp. 100901-100901.
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Mazaheri, H, Ong, HC, Masjuki, HH, Arslan, A, Chong, WT & Amini, Z 2023, 'Friction and wear characteristics of rice bran oil based biodiesel using calcium oxide catalyst derived from Chicoreus Brunneus shell', Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, vol. 45, no. 4, pp. 11015-11023.
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© 2019, © 2019 Taylor & Francis Group, LLC. In this study, an investigation has been carried out to examine the tribological features of Rice bran oil (RBO) based biodiesel as an agent to improve lubricity. RBOB was successfully generated by the means of transesterification and the studied fuels were pure biodiesel (RBOB100), 10% (RBOB10), 30% (RBOB30), and 50% (RBOB50) of biodiesel combined with diesel and pure diesel (RBOB0). Examination in agreement with the ASTM D 4172 method was carried out under following conditions for all examined fuels: a constant load of 40 kg, a steady sliding speed of 1200 rpm, a constant temperature of 75°C and a reaction time of 3600 s. Frictional torque was recorded on line in the course of wear examination. Optical microscopy was implemented to investigate the wear scars of tested balls. Result showed that friction diminished with the decrease of biodiesel concentration. Moreover, formation of wear scars increased with increasing the biodiesel concentration. It could be concluded that lubricity decreases due to increasing the biodiesel concentration. Surface morphology analysis showed that pure biodiesel and diesel formed adhesive wears. However, all the wears formed by biodiesel-diesel blends were fallen into the abrasive wear group.
Medawela, S, Armaghani, DJ, Indraratna, B, Kerry Rowe, R & Thamwattana, N 2023, 'Development of an advanced machine learning model to predict the pH of groundwater in permeable reactive barriers (PRBs) located in acidic terrain', Computers and Geotechnics, vol. 161, pp. 105557-105557.
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Medawela, S, Indraratna, B & Rowe, RK 2023, 'The reduction in porosity of permeable reactive barriers due to bio-geochemical clogging caused by acidic groundwater flow', Canadian Geotechnical Journal, vol. 60, no. 2, pp. 151-165.
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This study demonstrates the change in porosity of permeable reactive barrier (PRB) material when it reacts with acidic flow. The laboratory column test data obtained over 9 months prove that the porosity of a granular limestone assembly decreases significantly due to bio-geochemical clogging caused by a continuous flow of acidic groundwater. The variations in pH, the pressure measurements, ion concentrations, and the results from X-ray diffraction suggest that clogging at the outlet of the column is much less than at the inlet. About 57% of the total reduction in porosity of the column is attributed to chemical clogging, while the remainder is mainly due to biological clogging. In this paper, a mathematical approach is proposed to estimate the reduction of reactive surface area based on changes in the pore volume. These proposed equations suggest that at the end of experimentation, the dissolution of calcite and bio-geochemical clogging can reduce the total surface area of limestone aggregates by more than 70%. The rigorous approach presented in this paper to determine the dominant clogging component within a granular filter at a given time is vital in estimating the longevity of a PRB and for planning its maintenance.
Mehraj, S, Mushtaq, S, Parah, SA, Giri, KJ, Sheikh, JA, Gandomi, AH, Hijji, M, Gupta, BB & Muhammad, K 2023, 'RBWCI: Robust and Blind Watermarking Framework for Cultural Images', IEEE Transactions on Consumer Electronics, vol. 69, no. 2, pp. 128-139.
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Mehta, M, Bui, TA, Yang, X, Aksoy, Y, Goldys, EM & Deng, W 2023, 'Lipid-Based Nanoparticles for Drug/Gene Delivery: An Overview of the Production Techniques and Difficulties Encountered in Their Industrial Development', ACS Materials Au, vol. 3, no. 6, pp. 600-619.
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Meilianda, E, Mauluddin, S, Pradhan, B & Sugianto, S 2023, 'Decadal shoreline changes and effectiveness of coastal protection measures post-tsunami on 26 December 2004', Applied Geomatics, vol. 15, no. 3, pp. 743-758.
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Mekala, MS, Srivastava, G, Gandomi, AH, Park, JH & Jung, H-Y 2023, 'A Quantum-Inspired Sensor Consolidation Measurement Approach for Cyber-Physical Systems', IEEE Transactions on Network Science and Engineering, pp. 1-14.
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Meng, X, Li, X, Charteris, A, Wang, Z, Naushad, M, Nghiem, LD, Liu, H & Wang, Q 2023, 'Impacts of site real-time adaptive control of water-sensitive urban designs on the stormwater trunk drainage system', Journal of Water Process Engineering, vol. 53, pp. 103656-103656.
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Increased rainfall intensity due to climate change is expected to exacerbate flood inundation in urban areas. Water sensitive urban design (WSUD) provides a variety of benefits in stormwater quantity management, ranging from stormwater harvesting to flood mitigation. Currently, however, developed areas lack any system that can improve the management of existing stormwater harvesting facilities to increase stormwater storage capacity without enlarging the stormwater drainage system. This study modelled a new method, Site Real-Time Adaptive Control (SRAC), that combined existing stormwater harvesting infrastructure at both regional and site levels with the existing stormwater drainage system (SWDS) through a cloud computing platform to increase stormwater storage capacity and reduce runoff water to the surface. The research found that: (1) the SRAC can manage runoff water dynamically and reduce flood inundation. The proposed impact factor Mt could help designers to measure the recovery capacity between two continuous rainfall events; (2) the SRAC model could postpone the peak flow in the trunk drainage system by 8–10 min; (3) the SRAC model could remove most of the excess water during very frequent rainfall events, decreasing over 98 % excess flow in design events 1h1EY (14,650 m3) and 2h1EY (11,272 m3); (4) the SRAC model showed a 36–50 % reduction in total outfall volume in the 1 h rainfall events, a 42–50 % reduction in the 2 h rainfall events; (5) the SRAC model could increase the capacity of downstream water treatment plants and save 43 % of the stormwater trunk drainage demand.
Meng, X, Li, X, Nghiem, LD, Hatshan, MR, Lam, KL & Wang, Q 2023, 'Assessing the effectiveness of site real-time adaptive control for stormwater quality control', Journal of Water Process Engineering, vol. 56, pp. 104324-104324.
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Miao, J, Wei, Y, Wang, X & Yang, Y 2023, 'Temporal Pixel-Level Semantic Understanding Through the VSPW Dataset', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 9, pp. 11297-11308.
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Minh, H-L, Sang-To, T, Khatir, S, Wahab, MA, Gandomi, AH & Cuong-Le, T 2023, 'Augmented deep neural network architecture for assessing damage severity in 3D concrete buildings under temperature fluctuations based on K-means optimization', Structures, vol. 57, pp. 105278-105278.
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Mirakhorli, F, Razavi Bazaz, S, Warkiani, ME & Ralph, PJ 2023, 'Ultra-high throughput microfluidic concentrator for harvesting of Tetraselmis sp. (Chlorodendrophyceae, Chlorophyta)', Algal Research, vol. 72, pp. 103145-103145.
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Mirdad, A, Hussain, FK & Hussain, OK 2023, 'A systematic literature review on pharmaceutical supply chain: research gaps and future opportunities', International Journal of Web and Grid Services, vol. 19, no. 2, pp. 233-258.
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Mirzaeipoueinak, M, Mordechai, HS, Bangar, SS, Sharabi, M, Tipper, JL & Tavakoli, J 2023, 'Structure-function characterization of the transition zone in the intervertebral disc', Acta Biomaterialia, vol. 160, pp. 164-175.
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Mishra, AK, Singh, O, Kumar, A, Puthal, D, Sharma, PK & Pradhan, B 2023, 'Hybrid Mode of Operation Schemes for P2P Communication to Analyze End-Point Individual Behaviour in IoT', ACM Transactions on Sensor Networks, vol. 19, no. 2, pp. 1-23.
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The Internet of Behavior is the recent trend in the Internet of Things (IoT), which analyzes the behaviour of individuals using huge amounts of data collected from their activities. The behavioural data collection process from an individual to a data center in the network layer of the IoT is addressed by the Routing Protocol for Low-powered Lossy Networks (RPL) downward routing policy. A hybrid mode of operation in RPL is designed to minimize the limitations of standard modes of operations in the downward routing of RPL. The existing hybrid modes use the common parameters, such as routing table capacity, energy level, and hop-count for making storing mode decisions at each node. However, none of these works have utilized the deciding parameters, such as number of Destination-Oriented Directed Acyclic Graph (DODAG) children, rank, and transmission traffic density for this purpose. In this article, we propose two hybrid MOPs for RPL focusing on the aspect of efficient downward communication for the Internet of Behaviors. The first version decides the mode of each node based on the rank and number of DODAG children of the node. In addition, the proposed Mode of Operation (MOP) has the provision to balance the task of a storing node that is currently running on low power and computational resources by a handover mechanism among the ancestors. The second version of the hybrid MOP utilizes the upward and downward transmission traffic probabilities together with 170 rule or 1D cellular automata to decide the operating mode of a node. The analysis on the upper bound on communication shows that both proposed works have communication overhead nearly equal to the storing mode. The experimental results also infer that the proposed adaptive MOP have lower communication overhead compared with standard storing modes and existing schemes ARPL, MERPL, and HIMOPD.
Mishra, DK, Wang, J, Li, L, Zhang, J & Hossain, MJ 2023, 'Resilience-Driven Scheme in Multiple Microgrids with Secure Transactive Energy System Framework', IEEE Transactions on Industry Applications, pp. 1-11.
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Mishra, K, Rajareddy, GNV, Ghugar, U, Chhabra, GS & Gandomi, AH 2023, 'A Collaborative Computation and Offloading for Compute-intensive and Latency-sensitive Dependency-aware Tasks in Dew-enabled Vehicular Fog Computing: A Federated Deep Q-Learning Approach', IEEE Transactions on Network and Service Management, pp. 1-1.
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Mishra, R, Ong, HC & Lin, C-W 2023, 'Progress on co-processing of biomass and plastic waste for hydrogen production', Energy Conversion and Management, vol. 284, pp. 116983-116983.
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Mistry, G, Popat, K, Patel, J, Panchal, K, Ngo, HH, Bilal, M & Varjani, S 2023, 'Corrigendum to “New outlook on hazardous pollutants in the wastewater environment: Occurrence, risk assessment and elimination by electrodeionization technologies” [Environ. Res. 219 (2023) 115112]', Environmental Research, vol. 227, pp. 115693-115693.
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Mistry, G, Popat, K, Patel, J, Panchal, K, Ngo, HH, Bilal, M & Varjani, S 2023, 'New outlook on hazardous pollutants in the wastewater environment: Occurrence, risk assessment and elimination by electrodeionization technologies', Environmental Research, vol. 219, pp. 115112-115112.
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Mofijur, M, Ahmed, SF, Rony, ZI, Khoo, KS, Chowdhury, AA, Kalam, MA, Le, VG, Badruddin, IA & Khan, TMY 2023, 'Screening of non-edible (second-generation) feedstocks for the production of sustainable aviation fuel', Fuel, vol. 331, pp. 125879-125879.
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Mofijur, M, Hasan, MM, Sultana, S, Kabir, Z, Djavanroodi, F, Ahmed, SF, Jahirul, MI, Badruddin, IA & Khan, TMY 2023, 'Advancements in algal membrane bioreactors: Overcoming obstacles and harnessing potential for eliminating hazardous pollutants from wastewater', Chemosphere, vol. 336, pp. 139291-139291.
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Mohammadi, M, Rashidi, M, Yu, Y & Samali, B 2023, 'Integration of TLS-derived Bridge Information Modeling (BrIM) with a Decision Support System (DSS) for digital twinning and asset management of bridge infrastructures', Computers in Industry, vol. 147, pp. 103881-103881.
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Mohanty, N, Behera, BK & Ferrie, C 2023, 'Analysis of the Vehicle Routing Problem Solved via Hybrid Quantum Algorithms in the Presence of Noisy Channels', IEEE Transactions on Quantum Engineering, vol. 4, pp. 1-14.
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Mohapatra, H, Mohanta, BK, Nikoo, MR, Daneshmand, M & Gandomi, AH 2023, 'MCDM-Based Routing for IoT-Enabled Smart Water Distribution Network', IEEE Internet of Things Journal, vol. 10, no. 5, pp. 4271-4280.
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The work consists of two subapproaches. In the first approach, an analytical model is developed using trapezium fuzzy numbers in decision-making problems for an Internet of Things-based water distribution network. The second phase explains the integration of the previous phase with the MCDM-based location routing protocol (M-LRP). The water distribution network has three components static water source, the utility center (UC) which can be located in the proper position, and the consumer. The objective of this work is to select an optimal route between the UC and the consumer by considering multiple criteria. The simulation result shows that the proposed multicriterion-based decision-making (MCDM)-based routing protocol outperforms both existing MCDM-based and non-MCDM-based routing schemes. The proposed model outperforms the existing models like non-MCDM-based and MCDM-based routing protocols by 51% and 11%, respectively.
Mohapatra, S, Maneesha, S, Mohanty, S, Patra, PK, Bhoi, SK, Sahoo, KS & Gandomi, AH 2023, 'A stacking classifiers model for detecting heart irregularities and predicting Cardiovascular Disease', Healthcare Analytics, vol. 3, pp. 100133-100133.
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Cardiovascular Diseases (CVDs), or heart diseases, are one of the top-ranking causes of death worldwide. About 1 in every 4 deaths is related to heart diseases, which are broadly classified as various types of abnormal heart conditions. However, diagnosis of CVDs is a time-consuming process in which data obtained from various clinical tests are manually analyzed. Therefore, new approaches for automating the detection of such irregularities in human heart conditions should be developed to provide medical practitioners with faster analysis by reducing the time of obtaining a diagnosis and enhancing results. Electronic Health Records(EHRs) are often utilized to discover useful data patterns that help improve the prediction of machine learning algorithms. Specifically, Machine Learning contributes significantly to solving issues like predictions in various domains, such as healthcare. Considering the abundance of available clinical data, there is a need to leverage such information for the betterment of humankind. Researchers have built various predictive models and systems over the years to help cardiologists and medical practitioners analyze data to attain meaningful insights. In this work, a predictive model is proposed for heart disease prediction based on the stacking of various classifiers in two levels(Base level and Meta level). Various heterogeneous learners are combined to produce strong model outcomes. The model obtained 92% accuracy in prediction with precision score of 92.6%, sensitivity of 92.6%, and specificity of 91%. The performance of the model was evaluated using various metrics, including accuracy, precision, recall, F1-scores, and area under the ROC curve values.
Mohapatra, S, Maneesha, S, Mohanty, S, Patra, PK, Bhoi, SK, Sahoo, KS & Gandomi, AH 2023, 'Erratum to “A stacking classifiers model for detecting heart irregularities and predicting Cardiovascular Disease” [Healthc. Anal. 3 (2023) 100133]', Healthcare Analytics, vol. 4, pp. 100236-100236.
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Mohsan, AUH & Wei, D 2023, 'Advancements in Additive Manufacturing of Tantalum via the Laser Powder Bed Fusion (PBF-LB/M): A Comprehensive Review', Materials, vol. 16, no. 19, pp. 6419-6419.
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Additive manufacturing (AM) exhibits a prime increment in manufacturing technology development. The last few decades have witnessed massive improvement in this field of research, including the growth in the process, equipment, and materials. Irrespective of compelling technological advancements, technical challenges provoke the application and development of these technologies. Metal additive manufacturing is considered a prime sector of the industrial revolution. Various metal AM techniques, including Selective Laser Sintering (SLS), Laser Powder Bed Fusion (PBF-LB/M), and Electron Beam Powder Bed Fusion (PBF-EB/M), have been developed according to materials and process classifications. PBF-LB/M is considered one of the most suitable choices for metallic materials. PBF-LB/M of tantalum has become a hot topic of research in the current century owing to the high biocompatibility of tantalum and its high-end safety applications. PBF-LB/M of porous Ta can direct unexplored research prospects in biomedical and orthopedics by adapting mechanical and biomedical properties and pioneering implant designs with predictable features. This review primarily examines the current advancements in the additive manufacturing of tantalum and related alloys using the PBF-LB/M process. The analysis encompasses the evaluation of process parameters, mechanical properties, and potential biological applications. This will offer the reader valuable insights into the present state of PBF-LB/M for tantalum alloys.
Mojiri, A, Zhou, JL, Ozaki, N, KarimiDermani, B, Razmi, E & Kasmuri, N 2023, 'Occurrence of per- and polyfluoroalkyl substances in aquatic environments and their removal by advanced oxidation processes', Chemosphere, vol. 330, pp. 138666-138666.
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Moles, RJ, Perry, L, Naylor, JM, Center, J, Ebeling, P, Duque, G, Major, G, White, C, Yates, C, Jennings, M, Kotowicz, M, Tran, T, Bliuc, D, Si, L, Gibson, K, Basger, BJ, Bolton, P, Barnett, S, Hassett, G, Kelly, A, Bazarnik, B, Ezz, W, Luckie, K & Carter, SR 2023, 'Safer medicines To reduce falls and refractures for OsteoPorosis (#STOP): a study protocol for a randomised controlled trial of medical specialist-initiated pharmacist-led medication management reviews in primary care.', BMJ Open, vol. 13, no. 8, pp. e072050-e072050.
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INTRODUCTION: Minimal trauma fractures (MTFs) often occur in older patients with osteoporosis and may be precipitated by falls risk-increasing drugs. One category of falls risk-increasing drugs of concern are those with sedative/anticholinergic properties. Collaborative medication management services such as Australia's Home Medicine Review (HMR) can reduce patients' intake of sedative/anticholinergics and improve continuity of care. This paper describes a protocol for an randomised controlled trial to determine the efficacy of an HMR service for patients who have sustained MTF. METHOD AND ANALYSIS: Eligible participants are as follows: ≥65 years of age, using ≥5 medicines including at least one falls risk-increasing drug, who have sustained an MTF and under treatment in one of eight Osteoporosis Refracture Prevention clinics in Australia. Consenting participants will be randomised to control (standard care) or intervention groups. For the intervention group, medical specialists will refer to a pharmacist for HMR focused on reducing falls risk predominately through making recommendations to reduce falls risk medicines, and adherence to antiosteoporosis medicines. Twelve months from treatment allocation, comparisons between groups will be made. The main outcome measure is participants' cumulative exposure to sedative and anticholinergics, using the Drug Burden Index. Secondary outcomes include medication adherence, emergency department visits, hospitalisations, falls and mortality. Economic evaluation will compare the intervention strategy with standard care. ETHICS AND DISSEMINATION: Approval was obtained via the New South Wales Research Ethics and Governance Information System (approval number: 2021/ETH12003) with site-specific approvals granted through Human Research Ethics Committees for each research site. Study outcomes will be published in peer-reviewed journals. It will provide robust insight into effectiveness of a pharmacist-based intervention ...
Mora, A, Leiva, F, Cardenas, R, Rojas, F, Pereda, J, Aguilera, RP & Travieso, JC 2023, 'Optimal Switching Sequence MPC for Four-Leg Two-Level Grid-Connected Converters', IEEE Journal of Emerging and Selected Topics in Power Electronics, pp. 1-1.
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Moradi, F, Biloria, N & Prasad, M 2023, 'Analyzing the age-friendliness of the urban environment using computer vision methods', Environment and Planning B: Urban Analytics and City Science, vol. 50, no. 8, pp. 239980832311538-2308.
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The accelerated growth of cities and urban populations over recent decades and the complexity and diversity of urban areas demands proficient spatial affordance assessment especially for the vulnerable sections of the society. Lately machine learning and computer vision models have become highly competent in analyzing urban images for assessing the built environment. This study harnesses the potential of computer vision techniques to assess the age-friendliness of urban areas. The developed machine learning model utilizes Google’s Street View images and is trained using lived experience-based image ratings provided by elderly participants. Newly assigned urban images are accordingly rated for their level of age-friendliness by the model with an accuracy of 85%. This paper elaborates upon the associated literature review, explains the data collection approach and the developed machine learning model. The success of the implementation is also demonstrated, confirming the validity of the proposed methodology.
Morris, A, Wilson, S, Mitchell, E, Ramia, G & Hastings, C 2023, 'International students struggling in the private rental sector in Australia prior to and during the pandemic', Housing Studies, vol. 38, no. 8, pp. 1-22.
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Morshedi Rad, D, Hansen, WP, Zhand, S, Cranfield, C & Ebrahimi Warkiani, M 2023, 'A hybridized mechano-electroporation technique for efficient immune cell engineering', Journal of Advanced Research.
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Mostafaei, H, Bahmani, H, Mostofinejad, D & Wu, C 2023, 'A novel development of HPC without cement: Mechanical properties and sustainability evaluation', Journal of Building Engineering, vol. 76, pp. 107262-107262.
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Mostafaei, H, Keshavarz, Z, Rostampour, MA, Mostofinejad, D & Wu, C 2023, 'Sustainability Evaluation of a Concrete Gravity Dam: Life Cycle Assessment, Carbon Footprint Analysis, and Life Cycle Costing', Structures, vol. 53, pp. 279-295.
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Mostafaei, H, Mostofinejad, D, Ghamami, M & Wu, C 2023, 'A new approach of ensemble learning in fully automated identification of structural modal parameters of concrete gravity dams: A case study of the Koyna dam', Structures, vol. 50, pp. 255-271.
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Motahari, R, Alavifar, Z, Zareh Andaryan, A, Chipulu, M & Saberi, M 2023, 'A multi-objective linear programming model for scheduling part families and designing a group layout in cellular manufacturing systems', Computers & Operations Research, vol. 151, pp. 106090-106090.
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Mousavi, M, Taskhiri, MS & Gandomi, AH 2023, 'Standing tree health assessment using contact–ultrasonic testing and machine learning', Computers and Electronics in Agriculture, vol. 209, pp. 107816-107816.
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MS, K, Johnson, I, Ngo, H-H, Guo, W & Kumar, M 2023, 'Application of Chlorella vulgaris for nutrient removal from synthetic wastewater and MBR-treated bio-park secondary effluent: growth kinetics, effects of carbon and phosphate concentrations', Environmental Monitoring and Assessment, vol. 195, no. 3.
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Muhammad, K, Ser, JD, Magaia, N, Fonseca, R, Hussain, T, Gandomi, AH, Daneshmand, M & de Albuquerque, VHC 2023, 'Communication Technologies for Edge Learning and Inference: A Novel Framework, Open Issues, and Perspectives', IEEE Network, vol. 37, no. 2, pp. 246-252.
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Muhit, IB, Masia, MJ & Stewart, MG 2023, 'Failure analysis and structural reliability of unreinforced masonry veneer walls: Influence of wall tie corrosion', Engineering Failure Analysis, vol. 151, pp. 107354-107354.
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Munot S, S, Bray, JE, Redfern, J, Bauman, A, Marschner, S, Semsarian, C, Denniss, AR, Coggins, AR, Middleton, PM, Jennings, G, Angell, B, Kumar, S, Kovoor, P, Lai, K, Vukasovic, M, Nelson, M, Oppermann, I & Chow, CK 2023, 'Sex-related Disparity in Bystander Response and Survival Outcomes for Out-of-hospital Cardiac Arrest (OHCA) in New South Wales (NSW), Australia', Heart, Lung and Circulation, vol. 32, pp. S128-S129.
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Nama, S, Saha, AK, Chakraborty, S, Gandomi, AH & Abualigah, L 2023, 'Boosting particle swarm optimization by backtracking search algorithm for optimization problems', Swarm and Evolutionary Computation, vol. 79, pp. 101304-101304.
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Namisango, F, Kang, K & Rehman, J 2023, 'Examining the relationship between sociomaterial practices enacted in the organizational use of social media and the emerging role of organizational generativity', International Journal of Information Management, vol. 71, pp. 102643-102643.
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Naqvi, B, Perova, K, Farooq, A, Makhdoom, I, Oyedeji, S & Porras, J 2023, 'Mitigation strategies against the phishing attacks: A systematic literature review', Computers & Security, vol. 132, pp. 103387-103387.
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Navidpour, AH, Hosseinzadeh, A, Zhou, JL & Huang, Z 2023, 'Progress in the application of surface engineering methods in immobilizing TiO2 and ZnO coatings for environmental photocatalysis', Catalysis Reviews, vol. 65, no. 3, pp. 822-873.
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Nazari, H, Shrestha, J, Naei, VY, Bazaz, SR, Sabbagh, M, Thiery, JP & Warkiani, ME 2023, 'Advances in TEER measurements of biological barriers in microphysiological systems', Biosensors and Bioelectronics, vol. 234, pp. 115355-115355.
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Neshat, M, Nezhad, MM, Mirjalili, S, Garcia, DA, Dahlquist, E & Gandomi, AH 2023, 'Short-term solar radiation forecasting using hybrid deep residual learning and gated LSTM recurrent network with differential covariance matrix adaptation evolution strategy', Energy, vol. 278, pp. 127701-127701.
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Newsom, ET, Sadeghpour, A, Entezari, A, Vinzons, JLU, Stanford, RE, Mirkhalaf, M, Chon, D, Dunstan, CR & Zreiqat, H 2023, 'Design and evaluation of 3D-printed Sr-HT-Gahnite bioceramic for FDA regulatory submission: A Good Laboratory Practice sheep study', Acta Biomaterialia, vol. 156, pp. 214-221.
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Ngo, HH, Guo, W, Pandey, A, Varjani, S & Tsang, DCW 2023, 'Preface', Current Developments in Biotechnology and Bioengineering: Biochar Towards Sustainable Environment, pp. xix-xx.
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Ngo, QT, Jayawickrama, BA, He, Y & Dutkiewicz, E 2023, 'Multi-Agent DRL-Based RIS-Assisted Spectrum Sensing in Cognitive Satellite-Terrestrial Networks', IEEE Wireless Communications Letters, pp. 1-1.
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Ngo, QT, Phan, KT, Mahmood, A & Xiang, W 2023, 'Physical Layer Security in IRS-Assisted Cache-Enabled Satellite Communication Networks', IEEE Transactions on Green Communications and Networking, pp. 1-1.
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Nguyen, BM, Nguyen, T, Vu, Q-H, Tran, HH, Vo, H, Son, DB, Binh, HTT, Yu, S & Wu, Z 2023, 'A Novel Nature-inspired Algorithm for Optimal Task Scheduling in Fog-Cloud Blockchain System', IEEE Internet of Things Journal, pp. 1-1.
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Nguyen, CC, Thai, MT, Hoang, TT, Davies, J, Phan, PT, Zhu, K, Wu, L, Brodie, MA, Tsai, D, Ha, QP, Phan, H-P, Lovell, NH & Nho Do, T 2023, 'Development of a soft robotic catheter for vascular intervention surgery', Sensors and Actuators A: Physical, vol. 357, pp. 114380-114380.
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Nguyen, CT, Hoang, DT, Nguyen, DN, Xiao, Y, Niyato, D & Dutkiewicz, E 2023, 'MetaShard: A Novel Sharding Blockchain Platform for Metaverse Applications', IEEE Transactions on Mobile Computing, pp. 1-14.
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Nguyen, CT, Hoang, DT, Nguyen, DN, Xiao, Y, Pham, H-A, Dutkiewicz, E & Tuong, NH 2023, 'FedChain: Secure Proof-of-Stake-Based Framework for Federated-Blockchain Systems', IEEE Transactions on Services Computing, vol. 16, no. 4, pp. 2642-2656.
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Nguyen, DDN, Sood, K, Xiang, Y, Gao, L, Chi, L & Yu, S 2023, 'Toward IoT Node Authentication Mechanism in Next Generation Networks', IEEE Internet of Things Journal, vol. 10, no. 15, pp. 13333-13341.
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Nguyen, DT, Ho-Le, TP, Pham, L, Ho-Van, VP, Hoang, TD, Tran, TS, Frost, S & Nguyen, TV 2023, 'BONEcheck: A digital tool for personalized bone health assessment', Osteoporosis and Sarcopenia, vol. 9, no. 3, pp. 79-87.
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Nguyen, HAD, Ha, QP, Duc, H, Azzi, M, Jiang, N, Barthelemy, X & Riley, M 2023, 'Long Short-Term Memory Bayesian Neural Network for Air Pollution Forecast', IEEE Access, vol. 11, pp. 35710-35725.
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Nguyen, LN, Vu, MT, Vu, HP, Johir, MAH, Labeeuw, L, Ralph, PJ, Mahlia, TMI, Pandey, A, Sirohi, R & Nghiem, LD 2023, 'Microalgae-based carbon capture and utilization: A critical review on current system developments and biomass utilization', Critical Reviews in Environmental Science and Technology, vol. 53, no. 2, pp. 216-238.
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Nguyen, MK, Lin, C, Hoang, HG, Bui, XT, Ngo, HH, Le, VG & Tran, H-T 2023, 'Investigation of biochar amendments on odor reduction and their characteristics during food waste co-composting', Science of The Total Environment, vol. 865, pp. 161128-161128.
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Nguyen, QD & Castel, A 2023, 'Long-term durability of underground reinforced concrete pipes in natural chloride and carbonation environments', Construction and Building Materials, vol. 394, pp. 132230-132230.
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Nguyen, TT & Indraratna, B 2023, 'Influence of varying water content on permanent deformation of mud-fouled ballast', Transportation Geotechnics, vol. 38, pp. 100919-100919.
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The contamination of ballast by mud pumping is known to cause considerable reduction in the shear resistance as well as increased settlement of railroad foundations. However, how varying water content (w) of fouled ballast can affect this deterioration has not been properly understood. The current study thus adopts a large-scale cyclic triaxial test to examine permanent (plastic) deformation of mud-fouled ballast collected from a site with a history of mud pumping with a consideration of different water contents. In these tests, fouling content varies from 5 to 30 %, while the water content changes from 0 to 40 %. The results show that while increasing content of fines causes larger permanent settlement of ballast, varying water content of fines can influence this behaviour significantly. A salient finding of this study is the critical threshold of water content near to the liquid limit (LL) of fine soil (finer than 0.425 mm) that can cause a swift increase in ballast settlement. The results show that the peak permanent strain can increase by about 26 % compared to the dry state when w of fines reaches the LL. On the other hand, permanent strain of fouled ballast can decrease at the optimum water content of fines, if a sufficient mass of fines (>20 % by weight) is provided to reinforce the granular assembly. An empirical method is provided to estimate the ultimate settlement of mud-fouled tracks considering the moisture state that would be most beneficial in real-life applications.
Nguyen, TTH, Nguyen, XC, Nguyen, DLT, Nguyen, DD, Vo, TYB, Vo, QN, Nguyen, TD, Ly, QV, Ngo, HH, Vo, D-VN, Nguyen, TP, Kim, IT & Van Le, Q 2023, 'Converting biomass of agrowastes and invasive plant into alternative materials for water remediation', Biomass Conversion and Biorefinery, vol. 13, no. 6, pp. 5391-5406.
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Three types of biomass of invasive plants and agrowastes, namely, the wattle bark of Acacia auriculiformis (BA), mimosa (BM), and coffee husks (BC), were converted into biochars through slow pyrolysis and investigated for their ability to remove dyes in water. The properties of the materials were characterized using Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), and Brunauer–Emmett–Teller (BET) analysis. The BET surface area (total pore volume) of BC was 2.62 m2/g (0.007 cm3/g), far below those of BA and BM with 393.15 cm2/g (0.195 m3/g) and 285.53 cm2/g (0.153 m3/g), respectively. The optimal adsorption doses for the removal of methylene blue (MB) were found to be 2, 5, and 5 g/L for BC, BA, and BM, respectively. The suitable pH ranges for MB removal were 6–12 for BA, 7–12 for BC, and 2–10 for BM. The majority of MB (over 83%) was removed in the initial 30 min, followed by a more quasisteady state condition after the removal rate exceeded 90%. The experimental data were fitted with the kinetic models (PFO, PSO, Bangham, IDP), indicating that physicochemical adsorption, pore diffusion process, and multiple stages are the dominant mechanisms for the MB adsorption onto biochars. Finally, BA and BM showed similar adsorption efficiencies, while BC may not be favorable for use as an adsorbent due to its low surface area and low pore volume.
Ni, B-J, Thomas, KV & Kim, E-J 2023, 'Microplastics and nanoplastics in urban waters', Water Research, vol. 229, pp. 119473-119473.
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Ni, Q, Ji, JC & Feng, K 2023, 'Data-Driven Prognostic Scheme for Bearings Based on a Novel Health Indicator and Gated Recurrent Unit Network', IEEE Transactions on Industrial Informatics, vol. 19, no. 2, pp. 1301-1311.
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The prognosis of bearings is vital for condition-based maintenance of rotating machinery. This article proposes a systematic prognostic scheme for rolling element bearings. The proposed scheme infers the degradation progression by developing a novel health indicator (HI). This novel HI, derived from the spectral correlation, Wasserstein distance, and linear rectification, can reflect the changes in the probability distribution of all cyclic power-spectra over time. In other words, any form of variation in modulation characteristics can be revealed through the proposed novel indicator, even for the weak information buried by the internal or external noise. Furthermore, the developed HI can eliminate random fluctuations that often impair the remaining useful life (RUL) prediction accuracy. Then, a 3 ${\boldsymbol{\sigma }}$ criterion-based technique is introduced to divide health stages. After that, the gated recurrent unit network is employed to predict the RUL of the bearing system, integrated with the Bayesian optimization algorithm to tune the optimal hyperparameters adaptively. This renders the establishment of an intelligent prognosis model with high prediction accuracy and generalization ability. Finally, experimental validations are conducted using the run-to-failure datasets of bearings. The obtained results demonstrate that the proposed HI has better monotonicity, and the proposed prognostic scheme can predict the RUL with high accuracy.
Ni, Q, Ji, JC, Halkon, B, Feng, K & Nandi, AK 2023, 'Physics-Informed Residual Network (PIResNet) for rolling element bearing fault diagnostics', Mechanical Systems and Signal Processing, vol. 200, pp. 110544-110544.
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Ni, Z, Zhang, JA & Liu, RP 2023, 'Waveform Optimizations Using Virtual Arrays in Broadband Radar Communications', IEEE Wireless Communications Letters, vol. 12, no. 5, pp. 1-1.
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Ni, Z, Zhang, JA, Wu, K & Liu, RP 2023, 'Uplink Sensing Using CSI Ratio in Perceptive Mobile Networks', IEEE Transactions on Signal Processing, vol. 71, pp. 2699-2712.
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Ni, Z, Zhang, JA, Wu, K, Yang, K & Liu, RP 2023, 'Receiver Design in Full-Duplex Joint Radar-Communication Systems', IEEE Transactions on Communications, vol. 71, no. 7, pp. 4234-4246.
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Nie, X, Chai, B, Wang, L, Liao, Q & Xu, M 2023, 'Learning enhanced features and inferring twice for fine-grained image classification', Multimedia Tools and Applications, vol. 82, no. 10, pp. 14799-14813.
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AbstractFine-Grained Visual Categorization (FGVC) aims to distinguish between extremely similar subordinate-level categories within the same basic-level category. Existing research has proven the great importance of the discriminative features in FGVC but ignored the contributions for correct classification from other features, and the extracted features always contain more information about the obvious regions but less about subtle regions. In this paper, firstly, a novel module named forcing module is proposed to force the network to extract more diverse features for FGVC, which generates a suppression mask based on the class activation maps to suppress the most distinguishable regions, so as to force the network to extract other secondary distinguishable features as the final features. The forcing module consists of the original branch and the forcing branch. The original branch focuses on the primary discriminative regions while the forcing branch focuses on secondary discriminative regions. Secondly, in order to solve the problem that information of small-scale distinguishable features is lost seriously after multi-layer down-sampling, according to the class activation maps of the first prediction, the object is cropped and scaled as the second input. To reduce the prediction error, the first and second prediction probabilities are fused as the final prediction result. Experimental results indicate that the proposed method not only outperforms the baseline model by a large margin (3.7%, 5.9%, 3.1% respectively) on CUB-200-2011, Stanford-Cars, and FGVC-Aircraft, but also achieves state-of-the-art performance on FGVC-Aircraft.
Nie, X, Liu, L, He, L, Zhao, L, Lu, H, Lou, S, Xiong, R & Wang, Y 2023, 'Weakly-Interactive-Mixed Learning: Less Labelling Cost for Better Medical Image Segmentation', IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 7, pp. 3270-3281.
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Nimbalkar, S & Basack, S 2023, 'Pile group in clay under cyclic lateral loading with emphasis on bending moment: Numerical modelling', Marine Georesources and Geotechnology, vol. 41, no. 3, pp. 269-284.
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Pile foundations supporting major structures are often founded in soft compressible clays. Apart from usual super-structural loading, these piles are subjected to cyclic lateral loads originating from actions of waves, ship impacts, winds or moving vehicles. Such repetitive loading induces stress reversal in adjacent soft clay initiating progressive degradation in soil strength and stiffness. This not only deteriorates the pile capacity with unacceptable displacements, the bending moments also increase. Although past studies investigated the response of single pile under lateral cyclic loading, a detailed study on pile group in clay under cyclic lateral loading with emphasis on bending moment is of immense practical interest. This paper focuses on detailed study of the response of pile group in clay under cyclic lateral loading, with emphasis on bending moment, through numerical modelling via a three-dimensional dynamic finite element (FE) approach and simplified boundary element modelling (BEM). Comparisons of computed results with available test data imply that the results obtained by 3 D dynamic FE model are better than the BEM. Extensive parametric studies with field data indicate that pile bending moment has been significantly influenced by cyclic loading parameters (number of cycles, frequency and amplitude). Relevant conclusions are drawn from the entire study.
Nimmy, SF, Hussain, OK, Chakrabortty, RK, Hussain, FK & Saberi, M 2023, 'An optimized Belief-Rule-Based (BRB) approach to ensure the trustworthiness of interpreted time-series decisions', Knowledge-Based Systems, vol. 271, pp. 110552-110552.
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Nimmy, SF, Hussain, OK, Chakrabortty, RK, Hussain, FK & Saberi, M 2023, 'Interpreting the antecedents of a predicted output by capturing the interdependencies among the system features and their evolution over time', Engineering Applications of Artificial Intelligence, vol. 117, pp. 105596-105596.
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Nirbhav, Malik, A, Maheshwar, Jan, T & Prasad, M 2023, 'Landslide Susceptibility Prediction based on Decision Tree and Feature Selection Methods', Journal of the Indian Society of Remote Sensing, vol. 51, no. 4, pp. 771-786.
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Nirbhav, Malik, A, Maheshwar, Prasad, M, Saini, A & Long, NT 2023, 'A comparative study of different machine learning models for landslide susceptibility prediction: a case study of Kullu-to-Rohtang pass transport corridor, India', Environmental Earth Sciences, vol. 82, no. 7.
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Nirmala, M, Gandomi, AH, Babu, MR, Babu, LDD & Patan, R 2023, '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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Ojelade, OA, Zaman, SF & Ni, B-J 2023, 'Green ammonia production technologies: A review of practical progress', Journal of Environmental Management, vol. 342, pp. 118348-118348.
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Olszak, CM, Zurada, J & Kozanoglu, DC 2023, 'Introduction to the Business Intelligence for Innovative, Collaborative and Sustainable Development of Organizations in Digital Era Mini-track', Proceedings of the Annual Hawaii International Conference on System Sciences, vol. 2023-January, pp. 257-258.
Ong, KL, Stafford, LK, McLaughlin, SA, Boyko, EJ, Vollset, SE, Smith, AE, Dalton, BE, Duprey, J, Cruz, JA, Hagins, H, Lindstedt, PA, Aali, A, Abate, YH, Abate, MD, Abbasian, M, Abbasi-Kangevari, Z, Abbasi-Kangevari, M, Abd ElHafeez, S, Abd-Rabu, R, Abdulah, DM, Abdullah, AYM, Abedi, V, Abidi, H, Aboagye, RG, Abolhassani, H, Abu-Gharbieh, E, Abu-Zaid, A, Adane, TD, Adane, DE, Addo, IY, Adegboye, OA, Adekanmbi, V, Adepoju, AV, Adnani, QES, Afolabi, RF, Agarwal, G, Aghdam, ZB, Agudelo-Botero, M, Aguilera Arriagada, CE, Agyemang-Duah, W, Ahinkorah, BO, Ahmad, D, Ahmad, R, Ahmad, S, Ahmad, A, Ahmadi, A, Ahmadi, K, Ahmed, A, Ahmed, A, Ahmed, LA, Ahmed, SA, Ajami, M, Akinyemi, RO, Al Hamad, H, Al Hasan, SM, AL-Ahdal, TMA, Alalwan, TA, Al-Aly, Z, AlBataineh, MT, Alcalde-Rabanal, JE, Alemi, S, Ali, H, Alinia, T, Aljunid, SM, Almustanyir, S, Al-Raddadi, RM, Alvis-Guzman, N, Amare, F, Ameyaw, EK, Amiri, S, Amusa, GA, Andrei, CL, Anjana, RM, Ansar, A, Ansari, G, Ansari-Moghaddam, A, Anyasodor, AE, Arabloo, J, Aravkin, AY, Areda, D, Arifin, H, Arkew, M, Armocida, B, Ärnlöv, J, Artamonov, AA, Arulappan, J, Aruleba, RT, Arumugam, A, Aryan, Z, Asemu, MT, Asghari-Jafarabadi, M, Askari, E, Asmelash, D, Astell-Burt, T, Athar, M, Athari, SS, Atout, MMW, Avila-Burgos, L, Awaisu, A, Azadnajafabad, S, B, DB, Babamohamadi, H, Badar, M, Badawi, A, Badiye, AD, Baghcheghi, N, Bagheri, N, Bagherieh, S, Bah, S, Bahadory, S, Bai, R, Baig, AA, Baltatu, OC, Baradaran, HR, Barchitta, M, Bardhan, M, Barengo, NC, Bärnighausen, TW, Barone, MTU, Barone-Adesi, F, Barrow, A, Bashiri, H, Basiru, A, Basu, S, Basu, S, Batiha, A-MM, Batra, K, Bayih, MT, Bayileyegn, NS, Behnoush, AH, Bekele, AB, Belete, MA, Belgaumi, UI, Belo, L, Bennett, DA, Bensenor, IM, Berhe, K, Berhie, AY, Bhaskar, S, Bhat, AN, Bhatti, JS, Bikbov, B, Bilal, F, Bintoro, BS, Bitaraf, S, Bitra, VR, Bjegovic-Mikanovic, V, Bodolica, V, Boloor, A, Brauer, M, Brazo-Sayavera, J, Brenner, H, Butt, ZA, Calina, D, Campos, LA, Campos-Nonato, IR, Cao, Y, Cao, C, Car, J, Carvalho, M, Castañeda-Orjuela, CA, Catalá-López, F, Cerin, E, Chadwick, J, Chandrasekar, EK, Chanie, GS, Charan, J, Chattu, VK, Chauhan, K, Cheema, HA, Chekol Abebe, E, Chen, S, Cherbuin, N, Chichagi, F, Chidambaram, SB, Cho, WCS, Choudhari, SG, Chowdhury, R, Chowdhury, EK, Chu, D-T, Chukwu, IS, Chung, S-C, Coberly, K, Columbus, A & et al. 2023, 'Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021', The Lancet, vol. 402, no. 10397, pp. 203-234.
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Ortega, JS, Corrales-Orovio, R, Ralph, P, Egaña, JT & Gentile, C 2023, 'Photosynthetic microorganisms for the oxygenation of advanced 3D bioprinted tissues', Acta Biomaterialia, vol. 165, pp. 180-196.
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Ostermeier, FF, Jaehnert, J & Deuse, J 2023, 'Joint modelling of the order-dependent parts supply strategies sequencing, kitting and batch supply for assembly lines: insights from industrial practice', Production & Manufacturing Research, vol. 11, no. 1.
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Otavio Mendes, J, Merenda, A, Wilson, K, Fraser Lee, A, Della Gaspera, E & van Embden, J 2023, 'Substrate Morphology Directs (001) Sb2Se3 Thin Film Growth by Crystallographic Orientation Filtering', Small, p. e2302721.
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AbstractAntimony chalcogenide, Sb2X3 (X = S, Se), applications greatly benefit from efficient charge transport along covalently bonded (001) oriented (Sb4X6)n ribbons, making thin film orientation control highly desirable – although particularly hard to achieve experimentally. Here, it is shown for the first time that substrate nanostructure plays a key role in driving the growth of (001) oriented antimony chalcogenide thin films. Vapor Transport Deposition of Sb2Se3 thin films is conducted on ZnO substrates whose morphology is tuned between highly nanostructured and flat. The extent of Sb2Se3 (001) orientation is directly correlated to the degree of substrate nanostructure. These data showcase that nanostructuring a substrate is an effective tool to control the orientation and morphology of Sb2Se3 films. The optimized samples demonstrate high (001) crystallographic orientation. A growth mechanism for these films is proposed, wherein the substrate physically restricts the development of undesirable crystallographic orientations. It is shown that the surface chemistry of the nanostructured substrates can be altered and still drive the growth of (001) Sb2Se3 thin films – not limiting this phenomenon to a particular substrate type. Insights from this work are expected to guide the rational design of Sb2X3 thin film devices and other low‐dimensional crystal‐structured materials wherein performance is intrinsically linked to morphology and orientation.
Ou, L, Chang, Y-C, Wang, Y-K & Lin, C-T 2023, 'Fuzzy Centered Explainable Network for Reinforcement Learning', IEEE Transactions on Fuzzy Systems, pp. 1-11.
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Ou, S, Guo, Z, Ci, J, Gong, S & Wen, S 2023, 'Multistability of switched complex-valued neural networks with state-dependent switching rules', Neurocomputing, vol. 551, pp. 126499-126499.
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Ou, Y, Zhou, JL, Jia, Y, Liang, M, Hu, H & Ren, L 2023, 'Complete genome of Mycolicibacterium phocaicum RL-HY01, a PAEs-degrading marine bacterial strain isolated from Zhanjiang Bay, China', Marine Genomics, vol. 69, pp. 101019-101019.
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Ouyang, D, Wen, D, Qin, L, Chang, L, Lin, X & Zhang, Y 2023, 'When hierarchy meets 2-hop-labeling: efficient shortest distance and path queries on road networks.', VLDB J., vol. 32, no. 6, pp. 1263-1287.
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Ouyang, P, Rao, P, Wu, J, Cui, J, Nimbalkar, S & Chen, Q 2023, 'Hydromechanical Modeling of High-Voltage Electropulse-Assisted Fluid Injection for Rock Fracturing', Rock Mechanics and Rock Engineering, vol. 56, no. 6, pp. 3861-3886.
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Owen, B, Kechagidis, K, Bazaz, SR, Enjalbert, R, Essmann, E, Mallorie, C, Mirghaderi, F, Schaaf, C, Thota, K, Vernekar, R, Zhou, Q, Warkiani, ME, Stark, H & Krüger, T 2023, 'Lattice-Boltzmann modelling for inertial particle microfluidics applications - a tutorial review', Advances in Physics: X, vol. 8, no. 1.
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Pacholak, A, Żur-Pińska, J, Piński, A, Nguyen, QA, Ligaj, M, Luczak, M, Nghiem, LD & Kaczorek, E 2023, 'Potential negative effect of long-term exposure to nitrofurans on bacteria isolated from wastewater', Science of The Total Environment, vol. 872, pp. 162199-162199.
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Pal, PK, Jana, KC, Siwakoti, YP, Ali, JSM & Blaabjerg, F 2023, 'A Switched-Capacitor Multilevel Inverter with Modified Pulse-Width Modulation and Active DC-Link Capacitor Voltage Balancing', IEEE Journal of Emerging and Selected Topics in Power Electronics, pp. 1-1.
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Pan, Y, Li, J, Zong, Z, Wu, C & Qian, H 2023, 'Experimental and numerical study on ground shock propagation in calcareous sand', International Journal of Impact Engineering, vol. 180, pp. 104724-104724.
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Pang, C, Zhu, S, Shen, M, Liu, X & Wen, S 2023, 'Novel Exponential Stability Criteria for Switched Neutral-Type Neural Networks with Mixed Delays', IEEE Transactions on Circuits and Systems II: Express Briefs, pp. 1-1.
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Pang, J, Xia, J, Cheng, F, Zhou, C, Chen, X, Shen, C, Xing, H & Chang, X 2023, 'Surface Wave Dispersion Measurement with Polarization Analysis Using Multicomponent Seismic Noise Recorded by a 1-D Linear Array', Surveys in Geophysics, vol. 44, no. 6, pp. 1863-1895.
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Park, MJ, Pathak, NB, Wang, C, Tran, VH, Han, D-S, Hong, S, Phuntsho, S & Shon, HK 2023, 'Fouling of reverse osmosis membrane: Autopsy results from a wastewater treatment facility at central park, Sydney', Desalination, vol. 565, pp. 116848-116848.
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Parsa, SM, Norouzpour, F, Shoeibi, S, Shahsavar, A, Aberoumand, S, Said, Z, Guo, W, Ngo, HH, Ni, B-J, Afrand, M & Karimi, N 2023, 'A comprehensive study to find the optimal fraction of nanoparticle coated at the interface of solar desalination absorbers: 5E and GHGs analysis in different seasons', Solar Energy Materials and Solar Cells, vol. 256, pp. 112308-112308.
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Parsaei, K, Keshavarz, R, Boroujeni, RM & Shariati, N 2023, 'Compact Pixelated Microstrip Forward Broadside Coupler Using Binary Particle Swarm Optimization', IEEE Transactions on Circuits and Systems I: Regular Papers, pp. 1-10.
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Patibanda, R, Hill, C, Saini, A, Li, X, Chen, Y, Matviienko, A, Knibbe, J, van den Hoven, E & Mueller, FF 2023, 'Auto-Paizo Games: Towards Understanding the Design of Games That Aim to Unify a Player’s Physical Body and the Virtual World', Proceedings of the ACM on Human-Computer Interaction, vol. 7, no. CHI PLAY, pp. 893-918.
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Most digital bodily games focus on the body as they use movement as input. However, they also draw the player’s focus away from the body as the output occurs on visual displays, creating a divide between the physical body and the virtual world. We propose a novel approach – the 'Body as a Play Material' – where a player uses their body as both input and output to unify the physical body and the virtual world. To showcase this approach, we designed three games where a player uses one of their hands (input) to play against the other hand (output) by loaning control over its movements to an Electrical Muscle Stimulation (EMS) system. We conducted a thematic analysis on the data obtained from a field study with 12 participants to articulate four player experience themes. We discuss our results about how participants appreciated the engagement with the variety of bodily movements for play and the ambiguity of using their body as a play material. Ultimately, our work aims to unify the physical body and the virtual world.
Patibanda, R, Saini, A, Overdevest, N, Montoya, MF, Li, X, Chen, Y, Nisal, S, Andres, J, Knibbe, J, van den Hoven, E & Mueller, FF 2023, 'Fused Spectatorship: Designing Bodily Experiences Where Spectators Become Players', Proceedings of the ACM on Human-Computer Interaction, vol. 7, no. CHI PLAY, pp. 769-802.
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Spectating digital games can be exciting. However, due to its vicarious nature, spectators often wish to engage in the gameplay beyond just watching and cheering. To blur the boundaries between spectators and players, we propose a novel approach called 'Fused Spectatorship', where spectators watch their hands play games by loaning bodily control to a computational Electrical Muscle Stimulation (EMS) system. To showcase this concept, we designed three games where spectators loan control over both their hands to the EMS system and watch them play these competitive and collaborative games. A study with 12 participants suggested that participants could not distinguish if they were watching their hands play, or if they were playing the games themselves. We used our results to articulate four spectator experience themes and four fused spectator types, the behaviours they elicited and offer one design consideration to support each of these behaviours. We also discuss the ethical design considerations of our approach to help game designers create future fused spectatorship experiences.
Patmore, G, Balnave, N & Marjanovic, O 2023, 'Business Co-operatives in Australia: “Unlikely Soil for a Co-operative Movement”', Enterprise & Society, vol. 24, no. 1, pp. 149-173.
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While co-operatives are traditionally associated with workers, consumers, and farmers, the business model, with its emphasis on democracy and community, has also been adopted by small business owners, the self-employed, and professionals. These business co-operatives are distinct phenomenon, because they primarily consist of independent organizational entities that are not co-operatives and are generally in direct competition with one another. They are unique in that they bring together separate organizations that seek to combat market threats while adopting a philosophy based on co-operative principles. This article begins with an overview of the Australian co-operative landscape. It then defines the concept of business co-operatives and then draws upon the Visual Atlas of Australian Co-operatives History Project, which has developed a large database of Australian co-operatives over time and space, to examine the development of business co-operatives in Australia. It looks at where business co-operatives formed in the economy, the motivation underlying their formation, their average life spans, and their relationships with the broader co-operative movement. The article highlights the value of business co-operatives in introducing the values of participatory democracy and working for the common good into unanticipated markets and reinforcing the co-operative movement.
Paul, A, Shukla, N & Trianni, A 2023, 'Modelling supply chain sustainability challenges in the food processing sector amid the COVID-19 outbreak', Socio-Economic Planning Sciences, vol. 87, pp. 101535-101535.
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Peellage, WH, Fatahi, B & Rasekh, H 2023, 'Assessment of cyclic deformation and critical stress amplitude of jointed rocks via cyclic triaxial testing', Journal of Rock Mechanics and Geotechnical Engineering, vol. 15, no. 6, pp. 1370-1390.
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Pei, C, Qiu, Y, Li, F, Huang, X, Si, Y, Li, Y, Zhang, X, Chen, C, Liu, Q, Cao, Z, Ding, N, Gao, S, Alho, K, Yao, D & Xu, P 2023, 'The different brain areas occupied for integrating information of hierarchical linguistic units: a study based on EEG and TMS', Cerebral Cortex, vol. 33, no. 8, pp. 4740-4751.
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Abstract
Human language units are hierarchical, and reading acquisition involves integrating multisensory information (typically from auditory and visual modalities) to access meaning. However, it is unclear how the brain processes and integrates language information at different linguistic units (words, phrases, and sentences) provided simultaneously in auditory and visual modalities. To address the issue, we presented participants with sequences of short Chinese sentences through auditory, visual, or combined audio-visual modalities while electroencephalographic responses were recorded. With a frequency tagging approach, we analyzed the neural representations of basic linguistic units (i.e. characters/monosyllabic words) and higher-level linguistic structures (i.e. phrases and sentences) across the 3 modalities separately. We found that audio-visual integration occurs in all linguistic units, and the brain areas involved in the integration varied across different linguistic levels. In particular, the integration of sentences activated the local left prefrontal area. Therefore, we used continuous theta-burst stimulation to verify that the left prefrontal cortex plays a vital role in the audio-visual integration of sentence information. Our findings suggest the advantage of bimodal language comprehension at hierarchical stages in language-related information processing and provide evidence for the causal role of the left prefrontal regions in processing information of audio-visual sentences.
Peng, L, Qiu, H, Li, S, Xu, Y, Liang, C, Wang, N, Liu, Y & Ni, B-J 2023, 'The mitigation effect of free ammonia and free nitrous acid on nitrous oxide production from the full-nitrification and partial-nitritation systems', Bioresource Technology, vol. 372, pp. 128564-128564.
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Pérez-Romero, ME, Alfaro-García, VG, Merigó, JM & Flores-Romero, MB 2023, 'Covariance Logarithmic Aggregation Operators in Decision-Making Processes', Cybernetics and Systems, vol. 54, no. 2, pp. 220-238.
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Ping, J, Zhu, S, Shi, M, Wu, S, Shen, M, Liu, X & Wen, S 2023, 'Event-Triggered Finite-Time Synchronization Control for Quaternion-Valued Memristive Neural Networks by an Non-Decomposition Method', IEEE Transactions on Network Science and Engineering, pp. 1-10.
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Pipino, DF, Piccardi, M, Lopez-Villalobos, N, Hickson, RE & Vázquez, MI 2023, 'Fertility and survival of Swedish Red and White × Holstein crossbred cows and purebred Holstein cows', Journal of Dairy Science, vol. 106, no. 4, pp. 2475-2486.
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Pira, L & Ferrie, C 2023, 'An invitation to distributed quantum neural networks', Quantum Machine Intelligence, vol. 5, no. 2.
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AbstractDeep neural networks have established themselves as one of the most promising machine learning techniques. Training such models at large scales is often parallelized, giving rise to the concept of distributed deep learning. Distributed techniques are often employed in training large models or large datasets either out of necessity or simply for speed. Quantum machine learning, on the other hand, is the interplay between machine learning and quantum computing. It seeks to understand the advantages of employing quantum devices in developing new learning algorithms as well as improving the existing ones. A set of architectures that are heavily explored in quantum machine learning are quantum neural networks. In this review, we consider ideas from distributed deep learning as they apply to quantum neural networks. We find that the distribution of quantum datasets shares more similarities with its classical counterpart than does the distribution of quantum models, though the unique aspects of quantum data introduce new vulnerabilities to both approaches. We review the current state of the art in distributed quantum neural networks, including recent numerical experiments and the concept of circuit-cutting.
Power, T, Kennedy, P, Chen, H, Martinez-Maldonado, R, McGregor, C, Johnson, A, Townsend, L & Hayes, C 2023, 'Learning to Manage De-escalation Through Simulation: An Exploratory Study', Clinical Simulation in Nursing, vol. 77, pp. 23-29.
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Pradeepkumar, A, Cheng, HH, Liu, KY, Gebert, M, Bhattacharyya, S, Fuhrer, MS & Iacopi, F 2023, 'Low-leakage epitaxial graphene field-effect transistors on cubic silicon carbide on silicon', Journal of Applied Physics, vol. 133, no. 17.
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Epitaxial graphene (EG) on cubic silicon carbide (3C-SiC) on silicon holds the promise of tunable nanoelectronic and nanophotonic devices, some uniquely unlocked by the graphene/cubic silicon carbide combination, directly integrated with the current well-established silicon technologies. Yet, the development of graphene field-effect devices based on the 3C-SiC/Si substrate system has been historically hindered by poor graphene quality and coverage, as well as substantial leakage issues of the heteroepitaxial system. We address these issues by growing EG on 3C-SiC on highly resistive silicon substrates using an alloy-mediated approach. In this work, we demonstrate a field-effect transistor based on EG/3C-SiC/Si with gate leakage current 6 orders of magnitude lower than the drain current at room temperature, which is a vast improvement on the current literature, opening the possibility for dynamically tunable nanoelectronic and nanophotonic devices on silicon at the wafer level.
Pradhan, B, Dikshit, A, Lee, S & Kim, H 2023, 'An explainable AI (XAI) model for landslide susceptibility modeling', Applied Soft Computing, vol. 142, pp. 110324-110324.
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Pradhan, B, Lee, S, Dikshit, A & Kim, H 2023, 'Spatial flood susceptibility mapping using an explainable artificial intelligence (XAI) model', Geoscience Frontiers, vol. 14, no. 6, pp. 101625-101625.
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Pradhan, S, Qiu, X & Ji, J 2023, 'On Time–Frequency Domain Flexible Structure of Delayless Partitioned Block Adaptive Filtering Approach for Active Noise Control', Circuits, Systems, and Signal Processing, vol. 42, no. 12, pp. 7580-7595.
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Frequencydomain filtered-x least mean square algorithms can reduce the computational complexity of the time domain counterpart with long filters; however, they suffer from large block delay, additional quantization error due to large size transformations and implementation difficulties in existing DSP hardware. In this paper, a time–frequencydomain flexible structure is proposed using the partitioned block frequencydomain adaptive filtering technique, which has no signal path delay and is well suited for low-cost DSP implementation. The proposed structure divides the long filters into many equal partitions and carries out the control filter update in frequency domain while generating the control signal in both time and frequency domains, thereby eliminating the forward path delay completely while maintaining low computational complexity. The proposed structure has a potential benefit for controlling broadband noise, where the causality constraint is more important. The simulation results using the measured acoustic paths demonstrate that the proposed structure maintains similar control performance as that of the time domain algorithm but with much less computational complexity. Furthermore, the tracking performance of the proposed structure under different levels of measurement noise is investigated.
Pradhan, S, Zhang, G, Zhao, S, Niwa, K & Bastiaan Kleijn, W 2023, 'On eigenvalue shaping for two-channel decentralized active noise control systems', Applied Acoustics, vol. 205, pp. 109260-109260.
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Pratt, L, Johnston, A & Pietroni, N 2023, 'Bending the light: Next generation anamorphic sculptures.', Comput. Graph., vol. 114, pp. 210-218.
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Punetha, P & Nimbalkar, S 2023, 'An innovative rheological approach for predicting the behaviour of critical zones in a railway track', Acta Geotechnica, vol. 18, no. 10, pp. 5457-5483.
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AbstractThe poor performance of critical zones along a railway line has long been a subject of concern for rail infrastructure managers. The rapid deterioration of track geometry in these zones is primarily ascribed to limited understanding of the underlying mechanism and scarcity of adequate tools to assess the severity of the potential issue. Therefore, a comprehensive evaluation of their behaviour is paramount to improve the design and ensure adequate service quality. With this objective, a novel methodology is introduced, which can predict the differential plastic deformations at the critical zones and assess the suitability of different countermeasures in improving the track performance. The proposed technique employs a three-dimensional geotechnical rheological track model that considers varied support conditions of the critical zone. The approach is successfully validated with published field data and predictions from finite element analysis. This methodology is then applied to a bridge-open track transition zone, where it is observed that an increase in axle load exacerbates the track geometry degradation problem. The results show that the performance of critical zones with weak subgrade can be improved by increasing the granular layer thickness. Interpretation of the predicted differential settlement for different countermeasures exemplifies the practical significance of the proposed methodology.
Punetha, P & Nimbalkar, S 2023, 'Numerical investigation on dynamic behaviour of critical zones in railway tracks under moving train loads', Transportation Geotechnics, vol. 41, pp. 101009-101009.
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Puthal, D, Yeun, CY, Damiani, E, Mishra, AK, Yelamarthi, K & Pradhan, B 2023, 'Blockchain Data Structures and Integrated Adaptive Learning: Features and Futures', IEEE Consumer Electronics Magazine, pp. 1-8.
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Qi, L, Wang, K, Qi, Y, Yu, H, Jin, X, Li, X & Qi, Y 2023, 'Facile synthesis of ZnO films with anisotropic preferred orientations: An effective strategy for controllable surface and optical property', Journal of Alloys and Compounds, vol. 935, pp. 168125-168125.
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Qi, T, Lyu, B & Hoang, DT 2023, 'Pilot Sequences With Low Coherence and PAPR for Grant-Free Massive Access', IEEE Wireless Communications Letters, vol. 12, no. 7, pp. 1254-1258.
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Qi, Y & Indraratna, B 2023, 'Closure to “Influence of Rubber Inclusion on the Dynamic Response of Rail Track”', Journal of Materials in Civil Engineering, vol. 35, no. 8.
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Qi, Y & Indraratna, B 2023, 'The effect of adding rubber crumbs on the cyclic permanent deformation of waste mixtures containing coal wash and steel furnace slag', Géotechnique, vol. 73, no. 11, pp. 951-960.
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Among the numerous studies into the dynamic loading behaviour of rubber crumbs–soil/waste mixtures, the main focus is on how the content of rubber crumbs ([Formula: see text]) affects the damping ratio, shear modulus and total deformation. However, research into the influence of [Formula: see text] on the permanent strain rate ([Formula: see text]) and the deformation mechanism under repeated loading is very limited. In the current study, the cyclic deformation response for waste mixtures of steel furnace slag (SFS), coal wash (CW) and rubber crumbs (RC) is analysed and the test results reveal that [Formula: see text] has a significant influence on the initial [Formula: see text] and the slope of the permanent axial strain rate line, whereas cyclic deviator stress ([Formula: see text]) mainly affects the initial [Formula: see text]. The influence of [Formula: see text] and [Formula: see text] on the [Formula: see text] value of the waste mixture is incorporated in an empirical model, which enables prediction of the permanent deformation mechanism of SFS + CW + RC mixtures with wider-ranging amounts of RC and higher cyclic deviator stresses.
Qian, J, Mi, X, Chen, Z, Xu, W, Liu, W, Ma, R, Zhang, Y, Du, Y & Ni, B-J 2023, 'Efficient emerging contaminants (EM) decomposition via peroxymonosulfate (PMS) activation by Co3O4/carbonized polyaniline (CPANI) composite: Characterization of tetracycline (TC) degradation property and application for the remediation of EM-polluted water body', Journal of Cleaner Production, vol. 405, pp. 137023-137023.
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Qian, J, Zhang, Y, Chen, Z, Du, Y & Ni, B-J 2023, 'NiCo layered double hydroxides/NiFe layered double hydroxides composite (NiCo-LDH/NiFe-LDH) towards efficient oxygen evolution in different water matrices', Chemosphere, vol. 345, pp. 140472-140472.
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Qin, H, Mason, M & Stewart, MG 2023, 'Fragility assessment for new and deteriorated portal framed industrial buildings subjected to tropical cyclone winds', Structural Safety, vol. 100, pp. 102287-102287.
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Qin, S, Yang, N, Zhu, X & Wang, Z 2023, 'Analytical Approach for Load-Carrying Capacity Evaluation of Tibetan Timber Beam-column Joint', International Journal of Architectural Heritage, vol. 17, no. 10, pp. 1719-1735.
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Queti is an important component of Tibetan timber beam-column joint to transfer compression, shear, and bending moment from one structural component to another. The inclination of Queti is a common type of damage in Tibetan heritage buildings and it significantly reduces the load-carrying capacity and safety of the joint under vertical load. In this paper, an analytical model of the joint with Queti-inclination is proposed to predict the yield and ultimate loads of the joint and the corresponding failure modes. Laboratory tests have been conducted on typical Tibetan beam-column joints to verify the proposed model. A parametric study is also conducted on the effects of material property, Queti width and height, as well as the dowel height on the load-carrying capacity of the joint. Results obtained show that a weaker material property will significantly reduce the capacity of the joint. An increase in Queti width and dowel height have an ameliorative effect on the yield and ultimate loads, while the Queti height has the opposite effect.
Qin, Y, Jia, H, Liu, W, Lu, N, Ngo, HH & Wang, J 2023, 'Application of in-situ micro laser transmission on real-time monitoring of flocculation process', Journal of Water Process Engineering, vol. 51, pp. 103364-103364.
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Qiu, N, Wan, Y, Shen, Y & Fang, J 2023, 'Experimental and numerical studies on mechanical properties of TPMS structures', International Journal of Mechanical Sciences, pp. 108657-108657.
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Qiu, N, Zhang, J, Li, C, Shen, Y & Fang, J 2023, 'Mechanical properties of three-dimensional functionally graded triply periodic minimum surface structures', International Journal of Mechanical Sciences, vol. 246, pp. 108118-108118.
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Qiu, P, Gong, Y, Zhao, Y, Cao, L, Zhang, C & Dong, X 2023, 'An Efficient Method for Modeling Nonoccurring Behaviors by Negative Sequential Patterns With Loose Constraints', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 4, pp. 1864-1878.
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Qu, F, Zhao, H, Wu, K, Liu, Y, Zhao, X & Li, W 2023, 'Phase transformation and microstructure of in-situ concrete after 20-year exposure to harsh mining environment: A case study', Case Studies in Construction Materials, vol. 19, pp. e02287-e02287.
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Qu, H & Beydoun, G 2023, 'Preface', ACM International Conference Proceeding Series, p. viii.
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Qu, J, Zhao, X, Xiao, Y, Chang, X, Li, Z & Wang, X 2023, 'Adaptive Manifold Graph representation for Two-Dimensional Discriminant Projection', Knowledge-Based Systems, vol. 266, pp. 110411-110411.
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Quyet, TD, Choo, Y, Akther, N, Roobavannan, S, Norouzi, A, Gupta, V, Blumenstein, M, Vinh, NT & Naidu, G 2023, 'Selective rubidium recovery from seawater with metal-organic framework incorporated potassium cobalt hexacyanoferrate nanomaterial', Chemical Engineering Journal, vol. 454, pp. 140107-140107.
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Rabie, M, Ali, AYM, Abo-Zahhad, EM, Elkady, MF, El-Shazly, AH, Salem, MS, Radwan, A, Rajabzadeh, S, Matsuyama, H & Shon, HK 2023, 'New hybrid concentrated photovoltaic/membrane distillation unit for simultaneous freshwater and electricity production', Desalination, vol. 559, pp. 116630-116630.
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Radfar, P, Ding, L, de, LFLR, Aboulkheyr, H, Gallego-Ortega, D & Warkiani, ME 2023, 'Rapid metabolomic screening of cancer cells via high-throughput static droplet microfluidics.', Biosens Bioelectron, vol. 223, pp. 114966-114966.
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Effective isolation and in-depth analysis of Circulating Tumour Cells (CTCs) are greatly needed in diagnosis, prognosis and monitoring of the therapeutic response of cancer patients but have not been completely fulfilled by conventional approaches. The rarity of CTCs and the lack of reliable biomarkers to distinguish them from peripheral blood cells have remained outstanding challenges for their clinical implementation. Herein, we developed a high throughput Static Droplet Microfluidic (SDM) device with 38,400 chambers, capable of isolating and classifying the number of metabolically active CTCs in peripheral blood at single-cell resolution. Owing to the miniaturisation and compartmentalisation capability of our device, we first demonstrated the ability to precisely measure the lactate production of different types of cancer cells inside 125 pL droplets at single-cell resolution. Furthermore, we compared the metabolomic activity of leukocytes from healthy donors to cancer cells and showed the ability to differentiate them. To further prove the clinical relevance, we spiked cancer cell lines in human healthy blood and showed the possibility to detect the cancer cells from leukocytes. Lastly, we tested the workflow on 8 preclinical mammary mouse models including syngeneic 67NR (non-metastatic) and 4T1.2 (metastatic) models with Triple-Negative Breast Cancer (TNBC) as well as transgenic mouses (12-week-old MMTV-PyMT). The results have shown the ability to precisely distinguish metabolically active CTCs from the blood using the proposed SDM device. The workflow is simple and robust which can eliminate the need for specialised equipment and expertise required for single-cell analysis of CTCs and facilitate on-site metabolic screening of cancer cells.
Radfar, P, Ding, L, Es, HA & Warkiani, ME 2023, 'A Microfluidic Approach for Enrichment and Single-Cell Characterization of Circulating Tumor Cells from Peripheral Blood', pp. 141-150.
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Raggam, S, Mohammad, M, Choo, Y, Naidu, G, Zargar, M, Shon, HK & Razmjou, A 2023, 'Advances in metal organic framework (MOF) – Based membranes and adsorbents for lithium-ion extraction', Separation and Purification Technology, vol. 307, pp. 122628-122628.
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Lithium plays a vital role in energy storage which is crucial for the transition to renewable energy, where it enables a stable and continuous release of the harvested energy from batteries. From both primary and secondary sources, there are various cost-effective and environmentally friendly methods of obtaining Lithium. This review highlights the development of novel metal organic framework (MOF)-based technologies (i.e., thin film membranes, mixed matrix membranes and adsorbents) for Lithium-ion extraction from aqueous sources like brine or seawater. The synthesis methods and the performance of the MOF-based membranes and adsorbents are further discussed in detail. MOF-based membranes and adsorbents can achieve a high selectivity towards Lithium ions up to the range of ∼270 times higher than competing ions such as Potassium. However, these materials have drawbacks in terms of water stability or their requirement of highly sophisticated fabrication methods which need to be considered before scaling-up processes. ZIF-8, UiO-66 and HKUST-1 are among the most researched MOFs for the desired application in this work and future progress should be done to address the aforementioned issues. This review compares the development and performance of a variety of different MOF-based materials for Lithium-ion extraction which will give an insight into the commercialization of this material in the industry.
Raghavendra, U, Gudigar, A, Paul, A, Goutham, TS, Inamdar, MA, Hegde, A, Devi, A, Ooi, CP, Deo, RC, Barua, PD, Molinari, F, Ciaccio, EJ & Acharya, UR 2023, 'Brain tumor detection and screening using artificial intelligence techniques: Current trends and future perspectives', Computers in Biology and Medicine, vol. 163, pp. 107063-107063.
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Rahimi, I, Gandomi, AH, Nikoo, MR & Chen, F 2023, 'A comparative study on evolutionary multi-objective algorithms for next release problem', Applied Soft Computing, vol. 144, pp. 110472-110472.
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Rahimi, I, Nikoo, MR & Gandomi, AH 2023, 'Techno-economic analysis for using hybrid wind and solar energies in Australia', Energy Strategy Reviews, vol. 47, pp. 101092-101092.
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Raja, AK & Zhou, J 2023, 'AI Accountability: Approaches, Affecting Factors, and Challenges', Computer, vol. 56, no. 4, pp. 61-70.
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Rajabipour, A, Kutay, C, Guenther, J & Bazli, M 2023, 'Factors to be considered in the design of indigenous communities' houses, with a focus on Australian first nation housing in the Northern Territory', Development Engineering, vol. 8, pp. 100109-100109.
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Ramachandran, M, Patan, R, Kumar, A, Hosseini, S & Gandomi, AH 2023, 'Mutual Informative MapReduce and Minimum Quadrangle Classification for Brain Tumor Big Data', IEEE Transactions on Engineering Management, vol. 70, no. 8, pp. 2644-2655.
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Ramakrishna, VAS, Chamoli, U, Larosa, AG, Mukhopadhyay, SC, Gangadhara Prusty, B & Diwan, AD 2023, 'A biomechanical comparison of posterior fixation approaches in lumbar fusion using computed tomography based lumbosacral spine modelling', Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, vol. 237, no. 2, pp. 243-253.
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Extreme lateral interbody fusion (XLIF) may be performed with a standalone interbody cage, or with the addition of unilateral or bilateral pedicle screws; however, decisions regarding supplemental fixation are predominantly based on clinical indicators. This study examines the impact of posterior supplemental fixation on facet micromotions, cage loads and load-patterns at adjacent levels in a L4-L5 XLIF at early and late fusion stages. CT data from an asymptomatic subject were segmented into anatomical regions and digitally stitched into a surface mesh of the lumbosacral spine (L1-S1). The interbody cage and posterior instrumentation (unilateral and bilateral) were inserted at L4-L5. The volumetric mesh was imported into finite element software for pre-processing, running nonlinear static solves and post-processing. Loads and micromotions at the index-level facets reduced commensurately with the extent of posterior fixation accompanying the XLIF, while load-pattern changes observed at adjacent facets may be anatomically dependent. In flexion at partial fusion, compressive stress on the cage reduced by 54% and 72% in unilateral and bilateral models respectively; in extension the reductions were 58% and 75% compared to standalone XLIF. A similar pattern was observed at full fusion. Unilateral fixation provided similar stability compared to bilateral, however there was a reduction in cage stress-risers with the bilateral instrumentation. No changes were found at adjacent discs. Posterior supplemental fixation alters biomechanics at the index and adjacent levels in a manner that warrants consideration alongside clinical information. Unilateral instrumentation is a more efficient option where the stability requirements and subsidence risk are not excessive.
Ramakrishna, VAS, Chamoli, U, Mukhopadhyay, SC, Diwan, AD & Prusty, BG 2023, 'Measuring compressive loads on a ‘smart’ lumbar interbody fusion cage: Proof of concept', Journal of Biomechanics, vol. 147, pp. 111440-111440.
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Ramakrishnan, N, Tomamichel, M & Berta, M 2023, 'Moderate Deviation Expansion for Fully Quantum Tasks', IEEE Transactions on Information Theory, vol. 69, no. 8, pp. 5041-5059.
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Rambach, M, Youssry, A, Tomamichel, M & Romero, J 2023, 'Efficient quantum state tracking in noisy environments', Quantum Science and Technology, vol. 8, no. 1, pp. 015010-015010.
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Abstract Quantum state tomography, which aims to find the best description of a quantum state—the density matrix, is an essential building block in quantum computation and communication. Standard techniques for state tomography are incapable of tracking changing states and often perform poorly in the presence of environmental noise. Although there are different approaches to solve these problems theoretically, experimental demonstrations have so far been sparse. Our approach, matrix-exponentiated gradient (MEG) tomography, is an online tomography method that allows for state tracking, updates the estimated density matrix dynamically from the very first measurements, is computationally efficient, and converges to a good estimate quickly even with very noisy data. The algorithm is controlled via a single parameter, its learning rate, which determines the performance and can be tailored in simulations to the individual experiment. We present an experimental implementation of MEG tomography on a qutrit system encoded in the transverse spatial mode of photons. We investigate the performance of our method on stationary and evolving states, as well as significant environmental noise, and find fidelities of around 95% in all cases.
Ramu, YK, Thomas, PS, Sirivivatnanon, V & Vessalas, K 2023, 'Non-expansive delayed ettringite formation in low sulphate and low alkali cement mortars', Australian Journal of Civil Engineering, vol. 21, no. 1, pp. 68-79.
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Ran, H, Wen, S, Li, Q, Cao, Y, Shi, K & Huang, T 2023, 'Compact and Stable Memristive Visual Geometry Group Neural Network', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 2, pp. 987-998.
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Rao, P, Feng, W, Ouyang, P, Cui, J, Nimbalkar, S & Chen, Q 2023, 'Numerical Simulation of Pipeline Failure Mechanisms Under Lightning Strikes, Capturing Electric Disruption and Thermal Damage', Journal of Failure Analysis and Prevention, vol. 23, no. 5, pp. 2065-2074.
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Rashed, AO, Huynh, C, Merenda, A, Rodriguez-Andres, J, Kong, L, Kondo, T, Razal, JM & Dumée, LF 2023, 'Dry-spun carbon nanotube ultrafiltration membranes tailored by anti-viral metal oxide coatings for human coronavirus 229E capture in water', Journal of Environmental Chemical Engineering, vol. 11, no. 3, pp. 110176-110176.
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Although waterborne virus removal may be achieved using separation membrane technologies, such technologies remain largely inefficient at generating virus-free effluents due to the lack of anti-viral reactivity of conventional membrane materials required to deactivating viruses. Here, a stepwise approach towards simultaneous filtration and disinfection of Human Coronavirus 229E (HCoV-229E) in water effluents, is proposed by engineering dry-spun ultrafiltration carbon nanotube (CNT) membranes, coated with anti-viral SnO2 thin films via atomic layer deposition. The thickness and pore size of the engineered CNT membranes were fine-tuned by varying spinnable CNT sheets and their relative orientations on carbon nanofibre (CNF) porous supports to reach thicknesses less than 1 µm and pore size around 28 nm. The nanoscale SnO2 coatings were found to further reduce the pore size down to ∼21 nm and provide more functional groups on the membrane surface to capture the viruses via size exclusion and electrostatic attractions. The synthesized CNT and SnO2 coated CNT membranes were shown to attain a viral removal efficiency above 6.7 log10 against HCoV-229E virus with fast water permeance up to ∼4 × 103 and 3.5 × 103 L.m-2.h-1.bar-1, respectively. Such high performance was achieved by increasing the dry-spun CNT sheets up to 60 layers, orienting successive 30 CNT layers at 45°, and coating 40 nm SnO2 on the synthesized membranes. The current study provides an efficient scalable fabrication scheme to engineer flexible ultrafiltration CNT-based membranes for cost-effective filtration and inactivation of waterborne viruses to outperform the state-of-the-art ultrafiltration membranes.
Rashidi, M, Tashakori, S, Kalhori, H, Bahmanpour, M & Li, B 2023, 'Iterative-Based Impact Force Identification on a Bridge Concrete Deck.', Sensors (Basel), vol. 23, no. 22.
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Steel-reinforced concrete decks are prominently utilized in various civil structures such as bridges and railways, where they are susceptible to unforeseen impact forces during their operational lifespan. The precise identification of the impact events holds a pivotal role in the robust health monitoring of these structures. However, direct measurement is not usually possible due to structural limitations that restrict arbitrary sensor placement. To address this challenge, inverse identification emerges as a plausible solution, albeit afflicted by the issue of ill-posedness. In tackling such ill-conditioned challenges, the iterative regularization technique known as the Landweber method proves valuable. This technique leads to a more reliable and accurate solution compared with traditional direct regularization methods and it is, additionally, more suitable for large-scale problems due to the alleviated computation burden. This paper employs the Landweber method to perform a comprehensive impact force identification encompassing impact localization and impact time-history reconstruction. The incorporation of a low-pass filter within the Landweber-based identification procedure is proposed to augment the reconstruction process. Moreover, a standardized reconstruction error metric is presented, offering a more effective means of accuracy assessment. A detailed discussion on sensor placement and the optimal number of regularization iterations is presented. To automatedly localize the impact force, a Gaussian profile is proposed, against which reconstructed impact forces are compared. The efficacy of the proposed techniques is illustrated by utilizing the experimental data acquired from a bridge concrete deck reinforced with a steel beam.
Raza, A, Keshavarz, R, Dutkiewicz, E & Shariati, N 2023, 'Compact Multiservice Antenna for Sensing and Communication Using Reconfigurable Complementary Spiral Resonator', IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-9.
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Raza, MA, Abolhasan, M, Lipman, J, Shariati, N, Ni, W & Jamalipour, A 2023, 'Statistical Learning-Based Adaptive Network Access for the Industrial Internet of Things', IEEE Internet of Things Journal, vol. 10, no. 14, pp. 12219-12233.
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Razavi Bazaz, S, Zhand, S, Salomon, R, Beheshti, EH, Jin, D & Warkiani, ME 2023, 'ImmunoInertial microfluidics: A novel strategy for isolation of small EV subpopulations', Applied Materials Today, vol. 30, pp. 101730-101730.
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Razzaq, L, Abbas, MM, Waseem, A, Jauhar, TA, Fayaz, H, Kalam, MA, Soudagar, MEM, A.S.Silitonga, Samr-Ul-Husnain & Ishtiaq, U 2023, 'Influence of varying concentrations of TiO2 nanoparticles and engine speed on the performance and emissions of diesel engine operated on waste cooking oil biodiesel blends using response surface methodology', Heliyon, vol. 9, no. 7, pp. e17758-e17758.
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Reddy, R, Kulkarni, AJ, Krishnasamy, G, Shastri, AS & Gandomi, AH 2023, 'LAB: a leader–advocate–believer-based optimization algorithm', Soft Computing, vol. 27, no. 11, pp. 7209-7243.
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Rehman, J, Hawryszkiewycz, IT, Sohaib, O, Namisango, F & Dahri, AS 2023, 'How Professional Service Firms Derive Triple Value Bottomline: An IC Perspective.', J. Inf. Knowl. Manag., vol. 22, no. 01, pp. 2250087:1-2250087:1.
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The ever-increasing market turbulence has turned today’s corporate landscape more competitive and complex. Particularly during the last two decades, the increased utilization of Information & Communication Technologies (ICTs) globally transformed the services sector in terms of ease of business processes and improved client service delivery. However, in the current knowledge-based era, these tools & technologies would only be meaningful if these are appropriately utilized by a knowledgeable workforce. In other words, this knowledge age has changed the success mantra of business competitiveness by re-shifting the focus from ICT-based transformations to knowledge-based transformations, though the availability of ICT systems has further augmented the organizational capabilities. Moreover, truly capitalizing on these warrants a knowledge-enabled work culture and recognizing as such the strategic significance of in-house Intellectual Capital (IC) that serves as a prime mover of achieving a sustainable competitive advantage. However, the maximum potential of IC for deriving multi-stakeholder value has not been fully achieved. Therefore, by administering 12 face-to-face semi-structured interviews at Australian Professional Service Firms (PSFs), this research offers a novel perspective on IC valuation by presenting the concept of ‘Triple Value Bottomline’ coupled with ‘IC Best Practices for PSFs’. These collectively offer IC evaluation, measurement and management mechanisms. Overall, the findings reveal immense potential of IC for achieving diverse value outcomes for multi-stakeholders in PSFs.
Ren, J, Zhu, X & Li, S 2023, 'Multiple Damaged Cables Identification in Cable-Stayed Bridges Using Basis Vector Matrix Method.', Sensors (Basel), vol. 23, no. 2, pp. 860-860.
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A new damaged cable identification method using the basis vector matrix (BVM) is proposed to identify multiple damaged cables in cable-stayed bridges. The relationships between the cable tension stiffness and the girder bending strain of the cable-stayed bridge are established using a force method. The difference between the maximum bending strains of the bridges with intact and damaged cables is used to obtain the damage index vectors (DIXVs). Then, BVM is obtained by the normalized DIXV. Finally, the damage indicator vector (DIV) is obtained by DIXV and BVM to identify the damaged cables. The damage indicator is substituted into the damage severity function to identify the corresponding damage severity. A field cable-stayed bridge is used to verify the proposed method. The three-dimensional finite element model is established using ANSYS, and the model is validated using the field measurements. The validated model is used to simulate the strain response of the bridge with different damage scenarios subject to a moving vehicle load, including one, two, three, and four damaged cables with damage severity of 10%, 20%, and 30%, respectively. The noise effect is also discussed. The results show that the BVM method has good anti-noise capability and robustness.
Ren, L, Guo, Z, Zhang, L, Hu, H, Li, C, Lin, Z, Zhen, Z & Zhou, JL 2023, 'A novel aerobic denitrifying phosphate-accumulating bacterium efficiently removes phthalic acid ester, total nitrogen and phosphate from municipal wastewater', Journal of Water Process Engineering, vol. 52, pp. 103532-103532.
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Simultaneous removal of nitrogen, phosphate and emerging pollutants are critical for safe reuse of wastewater, but research in this field is limited. In the present study, a novel aerobic denitrifying phosphate-accumulating bacterial strain RL-GZ01 was found to be able to utilize phthalic acid esters (PAEs) as carbon resource for cell growth. Based on 16S rRNA gene analysis, physiological and biochemical characterization, and genome-based average nucleotide identity calculation, RL-GZ01 was identified as Rhodococcus pyridinivorans. Strain RL-GZ01 showed high DEHP degradation in alkaline conditions and good tolerance of salinity and organic solvents. The degradation of DEHP by RL-GZ01 fitted well with a modified Gompertz model (R2 = 0.9985). Metabolic intermediates of DEHP were identified via UHPLC-MS/MS analysis and the catabolic pathway was proposed thereafter. Genes and gene clusters contributed to the utilization of DEHP were analyzed through genomic analysis. Analysis of KEGG nitrogen metabolism pathway indicated that nitrate and nitrite were further transformed into ammonium which was further used for the biosynthesis of L-glutamine and L-glutamate. Strain RL-GZ01 was further identified as a denitrifying phosphate accumulating organism which can accumulate phosphate by generating polyphosphate. Finally, strain RL-GZ01 was applied to municipal wastewater treatment for simultaneous removal of nitrogen, phosphate and DEHP. The removal percentages of DEHP (5 mg/L), TN (71.2 mg/L), NH4+-N (70.9 mg/L), PO43−-P (10.89 mg/L) and COD (622.4 mg/L) by strain RL-GZ01 were 89.94 %, 64.45 %, 64.94 %, 76.30 % and 63.23 % within 84 h, respectively. These demonstrated the capability of strain RL-GZ01 for the biological treatment of wastewater containing PAEs.
Ren, L, Weng, L, Chen, D, Hu, H, Jia, Y & Zhou, JL 2023, 'Bioremediation of PAEs-contaminated saline soil: The application of a marine bacterial strain isolated from mangrove sediment', Marine Pollution Bulletin, vol. 192, pp. 115071-115071.
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Ren, Y, Ren, B, Zhang, X, Lv, T, Ni, W & Lu, G 2023, 'Impartial Cooperation in SWIPT-Assisted NOMA Systems With Random User Distribution', IEEE Transactions on Vehicular Technology, vol. 72, no. 8, pp. 10488-10504.
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Ren, Z, Cao, H, Desmond, P, Liu, B, Ngo, HH, He, X, Li, G, Ma, J & Ding, A 2023, 'Ions play different roles in virus removal caused by different NOMs in UF process: Removal efficiency and mechanism analysis', Chemosphere, vol. 313, pp. 137644-137644.
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Ren, Z, Ji, J, Zhu, Y, Hong, J & Feng, K 2023, 'Generative Adversarial Network With Dual Multiscale Feature Fusion for Data Augmentation in Fault Diagnosis', IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-17.
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Rennie, C, Huang, Y, Siwakoti, P, Du, Z, Padula, M, Bao, G, Tuch, BE, Xu, X & McClements, L 2023, 'In vitro evaluation of a hybrid drug-delivery nanosystem for fibrosis prevention in cell therapy for Type 1 diabetes', Nanomedicine, vol. 18, no. 1, pp. 53-66.
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Background: Implantation of insulin-secreting cells has been trialed as a treatment for Type 1 diabetes mellitus; however, the host immunogenic response limits their effectiveness. Methodology: The authors developed a core-shell nanostructure of upconversion nanoparticle-mesoporous silica for controlled local delivery of an immunomodulatory agent, MCC950, using near-infrared light and validated it in in vitro models of fibrosis. Results: The individual components of the nanosystem did not affect the proliferation of insulin-secreting cells, unlike fibroblast proliferation (p < 0.01). The nanosystem is effective at releasing MCC950 and preventing fibroblast differentiation (p < 0.01), inflammation (IL-6 expression; p < 0.05) and monocyte adhesion (p < 0.01). Conclusion: This MCC950-loaded nanomedicine system could be used in the future together with insulin-secreting cell implants to increase their longevity as a curative treatment for Type 1 diabetes mellitus.
Richter, R, Syberg, M, Deuse, J, Willats, P & Lenze, D 2023, 'Creating lean value streams through proactive variability management', International Journal of Production Research, vol. 61, no. 16, pp. 5692-5703.
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Roche, CD, Lin, H, Huang, Y, de Bock, CE, Beck, D, Xue, M & Gentile, C 2023, '3D bioprinted alginate-gelatin hydrogel patches containing cardiac spheroids recover heart function in a mouse model of myocardial infarction', Bioprinting, vol. 30, pp. e00263-e00263.
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Rony, ZI, Mofijur, M, Hasan, MM, Ahmed, SF, Almomani, F, Rasul, MG, Jahirul, MI, Loke Show, P, Kalam, MA & Mahlia, TMI 2023, 'Unanswered issues on decarbonizing the aviation industry through the development of sustainable aviation fuel from microalgae', Fuel, vol. 334, pp. 126553-126553.
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Roobavannan, S, Choo, Y, Truong, DQ, Han, DS, Shon, HK & Naidu, G 2023, 'Seawater lithium mining by zeolitic imidazolate framework encapsulated manganese oxide ion sieve nanomaterial', Chemical Engineering Journal, vol. 474, pp. 145957-145957.
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Roohani, I, Entezari, A & Zreiqat, H 2023, 'Liquid crystal display technique (LCD) for high resolution 3D printing of triply periodic minimal surface lattices bioceramics', Additive Manufacturing, vol. 74, pp. 103720-103720.
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Rooholahi, B, Siwakoti, YP, Eckel, H-G, Blaabjerg, F & Bahman, AS 2023, '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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Roopa, AK, Hunashyal, AM, Patil, AY, Kamadollishettar, A, Patil, B, Soudagar, MEM, Shahapurkar, K, Khan, TMY & Kalam, MA 2023, 'Study on Interfacial Interaction of Cement-Based Nanocomposite by Molecular Dynamic Analysis and an RVE Approach', Advances in Civil Engineering, vol. 2023, pp. 1-18.
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There is an increased demand for cement nanocomposites in the twenty-first century due to their composition, higher strength, high efficiency, and multiscale nature. As carbon nanotubes (CNTs) possess extremely high strength, resilience, and stiffness, inclusion of carbon nanotubes in small quantities to the concrete mix makes them a multifunctional material. A molecular level understanding is significant to capacitate the macrolevel properties of these composites. In the proposed work, molecular dynamics (MD) simulations are used to understand the behaviour of the composites at the atomic level and continuum mechanics with representative volume element (RVE) homogenization modelling is carried out for interfacial interaction study of composites. The mechanical properties such as Young’s modulus, shear modulus, and poisons are evaluated using previous methods of simulations for different compositions of nanomaterials in cement matrix. The FORCITE module of MD simulation and square RVE model is used to determine the mechanical, electrical properties, and elastic constants of the cement nanocomposite. The MD simulation describes the linking effect of CNT into cement matric, and the RVE modelling study reveals the pull-out effect of CNT from matrix. From experimental and analytical studies, it is found that increase in CNT till 0.5% weight fraction increases the mechanical properties about 12% and further increasing of CNT weight fraction causes a reduction in mechanical properties about 5% due to the agglomeration of nanotubes. The density of states method in MD simulation indicates that mobility of the electrons increases with an increase in carbon nanotube proportion in the composites. The experimental test results substantiate the analytical studies, and the error obtained from both approaches is less than 20%. From the analytical study, the average maximum Young’s modulus, shear modulus, and bulk modulus are obtained as 46 GPa, 31 GPa, and 32 ...
Rout, JK, Dalmia, A, Rath, SK, Mohanta, BK, Ramasubbareddy, S & Gandomi, AH 2023, 'Detecting Product Review Spammers Using Principles of Big Data', IEEE Transactions on Engineering Management, vol. 70, no. 7, pp. 2516-2527.
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Roy, SS, Roy, A, Samui, P, Gandomi, M & Gandomi, AH 2023, '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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Ruan, Z, Song, W, Zhang, Y, Yao, G & Guo, Y 2023, 'A Variable Switching Frequency Space Vector Pulsewidth Modulation Technique Using Virtual Flux Ripple', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 11, no. 2, pp. 2051-2060.
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Ruan, Z, Song, W, Zhao, L, Zhang, Y & Guo, Y 2023, 'A Variable Switching Frequency Space Vector Pulse Width Modulation Control Strategy of Induction Motor Drive System With Torque Ripple Prediction', IEEE Transactions on Energy Conversion, vol. 38, no. 2, pp. 993-1003.
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Rybarczyk, A, Smułek, W, Grzywaczyk, A, Kaczorek, E, Jesionowski, T, Nghiem, LD & Zdarta, J 2023, '3D printed polylactide scaffolding for laccase immobilization to improve enzyme stability and estrogen removal from wastewater.', Bioresour Technol, vol. 381, pp. 129144-129144.
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This study reports a biocatalytic system of immobilized laccase and 3D printed open-structure biopolymer scaffoldings. The scaffoldings were computer-designed and 3D printed using polylactide (PLA) filament. The immobilization of laccase onto the 3D printed PLA scaffolds were optimized with regard to pH, enzyme concentration, and immobilization time. Laccase immobilization resulted in a small reduction in reactivity (in terms of Michaelis constant and maximum reaction rate) but led to significant improvement in chemical and thermal stability. After 20 days of storage, the immobilized and free laccase showed 80% and 35% retention of the initial enzymatic activity, respectively. The immobilized laccase on 3D printed PLA scaffolds achieved 10% improvement in the removal of estrogens from real wastewater as compared to free laccase and showed the significant reusability potential. Results here are promising but also highlight the need for further study to improve enzymatic activity and reusability.
Saberi, Z, K. Hussain, O & Saberi, M 2023, 'Data-driven personalized assortment optimization by considering customers’ value and their risk of churning: Case of online grocery shopping', Computers & Industrial Engineering, vol. 182, pp. 109328-109328.
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Sadat Hosseini, A, Kabiri, A, Gandomi, AH & Shafieefar, M 2023, 'Genetic programming for the prediction of berm breakwaters recession', Ocean Engineering, vol. 279, pp. 114465-114465.
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Sadeghi, F, Mousavi, M, Zhu, X, Rashidi, M, Samali, B & Gandomi, AH 2023, 'Damage Detection of Composite Beams via Variational Mode Decomposition of Shear-Slip Data', Journal of Structural Engineering, vol. 149, no. 1.
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Sadeghirad, H, Bahrami, T, Layeghi, SM, Yousefi, H, Rezaei, M, Hosseini‐Fard, SR, Radfar, P, Warkiani, ME, O'Byrne, K & Kulasinghe, A 2023, 'Immunotherapeutic targets in non‐small cell lung cancer', Immunology, vol. 168, no. 2, pp. 256-272.
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AbstractNon‐small cell lung cancer (NSCLC) is one of the most common types of cancer in the world and has a 5‐year survival rate of ~20%. Immunotherapies have shown promising results leading to durable responses, however, they are only effective for a subset of patients. To determine the best therapeutic approach, a thorough and in‐depth profiling of the tumour microenvironment (TME) is required. The TME is a complex network of cell types that form an interconnected network, promoting tumour cell initiation, growth and dissemination. The stroma, immune cells and endothelial cells that comprise the TME generate a plethora of cytotoxic or cytoprotective signalling pathways. In this review, we discuss immunotherapeutic targets in NSCLC tumours and how the TME may influence patients' response to immunotherapy.
Saha, S, Kundu, B, Paul, GC & Pradhan, B 2023, 'Proposing an ensemble machine learning based drought vulnerability index using M5P, dagging, random sub-space and rotation forest models', Stochastic Environmental Research and Risk Assessment, vol. 37, no. 7, pp. 2513-2540.
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AbstractDrought is one of the major barriers to the socio-economic development of a region. To manage and reduce the impact of drought, drought vulnerability modelling is important. The use of an ensemble machine learning technique i.e. M5P, M5P -Dagging, M5P-Random SubSpace (RSS) and M5P-rotation forest (RTF) to assess the drought vulnerability maps (DVMs) for the state of Odisha in India was proposed for the first time. A total of 248 drought-prone villages (samples) and 53 drought vulnerability indicators (DVIs) under exposure (28), sensitivity (15) and adaptive capacity (10) were used to produce the DVMs. Out of the total samples, 70% were used for training the models and 30% were used for validating the models. Finally, the DVMs were authenticated by the area under curve (AUC) of receiver operating characteristics, precision, mean-absolute-error, root-mean-square-error, K-index and Friedman and Wilcoxon rank test. Nearly 37.9% of the research region exhibited a very high to high vulnerability to drought. All the models had the capability to model the drought vulnerability. As per the Friedman and Wilcoxon rank test, significant differences occurred among the output of the ensemble models. The accuracy of the M5P base classifier improved after ensemble with RSS and RTF meta classifiers but reduced with Dagging. According to the validation statistics, M5P-RFT model achieved the highest accuracy in modelling the drought vulnerability with an AUC of 0.901. The prepared model would help planners and decision-makers to formulate strategies for reducing the damage of drought.
Saha, S, Kundu, B, Saha, A, Mukherjee, K & Pradhan, B 2023, 'Manifesting deep learning algorithms for developing drought vulnerability index in monsoon climate dominant region of West Bengal, India', Theoretical and Applied Climatology, vol. 151, no. 1-2, pp. 891-913.
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Saha, SC, Ahmed, SF, Ahmed, B, Mehnaz, T & Musharrat, A 2023, 'A review of phase change materials in multi-designed tubes and buildings: Testing methods, applications, and heat transfer enhancement', Journal of Energy Storage, vol. 63, pp. 106990-106990.
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Sahin, SE, Gulhan, G, Barua, PD, Tuncer, T, Dogan, S, Faust, O & Acharya, UR 2023, 'PrismPatNet: Novel prism pattern network for accurate fault classification using engine sound signals', Expert Systems, vol. 40, no. 8.
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AbstractEngines are prone to various types of faults, and it is crucial to detect and indeed classify them accurately. However, manual fault type detection is time‐consuming and error‐prone. Automated fault type detection promises to reduce inter‐ and intra‐observer variability while ensuring time invariant attention during the observation duration. We have proposed an automated fault‐type detection model based on sound signals to realize these advantageous properties. We have named the detection model prism pattern network (PrismPatNet) to reflect the fact that our design incorporates a novel feature extraction algorithm that was inspired by a 3D prism shape. Our prism pattern model achieves high accuracy with low‐computational complexity. It consists of three main phases: (i) prism pattern inspired multilevel feature generation and maximum pooling operator, (ii) feature ranking and feature selection using neighbourhood component analysis (NCA), and (iii) support vector machine (SVM) based classification. The maximum pooling operator decomposes the sound signal into six levels. The proposed prism pattern algorithm extracts parameter values from both the signal itself and its decompositions. The generated parameter values are merged and fed to the NCA algorithm, which extracts 512 features from that input. The resulting feature vectors are passed on to the SVM classifier, which labels the input as belonging to 1 of 27 classes. We have validated our model with a newly collected dataset containing the sound of (1) a normal engine and (2) 26 different types of engine faults. Our model reached an accuracy of 99.19% and 98.75% using 80:20 hold‐out validation and 10‐fold cross‐validation, respectively. Compared with previous studies, our model achieved the highest overall classification accuracy even though our model was tasked with identifying significantly more fault classes. This performance indicates that our PrismPatNet model is re...
Sahoo, SS, Mohanty, S, Sahoo, KS, Daneshmand, M & Gandomi, AH 2023, 'A Three-Factor-Based Authentication Scheme of 5G Wireless Sensor Networks for IoT System', IEEE Internet of Things Journal, vol. 10, no. 17, pp. 15087-15099.
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Sajjad, MB, Indraratna, B, Ngo, T, Kelly, R & Rujikiatkamjorn, C 2023, 'A Computational Approach to Smoothen the Abrupt Stiffness Variation along Railway Transitions', Journal of Geotechnical and Geoenvironmental Engineering, vol. 149, no. 8.
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Sakhare, A, Punetha, P, Meena, NK, Nimbalkar, S & Dodagoudar, G-R 2023, 'Dynamic behaviour of integral abutment bridge transition under moving train loads', Transportation Geotechnics, vol. 40, pp. 100989-100989.
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Transition zones, such as bridge approaches, are discontinuities along a railway line that are highly prone to differential movement due to a rapid variation of support conditions along the track. The concrete approach slabs are often provided before and after the bridges to reduce this differential movement and provide a gradual variation in track stiffness. This paper provides insights into the dynamic behaviour of an integral abutment railway bridge (IARB) transition zone consisting of approach slab under moving train loads using finite element (FE) analyses. Firstly, the FE model is successfully validated against the published field data. Subsequently, the validated model is employed to investigate the influence of parameters such as approach slab geometry (length, thickness, inclination, and shape), backfill soil type, direction of train movement and train speed. Results show that the behaviour of IARB is sensitive to the length of the approach slab, backfill soil type and train speed. The findings of this study enhance the current understanding of the behaviour of IARBs subjected to moving train loading and identify the important parameters that influence their performance.
Sakti, AD, Anggraini, TS, Ihsan, KTN, Misra, P, Trang, NTQ, Pradhan, B, Wenten, IG, Hadi, PO & Wikantika, K 2023, 'Multi-air pollution risk assessment in Southeast Asia region using integrated remote sensing and socio-economic data products', Science of The Total Environment, vol. 854, pp. 158825-158825.
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Salah, A, Bekhit, M, Eldesouky, E, Ali, A & Fathalla, A 2023, 'Price Prediction of Seasonal Items Using Time Series Analysis', Computer Systems Science and Engineering, vol. 46, no. 1, pp. 445-460.
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Salgotra, R, Singh, S, Singh, U, Mirjalili, S & Gandomi, AH 2023, 'Marine predator inspired naked mole-rat algorithm for global optimization', Expert Systems with Applications, vol. 212, pp. 118822-118822.
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Salis, Z, Gallego, B, Nguyen, TV & Sainsbury, A 2023, 'Association of Decrease in Body Mass Index With Reduced Incidence and Progression of the Structural Defects of Knee Osteoarthritis: A Prospective Multi‐Cohort Study', Arthritis & Rheumatology, vol. 75, no. 4, pp. 533-543.
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ObjectiveTo define the association between change in body mass index (BMI) and the incidence and progression of the structural defects of knee osteoarthritis as assessed by radiography.MethodsRadiographic analyses of knees at baseline and at 4–5 years of follow‐up were obtained from the following 3 independent cohort studies: the Osteoarthritis Initiative (OAI) study, the Multicenter Osteoarthritis Study (MOST), and the Cohort Hip and Cohort Knee (CHECK) study. Logistic regression analyses using generalized estimating equations, with clustering of both knees within individuals, were used to investigate the association between change in BMI from baseline to 4–5 years of follow‐up and the incidence and progression of knee osteoarthritis.ResultsA total of 9,683 knees (from 5,774 participants) in an “incidence cohort” and 6,075 knees (from 3,988 participants) in a “progression cohort” were investigated. Change in BMI was positively associated with both the incidence and progression of the structural defects of knee osteoarthritis. The adjusted odds ratio (OR) for osteoarthritis incidence was 1.05 (95% confidence interval [95% CI] 1.02–1.09), and the adjusted OR for osteoarthritis progression was 1.05 (95% CI 1.01–1.09). Change in BMI was also positively associated with degeneration (i.e., narrowing) of the joint space and with degeneration of the femoral and tibial surfaces (as indicated by osteophytes) on the medial but not on the lateral side of the knee.ConclusionA decrease in BMI was independently associated with lower odds of incidence and progression of the structural defects of knee osteoarthritis and could be a component in preventing the onset or worsening of knee osteoarthritis.
Samadi, A, Ni, T, Fontananova, E, Tang, G, Shon, H & Zhao, S 2023, 'Engineering antiwetting hydrophobic surfaces for membrane distillation: A review', Desalination, vol. 563, pp. 116722-116722.
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Samal, PB, Chen, SJ & Fumeaux, C 2023, 'Flexible Hybrid-Substrate Dual-Band Dual-Mode Wearable Antenna', IEEE Transactions on Antennas and Propagation, pp. 1-1.
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Samal, PB, Chen, SJ & Fumeaux, C 2023, 'Wearable Textile Multiband Antenna for WBAN Applications', IEEE Transactions on Antennas and Propagation, vol. 71, no. 2, pp. 1391-1402.
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Samal, PB, Chen, SJ, Tung, TT, Losic, D & Fumeaux, C 2023, 'Efficiency-Driven Design for Planar Antennas With Lossy Materials', IEEE Open Journal of Antennas and Propagation, vol. 4, pp. 23-33.
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Samy, I, Han, X, Lazos, L, Li, M, Xiao, Y & Krunz, M 2023, 'Misbehavior Detection in Wi-Fi/LTE Coexistence Over Unlicensed Bands', IEEE Transactions on Mobile Computing, vol. 22, no. 8, pp. 4773-4791.
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Sanderson, B, Field, JD, Kocaballi, AB, Estcourt, LJ, Magrabi, F, Wood, EM & Coiera, EW 2023, 'Multicenter, multidisciplinary user‐centered design of a clinical decision‐support and simulation system for massive transfusion', Transfusion, vol. 63, no. 5, pp. 993-1004.
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AbstractBackgroundManaging critical bleeding with massive transfusion (MT) requires a multidisciplinary team, often physically separated, to perform several simultaneous tasks at short notice. This places a significant cognitive load on team members, who must maintain situational awareness in rapidly changing scenarios. Similar resuscitation scenarios have benefited from the use of clinical decision support (CDS) tools.Study Design and MethodsA multicenter, multidisciplinary, user‐centered design (UCD) study was conducted to design a computerized CDS for MT. This study included analysis of the problem context with a cognitive walkthrough, development of a user requirement statement, and co‐design with users of prototypes for testing. The final prototype was evaluated using qualitative assessment and the System Usability Scale (SUS).ResultsEighteen participants were recruited across four institutions. The first UCD cycle resulted in the development of four prototype interfaces that addressed the user requirements and context of implementation. Of these, the preferred interface was further developed in the second UCD cycle to create a high‐fidelity web‐based CDS for MT. This prototype was evaluated by 15 participants using a simulated bleeding scenario and demonstrated an average SUS of 69.3 (above average, SD 16) and a clear interface with easy‐to‐follow blood product tracking.DiscussionWe used a UCD process to explore a highly complex clinical scenario and develop a prototype CDS for MT that incorporates distributive situational awareness, supports multiple user roles, and allows simulated MT training. Evaluation of the impact of this prototype on the efficacy and efficiency of managing MT is currently underway...
Sang, L, Xu, M, Qian, S & Wu, X 2023, 'Adversarial Heterogeneous Graph Neural Network for Robust Recommendation', IEEE Transactions on Computational Social Systems, vol. 10, no. 5, pp. 2660-2671.
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Santra, SB, Chatterjee, D & Siwakoti, YP 2023, 'Coupled Inductor Based Soft Switched High Gain Bidirectional DC-DC Converter With Reduced Input Current Ripple', IEEE Transactions on Industrial Electronics, vol. 70, no. 2, pp. 1431-1443.
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Bidirectional dc-dc converter (BDC) is an integral part of energy storage interface, where high efficiency, high voltage transfer ratio and small input ripple current are essential for application, such as dc microgrid and electric vehicle. In this article, a new approach is proposed to achieve reduced input ripple current at the low voltage side, which potentially replaces the necessity of using interleaved circuit structure. Parallel coupled inductor and capacitor circuit are utilized for charging and discharging the two parallel paths in a complementary fashion using a mosfet. The proposed solution helps to increase the voltage conversion ratio unlike interleaved structure. Additionally, in proposed BDC, zero voltage switching turn on operation of all active switches are possible utilizing synchronous rectification principle in both the operating modes. Parallel path structure not only reduce the input current ripple, but also helps to share the input current, thereby reducing the coil size and associated losses. A 250 W BDC is designed to validate performance in 1kW dc microgrid system
Saputra, YM, Hoang, DT, Nguyen, DN, Tran, L-N, Gong, S & Dutkiewicz, E 2023, 'Dynamic Federated Learning-Based Economic Framework for Internet-of-Vehicles', IEEE Transactions on Mobile Computing, vol. 22, no. 4, pp. 2100-2115.
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Sarafianou, M, Choong, DSW, Chen, DS-H, Goh, DJ, Yao, Z, Sharma, J, Merugu, S, Ng, EJ & Lee, JE-Y 2023, 'Long-Range High-Resolution Imaging With Silicon-on-Nothing ScAlN pMUTs', IEEE Sensors Journal, vol. 23, no. 20, pp. 24254-24263.
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Saraswat, M, Chowdhury, C, Mandal, CK & Gandomi, AH 2023, 'Preface', Lecture Notes in Networks and Systems, vol. 551, p. v.
Sateesh, KA, Yaliwal, VS, Banapurmath, NR, Soudagar, MEM, Yunus Khan, TM, Harari, PA, El-Shafay, AS, Mujtaba, MA, Elfaskhany, A & Kalam, MA 2023, 'Effect of MWCNTs nano-additive on a dual-fuel engine characteristics utilizing dairy scum oil methyl ester and producer gas', Case Studies in Thermal Engineering, vol. 42, pp. 102661-102661.
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Savkin, AV, Huang, C & Ni, W 2023, 'Collision-Free 3-D Navigation of a UAV Team for Optimal Data Collection in Internet-of-Things Networks With Reconfigurable Intelligent Surfaces', IEEE Systems Journal, vol. 17, no. 3, pp. 4070-4077.
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Savkin, AV, Huang, C & Ni, W 2023, 'Joint Multi-UAV Path Planning and LoS Communication for Mobile-Edge Computing in IoT Networks With RISs', IEEE Internet of Things Journal, vol. 10, no. 3, pp. 2720-2727.
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Savkin, AV, Huang, C & Ni, W 2023, 'On-Demand Deployment of Aerial Base Stations for Coverage Enhancement in Reconfigurable Intelligent Surface-Assisted Cellular Networks on Uneven Terrains', IEEE Communications Letters, vol. 27, no. 2, pp. 666-670.
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Savkin, AV, Ni, W & Eskandari, M 2023, 'Effective UAV Navigation for Cellular-Assisted Radio Sensing, Imaging, and Tracking', IEEE Transactions on Vehicular Technology, vol. 72, no. 10, pp. 13729-13733.
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Savkin, AV, Verma, SC & Ni, W 2023, 'Autonomous UAV 3D trajectory optimization and transmission scheduling for sensor data collection on uneven terrains', Defence Technology.
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Sayem, ASM, Lalbakhsh, A, Esselle, KP, Moloudian, G, Buckley, JL & Simorangkir, RBVB 2023, 'Advancements, Challenges, and Prospects of Water-Filled Antennas', IEEE Access, vol. 11, pp. 8301-8323.
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Schneider, PJ & Rizoiu, M-A 2023, 'The effectiveness of moderating harmful online content', Proceedings of the National Academy of Sciences, vol. 120, no. 34.
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In 2022, the European Union introduced the Digital Services Act (DSA), a new legislation to report and moderate harmful content from online social networks. Trusted flaggers are mandated to identify harmful content, which platforms must remove within a set delay (currently 24 h). Here, we analyze the likely effectiveness of EU-mandated mechanisms for regulating highly viral online content with short half-lives. We deploy self-exciting point processes to determine the relationship between the regulated moderation delay and the likely harm reduction achieved. We find that harm reduction is achievable for the most harmful content, even for fast-paced platforms such as Twitter. Our method estimates moderation effectiveness for a given platform and provides a rule of thumb for selecting content for investigation and flagging, managing flaggers’ workload.
Sebayang, AH, Ideris, F, Silitonga, AS, Shamsuddin, AH, Zamri, MFMA, Pulungan, MA, Siahaan, S, Alfansury, M, Kusumo, F & Milano, J 2023, 'Optimization of ultrasound-assisted oil extraction from Carica candamarcensis; A potential Oleaginous tropical seed oil for biodiesel production', Renewable Energy, vol. 211, pp. 434-444.
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Sebayang, AH, Kusumo, F, Milano, J, Shamsuddin, AH, Silitonga, AS, Ideris, F, Siswantoro, J, Veza, I, Mofijur, M & Reen Chia, S 2023, 'Optimization of biodiesel production from rice bran oil by ultrasound and infrared radiation using ANN-GWO', Fuel, vol. 346, pp. 128404-128404.
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Seifollahi, S & Piccardi, M 2023, 'Taxonomy-Based Feature Extraction for Document Classification, Clustering and Semantic Analysis', pp. 575-586.
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Selleri, S & Bird, TS 2023, 'Gian Domenico Romagnosi’s Forgotten Experiment on the Magnetic Effect of Currents in 1802', IEEE Antennas and Propagation Magazine, vol. 65, no. 1, pp. 125-129.
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Seoni, S, Jahmunah, V, Salvi, M, Barua, PD, Molinari, F & Acharya, UR 2023, 'Application of uncertainty quantification to artificial intelligence in healthcare: A review of last decade (2013–2023)', Computers in Biology and Medicine, vol. 165, pp. 107441-107441.
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Sha, C, Yang, L, Cairney, JM, Zhang, J & Young, DJ 2023, 'Sulphur diffusion through a growing chromia scale and effects of water vapour', Corrosion Science, vol. 222, pp. 111410-111410.
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Shadmani, A, Nikoo, MR, Gandomi, AH, Wang, R-Q & Golparvar, B 2023, 'A review of machine learning and deep learning applications in wave energy forecasting and WEC optimization', Energy Strategy Reviews, vol. 49, pp. 101180-101180.
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Shadmani, A, Reza Nikoo, M, Etri, T & Gandomi, AH 2023, 'A multi-objective approach for location and layout optimization of wave energy converters', Applied Energy, vol. 347, pp. 121397-121397.
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Shahabuddin, M, Uddin, MN, Chowdhury, JI, Ahmed, SF, Uddin, MN, Mofijur, M & Uddin, MA 2023, 'A review of the recent development, challenges, and opportunities of electronic waste (e-waste)', International Journal of Environmental Science and Technology, vol. 20, no. 4, pp. 4513-4520.
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AbstractThis study reviews recent developments, challenges, and the prospect of electronic waste (e-waste). Various aspects of e-waste, including collection, pre-treatment, and recycling, are discussed briefly. It is found that Europe is the leading collector of e-waste, followed by Asia, America, Oceania, and Africa. The monetary worth of e-waste raw materials is estimated to be $57.0 billion. However, only $10.0 billion worth of e-waste is recycled and recovered sustainably, offsetting 15.0 million tonnes (Mt) of CO2. The major challenges of e-waste treatment include collection, sorting and inhomogeneity of waste, low energy density, prevention of further waste, emission, and cost-effective recycling. Only 78 countries in the world now have e-waste related legislation. Such legislation is not effectively implemented in most regions. Developing countries like south-eastern Asia and Northern Africa have limited or no e-waste legislation. Therefore, country-specific standards and legislation, public awareness, effective implementation, and government incentives for developing cost-effective technologies are sought to manage e-waste, which will play an important role in the circular economy.
Shaharuddin, S, Abdul Maulud, KN, Syed Abdul Rahman, SAF, Che Ani, AI & Pradhan, B 2023, 'The role of IoT sensor in smart building context for indoor fire hazard scenario: A systematic review of interdisciplinary articles', Internet of Things, vol. 22, pp. 100803-100803.
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Shakor, P, Nejadi, S, Paul, G & Gowripalan, N 2023, 'Effects of Different Orientation Angle, Size, Surface Roughness, and Heat Curing on Mechanical Behavior of 3D Printed Cement Mortar With/Without Glass Fiber in Powder-Based 3DP', 3D Printing and Additive Manufacturing, vol. 10, no. 2, pp. 330-355.
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Shamayleh, OA & Far, H 2023, 'Utilising artificial neural networks for prediction of properties of geopolymer concrete', Computers and Concrete, vol. 31, no. 4, pp. 327-335.
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The most popular building material, concrete, is intrinsically linked to the advancement of humanity. Due to the ever-increasing complexity of cementitious systems, concrete formulation for desired qualities remains a difficult undertaking despite conceptual and methodological advancement in the field of concrete science. Recognising the significant pollution caused by the traditional cement industry, construction of civil engineering structures has been carried out successfully using Geopolymer Concrete (GPC), also known as High Performance Concrete (HPC). These are concretes formed by the reaction of inorganic materials with a high content of Silicon and Aluminium (Pozzolans) with alkalis to achieve cementitious properties. These supplementary cementitious materials include Ground Granulated Blast Furnace Slag (GGBFS), a waste material generated in the steel manufacturing industry; Fly Ash, which is a fine waste product produced by coal-fired power stations and Silica Fume, a by-product of producing silicon metal or ferrosilicon alloys. This result demonstrated that GPC/HPC can be utilised as a substitute for traditional Portland cement-based concrete, resulting in improvements in concrete properties in addition to environmental and economic benefits. This study explores utilising experimental data to train artificial neural networks, which are then used to determine the effect of supplementary cementitious material replacement, namely fly ash, Ground Granulated Blast Furnace Slag (GGBFS) and silica fume, on the compressive strength, tensile strength, and modulus of elasticity of concrete and to predict these values accordingly.
Shamsi, A, Asgharnezhad, H, Bouchani, Z, Jahanian, K, Saberi, M, Wang, X, Razzak, I, Alizadehsani, R, Mohammadi, A & Alinejad-Rokny, H 2023, 'A novel uncertainty-aware deep learning technique with an application on skin cancer diagnosis', Neural Computing and Applications, vol. 35, no. 30, pp. 22179-22188.
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AbstractSkin cancer, primarily resulting from the abnormal growth of skin cells, is among the most common cancer types. In recent decades, the incidence of skin cancer cases worldwide has risen significantly (one in every three newly diagnosed cancer cases is a skin cancer). Such an increase can be attributed to changes in our social and lifestyle habits coupled with devastating man-made alterations to the global ecosystem. Despite such a notable increase, diagnosis of skin cancer is still challenging, which becomes critical as its early detection is crucial for increasing the overall survival rate. This calls for advancements of innovative computer-aided systems to assist medical experts with their decision making. In this context, there has been a recent surge of interest in machine learning (ML), in particular, deep neural networks (DNNs), to provide complementary assistance to expert physicians. While DNNs have a high processing capacity far beyond that of human experts, their outputs are deterministic, i.e., providing estimates without prediction confidence. Therefore, it is of paramount importance to develop DNNs with uncertainty-awareness to provide confidence in their predictions. Monte Carlo dropout (MCD) is vastly used for uncertainty quantification; however, MCD suffers from overconfidence and being miss calibrated. In this paper, we use MCD algorithm to develop an uncertainty-aware DNN that assigns high predictive entropy to erroneous predictions and enable the model to optimize the hyper-parameters during training, which leads to more accurate uncertainty quantification. We use two synthetic (two moons and blobs) and a real dataset (skin cancer) to validate our algorithm. Our experiments on these datasets prove effectiveness of our approach in quantifying reliable uncertainty. Our method achieved 85.65 ± 0.18 prediction accuracy, 83.03 ± 0.25 uncertainty accuracy, and 1.93 ± 0.3 expected calibration error outperformin...
Shan, B, Ni, W, Yuan, X, Yang, D, Wang, X & Liu, RP 2023, 'Graph learning from band-limited data by graph Fourier transform analysis', Signal Processing, vol. 207, pp. 108950-108950.
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Shan, B, Yuan, X, Ni, W, Wang, X, Liu, RP & Dutkiewicz, E 2023, 'Novel Graph Topology Learning for Spatio-Temporal Analysis of COVID-19 Spread', IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 6, pp. 2693-2704.
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Shan, B, Yuan, X, Ni, W, Wang, X, Liu, RP & Dutkiewicz, E 2023, 'Preserving the Privacy of Latent Information for Graph-Structured Data', IEEE Transactions on Information Forensics and Security, vol. 18, pp. 5041-5055.
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Shan, F, He, X, Armaghani, DJ & Sheng, D 2023, 'Effects of data smoothing and recurrent neural network (RNN) algorithms for real-time forecasting of tunnel boring machine (TBM) performance', Journal of Rock Mechanics and Geotechnical Engineering.
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Shan, F, He, X, Armaghani, DJ, Zhang, P & Sheng, D 2023, 'Response to Discussion on “Success and challenges in predicting TBM penetration rate using recurrent neural networks” by Georg H. Erharter, Thomas Marcher', Tunnelling and Underground Space Technology, vol. 139, pp. 105064-105064.
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Shanmugam, S, Mathimani, T, Rajendran, K, Sekar, M, Rene, ER, Chi, NTL, Ngo, HH & Pugazhendhi, A 2023, 'Perspective on the strategies and challenges in hydrogen production from food and food processing wastes', Fuel, vol. 338, pp. 127376-127376.
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Shao, R, Wu, C, Li, J & Liu, Z 2023, 'Repeated impact resistance of steel fibre-reinforced dry UHPC: Effects of fibre length, mixing method, fly ash content and crumb rubber', Composite Structures, vol. 321, pp. 117274-117274.
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Shao, R, Wu, C, Li, J, Liu, Z, Wu, P & Yang, Y 2023, 'Mechanical behaviour and environmental benefit of eco-friendly steel fibre-reinforced dry UHPC incorporating high-volume fly ash and crumb rubber', Journal of Building Engineering, vol. 65, pp. 105747-105747.
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This study evaluates the impact of high-volume fly ash (HVFA) and waste crumb rubber (CR) on the mechanical property and environmental benefit of steel fibre-reinforced dry UHPC (FR-DUHPC) designed in a previous study. FA was introduced at 20–60% by mass substitution for cement with fibre dosage of 1.5 vol. %. Then, waste CR with different meshes were added as partial/completed replacements of coarse and medium sand with three volume contents of fibres (0.5%, 1.0% and 1.5%). Test results indicated that in the case of 1.5% fibre reinforcement, the increase in FA content and the addition of CR aggregate markedly reduced the density, modulus of elasticity and strength behaviour, whereas had minimal effect on the post-peak ductility of the assessed mixtures under compression and bending loads. Owing to the adopted moist/steam curing and the continuous pozzolanic reaction, the contribution of FA effect to both strengths at various ages was apparently increased and 50% of cement substitution was considered to be the most suitable FA addition in this study. For rubberized concrete reinforced with 0.5–1.5% steel fibres, the mechanical properties increased gradually with fibre dosage and curing age. However, the effect was evidently weakened with the addition of finer CR aggregate, and increasing the fibre dosage contributed to more positive impact on ductility rather than the load-carrying capacity. In summary, the flexural property benefits derived from the inclusion of steel fibre, FA and waste CR, as well as the eco-friendly benefits derived from the cost saving, energy conservation and carbon emission reduction, render the developed lightweight concrete mixture to be broadly used in dry concrete applications with different strength requirements that are mainly subjected to bending loads during serviceability.
Sharma, K, Akther, N, Choo, Y, Zhang, P, Matsuyama, H, Shon, HK & Naidu, G 2023, 'Positively charged nanofiltration membranes for enhancing magnesium separation from seawater', Desalination, vol. 568, pp. 117026-117026.
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Sharma, M, Joshi, S, Prasad, M & Bartwal, S 2023, 'Overcoming barriers to circular economy implementation in the oil & gas industry: Environmental and social implications', Journal of Cleaner Production, vol. 391, pp. 136133-136133.
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This anticipated consumer demand has put unprecedented pressure on natural resources. Being the highest contributor in the energy transition, Oil & gas (O&G) industry needs to lessen the negative impact of climate change and natural disasters. To combat the impact of emissions and a move towards circularity, O&G industry has undertaken numerous initiatives including energy efficiency, process fuel improvements, and technological transformation etc. But due to certain barriers O&G industry is unable to embrace Circular Economy (CE) implementation in the firms. Therefore, this study has proposed a model to examine the existing critical barriers and suggest strategies to overcome the barriers. The current study has employed an extensive analysis using a hybrid methodology of Fuzzy-DEMATEL (F-DEMATEL) and Best Worst Method (BWM) for assessing the barriers and ranking the strategies. The results showed that ‘knowledge barriers’ are the most critical in the O&G industry that hampers the implementation of CE currently. Further, the strategies ‘Developing collaborative model’ and ‘Internal research and development, innovation’ are the two most significant strategies that may help to reduce the barriers to a minimum. The findings, social and environmental implications are beneficial for the stakeholders and policy-makers to support the transition to CE.
Sharma, R, Goel, T, Tanveer, M, Lin, CT & Murugan, R 2023, 'Deep-Learning-Based Diagnosis and Prognosis of Alzheimer’s Disease: A Comprehensive Review', IEEE Transactions on Cognitive and Developmental Systems, vol. 15, no. 3, pp. 1123-1138.
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Sharma, SK, Truong, DQ, Guo, J, An, AK, Naidu, G & Deka, BJ 2023, 'Recovery of rubidium from brine sources utilizing diverse separation technologies', Desalination, vol. 556, pp. 116578-116578.
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Shen, J, Li, H, Wang, L, Chen, G & Wen, S 2023, 'Fixed-Time Synergetic Control Based on SEAIQR Model for COVID-19 Epidemic', IEEE Transactions on Circuits and Systems II: Express Briefs, pp. 1-1.
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Shen, M, Ye, K, Liu, X, Zhu, L, Kang, J, Yu, S, Li, Q & Xu, K 2023, 'Machine Learning-Powered Encrypted Network Traffic Analysis: A Comprehensive Survey', IEEE Communications Surveys & Tutorials, vol. 25, no. 1, pp. 791-824.
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Shen, S, Wu, X, Sun, P, Zhou, H, Wu, Z & Yu, S 2023, 'Optimal privacy preservation strategies with signaling Q-learning for edge-computing-based IoT resource grant systems', Expert Systems with Applications, vol. 225, pp. 120192-120192.
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Shen, S, Xie, L, Zhang, Y, Wu, G, Zhang, H & Yu, S 2023, 'Joint Differential Game and Double Deep Q-Networks for Suppressing Malware Spread in Industrial Internet of Things', IEEE Transactions on Information Forensics and Security, vol. 18, pp. 5302-5315.
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Sheng, Z, Wen, S, Feng, Z-K, Gong, J, Shi, K, Guo, Z, Yang, Y & Huang, T 2023, 'A Survey on Data-Driven Runoff Forecasting Models Based on Neural Networks', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 7, no. 4, pp. 1083-1097.
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Shephard, RW & Maloney, SK 2023, 'A review of thermal stress in cattle', Australian Veterinary Journal, vol. 101, no. 11, pp. 417-429.
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Cattle control body temperature in a narrow range over varying climatic conditions. Endogenous body heat is generated by metabolism, digestion and activity. Radiation is the primary external source of heat transfer into the body of cattle. Cattle homeothermy uses behavioural and physiological controls to manage radiation, convection, conduction, and evaporative exchange of heat between the body and the environment, noting that evaporative mechanisms almost exclusively transfer body heat to the environment. Cattle control radiation by shade seeking (hot) and shelter (cold) and by huddling or standing further apart, noting there are intrinsic breed and age differences in radiative transfer potential. The temperature gradient between the skin and the external environment and wind speed (convection) determines heat transfer by these means. Cattle control these mechanisms by managing blood flow to the periphery (physiology), by shelter‐seeking and standing/lying activity in the short term (behaviourally) and by modifying their coats and adjusting their metabolic rates in the longer term (acclimatisation). Evaporative heat loss in cattle is primarily from sweating, with some respiratory contribution, and is the primary mechanism for dissipating excess heat when environmental temperatures exceed skin temperature (~36°C). Cattle tend to be better adapted to cooler rather than hotter external conditions, with Bos indicus breeds more adapted to hotter conditions than Bos taurus. Management can minimise the risk of thermal stress by ensuring appropriate breeds of suitably acclimatised cattle, at appropriate stocking densities, fed appropriate diets (and water), and with access to suitable shelter and ventilation are better suited to their expected farm environment.
Sheu, A, Blank, RD, Tran, T, Bliuc, D, Greenfield, JR, White, CP & Center, JR 2023, 'Associations of Type 2 Diabetes, Body Composition, and Insulin Resistance with Bone Parameters: The Dubbo Osteoporosis Epidemiology Study', JBMR Plus, vol. 7, no. 9.
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ABSTRACTType 2 diabetes (T2D) may be associated with increased risk of fractures, despite preserved bone mineral density (BMD). Obesity and insulin resistance (IR) may have separate effects on bone turnover and bone strength, which contribute to skeletal fragility. We characterized and assessed the relative associations of obesity, body composition, IR, and T2D on bone turnover markers (BTMs), BMD, and advanced hip analysis (AHA). In this cross‐sectional analysis of Dubbo Osteoporosis Epidemiology Study, 525 (61.3% women) participants were grouped according to T2D, IR (homeostasis model assessment insulin resistance [HOMA‐IR] </≥2.5), and BMI (</≥25 kg/m2): insulin‐sensitive lean (IS‐L), insulin‐sensitive overweight/obese (IS‐O), insulin‐resistant (IR), and T2D. BMD, AHA, and body composition, including visceral adipose tissue (VAT) (on dual‐energy x‐ray absorptiometry scan) and fasting BTMs, were assessed. Analyses performed using Bayesian model averaging and principal component analysis. T2D was associated with low BTMs (by 26%–30% [95% confidence interval [CI] 11%–46%] in women, 35% [95% CI 18%–48%] in men compared to IS‐L), which persisted after adjustment for VAT. BTMs were similar among IR/IS‐O/IS‐L. BMD was similar among T2D/IR/IS‐O; BMD was low only in IS‐L. All groups were similar after adjustment for BMI. Similarly, AHA components were lowest in IS‐L (attenuated following adjustment). On multivariate analysis, T2D was independently associated with BTMs. IR was also associated with C‐terminal telopeptide of type 1 collagen in men. Age and body size were the strongest independent contributors to BMD and AHA. VAT was inversely associated with section modulus, cross‐sectional area, cross‐sectional moment of inertia in women, and hip axis length in men. Low bone turnover is associated with T2D and IR (in men), while BMD and hip strength/geometry are predominantly associated with body size. VAT, indi...
Sheu, A, O’Connell, RL, Jenkins, AJ, Tran, T, Drury, PL, Sullivan, DR, Li, L, Colman, P, O’Brien, R, Kesäniemi, YA, Center, JR, White, CP & Keech, AC 2023, 'Factors associated with fragility fractures in type 2 diabetes: An analysis of the randomised controlled Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) study', Diabetes/Metabolism Research and Reviews, vol. 39, no. 5.
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AbstractAimsFracture risk is elevated in some type 2 diabetes patients. Bone fragility may be associated with more clinically severe type 2 diabetes, although prospective studies are lacking. It is unknown which diabetes‐related characteristics are independently associated with fracture risk. In this post‐hoc analysis of fracture data from the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) trial (ISRCTN#64783481), we hypothesised that diabetic microvascular complications are associated with bone fragility.Materials and MethodsThe FIELD trial randomly assigned 9795 type 2 diabetes participants (aged 50–75 years) to receive oral co‐micronised fenofibrate 200 mg (n = 4895) or placebo (n = 4900) daily for a median of 5 years. We used Cox proportional hazards models to identify baseline sex‐specific diabetes‐related parameters independently associated with incident fractures.ResultsOver 49,470 person‐years, 137/6138 men experienced 141 fractures and 143/3657 women experienced 145 fractures; incidence rates for the first fracture of 4∙4 (95% CI 3∙8–5∙2) and 7∙7 per 1000 person‐years (95% CI 6∙5–9∙1), respectively. Fenofibrate had no effect on fracture outcomes. In men, baseline macrovascular disease (HR 1∙52, 95% CI 1∙05–2∙21, p = 0∙03), insulin use (HR 1∙62, HR 1∙03–2∙55, p = 0∙03), and HDL‐cholesterol (HR 2∙20, 95% CI 1∙11–4∙36, p = 0∙02) were independently associated with fracture. In women, independent risk factors included baseline peripheral neuropathy (HR 2∙04, 95% CI 1∙16–3∙59, p = 0∙01) and insulin use (HR 1∙55, 95% CI 1∙02–2∙33, p = 0∙04).
Shi, AC, Maidi, AM, Shamsuddin, N, Kalam, MA & Begum, F 2023, 'Photonic crystal fibre sensor for alcohol detection with extremely low birefringence', International Journal of Applied Science and Engineering, vol. 20, no. 2, pp. 1-7.
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Shi, D, Zhu, L, Li, J, Zhang, Z & Chang, X 2023, 'Unsupervised Adaptive Feature Selection With Binary Hashing', IEEE Transactions on Image Processing, vol. 32, pp. 838-853.
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Shi, K, Cai, X, She, K, Wen, S, Zhong, S, Park, P & Kwon, O-M 2023, 'Stability Analysis and Security-Based Event-Triggered Mechanism Design for T-S Fuzzy NCS With Traffic Congestion via DoS Attack and Its Application', IEEE Transactions on Fuzzy Systems, vol. 31, no. 10, pp. 3639-3651.
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Shi, K, Peng, X, Lu, H, Zhu, Y & Niu, Z 2023, 'Multiple Knowledge-Enhanced Meteorological Social Briefing Generation', IEEE Transactions on Computational Social Systems, pp. 1-12.
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Shi, M, Zhao, X, Yin, X, Chang, X, Niu, F & Guo, J 2023, 'Multiview Latent Structure Learning: Local structure-guided cross-view discriminant analysis', Knowledge-Based Systems, vol. 276, pp. 110707-110707.
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Shi, T, Xiang, X, Lei, J, Liu, B, Wang, F, Chen, M, Yang, H, Li, L & Li, W 2023, 'Non-detection Zone Elimination and Detection Speed Improvement for DC Microgrids Islanding Detection with Adaptive Resonant Frequency', IEEE Journal of Emerging and Selected Topics in Power Electronics, pp. 1-1.
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Shi, Y, Han, Y, Hu, Q, Yang, Y & Tian, Q 2023, 'Query-Efficient Black-Box Adversarial Attack With Customized Iteration and Sampling', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 2, pp. 2226-2245.
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Shi, Z, Sun, X, Yang, Z, Cai, Y, Lei, G, Zhu, J & Lee, CHT 2023, 'Design Optimization of a Spoke Type Axial-Flux PM Machine for In-wheel Drive Operation', IEEE Transactions on Transportation Electrification, pp. 1-1.
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Shoeibi, A, Khodatars, M, Jafari, M, Ghassemi, N, Moridian, P, Alizadehsani, R, Ling, SH, Khosravi, A, Alinejad-Rokny, H, Lam, H-K, Fuller-Tyszkiewicz, M, Acharya, UR, Anderson, D, Zhang, Y & Górriz, JM 2023, 'Diagnosis of brain diseases in fusion of neuroimaging modalities using deep learning: A review.', Inf. Fusion, vol. 93, pp. 85-117.
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Shoorangiz, M, Nikoo, MR, Šimůnek, J, Gandomi, AH, Adamowski, JF & Al-Wardy, M 2023, 'Multi-objective optimization of hydrant flushing in a water distribution system using a fast hybrid technique', Journal of Environmental Management, vol. 334, pp. 117463-117463.
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Shrestha, J, Paudel, KR, Nazari, H, Dharwal, V, Bazaz, SR, Johansen, MD, Dua, K, Hansbro, PM & Warkiani, ME 2023, 'Advanced models for respiratory disease and drug studies.', Med Res Rev, vol. 43, no. 5, pp. 1470-1503.
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The global burden of respiratory diseases is enormous, with many millions of people suffering and dying prematurely every year. The global COVID-19 pandemic witnessed recently, along with increased air pollution and wildfire events, increases the urgency of identifying the most effective therapeutic measures to combat these diseases even further. Despite increasing expenditure and extensive collaborative efforts to identify and develop the most effective and safe treatments, the failure rates of drugs evaluated in human clinical trials are high. To reverse these trends and minimize the cost of drug development, ineffective drug candidates must be eliminated as early as possible by employing new, efficient, and accurate preclinical screening approaches. Animal models have been the mainstay of pulmonary research as they recapitulate the complex physiological processes, Multiorgan interplay, disease phenotypes of disease, and the pharmacokinetic behavior of drugs. Recently, the use of advanced culture technologies such as organoids and lung-on-a-chip models has gained increasing attention because of their potential to reproduce human diseased states and physiology, with clinically relevant responses to dru