Abdi, Y, Momeni, E & Armaghani, DJ 2023, 'Elastic modulus estimation of weak rock samples using random forest technique', Bulletin of Engineering Geology and the Environment, vol. 82, no. 5.
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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 2023, 'Regional-Scale Analysis of Vegetation Dynamics Using Satellite Data and Machine Learning Algorithms: A Multi-Factorial Approach', International Journal on Smart Sensing and Intelligent Systems, vol. 16, no. 1.
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Abstract Accurate vegetation analysis is crucial amid accelerating global changes and human activities. Achieving precise characterization with multi-temporal Sentinel-2 data is challenging. In this article, we present a comprehensive analysis of 2021's seasonal vegetation cover in Greater Sydney using Google Earth Engine (GEE) to process Sentinel-2 data. Using the random forest (RF) method, we performed image classification for vegetation patterns. Supplementary factors such as topographic elements, texture information, and vegetation indices enhanced the process and overcome limited input variables. Our model outperformed existing methods, offering superior insights into season-based vegetation dynamics. Multi-temporal Sentinel-2 data, topographic elements, vegetation indices, and textural factors proved to be critical for accurate analysis. Leveraging GEE and rich Sentinel-2 data, our study would benefit decision-makers involved in vegetation monitoring.
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, no. 1, 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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Aboutorab, H, Hussain, OK, Saberi, M, Hussain, FK & Prior, D 2023, 'Reinforcement Learning-Based News Recommendation System', IEEE Transactions on Services Computing, vol. 16, no. 6, pp. 4493-4502.
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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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Abtahi, H, Karimi, M & Maxit, L 2023, 'Numerical study on the estimation of the low-wavenumber wall pressure field using vibration data', INTER-NOISE and NOISE-CON Congress and Conference Proceedings, vol. 268, no. 6, pp. 2268-2275.
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It is known that the low-wavenumber components of the wall pressure field beneath a turbulent boundary layer is the main cause of structural vibration in low Mach number flows. This is because the structure filters the convective ridge of the turbulent boundary layer excitation at frequencies well above the aerodynamic coincidence frequency. This work aims to explore the possibility of estimating the low-wavenumber wall pressure filed by obtaining vibration data from a structure excited by a turbulent boundary layer. To achieve this, an analytical model of a simply-supported plate under turbulent boundary layer is developed and the acceleration data obtained from the plate is used to evaluate the wall pressure field in the low-wavenumber domain. The wall pressure field is modelled using the Goody and Mellen models for auto-spectrum density and cross-spectrum density, respectively. Finally, the estimated pressure field is compared with the input turbulent boundary layer to evaluate the accuracy of the proposed method.
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.
Adeoti, OS, Kandasamy, J & Vigneswaran, S 2023, 'Water infrastructure sustainability in Nigeria: a systematic review of challenges and sustainable solutions', Water Policy, vol. 25, no. 11, pp. 1094-1111.
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Abstract Using the PRISMA method, this systematic literature review synthesized findings from 15 studies to elucidate the key factors contributing to water infrastructure failure in Nigeria and propose evidence-based sustainable solutions. The study identified technical, financial, environmental, social, political, and institutional factors as predominant challenges in achieving water infrastructure sustainability. In response to these challenges, the researcher proposes a comprehensive ‘Sustainability Framework for Water Infrastructure’. This framework is designed to guide every stage of water infrastructure development, starting from pre-construction with an emphasis on inclusive project planning, followed by the construction phase where suitable techniques are utilized, and extending to the post-construction stage, focusing on efficient monitoring and management mechanisms. The study highlights the complexity of water infrastructure sustainability in Nigeria and underscores the urgent need for a structured and comprehensive approach to address this pressing issue.
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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Due to the increasing interest of people in the stock and financial market, the sentiment analysis of news and texts related to the sector is of utmost importance. This helps the potential investors in deciding what company to invest in and what are their long-term benefits. However, it is challenging to analyze the sentiments of texts related to the financial domain, given the enormous amount of information available. The existing approaches are unable to capture complex attributes of language such as word usage, including semantics and syntax throughout the context, and polysemy in the context. Further, these approaches failed to interpret the models' predictability, which is obscure to humans. Models' interpretability to justify the predictions has remained largely unexplored and has become important to engender users' trust in the predictions by providing insight into the model prediction. Accordingly, in this paper, we present an explainable hybrid word representation that first augments the data to address the class imbalance issue and then integrates three embeddings to involve polysemy in context, semantics, and syntax in a context. We then fed our proposed word representation to a convolutional neural network (CNN) with attention to capture the sentiment. The experimental results show that our model outperforms several baselines of both classic classifiers and combinations of various word embedding models in the sentiment analysis of financial news. The experimental results also show that the proposed model outperforms several baselines of word embeddings and contextual embeddings when they are separately fed to a neural network model. Further, we show the explainability of the proposed method by presenting the visualization results to explain the reason for a prediction in the sentiment analysis of financial news.
Adibi, T, Razavi, SE, Ahmed, SF, Hassanpour, H, Saha, SC & Muyeen, SM 2023, 'Predicting airfoil stalling dynamics using upwind numerical solutions to non-viscous equations', Results in Engineering, vol. 20, pp. 101472-101472.
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Aditiya, HB, Sebayang, AH, Silitonga, AS, Mulyaningsih, Y, Mulyaningsih, Y, Theofany, HC & Supriyanto 2023, 'Design of experiment (DoE) in reducing sugar optimization to produce third-generation bioethanol from Chlorella pyrenoidosa: Central composite design vs Box-Behnken design', AIP Conference Proceedings.
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Aditya, L, Vu, HP, Abu Hasan Johir, M, 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, p. 15.
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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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In this study, a composite electrospun nanofiber membrane was fabricated and used to treat a geothermal brine source with lithium enrichment. An in-situ growth technique was applied to incorporate silica nanoparticles on the surface of nanofibers with (3-Aminopropyl) triethoxysilane as the nucleation site. The fabricated composite nanofiber membrane was heat pressed to enhance the integration of the membrane and its mechanical stability. The fabricated membranes were tested to evaluate their performance in feedwater containing different concentrations of NaCl in the range of 0-100 g/L, and the wetting resistivity of the membranes was examined. Finally, the optimal membrane was applied to treat the simulated geothermal brine. The experimental results revealed that the in-situ growth of nanoparticles and coating of flourosilane agent dramatically improved the separation performance of the membrane with high salt rejection, and adequate flux was achieved. The heat-pressed membrane obtained >99% salt rejection and flux of 14-19 L/m2h at varying feedwater salinity (0-100 g/L), and the concentration of the Li during the 24 h test reached >1100 ppm from the initial 360 ppm. Evaluation of the energy efficiency of the membranes showed that the heat-pressed membrane obtained the optimum energy efficiency in the high concentration of salts. Additionally, the economic analysis indicated that MD could achieve a levelized cost of 2.9 USD/m3 of lithium brine concentration as the heat source is within the feed. Overall, this technology would represent a viable alternative to the solar pond to concentrate Li brine, enabling a compact, efficient, and continuous operating system.
Afsari, M, Park, MJ, Kaleekkal, NJ, Motsa, MM, Shon, HK & Tijing, L 2023, 'Janus Distillation Membrane via Mussel-Inspired Inkjet Printing Modification for Anti-Oil Fouling Membrane Distillation', Membranes, vol. 13, no. 2, pp. 191-191.
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In this work, inkjet printing technology was used to print a thin layer of a hydrophilic solution containing polydopamine as a binder and polyethyleneimine as a strong hydrophilic agent on a commercial hydrophobic membrane to produce a Janus membrane for membrane distillation. The pristine and modified membranes were tested in a direct-contact membrane distillation system with mineral oil-containing feedwater. The results revealed that an integrated and homogenous hydrophilic layer was printed on the membrane with small intrusions in the pores. The membrane, which contained three layers of inkjet-printed hydrophilic layers, showed a high underwater oil contact angle and a low in-air water contact angle. One-layer inkjet printing was not robust enough, but the triple-layer coated modified membrane maintained its anti-oil fouling performance even for a feed solution containing 70 g/L NaCl and 0.01 v/v% mineral oil concentration with a flux of around 20 L/m2h. This study implies the high potential of the inkjet printing technique as a facile surface modification strategy to improve membrane performance.
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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Ahmadi, S, Ebrahimi Warkiani, M, Rabiee, M, Iravani, S & Rabiee, N 2023, 'Carbon-based nanomaterials against SARS-CoV-2: Therapeutic and diagnostic applications', OpenNano, vol. 10, pp. 100121-100121.
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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. 69101-69116.
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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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Around 84 % of the global energy needs are met by fossil fuels, notwithstanding their several drawbacks. Dependence on fossil fuels can be reduced when biofuels become more widely used. Compared to fossil fuel, biofuel is substantially less combustible and derived from renewable resources. Biofuel production from non-edible feedstocks can be enhanced by utilizing nanotechnology. Biofuel research to date has produced promising results, but very few recent studies have underlined the use of nanotechnology to enhance the biofuel production process. This study comprehensively reviews the potential use of nanotechnology in improving biofuel production processes. It also highlights the factors that affect nanomaterial performance in the biofuel production process. The nickel oxide (NiO) nanoparticles (NPs) are shown to be highly efficient, with harvesting Chlorella vulgaris biomass at an efficiency of 98.75 % in 1 min at pH 7. In terms of cost-effectiveness, naked modified magnetic nanoparticles (MNPs) cost significantly less, ranging from £3-500 to £0.5–108 per kg following nanoparticle reactivation. Due to their toxicity, nanomaterials used in biofuel production systems have several detrimental effects on living organisms, the environment, and the economy. Developing non-toxic nanomaterials, utilizing cheaper nanoparticles, and doing additional research might increase knowledge availability and understanding to address the current issues.
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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Perovskite solar cells (PSCs) are highly efficient and are comparatively cheaper than the large silicon crystals primarily used in solar cells. Their outstanding photovoltaic performance makes them a potential alternative to silicon solar cells. While efficiency and photovoltaic performance have been investigated in recent decades, a knowledge gap on the degradation, economic feasibility and stability of PSCs exists, and their poor stability remains a barrier to commercialization. Thus, this review aims to fill this knowledge gap by focusing on approaches to improve PSCs’ thermal and chemical stability, and their economic viability under different conditions. The structure and manufacture of PSCs are also discussed along with an economic analysis of different perovskite devices. Improvements in thermal stability can be reached by incorporating inorganic materials into the PSC. A PSC model optimized with ZnO improves chemical stability by 8% and works well under low temperatures. To make PSCs more economically feasible, certain parts like counter electrodes (CE) and hole transport materials (HTMs) can be replaced with alternative elements like carbon and inorganic HTMs, respectively. PSCs with long durability and high conversion efficiency will expand the commercial prospects for this material. To bridge the lack of knowledge, further investigation is required on the sustainability and longevity of PSCs.
Ahmed, SF, Kabir, M, Mehjabin, A, Oishi, FTZ, Ahmed, S, Mannan, S, Mofijur, M, Almomani, F, Badruddin, IA & Kamangar, S 2023, 'Waste biorefinery to produce renewable energy: Bioconversion process and circular bioeconomy', Energy Reports, vol. 10, pp. 3073-3091.
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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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Akhtar, R, Hamza, A, Razzaq, L, Hussain, F, Nawaz, S, Nawaz, U, Mukaddas, Z, Jauhar, TA, Silitonga, AS & Saleel, CA 2023, 'Maximizing biodiesel yield of a non-edible chinaberry seed oil via microwave assisted transesterification process using response surface methodology and artificial neural network techniques', Heliyon, vol. 9, no. 11, pp. e22031-e22031.
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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-u...
Alajlouni, DA, Bliuc, D, Tran, TS, Blank, RD & Center, JR 2023, 'Muscle strength and physical performance contribute to and improve fracture risk prediction in older people: A narrative review', Bone, vol. 172, pp. 116755-116755.
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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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Alarkawi, D, Tran, T, Chen, W, March, LM, Blyth, FM, Blank, RD, Bliuc, D & Center, JR 2023, 'Denosumab and Mortality in a Real-World Setting: A Comparative Study', Journal of Bone and Mineral Research, vol. 38, no. 12, pp. 1757-1770.
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ABSTRACT Denosumab (Dmab) is increasingly prescribed worldwide. Unlike bisphosphonates (BPs), its effect on mortality has yet to be well explored. This study examined the association between Dmab and all-cause mortality compared with no treatment in subjects with a fracture and BPs in subjects without a fracture. The study population was from the Sax Institute's 45 and Up Study (n = 267,357), a prospective population-based cohort with questionnaire data linked to hospital admissions (Admitted Patients Data Collection [APDC] data were linked by the Centre for Health Record Linkage), medication records (Pharmaceutical Benefits Scheme [PBS] provided by Services Australia), and stored securely (secure data access was provided through the Sax Institute's Secure Unified Research Environment [SURE]). The new-user cohort design with propensity-score (PS) matching was implemented. In the fracture cohort, Dmab and oral BP users were matched 1:2 to no treatment (Dmab: 617 women, 154 men; oral BPs: 615 women, 266 men). In the no-fracture cohort, Dmab users were matched 1:1 with oral BPs and zoledronic acid (Zol) users (Dmab:oral BPs: 479 men, 1534 women; Dmab:Zol: 280 men, 625 women). Mortality risk was measured using sex-specific pairwise multivariable Cox models. In the fracture cohort, compared with no treatment, Dmab was associated with 48% lower mortality in women (hazard ratio [HR] = 0.52, 95% confidence interval [CI] 0.36–0.72) but not in men. Oral BPs were associated with 44% lower mortality in both sexes (women HR = 0.56, 95% CI 0.42–0.77; men HR = 0.56, 95% CI 0.40–0.78). In the no-fracture cohort, compared with BPs, Dmab was associated with 1.5- to 2.5-fold higher mortality than oral BPs (women HR = 1.49, 95% CI 1.13–1.98; men HR = 2.74; 95% CI 1.82–4.11) but similar mortality to Zol. Dmab in women and oral BPs were associated with lower post-fracture mortality than no treatment. However, Dmab users had gen...
Aldini, S, Singh, AK, Leong, D, Wang, Y-K, Carmichael, MG, Liu, D & Lin, C-T 2023, 'Detection and Estimation of Cognitive Conflict During Physical Human–Robot Collaboration', IEEE Transactions on Cognitive and Developmental Systems, vol. 15, no. 2, pp. 959-968.
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Robots for physical Human-Robot Collaboration (pHRC) often need to adapt their admittance and how they operate due to several factors. As the admittance of the system becomes variable throughout the workspace, it is not always straightforward for the operator to predict the robot’s behaviour. Previous work demonstrated that cognitive conflicts can be detected during one-dimensional tasks. This work assesses whether cognitive conflicts can also be detected during 2D tasks in pHRC and a classification problem is formulated. Different robot admittance profiles anticipating the stimulus translated into different levels of cognitive conflict. Several commonly used classification algorithms for EEG signals were evaluated to classify different levels of cognitive conflict. Results demonstrate that cognitive conflict level is lower when the admittance smoothly decreases before unexpected events when compared to conditions in which the admittance abruptly decreases before the stimulus. Among the classification algorithms, Convolutional Neural Network has shown the best results to classify different levels of cognitive conflict. Results suggest the feasibility of adaptive approaches for future pHRC control systems that close the loop on users through EEG signals. The detected human cognitive state can also be used to assess and improve the predictability of Human-Robot teams in various pHRC applications.
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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The paradigm of edge computing has formed an innovative scope within the domain of IoT through expanding the services of the cloud to the network edge to design distributed architectures and securely enhance decision-making applications. Due to the heterogeneous of edge Computing, edge applications are required to be developed as a set of lightweight and interdependent modules. As this concept aligns with the objectives of microservice architecture, effective implementation of microservices-based edge applications within IoT networks has the prospective of fully leveraging edge nodes capabilities. Deploying microservices at IoT edge faces plenty of challenges associated with security and privacy. Advances in AI, and the easy access to resources with powerful computing providing opportunities for deriving precise models and developing different intelligent applications at the edge of network. In this study, an extensive survey is presented for securing edge computing-based AI Microservices to elucidate the challenges of IoT management and enable secure decision-making systems at the edge. We present recent research studies on edge AI and microservices orchestration and highlight key requirements as well as challenges of securing Microservices at IoT edge. We also propose a Microservices-based edge framework that provides secure edge AI algorithms as Microservices utilizing the containerization technology.
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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This article investigates topology optimization of multi-layer multi-material composite structures under static loading. A moving iso-surface threshold optimization method, previously well defined for single or cellular materials, is extended to multi-layer multi-material structures using a physical response function discrepancy scheme. It is also integrated with an alternating active-phase algorithm as an alternative procedure. The proposed methods are applied to three types of objective functions, namely, minimizing compliance, maximizing mutual strain energy and minimizing full-stress designs. The corresponding response functions relevant to each optimization problem according to the proposed topology optimization methods are strain energy density, mutual strain energy density and von Mises stress, respectively. Examples are presented and compared with those available in the literature to verify the derived formulations on topology optimization for multi-layer multi-material structures. Highlights Optimization by integrating MIST with alternating active phase for multi-materials Extended MIST to topology optimization for multi-layer and multi-materials Multimaterial design to maximize mutual energy, minimize compliance and full stress.
Alghamdi, AM, Pileggi, SF & 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.
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, 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, vol. 72, no. 4, pp. 1367-1382.
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Deep neural networks have shown vulnerability to well-designed inputs called adversarial examples. Researchers in industry and academia have proposed many adversarial example defense techniques. However, they offer partial but not full robustness. Thus, complementing them with another layer of protection is a must, especially for mission-critical applications. This article proposes a novel online selection and relabeling algorithm (OSRA) that opportunistically utilizes a limited number of crowdsourced workers to maximize the machine learning (ML) system's robustness. The OSRA strives to use crowdsourced workers effectively by selecting the most suspicious inputs and moving them to the crowdsourced workers to be validated and corrected. As a result, the impact of adversarial examples gets reduced, and accordingly, the ML system becomes more robust. We also proposed a heuristic threshold selection method that contributes to enhancing the prediction system's reliability. We empirically validated our proposed algorithm and found that it can efficiently and optimally utilize the allocated budget for crowdsourcing. It is also effectively integrated with a state-of-the-art black box defense technique, resulting in a more robust system. Simulation results show that the OSRA can outperform a random selection algorithm by 60% and achieve comparable performance to an optimal offline selection benchmark. They also show that OSRA's performance has a positive correlation with system robustness.
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, 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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As artificial intelligence (AI) techniques are becoming more popular in landslide modeling, it is important to understand how decisions are made. Fairness, and transparency becomes ever more vital due to ethical concerns and trust. Despite the popularity of machine learning (ML) algorithms in landslide modeling, the explainability of these methods are often considered as black box. This paper aims to propose an explainable artificial intelligence (XAI) for landslide prediction using synthetic-aperture radar (SAR) time-series data, NDVI (normalized difference vegetation index) time-series data and other geo-environmental factors such as DEM (digital elevation model) derivatives. We employed a Shapley Additive Explanations (SHAP) approach to understand how and what decisions ML-based models are making. 37 features were extracted from various sources such as ALOS-PALSAR (ALOS Phased Array type L-band Synthetic Aperture Radar), ALOS-2 (SAR), Landsat-8, topographic maps, and DEM for landslide susceptibility mapping in a landslide prone area in Chukha, Bhutan as a test site. The result was then compared using two standard ML methods: random forest (RF) and support vector machine (SVM). As per results, the RF model outperformed (0.914) the SVM. Moreover, the higher reliability of the RF model was proved by the area under the curve (AUC) of 0.941. XAI results revealed, features like altitude, aspect, NDVI-2014, NDVI-2017, and NDVI-2018 were the most effective features for landslide prediction by both models. Interestingly, among those features, NDVI-2014, aspect, and NDVI-2017 negatively correlated with the landslide prediction; whereas positively correlated when SVM was utilized. This interpretation ability indicates the advantages of XAI over the conventional methods as it measures the impact, interaction and correlation of conditioning factors within a model. The current research finding can provide more transparency and explainability when working with MLs ...
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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A micro-credential is a proof of the student's knowledge, skills, and experience that can be used to progress towards a larger credential or degree that focuses on a particular field of study in the shortest amount of time. Micro-credentials are a new area in the education sector that has expanded significantly over recent years and have become a popular idea in the higher education sector. Since the Covid-19 pandemic, micro-credentials are the most recent innovation in online education, gaining traction in public and private universities throughout the world. This has resulted in many universities developing strategies to offer micro-credential-driven courses. Higher education institutions (HEIs) need to validate micro-credentials, but the validation is a long-drawn-out and cumbersome process, so blockchain technology can be used to easily validate the detailed information on each students’ micro-credentials. Unfortunately, to date, only scant scholarly research has been conducted on blockchain-based micro-credentialing systems in HEIs. This study provides a detailed overview of the state-of-the-art in the field of managing micro-credentials using blockchain technology. We start by outlining the various requirements that need to be met in a blockchain-based micro-credentialing system. We then use a systematic literature review (SLR) to retrieve relevant studies published between 2016–2022 and compare them to the defined requirements. We also analyse the relevant studies to determine the research gaps. This review will offer insight into micro-credentialing systems that have been proposed for HEIs over recent years.
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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Nonprofit organisations (NPOs) use data analytics and corresponding visualisations to discover and interpretpatterns of donations and donor behaviours, predict future funds, and analyse time series to undertake decisionsand resolve issues. Further detailed understanding of these activities in the context of NPOs is required forefficient and effective utilisation of data analytics. This article reports a systematic review of available literatureon data analytics applications in NPOs to answer three research questions: (1) What are the proposed approachesand frameworks for adopting and applying data analytics in NPOs? (2) What aspects of data analytics are usedfor NPO activities and missions? (3) What challenges and barriers face NPOs regarding the adoption and applicationof data analytics for their missions? We answered the three research questions by collecting and examiningdata and using it to develop a new taxonomy. The results show the utilisation of data analytics applicationsby NPOs has not been examined in depth, indicating the need for further research. This study contributesto 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, 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.
Alzoubi, YI & Aljaafreh, A 2023, 'Blockchain-Fog Computing Integration Applications: A Systematic Review', Cybernetics and Information Technologies, vol. 23, no. 1, pp. 3-37.
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AbstractThe Fog computing concept has been introduced to aid in the data processing of Internet of things applications using Cloud computing. Due to the profitable benefits of this combination, several papers have lately been published proposing the deployment of Blockchain alongside Fog computing in a variety of fields. A comprehensive evaluation and synthesis of the literature on Blockchain-Fog computing integration applications that have emerged in recent years is required. Although there have been several articles on the integration of Blockchain with Fog computing, the applications connected with this combination are still fragmented and require further exploration. Hence, in this paper, the applications of Blockchain-Fog computing integration are identified using a systematic literature review technique and tailored search criteria generated from the study objectives. This article found and evaluated 144 relevant papers. The findings of this article can be used as a resource for future Fog computing research and designs.
Alzubaidi, L, Al-Sabaawi, A, Bai, J, Dukhan, A, Alkenani, AH, Al-Asadi, A, Alwzwazy, HA, Manoufali, M, Fadhel, MA, Albahri, AS, Moreira, C, Ouyang, C, Zhang, J, Santamaría, J, Salhi, A, Hollman, F, Gupta, A, Duan, Y, Rabczuk, T, Abbosh, A & Gu, Y 2023, 'Towards Risk‐Free Trustworthy Artificial Intelligence: Significance and Requirements', International Journal of Intelligent Systems, vol. 2023, no. 1, pp. 1-41.
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Given the tremendous potential and influence of artificial intelligence (AI) and algorithmic decision‐making (DM), these systems have found wide‐ranging applications across diverse fields, including education, business, healthcare industries, government, and justice sectors. While AI and DM offer significant benefits, they also carry the risk of unfavourable outcomes for users and society. As a result, ensuring the safety, reliability, and trustworthiness of these systems becomes crucial. This article aims to provide a comprehensive review of the synergy between AI and DM, focussing on the importance of trustworthiness. The review addresses the following four key questions, guiding readers towards a deeper understanding of this topic: (i) why do we need trustworthy AI? (ii) what are the requirements for trustworthy AI? In line with this second question, the key requirements that establish the trustworthiness of these systems have been explained, including explainability, accountability, robustness, fairness, acceptance of AI, privacy, accuracy, reproducibility, and human agency, and oversight. (iii) how can we have trustworthy data? and (iv) what are the priorities in terms of trustworthy requirements for challenging applications? Regarding this last question, six different applications have been discussed, including trustworthy AI in education, environmental science, 5G‐based IoT networks, robotics for architecture, engineering and construction, financial technology, and healthcare. The review emphasises the need to address trustworthiness in AI systems before their deployment in order to achieve the AI goal for good. An example is provided that demonstrates how trustworthy AI can be employed to eliminate bias in human resources management systems. The insights and recommendations presented in this paper will serve as a valuable guide for AI researchers seeking to achieve trustworthiness in ...
Amar, M, Akram, N, Chaudhary, GQ, Kazi, SN, Soudagar, MEM, Mubarak, NM & Kalam, MA 2023, 'Energy, exergy and economic (3E) analysis of flat-plate solar collector using novel environmental friendly nanofluid', Scientific Reports, vol. 13, no. 1, p. 411.
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AbstractThe use of solar energy is one of the most prominent strategies for addressing the present energy management challenges. Solar energy is used in numerous residential sectors through flat plate solar collectors. The thermal efficiency of flat plate solar collectors is improved when conventional heat transfer fluids are replaced with nanofluids because they offer superior thermo-physical properties to conventional heat transfer fluids. Concentrated chemicals are utilized in nanofluids' conventional synthesis techniques, which produce hazardous toxic bi-products. The present research investigates the effects of novel green covalently functionalized gallic acid-treated multiwall carbon nanotubes-water nanofluid on the performance of flat plate solar collectors. GAMWCNTs are highly stable in the base fluid, according to stability analysis techniques, including ultraviolet–visible spectroscopy and zeta potential. Experimental evaluation shows that the thermo-physical properties of nanofluid are better than those of base fluid deionized water. The energy, exergy and economic analysis are performed using 0.025%, 0.065% and 0.1% weight concentrations of GAMWCNT-water at varying mass flow rates 0.010, 0.0144, 0.0188 kg/s. The introduction of GAMWCNT nanofluid enhanced the thermal performance of flat plate solar collectors in terms of energy and exergy efficiency. There is an enhancement in efficiency with the rise in heat flux, mass flow rate and weight concentration, but a decline is seen as inlet temperature increases. As per experimental findings, the highest improvement in energy efficiency is 30.88% for a 0.1% weight concentration of GAMWCNT nanofluid at 0.0188 kg/s compared to the base fluid. The collector's exergy efficiency increases with the rise in weight concentration while it decreases with an increase in flow rate. The highest exergy efficiency is achieved at 0.1% GAMWCNT concentration and 0.010 kg/s mass flow...
Amini, E, Nasiri, M, Pargoo, NS, Mozhgani, Z, Golbaz, D, Baniesmaeil, M, Nezhad, MM, Neshat, M, Astiaso Garcia, D & Sylaios, G 2023, 'Design optimization of ocean renewable energy converter using a combined Bi-level metaheuristic approach', Energy Conversion and Management: X, vol. 19, pp. 100371-100371.
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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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Soil moisture monitoring and irrigation scheduling are essential parameters in farming efficiency. Internet-of-Things (IoT) technology is a promising solution for automating irrigation procedures and improving farming efficiency by removing human faults. In this article, a new method is introduced to measure soil moisture level along with providing energy to run a low-power transmitter as an alarm signal. A combination of a metamaterial perfect absorber (MPA) and two rectifiers that are designed at different frequencies specifies 5% and 25% soil moisture levels. The sensor monitors the soil moisture continuously without consuming energy. Once the soil moisture becomes 5% of the first rectifier starts working and provides 65 $\text{uW}$ dc output, while the second rectifier is off. Increasing the soil moisture to 25%, the second rectifier creates 100 uW dc output when the first rectifier is off. The designed structure is fabricated on RO4003 in a $4$ $\times$ $4$ array. The measurement results are provided by performing a set of different experiments. Initially, the MPA’s absorption characteristics are validated facing different polarization and incident angles. Then, the sensing capability is proven by burying the proposed sensor under sand and measuring the dc outputs of rectifiers. A strong correlation between simulation and measurement results validates the design procedure.
Ampah, JD, Jin, C, Rizwanul Fattah, IM, Appiah-Otoo, I, Afrane, S, Geng, Z, Yusuf, AA, Li, T, Mahlia, TMI & Liu, H 2023, 'Investigating the evolutionary trends and key enablers of hydrogen production technologies: A patent-life cycle and econometric analysis', International Journal of Hydrogen Energy, vol. 48, no. 96, pp. 37674-37707.
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With rapid industrialization, rising fossil fuel consumption, and environmental concerns, developing clean and green energy is an inescapable option. Hydrogen has emerged as a significant potential energy carrier and a viable future replacement fuel for fossil fuels due to its renewable and pollution-free properties. Previous review papers have significantly contributed to the body of literature on the various technologies for producing hydrogen by revealing key insights into their working principles and conditions, as well as the economic and environmental aspects. In addition, they also highlighted the potential pathways to enable the application of these technologies in the context of carbon neutrality. However, these studies have not broken down the evolutionary patterns and developmental progress of either fossil fuel-based or renewable energy-based technologies used to produce hydrogen. In addition, the currently available literature does not contain the most recent research that focuses on the evolution and life cycle of each technology category from a chronological point of view. The key drivers, countries/regions, and their contributions to the field's development have received little attention. As a result, it is critical to monitor technological advances in hydrogen energy production and investigate the key enablers of these advancements. Against this backdrop, the current study employs patent analysis tools to achieve four primary goals: (1) to track the development trends in the field of hydrogen production from 2000 to 2019; (2) to identify and compare the recent development trends in the last five years according to the feedstock, i.e., fossil fuel, water, and biomass-based technologies; (3) to predict the technology life cycle of the two main groups of hydrogen production technologies (fossil and renewable); (4) to identify and compare the key drivers of hydrogen production technologies from a statistical standpoint. The findings of the ...
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.
Anaissi, A, Zandavi, SM, Suleiman, B, Naji, M & Braytee, A 2023, 'Multi-objective variational autoencoder: an application for smart infrastructure maintenance', Applied Intelligence, vol. 53, no. 10, pp. 12047-12062.
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AbstractMulti-way data analysis has become an essential tool for capturing underlying structures in higher-order data sets where standard two-way analysis techniques often fail to discover the hidden correlations between variables in multi-way data. We propose a multi-objective variational autoencoder (MO-VAE) method for smart infrastructure damage detection and diagnosis in multi-way sensing data based on the reconstruction probability of autoencoder deep neural network (ADNN). Our method fuses data from multiple sensors in one ADNN at which informative features are being extracted and utilized for damage identification. It generates probabilistic anomaly scores to detect damage, asses its severity and further localize it via a new localization layer introduced in the ADNN. We evaluated our method on multi-way laboratory-based and real-life structural datasets in the area of structural health monitoring for damage diagnosis purposes. The data was collected from our deployed data acquisition system on a cable-stayed bridge in Western Sydney, a reinforced concrete cantilever beam which replicates one of the major structural components on the Sydney Harbour Bridge and a laboratory based building structure obtained from Los Alamos National Laboratory (LANL). Experimental results show that the proposed method can accurately detect structural damage. It was also able to estimate the different levels of damage severity, and capture damage locations in an unsupervised aspect. Compared to the state-of-the-art approaches, our proposed method shows better performance in terms of damage detection and localization.
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 protocol23to 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.
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, Jiménez, P, Sousa, HS, Stewart, MG & Matos, JC 2023, 'Policies towards the resilience of road-based transport networks to wildfire events. The Iberian case', Transportation Research Procedia, vol. 71, pp. 61-68.
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Arango, E, Nogal, M, Sousa, HS, Matos, JC & Stewart, MG 2023, 'GIS-based methodology for prioritization of preparedness interventions on road transport under wildfire events', International Journal of Disaster Risk Reduction, vol. 99, pp. 104126-104126.
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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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Aregawi, BH, Nguyen, HC, Fu, C-C, Ong, HC, Barrow, CJ, Su, C-H, Wu, S-J, Juan, H-Y & Wang, F-M 2023, 'Biodiesel Production through Electrolysis Using an Ionic Liquid, 1-Ethyl-3-Methylimidazolium Chloride as a Supporting Electrolyte', International Journal of Energy Research, vol. 2023, pp. 1-11.
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Electrolysis is a promising approach for biodiesel production. However, low electrical conductivity of a reaction mixture results in a low reaction rate. Thus, this study developed a novel catalyst-free electrolysis process using an ionic liquid as a supporting electrolyte for biodiesel production. Various ionic liquids were assessed, and 1-ethyl-3-methylimidazolium chloride ([Emim]Cl) exhibited the highest electrical conductivity (4.59 mS/cm) and the best electrolytic performance for transesterification. Electrolysis in the presence of [Emim]Cl was subsequently optimized using response surface methodology to maximize biodiesel yield. A maximum biodiesel yield of 97.76% was obtained under the following optimal reaction conditions: electrolysis voltage, 19.42 V; [Emim]Cl amount, 4.43% ( ); water content, 1.62% ( ); methanol to oil molar ratio, 26.38 : 1; and reaction time, 1 h. Notably, [Emim]Cl could be efficiently reused for at least three cycles with a corresponding biodiesel yield of 94.81%. Moreover, the properties of the synthesized biodiesel complied with EN and ASTM standards. The findings of this study indicate that catalyst-free electrolysis using [Emim]Cl as a supporting electrolyte is an eco-friendly and efficient method for biodiesel production.
Armaghani, DJ, Ming, YY, Mohammed, AS, Momeni, E & Maizir, H 2023, 'Effect of SVM Kernel Functions on Bearing Capacity Assessment of Deep Foundations', Journal of Soft Computing in Civil Engineering, vol. 7, no. 3, pp. 111-128.
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Pile foundations are vastly utilized in construction projects where their capacities (pile bearing capacity, PBC) should be determined in different stages of construction. A highly reliable and accurate prediction model can lead to many advantages, such as reducing the construction cost, shortening the construction timeline, and providing safety construction. Hence, the aim of this study is the developments of statistical and artificial intelligence (AI) models for predicting bearing capacities of 141 piles. At the preliminary of the study, features or inputs of this study to predict PBC were selected trough simple regression analysis. Then, this study presents different kernels of support vector machine (SVM) technique, i.e., the dot, the radial basis function (RBF), the polynomial, the neural, and the ANOVA to predict the PBC. The aforementioned models were evaluated by several performance indices and their results were compared using a simple ranking system. The results showed that the SVM-RBF model is able to achieve the highest coefficient of determination, R2 values which are 0.967 and 0.993 for training and testing stages, respectively. It is important to mention that a multiple regression model was also employed to predict PBC values. The other SVM kernels were provided a high degree of accuracy for estimating PBC, however, the SVM-RBF model is recommended to be used as a powerful, highly reliable, and simple solution for PBC prediction.
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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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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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.
Ashyap, AYI, Dahlan, SH, Abidin, ZZ, Afzal, MU, Khee, YS, Majid, HA & Shah, SM 2023, 'Wearable antenna enabled AMC based on polydimethylsiloxane material for WBAN applications', AIP Conference Proceedings, vol. 2956, pp. 080001-080001.
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Asikin-Mijan, N, Juan, JC, Taufiq-Yap, YH, Ong, HC, Lin, Y-C, AbdulKareem-Alsultan, G & Lee, HV 2023, 'Towards sustainable green diesel fuel production: Advancements and opportunities in acid-base catalyzed H2-free deoxygenation process', Catalysis Communications, vol. 182, pp. 106741-106741.
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Asim, M, Mohammad, S, Kanwal, A, Uddin, GM, Khan, AA, Mujtaba, MA, Veza, I, Kalam, MA & Almomani, F 2023, 'Comparative study of the parameters affecting the performance of microchannels' heat exchangers: Latest advances review', Energy Science & Engineering, vol. 11, no. 10, pp. 3869-3887.
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AbstractMicrochannel heat exchangers are heat exchangers with a tube diameter of less than 1 mm. Conventional cooling approaches such as the forced‐air cooling technique fail in high technological compact systems because of the small‐sized surfaces of chips and circuits. In comparison, microchannel heat exchangers are being extensively utilized in compact‐sized devices where a high heat‐transfer medium is required. Moreover, consumers' continued desire for compact products has prompted researchers to study microchannel heat exchangers for their ability to boost the rate of heat transfer that ensures the safety of compact designs. This study presents the evaluation of performance parameters and the manufacturing aspects of microchannel heat exchangers. This study also examines how microchannel heat exchangers are affected by several parameters, including the type of working fluid used, Brownian motion, geometry of the channel, Reynolds number, Nusselt number, Knudsen number, wall resistance of the channel, the effect of gravity, and inlet and outlet arrangement for fluid. Investigating the various geometries for the microchannel indicates that the least pressure drop occurs in square shape cross‐section channels while the highest pressure drop occurs in channels with triangular cross‐sections. Moreover, it has been observed that, with the addition of nanoparticles to the working fluid, the thermal properties of the exchangers as well as the pressure drop increases while at the same time it reduces the boundary layer thickness. In addition, the Reynolds number affects the performance irrespective of the channel geometry. When the fluid is added with nanoparticles, like, Al2O3 and copper oxide (CuO) with different volumetric fractions (φ) of 0%, 0.5%, 1%, 1.5%, and 2%, the performance of the microchannel rises with rising the Reynolds number but conversely...
Asteris, PG, Kokoris, S, Gavriilaki, E, Tsoukalas, MZ, Houpas, P, Paneta, M, Koutzas, A, Argyropoulos, T, Alkayem, NF, Armaghani, DJ, Bardhan, A, Cavaleri, L, Cao, M, Mansouri, I, Mohammed, AS, Samui, P, Gerber, G, Boumpas, DT, Tsantes, A, Terpos, E & Dimopoulos, MA 2023, 'Early prediction of COVID-19 outcome using artificial intelligence techniques and only five laboratory indices', Clinical Immunology, vol. 246, pp. 109218-109218.
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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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Bahmani, H, Mostafaei, H, Ghiassi, B, Mostofinejad, D & Wu, C 2023, 'A comparative study of calcium hydroxide, calcium oxide, calcined dolomite, and metasilicate as activators for slag-based HPC', Structures, vol. 58, pp. 105653-105653.
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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 ...
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, Y, Henry, J, Cheng, E, Perry, S, Mawdsley, D, Wong, BBM, Kaslin, J & Wlodkowic, D 2023, 'Toward Real-Time Animal Tracking with Integrated Stimulus Control for Automated Conditioning in Aquatic Eco-Neurotoxicology', Environmental Science & Technology, vol. 57, no. 48, pp. 19453-19462.
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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, vol. ahead-of-print, no. ahead-of-print, 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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The monkeypox virus poses a new pandemic threat while we are still recovering from COVID-19. Despite the fact that monkeypox is not as lethal and contagious as COVID-19, new patient cases are recorded every day. If preparations are not made, a global pandemic is likely. Deep learning (DL) techniques are now showing promise in medical imaging for figuring out what diseases a person has. The monkeypox virus-infected human skin and the region of the skin can be used to diagnose the monkeypox early because an image has been used to learn more about the disease. But there is still no reliable Monkeypox database that is available to the public that can be used to train and test DL models. As a result, it is essential to collect images of monkeypox patients. The 'MSID' dataset, short form of 'Monkeypox Skin Images Dataset', which was developed for this research, is free to use and can be downloaded from the Mendeley Data database by anyone who wants to use it. DL models can be built and used with more confidence using the images in this dataset. These images come from a variety of open-source and online sources and can be used for research purposes without any restrictions. Furthermore, we proposed and evaluated a modified DenseNet-201 deep learning-based CNN model named MonkeyNet. Using the original and augmented datasets, this study suggested a deep convolutional neural network that was able to correctly identify monkeypox disease with an accuracy of 93.19% and 98.91% respectively. This implementation also shows the Grad-CAM which indicates the level of the model's effectiveness and identifies the infected regions in each class image, which will help the clinicians. The proposed model will also help doctors make accurate early diagnoses of monkeypox disease and protect against the spread of the disease.
Balakrishnan, HK, Dumée, LF, Merenda, A, Aubry, C, Yuan, D, Doeven, EH & Guijt, RM 2023, '3D Printing Functionally Graded Porous Materials for Simultaneous Fabrication of Dense and Porous Structures in Membrane‐Integrated Fluidic Devices', Small Structures, vol. 4, no. 5.
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3D printing provides access to complex multilevel architectures, though the capability to routinely print and integrate structures of controlled porosity is limited. Herein, grayscale digital light projection 3D printing of a polymerization‐induced phase separation ink is introduced to directly 3D print functionally graded porous within a single layer from the same ink formulation. The structural properties of materials printed from a single ink are tuned from an effectively dense to a porous material with interconnected pores up to 250 nm. Heterostructures with the physically dense structure of porosity 0.8% and porous structures with up to 23% can be concurrently formed within a layer, with high spatial resolution inherent of this 3D printing technique. Materials with densities from 1.01 to 1.21 g cm−3 are 3D printed in a wicking device and show wicking rates (H2O) from complete diffusion blockage up to 4.5 mm h−1. Furthermore, a proof‐of‐concept membrane‐integrated fluidic device is used for the elemental metal sensing of iron in soil. The presented single‐step fabrication of functionally graded materials with pixel‐based control within a single layer holds potential for manufacturing and integrating membranes or sorbents for environmental, biotechnology, and healthcare applications.
Bandara, MN, Rabhi, FA & Bano, M 2023, 'A knowledge-driven approach for designing data analytics platforms.', Requir. Eng., vol. 28, no. 2, pp. 195-212.
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Big data analytics technologies are rapidly expanding across all industry sectors as organisations try to make analytics an integral part of their everyday decision-making. Although there are many software tools and libraries to assist analysts and software engineers in developing solutions, organisations are looking for flexible analytics platforms that can address their specific objectives and requirements. To minimise costs, such platforms also need to co-exist with existing IT infrastructures and reuse knowledge and resources already accumulated within the organisation. To address such needs, this paper proposes the Data Analytics Solution Engineering (DASE) framework—a knowledge-driven approach supported by semantic web technologies for requirements engineering, design and development of new data analytics platforms. It includes a meta-model that captures data analytics platform requirements via a Knowledge Base, a set of guidelines that organisations can follow in engineering data analytics platforms and a reference architecture that demonstrates how to use these guidelines. We evaluate the DASE framework through two case studies and demonstrate how it can facilitate knowledge-based and requirements-driven data analytics platform engineering. The resulting data analytics platforms are observed to be user friendly, easy to maintain and flexible in handling changes to requirements. This work contributes to the body of knowledge in knowledge-driven requirements engineering, and data analytics platform engineering by providing a meta-model and a reference architecture that can be tailored to different analytics application domains.
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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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...
Barua, PD, Keles, T, Dogan, S, Baygin, M, Tuncer, T, Demir, CF, Fujita, H, Tan, R-S, Ooi, CP & Rajendra Acharya, U 2023, 'Automated EEG sentence classification using novel dynamic-sized binary pattern and multilevel discrete wavelet transform techniques with TSEEG database', Biomedical Signal Processing and Control, vol. 79, pp. 104055-104055.
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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. 38, no. 6, pp. 7796-7809.
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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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Basack, S, Nimbalkar, S & Zaman, M 2023, 'Recent developments in pile foundations: design, construction, innovations and case studies', International Journal of Geotechnical Engineering, vol. 17, no. 6, pp. 581-582.
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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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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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Begum, Z, Saleem, M, Islam, SU & Saha, SC 2023, 'Numerical Study of Natural Convection Flow in Rectangular Cavity with Viscous Dissipation and Internal Heat Generation for Different Aspect Ratios', Energies, vol. 16, no. 14, pp. 5267-5267.
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Numerical simulations have been performed to investigate the influence of constant volumetric heat generation and viscous dissipation on the unsteady natural convection flow of an incompressible Newtonian fluid contained in a rectangular cavity. The left vertical wall of the cavity is cooled, while the right vertical wall is heated, and the bottom and top walls are adiabatic. A numerical technique based on the implicit finite difference method (IFDM), along with an upwind finite difference scheme and an iterative successive over relaxation (SOR) technique, is employed to solve the governing equations numerically. The effect of physical parameters, namely the modified Rayleigh number (103≤Ra≤107), aspect ratio (1≤A≤4), Prandtl number (Pr=0.7, 1.0, 6.2, 15), volumetric internal heat generation parameter (Qλ=0, 1), and Eckert number (0≤Ec≤10−6), on the streamlines and isotherms are discussed graphically. Variations of maximum stream function, as well as average and local Nusselt number, are also discussed. The results show that the increase in Eckert number from 0 to 10−4 causes the average heat transfer to decrease, while Pr=0.71, Ra=104, and Qλ=0. Additionally, the average heat transfer decreases as the cavity width increases from 1 to 4, while Pr=0.71, Ra = 5×104, Ec=10−6 and Qλ=1. The results of the numerical model used here are in excellent accord with earlier findings.
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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Bennett, NS & Lim, B 2023, 'Assessing the Potential of Heat Pumps to Reduce the Radiator Size on Small Satellites', Energies, vol. 16, no. 10, pp. 4010-4010.
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Future small satellites will demand high-performance on-board electronics, requiring sophisticated approaches to heat rejection beyond simply increasing the radiator surface area. An interesting alternative approach is to increase the surface temperature of the radiator, using a heat pump. In this study, calculations were carried out to compute the theoretical radiator size reduction potential enacted by having a heat pump as part of a satellite’s thermal management system. The practical likelihood of a ‘typical’ vapor compression cycle (VCC) heat pump satisfying theoretical requirements was considered. In agreement with theoretical calculations, employing a ‘typical’ VCC heat pump could either increase or decrease the required radiator surface area. The choice of heat pump and its design is therefore crucial. A heat pump with a large temperature lift is essential for satellite radiator cooling applications, with the coefficient of performance (COP) being less important. Even with a low COP, such as 2.4, a ‘typical’ heat pump providing a large temperature lift, close to 60 °C, could reduce the satellite’s radiator surface area by a factor close to 1.4. This is a significant potential reduction. The decision on whether to pursue this approach compared to alternatives, such as deployable radiators, should consider the relative complexity, cost, weight, size, reliability, etc., of the two options. The focus of this study is VCC heat pumps; however, the results provide performance targets for less mature heat pump technologies, e.g., caloric devices, which could ultimately be applied in space.
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, Z, Zhang, L, He, X & Zhai, Y 2023, 'Effect of oblique incidence angle and frequency content of P and SV waves on the dynamic behavior of liquid tanks', Soil Dynamics and Earthquake Engineering, vol. 171, pp. 107929-107929.
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Bibin, C, Sheeja, R, Devarajan, Y, Raja, T, Hossain, I, Ouladsmane, M & Kalam, MA 2023, 'Process optimization study on the feedstock derived from Cerbera odollam seeds', Biomass Conversion and Biorefinery, vol. 13, no. 17, pp. 16253-16262.
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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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Biswas, K, Shivakumara, P, Pal, U, Lu, T, Blumenstein, M & Lladós, J 2023, 'Classification of aesthetic natural scene images using statistical and semantic features', Multimedia Tools and Applications, vol. 82, no. 9, pp. 13507-13532.
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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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Bliuc, D, Tran, T, Chen, W, Alarkawi, D, Alajlouni, DA, Blyth, F, March, L, Blank, RD & Center, JR 2023, 'Antiresorptive Medication Use Is not Associated With Acute Cardiovascular Risk: An Observational Study', The Journal of Clinical Endocrinology & Metabolism, vol. 108, no. 5, pp. e110-e119.
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AbstractContextBisphosphonates have been reported to be cardioprotective in some, but not all, studies. It is unknown whether denosumab (Dmab) use protects against cardiovascular events (CVEs).ObjectiveTo determine whether oral bisphosphonate (oBP) or Dmab use is associated with CVEs in persons with incident fracture.MethodsParticipants with an incident minimal trauma fracture from the Sax Institute’s 45 and Up Study, a population-based cohort from NSW, Australia, were followed between 2005/2009 and 2017. Questionnaire data were linked to hospital admissions (Admitted Patients Data Collection [APDC]) by the Centre for Health Record Linkage). Medicare Benefit Schedule (MBS) and Pharmaceutical Benefits Scheme (PBS) data sets were provided by Services Australia. Data was stored in a secure computing environment (Secure Unified Research Environment). Fractures, CVEs, and comorbidities were identified from the APDC and oBP and Dmab medication from the PBS. oBP and Dmab users were matched to never users (NoRx) by propensity scores. The main outcome measures were association between oBP and Dmab with CVE (acute myocardial infarction, unstable angina, cerebrovascular accident, and transient ischemic attack) and were determined using a stratified Cox's proportional hazards model.ResultsThere were 880 pairs of oBP and NoRx (616 women) and 770 pairs of Dmab and NoRx (615 women) followed for ∼4.3 years. CVE risk was similar for oBP and NoRx Hazard Ratios (HR) women, 0.88 [95% CI 0.65-1.18]; men, 1.07 [95% CI 0.72-1.57]). Similar findings were obtained for Dmab (Hazard Ratios (HR) women, 1.08 [95% CI 0.78-1.50]; men, 1.55 [95% CI 0.96-2.48]).Conclusion...
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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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, 'Front Cover: General Doping Chemistry of Carbon Materials (ChemNanoMat 4/2023)', ChemNanoMat, vol. 9, no. 4.
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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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The search for missing persons is a major challenge for investigations involving presumed deceased individuals. Currently, the most effective tool is the use of cadaver-detection dogs; however, they are limited by their cost, limited operation times, and lack of granular information reported to the handler. Thus, there is a need for discrete, real-time detection methods that provide searchers explicit information as to whether human-decomposition volatiles are present. A novel e-nose (NOS.E) developed in-house was investigated as a tool to detect a surface-deposited individual over time. The NOS.E was able to detect the victim throughout most stages of decomposition and was influenced by wind parameters. The sensor responses from different chemical classes were compared to chemical class abundance confirmed by two-dimensional gas chromatography - time-of-flight mass spectrometry. The NOS.E demonstrated its ability to detect surface-deposited individuals days and weeks since death, demonstrating its utility as a detection tool.
Bruza, PD, Fell, L, Hoyte, P, Dehdashti, S, Obeid, A, Gibson, A & Moreira, C 2023, 'Contextuality and context-sensitivity in probabilistic models of cognition', Cognitive Psychology, vol. 140, pp. 101529-101529.
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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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Büscher, J, Paranjape, A, Möhle, R, Polikarpov, M, Plettenberg, N, Zwinkau, R, Deuse, J & Schmitt, RH 2023, 'Bauteile ressourceneffizient reinigen mithilfe von KI', JOT Journal für Oberflächentechnik, vol. 63, no. 1, pp. 40-43.
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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, Z, Yang, F, Song, Y, Liu, Y, Liu, W, Wang, Q & Sun, X 2023, 'Semiconducting mineral induced photochemical conversion of PAHs in aquatic environment: Mechanism study and fate prediction', Science of The Total Environment, vol. 860, pp. 160382-160382.
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Semiconducting minerals (such as iron sulfides) are highly abundant in surface water, but their influences on the natural photochemical process of contaminants are still unknown. By simulating the natural water environment under solar irradiation, this work comprehensively investigated the photochemical processes of anthracene (a typical Polycyclic Aromatic Hydrocarbons) in both freshwater and seawater. The results show that the natural pyrite (NP) significantly promotes the degradation of anthracene under solar illumination via 1) NP induced photocatalytic degradation of anthracene, and 2) Fenton reaction due to the NP induced photocatalytic generation of H2O2. The material characterization and theoretical calculation reveal that the natural impurity in NP enlarges its band gap, which limits the utilization of solar spectra to shorter wavelength. The contribution of generated reactive intermediates on anthracene degradation follows the order of 1O2 >OH > O2- in freshwater and O2- >1O2 >OH in seawater. The photochemically generated H2O2 is a vital source for OH generation (from Fenton reaction). The steady-state concentration of OH, 1O2 and O2- in freshwater were monitored as 3.0 × 10-15 M, 1.1 × 10-13 M, and 4.5 × 10-14 M, respectively. However, the OH concentration in seawater can be negligible due to the quenching effects by halides, and the 1O2 and O2- concentrations are higher than that in freshwater. An anthracene degradation kinetic model was built based on the experimentally determined reactive intermediates concentration and its second order rate constant with anthracene. Moreover, the anthracene degradation pathway was proposed based on intermediates analysis and DFT calculation, and its toxicity evolution during the photochemical process was assessed by quantitative structure-activity relationship (QSAR) based prediction. This finding suggests that the natural semiconducting minerals can affect the fate and environmental risks of contaminants...
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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© 2020 International Council for Small Business. Ibero-American researchers show an increasing number of studies on entrepreneurship and innovation research. This article analyzes the journals and universities that published research on the discipline developed by Ibero-American authors between 1986 and 2015. The work uses the Web of Science database and provides several bibliometric indicators. The results show that the most outstanding researchers of the region come mainly from Spain and Portugal. In particular, Spanish researchers are the most productive and influential authors in the region. A small group of researchers from Chile, Argentina, and Mexico are also very influential. Latin American researchers must deepen their international academic networks.
Cao, C, Nogueira, L, Zhu, H, Keller, J, Best, G, Garg, R, Kohanbash, D, Maier, J, Zhao, S, Yang, F, Cujic, K, Darnley, R, DeBortoli, R, Drozd, B, Sun, P, Higgins, I, Willits, S, Armstrong, G, Zhang, J, Hollinger, G, Travers, M & Scherer, S 2023, 'Exploring the Most Sectors at the DARPA Subterranean Challenge Finals', Field Robotics, vol. 3, no. 1, pp. 801-836.
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Autonomous robot navigation in austere environments is critical to missions like “search and rescue”, yet it remains difficult to achieve. The recent DARPA Subterranean Challenge (SubT) highlights prominent challenges including GPS-denied navigation through rough terrains, rapid exploration in large-scale three-dimensional (3D) space, and interrobot coordination over unreliable communication. Solving these challenges requires both mechanical resilience and algorithmic intelligence. Here, we present our approach that leverages a fleet of custom-built heterogeneous robots and an autonomy stack for robust navigation in challenging environments. Our approach has demonstrated superior navigation performance in the SubT Final Event, resulting in the fastest traversal and most thorough exploration of the environment, which won the “Most Sectors Explored Award.” This paper details our approach from two aspects: mechanical designs of a marsupial ground-and-aerial system to overcome mobility challenges and autonomy algorithms enabling collective rapid exploration. We also provide lessons learned in the design, development, and deployment of complex but resilient robotic systems to overcome real-world navigation challenges.
Cao, J, Gu, Z & Hasan, I 2023, 'Exploring Accounting Research Topic Evolution: An Unsupervised Machine Learning Approach', Journal of International Accounting Research, vol. 22, no. 3, pp. 1-30.
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ABSTRACT This study explores the evolution of accounting research by utilizing an unsupervised machine learning approach. We aim to identify the latent topics of accounting from the 1980s up to 2018, the dynamics and emerging topics of accounting research, and the economic reasons behind those changes. First, based on 23,220 articles from 46 accounting journals, we identify 55 topics using the latent Dirichlet allocation model. To illustrate the connection between topics, we use HistCite to generate a citation map along a timeline. The citation clusters demonstrate the “tribalism” phenomenon in accounting research. We then implement the dynamic topic model to reveal the dynamics of topics to show changes in accounting research. The emerging research trends are identified from the topic analytics. We further explore the economic reasons and in-depth insights into the topic evolution, indicating the economic development embeddedness nature of accounting research. JEL Classifications: B26; M40.
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 in Finance: Challenges, Techniques, and Opportunities', ACM Computing Surveys, vol. 55, no. 3, pp. 1-38.
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AI in finance refers to the applications of AI techniques in financial businesses. This area has attracted attention for decades, with both classic and modern AI techniques applied to increasingly broader areas of finance, economy, and society. In contrast to reviews on discussing the problems, aspects, and opportunities of finance benefited from specific or some new-generation AI and data science (AIDS) techniques or the progress of applying specific techniques to resolving certain financial problems, this review offers a comprehensive and dense landscape of the overwhelming challenges, techniques, and opportunities of AIDS research in finance over the past decades. The challenges of financial businesses and data are first outlined, followed by a comprehensive categorization and a dense overview of the decades of AIDS research in finance. We then structure and illustrate the data-driven analytics and learning of financial businesses and data. A comparison, criticism, and discussion of classic versus modern AIDS techniques for finance follows. Finally, the open issues and opportunities to address future AIDS-empowered finance and finance-motivated AIDS research are discussed.
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, 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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In this study, we investigate the use of global information to speed up the learning process and increase the cumulative rewards of reinforcement learning (RL) in competition tasks. Within the framework of actor-critic RL, we introduce multiple cooperative critics from two levels of a hierarchy and propose an RL from the hierarchical critics (RLHC) algorithm. In our approach, each agent receives value information from local and global critics regarding a competition task and accesses multiple cooperative critics in a top-down hierarchy. Thus, each agent not only receives low-level details, but also considers coordination from higher levels, thereby obtaining global information to improve the training performance. Then, we test the proposed RLHC algorithm against a benchmark algorithm, that is, proximal policy optimization (PPO), under four experimental scenarios consisting of tennis, soccer, banana collection, and crawler competitions within the Unity environment. The results show that RLHC outperforms the benchmark on these four competitive tasks.
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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Machine learning provides many powerful and effective techniques foranalysing heterogeneous electronic health records (EHR). Administrative HealthRecords (AHR) are a subset of EHR collected for administrative purposes, andthe use of machine learning on AHRs is a growing subfield of EHR analytics.Existing reviews of EHR analytics emphasise that the data-modality of the EHRlimits the breadth of suitable machine learning techniques, and pursuablehealthcare applications. Despite emphasising the importance of data modality,the literature fails to analyse which techniques and applications are relevantto AHRs. AHRs contain uniquely well-structured, categorically encoded recordswhich are distinct from other data-modalities captured by EHRs, and they canprovide valuable information pertaining to how patients interact with thehealthcare system. This paper systematically reviews AHR-based research, analysing 70 relevantstudies and spanning multiple databases. We identify and analyse which machinelearning techniques are applied to AHRs and which health informaticsapplications are pursued in AHR-based research. We also analyse how thesetechniques are applied in pursuit of each application, and identify thelimitations of these approaches. We find that while AHR-based studies aredisconnected from each other, the use of AHRs in health informatics research issubstantial and accelerating. Our synthesis of these studies highlights theutility of AHRs for pursuing increasingly complex and diverse researchobjectives despite a number of pervading data- and technique-based limitations.Finally, through our findings, we propose a set of future research directionsthat can enhance the utility of AHR data and machine learning techniques forhealth informatics research.
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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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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Chandrakant Nikam, K, Jathar, L, Shelare, SD, Shahapurkar, K, Dambhare, S, Soudagar, MEM, Mubarak, NM, Ahamad, T & Kalam, MA 2023, 'Parametric analysis and optimization of 660 MW supercritical power plant', Energy, vol. 280, pp. 128165-128165.
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Chang, L, Feng, X, Yao, K, Qin, L & Zhang, W 2023, 'Accelerating Graph Similarity Search via Efficient GED Computation.', IEEE Trans. Knowl. Data Eng., vol. 35, pp. 4485-4498.
Chang, X, Ren, P, Xu, P, Li, Z, Chen, X & Hauptmann, A 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.
Chaturvedi, K, Braytee, A, Li, J & Prasad, M 2023, 'SS-CPGAN: Self-Supervised Cut-and-Pasting Generative Adversarial Network for Object Segmentation', Sensors, vol. 23, no. 7, pp. 3649-3649.
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This paper proposes a novel self-supervised based Cut-and-Paste GAN to perform foreground object segmentation and generate realistic composite images without manual annotations. We accomplish this goal by a simple yet effective self-supervised approach coupled with the U-Net discriminator. The proposed method extends the ability of the standard discriminators to learn not only the global data representations via classification (real/fake) but also learn semantic and structural information through pseudo labels created using the self-supervised task. The proposed method empowers the generator to create meaningful masks by forcing it to learn informative per-pixel and global image feedback from the discriminator. Our experiments demonstrate that our proposed method significantly outperforms the state-of-the-art methods on the standard benchmark datasets.
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.
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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Urban traffic forecasting is the cornerstone of the intelligent transportation system (ITS). Existing methods focus on spatial-temporal dependency modeling, while two intrinsic properties of the traffic forecasting problem are overlooked. First, the complexity of diverse forecasting tasks is nonuniformly distributed across various spaces (e.g., suburb versus downtown) and times (e.g., rush hour versus off-peak). Second, the recollection of past traffic conditions is beneficial to the prediction of future traffic conditions. Based on these properties, we propose a bidirectional spatial-temporal adaptive transformer (Bi-STAT) for accurate traffic forecasting. Bi-STAT adopts an encoder-decoder architecture, where both the encoder and the decoder maintain a spatial-adaptive transformer and a temporal-adaptive transformer structure. Inspired by the first property, each transformer is designed to dynamically process the traffic streams according to their task complexities. Specifically, we realize this by the recurrent mechanism with a novel dynamic halting module (DHM). Each transformer performs iterative computation with shared parameters until DHM emits a stopping signal. Motivated by the second property, Bi-STAT utilizes one decoder to perform the present → past recollection task and the other decoder to perform the present → future prediction task. The recollection task supplies complementary information to assist and regularize the prediction task for a better generalization. Through extensive experiments, we show the effectiveness of each module in Bi-STAT and demonstrate the superiority of Bi-STAT over the state-of-the-art baselines on four benchmark datasets. The code is available at https://github.com/chenchl19941118/Bi-STAT.git.
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, D, Wu, C, Li, J & Liao, K 2023, 'An overpressure-time history model of methane-air explosion in tunnel-shape space', Journal of Loss Prevention in the Process Industries, vol. 82, pp. 105004-105004.
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This study investigated methane-air explosion in tunnel-shape space and developed an overpressure-time history model based on numerical results. The findings revealed that for the progressively vented gas explosion with movable steel obstacles in a 20 m long tunnel, the inner peak overpressure increased as the activation pressure of the tunnel top cover got higher but remained below 6 bar. However, as the activation pressure increased to 8 bar or higher, the peak inner overpressure remained unchanged. As the segment cover panel became wider, the peak pressure was almost unchanged, but the pressure duration and impulse declined significantly. The peak pressure and impulse increased as the tunnel length vary from 10 to 30 m. With fixed tunnel length, higher blast pressure but lower impulse was observed as the inner obstacles were closer or the activation pressure of obstacles was higher. It is also found that a local enlarged space in the tunnel enhanced the peak pressure significantly. An overpressure time history model for the tunnel with fixed top cover and enlarged end zone was established. The model considered activation pressure of vent cover, area and length of vent opening, methane concentration, number and blockage ratio of fixed obstacles was developed to calculate the overpressure and corresponding time at characteristic points of the pressure-history curve. The cubic Hermite interpolation algorithm and a specially tuned formula consisting of the power and exponential function were used to interpolate pressure values between characteristic points. The proposed model can predict both the peak pressure and the overpressure time history with acceptable accuracy.
Chen, F, Zhou, J, Holzinger, A, Fleischmann, KR & Stumpf, S 2023, 'Artificial Intelligence Ethics and Trust: From Principles to Practice', IEEE Intelligent Systems, vol. 38, no. 6, pp. 5-8.
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Chen, H, Tian, K, Qing, T, Liu, X, Mao, J, Qin, J & Jiang, S 2023, 'Efficient removal of tetrabromobisphenol A through persulfate activation by α-MnO2 nanofiber coated with graphene oxide', Applied Surface Science, vol. 641, pp. 158445-158445.
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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, Indraratna, B, Vinod, JS, Ngo, T & Liu, Y 2023, 'Discrete element modelling of the effects of particle angularity on the deformation and degradation behaviour of railway ballast', Transportation Geotechnics, vol. 43, pp. 101154-101154.
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Railroad ballast exhibits distinct morphological characteristics represented by shape irregularity, corner angularity, and surface texture. Upon repeated train loading, the morphology of ballast undergoes inevitable degradation, particularly in terms of its corner sharpness, which can affect track performance and even pose a substantial threat to operational safety. These aspects have rarely been captured insightfully in most DEM studies on ballast. In contrast, this study examines the influence of particle angularity on the deformation and degradation behaviour of railway ballast upon repeated loading using the discrete element method (DEM). The angularity of ballast particles is captured and quantified using the CT scanning technology in conjunction with an image-based processing strategy, after which the irregularly shaped particles are reconstructed in the DEM. In this numerical procedure, aggregates with varying angularities are created by incorporating a particle degradation subroutine to capture corner abrasion and surface attrition of ballast to mimick real-life field processes. The macro-response of a typical ballasted track subjected to cyclic rail loading is investigated, and the results show that as the angularity increases, the permanent deformation of the track corresponds to a lower permanent strain rate and a higher resilient modulus. However, the opposite behaviour is observed if excessive breakage of the aggregates occurs that reduces the angularity of the individual particles. In this study, detailed microscopic analysis based on DEM in terms of interparticle interaction and associated vibration velocity has also been performed. The results offer distinct clarity to the essential micro-mechanisms embracing particle angularity, and the accompanying influence on the deformation and degradation characteristics of ballast is elucidated with greater insight.
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, J, Zhou, Z, Miao, Y, Liu, H, Huang, W, Chen, Y, Jia, L, Zhang, W & Huang, J 2023, 'Preparation of CS@BAC composite aerogel with excellent flame-retardant performance, good filtration for PM2.5 and strong adsorption for formaldehyde', Process Safety and Environmental Protection, vol. 173, pp. 354-365.
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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, L, Zhang, Y, Liang, J, Li, Y, Zhang, J, Fang, W, Zhang, P, Zhang, G & Hao Ngo, H 2023, 'Improvement of anaerobic digestion containing sulfur with conductive materials: Focusing on recent advances and internal biological mechanisms', Chemical Engineering Journal, vol. 472, pp. 144867-144867.
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Chen, N, Zhang, X, Du, Q, Huo, J, Wang, H, Wang, Z, Guo, W & Ngo, HH 2023, 'Advancements in swine wastewater treatment: Removal mechanisms, influential factors, and optimization strategies', Journal of Water Process Engineering, vol. 54, pp. 103986-103986.
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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, C, Zhong, Y, Tang, B, Xie, Q, Guo, R, Qiao, Z, Li, C, Ge, Y & Zhu, J 2023, 'Association between preoperative serum myoglobin and acute kidney injury after Stanford Type A aortic dissection surgery', Clinica Chimica Acta, vol. 541, pp. 117232-117232.
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BACKGROUND: Acute kidney injury (AKI) is a common complication after Type A aortic dissection (TAAD) surgery, and it is associated with poor outcomes. The nephrotoxic effect of myoglobin was established, but its correlation with AKI following TAAD repair still lacks sufficient evidence. We clarified the correlation between preoperative serum myoglobin (pre-sMyo) concentrations and AKI after TAAD surgery. METHOD: A retrospective analysis was performed on the perioperative data of 382 patients treated with TAAD surgery at Beijing Anzhen Hospital. AKI was defined and classified according to the criteria established by the Kidney Disease: Improving Global Outcomes Acute Kidney Injury Work Group. We attempted to determine the correlation between pre-sMyo concentrations and postoperative AKI. RESULTS: The incidences of Stage 1, 2, and 3 AKI were 37.3 % (57/153), 23.5 % (36/153), and 39.2 % (60/153), respectively. The pre-sMyo concentrations of the AKI group were significantly increased than the non-AKI group [43.1 (21.4, 107.5) vs 26.4 (18.0, 37.2), P < 0.001]. Pre-sMyo concentrations have a linear correlation with preoperative renal function-related indicators. The multivariable logistic regression analysis showed that Ln (pre-sMyo) was an independent risk factor for AKI. When the pre-sMyo concentration was at the fourth quartile [109.3 (64.8, 213.4) ng/ml], the risk of developing any-stage and severe AKI was significantly increased (OR = 4.333, 95 % CI: 2.364-7.943, P < 0.001; OR = 3.862, 95 %, CI: 2.011-7.419, P < 0.001). This difference persisted after adjustment (OR = 3.830, 95 % CI: 1.848-7.936, P < 0.001; OR = 2.330, 95 % CI: 1.045-5.199, P = 0.039). Furthermore, pre-sMyo concentrations were not affected by lower limb malperfusion, myocardial malperfusion, and cardiac tamponade. CONCLUSIONS: Increased pre-sMyo concentrations correlated with postoperative AKI in TAAD, which may increase the risk of developing any-stage AKI and severe AKI after TAAD surgery.
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, Chen, X, Zeng, RJ, Nie, W-B, Yang, L, Wei, W & Ni, B-J 2023, 'Instrumental role of bioreactors in nitrate/nitrite-dependent anaerobic methane oxidation-based biotechnologies for wastewater treatment: A review', Science of The Total Environment, vol. 857, no. Pt 3, pp. 159728-159728.
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Recently, the nitrate/nitrite-dependent anaerobic methane oxidation (n-DAMO) processes have become a research hotspot in the field of wastewater treatment. The n-DAMO processes could not only mitigate direct and indirect carbon emissions from wastewater treatment plants but also strengthen biological nitrogen removal. However, the applications of n-DAMO-based biotechnologies face practical difficulties mainly caused by the distinctive properties of n-DAMO microorganisms and the limited/availability of methane with poor solubility. In this sense, the choice of bioreactors will play important roles that influence the growth and functioning of n-DAMO microorganisms, thus enabling dedicated development of the n-DAMO processes and efficient applications of n-DAMO-based biotechnologies. Therefore, this paper aims to discuss the three commonly-applied types of bioreactors, covering the individual working principle and state-of-the-art removal performance of nitrogen as well as dissolved methane observed when adopted for n-DAMO-based biotechnologies. With noted limitations for each bioreactor type, several key perspectives were proposed which hopefully would inspire future investigation and practical applications of the n-DAMO processes.
Chen, X, Chen, Z, Ngo, HH, Mao, Y, Cao, K, Shi, Q, Lu, Y & Hu, H-Y 2023, 'Comparison of inactivation characteristics between Gram-positive and Gram-negative bacteria in water by synergistic UV and chlorine disinfection', Environmental Pollution, vol. 333, pp. 122007-122007.
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Chen, X, Feng, Z, Andrew Zhang, J, Wei, Z, Yuan, X & Zhang, P 2023, 'Sensing-Aided Uplink Channel Estimation for Joint Communication and Sensing', IEEE Wireless Communications Letters, vol. 12, no. 3, pp. 441-445.
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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, D, Nie, W-B, Yang, L, Wei, W, Ni, B-J & Chen, X 2023, 'Impacts of Biofilm Properties on the Start-Up and Performance of a Membrane Biofilm Reactor Performing Anammox and Nitrate/Nitrite-Dependent Anaerobic Methane Oxidation Integrated Processes: A Model-Based Investigation', ACS ES&T Water, vol. 3, no. 4, pp. 1141-1149.
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Even though the membrane biofilm reactor (MBfR) performing the anammox and nitrate/nitrite-dependent anaerobic methane oxidation (n-DAMO) integrated processes has been known to enable complete nitrogen removal, the effects of biofilm properties on such an MBfR are yet to be disclosed. In this work, a biofilm model was constructed to investigate the effects of the initial microbial composition of the biofilm, the initial biofilm thickness, the boundary layer thickness of the biofilm, and the diffusivity of solutes in the biofilm structure on the start-up process and steady-state performance of the MBfR performing anammox/n-DAMO. The results showed that the four biofilm properties would not affect the steady-state performance but would significantly regulate the start-up time of the MBfR. Unless the MBfR was operated under undesired operational conditions, inoculation of sludge comprised mainly of anammox bacteria or/and n-DAMO archaea to form a thin initial biofilm would accelerate the start-up process of the MBfR. Moreover, measures could be taken to reduce the boundary layer thickness and the diffusivity of solutes in the biofilm structure, the latter of which would also enhance methane utilization. This work would provide valuable practical guidance for the rapid establishment of the MBfR with a high-level treatment capacity based on anammox/n-DAMO.
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, Yao, L, Wang, X, Sun, A & Sheng, QZ 2023, 'Generative Adversarial Reward Learning for Generalized Behavior Tendency Inference', IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 10, pp. 9878-9889.
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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, X, Zhao, Q, Yang, L, Wei, W, Ni, B-J & Chen, X 2023, 'Impacts of granular sludge properties on the bioreactor performing nitrate/nitrite-dependent anaerobic methane oxidation/anammox processes', Bioresource Technology, vol. 386, pp. 129510-129510.
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Chen, Y & Jupp, JR 2023, 'Challenges to requirements management in complex rail transport projects', International Journal of Product Lifecycle Management, vol. 15, no. 2, pp. 139-177.
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Chen, Y, Huang, W, Jiang, X, Zhang, T, Wang, Y, Yan, B, Wang, Z, Chen, Q, Xing, Y, Li, D & Long, G 2023, 'UbiMeta: A Ubiquitous Operating System Model for Metaverse', International Journal of Crowd Science, vol. 7, no. 4, pp. 180-189.
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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, 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, 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-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. 71, no. 4, pp. 3000-3010.
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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, Shi, X, Zhang, J, Wu, L, Wei, W & Ni, B-J 2023, 'Nanoplastics are significantly different from microplastics in urban waters', Water Research X, vol. 19, pp. 100169-100169.
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Microplastics (MPs) and nanoplastics (NPs) are ubiquitous and intractable in urban waters. Compared with MPs, the smaller NPs have shown distinct physicochemical features, such as Brownian motion, higher specific surface area, and stronger interaction with other pollutants. Therefore, the qualitative and quantitative analysis of NPs is more challenging than that of MPs. Moreover, these characteristics endow NPs with significantly different environmental fate, interactions with pollutants, and eco-impacts from those of MPs in urban waters. Herein, we critically analyze the current advances in the difference between MPs and NPs in urban waters. Analytical challenges, fate, interactions with surrounding pollutants, and eco-impacts of MPs and NPs are comparably discussed., The characterizations and fate studies of NPs are more challenging compared to MPs. Furthermore, NPs in most cases exhibit stronger interactions with other pollutants and more adverse eco-impacts on living things than MPs. Subsequently, perspective in this field is proposed to stimulate further size-dependent studies on MPs and NPs. This review would benefit the understanding of the role of NPs in the urban water ecosystem and guide future studies on plastic pollution management.
Chen, Z, Wei, W, Chen, H & Ni, B-J 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, Yun, S, Wu, L, Zhang, J, Shi, X, Wei, W, Liu, Y, Zheng, R, Han, N & Ni, B-J 2023, 'Waste-Derived Catalysts for Water Electrolysis: Circular Economy-Driven Sustainable Green Hydrogen Energy', Nano-Micro Letters, vol. 15, no. 1, p. 4.
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AbstractThe sustainable production of green hydrogen via water electrolysis necessitates cost-effective electrocatalysts. By following the circular economy principle, the utilization of waste-derived catalysts significantly promotes the sustainable development of green hydrogen energy. Currently, diverse waste-derived catalysts have exhibited excellent catalytic performance toward hydrogen evolution reaction (HER), oxygen evolution reaction (OER), and overall water electrolysis (OWE). Herein, we systematically examine recent achievements in waste-derived electrocatalysts for water electrolysis. The general principles of water electrolysis and design principles of efficient electrocatalysts are discussed, followed by the illustration of current strategies for transforming wastes into electrocatalysts. Then, applications of waste-derived catalysts (i.e., carbon-based catalysts, transitional metal-based catalysts, and carbon-based heterostructure catalysts) in HER, OER, and OWE are reviewed successively. An emphasis is put on correlating the catalysts’ structure–performance relationship. Also, challenges and research directions in this booming field are finally highlighted. This review would provide useful insights into the design, synthesis, and applications of waste-derived electrocatalysts, and thus accelerate the development of the circular economy-driven green hydrogen energy scheme.
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, p. 210.
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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.
Chen, Z, Zheng, R, Zou, H, Wang, R, Huang, C, Dai, W, Wei, W, Duan, L, Ni, B-J & Chen, H 2023, 'Amorphous iron-doped nickel boride with facilitated structural reconstruction and dual active sites for efficient urea electrooxidation', Chemical Engineering Journal, vol. 465, pp. 142684-142684.
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Chen, Z, Zuo, W, Zhou, K, Li, Q, Huang, Y & E, J 2023, 'Multi-factor impact mechanism on the performance of high temperature proton exchange membrane fuel cell', Energy, vol. 278, pp. 127982-127982.
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In order to reveal the quantitative impacts of multi-factors on the performance of high-temperature proton exchange membranes fuel cell (HT-PEMFC), in this work, Taguchi experimental design, grey relational analysis, and analysis of variance are combined to explore the influence of working temperature, working pressure, anode stoichiometric ratio, GDL porosity, and membrane thickness on power density, system efficiency, and exergy efficiency of HT-PEMFC. Firstly, by Taguchi experimental design, the highest power density, system efficiency and exergy efficiency of HT-PEMFC arrives at 0.6895 W cm−2, 38.19% and 48.65%, respectively. Secondly, based on grey relational analysis and analysis of variance, the impact order of multi-factors on power density, system efficiency, and exergy efficiency of HT-PEMFC is determined. Finally, it is discovered that the membrane thickness makes the most significant contribution on the power density of HT-PEMFC, while the anode stoichiometry ratio makes the most significant contribution on the system efficiency and exergy efficiency of HT-PEMFC. This work provides a significant reference and valuable guidance for designing HT-PEMFC.
Chen, Z, Zuo, W, Zhou, K, Li, Q, Huang, Y & E, J 2023, 'Multi-objective optimization of proton exchange membrane fuel cells by RSM and NSGA-II', Energy Conversion and Management, vol. 277, pp. 116691-116691.
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This study optimized the performance of a proton exchange membrane fuel cell by combining the response surface methodology and non-dominated ranking genetic algorithm. Firstly, the design variables are determined, including operating pressure (p), operating temperature (T), Anode stoichiometry ratio (λa), thickness of the proton exchange membrane (Hmem) and gas diffusion layer (GDL) porosity (εGDL). The objective functions are also identified, including power density (P), system efficiency (η) and exergy efficiency. Then, the Box-Behnken design is employed to arrange the numerical investigations. Analysis of variance is used to verify the appropriateness and reliability of the constructed regression models. Response surface analysis is used to show the interaction between each pair of design parameters. Finally, the Pareto optimal frontier is obtained by non-dominated ranking genetic algorithm II and the regression models constructed by response surface methodology. The Pareto optimal solution offers a power density of 0.6327 W·cm−2, a system efficiency of 26.16% and an exergy efficiency of 43.94 %, which is 13.18 %, 7.06 % and 20.29 % better than the initial direct current channel, respectively. The corresponding design variables is p = 2.6498 atm, T = 341.621 K, λa = 1.1808, Hmem = 0.0577 mm and εGDL = 0.4908. This work provides a new multi-objective optimization method for designing more efficient proton exchange membrane fuel cells.
Cheng, F, Li, Y, Deng, L, Yang, Y & Yin, L 2023, 'Sub‐THz 3D printed lens based on diffractive neural network for low‐cost detection of orbital angular momentum states', Microwave and Optical Technology Letters, vol. 65, no. 8, pp. 2196-2200.
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AbstractAccurate detection of orbital angular momentum (OAM) is one of the critical techniques for OAM‐related communications. However, conventional OAM identification approaches are often complicated and costly. In this letter, a convenient method based on diffraction neural network (DNN) is presented for OAM states detection in the sub‐terahertz (sub‐THz) regime. The optimal phase distribution of the lens for OAM state detection is obtained from DNN training. Then, a prototype based on three‐dimensional printing is fabricated according to the training results. Numerical simulation and experiment are performed at 140 GHz to verify the proposed approach. The results show that the proposed method can accurately detect the OAM state with lower complexity. This convenient detection approach can provide a novel pathway for realizing low‐cost THz OAM receiving systems, promoting the development of future communication technology.
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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Cheng, Z, Zhu, T, Zhu, C, Ye, D, Zhou, W & Yu, PS 2023, 'Privacy and evolutionary cooperation in neural-network-based game theory', Knowledge-Based Systems, vol. 282, pp. 111076-111076.
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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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Choi, W, Fang, J, Kim, J, Love, N, Saeys, M & Wong, M 2023, 'ACS ES&T Engineering’s 2022 Excellence in Review Awards', ACS ES&T Engineering, vol. 3, no. 8, pp. 1053-1054.
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Choi, WWY, Sánchez, C, Li, JJ, Dinarvand, M, Adomat, H, Ghaffari, M, Khoja, L, Vafaee, F, Joshua, AM, Chi, KN, Guns, EST & Hosseini-Beheshti, E 2023, 'Extracellular vesicles from biological fluids as potential markers in castration resistant prostate cancer', Journal of Cancer Research and Clinical Oncology, vol. 149, no. 8, pp. 4701-4717.
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AbstractPurposeExtracellular vesicles (EV) secreted from cancer cells are present in various biological fluids, carrying distinctly different cellular components compared to normal cells, and have great potential to be used as markers for disease initiation, progression, and response to treatment. This under-utilised tool provides insights into a better understanding of prostate cancer.MethodsEV from serum and urine of healthy men and castration-resistant prostate cancer (CRPC) patients were isolated and characterised by transmission electron microscopy, particle size analysis, and western blot. Proteomic and cholesterol liquid chromatography-mass spectrometry (LC–MS) analyses were conducted.ResultsThere was a successful enrichment of small EV/exosomes isolated from serum and urine. EV derived from biological fluids of CRPC patients had significant differences in composition when compared with those from healthy controls. Analysis of matched serum and urine samples from six prostate cancer patients revealed specific EV proteins common in both types of biological fluid for each patient.ConclusionSome of the EV proteins identified from our analyses have potential to be used as CRPC markers. These markers may depict a pattern in cancer progression through non-invasive sample collection.
Chu, NH, Hoang, DT, Nguyen, DN, Van Huynh, N & Dutkiewicz, E 2023, 'Joint Speed Control and Energy Replenishment Optimization for UAV-Assisted IoT Data Collection With Deep Reinforcement Transfer Learning', IEEE Internet of Things Journal, vol. 10, no. 7, pp. 5778-5793.
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Unmanned aerial vehicle (UAV)-assisted data collection has been emerging as a prominent application due to its flexibility, mobility, and low operational cost. However, under the dynamic and uncertainty of IoT data collection and energy replenishment processes, optimizing the performance for UAV collectors is a very challenging task. Thus, this paper introduces a novel framework that jointly optimizes the flying speed and energy replenishment for each UAV to significantly improve the overall system performance (e.g., data collection and energy usage efficiency). Specifically, we first develop a Markov decision process to help the UAV automatically and dynamically make optimal decisions under the dynamics and uncertainties of the environment. Although traditional reinforcement learning algorithms such as Q-learning and deep Q-learning can help the UAV to obtain the optimal policy, they often take a long time to converge and require high computational complexity. Therefore, it is impractical to deploy these conventional methods on UAVs with limited computing capacity and energy resource. To that end, we develop advanced transfer learning techniques that allow UAVs to “share” and “transfer” learning knowledge, thereby reducing the learning time as well as significantly improving learning quality. Extensive simulations demonstrate that our proposed solution can improve the average data collection performance of the system up to 200% and reduce the convergence time up to 50% compared with those of conventional methods.
Chu, NH, Nguyen, DN, Hoang, DT, Pham, Q-V, Phan, KT, Hwang, W-J & Dutkiewicz, E 2023, 'AI-Enabled mm-Waveform Configuration for Autonomous Vehicles With Integrated Communication and Sensing', IEEE Internet of Things Journal, vol. 10, no. 19, pp. 16727-16743.
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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.
Chugh, D, Mittal, H, Saxena, A, Chauhan, R, Yafi, E & Prasad, M 2023, 'Augmentation of Densest Subgraph Finding Unsupervised Feature Selection Using Shared Nearest Neighbor Clustering', Algorithms, vol. 16, no. 1, pp. 28-28.
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Determining the optimal feature set is a challenging problem, especially in an unsupervised domain. To mitigate the same, this paper presents a new unsupervised feature selection method, termed as densest feature graph augmentation with disjoint feature clusters. The proposed method works in two phases. The first phase focuses on finding the maximally non-redundant feature subset and disjoint features are added to the feature set in the second phase. To experimentally validate, the efficiency of the proposed method has been compared against five existing unsupervised feature selection methods on five UCI datasets in terms of three performance criteria, namely clustering accuracy, normalized mutual information, and classification accuracy. The experimental analyses have shown that the proposed method outperforms the considered methods.
Ci, J, Guo, Z, Long, H, Wen, S & Huang, T 2023, 'Multiple asymptotical ω-periodicity of fractional-order delayed neural networks under state-dependent switching', Neural Networks, vol. 157, pp. 11-25.
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This paper presents theoretical results on multiple asymptotical ω-periodicity of a state-dependent switching fractional-order neural network with time delays and sigmoidal activation functions. Firstly, by combining the geometrical properties of activation functions with the range of switching threshold, a partition of state space is given. Then, the conditions guaranteeing that the solutions can approach each other infinitely in each positive invariant set are derived. Furthermore, the S-asymptotical ω-periodicity and the convergence of solutions in positive invariant sets are discussed. It is worth noting that the number of attractors increases to 3n from 2n in a neural network without switching. Finally, three numerical examples are given to substantiate the theoretical results.
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', Advanced Science, vol. 10, no. 7, pp. e2206271-2206271.
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AbstractCarbon 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 int...
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, Q, Zhao, X, Ni, W, Hu, Z, Tao, X & Zhang, P 2023, 'Multi-Agent Deep Reinforcement Learning-Based Interdependent Computing for Mobile Edge Computing-Assisted Robot Teams', IEEE Transactions on Vehicular Technology, vol. 72, no. 5, pp. 6599-6610.
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A group of robots can be assigned with different roles to collaboratively conduct interdependent tasks. The robots form a multi-robot system (MRS), where one robot's decision or action relies on the others'. This paper addresses the sequential decision problem of user association and resource allocation in a mobile edge computing (MEC)-enabled, wirelessly-connected MRS to maximize the time-averaged completion rate of interdependent computing tasks. The problem is challenging due to the partial observability of the network environment, and the delicate delay requirements of interdependent computing tasks. A new decentralized partially observable Markov decision process (Dec-POMDP) problem is reformulated, where edge servers act as intelligent agents and can make decentralized decisions about user association and resource management with their local information of the network state. By leveraging the multi-agent deep deterministic policy gradient (MADDPG) theory, a new cooperative multi-agent deep reinforcement learning (MADRL) model is developed to enable interdependent computing. Simulations show the merits of our approach in terms of task completion rate compared to existing techniques.
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 and 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, vol. 72, no. 12, pp. 16135-16147.
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Cui, Z, Sun, X, Pan, L, Liu, S & Xu, G 2023, 'Event-based incremental recommendation via factors mixed Hawkes process', Information Sciences, vol. 639, pp. 119007-119007.
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Cui, Z, Zhang, M, Yuan, Y, Jia, H, Hao Ngo, H & Wang, J 2023, 'Study on pre-concentration of trace heavy metal ions in water quality detection using FO-electroosmotic integrated chip', Chemical Engineering Journal, vol. 472, pp. 144968-144968.
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Curtis, K, Brown, J, Sharwood, LN, Risi, D, Eager, D, Holland, AJA, Beck, B, Erskine, C, Lockhart, K, Cooke, K, Adams, S, Teague, WJ & Mitchell, R 2023, 'Playground injury prevention: the need for consistent and national implementation of Australian safety standards', Australian and New Zealand Journal of Public Health, vol. 47, no. 2, pp. 100023-100023.
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OBJECTIVES: Hospitalisation rates for injury, including at playgrounds, have not changed in the past decade. There are nine Australian Standards specific to playgrounds. The impact (if any) of these standards on playground injury resulting in hospitalisation is unknown. METHODS: Retrospective data for patients under 18 years presenting to emergency departments and/or admitted between October 2015 and December 2019 due to an injury documented as occurring at a playground were retrieved by the Illawarra Shoalhaven Local Health District Planning, Information and Performance Department. Maintenance and Australian Standard (AS) compliance data for the 401 local playgrounds were requested from the four Local Governments in Illawarra Shoalhaven Local Health District. Descriptive statistics were used. RESULTS: A total of 548 children were treated in emergency departments and/or admitted following playground injury. There was an overall increase of 39.3% in playground injury across the study period, and expenditure rose from $43,478 in 2011 to $367,259 in 2019 (a 744.7% increase). CONCLUSIONS: Playground injury has not decreased in the Illawarra Shoalhaven. Data regarding maintenance and AS compliance are lacking. This is not unique to our region. IMPLICATIONS FOR PUBLIC HEALTH: Without a national approach to adequately resource and monitor playground injury, it is not possible to assess the impact of Australian Standards or any injury prevention program.
Cuzmar, RH, Mora, A, Pereda, J, Aguilera, RP, Poblete, P & Neira, S 2023, 'Computationally Efficient MPC for Modular Multilevel Matrix Converters Operating With Fixed Switching Frequency', IEEE Open Journal of the Industrial Electronics Society, vol. 4, pp. 748-761.
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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', Science of The Total Environment, 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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Few-shot learning (FSL) that aims to recognize novel classes with few labeled samples is troubled by its data scarcity. Though recent works tackle FSL with data augmentation-based methods, these models fail to maintain the discrimination and diversity of the generated samples due to the distribution shift and intra-class bias caused by the data scarcity, therefore greatly undermining the performance. To this end, we use causal mechanisms, which are constant among independent variables across data distribution, to alleviate such effects. In this sense, we decompose the image information into two independent components: sample-specific and class-agnostic information, and further propose a novel Counterfactual Generation Framework (CGF) to learn the underlying causal mechanisms to synthesize faithful samples for FSL. Specifically, based on the counterfactual inference, we design a class-agnostic feature extractor to capture the sample-specific information, together with a counterfactual generation network to simulate the data generation process from a causal perspective. Moreover, to leverage the power of CGF in counterfactual inference, we further develop a novel classifier that classifies samples based on their distributions of counterfactual generations. Extensive experiments demonstrate the effectiveness of CGF on four FSL benchmarks, e.g., 80.12/86.13% accuracy on 5-way 1/5-shot miniImageNet FSL tasks, significantly improving the performance. Our codes and models are available at https://github.com/eric-hang/CGF.
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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Davies, AJ, Shepherd, I & Leigh, E 2023, 'Enhancing leadership training in health services – an evidence-based practice-oriented approach', Leadership in Health Services, vol. 36, no. 1, pp. 24-38.
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PurposeGlobally, private and public organisations invest ever increasing amounts of money, time and effort to develop leadership capabilities in current and future leaders. Whilst such investment results in benefits for some, the full value of developmental strategies on offer is not always realised. Challenges inhibiting achievement of full value include struggling to identify learning programs that best fit with the organisational structure, culture, mission and vision and difficulties in maximising engagement of personnel at multiple levels of the management structure.Design/methodology/approachThe purpose of this study is to introduce a pathway for health services to develop and embed simulation-based educational strategies that provide targeted learning for leaders and teams. Aligning this approach to leadership development through presentation of case studies in which the model has been applied illustrates the pathway for application in the health-care sector.FindingsThe findings of the approach to leadership development are presented through the presentation of a case study illustrating application of the ADELIS model to simulation-based learning.Practical implicationsThe ADELIS model, outlined in this study, provides a guide for creating customised and flexible learning designs that apply simulation-based learning, enabling organisations to develop and provide leadership training for individuals, units and teams that is appropriately fit for purpose.Originality/valueThe key contributi...
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, no. Pt 1, pp. 160285-160285.
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Efficient recovery of nitrous oxide (N2O) through heterotrophic denitrification with the help of Fe(II)EDTA-NO as a chelating agent has been regarded as an ideal technology to treat nitric oxide (NO)-rich flue gas. In this study, an integrated NO-based biological denitrification model was developed to describe the sequential reduction of the NO fixed in Fe(II)EDTA-NO with organic carbon as the electron donor. With the inclusion of only the key pathways contributing to nitrogen transformation, the model was firstly developed and then calibrated/validated and evaluated using the data of batch tests mediated by the identified functional heterotrophic bacteria at various substrates concentrations and then used to explore the possibility of enhancing N2O recovery by altering the substrates condition and reactor setup. The results demonstrated that the optimal COD/N ratio decreased consistently from 1.5 g-COD/g-N at the initial NO concentration of 40 g-N/m3 to 1.0 g-COD/g-N at the initial NO concentration of 420 g-N/m3. Furthermore, sufficiently increasing the headspace volume of the reactor was considered an ideal strategy to obtain ideal N2O production of 86.6 % under the studied conditions. The production of high-purity N2O (98 %) confirmed the practical application potential of this integrated treatment technology to recover a valuable energy resource from NO-rich flue gas.
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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Desroches, PE, Fraysse, KS, Silva, SM, Firipis, K, Merenda, A, Han, M, Dumée, LF, Quigley, AF, Kapsa, RMI, O'Connel, CD, Moulton, SE & Greene, GW 2023, 'A surface-tethered dopant method to achieve 3D control over the growth of a nanometers-thin and intrinsically transparent polypyrrole film', Electrochimica Acta, vol. 463, pp. 142817-142817.
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The electrochemical growth of conductive polymer films is a convenient synthesis route but challenging to control due to local variability in the reaction kinetics. Here we report a new method for electropolymerizing highly reproducible conductive polypyrrole films that are just nanometers thick, highly conductive and possess intrinsic optical transparencies comparable to ITO. The synthesis method utilizes a surface-tethered dopant molecule, in this case a self-assembled monolayer of the highly anionic protein lubricin (LUB), to template and thus control the 3-dimensional growth of the polypyrrole when the electrochemical polymerization reaction is performed in a pyrrole monomer solution containing no additional dopant molecules or ions. Because the tethered dopant controls where and how much polypyrrole growth occurs, this method effectively decouples the fine film morphology, thickness, and spatial-growth from the polymerization reaction kinetics and represents a paradigm shift in the electrochemical polymerization of conductive polymer films.
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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Dharma, S, Silitonga, AS, Shamsuddin, AH, Sebayang, AH, Milano, J, Sebayang, R, Sarjianto, Ibrahim, H, Bahri, N, Ginting, B & Damanik, N 2023, 'Properties and corrosion behaviors of mild steel in biodiesel-diesel blends', Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, vol. 45, no. 2, pp. 3887-3899.
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Global warming in relation to fossil fuel pollution and their environmental impacts have become a major global concern. Biodiesel has entered the scene as an alternative fuel but it also generated controversy associated with increased residual fuel, increased acidity, oxidation, and corrosion. The main objective of this study was to observe the corrosion behavior of the mild steel immersed in J50C50 biodiesel-diesel fuel blends for up to 800 h at ambient temperature. The results showed corrosion rate at 800-h immersion are 0.0103, 0.0044, 0.0117, 0.0155, 0.2283 and 0.02524 mm/year, respectively, for B0, B10, B20, B30, B40 and B50. Mild steel coupon surface observation using SEM showed corrosion attacks are characterized by round holes on the metal surface. The addition of J50C50 biodiesel into diesel fuel accelerated the corrosion rate and acid value. Overall, corrosion observations conducted on mild steel suggested J50C50 biodiesel-diesel fuel blend is more corrosive compared with diesel fuel.
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. 1-12.
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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, 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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In group decision making (GDM), opinion dynamics is a useful tool to investigate consensus formation. Notably, consensus convergence speed is of key importance to manage the consensus formation in GDM with opinion dynamics. Recently, social network DeGroot (SNDG) model has been widely used in opinion dynamics. Based on this, this article dedicates to study how agents’ high self-confidence levels affect the consensus convergence speed in SNDG model. Interestingly, using theoretical analysis, we prove that: 1) the speed of consensus reaching is subject to the largest self-confidence level of opinion followers and 2) the speed of consensus reaching is also subject to the top two self-confidence levels of opinion leaders. Furthermore, through extensive simulation’, we find that the theoretical results are robust to the topological structure and the size of social networks.
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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Hysteretic models are used to describe the nonlinear memory-based relationship between the input and output of some physical systems. A long short-term memory neural network-based method is proposed to identify nonlinear hysteretic parameters. Either force or vibration response data are used as the input of the network and the nonlinear hysteresis parameters as the output. The principal component analysis technique is applied to eliminate the redundant dimensionality of the input data. The attention mechanism is utilized to enhance the generalization ability of the standard network. Three representative hysteretic models are employed to verify the effectiveness of the present method. Both numerical and experimental results demonstrate that the proposed method could yield accurate identification results in all cases, even when uncertain and limited input data are used. Compared with the sensitivity methods and heuristic algorithms, the proposed method is more computationally efficient and can obtain more accurate identification results.
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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Dinh, TQ, Nguyen, DN, Hoang, DT, Pham, TV & Dutkiewicz, E 2023, 'In-Network Computation for Large-Scale Federated Learning Over Wireless Edge Networks', IEEE Transactions on Mobile Computing, vol. 22, no. 10, pp. 5918-5932.
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Most conventional Federated Learning (FL) models are using a star network topology where all users aggregate their local models at a single server (e.g., a cloud server). That causes significant overhead in terms of both communications and computing at the server, delaying the training process, especially for large scale FL systems with straggling nodes. This paper proposes a novel edge network architecture that enables decentralizing the model aggregation process at the server, thereby significantly reducing the training delay for the whole FL network. Specifically, we design a highly-effective in-network computation framework (INC) consisting of a user scheduling mechanism, an in-network aggregation process (INA) which is designed for both primal- and primal-dual methods in distributed machine learning problems, and a network routing algorithm with theoretical performance bounds. The in-network aggregation process, which is implemented at edge nodes and cloud node, can adapt two typical methods to allow edge networks to effectively solve the distributed machine learning problems. Under the proposed INA, we then formulate a joint routing and resource optimization problem, aiming to minimize the aggregation latency. The problem turns out to be NP-hard, and thus we propose a polynomial time routing algorithm which can achieve near optimal performance with a theoretical bound. Simulation results showed that the proposed algorithm can achieve more than 99$\%$ of the optimal solution and reduce the FL training latency, up to 5.6 times w.r.t other baselines. The proposed INC framework can not only help reduce the FL training latency but also significantly decrease cloud’s traffic and computing overhead. By embedding the computing/aggregation tasks at the edge nodes and leveraging the multi-layer edge-network architecture, the INC framework can liberate FL from the star topology to enable ...
Do, PMT, Zhang, Q, Zhang, G & Lu, J 2023, 'meta-GRS: A Graph Neural Network for Cross-Domain Recommender System via Meta-Learning', Procedia Computer Science, vol. 225, pp. 2536-2545.
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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, B, Yu, Y, Feng, Y, Wu, D, Zhao, G, Liu, A & Gao, W 2023, 'Robust numerical solution for assessing corrosion of reinforced concrete structures under external power supply', Engineering Structures, vol. 294, pp. 116724-116724.
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Dong, M, Yao, L, Wang, X, Xu, X & Zhu, L 2023, 'Adversarial dual autoencoders for trust-aware recommendation', Neural Computing and Applications, vol. 35, no. 18, pp. 13065-13075.
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Recommender systems face longstanding challenges in gaining users’ trust due to the unreliable information caused by profile injection or human misbehavior. Traditional solutions to those challenges focus on leveraging users’ social relationships for inferring the user preference, i.e., recommending items according to the preference by user’s trusted friends; or adding random noise to the input to improve the robustness of the recommender systems. However, such approaches cannot defend the real-world noises like fake ratings. The recommender model is generally built upon all the user-item interactions, which incorporates the information from fake ratings or spammer groups, that neglects the reliability of the ratings. To address the above challenges, we propose an adversarial training approach in this work. In details, our approach includes two components: a predictor that infers the user preference; and a discriminator that enforces cohort rating patterns. In particular, the predictor applies an encoder-decoder structure to learn the shared latent information from sparse users’ ratings and trust relationships; the discriminator enforces the predictor to provide ratings as coherent with the cohort rating patterns. Our extensive experiments on three real-world datasets show the advantages of our approach over several competitive baselines.
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, S, Mei, F, Li, JJ & Xing, D 2023, 'Global Cluster Analysis and Network Visualization in Prosthetic Joint Infection: A Scientometric Mapping', Orthopaedic Surgery, vol. 15, no. 4, pp. 1165-1178.
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ObjectiveProsthetic joint infection (PJI) is the main reason of failure of total joint arthroplasty (TJA). This study aimed to investigate the global trends and network visualization in research of PJI.MethodsPublications in PJI search during 1980–2022 were extracted from the Science Citation Index‐Expanded of Web of Science Core Collection database (WoSCC). The source data was investigated and analyzed by bibliometric methodology. For network visualization, VOS viewer and R software was used to perform bibliographic coupling, co‐citation, co‐authorship and co‐occurrence analysis and to predict the publication trends in PJI research.ResultsThere were 7288 articles included. The number of publications and relative research interests increased gradually per year globally. The USA made the highest contributions in the world and with the highest H‐index and the most citations. Journal of Arthroplasty published the highest number of articles in this area. The Mayo Clinic, Thomas Jefferson University (Rothman Institute), Hospital Special Surgery and the Rush University were the most contributive institutions by network visualization. Included studies were divided into four clusters: bacterial pathogenic mechanism and antibacterial drugs study, TJA complications, risk factors and epidemiology of PJI, diagnosis of PJI, and revision surgical management. More articles in PJI could be published over the next few years.ConclusionThe number of publications about PJI will be increasing dramatically based on the global trends and network visualization. The USA made the highest contributions in PJI. Diagnosis and revision management may be the next hot spots in this field.
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.
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, Li, X, Sugumaran, V, Hu, Y & Xue, Y 2023, 'Dynamic model averaging-based procurement optimization of prefabricated components', Neural Computing and Applications, vol. 35, no. 36, pp. 25157-25173.
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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 effectiven...
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, Sui, Y, Liu, Z & Ai, J 2023, 'An Empirical Study of Fault Triggers in Deep Learning Frameworks', IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 4, pp. 2696-2712.
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Deep learning frameworks play a key rule to bridge the gap between deep learning theory and practice. With the growing of safety- and security-critical applications built upon deep learning frameworks, their reliability is becoming increasingly important. To ensure the reliability of these frameworks, several efforts have been taken to study the causes and symptoms of bugs in deep learning frameworks, however, relatively little progress has been made in investigating the fault triggering conditions of those bugs. This paper presents the first comprehensive empirical study on fault triggering conditions in three widely-used deep learning frameworks (i.e., TensorFlow, MXNET, and PaddlePaddle). We have collected 3,555 bug reports from GitHub repositories of these frameworks. A bug classification is performed based on fault triggering conditions, followed by the analysis of frequency distribution of different bug types and the evolution features. The correlations between bug types and fixing time are investigated. Moreover, we have also studied the root causes of Bohrbugs and Mandelbugs and investigated the important consequences of each bug type. Finally, the analysis of regression bugs in deep learning frameworks is conducted. We have revealed 12 important findings based on our empirical results and have provided 10 implications for developers and users.
Du, Z, Yang, M, Yang, Y, Zhang, X, Chen, H, Ngo, HH & Liu, Q 2023, 'Sulfur-Modified Biochar Efficiently Removes Cr(VI) from Water by Sorption and Reduction', Environmental Engineering Science, vol. 40, no. 9, pp. 362-372.
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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, 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, vol. 20, no. 4, pp. 4354-4368.
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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, vol. 25, no. 4, pp. 2892-2950.
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Duan, S, Sasaki, S, Han, D, Zhang, G, Li, D, Feng, C, Wang, X, Tamiaki, H, Chung, S, Cho, K, Li, G & Lu, S 2023, 'Natural Bio‐additive Chlorophyll Derivative Enables 17.30% Efficiency Organic Solar Cells', Advanced Functional Materials, vol. 33, no. 37.
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AbstractAdditive‐assisted donor and acceptor domain regulation is regarded as an effective strategy to further release the potential photovoltaic performance of the existing organic solar cells (OSCs). Meanwhile, it is also critical to find high‐efficient, stable, non‐toxic, and low‐cost biological materials as bio‐additives to replace the traditional toxic halogen‐based additives. In this study, bio‐additives derived from a natural chlorophyll pigment named as ZnChl and H2Chl are employed to optimize the morphology and molecular stack of the PM6:Y6 active layer. The eutectic molecular stack of the blends is more ordered and tighter after introducing the bio‐additive chlorophyll derivatives to the system compared to the pristine PM6:Y6 blends. Owing to such a fine‐tuned donor‐acceptor microstructure network, the photovoltaic performance of the H2Chl bio‐additive‐based OSC achieves a 17.30% PCE and ZnChl‐based device obtains an efficiency of 16.61%, which is much higher than that of the control device with a 15.97% PCE. The result proves the feasibility of introducing environmental‐ and eco‐friendly chlorophyll derivatives as bio‐additives to further improve the photovoltaic performance of the OSCs.
Duan, T, Yang, Q, Sun, Z, Chen, Q, Zhang, G, Hu, D, Oh, J, Yang, C, Lv, J, Feng, B, Kan, Z, Chen, S, Zhong, C, Lu, S & Yang, K 2023, 'Oligothiophene electron donor and electron acceptor for all small molecule organic solar cells with efficiency over 9%', Chemical Engineering Journal, vol. 456, pp. 141006-141006.
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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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Eager, D, Zhou, S, Barker, R, Catchpoole, J & Sharwood, LN 2023, 'A Public Health Review into Two Decades of Domestic Trampoline Injuries in Children within Queensland, Australia', International Journal of Environmental Research and Public Health, vol. 20, no. 3, pp. 1742-1742.
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Trampolining as an activity brings enjoyment and many health benefits, but at the same time it carries an injury risk. Most domestic trampoline users are children who are developing in skill, cognition, risk perception, physical strength and resilience to injury. Several common patterns of child trampoline injuries have been identified and countermeasures outlined in standards have been taken to reduce higher risk injury mechanisms, such as entrapment and falls from the trampoline through design, product and point of sale labelling. In Australia, the first national trampoline standard was published in 2003 which introduced improvements in trampoline design and requirements for labelling and padding. This work investigated the potential impact of these and subsequent changes based on almost two decades of emergency department trampoline injury data collected in Queensland, Australia. These data describe the changing representative proportion and pattern of trampoline injuries in Queensland over time by age, mechanism, gender, severity and nature of injury of injured persons up to the age of 14 years. The interrelationships between different injury characteristics were also analysed to propose the main factors influencing injury occurrence and severity. These findings seem to indicate that safety evolution in the form of enclosure nets, frame impact attenuation and entrapment protection have likely improved domestic trampoline safety. Other factors, such as adult supervision, minimum age and avoidance of multiple users, could further reduce injury but are harder to influence in the domestic setting.
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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This paper proposes a novel label ranker network to learn the relationship between labels to solve ranking and classification problems. The Preference Neural Network (PNN) uses spearman correlation gradient ascent and two new activation functions, positive smooth staircase (PSS), and smooth staircase (SS) that accelerate the ranking by creating almost deterministic preference values. PNN is proposed in two forms, fully connected simple Three layers and Preference Net (PN), where the latter is the deep ranking form of PNN to learning feature selection using ranking to solve images classification problem. PN uses a new type of ranker kernel to generate a feature map. PNN outperforms five previously proposed methods for label ranking, obtaining state-of-the-art results on label ranking, and PN achieves promising results on CFAR-100 with high computational efficiency.
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>
Elmakki, T, Zavahir, S, Gulied, M, Qiblawey, H, Hammadi, B, Khraisheh, M, Shon, HK, Park, H & Han, DS 2023, 'Potential application of hybrid reverse electrodialysis (RED)-forward osmosis (FO) system to fertilizer-producing industrial plant for efficient water reuse', Desalination, vol. 550, pp. 116374-116374.
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This study presents an experimental investigation and a parametric analysis of the applicability of agricultural fertigation and power generation using a reverse electrodialysis-forward osmosis (RED-FO) hybrid system, with a water stream discharged from a fertilizer-producing plant. The results of this study demonstrated the possibility of achieving high salinity power generation from the RED system utilizing high-salinity brine and low-salinity ammonia solution that simulates reverse osmosis (RO) brine and wastewater streams released by the fertilizer-producing industry. The feasibility of stream dilution for fertigation application is demonstrated when the resulting moderately saline RED effluent is introduced into the FO process as a draw solution. The effect of external load addition, flow velocities variation, and concentration changes of the working solutions on the overall stack internal resistance and, thereby, RED performance was evaluated. As such, the lowest internal resistance converged to a threshold value of 4.03 Ω, giving the highest gross power density of 2.17 W/m2 when a flow velocity of 1.18 cm/s, 10 Ω external load, and 0.015 M (NH4)2SO4/1 M NaCl solution pair were utilized. In addition, the effect of the number of ion exchange membrane pairs and wastewater stream recycling was studied and optimized to amplify the osmotically generated power. As a result, the most consistent power generation was achieved when using 20 pairs of membrane cells in a single-pass flow mode operation. The applicability of the RED effluent to a subsequent FO system as a draw solution (DS) was investigated, showing a dilution rate (17 %) and a conductivity (1–2 mS/cm of DS) suitable for agricultural fertigation applications.
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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Ercan, T, Sedehi, O, Katafygiotis, LS & Papadimitriou, C 2023, 'Information theoretic-based optimal sensor placement for virtual sensing using augmented Kalman filtering', Mechanical Systems and Signal Processing, vol. 188, pp. 110031-110031.
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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 variou...
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, vol. 11, no. 4, pp. 3483-3496.
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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, Guan, R, Ou, K, Fu, Q, Liu, Q, Li, D-S, Zheng, H & Sun, Y 2023, 'Direct Ink Writing 3D Printing of Graphene/Al2O3 Composite Ceramics with Gradient Mechanics', Advanced Engineering Materials, vol. 25, no. 8.
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A new graphene (G)/Al2O3 composite ceramic with tunable mechanics is prepared by direct ink writing (DIW) 3D‐printing technology. It is found that the bending strength, fracture toughness, and hardness all increase with increasing in content of G. The bending strength, fracture toughness, and hardness of G/Al2O3 composite ceramic (4.0 wt‰) are improved to be 45.0%, 40.6%, and 21.9% comparing to Al2O3 ceramic, respectively. The result is attributed to good reinforcement of G and inhibition of Al2O3 phase growth by G. Furthermore, the gear wheel with gradient mechanics is also designed and fabricated by the DIW 3D‐printing technology from various G/Al2O3 composite gels. It exhibits excellent wear resistance and low generation of heat during rotational friction. Herein, a new method is provided to fabricate G‐based ceramics with gradient structure and mechanics for various applications.
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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To solve heavy metals leaching problem in the utilization of various industrial solid wastes, this work investigated the heavy metals immobilization of ternary geopolymer prepared by nickel slag (NS), lithium slag (LS), and metakaolin (MK). Compressive strength was measured to determine the optimum and appropriate mix proportions. The leaching characteristics of typical heavy metals (Cu (Ⅱ), Pb (Ⅱ), and Cr (Ⅲ)) in acid, alkali, and salt environments were revealed by Inductively Coupled Plasma (ICP). The heavy metals immobilization mechanism was explored by Mercury Intrusion Porosimetry (MIP), X-ray Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), and Scanning Electron Microscopy (SEM) tests. The experimental results show that the group with a mass ratio of NS, LS and MK of 1:1:8 exhibits the highest compressive strength, which reaches 69.1 MPa at 28 d. The ternary geopolymer possesses a desirable capacity for immobilizing inherent heavy metals, where the immobilization rates of Cu and Pb reach 96.69 %, and that of Cr reaches 99.97 %. The leaching concentrations of Cr and Pb increase when the samples are exposed to acidic and alkaline environments. Cu and Pb are mainly physically encapsulated in geopolymer. Additionally, immobilization of Cr mainly involves physical encapsulation and chemical bonding.
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, X, Li, Y, Chen, L, Li, B & Sisson, SA 2023, 'Hawkes Processes With Stochastic Exogenous Effects for Continuous-Time Interaction Modelling', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 2, pp. 1848-1861.
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Continuous-time interaction data is usually generated under time-evolving environment. Hawkes processes (HP) are commonly used mechanisms for the analysis of such data. However, typical model implementations (such as e.g. stochastic block models) assume that the exogenous (background) interaction rate is constant, and so they are limited in their ability to adequately describe any complex time-evolution in the background rate of a process. In this paper, we introduce a stochastic exogenous rate Hawkes process (SE-HP) which is able to learn time variations in the exogenous rate. The model affiliates each node with a piecewise-constant membership distribution with an unknown number of changepoint locations, and allows these distributions to be related to the membership distributions of interacting nodes. The time-varying background rate function is derived through combinations of these membership functions. We introduce a stochastic gradient MCMC algorithm for efficient, scalable inference. The performance of the SE-HP is explored on real world, continuous-time interaction datasets, where we demonstrate that the SE-HP strongly outperforms comparable state-of-the-art methods. We introduce a stochastic gradient MCMC algorithm for efficient, scalable inference. The performance of the SE-HP is explored on real world, continuous-time interaction datasets, where we demonstrate that the SE-HP strongly outperforms comparable state-of-the-art methods.
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, vol. 72, no. 12, pp. 16195-16207.
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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, Liu, F & Zhang, G 2023, 'Semi-Supervised Heterogeneous Domain Adaptation: Theory and Algorithms', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 1, pp. 1087-1105.
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Semi-supervised heterogeneous domain adaptation (SsHeDA) aims to train a classifier for the target domain, in which only unlabeled and a small number of labeled data are available. This is done by leveraging knowledge acquired from a heterogeneous source domain. From algorithmic perspectives, several methods have been proposed to solve the SsHeDA problem; yet there is still no theoretical foundation to explain the nature of the SsHeDA problem or to guide new and better solutions. Motivated by compatibility condition in semi-supervised probably approximately correct (PAC) theory, we explain the SsHeDA problem by proving its generalization error that is, why labeled heterogeneous source data and unlabeled target data help to reduce the target risk. Guided by our theory, we devise two algorithms as proof of concept. One, kernel heterogeneous domain alignment (KHDA), is a kernel-based algorithm; the other, joint mean embedding alignment (JMEA), is a neural network-based algorithm. When a dataset is small, KHDA's training time is less than JMEA's. When a dataset is large, JMEA is more accurate in the target domain. Comprehensive experiments with image/text classification tasks show KHDA to be the most accurate among all non-neural network baselines, and JMEA to be the most accurate among all baselines.
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 Science, Medicine and Rehabilitation, vol. 15, no. 1, p. 114.
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Abstract 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 ne...
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, no. 1, 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 different karate skills. The results of the proposed system are identical to the decisions of the expert panels and are thus suitable for real‐time decisions.
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, 'RETRACTED ARTICLE: Hollow fiber membrane contactor based carbon dioxide absorption − stripping: a review', Macromolecular Research, vol. 31, no. 4, pp. 299-325.
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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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Fei, Z, Wang, X, Wu, N, Huang, J & Zhang, JA 2023, 'Air-Ground Integrated Sensing and Communications: Opportunities and Challenges', IEEE Communications Magazine, vol. 61, no. 5, pp. 55-61.
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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 & 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, A, Onggowarsito, C, Mao, S, Qiao, GG & Fu, Q 2023, 'Divide and Conquer: A Novel Dual‐Layered Hydrogel for Atmospheric Moisture Harvesting', ChemSusChem, vol. 16, no. 14, p. e202300137.
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AbstractAtmospheric water harvesting (AWH) has been recognized as a next‐generation technology to alleviate water shortages in arid areas. However, the current AWH materials suffer from insufficient water adsorption capacity and high‐water retention, which hinder the practical application of AWH materials. In this study, we developed a novel dual‐layered hydrogel (DLH) composed of a light‐to‐heat conversion layer (LHL) containing novel polydopamine‐manganese nanoparticles (PDA−Mn NPs) and a water adsorption layer (WAL) made of 2‐(acryloyloxyethyl) trimethylammonium chloride (AEtMA). The WAL has a strong ability to adsorb water molecules in the air and has a high‐water storage capacity, and the PDA−Mn NPs embedded in the LHL have excellent photothermal conversion efficiency, leading to light‐induced autonomous water release. As a result, the DLH displays a high‐water adsorption capacity of 7.73 g g−1 under optimal conditions and could near‐quantitatively release captured water within 4 h sunlight exposure. Coupled with its low cost, we believed that the DLH will be one of the promising AWH materials for practical applications.
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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Gearbox plays a vital role in a wide range of mechanical power transmission systems in many industrial applications, including wind turbines, vehicles, mining and material handling equipment, oil and gas processing equipment, offshore vessels, and aircraft. As an inevitable phenomenon during gear service life, gear wear affects the durability of gear tooth and reduces the remaining useful life of the gear transmission system. The propagation of gear wear can lead to severe gear failures such as gear root crack, tooth spall, and tooth breakage, which can further cause unexpected equipment shutdown or hazardous incidents. Therefore, it is necessary to monitor gear wear propagation progression in order to perform predictive maintenance. Vibration analysis is a widely used and effective technique to monitor the operating condition of rotating machinery, especially for the diagnosis of localized failures such as gear root crack and tooth surface spalling. However, vibration-based techniques for gear wear analysis and monitoring are very limited, mainly due to the difficulties in identifying the complex vibration characteristics induced by gear wear propagation. Understanding the effect of gear wear on vibration characteristics is essential to develop vibration-based techniques for monitoring and tracking gear wear evolution. However, no research work has been previously published to summarize the research progress in vibration-based gear wear monitoring and prediction. To fill the research gap, this review paper aims to conduct a state-of-the-art comprehensive review on vibration-based gear wear monitoring, including studying the gear surface features caused by different gear wear mechanisms, investigating the relationships between gear surface features and vibration characteristics, and summarizing the current research progress of vibration-based gear wear monitoring. This review also makes some recommendations for future research work in this area. It is e...
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, 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, Hao Ngo, H, Guo, W, Woong Chang, S, Duc Nguyen, D, Thanh Bui, X, Zhang, X, Ma, XY & Ngoc Hoang, B 2023, 'Biohydrogen production, storage, and delivery: A comprehensive overview of current strategies and limitations', Chemical Engineering Journal, vol. 471, pp. 144669-144669.
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The development of biohydrogen is crucial for achieving a sustainable and eco-friendly society and reducing dependence on traditional fossil fuels. Biohydrogen production, storage, and delivery are three essential components of the biohydrogen economy. Strategies like dark fermentation and photo-fermentation have been widely studied for biohydrogen production. At the same time, hydrogen storage options have also been explored, including compressed, liquid, and material-based hydrogen. However, many of the technologies aimed at developing the biohydrogen economy are still immature, and the current biohydrogen economy is facing challenges like low biohydrogen production, high hydrogen storage costs, and unsatisfactory hydrogen delivery efficiency. Therefore, this review aims to present a comprehensive overview of the latest technologies for biohydrogen production, storage, and delivery, while highlighting their respective benefits and drawbacks. Furthermore, the review proposes perspectives on the challenges facing current biohydrogen production, storage, and delivery technologies, as well as suggesting further research directions to improve these technologies for widespread implementation of the biohydrogen economy.
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?', Journal of Hazardous Materials, 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, X, Zhang, Y, Meng, MH, Li, Y, Joe, CE, Wang, Z & Bai, G 2023, 'Detecting contradictions from IoT protocol specification documents based on neural generated knowledge graph', ISA Transactions, vol. 141, pp. 10-19.
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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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Fernandez, E, Hossain, MJ, Nawazish Ali, SM & Sharma, V 2023, 'An efficient P2P energy trading platform based on evolutionary games for prosumers in a community', Sustainable Energy, Grids and Networks, vol. 34, pp. 101074-101074.
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Fonseka, C, Ryu, S, Choo, Y, Naidu, G, Kandasamy, J, Thiruvenkatachari, R, Foseid, L, Ratnaweera, H & Vigneswaran, S 2023, 'Selective recovery of europium from real acid mine drainage by using novel amine based modified SBA15 adsorbent and membrane distillation system', Journal of Water Process Engineering, vol. 56, pp. 104551-104551.
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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.
Fu, J, Abharian, S, Sarfarazi, V, Haeri, H, Rasekh, H & Xu, L 2023, 'The rock fracturing in the jointed tunnel face ground with TBM: Experimental and numerical study', Theoretical and Applied Fracture Mechanics, vol. 125, pp. 103933-103933.
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G. Asteris, P, Jahed Armaghani, D, Cavaleri, L & Nguyen, H 2023, 'Introduction to the Special Issue on Soft Computing Techniques in Materials Science and Engineering', Computer Modeling in Engineering & Sciences, vol. 135, no. 2, pp. 839-841.
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Gabryelczyk, A, Yadav, S, Swiderska-Mocek, A, Altaee, A & Lota, G 2023, 'From waste to energy storage: calcinating and carbonizing chicken eggshells into electrode materials for supercapacitors and lithium-ion batteries', RSC Advances, vol. 13, no. 34, pp. 24162-24173.
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The study explores waste-derived sustainable materials from the eggshell's inner and outer layers. The materials work as an inert scaffold to reduce the carbon content in supercapacitors and as a Li-ion anode with a specific capacity of 280 mA h g−1.
Gadde, S, Kleynhans, A, Holien, JK, Bhadbhade, M, Nguyen, PLD, Mittra, R, Yu, TT, Carter, DR, Parker, MW, Marshall, GM, Cheung, BB & Kumar, N 2023, 'Pyrimido[1,2-a]benzimidazoles as inhibitors of oncoproteins ubiquitin specific protease 5 and MYCN in the childhood cancer neuroblastoma', Bioorganic Chemistry, vol. 136, pp. 106462-106462.
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Galat, D & Rizoiu, MA 2023, 'Enhancing Biomedical Text Summarization and Question-Answering: On the Utility of Domain-Specific Pre-Training University of Technology Sydney participation in BioASQ Task 11b Phase B', CEUR Workshop Proceedings, vol. 3497, pp. 102-113.
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Biomedical summarization requires large datasets to train for text generation. We show that while transfer learning offers a viable option for addressing this challenge, an in-domain pre-training does not always offer advantages in a BioASQ summarization task. We identify a suitable model architecture and use it to show a benefit of a general-domain pre-training followed by a task-specific fine-tuning in the context of a BioASQ summarization task, leading to a novel three-step fine-tuning approach that works with only a thousand in-domain examples. Our results indicate that a Large Language Model without domain-specific pre-training can have a significant edge in some domain-specific biomedical text generation tasks.
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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The efficacy of the Standalone Electrokinetic (EK) process in soil PFAS removal is negligible, primarily due to the intersecting mechanisms of electromigration and electroosmosis transportation. Consequently, the redistribution of PFAS across the soil matrix occurs, hampering effective remediation efforts. Permeable reactive barrier (PRB) has been used to capture contaminants and extract them at the end of the EK process. This study conducted laboratory-scale tests to evaluate the feasibility of the iron slag PRB enhanced-EK process in conjunction with Sodium Cholate (NaC) biosurfactant as a cost-effective and sustainable method for removing PFOA from the soil. A 2 cm iron slag-based PRB with a pH of 9.5, obtained from the steel-making industry, was strategically embedded in the middle of the EK reactors to capture PFOA within the soil. The main component of the slag, iron oxide, exhibited significant adsorption capacity for PFOA contamination. The laboratory-scale tests were conducted over two weeks, revealing a PFOA removal rate of more than 79% in the slag/activated carbon PRB-EK test with NaC enhancement and 70% PFOA removal in the slag/activated carbon PRB-EK without NaC. By extending the duration of the slag/AC PRB-EK test with NaC enhancement to three weeks, the PFOA removal rate increased to 94.09%, with the slag/AC PRB capturing over 87% of the initial PFOA concentration of 10 mg/L. The specific energy required for soil decontamination by the EK process was determined to be 0.15 kWh/kg. The outcomes of this study confirm the feasibility of utilizing iron slag waste in the EK process to capture PFOA contaminants, offering a sustainable approach to soil decontamination. Combining iron slag PRB and NaC biosurfactant provides a cost-effective and environmentally friendly method for efficient PFOA removal from soil.
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, M, Coleman, M, Ngo, T-V, Sorrelle, N, Dominguez, A, Toombs, J, Lewis, C, Fang, Y, Mora, F, Ortega, D, Wellstein, A & Brekken, R 2023, 'Abstract B016: Pleiotrophin drives a pro-metastatic immune niche within the breast tumor microenvironment', Cancer Research, vol. 83, no. 2_Supplement_2, pp. B016-B016.
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Abstract 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-kB 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 + chemotherapy in reducing metastatic burden in mice. These findings establish PTN as a previously unrecognized driver of a pro-metastatic immune niche and thus represents a promising therapeutic target for the treatment of metastatic breast cancer. Citation Format: Debolina Ganguly, Marcel Schmidt, Morgan Coleman, Tuong-Vi Ngo, Noah Sorrelle, Adrian Dominguez, Jason Toombs, Cheryl Lewis, Yisheng Fang, Fatima Mora, David Ortega, Anton Wellstein, Rolf Brekken. Pleiotrophin drives a pro-metastatic immune niche within the breast tumor microenvironment [abstract]. In: Proceedings of the AACR Special Conference: Cancer Metastasis; 2022 Nov 14-17; Portland, OR. Philadelphia (PA): AACR; Cancer Res 2022;83(2 Suppl_2):Abstract nr B016.
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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Fully automated vehicles collect information about their road environments to adjust their driving actions, such as braking and slowing down. The development of artificial intelligence (AI) and the Internet of Things (IoT) has improved the cognitive abilities of vehicles, allowing them to detect traffic signs, pedestrians, and obstacles for increasing the intelligence of these transportation systems. Three-dimensional (3D) object detection in front-view images taken by vehicle cameras is important for both object detection and depth estimation. In this paper, a joint channel attention and multidimensional regression loss method for 3D object detection in automated vehicles (called CAMRL) is proposed to improve the average precision of 3D object detection by focusing on the model’s ability to infer the locations and sizes of objects. First, channel attention is introduced to effectively learn the yaw angles from the road images captured by vehicle cameras. Second, a multidimensional regression loss algorithm is designed to further optimize the size and position parameters during the training process. Third, the intrinsic parameters of the camera and the depth estimate of the model are combined to reduce the object depth computation error, allowing us to calculate the distance between an object and the camera after the object’s size is confirmed. As a result, objects are detected, and their depth estimations are validated. Then, the vehicle can determine when and how to stop if an object is nearby. Finally, experiments conducted on the KITTI dataset demonstrate that our method is effective and performs better than other baseline methods, especially in terms of 3D object detection and bird’s-eye view (BEV) evaluation.
Gao, H, Hussain, W, Durán Barroso, RJ, Arshad, J & Yin, Y 2023, 'Guest Editorial: Machine learning applied to quality and security in software systems', IET Software, vol. 17, no. 4, pp. 345-347.
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Gao, H, Luo, B, Barroso, RJD & Hussain, W 2023, 'Guest Editorial Special Issue on Computational Intelligence to Edge AI for Ubiquitous IoT Systems', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 7, no. 1, pp. 36-38.
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Gao, H, Qiu, B, Barroso, RJD, Hussain, W, Xu, Y & Wang, X 2023, 'TSMAE: A Novel Anomaly Detection Approach for Internet of Things Time Series Data Using Memory-Augmented Autoencoder', IEEE Transactions on Network Science and Engineering, vol. 10, no. 5, pp. 2978-2990.
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With the development of the Internet of Things, it has been widely studied and deployed in industrial manufacturing, intelligent transportation, and healthcare systems. The time-series feature of the IoT makes the data density and the data dimension higher, where anomaly detection is important to ensure hardware and software security. However, the traditional anomaly detection algorithm has difficulty meeting this demand, not only in complexity but also accuracy. Sometimes the anomaly can be well reconstructed, resulting in a low reconstruction error. In this paper, we propose a memory-augmented autoencoder approach for detecting anomalies in IoT data, which aims to use reconstruction errors to determine data anomalies. First, a memory mechanism is introduced to suppress the generalization ability of the model, and a memory-augmented autoencoder TSMAE is designed for time-series data anomaly detection. Second, by adding penalties and derivable rectifier functions to loss to make the addressing vector sparse, memory modules are encouraged to extract typical normal patterns, thus inhibiting model generalization ability. Finally, through experiments on ECG and Wafer datasets, the validity of TSMAE is verified, and the rationality of hyperparameter setting is discussed through visualizing the memory module addressing vector.
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, L, Liu, P, Jiang, Y, Xie, W, Lei, J, Li, Y & Du, Q 2023, 'CBFF-Net: A New Framework for Efficient and Accurate Hyperspectral Object Tracking', IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-14.
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Gao, M, Shen, R, Shi, L, Qi, W, Li, J & Li, Y 2023, 'Task Partitioning and Offloading in DNN-Task Enabled Mobile Edge Computing Networks', IEEE Transactions on Mobile Computing, vol. 22, no. 4, pp. 2435-2445.
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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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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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Gavinsky, D, Lee, T, Santha, M & Sanyal, S 2023, 'Optimal Composition Theorem for Randomized Query Complexity', THEORY OF COMPUTING, vol. 19.
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For any set S, any relation F subset of {0, 1}(n) x S and any partial Boolean function, defined on a subset of {0, 1}(m), we show thatR-1/3 (f o g(n)) is an element of Omega (R-4/9(f) center dot root R-1/3(g)),where R-epsilon(center dot) stands for the bounded-error randomized query complexity with error at most epsilon, and f o g(n) subset of ({0, 1}(m))(n) x S denotes the composition of 5 with = instances of g. This result is new even in the special case when S = {0, 1} and g is a total function. We show that the new composition theorem is optimal for the general case of relations: A relation f(0) and a partial Boolean function g(0) are constructed, such that R-4/9 (f(0)) is an element of Theta(root n), R-1/3(g(0)) is an element of Theta (n) and R-1/3(f(0) o g(0)(n)) is an element of Theta (n).The theorem is proved via introducing a new complexity measure, max-conflict complexity, denoted by chi(center dot). Its investigation shows that (chi) over bar (g) is an element of Omega(R-1/3(g)) for any partial Boolean function g and (R-1/3(f o g(n)) is an element of Omega(R-4/9(f) center dot (chi) over bar (g)) for any relation f, which readily implies the composition statement. It is further shown that (chi) over bar (g) is always at least as large as the sabotage complexity of g (introduced by Ben-David and Kothari in 2016).
Ge, M, Pineda, J & Sheng, D 2023, 'Competing effects of wetting and volume change on G0 in compacted loess', Géotechnique Letters, vol. 13, no. 4, pp. 182-190.
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This paper explores the relative contributions of wetting (suction reduction) and its associated volume change on the small-strain shear stiffness, G0, in compacted loess from Xi’an, China. Results from one-dimensional compression tests with measurements of the shear wave velocity upon wetting and loading paths are presented. The experimental results show that the softening caused by wetting compete with the densification caused by plastic deformation and their effects on G0 are strongly controlled by stress level applied prior to wetting. Below the compaction stress, suction effects are dominant and G0 reduces irrespective of the magnitude of the collapse strain. With the increase in the stress level, the reduction in G0 caused by saturation is compensated by the plastic deformation triggered by soil collapse. This behaviour is clearly observed when the soil is first loaded to the compaction stress, where the maximum collapse strain is measured upon wetting. Volume change is dominant once the compaction stress is exceeded so that G0 tends to increase upon wetting. A wetting-induced stiffness factor D is defined to demonstrate that the change in G0 varies linearly with the stress level and this behaviour is independent of the compaction conditions.
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. 33-41.
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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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Ghanizadeh, AR, Ghanizadeh, A, Asteris, PG, Fakharian, P & Armaghani, DJ 2023, 'Developing bearing capacity model for geogrid-reinforced stone columns improved soft clay utilizing MARS-EBS hybrid method', Transportation Geotechnics, vol. 38, pp. 100906-100906.
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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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Gully erosion possess a serious hazard to critical resources such as soil, water, and vegetation cover within watersheds. Therefore, spatial maps of gully erosion hazards can be instrumental in mitigating its negative consequences. Among the various methods used to explore and map gully erosion, advanced learning techniques, especially deep learning (DL) models, are highly capable of spatial mapping and can provide accurate predictions for generating spatial maps of gully erosion at different scales (e.g., local, regional, continental, and global). In this paper, we applied two DL models, namely a simple recurrent neural network (RNN) and a gated recurrent unit (GRU), to map land susceptibility to gully erosion in the Shamil-Minab plain, Hormozgan province, southern Iran. To address the inherent black box nature of DL models, we applied three novel interpretability methods consisting of SHaply Additive explanation (SHAP), ceteris paribus and partial dependence (CP-PD) profiles and permutation feature importance (PFI). Using the Boruta algorithm, we identified seven important features that control gully erosion: soil bulk density, clay content, elevation, land use type, vegetation cover, sand content, and silt content. These features, along with an inventory map of gully erosion (based on a 70 % training dataset and 30 % test dataset), were used to generate spatial maps of gully erosion using DL models. According to the Kolmogorov-Smirnov (KS) statistic performance assessment measure, the simple RNN model (with KS = 91.6) outperformed the GRU model (with KS = 66.6). Based on the results from the simple RNN model, 7.4 %, 14.5 %, 18.9 %, 31.2 % and 28 % of total area of the plain were classified as very-low, low, moderate, high and very-high hazard classes, respectively. According to SHAP plots, CP-PD profiles, and PFI measures, soil silt content, vegetation cover (NDVI) and land use type had the highest impact on the model's output. Overall, the DL modell...
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.
Ghorbanpour, SM, Richards, C, Pienaar, D, Sesperez, K, Aboulkheyr Es., H, Nikolic, VN, Karadzov Orlic, N, Mikovic, Z, Stefanovic, M, Cakic, Z, Alqudah, A, Cole, L, Gorrie, C, McGrath, K, Kavurma, MM, Ebrahimi Warkiani, M & McClements, L 2023, 'A placenta-on-a-chip model to determine the regulation of FKBPL and galectin-3 in preeclampsia', Cellular and Molecular Life Sciences, vol. 80, no. 2.
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AbstractPreeclampsia is a pregnancy-specific cardiovascular disorder, involving significant maternal endothelial dysfunction. Although inappropriate placentation due to aberrant angiogenesis, inflammation and shallow trophoblast invasion are the root causes of preeclampsia, pathogenic mechanisms are poorly understood, particularly in early pregnancy. Here, we first confirm the abnormal expression of important vascular and inflammatory proteins, FK506-binding protein-like (FKBPL) and galectin-3 (Gal-3), in human plasma and placental tissues from women with preeclampsia and normotensive controls. We then employ a three-dimensional microfluidic placental model incorporating human umbilical vein endothelial cells (HUVECs) and a first trimester trophoblast cell line (ACH-3P) to investigate FKBPL and Gal-3 signaling in inflammatory conditions. In human samples, both circulating (n = 17 controls; n = 30 preeclampsia) and placental (n ≥ 6) FKBPL and Gal-3 levels were increased in preeclampsia compared to controls (plasma: FKBPL, p < 0.0001; Gal-3, p < 0.01; placenta: FKBPL, p < 0.05; Gal-3, p < 0.01), indicative of vascular dysfunction in preeclampsia. In our placenta-on-a-chip model, we show that endothelial cells are critical for trophoblast-mediated migration and that trophoblasts effectively remodel endothelial vascular networks. Inflammatory cytokine tumour necrosis factor-α (10 ng/mL) modulates both FKBPL and Gal-3 signaling in conjunction with trophoblast migration and impairs vascular network formation (p < 0.005). Our placenta-on-a-chip recapitulates aspects of inappropriate placental development and vascular dysfunction in preeclampsia.
Gill, AQ 2023, 'The digital ecosystem information framework: Insights from action design research', Journal of Information Science.
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Digital ecosystem (DE) is a dynamic configuration of informational organisms, individual and organisational actors, which interact in the digitally networked and federated environment. Traditional approaches are challenged by the need for handling information in complex DE where information flows beyond the boundary of a single actor. This article presents the informational organism-interaction centric digital ecosystem information (DEi) framework for information operations, management, and governance. The DEi framework emerged based on the insights obtained through the application of well-known thematic network analysis and abstraction, reflection and learning techniques to 15 action design research projects across nine different industry partners in Australia. The DEi framework includes 27 topics that are organised into nine key knowledge and three focus areas. The DEi framework can be used by researchers and practitioners as a resource for designing digital information capabilities as appropriate to their context.
Gilmore, N, Britz, T, Maartensson, E, Orbegoso-Jordan, C, Schroder, S & Malerba, M 2023, 'Continental-scale assessment of micro-pumped hydro energy storage using agricultural reservoirs', Applied Energy, vol. 349, pp. 121715-121715.
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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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There is a considerable body of literature on the challenges that are encountered in the transition from technical engineering research to engineering education research. These challenges include conceptual difficulties, shifts in identities and in paradigms, and changes of cultural and social capital. Many of the studies in this area emphasise the importance of having a network of engineering education researchers, but there is little research on what such a network would look like. Our research builds on this by investigating how the Centre for Research in Engineering & IT Education (CREITE) has established conditions which enable the development of engineering education research capabilities across several universities in NSW. Our novel research approach views six case studies of CREITE members through the lens of three practice theories: community of practice; Bourdieu’s theory of practice; and the theory of practice architecture. The findings reveal a kaleidoscopic understanding of what constrains and enables engineering educators to engage with the field of EER, and the pivotal role played by a research group such as CREITE.
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, Ball, JE & Surawski, N 2023, 'An initial parameter estimation approach for urban catchment modelling', Urban Water Journal, vol. 20, no. 2, pp. 171-183.
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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, vol. 22, no. 11, pp. 8114-8127.
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In this paper, we focus on a wireless-powered sensor network coordinated by a multi-antenna access point (AP). Each node can generate sensing information and report the latest information to the AP using the energy harvested from the AP’s signal beamforming. We aim to minimize the average age-of-information (AoI) by adapting the nodes’ scheduling and the transmission control strategies jointly. To reduce the transmission delay, an intelligent reflecting surface (IRS) is used to enhance the channel conditions by controlling the AP’s beamforming strategy and the IRS’s phase shifting matrix. Considering dynamic data arrivals at different sensing nodes, we propose a hierarchical deep reinforcement learning (DRL) framework to for AoI minimization in two steps. The users’ transmission scheduling is firstly determined by the outer-loop DRL approach, e.g. the DQN or PPO algorithm, and then the inner-loop optimization is used to adapt either the uplink information transmission or downlink energy transfer to all nodes. A simple and efficient approximation is also proposed to reduce the inner-loop rum time overhead. Numerical results verify that the hierarchical learning framework outperforms typical baselines in terms of the average AoI and proportional fairness among different nodes.
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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This paper focuses on the study of synchronization problem for T-S fuzzy memristive neural networks with time delay. First, a delay-independent nonlinear fuzzy control is designed. Second, under the designed fuzzy control, two kinds of finite-time synchronization criteria are obtained by comparison method and Lyapunov function method, respectively. Furthermore, the settling time is estimated. Finally, a numerical simulation example is provided to demonstrate the effectiveness and feasibility of the theoretical results, and an application of the obtained theories is also given in the pseudorandom number generator (PRNG).
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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In the presence of stochastic perturbations and time delay, the aperiodic event-triggered synchronization control for neural networks is studied in this paper. First, a suitable linear event-triggered control is designed to ensure the synchronization of the delayed stochastic neural networks in mean square. Then, by using Itô-differential formula, the synchronization criteria are derived under the event-triggered control. Moreover, a new method is proposed to prove that Zeno behavior does not occur, which ensures that the event-triggered control is feasible. Finally, an example and its simulation are provided to substantiate the effectiveness of the theory.
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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In this paper, we employ multiple UAVs coordinated by a base station (BS) to help the ground users (GUs) to offload their sensing data. Different UAVs can adapt their trajectories and network formation to expedite data transmissions via multi-hop relaying. The trajectory planning aims to collect all GUs' data, while the UAVs' network formation optimizes the multi-hop UAV network topology to minimize the energy consumption and transmission delay. The joint network formation and trajectory optimization is solved by a two-step iterative approach. Firstly, we devise the adaptive network formation scheme by using a heuristic algorithm to balance the UAVs' energy consumption and data queue size. Then, with the fixed network formation, the UAVs' trajectories are further optimized by using multi-agent deep reinforcement learning without knowing the GUs' traffic demands and spatial distribution. To improve the learning efficiency, we further employ Bayesian optimization to estimate the UAVs' flying decisions based on historical trajectory points. This helps avoid inefficient action explorations and improves the convergence rate in the model training. The simulation results reveal close spatial-temporal couplings between the UAVs' trajectory planning and network formation. Compared with several baselines, our solution can better exploit the UAVs' cooperation in data offloading, thus improving energy efficiency and delay performance.
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. 45, no. 9, pp. 11053-11066.
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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.
Gong, Y, Yin, J, Zhang, T, Yin, W, Sun, L, Liang, Q & Wang, Q 2023, 'Ferrous sulfide nanoparticles control mercury speciation and bioavailability to methylating bacteria in contaminated groundwater: Impacts of mercury species', Chemical Engineering Journal, vol. 455, pp. 140612-140612.
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Mercury speciation in groundwater affects its removal effectiveness and methylation potential. Yet, most studies focus on the removal of inorganic dissolved Hg(II) and few studies explored the mercury methylation before and after the treatment. This study comprehensively explored the removal performance of three model mercury species, namely, dissolved inorganic divalent Hg (Hg(II), including free Hg2+ and Hg2+ complexes with Cl− and OH−), Hg2+ bound to dissolved organic matter (Hg-DOM), and HgS nanoparticles by FeS nanoparticles and further investigated the resultant impacts on the microbial methylation of Hg. Among three different stabilizers (starch, carboxymethyl cellulose (CMC), carboxymethyl starch (CMS)), CMC stabilized FeS nanoparticles (CMC-FeS) demonstrated best physical stability and highest mercury uptake. The CMC-FeS nanoparticles efficiently immobilized the three mercury species within 20 h. The sorption isotherm data of Hg(II) and Hg-DOM were well fitted by the dual-mode isotherm model and the maximum sorption capacities were 3358.28 and 2396.38 mg/g, respectively. Hg(II) and Hg-DOM were predominantly removed via ion exchange, chemical precipitation, and surface complexation whereas HgS was mainly immobilized through heteroaggregation. The simple treatment greatly reduced the bioavailable Hg species, thereby diminishing the net MeHg production by 70.2 %, 32.7 %, and 11.3 %, respectively. This study provides compelling evidence that FeS nanoparticles efficiently removed various mercury species in groundwater and remarkably inhibited the microbial methylation of mercury.
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 Microbiology Reviews, vol. 47, no. 2, p. fuad004.
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Abstract 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', Scientific Reports, vol. 13, no. 1, p. 8243.
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AbstractVaccine 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.
Gowd, SC, Ganeshan, P, Vigneswaran, VS, Hossain, MS, Kumar, D, Rajendran, K, Ngo, HH & Pugazhendhi, A 2023, 'Economic perspectives and policy insights on carbon capture, storage, and utilization for sustainable development', Science of The Total Environment, vol. 883, pp. 163656-163656.
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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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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, 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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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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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 Trans. Ind. Electron., 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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Currently, there is increasing interest in analog multibeam antennas whose beams can be flexibly steered to arbitrary directions. In a previous paper, we presented the theoretical framework for synthesizing individually steerable multiple beams using generalized joined coupler (GJC) matrices. The synthesis method was to optimize the array excitation vectors to approximate known distributions. In this article, we present a more robust optimization method to optimize the multibeams directly in order to control the half-power beamwidth, the sidelobe levels, and nulls for mitigating system interference. The effectiveness of the proposed method is demonstrated by numerical examples. We reveal how the quality of the multiple beams is inherently determined by the dimensions of the GJC matrix. Experimental results of a 3 10 Nolen-like GJC matrix are presented for the first time to validate the proposed method in realizing low sidelobe multibeams.
Guo, J, Lou, H, Yu, J, Li, R, Fang, W, Liu, J, Long, P, Ying, S & Ying, M 2023, 'isQ: An Integrated Software Stack for Quantum Programming', IEEE Transactions on Quantum Engineering, vol. 4, pp. 1-16.
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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.
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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Telecare medical information system (TMIS) is used to connect patients and doctors who are at a different location from each other. The authentication of the user and system is very crucial as the medical data of the user is stored on the server. Many systems have been developed in order to achieve this goal. We show some vulnerabilities of existing systems in this paper. We then propose a secure authentication mechanism to achieve the same goal. Machine learning and the nonce-based system is used for authentication of the entity and to prove the freshness of transmitted messages. Smart card blocking mechanisms have been included in each phase of the proposed system to prevent unauthorized access of data. The proposed system has been evaluated formally with the AVISPA tool. Then the proposed model has also been checked against different attacks and evaluated for different functionalities. We provide relative analysis with some recently proposed models and show our proposed system is relatively more efficient and secure.
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. 411-413.
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AbstractThis Special Issue of theRoboticais 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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Haghighian, SK, Yeh, H-G, Marangalu, MG, Kurdkandi, NV, Abbasi, M & Tarzamni, H 2023, 'A Seventeen-Level Step-Up Switched-Capacitor-Based Multilevel Inverter With Reduced Charging Current Stress on Capacitors for PV Applications', IEEE Access, vol. 11, no. 99, pp. 118124-118143.
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Haghighitalab, A, Dominici, M, Matin, MM, Shekari, F, Ebrahimi Warkiani, M, Lim, R, Ahmadiankia, N, Mirahmadi, M, Bahrami, AR & Bidkhori, HR 2023, 'Extracellular vesicles and their cells of origin: Open issues in autoimmune diseases', Frontiers in Immunology, vol. 14, p. 1090416.
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The conventional therapeutic approaches to treat autoimmune diseases through suppressing the immune system, such as steroidal and non-steroidal anti-inflammatory drugs, are not adequately practical. Moreover, these regimens are associated with considerable complications. Designing tolerogenic therapeutic strategies based on stem cells, immune cells, and their extracellular vesicles (EVs) seems to open a promising path to managing autoimmune diseases’ vast burden. Mesenchymal stem/stromal cells (MSCs), dendritic cells, and regulatory T cells (Tregs) are the main cell types applied to restore a tolerogenic immune status; MSCs play a more beneficial role due to their amenable properties and extensive cross-talks with different immune cells. With existing concerns about the employment of cells, new cell-free therapeutic paradigms, such as EV-based therapies, are gaining attention in this field. Additionally, EVs’ unique properties have made them to be known as smart immunomodulators and are considered as a potential substitute for cell therapy. This review provides an overview of the advantages and disadvantages of cell-based and EV-based methods for treating autoimmune diseases. The study also presents an outlook on the future of EVs to be implemented in clinics for autoimmune patients.
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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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.
Han, Z, Xu, C, Ma, S, Hu, Y, Zhao, G & Yu, S 2023, 'DTE-RR: Dynamic Topology Evolution-Based Reliable Routing in VANET', IEEE Wireless Communications Letters, vol. 12, no. 6, pp. 1061-1065.
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Han, Z, Xu, C, Zhao, G, Wang, S, Cheng, K & Yu, S 2023, 'Time-Varying Topology Model for Dynamic Routing in LEO Satellite Constellation Networks', IEEE Transactions on Vehicular Technology, vol. 72, no. 3, pp. 3440-3454.
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With the characteristics of low-latency, seamless coverage and high bandwidth, the Low Earth Orbit (LEO) satellite network has been the promising technology for the sixth-generation mobile communication (6G) networks, especially, the inter-satellite link can improve the flexibility of inter-satellite networking and routing. However, in the existing works, since the impact of link attributes on the satellite topology has not been well investigated, it is difficult to avoid the loss of topology information for the transmission path selection, which may aggravate the unreliability of routing path. In this paper, we propose a novel time-varying topology model for the LEO satellite network, to improve the adaptability of the satellite routing. Firstly, the weighted time-space evolution graph based on the link attributes is established to construct the time-varying topology model of LEO satellite networks. Then, the utility function of the link attributes is modelled and the multi-attribute decision-making is introduced to calculate the weight of each link attribute for the quantification of the link utility. Finally, based on the topology model, the inter-satellite link utility-based dynamic routing algorithm is proposed to improve the adaptability of satellite routing. The simulation results demonstrate that the proposed routing algorithm outperforms the existing routing algorithms in terms of packet drop rate, end-to-end delay and throughput.
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, 'Hydromechanical state of soil fluidisation: a microscale perspective', Acta Geotechnica, vol. 18, no. 3, pp. 1149-1167.
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AbstractThis paper investigates soil fluidisation at the microscale using the discrete element method (DEM) in combination with the lattice Boltzmann method (LBM). Numerical simulations were carried out at varying hydraulic gradients across the granular assembly of soil. The development of local hydraulic gradients, the contact distribution, and the associated fabric changes were investigated. Microscale findings suggest that a critical hydromechanical state inducing fluid-like instability of a granular assembly can be defined by a substantial increase in grain slip associated with a rapid reduction in interparticle contacts. Based on these results, a new micromechanical criterion is proposed to characterise the transformation of granular soil from a hydromechanically stable to an unstable state. The constraint ratio (ratio of the number of constraints to the number of degrees of freedom) is introduced to portray the relative slippage between particles and the loss of interparticle contacts within the granular fabric. Its magnitude of unity corresponds to the condition of zero effective stress, representing the critical hydromechanical state. In practical terms, the results of this study reflect the phenomenon of subgrade mud pumping that occurs in railways when heavy-haul trains pass through at certain axle loads and speeds.
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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Hasan, HA, Hacheem, ZA, Almurshedi, AD & Khabbaz, H 2023, 'The Influence of Styrene Butadiene Latex on Sandy Soil Reinforced by Soil Mixed Columns under Raft Foundation', Mathematical Modelling of Engineering Problems, vol. 10, no. 3, pp. 733-739.
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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 Trans. Ind. Electron., 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.
Hasanpour, S, Siwakoti, YP & Blaabjerg, F 2023, 'A New Soft-Switched High Step-Up Trans-Inverse DC/DC Converter Based on Built-In Transformer', IEEE Open Journal of Power Electronics, vol. 4, pp. 381-394.
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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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Hassan, Z, Mahmood, M, Ahmed, N, Saeed, MH, Khan, R, Abbas, MM, Kalam, MA, Almomani, F & Abdelsalam, E 2023, 'Techno‐economic assessment of evacuated flat‐plate solar collector system for industrial process heat', Energy Science & Engineering, vol. 11, no. 6, pp. 2185-2201.
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AbstractIn the industrial sector, hot water applications constitute a significant share of final energy consumption. This creates a wide demand‐supply energy gap that must be bridged by integrating renewable sources with conventional fuels. This paper presents the performance analysis of a solar water heating system based on an evacuated flat‐plate collector (EFPC) with a surface area of 4 m2. A water–glycol mixture was used as the heat transfer fluid (HTF) with mass flow rates of 0.03, 0.0336, and 0.0504 kg/s under a vacuum pressure of –0.8 bar created inside the collector. A detailed numerical model was developed in MATLAB for the proposed EFPC system, followed by experimental validation. A maximum root mean square error of 2.81 for the absorber temperature and a percentage error of 6.62 was observed for the thermal efficiency in model validation. This substantiates the model's capability to predict actual system performance with reasonable accuracy. The maximum thermal efficiency of the EFPC is 78% with a maximum fluid outlet temperature of 98°C in June and 69°C in January. The maximum useful energy extracted is 1300 W in January. Additionally, the effect of design parameters on system performance such as mass flow rates, collector areas, tube spacing, and different HTF mixtures is simulated. Lastly, an economic analysis of the EFPC was conducted for hot water demand in a textile industry. The results revealed a payback period of 7.4 years, which highlights the feasibility of this system.
Hastings, C & Craig, L 2023, 'Accumulating Financial Vulnerability, Not Financial Security: Social Reproduction and Older Women’s Homelessness', Housing, Theory and Society, vol. 40, no. 3, pp. 356-376.
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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. 39-63.
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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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Hazrat, MA, Rasul, MG, Khan, MMK, Ashwath, N, Fattah, IMR, Ong, HC & Mahlia, TMI 2023, 'Biodiesel production from transesterification of Australian Brassica napus L. oil: optimisation and reaction kinetic model development', Environment, Development and Sustainability, vol. 25, no. 11, pp. 12247-12272.
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AbstractEdible oil-based feedstocks based biodiesel is still leading the industry around the world. Canola oil (Brassica napus L.) contributes significantly to that race. Process optimisation and the development of reaction kinetic models of edible oil feedstocks are still required since the knowledge of kinetics is needed for designing industrial facilities and evaluating the performance of catalysts during transesterification or other related processes in a biorefinery. This research focuses on the transesterification process for biodiesel production because of its higher output efficiency, reactivity with feedstock, techno-economic feasibility in terms of FFA content, and environmental sustainability. The response surface method with the Box–Behnken model was used to optimise the process. Multivariate analysis of variance (ANOVA) was also performed to investigate the effectiveness of the regression model. The optimal process conditions were found to be 5.89 M methanol, 0.5% (w/w) KOH, 60 °C and 120 min. The predicted yield was 99.5% for a 95% confidence interval (99.1, 99.9). The experimental yield was 99.6% for these conditions. Two different kinetic models were also developed in this study. The activation energy was 16.9% higher for the pseudo-first-order irreversible reaction than for the pseudo-homogenous irreversible reaction. Such a comprehensive analysis will assist stakeholders in evaluating the technology for industrial development in biodiesel fuel commercialisation.
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, D, Gui, Y, Li, W, Tao, Y, Zou, C, Sui, Y & Xue, J 2023, 'A Container-Usage-Pattern-Based Context Debloating Approach for Object-Sensitive Pointer Analysis', Proceedings of the ACM on Programming Languages, vol. 7, no. OOPSLA2, pp. 971-1000.
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In this paper, we introduce DebloaterX, a new approach for automatically identifying context-independent objects to debloat contexts in object-sensitive pointer analysis ( k obj). Object sensitivity achieves high precision, but its context construction mechanism combines objects with their contexts indiscriminately. This leads to a combinatorial explosion of contexts in large programs, resulting in inefficiency. Previous research has proposed a context-debloating approach that inhibits a pre-selected set of context-independent objects from forming new contexts, improving the efficiency of k obj. However, this earlier context-debloating approach under-approximates the set of context-independent objects identified, limiting performance speedups. We introduce a novel context-debloating pre-analysis approach that identifies objects as context-dependent only when they are potentially precision-critical to k obj based on three general container-usage patterns. Our research finds that objects containing no fields of ”abstract” (i.e., open) types can be analyzed context-insensitively with negligible precision loss in real-world applications. We provide clear rules and efficient algorithms to recognize these patterns, selecting more context-independent objects for better debloating. We have implemented DebloaterX in the Qilin framework and will release it as an open-source tool. Our experimental results on 12 standard Java benchmarks and real-world programs show that DebloaterX selects 92.4% of objects to be context-independent on average, enabling k obj to run significantly faster (an average of 19.3x when k = 2 and 150.2x when ...
He, H, Chen, Z, Liu, H, Liu, X, Guo, Y & Li, J 2023, 'Practical Tracking Method based on Best Buddies Similarity', Cyborg and Bionic Systems, vol. 4.
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Visual tracking is a crucial skill for bionic robots to perceive the environment and control their movement. However, visual tracking is challenging when the target undergoes nonrigid deformation because of the perspective change from the camera mounted on the robot. In this paper, a real-time and scale-adaptive visual tracking method based on best buddies similarity (BBS) is presented, which is a state-of-the-art template matching method that can handle nonrigid deformation. The proposed method improves the original BBS in 4 aspects: (a) The caching scheme is optimized to reduce the computational overhead, (b) the effect of cluttered backgrounds on BBS is theoretically analyzed and a patch-based texture is introduced to enhance the robustness and accuracy, (c) the batch gradient descent algorithm is used to further speed up the method, and (d) a resample strategy is applied to enable the BBS to track the target in scale space. The proposed method on challenging real-world datasets is evaluated and its promising performance is demonstrated.
He, H, Teng, J, Zhang, S & Sheng, D 2023, 'Determining frost heave classification by using ratio of frost heave to square root of time', Yantu Gongcheng Xuebao/Chinese Journal of Geotechnical Engineering, vol. 45, no. 12, pp. 2519-2528.
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The frost heave classification is the critical index for the design of foundation engineering in cold regions. At present, it is considered as a basic property of materials. Many criteria belong to empirical or semi-empirical methods and lack theoretical support. The frost heave tests are tedious and long time-consuming, and are not easily operated. To propose a rational and simple frost heave classification index, from the frost heave mechanism, an analytical model for unsaturated frozen soil is established and validated. Then a new frost heave classification index R (mm/h0.5), which is the ratio of frost heave to square root of time, is identified based on the proposed model. Through comparison with the large number of frost heave results, the value of R less than 0.21 indicates the low frost heave classification, that between 0.21 and 1.18 represents the medium heave classification, and that greater than 1.18 means the high frost heave classification. From a statistical probability perspective, the probability density distribution of the values of each classification index is analyzed, and their trends are also compared. It is found that the concentration and stability of the new index R are the highest during freezing process. The new index R has theoretical support and simultaneously couples the basic soil properties and freezing environmental factors. It breaks through the limitation of the existing indexes, and enriches the frost heave classification system, and provides theoretical support for the engineering design in cold regions.
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, Xu, G, Jameel, S, Wang, X & Chen, H 2023, 'Graph-Aware Deep Fusion Networks for Online Spam Review Detection', IEEE Transactions on Computational Social Systems, vol. 10, no. 5, pp. 2557-2565.
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He, T, Wu, M, Aguilera, RP, Lu, DD-C, Liu, Q & Vazquez, S 2023, 'Low Computational Burden Model Predictive Control for Single-Phase Cascaded H-Bridge Converters Without Weighting Factor', IEEE Transactions on Industrial Electronics, vol. 70, no. 3, pp. 2396-2406.
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In this article, a low computational burden model predictive control (MPC) strategy without weighting factor is proposed for the single-phase cascaded H-bridge CHB converters. To reduce the switching state candidates, a hierarchy control algorithm is proposed. The grid current is controlled by selecting a subregion from the designed 2-D control plane, instead of the entire area. Two vectors are chosen in one sampling period for more accurate tracking. Then, the voltage balancing is achieved by selecting the optimal switching state from the subregion candidates to form the above two vectors. The cost function can be constructed of one variable: load voltage. Therefore, the weighting factor can be eliminated. No tuning or retuning processes are required in the proposed method. To reduce the computational time further, the principle of eliminating the switching state candidates operating the same voltage balancing performance is proposed. Conventional and proposed MPC methods are verified by experimental tests via a laboratory setup of a three-cell connected CHB converter. Steady-state and transient operations demonstrate that the proposed method guarantees less distortion grid current and shorter execution time (reduced from 15 to 3 μ s). Fast response speed to variations in voltage reference and load resistance can be achieved t.
He, W, Xu, G & Razzak, I 2023, 'Social responses to the COVID-19 pandemic', Behaviour & Information Technology, vol. 42, no. 2, pp. 171-173.
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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, Y, Liu, P, Zhu, L & Yang, Y 2023, 'Filter Pruning by Switching to Neighboring CNNs With Good Attributes', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 10, pp. 8044-8056.
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Filter pruning is effective to reduce the computational costs of neural networks. Existing methods show that updating the previous pruned filter would enable large model capacity and achieve better performance. However, during the iterative pruning process, even if the network weights are updated to new values, the pruning criterion remains the same. In addition, when evaluating the filter importance, only the magnitude information of the filters is considered. However, in neural networks, filters do not work individually, but they would affect other filters. As a result, the magnitude information of each filter, which merely reflects the information of an individual filter itself, is not enough to judge the filter importance. To solve the above problems, we propose meta-attribute-based filter pruning (MFP). First, to expand the existing magnitude information-based pruning criteria, we introduce a new set of criteria to consider the geometric distance of filters. Additionally, to explicitly assess the current state of the network, we adaptively select the most suitable criteria for pruning via a meta-attribute, a property of the neural network at the current state. Experiments on two image classification benchmarks validate our method. For ResNet-50 on ILSVRC-2012, we could reduce more than 50% FLOPs with only 0.44% top-5 accuracy loss.
He, Y, Wang, K, Zhang, W, Lin, X & Zhang, Y 2023, 'Scaling Up k-Clique Densest Subgraph Detection', Proceedings of the ACM on Management of Data, vol. 1, no. 1, pp. 1-26.
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In this paper, we study the k-clique densest subgraph problem, which detects the subgraph that maximizes the ratio between the number of k-cliques and the number of vertices in it. The problem has been extensively studied in the literature and has many applications in a wide range of fields such as biology and finance. Existing solutions rely heavily on repeatedly computing all the k-cliques, which are not scalable to handle large k values on large-scale graphs. In this paper, by adapting the idea of 'pivoting', we propose the SCT*-Index to compactly organize the k-cliques. Based on the SCT*-Index, our SCTL algorithm can directly obtain the k-cliques from the index and efficiently achieve near-optimal approximation. To further improve SCTL, we propose SCTL* that includes novel graph reductions and batch-processing optimizations to reduce the search space and decrease the number of visited k-cliques, respectively. As evaluated in our experiments, SCTL* significantly outperform existing approaches by up to two orders of magnitude. In addition, we propose a sampling-based approximate algorithm that can provide reasonable approximations for any k value on billion-scale graphs. Extensive experiments on 12 real-world graphs validate both the efficiency and effectiveness of the proposed techniques.
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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Helalian, H, Zhu, X & Atarodi, M 2023, 'A Multioutput and Highly Efficient GaN Distributed Power Amplifier for Compact Subarrays in Wideband Phased Array Antennas', IEEE Transactions on Microwave Theory and Techniques, vol. 71, no. 11, pp. 4800-4813.
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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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In this letter, we investigate a novel optimization approach to direction-of-arrival (DoA) estimation for a lens antenna array. Inspired by a property of the sinc function and ${\ell _{2}}$-norm optimization, we develop the gradient descent-based spatial spectrum reconstruction (GD-SSR) to estimate the DoAs based on the sum signal covariance vector (SSCV). Our proposed algorithm does not require a priori knowledge of signal number and has a lower complexity compared with existing techniques while achieving a better estimation performance, even in a low-SNR regime. In addition, the proposed model does not require any pretraining process as prior learning-based methods. The simulation results show that our scheme not only outperforms other techniques but also resolves the angular ambiguity problem.
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', Pediatric Research, vol. 94, no. 2, pp. 466-476.
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Abstract 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 ti...
Hoke, JC, Ippoliti, M, Rosenberg, E, Abanin, D, Acharya, R, Andersen, TI, Ansmann, M, Arute, F, Arya, K, Asfaw, A, Atalaya, J, Bardin, JC, Bengtsson, A, Bortoli, G, Bourassa, A, Bovaird, J, Brill, L, Broughton, M, Buckley, BB, Buell, DA, Burger, T, Burkett, B, Bushnell, N, Chen, Z, Chiaro, B, Chik, D, Cogan, J, Collins, R, Conner, P, Courtney, W, Crook, AL, Curtin, B, Dau, AG, Debroy, DM, Del Toro Barba, A, Demura, S, Di Paolo, A, Drozdov, IK, Dunsworth, A, Eppens, D, Erickson, C, Farhi, E, Fatemi, R, Ferreira, VS, Burgos, LF, Forati, E, Fowler, AG, Foxen, B, Giang, W, Gidney, C, Gilboa, D, Giustina, M, Gosula, R, Gross, JA, Habegger, S, Hamilton, MC, Hansen, M, Harrigan, MP, Harrington, SD, Heu, P, Hoffmann, MR, Hong, S, Huang, T, Huff, A, Huggins, WJ, Isakov, SV, Iveland, J, Jeffrey, E, Jiang, Z, Jones, C, Juhas, P, Kafri, D, Kechedzhi, K, Khattar, T, Khezri, M, Kieferová, M, Kim, S, Kitaev, A, Klimov, PV, Klots, AR, Korotkov, AN, Kostritsa, F, Kreikebaum, JM, Landhuis, D, Laptev, P, Lau, K-M, Laws, L, Lee, J, Lee, KW, Lensky, YD, Lester, BJ, Lill, AT, Liu, W, Locharla, A, Martin, O, McClean, JR, McEwen, M, Miao, KC, Mieszala, A, Montazeri, S, Morvan, A, Movassagh, R, Mruczkiewicz, W, Neeley, M, Neill, C, Nersisyan, A, Newman, M, Ng, JH, Nguyen, A, Nguyen, M, Niu, MY, O’Brien, TE, Omonije, S, Opremcak, A, Petukhov, A, Potter, R, Pryadko, LP, Quintana, C, Rocque, C, Rubin, NC, Saei, N, Sank, D, Sankaragomathi, K, Satzinger, KJ, Schurkus, HF, Schuster, C, Shearn, MJ, Shorter, A, Shutty, N, Shvarts, V, Skruzny, J, Smith, WC, Somma, R, Sterling, G, Strain, D, Szalay, M, Torres, A, Vidal, G, Villalonga, B, Heidweiller, CV, White, T, Woo, BWK, Xing, C, Yao, ZJ, Yeh, P, Yoo, J, Young, G, Zalcman, A, Zhang, Y, Zhu, N, Zobrist, N, Neven, H, Babbush, R, Bacon, D, Boixo, S, Hilton, J, Lucero, E, Megrant, A, Kelly, J, Chen, Y, Smelyanskiy, V, Mi, X, Khemani, V & Roushan, P 2023, 'Measurement-induced entanglement and teleportation on a noisy quantum processor', Nature, vol. 622, no. 7983, pp. 481-486.
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AbstractMeasurement has a special role in quantum theory1: by collapsing the wavefunction, it can enable phenomena such as teleportation2 and thereby alter the ‘arrow of time’ that constrains unitary evolution. When integrated in many-body dynamics, measurements can lead to emergent patterns of quantum information in space–time3–10 that go beyond the established paradigms for characterizing phases, either in or out of equilibrium11–13. For present-day noisy intermediate-scale quantum (NISQ) processors14, the experimental realization of such physics can be problematic because of hardware limitations and the stochastic nature of quantum measurement. Here we address these experimental challenges and study measurement-induced quantum information phases on up to 70 superconducting qubits. By leveraging the interchangeability of space and time, we use a duality mapping9,15–17 to avoid mid-circuit measurement and access different manifestations of the underlying phases, from entanglement scaling3,4 to measurement-induced teleportation18. We obtain finite-sized signatures of a phase transition with a decoding protocol that correlates the experimental measurement with classical simulation data. The phases display remarkably different sensitivity to noise, and we use this disparity to turn an inherent hardware limitation into a useful diagnostic. Our work demonstrates an approach to realizing measurement-induced physics at scales that are at the limits of current NISQ processors.
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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Honda, T, Tran, T, Popplestone, S, Draper, CE, Yousafzai, AK, Romero, L & Fisher, J 2023, 'Parents’ mental health and the social-emotional development of their children aged between 24 and 59 months in low-and middle-income countries: A systematic review and meta-analyses', SSM - Mental Health, vol. 3, pp. 100197-100197.
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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, p. 155.
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Knowledge of the degree and depth of the excavation damaged zone (EDZ) resulting from drill-and-blast excavations has important influences on the design and construction of deep underground tunnels. However, the spatial distribution of EDZ around underground tunnels subjected to the combined effect of blast loading and in situ stress unloading is still under discussion. In this study, the dependences of the spatial distribution of EDZ in an underground tunnel on the variations of in situ stress conditions, excavation dimensions and rock strength were investigated using a combined method of field measuring and numerical modelling. Field measurements of EDZ in an underground mine were first conducted using the non-metallic acoustic technique. Subsequently, a blast excavation model concerning the combined effect of blast loading and in situ stress unloading was developed and calibrated against the field-measured data. Finally, the EDZs induced by various in situ stress conditions, tunnel shapes, and dimensions as well as rock strengths were simulated. The field measurement results indicate that larger highly damaged zones are generated at the roadway crown and sidewall, whereas smaller intensively damaged zones are induced at the roadway shoulder. The average depth of EDZ around the tested roadways, with an overburden of 355 m to 915 m, varies from 0.36 m to 1.72 m. The numerical results show that in situ stress, excavation dimension, and rock strengths significantly impact the depth of EDZ. More specifically, lateral pressure coefficient and the shape of the roadway cross section play a predominant role in controlling the distribution pattern of EDZ, and the magnitude of in situ stress, excavation dimension, and rock strength mainly contribute to the depth of EDZ. Furthermore, the special phenomenon of zonal disintegration in the surrounding rock mass occurs around highly pre-stressed underground roadways.
Hong, Z, Tao, M, Zhao, M, Zhou, J, Yu, H & Wu, C 2023, 'Numerical modelling of rock fragmentation under high in-situ stresses and short-delay blast loading', Engineering Fracture Mechanics, vol. 293, pp. 109727-109727.
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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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Hoque, M, Alam, M, Wang, S, Zaman, JU, Rahman, MS, Johir, MAH, Tian, L, Choi, J-G, Ahmed, MB & Yoon, M-H 2023, 'Interaction chemistry of functional groups for natural biopolymer-based hydrogel design', Materials Science and Engineering: R: Reports, vol. 156, pp. 100758-100758.
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Horry, MJ, Chakraborty, S, Pradhan, B, Paul, M, Zhu, J, Barua, PD, Mir, HS, Chen, F, Zhou, J & Acharya, UR 2023, 'Full-Resolution Lung Nodule Localization From Chest X-Ray Images Using Residual Encoder-Decoder Networks', IEEE Access, vol. 11, pp. 143016-143036.
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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. 525-540.
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AbstractHigh-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, A, Molla, MM, Kamrujjaman, M, Mohebujjaman, M & Saha, SC 2023, 'MHD Mixed Convection of Non-Newtonian Bingham Nanofluid in a Wavy Enclosure with Temperature-Dependent Thermophysical Properties: A Sensitivity Analysis by Response Surface Methodology', Energies, vol. 16, no. 11, pp. 4408-4408.
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The numerical investigation of magneto-hydrodynamic (MHD) mixed convection flow and entropy formation of non-Newtonian Bingham fluid in a lid-driven wavy square cavity filled with nanofluid was investigated by the finite volume method (FVM). The numerical data-based temperature and nanoparticle size-dependent correlations for the Al2O3-water nanofluids are used here. The physical model is a two-dimensional wavy square cavity with thermally adiabatic horizontal boundaries, while the right and left vertical walls maintain a temperature of TC and TH, respectively. The top wall has a steady speed of u=u0. Pertinent non-dimensional parameters such as Reynolds number (Re=10,100,200,400), Hartmann number (Ha=0,10,20), Bingham number (Bn=0,2,5,10,50,100,200), nanoparticle volume fraction (ϕ=0,0.02,0.04), and Prandtl number (Pr=6.2) have been simulated numerically. The Richardson number Ri is calculated by combining the values of Re with a fixed value of Gr, which is the governing factor for the mixed convective flow. Using the Response Surface Methodology (RSM) method, the correlation equations are obtained using the input parameters for the average Nusselt number (Nu¯), total entropy generation (Es)t, and Bejan number (Beavg). The interactive effects of the pertinent parameters on the heat transfer rate are presented by plotting the response surfaces and the contours obtained from the RSM. The sensitivity of the output response to the input parameters is also tested. According to the findings, the mean Nusselt numbers (Nu¯) drop when Ha and Bn are increased and grow when Re and ϕ are augmented. It is found that (Es)t is reduced by raising Ha, but (Es)t rises with the augmentation of ϕ and Re. It is also found that the ϕ and Re numbers have a positive sensitivity to the Nu¯, while the sensitivity of the Ha and Bn numbers is negative.
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, MA, Islam, MR, Hossain, MA & Hossain, MJ 2023, 'Control strategy review for hydrogen-renewable energy power system', Journal of Energy Storage, vol. 72, pp. 108170-108170.
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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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The demand for lithium (Li) will grow from about 500,000 metric tons of lithium carbonate equivalent in 2021 to 3–4 million metric tons in 2030. To meet the Li demand, the separation of Li-mixed monovalent and divalent cations is critical for Li extraction from an aqueous medium. Capacitive deionization (CDI) and membrane capacitive deionization (MCDI) have recently emerged as viable water treatment technologies, yet ion-specific selective recovery using CDI systems is still under-investigated. In this study, the electrode surface of each system was modified to improve Li+ selectivity. Metal-organic frameworks (MOF), particularly zeolitic imidazolate framework-8 (ZiF-8), have shown substantial promise due to their tunable pore size and pore channel chemistry. Through an aqueous medium-based surface modification, we offer a simple technique of synthesizing ZiF-8 on carbon electrodes and underneath the cation exchange membrane (CEM). The bare CDI and MCDI systems initially showed poor selectivity towards Li+ in the mono and divalent ion incorporated simulated solutions. The relative selectivity (ρMLi; (M = metal ions)) in the CDI system was estimated as 0.73, 0.43, 0.67, and 0.58 for Na+, K+, Mg2+, and Ca2+, respectively, which was 0.93, 0.97, 0.39, and 0.30 in the MCDI system. In the case of bare activated carbon (AC) electrodes, the difference of hydration enthalpy played a critical role in Li+ selectivity towards other monovalent ions. However, despite having high hydration enthalpy, the Mg2+ and Ca2+ showed low Li+ selectivity due to the superior charge density of divalent ions. On the other hand, after the modification of AC electrodes with in-situ growth of ZiF-8 on the surface, the Li+ selectivity for monovalent Na+ and K+ was estimated at 3.08 and 1.12, respectively, which is 4.2 and 2.6 times higher than the bare AC electrode, respectively. Besides, compared to Na+, the trade-off between the low dehydration energy of K+ and the rapid ion transit ...
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, 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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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, C-W, Lin, C, Nguyen, MK, Hussain, A, Bui, X-T & Ngo, HH 2023, 'A review of biosensor for environmental monitoring: principle, application, and corresponding achievement of sustainable development goals', Bioengineered, vol. 14, no. 1, pp. 58-80.
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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, Q-L & 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, Li, G, Liu, Z, Wu, Y, Gong, C, Zhu, L & Yang, Y 2023, 'Exploring viewport features for semi-supervised saliency prediction in omnidirectional images', Image and Vision Computing, vol. 129, pp. 104590-104590.
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Huang, M, Zhou, S, Ma, D-D, Wei, W, Zhu, Q-L & 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, S, Tsang, IW, Xu, Z & Lv, J 2023, 'Latent Representation Guided Multi-View Clustering', IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 7, pp. 7082-7087.
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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, X, Tuyen Le, A & Guo, YJ 2023, 'Joint Analog and Digital Self-Interference Cancellation for Full Duplex Transceiver With Frequency-Dependent I/Q Imbalance', IEEE Transactions on Wireless Communications, vol. 22, no. 4, pp. 2364-2378.
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An effective and practical joint analog and digital self-interference cancellation (SIC) scheme without additional signalling overhead for an I/Q imbalanced full duplex transceiver is proposed in this paper. This scheme combines an I/Q imbalanced analog least mean square (ALMS) loop at the transceiver radio frequency frontend and a two-stage digital signal processing (DSP) at the digital baseband to achieve excellent SIC performance with low complexity. The steady state weighting coefficients of the I/Q imbalanced ALMS loop with periodical transmitted signal and the loop’s convergence behaviour are firstly analysed. The residual SI is then modelled as the output of a time-varying widely linear system. With a track/hold control mechanism applied to the ALMS loop, the system model for digital SIC is further presented, followed by the DSP algorithms suitable for real-time implementation. The noise enhancement in each stage digital cancellation is also analysed and formulated. Finally, simulation results are provided to verify the theoretical analyses and demonstrate the overall SIC performance.
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, Li, Y, Jourjon, G, Seneviratne, S, Thilakarathna, K, Cheng, A, Webb, D & Xu, RYD 2023, 'Calibrated reconstruction based adversarial autoencoder model for novelty detection', Pattern Recognition Letters, vol. 169, pp. 50-57.
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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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Handwritten image-based writer age estimation is a challenging task due to the various writing styles of different individuals, use of different scripts, varying alignment, etc. Unlike age estimation using face recognition in biometrics, handwriting-based age classification is reliable and inexpensive because of the plain backgrounds of documents. This paper presents a novel model for deriving the phase spectrum based on the Harmonic Wavelet Transform (HWT) for age classification on handwritten images from 11 to 65 years. This includes 11 classes with an interval of 5 years. In contrast to the Fourier transform, which provides a noisy phase spectrum due to loss of time variations, the proposed HWT-based phase spectrum retains time variations of phase and magnitude. As a result, the proposed HWT-based phase spectrum preserves vital information of changes in handwritten images. In order to extract such information, we propose new phase statistics-based features for age classification based on the understanding that as age changes, writing style also changes. The features and the input images are fed to a VGG-16 model for age classification. The proposed method is tested on our own dataset and three standard datasets, namely, IAM-2, KHATT and that of Basavaraja et al. to demonstrate the effectiveness of the proposed model compared to the existing methods in terms of classification rate. The results of the proposed and existing methods on different datasets show that the proposed method outperforms the existing methods in terms of classification rate.
Huang, Z, Wen, J, Chen, S, Zhu, L & Zheng, N 2023, 'Discriminative Radial Domain Adaptation', IEEE Transactions on Image Processing, vol. 32, pp. 1419-1431.
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Hugon-Rodin, J, Carrière, C, Trillot, N, Drillaud, N, Biron, C, Barbay, V, Chamouni, P, Lavenu Bombled, C, Lebreton, A, Wieland, A, Moussa, M, Brungs, T, Tardy, B, Desconclois, C, Beurrier, P, Gay, V, Clasyssens, S, De Maistre, E, Simurda, T & Casini, A 2023, 'OC 75.2 Obstetrical Complications in Hereditary Fibrinogen Disorders: The Fibrinogest Study', Research and Practice in Thrombosis and Haemostasis, vol. 7, pp. 100477-100477.
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Hume, RD, Kanagalingam, S, Deshmukh, T, Chen, S, Mithieux, SM, Rashid, FN, Roohani, I, Lu, J, Doan, T, Graham, D, Clayton, ZE, Slaughter, E, Kizana, E, Stempien-Otero, AS, Brown, P, Thomas, L, Weiss, AS & Chong, JJH 2023, 'Tropoelastin Improves Post-Infarct Cardiac Function', Circulation Research, vol. 132, no. 1, pp. 72-86.
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Background: Myocardial infarction (MI) is among the leading causes of death worldwide. Following MI, necrotic cardiomyocytes are replaced by a stiff collagen-rich scar. Compared to collagen, the extracellular matrix protein elastin has high elasticity and may have more favorable properties within the cardiac scar. We sought to improve post-MI healing by introducing tropoelastin, the soluble subunit of elastin, to alter scar mechanics early after MI. METHODS AND RESULTS: We developed an ultrasound-guided direct intramyocardial injection method to administer tropoelastin directly into the left ventricular anterior wall of rats subjected to induced MI. Experimental groups included shams and infarcted rats injected with either PBS vehicle control or tropoelastin. Compared to vehicle treated controls, echocardiography assessments showed tropoelastin significantly improved left ventricular ejection fraction (64.7±4.4% versus 46.0±3.1% control) and reduced left ventricular dyssynchrony (11.4±3.5 ms versus 31.1±5.8 ms control) 28 days post-MI. Additionally, tropoelastin reduced post-MI scar size (8.9±1.5% versus 20.9±2.7% control) and increased scar elastin (22±5.8% versus 6.2±1.5% control) as determined by histological assessments. RNA sequencing (RNAseq) analyses of rat infarcts showed that tropoelastin injection increased genes associated with elastic fiber formation 7 days post-MI and reduced genes associated with immune response 11 days post-MI. To show translational relevance, we performed immunohistochemical analyses on human ischemic heart disease cardiac samples and showed an increase in tropoelastin within fibrotic areas. Using RNA-seq we also demonstrated the tropoelastin gene ELN is upregulated in human isc...
Hunt, H, Indraratna, B & Qi, Y 2023, 'Ductility and energy absorbing behaviour of coal wash – rubber crumb mixtures', International Journal of Rail Transportation, vol. 11, no. 4, pp. 508-528.
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The reuse of waste materials, such as coal wash (CW) and rubber crumbs (RC), is becoming increasingly popular in large-scale civil engineering applications, which is environmentally friendly and economically attractive. In this study, the ductility and strain energy density of CW-RC mixtures with different RC contents compacted to the same initial void ratio and subjected to triaxial shearing are evaluated. As expected, the ductility and energy absorbing capacity of the waste mixture are improved with RC addition. This makes the use of CW-RC mixtures in substructure applications a promising development for future rail design where loads are expected to increase. Furthermore, empirical models for the shear strength and strain energy density based on the RC content are proposed. These models may be used as a guide to approximate the sheacr strength and strain energy density of these compacted CW-RC mixtures prior to the undertaking of extensive triaxial tests.
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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Instead of the conventional perception of nitrous oxide (N2O) as a potent greenhouse gas whose production should be minimized, this work aimed to assess N2O recovery as a potential energy source from nitric oxide (NO) in the form of Fe(II)EDTA-NO through element sulfur (S0) or thiosulfate (S2O32−)-driven NO-based autotrophic denitrification (SNADS0 or SNADS2O3). A mathematical model was proposed to describe substrate dynamics related to N2O production and reduction and was successfully calibrated and validated using batch experimental data from lab-scale SNADS0 and SNADS2O3 systems under different substrates conditions. The model was subsequently employed to assess the potential of N2O accumulation and recovery by altering the S/N mass ratio and the ratio of gas volume to liquid volume of the system. The simulation results suggested that with a S/N mass ratio of nearly 1.0, high-purity N2O could be more rapidly and efficiently recovered from Fe(II)EDTA-NO in the SNADS0 and SNADS2O3 systems with a higher ratio of gas volume to liquid volume (i.e., a N2O recovery efficiency of up to 80.2%−84.9% reached within 3.1 h−3.5 h under the studied conditions). Comparatively, the SNADS0 process showed an economic and viable advantage for practical applications to the efficient treatment and resource utilization of NO-containing flue gas.
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, A, Wu, SC, Le, T-H, Huang, W-Y, Lin, C, Bui, X-T & Ngo, HH 2023, 'Enhanced biodegradation of endocrine disruptor bisphenol A by food waste composting without bioaugmentation: Analysis of bacterial communities and their relative abundances', Journal of Hazardous Materials, vol. 460, pp. 132345-132345.
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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.
Huynh, TQ, Nguyen, TT & Nguyen, H 2023, 'Base resistance of super-large and long piles in soft soil: performance of artificial neural network model and field implications', Acta Geotechnica, vol. 18, no. 5, pp. 2755-2775.
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AbstractThis study aims to examine the performance of artificial neural network (ANN) model based on 1137 datasets of super-large (1.0–2.5 m in equivalent diameter) and long (40.2–99 m) piles collected over 37 real projects in the past 10 years in Mekong Delta. Five key input parameters including the load, the displacement, the Standard Penetration Test value of the base soil, the distance between the loading point and pile toe, and the axial stiffness are identified via assessing the results of field load tests. Key innovations of this study are (i) use of large database to evaluate the effect that random selection of training and testing datasets can have on the predicted outcomes of ANN modelling, (ii) a simple approach using multiple learning rates to enhance training process, (iii) clarification of the role that the selected input factors can play in the base resistance, and (iv) new empirical relationships between the pile load and settlement. The results show that the random selection of training and testing datasets can affect significantly the predicted results, for example, the confidence of prediction can drop under 80% when an averageR2 > 0.85 is required. The analysis indicates predominant role of the displacement in governing the base resistance of piles, providing significant implication to practical designs.
Ibrahim, IA & Hossain, MJ 2023, 'Short-term multivariate time series load data forecasting at low-voltage level using optimised deep-ensemble learning-based models', Energy Conversion and Management, vol. 296, pp. 117663-117663.
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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, Kalam, MA, Mujtaba, MA & Almomani, F 2023, 'A review of recent advances in the synthesis of environmentally friendly, sustainable, and nontoxic bio-lubricants: Recommendations for the future implementations', Environmental Technology & Innovation, vol. 32, pp. 103366-103366.
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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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Purpose: The transient analysis of the thermal response and frictional loss characteristics for flow-modulated conjugate heat transfer phenomena has been investigated in the present study. The flow domain is a partitioned cavity of a hexagonal structure equipped with a multi-blade flow modulator. The clockwise rotating blade is adiabatic and stirrers the internal flow along with the natural convection caused by the bottom heated floor of uniform heat flux. The conjugate behavior is introduced through the solid subdomains consisting of two brick-made partitions and one glass partition of uniform thickness. The material of the partition wall reflects the physical aspects of industrial applications. Approach: The two-dimensional unsteady continuity, momentum, and energy equations are expressed in a non-dimensional form where the buoyant force is modeled through the Boussinesq approximation. The Arbitrary Lagrangian Euler (ALE) finite element is adopted to solve the moving mesh problem by formulating a free triangular discretization scheme. Parametric computational investigations are carried out for air as the working fluid (Pr = 0.71) and 3 different configurations of the rotating modulator while varying the other parameters, i.e., Reynolds number (Re) and Rayleigh number (Ra) for a fixed Biot number (Bi = 104). This dynamic mesh problem encompasses a wide range of parameters, i.e., (100 ≤ Re ≤ 103), and (104 ≤ Ra ≤ 106) for Bi = 104 to evaluate the thermodynamic response of the present thermo-fluid system. Various thermo-fluid system responses are visualized through the spatially average Nusselt number evaluated on the heated surface, system effectiveness, average thermal storage capacity, and frictional power loss of the flow domain. The thermal response is fragmented into individual responses in terms of component signal frequency by the Fast Fourier Transform (FFT) analysis. Findings: According to the current analysis, increasing the number of blades i...
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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Imran, S, Mujtaba, MA, Zafar, MM, Hussain, A, Mehmood, A, Farwa, UE, Korakianitis, T, Kalam, MA, Fayaz, H & Saleel, CA 2023, 'Assessing the potential of GHG emissions for the textile sector: A baseline study', Heliyon, vol. 9, no. 11, pp. e22404-e22404.
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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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The potential of the least-exploited date pam waste was presented as feedstock for bio-oil production. The surface fibers of the date palm are widely available as waste material in the Gulf region, the Middle East, and Africa. Chemical composition analysis and physiochemical characterization showed that surface fibers are valuable feedstock for energy production. Surface fibers were analyzed thermogravimetrically at different heating rates (10, 20, and 30 °C /min) in an inert atmosphere. Decomposition was carried out in three stages: dehydration, devolatilization, and solid combustion. Kinetic analysis was performed on the devolatilization region using the Coats–Redfern model–fitting method using twenty–one reaction mechanisms from four different solid-state reaction mechanisms. Two diffusion models: one–way transport (g(x) = α2) and Valensi equation (g(x) = α+(1-α) × ln(1-α)) showed the highest regression coefficient (R2) with the experimental data. The activation energy (Ea) and the pre-exponential factor (A) was estimated to be 91.40 kJ/mol and 1.59 × 103 –29.39 × 103 min−1, respectively. The kinetic parameters were found to be dependent on the heating rate. The surface fibers' thermodynamic parameters ΔH, ΔG, and ΔS were 80–97, 151–164, and −0.17- −0.18 kJ/mol, respectively. This indicates that the pyrolysis of surface fibers is endothermal and not spontaneous. Since there is not much experimental work on the pyrolysis of surface fibers available in the literature, the reported results are crucial for designing the pyrolysis process.
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, vol. 48, no. 99, pp. 39531-39552.
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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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The resilient modulus (MR) of ballast is one of the key output parameters in any rail design project because it controls the elastic magnitude of track deformation under cyclic loading. This study investigates the response of MR under cyclic conditions as a function of four key parameters, i.e., the loading magnitude, the number of loading cycles, the loading frequency, and the confining pressure. To do so, two non-linear predictive models, namely, the artificial neural network (ANN), and the adaptive neuro-fuzzy inference system (ANFIS), are used to predict the MR values under different loading conditions. To evaluate and predict MR, an experimental database with 196 data samples is considered in this study. A series of sensitivity analyses is carried out to investigate the most effective parameters in each non-linear model and also predict the highest performance model. Although the results from the primary validation phase are satisfactory for the ANN and ANFIS models, ANFIS proves better (i.e., the coefficient of determination = 0.709) at estimating the MR during the secondary validation phase, using an independent dataset. Hence, it can be used as a powerful and practical model for predicting the magnitude of MR. On the basis of the ANFIS model, this study also offers some design considerations in terms of MR of ballast under a practical range of cyclic loading parameters.
Inwumoh, J, Baguley, C, Madawala, U & Gunawardane, K 2023, 'A novel fault location strategy based on Bi‐LSTM for MMC‐HVDC systems', The Journal of Engineering, vol. 2023, no. 10.
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AbstractThe integration of modular multilevel converters (MMCs) with high voltage direct current (HVDC) transmission systems is an efficient method for transporting electricity from distant renewable energy sources to demand centres. However, MMC‐HVDC systems face reliability challenges during DC overcurrent faults, often caused by component failures that can lead to HVDC network shutdowns. Consequently, a reliable fault location approach is crucial for grid protection and restoration, aiding in fault isolation and alternate power flow identification. Conventional fault location methods struggle with manual protective threshold setting, susceptibility to fault resistance and noise, and the need for communication channels, resulting in signal delays. In multi‐terminal HVDC networks, fault location becomes even more complex due to poor selectivity and sensitivity in traditional schemes. This study proposes a robust fault location approach based on bidirectional long short‐term memory (bi‐LSTM). The method offers a simplified decision‐making model with low computational requirements, utilizing fault features from one end of the network, eliminating the need for a communication channel. Remarkably, this approach achieves high fault location accuracy, even with varying fault types, resistances, and noise levels, as demonstrated by an MSE of 0.006 and a percentage error below 1% in simulations conducted using a real‐time simulator with MATLAB/Simulink.
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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The International Maritime Organization (IMO) has placed stricter controls on several aspects of global maritime transport operations to protect the environment. In light of this, the goal of this study is to examine and assess the different prospective paths and technologies that will assist the shipping industry in decarbonizing its operations. We consider how the utilisation of various alternative energy sources reduces greenhouse gas (GHG) emissions from marine transportation and contributes to the promotion of the United Nations Sustainable Development Goals (SGDs). The complexities associated with maritime industry operations using alternative energy sources are also explored. Biofuel as an alternative energy source, including biomethanol and biodiesel, can reduce greenhouse gas emissions in the shipping industry by 25% to 100%. However, the current supply of biofuels can only meet about 15% of the total demand which is not sufficient to sustainably power the entire marine fleet. There are several issues associated with these biofuels, including oxidation, ecological consequences, feedstock availability, technical and operational constraints, and economic factors that must be addressed before their full potential may be achieved.
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, 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.
Iyer, S, Blair, A, White, C, Dawes, L, Moses, D & Sowmya, A 2023, 'Vertebral compression fracture detection using imitation learning, patch based convolutional neural networks and majority voting', Informatics in Medicine Unlocked, vol. 38, pp. 101238-101238.
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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, 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, 'Optimal curing resource allocation for epidemic spreading processes', Automatica, vol. 150, pp. 110851-110851.
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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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Jahan, T, Yasmin, S, Ali Shaikh, MA, Ibn Yousuf, MJ, Islam, MS, Islam Choudhury, MT & Kabir, MH 2023, 'Development and validation of a modified QuEChERS method coupled with LC-MS/MS for simultaneous determination of difenoconazole, dimethoate, pymetrozine, and chlorantraniliprole in brinjal collected from fields and markets places to assess human health risk', Heliyon, vol. 9, no. 4, pp. e14972-e14972.
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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.
Jakubowski, K, Chacon, A, Tran, LT, Stopic, A, Garbe, U, Bevitt, J, Olsen, S, Franklin, DR, Rosenfeld, A, Guatelli, S & Safavi-Naeini, M 2023, 'A Monte Carlo model of the Dingo thermal neutron imaging beamline', Scientific Reports, vol. 13, no. 1, p. 17415.
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AbstractIn this study, we present a validated Geant4 Monte Carlo simulation model of the Dingo thermal neutron imaging beamline at the Australian Centre for Neutron Scattering. The model, constructed using CAD drawings of the entire beam transport path and shielding structures, is designed to precisely predict the in-beam neutron field at the position at the sample irradiation stage. The model’s performance was assessed by comparing simulation results to various experimental measurements, including planar thermal neutron distribution obtained in-beam using gold foil activation and $$^{10}$$ 10 B$$_{4}$$ 4 C-coated microdosimeters and the out-of-beam neutron spectra measured with Bonner spheres. The simulation results demonstrated that the predicted neutron fluence at the field’s centre is within 8.1% and 2.1% of the gold foil and $$^{10}$$ 10 B$$_{4}$$Acta Biomaterialia, vol. 156, pp. 75-87.
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Jansing, S, Brockmann, B, Möhle, R, Patzelt, D & Deuse, J 2023, 'Potenziale von Motion Capturing bei der Erstellung von Ausführungsanalysen', Zeitschrift für wirtschaftlichen Fabrikbetrieb, vol. 118, no. 1-2, pp. 74-78.
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Abstract Neben der zeitökonomischen Gestaltung gewinnt die ergonomische Optimierung von Arbeitssystemen zunehmend an Bedeutung. Hohe Personalaufwände zur Erstellung bewegungsökonomischer Analysen sind jedoch Hemmnisse in deren industriellen Umsetzung. Markerloses Motion Capturing bietet Potenzial zur aufwandsreduzierten Erstellung entsprechender Analysen auf Basis des Prozessbausteinsystems MTM-Human Work Design. Der Beitrag beschreibt, wie Maschinelles Lernen unter Nutzung abstrahierter Videodaten zur Bewegungsanalyse eingesetzt werden kann.
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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Jauregi Unanue, I, Haffari, G & Piccardi, M 2023, 'T3L: Translate-and-Test Transfer Learning for Cross-Lingual Text Classification', Transactions of the Association for Computational Linguistics, vol. 11, pp. 1147-1161.
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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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Ji, J, Zhang, Y & Yang, C 2023, 'Hepatic caudate epithelioid angiomyolipoma mimicking hydatid cyst', Asian Journal of Surgery, vol. 46, no. 4, pp. 1700-1701.
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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, Liu, Q, He, Y, Wu, Q, Liu, RP & Sun, Y 2023, 'Efficient end-to-end failure probing matrix construction in data center networks', Journal of Communications and Networks, vol. 25, no. 4, pp. 532-543.
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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. 1583-1598.
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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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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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Joseph, S, Dhandapani, Y, Geddes, DA, Zhao, Z, Bishnoi, S, Vieira, M, Martirena, F, Castel, A, Kanavaris, F, Bansal, T & Riding, KA 2023, 'Mechanical properties of concrete made with calcined clay: a review by RILEM TC-282 CCL', Materials and Structures, vol. 56, no. 4.
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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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Jouneghani, HG, Fanaie, N, Kalaleh, MT & Mortazavi, M 2023, 'Determining elastic lateral stiffness of steel moment frame equipped with elliptic brace', Steel and Composite Structures, vol. 46, no. 3, pp. 293-318.
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This study aims to examine the elastic stiffness properties of Elliptic-Braced Moment Resisting Frame (EBMRF) subjected to lateral loads. Installing the elliptic brace in the middle span of the frames in the facade of a building, as a new lateral bracing system not only it can improve the structural behavior, but it provides sufficient space to consider opening it needed. In this regard, for the first time, an accurate theoretical formulation has been developed in order that the elastic stiffness is investigated in a two-dimensional single-story single-span EBMRF. The concept of strain energy and Castigliano’s theorem were employed to perform the analysis. All influential factors were considered, including axial and shearing loads in addition to the bending moment in the elliptic brace. At the end of the analysis, the elastic lateral stiffness could be calculated using an improved relation through strain energy method based on geometric properties of the employed sections as well as specifications of the utilized materials. For the ease of finite element (FE) modeling and its use in linear design, an equivalent element was developed for the elliptic brace. The proposed relation was verified by different examples using OpenSees software. It was found that there is a negligible difference between elastic stiffness values derived by the developed equations and those of numerical analysis using FE method.
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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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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The present investigation explored two novel adsorbents, i.e., hydroxyapatite (HAP) and moringa-modified activated carbon (MMAC) from eggshell and moringa (Moringa oleifera) seeds, respectively, for the treatment of tannery effluents (TE). Particular emphasis was given to Cr(VI) adsorption when varying equilibration time, effluent pH, initial concentration of Cr(VI) and temperature. The adsorbents' characteristics suggested significant Cr(VI) accumulation onto the adsorbent's surface, whereas adsorption modelling recommended pseudo-second-order (PSO) and Langmuir models fitted well with the experimental data based on the regression coefficient (R2) values with minimum errors. The surface complexation model (SCM) indicated that speciation of Cr(VI) sorbed complexes formed an inner-sphere compound dominated by acidic pH, validated by pHpzc. The maximum adsorption capacities (qmax) of Cr(VI) were accounted to be 295 and 280 mg/g for HAP and MMAC, correspondingly. Interestingly, both adsorbents effectively removed other metallic ions; Fe, Pd, Cu and Zn removal was 85 %, while Cd, Ni, and Mn removal was 70 %. The Cr(VI) adsorption processes followed chemisorption mechanisms dominated by the surface complexation phenomenon. The performance of a 100 L packed-bed reactor was evaluated, and the breakthrough time of Cr(VI) adsorption for both adsorbents was 15 min. The adsorbents had splendid regeneration capacities and could be re-used numerous times. In essence, the present study concludes that both adsorbents are highly effective at removing Cr(VI) and other contaminants. The adsorbents are innovative and economical and can be one of the breakthrough feasible options for treating toxic contaminants in a large-scale TE.
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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Currently, there exists a significant number of green hydrogen production (GHP) technologies based on scaling-up issues (SCUI). Optimal prediction and process optimization could be one of the most substantial SCUI of GHP. Machine learning (ML)-based prediction and optimization of GHP technologies from water industries for a circular economy (CRE) could be a plausible solution for these SCUI. We studied a detailed techno-economic and environmental feasibility study, which recommended proton exchange membrane (PEM) and dark fermentation (DF) as the most promising and environment-friendly technologies for GHP. Thus, the present investigation aims to apply different ML models to predict and optimize the GHP of DF and PEM technologies to solve the SCUI. The results revealed K-nearest neighbor and random forest are the best-fitted models to predict GHP for DF and PEM, correspondingly based on the regression co-efficient (R2), root mean squared error (RMSE) and mean absolute error (MEA). The permutation variable index (PVI) recommended that chemical oxygen demand (COD), butyrate, temperature, pH and acetate/butyrate ratio are the most influential process parameters in decreasing order for DF, while temperature, cell areas, cell pressure, cell voltage and catalysts loadings are the most effective process parameters for PEM in reducing order. The partial dependency analysis (PDA) demonstrated GHP increases with increasing COD values up to 10 mg/L, and the optimal temperature range in the DF process is between 25 and 30 °C. On the other hand, cell temperature up to 35 °C should be considered optimum for PEM, and 40–70 cm2 cell areas could produce a significant GHP. In summary, the present study underscores the potential of machine learning (ML) and artificial intelligence (AI) as promising techniques for optimizing GHP, ultimately addressing scaling-up challenges in large-scale industrial GHP production and ensuring a sustainable hydrogen economy (HE).
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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Kalhori, H, Rafiee, R, Ye, L, Halkon, B & Bahmanpour, M 2023, 'Randomized Kaczmarz and Landweber algorithms for impact force identification on a composite panel', International Journal of Impact Engineering, vol. 176, pp. 104576-104576.
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Kamran, M, Wattimena, RK, Armaghani, DJ, Asteris, PG, Jiskani, IM & Mohamad, ET 2023, 'Intelligent based decision-making strategy to predict fire intensity in subsurface engineering environments', Process Safety and Environmental Protection, vol. 171, pp. 374-384.
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Kanavaris, F, Vieira, M, Bishnoi, S, Zhao, Z, Wilson, W, Tagnit Hamou, A, Avet, F, Castel, A, Zunino, F, Visalaksh, T, Martirena, F, Bernal, SA, Juenger, MCG & Riding, K 2023, 'Correction: Standardisation of low clinker cements containing calcined clay and limestone: a review by RILEM TC-282 CCL', Materials and Structures, vol. 56, no. 10.
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Kanavaris, F, Vieira, M, Bishnoi, S, Zhao, Z, Wilson, W, Tagnit Hamou, A, Avet, F, Castel, A, Zunino, F, Visalakshi, T, Martirena, F, Bernal, SA, Juenger, MCG & Riding, K 2023, 'Standardisation of low clinker cements containing calcined clay and limestone: a review by RILEM TC-282 CCL', Materials and Structures, vol. 56, no. 9.
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AbstractMaterials used in concrete construction are highly regulated through national standards that set minimum material reactivity, composition, and performance. Advances have shown that the combination of calcined clay and limestone fines in cementitious systems can have a synergistic reaction that allows for high levels of clinker replacement while maintaining adequate mechanical properties and durability. Recent modifications to national standards and codes have been made to allow for the use of calcined clay and limestone fines in concrete, albeit with some restrictions on use. Building codes also impose limits such as maximum water-to-cement/binder)-ratio, minimum strength, and minimum cement content as means to meet design service life requirements in lieu of measuring durability properties. This paper reviews the major standards and codes related to calcined clay materials and their use in concrete and suggests changes that could increase adoption and clinker replacement. It is hoped that this review will provide insights that can help facilitate the wider adoption of these materials in the construction industry as well as to identify potential changes in standards or creation of new ones which might be needed to enable the rapid widespread uptake of this promising technology.
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 educational background perspective using multi-group analysis', Entrepreneurial Business and Economics Review, vol. 11, no. 1, pp. 113-126.
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Kang, X, Li, C, Ding, W, Ma, Y, Zhou, X, Gao, S, Chen, C, Liu, W, He, Z, Li, X & Jiang, G 2023, 'Optimization of biological enzymes combined with Fe2+-activated advanced oxidation process for waste activated sludge conditioning using the response surface method', Journal of Water Process Engineering, vol. 53, pp. 103634-103634.
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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 Domenico, E, 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 Uelft, M, Osei-Sarpong, C, van den Berge, M, Donker, HC, Groen, HJM, Sohal, SS, Klein, J, Schreiber, T, Feißt, A, Yildirim, AÖ, 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 Reports, 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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Kaplan, E, Chan, WY, Altinsoy, HB, Baygin, M, Barua, PD, Chakraborty, S, Dogan, S, Tuncer, T & Acharya, UR 2023, 'PFP-HOG: Pyramid and Fixed-Size Patch-Based HOG Technique for Automated Brain Abnormality Classification with MRI', Journal of Digital Imaging, vol. 36, no. 6, pp. 2441-2460.
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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, vol. 200, pp. 682-692.
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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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Karimi, M & Maxit, L 2023, 'Acoustic source localisation using vibroacoustic beamforming', Mechanical Systems and Signal Processing, vol. 199, pp. 110454-110454.
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Karmaker, AK, Behrens, S, Hossain, MJ & Pota, H 2023, 'Multi-stakeholder perspectives for transport electrification: A review on placement and scheduling of electric vehicle charging infrastructure', Journal of Cleaner Production, vol. 427, pp. 139145-139145.
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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.
Katzmarek, DA, Yang, Y, Ghasemian, MB, Kalantar-Zadeh, K, Ziolkowski, RW & Iacopi, F 2023, 'Characteristics of Epitaxial Graphene on SiC/Si Substrates in the Radio Frequency Spectrum', IEEE Electron Device Letters, vol. 44, no. 2, pp. 297-300.
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Kaur, P, Bohidar, HB, Nisbet, DR, Pfeffer, FM, Rifai, A, Williams, R & Agrawal, R 2023, 'Waste to high-value products: The performance and potential of carboxymethylcellulose hydrogels via the circular economy', Cellulose, vol. 30, no. 5, pp. 2713-2730.
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KB, H, S, V, G, N, R, P, Alwetaishi, M, Alahmadi, AA, Alzaed, AN, MA, K & Shahapurkar, K 2023, 'Effects of machining parameters on H13 die steel using CNC drilling machine', Composites and Advanced Materials, vol. 32.
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In order to enhance the fitness of the product and in order to improve productivity in turning operations, greater amount of challenges have been faced. In this paper, we have made a comparative analysis of HSS and carbide coated HSS drills while machining with H13 steel plates. For the drilling operation, process parameters were analysed using the Taguchi design of experiments. The response performance characteristics of surface roughness of H13 die steel plates for the drilling settings, cutting speed (rpm), and feed rate (mm/min) is optimized. The design of the experiment was conducted using the Taguchi technique for the L18 orthogonal array, and an analysis of variance was observed. The effect of drilling settings on the quality of drilled holes is examined; variation in surface roughness for various levels of speed and feed and the different combinations of these levels will form an L18 orthogonal array design of experiment by Taguchi analysis. A total of 36 cutting tests were performed with two different drill bits; here three different cutting speeds of 300, 600, and 900 rpm were taken with a feed rate of 0.02, 0.04, and 0.06 mm/rev combinations. The response of SN ratio for surface roughness of HSS and carbide tool has been found out for different levels of speed and feed. From this Taguchi analysis, it is identified that the optimal parameter. As a result, the factors are analysed, and optimized parameters have been concluded for H13 material using HSS, and carbide tools were examined both statistically and experimentally. The carbide coated drill bit gives 60% better surface roughness value based on experimental data obtained. The surface roughness value based on experimentation for HSS tool was found to be 34.16% and carbide coated drill bit was 23.40%.
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.
Kedziora, DJ, Musiał, A, Rudno-Rudziński, W & Gabrys, B 2023, 'Harnessing data augmentation to quantify uncertainty in the early estimation of single-photon source quality', Machine Learning: Science and Technology, vol. 4, no. 4, pp. 045042-045042.
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Abstract Novel methods for rapidly estimating single-photon source (SPS) quality have been promoted in recent literature to address the expensive and time-consuming nature of experimental validation via intensity interferometry. However, the frequent lack of uncertainty discussions and reproducible details raises concerns about their reliability. This study investigates the use of data augmentation, a machine learning technique, to supplement experimental data with bootstrapped samples and quantify the uncertainty of such estimates. Eight datasets obtained from measurements involving a single InGaAs/GaAs epitaxial quantum dot serve as a proof-of-principle example. Analysis of one of the SPS quality metrics derived from efficient histogram fitting of the synthetic samples, i.e. the probability of multi-photon emission events, reveals significant uncertainty contributed by stochastic variability in the Poisson processes that describe detection rates. Ignoring this source of error risks severe overconfidence in both early quality estimates and claims for state-of-the-art SPS devices. Additionally, this study finds that standard least-squares fitting is comparable to using a Poisson likelihood, and expanding averages show some promise for early estimation. Also, reducing background counts improves fitting accuracy but does not address the Poisson-process variability. Ultimately, data augmentation demonstrates its value in supplementing physical experiments; its benefit here is to emphasise the need for a cautious assessment of SPS quality.
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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Kha, J, Karimi, M, Maxit, L, Skvortsov, A & Kirby, R 2023, 'Sound radiation from a cylindrical shell in an underwater waveguide', INTER-NOISE and NOISE-CON Congress and Conference Proceedings, vol. 268, no. 8, pp. 403-409.
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Sound radiation from cylindrical structures immersed in an underwater acoustic waveguide, that is, a fluid layer with an upper free surface and lower rigid floor, is a challenging engineering problem to analyze due to the complex interaction of the radiation from the excited structure and its subsequent propagation within an acoustic waveguide. To contribute to the understanding of this phenomenon, an analytical model is presented. The model involves an infinitely-long three-dimensional cylindrical shell that is point-excited and submerged in a perfect underwater waveguide, where the reflections off the free surface and rigid floor have no absorption. The equations of motion of the shell are given by thin shell theory with heavy fluid loading. The image-source method is applied directly to the acoustic boundary conditions to account for the vibroacoustic coupling of the shell and waveguide. Together, these techniques combine into an analytical framework that can evaluate both the vibration of the shell and its acoustic radiation. Results include the far-field radiated sound pressure with observations of the relative and combined effect of the waveguide boundaries.
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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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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Khan, TA, Ling, SH & Rizvi, AA 2023, 'Optimisation of electrical Impedance tomography image reconstruction error using heuristic algorithms.', Artif. Intell. Rev., vol. 56, pp. 15079-15099.
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 Kashani, MR, Wang, Q, Khatebasreh, M, Li, X, Sheikh Asadi, AM, Boczkaj, G & Ghanbari, F 2023, 'Sequential treatment of landfill leachate by electrocoagulation/aeration, PMS/ZVI/UV and electro-Fenton: Performance, biodegradability and toxicity studies', Journal of Environmental Management, 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.
Khoa, TV, Hoang, DT, Trung, NL, Nguyen, CT, Quynh, TTT, Nguyen, DN, Ha, NV & Dutkiewicz, E 2023, 'Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks', IEEE Internet of Things Journal, vol. 10, no. 10, pp. 8578-8589.
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Federated Learning (FL) has recently become an effective approach for cyberattack detection systems, especially in Internet-of-Things (IoT) networks. By distributing the learning process across IoT gateways, FL can improve learning efficiency, reduce communication overheads and enhance privacy for cyberattack detection systems. However, one of the biggest challenges for deploying FL in IoT networks is the unavailability of labeled data and dissimilarity of data features for training. In this paper, we propose a novel collaborative learning framework that leverages Transfer Learning (TL) to overcome these challenges. Particularly, we develop a novel collaborative learning approach that enables a target network with unlabeled data to effectively and quickly learn “knowledge” from a source network that possesses abundant labeled data. It is important that the state-of-the-art studies require the participated datasets of networks to have the same features, thus limiting the efficiency, flexibility as well as scalability of intrusion detection systems. However, our proposed framework can address these problems by exchanging the learning “knowledge” among various deep learning models, even when their datasets have different features. Extensive experiments on recent real-world cybersecurity datasets show that the proposed framework can improve more than 40% as compared to the state-of-the-art deep learning based approaches.
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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Khorshidi, MS, Izady, A, Al-Maktoumi, A, Chen, M, Nikoo, MR & Gandomi, AH 2023, 'Information-theoretic summary statistics for diagnostic calibration of the groundwater models using approximate Bayesian computation', Environmental Earth Sciences, vol. 82, no. 23.
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This paper presents a novel approach to analyzing uncertainty in complex groundwater models based on the approximate Bayesian computation (ABC) framework and information-theoretic summary statistics. Two summary statistics using the concepts of mutual information and variation of information are formulated as distance function measures of the ABC. These signatures are utilized within an ABC rejection (ABC-REJ) algorithm to measure the similarity and dissimilarity of the generated samples to the true posterior distribution of the groundwater model parameters. This method was applied to groundwater model calibration and uncertainty analysis in an arid region of Oman with a complex hydrogeological setting and a hardrock-alluvial aquifer system. MODFLOW unstructured-grid was used for modelling groundwater dynamics. A three-dimensional stratigraphic model was developed based on borehole data, and five-layer grid cells were defined according to the material and elevations of the stratigraphic model. Results show that the model reproduces the observed data behaviour very well, including peaks and abrupt declines in the head, as well as the trend of fluctuations in the observation wells. A notable match between the observed and simulated heads indicates the accuracy of the ABC-REJ algorithm based on summary statistics for calibrating and analyzing groundwater models.
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 & Gabrys, B 2023, 'Random Hyperboxes', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 2, pp. 1008-1022.
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This article proposes a simple yet powerful ensemble classifier, called Random Hyperboxes, constructed from individual hyperbox-based classifiers trained on the random subsets of sample and feature spaces of the training set. We also show a generalization error bound of the proposed classifier based on the strength of the individual hyperbox-based classifiers as well as the correlation among them. The effectiveness of the proposed classifier is analyzed using a carefully selected illustrative example and compared empirically with other popular single and ensemble classifiers via 20 datasets using statistical testing methods. The experimental results confirmed that our proposed method outperformed other fuzzy min-max neural networks (FMNNs), popular learning algorithms, and is competitive with other ensemble methods. Finally, we identify the existing issues related to the generalization error bounds of the real datasets and inform the potential research directions.
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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Kivrak, T, Nayak, J, Gelen, MA, Barua, PD, Baygin, M, Pamukcu, HE, Dogan, S, Tuncer, T & Acharya, UR 2023, 'EfDenseNet: Automated Pulmonary Hypertension Detection Model Based on EfficientNetb0 and DenseNet201 Using CT Images', IEEE Access, vol. 11, pp. 142711-142724.
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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, 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.
Korkitsuntornsan, W, Indraratna, B, Rujikiatkamjorn, C & Nguyen, TT 2023, 'Depth-dependent soil fluidization under cyclic loading—an experimental investigation', Canadian Geotechnical Journal, vol. 60, no. 6, pp. 946-950.
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Past studies have shown that shallow subgrade soil can transform to a slurry (i.e., fluidization) under unfavourable cyclic loading. However, the depth-dependent behaviour of soil parameters during this process has not been properly understood. The current study utilised a large-scale cylindrical test rig, where instrumentation was installed to observe the soil behaviour along the depth of the test specimens under cyclic loading, to examine and quantify the onset of soil fluidization. The results show that excess pore water pressure tends to rise more at the upper layers causing zero-effective stress, while void ratio expands rapidly within the deteriorated soil fabric, making the water content approach the liquid limit of soil when internal moisture migration occurs from the bottom to the top of the specimen. The larger the cyclic load, the deeper the fluidized zone and the faster the fluidization. The study also suggests that the zero-effective stress condition alone cannot interpret the inception of soil fluidization; hence, the change in void ratio and the liquidity index during the application of cyclic loading should also be considered in tandem.
Kosikova, AM, Sedehi, O, Papadimitriou, C & Katafygiotis, LS 2023, 'Bayesian structural identification using Gaussian Process discrepancy models', Computer Methods in Applied Mechanics and Engineering, vol. 417, pp. 116357-116357.
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Kozanoglu, DC, Daim, TU & Contreras-Cruz, A 2023, 'Unraveling the Dynamics of Immigrant Engineers’ Full-Utilization in Australia', IEEE Transactions on Engineering Management, vol. 70, no. 11, pp. 3776-3791.
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The study aims to improve our understanding of the full-utilization of immigrant engineers by answering three research questions: (1) what are the economic and social costs of the under-utilization of immigrant engineers, (2) what factors determine immigrant engineers’ employment, and (3) what might be potential solutions to tackle with their under-utilization? We adopt the intersectionality theory to observe a rich set of social factors influential in immigrant engineers’ under-utilization by using 188 surveys and 14 interviews of immigrant engineers living in Australia. The paper concludes with the findings’ theoretical and policy implications, followed by suggestions for future studies.
Krishnan, S, DeMaere, MZ, Beck, D, Ostrowski, M, Seymour, JR & Darling, AE 2023, 'Rhometa: Population recombination rate estimation from metagenomic read datasets', PLOS Genetics, vol. 19, no. 3, pp. e1010683-e1010683.
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Prokaryotic evolution is influenced by the exchange of genetic information between species through a process referred to as recombination. The rate of recombination is a useful measure for the adaptive capacity of a prokaryotic population. We introduce Rhometa (https://github.com/sid-krish/Rhometa), a new software package to determine recombination rates from shotgun sequencing reads of metagenomes. It extends the composite likelihood approach for population recombination rate estimation and enables the analysis of modern short-read datasets. We evaluated Rhometa over a broad range of sequencing depths and complexities, using simulated and real experimental short-read data aligned to external reference genomes. Rhometa offers a comprehensive solution for determining population recombination rates from contemporary metagenomic read datasets. Rhometa extends the capabilities of conventional sequence-based composite likelihood population recombination rate estimators to include modern aligned metagenomic read datasets with diverse sequencing depths, thereby enabling the effective application of these techniques and their high accuracy rates to the field of metagenomics. Using simulated datasets, we show that our method performs well, with its accuracy improving with increasing numbers of genomes. Rhometa was validated on a real S. pneumoniae transformation experiment, where we show that it obtains plausible estimates of the rate of recombination. Finally, the program was also run on ocean surface water metagenomic datasets, through which we demonstrate that the program works on uncultured metagenomic datasets.
Kronowetter, F, Pretsch, L, Chiang, YK, Melnikov, A, Sepehrirahnama, S, Oberst, S, Powell, DA & Marburg, S 2023, 'Sound attenuation enhancement of acoustic meta-atoms via coupling', The Journal of the Acoustical Society of America, vol. 154, no. 2, pp. 842-851.
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Arrangements of acoustic meta-atoms, better known as acoustic metamaterials, are commonly applied in acoustic cloaking, for the attenuation of acoustic fields or for acoustic focusing. A precise design of single meta-atoms is required for these purposes. Understanding the details of their interaction allows improvement of the collective performance of the meta-atoms as a system, for example, in sound attenuation. Destructive interference of their scattered fields, for example, can be mitigated by adjusting the coupling or tuning of individual meta-atoms. Comprehensive numerical studies of various configurations of a resonator pair show that the coupling can lead to degenerate modes at periodic distances between the resonators. We show how the resonators' separation and relative orientation influence the coupling and thereby tunes the sound attenuation. The simulation results are supported by experiments using a two-dimensional parallel-plate waveguide. It is shown that coupling parameters like distance, orientation, detuning, and radiation loss provide additional degrees of freedom for efficient acoustic meta-atom tuning to achieve unprecedented interactions with excellent sound attenuation properties.
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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Kumar, R, Kumar, R, Sharma, N, Khurana, N, Singh, SK, Satija, S, Mehta, M & Vyas, M 2023, 'Corrigendum to “Pharmacological evaluation of bromelain in mouse model of Alzheimer’s disease” [NeuroToxicology 90 (2022) 19–34]', NeuroToxicology, vol. 99, pp. 332-332.
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Kumari, P, Bahadur, N, Conlan, XA, Zeng, X, Kong, L, O'Dell, LA, Sadek, A, Merenda, A & Dumée, LF 2023, 'Stimuli-responsive heterojunctions based photo-electrocatalytic membrane reactors for reactive filtration of persistent organic pollutants', Chemical Engineering Journal, vol. 452, pp. 139374-139374.
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The design of semiconducting metal oxide heterojunctions is promising to overcome conventional limitations associated to photocatalysis or electrocatalysis, such as fast recombination of electron-hole pairs and poor long-term stability leading to low catalytic performance. A route to tackle this issue is to design catalysts at the atomic levels by arranging order and controlling nanoscale interfaces to yield catalytic materials with greater response rates and stability to dissolution or corrosion. The present study focuses on the formation of such nanoscale heterojunctions between TiO2 and ZnO via atomic layer deposition across conductive and porous stainless-steel substrates to develop enhanced photo-electro-responsive catalytic membrane reactors. The heterojunction nano-sheet based structures produced higher density of electron and hole pairs and offered efficient separation of charges, longer lifetime of photo-generated electrons compared to single metal oxides, resulting in enhanced photocurrent efficiency. The tailoring of both the nanoscale dimensions of the metal oxide layers and the stacking of these inorganic nano-sheets led to the development of multi-heterojunctions, of a few tens of nanometres, deposited across conductive porous substrates. The high electron mobility across the heterojunction nano-sheets increased the oxygen evolution potential from 1.4 to 1.7 eV, leading to enhanced electrochemical reactions, as well as offered photocurrent densities 2–3 times higher than pristine single metal oxide membranes. The formation of type II heterojunction structures between TiO2 and ZnO leads to band alignment at the interface, yielding an efficient charge separation mechanism and high catalytic performance. A prototype of novel cross-flow filtration module was designed in this study to support the coupling of photo-electrocatalysis on the membrane surface and simultaneous pressure driven membrane processes. The designed 3D printed modules demonstrated hig...
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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Kurdkandi, NV, Marangalu, MG, Naderi, Y, Husev, O, Hosseini, SH, Siwakoti, Y & Mehrizi‐Sani, A 2023, 'An improved nine‐level switched capacitor‐based inverter with voltage boosting capability and limitation of capacitor current spikes for PV applications', IET Renewable Power Generation, vol. 17, no. 3, pp. 725-749.
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Kwon, I, Li, J & Prasad, M 2023, 'Lightweight Video Super-Resolution for Compressed Video', Electronics, vol. 12, no. 3, pp. 660-660.
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Video compression technology for Ultra-High Definition (UHD) and 8K UHD video has been established and is being widely adopted by major broadcasting companies and video content providers, allowing them to produce high-quality videos that meet the demands of today’s consumers. However, high-resolution video content broadcasting is not an easy problem to be resolved in the near future due to limited resources in network bandwidth and data storage. An alternative solution to overcome the challenges of broadcasting high-resolution video content is to downsample UHD or 8K video at the transmission side using existing infrastructure, and then utilizing Video Super-Resolution (VSR) technology at the receiving end to recover the original quality of the video content. Current deep learning-based methods for Video Super-Resolution (VSR) fail to consider the fact that the delivered video to viewers goes through a compression and decompression process, which can introduce additional distortion and loss of information. Therefore, it is crucial to develop VSR methods that are specifically designed to work with the compression–decompression pipeline. In general, various information in the compressed video is not utilized enough to realize the VSR model. This research proposes a highly efficient VSR network making use of data from decompressed video such as frame type, Group of Pictures (GOP), macroblock type and motion vector. The proposed Convolutional Neural Network (CNN)-based lightweight VSR model is suitable for real-time video services. The performance of the model is extensively evaluated through a series of experiments, demonstrating its effectiveness and applicability in practical scenarios.
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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Lalbakhsh, A, Yahya, SI, Moloudian, G, Hazzazi, F, Sobhani, SN, Assaad, M & Chaudhary, MA 2023, 'Hybrid Encoding Method for Radio Frequency Identification in the Internet of Things Systems', IEEE Access, vol. 11, pp. 122554-122565.
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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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Larpruenrudee, P, Do, DK, Bennett, NS, Saha, SC, Ghalambaz, M & Islam, MS 2023, 'Computational Fluid Dynamics Analysis of Spray Cooling in Australia', Energies, vol. 16, no. 14, pp. 5317-5317.
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Spray cooling technology offers high levels of uniform heat removal with very low fluid volumes and has found recent application in relatively small-scale use cases. Since it is a complex process, research can enable spray cooling to be applied more widely and at larger scales, such as in HVAC, as a means to operate more efficiently. Weather conditions are one of the main parameters that directly affect the effectiveness of spray cooling. This study investigates the spray cooling performance for temperature and humidity conditions in six Australian cities. ANSYS Fluent (2021 R1) software is applied for the numerical simulation. The numerical model is first validated with the available literature before a numerical simulation is conducted to assess each city throughout the year. These include Adelaide, Brisbane, Darwin, Melbourne, Perth, and Sydney. The spray cooling pattern, temperature, and humidity distribution, as well as the evaporation effect on different regions in Australia, is simulated and analysed based on the CFD technique. The results from this study indicate that weather conditions influence spray cooling for all cities, especially in summer. Along the wind tunnel, the temperature significantly drops at the spray cooling area, while the humidity increases. Due to the effect of spray cooling inside the wind tunnel, the temperature at the outlet is still lower than the inlet for all cases. However, the humidity at the outlet is higher than the inlet for all cases.
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 Nguyen, K, Thi Trinh, H, Nguyen, TT & Nguyen, HD 2023, 'Comparative study on the performance of different machine learning techniques to predict the shear strength of RC deep beams: Model selection and industry implications', Expert Systems with Applications, vol. 230, pp. 120649-120649.
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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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Le, T, Desa, S & Khabbaz, H 2023, 'The Influence Of Bagasse Fly Ash Particle Size In Controlling Expansive Soils In Combination With Hydrated Lime', Australian Geomechanics Journal, vol. 58, no. 1, pp. 47-57.
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Sugarcane is the second largest export crop in Australia. Industrial production of sugar, from sugarcane, results in bagasse fly ash (BFA), a by-product from the cogeneration in sugar milling operations that generate electricity by steam. The chemical and physical properties of BFA highlight its potential as a promising pozzolan for the stabilization of expansive soils, due primarily to a high content and surface area of the amorphous silicate found in BFA. Silicate in bagasse fly ash reacts extensively with calcium hydrate in lime to produce hydrated products via pozzolanic reactions, this results in a hardening of the material to which BFA and lime have been added. This reaction has been studied to be a function of the size of BFA particles and conditions of the curing process. This study explored the variables that influence the reaction and evaluated shrinkage and compressive strength of the mixtures to which bagasse fly ash, in the form of different particle size distributions, and hydrated lime are added. The maximum BFA particles sizes considered within this study include 75, 150 and 425 μm; curing times of 7 and 28 days are also explored. A suite of testing, including Atterberg limits, linear shrinkage (LS), and unconfined compressive strength (UCS) tests were completed on the prepared mixtures. The findings indicate that bagasse fly ash with a maximum size of 425 μm yields a higher UCS and lower LS, compared to finer BFA particle mixtures. The ash with a maximum particle size of 425-μm also improves the ductility of treated soils and accelerates their strength gain, compared to soil- lime stabilized samples. The results of the study build towards a better understanding of BFA, and the ways in which such a material maybe engineered to replace concrete in road work projects and other applications involving expansive soils.
Le, T-H, Tran, D-T, Vu, T-P-T & Nghiem, LD 2023, 'A novel tertiary magnetic ZnFe2O4/BiOBr/rGO nanocomposite catalyst for photodegrading organic contaminants by visible light', Science of The Total Environment, vol. 891, pp. 164358-164358.
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Le, TP, Meroni, C, Sturmfels, B, Werner, RF & Ziegler, T 2023, 'Quantum Correlations in the Minimal Scenario', QUANTUM, vol. 7, pp. 1-59.
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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Lee, T & Shraibman, A 2023, 'Around the log-rank conjecture', Israel Journal of Mathematics, vol. 256, no. 2, pp. 441-477.
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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, J, Xu, S, Xie, W, Zhang, J, Li, Y & Du, Q 2023, 'A Semantic Transferred Priori for Hyperspectral Target Detection With Spatial–Spectral Association', IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-14.
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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-...
Lei, Y, Ye, D, Shen, S, Sui, Y, Zhu, T & Zhou, W 2023, 'New challenges in reinforcement learning: a survey of security and privacy', Artificial Intelligence Review, vol. 56, no. 7, pp. 7195-7236.
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Reinforcement learning is one of the most important branches of AI. Due to its capacity for self-adaption and decision-making in dynamic environments, reinforcement learning has been widely applied in multiple areas, such as healthcare, data markets, autonomous driving, and robotics. However, some of these applications and systems have been shown to be vulnerable to security or privacy attacks, resulting in unreliable or unstable services. A large number of studies have focused on these security and privacy problems in reinforcement learning. However, few surveys have provided a systematic review and comparison of existing problems and state-of-the-art solutions to keep up with the pace of emerging threats. Accordingly, we herein present such a comprehensive review to explain and summarize the challenges associated with security and privacy in reinforcement learning from a new perspective, namely that of the Markov Decision Process (MDP). In this survey, we first introduce the key concepts related to this area. Next, we cover the security and privacy issues linked to the state, action, environment, and reward function of the MDP process, respectively. We further highlight the special characteristics of security and privacy methodologies related to reinforcement learning. Finally, we discuss the possible future research directions within this area.
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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Leong, D, Do, TT-T & Lin, C-T 2023, 'Ventral and Dorsal Stream EEG Channels: Key Features for EEG-Based Object Recognition and Identification', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 31, pp. 4862-4870.
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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, 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, Wang, G, Wang, B, Liang, X, Li, Z & Chang, X 2023, 'DS-Net++: Dynamic Weight Slicing for Efficient Inference in CNNs and Vision Transformers', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 4, pp. 4430-4446.
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Dynamic networks have shown their promising capability in reducing theoretical computation complexity by adapting their architectures to the input during inference. However, their practical runtime usually lags behind the theoretical acceleration due to inefficient sparsity. In this paper, we explore a hardware-efficient dynamic inference regime, named dynamic weight slicing, that can generalized well on multiple dimensions in both CNNs and transformers (e.g. kernel size, embedding dimension, number of heads, etc.). Instead of adaptively selecting important weight elements in a sparse way, we pre-define dense weight slices with different importance level by nested residual learning. During inference, weights are progressively sliced beginning with the most important elements to less important ones to achieve different model capacity for inputs with diverse difficulty levels. Based on this conception, we present DS-CNN++ and DS-ViT++, by carefully designing the double headed dynamic gate and the overall network architecture. We further propose dynamic idle slicing to address the drastic reduction of embedding dimension in DS-ViT++. To ensure sub-network generality and routing fairness, we propose a disentangled two-stage optimization scheme. In Stage I, in-place bootstrapping (IB) and multi-view consistency (MvCo) are proposed to stablize and improve the training of DS-CNN++ and DS-ViT++ supernet, respectively. In Stage II, sandwich gate sparsification (SGS) is proposed to assist the gate training. Extensive experiments on 4 datasets and 3 different network architectures demonstrate our methods consistently outperform the state-of-the-art static and dynamic model compression methods by a large margin (up to 6.6%). Typically, we achieves 2-4× computation reduction and up to 61.5% real-world acceleration on MobileNet, ResNet-50 and Vision Transformer, with minimal accuracy drops on ImageNet. Code release: https://github.com/changlin31/DS-Net.
Li, C, Zhou, J, Du, K, Armaghani, DJ & Huang, S 2023, 'Prediction of Flyrock Distance in Surface Mining Using a Novel Hybrid Model of Harris Hawks Optimization with Multi-strategies-based Support Vector Regression', Natural Resources Research, vol. 32, no. 6, pp. 2995-3023.
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Li, D, Ma, XY, Zhang, S, Wang, YK, Han, Y, Chen, R, Wang, XC & Ngo, HH 2023, 'Aquatic photolysis of high-risk chemicals of emerging concern from secondary effluent mediated by sunlight irradiation for ecological safety and the enhanced methods', Water Research, vol. 238, pp. 120002-120002.
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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, F, Yang, J-J, Sun, Z-Y, Wang, L, Qi, L-Y, A, S, Liu, Y-Q, Zhang, H-M, Dang, L-F, Wang, S-J, Luo, C-X, Nian, W-F, O’Conner, S, Ju, L-Z, Quan, W-P, Li, X-K, Wang, C, Wang, D-P, You, H-L, Cheng, Z-K, Yan, J, Tang, F-C, Yang, D-C, Xia, C-W, Gao, G, Wang, Y, Zhang, B-C, Zhou, Y-H, Guo, X, Xiang, S-H, Liu, H, Peng, T-B, Su, X-D, Chen, Y, Ouyang, Q, Wang, D-H, Zhang, D-M, Xu, Z-H, Hou, H-W, Bai, S-N & Li, L 2023, 'Plant-on-chip: Core morphogenesis processes in the tiny plant Wolffia australiana', PNAS Nexus, vol. 2, no. 5.
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Abstract A plant can be thought of as a colony comprising numerous growth buds, each developing to its own rhythm. Such lack of synchrony impedes efforts to describe core principles of plant morphogenesis, dissect the underlying mechanisms, and identify regulators. Here, we use the minimalist known angiosperm to overcome this challenge and provide a model system for plant morphogenesis. We present a detailed morphological description of the monocot Wolffia australiana, as well as high-quality genome information. Further, we developed the plant-on-chip culture system and demonstrate the application of advanced technologies such as single-nucleus RNA-sequencing, protein structure prediction, and gene editing. We provide proof-of-concept examples that illustrate how W. australiana can decipher the core regulatory mechanisms of plant morphogenesis.
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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The poor quality of underwater images has become a widely-known cause affecting the performance of the underwater development projects, including mineral exploitation, driving photography, and navigation for autonomous underwater vehicles. In recent years, deep learning-based techniques have achieved remarkable successes in image restoration and enhancement tasks. However, the limited availability of paired training data (underwater images and their corresponding clear images) and the requirement for vivid color correction remain challenging for underwater image enhancement, as almost all learning-based methods require paired data for training. In this study, instead of creating the time-consuming paired data, we explore the unsupervised training strategy. Specifically, we introduce a universal cross-domain GAN-based framework to generate high-quality images to address the dependence on paired training data. To ensure the vivid colorfulness, the color loss is designed to constrain the training process. Also, a feature fusion module (FFM) is proposed to increase the capacity of the whole model as well as the dual discriminator channel adopted in the architecture. Extensive quantitative and perceptual experiments show that our approach overcomes the limitation of paired data and obtains superior performance over the state-of-the-art on several underwater benchmarks in terms of both accuracy and model deployment.
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, 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, 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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In recent years, algorithms based on human pose estimation have been applied more and more in low power embedded system. However, the keypoints detection under occlusion is not well solved, resulting in poor effect in practical application on embedded devices. In this paper, we propose a novel Simple and Effective Network (SEN) to deal with the multi-person pose estimation problem of low power embedded system via detecting occlusion keypoints quite well to a certain extent. This model is easy to apply to embedded devices, and has the characteristics of simplicity, strong expansibility and wide application, which are very important in the world of Internet of things. Our model contains three novel modules: Feature Fusion Module (FFM), Channel Enhancement Attention Module (CEAM), and Feature Enhancement Module (FEM). The FFM fuses the shallow and deep feature maps, bringing rich context information to the model. At the same time, it can alleviate the problem of information loss caused by downsampling operations and locate the keypoints more accurately. The FEM and the CEAM act on the deep feature maps of the network, which helps to infer the keypoints of occlusion or invisibility. Related experiments explain that the raised means is effective and achieves the superior performance over two benchmark datasets: the COCO keypoints detection dataset and the MPII Human Pose dataset.
Li, J, Hajimohammadi, A, Yu, Y, Lee, BY & Kim, T 2023, 'Mechanism of PVA Fiber Influence in Foam Concrete: From Macroscopic to Microscopic View', Journal of Materials in Civil Engineering, vol. 35, no. 12.
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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 Research, 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, vol. 45, no. 10, pp. 11753-11765.
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Li, J, Wan, D, Jin, S, Ren, H, Wang, Y, Huang, J, Li, H & Zhang, G 2023, 'Fast treatment and recycling method of large-scale vegetable wastes', Science of The Total Environment, vol. 892, pp. 164308-164308.
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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, 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, Lau, BPL, Yuan, X, Ni, W, Guizani, M & Yuen, C 2023, 'Toward Ubiquitous Semantic Metaverse: Challenges, Approaches, and Opportunities', IEEE Internet of Things Journal, vol. 10, no. 24, pp. 21855-21872.
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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, 'Multi-Source Domain Adaptation with Incomplete Source Label Spaces', Procedia Computer Science, vol. 225, pp. 2343-2350.
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Li, K, Lu, J, Zuo, H & Zhang, G 2023, 'Source-Free Multidomain Adaptation With Fuzzy Rule-Based Deep Neural Networks', IEEE Transactions on Fuzzy Systems, vol. 31, no. 12, pp. 4180-4194.
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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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PurposeAlthough most Chinese ethnic minority groups (EMGs) hold conservative thinking to online-startups, the new entrepreneurial model is booming on live streaming p