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2023 International Conference on Power, Energy and Innovations, ICPEI 2023

作者： Sakulphaisan, Guntinan Son-In, Sirirat Junhuathon, Nitikorn Muankhaw, Theerapol Pathumwan Institute of Technology Faculty of Science and Technology Industrial Technology and Innovation Management Bangkok Thailand Bansomdejchaopraya Rajabhat University Faculty of Engineering and Industrial Technology Electrical Engineering and Automation Control Systems Bangkok Thailand Rajamangala University of Technology Faculty of Engineering Department of Electrical Engineering Thanyaburi Pathumthani Thailand

ISBN: (纸本)9798350344585

This study conducts a comprehensive analysis of optimizing the speed profile of an AE-bus, a critical step in enhancing its operational efficiency. It encompasses three distinct case studies, each targeting specific objectives. The first case emphasizes energy consumption reduction, contributing to overall resource efficiency. The second focuses on eco-conscious speed profiles within predefined travel durations, balancing environmental sustainability and timeliness. The third case prioritizes travel time minimization for streamlined efficiency. Detailed insights into speed adjustments, bus positions, and energy consumption patterns are provided for each case. The findings reveal tailored approaches for energy conservation, speed, and travel time optimization. Additionally, emissions analysis demonstrates that electric buses significantly reduce CO2 emissions compared to conventional NGV buses, showcasing their potential for environmental benefits. © 2023 IEEE.

关键词： Electric buses

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Frontiers in high entropy alloys and high entropy functional materials

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Rare Metals 2024年第10期43卷 4639-4776页

作者： Wen-Tao Zhang Xue-Qian Wang Feng-Qi Zhang Xiao-Ya Cui Bing-Bing Fan Jia-Ming Guo Zhi-Min Guo Rui Huang Wen Huang Xu-Bo Li Meng-Ru Li Yan Ma Zhi-Hua Shen Yong-Gang Sun De-Zhuang Wang Fei-Yang Wang Li-Qiang Wang Nan Wang Tian-Li Wang Wei Wang Xiao-Yang Wang Yi-Han Wang Fu-Jie Yu Yu-Zhen Yin Ling-Kun Zhang Yi Zhang Jian-Yang Zhang Qi Zhao Yu-Ping Zhao Xin-Dong Zhu Yasir Sohail Ya-Nan Chen Tao Feng Qi-Long Gao Hai-Yan He Yong-Jiang Huang Zeng-Bao Jiao Hua Ji Yao Jiang Qiang Li Xiao-Ming Li Wei-Bing Liao Huai-Jun Lin Hui Liu Qi Liu Qing-Feng Liu Wei-Di Liu Xiong-Jun Liu Yang Lu Yi-Ping Lu Wen Ma Xue-Fei Miao Jie Pan Qing Wang Hong-Hui Wu Yuan Wu Tao Yang Wei-Ming Yang Qian Yu Jin-Yu Zhang Zhi-Gang Chen Liang Mao Yang Ren Bao-Long Shen Xun-Li Wang Zhe Jia He Zhu Zhen-Duo Wu Si Lan School of Materials Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China Jiangsu Key Laboratory for Advanced Metallic Materials School of Materials Science and EngineeringSoutheast UniversityNanjing 211189China Department of Physics City University of Hong KongHongKong 999077China School of Materials Science and Engineering Tianjin UniversityTianjin 300072China School of Physics and Microelectronics Zhengzhou UniversityZhengzhou 450001China Department of Mechanical Engineering Research Institute for Advanced ManufacturingThe Hong Kong Polytechnic UniversityHong Kong 100872China Center of Electron Microscopy and State Key Laboratory of Silicon Materials School of Materials Science and EngineeringZhejiang UniversityHangzhou 310027China Key Laboratory of Solidification Control and Digital Preparation Technology(Liaoning Province) School of Materials Science and EngineeringDalian University of TechnologyDalian 116024China Institute of Advanced Wear&Corrosion Resistance and Functional Materials Jinan UniversityGuangzhou 510632China School of Mechanics and Civil Engineering China University of Mining and TechnologyXuzhou 221116China School of Physical Sciences Great Bay UniversityDongguan 523000China School of Materials Science and Engineering Harbin Institute of TechnologyHarbin 150001China State Key Laboratory of Materials-Oriented Chemical Engineering College of Chemical EngineeringNanjing Tech UniversityNanjing 211816China Beijing Advanced Innovation Center for Materials Genome Engineering Research Institute for Carbon NeutralityUniversity of Science and Technology BeijingBeijing 100083China Department of Mechanical Engineering The University of Hong KongHong Kong 999077China School of Computing Macquarie UniversitySydney 2113Australia Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province College of Physics and Optoelectronic EngineeringShenzhen UniversityShenzhen 518060China State Key Laborato

Owing to their exceptional properties,high-entropy alloys(HEAs)and high-entropy materials have emerged as promising research areas and shown diverse ***,the recent advances in the field are comprehensively reviewed,organized into five *** first section introduces the background of HEAs,covering their definition,significance,application prospects,basic properties,design principles,and *** subsequent section focuses on cutting-edge high-entropy structural materials,highlighting developments such as nanostructured alloys,grain boundary engineering,eutectic systems,cryogenic alloys,thin films,micro-nano-lattice structures,additive manufacturing,high entropy metallic glasses,nano-precipitate strengthened alloys,composition modulation,alloy fibers,and refractory *** the following section,the emphasis shifts to functional materials,exploring HEAs as catalysts,magneto-caloric materials,corrosion-resistant alloys,radiation-resistant alloys,hydrogen storage systems,and materials for ***,the review encompasses functional high-entropy materials outside the realm of alloys,including thermoelectric,quantum dots,nanooxide catalysts,energy storage materials,negative thermal expansion ceramics,and high-entropy wave absorption *** paper concludes with an outlook,discussing future directions and potential growth areas in the *** this comprehensive review,researchers,engineers,and scientists may gain valuable insights into the recent progress and opportunities for further exploration in the exciting domains of high-entropy alloys and functional materials.

关键词： High entropy alloys Cutting-edge structural materials Cutting-edgefunctional materials

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Interpretation of possible biogas production capacity by investigating the effects of anaerobic digester tank geometry and angular velocity on flow characteristics

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Environmental science and Pollution Research 2024年第50期31卷 60220-60234页

作者： Celik, Ahmet Fırat Elibol, Emre Askin Turgut, Oguz Senol, Halil Sillanpää, Mika Institute of Natural and Applied Sciences Gazi University Ankara Turkey Department of Mechanical Engineering Faculty of Engineering Giresun University Giresun Turkey Department of Mechanical Engineering Faculty of Engineering Gazi University Ankara Turkey Department of Energy Systems Engineering Faculty of Engineering Giresun University Giresun Turkey Functional Materials Group Gulf University for Science and Technology Mubarak Al-Abdullah32093 Kuwait Adnan Kassar School of Business Lebanese American University Beirut Lebanon Centre of Research Impact and Outcome Chitkara University Institute of Engineering and Technology Rajpura-140401 Punjab India Division of Research & amp Development Lovely Professional University Phagwara-144411 Punjab India Department of Chemical Engineering School of Mining Metallurgy and Chemical Engineering University of Johannesburg P. O. Box 17011 Doornfontein2028 South Africa Department of Civil Engineering University Centre for Research & ampDevelopment Chandigarh University Punjab Gharuan Mohali India Sustainability Cluster School of Advanced Engineering UPES Bidholi Uttarakhand Dehradun248007 India

Mixing performance in reactors producing biogas through anaerobic digestion is one of the parameters that directly affect biogas yield. The most commonly used mixing model for bioreactors in biogas-production processes is mechanical mixing. In the present study, we focus on the geometry of the tank, where the mechanical mixing actually takes place. In this context, by using the six-blade standard Rushton impeller in two different types of tank, flow patterns involving velocity, dead zone volume, turbulent kinetic energy, and turbulent eddy dissipation rate in the angular velocity range of 25–100 rpm were observed, and the possible effects of the results on biogas production were interpreted. A new impeller design was proposed that maximizes the interface between the fluid inside the reactor tank and the impeller, which has the potential to reduce the dead zone volume to significantly lower levels. Our results showed that the lowest dead zone volume was achieved for a 60° slope reactor tank compared to the conventional 90° slope reactor tank at an angular velocity of 100 rpm. The dead zone volume decreased to 0.000094 m3 at 100 rpm in the 60° slope reactor tank with a total volume of 0.0305 m3, which by comparison was 0.000374 m3 in the 90° slope reactor tank. The magnitudes of both maximum turbulent kinetic energy and maximum turbulent eddy dissipation were higher in the 60° slope reactor tank at all angular velocities examined, which would be expected to enhance mixing performance. It is hoped that the reader will benefit from the results of this study;however, further studies should be conducted on the use of actual biowaste as the working fluid instead of water. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Angular velocity

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A novel method for the detection and classification of multiple diseases using transfer learning-based deep learning techniques with improved performance

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Neural Computing and Applications 2024年第30期36卷 18979-18997页

作者： Natarajan, Krishnamoorthy Muthusamy, Suresh Sha, Mizaj Shabil Sadasivuni, Kishor Kumar Sekaran, Sreejith Charles Gnanakkan, Christober Asir Rajan A.Elngar, Ahmed School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Tamil Nadu Perundurai Erode India Centre for Advanced Materials Qatar University Doha Qatar Department of Mechanical and Industrial Engineering Qatar University PO Box 2713 Doha Qatar Department of Electrical Engineering National Institute of Technology Assam Silchar India Department of Electrical and Electronics Engineering Puducherry Technological University Puducherry India Faculty of Computers and Artificial Intelligence Beni-Suef University Beni-Suef City62511 Egypt

A disease is a distinct abnormal state that significantly affects the functioning of all or part of an individual and is not caused by external harm. Diseases are frequently understood as medical conditions connected with distinct indications and symptoms. According to a fairly wide categorization, diseases can also be categorized as mental disorders, deficient diseases, genetic diseases, degenerative diseases, self-inflicted diseases, infectious diseases, non-infectious diseases, social diseases, and physical diseases. Prevention of the diseases is of multiple instances. Primary prevention seeks to prevent illness or harm before it ever happens. Secondary prevention tries to lessen the effect of an illness or damage that has already happened. This is done through diagnosing and treating illness or injury as soon as feasible to stop or delay its course, supporting personal ways to avoid recurrence or reinjury, and implementing programs to restore individuals to their previous health and function to prevent long-term difficulties. Tertiary prevention tries to lessen the impact of a continuing sickness or injury that has enduring repercussions. Diagnosis of the disease at an earlier stage is important for the treatment of the disease. Hence, in this study, deep learning algorithms, such as VGG16, EfficientNetB4, and ResNet, are utilized to diagnose various diseases, such as Alzheimer's, brain tumors, skin diseases, and lung diseases. Chest X-rays, MRI scans, CT scans, and skin lesions are used to diagnose the mentioned diseases. Transfer learning algorithms, such as VGG16, VGG19, ResNet, InceptionV3, and EfficientNetB4, are utilized to categorize various diseases. EfficientNetB4 with the learning rate annealing, having obtained an accuracy of 94.04% on the test dataset, is observed. As a consequence, we observed that every network has unique particular skills on the multi-disease dataset, which includes chest X-rays, MRI scans, etc., © The Author(s) 2024.

关键词： Diseases

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Improved Prediction of Metamaterial Antenna Bandwidth Using Adaptive Optimization of LSTM

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Computers, Materials & Continua 2022年第10期73卷 865-881页

作者： Doaa Sami Khafaga Amel Ali Alhussan El-Sayed M.El-kenawy Abdelhameed Ibrahim Said H.Abd Elkhalik Shady Y.El-Mashad Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Systems Engineering Faculty of Engineering at ShoubraBenha UniversityEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

The design of an antenna requires a careful selection of its parameters to retain the desired ***,this task is time-consuming when the traditional approaches are employed,which represents a significant *** the other hand,machine learning presents an effective solution to this challenge through a set of regression models that can robustly assist antenna designers to find out the best set of design parameters to achieve the intended *** this paper,we propose a novel approach for accurately predicting the bandwidth of metamaterial *** proposed approach is based on employing the recently emerged guided whale optimization algorithm using adaptive particle swarm optimization to optimize the parameters of the long-short-term memory(LSTM)deep *** optimized network is used to retrieve the metamaterial bandwidth given a set of *** addition,the superiority of the proposed approach is examined in terms of a comparison with the traditional multilayer perceptron(ML),Knearest neighbors(K-NN),and the basic LSTM in terms of several evaluation criteria such as root mean square error(RMSE),mean absolute error(MAE),and mean bias error(MBE).Experimental results show that the proposed approach could achieve RMSE of(0.003018),MAE of(0.001871),and MBE of(0.000205).These values are better than those of the other competing models.

关键词： Metamaterial antenna long short term memory(LSTM) guided whale optimization algorithm(Guided WOA) adaptive dynamic particle swarm algorithm(AD-PSO)

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Improved material descriptors for bulk modulus in intermetallic compounds via machine learning

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Rare Metals 2023年第7期42卷 2396-2405页

作者： De-Xin Zhu Kun-Ming Pan Yuan Wu Xiao-Ye Zhou Xiang-Yue Li Yong-Peng Ren Sai-Ru Shi Hua Yu Shi-Zhong Wei Hong-Hui Wu Xu-Sheng Yang National Joint Engineering Research Center for Abrasion Control and Molding of Metal Materials Henan Key Laboratory of High-Temperature Structural and Functional MaterialsHenan University of Science and TechnologyLuoyang471003China Longmen Laboratory Luoyang471003China State Key Laboratory for Advanced Metals and Materials University of Science and Technology BeijingBeijing100083China Guangdong Province Key Laboratory of Durability for Marine Civil Engineering School of Civil EngineeringShenzhen UniversityShenzhen518060China Department of Industrial and Systems Engineering The Hong Kong Polytechnic UniversityHong Kong999077China

Bulk modulus is an important mechanical property in the optimal design and selection of intermetallic *** this study,bulk modulus datasets of intermetallic compounds were collected,and the features affecting the bulk modulus of intermetallics were screened via feature *** features B_(cal),dB_(avg),and TIE(corresponding to calculated bulk modulus,mean bulk modulus,and third ionization energy,respectively)were found to be the dominant factors influencing bulk modulus and can be extended to other multi-component ***,we predicted the bulk modulus with an accuracy of 95%using surrogate machine learning models with the selected features,and these features were also demonstrated to be effective for high-entropy ***,symbolic regression provided an expression for the relationship between bulk modulus and the screened *** machine learning models provide a new approach for optimizing and predicting the bulk moduli of intermetallic compounds.

关键词： Bulk modulus Intermetallic compounds Machine learning Symbolic regression

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Enhancing Mild Cognitive Impairment Detection through Efficient Magnetic Resonance Image Analysis

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Computers, Materials & Continua 2024年第8期80卷 2081-2098页

作者： Atif Mehmood Zhonglong Zheng Rizwan Khan Ahmad Al Smadi Farah Shahid Shahid Iqbal Mutasem K.Alsmadi Yazeed Yasin Ghadi Syed Aziz Shah Mostafa M.Ibrahim Department of Computer Science and Technology Zhejiang Normal UniversityJinhua321002China Zhejiang Institute of Photoelectronics&Zhejiang Institute for Advanced Light Source Zhejiang Normal UniversityJinhua321004China Department of Data Science and Artificial Intelligence Zarqa UniversityZarqa13100Jordan School of Chemical and Environmental Engineering College of ChemistryChemical Engineering andMaterials ScienceSoochow UniversitySuzhou215123China Department of Management Information Systems College of Applied Studies and Community ServiceImam Abdulrahman Bin Faisal UniversityDammam32244Saudi Arabia Department of Computer Science Al Ain UniversityAbu Dhabi999041United Arab Emirates Department of Electrical Engineering Faculty of EngineeringMinia UniversityMinia61519Egypt Healthcare Technology and Innovation Theme Faculty Research Centre for Intelligent HealthcareCoventry UniversityCoventryCV48BWUK

Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease(AD).Mild cognitive impairment(MCI)is a condition that falls between the spectrum of normal cognitive function and ***,previous studies have mainly used handcrafted features to classify MCI,AD,and normal control(NC)*** paper focuses on using gray matter(GM)scans obtained through magnetic resonance imaging(MRI)for the diagnosis of individuals with MCI,AD,and *** improve classification performance,we developed two transfer learning strategies with data augmentation(i.e.,shear range,rotation,zoom range,channel shift).The first approach is a deep Siamese network(DSN),and the second approach involves using a cross-domain strategy with customized *** performed experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset to evaluate the performance of our proposed *** experimental results demonstrate superior performance in classifying the three binary classification tasks:NC ***,NC ***,and MCI ***,we achieved a classification accuracy of 97.68%,94.25%,and 92.18%for the three cases,*** study proposes two transfer learning strategies with data augmentation to accurately diagnose MCI,AD,and normal control individuals using GM *** findings provide promising results for future research and clinical applications in the early detection and diagnosis of AD.

关键词： Alzheimer’s disease mild cognitive impairment normal control transfer learning classification augmentation

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Investigating the Determinants of Compliance Intention in Behavior Change Support systems 29

Investigating the Determinants of Compliance Intention in Be...

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29th Annual Americas Conference on Information systems: Diving into Uncharted Waters, AMCIS 2023

作者： Ekpezu, Akon Obu Oinas-Kukkonen, Harri Wiafe, Isaac Faculty of Information Technology and Electrical Engineering Oulu Advanced Research on Service and Information Systems University of Oulu Finland Department of Computer Science University of Ghana Ghana

ISBN: (纸本)9781713893592

The effectiveness of behavior change support systems (BCSS) in promoting health and well-being is unflinching. However, its long-term effectiveness is hindered by non-compliance. Research in BCSS that focuses on compliance is scarce, particularly the determinants of compliance intention are not well established. To address this gap, this study investigated the determinants of compliance intention in BCSS by extending the persuasive system design model to include competence, effectiveness, enjoyment, and persuasiveness. By surveying a sample of prospective BCSS users (N=234), partial least squares structural equation modeling was used to evaluate the proposed model. The findings suggest that the prospective users’ perceptions of primary task support and dialogue support, as well as social support and credibility support significantly influenced their perceptions of self-competence and system effectiveness respectively. These perceptions influenced enjoyment, then persuasiveness, and finally compliance intention. Future studies will validate these findings on the compliance behavior of BCSS. © 2023 29th Annual Americas Conference on Information systems, AMCIS 2023. All rights reserved.

关键词： Behavior Change Support systems Compliance Intention Persuasive systems Design Persuasive Technology Physical Activity

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Real-time monitoring of fast gas dynamics with a single-molecule resolution by frequency-comb-referenced plasmonic phase spectroscopy

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PhotoniX 2024年第1期5卷 357-372页

作者： Duy-Anh Nguyen Dae Hee Kim Geon Ho Lee San Kim Dong-Chel Shin Jongkyoon Park Hak-Jong Choi Seung-Woo Kim Seungchul Kim Young-Jin Kim Department of Mechanical Engineering Korea Advanced Institute of Science and Technology(KAIST)Science TownDaejeon 34141South Korea Department of Mechatronic Engineering Faculty of Mechanical EngineeringHo Chi Minh City University of Technology(HCMUT)Vietnam National UniversityHo Chi Minh CityVietnam Department of Cogno-Mechatronics Engineering College of Nanoscience and NanotechnologyPusan National UniversityBusan 46241South Korea Department of Optics and Mechatronics Engineering College of Nanoscience and NanotechnologyPusan National UniversityBusan 46241South Korea School of Mechanical and Aerospace Engineering Nanyang Technological University(NTU)50 Nanyang AvenueSingapore 639798Singapore Institute of Machinery and Materials Nano-Convergence Mechanical Systems Research DivisionKorea156Gajeongbuk-RoYuseong-GuDaejeon 34103South Korea

Surface plasmon resonance(SPR)sensors are based on photon-excited surface charge density oscillations confined at metal-dielectric interfaces,which makes them highly sensitive to biological or chemical molecular bindings to functional metallic *** nanostructures further concentrate surface plasmons into a smaller area than the diffraction limit,thus strengthening photon-sample ***,plasmonic sensors based on intensity detection provide limited resolution with long acquisition time owing to their high vulnerability to environmental and instrumental ***,we demonstrate fast and precise detection of noble gas dynamics at single molecular resolution via frequency-comb-referenced plasmonic phase *** photon-sample interaction was enhanced by a factor of 3,852 than the physical sample thickness owing to plasmon resonance and thermophoresis-assisted optical confinement *** utilizing a sharp plasmonic phase slope and a high heterodyne information carrier,a small atomic-density modulation was clearly resolved at 5 Hz with a resolution of 0.06 Ar atoms per nano-hole(in 10^(11)RIU)in Allan deviation at 0.2 s;a faster motion up to 200 Hz was clearly *** fast and precise sensing technique can enable the in-depth analysis of fast fluid dynamics with the utmost resolution for a better understanding of biomedical,chemical,and physical events and interactions.

关键词： Frequency comb Nanohole array Single molecule detection Refractive index High resolution High speed Traceability to time standard

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Novel Optimized Feature Selection Using Metaheuristics Applied to Physical Benchmark Datasets

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Computers, Materials & Continua 2023年第2期74卷 4027-4041页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Fadwa Alrowais Sunil Kumar Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt School of Computer Science University of Petroleum and Energy StudiesDehradun248001India Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

In data mining and machine learning,feature selection is a critical part of the process of selecting the optimal subset of features based on the target *** are 2n potential feature subsets for every n features in a dataset,making it difficult to pick the best set of features using standard ***,in this research,a new metaheuristics-based feature selection technique based on an adaptive squirrel search optimization algorithm(ASSOA)has been *** using metaheuristics to pick features,it is common for the selection of features to vary across runs,which can lead to *** of this,we used the adaptive squirrel search to balance exploration and exploitation duties more evenly in the optimization *** the selection of the best subset of features,we recommend using the binary ASSOA search strategy we developed *** to the suggested approach,the number of features picked is reduced while maximizing classification accuracy.A ten-feature dataset from the University of California,Irvine(UCI)repository was used to test the proposed method’s performance *** other state-of-the-art approaches,including binary grey wolf optimization(bGWO),binary hybrid grey wolf and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hybrid GWO and genetic algorithm 4028 CMC,2023,vol.74,no.2(bGWO-GA),binary firefly algorithm(bFA),and *** results confirm the superiority and effectiveness of the proposed algorithm for solving the problem of feature selection.

关键词： Metaheuristics adaptive squirrel search algorithm optimization methods binary optimizer

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