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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Vengidusamy, S. Kottaimalai, R. Pallikonda Rajasekaran, M. Arunprasath, T. Govindaraj, Vishnuvarthanan Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Tamilnadu Krishnankoil India Kalasalingam Academy of Research and Education Department of Biomedical Engineering Tamilnadu Krishnankoil India School of Computer Science and Engineering VIT Bhopal University Data Science Division Madhya Pradesh India

ISBN: (纸本)9798331530013

Neurodevelopmental disorders (NDD) are conditions that impact brain function and interfere with neurological development, resulting in difficulties with social, cognitive, and emotional skills. The most common NDD include autism spectrum disorder (ASD). It is a complicated NDD marked by ongoing difficulties in social communication and interaction, as well as restricted or repetitive behaviors. The origins of ASD are intricate, encompassing a combination of environmental, genetic, and neurobiological factors. This research delves into the latest insights regarding ASD, stressing the critical role of early diagnosis and intervention. To improve long-term outcomes. The study also examines neuroimaging methods, genetic markers, and behavioural assessments that contribute to more precise diagnoses. Furthermore, the paper addresses the variability within ASD, highlighting the need for tailored treatment approaches and the vital role of individualized educational and therapeutic strategies. Despite significant progress, there remain gaps in our comprehension of the underlying mechanisms of ASD, and this research emphasizes the necessity for continued interdisciplinary collaboration. The findings aim to enhance diagnostic accuracy, inform treatment strategies, and improve the overall and improve the overall well-being of life for individuals with ASD and their families. © 2024 IEEE.

关键词： Convolutional neural networks

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Games for Artificial Intelligence research: A Review and Perspectives

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 5949-5968页

作者： Hu, Chengpeng Zhao, Yunlong Wang, Ziqi Du, Haocheng Liu, Jialin Research Institute of Trustworthy Autonomous System Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China LightSpeed Studios Tencent Shenzhen518057 China

Games have been the perfect test beds for artificial intelligence (AI) research for the characteristics that widely exist in real-world scenarios. Learning and optimization, decision-making in dynamic and uncertain environments, game theory, planning and scheduling, design, and education are common research areas shared between games and real-world problems. Numerous open-source games or game-based environments have been implemented to study AI. In addition to single- or multiplayer, collaborative, and adversarial games, there has also been growing interest in implementing platforms for creative design in recent years. Those platforms provide ideal benchmarks for exploring and comparing AI ideas and techniques. This article reviews the games and game-based platforms for AI research, provides guidance on matching particular types of AI with suitable games for testing and matching particular needs in games with suitable AI techniques, discusses the research trend induced by the evolution of those games and platforms, and gives an outlook. © 2024 IEEE.

关键词： Reinforcement learning

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Prediction of Geopolymer Concrete Compressive Strength Using Convolutional Neural Networks

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computer Modeling in engineering & sciences 2024年第5期139卷 1455-1486页

作者： Kolli Ramujee Pooja Sadula Golla Madhu Sandeep Kautish Abdulaziz S.Almazyad Guojiang Xiong Ali Wagdy Mohamed Department of Civil Engineering VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Information Technology VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Computer Science LBEF Campus(Asia Pacific University of Technology&InnovationMalaysia)Kathmandu44600Nepal Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmmanJordan

Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental *** attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation *** this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering *** achieve this goal,a new approach using convolutional neural networks(CNNs)has been *** study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly *** selection of optimal input parameters is guided by two distinct *** first criterion leverages insights garnered from previous research on the influence of individual features on compressive *** second criterion scrutinizes the impact of these features within the model’s predictive *** to enhancing the CNN model’s performance is the meticulous determination of the optimal *** a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s *** model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score ***,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction *** unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rat

关键词： Class F fly ash compressive strength geopolymer concrete prediction deep learning convolutional neural network

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Passive activity enhances residual control ability in patients with complete spinal cord injury

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Neural Regeneration research 2025年第8期20卷 2337-2347页

作者： Yanqing Xiao Mingming Gao Zejia He Jia Zheng Hongming Bai Jia-Sheng Rao Guiyun Song Wei Song Xiaoguang Li Beijing Key Laboratory for Biomaterials and Neural Regeneration Beijing Advanced Innovation Center for Biomedical EngineeringSchool of Biological Science and Medical EngineeringBeihang UniversityBeijingChina Department of Rehabilitation Evaluation China Rehabilitation Research CenterBeijingChina Cardiac Center Beijing Children’s HospitalCapital Medical UniversityBeijingChina The State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijingChina Department of Rehabilitation Engineering China Rehabilitation Science InstituteBeijingChina

Patients with complete spinal cord injury retain the potential for volitional muscle activity in muscles located below the spinal injury ***,because of prolonged inactivity,initial attempts to activate these muscles may not effectively engage any of the remaining neurons in the descending pathway.A previous study unexpectedly found that a brief clinical round of passive activity significantly increased volitional muscle activation,as measured by surface *** this study,we further explored the effect of passive activity on surface electromyographic signals during volitional control tasks among individuals with complete spinal cord *** patients with chronic complete thoracic spinal cord injury were *** electromyography data from eight major leg muscles were acquired and compared before and after the passive activity *** results indicated that the passive activity led to an increased number of activated volitional muscles and an increased frequency of *** the cumulative root mean square of surface electromyography amplitude for volitional control of movement showed a slight increase after passive activity,the difference was not statistically *** findings suggest that brief passive activity may enhance the ability to initiate volitional muscle activity during surface electromyography tasks and underscore the potential of passive activity for improving residual motor control among patients with motor complete spinal cord injury.

关键词： complete spinal cord injury cycle training epidural electrical stimulation motor training passive activity physiological state spinal cord circuit surface electromyography volitional control task

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Precise Planar-Twisted Molecular engineering to Construct Semiconducting Polymers with Balanced Absorption and Quantum Yield for Efficient Phototheranostics

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research 2024年第2期2023卷 227-238页

作者： Xiang Su Zhirong Bao Wei Xie Deliang Wang Ting Han Dong Wang Ben Zhong Tang Center for AIE Research Shenzhen Key Laboratory of Polymer Science and TechnologyGuangdong Research Center for Interfacial Engineering of Functional MaterialsCollege of Materials Science and EngineeringShenzhen UniversityShenzhen 518060China School of Biomedical and Pharmaceutical Sciences Guangdong University of TechnologyGuangzhou 510006China Department of Radiation and Medical Oncology Hubei Key Laboratory of Tumor Biological BehaviorsHubei Cancer Clinical Study CenterZhongnan Hospital of Wuhan UniversityWuhan 430071China Department of Materials Chemistry Huzhou UniversityHuzhou 313000China School of Science and Engineering Shenzhen Institute of Aggregate Science and TechnologyThe Chinese University of Hong KongShenzhenGuangdong 518172China

Semiconducting polymers(SPs)have shown great feasibility as candidates for near-infrared-II(NIR-Ⅱ)fluorescence imaging-navigated photothermal therapy due to their strong light-harvesting ability and flexible ***,the fluorescence signal of traditional SPs tends to quench in their aggregate states owing to the strongπ-πstacking,which can lead to the radiative decay pathway shutting *** address this issue,aggregation-induced emission effect has been used as a rational tactic to boost the aggregate-state fluorescence of NIR-Ⅱ*** this contribution,we developed a precise molecular engineering tactic based on the block copolymerizations that integrate planar and twisted segments into one conjugated polymer backbone,providing great flexibility in tuning the photophysical properties and photothermal conversion capacity of *** monomers featured with twisted and planar architectures,respectively,were tactfully incorporated via a ternary copolymerization approach to produce a series of new *** optimal copolymer(SP2)synchronously shows desirable absorption ability and good NIR-Ⅱquantum yield on the premise of maintaining typical aggregation-induced emission characteristics,resulting in balanced NIR-Ⅱfluorescence brightness and photothermal ***-dispersible nanoparticles fabricated from the optimal SP2 show efficient photothermal therapeutic effects both in vitro and in *** in vivo investigation reveals the distinguished NIR-Ⅱfluorescence imaging performance of SP2 nanoparticles and their photothermal ablation toward tumor with prominent tumor accumulation ability and excellent biocompatibility.

关键词： property conducting twisted

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Exploring the Predictive Capabilities of AlphaFold Using Adversarial Protein Sequences

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3384-3392页

作者： Alkhouri, Ismail R. Jha, Sumit Beckus, Andre Atia, George Jha, Susmit Ewetz, Rickard Velasquez, Alvaro OrlandoFL32816 United States Michigan State University Computational Mathematics Science and Engineering Department East LansingMI48824 United States University of Michigan Electrical Engineering and Computer Science Department Ann ArborMI48109 United States Florida International University Knights Foundation School of Computing and Information Sciences MiamiFL33199 United States Air Force Research Laboratory Rome Information Directorate NY13441 United States Department Computer Science Department OrlandoFL32816 United States SRI International Computer Science Laboratory Menlo ParkCA94025 United States Electrical and Computer Engineering Department OrlandoFL32816 United States University of Colorado Computer Science Department BoulderCO80309 United States

Protein folding neural networks (PFNNs) such as AlphaFold predict remarkably accurate structures of proteins compared to other approaches. However, the robustness of such networks has heretofore not been fully explored. This is particularly relevant given the broad social implications of such technologies and the fact that biologically small perturbations to noncritical residues of a protein sequence do not typically lead to drastic changes in the protein structure. Our study demonstrates that, similar to adversarial methods in machine learning, small changes to protein sequences can result in significant differences in the predicted protein structures using AlphaFold as determined by large distance measures. Despite this, our findings using multiple protein sequences suggest that AlphaFold is able to accurately predict the domain structure and folding regions of a protein. To gauge structural differences, we employ two alignment-based measures [root-mean-square deviation (RMSD) and the global distance test (GDT) similarity], and one alignment-free measure, which is an effective graph-based structure representation (GraSR) method. We prove that the problem of minimally perturbing protein sequences is NP-complete. Based on the well-established block substitution matrices (BLOSUM62) sequence alignment scoring matrix, we generate adversarial sequences. In our experimental evaluation, we consider 111 proteins (including 29 COVID-19 sequences) in the Universal Protein resource (UniProt), a central resource for protein data. Our findings suggest that, despite the high RMSD values returned by AlphaFold, it is capable of handling the BLOSUM adversarial sequences considered in our analysis, as evidenced by the preservation of the folded regions and the GraSR results. © 2020 IEEE.

关键词： Neural networks

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Community Smell Occurrence Prediction on Multi-Granularity by Developer-Oriented Features and Process Metrics

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Journal of computer science & Technology 2022年第1期37卷 182-206页

作者： Zi-Jie Huang Zhi-Qing Shao Gui-Sheng Fan Hui-Qun Yu Xing-Guang Yang Kang Yang Department of Computer Science and Engineering East China University of Science and TechnologyShanghai 200237China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai 200237China Shanghai Engineering Research Center of Smart Energy Shanghai 200237China

Community smells are sub-optimal developer community structures that hinder *** studies performed smell prediction and provided refactoring guidelines from a top-down aspect to help community ***,refactoring smells also requires bottom-up effort from every ***,supportive measures and guidelines for them are not available at a fine-grained *** recent work revealed developers'personalities and working states could influence community smells'emergence and variation,we build prediction models with experience,sentiment,and development process features of developers considering three smells including Organizational Silo,Lone Wolf,and Bottleneck,as well as two related classes including smelly developer and smelly *** predict the five classes in the individual granularity,and we also generate forecasts for the number of smelly developers in the community *** proposed models achieve F-measures ranging from 0.73 to 0.92 in individual-wide within-project,time-wise,and cross-project prediction,and mean R2 performance of 0.68 in community-wide Smelly Developer *** also exploit SHAP(SHapley Additive exPlanations)to assess feature importance to explain our *** conclusion,we suggest developers with heavy workload should foster more frequent communication in a straightforward and polite way to build healthier communities,and we recommend community shepherds to use the forecasting model for refactoring planning.

关键词： community smell developer sentiment socio-technical analysis empirical software engineering

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Finger Width Effects on Performance of GaN HEMTs 2

Finger Width Effects on Performance of GaN HEMTs

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2nd International Microwave and Antenna Symposium, IMAS 2024

作者： Nouri, Soheil Avval, Amirreza G. Darwish, Ali El-Ghazaly, Samir M. University of Arkansas Department of Electrical Engineering and Computer Science Fayetteville United States Army Research Laboratory AdelphiMD United States

ISBN: (纸本)9798350356359

In this study, we characterize GaN-HEMT devices and present both DC and AC measurement outcomes for the multi-finger configurations. Specifically, we introduce a new device featuring wide electrodes to assess and compare the performance of various multi-finger arrangements. This particular transistor comprises two fingers, each measuring 150 μm in width. We analyze and compare the s-parameters of structures with varying widths, extending our frequency range up to 67 GHz. This newly developed wide-finger configuration is integral to our ongoing efforts in developing optimized modeling approaches and improved transistor designs. The comparative analysis of different finger widths lays the groundwork for future explorations into high-frequency phenomena, including wave propagation effects and their impact on the performance of millimeter-wave transistors. © 2024 IEEE.

关键词： Gallium nitride

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Global video object segmentation with spatial constraint module

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Computational Visual Media 2023年第2期9卷 385-400页

作者： Yadang Chen Duolin Wang Zhiguo Chen Zhi-Xin Yang Enhua Wu Engineering Research Center of Digital Forensics Ministry of EducationSchool of Computer and SoftwareNanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Internet of Things for Smart City Department of Electromechanical EngineeringUniversity of MacaoMacao 999078China State Key Laboratory of Computer Science Institute of SoftwareUniversity of Chinese Academy of SciencesBeijing 100190China Faculty of Science and Technology University of MacaoMacao 999078China

We present a lightweight and efficient semisupervised video object segmentation network based on the space-time memory *** some extent,our method solves the two difficulties encountered in traditional video object segmentation:one is that the single frame calculation time is too long,and the other is that the current frame’s segmentation should use more information from past *** algorithm uses a global context(GC)module to achieve highperformance,real-time *** GC module can effectively integrate multi-frame image information without increased memory and can process each frame in real ***,the prediction mask of the previous frame is helpful for the segmentation of the current frame,so we input it into a spatial constraint module(SCM),which constrains the areas of segments in the current *** SCM effectively alleviates mismatching of similar targets yet consumes few additional *** added a refinement module to the decoder to improve boundary *** model achieves state-of-the-art results on various datasets,scoring 80.1%on YouTube-VOS 2018 and a J&F score of 78.0%on DAVIS 2017,while taking 0.05 s per frame on the DAVIS 2016 validation dataset.

关键词： video object segmentation semantic segmentation global context(GC)module spatial constraint

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Silk fibroin-based inks for in situ 3D printing using a double crosslinking process

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Bioactive Materials 2024年第5期35卷 122-134页

作者： Francesca Agostinacchio Vincent Fitzpatrick Sandra Dire David LKaplan Antonella Motta National Interuniversity Consortium of Material Science and Technology FlorenceItaly BIOtech Research Center and European Institute of Excellence on Tissue Engineering and Regenerative Medicine Department of Industrial EngineeringUniversity of TrentoTrentoItaly Materials Chemistry Group&“Klaus Müller”Magnetic Resonance Laboratory Department of Industrial EngineeringUniversity of TrentoTrentoItaly Department of Biomedical Engineering Tufts UniversityMedfordMA02155USA

The shortage of tissues and organs for transplantation is an urgent clinical *** situ 3D printing is an advanced 3D printing technique aimed at printing the new tissue or organ directly in the *** ink for this process is central to the outcomes,and must meet specific requirements such as rapid gelation,shape integrity,stability over time,and adhesion to surrounding healthy *** natural materials,silk fibroin exhibits fascinating properties that have made it widely studied in tissue engineering and regenerative ***,further improvements in silk fibroin inks are needed to match the requirements for in situ 3D *** the present study,silk fibroin-based inks were developed for in situ applications by exploiting covalent crosslinking process consisting of a pre-photo-crosslinking prior to printing and in situ enzymatic *** different silk fibroin molecular weights were characterized and the synergistic effect of the covalent bonds with shear forces enhanced the shift in silk secondary structure towardβ-sheets,thus,rapid *** hydrogels exhibited good mechanical properties,stability over time,and resistance to enzymatic degradation over 14 days,with no significant changes over time in their secondary structure and swelling ***,adhesion to tissues in vitro was demonstrated.

关键词： Silk fibroin inks In situ 3D printing Covalent crosslinking Regenerative medicine Rapid gelation

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