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Integration of Continuous Improvement Approach in Sustainable E-Government Services Towards Citizen Satisfaction in Health Sector Development of Conceptual Model

Integration of Continuous Improvement Approach in Sustainabl...

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Automation and Computation (AUTOCOM), International Conference on

作者： R. Anandhakrishnan D. Vimala S. Vasantha Department of Biotechnology V.S.B. College of Engineering Karur Tamil Nadu India School of Management Studies Vels Institute of Science Technology & Advanced Studies(VISTAS) Chennai Tamil Nadu India Department of MBA Saveetha Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

E-government success relies on high-quality e-services. Understanding e-government service quality is crucial for improving existing and future implementations. As the globe advances in communication and information technology, study on e-government services especially in health sector since covid-19 is gaining momentum. This study introduces a conceptual model for measuring the impact of continuous improvement approach on sustainable e-government services in health sector. The suggested model identifies five stream dimensions to assess the quality of e-government services based on a survey of relevant literature. This article focuses on the five dimension of continuous improvement approach the efficiency, accessibility, and sustainability of e-government services. This research provides a conceptual model for integrating continuous improvement approach to promote sustainable long-term e-government services in the health care area. Governments may use AI-driven analytics, predictive modeling, and automation to improve administrative procedures, personalise healthcare services, and enable data-driven decision-making. The concept of continuous improvement approach is central to the model, in which AI algorithms continuously analyze data from various sources, such as electronic health records, patient feedback and performance metrics. The outcome of the conceptual model is to identify the effect of integrating continuous improvement approach in the healthcare sector to achieve sustainable e-government services that optimizes resource allocation, improves service quality, and fosters innovation for Citizen Satisfaction. This research adds to the literature on e-government and healthcare management by presenting a new conceptual model that combines ideas of continuous improvement. Future study should concentrate on empirical validation and implementation methodologies to determine the suggested model's usefulness and scalability in real-world scenarios. Overall, incorpor

关键词： Analytical models Technological innovation Automation Medical services Predictive models Data models Vaccines Continuous improvement Sustainable development Electronic government

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Genome-wide primer scan (GPS): a python package for a flexible, reliable and large-scale primer design toolkit

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Frontiers of Computer Science 2025年第2期19卷 127-128页

作者： Wencong HE Yan ZHUO Chen WANG Yemei HUANG Xuelei ZANG Chen YANG Hengyu DENG Yangyu ZHOU Jing LIU Ping ZHANG Xinying XUE Liye ZHANG School of Life Science and Technology ShanghaiTech UniversityShanghai 201210China Shanghai Institute for Advanced Immunochemical Studies ShanghaiTech UniversityShanghai 201210China Department of Respiratory and Critical Care Emergency and Critical Care Medical CenterBeijing Shijitan HospitalCapital Medical UniversityBeijing 100038China Medical School of Chinese PLA Beijing 100853China Department of Respiratory and Critical Care Shandong Second Medical UniversityWeifang 261053China Beijing Key Laboratory of Genetic Engineering Drug and Biotechnology College of Life SciencesBeijing Normal UniversityBeijing 100875China

1 Introduction Microbial infections represent significant global health challenges with far-reaching *** contemporary medical practice,quantitative polymerase chain reaction(qPCR)stands as an important technique for pathogen detection,and the success of qPCR-based diagnosis fundamentally depends on the precision of the qPCR primers employed[1].However,to our knowledge,no easy-to-use qPCR primer design tool with flexible,customized options is *** most clinical contexts,detecting the presence of a pathogen within a given clinical sample is the primary ***,well-designed primers should achieve dual objectives:high sensitivity and specificity for detecting target pathogens,while avoiding false positives,even when testing evolutionarily closely related ***,a constellation of software and program packages towards qPCR primer design is available(for example,ssPrimer).However,these tools typically operate on a single gene or region,necessitating user-driven selection of the region or gene[2,3].In this context,we introduce the first one-step solution,to our knowledge,for scanning entire genomes to identify the best candidate primer *** addition,our framework allows users to efficiently search against a large number of genomes(over 1,800 genomes in our test case)to identify specific primers.

关键词： python false typically

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AI-Enhanced Healthcare: Densenet Based Alzheimer's Disease Prediction and Online Consultation for Accessible Medical Support

AI-Enhanced Healthcare: Densenet Based Alzheimer's Disease P...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Wilfred Blessing N. R Hemalatha Gunasekaran Hariharan B. Jaber Saleh Salim Al Jabri Kavitha S. J Sutherlin Subitha G. College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Department of Computer Science and Engineering Stella Mary's College of Engineering Aruthenganvilai Tamil Nadu India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Improvements in patient care and early disease detection possibilities are two of the ways artificial intelligence is transforming the healthcare industry. Alzheimer's disease, a neurodegenerative disease that worsens with time, is among the most difficult diseases to exactly identify in early stages. Patients thus delay in treatment and experience a decline in their quality of life. Research is still under progress, thus knowledge about how AI-driven prediction models could be combined with readily available healthcare services remains lacking. This work presents a special artificial intelligence-powered system consisting of DenseNet-based deep learning for Alzheimer's disease prediction with an online consultation platform. The proposed approach analyzes brain MRI data and classifies Alzheimer's stages with higher accuracy using a DenseNet architecture, well-known for its exceptional performance in the field of feature extraction. Comprising three thousand magnetic resonance imaging (MRI) images, the dataset has been preprocessed for noise reduction and tuned for models performance. At 98.5%, the DenseNet model exceeded other methods including VGGNet and ResNet in accuracy.

关键词： Accuracy Magnetic resonance imaging Redundancy Medical services Predictive models Brain modeling Real-time systems Alzheimer's disease Medical diagnostic imaging Overfitting

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Artificial Intelligence in Health Care Computing and Assessments in Oman: an Effectual Study

Artificial Intelligence in Health Care Computing and Assessm...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Health care plays a vital responsibility in a human life span. World countries targets to provide better care for their citizens. Digital transformations and innovations in Artificial Intelligence (AI) technologies helps in deep for the novel solutions to speed-up the decision making process in health management and patient consulting. AI branches including Machine Learning (ML), Deep Learning (DL), Neural Networks (NN), Natural Language Processing (NLP) and so forth can support innovations in heal care domain. This paper presents the comprehensive and noble review on recent existing tools and techniques and also recommends advanced techniques for health care industry that are to be used in Sultanate of Oman. Furthermore, overcoming AI challenges are resulted and presented through the study. This work mainly aimed to produce the multiple dimensions and challenges that helps to develop AI powered real-life applications in health care support system.

关键词： Deep learning Technological innovation Ethics Reviews Decision making Surgery Medical services Natural language processing Medical diagnostic imaging Diseases

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Explanations of Deep Language Models Explain Language Representations in the Brain

arXiv

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arXiv 2025年

作者： Rahimi, Maryam Yaghoobzadeh, Yadollah Daliri, Mohammad Reza Biomedical Engineering Department School of Electrical Engineering Iran University of Science and Technology Tehran Iran Electrical and Computer Engineering Department University of Tehran Tehran Iran School of Cognitive Sciences Institute for Research in Fundamental Sciences Tehran Iran Tehran Institute for Advanced Studies Khatam University Tehran Iran

Recent advances in artificial intelligence have given rise to large language models (LLMs) that not only achieve human-like performance but also share computational principles with the brain’s language processing mechanisms. While previous research has primarily focused on aligning LLMs’ internal representations with neural activity, we introduce a novel approach that leverages explainable AI (XAI) methods to forge deeper connections between the two domains. Using attribution methods, we quantified how preceding words contribute to an LLM’s next-word predictions and employed these explanations to predict fMRI recordings from participants listening to the same narratives. Our findings demonstrate that attribution methods robustly predict brain activity across the language network, surpassing traditional internal representations in early language areas. This alignment is hierarchical: early-layer explanations correspond to the initial stages of language processing in the brain, while later layers align with more advanced stages. Moreover, the layers more influential on LLM next-word prediction—those with higher attribution scores—exhibited stronger alignment with neural activity. This work establishes a bidirectional bridge between AI and neuroscience. First, we demonstrate that attribution methods offer a powerful lens for investigating the neural mechanisms of language comprehension, revealing how meaning emerges from preceding context. Second, we propose using brain alignment as a metric to evaluate the validity of attribution methods, providing a framework for assessing their biological plausibility. © 2025, CC BY-SA.

关键词： Computational neuroscience

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Generation,interrogation,and future applications of microglia-containing brain organoids

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Neural Regeneration Research 2025年第12期20卷 3448-3460页

作者： Julia Di Stefano Federica Di Marco Ilaria Cicalini Una FitzGerald Damiana Pieragostino Marleen Verhoye Peter Ponsaerts Elise Van Breedam Laboratory of Experimental Hematology Vaccine and Infectious Disease Institute(Vaxinfectio)University of AntwerpWilrijkBelgium Bio-Imaging Lab University of AntwerpWilrijkBelgium Center for Advanced Studies and Technology(CAST) G.d’Annunzio University of Chieti-PescaraChietiItaly Department of Innovative Technologies in Medicine and Dentistry University“G.d’Annunzio”of Chieti-PescaraChietiItaly CÚRAM Center for Research in Medical DevicesBiomedical EngineeringUniversity of GalwayIreland Galway Neuroscience Center University of GalwayIreland μNEURO Research Center of Excellence University of AntwerpWilrijkBelgium

Brain organoids encompass a large collection of in vitro stem cell–derived 3D culture systems that aim to recapitulate multiple aspects of in vivo brain development and ***,this review provides a brief introduction to the current state-of-the-art for neuroectoderm brain organoid development,emphasizing their biggest advantages in comparison with classical two-dimensional cell cultures and animal ***,despite their usefulness for developmental studies,a major limitation for most brain organoid models is the absence of contributing cell types from endodermal and mesodermal *** such,current research is highly investing towards the incorporation of a functional vasculature and the microglial immune *** this review,we will specifically focus on the development of immune-competent brain *** summarizing the different approaches applied to incorporate microglia,it is highlighted that immune-competent brain organoids are not only important for studying neuronal network formation,but also offer a clear future as a new tool to study inflammatory responses in vitro in 3D in a brainlike ***,our main focus here is to provide a comprehensive overview of assays to measure microglial phenotype and function within brain organoids,with an outlook on how these findings could better understand neuronal network development or restoration,as well as the influence of physical stress on microglia-containing brain ***,we would like to stress that even though the development of immune-competent brain organoids has largely evolved over the past decade,their full potential as a pre-clinical tool to study novel therapeutic approaches to halt or reduce inflammation-mediated neurodegeneration still needs to be explored and validated.

关键词： 3D cell culture brain organoids immune response immunocompetent model in vitro model microglia neural organoids neuroimmunology neuroinflammation

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Rate-splitting Multiple Access for Hierarchical HAP-LAP Networks under Limited Fronthaul

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IEEE Transactions on Vehicular technology 2025年

作者： Kim, Jeongbin Jeong, Seongah Yoo, Seonghoon Son, Woong Kang, Joonhyuk School of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of School of Advanced Fusion Studies University of Seoul Seoul02504 Korea Republic of C4I Research and Development Laboratory LIG Nex1 Company Ltd. Yongin16911 Korea Republic of

In this correspondence, we propose hierarchical high-altitude platform (HAP)-low-altitude platform (LAP) networks with the aim of maximizing the sum-rate of ground user equipments (UEs). The multiple aerial radio units mounted on HAPs and LAPs are managed by the central unit (CU) via constrained fronthaul links. The limitation of fronthaul capacity can be addressed through quantization, employing the network multiple-input multiple-output (MIMO) architecture. For spectral efficiency, we adopt the rate-splitting multiple access (RSMA), leveraging the advantages of both space-division multiple access (SDMA) and non-orthogonal multiple access (NOMA). To achieve this, we jointly optimize multiple-antenna rate-splitting transmission, fronthaul quantization design and UAV placement using an alternating optimization (AO) approach coupled with successive convex approximation (SCA) and the weighted minimum mean square error (WMMSE) method. Numerical results validate the superior performance of the proposed method compared to benchmark schemes, including partial optimizations or those without the assistance of LAPs. © 2025 IEEE. All rights reserved,

关键词： Mean square error

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Hot Corrosion Behavior of Lapo4-Ysz Thermal Barrier Coating Exposed to Molten Calcium-Magnesium-Alumino-Silicate (Cmas) at 1250°C-1350°C

SSRN

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SSRN 2025年

作者： Deng, Panhao Huang, Wenzhi Zhong, Ruiqi Yu, Yiping Lou, Junhua Deng, Zixin Zhou, Xin Science and Technology on Advanced Ceramic Fibers and Composites Laboratory College of Aerospace Science and Engineering National University of Defense Technology Changsha410073 China Qingdao Brunch of Naval Aeronautics University Qingdao China College of Intelligence Science and Technology National University of Defense Technology Changsha410073 China Institute for Advanced Studies in Precision Materials Yantai University Yantai264005 China National University of Defense Technology China

The calcium-magnesium-alumina-silicate (CMAS) corrosion behavior of LaPO4-YSZ thermal barrier coatings (TBCs) was investigated at 1250°C-1350°C for 4 hours. Corrosion was observed to occur primarily at the grain boundaries, and the corrosion depth increased with increasing temperature. The melt infiltration reaction layer formed after CMAS corrosion was primarily composed of ZrO2, residual CMAS, and La-apatite (Ca2+xLa8-x(SiO4)6-x(PO4)xO2), regardless of the temperature. It was worth noting that a continuous dense reaction layer was formed after the corrosion test at 1250 °C, which further delayed the infiltration of CMAS. However, the molten CMAS could infiltrate into the coating more easily when the temperature exceeds 1300 °C, leading to more severe corrosion. The results show that the CMAS corrosion behaviors of coating at different temperatures were primarily controlled by the infiltration and reaction processes;When temperatures exceed 1300°C, CMAS infiltration becomes the dominant process due to the reduced viscosity of the CMAS melt. © 2025, The Authors. All rights reserved.

关键词： Thermal barrier coatings

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Synergistic Integration of 5G-enabled Cloud Native Infrastructures With advanced Technologies for Urban Safety Enhancement

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IEEE Transactions on Consumer Electronics 2025年

作者： Sharma, Ashutosh Sharma, Amit Raju, Kola Narasimha Keshta, Ismail Guo, Kai Henan University of Science and Technology Business School Henan China University of Petroleum and Energy Studies School of Computer Science Dehradun India INSA CVL PRISME Laboratory University of Orleans France Loire Valley Institute for Advanced Studies LE STUDIUM Orlèans France Chitkara University Chitkara University Institute of Engineering and Technology Punjab India Visakhapatnam India AlMaarefa University College of Applied Sciences Riyadh Saudi Arabia

Urban safety remains an important concern in modern cities, which makes it necessary for advanced approaches to address the progressing challenge such as fire risk assessment and security enhancement. This study explores the important integration of 5G-enabled cloud-native infrastructures with the Internet of Things (IoT), and Artificial Intelligence (AI) algorithm for empowering urban safety. Kubernetes is used as an essential component of the Cloud Native Computing Foundation (CNCF) ecosystem, for managing scalable and resilient infrastructures. This integration of 5G-enabled systems to cloud-native infrastructures proposes an efficient solution that offers high-speed connectivity, low latency, and the scalability required for real-time data processing and communication. In the proposed work, IoT devices are strategically deployed across urban areas to achieve comprehensive coverage and facilitate real-time data collection and monitoring, ideally integrated with Kubernetes-managed clusters to ensure data transmission and processing. Machine Learning (ML) and Deep Learning (DL) based model is trained on the collected data from the field, using Kubernetes for scalable and automated deployment providing real-time data analysis, accurate prediction, and decision-making. The proposed 5G integrated framework provides proactive risk management and enhanced emergency response strategies. The integration of 5G-enabled systems led to a significant reduction in latency, typically to 20 milliseconds, enhancing real-time performance and decision-making processes. This study introduces a framework designed to reduce latency to 20 ms and improve data transmission rates by 25%. Real-world applications, including fire detection and traffic management, demonstrated the systems effectiveness, achieving sensitivity and recall rates of 97% and 96%, respectively. These results underscore the potential of the proposed framework to enhance urban safety while addressing scalability and in

关键词： 5G mobile communication systems

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Polyoxometalated metal-organic framework superstructure for stable water oxidation

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Science (New York, N.Y.) 2025年第6745期388卷 430-436页

作者： Yue, Kaihang Lu, Ruihu Gao, Mingbin Song, Fei Dai, Yao Xia, Chenfeng Mei, Bingbao Dong, Hongliang Qi, Ruijuan Zhang, Daliang Zhang, Jiangwei Wang, Ziyun Huang, Fuqiang Xia, Bao Yu Yan, Ya State Key Laboratory of High Performance Ceramics and Superfine Microstructure Shanghai Institute of Ceramics Chinese Academy of Sciences Shanghai China School of Chemistry and Chemical Engineering State Key Laboratory of Materials Processing and Die & Mould Technology Key Laboratory of Material Chemistry for Energy Conversion and Storage (Ministry of Education) Hubei Key Laboratory of Material Chemistry and Service Failure Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology (HUST) Wuhan China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing China School of Chemical Sciences University of Auckland Auckland New Zealand National Engineering Laboratory for Methanol to Olefins Dalian Institute of Chemical Physics Chinese Academy of Sciences Dalian China Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai Synchrotron Radiation Facility Shanghai China Center for High Pressure Science and Technology Advanced Research Shanghai China Key Laboratory of Polar Materials and Devices (MOE) Department of Electronics East China Normal University Shanghai China Multiscale Porous Materials Center Institute of Advanced Interdisciplinary Studies and School of Chemistry and Chemical Engineering Chongqing University Chongqing China College of Energy Material and Chemistry Inner Mongolia Key Laboratory of Low Carbon Catalysis Inner Mongolia University Hohhot China State Key Lab of Metal Matrix Composites School of Materials Science and Engineering Shanghai Jiao Tong University Shanghai China Center for Next-Generation Energy Materials and School of Chemical Engineering Sungkyunkwan University (SKKU) Seobu-ro Jangan-guGyeonggi-do Suwon South Korea

Stable, nonprecious catalysts are vital for large-scale alkaline water electrolysis. Here, we report a grafted superstructure, MOF@POM, formed by self-assembling a metal-organic framework (MOF) with polyoxometalate (POM). In situ electrochemical transformation converts MOF into active metal (oxy)hydroxides to produce a catalyst with a low overpotential of 178 millivolts at 10 milliamperes per square centimeter in alkaline electrolyte. An anion exchange membrane water electrolyzer incorporating this catalyst achieves 3 amperes per square centimeter at 1.78 volts at 80°C and stable operation at 2 amperes per square centimeter for 5140 hours at room temperature. In situ electrochemical spectroscopy and theoretical studies reveal that the synergistic interactions between metal atoms create a fast electron-transfer channel from catalytic iron and cobalt sites, nickel, and tungsten in the polyoxometalate to the electrode, stabilizing the metal sites and preventing dissolution.

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