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Performance Evaluation of Pond Ash-Enhanced Flowable Fill for Plastic Concrete Cutoff Walls in Earthen Dams Using Advanced Machine Learning Models

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Arabian Journal for science and Engineering 2025年 1-25页

作者： Dev, K. Lini Kumar, Divesh Ranjan Wipulanusat, Warit Department of Civil Engineering National Institute of Technology Patna India Research Unit in Data Science and Digital Transformation Department of Civil Engineering Faculty of Engineering Thammasat School of Engineering Thammasat University Pathumthani Thailand

Structural damage and foundation leakage are major concerns for earthen dams. To minimize seepage, cutoff walls are typically installed beneath the dam core to act as impermeable barriers. While concrete cutoff walls are widely used, their limited ductility and strength incompatibility with foundation soil present design challenges. Plastic concrete, a modified form of conventional concrete incorporating bentonite and pond ash, offers improved ductility and reduced brittleness, making it a suitable alternative. This study investigates the use of pond ash-based flowable fill as a replacement for normal concrete in plastic concrete cutoff walls. The unconfined compressive strength (UCS) of plastic concrete mixes was analyzed using four advanced regression machine learning algorithms: multivariate adaptive regression splines, extreme neural network (ENN), extreme gradient boosting (XGBoost), and gradient boosting machine (GBM). Several performance indices were used to evaluate model accuracy. The MARS model achieved the highest accuracy, with R² = 0.990 for training and R² = 0.963 for testing, followed by XGBoost, GBM, and ENN. SHAP analysis revealed that curing period has the most significant positive effect on UCS, followed by water and cement contents, while bentonite showed the least impact. Key properties were evaluated to determine an optimal mix design. This research enhances the understanding of CLSM-based plastic concrete and supports its application in cutoff walls by developing accurate UCS prediction models, contributing to the improved suitability and sustainability of dam foundation systems. © King Fahd University of Petroleum & Minerals 2025.

关键词： Compressive strength Cutoff walls Flowability Multivariate adaptive regression splines (MARS) Permeability Shear strength

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Cloud-magnetic resonance imaging system:In the era of 6G and artificial intelligence

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Magnetic Resonance Letters 2025年第1期5卷 52-63页

作者： Yirong Zhou Yanhuang Wu Yuhan Su Jing Li Jianyu Cai Yongfu You Jianjun Zhou Di Guo Xiaobo Qu Department of Electronic Science Intelligent Medical Imaging R&D CenterFujian Provincial Key Laboratory of Plasma and Magnetic ResonanceNational Institute for Data Science in Health and MedicineXiamen UniversityXiamen361104China Department of Electronic Science Key Laboratory of Digital Fujian on IoT CommunicationXiamen UniversityXiamen361104China Shanghai Electric Group Co. LtdShanghai200002China China Telecom Group Quanzhou362018China China Mobile Group Xiamen361009China Department of Radiology Zhongshan Hospital(Xiamen)Fudan UniversityXiamen Municipal Clinical Research Center for Medical ImagingFujian Province Key Clinical Specialty Construction Project(Medical Imaging Department)Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial IntelligenceXiamen361006China School of Computer and Information Engineering Xiamen University of TechnologyXiamen361024China

Magnetic resonance imaging(MRI)plays an important role in medical diagnosis,generating petabytes of image data annually in large *** voluminous data stream requires a significant amount of network bandwidth and extensive storage ***,local data processing demands substantial manpower and hardware *** isolation across different healthcare institutions hinders crossinstitutional collaboration in clinics and *** this work,we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud computing,6G bandwidth,edge computing,federated learning,and blockchain *** system is called Cloud-MRI,aiming at solving the problems of MRI data storage security,transmission speed,artificial intelligence(AI)algorithm maintenance,hardware upgrading,and collaborative *** workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw data(ISMRMRD)***,the data are uploaded to the cloud or edge nodes for fast image reconstruction,neural network training,and automatic ***,the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other *** Cloud-MRI system will save the raw imaging data,reduce the risk of data loss,facilitate inter-institutional medical collaboration,and finally improve diagnostic accuracy and work efficiency.

关键词： Magnetic resonance imaging Cloud computing 6G bandwidth Artificial intelligence Edge computing Federated learning Blockchain

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Educational data mining: a 10-year review

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Discover Computing 2025年第1期28卷 1-25页

作者： Kalita, Emi Oyelere, Solomon Sunday Gaftandzhieva, Silvia Rajesh, Kandala N. V. P. S. Jagatheesaperumal, Senthil Kumar Mohamed, Asmaa Elbarawy, Yomna M. Desuky, Abeer S. Hussain, Sadiq Cifci, Mehmet Akif Theodorou, Paraskevi Hilčenko, Slavoljub Hazarika, Jiten Ali, Tazid Centre for Computer Science and Applications Dibrugarh University Dibrugarh 786004 India Department of Computer Science Electrical and Space Engineering Luleå University of Technology Skellefteå 931 87 Sweden Department of Computer Science Faculty of Environment Science and Economy University of Exeter Exeter United Kingdom Research Group On Data Artificial Intelligence and Innovations for Digital Transformation Johannesburg Business School (JBS) University of Johannesburg Johannesburg South Africa Faculty of Mathematics and Informatics University of Plovdiv “Paisii Hilendarski” Plovdiv Bulgaria School of Electronics Engineering VIT-AP University Vijayawada India Department of Electronics & Communication Engineering Mepco Schlenk Engineering College Sivakasi 626005 India Department of Mathematics Faculty of Science Al-Azhar University (Girls Branch) Cairo 11754 Egypt Department of Computer Engineering Bandirma Onyedi Eylul University Balikesir 10200 Turkey Engineering and Informatics Department Klaipėdos Valstybinė Kolegija/Higher Education Institution Klaipeda 92294 Lithuania Department of Digital Systems University of Piraeus Piraeus Greece College for Vocational Education of Preschool Teachers and Coaches Subotica Serbia Department of Statistics Dibrugarh University Dibrugarh 786004 India

This systematic review comprehensively examines the application and impacts of Educational data Mining (EDM) over the past decade. It explores the use of various data mining tools and techniques, statistics, and machine learning algorithms in education. The review discusses how EDM helps understand and improve the learning experience, educational strategies, and institutional efficiency. It highlights the iterative process of EDM, its applications, and the benefits it offers to different stakeholders, including students, teachers, and educational institutions. The paper also discusses the challenges related to data ethics, privacy, and security in EDM. Key sections include a methodology for conducting the systematic review, exploring different data mining techniques and learning styles, and using Artificial Intelligence in EDM. The review concludes with a discussion of findings, future research directions, and a summary of the study’s contributions and limitations. © The Author(s) 2025.

关键词： Artificial intelligence in education Education data mining Explainability in education Multimodal learning analytics

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Teaching accounting principles using augmented reality and artificial intelligence-generated IsiZulu language translations

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International Journal of Educational research Open 2025年 8卷

作者： Ade-Ibijola, Abejide Sukhari, Aneetha Oyelere, Solomon Sunday Research Group on Data Artificial Intelligence and Innovations for Digital Transformation Johannesburg Business School University of Johannesburg Johannesburg South Africa College of Accounting Sciences University of South Africa Pretoria South Africa Department of Computer Science University of Exeter EX4 4PY Exeter United Kingdom & Johannesburg Business School University of Johannesburg Johannesburg South Africa

One of the sustainable development goals identified by the united Nations (UN) aims to ensure inclusive, equitable, quality education and promote lifelong learning opportunities for all. The report issued by the UN in 2022 highlighted that the global education crisis for vulnerable groups has worsened due to the COVID-19 pandemic, war, lack of infrastructure, and little or no psychosocial support. The prevalence of English as the medium of instruction in higher education, is a challenge for students who speak English as a second language. In South Africa, and Africa as a whole, the majority of the population speak in their mother tongue as a first language, which is problematic for students enrolled at English medium universities. This study focuses on accounting education from a South African perspective. Earlier research found that the number of African chartered accountants in South Africa are inadequately represented and cited one of the reasons as being a language barrier. We propose that the translation of accounting concepts into indigenous African languages can improve the understanding of accounting principles which will allow for inclusive, equitable, and quality education for all. In doing so, we develop a prototype application, which encompasses augmented reality and artificial intelligence-generated language translation functionalities to teach accounting principles in an indigenous African language, namely, IsiZulu. The application, named Zulu Augmented Reality Translation (ZART), aids the learning process of accounting principles, by providing an engaging and interactive learning experience in IsiZulu. The paper commences with a literature review on the teaching pedagogy and challenges in accounting education. Thereafter, the design science research applied in the development of ZART is discussed. Using questionnaires, an experiment is conducted to evaluate the usability of ZART, students’ perceptions and pedagogical experiences of using ZART. The res

关键词： Accounting education Artificial intelligence-generated language translations Augmented reality Design science research IsiZulu Sustainable development goal 4

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Intelligent System for the Dissemination and Distribution of Content in Social Networks Using Process Automation by Means of Software-Based Robots (RPA)

Intelligent System for the Dissemination and Distribution of...

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International Conference on Management, Tourism and Technologies, ICMTT 2024

作者： Quisi-Peralta, Diego F. Salgado-Guerrero, Juan P. Murillo-Valarezo, Jordan F. Pulla-Sanchez, Daniel J. Paguay-Palaguachi, Luis D. Angamarca-Deleg, Verónica E. Research Group on Digital Transformation and Data Science Universidad Politécnica Salesiana Cuenca Azuay010102 Ecuador

ISBN: (纸本)9783031748271

Social media content dissemination systems have become especially important nowadays, due to the growing popularity of social networks as a means of communication and promotion. In this context a social media content dissemination system that uses process automation techniques. The system allows users to create content using templates and distribute it on various social network platforms in an automated and personalized way. Algorithms are used to analyze user interaction patterns with the content to optimize the dissemination strategy. In addition, an intuitive and easy-to-use user interface is implemented so that users can interact with the system effectively. The results see significant improvement in the interaction rate and effectiveness of content dissemination in social networks. This system represents an innovative solution for the dissemination of content on social networks, optimizing the publication time by 50% and the generation of content by 25%, since it is all done from an integrated system that includes templates. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Chatbots

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Medical history predicts phenome-wide disease onset and enables the rapid response to emerging health threats (vol 16, 585, 2025)

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NATURE COMMUNICATIONS 2025年第1期16卷 1-15页

作者： Steinfeldt, Jakob Wild, Benjamin Buergel, Thore Pietzner, Maik Upmeier zu Belzen, Julius Vauvelle, Andre Hegselmann, Stefan Denaxas, Spiros Hemingway, Harry Langenberg, Claudia Landmesser, Ulf Deanfield, John Eils, Roland Department of Cardiology Angiology and Intensive Care Medicine Deutsches Herzzentrum der Charité (DHZC) Berlin Germany Charité – Universitätsmedizin Berlin corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin Klinik/Centrum Berlin Germany Computational Medicine Berlin Institute of Health (BIH) Charite - University Medicine Berlin Berlin Germany Friede Springer Cardiovascular Prevention Center@Charite Charite - University Medicine Berlin Berlin Germany Institute of Cardiovascular Sciences University College London London UK Berlin Institute of Health (BIH) Charite - University Medicine Berlin Berlin Germany DZHK (German Centre for Cardiovascular Research) Partner Site Berlin Berlin Berlin Germany MRC Epidemiology Unit Institute of Metabolic Science University of Cambridge Cambridge UK Precision Health University Research Institute Queen Mary University of London and Barts NHS Trust London UK Center for Digital Health Berlin Institute of Health (BIH) Charite - University Medicine Berlin Berlin Germany Health Data Science Unit Heidelberg University Hospital and BioQuant Heidelberg Germany Institute of Health Informatics University College London London UK British Heart Foundation Data Science Centre London UK Health Data Research UK London UK National Institute for Health Research Biomedical Research Centre at University College London Hospitals London UK Institute for Medical Engineering and Science Massachusetts Institute of Technology Massachusetts USA Pattern Recognition and Image Analysis Lab University of Münster Münster Germany

The COVID-19 pandemic exposed a global deficiency of systematic, data-driven guidance to identify high-risk individuals. Here, we illustrate the utility of routinely recorded medical history to predict the risk for 1741 diseases across clinical specialties and support the rapid response to emerging health threats such as COVID-19. We developed a neural network to learn from health records of 502,489 UK Biobank participants. Importantly, we observed discriminative improvements over basic demographic predictors for 1546 (88.8%) endpoints. After transferring the unmodified risk models to the All of US cohort, we replicated these improvements for 1115 (78.9%) of 1414 investigated endpoints, demonstrating generalizability across healthcare systems and historically underrepresented groups. Ultimately, we showed how this approach could have been used to identify individuals vulnerable to severe COVID-19. Our study demonstrates the potential of medical history to support guidance for emerging pandemics by systematically estimating risk for thousands of diseases at once at minimal cost.

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Machine Learning Models for Early Prediction of COVID-19 Infections Based on Clinical Signs

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SN Computer science 2024年第1期5卷 1-10页

作者： Ben Ammar, Boulbaba Salem, Ali Ben Said, Mouna Ben Aouicha, Mohamed Data Engineering and Semantics Research Unit Faculty of Science University of Sfax Sfax Tunisia Digital Research Center of Sfax Ministry of Higher Education and Scientific Research Sfax Tunisia Signals systeMs aRtificial intelligence and neTworkS Laboratory (SM@RTS) Digital Research Center of Sfax Sfax Tunisia

Nowadays, the appearance of common symptoms, such as cough, fever, and loss of smell and taste, is the starting point of a battle against the coronavirus. The first standard method of COVID-19 infection assertion has become the RT-PCR test, which is however an uncomfortable solution for both patients and medical staff due to its high cost, timeliness, and false-negative result issue. This has raised the need for reliable automatic detection systems that aid in the early prediction of the COVID-19 infections with a lower cost. In this work, we aim at profiting from the Machine Learning (ML) advances to provide a reliable and low-cost COVID-19 prediction system. This system is based on the disease starting point, which is the patients’ clinical symptoms, that are still under-explored. We developed seven predictive models using traditional ML classification algorithms using a public dataset of obvious high-risk factors from patients’ clinical signs. The dataset has first undergone a pre-processing phase consisting of feature engineering and dataset resampling to deal with imbalanced dataset issue. Our best classification model is able to detect true positives and true negatives and weed out false positive and false negatives with an accuracy of 93%. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Clinical signs COVID-19 Early diagnosis Machine learning

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Author Correction: Five ways to enhance the diversity and quality of health data

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Nature medicine 2025年 2025 Feb 28页

作者： Paola Quattroni Ash Routen Joe Alderman Claire Argent Samuel Cusworth Hajira Dambha-Miller Rose Drummond Alastair Denniston Poushali Ganguli Nicola Hamilton Uwaye Ideh Sara Khalid Aden Kwok Elinor Laws Xiaoxuan Liu Vahe Nafilyan Jo Palmer Marta Pineda-Moncusi Alexia Sampri Holly Tibble Fatemeh Torabi Jonathan Valabhji Diana Withrow Claire Welsh Angela M Wood Ashley Akbari Kamlesh Khunti Health Data Research UK London UK. Diabetes Research Centre Leicester Diabetes Research Centre University of Leicester Leicester UK. NIHR Birmingham Biomedical Research Centre Birmingham UK. STANDING Together Initiative University Hospitals Birmingham NHS Foundation Trust Birmingham UK. Aldermoor Health Centre University of Southampton Southampton UK. Office for National Statistics London UK. Kings College London London UK. Understanding Patient Data London UK. Centre for Statistics in Medicine Botnar Research Centre Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences (NDORMS) University of Oxford Oxford UK. University of Oxford Applied Health Research Unit Nuffield Department of Population Health Oxford UK. British Heart Foundation Cardiovascular Epidemiology Unit Department of Public Health and Primary Care National Institute for Health Research Blood Transplant Research Unit in Donor Health and Genomics and British Heart Foundation Centre of Research Excellence University of Cambridge Cambridge UK. Usher Institute College of Medicine and Veterinary Medicine University of Edinburgh Edinburgh UK. Population Data Science Swansea University Medical School Faculty of Medicine Health and Life Science Swansea University Swansea UK. Department of Metabolism Digestion and Reproduction Faculty of Medicine Chelsea and Westminster Hospital Campus Imperial College London London UK. Nuffield Department of Primary Care Health Sciences Radcliffe Observatory Quarter University of Oxford Oxford UK. National Disease Registration Service Data and Analytics Transformation Directorate NHS England London UK. Diabetes Research Centre Leicester Diabetes Research Centre University of Leicester Leicester UK. kk22@le.ac.uk.

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NEC's Solutions for Venues in the New Normal Era

NEC Technical Journal

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NEC Technical Journal 2021年第1期15卷 109-113页

作者： Kazufumi, Hoshi Masayuki, Ariyoshi Seigo, Miyawaki Tsubasa, Nabetani Gaku, Nakano Trading and Service Industries Solutions Division Japan Data Science Research Laboratories Japan PS Network Business Promotion Division Japan Digital Business Platform Unit Japan Biometrics Research Laboratories Japan

The impact of the novel coronavirus (COVID-19) pandemic is unprecedented in recent history. That impact has been especially severe for venues that attract mass gatherings, such as at sports events and concerts. Although remote viewing is becoming increasingly popular, there is still great demand for the shared feeling and crowds that can be experienced through physical viewings. In this paper, we will discuss various solutions now under development at NEC, which are intended to make it possible for people to visit these venues in person and enjoy their experience safely and securely in the new normal era. Those solutions include touchless, walk-through security check, entry confirmation and on-site monitoring of visitors, enhanced on-site entertainment using smartphone apps, visitor density visualization to avoid overcrowding, and social distancing measurement technology to confirm that visitors are maintaining a safe distance between one another. © 2021 NEC Mediaproducts. All rights reserved.

关键词： Face recognition

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Monitoring metrics over time: Why clinical trialists need to systematically collect site performance metrics

Research methods in medicine & health sciences

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research methods in medicine & health sciences 2023年第4期4卷 124-135页

作者： Victoria Yorke-Edwards Carlos Diaz-Montana Macey L Murray Matthew R Sydes Sharon B Love MRC Clinical Trials Unit at UCL Institute of Clinical Trials and Methodology University College London London UK. Health Data Research UK London UK. NHS DigiTrials Data Services Directorate NHS Digital Leeds UK. British Heart Foundation Data Science Centre Health Data Research UK London UK.

Background:Over the last decade, there has been an increasing interest in risk-based monitoring (RBM) in clinical trials, resulting in a number of guidelines from regulators and its inclusion in ICH GCP. However, there is a lack of detail on how to approach RBM from a practical perspective, and insufficient understanding of best practice. Purpose:We present a method for clinical trials units to track their metrics within clinical trials using descriptive statistics and visualisations. research Design:We suggest descriptive statistics and visualisations within a SWAT methodology. Study Sample:We illustrate this method using the metrics from TEMPER, a monitoring study carried out in three trials at the MRC Clinical Trials unit at UCL. data Collection:The data collection for TEMPER is described in . Results:We show the results and discuss a protocol for a Study-Within-A-Trial (SWAT 167) for those wishing to use the method. Conclusions:The potential benefits metric tracking brings to clinical trials include enhanced assessment of sites for potential corrective action, improved evaluation and contextualisation of the influence of metrics and their thresholds, and the establishment of best practice in RBM. The standardisation of the collection of such monitoring data would benefit both individual trials and the clinical trials community.

关键词： Risk-based monitoring Study-Within-A-Trial (SWAT) central monitoring centralised monitoring clinical trials

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