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  1. 15621

    Network analysis of chronic disease among middle-aged and older adults in China: a nationwide survey by Chen Chen, Chen Chen, Hongfeng Wu, Likun Yang, Ke Kan, Xinping Zhang, Su Zhang, Rufu Jia, Xian Li

    Published 2025-04-01
    “…Community detection identified groups of closely related diseases using the Louvain algorithm. Multivariable linear regression with forward stepwise selection explored factors influencing chronic disease co-morbidity. …”
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    Article
  2. 15622

    DIGITAL TOOLS FOR MATCHING QUALIFICATIONS TO THE LEVELS OF THE NATIONAL QUALIFICATIONS FRAMEWORK by Volodymyr Kovtunets, Sergiy Londar, Serhii Melnyk, Oles Kovtunets

    Published 2024-04-01
    “…Both Excel and API are accessible for users at the website of the Institute of Educational Analytics in Kyiv. The novelty of the article is that for the first time in national and international practice, it proposes an alternative/supplementary algorithmic method for determining the level of certain full and/or partial professional qualifications by the National Qualifications Framework, thus creating prerequisites for further automation of the activities of professional standards developers. …”
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  3. 15623

    Artificial Intelligence in Biomedical Sciences: A Scoping Review by Rasha Abu-El-Ruz, Ali Hasan, Dima Hijazi, Ovelia Masoud, Atiyeh M. Abdallah, Susu M. Zughaier, Maha Al-Asmakh, Maha Al-Asmakh

    Published 2025-08-01
    “…Scope (5): AI in biomedical sciences education, which was limited to only six studies, indicating a gap in research on the educational application of AI in biomedical sciences. …”
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    Article
  4. 15624
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  6. 15626

    Intelligent generation and optimization of resources in music teaching reform based on artificial intelligence and deep learning by Ding Cheng, Xiaoyu Qu

    Published 2025-08-01
    “…Therefore, a Melody Generation Model in Music Education Based on Actor-Critic Framework (AC-MGME) is proposed. …”
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  7. 15627
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  12. 15632

    Identification of veterinary and medically important blood parasites using contrastive loss-based self-supervised learning by Supasuta Busayakanon, Morakot Kaewthamasorn, Natchapon Pinetsuksai, Teerawat Tongloy, Santhad Chuwongin, Siridech Boonsang, Veerayuth Kittichai

    Published 2024-11-01
    “…As a result, multi-class classification prediction demonstrated that model performance exceeded 91% for the F1 score, except for the early stage of Trypanosoma evansi, which showed an F1 score of 87%. …”
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  13. 15633

    Age and sex specific thresholds for risk stratification of cardiovascular disease and clinical decision making: prospective open cohort study by Matthew Sperrin, Angela M Wood, Lois Kim, Zhe Xu, Emanuele Di Angelantonio, Stephen Kaptoge, Juliet Usher-Smith, Matthew Arnold, Lisa Pennells, Ryan Chung

    Published 2024-12-01
    “…Objective To quantify the potential advantages of using 10 year risk prediction models for cardiovascular disease, in combination with risk thresholds specific to both age and sex, to identify individuals at high risk of cardiovascular disease for allocation of statin treatment.Design Prospective open cohort study.Setting Primary care data from the UK Clinical Practice Research Datalink GOLD, linked with hospital admissions from Hospital Episode Statistics and national mortality records from the Office for National Statistics in England, 1 January 2006 to 31 May 2019.Participants 1 046 736 individuals (aged 40-85 years) with no cardiovascular disease, diabetes, or a history of statin treatment at baseline using data from electronic health records.Main outcome measures 10 year risk of cardiovascular disease, calculated with version 2 of the QRISK cardiovascular disease risk algorithm (QRISK2), with two main strategies to identify individuals at high risk: in strategy A, estimated risk was a fixed cut-off value of ≥10% (ie, as per the UK National Institute for Health and Care Excellence guidelines); in strategy B, estimated risk was ≥10% or ≥90th centile of age and sex specific risk distributions.Results Compared with strategy A, strategy B stratified 20 241 (149.8%) more women aged ≤53 years and 9832 (150.2%) more men aged ≤47 years as having a high risk of cardiovascular disease; for all other ages the strategies were the same. …”
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  14. 15634

    4D-CTA image and geometry dataset for kinematic analysis of abdominal aortic aneurysmsZenodo by Mostafa Jamshidian, Adam Wittek, Saeideh Sekhavat, Farah Alkhatib, Jens Carsten Ritter, Paul M. Parizel, Donatien Le Liepvre, Florian Bernard, Ludovic Minvielle, Antoine Fondanèche, Jane Polce, Christopher Wood, Karol Miller

    Published 2025-08-01
    “…The images were acquired at Fiona Stanley Hospital in Western Australia and provided to the researchers at the Intelligent Systems for Medicine Laboratory at The University of Western Australia (ISML-UWA), where image-based AAA kinematic analysis was performed using a newly created algorithm, as described in [1]. The AAA geometries were extracted using an automated image processing pipeline comprising AI-based segmentation with PRAEVAorta software by NUREA (https://www.nurea-soft.com/), automated post-processing with the ISML-UWA in-house code (https://arxiv.org/abs/2403.07238), and surface model extraction using the freely available BioPARR (Biomechanics-based Prediction of Aneurysm Rupture Risk) (https://bioparr.mech.uwa.edu.au/) and 3D Slicer (https://www.slicer.org/) software packages [2,3]. …”
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  15. 15635
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    Effect of Molarity of Sodium Hydroxide on the Strength Behavior of Fiber-Reinforced Geopolymer Concrete Exposed to Elevated Temperature by Abbasali Saffar, Mohammad Ehsanifar, Seyed Mohammad Mirhoseini, Mohammad Javad Taheri Amiri

    Published 2024-05-01
    “…Beside, post-fire strength of FRGPC was predicted using artificial neural network (ANN) and support vector machines (SVM) with the integration of water cycle algorithm (WCA). …”
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  18. 15638

    Literary Texts Utilization in Distant Teaching of Russian Language by Zoya Şengüder

    Published 2022-10-01
    “…The topicality level of this study is determined by the need to understand new educational conditions, improve the process of teaching Russian as a foreign language as well as to improve the quality of education and motivation of foreign students learning Russian remotely.…”
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