Performance Comparison of Genomic Best Linear Unbiased Prediction and Four Machine Learning Models for Estimating Genomic Breeding Values in Working Dogs by Joseph A. Thorsrud, Katy M. Evans, Kyle C. Quigley, Krishnamoorthy Srikanth, Heather J. Huson

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Psychosocial Differences Between Female and Male Students in Learning Patterns and Mental Health-Related Indicators in STEM vs. Non-STEM Fields by María Natividad Elvira-Zorzo, Miguel Ángel Gandarillas, Mariacarla Martí-González

“…Using a holistic approach, five learning dimensions comprising a diversity-in-learning (DinL) construct were analyzed: Coping with Difficulties, Effort, Autonomy, Understanding/Career Interest, and Social Context. …”
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Multiscale guided attention network for optic disc segmentation of retinal images by A Z M Ehtesham Chowdhury, Andrew Mehnert, Graham Mann, William H. Morgan, Ferdous Sohel

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Defining the cardiac surgical learning curve: a longitudinal cumulative analysis of a surgeon’s experience and performance monitoring in the first decade of practice by Shantel Chang, Ian Smith, Christopher Cole

“…Abstract Background Individual surgeons’ learning curves are a crucial factor impacting patient outcomes. …”
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Helping tools for the regular expression author for test questions in LMS Moodle by O. A. Sychev, G. V. Terehov

“…There are many programs developed to help composing and learning of the regular expressions, but they are using different forms of regular expression visualization. …”
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Real time intelligent garbage monitoring and efficient collection using Yolov8 and Yolov5 deep learning models for environmental sustainability by Mohammed M. Abo-Zahhad, Mohammed Abo-Zahhad

“…Nationally, governments have initiated several programs to improve cleanliness by developing systems that alert businesses when it’s time to empty the bins. …”
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Dual pathways of forest landscape dynamics in China: Integrating ecological land evolution Index (ELEI) and machine learning to decipher fragmentation and restoration by Yifan Xie, Sui Liu, Bowen Pang, Nannan Wang, Jiwei Xu, He Huang, Yao Cui, Jiaming Yang, Yaolin Liu, Yanfang Liu

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Determining the Teachers' Attitude towards "Mistake" and "Making Mistakes" in the Learning Process and Improving the Culture facing it (case study: elementary school students) by Mazhar Babaee, Hedyeh Azagh

“…Some teachers have positive and constructive ideas about making mistakes and they see it as an opportunity to learn and as a stepping stone to progress and achieve success. …”
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The Media Thematic Dictionary and Its Application in Improving Speaking Skills l Wasa’il al-Qamus al-Maudhu‘i wa Tathbiqu fi Tarqiyati Maharah al-Kalam by Muhammad Syaifullah, Siti Mubarokah, Nailul Izzah, Wakhidati Nurrohmah Putri, Kholida Nur, Syuhadak Syuhadak

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Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study by Ziyao Meng, MEng, Zhouyu Guan, MD, Shujie Yu, MBBS, Yilan Wu, BSc, Yaoning Zhao, BSc, Jie Shen, PhD, Cynthia Ciwei Lim, MMed, Tingli Chen, MD, Dawei Yang, PhD, An Ran Ran, PhD, Feng He, MSc, Haslina Hamzah, BSc, Sarkaaj Singh, MSc, Anis Syazwani Abd Raof, MSc, Jian Wen Samuel Lee-Boey, MBBS, ProfMBBS Soo-Kun Lim, MBBS, ProfMD Xufang Sun, MBBS, Shuwang Ge, MD, ProfMD Gang Xu, MD, Prof Hua Su, MD, Yang Cheng, MD, Feng Lu, PhD, ProfPhD Xiaofei Liao, PhD, ProfPhD Hai Jin, PhD, Chenxin Deng, MMed, Lei Ruan, MD, ProfMD Cuntai Zhang, MD, Chan Wu, MD, ProfMD Rongping Dai, MD, Yixiao Jin, MSc, Wenxiao Wang, PhD, Tingyao Li, BSc, Ruhan Liu, PhD, Jiajia Li, PhD, Jia Shu, BEng, Yuwei Lu, MBBS, Xiangning Wang, MD, Qiang Wu, MD, Yiming Qin, BEng, Jin Tang, MD, Xiaohua Sheng, MD, Qiong Jiao, MD, ProfPhD Xiaokang Yang, MD, ProfPhD Minyi Guo, MD, Gareth J McKay, PhD, ProfPhD Ruth E Hogg, PhD, Gerald Liew, MBBS, Evelyn Yi Lyn Chee, PhD, ProfPhD Wynne Hsu, PhD, ProfPhD Mong Li Lee, PhD, Simon Szeto, MBChB, ProfMD Andrea O Y Luk, MBChB, ProfMD Juliana C N Chan, MBChB, Carol Y Cheung, PhD, Gavin Siew Wei Tan, FRCSEd, Yih-Chung Tham, PhD, ProfMD PhD Ching-Yu Cheng, PhD, Charumathi Sabanayagam, MD PhD, ProfPhD Lee-Ling Lim, MD PhD, ProfMD PhD Weiping Jia, MD PhD, ProfMD PhD Huating Li, MD PhD, Bin Sheng, Prof, Tien Yin Wong, Prof

“…Funding: National Key R & D Program of China, National Natural Science Foundation of China, Beijing Natural Science Foundation, Shanghai Municipal Key Clinical Specialty, Shanghai Research Centre for Endocrine and Metabolic Diseases, Innovative research team of high-level local universities in Shanghai, Noncommunicable Chronic Diseases-National Science and Technology Major Project, Clinical Special Program of Shanghai Municipal Health Commission, and the three-year action plan to strengthen the construction of public health system in Shanghai.…”
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Attack resilient IoT security framework using multi head attention based representation learning with improved white shark optimization algorithm by Jawhara Aljabri

“…Artificial intelligence (AI), mostly machine learning (ML) and deep learning (DL), has been employed to construct a data-driven intelligent IDS. …”
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Machine learning assists regulated cell death crucial biomarker selection in adenocarcinoma of the lung: biological data testing and cell assay determination by Han Ning, Ying Jiang, Mengli Zheng, Gao Yang, Lianjun Ma, Yachao Zhao

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Preliminary Development of Global–Local Balanced Vision Transformer Deep Learning with DNA Barcoding for Automated Identification and Validation of Forensic Sarcosaphagous Flies by Yixin Ma, Lin Niu, Bo Wang, Dianxin Li, Yanzhu Gao, Shan Ha, Boqing Fan, Yixin Xiong, Bin Cong, Jianhua Chen, Jianqiang Deng

“…In our previous study, we developed a GLB-ViT (Global–Local Balanced Vision Transformer)-based deep learning model for fly species identification, which demonstrated improved identification capabilities. …”
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Prediction of PD-L1 tumor positive score in lung squamous cell carcinoma with H&E staining images and deep learning by Qiushi Wang, Xixiang Deng, Pan Huang, Qiang Ma, Lianhua Zhao, Yangyang Feng, Yiying Wang, Yuan Zhao, Yan Chen, Peng Zhong, Peng He, Mingrui Ma, Peng Feng, Hualiang Xiao

“…Therefore, the application of deep learning models to segment and quantitatively predict PD-L1 expression in digital sections of Hematoxylin and eosin (H&E) stained lung squamous cell carcinoma is of great significance.MethodsWe constructed a dataset comprising H&E-stained digital sections of lung squamous cell carcinoma and used a Transformer Unet (TransUnet) deep learning network with an encoder-decoder design to segment PD-L1 negative and positive regions and quantitatively predict the tumor cell positive score (TPS).ResultsThe results showed that the dice similarity coefficient (DSC) and intersection overunion (IoU) of deep learning for PD-L1 expression segmentation of H&E-stained digital slides of lung squamous cell carcinoma were 80 and 72%, respectively, which were better than the other seven cutting-edge segmentation models. …”
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Association between accelerometer-measured physical activity volume and sleep duration in older adults: a cross-sectional interpretable machine learning analysis by XiaoTao Cai, Yi Xian, YuXin Zhou, TongYi Liu, Xinyue Zhang, Qing Chen

“…Analysis of the derivation cohort included weighted univariate analysis, weighted multivariate logistic regression, and interpretable machine learning analysis. The machine learning interpretability process involved dividing a 20% internal validation test set, using the grid search method and five-fold cross-validation to construct RF, GBDT, XGBoost, and LightGBM models, as well as a two-layer stacked ensemble model for model comparison, with external validation of the optimal model’s performance. …”
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Multiobjective integrated scheduling of disassembly and reprocessing operations considering product structures and stochastic processing time via reinforcement learning-based evolu... by Yaping Fu, Fuquan Wang, Zhengyuan Li, Guangdong Tian, Duc Truong Pham, Hao Sun

“…First, a stochastic programming model is developed to minimize maximum completion time and total tardiness. …”
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Editorial: Computational bioacoustics and automated recognition of bird vocalizations: new tools, applications and methods for bird monitoring by Yizhar Lavner, Cristian Pérez-Granados, Cristian Pérez-Granados

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Do pensamento computacional desplugado ao plugado no processo de aprendizagem da matemática / Computational thinking unplugged to plugged in the mathematic learning process by Ingrid Santella, Adriana Aparecida de Lima Terçariol, Elisangela Aparecida Bulla Ikeshoji

“…. /// This article is an excerpt from the dissertation entitled «Computational thinking in the mathematics learning process in the final years of elementary school», linked to the Master Program in Management and Educational Practices (PROGEPE) at Nove de Julho University, in particular, to the Research and intervention line learning methodology and teaching pratices (LIMAPE). …”
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Overview of Computational Toxicology Methods Applied in Drug and Green Chemical Discovery by Jose I. Bueso-Bordils, Gerardo M. Antón-Fos, Rafael Martín-Algarra, Pedro A. Alemán-López

“…Currently, there are numerous commercially available and free web-based programs for toxicity prediction, which can be used to construct various predictive models. …”
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Unveiling the role of IL7R in metabolism-associated fatty liver disease leading to hepatocellular carcinoma through transcriptomic and machine learning approaches by Priyadharshini Annadurai, Arnold Emerson Isaac

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