Virtual game-based learning environments to promote self-regulated learning skills in foreign language learners [version 1; peer review: 1 approved, 2 approved with reservations] by Maira Alejandra Noriega Cortes, Laura Carreño-Bolivar

“…Background This mixed-method action research study explores the impact of a gamified virtual learning tool on elementary students’ self-regulated learning (SRL) skills in English language acquisition. …”
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Approaches to teaching evidence-based medicine in residency: a systematic review by Kathleen Mathieson, Megan Weemer, Laura Lipke

“…Background Studies of evidence-based medicine (EBM) curricula in graduate medical education are common, but little consensus exists on the best methods to teach EBM.Objective The purpose of the current study was to evaluate EBM teaching approaches for graduate medical trainees and to update a 2014 systematic review.Methods We conducted a systematic literature search of major health and education databases for articles published from January 2014 through October 2022. …”
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Optimization research on UAV semantic communication system based on SVD-MADRL by Yibo Yang, Yahe Tan, Liu Liu

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A Lightweight Two-Step Detection Method for Real-Time Small UAV Detection by Sungkyu Jung, Jaeyeon Jang, Chang Ouk Kim

“…In response to this demand, various studies have explored deep learning-based UAV detection approaches that can attain increased detection accuracy and efficiency. …”
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Screening Model for Bladder Cancer Early Detection With Serum miRNAs Based on Machine Learning: A Mixed‐Cohort Study Based on 16,189 Participants by Cong Lai, Zhensheng Hu, Jintao Hu, Zhuohang Li, Lin Li, Mimi Liu, Zhikai Wu, Yi Zhou, Cheng Liu, Kewei Xu

“…ABSTRACT Background Early detection of bladder cancer (BCa) can have a positive impact on patients' prognosis. However, there is currently no widely accepted method for early screening of BCa. …”
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Exploring emotions and perspectives of families with autism spectrum disorder individuals on sports participation: a case study on Weibo using machine learning method by Shan He, Fengshi Jing, Hao Ren, Yanning Xiao, Xia Sebastiane Chen, Ruihong Tang, Yue Qiu, Junwei Zeng, Tianyu Gao, Junhao Huang

“…Objective This study investigates family sentiments and attitudes toward sports participation by ASD individuals, leveraging longitudinal data from Sina Weibo and machine learning–based sentiment analysis. Methods We collected Weibo posts containing the keyword “autism” from January 2020 to April 2023, filtered for sports‑related content, and divided the dataset into ten four‑month intervals. …”
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Similarity based city data transfer framework in urban digitization by Haoxiang Wang, Xiaoping Che, Enyao Chang, Chenxin Qu, Ganghua Zhang, Zihan Zhou, Zhenlin Wei, Gengyu Lyu, Pengfei Li

“…To solve this problem, we propose a similarity-based cross-city transfer learning method named TransCSM, which embeds the urban similarity into an adaptation transfer learning framework to achieve desired data transfer. …”
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Restricted Label-Based Self-Supervised Learning Using SAR and Multispectral Imagery for Local Climate Zone Classification by Amjad Nawaz, Wei Yang, Hongcheng Zeng, Yamin Wang, Jie Chen

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Refining the Feasibility of Machine‐Learning‐Based Diagnostic Model Utilizing Gut Microbiota Analysis for Colorectal Cancer Screening by Shintaro Okumura, Yusuke Konishi, Taku Kitano, Tomonori Matsumoto, Kazutaka Obama, Satoshi Nagayama, Eiji Hara

“…ABSTRACT Background Recently, we developed a colorectal cancer (CRC) diagnostic model based on a machine learning algorithm with gut microbiota analysis. …”
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Machine learning–based quantification of overall and internal ultrasound characteristics for diagnosing malignant partially cystic thyroid nodules by Yutong Zhang, Jue Jiang, Aqian Chen, Dong Zhang, Dong Zhang, Lirong Wang, Xin Yuan, Xin He, Shanshan Yu, Juan Wang, Qi Zhou

“…The model demonstrated an accuracy of 0.91 (0.85-0.95), a sensitivity of 0.88 (0.73-0.97), a specificity of 0.92 (0.85-0.96), a negative predictive value of 0.96 (0.91-0.99), and a positive predictive value of 0.77 (0.61-0.89). This comprehensive model significantly outperformed the model utilizing only overall nodule characteristics (AUC = 0.85, P = 2.35e-6), and demonstrated comparable effectiveness to the model based solely on internal characteristics (AUC = 0.93, P = 1.01e-1).DiscussionThe results support the clinical utility of an ML-driven approach that integrates comprehensive ultrasound metrics for the reliable identification of malignant PCTNs.…”
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Problem Solving Approach Based on Blended Learning on Trigonometric Comparison of Right Triangles on Mathematical Concept Understanding Ability by Yosafat Ardian Kristiarta, Wahyu Setyaningrum, Marsigit

“…Statistical tests and classroom observations show positive results, indicating that this approach is the right choice to help students learn mathematics. …”
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MRI-based machine learning radiomics for prediction of HER2 expression status in breast invasive ductal carcinoma by Hong-Jian Luo, Jia-Liang Ren, Li mei Guo, Jin liang Niu, Xiao-Li Song

“…Objective: This study aimed to explore the effectiveness of a multisequence magnetic resonance imaging (MRI)-based machine learning radiomics model in classifying the expression status of HER2, including HER2-positive, HER2-low, and HER2 completely negative (HER2-zero), among patients with IDC. …”
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Deep learning based analysis of G3BP1 protein expression to predict the prognosis of nasopharyngeal carcinoma. by Linshan Zhou, Mu Yang, Jiadi Luo, Hongjing Zang, Songqing Fan, Yuting Zhan

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App-based symptom tracking to optimize SARS-CoV-2 testing strategy using machine learning. by Leila F Dantas, Igor T Peres, Leonardo S L Bastos, Janaina F Marchesi, Guilherme F G de Souza, João Gabriel M Gelli, Fernanda A Baião, Paula Maçaira, Silvio Hamacher, Fernando A Bozza

“…Hence, we aim to use the combination of symptoms to build a predictive model as a screening tool to identify people and areas with a higher risk of SARS-CoV-2 infection to be prioritized for testing.<h4>Materials and methods</h4>We performed a retrospective analysis of individuals registered in "Dados do Bem," a Brazilian app-based symptom tracker. …”
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Community-Based Tobacco Smoking Cessation Programmes Among Adolescents in Sarawak: Lesson Learned from Process Evaluation by Siddiq Muhammad, Md Mizanur Rahman, Sabrina Binti Lukas, Kamarudin Bin Kana, Merikan Bin Aren, Rudy Ngau Ajeng, Mohd Faiz Gahamat

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Advancing deep learning-based segmentation for multiple lung cancer lesions in real-world multicenter CT scans by Xavier Rafael-Palou, Ana Jimenez-Pastor, Luis Martí-Bonmatí, Carlos F. Muñoz-Nuñez, Mario Laudazi, Ángel Alberich-Bayarri

“…Relevance statement AI-driven segmentation comprehensively captures lesion burden, enhancing lung cancer assessment and disease monitoring Key Points Automatic multi-instance lung cancer lesion segmentation is underexplored yet crucial for disease assessment. Developed a deep learning-based segmentation pipeline trained on multi-center real-world data, which reached 85% sensitivity at external validation. …”
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Effects of the Intervention Programme for a Mexican Adolescent with Absence Epilepsy and Learning Difficulties, Based on the Syndromic Analysis Methodology by Hansel Soto Hernández, Yulia Solovieva, Ernesto V. Ramírez Arroyo, Izabel Hazin

“…The case study refers to a Mexican 13-year-old male student, left-handed. Methods. The type of study was a single case study based on the theoretical-methodological assumptions of historical-cultural neuropsychology. …”
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An Intelligent Smart Dynamic Feature Analysis Based Approach by Utilizing Deep Learning to Improve the Breast Cancer Detection by Seong-O Shim, Lal Hussain, Eesa Alsolami, Monagi H. Alkinani

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A Machine Learning Model Based on MRI Radiomics to Predict Response to Chemoradiation Among Patients with Rectal Cancer by Filippo Crimì, Carlo D’Alessandro, Chiara Zanon, Francesco Celotto, Christian Salvatore, Matteo Interlenghi, Isabella Castiglioni, Emilio Quaia, Salvatore Pucciarelli, Gaya Spolverato

“…Background: With rectum-sparing protocols becoming more common for rectal cancer treatment, this study aimed to predict the pathological complete response (pCR) to preoperative chemoradiotherapy (pCRT) in rectal cancer patients using pre-treatment MRI and a radiomics-based machine learning approach. Methods: We divided MRI-data from 102 patients into a training cohort (<i>n</i> = 72) and a validation cohort (<i>n</i> = 30). …”
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Machine learning-driven insights into self-healing silicon-based anodes for high-performance lithium-ion batteries by Mahta Moazzenzadeh, Mahmoud Samadpour

“…SHAP analysis revealed that ether functional groups, donor and acceptor hydrogen bonds, and dual-interconnected rings have the most positive impact on preserving battery capacity. In this study, we introduce a set of design principles for selecting functional groups aimed at enhancing the self-healing capabilities and prolonging the lifespan of Si-based LIBs. …”
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