Virtual patient simulation in an interactive educational module on intimate partner violence: nursing students’ experiences—a mixed-methods study by Joachim Eckerström, Joachim Eckerström, Sophie Mårtensson, Margaretha Larsson, Rajna Knez, Rajna Knez, Rajna Knez, Madeleine Ljudvåg, Samir El Alaoui, Karin Dahlström, Karin Dahlström, Ylva Elvin Nowak, Ylva Elvin Nowak, Terese Stenfors, Nitya Jayaram-Lindström, Marianne Kristiansson, Uno Fors, Karolina Sörman

“…However, some participants noted that interacting with a VP on a screen was less emotional compared to real human interactions.ConclusionThe interactive educational module, integrated into the regular nursing program, was positively received by the students. Overall, the VP was considered beneficial for learning about IPV, easy to navigate, and provided a valuable opportunity for practice.…”
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How does student creativity depend on teaching methods at the institute? Modern information technologies for education and creativity as manifestations of the specifics of the psyc... by Ainash Davletova, Aidana Nazarova, Ainash Alzhanova, Yuliya Lazareva

“…This study analyzes the impact of project-based learning (PBL) and SCAMPER techniques on fostering creativity among university students. …”
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Analyzing social psychological impact on emotional expression through peer communication using crayfish optimization algorithm with deep learning model by Umkalthoom Alzubaidi

“…NLP methods enhance the performance of learning-based models by combining the syntactic and semantic features of the text. …”
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Development of an online psychological intervention to prevent depression in patients with atrial fibrillation: lessons learned in a user-centered design study by Charlotte Helmark, Robert Ahm, Axel Brandes, Søren J. Skovbakke, Jens Cosedis Nielsen, Lisbeth Frostholm, Rod S. Taylor, Susanne S. Pedersen

“…Primary lessons learned were (1) engaging patients from the start to ensure the intervention becomes meaningful; (2) using positive phrasing in all patient-related materials to promote motivation; (3) incorporating patients’ choice of preference where possible to personalize the intervention. …”
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Chronic stress in practice assistants: An analytic approach comparing four machine learning classifiers with a standard logistic regression model. by Arezoo Bozorgmehr, Anika Thielmann, Birgitta Weltermann

“…The performance of these models was compared in terms of predictive accuracy based on the 'operating area under the curve' (AUC), sensitivity, and positive predictive value.…”
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Does self-paced learning in mobile flood protection unit construction in virtual reality have advantages over traditional measures? by Patrick Querl, Raymond Leonardo Chandra, Djamel Berkaoui, Koen Castermans, Heribert Nacken

“…It aims to investigate the potential of an immersive virtual lab employing self-paced learning for teaching complex tasks. The focus is on the construction of a Mobile Flood Protection Unit (MFPU), traditionally taught through written instructions or video tutorials.MethodsAn experiment was conducted involving 48 students who were divided into two groups. …”
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A Cross-sectional Study on Stature Estimation from Arm Lengths among North Indian Population using Machine Learning by Arunima Dutta, Gyamar Anya

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A Data-Driven Approach to Assessing Hepatitis B Mother-to-Child Transmission Risk Prediction Model: Machine Learning Perspective by Dung Nguyen Tien, Huong Thi Thu Bui, Tram Hoang Thi Ngoc, Thuy Thi Pham, Dac Trung Nguyen, Huyen Nguyen Thi Thu, Thi Thu Hang Vu, Thi Lan Anh Luong, Lan Thu Hoang, Ho Cam Tu, Nina Körber, Tanja Bauer, Lam Khanh Ho

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Analysis of Effective Classroom Management Strategies to Create a Conducive Learning Environment for Elementary School Students through Document Studies by Bambang Afriadi, Fitri Fitri

“…The implications of this study underscore the importance of teacher professional development in adopting adaptive and evidence-based classroom management strategies to enhance the quality of learning at the primary education level.…”
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Machine learning-assisted source identification and probabilistic ecological-health risk assessment of heavy metal(loid)s in urban park soils by Jun Li, Xu Li, Xi-Sheng Tai, Xin-Ying Tuo, Fa-Yuan Zhou, Yi-Jing Rong, Fei Zang

“…This study combines multivariate statistical methods, machine learning techniques, and positive matrix factorization (PMF) with Monte Carlo simulation to investigate HM sources, probabilistic pollution levels, source-based ecological risks, and population-specific health hazards in seven urban parks in a nickel-based mining city in China. …”
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Interpretable noninvasive diagnosis of tuberculous pleural effusion using LGBM and SHAP: development and clinical application of a machine learning model by Bihua Yao, Xingyu Yu, Liannv Qiu, Er-min Gu, Siyu Mao, Lei Jiang, Jijun Tong, Jianguo Wu

“…This study seeks to design and validate an interpretable machine learning model based on routine laboratory data to enable noninvasive and rapid TPE diagnosis. …”
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MS2Lipid: A Lipid Subclass Prediction Program Using Machine Learning and Curated Tandem Mass Spectral Data by Nami Sakamoto, Takaki Oka, Yuki Matsuzawa, Kozo Nishida, Jayashankar Jayaprakash, Aya Hori, Makoto Arita, Hiroshi Tsugawa

“…However, annotation confidence still relies on manual curation by analytical chemists, despite the development of various software tools for automatic spectral processing based on rule-based fragment annotations. <b>Methods</b>: In this study, we present a novel machine learning model, MS2Lipid, for the prediction of known lipid subclasses from MS/MS queries, providing an orthogonal approach to existing lipidomics software programs in determining the lipid subclass of ion features. …”
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AI-Driven Comprehensive SERS-LFIA System: Improving Virus Automated Diagnostics Through SERS Image Recognition and Deep Learning by Shuai Zhao, Meimei Xu, Chenglong Lin, Weida Zhang, Dan Li, Yusi Peng, Masaki Tanemura, Yong Yang

“…On this basis, a negative–positive discrimination method combining SERS scanning imaging with a deep learning model (ResNet-18) was developed to analyze probe distribution patterns near the T line. …”
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Machine learning for predicting neoadjuvant chemotherapy effectiveness using ultrasound radiomics features and routine clinical data of patients with breast cancer by Pu Zhou, Pu Zhou, Hongyan Qian, Pengfei Zhu, Jiangyuan Ben, Jiangyuan Ben, Guifang Chen, Qiuyi Chen, Lingli Chen, Jia Chen, Ying He, Ying He

“…BackgroundThis study explores the clinical value of a machine learning (ML) model based on ultrasound radiomics features of primary foci, combined with clinicopathologic factors to predict the pathological complete response (pCR) of neoadjuvant chemotherapy (NAC) for patients with breast cancer (BC).MethodWe retrospectively analyzed ultrasound images and clinical information from 231 participants with BC who received NAC. …”
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Screening and Management of Obstructive Sleep Apnea and Daytime Sleepiness Among Professional Drivers in Tunisia: Protocol for a Machine Learning Study by Sameh Msaad, Nesrine Kammoun, Rahma Gargouri, Rim Khemakhem, Amira Triki, Narjes Abid, Sonia Fehri, Kaouthar Kallel, Rim Kammoun, Leila Douik EL Gharbi, Sonia MaalejBellaj, Heni Bouhamed, Ahmed Abdelghani, Chiraz Aichaouia, Mohamed Turki, Samy Kammoun

“…We will also evaluate the risk of accidents associated with OSA and EDS before and after the treatment. MethodsThis will be a population-based and prospective study of about 3000 professional drivers. …”
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IMPLEMENTASI MEDIA CARD SORTING UNTUK MEMBENTUK SIKAP KERJASAMA SISWA DALAM PEMBELAJARAN IPA DI MI by aulia_ainur_rofiq, Nur Khosiah

“…The findings indicate that the use of card sorting media in science learning at MI Nurul Islam positively impacts students, particularly by enhancing their collaboration and active involvement. …”
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Stock market trend prediction using deep neural network via chart analysis: a practical method or a myth? by Erfan Radfar

“…We explore the dynamics of the stock market and prominent classical methods and deep learning-based approaches that are used to forecast prices and market trends. …”
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Clinical-radiomics hybrid modeling outperforms conventional models: machine learning enhances stratification of adverse prognostic features in prostate cancer by Minghan Jiang, Minghan Jiang, Zeyang Miao, Run Xu, Mengyao Guo, Xuefeng Li, Guanwu Li, Peng Luo, Su Hu, Su Hu

“…ObjectiveThis study aimed to develop MRI-based radiomics machine learning models for predicting adverse pathological prognostic features in prostate cancer and to explore the feasibility of integrating radiomics with clinical characteristics to improve preoperative risk stratification, addressing the limitations of conventional clinical models.MethodsA retrospective cohort of 137 prostate cancer patients between January 2021 and April 2023 with preoperative MRI and postoperative pathology data was divided into adverse-feature-positive (n=85) and negative (n=52) groups. …”
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Identification of maize kernel varieties based on interpretable ensemble algorithms by Chunguang Bi, Chunguang Bi, Xinhua Bi, Jinjing Liu, Hao Xie, Shuo Zhang, He Chen, Mohan Wang, Lei Shi, Lei Shi, Shaozhong Song

“…Traditional single models show limitations in processing large-scale multimodal data.MethodsThis study constructed an interpretable ensemble learning model for maize seed variety identification through improved differential evolutionary algorithm and multimodal data fusion. …”
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Lymph Node Involvement Prediction Using Machine Learning: Analysis of Prostatic Nodule, Prostatic Gland, and Periprostatic Adipose Tissue (PPAT) by Eliodoro Faiella, Giulia D’amone, Raffaele Ragone, Matteo Pileri, Elva Vergantino, Bruno Beomonte Zobel, Rosario Francesco Grasso, Domiziana Santucci

“…This study included patients who underwent prostatectomy and lymphadenectomy with complete histological examination and previous staging mp-MRI and were divided into two groups based on lymph node status (positive/negative). Data were collected from each patient, including clinical information, radiomics, and semantic data (such as tumor MRI characteristics, histological tumor details, and lymph node status (LNS)). …”
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