Interpretable machine learning approaches for predicting prostate cancer by using multiple heavy metal exposures based on the data from NHANES 2003–2018 by Zu-Ming You, Yuan-Sheng Li, Fan-Shuo Meng, Rui-Xiang Zhang, Chen-Xi Xie, Zhijiang Liang, Ji-Yuan Zhou

“…Among the eight ML models evaluated, the random forest (RF) algorithm showed superior performance, achieving an accuracy of 72.835 %, an area under the receiver operating characteristic curve (AUC) of 0.869, an F1 score of 0.145, a G-mean of 0.749, and a Youden index of 0.498 in the test set. Four interpretable methods were integrated into the ML model. RF found that specific levels of blood lead (Pb) (0.449–29.964 µg/dL), urinary cesium (Cs) (1.822–270.426 µg/L), and urinary antimony (Sb) (0.015–4.953 µg/L) were positively associated with the PCA risk, while blood cadmium (Cd) (0.247–9.025 µg/L) showed a negative association. …”
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Design and Implementation of a Deep Learning-Based Hand Gesture Recognition System for Rehabilitation Internet-of-Things (RIoT) Environments Using MediaPipe by Nurul Hanis Mohd Dhuzuki, Ahmad Anwar Zainuddin, Nur Anis Sofea Kamarul Zaman, Alin Nur Maisarah Ahmad Razmi, Wonderful Shammah Kaitane, Asmarani Ahmad Puzi, Mohd Naqiuddin Johar, Maslina Yazid, Nor Azlin Mohd Nordin, Shahrul Naim Sidek, Hasan Firdaus Mohd Zaki

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Facing depression: evaluating the efficacy of the EmpkinS-EKSpression reappraisal training augmented with facial expressions – protocol of a randomized controlled trial by Marie Keinert, Lena Schindler-Gmelch, Lydia Helene Rupp, Misha Sadeghi, Klara Capito, Malin Hager, Farnaz Rahimi, Robert Richer, Bernhard Egger, Bjoern M. Eskofier, Matthias Berking

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Advancing offline magnetic resonance-guided prostate radiotherapy through dedicated imaging and deep learning-based automatic contouring of targets and neurovascular structures by Philipp Schubert, Matthias May, Daniel Höfler, Hans-Peter Fautz, Jana Hutter, Ricarda Merten, Sina Mansoorian, Thomas Weissmann, Lisa Deloch, Miriam Schonath, Nathalia Belmas, Felix Grabenbauer, Benjamin Frey, Udo Gaipl, Bernd-Niklas Axer, Juliane Szkitsak, Michael Uder, Christoph Bert, Rainer Fietkau, Florian Putz

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PR-CLIP: Cross-Modal Positional Reconstruction for Remote Sensing Image–Text Retrieval by Jihong Guan, Yulou Shu, Wengen Li, Zihan Song, Yichao Zhang

“…To address this issue, we propose a novel cross-modal retrieval model named PR-CLIP, which leverages a cross-modal positional information reconstruction task to learn position-aware correlations between modalities. …”
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A Texture-Enhanced Deep Learning Network for Cloud Detection of GaoFen/WFV by Integrating an Object-Oriented Dynamic Threshold Labeling Method and Texture-Feature-Enhanced Attentio... by Bo Zhong, Xiao Tang, Xiaobo Luo, Shanlong Wu, Kai Ao

“…Additionally, the efficiency of collecting training samples for prevalent deep learning-based methods heavily relies on large-scale pixel-level annotations, which are both time-consuming and labor-intensive. …”
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Structural Damage Detection Using an Unmanned Aerial Vehicle-Based 3D Model and Deep Learning on a Reinforced Concrete Arch Bridge by Mary C. Alfaro, Rodrigo S. Vidal, Rick M. Delgadillo, Luis Moya, Joan R. Casas

“…An 88.4% accuracy was achieved in crack detection, identifying 18 damage points, of which 3 turned out to be false positives. Additionally, it was determined that the left pillar in the southern area of the bridge presented the highest concentration of damage, which underlines the effectiveness of the method used.…”
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TSMixer- and Transfer Learning-Based Highly Reliable Prediction with Short-Term Time Series Data in Small-Scale Solar Power Generation Systems by Younjeong Lee, Jongpil Jeong

“…We present a transfer learning-based prediction system design that can maintain high prediction performance even in data-poor environments. …”
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Hybrid IoT-CAD system: optimized feature selection based gated recurrent residual deep learning for cyber attack detection in IoT networks by Susheela Vishnoi, Sunil Kumar, Saikat Samanta

“…In this work, a novel approach is introduced that combines deep learning (DL) and machine learning (ML) to improve cyber-attack detection in IoT networks, which solves cybersecurity issues. …”
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Machine learning based multi-stage intrusion detection system and feature selection ensemble security in cloud assisted vehicular ad hoc networks by C. Christy, A. Nirmala, A. Mary Odilya Teena, A. Isabella Amali

“…Solving these issues will require an advanced Intrusion Detection System (IDS) with real-time threat recognition and neutralization capabilities. A new method for improving VANET security, a multi-stage Lightweight IntrusionDetection System Using Random Forest Algorithms (MLIDS-RFA), focuses on feature selection and ensemble models based on machine learning (ML). …”
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High-performance machine-learning-based calibration of low-cost nitrogen dioxide sensor using environmental parameter differentials and global data scaling by Slawomir Koziel, Anna Pietrenko-Dabrowska, Marek Wojcikowski, Bogdan Pankiewicz

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Improving Critical Thinking Abilities on HOTs Questions through PJBL Model for Students of Elementary School Teacher Education by Putri Nuraini Sari, Wuri Wuryandani, Rahayu Condro Murti

“…Based on the research results, the two studies increased critical thinking skills by 66.66% pre-action and 16.66% post-action, increasing to 85.3% in the method Project Based Learning. …”
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Game on or game over? Gamification from 360-degree perspective, perception, confidence, and challenges in simulation based nursing education: mixed-method study by Mohamed Hashem Kotp, Hasan Ahmed Awad Bassyouny, Mohamed Ahmed Aly, Rasha Kadri Ibrahim, Abdelaziz Hendy, Ahmed Shaaban Attia, Ahmed Khalaf Mekdad, Ahmed Ali Hafez, Sally Mohammed Farghaly Abdelaliem, Nadiah A. Baghdadi, Ahmed Hendy, Hossam Ali Ismail

“…Abstract Background Gamification has emerged as a transformative approach in nursing education, especially within simulation-based learning environments. It is recognized for enhancing student engagement, knowledge retention, and confidence. …”
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Study on video action recognition based on augment negative example multi-granularity discrimination model by LIU Liangzhen, YANG Yang, XIA Yingjie, KUANG Li

“…An augment negative example discrimination paradigm based on contrastive learning was proposed to improve the model’s fine-grained discrimination ability of video actions. …”
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Usability Evaluation of a Digital Teleradiology Case Study Using the System Usability Scale in the WFDT Project by Anna BOEHM, Thomas LUX

“…The teleradiology content is presented through case studies oriented towards case-based learning. Aim: The objective of usability testing is to identify potential areas for improvement and ensure these are incorporated into subsequent development and quality assurance processes. …”
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Comparison of myopia-related behaviors among Chinese school-aged children and associations with parental awareness of myopia control: a population-based, cross-sectional study by Chaoying Ye, Chaoying Ye, Chaoying Ye, Yujie Wang, Yujia Liu, Yujia Liu, Yujia Liu, Xingxue Zhu, Xingxue Zhu, Xingxue Zhu, Jianmin Shang, Jianmin Shang, Jianmin Shang, Xiaomei Qu, Xiaomei Qu, Xiaomei Qu

“…Therefore, we assessed the myopia-related behaviors and parental awareness of school-aged children in different city tiers of China with different refractive statuses.MethodsA population-based, cross-sectional study was conducted on Chinese children from 110 cities aged 7–15 years. …”
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StyleMI: An Image Processing-Based Method for Detecting Unauthorized Style Mimicry in Fine-Tuned Diffusion Models in a More Realistic Scenario by Xinjun Zhang, Shixin Zuo, Yu Sang

“…The key insight is that images generated by a diffusion model inherently carry information of its training dataset. A style comparison-based method is proposed, which consists of a LoRA-based style transfer module and a CLIP-based style extraction and comparison module. …”
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A group-forming course content-focused icebreaker for life science classrooms by Adam J. Kleinschmit, Rebecca L. Sparks-Thissen, Sumali Pandey

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It’s About Relations: Goals and Limitations of School-Based Youth Work in Finland by Tomi Kiilakoski, Antti Kivijärvi, Eila Kauppinen

“… In recent years, there have been increasing public investments in school-based youth work in Finland. Therefore, in this article, we examine the evolution and the current goals and methods of school-based youth work in Finland using two datasets: 27 thematic interviews conducted in 2013–2014 and two quantitative surveys from 2021 and 2023. …”
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Daily activities and training experiences of urology residents during the coronavirus disease 2019 pandemic in Indonesia: A nationwide survey by Ponco Birowo, Nur Rasyid, Chaidir A. Mochtar, Bambang S. Noegroho, H.R. Danarto, Besut Daryanto, Lukman Hakim, Dyandra Parikesit, Fakhri Rahman, S. Cahyo Ariwicaksono

“…The respondents thought that educational activities using web-based video conferences and smart learning methods were effective methods of learning. …”
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