A scoping review of nursing interventions of improving resilience among university students during online learning by Rohman Hikmat, Iyus Yosep, Ai Mardhiyah

“…The authors identified 14 articles discussing nursing interventions to improve resilience in university students during online learning. Five main types of interventions were identified: (1) resilience-based intervention programs, (2) mindfulness and yoga-based interventions, (3) positive psychology and journaling-based interventions, (4) social skills and problem-solving training, and (5) simulation-based and curricular interventions. …”
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EFEKTIFITAS PENGGUNAAN MEDIA PEMBELAJARAN MATEMATIKA BERBAHAN SAMPAH UNTUK MENGURANGI LEARNING LOSS AKIBAT PANDEMI COVID-19 by Yohanis Ndapa Deda, Hermina Disnawati

“…Based on the results of this study, it can be concluded that the use of waste-based MPMBS in learning integer operations can improve learning achievement while reducing learning loss in the community of Sunday school children in Kefemenanu. …”
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Nursing Students’ Perception of Nursing as a Career, Outcome Expectations, Job Satisfaction and Informal Workplace Learning by Veronika Anselmann, Sebastian Anselmann

“…<b>Conclusions</b>: Nursing vocational education and training is a vital socialization process in which supervisors can arrange a positive learning climate.…”
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BeatProfiler: Multimodal In Vitro Analysis of Cardiac Function Enables Machine Learning Classification of Diseases and Drugs by Youngbin Kim, Kunlun Wang, Roberta I. Lock, Trevor R. Nash, Sharon Fleischer, Bryan Z. Wang, Barry M. Fine, Gordana Vunjak-Novakovic

“…To accurately classify between these biological signals, we apply feature-based ML and deep learning models (temporal convolutional-bidirectional long short-term memory model or TCN-BiLSTM). …”
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Inclusive Excellence in Practice: Integrating Equitable, Consequential Learning and an Inclusive Climate in Higher Education Classrooms and Institutions by Susannah Davis, Susan Nolen, Milo Koretsky

“…The I2 Framework posits that DEIJ and learning goals need to be addressed simultaneously and at multiple, intersecting organizational levels. …”
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An Automated Framework for Lane Closure Detection on Highway Using Connected Vehicle Data and Machine Learning Models by Ashutosh Dumka, Raghupathi Kandiboina, Aparna Joshi, Skylar Knickerbocker, Neal Hawkins, Anuj Sharma

“…Lane closures on highways present significant challenges, including traffic disruptions, increased crash risks, and economic losses. The traditional methods, which use primarily manual reporting or sensor-based methods, can be error-prone, inefficient, and costly. …”
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Enhanced Global Ionospheric Mapping Using Deep Ensemble Neural Networks With Uncertainty Quantification by Shuyin Mao, Yuanxin Pan, Grzegorz Kłopotek, Matthias Schartner, Hana Krásná, Aletha deWitt, Benedikt Soja

“…To develop the machine learning model, we first determined the VTEC time series based on the carrier‐to‐code leveling method using multi‐GNSS observations from global IGS stations. …”
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Developing and Testing a Framework for Learning Online Collaborative Creativity in Medical Education: Cross-Sectional Study by Shairah Radzi, Joo Seng Tan, Preman Rajalingam, Jennifer Cleland, Sreenivasulu Reddy Mogali

“…MethodsFLOCC builds on established pedagogical approaches such as design thinking and integrates sociocultural learning methods (team-based learning [TBL] and problem-based learning [PBL]). …”
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MVT-Net: A novel cervical tumour segmentation using multi-view feature transfer learning. by Yao Yao, Yunzhi Chen, An Yang, Ye Ye, Lichun Wei, Shuiping Gou, Hua Yang

“…Accurately segmenting cervical tumours in MR images remains a challenging task due to the complex characteristics of tumours and the limitations of traditional methods. To address these challenges, this study proposes a novel cervical tumour segmentation model based on multi-view feature transfer learning, named MVT-Net. …”
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Detection of hypertension from pharyngeal images using deep learning algorithm in primary care settings in Japan by Takeo Nakayama, Yusuke Tsugawa, Hiroshi Yoshihara, Memori Fukuda, Sho Okiyama

“…Pharyngeal images include vascular morphological information and may therefore be useful for identifying hypertension.Objectives This study sought to develop a deep learning-based artificial intelligence algorithm for identifying hypertension from pharyngeal images.Methods We conducted a secondary analysis of data from a clinical trial, in which demographic information, vital signs and pharyngeal images were obtained from patients with influenza-like symptoms in multiple primary care clinics in Japan. …”
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Growing empathy through art therapy, life story, and relationships: experiential learning in adult day services by L. Blake Peeples, L. Blake Peeples, L. Blake Peeples, Benjamin C. Thompson, Benjamin C. Thompson, Jackie B. Tucker, Jackie B. Tucker, LaDerrick Smith, Amy Brown, Keisha D. Carden, Keisha D. Carden, Keisha D. Carden, Anne Halli-Tierney, Angel C. Duncan, Neelum T. Aggarwal, Jessica Y. Allen, Rebecca S. Allen, Rebecca S. Allen, Rebecca S. Allen, Daniel C. Potts, Daniel C. Potts, Daniel C. Potts

“…Appeals have therefore been made for the development of evidence-based empathy-enhancing experiential learning and training models. …”
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A machine learning approach for the diagnosis of obstructive sleep apnoea using oximetry, demographic and anthropometric data by Zhou Hao Leong, Shaun Ray Han Loh, Leong Chai Leow, Thun How Ong, Song Tar Toh

“…More efficient diagnostic methods are needed, even in tertiary settings. Machine learning (ML) models have strengths in disease prediction and early diagnosis. …”
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Enhancing power efficiency in BLDC motor drives for drones using multiview learning with hybrid optimization algorithms by Anushree Gopalakrishnan, Rani Thottungal

“…Employing multi-view learning techniques, it evaluates various performance metrics such as torque speed and power, revealing substantial improvements over the existing methods. …”
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Stuck Pipe Detection in Oil and Gas Drilling Operations Using Deep Learning Autoencoder for Anomaly Diagnosis by Hasan N. Al-Mamoori, Jialin Tian, Haifeng Ma

“…Additionally, the model attained a precision of 97.12%, a recall of 91.34%, and a F1-score of 94.15%, significantly reducing false positives and false negatives. The findings highlight the potential of deep learning-based approaches in improving real-time anomaly detection, offering a scalable and cost-effective solution for mitigating drilling disruptions. …”
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DriftShield: Autonomous Fraud Detection via Actor-Critic Reinforcement Learning With Dynamic Feature Reweighting by Jialei Cao, Wenxia Zheng, Yao Ge, Jiyuan Wang

“…Financial fraud detection systems confront the persistent challenge of concept drift, where fraudulent patterns evolve continuously to evade detection mechanisms. Traditional rule-based methods and static machine learning models require frequent manual updates, failing to autonomously adapt to emerging fraud strategies. …”
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Examining the effect of knowledge-based leadership on organizational innovation with the mediating role of illusion of control and rationality by davar Dirmina, Gholamhasan Shirdel, Seyyed Javad Iranban

“…The statistical method used in this study was the structural modeling method based on the partial least squares approach and Smart PLS3 software. …”
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Using machine learning to assist decision making in the assessment of mental health patients presenting to emergency departments by Oliver Higgins, Rhonda L. Wilson, Stephan K. Chalup

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A novel diagnostic framework for breast cancer: Combining deep learning with mammogram-DBT feature fusion by Nishu Gupta, Jan Kubicek, Marek Penhaker, Mohammad Derawi

“…Results: The proposed FENN-based framework achieved superior classification performance compared to existing methods. …”
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Undergraduate Nursing Students’ Experiences of Virtual Learning during the COVID-19 Pandemic: A Qualitative Study by Zahra Asgari Tapeh, Azar Darvishpour

“…With the emergence of the COVID-19 pandemic, schools and universities were closed, and virtual education replaced face-to-face classes. This learning method was a new and different experience for nursing students. …”
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Toward Robust Lung Cancer Diagnosis: Integrating Multiple CT Datasets, Curriculum Learning, and Explainable AI by Amira Bouamrane, Makhlouf Derdour, Akram Bennour, Taiseer Abdalla Elfadil Eisa, Abdel-Hamid M. Emara, Mohammed Al-Sarem, Neesrin Ali Kurdi

“…This study proposes a novel model based on deep learning to enhance lung cancer diagnosis quality, understandability, and generalizability. …”
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