Implementing a One Health curriculum across multiple colleges: Challenges and lessons learned by Jennifer Wishnie, Julia Jernberg, Heidi E. Brown, Bonnie Buntain

“…If we wish to address complex global OH challenges effectively, universities need to incentivize cross-disciplinary teaching beyond ‘guest lectures’; methods to co-convene courses across colleges need to be established; and funding needs to move beyond OH research to also support OH teaching and learning. One Health impact statement As the importance of training transdisciplinary One Health (TD OH) practitioners grows, successful models for co-creating and presenting college coursework are increasingly essential. …”
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A Deep Learning-Based Time-Frequency Scheme for Ship Detection Using HFSWR by Da Huang, Hao Zhou, Yingwei Tian, Zhiqing Yang, Weimin Huang

“…To address this challenge, a deep learning (DL)-based scheme tailored for identifying ship targets in the time-frequency (TF) domain is presented. …”
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Deep-Learning-Driven Insights into Nitrogen Leaching for Sustainable Land Use and Agricultural Practices by Caixia Hu, Jie Li, Yaxu Pang, Lan Luo, Fang Liu, Wenhao Wu, Yan Xu, Houyu Li, Bingcang Tan, Guilong Zhang

“…Nitrate leaching from soil presents a significant threat to soil health, as it can result in nutrient loss, soil acidification, and structural damage. …”
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A Deep Learning Approach Based on Interpretable Feature Importance for Predicting Sports Results by Bendiaf Messaoud, Khelifi Hakima, Mohdeb Djamila, Belazzoug Mouhoub, Saifi Abdelhamid

“…However and recently there has been an increased interest in predicting match outcomes using statistical techniques and machine learning. These algorithms can learn from historical data to identify complex relationships between different variables, and then make predictions about the outcome of future matches. …”
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Forecasting invasive mosquito abundance in the Basque Country, Spain using machine learning techniques by Vanessa Steindorf, Hamna Mariyam K. B., Nico Stollenwerk, Aitor Cevidanes, Jesús F. Barandika, Patricia Vazquez, Ana L. García-Pérez, Maíra Aguiar

“…The establishment of mosquito species in new areas, coupled with rising mosquito populations and viremic imported cases, presents challenges for public health systems in non-endemic regions. …”
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Constructing inflammatory bowel disease diagnostic models based on k-mer and machine learning by Liwei Li, Zheng Liu, Jiamin Qin, Guang Xiong, Chongze Yang, Fuqing Cai, Jiean Huang

“…UC. Across all machine learning frameworks, the FFNN consistently attained the highest ROC AUC, underscoring its superior diagnostic performance.ConclusionThe integration of k-mer-based feature extraction with machine learning offers a non-invasive, highly accurate approach for IBD diagnosis, surpassing traditional microbiota-based models. …”
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Mechanism-learning prediction model for pitting depth of buried pipeline based on HMOGWO-RF by Fulin SONG, Hong ZHAO, Xingyuan MIAO

“…To ensure the safe operation of buried pipelines, accurately predicting the degree of corrosion is crucial. Methods This paper presents a prediction model for the pitting depth of buried pipelines, guided by the corrosion mechanism and combining a Random Forest (RF) algorithm with a Multi-Objective Optimization process. …”
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Transferring Learned ECG Representations for Deep Neural Network Classification of Atrial Fibrillation with Photoplethysmography by Jayroop Ramesh, Zahra Solatidehkordi, Raafat Aburukba, Assim Sagahyroon, Fadi Aloul

“…However, the scarcity of large-scale public PPG datasets acquired from wearable devices hinders the development of intelligent automatic AF detection algorithms unaffected by motion artifacts, saturated ambient noise, inter- and intra-subject differences, or limited training data. In this work, we present a deep learning framework that leverages convolutional layers with a bidirectional long short-term memory (CNN-BiLSTM) network and an attention mechanism for effectively classifying raw AF rhythms from normal sinus rhythms (NSR). …”
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Reinforcement learning based route optimization model to enhance energy efficiency in internet of vehicles by Quadeer Hussain, Ahmad Shukri Mohd Noor, Muhammad Mukhtar Qureshi, Jianqiang Li, Atta-ur Rahman, Aghiad Bakry, Tariq Mahmood, Amjad Rehman

“…In the realm of IoV, we propose OptiE2ERL, an advanced Reinforcement Learning (RL) based model designed to optimize energy efficiency and routing. …”
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Bringing Machine Learning Classifiers Into Critical Cyber-Physical Systems: A Matter of Design by Burcu Sayin, Tommaso Zoppi, Nicolo Marchini, Fahad Ahmed Khokhar, Andrea Passerini

“…Machine Learning (ML) models are increasingly used by domain experts to tackle classification tasks, aiming for high predictive accuracy. …”
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TECHNOLOGY OF SELF-DETERMINED LEARNING AS A NEW FORMAT OF CONTINUING PROFESSIONAL EDUCATION OF TEACHERS by G. A. Ignatieva, O. V. Tulupova, S. V. Matchinа

“…The essence of such transformations generally consists in necessary rejection of a subject-information learning model and transition to designing the model of vocational self-development and self-determination.The aim of this research was to reveal the essence of teachers’ professional development as the process of positional self-determination and formation of managerial position. …”
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Machine Learning in the Management of Patients Undergoing Catheter Ablation for Atrial Fibrillation: Scoping Review by Aijing Luo, Wei Chen, Hongtao Zhu, Wenzhao Xie, Xi Chen, Zhenjiang Liu, Zirui Xin

“… BackgroundAlthough catheter ablation (CA) is currently the most effective clinical treatment for atrial fibrillation, its variable therapeutic effects among different patients present numerous problems. Machine learning (ML) shows promising potential in optimizing the management and clinical outcomes of patients undergoing atrial fibrillation CA (AFCA). …”
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Educational Interventions to Promote Self-Regulated Learning in Vocational Schools - A Systematic Review by Mathias Mejeh, Corinne Grieder

“… Purpose: In the evolving landscape of the 21st century, characterized by dynamic global challenges such as health crises, climate change, and rapid technological advancements, the imperative of lifelong learning has become more pronounced than ever. Self-Regulated Learning (SRL) plays a pivotal role in lifelong learning, involving independent, self-directed behaviors to enhance knowledge and skills. …”
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Screening for severe coronary stenosis in patients with apparently normal electrocardiograms based on deep learning by Zhengkai Xue, Shijia Geng, Shaohua Guo, Guanyu Mu, Bo Yu, Peng Wang, Sutao Hu, Deyun Zhang, Weilun Xu, Yanhong Liu, Lei Yang, Huayue Tao, Shenda Hong, Kangyin Chen

“…A total of 392 patients, including 138 with severe stenosis, were selected for the study. Deep learning (DL) models were trained from scratch and using pre-trained parameters via transfer learning. …”
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Global and Local Interpretable Machine Learning Allow Early Prediction of Unscheduled Hospital Readmission by Rafael Ruiz de San Martín, Catalina Morales-Hernández, Carmen Barberá, Carlos Martínez-Cortés, Antonio Jesús Banegas-Luna, Francisco José Segura-Méndez, Horacio Pérez-Sánchez, Isabel Morales-Moreno, Juan José Hernández-Morante

“…Personalized prevention strategies could be developed to improve the management of these patients. The aim of the present work was to develop local predictive models using interpretable machine learning techniques to early identify individual unscheduled hospital readmissions. …”
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Lung Cancer Management: Revolutionizing Patient Outcomes Through Machine Learning and Artificial Intelligence by Taghi Riahi, Bahareh Shateri‐Amiri, Amirhossein Hajialiasgary Najafabadi, Sina Garazhian, Hanieh Radkhah, Diar Zooravar, Sahar Mansouri, Roya Aghazadeh, Mohammadreza Bordbar, Shirin Raiszadeh

“…Conclusion This research presents a reliable deep learning framework for lung cancer detection that outperforms traditional ML approaches on external validation. …”
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Spectroscopic photoacoustic denoising framework using hybrid analytical and data-free learning method by Fangzhou Lin, Shang Gao, Yichuan Tang, Xihan Ma, Ryo Murakami, Ziming Zhang, John D. Obayemi, Winston O. Soboyejo, Haichong K. Zhang

“…Additionally, training data is not always accessible for learning-based methods. In this work, we propose a Spectroscopic Photoacoustic Denoising (SPADE) framework using hybrid analytical and data-free learning method. …”
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Digital mapping of peat thickness and extent in Finland using remote sensing and machine learning by Jonne Pohjankukka, Timo A. Räsänen, Timo P. Pitkänen, Arttu Kivimäki, Ville Mäkinen, Tapio Väänänen, Jouni Lerssi, Aura Salmivaara, Maarit Middleton

“…In this study, we present a workflow for producing peat occurrence maps for the whole of Finland. …”
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The utility of combining deep learning with metabarcoding to model biodiversity dynamics at a national scale by Adrian Baggström, Robert Goodsell, Laura van Dijk, Ela Iwaszkiewicz-Eggebrecht, Andreia Miraldo, Ayco J.M. Tack, Tobias Andermann

“…By combining detailed biodiversity surveys, geospatial data, and machine learning, we can model biodiversity with the aim of gaining insights into how these complex patterns behave. …”
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Machine Learning Approach to Aerodynamic Analysis of NACA0005 Airfoil: ANN and CFD Integration by Taiba Kouser, Dilek Funda Kurtulus, Srikanth Goli, Abdulrahman Aliyu, Imil Hamda Imran, Luai M. Alhems, Azhar M. Memon

“…This study presents a machine learning approach to predict the unsteady aerodynamic performance of a NACA0005 airfoil. …”
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