Application of supervised machine learning and unsupervised data compression models for pore pressure prediction employing drilling, petrophysical, and well log data by Abu Bakker Siddique, Tanveer Alam Munshi, Nazmul Islam Rakin, Mahamudul Hashan, Sushmita Sarker Chnapa, Labiba Nusrat Jahan

“…Hybrid stacking improves performance even without hyperparameter tuning. …”
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Structural optimization and performance evaluation of a sugarcane leaf mulching machine by Weihua Huang, Shuo Wang, Chang Ge, Lijiao Wei, Dongjie Du, Zhaojun Niu, Ming Li, Zhenhui Zheng

“…Key parameters—such as blade structure, rotational speed, and arrangement—were optimized to improve mulching quality and pick-up efficiency. Further, a balance analysis of the cutter roller was conducted, incorporating MATLAB optimization algorithms and a fuzzy reliability function to enhance the roller’s structural integrity and reduce weight. …”
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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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Machine learning-based prediction of carotid intima–media thickness progression: a three-year prospective cohort study by An Zhou, Kui Chen, Kui Chen, Yonghui Wei, Qu Ye, Qu Ye, Yuanming Xiao, Rong Shi, Jiangang Wang, Wei-Dong Li

“…After Platt scaling, the calibration improved significantly across all the models. Decision curve analysis revealed a positive net benefit across a wide threshold range (0.01–0.5). …”
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Assistance of Artificial Intelligence in Diagnosis of Vitreoretinal Lymphoma on Optical Coherence Tomography by Aidi Lin, Yuanyuan Peng, Tian Lin, Jun Dai, Jizhu Li, Tingkun Shi, Xixuan Ke, Xulong Liao, Danqi Fang, Man Chen, Huiyu Liang, Shirong Chen, Honghe Xia, Jingtao Wang, Zehua Jiang, Tao Li, Dan Liang, Shanshan Yu, Jing Luo, Ling Gao, Dawei Sun, Yih Chung Tham, Xinjian Chen, Haoyu Chen

“…AI assistance significantly improves the F1 scores of doctors by 6.16–14.46% (p < 0.001). …”
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Machine learning for prediction of childhood mental health problems in social care by Ryan Crowley, Katherine Parkin, Emma Rocheteau, Efthalia Massou, Yasmin Friedmann, Ann John, Rachel Sippy, Pietro Liò, Anna Moore

“…Clinical prediction tools could improve these early intervention efforts. Aims Characterise a novel cohort consisting of children in social care and develop effective machine learning models for prediction of childhood mental health problems. …”
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A real-world pharmacovigilance study of Sorafenib based on the FDA Adverse Event Reporting System by Dongdong Zhang, Dongdong Zhang, Dongdong Zhang, Ying Cai, Ying Cai, Ying Cai, Yixin Sun, Peiji Zeng, Wei Wang, Wenhui Wang, Xiaohua Jiang, Yifan Lian, Yifan Lian, Yifan Lian

“…The results of this study emphasize the critical importance of continuous and vigilant surveillance for the timely identification and effective management of AEs to improve the overall patient safety and wellbeing in the context of Sorafenib therapy.…”
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SSVEP Enhancement in Mixed Reality Environment for Brain&#x2013;Computer Interfaces by Jieyu Wu, Feng He, Xiaolin Xiao, Runyuan Gao, Lin Meng, Xiuyun Liu, Minpeng Xu, Dong Ming

“…The purpose of this study was to optimize stimulus colors in order to improve the MR-BCI system&#x2019;s performance. In the MR environment, a 10-command SSVEP-BCI was deployed. …”
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The diagnostic and prognostic capability of artificial intelligence in spinal cord injury: A systematic review by Saran Singh Gill, Hariharan Subbiah Ponniah, Sho Giersztein, Rishi Miriyala Anantharaj, Srikar Reddy Namireddy, Joshua Killilea, DanieleS.C. Ramsay, Ahmed Salih, Ahkash Thavarajasingam, Daniel Scurtu, Dragan Jankovic, Salvatore Russo, Andreas Kramer, Santhosh G. Thavarajasingam

“…Results: For prognostication, 11 studies predicted outcomes including AIS improvement (30%), mortality and ambulatory ability (20% each), and discharge or length of stay (10%). …”
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Mapping Mountain Permafrost via GPR-Augmented Machine Learning in the Northeastern Qinghai–Tibet Plateau by Yao Xiao, Guangyue Liu, Guojie Hu, Defu Zou, Ren Li, Erji Du, Tonghua Wu, Xiaodong Wu, Guohui Zhao, Yonghua Zhao, Lin Zhao

“…Additional experiments confirmed that integration of GPR and high-elevation constraint samples significantly improved model generalization, especially in underrepresented terrain zones. …”
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Heat recovery integration in a hybrid geothermal-based system producing power and heating using machine learning approach to maximize outputs by Hatem Gasmi, Azher M. Abed, Ashit Kumar Dutta, Fahad M. Alhomayani, Ibrahim Mahariq, Fahad Alturise, Salem Alkhalaf, Tamim Alkhalifah, Yasser Elmasry, Baseem Khan

“…These results highlight the potential of combining advanced thermodynamic systems with machine learning methodologies to improve the efficiency and effectiveness of renewable energy sources.…”
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Interpretable machine learning approach for electron antineutrino selection in a large liquid scintillator detector by A. Gavrikov, V. Cerrone, A. Serafini, R. Brugnera, A. Garfagnini, M. Grassi, B. Jelmini, L. Lastrucci, S. Aiello, G. Andronico, V. Antonelli, A. Barresi, D. Basilico, M. Beretta, A. Bergnoli, M. Borghesi, A. Brigatti, R. Bruno, A. Budano, B. Caccianiga, A. Cammi, R. Caruso, D. Chiesa, C. Clementi, S. Dusini, A. Fabbri, G. Felici, F. Ferraro, M.G. Giammarchi, N. Giudice, R.M. Guizzetti, N. Guardone, C. Landini, I. Lippi, S. Loffredo, L. Loi, P. Lombardi, C. Lombardo, F. Mantovani, S.M. Mari, A. Martini, L. Miramonti, M. Montuschi, M. Nastasi, D. Orestano, F. Ortica, A. Paoloni, E. Percalli, F. Petrucci, E. Previtali, G. Ranucci, A.C. Re, M. Redchuck, B. Ricci, A. Romani, P. Saggese, G. Sava, C. Sirignano, M. Sisti, L. Stanco, E. Stanescu Farilla, V. Strati, M.D.C. Torri, A. Triossi, C. Tuvè, C. Venettacci, G. Verde, L. Votano

“…This method provides insights into the decision-making process of the model and offers valuable information for improving and updating traditional event selection approaches.…”
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Serum metabolic fingerprints encode functional biomarkers for ovarian cancer diagnosis: a large-scale cohort studyResearch in context by Wanshan Liu, Xiaoxiao Hu, Zhouzhou Bao, Yanyan Li, Juxiang Zhang, Shouzhi Yang, Yida Huang, Ruimin Wang, Jiao Wu, Xiaoyu Xu, Qi Sang, Wen Di, Huaiwu Lu, Xia Yin, Kun Qian

“…This panel achieved consistent areas under the curve (AUCs) of 0.87–0.89 for distinguishing between malignant and benign ovarian masses across all cohorts, and improved to AUCs of 0.95–0.99 when combined with risk of ovarian malignancy algorithm (ROMA). …”
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Securing fruit trees future: AI-driven early warning and predictive systems for abiotic stress in changing climate by Muhammad Ahtasham Mushtaq, Muhammad Ateeq, Muhammad Ikram, Shariq Mahmood Alam, Muhammad Mohsin Kaleem, Muhammad Atiq Ashraf, Muhammad Asim, Khalid F. Almutairi, Mahmoud F. Seleiman, Fareeha Shireen

“…Specifically, multi-omics, data accessibility, algorithmic biases, the cost of implementation and requirements for robust training programs need to integrate for sustainable agriculture. …”
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Modeling and Analysis of a Cutting Robot for the “Excavation–Backfill–Retention” Integrated Mining and Excavation Equipment by Hongwei Ma, Wenda Cui, Chuanwei Wang, Xusheng Xue, Qinghua Mao, Haotian Wang, Limeng Xue, Hao Su, Zukun Yu, Jiashuai Cheng, Yifeng Guo, Kexiang Ma

“…These findings provide a theoretical foundation for the development of adaptive cutting strategies in mining operations, contributing to improved performance and efficiency in complex mining conditions.…”
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Mucocutaneous Leishmaniasis: Knowledge, Attitudes, and Practices Among Paraguayan Communities, Patients, and Health Professionals by Mónica Ruoti, Rolando Oddone, Nathalie Lampert, Elizabeth Orué, Michael A. Miles, Neal Alexander, Andrea M. Rehman, Rebecca Njord, Stephanie Shu, Susannah Brice, Bryony Sinclair, Alison Krentel

“…Several important cost-effective measures are indicated that may improve control of CL. Community awareness could be enhanced through existing community structures. …”
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Modeling pine forest growing stock volume in subtropical regions of China using airborne Lidar data by Zige Lan, Xiandie Jiang, Guiying Li, Yagang Lu, Hongwen Yao, Dengsheng Lu

“…More research is needed to quantitatively examine different contribution of sample sizes, modeling algorithms, variables from different sources, and stratification factors on modeling results, so that we can design an optimal procedure for GSV modeling using airborne Lidar data.…”
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Prediction of acute and chronic kidney diseases during the post-covid-19 pandemic with machine learning models: utilizing national electronic health records in the USResearch in co... by Yue Zhang, Nasrollah Ghahramani, Runjia Li, Vernon M. Chinchilli, Djibril M. Ba

“…Incorporating the number of COVID-19 infections in the past year showed improved prediction performance and should be considered in future models for kidney disease prediction. …”
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Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews by He-Li Xu, Ting-Ting Gong, Xin-Jian Song, Qian Chen, Qi Bao, Wei Yao, Meng-Meng Xie, Chen Li, Marcin Grzegorzek, Yu Shi, Hong-Zan Sun, Xiao-Han Li, Yu-Hong Zhao, Song Gao, Qi-Jun Wu

“…The present findings highlight that a concerted effort from the research community, clinicians, and policymakers is required to overcome existing hurdles and translate this potential into improved patient outcomes and health care delivery. …”
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Automated Lightweight Model for Asthma Detection Using Respiratory and Cough Sound Signals by Shuting Xu, Ravinesh C. Deo, Oliver Faust, Prabal D. Barua, Jeffrey Soar, Rajendra Acharya

“…Additionally, a majority voting ensemble technique was employed to aggregate the predictions of the various classifiers for improved diagnostic reliability. We applied Gabor time–frequency transformation for feature extraction and NCA) for feature selection to optimise predictive accuracy. …”
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