Prevalence estimates of trafficking in persons using statistical definitions: a cross-sectional high-risk community survey in Cape Town, South Africa by Rumi Kato Price, Annah K Bender, Floriana H Milazzo, Edna G. Rich, Nicolette V. Roman, Sheldon X Zhang, Erica L Koegler

“…Secondary outcome measures included individual and summary measures from the two screeners.Results Our PRIF algorithm yielded a TIP lifetime prevalence rate of 17.0% and past 12-month rate of 2.9%. …”
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Diabetes Risk Assessment: A Comparative Study of Decision Trees and Ensemble Learning Models by Lei Tianxing

“…This high accuracy highlights the effectiveness of machine learning algorithms in predicting diabetes risk. The findings underscore the potential of these methods in enhancing diabetes management and offer a valuable contribution to the field of medical predictive analytics.…”
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Efficient search, mapping, and optimization of multi‐protein genetic systems in diverse bacteria by Iman Farasat, Manish Kushwaha, Jason Collens, Michael Easterbrook, Matthew Guido, Howard M Salis

“…We validated the algorithm's predictions by characterizing 646 genetic system variants, encoded in plasmids and genomes, expressed in six gram‐positive and gram‐negative bacterial hosts. …”
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Crystal structure generation with autoregressive large language modeling by Luis M. Antunes, Keith T. Butler, Ricardo Grau-Crespo

“…Seeding structure prediction algorithms with quality generated candidates can overcome a major bottleneck. …”
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A Stochastic Approach to Energy Cost Minimization in Smart-Grid-Enabled Data Center Network by Abolfazl Ghassemi, Pejman Goudarzi, Mohammad R. Mirsarraf, T. Aaron Gulliver

“…These results illustrate significant operational/energy cost reductions for a data center network over the conventional approach which optimizes the predicted values of stochastic parameters under a fixed QoS constraint.…”
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Machine learning enhanced expert system for detecting heart failure decompensation using patient reported vitals and electronic health records by Shumit Saha, Heather Ross, Pedro Elkind Velmovitsky, Chloe X. Wang, Julie K. K. Vishram-Nielsen, Cedric Manlhiot, Bo Wang, Joseph A. Cafazzo

“…Further, patients’ electronic health records (EHR) were not used when developing the rules-based algorithm. This study aimed to enhance Medly’s performance with machine learning and include a richer set of data, including EHR, for predicting decompensated HF episodes. …”
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IRGL-RRI: interpretable graph representation learning for plant RNA–RNA interaction discovery by Qingquan Liao, Xuchong Liu, Wei Zhao, Yu Tong, Fangzheng Xu, Xinxin Liu, Yifan Chen

“…To address this, this study proposes an interpretable graph representation model for accurate plant RRI prediction. The model enriches sample information by extracting features of different bases from plant RNA data and reconstructs these features using an algorithmic hierarchy approach to capture more complex patterns. …”
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Enhanced Gold Ore Classification: A Comparative Analysis of Machine Learning Techniques with Textural and Chemical Data by Fabrizzio Rodrigues Costa, Cleyton de Carvalho Carneiro, Carina Ulsen

“…Specific computational methods, such as machine learning algorithms, can assist mining professionals in quickly and consistently identifying and addressing classification issues related to mineralized horizons, as well as uncovering key variables that impact predictive outcomes, many of which were previously difficult to observe. …”
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Methodology of risks assessment of information security evaluation in e-economy systems by L. M. Lynkov, T. N. Beliatskaya, V. S. Knyazkova

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Optimizing Immunotherapy: The Synergy of Immune Checkpoint Inhibitors with Artificial Intelligence in Melanoma Treatment by Mohammad Saleem, Abigail E. Watson, Aisha Anwaar, Ahmad Omar Jasser, Nabiha Yusuf

“…Immune checkpoint inhibitors (ICIs) have transformed melanoma treatment; however, predicting patient responses remains a significant challenge. …”
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Explainable AI-driven intelligent system for precision forecasting in cardiovascular disease by Anas Bilal, Abdulkareem Alzahrani, Khalid Almohammadi, Muhammad Saleem, Muhammad Sajid Farooq, Raheem Sarwar

“…IntroductionCardiovascular diseases (CVDs) are complex and affect a large part of the world’s population; early accurate and timely prediction is also complicated. Typically, predicting CVDs involves using statistical models and other forms of standard machine learning. …”
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Development and validation of a chronic kidney disease progression model using patient-level simulations by Mafalda Ramos, Laetitia Gerlier, Anastasia Uster, Louise Muttram, Dominik Steubl, Andrew H Frankel, Mark Lamotte

“…A series of literature reviews informed the selection of risk factors and identified existing risk equations/algorithms for kidney replacement therapy (KRT), CV events, other CKD-related complications, and mortality. …”
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A Review of Vehicle Dynamics and Control Approaches for Mitigating Motion Sickness in Autonomous Vehicles by Ilhan Yunus, Georgios Papaioannou, Jenny Jerrelind, Lars Drugge

“…Effective mitigation requires accurate and reliable prediction. In this context, motion-based prediction approaches, recognised for their practicality, cost-effectiveness, and promising results, are examined in detail with particular focus on ISO-based methods and sensory conflict theory-based models. …”
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Unsplit Youngs Method for Tracking the Moving Interface of Expansible Grout by Xiaolong Li, Tiantian Zhang, Meimei Hao, Bei Zhang, Wenyi Yao, Zhenzhou Shen

“…The numerical method based on the unsplit algorithm was validated by examples of predicting the propagation of expansible grout in a fracture. …”
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Rapid diagnosis of bacterial vaginosis using machine-learning-assisted surface-enhanced Raman spectroscopy of human vaginal fluids by Xin-Ru Wen, Jia-Wei Tang, Jie Chen, Hui-Min Chen, Muhammad Usman, Quan Yuan, Yu-Rong Tang, Yu-Dong Zhang, Hui-Jin Chen, Liang Wang

“…Multiple ML models were constructed and optimized, with the convolutional neural network (CNN) model achieving the highest prediction accuracy at 99%. Gradient-weighted class activation mapping (Grad-CAM) was used to highlight important regions in the images for prediction. …”
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METHOD FOR NUMERICAL MODELING OF UNSTEADY SEPARATED FLOW AROUND AIRFOILS MOVING CLOSE TO FLAT SCREEN by T. V. Pogrebnaya, S. D. Shipilov

“…The algorithm is based on the combination of discrete vortex method and turbulent boundary layer equations. …”
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Optimal design of frame structures equipped with viscous dampers using machine learning techniques by Yi Wen, Jianze Wang, Jun Xu, Kaoshan Dai, Yuanfeng Shi, Reza Sharbati

“…The built models achieved an average R2 of 0.955 for the testing dataset, indicating a high level of predictive accuracy. Then, with the target of minimizing seismic responses of the structural system, the developed DP-based optimization algorithm effectively identifies the optimal damper arrangement for the considered three structure cases. …”
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A novel multiscale feature enhancement network using learnable density map for red clustered pepper yield estimation by Chenming Cheng, Chenming Cheng, Jin Lei, Jin Lei, Zicui Zhu, Lijian Lu, Lijian Lu, Zhi Wang, Zhi Wang, Jiali Tao, Jiali Tao, Xinyan Qin, Xinyan Qin

“…The proposed method provides an robust algorithmic support for efficient and intelligent yield estimation in RCP.…”
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Stratified allocation method for water injection based on machine learning: A case study of the Bohai A oil and gas field by Changlong Liu, Pingli Liu, Qiang Wang, Lu Zhang, Zechao Huang, Yuande Xu, Shaojiu Jiang, Le Zhang, Changxiao Cao

“…Second, the training and prediction effects of three machine learning prediction models—support vector machine, BP neural network, and random forest—were compared, and the BP neural network was selected as the machine learning mathematical model for injection allocation optimization. …”
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The association between cystatin C and hypertension risk in diabetes patients: A multi-cohort cross-sectional study by Ye Kuang, Jia Wang, Yang Wang, Chuanmei Peng, Pei He, Yong Ji, Jinrong Tian, Yong Yuan, Lei Feng

“…Analyzing 5210 DM patients from three cohorts, this study identified serum cystatin C (CysC) as an independent risk factor for DM + HTN through univariate and multivariate logistic regression. A risk prediction model incorporating CysC concentration was developed and adjusted for age, sex, race, education, body mass index, smoking status, and drinking status. …”
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