Accuracy and clinical effectiveness of risk prediction tools for pressure injury occurrence: An umbrella review. by Bethany Hillier, Katie Scandrett, April Coombe, Tina Hernandez-Boussard, Ewout Steyerberg, Yemisi Takwoingi, Vladica M Veličković, Jacqueline Dinnes

“…The 19 SRs of prognostic accuracy evaluated 70 tools (39 scales and 31 machine learning (ML) models), with the Braden, Norton, Waterlow, Cubbin-Jackson scales (and modifications thereof) the most evaluated tools. …”
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Pretrained E(3)-equivariant message-passing neural networks with multi-level representations for organic molecule spectra prediction by Yuzhi Xu, Daqian Bian, Cheng-Wei Ju, Fanyu Zhao, Pujun Xie, Yuanqing Wang, Wei Hu, Zhenrong Sun, John Z. H. Zhang, Tong Zhu

“…Compared to state-of-the-art machine learning models, EnviroDetaNet excels in various predictive tasks and maintains high accuracy even with a 50% reduction in training data, demonstrating strong generalization capabilities. …”
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Optimizing Outdoor Micro-Space Design for Prolonged Activity Duration: A Study Integrating Rough Set Theory and the PSO-SVR Algorithm by Jingwen Tian, Zimo Chen, Lingling Yuan, Hongtao Zhou

“…The innovative contribution of this study lies in the proposed data-driven optimization method that integrates machine learning and KE. This method not only offers a new theoretical perspective for OMS design but also establishes a scientific framework to accurately incorporate users’ emotional needs into the design process. …”
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Feasibility test of per-flight contrail avoidance in commercial aviation by Aaron Sonabend-W, Carl Elkin, Thomas Dean, John Dudley, Noman Ali, Jill Blickstein, Erica Brand, Brian Broshears, Sixing Chen, Zebediah Engberg, Mark Galyen, Scott Geraedts, Nita Goyal, Rebecca Grenham, Ulrike Hager, Deborah Hecker, Marco Jany, Kevin McCloskey, Joe Ng, Brian Norris, Frank Opel, Juliet Rothenberg, Tharun Sankar, Dinesh Sanekommu, Aaron Sarna, Ole Schütt, Marc Shapiro, Rachel Soh, Christopher Van Arsdale, John C. Platt

“…Predictions for regions prone to contrail formation came from a physics-based simulation model and a machine learning model. Participating pilots made altitude adjustments based on contrail formation predictions for flights assigned to the treatment group. …”
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Research on the High Stability of an Adaptive Controller Based on a Neural Network for an Electrolysis-Free-Capacitor Motor Drive System by Danyang Bao, Haorui Shen, Wenxiang Ding, Hao Yuan, Yingying Guo, Zhendong Song, Tao Gong

“…Key innovations include: (1) BP neural network integration for dynamic parameter optimization, (2) impulse voltage suppression through adaptive control matching, and (3) enhanced transient response via machine learning-enhanced speed regulation. The test results demonstrate a 63% reduction in bus voltage fluctuations and 35% improvement in load transition responses compared to conventional PID-based systems, proving the strategy’s practical viability for industrial drive applications.…”
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Multi-decadal spatiotemporal dynamics of alpine plant functional types (PFTs) inferred from Landsat-derived fractional cover across the Yarlung Zangbo river basin, China by Qichi Yang, Lihui Wang, Xiaoqi Li, Xue Yan, Jinliang Huang, Yun Du, Feng Ling

“…In this study, we employed a regression-based unmixing model using synthetic data to develop a multi-temporal machine learning model aimed to estimate the fractions of alpine plant functional types (PFTs) from 1984 to 2024 in the Yarlung Zangbo River Basin (YZRB), China. …”
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An Integrated Algorithm with Feature Selection, Data Augmentation, and XGBoost for Ovarian Cancer by Jingxun Cai, Zne-Jung Lee, Zhihxian Lin, Chih-Hung Hsu, Yun Lin

“…With the rapid development of machine learning, numerous efficient classification prediction models have emerged. …”
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Optimization of Sensor Targeting Configuration for Intelligent Tire Force Estimation Based on Global Sensitivity Analysis and RBF Neural Networks by Yu Zhang, Guolin Wang, Haichao Zhou, Jintao Zhang, Xiangliang Li, Xin Wang

“…To address the demand for accurate tire force prediction in active safety control systems under various operating conditions, this paper proposes an intelligent tire force estimation method, integrating sensor-measured dynamic response parameters and machine learning techniques. A 205/55 R16 radial tire was selected as the research object, and a finite element model was established using the parameterized modeling approach with the ABAQUS finite element simulation software. …”
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Integrated edge-to-exascale workflow for real-time steering in neutron scattering experiments by Junqi Yin, Viktor Reshniak, Siyan Liu, Guannan Zhang, Xiaoping Wang, Zhongcan Xiao, Zachary Morgan, Sylwia Pawledzio, Thomas Proffen, Christina Hoffmann, Huibo Cao, Bryan C. Chakoumakos, Yaohua Liu

“…We introduce a computational framework that integrates artificial intelligence (AI), machine learning, and high-performance computing to enable real-time steering of neutron scattering experiments using an edge-to-exascale workflow. …”
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Identifying Substance Use and High-Risk Sexual Behavior Among Sexual and Gender Minority Youth by Using Mobile Phone Data: Development and Validation Study by Mehrab Beikzadeh, Ian W Holloway, Kimmo Kärkkäinen, Chenglin Hong, Cory Cascalheira, Elizabeth S C Wu, Callisto Boka, Alexandra C Avendaño, Elizabeth A Yonko, Majid Sarrafzadeh

“…MethodsWe developed a mobile phone app to collect participants’ messaging, location, and app use data and trained a machine learning model to predict risk behaviors for STI and HIV transmission. …”
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Neuromorphic imaging cytometry on human blood cells by Ziyao Zhang, Haoxiang Yang, Jiayin Li, Shin Wei Chong, Jason K Eshraghian, Ken-Tye Yong, Daniele Vigolo, Helen M McGuire, Omid Kavehei

“…Recently, this sensor has been adopted to address the limitations in IFC with prominent results in diverse modalities and machine learning approaches. Such a dataset serves as a baseline of healthy cell groups for both diagnostic and research purposes. …”
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ARTIFICIAL INTELLIGENCE AND BIG DATA ANALYSIS IN CRIME PREVENTION AND COMBAT by George-Marius ȚICAL

“…The development of explainable predictive models, the reduction of biases, and the adoption of clear international regulations are essential. …”
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Assessing the economic impact of climate risk on green and low-carbon transformation by Chen Qin, Hongli Lou, Li Li

“…To bridge this gap, we propose a novel framework that integrates the Integrated Green Transition Model (IGTM) and the Sustainable Transition Optimization Framework (STOF).MethodsIGTM employs agent-based modeling and network dynamics to simulate the cascading impacts of green policies on energy systems and socio-economic outcomes, while STOF leverages advanced optimization and machine learning techniques to balance economic growth, emission reductions, and social equity under diverse scenarios.ResultsBy synthesizing these approaches, our study provides actionable insights into the economic impact of climate risk and offers robust strategies for optimizing investments in renewable energy and policy interventions. …”
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Predicting depression risk in middle-aged and elderly adults in China using CNN-BiLSTM-Attention mechanism and LSTM+SHAP framework by Shengxian Bi, Gang Li, Huawei Tan, Yingchun Chen, Dandan Guo

“…However, current research predominantly employs machine learning (ML) methods to predict depression risk, often overlooking the spatiotemporal heterogeneity of this risk. …”
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Self-lubricative performance of laser processed graphene structures for space applications via digital twin approach by Praveen Kumar Kanti, Prashantha Kumar H. G, V．Vicki Wanatasanappan, Hanen Karamti, Vijayalaxmi Mishra, Majed Alsubih, Prabhu Paramasivam, Abinet Gosaye Ayanie

“…Digital twin approach by machine learning, particularly Extreme Gradient Boosting (XGBoost), achieved an R² score of 0.92, confirming the model's accuracy in wear loss prediction. …”
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A deep neural network framework for estimating coastal salinity from SMAP brightness temperature data by Yidi Wei, Qing Xu, Qing Xu, Xiaobin Yin, Xiaobin Yin, Yan Li, Yan Li, Kaiguo Fan

“…The framework leverages machine learning interpretability tools (Shapley Additive Explanations, SHAP) to optimize input feature selection and employs a grid search strategy for hyperparameter tuning.Results and discussionSystematic validation against independent in-situ measurements demonstrates that the baseline DNN model constructed for the entire region and time period outperforms conventional algorithms including K-Nearest Neighbors, Random Forest, and XGBoost and the standard SMAP SSS product, achieving a reduction of 36.0%, 33.4%, 40.1%, and 23.2%, respectively in root mean square error (RMSE). …”
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Development of an upper limb muscle strength rehabilitation assessment system using particle swarm optimisation by Chuangan Zhou, Siqi Wang, Meiyi Wu, Wei Lai, Junyu Yao, Xingyue Gou, Hui Ye, Jun Yi, Dong Cao

“…Machine learning models, including Backpropagation Neural Network (BPNN), Support Vector Machines (SVM), and particle swarm optimization algorithms (PSO-BPNN, PSO-SVR), were applied for regression analysis. …”
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LILRA5+ macrophages drive early oxidative stress surge in sepsis: a single-cell transcriptomic landscape with therapeutic implications by Peng Xu, Haoze Li, Zuo Tao, Zixuan Zhang, Xiaohuan Wang, Cheng Zhang

“…High-dimensional weighted gene co-expression network analysis (hdWGCNA), combined with multiple machine learning methods, was used for the selection of hub genes. …”
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Mechanisms and management of self-resolving lumbar disc herniation: bridging molecular pathways to non-surgical clinical success by Yan Zhao, Zhiwei Jia, Abudunaibi Aili, Aikeremujiang Muheremu

“…Future research should focus on elucidating the molecular mechanisms of resorption, regulation of inflammatory response, macrophage polarization, matrix degradation, immune privilege and neovascularization, developing advanced imaging techniques to predict resorption potential, and exploring personalized treatment strategies based on machine learning and deep learning prediction models.…”
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Unlocking precision medicine: clinical applications of integrating health records, genetics, and immunology through artificial intelligence by Yi-Ming Chen, Tzu-Hung Hsiao, Ching-Heng Lin, Yang C. Fann

“…Through the synergistic approach of integrating AI across diverse data sets, clinicians gain a holistic view of patient health and potential risks. Machine learning models excel at identifying high-risk patients, predicting disease activity, and optimizing therapeutic strategies based on clinical, genomic, and immunological profiles. …”
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