Using machine learning to identify key predictors of maternal success in sheep for improved lamb survival by Ebru Emsen, Bahadir Baran Odevci, Muzeyyen Kutluca Korkmaz

“…Several machine learning algorithms, including Random Forest, Decision Trees, Logistic Regression, and Support Vector Machines (SVM), were evaluated for predictive accuracy. The Random Forest model achieved the highest accuracy (67.2%) and demonstrated the best overall performance with a 0.41 Kappa statistic and the lowest mean absolute error (0.59). …”
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Prognostic nutritional index and diabetic peripheral neuropathy in type 2 diabetes: a machine learning approach by Ya Wu, Danmeng Dong, Yang Liu, Xiaoyun Xie

“…Results Both RF and XGBoost models exhibited strong performance. The RF model achieved a recall of 78.4%, specificity of 87.8%, and accuracy of 84.0%, while the XGBoost model showed a recall of 77.4%, specificity of 92.1%, and accuracy of 84.8%. …”
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Machine Learning Reconstruction of Wyrtki Jet Seasonal Variability in the Equatorial Indian Ocean by Dandan Li, Shaojun Zheng, Chenyu Zheng, Lingling Xie, Li Yan

“…The results revealed that wind stress components, sea surface temperature, and wind stress curl serve as the primary drivers of its seasonal dynamics. The XGBoost model demonstrated superior performance in reconstructing WJ’s seasonal variations, achieving coefficients of determination (<i>R</i>²) exceeding 0.97 across all seasons and maintaining root mean square errors (RMSE) below 0.2 m/s across all seasons. …”
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Automated Pneumothorax Segmentation with a Spatial Prior Contrast Adapter by Yiming Jia, Essam A. Rashed

“…First, we integrate an SPCA utilizing the MedSAM foundation model to incorporate spatial prior information of the lung region, effectively guiding the segmentation network to focus on anatomically relevant areas. …”
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Fast evaluation on the fatigue level of copper contact wire based on laser induced breakdown spectroscopy and supervised machine learning for high speed railway by Wenfu Wei, Langyu Xia, Zefeng Yang, Huan Zhang, Like Pan, Jian Wu, Guangning Wu

“…Results have shown that the standard normal variable transform–principal component analysis–genetic algorithm improve support vector machine (SNV‐PCA‐GASVM) model have presented a most satisfactory performance than the others. …”
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Assessment of geotechnical behavior of gypseous soil under leaching effect using machine learning by Saif M. Hassan Al-Riahi, Nur Irfah Mohd Pauzi, Mohammed Y. Fattah, Hasan Ali Abbas

“…Model performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (R). …”
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3D Visualization System of Breast Magnetic Resonance Images Based on Deep Learning and Volume Rendering by Rong Chen, Stephen Karungaru, Kenji Terada, Changyan Xu

“…Experimental results demonstrate that the improved ray-casting algorithm accelerates the rendering process while maintaining visualization quality. The MFF-Vnet model demonstrated promising segmentation performance on a breast DCE-MRI dataset containing expert-annotated lesions, achieving a Dice Similarity Coefficient (DSC) of up to 83.77%. …”
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Advanced machine learning techniques for predicting compressive strength and ultrasonic pulse velocity of concrete incorporating industrial by-products by Ehsan Mohsennia, Alireza Javid, Vahab Toufigh

“…A robust dataset, comprising 162 structured IBP concrete samples and 524 data points from existing literature, enabled rigorous training and validation of sophisticated ML models. Among the models tested, the CatBoost (CB) algorithm, optimized with the Whale Optimization Algorithm (WOA), exhibited outstanding predictive performance. …”
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AI bot to detect fake COVID‐19 vaccine certificate by Muhammad Arif, Shermin Shamsudheen, F Ajesh, Guojun Wang, Jianer Chen

“…The evaluation of the model is taken over performance measures like accuracy, specificity, sensitivity, detection rate, recall, f1‐score, and computation time over state‐of‐art models such as SVM, RNN, VGG16, Alexnet, and CNN in which the proposed model (D201‐LBP) outperforms with an accuracy of 0.94.…”
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Soft Computing Solutions for Reducing the Carbon Footprint of Fly Ash Based Concrete. Advances in Civil Engineering by Awoyera, Paul O., Adetola, Joshua, Nayeemuddin, Mohammed, Mewada, Hiren, George Fadugba, Olaolu

“…The construction industry significantly contributes to environmental degradation,with many structures exhibiting high carbon footprints throughout their construction processes and lifespans.Activities such as cement hydration and other commoncon-struction practices substantially influence environmental conditions overtime,necessitating a critical evaluation of material and design choices.This study reported the environmental impact of fly ash(FA),which is largely used to enhance concrete strength.A prediction of two end point indicators,that is,global warming potential(GWP)and CO2 emission using soft computing methods are presented,which are particularly effective for handling complex,non linear relationships in environmental data.To achieve this, two machine learning approaches,the random forest(RF)and decision tree(DT)models,are employed to assess the environ- mental impact of structural materials and designs.Two data sets were obtained from reputable databases,including ResearchGate, Science Direct, Semantic Scholar,and Mendeley Data.The models are trained to explore the potential for optimizing structural designs and material selection stominimize environmental impacts.Feature importance is analyzed using Shapley values,providing insights into the most influential factors affecting GWP and CO2 emission Model performance is evaluated using R2 and root mean square error(RMSE) metrics. …”
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Research on Hyperspectral Inversion of Soil Organic Carbon in Agricultural Fields of the Southern Shaanxi Mountain Area by Yunhao Han, Bin Wang, Jingyi Yang, Fang Yin, Linsen He

“…The results indicate that (1) the Spectral Space Transformation (SST) algorithm effectively eliminates environmental interference on image spectra, enhancing SOC prediction accuracy; (2) continuous wavelet transform significantly reduces data noise compared to other spectral processing methods, further improving SOC prediction accuracy; and (3) among feature band selection methods, the CARS algorithm demonstrated the best performance, achieving the highest SOC prediction accuracy when combined with the random forest model. …”
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Optimizing mobile cognitive assessment reduces administration time while maintaining screening accuracy in older adults by Hyun Jeong Ko, Jin Sung Kim, Whani Kim, Byung Hun Yun, So Yoon Park, Dong Han Kim, Ui Jun Kwon, Sang Kwon Lim, Bo Ri Kim, Jee Hang Lee, Geon Ha Kim, Jin Woo Kim

“…Initially, 304 older adults (272 healthy, 32 cognitively impaired) completed both a pencil-and-paper Cognitive Impairment Screening Test (CIST) and a full-length DACI. The best-performing CatBoost model achieved an area under the curve (AUC) of 0.813, with sensitivity of 0.903, requiring an average completion time of 321 s. …”
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Development of a GPT-4–Powered Virtual Simulated Patient and Communication Training Platform for Medical Students to Practice Discussing Abnormal Mammogram Results With Patients: M... by Dan Weisman, Alanna Sugarman, Yue Ming Huang, Lillian Gelberg, Patricia A Ganz, Warren Scott Comulada

“…It was iteratively refined through an agile design process involving repeated cycles of testing, documenting issues, and revising the prompt. As an exploratory feature, the simulation used GPT-4 to provide written feedback to learners about their performance communicating with the VSP and their adherence to guidelines for difficult conversations. …”
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Monitoring Construction Workers’ Mental Workload Due to Heat Exposure Using Heart Rate Variability and Eye Movement: A Study on Pipe Workers by Shiyi He, Dongsheng Qi, Enkai Guo, Liyun Wang, Yewei Ouyang, Lan Zheng

“…This study aimed to measure workers’ mental workload caused by heat exposure using heart rate variability (HRV) and eye movement features. Inexperienced pipe workers (<i>n</i> = 30) were invited to perform an installation task in a normothermic (26 °C, 50% RH) and a hyperthermic (33 °C, 50% RH) condition. …”
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Frequency function and damping function in assessment of dynamic processes in mechanical oscillatory systems with symmetry by A. V. Eliseev

“…A criterion for classifying frequency functions and damping functions based on the topological features of the graphs of the corresponding functions is proposed.Discussion and Conclusions. …”
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RETRACTED: Simplicial Complex-Enhanced Manifold Embedding of Spatiotemporal Data for Structural Health Monitoring by Nan Xu, Zhiming Zhang, Yongming Liu

“…Conventionally, both data-driven and physics-based models for structural damage detection have relied on handcrafted features, which are susceptible to the practitioner’s expertise and experience in feature selection. …”
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LMD&#x00B2;F-Net: Layered Multi-Scale Dual-Branch Dual-Temporal Fusion Network for Medical Image Segmentation by Siyuan Ye, Yan Wei

“…We propose a new architecture called LMD2F-Net, which combines MaxViT&#x2019;s multi-axis attention and Swin Transformer&#x2019;s global context modeling. This design enhances both local feature extraction and global context understanding. …”
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Dynamic Simulation of Solar-Assisted Medium-Depth Ground Heat Exchanger Direct Heating System by Le Chang, Lingjun Kong, Yangyang Jing, Wenshuo Zhang, Sifang Fu, Xueming Lu, Haiqing Yao, Xiaona Xie, Ping Cui

“…In this study, we propose a renewable energy heating system incorporating a solar-assisted medium-depth ground heat exchanger (MDGHE). A dynamic simulation model of the solar-assisted MDGHE system was developed in TRNSYS, featuring a novel MDGHE module specifically developed to improve simulation accuracy. …”
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Predicting weather-related power outages in large scale distribution grids with deep learning ensembles by L. Prieto-Godino, C. Peláez-Rodríguez, J. Pérez-Aracil, J. Pastor-Soriano, S. Salcedo-Sanz

“…The top-performing models using this methodology achieve detection rates of 38% and 73%, with false alarm rates of 1% and 3%, respectively. …”
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Cascade Nonlinear Observer-Based Speed-Sensorless Adaptive Twisting Sliding Mode Control of Linear Induction Motor by Lei Zhang, Xiaodong Xie, Dabiao Wu, Zicheng Wang, Jianli Wang, Jiaxin Jing, Huazhen Deng, Junkai Li, Jie Huang, Jingli Huang

“…This paper presents a novel adaptive twisting sliding mode control strategy combined with a speed-sensorless cascade nonlinear observer for the high-performance control of linear induction motors (LIMs). …”
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