Comparison of lower limb kinematic and kinetic estimation during athlete jumping between markerless and marker-based motion capture systems by Changzhi Yang, Linyu Wei, Xi Huang, Lili Tu, Yanjia Xu, Xiaolong Li, Zhe Hu

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Multi-time scale analysis of human activity patterns on the Qinghai–Tibet Plateau using location request data by Minglu Che, Yanyun Nian, Chengyao Wang, Wenhui Zhang, Jie Chen, Bingzhi Liao, Tao Pei

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Neuro-fuzzy techniques for modeling process parameters in pulsed electrochemical machining of steel by Irvin Uriel Nopalera Angeles, Everardo Efrén Granda Gutiérrez, Roberto Alejo Eleuterio, René Arnulfo García Hernández, Ángel Hernández Castañeda, María Guadalupe Pineda Arizmendi

“…In this way, a root mean square error in the prediction of the material removal rate of 0.394 and a coefficient of determination  of 0.845 were obtained. …”
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Machine learning identification of a novel vasculogenic mimicry-related signature and FOXM1’s role in promoting vasculogenic mimicry in clear cell renal cell carcinoma by Chao Xu, Sujing Zhang, Jingwei Lv, Yilong Cao, Yao Chen, Hao Sun, Shengtao Dai, Bowei Zhang, Meng Zhu, Yuepeng Liu, Junfei Gu

“…The predictive value of VRG_score for ccRCC prognosis and treatment response was assessed. …”
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ПРОГНОЗИРОВАНИЕ РИСКА НАЛИЧИЯ ВОСПАЛИТЕЛЬНЫХ ЗАБОЛЕВАНИЙ КИШЕЧНИКА У ПАЦИЕНТОВ БЕЗ ЛАБОРАТОРНЫХ ПРИЗНАКОВ ВОСПАЛЕНИЯ... by Yuliya Abbyasovna Khalitova, Yuliya Valerevna Myakisheva, Artem Viktorovich Lyamin, Alena Anatolevna Ereshchenko, Anna Vitalevna Yanchenko

“…An algorithm for predicting the risk of IBD in patients without non-specific laboratory criteria of inflammation was proposed. …”
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Capturing spatiotemporal variation in salt marsh belowground biomass, a key resilience metric, through geoinformatics by Kyle D. Runion, Deepak R. Mishra, Merryl Alber, Mark A. Lever, Jessica L. O'Connell

“…Abstract The Belowground Ecosystem Resiliency Model (BERM) is a geoinformatics tool that was developed to predict belowground biomass (BGB) of Spartina alterniflora in salt marshes based on remote sensing of aboveground characteristics and other readily available hydrologic, climatic, and physical data. …”
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Unravelling the importance of spatial and temporal resolutions in modeling urban air pollution using a machine learning approach by Alireza Zhalehdoost, Mohammad Taleai

“…Among the evaluated algorithms, MLP consistently achieves the highest predictive accuracy across both temporal and spatial scenarios. …”
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Neural Network Optimization of Mechanical Properties of ABS-like Photopolymer Utilizing Stereolithography (SLA) 3D Printing by Abdulkader Ali Abdulkader Kadauw

“…This approach uses machine learning algorithms to analyze and predict the relationships between various printing parameters and the resulting mechanical properties, thereby allowing the engineering of better materials specifically designed for targeted applications. …”
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Development and validation of novel machine learning-based prognostic models and propensity score matching for comparison of surgical approaches in mucinous breast cancer by Chunmei Chen, Jundong Wu, Yutong Fang, Yong Li, Qunchen Zhang

“…We have successfully developed 6 optimal prognostic models utilizing the XGBoost algorithm to accurately predict the survival of MBC patients. …”
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Development of an interpretable model for foot soft tissue stiffness based on gait plantar pressure analysis by Xiaotian Bai, Xiao Hou, Dazhi Lv, Dazhi Lv, Jialin Wei, Jialin Wei, Yiling Song, Zhengyan Tang, Hongfeng Huo, Hongfeng Huo, Jingmin Liu

“…A backpropagation neural network, optimized by integrating particle swarm optimization and genetic algorithm, was constructed to predict foot soft tissue stiffness using plantar pressure data collected during walking. …”
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Conservative Treatment and Rehabilitation Training for Rectus Femoris Tear in Basketball Training Based on Computer Vision by Yupeng Zhang, Gaowei Zhao

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Cinematic Rendered Computed Tomography Imaging Enhances 3D Visualization of Upper Extremity Arteriovenous Malformation by Balazs C. Lengyel, Alan B. Lumsden, Ponraj Chinnadurai

“…Inspired by the quality of computer-rendered images in the animated movie industry, cinematic rendering (CR) is a novel image visualization technique using proprietary rendering algorithms that simulate the propagation and interaction of light rays passing through 3-dimensional (3D) volumetric data, resulting in a photorealistic representation of bodily organs and vasculature.1,2 Due to its more realistic representative and intuitive format, it has become part of medical education, especially 3D anatomy teaching, patient education, and communication.3,4 It also plays an important role in the diagnostics and treatment planning for complex vascular pathologies, especially malformations.5 This short report highlights the additional diagnostic and clinical value of this image visualization technique by showing its value in treatment planning in a case of arteriovenous malformation (Figure 1).…”
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A Comprehensive Survey on Coverage Path Planning for Mobile Robots in Dynamic Environments by K. P. Jayalakshmi, Vishnu G. Nair, Dayakshini Sathish

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PFRNet: A Small Object Detection Method Based on Parallel Feature Extraction and Attention Mechanism by Hai Lin, Ji Wang, Jingguo Li

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FGA Temperature Control for Incubating Egg by Ismail Yusuf, Yusram Yusuf, Nur Iksan

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Current Trends in the Radiodiagnosis of Hodgkin’s Lymphoma in Children by K. A. Khasanova, I. E. Tyurin

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Spectroscopy-Based Methods and Supervised Machine Learning Applications for Milk Chemical Analysis in Dairy Ruminants by Aikaterini-Artemis Agiomavriti, Maria P. Nikolopoulou, Thomas Bartzanas, Nikos Chorianopoulos, Konstantinos Demestichas, Athanasios I. Gelasakis

“…Currently, the advancements and utilization of spectroscopy-based techniques combined with machine learning algorithms have made the development of analytical tools and real-time monitoring and prediction systems in the dairy ruminant sector feasible. …”
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Integrative analysis of signaling and metabolic pathways, immune infiltration patterns, and machine learning-based diagnostic model construction in major depressive disorder by Lei Tang, Liling Wu, Mengqin Dai, Nian Liu, Lu liu

“…By comparing the enrichment results across the five datasets, we found that the cell-killing signaling pathway was consistently present in the enriched signaling pathways of all datasets, suggesting that this pathway may play a crucial role in the pathogenesis of MDD. The random forest algorithm (AUC = 0.788) was selected as the optimal algorithm from 113 machine learning algorithms, leading to the development of a robust and predictive MDD algorithm, highlighting the important role of NPL in MDD. …”
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In-depth insight into structure-reactivity/regioselectivity relationship of Lewis acid catalyzed cascade 4πe-cyclization/dicycloexpansion reaction by Ka Lu, Pan-Pan Zhou, Yong-Qiang Tu, Fu-Min Zhang, Xiao-Ming Zhang, Kai Li, Kun Fang, Yun-Peng Wang, Zi-Hao Li, Jia-Qi Li

“…Then, DFT calculations combined with machine learning algorithms establish a prediction model for first cycloexpansion (i e. regioselectivity). …”
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Segmentation versus detection: Development and evaluation of deep learning models for prostate imaging reporting and data system lesions localisation on Bi‐parametric prostate magn... by Zhe Min, Fernando J. Bianco, Qianye Yang, Wen Yan, Ziyi Shen, David Cohen, Rachael Rodell, Dean C. Barratt, Yipeng Hu

“…The ground‐truth (GT) perspective lesion‐level sensitivity and prediction‐perspective lesion‐level precision are reported, to quantify the ratios of true positive voxels being detected by algorithms over the number of voxels in the GT labelled regions and predicted regions. …”
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