Interpretable Prediction and Analysis of PVA Hydrogel Mechanical Behavior Using Machine Learning by Liying Xu, Siqi Liu, Anqi Lin, Zichuan Su, Daxin Liang

“…However, rational design remains challenging due to complex structure–property relationships involving multiple formulation parameters. This study presents an interpretable machine learning framework for predicting PVA hydrogel tensile strain properties with emphasis on mechanistic understanding, based on a comprehensive dataset of 350 data points collected from a systematic literature review. …”
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Machine Learning-Based Approach for Hydrogen Economic Evaluation of Small Modular Reactors by Juyoul Kim, Mujuni Rweyemamu, Boldsaikhan Purevsuren

“…Furthermore, we employ a machine learning-based approach to predict important parameters that affect the hydrogen production cost. …”
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Investigating lightweight and interpretable machine learning models for efficient and explainable stress detection by Debasish Ghose, Ayan Chatterjee, Indika A. M. Balapuwaduge, Yuan Lin, Soumya P. Dash

“…However, achieving high accuracy in stress detection through machine learning (ML), using a reduced set of statistical features extracted from HRV, remains a significant challenge. …”
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Cutting Force Estimation Using Milling Spindle Vibration-Based Machine Learning by Je-Doo Ryu, Hoon-Hee Lee, Kyoung-Nam Ha, Sung-Ryul Kim, Min Cheol Lee

“…Cutting force is a key parameter for evaluating tool wear, but conventional force sensors are costly and difficult to implement. …”
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A comparative ensemble approach to bedload prediction using metaheuristic machine learning by Ajaz Ahmad Mir, Mahesh Patel, Fahad Albalawi, Mohit Bajaj, Milkias Berhanu Tuka

“…The current study introduces a novel comparative ensemble approach using metaheuristic machine learning (ML) models to enhance the accuracy of bedload prediction using data from laboratory flume experiments. …”
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Research on Export Oil and Gas Concentration Prediction Based on Machine Learning Methods by Xiaochuan Wang, Baikang Zhu, Huajun Zheng, Jiaqi Wang, Zhiwei Chen, Bingyuan Hong

“…This paper investigates the prediction of outlet oil and gas concentration based on the process parameters of oil and gas recovery devices in oil depots. …”
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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

“…To address the scarcity of in situ observational data, this study developed a satellite remote sensing-driven multi-parameter coupled model and reconstructed the WJ’s seasonal variations using the XGBoost machine learning algorithm. …”
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Predicting Equatorial Spread F at JICAMARCA Sector Via Supervised Machine Learning by Shunzu Gao, Chao Xiong, Hongtao Cai, Qian Pan, Weijia Zhan, Hong Zhang, Yuhao Zheng

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Classification of Structural and Functional Development Stage of Cardiomyocytes Using Machine Learning Techniques by V. R. Bondarev, K. O. Ivanko, N. G. Ivanushkina

“…Methods that prevent the alteration of the transverse T-tubule, which is an important parameter for correct classification of the development of cardiomyocytes, are used. …”
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Microbial Safety of Milk from Vending Machines in the Informal Settlements of Nairobi, Kenya by Kevin W. Holi, Lucy G. Njue, George O. Abong’

“…This study sought to determine the microbial safety of milk obtained from vending machines that were located in Nairobi’s informal settlements of Kibra and Dagoretti North. 37 milk samples were collected both from the storage tanks and vending machines located in the study area using a cross-sectional design and tested for microbial safety. …”
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Analysis of hydraulic breakdown and seepage of tail sealing system in shield tunnel machines by Dalong Jin, Zhuoyu Li, Dajun Yuan, Yangyang Gan, Jian Chen, Haipeng Guo

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Letter and Person Recognition in Freeform Air-Writing Using Machine Learning Algorithms by Huseyin Kunt, Zeki Yetgin, Furkan Gozukara, Turgay Celik

“…Furthermore, the study is also original due to its publicly available air-writing dataset on the Turkish alphabet and also applying various machine learning algorithms. The experimental results show that SubSpace KNN is superior to the others under the suggested parameter settings.…”
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Prediction of Proton Pressure in the Outer Part of the Inner Magnetosphere Using Machine Learning by S. Y. Li, E. A. Kronberg, C. G. Mouikis, H. Luo, Y. S. Ge, A. M. Du

“…We trained several different machine‐learning‐based models and compared their performances with observations. …”
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Machine Learning‐Enhanced Optimization for High‐Throughput Precision in Cellular Droplet Bioprinting by Jaemyung Shin, Ryan Kang, Kinam Hyun, Zhangkang Li, Hitendra Kumar, Kangsoo Kim, Simon S. Park, Keekyoung Kim

“…To address these obstacles, machine learning is employed to optimize five critical printing parameters (i.e., bioink viscosity, nozzle size, printing time, printing pressure, and cell concentration), and develop algorithms capable of immediate cellular droplet size prediction. …”
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Machine learning approach for water quality predictions based on multispectral satellite imageries by Vicky Anand, Bakimchandra Oinam, Silke Wieprecht

“…The main objective of this study to retrieve and map the water quality parameters from Sentinel-2 and ResourceSat-2 [Linear Imaging Self-Scanning Sensor (LISS)–IV] multi-spectral satellite data, using Support Vector Machines (SVM), Random Forests (RF), and Multi-Linear regression (MLR) models. …”
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Application of Machine Tool Thermal Error Compensation in Digital Twin-based System by MA Chi, LI Minging, LIU Jialan, HE Jialong, HUA Chunlei, WANG Liang

“…Thermal error significantly affects machining accuracy, demanding careful control. A robust system integrating a highly accurate thermal error model is the key to this control. …”
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Design of and Experimentation on an Intelligent Intra-Row Obstacle Avoidance and Weeding Machine for Orchards by Weidong Jia, Kaile Tai, Xiang Dong, Mingxiong Ou, Xiaowen Wang

“…The optimal operational parameter combination determined by this study for the weeding machine is as follows: forward speed of 0.5 m/s, hydraulic cylinder extension speed of 11.5 cm/s, and hydraulic cylinder retraction speed of 8 cm/s. …”
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Metering Automation System 3.0 Base Version Based on Machine Learning by Sheng Li, Leping Zhang, Hang Dai, Lukun Zeng, Yuan Ai, Shuang Qi, Yuanzhai Cui

“…However, traditional machine learning methods and standalone deep learning architectures struggle to balance spatiotemporal feature extraction, computational efficiency, and deployment constraints for high-frequency multivariate metering data. …”
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Quantifying training response in cycling based on cardiovascular drift using machine learning by Artur Barsumyan, Artur Barsumyan, Raman Shyla, Anton Saukkonen, Christian Soost, Jan Adriaan Graw, Rene Burchard, Rene Burchard, Rene Burchard

“…PurposeThe most important parameter influencing performance in endurance sports is aerobic fitness, the quality of the cardiovascular system for efficient oxygen supply of working muscles to produce mechanical work. …”
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Multi-Sensor Fusion and Machine Learning for Forest Age Mapping in Southeastern Tibet by Zelong Chi, Kaipeng Xu

“…For undisturbed forests, we compared 12 machine-learning models and selected the Random Forest model for age prediction. …”
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