Complete Blood Count and Monocyte Distribution Width–Based Machine Learning Algorithms for Sepsis Detection: Multicentric Development and External Validation Study by Andrea Campagner, Luisa Agnello, Anna Carobene, Andrea Padoan, Fabio Del Ben, Massimo Locatelli, Mario Plebani, Agostino Ognibene, Maria Lorubbio, Elena De Vecchi, Andrea Cortegiani, Elisa Piva, Donatella Poz, Francesco Curcio, Federico Cabitza, Marcello Ciaccio

“…ObjectiveThis study aims to investigate the use of machine learning (ML) to overcome the limitations mentioned earlier by combining different parameters and therefore improving sepsis detection. …”
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Development of a Fault-Tolerant Permanent Magnet Synchronous Motor Using a Machine-Learning Algorithm for a Predictive Maintenance Elevator by Vasileios I. Vlachou, Theoklitos S. Karakatsanis

“…These signals are preprocessed and analyzed using advanced machine-learning techniques, specifically a Random Forest classifier, to distinguish between Normal, Marginal, and Critical states of motor health. …”
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Low Voltage Ride-Through Improvement of a Grid-Connected PV Power System Using a Machine Learning Control System by Altan Gencer

“…To forecast the best control parameters using real time, including both the fault and normal operation conditions of the grid-connected PVPP system, the ML approach is trained on historical data. …”
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In-situ Al–Al2O3 composite from recycled Al-7075 machining chips: Processing, microstructure and hardness evolution by J. Esguerra Arce, A. Esguerra Arce, G. Schmitz

“…Production of powders by grinding has recently been proposed as an energy efficient strategy for recycling short machining chips. Considering that low energy ball milling of Al-7075 machining chips produces crystalline aluminum oxide in addition to the metallic phase, this research investigates whether alumina can act as reinforcement and clarifies how ball milling-induced porosity and microstructural parameters affect hardness. …”
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Multi-Criteria Assessment of Flood Risk on Railroads Using a Machine Learning Approach: A Case Study of Railroads in Minas Gerais by Fernanda Oliveira de Sousa, Victor Andre Ariza Flores, Christhian Santana Cunha, Sandra Oda, Hostilio Xavier Ratton Neto

“…The database required for the machine learning model can be created with these hydrological data. …”
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Enhanced NDVI prediction accuracy in complex geographic regions by integrating machine learning and climate data—a case study of Southwest basin by Zehui Zhou, Jiaxin Jin, Bin Yong, Weidong Huang, Lei Yu, Peiqi Yang, Dianchen Sun

“…The normalized difference vegetation index (NDVI) is a vital metric for assessing vegetation growth, yet accurate prediction remains challenging, particularly in regions with complex geographic and climatic conditions. Machine learning methods offer promise but are often hindered by sensitivity to model structure, input parameters, and training samples. …”
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The selection of washing machine programs with fuzzy dematel and moora-ratio multi-criteria decision-making methods considering environmental and cost criteria by Fatma Şener Fidan, Emel Kızılkaya Aydoğan, Nigmet Uzal

“…The washing machine is the prevalent white household equipment in contemporary society. …”
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Simulation model of a chair vibration protective mechanism with a part of quasi-zero-stiffness for the operator of a road-building machine by Korytov M.S., Sherbakov V.S., Pochekueva I.E.

“…Vibrations of construction and road machinery components that occur during their operation are a harmful production factor for machine operators, therefore, their reduction can be considered a very urgent task. …”
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A State-of-the-Art Review on Micro-Machining of Nitinol Shape Memory Alloys and Optimization of Process Variables Considering the Future Trends of Research by Souradeep Dutta, Deba Kumar Sarma, Jay Vora, Rakesh Chaudhari, Abhijit Bhowmik, Priyaranjan Samal, Sakshum Khanna

“…The optimization of process parameters using different methods during conventional and non-conventional micro-machining of NiTi SMAs is also analyzed. …”
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Predicting Surface Roughness and Grinding Forces in UNS S34700 Steel Grinding: A Machine Learning and Genetic Algorithm Approach to Coolant Effects by Mohsen Dehghanpour Abyaneh, Parviz Narimani, Mohammad Sadegh Javadi, Marzieh Golabchi, Samareh Attarsharghi, Mohammadjafar Hadad

“…These findings suggest that machine learning techniques are versatile and precise when dealing with datasets. …”
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Developing a semi-automated technique of surface water quality analysis using GEE and machine learning: A case study for Sundarbans by Sheikh Fahim Faysal Sowrav, Sujit Kumar Debsarma, Mohan Kumar Das, Khan Mohammad Ibtehal, Mahfujur Rahman, Noshin Tabassum Hridita, Atika Afia Broty, Muhammad Sajid Anam Hoque

“…This study presents a semi-automated approach for assessing water quality in the Sundarbans, a critical and vulnerable ecosystem, using machine learning (ML) models integrated with field and remotely-sensed data. …”
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Estimated Disease Progression Trajectory of White Matter Disruption in Unilateral Temporal Lobe Epilepsy: A Data-Driven Machine Learning Approach by Daichi Sone, Noriko Sato, Yoko Shigemoto, Iman Beheshti, Yukio Kimura, Hiroshi Matsuda

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Toward a Next Generation Particle Precipitation Model: Mesoscale Prediction Through Machine Learning (a Case Study and Framework for Progress) by Ryan M. McGranaghan, Jack Ziegler, Téo Bloch, Spencer Hatch, Enrico Camporeale, Kristina Lynch, Mathew Owens, Jesper Gjerloev, Binzheng Zhang, Susan Skone

“…Abstract We advance the modeling capability of electron particle precipitation from the magnetosphere to the ionosphere through a new database and use of machine learning (ML) tools to gain utility from those data. …”
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Interpretable Machine Learning for Multi-Energy Supply Station Revenue Forecasting: A SHAP-Driven Framework to Accelerate Urban Carbon Neutrality by Zhihui Zhao, Minjuan Wang, Jin Wei, Xiao Cen, Shengnan Du, Ziwen Wu, Huanying Liu, Weiqiang Wang

“…This study proposes a novel Shapley additive explanations (SHAP)-driven machine learning framework for multi-energy supply station revenue forecasting. …”
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Supervised machine learning statistical models for visual outcome prediction in macular hole surgery: a single-surgeon, standardized surgery study by Kanika Godani, Vishma Prabhu, Priyanka Gandhi, Ayushi Choudhary, Shubham Darade, Rupal Kathare, Prathiba Hande, Ramesh Venkatesh

“…Abstract Purpose To evaluate the predictive accuracy of various machine learning (ML) statistical models in forecasting postoperative visual acuity (VA) outcomes following macular hole (MH) surgery using preoperative optical coherence tomography (OCT) parameters. …”
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Machine learning classifiers to detect data pattern change of continuous emission monitoring system: A typical chemical industrial park as an example by Zhefeng Xu, Xiahong Shi, Wei Shu, Yilu Xin, Xuan Zan, Zhaonian Si, Jinping Cheng

“…By categorizing outlets into 12 datasets based on monitoring parameters, 17 machine learning models were evaluated to identify emission patterns and detect potential data anomalies. …”
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Generating 1 km Seamless Land Surface Temperature from China FY3C Satellite Data Using Machine Learning by Xinhan Liu, Weiwei Zhu, Qifeng Zhuang, Tao Sun, Ziliang Chen

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Quantifying optimal inner limiting membrane peeling in macular hole surgery: a machine learning framework for predictive modeling and schematic visualization by Xiang Zhang, Hongjie Ma, Song Lin, Ledong Zhao, Lu Chen, Zetong Nie, Zhaoxiong Wang, Chang Liu, Xiaorong Li, Wenbo Li, Bojie Hu

“…Preoperative and postoperative optical coherence tomography (OCT) images were used to measure key MH parameters, including minimum diameter (MIN), base width (BASE), temporal length (T), nasal length (N), and height (H). …”
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Improving the Performance of Electrotactile Brain–Computer Interface Using Machine Learning Methods on Multi-Channel Features of Somatosensory Event-Related Potentials by Marija Novičić, Olivera Djordjević, Vera Miler-Jerković, Ljubica Konstantinović, Andrej M. Savić

“…We systematically tested classification performance using machine learning algorithms, including logistic regression, k-nearest neighbors, support vector machines, random forests, and artificial neural networks. …”
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Effects of sandblasting and acid etching on the surface properties of additively manufactured and machined titanium and their consequences for osteoblast adhesion under different s... by Osman Akbas, Amit Gaikwad, Amit Gaikwad, Leif Reck, Nina Ehlert, Nina Ehlert, Anne Jahn, Jörg Hermsdorf, Andreas Winkel, Andreas Winkel, Meike Stiesch, Meike Stiesch, Andreas Greuling

“…For this purpose, the parameters cell adhesion, morphology, and membrane integrity were investigated using confocal laser microscopy and LDH assay.ResultsInitial high roughness of AM titanium surfaces was decreased by sandblasting, while initial smooth machined surfaces (MM) increased in roughness. …”
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