Robust machine learning models for calculating the carbon dioxide desublimation point within natural gas mixtures at low temperature conditions by Walid Abdelfattah, Munthar Kadhim Abosaoda, Dharmesh Sur, Menon Soumya V, Prabhat Kumar Sahu, Kamred Udham Singh, R. Sivaranjani, Rohit Chauhan, Siya Singla, Fereydoon Ranjbar

“…This investigation aimed to develop predictive tools utilizing machine learning approaches to estimate CDDT within natural gas mixtures. …”
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Deciphering organic substrate impacts in Anammox systems: A machine learning driven framework for predictive classification and process mechanism analysis by Zemin Li, Yulun Wu, Tao Chen, Bo Yan, Chaohai Wei

“…Mechanistic interpretation through molecular-level inhibition analysis further validates the scientific rationale for employing Biochemical Oxygen Demand/Chemical oxygen demand (BOD/COD) ratio as a comprehensive indicator of carbon source effects on Anammox systems. In summary, machine learning framework effectively integrates and optimizes the regulation of material stoichiometry, environmental parameters, and microbial functionality, thereby advancing the development of energy-efficient nitrogen removal technologies and enhancing the evaluation system for wastewater treatment processes.…”
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Machine learning and response surface methodology forecasting comparison for improved spray dry scrubber performance with brine sludge-derived sorbent by B.J. Chepkonga, L. Koech, R.S. Makomere, H.L. Rutto

“…Three machine learning (ML) models, multilayer perceptron (MLP), support vector regressor (SVR), and light gradient boosting machine (LightGBM), were assessed for their output estimation accuracy and compared to the CCD prediction model. …”
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Assessing the risk of high-grade squamous intraepithelial lesions (HSIL+) in women with LSIL biopsies: a machine learning-based study by Dongmei Li, Zhichao Wang, Yan Liu, Meiyuan Zhou, Bo Xia, Lin Zhang, Keming Chen, Yong Zeng

“…Logistic regression was used to identify demographic and pathological parameters associated with missed diagnoses of HSIL+. …”
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Machine learning approach for 2D abrasion mapping in Sediment Bypass Tunnels: a case study of Koshibu SBT, Japan by Ahmed Emara, Sameh A. Kantoush, Mohamed Saber, Tetsuya Sumi, Vahid Nourani, Emad Mabrouk

“…Overall, this study demonstrates the potential of machine learning algorithms for predicting tunnel abrasion in SBTs.Paper highlightsThis study introduces a validated 2D model for tunnel abrasion based on field data, contributing to improved sediment management in SBTs.ASM Model efficiently predicts abrasion mapping in SBT, achieving 86.4% overall accuracy.High sensitivity and specificity in distinguishing abraded and non-abraded areas.Captures four complex abrasion patterns in straight and curved sections but is limited to relatively small wave-like patterns.Geometric and hydraulic parameters, particularly the elongated distance and flow velocity, exhibit significant impacts in the ASM model.…”
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Auxiliary identification of depression patients using interpretable machine learning models based on heart rate variability: a retrospective study by Min Yang, Huiqin Zhang, Minglan Yu, Yunxuan Xu, Bo Xiang, Xiaopeng Yao

“…The objective of this study was to explore the association between heart rate variability (HRV) and depression, with the aim of establishing and validating machine learning models for the auxiliary diagnosis of depression. …”
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Mitochondrial respiratory chain and Krebs cycle enzyme function in human donor livers subjected to end-ischaemic hypothermic machine perfusion. by Hamid Abudhaise, Jan-Willem Taanman, Peter DeMuylder, Barry Fuller, Brian R Davidson

“…Oxygenation during hypothermic machine perfusion (HMP) was proposed to protect the mitochondria but the mechanism is unclear. …”
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Inflammation-Driven Prognosis in Advanced Heart Failure: A Machine Learning-Based Risk Prediction Model for One-Year Mortality by Zhou M, Du X

“…Min Zhou,1 Xiue Du2 1Department of Intensive Care Unit, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, 221004, People’s Republic of China; 2Department of Intensive Care Unit, Suining County People’s Hospital, Xuzhou, Jiangsu, 221200, People’s Republic of ChinaCorrespondence: Min Zhou, Email 15050047978@163.comBackground: To develop a machine learning (ML)-based prediction model focused on the one-year mortality risk in patients with advanced heart failure (AdHF), aiming to improve prediction accuracy by integrating inflammatory biomarkers and clinical parameters, assist clinical decision-making, and enhance patient outcomes.Methods: A retrospective cohort study. …”
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Early Fault Detection in Electro-Pneumatic Actuators using Mathematical Modelling and Machine Learning: A Bottling Company Case Study by Samuel Olufemi Amudipe, Adeyinka Moses Adeoye, Aderonke Oluwaseunfunmi Akinwumi, Rotimi Adedayo Ibikunle, Segun Adebayo

“…Real-time measurement points were validated through a baseline reference and machine learning models based on support vector machines received training data from labelled sets. …”
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A Cross-sectional Study on Stature Estimation from Arm Lengths among North Indian Population using Machine Learning by Arunima Dutta, Gyamar Anya

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Innovative cone resistance and sleeve friction prediction from geophysics based on a coupled geo-statistical and machine learning process by A. Bolève, R. Eddies, M. Staring, Y. Benboudiaf, H. Pournaki, M. Nepveaux

“…Geotechnical parameters derived from an intrusive cone penetration test (CPT) are used to asses mechanical properties to inform the design phase of infrastructure projects. …”
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Machine-learning-assisted inference of the particle charge fraction and the ion-induced nucleation rates during new particle formation events by P. Wang, Y. Zhao, J. Wang, J. Wang, V.-M. Kerminen, J. Jiang, C. Li

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Comparative study on risk prediction model of type 2 diabetes based on machine learning theory: a cross-sectional study by Shu Wang, Shuang Wang, Rong Chen, Ling Luo, Qiaoli Zhang, Danli Kong, Rudai Cao, Chunwen Lin, Jialu Huang, Haibing Yu, Yuan Lin Ding

“…Objectives To compare the prediction effects of six models based on machine learning theories, which can provide a methodological reference for predicting the risk of type 2 diabetes mellitus (T2DM).Setting and participants This study was based on the monitoring data of chronic disease risk factors in Dongguan residents from 2016 to 2018. …”
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Stratified allocation method for water injection based on machine learning: A case study of the Bohai A oil and gas field by Changlong Liu, Pingli Liu, Qiang Wang, Lu Zhang, Zechao Huang, Yuande Xu, Shaojiu Jiang, Le Zhang, Changxiao Cao

“…This paper established an automatic allocation method aimed at optimizing injection strategies based on the reservoir injection allocation scheme and utilizing real-time online data from intelligent layered injection wells by combining numerical simulation with artificial intelligence and machine learning algorithms. First, according to the basic parameters of block B in the Bohai A oil and gas field, a reservoir numerical simulation model was established, and historical fitting was carried out. …”
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Interpretable noninvasive diagnosis of tuberculous pleural effusion using LGBM and SHAP: development and clinical application of a machine learning model by Bihua Yao, Xingyu Yu, Liannv Qiu, Er-min Gu, Siyu Mao, Lei Jiang, Jijun Tong, Jianguo Wu

“…The model was built upon 18 routine laboratory parameters, including pleural fluid and serum biomarkers, with multiple machine learning (ML) algorithms evaluated. …”
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Permanent Magnet Axial Length Optimization for Transverse Magnetic Flux Generator with Disk Rotor by Duniev O., Yehorov A., Masliennikov A., Stamann M., Dobzhanskyi O.

“…Based on the analysis of the transverse flux machine designs, they were found to have a relative design simplicity and a high-power density. …”
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Development and Validation of an Interpretable Machine Learning Model for Prediction of the Risk of Clinically Ineffective Reperfusion in Patients Following Thrombectomy for Ischem... by Hu X, Qi D, Li S, Ye S, Chen Y, Cao W, Du M, Zheng T, Li P, Fang Y

“…The final model included ten parameters: EVT attempts, diabetes mellitus, previous ischemic stroke, National Institutes of Health Stroke Scale (NIHSS score), preoperative infarction in the basal ganglia, baseline diastolic blood pressure, clot burden score (CBS)/basilar artery on computed tomography angiography (BATMAN) score, stroke cause, collateral grade, and MLS.Conclusion: We developed and validated the first interpretable machine learning model for CIR prediction after EVT, surpassing traditional methods. …”
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A systematic comparison of short-term and long-term mortality prediction in acute myocardial infarction using machine learning models by Yawei Yang, Junjie Tang, Liping Ma, Feng Wu, Xiaoqing Guan

“…Abstract Background and objective The machine learning (ML) models for acute myocardial infarction (AMI) are considered to have better predictive ability for mortality compared to conventional risk scoring models. …”
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A Data-Driven Approach to Assessing Hepatitis B Mother-to-Child Transmission Risk Prediction Model: Machine Learning Perspective by Dung Nguyen Tien, Huong Thi Thu Bui, Tram Hoang Thi Ngoc, Thuy Thi Pham, Dac Trung Nguyen, Huyen Nguyen Thi Thu, Thi Thu Hang Vu, Thi Lan Anh Luong, Lan Thu Hoang, Ho Cam Tu, Nina Körber, Tanja Bauer, Lam Khanh Ho

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Preparation of land subsidence susceptibility map using machine learning methods based on decision tree (case study: Isfahan–Borkhar) by Negar Ghasemi, Iman Khosravi, Ali Bahrami

“…All input datasets (as input factors for machine learning algorithms) were co-registered to match the resolution of the InSAR-derived maps (100 meters).Machine learning algorithms: Three machine learning algorithms including decision tree (DT), random forest (RF) and extreme gradient boosting (XGBoost) were tested. …”
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