Optimization of Multi-Source Remote Sensing Soil Salinity Estimation Based on Different Salinization Degrees by Huifang Chen, Jingwei Wu, Chi Xu

“…Subsequently, machine learning methods such as random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), and multiple linear regression (MLR) were employed, in combination with sensitive spectral indices, to develop a multi-source remote sensing soil salinity estimation model optimized for different salinization degrees (mild or lower salinization vs. moderate or higher salinization). …”
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Multi-dimensional spatial and temporal dynamics of environmental stress and driving factors in the Baiyangdian Basin, China by Siyao Li, Zihua Yuan, Xiao Liu, Xing Gao, Hui Du

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Determine the Importance of Effective Factors in site selection Rainwater harvesting in the Tajerre Kashan Basin by maryam aghaie, siamak dokhani, ebrahim omidvar

“…Also, to investigate the relationship between these variables and weighting, each of the effective layers of multi-variable regression was used by the stepwise method The results showed that the linear multivariate regression model with an explanation coefficient of 0.993 was able to estimate the penetration factor values well In terms of grade of importance, the curve number variables with a coefficient of -2.433, depth of soil with a coefficient of 0.3488, and rubble and gravel percent with a coefficient of 0.057, were the most important, and other factors were not significant. …”
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Multi-morbidity and blood pressure trajectories in hypertensive patients: A multiple landmark cohort study. by Jenny Tran, Robyn Norton, Dexter Canoy, Jose Roberto Ayala Solares, Nathalie Conrad, Milad Nazarzadeh, Francesca Raimondi, Gholamreza Salimi-Khorshidi, Anthony Rodgers, Kazem Rahimi

“…Time-updated multivariable linear regression analyses showed that the presence of more comorbidities was associated with lower SBP during follow-up. …”
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Deciphering Car Crash Dynamics in Greater Melbourne: a Multi-Model Machine Learning and Geospatial Analysis by Christopher JOHNSON, ZHOU Heng, Richard TAY, SUN Qian(Chayn)

“…By harnessing Random Forest with SHAP (Shapley Additive Explanations), GLR (Generalized Linear Regression), and GWR (Geographically Weighted Regression), our research not only highlighted pivotal contributing elements but also enriched our findings by capturing often overlooked complexities. …”
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Work engagement and associated factors among Chinese nurses: a multi-centre cross-sectional study by Yuqi Zhang, Lanjun Zhang, Lu Luo, Yan Li, Yu Sun, Zhimin Dai, Xiaofan Bu, Ting Pan

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Research on Sealing Performance of Supercritical Hydrogen Cylindrical Seals Based on Multi-Objective Optimization of Spiral Grooves by Xueliang Wang, Zegan Gao, Wei Zhang, Junjie Lu, Min Jiang, Leibo Wu, Xuejing Wu

“…A multivariate linear regression analysis model is established. Subsequently, the NSGAII algorithm is used to perform multi-objective optimization design under operational conditions. …”
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Construction of Multi-Sample Public Building Carbon Emission Database Model Based on Energy Activity Data by Yue Guo, Xin Zheng, Wei Wei, Yuancheng He, Xiang Peng, Fei Zhao, Hailong Wu, Wenxin Bi, Hongyang Yan, Xiaohan Ren

“…Office buildings have been selected as representative samples to carry out baseline modeling and variable selection using linear regression analysis. …”
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Acculturation and Plasma Fatty Acid Concentrations in Hispanic and Chinese-American Adults: The Multi-Ethnic Study of Atherosclerosis. by Cassandra S Diep, Rozenn N Lemaitre, Tzu-An Chen, Tom Baranowski, Pamela L Lutsey, Ani W Manichaikul, Stephen S Rich, David E St-Jules, Brian T Steffen, Michael Y Tsai, David S Siscovick, Alexis C Frazier-Wood

“…Acculturation was determined from three proxy measures: nativity, language spoken at home, and years in the U.S., with possible scores ranging from 0 (least acculturated) to 5 (most acculturated) points. α-Linolenic acid, linoleic acid, eicosapentaenoic acid, docosahexaenoic acid, and arachidonic acid were measured in fasting plasma. Linear regression models were conducted in race/ethnicity-stratified analyses, with acculturation as the predictor and plasma phospholipid fatty acids as the outcome variables. …”
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Spiritual care competence and associated factors among nurses: a multi-center cross-sectional study by Yalemwork Getahun Azanaw, Ashenafi Worku Woretaw, Tiruye Azene, Desalegn Getachew Ayele, Tsehayu Melak Siyoum, Tizita Gebeyehu, Kalkidan Bazezew, Kassye Ahmed Zeleke, Esayas Alemsht, Sintayehu Simie Tsega, Melkamu Tilahun Dessie, Ashenafi Fekad Getahun, Astewil Moges Bazezew

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PENGARUH JOB SATISFACTION DAN EMPLOYEE ENGAGEMENT TERHADAP TURNOVER INTENTION PADA PT. HARAPAN JAYA MULTI BISNIS by Jhana Vasabha Winarso

“… The purpose of this research is to analyze the effect of job satisfaction and employee engagement on turnover intention at PT. Harapan Jaya Multi Bisnis. The method that will be used in this research is quantitative research method by using Multiple Linear Regression. …”
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Modeling and Numerical Prediction on Mechanical Behaviors of Hybrid Fiber Reinforced Polymer Bio Composites Using Fuzzy Logic Algorithm by Vinoth Viswanathan, Sathiyamurthy Subbarayan, Ananthi Narayanaswamy, Devi Panneerselvam, Prabhakaran Jayasankar

“…A triangular membership function was employed to define all these variables. The effectiveness of the nonlinear regression analysis and fuzzy logic model was evaluated through confirmatory experiments. …”
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Universal Modeling for Non-Destructive Testing of Soluble Solids Content in Multi-Variety Blueberries Based on Hyperspectral Imaging Technology by Lingqi Meng, Guoliang Chen, Dayang Liu, Ning Tian

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Machine learning and parametrisation of multi-cell structures of secondary circulation in a tight open channel bend using LES by H. Katie Schreiner, Abdolmajid Mohammadian, Colin D. Rennie

“…The strength and position of the sub-cells are then modelled using decision trees, multiple linear regression, multi-layer perceptrons, and adaptive neuro-fuzzy inference systems to obtain parametric models of secondary circulation development in a channel bend. …”
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Assessing equity of care across metastatic breast cancer treatment junctures: a multi-site retrospective cohort study by Carolyn Brown, Hyeun Ah Kang, Michael Johnsrud, Jamie Terry, Gury Doshi, Stephanie Broussard, Chinelo Orji

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Development of Non-Invasive Continuous Glucose Prediction Models Using Multi-Modal Wearable Sensors in Free-Living Conditions by Thilini S. Karunarathna, Zilu Liang

“…We evaluated the effectiveness of various ML regression techniques, including linear regression, ridge regression, random forest regression, and XGBoost regression, in terms of prediction and clinical accuracy. …”
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The Role of Country- and Firm-Level Factors in Determining Firms’ Environmental, Social, and Governance (ESG) Performance: A Machine Learning Approach by Eman Abdelfattah, Mahfuja Malik, Syed Muhammad Ishraque Osman

“…We employed ten supervised machine learning models—decision tree, stochastic gradient descent, random forest, adaptive boosting, extra trees, extreme gradient boosting, k-nearest neighbors, multiple linear regression, transformer-based regression, and multi-layer perceptron—and evaluated their effectiveness in ESG prediction. …”
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Self-care practices and diabetes distress among type 2 diabetic patients in Lebanon amidst a multi-crisis context by Ramona Nasr, Eman Swaidan, Chadia Haddad, Wissam Jreije, Abir Abdel Rahman, Joanne Karam

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Chronic conditions and sleep problems among adults aged 50 years or over in nine countries: a multi-country study. by Ai Koyanagi, Noe Garin, Beatriz Olaya, Jose Luis Ayuso-Mateos, Somnath Chatterji, Matilde Leonardi, Seppo Koskinen, Beata Tobiasz-Adamczyk, Josep Maria Haro

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Evaluating Sugarcane Yield Estimation in Thailand Using Multi-Temporal Sentinel-2 and Landsat Data Together with Machine-Learning Algorithms by Jaturong Som-ard, Savittri Ratanopad Suwanlee, Dusadee Pinasu, Surasak Keawsomsee, Kemin Kasa, Nattawut Seesanhao, Sarawut Ninsawat, Enrico Borgogno-Mondino, Filippo Sarvia

“…Moreover, in order to generate the sugarcane yield estimation maps, only 75 sampling plots were selected and surveyed to provide training and validation data for several powerful machine-learning algorithms, including multiple linear regression (MLR), stepwise multiple regression (SMR), partial least squares regression (PLS), random forest regression (RFR), and support vector regression (SVR). …”
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