Cross-Year Rapeseed Yield Prediction for Harvesting Management Using UAV-Based Imagery by Yanni Zhang, Yaxiao Niu, Zhihong Cui, Xiaoyu Chai, Lizhang Xu

“…Accurate estimation of rapeseed yield is crucial for harvesting decisions and improving efficiency and output. Machine learning (ML) models driven by remote sensing data are widely used for yield prediction. …”
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Exploring the spatiotemporal influence of climate on American avian migration with random forests by I. Avery Bick, Vegar Bakkestuen, Marius Pedersen, Kiran Raja, Sarab Sethi

“…These results suggest that analyses of machine learning model metrics may be useful for identifying spatiotemporal climatic cues that affect migratory behavior. …”
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Lipid-Metabolism-Related Gene Signature Predicts Prognosis and Immune Microenvironment Alterations in Endometrial Cancer by Zhangxin Wu, Yufei Nie, Deshui Kong, Lixiang Xue, Tianhui He, Kuaile Zhang, Jie Zhang, Chunliang Shang, Hongyan Guo

“…<b>Methods</b>: A total of 552 UCEC and 35 normal tissue samples from The Cancer Genome Atlas (TCGA) database were analyzed to identify differentially expressed lipid-metabolism-related genes (DE-LMRGs). A prognostic risk model was established using univariate Cox analysis, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression, and its clinical utility was assessed through nomogram construction. …”
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Enhancing access to specialist appointments in tertiary healthcare in Shanghai, China: a structured reservation pathway using digital health technologies by Li Li, Tao Zheng, Enhong Dong, Minjie Chen, Xiaojing Zhao, Binyuan Zhang, Xuji Zhao, Weijun Shao, Yiling Fan

“…The outcome analysis employed a mixed-methods approach, integrating quantitative analysis with statistical and machine learning techniques, including multivariate logistic regression, random forest (RF) and artificial neural network (ANN) analysis.Setting This study was conducted at Renji Hospital, a premier general tertiary care institution in Shanghai, China, where the innovative PRP system was implemented. …”
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Prediction of Pile Bearing Capacity Using Opposition-Based Differential Flower Pollination-Optimized Least Squares Support Vector Regression (ODFP-LSSVR) by Nhat-Duc Hoang, Xuan-Linh Tran, Thanh-Canh Huynh

“…This study proposes a data-driven model for coping with the problem of interest that hybridizes machine learning and metaheuristic approaches. …”
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Exploring the comorbidity association and biological mechanisms of chronic rhinosinusitis and chronic obstructive pulmonary disease by Shihan Liu, Jinxiong Yang, Yiyi Lin, Lingli Zhang, Wenlong Luo

“…Machine-learning models (glmnet, ranger, and xgboost) were used to analyze the NHANES data to determine the best model. …”
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Decoding the Narcissistic Brain by Zhiwei Zhou, Chengli Huang, Esther M. Robins, Douglas J. Angus, Constantine Sedikides, Nicholas J. Kelley

“…We attempted to do so by applying a machine learning approach (multivariate pattern analysis) to the resting-state EEG data of 162 participants who also completed a comprehensive battery of narcissism scales assessing agentic, admirative, rivalrous, communal, and vulnerable forms. …”
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Optimization of Reservoir Water Quality Parameters Retrieval and Treatment Using Remote Sensing and Artificial Neural Networks by Alice Nureen Adhiambo Omondi, Yashon Ouma, Simon Njoroge Mburu, Cleophas Mecha Achisa

“…The study utilized empirical multivariate regression modelling (EMRM) of the spectral reflectances from satellite data for the retrieval of Chla-a, Turbidity, and total suspended solids (TSS) concentrations in an inland water body. …”
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The association between cystatin C and hypertension risk in diabetes patients: A multi-cohort cross-sectional study by Ye Kuang, Jia Wang, Yang Wang, Chuanmei Peng, Pei He, Yong Ji, Jinrong Tian, Yong Yuan, Lei Feng

“…Analyzing 5210 DM patients from three cohorts, this study identified serum cystatin C (CysC) as an independent risk factor for DM + HTN through univariate and multivariate logistic regression. A risk prediction model incorporating CysC concentration was developed and adjusted for age, sex, race, education, body mass index, smoking status, and drinking status. …”
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Neurobehavioral mechanisms of fear and anxiety in multiple sclerosis by Lil Meyer-Arndt, Rebekka Rust, Judith Bellmann-Strobl, Tanja Schmitz-Hübsch, Lajos Marko, Sofia Forslund, Michael Scheel, Stefan M. Gold, Stefan Hetzer, Friedemann Paul, Martin Weygandt

“…Results Consistent with findings in non-MS anxiety populations, PwMS with anxiety exhibit fear overgeneralization, perceiving non-threating stimuli as threatening. A machine learning model trained on HPs in a multivariate pattern analysis (MVPA) cross-decoding approach accurately predicts behavioral fear generalization in both MS groups using whole-brain fMRI fear response patterns. …”
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A new signature associated with anoikis predicts the outcome and immune infiltration in nasopharyngeal carcinoma by Yonglin Luo, Wenyang Wei, Yaxuan Huang, Jun Li, Weiling Qin, Quanxiang Hao, Jiemei Ye, Zhe Zhang, Yushan Liang, Xue Xiao, Yonglin Cai

“…Results Three differentially expressed ARGs (CDC25C, E2F1 and RBL2) with prognostic value were identified by the intersection of multiple machine learning algorithms. A risk score based on t 3-ARG feature was developed to stratify NPC patients into two distinct risk groups using the optimal model, Random Survival Forest. …”
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A nomogram for predicting the risk of tigecycline-associated drug-induced liver injury in a Chinese population by Xin Zhang, Lanfang Li, Chuanpeng Zhang, Huixian Zhang, Haining Huang

“…Candidate variables were selected using least absolute shrinkage and selection operator (Lasso) regression and support vector machine recursive feature elimination (SVM-RFE), followed by univariate and multivariate logistic regression to identify independent risk factors, which were visualized in a nomogram. …”
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Rapid Nondestructive Detection of Welsh Onion, Onion, and Chinese Chives Seeds Based on Hyperspectral Imaging Technology by Sisi Zhao, Danqi Zhao, Jiangping Song, Huixia Jia, Xiaohui Zhang, Wenlong Yang, Haiping Wang

“…Then the dimensionality was reduced by Principal Component Analysis (PCA). Four classification models, Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), Random Forest (RF), and k-Nearest Neighbor (KNN), were used to classify seeds quickly and accurately. …”
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Estimation of Subtropical Forest Aboveground Biomass Using Active and Passive Sentinel Data with Canopy Height by Yi Wu, Yu Chen, Chunhong Tian, Ting Yun, Mingyang Li

“…This study utilizes four machine learning algorithms, namely Multivariate Stepwise Regression (MSR), K-Nearest Neighbors (k-NN), Artificial Neural Network (ANN), and Random Forest (RF), to estimate forest aboveground biomass (AGB) in Chenzhou City, Hunan Province, China. …”
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Sensitive Wavelengths Selection in Identification of Ophiopogon japonicus Based on Near-Infrared Hyperspectral Imaging Technology by Zhengyan Xia, Chu Zhang, Haiyong Weng, Pengcheng Nie, Yong He

“…And a nonlinear calibration model, support vector machine (SVM), was also provided for comparison. …”
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Spatial characterization of tertiary lymphoid structures as predictive biomarkers for immune checkpoint blockade in head and neck squamous cell carcinoma by Daniel A. Ruiz-Torres, Michael E. Bryan, Shun Hirayama, Ross D. Merkin, Evelyn Luciani, Thomas J. Roberts, Manisha Patel, Jong C. Park, Lori J. Wirth, Peter M. Sadow, Moshe Sade-Feldman, Shannon L. Stott, Daniel L. Faden

“…The presence of TLS within 100 µm of the tumor was associated with improved overall (p = 0.04) and progression-free survival (p = 0.03). A multivariate machine learning model identified TLS density as a leading predictor of response to ICB with 80% accuracy. …”
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C-reactive protein-triglyceride glucose index predicts stroke incidence in a hypertensive population: a national cohort study by Songyuan Tang, Han Wang, Kunwei Li, Yaqing Chen, Qiaoqi Zheng, Jingjing Meng, Xin Chen

“…The Boruta algorithm validated CTI as a crucial indicator of stroke risk. The Support Vector Machine (SVM) survival model exhibited the best predictive performance for stroke risk in hypertensive patients, with an area under the curve (AUC) of 0.956. …”
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Exploration of Fully‐Automated Body Composition Analysis Using Routine CT‐Staging of Lung Cancer Patients for Survival Prognosis by Marc‐David Künnemann, Christian Römer, Anne Helfen, Annalen Bleckmann, Marcel Kemper, Walter Heindel, Tobias J. Brix, Michael Forsting, Johannes Haubold, Marcel Opitz, Martin Schuler, Felix Nensa, Katarzyna Borys, René Hosch

“…Kaplan–Meier survival analysis, Cox proportional hazards modelling and machine learning‐based survival prediction were performed. …”
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Designing empirical fourier decomposition reinforced with multiscale increment entropy and deep learning to forecast dry bulb air temperature by Mohammed Diykh, Mumtaz Ali, Abdulhaleem H. Labban, Ramendra Prasad, Mehdi Jamei, Shahab Abdulla, Aitazaz Ahsan Farooque

“…This paper aims to design an intelligent model namely MEFD-MSIE-FCNN to forecast DBTair which integrates multivariate empirical Fourier decomposition (MEFD), multiscale increment entropy (MSIE), and FCSM model that integrates a fully connected neural network FCNN with long short-term memory (LSTM) to forecast DBTair. …”
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Concentrations of Serum Per- and Polyfluoroalkyl Substances and Lipid Health in Adolescents: A Cross-Sectional Study from the Korean National Environmental Health Survey 2018–2020... by Min-Won Shin, Habyeong Kang, Shin-Hye Kim

“…Serum concentrations of PFAS, including perfluorooctanoic acid (PFOA), perfluorooctane sulfonic acid (PFOS), perfluorononanoic acid (PFNA), perfluorohexane sulfonic acid (PFHxS), and perfluorodecanoic acid (PFDeA), and lipid profiles were assessed. In multivariate regression models, PFDeA and PFNA were positively associated with elevated total cholesterol and low-density lipoprotein cholesterol levels, and PFDeA was associated with hypercholesterolemia risk in boys. …”
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