Dose–Response Functions for Assessing Corrosion Risks to Urban Heritage Materials from Air Pollution Under Climate Change: Insights from Europe and China by Zhe Bai, Yu Yan

“…While DRFs offer clear quantitative estimates, their empirical nature and simplified assumptions necessitate complementary methods, including sensor networks, remote sensing, and machine learning models. Future research should integrate multivariate modelling, expand empirical data, and couple DRFs with real-time monitoring to better protect urban heritage materials amid environmental change.…”
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NMR-based metabolomic approach to estimate chemical and sensorial profiles of olive oil by Gaia Meoni, Leonardo Tenori, Francesca Di Cesare, Stefano Brizzolara, Pietro Tonutti, Chiara Cherubini, Laura Mazzanti, Claudio Luchinat

“…By integrating NMR data with traditional chemical analyses and sensory evaluation, we developed multivariate models to evaluate the predictive power of NMR spectra coupled with machine learning algorithms for 50 distinct olive oil quality parameters, including physicochemical properties, fatty acid composition, total polyphenols, tocopherols, and sensory attributes. …”
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Predicting operative mortality in patients who undergo elective open thoracoabdominal aortic aneurysm repairCentral MessagePerspective by Kyle W. Blackburn, BS, Susan Y. Green, MPH, Allen Kuncheria, BA, Meng Li, PhD, Adel M. Hassan, BA, Brittany Rhoades, PhD, Scott A. Weldon, MA, Subhasis Chatterjee, MD, Marc R. Moon, MD, Scott A. LeMaire, MD, Joseph S. Coselli, MD

“…Using clinical and selected operative variables, we built 4 predictive models: multivariable logistic regression (MLR), random forest, support vector machine, and gradient boosting machine. …”
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Impact of ITH on PRAD patients and feasibility analysis of the positive correlation gene MYLK2 applied to PRAD treatment by Chuanyu Ma, Chuanyu Ma, Guandu Li, Xiaohan Song, Xiaochen Qi, Tao Jiang

“…The results of the CMap data suggested that NU.1025 was the most likely drug to treat PRAD. The results of our machine learning model constructed based on ITH-score suggest that the random survival forest (RSF) model performs well in both the training and validation sets and has the potential to be used as a clinical prediction model. …”
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Cloud-Driven Data Analytics for Growing Plants Indoor by Nezha Kharraz, István Szabó

“…Built with Apache NiFi (Niagara Files), the pipeline facilitates real-time ingestion, processing, and storage of IoT sensor data measuring light, moisture, and nutrient levels. Machine learning models, including SVM (Support Vector Machine), Gradient Boosting, and DNN (Deep Neural Networks), analyzed 12 weeks of sensor data to predict growth trends and optimize thresholds. …”
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An empirical study of the naïve REINFORCE algorithm for predictive maintenance by Rajesh Siraskar, Satish Kumar, Shruti Patil, Arunkumar Bongale, Ketan Kotecha, Ambarish Kulkarni

“…Our broad goal was to study model performance under four scenarios: (1) simulated tool-wear data, (2) actual tool-wear data (benchmark IEEEDataPort PHM Society datasets), (3) univariate state with added noise levels and a random chance of break-down, and finally (4) complex multivariate state. …”
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Special Issue on Contemporary Research Studies in Operations Research, Business Analytics, and Business Intelligence by Viswanath Kumar Ganesan, S. Vinodh, Malolan Sundararaman, M. Vimala Rani, M. Mathirajan

“…Advanced Scheduling Algorithms: Creating advanced meta-heuristic algorithms using efficient dispatching rule-based heuristics for dynamic scheduling of non-identical parallel batch processing machines with eligibility constraints. 8. Hybrid Anomaly Detection: Proposing a hybrid framework that combines multivariate extension of Hierarchical Temporal Memory with multivariate Sequential Probability Ratio Test for enhanced anomaly detection. 9. …”
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TECHNOLOGICAL ADVANCES IN ELECTROPLATING: ARTIFICIAL INTELLIGENCE TO PREDICT ZINC COATING THICKNESS ON SAE 1008 LOW CARBON STEELS by Luciano M. L. de Oliveira, Fabiana L. da Silva, Paulo R. Janissek, Juliano C. Toniolo

“…Statistical analysis and supervised machine learning algorithms, including multivariate regression, random forest, and extreme gradient boosting (XGBoost), were employed to develop prediction models. …”
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The aggrephagy-related gene TUBA1B influences clinical outcomes in glioma patients by regulating the cell cycle by Zesheng Sun, Pengcheng Huang, Jialiang Lin, Guiping Jiang, Jian Chen, Qianqian Liu

“…TUBA1B, identified as a key gene through machine learning, was incorporated into a nomogram model using multivariate Cox regression. …”
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Associations between the intake of single and multiple dietary vitamins and depression risk among populations with chronic kidney disease by Chunli Yu, Kun Liu, Weiguo Yao, Dingzhong Tang

“…This study aimed to explore the effects of individual vitamin intakes and the joint effect of the intake of multiple vitamins (including vitamins A, B1, B2, B6, B12, C, D, E, and K) on depression risk in participants with CKD.MethodsA total of 3,123 participants with CKD (weighted n = 25,186,480) from the National Health and Nutrition Examination Survey database from 2007 to 2014 were included. Weighted multivariate logistic regression models were utilized to analyze the associations of individual dietary vitamin intakes with depression risk. …”
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Integrating Drone Truthing and Functional Classification of Remote Sensing Time Series for Supervised Vegetation Mapping by Giacomo Quattrini, Simone Pesaresi, Nicole Hofmann, Adriano Mancini, Simona Casavecchia

“…Ground truthing plays a crucial role in ensuring the accuracy of supervised remote sensing maps, as it provides the high-quality reference data needed for model training and validation. However, traditional ground truthing methods are labor-intensive, time-consuming and restricted in spatial coverage, posing challenges for large-scale or complex landscapes. …”
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Assessing Milk Authenticity Using Protein and Peptide Biomarkers: A Decade of Progress in Species Differentiation and Fraud Detection by Achilleas Karamoutsios, Pelagia Lekka, Chrysoula Chrysa Voidarou, Marilena Dasenaki, Nikolaos S. Thomaidis, Ioannis Skoufos, Athina Tzora

“…Robust chemometric approaches are increasingly integrated with proteomic pipelines to handle high-dimensional datasets and enhance classification performance. Multivariate techniques, such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), are frequently employed to extract discriminatory features and model adulteration scenarios. …”
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Advanced feature engineering in Acute:Chronic Workload Ratio (ACWR) calculation for injury forecasting in elite soccer. by Jaime B Matas-Bustos, Antonio M Mora-García, Moisés de Hoyo Lora, Alejandro Nieto-Alarcón, Francisco T Gonzalez-Fernández

“…Our approach makes traditional workload metrics suitable for modern machine learning. Using real-world data from an elite soccer team competing in LaLiga (Spain's top division) and UEFA tournaments, we conducted exploratory and confirmatory analyses comparing multivariate models trained on FWF-derived features against those using traditional ACWR calculations. …”
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Intelligent ESG portfolio optimization: A multi-objective AI-driven framework for sustainable investments in the Indian stock market by Apurv Gaurav, Kripamay Baishnab, Piyush Kumar Singh

“…It demonstrates on 30 randomly selected ESG-ranked Indian stocks from diverse sets, and simulates a retail portfolio scenario by leveraging advanced machine learning and optimization techniques. In first stage, a Multivariate Bidirectional Long Short-Term Memory (MBi-LSTM) network is utilized to enhance return prediction accuracy, capturing the market’s nonlinear dynamics. …”
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SPP1 expression indicates outcome of immunotherapy plus tyrosine kinase inhibition in advanced renal cell carcinoma by Xianglai Xu, Jinglai Lin, Jiahao Wang, Ying Wang, Yanjun Zhu, Jiajun Wang, Jianming Guo

“…A significant increase in the abundance of Tregs was observed in tumors with high levels of SPP1. Additionally, a machine-learning-based model was constructed to predict the benefit of IO-TKI treatment. …”
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ReliefSeq: a gene-wise adaptive-K nearest-neighbor feature selection tool for finding gene-gene interactions and main effects in mRNA-Seq gene expression data. by Brett A McKinney, Bill C White, Diane E Grill, Peter W Li, Richard B Kennedy, Gregory A Poland, Ann L Oberg

“…Relief-F is a nonparametric, nearest-neighbor machine learning method that has been successfully used to identify relevant variables that may interact in complex multivariate models to explain phenotypic variation. …”
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Transformer-Based Downside Risk Forecasting: A Data-Driven Approach with Realized Downward Semi-Variance by Yuping Song, Yuetong Zhang, Po Ning, Jiayi Peng, Chunyu Kao, Liang Hao

“…In this paper, the RDS rolling prediction performance of the traditional econometric model, machine learning model, and deep learning model is discussed in combination with various relevant influencing factors, and the sensitivity analysis is further carried out with the rolling window length, prediction length, and a variety of evaluation methods. …”
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Predicting High-Grade Patterns in Stage I Solid Lung Adenocarcinoma: A Study of 371 Patients Using Refined Radiomics and Deep Learning-Guided CatBoost Classifier by Hong Zheng MS, Wei Chen MS, Jun Liu MD, Lian Jian MD, Tao Luo BS, Xiaoping Yu MD

“…Employing redundancy and the least absolute shrinkage and selection operator regression, a radiomics model was developed. Subsequently, radiomics refinement and deep learning features were employed using a machine learning algorithm to construct the RRDLC-Classifier, which aims to predict high-grade patterns in clinical stage I solid LADC. …”
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A new online dynamic nomogram based on the inflammation burden index to predict cardiac injury after antitumor therapy in lung cancer patients by Yumin Wang, Chunyan Huan, Huijuan Pu, Guodong Wang, Yan Liu, Xiuli Zhang, Chengyang Li, Jie Liu, Wanling Wu, Defeng Pan

“…Statistical analysis using SPSS 22.0 and R 4.4.1, including machine learning algorithms and multivariate logistic analysis, identified independent predictors of cardiac injury. …”
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Non-small cell lung cancer patients treated with Anti-PD1 immunotherapy show distinct microbial signatures and metabolic pathways according to progression-free survival and PD-L1 s... by David Dora, Balazs Ligeti, Tamas Kovacs, Peter Revisnyei, Gabriella Galffy, Edit Dulka, Dániel Krizsán, Regina Kalcsevszki, Zsolt Megyesfalvi, Balazs Dome, Glen J. Weiss, Zoltan Lohinai

“…We confirmed the predictive role of PFS-related key bacteria with multivariate statistical models (Lasso- and Cox-regression) and validated on an additional patient cohort (n = 60). …”
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