Machine learning approaches for predicting the structural number of flexible pavements based on subgrade soil properties by Asadullah Ziar

“…Abstract This study presents a machine learning approach to predict the structural number of flexible pavements using subgrade soil properties and environmental conditions. …”
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Association between the nutritional inflammation index and mortality among patients with sepsis: insights from traditional methods and machine learning-based mortality prediction by Yuanshuo Ge, Ding Hu, Zhe Wang, Cheng Zhang

“…However, its prognostic significance in sepsis remains unclear. This study aims to evaluate the association between ANLR and mortality in sepsis patients using both traditional statistical methods and machine learning models. …”
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Study of aging life for EMU arresters based on aging tests and principal component analysis method by LI Ying, SONG Yanjun, XU Yuqi, LIU Kai, CHEN Kui, GAO Guoqiang

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Machine Learning-Driven Discovery and Evaluation of Antimicrobial Peptides from <i>Crassostrea gigas</i> Mucus Proteome by Jingchen Song, Kelin Liu, Xiaoyang Jin, Ke Huang, Shiwei Fu, Wenjie Yi, Yijie Cai, Ziniu Yu, Fan Mao, Yang Zhang

“…We conducted proteome sequencing of <i>C. gigas</i> mucous proteins, used the iAMPCN model for peptide activity prediction, and evaluated the antimicrobial, hemolytic, and cytotoxic capabilities of six peptides. …”
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Performance, emissions, and combustion analysis of catalytic hydrothermal liquefaction derived fuel from Arthrospira platensis in IC engines: A predictive approach using least squa... by Mozas Santhose Kumar J, Prakash Ramakrishnan, Padmanathan Panneerselvam

“…A multiple linear regression model, developed using the least squares method, predicted blend ratio (R² = 0.96, RMSE = 2.20) and load (R² = 0.9995, RMSE = 1.11) based on performance and emission parameters. …”
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Evaluation of postoperative delirium in cardiac surgery patients with the SDACS screening tool: a multicenter-multiphase study by Hosein Mahmoudi, Athanasios Chalkias, Ali Moradi, Seyed Tayeb Moradian, Seyed Mohammad Reza Amouzegar, Amir Vahedian-Azimi

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A Team-Compatibility Decision Support System for the National Football League by Young W.A., Weckman G.R.

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Soil moisture forecasting from sensors-based soil moisture, weather and irrigation observations: A systematic review by Iustina Ivanova

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Methods of a Priori Statistical Analysis of Disturbed Motion of Aircraft in Turbulent Environments by Alexander S. Ermilov, Olga A. Saltykova

“…The article discusses the methods of a priori statistical analysis used for predicting perturbed motion of aircraft in turbulent environments. …”
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Combining Environmental Variables and Machine Learning Methods to Determine the Most Significant Factors Influencing Honey Production by Johanna Ramirez-Diaz, Arianna Manunza, Tiago Almeida de Oliveira, Tania Bobbo, Francesco Nutini, Mirco Boschetti, Maria Grazia De Iorio, Giulio Pagnacco, Michele Polli, Alessandra Stella, Giulietta Minozzi

“…This study aimed to identify the most important features and predict Total Honey Harvest (THH) by combining machine learning (ML) methods with climatic conditions and environmental factors recorded from the winter before and during the harvest season. …”
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Micromechanical modeling and evaluation of CNTs reinforced magnesium matrix composites based on stress softening modified constitutive model by Mingjie Shen, Baojian Han, Tao Ying, Jingya Wang, Liping Zhou, Xiangzhen Xue, Yang Gao, Zongyang Yang

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Comprehensive evaluation of U-Net based transcranial magnetic stimulation electric field estimations by Taylor A Berger, Kathleen Mantell, Zachary Haigh, Nipun Perera, Ivan Alekseichuk, Alexander Opitz

“…Our findings indicate that while deep learning has the potential to significantly accelerate electric field predictions, the precision it achieves needs to be evaluated for the specific TMS application.…”
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Enhancing phase change thermal energy storage material properties prediction with digital technologies by Minghao Yu, Jing Liu, Cheng Chen, Mingyue Li

“…The method integrates MD simulations for atomic-level interactions using Lennard-Jones and embedded-atom method (EAM) potentials, FEM-based continuum mechanics for stress-strain analysis and thermal response evaluation, and ML techniques trained on multiscale descriptors (e.g., bond energy, stress tensor, coordination number) to model nonlinear property relations and accelerate design iteration. …”
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Construction and evaluation of a triage assessment model for patients with acute non-traumatic chest pain: mixed retrospective and prospective observational study by Xuan Zhou, Gangren Jian, Yuefang He, Yating Huang, Jie Zhang, Shengfang Wang, Yunxian Wang, Ruofei Zheng

“…A nomogram was built based on the predictors, and an internal evaluation was performed using bootstrap resampling methods. …”
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Experimental and Numerical Study to Enhance Granule Control and Quality Predictions in Pharmaceutical Granulations by Maroua Rouabah, Inès Esma Achouri, Sandrine Bourgeois, Stéphanie Briançon, Claudia Cogné

“…A systematic model employing the discrete element method (DEM) was developed herein to gain insights into and better control this process. …”
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Development of a Numerical Prediction Method for the Strain Energy Density of Welded Joints Using Structural Stresses Derived from Nodal Forces by Simone Lucertini, Giulia Morettini, Filippo Cianetti

“…The analysis considers plates of different thicknesses and weld sizes. The proposed method combines the two mentioned approaches to exploit the advantages of both, allowing a fast preliminary investigation of numerous and complex welded joints within a simplified model, with a coarse mesh and no geometric pre-processing required, while keeping a good adherence to the precision benefits given by local energy evaluations. …”
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Optimized customer churn prediction using tabular generative adversarial network (GAN)-based hybrid sampling method and cost-sensitive learning by I Nyoman Mahayasa Adiputra, Paweena Wanchai, Pei-Chun Lin

“…Methods To optimize the performance of classical machine learning on customer churn prediction tasks, this study introduces an extension framework called CostLearnGAN, a tabular generative adversarial network (GAN)-hybrid sampling method, and cost-sensitive Learning. …”
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Predicting Protein Interactions Using a Deep Learning Method-Stacked Sparse Autoencoder Combined with a Probabilistic Classification Vector Machine by Yanbin Wang, Zhuhong You, Liping Li, Li Cheng, Xi Zhou, Libo Zhang, Xiao Li, Tonghai Jiang

“…To further evaluate the performance of our method, we compare it with the support vector machine- (SVM-) based method. …”
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Detection of FAM172A expressed in circulating tumor cells is a feasible method to predict high-risk subgroups of colorectal cancer by Chun-Hui Cui, Ri-hong Chen, Duan-Yang Zhai, Lang Xie, Jia Qi, Jin-Long Yu

“…Mesenchymal circulating tumor cells and FAM172A detection may predict highrisk stage II colorectal cancer. Our research proved that circulating tumor cells were feasible surrogate samples to detect gene expression and could serve as a predictive biomarker for tumor evaluation.…”
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Nonlinear mixed‐effects modeling as a method for causal inference to predict exposures under desired within‐subject dose titration schemes by Christian Bartels, Martina Scauda, Neva Coello, Thomas Dumortier, Björn Bornkamp, Giusi Moffa

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