Unveiling postpartum PTSD: predicting risk factors using decision trees and logistic regression in Chinese women by Xiao Fei Nie, Lan Lan Xu, Wen Ping Guo, Jin Hui Li, Li Cheng, Tao Tao Zhang, Jun-Yan Li

“…However, no studies have yet integrated both approaches to investigate postpartum posttraumatic stress disorder (PP-PTSD). This study aims to explore the factors associated with postpartum posttraumatic stress disorder (PP-PTSD) in Chinese women using decision tree and logistic regression models, while also comparing the predictive performance of both approaches. …”
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Development of a high-performing, cost-effective and inclusive Afrocentric predictive model for stroke: a meta-analysis approach  by M Nweke, P Oyirinnaya, P Nwoha, SB Mitha, N Mshunqane, N Govender, M Ukwuoma, SC Ibeneme

“…Abstract Background Predicting stroke risk is critical for preventive interventions. …”
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A Hybrid Model for Predicting Steering Brake Squeal Based on Multibody Dynamics and Finite Element Methods by Lijun Zhang, Yongchao Dong, Dejian Meng, Wenbo Li

“…Under the conditions of squealing, the loading of sprung mass is transferred, and lateral force is generated on the tire, resulting in stress and deformation of the suspension system. To predict the steering brake squeal propensity and explore its mechanism, we established a hybrid model of multibody dynamics and finite element methods to transfer the displacement values of each suspension connection point between two models. …”
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Adverse outcome pathway (AOP) framework for predicting toxic mechanisms in E-cigarette-induced lung injury by Yan Chen, Hongqian Jiang, Wei Liu, Zhenguang Du, Zhenyuan Wang, Zhicheng Zhou, Fusheng Chi

“…This AOP framework establishes a predictive paradigm linking structural properties of nicotine delivery systems to signaling perturbations, advancing toxicity assessment for next-generation tobacco products.…”
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Incorporating soil moisture data into a machine learning framework improved the predictive accuracy of corn yields in the U.S. by Bishwoyog Bhattarai, Zachary Leasor, André Fróes de Borja Reis

“…We hypothesize that incorporating soil moisture and temperature data from land surface models into a ML framework will enhance accuracy of corn yield predictions. To test this, we employed XGBoost algorithm using long-term (2010–2022) yield data collected from multi-environment corn trials conducted by the Variety Testing Program at the University of Missouri, along with weather data from Daymet database, soil data from North American Land Data Assimilation System (NLDAS-2), and POLARIS soil database. …”
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Forming limit prediction of advanced high-strength steels (AHSS) using an enhanced ductile damage model by Nguyen H. Hao

“… This paper presents prediction of forming limits of Advanced High-Strength Steels (AHSS) DP980 by adopting an enhanced ductile damage model to consider sheet metal anisotropy. …”
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Improved Deflection Prediction Model for PSC Box Girder with Stay Cable System during Tensioning Phase by Gangnian Xu, Weimin Xu, Zhanhong Wang, Ruishuo Zhang

“…To improve the accuracy of deflection prediction for prestressed concrete (PSC) box-girder bridges with a stay cable system (SCS) during tensioning, this study employs the Latin hypercube sampling (LHS) technique to sample random variables affecting long-term deflection of the main girder. …”
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Using machine learning to predict anesthetic dose in fish: a case study using nutmeg oil by Mert Minaz, Cem Alparslan, Akif Er

“…Application of anesthetic chemicals in aquaculture is important to minimize stress under normal operations such as handling, transport, and artificial breeding. …”
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Suppression of p53-inducible gene 3 is significant for glioblastoma progression and predicts poor patient prognosis by Jishu Quan, Yong Li, Meihua Jin, Dunfu Chen, Xuezhe Yin, Ming Jin

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Histogram-based gradient boosting machine with SHAP-driven interpretability for predicting intensity of urban heat Island effect by Nhat-Duc Hoang

“…Abstract This study presents a novel framework for the analysis and prediction of urban heat stress. To conduct the analysis, urban Land Surface Temperature (LST) data were retrieved from Landsat 8 during the dry seasons in 2006, 2014, and 2022. …”
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Predicting yellow mosaic disease severity in yardlong bean using visible imaging coupled with machine learning model by Abhishek Kumar Dubey, Prakash Kumar Jha, Kumari Shubha, RN Singh, Manisha Tamta, Sonam Sah, Santosh Kumar, Sanjeev Kumar, Rakesh Kumar, Kirti Saurabh, Rajeev Kumar, Anup Das, P. V. V. Prasad, Arbind Kumar Choudhary

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A Metaheuristic-Trained Wavelet Neural Network for Predicting of Soil Liquefaction Based on the Standard Penetration Test Results by Ali Reza Ghanizadeh, Ahmad Aziminejad

“…The developed WNN model is able to predict liquefaction with an overall accuracy of 96.52% based on two input variables, namely modified cyclic stress ratio (CSR7.5) and SPT number (N1,60). …”
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Predicting soil chemical characteristics in the arid region of central Iran using remote sensing and machine learning models by Azita Molaeinasab, Hossein Bashari, Mostafa Tarkesh Esfahani, Saeid Pourmanafi, Norair Toomanian, Bahareh Aghasi, Ahmad Jalalian

“…For CaSO4, Band 5 (B5) and TCB were the most effective, whereas SO4 predictions were driven by TCB along with Bands 5 and 7. …”
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Mobile applications for anxiety and stress reduction: potential for preventing noncommunicable diseases by K. A. Zamyatin, D. I. Nozdrachev, M. N. Solovieva

“…There are following key features of the Russianlanguage field of mobile applications for anxiety and stress reduction: a small number of applications, high prevalence of using breathing exercises, meditations and sound therapy, an extremely low disease specificity and focus mainly on helping with general symptoms of anxiety.…”
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Optimizing Tourism Offers with COPRAS-SVNS for Stress Reduction and Sustainability Promotion by Freddy Anaximandro Álvarez Lema, Manuel Antonio Abarca Zaquinaula, Milton Alberto Sampedro Arrieta, Rodolfo Matius Mendoza Poma

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From Crisis to Algorithm: Credit Delinquency Prediction in Peru Under Critical External Factors Using Machine Learning by Jomark Noriega, Luis Rivera, Jorge Castañeda, José Herrera

“…Among the evaluated models, <i>CNN</i> and <i>XGB</i> consistently demonstrate superior adaptability, defined as their ability to maintain strong predictive performance across diverse stress scenarios—including pandemic, climate, and unrest contexts—and to dynamically adjust to varying input distributions and portfolio conditions. …”
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The predictive utility of the E-PASS score for postoperative complications in robot-assisted partial nephrectomy: a retrospective cohort study by Cagatay Ozsoy, Erhan Ates, Resat Inal, Mucahit Gelmis, Sahin Kilic, Mutlu Ates

“…The Estimation of Physiologic Ability and Surgical Stress (E-PASS) score, originally developed for gastrointestinal surgery, combines physiological and surgical parameters for the prediction of postoperative risk. …”
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Integrated phenotypic analysis, predictive modeling, and identification of novel trait-associated loci in a diverse Theobroma cacao collection by Insuck Baek, Minhyeok Cha, Seunghyun Lim, Brian M. Irish, Sookyung Oh, Jishnu Bhatt, Rakesh K. Upadhyay, Moon S. Kim, Lyndel W. Meinhardt, Sunchung Park, Ezekiel Ahn

“…We also developed machine learning (ML) models for yield prediction and identified yield-associated SNP markers. …”
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Vowel Reduction In The English Of Educated Edo (nigerian) English Speakers by Julianah Ajoke Akindele

“… Educated Edo English (EEE) is a sub-group of Nigerian English (NigE), one of the new Englishes. …”
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Drug-Drug interactions prediction calculations between cardiovascular drugs and antidepressants for discovering the potential co-medication risks. by Tie Hua Zhou, Tian Yu Jin, Xi Wei Wang, Ling Wang

“…Predicting Drug-Drug Interactions (DDIs) enables cost reduction and time savings in the drug discovery process, while effectively screening and optimizing drugs. …”
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