TEENAGE PREGNANCY PREDICTION INDEX DURING THE ONLINE LEARNING PERIOD OF THE COVID-19 PANDEMIC by Eny Qurniyawati, Santi Martini, Fariani Syahrul, Jayanti Dian Eka Sari, Rahayu Lubis, Nayla Mohamed Gomaa Nasr

“…Aims: To determine the predictive index of risk variables for teen pregnancy throughout the COVID-19 pandemic's online learning period. …”
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Biomarker panels for improved risk prediction and enhanced biological insights in patients with atrial fibrillation by Pascal B. Meyre, Stefanie Aeschbacher, Steffen Blum, Tobias Reichlin, Moa Haller, Nicolas Rodondi, Andreas S. Müller, Alain Bernheim, Jürg Hans Beer, Giorgio Moschovitis, André Ziegler, Bianca Wahrenberger, Elia Rigamonti, Giulio Conte, Philipp Krisai, Leo H. Bonati, Stefan Osswald, Michael Kühne, David Conen

“…We identify 5 biomarkers including D-dimer, growth differentiation factor 15 (GDF-15), interleukin-6 (IL-6), N-terminal pro-B-type natriuretic peptide (NT-proBNP), and high-sensitivity troponin T (hsTropT) that independently predict cardiovascular death, stroke, myocardial infarction, and systemic embolism, significantly enhancing predictive accuracy. …”
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Prediction method of surface deformation around soft base dump based on viscoelastic theory by Dong WANG, Li YIN, Lanzhu CAO, Yiming FU, Liguo ZHANG, Chunhui ZHANG, Chunjian DING

“…A series of quantitative data is obtained through calculations and predictions of surface deformation on layered viscoelastic substrates under pile load actions in the discharging yard. …”
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Optimizing corn yield prediction: Integrating multi-temporal UAS data and machine learning by Huihui Zhang, Yuting Zhou, Shengfang Ma, Kevin Yemoto

“…The integration of VNIR and LWIR imagery improves yield prediction accuracy, particularly in water-stressed conditions, highlighting the importance of advanced sensing technologies and complex models such as RF and GB. …”
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Multi‐trait multi‐environment genomic prediction of preliminary yield trial in pulse crop by Rica Amor Saludares, Sikiru Adeniyi Atanda, Lisa Piche, Hannah Worral, Francoise Dariva, Kevin McPhee, Nonoy Bandillo

“…Multi‐trait multi‐environment enabled genomic prediction (MTME‐GP) offers a valuable alternative to predict missing phenotypes of selection candidates for multiple traits and diverse environments. …”
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MODIFIABLE AND NON-MODIFIABLE RISK FACTORS IN PREDICTING THE RECURRENCE OF ATRIAL FIBRILLATION IN PATIENTS WITH ARTERIAL HYPERTENSION by M. S. Arakelyan, N. G. Poteshkina, P. A. Mogutova

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Advancements in predicting soil liquefaction susceptibility: a comprehensive analysis of ensemble and deep learning approaches by Divesh Ranjan Kumar, Warit Wipulanusat

“…The BI-LSTM model has the highest accuracy, with 0.9791 in training and 0.8889 in testing, indicating strong predictive ability and good generalizability. LSTM follows closely with training and testing accuracies of 0.9433 and 0.8750, respectively, offering consistent performance. …”
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Comparison of hemodynamic effects and negative predictive value of normal adenosine gated myocardial perfusion scan with or without caffeine abstinence by Maseeh uz Zaman, Nosheen Fatima, Areeba Zaman, Unaiza Zaman, Rabia Tahseen

“…The aim of this study was to compare the hemodynamic changes and negative predictive value (NPV) of normal MPIs with adenosine stress performed with or without caffeine abstinence. …”
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CS03 | Khorana risk score and genomic profiling for prediction of venous thromboembolism in ovarian cancer

“…Figure 1. Evaluation of the predictive role of genomic mutations in patients with ovarian cancer. a. …”
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Compression-compression fatigue life prediction model of T300/69 laminates under edge impact by ZHANG Peng, LIU Jianhui, WEI Yaobing

“…Therefore, it is of practical engineering significance to establish a fatigue life prediction model for low-velocity impact at the edge. …”
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Contact fatigue limit prediction method for heavy-duty gears considering hardness gradient characteristics by Boyu Zhang, Yongyong He, Zhengwei Wang

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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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Evaluating the feasibility of using crystalline patterns induced by PBF-LB to predict strength enhancing orientations by José David Pérez-Ruiz, Luis Norberto López de Lacalle, Wilmer Velilla-Díaz, Jaime A. Mesa, Gaizka Gómez, Heriberto Maury, Gorka Urbikain, Haizea Gonzalez

“…A distinctive feature of the PBF-LB process is its capacity to develop crystalline patterns, which can be utilized to predict strength-enhancing orientations of the produced components. …”
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Biomarkers for intensive care unit-acquired weakness: a systematic review for prediction, diagnosis and prognosis by Jiamei Song, Ting Deng, Qingmei Yu, Xun Luo, Yanmei Miao, Leiyu Xie, Yongming Mei, Peng Xie, Shaolin Chen

“…Biomarkers offer potential for diagnosing, predicting, and prognosticating ICU-AW, but a comprehensive synthesis of the available evidence is still lacking. …”
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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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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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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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