Interpretable Machine Learning Models for PISA Results in Mathematics by Ismael Gomez-Talal, Luis Bote-Curiel, Jose Luis Rojo-Alvarez

“…By preprocessing the PISA dataset, we categorized students into Low, Medium, and High proficiency levels and employed various binary classification models to discern predictive patterns. …”
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The potential habitat of Phlomoides rotata in Tibet was based on an optimized MaxEnt model by Jun-Wei Wang, Jun-Wei Wang, Min Xu, Norbu Ngawang, Yonghao Chen, Ngawang Bonjor, Xiaoyan Jia, Zhefei Zeng, Zhefei Zeng, La Qiong, La Qiong

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Temporal Synchrony in Bodily Interaction Enhances the Aha! Experience: Evidence for an Implicit Metacognitive Predictive Processing Mechanism by Jiajia Su, Haosheng Ye

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Patterns of Factors in the National Institute on Aging Health Disparities Research Framework Domains and Mild Cognitive Impairment Risk by Maria Vassilaki, MD, PhD, Jeremiah A. Aakre, MPH, Timothy G. Lesnick, MS, Walter K. Kremers, PhD, Jonathan Graff-Radford, MD, David S. Knopman, MD, Thomas H. Mosley, PhD, B. Gwen Windham, MD, MHS, Michael E. Griswold, PhD, Yonas E. Geda, MD, MSc, Val J. Lowe, MD, Clifford R. Jack, Jr., MD, Ronald C. Petersen, MD, PhD, Prashanthi Vemuri, PhD

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Damage Assessment of Low-Velocity Impacted Sandwich Composite Structures Using X-Ray Micro-Computed Tomography by Tendai Chipanga, Ouassini Nemraoui, Fareed Ismail

“…High-level impacts resulted in near or full perforations, with more pronounced delamination at the bottom interface, and fibre fractures in the impact zone, displaying a distinctive diamond-like damage pattern. These findings can be instrumental in developing a predictive impact damage model.…”
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Lipid profile in hospitalized patients with COVID-19 depending on the outcome of its acute phase: data from the international registry "Dynamics analysis of comorbidities in SARS-C... by G. P. Arutyunov, E. I. Tarlovskaya, A. G. Arutyunov, Yu. N. Belenkov, A. O. Konradi, Yu. M. Lopatin, A. P. Rebrov, S. N. Tereshchenko, A. I. Chesnikova, G. G. Airapetyan, A. P. Babin, I. G. Bakulin, N. V. Bakulina, L. A. Balykova, A. S. Blagonravova, M. V. Boldina, M. I. Butomo, A. R. Vaisberg, A. S. Galyavich, V. V. Gomonova, N. Yu. Grigorieva, I. V. Gubareva, I. V. Demko, A. V. Evzerikhina, A. V. Zharkov, A. A. Zateyshchikova, U. K. Kamilova, Z. F. Kim, T. Yu. Kuznetsova, A. N. Kulikov, N. V. Lareva, E. V. Makarova, S. V. Malchikova, S. V. Nedogoda, M. M. Petrova, I. G. Pochinka, K. V. Protasov, D. N. Protsenko, D. Yu. Ruzanov, S. A. Saiganov, A. Sh. Sarybaev, N. M. Selezneva, A. B. Sugraliev, I. V. Fomin, O. V. Khlynova, O. Yu. Chizhova, I. I. Shaposhnik, D. A. Schukarev, A. K. Abdrakhmanova, S. A. Avetisyan, O. G. Avoyan, K. K. Azaryan, G. T. Aimakhanova, D. A. Aiypova, A. Ch. Akunov, M. K. Alieva, A. R. Almukhambedova, A. V. Aparkina, O. R. Aruslanova, E. Yu. Ashina, O. Yu. Badina, O. Yu. Barysheva, T. I. Batluk, A. S. Batchaeva, R. A. Bashkinov, A. M. Bitieva, I. U. Bikhteev, N. A. Borodulina, M. V. Bragin, V. A. Brazhnik, A. M. Budu, G. A. Bykova, K. R. Vagapova, D. D. Varlamova, N. N. Vezikova, E. A. Verbitskaya, O. E. Vilkova, E. A. Vinnikova, V. V. Vustina, E. A. Galova, V. V. Genkel, D. B. Giller, E. I. Gorshenina, E. V. Grigoryeva, E. Yu. Gubareva, G. M. Dabylova, A. I. Demchenko, O. Yu. Dolgikh, M. Y. Duishobaev, D. S. Evdokimov, K. E. Egorova, A. N. Ermilova, A. E. Zheldybaeva, N. V. Zarechnova, Yu. D. Zimina, S. Yu. Ivanova, E. Yu. Ivanchenko, M. V. Ilyina, M. V. Kazakovtseva, E. V. Kazymova, Yu. S. Kalinina, N. A. Kamardina, A. M. Karachenova, I. A. Karetnikov, N. A. Karoli, M. Kh. Karsiev, D. S. Kaskaeva, K. F. Kasymova, J. B. Kerimbekova, E. S. Kim, N. V. Kiseleva, D. A. Klimenko, A. V. Klimova, O. V. Kovalishena, S. V. Kozlov, E. V. Kolmakova, T. P. Kolchinskaya, M. I. Kolyadich, O. V. Kondryakova, M. P. Konoval, D. Yu. Konstantinov, E. A. Konstantinova, V. A. Kordyukova, E. V. Koroleva, A. Yu. Kraposhina, T. V. Kryukova, A. P. Kuznetsova, T. Yu. Kuzmina, K. V. Kuzmichev, Ch. K. Kulchoroeva, T. V. Kuprina, I. M. Kuranova, L. V. Kurenkova, N. Yu. Kurchugina, N. A. Kushubakova, V. I. Levankova, A. A. Ledyaeva, T. V. Lisun, V. E. Lisyanskaya, N. A. Lyubavina, N. A. Magdeeva, K. V. Mazalov, V. I. Mayseenko, A. S. Makarova, A. M. Maripov, N. V. Markov, A. A. Marusina, E. S. Melnikov, A. I. Metlinskaya, N. B. Moiseenko, F. N. Muradova, R. G. Muradyan, Sh. N. Musaelyan, E. S. Nekaeva, N. M. Nikitina, S. E. Nifontov, E. Yu. Obolentseva, A. A. Obukhova, B. B. Ogurlieva, A. A. Odegova, Yu. V. Omarova, N. A. Omurzakova, Sh. O. Ospanova, V. A. Pavlova, E. V. Pakhomova, L. D. Petrov, S. S. Plastinina, D. A. Platonov, V. A. Pogrebetskaya, D. V. Polyakov, D. S. Polyakov, E. V. Ponomarenko, L. L. Popova, A. A. Potanin, N. A. Prokofieva, Yu. D. Rabik, N. A. Rakov, A. N. Rakhimov, N. A. Rozanova, S. Serikbolkyzy, Ya. A. Sidorkina, A. A. Simonov, V. V. Skachkova, R. D. Skvortsova, D. S. Skuridin, D. V. Solovieva, I. A. Solovieva, I. M. Sukhomlinova, A. G. Sushilova, D. R. Tagaeva, Yu. V. Titoikina, E. P. Tikhonova, D. S. Tokmin, A. A. Tolmacheva, M. S. Torgunakova, K. V. Trenogina, N. A. Trostyanetskaya, D. A. Trofimov, M. A. Trubnikova, A. A. Tulichev, A. T. Tursunova, N. D. Ulanova, O. V. Fatenkov, O. V. Fedorishina, T. S. Fil, I. Yu. Fomina, I. S. Fominova, I. A. Frolova, S. M. Tsvinger, V. V. Tsoma, M. B. Cholponbaeva, T. I. Chudinovskikh, I. V. Shavrin, O. A. Shevchenko, D. R. Shikhaliev, E. A. Shishkina, K. Yu Shishkov, S. Yu. Shcherbakov, G. V. Shcherbakova, E. A. Yausheva

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Burden, patterns, and risk factors of esophageal cancer mortality in China, 2008-2021: a national mortality surveillance system data analysis by Yunfei Jiao, Tinglu Wang, Lin Fu, Ye Gao, Zhiyuan Cheng, Lei Xin, Jinfang Xu, Han Lin, Wei Wang, Maigeng Zhou, Jinlei Qi, Zhaoshen Li, Luowei Wang

“…Nationwide, individuals with agriculture-related occupations and low educational levels had significantly high risks of EC death. …”
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Prediction of Urban Construction Land Carbon Effects (UCLCE) Using BP Neural Network Model: A Case Study of Changxing, Zhejiang Province, China by Qinghua Liao, Xiaoping Zhang, Zixuan Cui, Xunxi Yin

“…The results demonstrate that the BP neural network model effectively predicts the different types of UCLCE, with an average error rate of 30.10%. (1) The total effect and intensity effect exhibit different trends in the study area, and a carbon effect table for different types of UCL is established. (2) The spatial distribution characteristics of UCLCE reveal a distinct reverse-L pattern (“┙”-shaped layout) with positive spatial correlation (Moran’s I = 0.11, <i>p</i> < 0.001). (3) The model’s core practical value lies in enabling forward-looking assessment of carbon effects in urban planning schemes and precise quantification of emissions reduction benefits. …”
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Histological Grade, Tumor Breadth, and Hypertension Predict Early Recurrence in Pediatric Sarcoma: A LASSO-Regularized Micro-Cohort Study by Alexander Fiedler, Mehran Dadras, Marius Drysch, Sonja Verena Schmidt, Flemming Puscz, Felix Reinkemeier, Marcus Lehnhardt, Christoph Wallner

“…PCA mapping revealed distinct outlier patterns correlating with high-risk profiles. <b>Conclusions</b>: Even in a small cohort, classical prognostic markers, such as tumor grade and size, retained predictive relevance, while hypertension emerged as a novel, potentially modifiable cofactor or indicator for recurrence. …”
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Simulation of Spatial and Temporal Patterns of Suitable Wintering Habitat for Hooded Crane (<i>Grus monacha</i>) Under Climate and Land Use Change Scenarios by Zeng Jiang, Mingqin Shao, Jianying Wang

“…In order to clarify the trends in the current and future suitable wintering areas for hooded cranes (<i>Grus monacha</i>), the MaxEnt model was applied to predict the distribution patterns and trends of hooded cranes based on 94 occurrence records and 23 environmental variables during the wintering periods from 2015 to 2024. …”
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Optimizing HVAC energy efficiency in low-energy buildings: a comparative analysis of reinforcement learning control strategies under Tehran climate conditions by Mohammad Anvar Adibhesami, Amir Hassanzadeh

“…This indicates enhanced adaptability to consistent daily trends and irregular periodicities, such as weather patterns. The proposed reinforcement learning method achieved energy savings of 10–15 percent compared to both rule-based and model predictive control and approximately 10 percent improvement over rule-based control, while employing fewer building features than existing state-of-the-art control systems.…”
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Machine learning-based prediction of optimal antenatal care utilization among reproductive women in Nigeria by Jamilu Sani, Adeyemi Oluwagbemiga, Mohamed Mustaf Ahmed

“…Machine learning (ML) offers a promising alternative that uncovers hidden patterns and improves prediction accuracy. Methods: This study used data from the 2018 Nigeria Demographic and Health Survey (NDHS), a nationally representative data set. …”
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Reconstruction of shrimp catches in Brazil based on generalized linear models by Matheus Lourenço Soares Silva, Humber Agrelli Andrade

“…The general category was the most relevant variable, whereas temperature indices showed reduction patterns in catches over time, which may indicate the likely effects of temperature increase on shrimp fisheries. …”
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Modeling student satisfaction in online learning using random forest by Jinlei Li, Xiaowei Chen

“…Partial dependence plots revealed threshold and saturation effects, highlighting complex nonlinear patterns missed by traditional linear models. However, performance declined in predicting low-satisfaction cases (AUC = 0.70), likely due to subgroup underrepresentation. …”
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A PSO-CNN-LSTM Model for Seismic Facies Analysis: Methodology and Applications by Luyao Liao, Huailai Zhou, Junping Liu, Jie Zhou, Donghang Zhang, Jian Wang

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The Space Conceptual Models and Water Vapor Characteristics of Typical Rainstorms during Plum Rain Season by Ying Tian, Rong Yao, Enrong Zhao, Qian Yao, Xiaolong Pan

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Modelling the protective efficacy of alternative delivery schedules for intermittent preventive treatment of malaria in infants and children. by Matthew Cairns, Azra Ghani, Lucy Okell, Roly Gosling, Ilona Carneiro, Francis Anto, Victor Asoala, Seth Owusu-Agyei, Brian Greenwood, Daniel Chandramohan, Paul Milligan

“…<h4>Methods and findings</h4>A mathematical model was developed to estimate the protective efficacy (PE) of IPT against clinical malaria in children aged 2-24 months, using entomological and epidemiological data from an EPI-IPTi trial in Navrongo, Ghana to parameterise the model. …”
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First-trimester triglyceride-glucose index and birth weight: a retrospective cohort mediation analysis of preterm birth and gestational complications by Jinhui Cui, Hui Jiang, Fei Huang, Mengjun Xie, Ziyi Cui, Xinyuan Chen, Liping OUYang, Ping Li, Yanling Wang

“…Abstract Background Insulin resistance during pregnancy, while physiologically adaptive to enhance fetal nutrient supply, becomes pathological when excessive, contributing to low birth weight (LBW). The triglyceride-glucose (TyG) index, a biomarker of insulin resistance, predicts gestational complications, but its pathways to birth weight disparities remain unclear. …”
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Drivers and forecasting of carbon emissions with extended LMDI and Bagging models: A case study of China's Bohai Rim region. by Wen Yu, Jianguo Lin, Shusheng Yin

“…Similarly, modal shifts and energy efficiency advancements positively impact emission reduction in the transportation sector. Furthermore, trends in carbon emissions are predicted using a bagging ensemble model for the Bohai Rim region's two municipalities and three provinces from 2024 to 2060. …”
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Insights of the asynchronous strain evolution of lean coal in uniaxial compression creep by Tingting Cai, Jia Liu, Zengchao Feng, Kai Wang, Yongxing Shen, Yulong Jiang, Bichuan Zhang

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