Which Parameter Related to Low-Density Lipoprotein Cholesterol is Superior for Predicting the Recurrence of Myocardial Infarction in Young Patients with Previous Coronary Heart Dis... by Feng Xu, Hao-Ran Xing, Hong-Xia Yang, Jin-Wen Wang, Xian-Tao Song, Hui-Juan Zuo

“…While LDL-C <1.8 mmol/L at baseline showed a slightly lower cumulative incidence of MI than LDL-C ≥1.8 mmol/L, the difference was not statistically significant (log-rank p = 0.335). Reductions in LDL-C levels of ≥50% and the patterns of change did not correlate with decreased MI risk. …”
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Using Permutation-Based Feature Importance for Improved Machine Learning Model Performance at Reduced Costs by Adam Khan, Asad Ali, Jahangir Khan, Fasee Ullah, Muhammad Faheem

“…These selected features were used to retrain the ML models without hyperparameters (default settings) to determine whether similar performance could be achieved at low computational cost. …”
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Land Use Optimization and Simulation in the Dongjiang River Basin with a Low-Carbon Orientation by He Yanhu, Wu Luyan, Lin Zeyu

“…A CA-Markov model was employed to simulate the land use spatial pattern in the river basin for 2025. …”
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Monitoring and prediction spatiotemporal vegetation changes using NDVI index and CA-Markov model (case study: Kermanshah city) by Shadman Darvishi, Karim Solaimani

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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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A Hybrid Model for Soybean Yield Prediction Integrating Convolutional Neural Networks, Recurrent Neural Networks, and Graph Convolutional Networks by Vikram S. Ingole, Ujwala A. Kshirsagar, Vikash Singh, Manish Varun Yadav, Bipin Krishna, Roshan Kumar

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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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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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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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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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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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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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Facilitating a high-quality dietary pattern induces shared microbial responses linking diet quality, blood pressure, and microbial sterol metabolism in caregiver-child dyads by Emily B. Hill, Li Chen, Michael T. Bailey, Amrik Singh Khalsa, Ross Maltz, Kelly Kelleher, Colleen K. Spees, Jiangjiang Zhu, Brett R. Loman

“…Herein, we targeted low-resource caregiver-child dyads for a 10-week, randomized, controlled, multifaceted lifestyle intervention including: nutrition and physical activity education, produce harvesting, cooking demonstrations, nutrition counseling, and kinetic activites; to evaluate its effects on dietary patterns, CVD risk factors, and microbiome composition. …”
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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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FST and genetic diversity in an island model with background selection. by Asad Hasan, Michael C Whitlock

“…Here, we derive novel theory to predict the effects of migration on background selection experienced by a subpopulation and extend previous theory from the interference selection regime to make predictions in an island model. …”
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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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Spatiotemporal trends of Type 2 diabetes due to low physical activity from 1990 to 2019 and forecasted prevalence in 2050: A Global Burden of Disease Study 2019 by Shujin Fan, Jin Xu, Jinli Wu, Li Yan, Meng Ren

“…Estimated Annual Percentage Change (EAPC) assessed trends, and Bayesian models predicted future patterns. Results: In 2019, LPA accounted for a substantial 8.5% of T2DM deaths and 6.9% of DALYs, representing a noticeable rise since 1990. …”
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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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