Simulating the Carbon, Nitrogen, and Phosphorus of Plant Above-Ground Parts in Alpine Grasslands of Xizang, China by Mingxue Xiang, Gang Fu, Jianghao Cheng, Tao Ma, Yunqiao Ma, Kai Zheng, Zhaoqi Wang

“…Therefore, the random forest algorithm based on climate data and/or the NDVImax demonstrated superior predictive performance in modeling these biogeochemical parameters.…”
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Evaluation of the impact of COVID-19 on hepatitis B in Henan Province and its epidemic trend based on Bayesian structured time series model by Xinxiao Li, Yanyan Li, Shushuo Xu, Penghao Wang, Meng Hu, Haibin Li, Yongbin Wang

“…To assess how well the BSTS algorithm performs predictions, we split the observations into various training and testing ranges. …”
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Association of estimated glucose disposal rate with atrial fibrillation, heart failure and cardiovascular mortality in patients with diabetes: a prospective cohort study from the U... by Zhen Tan, Yijun Liu, Lei Liu, Shuang Li, Xinrui Xue, Xiaoping Li, Hongqiang Ren

“…Polygenic Risk Score (PRS) analysis evaluated the joint effects of eGDR and PRS. Boruta algorithm filtered key predictive variables. Subgroup analysis was performed using cardiovascular high-risk factors, and mediation analysis explored the relationships of eGDR with the outcomes.ResultsSubjects with higher eGDR were more likely to be female, younger, more physically active, non-smoker, and non-drinker. …”
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PDT-regulated immune gene prognostic model reveals tumor microenvironment in colorectal cancer liver metastases by Jiachi Xu, Hui Zhou, Zhongtao Liu, Yunpeng Huang, Zijian Zhang, Heng Zou, Yongxiang Wang

“…By using the weighted gene co-expression network analysis (WGCNA) algorithm, key gene modules related to metastasis in colorectal cancer were identified. …”
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DETERMINATION OF TEMPERATURE-TIME PARAMETERS OF GROWING SINGLE CRYSTALS β-BaB₂O₄ BY MODIFIED CZOCHRALSKI METHOD by S. A. Guretskii, I. M. Kolesova, A. V. Kravtsov, A. A. Linkevish, K. L. Trukhanava, V. B. Malyutin, V. L. Kolpashikov, S. YU. Yanovsky

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Enhancing Lung Ultrasound Diagnostics: A Clinical Study on an Artificial Intelligence Tool for the Detection and Quantification of A-Lines and B-Lines by Mahdiar Nekoui, Seyed Ehsan Seyed Bolouri, Amir Forouzandeh, Masood Dehghan, Dornoosh Zonoobi, Jacob L. Jaremko, Brian Buchanan, Arun Nagdev, Jeevesh Kapur

“…In quantifying B-lines, the algorithm achieved a weighted kappa score of 0.77 (95% CI 0.74 to 0.80) and an ICC of 0.87 (95% CI 0.85 to 0.89), showing substantial agreement between the ground truth and predicted B-line counts. …”
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Mental health of university students twenty months after the beginning of the full-scale Russian-Ukrainian war by Marina Polyvianaia, Yulia Yachnik, Jörg M. Fegert, Emily Sitarski, Nataliia Stepanova, Irina Pinchuk

“…Correlation between variables was calculated with Pearson correlation, adjusted with Benjamini-Hochberg procedure. To develop the predictive model the XGBoost algorithm was employed, additionally, the SHAP algorithm was utilized. …”
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Enhancing cotton irrigation with distributional actor–critic reinforcement learning by Yi Chen, Meiwei Lin, Zhuo Yu, Weihong Sun, Weiguo Fu, Liang He

“…Accurate predictions of irrigation’s impact on crop yield are crucial for effective decision-making. …”
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Fourier Analysis of CMFD Method in Cylindrical Geometry by WEN Yuchen, HAO Chen, WANG Yizhen

“…The coarse mesh finite difference (CMFD) method has been widely adopted as an acceleration algorithm for neutron transport calculations, effectively reducing computational costs and improving convergence. …”

Systematic pan-cancer analysis identifies PKNOX1 as a potential prognostic and immunological biomarker and its functional validation by Kan Liu, Yongkang Xu, Ye Mao, Shumin Fu, Shenglan Huang, Jianbing Wu

“…The correlations between PKNOX1 expression and MDSC immune infiltration and immune cells were analyzed using the TIDE algorithm and the ESTIMATE algorithm. PKNOX1 -interacting proteins and expression-related genes were analysed via the STRING and TIMER 2.0 platforms, and the functions of PKNOX1 in tumors and the cell pathways involved were predicted via KEGG enrichment analysis. …”
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Mapping landforms of a hilly landscape using machine learning and high-resolution LiDAR topographic data by Netra R. Regmi, Nina D.S. Webb, Jacob I. Walter, Joonghyeok Heo, Nicholas W. Hayman

“…We used 4200 landform observations (600 per landform) and eight topographic indices derived from 2 m, 5 m and 10 m resolution LiDAR DEM in random forest algorithm to develop 2 m, 5 m and 10 m resolution landform models. …”
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Modeling microbial drug-resistance: from mathematics to pharmacoeconomics by Yu. M. Gomon, M. A. Arepyeva, Yu. E. Balykina, A. S. Kolbin, A. A. Kurylev, M. A. Proskurin, S. V. Sidorenko

“…The proposed mathematical model allows one to predict the changes in microbial  drug-resistance and choose the optimal algorithm of AMA consumption able to restrain the growth of drug-resistance.…”
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Integration of the Adaptive Approach in HVAC System Operation: A Case Study by Pablo Aparicio-Ruiz, J. C. Ragel-Bonilla, Elena Barbadilla-Martín, José Guadix

“…Therefore, this paper investigates how the application of a setpoint based on adaptive comfort control (ACC) would affect occupants’ comfort considering mixed mode operation and based on the results of a longitudinal field study in an academic office building of a tertiary educational institution in southern Spain. The manuscript analyses the Thermal Preference Vote over 12 months in a mixed mode room with an HVAC system whose setpoint is adjusted with a previously calculated adaptive algorithm for the building. …”
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Numerical Simulation of Earthquake Impacts on Marine Structures: A Comprehensive Review by Adel Kabi, Jersson X. Leon-Medina, Francesc Pozo

“…Furthermore, integrating laboratory and field data into unified frameworks will significantly improve the precision and practicality of these models, offering robust tools for predicting earthquake and wave impacts on marine environments.…”
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Quantum machine learning regression optimisation for full-scale sewage sludge anaerobic digestion by Yomna Mohamed, Ahmed Elghadban, Hei I Lei, Amelie Andrea Shih, Po-Heng Lee

“…We, thus, propose a hybrid quantum-classical machine learning (Q-CML) regression algorithm using a quantum circuit learning (QCL) strategy. …”
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The Use of General Inverse Problem Platform (GRIPP) as a Robust Backtracking Solution by Nikolas Gomes Silveira de Souza, Jader Lugon, Alexandre Macedo Fernandes, Ramiro Joaquim de Jesus Neves, Antônio José da Silva Neto

“…The optimization yielded a solution with a mean error of 0.019 degrees between predicted and observed tracer locations. This demonstrates the potential of GRIPP and GenSA for solving practical environmental problems in coastal regions. …”
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Machine learning-based multiphysics model for corrosion fatigue crack propagation in aluminum alloy by Hongkun Wang, Zhenshuang Wu, Dongmei Jiang, Haitao Wang, En-Hou Han

“…The present study predicted the corrosion fatigue crack growth rate of 7075-T7651 aluminum alloy under cyclic sinusoidal loading in an aqueous solution. …”
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Intelligent design of mixture proportions of manufactured sand concrete from environmental, economical and mechanical perspectives by Junfei Zhang, Changhai Xu, Lei Zhang, Ling Wang

“…This study proposes a multi-objective optimization (MOO) method based on machine learning (ML) and the non-dominated sorting genetic algorithm II (NSGA-II) to optimize MSC mixtures. The results indicate that the extremely randomized trees (ERT) model exhibits the best predictive performance for UCS, with an R value of 0.988 on the test set. …”
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Numerical modeling of electromagnetic wave propagation in spatially-varying evaporation duct conditions via 3D parabolic equation method by Hanjie Ji, Hanjie Ji, Lixin Guo, Yan Zhang, Tianhang Nie, Yiwen Wei, Jinpeng Zhang, Qingliang Li, Xiangming Guo, Yusheng Zhang

“…An even-odd splitting Fourier transform algorithm enables efficient computational analysis of EM wave propagation across azimuthal planes. …”
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Robust Representation Learning Based on Deep Mutual Information for Scene Classification Against Adversarial Perturbations by Linjuan Li, Gang Xie, Haoxue Zhang, Xinlin Xie, Heng Li

“…However, deep learning models used for scene classification are highly vulnerable to adversarial samples, resulting in incorrect predictions and posing significant risks. While most current methods focus on improving adversarial robustness, they face a trade-off that compromises accuracy on clean, unperturbed images. …”
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