Soil quality variation associated with land cover in the Peruvian jungle of the Junín region by Carlos Carbajal, Fernanda Moya-Ambrosio, Antony Barja, Elvis Ottos-Diaz, Cinthya Aguilar-Tito, Orlando Advíncula-Zeballos, Juancarlos Cruz-Luis, Richard Solórzano-Acosta

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ECG Screening in Athletes: A Systematic Review of Sport, Age, and Gender Variations by Adela Caramoci, Alina Maria Smaranda, Teodora Simina Drăgoiu, Ioana Anca Bădărău

“…Methods: A systematic review of observational studies published between 2015 and 2025 was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. …”
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Tropospheric ozone trends and attributions over East and Southeast Asia in 1995–2019: an integrated assessment using statistical methods, machine learning models, and multiple chem... by X. Lu, X. Lu, Y. Liu, Y. Liu, J. Su, J. Su, X. Weng, X. Weng, X. Weng, T. Ansari, Y. Zhang, G. He, G. He, Y. Zhu, Y. Zhu, H. Wang, H. Wang, G. Zeng, G. Zeng, J. Li, J. Li, C. He, C. He, S. Li, S. Li, T. Amnuaylojaroen, T. Butler, Q. Fan, Q. Fan, S. Fan, S. Fan, G. L. Forster, G. L. Forster, M. Gao, J. Hu, Y. Kanaya, M. T. Latif, K. Lu, P. Nédélec, P. Nowack, P. Nowack, B. Sauvage, X. Xu, L. Zhang, K. Li, J.-H. Koo, T. Nagashima

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Internal Benchmarking of Thermal Power Plants оf Electric Power Systems by E. M. Farhadzadeh, A. Z. Muradaliyev, Y. Z. Farzaliyev, U. K. Ashurova

“…The results of the studies performed using the simulation method made it possible to establish that the smallest correlation occurs between the integral indicator calculated as the arithmetic mean of random variables and the integral indicator calculated as the coefficient of variation of the same random variables. …”
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Predicting the impact of graphene oxide and its shapes on the thermal efficiency of Polyalphaolefin-40 using a mathematical model for wind turbine gearboxes by Aamina Ahsan, Amir Zanj, Ahsan Zafar, Najam Iqbal, Adeena Fatima, Wajid Khan, Muhammad Zain Yousaf, Baseem Khan, Josep M. Guerrero

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Low-carbon optimization scheduling of IES based on enhanced diffusion model for scenario deep generation by Danhao Wang, Daogang Peng, Dongmei Huang, Huirong Zhao, Bogang Qu

“…With the widespread integration of renewable energy sources into IES (Integrated Energy Systems), the uncertainties in renewable energy outputs and the volatility of user loads present new challenges for energy scheduling optimization. …”
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AngleCam: Predicting the temporal variation of leaf angle distributions from image series with deep learning by Teja Kattenborn, Ronny Richter, Claudia Guimarães‐Steinicke, Hannes Feilhauer, Christian Wirth

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A Systematic Review of the Advances and New Insights into Copy Number Variations in Plant Genomes by Saimire Silaiyiman, Jiaxuan Liu, Jiaxin Wu, Lejun Ouyang, Zheng Cao, Chao Shen

“…Copy number variations (CNVs), as an important structural variant in genomes, are widely present in plants, affecting their phenotype and adaptability. …”
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Nonlinear Dynamic Process Monitoring Based on Discriminative Denoising Autoencoder and Canonical Variate Analysis by Jun Liang, Daoguang Liu, Yinxiao Zhan, Jiayu Fan

“…In this article, we present a novel process monitoring approach, CVA-<i>DisDAE</i>, which integrates an improved Denoising Autoencoder (<i>DAE</i>) with Canonical Variate Analysis (CVA) to address the challenges posed by dynamic behaviors and nonlinear relationships in industrial processes. …”
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Deep plug-and-play denoising prior with total variation regularization for low-dose CT by Yinjin Ma, Yajuan Zhang, Lin Chen, Qiang Jiang, Fengjuan Shi, Biao Wei

“…We then train the DRBNet using a hybrid loss function combining L1 and multi-scale structural similarity (M-SSIM) losses, while regularizing the training with total variation (TV). After training, the DRBNet is integrated as a deep denoiser prior into a half-quadratic splitting-based method to solve the LDCT image denoising problem. …”
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Single Underwater Image Restoration Using Variational Framework Guided by Imaging Model With Noise by Chenggang Dai, Mingxing Lin

“…To address these corruptions, a novel method is proposed to reconstruct high-quality underwater images, which is designed by integrating imaging model with noise and variational framework. …”
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Harnessing dual variational autoencoders to decode microbe roles in diseases for traditional medicine discovery by Qing Ye, Yaxin Sun, Yaxin Sun, Yaxin Sun

“…This method innovatively integrates double variational autoencoders and multi-information fusion techniques. …”
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Localising loci underlying complex trait variation using Regional Genomic Relationship Mapping. by Yoshitaka Nagamine, Ricardo Pong-Wong, Pau Navarro, Veronique Vitart, Caroline Hayward, Igor Rudan, Harry Campbell, James Wilson, Sarah Wild, Andrew A Hicks, Peter P Pramstaller, Nicholas Hastie, Alan F Wright, Chris S Haley

“…Motivated by studies that demonstrate that loci contributing to trait variation may contain a number of different alleles, we have developed an analytical approach termed Regional Genomic Relationship Mapping that, like linkage-based family methods, integrates variance contributed by founder gametes within a pedigree. …”
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Daily Runoff Prediction Model Based on Multivariate Variational Mode Decomposition and Correlation Reconstruction by DING Jie, TU Peng-fei, FENG Yu, ZENG Huai-en

“…The original data included daily runoff from January 2005 to December 2012. [Methods] This study first employed Multivariate Variational Mode Decomposition(MVMD) to decompose the original daily runoff data from the two stations, reducing data complexity. …”
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DVAEGMM: Dual Variational Autoencoder With Gaussian Mixture Model for Anomaly Detection on Attributed Networks by Wasim Khan, Mohammad Haroon, Ahmad Neyaz Khan, Mohammad Kamrul Hasan, Asif Khan, Umi Asma Mokhtar, Shayla Islam

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Spatial variation of soil quality in key grain-producing areas of Qitai County, Xinjiang by FAN Zikang, WANG Chunxia, YU Jing, QIN Da, YANG Yuefa, WANG Hongxin

“…Classical statistical methods were used to analyze the correlations between indicators, and geostatistical methods were used to assess the spatial variation of each indicator. …”
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Influence of Modal Decomposition Algorithms on Nonlinear Time Series Machine Learning Prediction Models in Engineering: A Case Study of Subway Tunnel Settlement by Qingmeng Shen, Yuming Wu, Limin Wan, Qian Chen, Yue Li, Zichao Liao, Wenbo Wang, Feng Li, Tao Li, Jiajun Shu

“…The settlement values of subway tunnels during the construction period exhibit significant nonlinear and spatial–temporal variation characteristics. To overcome the problems of historical data interference and spatiotemporal characteristics in tunnel settlement prediction models, this paper proposes a tunnel settlement prediction method based on data decomposition, reconstruction, and optimization. …”
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Photon sensor-based monitoring of spatial variations in canopy FIPAR for crop growth assessment by Jian Wang, Zhenggui Zhang, Xin Li, Lu Feng, Xiaofei Li, Minghua Xin, Shiwu Xiong, Yingchun Han, Shijie Zhang, Xiaoyu Zhi, Beifang Yang, Guoping Wang, Yaping Lei, Zhanbiao Wang, Yabing Li

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Evaluating Spatio-Temporal Kriging with Machine Learning Considering the Sources of Spatio-Temporal Variation by Min Jeong, Hyeongmo Koo

“…Integrating spatio-temporal kriging with machine learning improves estimation accuracy by addressing complex spatial and temporal variations in spatio-temporal phenomena. …”
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Achieving excellent mechanical properties in CoNiCrMo multi-principal element alloys: A comprehensive approach integrating mean square atomic displacement and machine learning meth... by Yang Long, Wei Fang, Haoyang Yu, Tiexu Peng, Cong Li, Weiguang Liu, Chang Liu, Qinghe Jia, Yushui Chen, Jia Li, Fuxing Yin

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