Prediction of Temperature Distribution on an Aircraft Hot-Air Anti-Icing Surface by ROM and Neural Networks by Ziying Chu, Ji Geng, Qian Yang, Xian Yi, Wei Dong

“…Two models, AlexNet combined with Proper Orthogonal Decomposition (POD-AlexNet) and multi-CNNs with GRU (MCG), are proposed by comparing several classic neural networks. …”
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Microbial biomass – not diversity – drives soil carbon and nitrogen mineralization in Spanish holm oak ecosystems by Elisa Bruni, Jorge Curiel Yuste, Lorenzo Menichetti, Omar Flores, Daniela Guasconi, Bertrand Guenet, Ana-Maria Hereș, Aleksi Lehtonen, Raisa Mäkipää, Marleen Pallandt, Leticia Pérez-Izquierdo, Etienne Richy, Mathieu Santonja, Boris Tupek, Stefano Manzoni

“…For this reason, most models predicting soil organic matter (SOM) dynamics at the ecosystem level do not explicitly describe the role of microorganisms as mediators of SOM decomposition. …”
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Assessing soil quality in association with frozen ground in the source areas of the Yangtze and Yellow Rivers, Qinghai-Tibet Plateau by Shizhen Li, Dongliang Luo, Jinniu Wang, Yanqiang Wei, Ziqiang Yuan

“…The regional average SQI in the SAYYR was 0.44, indicating moderate soil quality. Diagnostic modeling identified TN as the primary constraint on soil quality in the SAYYR, with an average obstacle rate of 22.4%. …”
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AN ANALYSIS OF THE DYNAMICS OF INVESTMENT SAVING AND ECONOMIC GROWTH IN TURKEY: 1950-2004 by Ercan Sarıdoğan, Sefer Şener, Deniz Şükrüoğlu

“…The methodology we applied depends on the time series econometric techniques which include analyzing the stationarity of the variables, cointegration, vector error correction mechanism, and Granger causality and vector auto regressive models with impulse-response and variance decomposition techniques. …”
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Allometric Equations for Aboveground Biomass Estimation in Natural Forest Trees: Generalized or Species-Specific? by Yuxin Shang, Yutong Xia, Xiaodie Ran, Xiao Zheng, Hui Ding, Yanming Fang

“…Key findings resolve the following hypotheses: (1) absence of strong phylogenetic signals validates generalized models for phylogenetically diverse communities; (2) ontogenetic regulation dominates error magnitude, particularly in early developmental stages; (3) differential modeling is recommended: species-specific equations for pure forests/seedlings vs. generalized equations for mixed mature forests. …”
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Short-Term Power Load Prediction Based on Level Processing Method and Improved GWO Algorithm by Yuntong Li

“…Comparative experiments are conducted among the proposed model, the long short-term memory model, as well as the variational mode decomposition model. …”
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Ensemble machine learning for predicting academic performance in STEM education by Aklilu Mandefro Messele

“…The model's effectiveness was assessed through metrics like accuracy, precision, recall, and F1 score. …”
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Motor imagery EEG signal classification using novel deep learning algorithm by Sathish Mathiyazhagan, M. S. Geetha Devasena

“…However, these technologies face challenges and exhibit reduced performances due to signal noise, inter-subject variability, and real-time processing demands. Thus, to overcome these limitations a novel model is presented in this research work for motor imagery (MI) EEG signal classification. …”
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Environmental Data Analytics for Smart Cities: A Machine Learning and Statistical Approach by Ali Suliman AlSalehy, Mike Bailey

“…This study analyzes spatiotemporal CO patterns and builds accurate predictive models using five years (2018–2022) of data from ten monitoring stations, combined with meteorological variables. …”
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A comprehensive review of machine learning applications in forecasting solar PV and wind turbine power output by Ian B. Benitez, Jai Govind Singh

“…Key features for SPVPO forecasting include solar irradiance, ambient temperature, and prior SPVPO, while wind speed, turbine speed, and prior wind power output are crucial for WTPO forecasting. Moreover, ensemble models, support vector machines, Gaussian processes, hybrid artificial neural networks, and decomposition-based hybrid models exhibit promising forecasting accuracy and reliability. …”
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Assessing CO2 Fluxes for European Peatlands in ORCHIDEE‐PEAT With Multiple Plant Functional Types by Liyang Liu, Chunjing Qiu, Yi Xi, Elodie Salmon, Aram Kalhori, Rebekka R. E. Artz, Christophe Guimbaud, Matthias Peichl, Joshua L. Ratcliffe, Koffi Dodji Noumonvi, Efrén López‐Blanco, Jiří Dušek, Tiina Markkanen, Torsten Sachs, Mika Aurela, Thu‐Hang Nguyen, Annalea Lohila, Ivan Mammarella, Philippe Ciais

“…Model parameters controlling photosynthesis, autotrophic respiration, and carbon decomposition have been optimized using eddy‐covariance observations from 14 European peatlands and a Bayesian optimization approach. …”
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Few-shot bearing fault diagnosis method based on an EEMD parallel neural network and a relation network by Cunsheng Zhao, Bo Tong, Chao Zhou, Qingrong Fan

“…Original signal decomposition, STFT transformation and splicing effectively improve the randomness and blindness of convolution operations, improve the accuracy of fault feature extraction in RN, and thus improve the overall diagnostic performance of the model. …”
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Process‐Based Machine Learning Observationally Constrains Future Regional Warming Projections by Sophie Wilkinson, Peer Nowack, Manoj Joshi

“…Combining the historically constrained Ridge‐ERA5 coefficients with inputs from CMIP6 future projections enables a derivation of observational constraints on regional warming. Although the multi‐model mean falls within the constrained range of temperatures in all tested regions, a subset of models which predict the greatest degree of warming tend to be excluded and decomposition of the constraint into predictor variable contributions suggests error‐cancellation of feedbacks in some models and regions.…”
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Linear control strategy of the dilution rate for stability in the anaerobic digestion process by Orlando Harker, Adolfo Andrés Jaramillo, Patrick U. Okoye

“…In this work, a compensator is designed, for which the approximate linearization technique of the AM2 nonlinear model is proposed, to obtain the linearized model from which a classic lag-lead control strategy is proposed, using the dilution rate as a control variable. …”
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STRUCTURAL GLOBAL SENSITIVITY METHOD BASED ON PARTIAL DERIVATIVE WHOLE DOMAIN INTEGRAL by TU LongWei, LIU Jie, LIU GuangZhao, ZHANG Zheng

“…Then,local sensitivity method based on partial derivative was extended to a global sensitivity method by integrating partial derivatives of model variables in variable sapces. In addition,the paper redefined a more conveniently calculated sensitivity indice that can achieve effective decomposition for the high-order sensitivity indices,and the sensitivity results directly corresponded to model variables without the high-order indices,which had more practical engineering significance. …”
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Analytical investigation on resolution calculation method for nonlinear temperature load of steel-concrete composite girders by Chun-Ming Zhang, Wei-Hong Wu, Wei Xian, Wei Xian

“…The analysis encompasses temperature-induced self-restraint stresses, secondary stresses, and axial deformation in variable-section continuous composite girders. Key findings reveal that code-specified thermal stresses exhibit opposing polarity characteristics at specific locations compared to other models. …”
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Uncertainty evaluation of surface profile measurement error based on adaptive sparse grid polynomial chaos expansion by Ke Zhang, Xinya Zheng, Ruiyu Zhang

“…This model is optimized using Cholesky decomposition to handle variable correlations, sparse grid integration for efficient generation of multidimensional integration points, and maximum entropy method for direct reconstruction of probability distributions, thereby achieving efficient evaluation of the measurement error and uncertainty of surface profile. …”
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Graphene nanoplatelets-reinforced polyetherimide foams prepared by water vapor-induced phase separation by H. Abbasi, M. Antunes, J. I. Velasco

“…The present work considers the preparation of medium-density polyetherimide foams reinforced with variable amounts of graphene nanoplatelets (1–10 wt%) by means of water vapor-induced phase separation (WVIPS) and their characterization . …”
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Transmission and Generation Expansion Planning Considering Virtual Power Lines/Plants, Distributed Energy Injection and Demand Response Flexibility from TSO-DSO Interface by Flávio Arthur Leal Ferreira, Clodomiro Unsihuay-Vila, Rafael A. Núñez-Rodríguez

“…A data-driven distributionally robust optimization-DDDRO approach is proposed to consider uncertainties of demand and variable renewable energy generation. The column and constraint generation algorithm and duality-free decomposition method are adopted. …”
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Weather Phenomena Monitoring: Optimizing Solar Irradiance Forecasting With Temporal Fusion Transformer by Xinyang Hu

“…The proposed model decomposes the raw solar irradiance sequence into intrinsic mode functions using VMD and optimizes the TFT using the variable screening network and GRU-based encoder-decoder. …”
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