Wind Power Prediction Based on a Hybrid Model of ICEEMDAN and ModernTCN-Informer by Jun He, Zijian Cheng, Zijie Zhong, Lizhuo Liang, Jianhui Ye

“…This paper proposes a hybrid forecasting model based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) combined with ModernTCN-Informer. …”
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Total factor carbon emission productivity in China’s manufacturing industry: a study considering indirect carbon emissions by Zhijie Hao, Xiaotian Wang

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Robust Controller Designing for an Air-Breathing Hypersonic Vehicle with an HOSVD-Based LPV Model by Xing He, Wei Jiang, Caisheng Jiang

“…Secondly, using the tensor product model transformation method, the obtained LPV model is converted into the polytopic LPV model via high-order singular value decomposition (HOSVD). …”
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Sensitivity Analysis of the Thermal Structure Within Subduction Zones Using Reduced‐Order Modeling by Gabrielle M. Hobson, Dave A. May

“…We simulate temperature for profiles of the Cascadia, Nankai and Hikurangi subduction zones using a 2D coupled kinematic‐dynamic thermal model. We then build reduced‐order models (ROMs) for temperature using the interpolated Proper Orthogonal Decomposition (iPOD). …”
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Construction of a health literacy prediction model for diabetic patients: A multicenter study by Zepeng Wang, Junyi Shi, Fangyuan Jiang, Kui Jiang, Yalan Chen

“…Calibration curves, decomposition plots, and partial dependence plots were drawn to evaluate and interpret the models. …”
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Cooperative Hybrid Modelling and Dimensionality Reduction for a Failure Monitoring Application in Industrial Systems by Morgane Suhas, Emmanuelle Abisset-Chavanne, Pierre-André Rey

“…Additionally, Singular Value Decomposition (SVD) is employed for the purposes of feature extraction and dimensionality reduction, thereby enhancing the model’s capacity to generalise with limited training data. …”
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Modeling soil respiration in summer maize cropland based on hyperspectral imagery and machine learning by Fanchao Zeng, Fanchao Zeng, Jinwei Sun, Huihui Zhang, Lizhen Yang, Xiaoxue Zhao, Jing Zhao, Xiaodong Bo, Yuxin Cao, Fuqi Yao, Fenghui Yuan, Fenghui Yuan

“…The XGBoost model can also effectively capture the impact of drought treatments on SR.DiscussionThe XGBoost model’s tree-based structure allows it to effectively capture complex interactions and nonlinear patterns within variables, while its high sensitivity to changes in SR rates under drought conditions makes it more reliable for modeling SR across different growth stages compared to the linear-based MLR model. …”
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A Fault Diagnosis Model Based on LCD-SVD-ANN-MIV and VPMCD for Rotating Machinery by Songrong Luo, Junsheng Cheng, Kexiang Wei

“…However, traditional class discrimination methods such as SVM and ANN fail to capitalize the interactions among the feature variables. Variable predictive model-based class discrimination (VPMCD) can adequately use the interactions. …”
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A Hybrid Deep Learning Model for Link Dynamic Vehicle Count Forecasting with Bayesian Optimization by Chunguang He, Dianhai Wang, Yi Yu, Zhengyi Cai

“…This paper presents a hybrid deep learning method that combines the gated recurrent unit (GRU) neural network model with automatic hyperparameter tuning based on Bayesian optimization (BO) and the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) model. …”
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Modelling the factors associated with quality of life in women with osteoporosis: A cross-sectional study by Rahmatollah Moradzadeh, Maryam Zamanian, Maliheh Taheri

“…Final regression coefficients were obtained based on the total effects of estimations (decompositions of effects into direct, indirect and total effects) by structural equation model (SEM) analysis. …”
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Building electrical consumption patterns forecasting based on a novel hybrid deep learning model by Nasser Shahsavari-Pour, Azim Heydari, Farshid Keynia, Afef Fekih, Aylar Shahsavari-Pour

“…Specifically, the proposed model comprises three key components: (i) a mutual information-based feature selection method to identify the most significant input variables influencing energy consumption; (ii) a variational mode decomposition (VMD) approach to decompose the original energy consumption signal into intrinsic mode functions (IMFs), capturing relevant trends and eliminating noise; and (iii) a long short-term memory (LSTM) neural network to perform time-series forecasting of the target energy consumption values. …”
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A Novel Flexible Model for the Extraction of Features from Brain Signals in the Time-Frequency Domain by R. Heideklang, G. Ivanova

“…However, the data typically exhibit intra- and interindividual variability. Existing algorithms often do not take into account this variability, for instance by using fixed frequency bands. …”
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Chaotic billiards optimized hybrid transformer and XGBoost model for robust and sustainable time series forecasting by Reham H. Mohammed, Asmaa Mohamed El-saieed

“…The model forecasts wind speed values on an hourly basis, up to 24 h ahead. …”
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Fast climate impact emulation for global temperature scenarios with the rapid impact model emulator (RIME) by Edward Byers, Michaela Werning, Mahé Perrette, Niklas Schwind, Volker Krey, Keywan Riahi, Carl-Friedrich Schleussner

“…Climate model emulation has long been applied to assess the global climate outcomes of integrated assessment model (IAM) emissions scenarios, but is typically limited to first-order climate variables like mean surface air temperatures at limited regional resolution. …”
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Efficient Phase-Field Modeling of Quasi-Static and Dynamic Crack Propagation Under Mechanical and Thermal Loadings by Lotfi Ben Said, Hamdi Hentati, Mohamed Turki, Alaa Chabir, Sattam Alharbi, Mohamed Haddar

“…This proposed approach was experimentally tested through the examination of crack propagation paths in brittle materials that were subjected to variable mechanical and thermal loads. This work focused on the integration of a spectral decomposition-based phase-field model with thermo-mechanical coupling for dynamic fracture, supported by benchmark validation and the comparative assessment of energy decomposition strategies. …”
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THE IMPACT OF EXTERNAL SHOCKS ON THE RUSSIAN ECONOMY by M. G. Tiunova

“…The contribution of external shocks to the dynamics of macroeconomic indicators is determined on the basis of the decomposition of the error variance of the model endogenous variables forecast. …”
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Analysis Method of Agricultural Total Factor Productivity Based on Stochastic Block Model (SBM) and Machine Learning by Yanzi Li, Cai Chen, Fuqiang Liu, Jian Wang

“…The time-varying variations of the national agricultural inefficiency value and its source decomposition under variable scale returns are then determined using the SBM-based algorithm of agricultural total factor productivity and the obtained sample data. …”
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DOES ISLAMIC FINANCE DRIVE ECONOMIC GROWTH IN INDONESIA? AN ANALYSIS USING VECTOR ERROR CORRECTION MODEL by Eko Kurniawan, Lina Nugraha Rani, Tanza Dona Pertiwi

“…The analysis is conducted using the vector error correction model (VECM), beginning with stationarity testing, optimal lag selection, cointegration testing, model estimation, and variance decomposition analysis. …”
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Macroeconomic Dynamics in Pakistan: A VAR Model Analysis of GDP, Inflation, Imports, Exports, and Interest Rates by Waseem Khoso, Muhammad Rafique Doudpoto

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Retrofitting Transportation Network Using a Fuzzy Random Multiobjective Bilevel Model to Hedge against Seismic Risk by Lu Gan, Jiuping Xu

“…After establishing the model, a fuzzy random variable transformation approach and fuzzy variable approximation decomposition are used to deal with the uncertainty. …”
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