Extension of the Multi-TP Model Transformation to Functions with Different Numbers of Variables by Péter Baranyi

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Forecasting water quality indices using generalized ridge model, regularized weighted kernel ridge model, and optimized multivariate variational mode decomposition by Marjan Kordani, Mohsen Bagheritabar, Iman Ahmadianfar, Arvin Samadi-Koucheksaraee

“…The light gradient boosting machine model (LGBM) is also implemented to select the influential input variables. …”
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Trends and factors contributing to changes in childhood stunting in Bangladesh from 2012 to 2019: A multivariate decomposition modelling. by Satyajit Kundu, Richard Gyan Aboagye, Syed Sharaf Ahmed Chowdhury, Md Hasan Al Banna, Md Ashfikur Rahman, Rakhi Dey, Faruk Ahmed

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Do Exogenous Shocks in Macroeconomic Variables Respond to Changes in Stock Prices? by H. Srivastava, P. Solomon, S. P. Singh

“…The study applies two econometric models such as «Variance Decomposition» (VDC) and «Impulse Response Function» (IRF) for examining the exogenous shocks in macroeconomic variables respond to changes in stock prices. …”
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A multi-channel spatiotemporal SegNet model for short term wind power prediction with sequence decomposition and reconstruction by Xingdou Liu, Liang Zou, Li Zhang, Jiangong Wang, Zhiyun Han, Yong Li

“…This article proposes a multi-channel spatiotemporal SegNet (MCST-SegNet) model that achieves synchronous power prediction for all wind turbines in a wind farm. …”
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Reservoir water level prediction using combined CEEMDAN-FE and RUN-SVM-RBFNN machine learning algorithms by Lan-ting Zhou, Guan-lin Long, Can-can Hu, Kai Zhang

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Domain knowledge-driven decomposition-based large-scale optimization for ship cabin structures by Puyu JIANG, Jun LIU, Qiangjun LUO, Yuansheng CHENG

“…ObjectivesThis paper proposes a domain knowledge-driven large-scale optimization algorithm for ship cabin structures based on a decomposition optimization framework. MethodsThe proposed algorithm combines domain mechanical knowledge with a general black box optimization algorithm, groups the design variables into location variables and size variables, and decomposes the original problem into a series of low-dimensional subproblems. …”
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Systematic Literature Review for Robust Mixed-Integer Linear Programming Models Using Benders Decomposition in Facility Location Problems by Fadiah Hasna Nadiatul Haq, Diah Chaerani, Anita Triska

“…The robust Mixed-Integer Linear Programming (MILP) model is an approach to address uncertainty in linear optimization involving integer and continuous variables, which can be solved using the Benders Decomposition method. …”
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Photovoltaic solar energy prediction using the seasonal-trend decomposition layer and ASOA optimized LSTM neural network model by Venkatachalam Mohanasundaram, Balamurugan Rangaswamy

“…Parameter standardization employing ASOA, the RSTL decomposition approach, and the conceptual model of LSTM networks are all presented in this research work. …”
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Application Of The Denitrification-Decomposition (DNDC) Model To Retrospective Analysis Of The Carbon Cycle Components In Agrolandscapes Of The Central Forest Zone Of European Russ... by Olga E. Sukhoveeva, Dmitry V. Karelin

“…We applied the process-based simulation model DNDC (DeNitrification-DeComposition) recommended  by UNCCC and world widely used.  …”
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Analysis of the Impact of Global Oil Prices On GDP (on the Example of the Azerbaijan Republic) by N. S. Ayyubova

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Analysis of effects of meteorological variables on dengue incidence in Bangladesh using VAR and Granger causality approach by Md. Jamal Hossain, Nazia Sultana, Anwesha Das, Fariea Nazim Jui, Md. Kamrul Islam, Md. Mijanoor Rahman, Mohammad Mafizur Rahman

“…While previous studies have examined the relationship between dengue and meteorological variables using single model approaches, this study employs advanced econometric techniques to capture dynamic interactions. …”
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Resampling Multi-Resolution Signals Using the Bag of Functions Framework: Addressing Variable Sampling Rates in Time Series Data by David Orlando Salazar Torres, Diyar Altinses, Andreas Schwung

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EXCHANGE RATE DYNAMICS AND FORECASTING ACCURACY IN EMERGINGECONOMIES: INTEGRATING ENSEMBLE LEARNING MODELS WITH STRUCTURAL TIME SERIES DECOMPOSITION FOR THE USD/ZAR RATE by Agbeyinka Yinka Ibrahim

“…The study offers relevant policy insights for monetary authorities and financial market participants, and recommends the integration of hybrid modeling frameworks that combine machine learning with structural macroeconomic variables. …”
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Spatiotemporal Analysis and Anomalous Trends of Asia AOD (2001–2024): Insights from a Deep Learning Fusion Model and EOF Decomposition by Yu Ding, Wenjia Ni, Jiaxin Dong, Jie Yang, Shiyao Meng, Siwei Li

“…To overcome these challenges, this study employs the deep learning model TabNet, incorporating Digital Elevation Model (DEM) data and ERA5 meteorological variables, to fuse MERRA-2 AOD with MODIS MAIAC AOD observations. …”
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Bearing Fault Diagnosis under Transient Conditions: Using Variational Mode Decomposition and the Symmetrized Dot Pattern-Based Convolutional Neural Network Model by Jide Jia, Jianmin Mei, Chuang Sun, Fengjuan Yang

“…An effective bearing fault diagnosis method for gearbox applications under variable operating conditions is proposed, utilizing variational mode decomposition (VMD) for feature extraction, symmetrized dot pattern (SDP) for visual representation, and convolutional neural network (CNN) for deep feature extraction and classification. …”
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Discretization-independent surrogate modeling of physical fields around variable geometries using coordinate-based networks by James Duvall, Karthik Duraisamy

“…These concepts are leveraged and adapted in the context of physical-field surrogate modeling. Two methods toward generalization are proposed and compared: design-variable multilayer perceptron (DV-MLP) and design-variable hypernetworks (DVH). …”
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Short-term residential electricity consumption forecast considering the cumulative effect of temperature, dual decomposition technology and integrated deep learning by Lanlan Wang, Yong Lin, Tingting Song, Yuchun Chen, Kai Li, Junchao Ran

“…We have developed a Variable Modal Decomposition (VMD) data decomposition technique optimized for non-governmental organizational models, which improves the problem of subjectivity in parameter setting in traditional VMD, thus enhancing the performance and accuracy in data decomposition. …”
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Coupling Remote Sensing With a Process Model for the Simulation of Rangeland Carbon Dynamics by Yushu Xia, Jonathan Sanderman, Jennifer D. Watts, Megan B. Machmuller, Andrew L. Mullen, Charlotte Rivard, Arthur Endsley, Haydee Hernandez, John Kimball, Stephanie A. Ewing, Marcy Litvak, Tomer Duman, Praveena Krishnan, Tilden Meyers, Nathaniel A. Brunsell, Binayak Mohanty, Heping Liu, Zhongming Gao, Jiquan Chen, Michael Abraha, Russell L. Scott, Gerald N. Flerchinger, Patrick E. Clark, Paul C. Stoy, Anam M. Khan, E. N. Jack Brookshire, Quan Zhang, David R. Cook, Thomas Thienelt, Bhaskar Mitra, Marguerite Mauritz‐Tozer, Craig E. Tweedie, Margaret S. Torn, Dave Billesbach

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Long-time dynamics of a random higher-order Kirchhoff model with variable coefficient rotational inertia by Penghui Lv, Yuxiao Cun, Guoguang Lin

“…The findings promote the relevant conclusions of the non-autonomous stochastic higher-order Kirchhoff model and provide a theoretical basis for its subsequent application and research.…”
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