Mathematical modeling of thermal decomposition of resins in the process of reversed gasification of plant biomass by I. G. Donskoy

“…METHODS. To this end, mathematical models are used in different statements: the decomposition of the tar is considered in the approximations of one- and two-reaction kinetic scheme; to assess the influence of the bed height and temperature, the convection-diffusionreaction equation with a given temperature distribution along the length of the reaction zone is used; the temperature of the gasification process is estimated from experimental data and thermodynamic calculations. …”
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Vibration Signal Forecasting on Rotating Machinery by means of Signal Decomposition and Neurofuzzy Modeling by Daniel Zurita-Millán, Miguel Delgado-Prieto, Juan José Saucedo-Dorantes, Jesus Adolfo Cariño-Corrales, Roque A. Osornio-Rios, Juan Antonio Ortega-Redondo, Rene de J. Romero-Troncoso

“…This work proposes a vibration signal prognosis methodology, applied to a rotating electromechanical system and its associated kinematic chain. The method combines the adaptability of neurofuzzy modeling with a signal decomposition strategy to model the patterns of the vibrations signal under different fault scenarios. …”
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A Decomposition-Integration Framework of Carbon Price Forecasting Based on Econometrics and Machine Learning Methods by Zhehao Huang, Benhuan Nie, Yuqiao Lan, Changhong Zhang

“…This paper presents an advanced decomposition-integration framework which seamlessly integrates econometric models with machine learning techniques to enhance carbon price forecasting. …”
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A structural health monitoring data reconstruction method based on VMD and SSA-optimized GRU model by Xiaoliang Jia, Guoyan Zhang, Zhiqiang Wang, Huacong Li, Jing Hu, Songlin Zhu, Caiwei Liu

“…To address this challenge, this study develops a method combining Variational Mode Decomposition (VMD) and Sparrow Search Algorithm (SSA)-optimized Gate Recurrent Unit (GRU) for recovering structural response data. …”
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Mathematical Dynamics of the (SVEITR) Model, the Impact of Treatment and Vaccination on Cholera Spread. by kazeem Abidoye ODEYEMI, Mutairu Kayode Kolawole

“…Through a numerical simulation of Laplace decomposition method the result reveal that treatment rate reduces the emanation of the disease and vaccination plays a vital role in curbing aftermath effect of wide-spread of the disease. …”
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Fast computation of electro-thermal coupling field of ± 400 kV converter transformer valve side bushing based on proper orthogonal decomposition method by Zhilin Chu, Qingyu Wang, Huidong Tian, Siyuan Liu, Zhijie Gao, Songbo Tian, Peng Liu, Zongren Peng

“…The paper adopts the finite element method to construct a model for electro-thermal coupling field of the converter transformer valve side bushing. …”
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A Novel Renewable Energy Scenario Generation Method Based on Multi-Resolution Denoising Diffusion Probabilistic Models by Donglin Li, Xiaoxin Zhao, Weimao Xu, Chao Ge, Chunzheng Li

“…To address this gap, this paper proposes a novel renewable energy scenario generation method based on a multi-resolution diffusion model. …”
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GPU Parallelization Nested Decomposition Method for Solving Large Linear Systems in Reservoir Numerical Simulation by Xin Shi, Yuan Di

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BS-CP: Efficient streaming Bayesian tensor decomposition method via assumed density filtering. by Jiaqi Liu, Qiwu Wu, Lingzhi Jiang, Renjun Zhan, Xiaochuan Zhao, Husheng Wu, Weicong Tan

“…CANDECOMP/PARAFAC (CP) is a popular tensor decomposition model, which is both theoretically advantageous and numerically stable. …”
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ED-Stacking：A Construction Method of Few-shot Prediction Model for Beef Microbial Growth Based on Ensemble Learning by Hanqiang LI, Yi CHEN, Yufei GAO, Kun HOU, Liping SONG, Jing LI

“…In this paper proposed a construction method of few-shot predictive model for microbial growth in beef, called ED-Stacking, which was based on time series decomposition and ensemble learning, for early warning of microbial risks in food. …”
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A photovoltaic power forecasting method based on the LSTM-XGBoost-EEDA-SO model by Ying Xu, Xinrong Ji, Zhengyang Zhu

“…Experimental results demonstrate that the proposed model significantly outperforms standalone benchmark methods. …”
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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

“…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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Decomposition of Intensity and Sustainable Use Countermeasures for the Energy Resources of the Northwestern Five Provinces of China Using the Logarithmic Mean Divisia Index (LMDI) Method and Three Convergence Models by Zhenxu Zhang, Junsong Jia, Chenglin Zhong, Chengfang Lu, Min Ju

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Accelerated Restricted Additive Schwarz method for asynchronous processing by A. Ben bouzid, Y. Khali, S. Khallouq, N. Nagid

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Decomposition of Technological Processes for Evaluating the Performance of Production Line for Pre Sowing Treatment of Seeds by E. I. Kubeyev, B. S. Antropov

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DECOMPOSITION OF KAZAKHSTAN’S ECONOMIC GROWTH AND MODELING OF LABOR, CAPITAL AND TOTAL FACTOR PRODUCTIVITY CONTRIBUTIONS by S. N. Alpysbayeva, S. Zh. Shuneyev, G. V. Stroyeva

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Stock Market Index Prediction Using CEEMDAN-LSTM-BPNN-Decomposition Ensemble Model by John Kamwele Mutinda, Abebe Geletu

“…The findings highlight the importance of combining advanced decomposition methods and deep learning models for financial forecasting. …”
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Evaluation of Eight Decomposition-Hybrid Models for Short-Term Daily Reference Evapotranspiration Prediction by Yunfei Chen, Zuyu Liu, Ting Long, Xiuhua Liu, Yaowei Gao, Sibo Wang

“…However, the nonlinear and non-stationary characteristics of ET_o time series pose challenges for conventional prediction models. Given this, in this study we evaluate eight decomposition-hybrid models that integrate various decomposition techniques with a long short-term memory (LSTM) network to enhance short-term (5-day, 7-day, and 10-day) ET_o forecasting. …”
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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

“…Finally, the integrated prediction combining fluctuation and random terms under condition 5 yielded R2 of 0.87 and 0.93 for the overall prediction at Ankang and Baihe stations, respectively, demonstrating excellent model performance. [Conclusions](1) The MVMD decomposition method can control the number of decomposition layers, ensuring complete signal feature extraction without overfitting while improving processing speed.(2) Pearson correlation coefficient method enhances prediction accuracy through decomposed data classification.(3) The MEA-BP can improve signal-to-noise ratio, adapt to complex environments, enhance learning efficiency and generalization ability, and reduce computational complexity.(4) The GWO-ELM algorithm integrates grey wolf optimizer with extreme learning machine, providing a fast and adaptive solution for time-series prediction with reduced overfitting and improved efficiency.(5) The overall combined model can efficiently and stably process large amount of data while ensuring high accuracy.…”
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A Hybrid Model for Carbon Price Forecasting Based on Secondary Decomposition and Weight Optimization by Yongfa Chen, Yingjie Zhu, Jie Wang, Meng Li

“…The empirical results of the carbon markets in Hubei and Guangdong indicate that the proposed method outperforms the benchmark model in terms of prediction accuracy and robustness, and the method has been tested by Diebold Mariano (DM). …”
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