Spectrum processing method for measuring low levels of specific activity of 137Cs with a NaI(Tl) detector in the presence of natural radionuclides by V. S. Repin, K. A. Sednev

“…The method involves the modeling the spectral regions for each natural radionuclide in the area of 137mBa peak and subtracting the simulated regions and the background spectrum from the total spectrum under the peak of 137mBa. …”
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Multiband Prediction Model for Financial Time Series with Multivariate Empirical Mode Decomposition by Md. Rabiul Islam, Md. Rashed-Al-Mahfuz, Shamim Ahmad, Md. Khademul Islam Molla

“…The simulation results show that the MEMD-ARMA-based method performs better than the other methods.…”
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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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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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Optimization Method for Combination of Residual Current Protection and Coincidence Brake Logic in a Low-Voltage Distribution System by XU Han, ZHU Sanli, ZHANG Tengfei, CHEN Shu, LIU Mingxiang

“…The simulation analysis shows that the error of the classification results of the model after seven iterations is only 9.49×10-9 with a correlation coefficient of 0.99, confirming the feasibility of the proposed method.…”
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Global index of the acoustic quality of sacral buildings at incomplete information by Krzysztof KOSAŁA

“…The decomposition versus singular values was done on the previously built empirical model of the index observation matrix of sacral objects. …”
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A Novel Boundary-Type Meshless Method for Modeling Geofluid Flow in Heterogeneous Geological Media by Jing-En Xiao, Cheng-Yu Ku, Chih-Yu Liu, Wei-Chung Yeih

“…To deal with the subsurface flow problems of heterogeneous geological media, the domain decomposition method was adopted so that flux conservation and the continuity of pressure potential at the interface between two consecutive layers can be considered in the numerical model. …”
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Tensor Network Methods for Hyperparameter Optimization and Compression of Convolutional Neural Networks by A. Naumov, A. Melnikov, M. Perelshtein, Ar. Melnikov, V. Abronin, F. Oksanichenko

“…TetraOpt consistently demonstrated superior performance, effectively exploring the global optimization space and identifying configurations with higher accuracies. For model compression, we introduce a novel iterative method that combines CP, SVD, and Tucker tensor decompositions. …”
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Dynamical and Computational Analysis of Fractional Korteweg–de Vries-Burgers and Sawada–Kotera Equations in Terms of Caputo Fractional Derivative by N. S. Alharthi

“…The homotopy perturbation transform method (HPTM) and the Yang transform decomposition method (YTDM) are two sophisticated techniques employed to derive analytical solutions. …”
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Reduced Order Modeling for Thermal Simulations of Electric Components With Surface-to-Surface Radiation by Matteo Zorzetto, Riccardo Torchio, Francesco Lucchini, Stefano Massei, Leonardo Robol, Fabrizio Dughiero

“…The Model Order Reduction is performed via the Proper orthogonal decomposition, coupled with the Discrete Empirical Interpolation Method to handle the nonlinearity introduced by the surface-to-surface radiation. …”
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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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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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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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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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Dynamic Stochastic Model Optimization for Underwater Acoustic Navigation via Singular Value Decomposition by Jialu Li, Junting Wang, Tianhe Xu, Jianxu Shu, Yangfan Liu, Yueyuan Ma, Yangyin Xu

“…To address this challenge, we propose a depth-constrained adaptive stochastic model optimization method based on singular value decomposition (SVD). …”
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Research on nonlinear constraint assessment model of power grid based on generalized Benders decomposition by Bo Jia, Le Kang, Yide Xie, Yue Xu

“…The generalized Benders decomposition method is used to decompose the model into main problem and sub-problems, the main problem is solved iteratively by linear evaluation, and the sub-problems are solved iteratively by interior point method. …”
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Uncertainty Decomposition to Understand the Influence of Water Systems Model Error in Climate Vulnerability Assessments by Scott Steinschneider, Jonathan D. Herman, John Kucharski, Marriah Abellera, Peter Ruggiero

“…The method is applied to a reduced complexity multi‐reservoir systems model of the Sacramento‐San Joaquin River Basin in California to demonstrate the decomposition of flood risk and water supply uncertainties under an ensemble of climate change scenarios. …”
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Assessing Vertical Equity in Defined Benefit Pension Plans: An Application to Switzerland by Tanja Kirn, Gijs Dekkers

“…We use an aligned dynamic microsimulation model to apply this method to the first pillar of the Swiss pension system and highlight the following three key effects: (1) the impact of the accrual rate on vertical equity; (2) the assessment of actuarial neutrality through the comparison of migrants with the non-migrant population; and (3) vertical equity across marital statuses. …”
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Based on Agent Model and K-Core Decomposition to Analyze the Diffusion of Mass Incident in Microblog by Jun Pan, Huizhang Shen, Zhong Chen

“…So, in this paper, we aim at introducing K-Core decomposition method from complex network to the analysis on how to manage the diffusion of mass incident in Weibo based on agent based model which can simulate Weibo user’s actions when mass incident happens. …”
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