Destripe Any Scale: Effective Stripe Removal Via Multiscale Decomposition Using the Luojia3-02 Stripe-Noise Dataset by Ru Chen, Mi Wang, Yingdong Pi, Ru Wang, Tao Peng, Fan Yang, Rongfan Dai

“…Compared with traditional methods, our approach exhibits superior robustness and adaptability in handling complex, large-swath scenarios, demonstrating the advantages of integrating deep-learning techniques with multiscale decomposition for stripe-noise removal.…”
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Global, regional, and national burden of ischemic heart disease attributable to lead exposure, 1990–2021: decomposition, frontier, and projection analysis by Xinyue Wen, Xinyue Wen, Lichun Qiao, Feidan Deng, Jingxuan Zhou, Miaoqian Li, Lin Wang, Huan Deng, Abebe Feyissa Amhare, Jing Han, Yijie Guo

“…Decomposition analysis, frontier analysis, and Bayesian age-period cohort (BAPC) models were applied to assess trends from 1990 to 2021.ResultsIn 2021, deaths and DALYs attributable to lead exposure reached 590,370.03 and 11,854,611.43, respectively, though age-standardized rates (ASRs) declined. …”
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Automated ECG Arrhythmia Classification Using Feature Images with Common Matrix Approach-Based Classifier by Ali Kirkbas, Aydin Kizilkaya

“…To offer a reasonable solution to this problem, a technique that combines a common matrix approach (CMA)-based classifier model with the Fourier decomposition method (FDM) is proposed. …”
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Optimal Forecast Combination for Japanese Tourism Demand by Yongmei Fang, Emmanuel Sirimal Silva, Bo Guan, Hossein Hassani, Saeed Heravi

“…Initially, the time series data were decomposed into high and low frequencies using the Ensemble Empirical Mode Decomposition (EEMD) technique. Following this, Autoregressive Integrated Moving Average (ARIMA), Neural Network (NN), and Support Vector Machine (SVM) forecasting models were applied to each decomposed component individually. …”
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Spectral solutions for QS with distribution laws in the form of probabilistic mixtures by Veniamin N. Tarasov, Nadezhda F. Bakhareva

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Efficiency multi-agent model assisted Moea/D algorithm for optimization design for building taking into account annual energy consumption and annual user discomfort hours by Hua Deng, Kai Zhou

“…Then it introduces a multi-agent model auxiliary mechanism to improve the decomposition based multi-objective evolutionary optimization algorithm, and then solves the multi-objective optimization model for building energy efficiency. …”
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Multi-step prediction of coal mine adit deformation based on time convolutional long short-term memory by Wenli JI, Xin DAN, Chenyang MA, Jing CHAI, Yuyi WU, Fengqi QIU, Wentao LIU, Wulin LEI, Yongliang LIU

“…Based on the application of fiber optic monitoring for adit deformation, A multi-step prediction model is proposed which is built with Ensemble Empirical Mode Decomposition (EEMD) integrated with TCN-LSTM deep learning network for adit deformation. …”
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Optimal Coverage Path Planning for Unmanned Surface Vehicles Using Flexible Formation Tracking Control by Minsung Park, Bo Woo Nam

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Comparative analysis of water network minimization in industrial processes: Regeneration vs. non-regeneration methods by Khalifa Azza Mohamed, Elsayed Nadia Ali, Awad Mostafa

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Multi-dimensional water quality indicators forecasting from IoT sensors: A tensor decomposition and multi-head self-attention mechanism. by Li Bo, Lv Junrui, Luo Xuegang

“…To overcome these limitations, we propose TGMHA (Tensor Decomposition and Gated Neural Network with Multi-Head Self-Attention), a novel hybrid model that integrates three key innovations: 1) Tensor-based Feature Extraction: We combine Standard Delay Embedding Transformation (SDET) with Tucker tensor decomposition to reconstruct raw time series into low-rank tensor representations, capturing latent spatio-temporal patterns while suppressing sensor noise. 2) Multi-Head Self-Attention for Inter-Indicator Dependencies: A multi-head self-attention mechanism explicitly models complex inter-dependencies among diverse water quality indicators (e.g., pH, dissolved oxygen, conductivity) via parallel feature subspace learning. 3) Efficient Long-Term Dependency Modeling: An encoder-decoder architecture with gated recurrent units (GRUs), optimized by adaptive rank selection, ensures efficient modeling of long-term dependencies without compromising computational performance. …”
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A novel method based on the SCNGO‐ICEEMDAN and MCNN‐BiLSTM model for fault diagnosis of motor bearings for more electric aircraft by Dongsheng Yuan, Feng Liu, Zhonggang Yin, Yanqing Zhang, Yanping Zhang, Peien Luo

“…In this article, a Northern Goshawk algorithm using a fusion subtraction optimiser and Cauchy strategy (SCNGO) is proposed to optimise the number of white noise additions and amplitude weights in the improved full set empirical mode decomposition method based on adaptive noise (ICEEMDAN). …”
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Convex Optimization of Markov Decision Processes Based on Z Transform: A Theoretical Framework for Two-Space Decomposition and Linear Programming Reconstruction by Shiqing Qiu, Haoyu Wang, Yuxin Zhang, Zong Ke, Zichao Li

“…We develop a Z-transformation-based dual-space decomposition method to reconstruct MDPs into a solvable linear programming form, resolving the inherent instability of traditional models caused by uncertain initial conditions and non-stationary state transitions. …”
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What explains differences in average wait time in the emergency department among different racial and ethnic populations: A linear decomposition approach by Hao Wang, Nethra Sambamoorthi, Richard D. Robinson, Heidi Knowles, Jessica J. Kirby, Amy F. Ho, Trevor Takami, Usha Sambamoorthi

“…We investigate factors contributing to longer wait times for NHB and Hispanic patients using a linear decomposition approach. Methods This retrospective observational study included patients presenting to one tertiary hospital ED from 2019 to 2021. …”
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A multivariate non-linear decomposition analysis of urban-rural disparities in overweight/obesity among men aged 20–49 in Ghana by Prince Tsekpetse, Samuel Salu, Joshua Shiuma, Brenda Nambozo, Brian Tonny Makoko, Bright Opoku Ahinkorah

“…A multivariate nonlinear decomposition model assessed the contributions of sociodemographic characteristics to urban–rural disparities in overweight/obesity. …”
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A Model Based on Neural Network to Predict Surface Settlement During Subway Station Construction: A Case Study of the Dongba-Zhongjie Station in Beijing, China by Jiaqi Zhang, Hua Jiang, Jinsen Wang, Jili Feng

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Partial Discharge Type Identification of 10 kV T-Type Terminal Based on Empirical Mode Decomposition and Deep Convolution Neural Network by Shude Cai, Chunhua Fang, Yongyu Guo, Jialiang Liu, Gu Zhou

“…The deep learning network model is used to identify the defect type of the IMF signal. …”
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Temporal Evolution of the Hydrodynamics of a Swimming Eel Robot Using Sparse Identification: SINDy-DMD by Mostafa Sayahkarajy, Hartmut Witte

“…In this study, we employ machine learning strategies to investigate the temporal evolution of the system and discover a data-driven model. Three methods were studied, including dynamic mode decomposition (DMD), sparse system identification (SINDy using PySINDy package), and autoencoder neural network (AE NN), as a general function approximator. …”
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Multi-Scale Ground Deformation in Beijing Plain Revealed by a Joint 2D-FFT and MGWR Decomposition of InSAR Observation by Nian Feng, Lei Xie, Wenbin Xu, Miao Yu

“…This article proposes a novel multiscale InSAR deformation decomposition method that integrates two-dimensional fast Fourier transform with a multiscale geographically weighted regression model. …”
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In vivo estimation of motor unit intrinsic properties in individuals with spinal cord injury by Zhihao Duan, Asta Kizyte, Emelie Butler Forslund, Elena M. Gutierrez-Farewik, Pawel Herman, Ruoli Wang

“…This study proposed an integrated approach using high-density electromyography (HD-EMG) decomposition and motor neuron (MN) modelling to estimate the intrinsic properties of MUs in vivo and investigated alterations of these properties in persons with SCI. …”
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Decomposition and comparative analysis of urban-rural disparity in attitude towards advance care planning among Chinese adults: A nationwide study by Xue Wang, Yibo Wu, Lina Ge, Meizhen Zhao, Yi Ma, Shuang Zang

“…A comprehensive understanding of contributing factors, especially through decomposition and comparative analysis, remains limited. …”
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