Socioeconomic Inequality in Chronic Complications of Type 2 Diabetes Mellitus in Iran: Concentration Index and Decomposition Approach by Sedigheh Mafakheri, Erfan Ayubi, Shiva Borzouei, Vajiheh Ramezani Doroh, Salman Khazaei

“…The purpose of this study is to examine SES inequality in chronic complications among T2DM patients using methods of decomposing inequality. Methods: This cross-sectional study included patients with T2DM receiving care at the diabetes clinic in Hamadan City, Iran, between April and September 2023. …”
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Enhancing Unmanned Aerial Vehicle Object Detection via Tensor Decompositions and Positive–Negative Momentum Optimizers by Ruslan Abdulkadirov, Pavel Lyakhov, Denis Butusov, Nikolay Nagornov, Dmitry Reznikov, Anatoly Bobrov, Diana Kalita

“…In this paper, we propose the Yolov8 architecture with decomposed layers via canonical polyadic and Tucker methods for accelerating the solving of the object detection problem in satellite images. …”
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Research on Fault Diagnosis Method of High-Speed EMU Air Compressor Based on ICEEMDAN and Wavelet Threshold Combined Noise Reduction by Liqiang Peng, Akang Guo, Shuzhao Zhang

“…The t-SNE manifold learning algorithm is applied for secondary feature extraction, creating an MPGA-SVM (Multi-Objective Genetic Algorithm-Support Vector Machine) fault diagnosis model. Experimental results demonstrate that the ICEEMDAN and wavelet thresholding denoising method improves the signal-to-noise ratio by 6.5% and reduces the mean square error by 16.1% compared to the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and wavelet thresholding denoising method. t-SNE, in comparison to ISOMAP and LLE, produces a minimum intra-class distance of 1.28 and a maximum inter-class distance of 43.1. …”
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An Interpretable Method for Asphalt Pavement Skid Resistance Performance Evaluation Under Sand-Accumulated Conditions Based on Multi-Scale Fractals by Yuhan Weng, Zhaoyun Sun, Huiying Liu, Yingbin Gu

“…The SHapley Additive exPlanations (SHAP) method is used to analyze the optimal model’s interpretability. …”
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Diagnosis Method for High-Speed Train Axle Box Bearing Slight Faults Based on Improved SAE and Temperature-Vibration Fusion by XU Xiao, SONG Dongli, WANG Zifan

“…［Method］ First, an AE (auto encoder) driven bearing temperature feature extraction method is designed to obtain the abnormal bearing temperature features, and EMD (empirical modal decomposition) method is used to process the vibration signal，so as to obtain the statistical features of the effective vibration IMF (intrinsic modal function). …”
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Fast Determination and Source Apportionment of Eight Polycyclic Aromatic Hydrocarbons in PM10 Using the Chemometric-Assisted HPLC-DAD Method by Ting Hu, Yitao Xia, You Wang, Li Lin, Rong An, Ling Xu, Xiangdong Qing

“…Subsequently, the second-order calibration method based on alternating trilinear decomposition (ATLD) was employed to handle the three-way HPLC-DAD data. …”
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Discrete Physics-Informed Training for Projection-Based Reduced-Order Models with Neural Networks by Nicolas Sibuet, Sebastian Ares de Parga, Jose Raul Bravo, Riccardo Rossi

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Short-Term Load Forecasting Based on Complementary Ensemble Empirical Mode Decomposition and Long Short-Term Memory by Huiru ZHAO, Yihang ZHAO, Sen GUO

“…At the same time, the prediction results of complementary ensemble empirical mode decomposition and long short-term memory are compared with those of long short-term memory model under other decomposition methods, which has verified that the complementary ensemble empirical mode decomposition method is effective in improving the prediction accuracy.…”
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An Assessment of Local Geometric Uncertainties in Polysilicon MEMS: A Genetic Algorithm and POD-Kriging Surrogate Modeling Approach by Ananya Roy, Francesco Rizzini, Gabriele Gattere, Carlo Valzasina, Aldo Ghisi, Stefano Mariani

“…In this paper, an approach that combines genetic algorithms and proper orthogonal decomposition with kriging surrogate modeling was proposed to accurately predict over-etch measures through an on-chip test device. …”
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Singular Value Decomposition-Based Adaptive Sampling Approximate Message Passing Net for Sparse-View CT Reconstruction by Zhenhua Wu, Jiafei Xu, Lixia Yang

“…However, the challenges of handling a reduced number of projection views persist for both iterative estimation and deep neural reconstruction methods. In this paper, to address these challenges, we present a singular value decomposition-based adaptive sampling approximate message passing network (ASAMP-Net) sparse-view CT imaging method. …”
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Feature Extraction and Attribute Recognition of Aerosol Particles from In Situ Light-Scattering Measurements Based on EMD-ICA Combined LSTM Model by Heng Zhao, Yanyan Zhang, Dengxin Hua, Jiamin Fang, Jie Zhang, Zewen Yang

“…Therefore, we propose a feature extraction and attribute recognition method from in situ light-scattering measurements based on Bayesian Optimization, wavelet scattering transform, and long short-term memory neural network (BO-WST-LSTM), with empirical mode decomposition (EMD) and independent component analysis (ICA) algorithm for signal preprocessing. …”
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A multiple pattern complex event detection scheme based on decomposition and merge sharing for massive event streams by Jianhua Wang, Bang Ji, Feng Lin, Shilei Lu, Yubin Lan, Lianglun Cheng

“…The experimental results show that the proposed detection method in this article outperforms some general detection methods in detection model and detection algorithm in multiple pattern complex event detection as a whole.…”
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Branch error reduction criterion-based signal recursive decomposition and its application to wind power generation forecasting. by Fen Xiao, Siyu Yang, Xiao Li, Junhong Ni

“…To address this issue, a branch error reduction (BER) criterion is proposed in this study that is based on which a mode number adaptive VMD-based recursive decomposition method is used. This decomposition method is combined with commonly used single forecasting models and applied to the wind power generation forecasting task. …”
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Parallel convolutional neural network and empirical mode decomposition for high accuracy in motor imagery EEG signal classification. by Jaipriya D, Sriharipriya K C

“…In this work, we propose a novel method that addresses these challenges by employing empirical mode decomposition (EMD) for feature extraction and a parallel convolutional neural network (PCNN) for feature classification. …”
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Ultra-Short-Term Photovoltaic Power Interval Forecasting Based on Time-Series Decomposition and Conformal Quantile Regression by Qianjin GUI, Wenfa XU, Xiaoyang LI, Lirong LUO, Haifeng YE, Zhengfeng WANG

“…Then, piecewise linear models, Fourier series decomposition models, and AR-Net models are respectively employed to fit the three subseries, with the Fourier series decomposition model enhancing the fitting capability for daily and seasonal periodicities of PV power. …”
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AMDCnet: attention-gate-based multi-scale decomposition and collaboration network for long-term time series forecasting by Shikang Hou, Song Sun, Tao Yin, Zhibin Zhang, Meng Yan

“…Furthermore, the intricate structure of multi-scale time series complicates the effective extraction of features at different temporal resolutions.MethodTo address these limitations, we propose AMDCnet, a multi-scale-based time series decomposition and collaboration network designed to enhance the model's capacity for decomposing and integrating data across varying time scales. …”
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Enhancing artificial neural network learning efficiency through Singular value decomposition for solving partial differential equations by Alfi Bella Kurniati, Maharani A. Bakar, Nur Fadhilah Ibrahim, Hanani Farhah Harun

“…In response, we introduce the matrix decomposition method into the ANN learning process, rooted in Singular Value Decomposition (SVD). …”
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SFPFMformer: Short-Term Power Load Forecasting for Proxy Electricity Purchase Based on Feature Optimization and Multiscale Decomposition by Chengfei Qi, Yanli Feng, Junling Wan, Xinying Mao, Peisen Yuan

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Multiscale Fuzzy Temporal Pattern Mining: A Block-Decomposition Algorithm for Partial Periodic Associations in Event Data by Aihua Zhu, Haote Zhang, Xingqian Chen, Dingkun Zhu

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Optimizing Fleet Size in Point-to-Point Shared Demand Responsive Transportation Service: A Network Decomposition Approach by Fudong Xie, Ce Wang, Housheng Duan

“…To solve this practical problem, we resort to the Model Predictive Control method (MPC) and propose a network decomposition approach that first converts the transportation network to a nodal tree structure and then develops a Nodal Tree Recourse with Dependent Arc Capacities (NTRDAC) algorithm to obtain the exact value of the expected recourse functions. …”
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