Multivariate decomposition of shift toward public facilities for inpatient care in rural India: evidence from National Sample Survey by Sandeep Sharma, E Lokesh Kumar, Atul Kotwal

“…This study conducted a decomposition analysis to identify the underlying causes that contributed to this increase in public facility utilization.Materials and methodsThe study used the latest available unit-level data from the 2014 and 2017–2018 NSS Health Survey. …”
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Decomposition analysis of public health service utilization and health disparities among urban and rural older adult migrants in China by Bo Dong, MengHan Jiang, Jing Zong

“…Finally, the Blinder–Oaxaca decomposition method was used to quantify the extent to which various factors contributed to urban–rural disparities in health service utilization and health outcomes.ResultsRural older adult migrants exhibited slightly higher utilization rates of public health services—health education, health records, and family doctor contracting—compared to their urban counterparts. …”
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Anomaly detection method for cyber physical power system based on bilateral data fusion by Tianlei Zang, Shijun Wang, Chuangzhi Li, Yunfei Liu, Yujian Xiao, Zian Wang, Xueying Yu

“…The novel model can depict data decomposition and feature extraction from both cyber and physical domains. …”
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Enhancing the Opportunistic Bone Status Assessment Using Radiomics Based on Dual-Energy Spectral CT Material Decomposition Images by Qiye Cheng, Jingyi Zhang, Mengting Hu, Shigeng Wang, Yijun Liu, Jianying Li, Wei Wei

“…Conclusions: Bone status assessment can be accurately conducted using density values from HAP (Water), HAP (Fat), Ca (Water), and Ca (Fat) MD images. However, radiomics models derived from MD images surpass traditional density measurement methods in evaluating bone status, highlighting their superior diagnostic potential.…”
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RM-MOCO: A Fast-Solving Model for Neural Multi-Objective Combinatorial Optimization Based on Retention by Huiqing Wei, Fei Han, Qing Liu, Henry Han

“…In this paper, following the idea of decomposition strategy and neural combinatorial optimization, a novel fast-solving model for MOCO based on retention is proposed. …”
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Advancements in energetic metal-organic frameworks, alkali and alkaline earth metal salts, and transition metal complexes: Predictive models for detonation velocity, heat, and pres... by Mohammad Hossein Keshavarz, Nasser Hassanzadeh, Mohammad Jafari

“…The new model enhances predictive reliability for detonation velocities, aligning more closely with experimental results, as evidenced by a root mean square error (RMSE) of 0.68 km/s compared to 1.12 km/s for existing methods. …”
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Solar Flare Prediction Using Long Short-term Memory (LSTM) and Decomposition-LSTM with Sliding Window Pattern Recognition by Zeinab Hassani, Davud Mohammadpur, Hossein Safari

“…To address class imbalance, resampling methods are applied. LSTM and DLSTM models are trained on sequences of peak fluxes and waiting times from irregular time series, while LSTM and DLSTM, integrated with an ensemble approach, are applied to sliding windows of regularized time series with a 3 hr interval. …”
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Numerical simulation of an effective transform mechanism with convergence analysis of the fractional diffusion-wave equations by Nazek A. Obeidat, Mahmoud S. Rawashdeh, Malak Q. Al Erjani

“…In the current study, we solve two very important mathematical models, such as the time fractional-order space-fractional telegraph and diffusion-wave equations using a reliable technique called the Adomian decomposition natural method (ADNM), which combines Adomian decomposition and natural transform. …”
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Bridge Deck Modal Parameters Identification Using Traffic Loads by Meisam Talebi, Zahra Tabrizian, Gholamreza Ghodrati Amiri

“…Specifically, the free vibration response is estimated using the Random Decrement (RD) technique combined with Empirical Mode Decomposition (EMD). The natural frequencies and mode shapes are extracted using Frequency Domain Decomposition (FDD). …”
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Fault Diagnosis of Gearboxes Based on AO-VMD and IAO-SVM by Wang Bo, Nan Xinyuan

“…Aiming at the problems of improving the adaptability of variational mode decomposition (VMD) and in order to optimize the intrinsic mode function (IMF) and multi-fault classification, a gearbox fault diagnosis method is proposed, with which the Aquila optimizer (AO) optimizes VMD, the comprehensive evaluation model optimizes IMF, and improves the Aquila optimizer optimization support vector machine (IAO-SVM). …”
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Improved method of non-intrusive load monitoring based on compressed sensing by Bo Yuan, Hong Liu, Shaoyun Ge, Guoping Liu

“…Experiment shows that the NILM-CS and its improved methods proposed in this paper are reasonable. The load decomposition accuracy of NILM-CS is greater than 92 %, and the load identification accuracy is close to 90 %, whose effect is similar to traditional compression method but reduces sampling frequency by 50 %, and is superior to traditional NILM with same-frequency sampling by 1 ∼ 5 percentage points. …”
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Coordinated Reactive Power–Voltage Control in Distribution Networks with High-Penetration Photovoltaic Systems Using Adaptive Feature Mode Decomposition by Yutian Fan, Yiqiang Yang, Fan Wu, Han Qiu, Peng Ye, Wan Xu, Yu Zhong, Lingxiong Zhang, Yang Chen

“…This paper proposes a coordinated reactive power and voltage optimization method based on Filtered Multiband Decomposition (FMD). …”
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Wind speed prediction for trains on bridges using enhanced variational mode decomposition assisted feature extraction and physical auxiliary mechanism by Zhilan Zhu, Yuan Jiang, Haicui Wang, Shuoyu Liu

“…This method not only can provide the accurately deterministic prediction, but also can produce the desired probabilistic prediction. …”
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Urban Traffic State Estimation with Online Car-Hailing Data: A Dynamic Tensor-Based Bayesian Probabilistic Decomposition Approach by Wenqi Lu, Ziwei Yi, Dongyu Luo, Yikang Rui, Bin Ran, Jianqing Wu, Tao Li

“…Afterward, the Gibbs sampling method is introduced to calibrate the parameters of the DTBPD models. …”
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Comprehensive thermal properties, kinetic, and thermodynamic analyses of biomass wastes pyrolysis via TGA and Coats-Redfern methodologies by Ocident Bongomin, Charles Nzila, Josphat Igadwa Mwasiagi, Obadiah Maube

“…This study comprehensively analyzes the thermal decomposition characteristics as well as the kinetic and thermodynamic parameters of five biomass wastes, including coffee husk, groundnut shell, macadamia nutshell, rice husk, and tea waste, using Thermogravimetric Analysis (TGA) and the Coats-Redfern method. …”
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Future variation and uncertainty source decomposition in deep learning bias-corrected CMIP6 global extreme precipitation historical simulation by Xiaohua Xiang, Yongxuan Li, Xiaoling Wu, Zhu Liu, Lei Wu, Biqiong Wu, Chuanxin Jin, Zhiqiang Zeng

“…Implementation of CNNs as a BC method could significantly reduce model uncertainty but at the cost of increasing the proportion of scenario uncertainty and internal variability. …”
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Using a seasonal and trend decomposition algorithm to improve machine learning prediction of inflow from the Yellow River, China, into the sea by Shuo Wang, Shuo Wang, Ke Yang, Ke Yang, Hui Peng, Hui Peng

“…However future data leakage from decomposition items was ignored in many studies. It is necessary to develop the right method to operate time decomposition to avoid future data leakage. …”
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Bridge Damage Identification Using Time-Varying Filtering-Based Empirical Mode Decomposition and Pre-Trained Convolutional Neural Networks by Shenghuan Zeng, Jian Cui, Ding Luo, Naiwei Lu

“…The proposed method enhances the key frequency-domain features of signals and suppresses the interference of non-stationary noise on model training through adaptive denoising and time–frequency reconstruction. …”
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Graphical Empirical Mode Decomposition–Convolutional Neural Network-Based Expert System for Early Corrosion Detection in Truss-Type Bridges by Alan G. Lujan-Olalde, Angel H. Rangel-Rodriguez, Andrea V. Perez-Sanchez, Martin Valtierra-Rodriguez, Jose M. Machorro-Lopez, Juan P. Amezquita-Sanchez

“…To enhance the computational efficiency of the method without compromising accuracy, different CNN architectures and image sizes are tested to propose a low-complexity model. …”
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A Posteriori Error Analysis for a Lagrange Multiplier Method for a Stokes/Biot Fluid-Poroelastic Structure Interaction Model by Houédanou Koffi Wilfrid

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