Decomposition Method Research Articles

Four-way decomposition of the effects of nutrient supplement and physical exercise on depression among older Chinese: a nationwide cross-sectional analysis

BackgroundStudies on the associations between nutrient supplement, physical exercise, and depression among persons 65 years and older in China are still lacking. We aimed to investigate the association between nutrient supplement and depression and to explore the role of physical exercise in this association.MethodsA total of 8,427 older Chinese adults from the Chinese Longitudinal Healthy Longevity Survey were included in this study. The Center for Epidemiologic Studies Depression Scale was used to assess participants’ depression, and nutrient supplement and physical exercise were evaluated using self-reports. Linear regression and logistic regression were performed to explore the association between nutrient supplement and depression, and a four-way decomposition method was used to explore the effects of nutrient supplement and physical exercise on depression.ResultsA total of 26.2% of participants suffered from depression. Prior to decomposition, multivariable linear regression and logistic regression models showed that older adults who received nutrient supplement had lower depression score (β: -0.704; 95% confidence interval [CI]: -1.004, -0.404) and a lower risk of depression (odds ratio: 0.710; 95% CI: 0.564, 0.894), especially for calcium and multivitamins. Four-way decomposition analysis suggested that physical exercise mediated the associations between nutrient (protein, calcium, iron, zinc, multivitamins, vitamin A/D, docosahexaenoic acid, and others) supplement and depression (all P < 0.05).ConclusionsCalcium or multivitamin supplements were associated with inverse depression, and the reason why this association existed could be partly explained by the mediating effect of physical exercise.

Re-examining Wage Disparities Across Segments of the Indian Labor Market

This article examines income disparities by considering both employer and employee perspectives of informality to scrutinize labor market segmentation in India. Additionally, the research investigates the impacts of varying degrees of urbanization, migration patterns, and industrial structures on formal and informal employment in the labor force. Utilizing data from the National Sample Survey Employment and Unemployment Survey rounds, specifically the 2004–2005 (61st) and 2011–2012 (68th) rounds, along with the Periodic Labour Force Survey 2018–2019 round, quantile regression models and the Recentred Influence Function decomposition method were employed to analyze wage inequality. A selection-corrected wage equation and its estimate were also presented by applying Bourguignon, Fournier, and Gurgand’s model-based selection correction. The study’s findings indicate that by 2019, the labor market had divided into two groups: organized formal workers and others. Further, the study reports wage convergence at lower quantiles and a widening gap at higher quantiles between organized formal and unorganized informal workers. Organized formal workers witness rising earnings, indicating positive wage growth, yet their share in overall employment remains small, which is significant given low job creation and high educated unemployment. At higher wage quantiles, there is a premium for being an informal worker in the organized sector compared to the unorganized sector, indicating a queue for formal employment opportunities in the organized sector. Despite the service sector’s significant role in Indian growth, it lags in creating formal job opportunities, unlike the manufacturing and construction sectors, which emphasize their substantial contribution to formal job generation. This study underlines that categorizing a firm as merely organized or a worker as formally employed is insufficient. Upgrades in these classifications should be accompanied by premiums, with wages being a key consideration. JEL Classification J31, J42, J62, J71

Research on Unmanned Aerial Vehicle Emergency Support System and Optimization Method Based on Gaussian Global Seagull Algorithm

In emergency rescue scenarios, drones can be equipped with different payloads as needed to aid in tasks such as disaster reconnaissance, situational awareness, communication support, and material assistance. However, rescue missions typically face challenges such as limited reconnaissance boundaries, heterogeneous communication networks, complex data fusion, high task latency, and limited equipment endurance. To address these issues, an unmanned emergency support system tailored for emergency rescue scenarios is designed. This system leverages 5G edge computing technology to provide high-speed and flexible network access along with elastic computing power support, reducing the complexity of data fusion across heterogeneous networks. It supports the control and data transmission of drones through the separation of the control plane and the data plane. Furthermore, by applying the Tammer decomposition method to break down the system optimization problem, the Global Learning Seagull Algorithm for Gaussian Mapping (GLSOAG) is proposed to jointly optimize the system’s energy consumption and latency. Through simulation experiments, the GLSOAG demonstrates significant advantages over the Seagull Optimization Algorithm (SOA), Particle Swarm Optimization (PSO), and Beetle Antennae Search Algorithm (BAS) in terms of convergence speed, optimization accuracy, and stability. The system optimization approach effectively reduces the system’s energy consumption and latency costs. Overall, our work alleviates the pain points faced in rescue scenarios to some extent.

Synthesis of barium titanate with narrow particle size distribution by the oxalate thermal decomposition method

Synthesis of barium titanate with narrow particle size distribution by the oxalate thermal decomposition method

Model Decomposition-Based Approach to Optimizing the Efficiency of Wireless Power Transfer Inside a Metal Enclosure

This paper describes a numerically efficient method for optimizing the high power transfer efficiency (PTE) of a resonant cavity-based Wireless Power Transfer (WPT) system for the wireless charging of smart clothing. The WPT system under study unitizes a carbon steel closet intended to store smart clothing overnight as a resonant cavity. The WPT system is designed to operate at 865.5 MHz; however, the operating frequency can be adjusted over a wide range. The main reason behind choosing a resonant cavity-based WPT system is that it has several advantages over the competitive WPT methods. Specifically, in contrast to its Far-field Power Transfer (FPT) and Inductive Power Transfer (IPT) counterparts, resonant cavity-based WPTs do not exhibit path loss and significant PTE sensitivity to the distance between the Tx and Rx coils and misalignment, respectively. The non-uniformity of the fields within the closet is addressed by using an optimized Yagi-like transmitting antenna with an additional element affecting the waveguide mode phases. The changes in the mode phases increase the volume inside the cavity, where the PTE values are higher than 50% (the high PTE region). In the present study, the model decomposition method is adapted to substantially accelerate the process of finding the optimal WPT system parameters. Additionally, the decomposition method explains the mechanism responsible for extending the high PTE region. The generalized scattering matrices are computed using the full-wave simulator Ansys HFSS for three sub-models. Then, the calculated S matrices are combined to evaluate the system’s PTE. The decomposition method is validated against full-wave simulations of the original WPT system’s model for several different parameter value combinations. The simulated results obtained for a sub-optimal model are experimentally verified by measuring the PTE of a real-life closet-based WPT system. The measured and calculated results are found to be in close agreement with the maximum measured PTE, as high as 60%.

Fertility Decline in Malawi: A Decomposition Analysis

Fertility in Malawi has been declining since the late 1980s. Total Fertility Rate is reported to have declined by 1.1 children per woman from 5.7 in 2010 to 4.6 in 2015. This reduction indicates a continuing rapid decline in fertility in the country. In view of the foregoing, this study aims to examine the influence of the age structure of the population, nuptiality, and marital fertility on fertility in Malawi. This study used the Malawi Demographic and Health Survey data from 1992, 2000, 2004, 2010, and 2015–2016. Standardization and decomposition methods were used to re-examine fertility trends in Malawi. The results confirms that fertility is declining in Malawi and the fall is clustered among women younger than 30 years of age. The contribution of marital fertility to the decline of Total Fertility Rate was 65.0% during the period 1992–2000, 89.5% during 2000–2004, 2.6% during 2004–2010, and 4.3% during 2010–2015, respectively. Over the same period, the respective contribution of the proportion married was 29.4%, 10.8%, 77.9%, and 92.6%. These percentages suggest that marital fertility followed by proportion of married were important factors before 2005. After 2005, fertility levels and patterns in Malawi are largely influenced by nuptiality. However, there is need for further studies to explain the marriage and fertility nexus in the Malawian context.

Familial co-aggregation and shared familiality among neurodevelopmental problems and with aggressive behavior, depression, anxiety, and substance use.

To refine the knowledge on familial transmission, we examined the (shared) familial components among neurodevelopmental problems (i.e. two attention-deficit/hyperactivity-impulsivity disorder [ADHD] and six autism spectrum disorder [ASD] subdomains) and with aggressive behavior, depression, anxiety, and substance use. Data were obtained from a cross-sectional study encompassing 37 688 participants across three generations from the general population. ADHD subdomains, ASD subdomains, aggressive behavior, depression, anxiety, and substance use were assessed. To evaluate familial (co-)aggregation, recurrence risk ratios (λR) were estimated using Cox proportional hazards models. The (shared) familiality (f2), which is closely related to (shared) heritability, was assessed using residual maximum likelihood-based variance decomposition methods. All analyses were adjusted for sex, age, and age2. The familial aggregation and familiality of neurodevelopmental problems were moderate (λR = 2.40-4.04; f2 = 0.22-0.39). The familial co-aggregation and shared familiality among neurodevelopmental problems (λR = 1.39-2.56; rF = 0.52-0.94), and with aggressive behavior (λR = 1.79-2.56; rF = 0.60-0.78), depression (λR = 1.45-2.29; rF = 0.43-0.76), and anxiety (λR = 1.44-2.31; rF = 0.62-0.84) were substantial. The familial co-aggregation and shared familiality between all neurodevelopmental problems and all types of substance use were weak (λR = 0.53-1.57; rF = -0.06-0.35). Neurodevelopmental problems belonging to the same disorder were more akin than cross-disorder problems. That said, there is a clear (shared) familial component to neurodevelopmental problems, in part shared with other psychiatric problems (except for substance use). This suggests that neurodevelopmental disorders, disruptive behavior disorders, and internalizing disorders share genetic and environmental risk factors.

Gender Wage Gap Analysis in Riau Province Using the Blinder-Oaxaca Decomposition Method

This study investigates the gender wage gap in Riau Province using the Blinder-Oaxaca decomposition method to examine its underlying factors. The analysis reveals a significant wage disparity, where women earn, on average, 29.56% less than men, despite higher educational attainment among women. The decomposition results highlight that only 2.44% of the gap is explained by observable factors such as education and employment type, while 76.76% remains unexplained, indicating the prevalence of discrimination and unmeasured structural barriers. This study identifies unique regional dynamics, including the paradox of women in urban areas earning less than their rural counterparts, contrary to global trends. Additionally, the study underscores the role of formal employment in reducing wage disparities, with women in formal jobs earning significantly more than those in informal sectors. These findings suggest that while improving education and transitioning women into formal employment can partially address wage disparities, structural reforms targeting workplace discrimination and cultural norms are essential for achieving gender wage equity. The study contributes to the literature by providing localized insights into gender wage gaps in Riau Province, emphasizing the need for targeted policy interventions to enhance equity in labor market outcomes.

Adaptive Ramanujan package decomposition method: a novel precise health indicator of rolling bearings

ABSTRACT In this paper, an adaptive Ramanujan packet decomposition (ARPD) method based on precise envelope segmentation is proposed to improve the accuracy of weak fault feature extraction and realise early non-destructive testing and condition evaluation of rolling bearings. Firstly, the power spectral density of the original signal is envelope-processed by accurate envelope segmentation method, so as to reduce the extreme points produced by interference frequency, and then realise the optimal frequency band division and reduce the influence of noise components. Secondly, the Ramanujan spectrum signal-to-noise ratio (RSSNR) index is introduced to quantify the fault characteristic information of the corresponding component in each frequency band. The largest component of the index is selected as the optimal mode, and the current number of layers is defined as the optimal number of modes. Finally, through the idea of iterative decomposition and RSSNR index, the problem that the optimal mode number is difficult to determine accurately is eliminated, and the optimal mode number is determined adaptively. The results of simulation and experimental signal analysis show that ARPD method can accurately divide the frequency bands and has excellent weak fault feature extraction ability, so it is an effective early non-destructive testing and condition evaluation method for rolling bearings.

Data-Driven Voltage Control Method of Active Distribution Networks Based on Koopman Operator Theory

The advent of large-scale distributed generation (DG) has introduced several challenges to the voltage control of active distribution networks (ADNs). These challenges include the heterogeneity of control devices, the complexity of models, and their inherent fluctuations. To maintain ADN voltage stability more economically and quickly, a data-driven ADN voltage control scheme is proposed in this paper. Firstly, based on the multi-run state sensitivity matrix, buses with similar voltage responses are clustered, and critical buses are selected to downsize the scale of the model. Secondly, a linear voltage-to-power dynamics model in high-dimensional state space is trained based on the offline data of critical bus voltages, DGs, and energy storage system (ESS) outputs, utilizing the Koopman theory and the Extended Dynamic Mode Decomposition (EDMD) method. A linear model predictive voltage controller, which takes ADN stability and control cost into account, is also proposed. Finally, the effectiveness and applicability of the method are verified by applying it to an improved 33-bus ADN system. The proposed control method can respond more quickly and accurately to the voltage fluctuation problems caused by source-load disturbances and short-circuit faults.

SOLUTION OF THE SPECTRUM ALLOCATION PROBLEM FOR A LINEAR CONTROL SYSTEM WITH CLOSED FEEDBACK

The method for constructing a feedback matrix to solve the problem spectrum allocation (spectrum control, pole assignment) for linear dynamic system is given. A new proof of the well-known theorem about the connection between the complete controllability of a dynamic system with existence of the feedback matrix is formed in the process of construction by the cascade decomposition method. The entire set of arbitrary elements that influence the non-uniqueness of the matrix is identified. Examples of constructing a feedback matrix in the case of a real spectrum and in the presence of complex conjugate eigenvalues, as well as for the case of multiple eigenvalues, are given. The stability of the specified spectrum under small perturbations of the system parameters with a fixed feedback matrix is studied.

Perturbational Decomposition Analysis for Quantum Ising Model with Weak Transverse Fields.

This work presents a perturbational decomposition method for simulating quantum evolution under the one-dimensional Ising model with both longitudinal and transverse fields. By treating the transverse field terms as perturbations in the expansion, our approach is particularly effective in systems with moderate longitudinal fields and weak to moderate transverse fields relative to the coupling strength. Through systematic numerical exploration, we characterize parameter regimes and evolution time windows where the decomposition achieves measurable improvements over conventional Trotter decomposition methods. The developed perturbational approach and its characterized parameter space may provide practical guidance for choosing appropriate simulation strategies in different parameter regimes of the one-dimensional Ising model.

Understanding Emission Trends, Regional Distribution Differences, and Synergistic Emission Effects in the Transportation Sector in Terms of Social Factors and Energy Consumption

China’s transportation sector plays a significant role in reducing carbon dioxide (CO2) and air pollution. Previous studies have predominantly utilized scenario analysis to forecast emissions for the next 30 to 50 years based on coefficients from a base year. To elucidate the current state of gas emissions in the transportation sector, this study employed panel data for 10 types of gas emissions from 2001 to 2020, analyzing their emission characteristics, tendencies, and synergistic effects. Utilizing the Kaya equation and the logarithmic mean division index (LMDI) decomposition method, we developed a model of pollutant emissions that considers the synergistic effects, pollution emission intensity, energy mix, energy consumption intensity, and population. The results show that all pollutants in the transportation sector decreased except for NH3 and CO2. There was a synergistic effect between air pollutants and CO2 emissions, but the reduction was not significant. From 2013 to 2020, the transportation sector shifted from a high emission intensity with low synergy to a low emission intensity with high synergy. The results indicate that off-road mobile vehicles, on-road diesel vehicles, and motorcycles became the main source of emissions from transportation in certain provinces, and a key area requiring attention in policy development. Gasoline consumption was identified as the primary contributor to the significant increase in synergistic emission variability in the transportation sector. These results provide policymakers with practical ways to optimize emission reduction pathways.

Determinants of Access to Higher Education: Evidence from Jharkhand, India

This paper examines the access to higher education across socio-religious groups in the state of Jharkhand in India. It also examines the factors affecting access to higher education and the role of students’ social background in explaining the inequality in participation in higher education. The analysis is based on cross tabulation, logistic regression and Fairlie decomposition method. The analysis shows that tribals, Muslims and Scheduled Castes are the worst performing groups in the state. The most prominent factor behind the vulnerable condition of tribals is their high concentration in rural areas as there is a remarkable gap in their performance between rural and urban areas. A large part of the gap between the privileged and the underprivileged groups could not be explained by endowment factors, namely, household size, education of the head of household and income background. The results suggest that incentives created due to family background leads to different outcomes among different socio-religious groups.

Cubic–quartic optical solitons with Fokas–Lenells equation by the improved Adomian decomposition method

Cubic–quartic optical solitons with Fokas–Lenells equation by the improved Adomian decomposition method

An optimized LSTM model for clean coal ash content prediction in dense medium separation scenarios on the basis of the dual decomposition method

ABSTRACT To increase the accuracy of clean coal ash content prediction during the dense medium separation process and address the time lag issue encountered when measuring clean coal ash content, a prediction model based on WaOA-VMD-SGMD-WaOA-LSTM was proposed. The model adopts the dual decomposition techniques of optimized Variational Mode Decomposition (VMD) and Symplectic Geometric Mode Decomposition (SGMD), which can completely decompose the original clean coal ash data, and uses the Walrus optimization algorithm (WaOA) to optimize the hyperparameters of the Long Short-Term Memory (LSTM) model. In the process of model construction, the characteristic data of ore content ( Z 2 ), raw coal ash ( Z 3 ), heavy mesoporous cyclone pressure ( Z 4 ), heavy mesoporous suspension density ( Z 5 ), and magnetic content ( Z 6 ) were combined with the decomposed cleaned coal ash grouping S-IMF0~S-IMFn, CO-IMF1, and CO-IMF2 as input variables to construct multiple LSTM prediction models. Finally, the prediction value is superimposed to realize the prediction of clean coal ash content. Based on industrial data of a coal preparation plant in Shanxi, China, the results show that the coefficient of determination ( R 2 ) of the model is 0.9974. After adding secondary decomposition technology, the average absolute error was reduced by 60.99% compared with that of the single decomposition strategy.

Tuning specific absorption rate of Mn ferrite nanoparticles synthesized by a thermal decomposition method with different Mn concentrations.

A thermal decomposition method is used to synthesize monodisperse Mn ferrite nanoparticles (NPs) by changing Mn concentration from 0.1 to 0.8 mmol. The effects of Mn concentration on structural, compositional, morphological, magnetic, and hyperthermia properties are investigated. Transmission electron microscopic images show that the morphology of the NPs changes from flower-like to polygonal with increasing the Mn concentration. The saturation magnetization reaches a maximum value of 48.32 emu/g and a minimum value of 11.09 emu/g with changing the Mn concentration, whereas the coercivity value decreases from 12.6 to 5.3 Oe. The first-order reversal curve (FORC) analysis enables the estimation of superparamagnetic contribution of the Mn ferrite NPs in the range of 21-59.5%. The highest specific absorption rate (SAR) value is obtained to be 385.37 W/g for Mn0.1Fe2.9O4 NPs with the maximum superparamagnetic contribution using a manganese concentration of 0.4 mmol.

Uncovering population contributions to the extracellular potential in the mouse visual system using Laminar Population Analysis.

The local field potential (LFP), the low-frequency part of the extracellular potential, reflects transmembrane currents in the vicinity of the recording electrode. Thought mainly to stem from currents caused by synaptic input, it provides information about neural activity complementary to that of spikes, the output of neurons. However, the many neural sources contributing to the LFP, and likewise the derived current source density (CSD), can often make it challenging to interpret. Efforts to improve its interpretability have included the application of statistical decomposition tools like principal component analysis (PCA) and independent component analysis (ICA) to disentangle the contributions from different neural sources. However, their underlying assumptions of, respectively, orthogonality and statistical independence are not always valid for the various processes or pathways generating LFP. Here, we expand upon and validate a decomposition algorithm named Laminar Population Analysis (LPA), which is based on physiological rather than statistical assumptions. LPA utilizes the multiunit activity (MUA) and LFP jointly to uncover the contributions of different populations to the LFP. To perform the validation of LPA, we used data simulated with the large-scale, biophysically detailed model of mouse V1 developed by the Allen Institute. We find that LPA can identify laminar positions within V1 and the temporal profiles of laminar population firing rates from the MUA. We also find that LPA can estimate the salient current sinks and sources generated by feedforward input from the lateral geniculate nucleus (LGN), recurrent activity in V1, and feedback input from the lateromedial (LM) area of visual cortex. LPA identifies and distinguishes these contributions with a greater accuracy than the alternative statistical decomposition methods, PCA and ICA. The contributions from different cortical layers within V1 could however not be robustly separated and identified with LPA. This is likely due to substantial synchrony in population firing rates across layers, which may be reduced with other stimulus protocols in the future. Lastly, we also demonstrate the application of LPA on experimentally recorded MUA and LFP from 24 animals in the publicly available Visual Coding dataset. Our results suggest that LPA can be used both as a method to estimate positions of laminar populations and to uncover salient features in LFP/CSD contributions from different populations.

Solitary Wave Solution of Korteweg–De Vries Equation by Double Laplace Transform with Decomposition Method

Solitary Wave Solution of Korteweg–De Vries Equation by Double Laplace Transform with Decomposition Method

Unsteady Flow Field Analysis of a Compressor Cascade Based on Dynamic Mode Decomposition

Traditional flow field modeling methods are limited by high computational complexity, making them difficult to apply in practical engineering. This study applies the Dynamic Mode Decomposition (DMD) method to perform reduced-order modeling of unsteady flow fields over an airfoil and a compressor cascade. As a data-driven modal decomposition technique, DMD extracts low-dimensional modes from high-dimensional spatiotemporal data, preserving key dynamic characteristics and significantly reducing computational costs. Numerical simulations were conducted to generate time snapshots, forming matrices of pressure and Mach number snapshots. DMD analysis identified a few dominant modes and their eigenvalues, capturing the primary dynamic behavior of the flow field. The results demonstrate that these modes effectively reconstruct the system’s main characteristics, reducing the need for extensive computational resources and time. The DMD method not only improves modeling efficiency, but also accurately reconstructs complex flow structures. This study validates the feasibility and effectiveness of DMD in reduced-order modeling for unsteady flow fields and includes error analysis for further evaluation.