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Brain development during childhood involves significant structural, functional, and connectivity changes, reflecting the interplay between modularity, information interaction, and functional segregation. This study aims to understand the dynamic properties of brain connectivity and their impact on cognitive development, focusing on temporal co-occurrence diversity patterns. We recruited 481 children aged 6 to 12 years from the Healthy Brain Network database. Functional MRI data were used to construct dynamic functional connectivity matrices with a sliding window approach. Modular structures were identified using multilayer network community detection, and the Dagum Gini coefficient decomposition technique, which uniquely allows for multi-faceted exploration of modular temporal co-occurrence diversities, quantified these diversities. Mediation analysis assessed the impact on small-world properties. Temporal co-occurrence diversity in brain networks increased with age, especially in the default mode, frontoparietal, and salience networks. These changes were driven by disparities within and between communities. The small-world coefficient increased with age, indicating improved information processing efficiency. To validate the impact of changes in spatiotemporal interaction disparities during childhood on information transmission within brain networks, we used mediation analysis to verify its effect on alterations in small-world properties. This study highlights the critical developmental changes in brain modularity and spatiotemporal interaction patterns during childhood, emphasizing their role in cognitive maturation. These insights into neural mechanisms can inform the diagnosis and intervention of developmental disorders. • Used multilayer networks to construct dynamic functional connectivity matrices from fMRI. • Dagum Gini coefficient explored brain network disparities, showing age-related increases. • Spatiotemporal interaction disparities increased within and between communities with age. • Mediation analysis showed spatiotemporal interaction mediates age and small-world properties. [ABSTRACT FROM AUTHOR]

Background: In the context of public health emergencies, the presence of medical and health talents (MHT) is critically important for support in any country or region. This study aims to analyze the spatial and temporal distributions and evolution of MHT in China and propose strategies and recommendations for promoting a balanced distribution. Methods: This research used data from 31 provinces in China to construct a multidimensional index system for measuring the agglomeration level of MHT. The indices include talent agglomeration density (TAD), talent agglomeration scale (TAS), talent agglomeration intensity (TAI), and talent agglomeration equilibrium (TAE). Using provincial data from the years 1982, 1990, 2000, 2010, and 2020, a spatiotemporal analysis of the MHT agglomeration levels was conducted. Furthermore, the regional dynamic distribution of MHT was analyzed using kernel density estimation diagrams. The spatial autocorrelation of MHT was assessed through global and local Moran's I, and the spatial gap and decomposition of MHT were analyzed using the Dagum Gini coefficient. Results: From the temporal level, the TAD and TAI of MHT showed an increasing trend over the studied period, whereas TAS decreased and TAE first increased and then decreased from 1982 to 2020. At the spatial level, the TAD, TAS, TAI, and TAE of MHT exhibited varied patterns among the eastern, central, and western regions of China, showing significant geographical disparities, generally demarcated by the Hu Huanyong Line. The regional dynamic distribution level of MHT in the country and the three regions were expanding. Spatial autocorrelation analysis using global and local Moran's I for TAD, TAS, TAI, and TAE demonstrated significant regional differences. The Dagum Gini coefficient of TAD, TAS, TAI, and TAE revealed divergent trends in regional disparities, with overall declines in disparities for TAD and TAI, a slight increase for TAS, and fluctuating patterns for TAE. Conclusions: From a temporal perspective, the overall number of MHT in China has been increasing annually at the national and provincial levels. From the spatial perspective, TAD, TAS, TAI, and TAE exhibit significant differences among the three regions. Kernel analysis reveals that the distribution differences are gradually expanding in national level and varying in regional level. Moreover, the global and local Moran's I indices reveal varying spatial autocorrelation for TAD, TAS, TAI, and TAE. The Dagum Gini coefficients of TAD, TAS, TAI, and TAE show different patterns of decomposition. [ABSTRACT FROM AUTHOR]

This study aims to develop an efficient model to predict the arrears behavior of electricity users by integrating multi-scale feature learning with a backpropagation (BP) neural network. The goal is to provide accurate early warning systems and enhanced risk management tools for power companies. The BP neural network algorithm adjusts weights to minimize prediction errors, while multi-scale feature learning captures the diversity and regularity of user behavior by extracting data from various time dimensions, such as daily, weekly, and monthly intervals. First, electricity usage and weather data from the UMass Smart Dataset are preprocessed, including steps such as data cleaning, standardization, and normalization. Next, features are extracted across three time scales—daily, weekly, and monthly. These features are then input into the BP neural network model using the multi-scale feature learning method. A hierarchical neural network structure is designed to address the characteristics of different scales in distinct layers. Key model parameters are optimized, and a sensitivity analysis is conducted. The experimental results demonstrate that the BP neural network model incorporating multi-scale features outperforms traditional BP neural network models and other control models in several evaluation metrics. Specifically, the Gini coefficient is 0.55, the Kolmogorov-Smirnov statistic is 0.60, the Matthews correlation coefficient is 0.45, and specificity is 0.82. These results indicate that the proposed method offers significant improvements in capturing user behavior patterns and enhancing prediction accuracy. The study concludes that the effective fusion of multi-scale features not only enhances the model's prediction performance but also strengthens its generalization ability. This method provides an advanced risk management tool for power companies, helping to increase the operational efficiency of smart grids and encouraging further research toward greater intelligence in the field. [ABSTRACT FROM AUTHOR]

Integrating machine learning techniques within metaheuristics has shown promise for effectively solving combinatorial problems like the Traveling Salesman Problem (TSP). However, key challenges remain in initializing metaheuristics to balance exploration and exploitation across vast search spaces. This paper introduces a novel clustering-driven technique for constructing high-quality initial solutions to Euclidean TSP instances. Our method uses hierarchical hybrid clustering with K-means, affinity propagation, and density peaks clustering to recursively partition cities into a compressed quadtree structure. A rigorous assessment using the Davies–Bouldin index and Gini coefficient optimizes intra- and inter-cluster quality and balance at each level. The multi-tiered decomposition strategically abstracts complex optimization landscapes into localized clusters that are solved efficiently in parallel within each using heuristics such as nearest neighbor and ant colony optimization. A genetic networking heuristic then interconnects independent intra-cluster solutions to construct unified inter-cluster routes. The clustering-guided initialization provides a diverse population of initialized tours that balance global exploration against localized exploitation. To validate our method, we conduct experiments using the generated solutions to seed a simulated annealing metaheuristic. This experimental evaluation will demonstrate this technique's ability to initialize metaheuristics for TSP instances closer to optimality compared to traditional methods. [ABSTRACT FROM AUTHOR]

The coupled development of new-type urbanization (NTU) and rural revitalization (RR) represents a critical proposition put forth by China for forging a novel paradigm of urban-rural relationship. Initially, this study employs the entropy method to quantify NTU and RR. Subsequently, it carries out a comprehensive analysis concerning their coupled relationship with the relative development degree model (RDDM), coupled coordination degree model (CCDM), Dagum Gini coefficient, kernel density estimation, and Tobit model. The findings drawn from the study indicate from 2011 to 2022, NTU and RR in the Yangtze River Economic Belt (YREB) have exhibited a consistent upward trajectory, but lagging NTU disorders are widely distributed and numerous. The coupled coordination degree (CCD) of NTU and RR constantly improves, transitioning from moderate imbalance to primary coordination, exhibiting a spatial distribution of a "high in the east and low in the west". The relative disparity between the coupled development of NTU and RR demonstrates a slowly narrowing trend, whereas the absolute disparity indicates an expanding trend. Among the influencing factors, the development of the agricultural industry exerts the most significant positive impact on the coupled development, whereas the level of financial support for agriculture exerts a dampening effect, which is heterogeneous in nature. [ABSTRACT FROM AUTHOR]

Ecological Welfare Performance (EWP) is a core issue in sustainable development and ecological civilization. Although Guangdong Province is one of China's most developed economies and most highly urbanized areas, there is a gap in the evaluation of the ecological welfare performance of the province. Previous studies have mainly calculated the performance of ecological welfare from a static viewpoint or only from the perspective of network efficiency, and few researchers have considered dynamic characteristics and network structures. This research focuses on 21 prefecturelevel cities in Guangdong Province and measures their ecological welfare performance by applying the dynamic network slacks-based measure (DNSBM) model. Using the Dagum Gini coefficient, this study identifies the sources and contributions of differences in ecological welfare performance among regions in Guangdong Province. The results reveal a low overall level of ecological welfare, with levels of ecological welfare showing an uneven distribution among the 21 cities. EWP and economic growth are spatially mismatched. The overall variations in the ecological welfare performance in the province are mainly due to ultra-high-density contributions. Therefore, we recommend that Guangdong Province strengthen overall regional coordinated development and promote high-quality "shared" development in the northern, eastern, and western areas. [ABSTRACT FROM AUTHOR]

The standard interpretation of inequality uses a number, such as the Gini coefficient, to compare income inequality across countries. These numbers apply universal upper limits to the maximum feasible inequality (Gini = 100) in vastly diverse economies even though floors for socially acceptable living standards vary quite a bit in different societies. I develop a new measure of income inequality — the Nationally Representative Inequality Extraction Ratio (NR IER) — and apply it to 112 countries. The NR IER uses country-by-country social and economic parameters to measure the distance between the actual income distribution and the country-specific feasible limit (a counterfactual distribution). I ground the counterfactual distribution in a functional income concept, corresponding to Marx's concept of exploitation. NR IERs are inversely related to per-capita income and exceed the feasible limits in the world's poorest countries. However, I find little variation in extractive inequality between closed autocracies (e.g., China) — where corruption is expectedly extractive — and liberal democracies (e.g., USA). Controlling for different political regimes, the NR IER explains over 60 per cent of a person's income anywhere in the world. [ABSTRACT FROM AUTHOR]

China is struggling to facilitate the application of renewable portfolio standards to realize sustainable economic growth. As such, improving the current distribution mechanism is crucial. In this paper, the context‐dependent data envelopment analysis and multi‐objective linear programming are combined to allocate the renewables quota for each province. This integrated approach can maximize total electricity generation while minimizing the total CO2 emission with considering the disparity of production technology level. Then, the extended Gini coefficient is employed to assess the fairness of new quota mechanism. We find that (1) the eastern region is the most efficient during the power generation process. During 2016–2019, the efficiency in the western region presents an upward trend. (2) The allocation results indicate that Inner Mongolia and Qinghai have the greatest pressure to absorb renewable energy electricity, while Guangdong and Guizhou can instead reduce the most. Shandong and Inner Mongolia face the greatest burden in conserving non‐renewable electricity. (3) Compared to 2020, the newly allocated scheme can mitigate inequality, with the Gini coefficient changing from 0.264 in 2020 to 0.248 after the allocation. Meanwhile, the reallocation reduces the Gini coefficient related to renewable electricity, non‐renewable electricity, and CO2 emissions by 0.003, 0.028, and 0.073, respectively at the 2020 level. [ABSTRACT FROM AUTHOR]

We measure the regional gaps in green total factor productivity (GTFP) growth by using the Dagum's Gini coefficient based on panel data for 306 cities from 1996 to 2017, then adopt a geographical detector to test the contribution of resource misallocation to the unbalanced growth in GTFP. The results show that Chinese agricultural GTFP continues to grow, but the overall growth gap has expanded year by year, mainly due to the inter-provincial gap. Compared with land, labor and machinery, fertilizer misallocation is the main factor driving the unbalanced growth in GTFP. Moreover, the interaction contribution of fertilizer misallocation with any one resource misallocation is higher than that in a single factor. Finally, resource misallocation also leads to unbalanced growth in technological progress and technical efficiency, but more so for the latter. Our research helps to provide a new solution to the "dilemma" of food security and ecological security from the perspective of optimizing resource allocation. [ABSTRACT FROM AUTHOR]

The accuracy of spatial clustering detection is crucial for public health policy development and identifying etiological clues. Circular and flexibly-shaped scan statistics are widely used for disease cluster detection, but differences in results arise mainly due to parameter sensitivity and variations in the scanning window shapes. This study aims to analyze the impact of parameter settings on the results of these methods and compare their performance in disease clustering detection. Using tuberculosis data from Wuhan, China (2015–2019), the study identified the optimal parameter settings—MSWS and K-value—for each method to ensure accurate clustering. A comprehensive comparison was made using two quantitative indicators, the LLR value and cluster size, as well as clustering visualizations. The results show that the optimal MSWS parameter for SaTScan is determined through a Gini coefficient-based stepwise-threshold-reduction approach, while a K-value of 30 is ideal for FleXScan. SaTScan tends to produce more regular clusters, while FleXScan often generates more irregular clusters. FleXScan detects fewer clusters but with higher LLR values and larger average cluster sizes, although the maximum cluster size is smaller. These findings provide valuable insights for optimizing disease clustering detection methods and enhancing public health interventions.

Establishing a new type of electricity system based on rooftop photovoltaics (PV) can facilitate the energy transition in rural China. Research on the mismatch between the PV supply and rural household demand is vital to the widespread adoption of PV microgrid systems. Currently, typical load patterns (TLPs) in rural areas lack accurate characterization and mismatch assessment methods disregard PV curtailment. Therefore, this study proposes a hybrid deep learning-based analytical framework to quantify short-term mismatches between PV power generation and TLPs throughout the day and applies it to a real rural dataset. This study employs the variational autoencoder (VAE) model for dimensionality reduction and feature extraction of high-resolution load data and compares it with traditional methods. In addition, we employed the k-medoids method to uncover TLPs and utilized decision trees to enhance interpretability. The results show that (1) The VAE model exhibits superior dimensionality reduction and feature extraction capabilities on both public and measured datasets and compared to other models, it can reconstruct peak loads more effectively. (2) Three types of TLPs were identified within the rural dataset, with the outdoor average daily wet-bulb temperature being the major influencing factor. (3) Significant differences existed in the mismatch levels between the three types of TLPs and PV power generation. The Lorenz curves and Gini coefficients can effectively quantify the mismatch between PV power generation and TLPs. The proposed framework provides theoretical support for optimizing PV microgrid systems design in rural areas and developing demand-side response strategies. [Display omitted] • A framework for intra-day short-term mismatch analysis of photovoltaic-load is proposed. • The variational autoencoder model is used for dimensionality reduction in high-resolution load data. • Utilized decision trees for seasonal classification and interpretability of rural TLPs. • The impact of photovoltaic capacity and data temporal resolution on mismatch is examined. [ABSTRACT FROM AUTHOR]

The increasing demand for air pollution control has driven the application of low-cost sensors (LCS) in air quality monitoring, enabling higher observation density and improved air quality predictions. However, the inherent limitations in data quality from LCS necessitate the development of effective methodologies to optimize their application. This study established a hybrid framework to enhance the accuracy of spatiotemporal predictions of PM 2.5 , standard instrument measurements were employed as reference data for the remote calibration of LCS. To account for local emission characteristics, the calibration model was trained using statistical values from LCS during periods of reduced anthropogenic emissions. This calibration approach significantly improved data quality, increasing R2 values of LCS data from 0.60 to 0.85. Subsequently, an advanced predictive model, STXGBoost, was developed, combining Kriging interpolation technology with high-density LCS data to integrate temporal trends and geographic spatial correlations. The STXGBoost model effectively captured the spatiotemporal variability of PM 2.5 data, producing accurate and high spatiotemporal resolution PM 2.5 prediction maps, with R2 values of 0.96, 0.92, and 0.89 for 1-h, 4-h, and 48-h predictions, respectively. These findings demonstrate the feasibility of generating high-resolution urban air pollution maps by integrating high-density ground monitoring data with advanced computational approaches. This framework provides valuable support for precise management and informed decision-making in urban atmospheric environments. [Display omitted] • Using machine learning to calibrate large-scale low cost sensors in Zhengzhou. • Train model using statistical data from low anthropogenic emissions periods. • Using Kriging-STXGBoost model based on dense air sensors to predict PM 2.5 maps. • Gini coefficient can reflect the variation of air pollution. [ABSTRACT FROM AUTHOR]

Although much research has focused on CO 2 emissions driven by household consumption, significant challenges remain in capturing complex regional variations and the indirect contributions of disaggregated energy sectors and different income groups. In this study, an affinity-propagation multi-regional input-output (AP-MRIO) model is developed through incorporating Dagum Gini coefficient (DGC) and affinity propagation (AP) clustering within a multi-regional input-output (MRIO) modeling framework. AP-MRIO not only traces CO 2 emissions from China's provincial household consumption, particularly within disaggregated energy sectors, but also reveals the interaction between sector emissions and income levels. Results obtained disclose that (i) within energy sectors, thermal power, electric power distribution, and petroleum are major emitters (accounting for 68.7 %, 17.5 %, and 6.6 %, respectively); in comparison, CO 2 emissions from renewable energy sectors (hydropower, nuclear, wind, and solar) are lower (2.7 %); (ii) urban middle- and high-income households contribute significantly to CO 2 emissions (57.1 %), and notable carbon inequalities exist both within and between regions for energy sectors; (iii) some provinces (e.g., Inner Mongolia, Liaoning, and Heilongjiang) should prioritize reducing per capita emissions from the non-renewable energy sectors, and other provinces (e.g., Ningxia, Guangdong, and Hunan) should further promote the development of renewable energy and focus on emissions embodied in the use of intermediate products. [ABSTRACT FROM AUTHOR]

• The sliding filter algorithm effectively reduces noise and redundant information in the data. • Use CART decision tree to output accurate anomaly detection results. • A novel detection method is proposed by combining sliding filters and decision tree techniques. • The longest time for detecting anomalies in research methods is only 8 s. In the actual measurement of three-phase electricity meters, the presence of electromagnetic interference, environmental temperature, and other factors increases the noise in the measurement data, which affects the accuracy of anomaly detection. In this regard, a fast detection method for three-phase meter measurement anomalies based on sliding filters and decision trees is studied. Firstly, sliding filtering and sliding window matrix methods are used to reduce the dimensionality and filter the measurement data of three-phase electric meters. Then, a cascaded random forest is constructed using a CART decision tree, and the filtered three-phase meter measurement data is input into the cascaded random forest. The CART decision tree uses a binary method to partition the nodes. Finally, using the Gini coefficient as a measurement indicator, a cascaded structure is formed through layered stacking to output rapid detection results of outliers in three-phase electricity meter measurements. The experimental results show that this method can quickly detect measurement abnormalities of three-phase energy meters, timely detect meter flying anomalies and unexpected mutations, and improve the fault handling efficiency of three-phase energy meters. [ABSTRACT FROM AUTHOR]

Environmental, social and corporate governance (ESG), as one of the guarantee systems for improving the construction of a beautiful China, has an impact on regional environmental management). Clarifying the coupling coordination relationship between the environmental performance of regional administrations and the ESG performance of listed enterprises may help achieve high levels of ecological preservation and economic growth. This study employs three methods to measure the degree of coordinated coupling between the environmental performance of regional administrations and the ESG effectiveness of listed firms. The methods used are the Dagum Gini coefficient decomposition method, the model of coordinated coupling, and the geographic Kernel density estimation method. The findings show that: (i) there is an overall higher trend in the level of coordinated coupling between the environmental performance of regional administrations and the ESG effectiveness of listed firms. This degree of coupling has evolved from near-disorder to intermediate coordination. (ii) There is an uneven spatial distribution in the level of coordinated coupling between the environmental performance of regional administrations and the ESG effectiveness of listed businesses, with inter-regional differences serving as the primary cause of spatial variation. (iii) In most provinces, there is a geographical link between the coordinated coordination of the environmental performance of regional administrations and the ESG effectiveness of listed businesses, provided that spatial elements and temporal span are taken into account. These findings give practical recommendations for regional administrations' environmental stewardship as well as important insights into the attainment of sustainable economic and social growth. • Build a model to evaluate the synergistic development effect between government environmental governance and corporate performance • Discuss the developmental trend and spatial differences of the coupling coordination degree • The central and northeastern regions showing polarization trends • Interregional differences are the main source of differences in coupling coordination degree among China's four major regions [ABSTRACT FROM AUTHOR]