Kernel Density Estimation Method Research Articles

Evaluation of the Regional Livestock High Quality Development in China Based on Spatial–Temporal Heterogeneity

The livestock high-quality development (LHIQUD) is an important guarantee for promoting livestock modernization and sustainability. The scientific evaluation of LHIQUD and its regulations is significant to solving the key problems in livestock sustainability and promoting LHIQUD. This paper holds that LHIQUD is influenced by the cutting-edge view of economic development and ecological civilization construction, driven by innovation and the change in quality and efficiency. Its fundamental goal is to satisfy people’s growing demand for safe, high-quality agricultural products. Finally, it will realize the sharing of development achievements and enhance competitiveness of the livestock. Based on this, the study sets up an evaluation system for LHIQUD, including indicators of quality and efficiency improvement, coordination and sharing, green development, and innovative development. The entropy method, exploratory spatial data analysis, and kernel density estimation method are used to evaluate the regional LHIQUD of China from 2010 to 2019. The dynamic evolution and spatial auto-correlation analysis results show that (a) Regional LHIQUD in China has generally improved, and there is a decreasing trend from the East, Northeast, and central region to the West, with the characteristics of spatial non-equilibrium. (b) LHIQUD is positively correlated with the regional economic development. (c) The spatial auto-correlation of LHIQUD is not obvious, generally showing a weak agglomeration pattern. Provincial LHIQUD is interdependent in geographical space, with agglomeration characteristics. The findings of this study are invaluable for governments at all levels to accurately comprehend the true state of regional LHIQUD in China, thereby providing a solid foundation for formulating corresponding policies.

Nonparametric Threshold Estimation of Autocorrelated Statistics in Multivariate Statistical Process Monitoring

ABSTRACTMultivariate statistical process monitoring is commonly used to detect abnormal process behavior in real time. Multiple process variables are monitored simultaneously, and alarms are issued when monitoring statistics exceed a predetermined threshold. Traditional approaches use a parametric threshold based on the assumptions of independence and multivariate normality of the process data, which are often violated in complex processes with high sampling frequencies, leading to excessive false alarms. Some approaches for improved threshold selection have been proposed, but they assume independence of the monitoring statistics, which are often autocorrelated. In this paper, we compare the performance of nonparametric estimators for computing thresholds from autocorrelated monitoring statistics through simulation. The false alarm rate and in‐control average run length of each estimator under different distributions, sample sizes, and autocorrelation levels and types are found. Estimator performance is found to depend on sample size and the strength of autocorrelation. The class of kernel density estimation (KDE) methods tends to perform better than estimators that use bootstrapping, and the proposed adjusted KDE methods that account for autocorrelation are recommended for general use. A case study to monitor a wastewater treatment facility further illustrates the performance of nonparametric and parametric thresholds when applied to real‐world systems.

Updating kernel density estimation method for quantitative damage diagnosis under vibration conditions using guided waves

Updating kernel density estimation method for quantitative damage diagnosis under vibration conditions using guided waves

Measurement of population agglomeration, dynamic change characteristics, and motivations in metropolitan agglomerations-A case study of the Xi'an metropolitan area.

This article compares the population agglomeration characteristics of the Xi'an metropolitan area in western China with those of metropolitan areas in other regions officially approved by the Chinese government. The kernel density estimation method and Markov chain model were used to conduct the study. The results revealed that from 2010 to 2020, the population agglomeration level of the Xi'an metropolitan area showed a trend of first increasing and then decreasing. The absolute gap in the population agglomeration level between cities within the metropolitan area gradually narrowed, and the polarization phenomenon of population agglomeration was not obvious. Compared with metropolitan agglomerations such as Nanjing, Wuhan, Fuzhou, Changsha-Zhuzhou-Xiangtan, Chongqing, and Chengdu, the Xi'an metropolitan agglomeration had a lower population agglomeration level, with a significant gap. Moreover, there was an obvious "club convergence" phenomenon in the population agglomeration levels of different urban agglomerations. The probability of the population agglomeration level remaining stable was at least 53.85%, indicating that there was a "Matthew effect" in which the rich become richer and the poor become poorer. Through the convergence models of α and β, the analysis suggested that there was no significant α convergence between the population agglomeration level of the Xi'an metropolitan agglomeration and that of other metropolitan agglomerations. Instead, there was a significant β divergence, indicating that the gap between the Xi'an metropolitan agglomeration's population agglomeration level and that of other metropolitan agglomerations is gradually widening. An integrated theoretical framework of population agglomeration was constructed from three dimensions: producers, consumers, and social people. An empirical analysis was conducted on the causes of population agglomeration in the Xi'an metropolitan area and other metropolitan areas. The multiple regression results showed that the income level, public consumption expenditure level, education level, comfortable living environment, and educational level were important factors leading to differences in population agglomeration. The geographic detector results showed that factors in the consumer dimension were the main reasons for population agglomeration in metropolitan areas.

Research on the Nested Structure and Substitution Elasticity of China’s Power Energy Sources

In alignment with China’s “carbon peak and carbon neutrality” goals, carbon reduction and energy structure transformation are central priorities. As a major emitter, the power industry plays a key role in this transition, and identifying effective pathways for its green energy transformation is essential to driving broader industrial green transformation and ensuring sustainable development. This article calculates the elasticity of substitution between clean and non-clean energy within China’s power sector from 1993 to 2021, employing the kernel density estimation method. By further comparing the goodness-of-fit across various nested structures of clean energy sources, the study identifies the optimal internal nested structure and examines the interactions among its components. The results underscore two key insights: on the one hand, a robust substitutive relationship exists between clean and non-clean energy, with the substitution elasticity of 1.646, exhibiting pronounced regional heterogeneity characterized as “weaker in the east and stronger in the west”; on the other hand, the optimal nested structure of clean energy is identified as (hydropower + nuclear power)—wind power—solar power. In this structure, the elements display a substitutive relationship in the Eastern Region, while in the Western Region, they exhibit a complementary relationship.

Empirical Insights into Centroid Optimization in Transportation Planning: A Novel Approach

In complex urban environments, sophisticated analytical techniques are essential to improve the accuracy of transportation modeling and the precision of travel time predictions. This research endeavors to critically examine various multi-zone centroid positioning strategies, with the goal of unveiling the method that most authentically represents the dynamics of urban travel. Anchored by a comprehensive case study of the city of Istanbul in Turkey, the investigation utilizes various analytical tools meticulously tailored to account for the intricate interplay of demographic and geographic variables. This methodical approach enables the identification of optimal centroid locations by evaluating various methods, including a novel approach called kernel density estimation (KDE), as well as five other established other methods. The results are then aligned with real-world travel time data through rigorous statistical analysis to ensure accurate predictions of travel times. The KDE method demonstrated its more acceptable correlation coefficient and lower mean absolute error, root mean square error, and mean absolute percentage error metrics, establishing its superiority in model performance. By highlighting the paramount importance of accurately placed centroids within urban transportation models, this research’s findings contribute significantly to the evolution of transportation planning methodologies and the advancement of urban mobility models through a comparative analysis of optimal methods.

Unveiling the Magnetized Nature of X‐Ray Binaries: Cyclotron Line Insights Title

ABSTRACTStudying the variations of measured cyclotron lines plays a crucial role in gaining insights into the physical processes of accretion in magnetized neutron stars. Our research focuses on the formation and distribution of magnetic fields in various high‐mass x‐ray binaries (HMXBs). Comparing these sources reveals valuable information about their origin and evolutionary history. It is worth noting that the presence of varying cyclotron lines can be attributed to the influence of accretion dynamics, which provides valuable information about the characteristics of the magnetic field. In addition, by visualizing data, we employ the Kernel Density Estimation method, to provide a robust framework for understanding the distribution of magnetic fields. We observed clear patterns of clustering in the energy range of (10–35) keV, which corresponds to magnetic fields of around G. This clustering indicates a shared magnetic field characteristics across these systems, suggesting a possible common origin or similar environmental and accretion condi tions. Finally, our research investigates the behavior of cyclotron lines in HMXBs, illuminating the accretion process and magnetic field properties to better understand their role as indicators of dynamical evolution.

Propagation of uncertainty with arbitrarily distributions by kernel density estimation method

Propagation of uncertainty with arbitrarily distributions by kernel density estimation method

A Hybrid Intelligent Algorithm for Deterministic and Probabilistic Interval Predictions of a High-Speed Railway Wind Warning System

A wind warning system (WWS) is designed to ensure the operational safety of trains amid various wind environments. The system integrates the Internet, cloud computing, and virtual instrumentation technologies to automatically collect wind speed data and intelligently output alert information. To precisely predict the wind speed, a hybrid intelligent algorithm termed VMD–SSA–GRU comprising variational mode decomposition (VMD), sparrow search algorithm (SSA), and gated recurrent unit (GRU) is proposed and integrated into the WWS. Besides, the quantile regression (QR) is applied to wind speed uncertainty estimation, and the probability density functions of partial intervals are further evaluated by an improved kernel density estimation method. Two typical wind speed datasets (typhoon and normal wind conditions) measured by the WWS are used for validation of the applicability of the developed forecasted model. Through comparison to seven single and four combined models, the developed model presents the highest forecast accuracy in both definitive and uncertainty predictions even in typhoon wind conditions. The study demonstrates that the WWS integrated with the proposed hybrid intelligent algorithm can provide sensible warning for the safety of train operation.

MEMO-QCD: quantum density estimation through memetic optimisation for quantum circuit design

This paper presents a strategy for efficient quantum circuit design for density estimation. The strategy is based on a quantum-inspired algorithm for density estimation and a circuit optimisation routine based on memetic algorithms. The model maps a training dataset to a quantum state represented by a density matrix through a quantum feature map. This training state encodes the probability distribution of the dataset in a quantum state, such that the density of a new sample can be estimated by projecting its corresponding quantum state onto the training state. We propose the application of a memetic algorithm to find the architecture and parameters of a variational quantum circuit that implements the quantum feature map, along with a variational learning strategy to prepare the training state. Demonstrations of the proposed strategy show an accurate approximation of the Gaussian kernel density estimation method through shallow quantum circuits illustrating the feasibility of the algorithm for near-term quantum hardware.

Spatio-temporal evolution characteristics and influencing factors of carbon emission reduction potential of the transportation industry in China

AbstractScientific estimation of China's transport carbon reduction potential (CERP) and exploration of its temporal and spatial characteristics and influencing factors are important bases for formulating emission reduction policies. Based on the dual perspective of equity and efficiency, we assess the CERP of the transportation industry in 30 provinces in China from 2010 to 2019 and analyze its spatio-temporal evolution characteristics using the Kernel density estimation method. Then, the Dagum Gini coefficient is used to measure the regional differences and sources of the CERP, and its influencing factors are analyzed with the help of the random effects panel Tobit regression model. The results show that: (1) from 2000 to 2019, the CERP of the transportation industry in China and the three regions show an oscillating upward trend, with a spatial pattern of "high in the west and low in the east". (2) The overall difference in the CERP of the transportation industry shows a fluctuating downward trend, and the difference in hypervariable density has the highest contribution rate. The inter-regional differences between the West and other regions are the largest, and the intra-regional difference pattern shows the pattern of western > central > east. (3) The technological improvement, industrial structure, and scale of transportation industry development have a significant positive impact on the CERP of the transportation industry. The influence of urbanization influence and environmental regulation have a significant negative influence.

A Lightweight Kernel Density Estimation and Adaptive Synthetic Sampling Method for Fault Diagnosis of Rotating Machinery with Imbalanced Data

Rotating machinery is widely used across various industries, making its reliable operation crucial for industrial production. However, in real-world settings, intelligent fault diagnosis faces challenges due to imbalanced fault data and the complexity of neural network models. These challenges are particularly pronounced when defining decision boundaries accurately and managing limited computational resources in real-time machine monitoring. To address these issues, this study presents KDE-ADASYN-based MobileNet with SENet (KAMS), a lightweight convolutional neural network designed for fault diagnosis in rotating machinery. KAMS effectively handles data imbalances commonly found in industrial applications and is optimized for real-time monitoring. The model employs the Kernel Density Estimation Adaptive Synthetic Sampling (KDE-ADASYN) algorithm for oversampling to balance the data, applies fast Fourier transform (FFT) to convert time-domain signals into frequency-domain signals, and utilizes a 1D-MobileNet network enhanced with a Squeeze-and-Excitation (SE) block for feature extraction and fault diagnosis. Experimental results across datasets with varying imbalance ratios demonstrate that KAMS achieves excellent performance, maintaining nearly 90% accuracy even on highly imbalanced datasets. Comparative experiments further demonstrate that KAMS not only delivers exceptional diagnostic performance but also significantly reduces network parameters and computational resource requirements.

Spatial–Temporal Pattern of Urban Land Green Use Efficiency and Its Influencing Factors in Guizhou Province, China, Based on County Units

Urban land is an important and indispensable support for urban development. However, the continued development of urbanization and industrialization has given rise to many problems, increasing the burden of urban land and leading to inefficient urban land use. The study of urban land green use efficiency (ULGUE) helps to explore the path to alleviate urban land-use pressure and enhance the land green utilization efficiency, and provides important decision-making references for urban greening construction and development. In this paper, 88 districts and counties in Guizhou Province were taken as the research units. Then, their efficiency values and spatial–temporal evolution characteristics were analyzed by applying the non-expected output super-efficiency SBM model, the kernel density estimation method, and spatial autocorrelation analysis, respectively. In order to identify its influencing factors, the main correlation analysis was carried out using the Tobit model. The research findings are summarized as follows: (1) From the time-series evolution characteristics, the ULGUE among the 88 county cities in Guizhou Province had obvious polarization characteristics. In terms of the numbers, there was a gradual downward trend in the number of high-efficiency cities. (2) Based on the spatial evolution characteristics, there was a positive correlation in the ULGUE among county cities in Guizhou Province, and the high- and low-value areas were comparatively more widely located in the central–east and west sides of Guizhou Province, respectively. (3) Among the factors influencing the level of the ULGUE, the level of economic development, industrial structure upgrading, and environmental regulation mainly played a positive facilitating role, while the material standard of living of the population, the GDP content, the green development level, government financial support, and the population size played a negative inhibiting role. The study of the ULGUE in Guizhou Province provides ideas for urban construction and is of great significance for the green transformation of urban development. At the same time, it can provide a reference for China’s western development.

Exploring Sustainable Innovation Level, Spatial Inequities, and Convergence Trends in China’s Wood Industry

The importance of sustainable innovation in the wood industry is growing, but there is a lack of comprehensive analysis of its evolution, regional differences, and patterns of convergence in China. Based on the panel data of 31 provinces in China from 2011 to 2021, the sustainable innovation index of the wood industry is measured by the projection pursuit method. On this basis, the kernel density estimation method and Dagum Gini coefficient are used to study the dynamic evolution trend, regional differences, and sources of the index, and the convergence characteristics are examined using the coefficient of variation method. The study shows that (1) China’s overall wood industry sustainable innovation index shows a decreasing trend from 2011 to 2021. (2) The differences in the four regions mainly come from inter-regional differences. (3) The index shows significant nonequilibrium characteristics and progressive evolution patterns, and the spatial agglomeration is significant. The magnitude of the index deviation from the average did not decrease over time for the northern and southwestern forest regions. (4) The wood industry sustainable innovation index of the four major forest regions has obviously converged to the same level; under the condition of considering multifactors differentiation, the growth rate of the index of the lower regions is significantly higher than that of the higher regions. The study concludes that current regional imbalances in sustainable innovation in China impede progress and equitable distribution of benefits in the wood industry, and that the impact of regional differences on the β-convergence of sustainable innovation varies according to specific regional characteristics and conditions. These findings provide important theoretical contributions and practical guidance for the development of targeted innovation strategies for the sustainable development of the wood industry, as well as for the promotion of balanced regional development.

Kernel density estimation method to determine coincident design day for air-conditioning system design in marine climate of China

Kernel density estimation method to determine coincident design day for air-conditioning system design in marine climate of China

Spatio-Temporal Evolution and Interactive Relationship Between Digital Economy and Green Development in China

A digital economy parallel with green development holds profound significance for achieving sustainability. The primary objective of this study was to explore the synergistic interaction effects between the digital economy and green development in China and forecast their future development. This study analyzed the spatio-temporal characteristics of provincial digital economy and green development in China by integrating a combined assignment method, an unconditional spatial kernel density estimation method, and a standard deviation ellipse model. The interplay between the digital economy and green development was examined using a panel vector autoregression model. Additionally, digital economy and green development levels were forecasted using univariate time series and radial basis function kernel ε-support vector regression models. The results indicate that both the digital economy and green development levels in China exhibited an upward trend from 2013 to 2021, with the digital economy increasing at a faster rate. However, both domains demonstrated regional disparities in their development processes. The mutual interaction between the digital economy and green development intensified as the lag period increased. The digital economy contributed 21% to green development, whereas green development contributed 18% to the digital economy. The initial effect of the digital economy on green development was negative, however, this impact gradually diminished over time. Additionally, the influence of green development on the digital economy was shown to follow a consistent trend of transitioning from negative to positive across the eastern, central, and western regions. Therefore, it can be seen that the digital economy exerts a sustainable impact on green development, albeit with a one-phase lag. This research provides a scientific basis for the deep integration of the digital economy and green development, thereby fostering sustainable socioeconomic growth.

Resilience Assessment of Flood Disasters in Zhengzhou Metropolitan Area Based on the PSR Model

Flood disasters occur frequently and cause great losses. Improving the resilience of urban flood disasters is of great significance to improving disaster prevention and mitigation in the region. The metropolitan area is the center of regional economic development and the key to strengthening the construction of local resilience. However, there is little research on resilience in the metropolitan area. Taking nine cities in the Zhengzhou metropolitan area as the research object, this paper uses the pressure state response (PSR) model to build the evaluation system of the Zhengzhou metropolitan area’s flood disaster resilience and comprehensively uses the entropy weight method, analytic hierarchy process, kernel density estimation method, and factor contribution model to measure the temporal and spatial evolution characteristics of Zhengzhou metropolitan area’s flood disaster resilience from 2010 to 2022, excavating the development trend of the level of flood disaster resilience of members in the Zhengzhou metropolitan area, and explore the driving factors affecting the resilience of the Zhengzhou metropolitan area’s flood disaster. The results show that (1) from 2010 to 2022, the development trend of flood disaster resilience among the Zhengzhou metropolitan area members has obvious differences, the change of pressure resilience is stable, and the state resilience and response resilience increase as a whole; (2) the results show that the resilience of flood disaster in the Zhengzhou metropolitan area has obvious change characteristics in time and space, and the overall trend is to take Zhengzhou as the core to drive the surrounding members’ upward development; (3) in the driving factor analysis, the number of ordinary colleges and universities and the proportion of public security expenditure in fiscal expenditure are the main influencing factors in the resilience evaluation index. The Zhengzhou metropolitan area is the key area of economic development in Henan Province. The research results provide a reference for improving the resilience level of the Zhengzhou metropolitan area and strengthening the prevention and control of flood disasters.

Multi-objective energy dispatch with deep reinforcement learning for wind–solar–thermal–storage hybrid systems

With the intensification of environmental pollution and energy shortage, wind–solar–thermal–storage hybrid systems have been widely considered in the advancement of sustainable energy sources. To reduce the fuel cost and carbon emissions while tracking the demanded load power, this paper proposes a novel energy dispatch strategy based on deep reinforcement learning to achieve the multi-objective task for wind–solar–thermal–storage hybrid systems. Firstly, considering the huge amount of annual load data, a characteristic day acquisition model is constructed to reduce the computational burden. To deal with the uncertainties of wind and solar data, a kernel density estimation method is proposed to obtain the seasonal power generation expectation. Then, a deep Reinforcement Learning method based on Deep Deterministic Policy Gradient is developed to achieve the multi-objective energy dispatch strategy by considering the carbon trading and fuel cost. Finally, simulation results verify that the designed method can effectively achieve multi-objective task and have better robustness performance compared with other methods.

Measurement, dynamic evolution and pollution emission effects of the coupling of green finance and digital technology-evidence from China

This study uses the game theory combination weighting method to measure the level of coordinated development of green finance and digital technology coupling in China. An analysis was conducted using the Kernel density estimation method, traditional Markov chain model, and spatial Markov chain model to study the dynamic evolution characteristics and trends of the coupling and coordination level of green finance and digital technology in Chinese provinces. The results showed an upward trend in the coupling and coordination level of green finance and digital technology in the entire sample of Chinese, eastern, central, western, and northeastern provinces. Additionally, there is an observable club convergence phenomenon in the coordinated development of green finance and digital technology. The phenomenon of high-level convergence and low-level convergence is significant. The areas on the diagonal that are low-level and high-level have a higher probability of remaining stable. The Markov chain model and the spatial Markov chain model both show the “Matthew effect” in the level of coupling and coordinated development of green finance and digital technology. Empirical testing suggests that the coordinated horizontal development of green finance and digital technology can significantly promote pollution emission. Further analysis found that the coupling and coordination of green finance and digital technology achieves pollution emission by driving green innovation.

The spatio-temporal characteristics and dynamic evolution of coupling and coordinated development of innovation factor allocation and green economy in China

IntroductionMeasuring the coupling coordination degree between the innovation factor allocation system and the green economy development system, and deeply exploring the changing trends in the coupling coordination between innovation factors allocation, including their subsystems, and green economy development, are essential prerequisites for achieving a high degree of coupling coordination between the two systems.MethodsThis paper, based on panel data from 30 provincial regions in China from 2013 to 2022, measures the coupling coordination degree between innovation factor allocation and green economy development, including their subsystems, and explores the spatial differentiation characteristics of this coupling coordination across the provinces in China and its three major regions. First, an evaluation index system for the innovation factor allocation system and the green economy development system is constructed from both input and output perspectives. The entropy weighted TOPSIS method and the coupling coordination degree model are then employed to explore the temporal characteristics of the coupling coordination between innovation factor allocation and green economy development. Second, Dagum's Gini coefficient is used to reveal the directional distribution of coupling coordination and the sources of spatial disparities. Third, kernel density estimation and spatial Markov chain methods are applied to uncover the dynamic evolution of this coupling coordination.Results and discussionThe study finds that the overall coupling coordination degree between innovation factor allocation and green economy development in China is relatively low, but it exhibits a continuous upward trend. From the perspective of regional disparities, the coupling level between innovation factor allocation and green economy development across Chinese provinces shows an imbalanced spatial distribution, characterized by higher levels in the east and lower levels in the west, with spatial disparities tending to widen. Inter-regional differences are the primary source of these spatial disparities, followed by intra-regional differences. While internal disparities within Eastern China are narrowing, they are expanding within the Central and Western China. The development processes in these regions are often influenced by neighboring spatial spillover effects. Finally, recommendations are proposed to enhance the coupling coordination degree between innovation factor allocation and green economy development in China.