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Animal diel activity patterns can aid understanding of (a) how species behaviourally adapt to anthropogenic and natural disturbances, (b) mechanisms of species co‐existence through temporal partitioning, and (c) community or ecosystem effects of diel activity shifts. Activity patterns often vary spatially, a feature ignored by the kernel density estimators (KDEs) currently used for estimating diel activity. Ignoring this source of heterogeneity may lead to biased estimates of uncertainty and misleading conclusions regarding the drivers of diel activity. Thus, there is a need for more flexible statistical approaches for estimating activity patterns and testing hypotheses regarding their biotic and abiotic drivers. We illustrate how trigonometric terms and cyclic cubic splines combined with hierarchical models can provide a valuable alternative to KDEs. Like KDEs, these models accommodate circular data, but they can also account for site‐to‐site and other sources of variability, correlation amongst repeated measures, and variable sampling effort. They can also more readily quantify and test hypotheses related to the effects of covariates on activity patterns. Through empirical case studies, we illustrate how hierarchical models can quantify changes in activity levels due to seasonality and in response to biotic and abiotic factors (e.g. anthropogenic stressors and co‐occurrence). We also describe frequentist and Bayesian approaches for quantifying site‐specific (conditional) and population‐averaged (marginal) activity patterns. We provide guidelines and tutorials with detailed step‐by‐step instructions for fitting and interpreting hierarchical models applied to time‐stamped data, such as those recorded by camera traps and audio recorders. We conclude that this approach offers a viable, flexible, and effective alternative to KDEs when modelling animal activity patterns. [ABSTRACT FROM AUTHOR]

Abstract.Auxiliary information is widely used in various studies to improve the precision of estimation. In this article, we extend the application of auxiliary information within the context of randomized response techniques (RRT), building upon the prior research on kernel density estimation (KDE) under additive RRT models. Inspired by Mostafa and Ahmad (2019), we proposed a kernel density estimator that incorporates an auxiliary variable to enhance the accuracy of estimating the distribution of a sensitive variable. Extensive simulations are conducted to evaluate the performance of this proposed methodology, highlighting the advantages of utilizing auxiliary information and the impact of factors such as noise levels, sample size, and the correlation between the auxiliary variable and the sensitive variable. [ABSTRACT FROM AUTHOR]

This study investigates lead provenance and circulation patterns in Han Dynasty (202BC-220AD) China through the analysis of lead glazed pottery. Four objects were studied using a combination of typological study, elemental chemistry and lead isotope ratio analysis. The results for each object were compared with databases of ‘lead mining districts’ (lead deposits) and ‘lead usage districts’ (lead-containing artifacts unearthed in different spatial and temporal ranges) to assess the lead sources used for each sample and offers a spatial-temporal range of the use of these lead resources. Three distinct groups of lead and their possible circulating spatial-temporal scales are identified across six samples in this study. A possible change in lead supply networks between the Western Han Dynasty (202BC-9AD) and the Eastern Han Dynasty (25AD-220AD) is proposed. This study also highlights the probable changes in the movement of lead resources from the Western Han Dynasty to the Tang Dynasty (618AD-690AD), suggesting improvements in long-distance transport capabilities, and the development of economic divisions and exchange connections in ancient Chinese society. Our findings contribute to a deeper understanding of the economic and political dynamics during the Han Dynasty and emphasize the significance of lead isotope analysis of glazed pottery in exploring resource movement. [ABSTRACT FROM AUTHOR]

The equalization of marine public services is an effective way to achieve harmonious coexistence of the sea. In this paper, a variable fuzzy recognition model is used to measure the equalization level of marine public services in 11 provinces and cities in China's coastal areas from 2006 to 2019. The Dagum Gini coefficient, kernel density estimation model, and convergence model are used to study their regional differences, distribution dynamics, and convergence characteristics. The results show that the equalization level of marine public services in China's coastal areas increased year by year from 2006 to 2019. In terms of spatial distribution, the equalization level of marine public services in coastal areas presents an unbalanced distribution pattern. The overall regional differences in the equalization level of marine public services in China's coastal areas have narrowed, and the inter-regional differences are the main reasons for the overall differences. The absolute difference in the equalization level of marine public services in China's coastal areas shows an expanding trend. The equalization level of marine public services in China's coastal areas has α convergence and β convergence. [ABSTRACT FROM AUTHOR]

Accurate indoor localization remains a significant challenge due to the complex nature of indoor environments. This paper proposes a novel method for constructing a radio map (RM) based on Kernel density estimation (KDE) and human trajectories (HT) to enhance indoor localization accuracy. The proposed method utilizes historical HT data in RM construction to capture the spatial variability and complexity of indoor environments, which is crucial for accurate localization. By employing KDE, kernel density maps are generated, identifying high-density regions where additional interpolated fingerprints are strategically placed to improve localization accuracy. In contrast to the conventional method of uniformly placing interpolated points (IPs), the proposed approach better models natural walking patterns and trajectories, thereby enhancing the uniqueness and accuracy of user position identification. Through extensive experiments with various HT patterns, the proposed KDE-RM optimization method consistently outperforms the conventional approach of evenly distributed IPs using Kriging and inverse distance weighting interpolation by up to 36.4%. This demonstrates the effectiveness and potential of the proposed method as a valuable tool for enhancing indoor localization. [ABSTRACT FROM AUTHOR]

Object: Promoting the accessibility and equity of healthcare services, as well as enhancing service capacity, are crucial for building a sound healthcare system. Particularly in the past two years of the normalized COVID-19 situation, this issue has garnered widespread attention in the academic community. This study aims to investigate and analyze the characteristics and trends of the spatial-temporal evolution of healthcare service supply levels in China. It also seeks to explore the influencing factors and pathways for development, with the goal of optimizing the allocation of healthcare resources. Methods: This article uses the entropy weight TOPSIS method combined with Dagum Gini coefficient and Kernel density to evaluate the supply level of healthcare services in 31 provinces and cities in China from 2012 to 2020, and explores its development and spatial pattern characteristics. Then, through Moran index, panel regression model and spatial econometric testing, the spatial correlation problem and its influencing factors are further analyzed, and targeted policy recommendations are proposed based on it, laying the foundation for further promoting the balanced development of healthcare service supply capacity. Results: (1) Healthcare services supply levels in various provinces and cities in China have significantly increased, with a shift in spatial distribution from 'higher in the east and lower in the west' to 'convergence between east and west, with lower levels in the central regions.' (2) Relative differences among regions are narrowing annually, primarily due to interactions between the four regions rather than within each region, with expanding impact of overlapping regions. (3) Absolute differences among regions are also decreasing, moving towards uniformity with a contraction of extension and a restraint on the trend towards multipolarization. (4) Spatial correlation between adjacent regions is weakening, eventually becoming non-significant, with fading spatial effects. (5) The correlation between local economic development, population factors, institutional arrangements, and the current state of supply is significant, and the research design and conclusions remain robust even after thorough consideration of spatial effects. The study explores the development pathways based on the objective existence of regional development and the controllable government actions. Conclusion: The overall level of healthcare service supply in China has improved, but regional differences still exist. The objective level of regional development and the subjective behavior of local governments have a significant impact on the supply of healthcare services. Therefore, it is recommended that each region adapt to local conditions, identify its own strengths and weaknesses, coordinate resource supply and demand, consider the impact of key factors, and optimize the allocation of healthcare development resources. [ABSTRACT FROM AUTHOR]

Numerous studies have utilized remote sensing techniques to analyze seismic data in active areas. Point density techniques, widely used in remote sensing, examine the spatial distribution of point clouds related to specific variables. Applying these techniques to complex tectonic settings, such as the East Anatolian Fault Zone, helps identify major active fractures using both surface and deep information. This study employed kernel density estimation (KDE) to compare two distinct point-cloud populations from the seismic event along the Türkiye–Syria border on 6 February 2023, providing insights into the main active orientations supporting the Global Tectonics framework. This study considered two populations of seismic foci point clouds containing over 40,000 events, recorded by the Turkish Disaster and Emergency Management Authority (AFAD) and Kandilli Observatory and Earthquake Research Institute (KOERI). These populations were divided into two datasets: crude and relocated-filtered. Kernel density analysis demonstrated that both datasets yielded similar geological interpretations. The high-density cores of both datasets perfectly matched, exhibiting identical structures consistent with geological knowledge. Areas with a minimal concentration of earthquakes at depth were also identified, separating different crustal strength levels. [ABSTRACT FROM AUTHOR]

AbstractIn this paper, we develop a novel efficient and robust nonparametric regression estimator under a framework of a feedforward neural network (FNN). There are several interesting characteristics for the proposed estimator. First, the loss function is built upon an estimated maximum likelihood function, which integrates the information from observed data as well as the information from the data distribution. Consequently, the resulting estimator has desirable optimal properties, such as efficiency. Second, different from the traditional maximum likelihood estimation (MLE), the proposed method avoids the specification of the distribution, making it adaptable to various distributions such as heavy tails and multimodal or heterogeneous distributions. Third, the proposed loss function relies on probabilities rather than direct observations as in least square loss, contributing to the robustness of the proposed estimator. Finally, the proposed loss function involves a nonparametric regression function only. This enables the direct application of the existing packages, simplifying the computational and programming requirements. We establish the large sample property of the proposed estimator in terms of its excess risk and minimax near-optimal rate. The theoretical results demonstrate that the proposed estimator is equivalent to the true MLE where the density function is known in terms of excess risk. Our simulation studies show that the proposed estimator outperforms the existing methods based on prediction accuracy, efficiency and robustness. Particularly, it is comparable to the MLE with the known density and even gets slightly better as the sample size increases. This implies that the adaptive and data-driven loss function from the estimated density may offer an additional avenue for capturing valuable information. We further apply the proposed method to four real data examples, resulting in significantly reduced out-of-sample prediction errors compared to existing methods. [ABSTRACT FROM AUTHOR]

Intangible cultural heritage (ICH) reflects a region's history and culture, serving as a significant indicator of regional identity and cohesion. The Grand Canal Basin in China is rich in historical traditions, containing a rich array of ICH resources. Analyzing the spatiotemporal distribution characteristics and influencing factors of ICH within the Grand Canal Basin of China can provide a scientific basis for developing cultural industries and promoting sustainable regional economic growth. This study employed GIS-based spatial analysis methods, including kernel density estimation, the mean nearest neighbor index, and standard deviation ellipse, to investigate the spatiotemporal distribution of 504 national-level ICH items (including extensions) in the Grand Canal Basin of China. The results demonstrate the significant spatial clustering of ICH, with concentrations in high-density regions, particularly at the northern and southern ends of the canal. There is significant regional disparity in the distribution of ICH, with an uneven quantity and structure, predominantly featuring traditional skills and traditional drama categories. The average centroid shift of ICH exhibits a north-to-south oscillatory trajectory. However, overall, it demonstrates a southward-moving trend. This study also underscores the impacts of urbanization, population density, economic development, and transportation infrastructure on ICH distribution. Among these factors, urbanization exerts the strongest influence on the spatial distribution of ICH. The impact of the natural environment is relatively minor; however, it remains a significant element that cannot be overlooked during development. This research offers valuable data and insights for local governments and institutions to formulate evidence-based strategies for the protection and sustainable utilization of ICH resources, promoting sustainable cultural and economic development in the Grand Canal Basin. [ABSTRACT FROM AUTHOR]

Exploring the interaction and coupling effects within the population‒land‒water‒industry (PLWI) system is conducive to promoting high-quality regional sustainable development. Taking the lower Yellow River during the period from 2000 to 2020 as a research sample, this study used the entropy weight TOPSIS method, the coupling coordination degree (CCD) model and kernel density estimation to synthetically evaluate the CCD of the PLWI system. The GeoDetector model was applied to explore the factors influencing the CCD of the PLWI system considering the nonlinear relationship. The major results can be summarized as follows: (1) From 2000 to 2020, the comprehensive development index (CDI) of the population, land, water and industry subsystems followed a gradual upward trend in the lower Yellow River, increasing by 0.293, 0.033, 0.111 and 0.369, respectively. However, the CDI of the land subsystem varied greatly between regions. Some cities, such as Jinan, Jining and Binzhou, experienced large declines in the CDI of the land subsystem, from 0.433, 0.534 and 0.572 to 0.358, 0.481 and 0.522, respectively. (2) The CCD of the PLWI system in the lower Yellow River showed an upward trend, increasing from 0.481 to 0.678, and became more concentrated during 2000–2020. Most of the region transitioned from near disorder to primary coordination. (3) Factors such as number of health technicians per 10,000 people, average salary, number of college students per 10,000 people, per capita GDP and per capita education expenditure were critical to the coordinated development of the PLWI system, the explanatory powers were 0.644, 0.639, 0.610, 0.498 and 0.455, respectively. Finally, this study proposed three policy recommendations to improve coupling coordination in the lower Yellow River Basin: Improving population quality, promoting green technology and rational land planning. [ABSTRACT FROM AUTHOR]

The foraging strategies of sympatric ungulates with similar ecological niches are important for understanding ecological niche differentiation, resource utilization, competition, and coexistence and for understanding the ecological impacts on plant communities in the ecosystem. The behavior of the wapiti (Cervus elaphus) and Siberian roe deer (Capreolus pygargus) foraging on Japanese yew (Taxus cuspidata) has affected its succession and renewal in the northeastern forests of China, which has become an urgent problem for the relevant departments. This study analyzed the foraging strategies of the wapiti and Siberian roe deer on Japanese yew from July 2021 to January 2024 using field investigations and infrared camera monitoring in the Muling National Nature Reserve, Heilongjiang Province, China. It was found that the wapiti and Siberian roe deer have different foraging strategies in terms of time, space, and behavior. Temporally, they both preferred to forage for the saplings of the Japanese yew during the winter season, the degree of overlap in foraging rhythms was medium (Dhat1 = 0.67), and the diurnal foraging activity index (DRAI) of the wapiti was larger than that of the Siberian roe deer. Spatially, the suitable foraging habitat of the Siberian roe deer was twice that of the wapiti, and their overlap was low in the location and direction of saplings and the distance of the seed tree. Behaviorally, the foraging intensity of the wapiti was high, and that of Siberian roe deer was low. Foraging reduced the average primary branch height, number of new branches, and length of lateral branches of saplings, and the influence of the wapiti was significantly greater than that of the Siberian roe deer. This study provides a scientific basis for solving the conservation and management problems of the deer animals foraging on Japanese yew and contributes to further understanding of the competition‐coexistence mechanism of sympatric species. [ABSTRACT FROM AUTHOR]

In response to resource constraints, power organizations are increasingly adopting renewable energy solutions. However, the inherent volatility and intermittency of renewable sources present challenges in effectively harnessing their potential during dispatch processes. This study proposes a cooperative distribution strategy that integrates an energy storage system with wind energy. Energy storage system charging stage, while in the discharge stage, optimal income is jointly pursued during power generation periods with wind energy. To ensure cooperation stability, the benefits distribution process not only incorporates marginal contribution value, but also employs a conditional value at risk constructed through an enhanced Kernel density estimation bandwidth calculation strategy in the allocation strategy. The implicit contribution value of the energy storage system to overall power dispatching is established using the conditional value at risk and other relevant factors. The experimental results demonstrate that the proposed strategic model is efficacious in achieving a notable reduction in the total power generation cost, amounting to 0.81%. Moreover, the model successfully mitigates curtailment, yielding a substantial decrease in the curtailment rate by 5.84%. Moreover, the redistribution aspect enhances the enthusiasm of energy storage system participation in cooperation. This model establishes a theoretical foundation for future collaborative strategies in wind and storage joint operations. [ABSTRACT FROM AUTHOR]

As an important part of the comprehensive transportation system, ports play an important role in economic development. In the context of the '3060 double carbon' target, the high-quality development (HQD) of ports is receiving attention from many parties. The aim of this paper is to examine the dynamic evolution of HQD in China's coastal ports from the perspective of green total factor productivity (GTFP). Firstly, the calculation standards of undesirable outputs are given, and integrated environmental dumping indicator is calculated by the combination weight calculation model based on game theory. Then, the super-efficiency DEA-GML (Data Envelopment Analysis-Data Envelopment Analysis-Global Malmquist-Luenberger) index model based on directional distance function (DDF) is used to measure the GTFP growth of ports. Furthermore, the dynamic evolution of GTFP growth of ports is explored using the kernel density estimation. The findings show that the GTFP growth of coastal ports has continued to improve during the examined period. However, there is the problem that port construction inputs cannot bring effective outputs, and there is a bifurcation that tends to stabilize within coastal ports. Finally, targeted policy implications are given to promote HQD of ports. [ABSTRACT FROM AUTHOR]

Information-theoretic measures play a vital role in training learning systems. Many researchers proposed non-parametric entropy estimators that have applications in adaptive systems. In this work, a kernel density estimator using Kapur entropy of order a and type ß has been proposed and discussed with the help of theorems and properties. From the results, it has been observed that the proposed density measure is consistent, minimum, and smooth for the probability density function (PDF) underlying given conditions and validated with the help of theorems and properties. The objective of the paper is to understand the theoretical viewpoint behind the underlying concept. [ABSTRACT FROM AUTHOR]

Growing economy has boomed tourism, but intelligent travel planning services restrict long-term and stable tourism development. Typically, travel planning requires substantial time and cost. And currently, less focus on user preferences in most tourist attraction recommendations also results in low efficiency. In this paper, firstly, the K-means algorithm is introduced for clustering analysis of user behavior or interests, so as to better understand user preferences. Gaussian kernel density estimation and similarity measurement are also adopted to improve the traditional K-means algorithm, which provides the foundation for a tourist attraction recommendation model. Then, to further improve transportation route planning, the study introduces the ant colony algorithm, adaptive crossover strategy and local search algorithm to enhance the traditional genetic algorithm for an optimized travel path planning model. The outcomes show that the improved clustering algorithm possesses the highest accuracy of 0.96 and 0.78 in Iris and Glass datasets respectively, along with a sum of squared errors of 96.73 and 476.48 respectively. The shortest running time in the Yeast data-set is 1.22 s. The improved clustering algorithm with 50 nearest neighbors has an average absolute error value of 0.749, and its longest running time does not exceed 1 s. In summary, the model developed in this study is highly applicable to personalized recommendation services and efficient travel routes. [ABSTRACT FROM AUTHOR]

Territoriality is a form of social dominance concerning the use of space that ensures the territory owner primary access to critical resources. The territory is defended with visual displays, advertisement calls, physical attacks, or chemical signals. The territory is frequently estimated by mapping locations where an animal is observed engaging in territorial behavior or by tracking. However, these approaches may over- or underestimate the areas defended. Thus, the use of approaches explicitly determining defended areas is critical to properly characterize the territory. Intrusion experiments can elicit a response in territory holders, allowing one to characterize their aggressive responses; however, the aggressive response depends on the species. We describe an approach to experimentally estimate the territory size using playback experiments in a species that exhibits a stereotypical phonotactic response: the nurse frog, Allobates aff. trilineatus and develop a new behavioral index that allows assessing territory size in response to playbacks for a species with non-stereotyped phonotactic response: the endangered Lehmann's poison frog, Oophaga lehmanni. We conducted 772 playback experiments on 18 males of A. aff. trilineatus, and 222 on nine males of O. lehmanni. We analyzed the results of playback experiments with three different area estimators regularly used to estimate space use and evaluated whether these estimates are correlated. The shape and size of territories varied among individuals and estimators in both species. Although we found that the absolute size of the territory depends on the method used, estimates were strongly correlated, meaning that different estimators similarly describe variation in territory size among males. Choosing an analysis method may not be particularly important for studying the characteristics of territoriality over space and time but using a systematic and standardized experimental approach that also incorporates the particularities of the aggressive response of each species is essential to understand the evolution of space use by poison frogs and other territorial species. [ABSTRACT FROM AUTHOR]

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AbstractA wide variety of location and scale estimators have been developed for light-tailed distributions. Despite indisputable importance in business, finance, cybersecurity, etc., statistical estimation and inference in the presence of heavy tails have received less attention in the literature. We adopt the Kernel-Weighted Average (KWA) approach to location and scale estimation and present a set of extensive comparisons with five prominent competitors. Unlike nonparametric kernel density estimation, the optimally tuned bandwidth for KWA estimators does not necessarily converge to zero as sample size grows. We also perform a large-scale Monte Carlo simulation to search for the optimal bandwidth that minimizes the mean squared error (MSE) of KWA location and scale estimators with simulated samples from Student’s t-distribution with degrees of freedom (df) 1,2,…,30. We further develop an adaptive technique to estimate the df that best match the observed samples using Cramér-von Mises test of goodness-of-fit. Unlike many existing methodologies, our approach is data-driven and exhibits excellent statistical performance. To illustrate this, we apply it to three real-world financial datasets containing daily closing prices of AMC Entertainment (AMC), GameStop (GME) and Meta Platforms (META) stocks to calibrate a geometric random walk model with Student’s t log-increments. [ABSTRACT FROM AUTHOR]

Background: This study aims to explore the development status of the supply level of professional public health resources in Beijing Municipality, analyze the areal differences and spatial distribution characteristics of the supply level in 16 districts, and provide a scientific basis for promoting the balanced development of the supply level of professional public health resources in each district of Beijing Municipality. Methods: Based on panel data from Statistical Yearbook of Health Work in Beijing Municipality and Health and Family Planning Work in Beijing Municipality from 2014 to 2022. Using the entropy method to measure the supply level of professional public health resources in Beijing, employing the Dagum Gini coefficient and Kernel density estimation method to analyze the spatial differentiation characteristics and dynamic evolution process of the supply level, and using heat maps to display the spatial distribution of the supply level in various districts of Beijing. Results: The Dagum Gini coefficient of the supply level of professional public health resources in Beijing Municipality decreased continuously from 0.3419 in 2014 to 0.29736 in 2020, then gradually increased, showing a trend of initially decreasing and then increasing overall spatial differences. The spatial differences mainly stem from differences between areas. The kernel density curve shows that the supply level of professional public health resources in Beijing Municipality gradually increased, slightly decreased after 2021, and did not form a situation of two or multi-level differentiation. Conclusion: From 2014 to 2022, the supply level of professional public health resources in Beijing Municipality showed an overall upward trend, but attention should be paid to the decline after 2021; spatial differences initially decreased and then increased, and the differences between areas is the main source of the overall difference in Beijing. Therefore, the Beijing Municipal Government should focus on narrowing the differences between areas, determine the allocation and management of public health resources based on the actual situation of core areas, promote coordinated development within and outside areas, and thus enhance the supply level of professional public health resources. [ABSTRACT FROM AUTHOR]

Introduction: In the search for sustainable development, urban ecodevelopment is becoming a core agenda for all countries. China's low-carbon city pilot (LCCP) policy is an important initiative to promote urban low-carbon governance. And exploring the direction of LCCP policy is an important step towards sustainable urban development. Methods: Super-SBM is employed to calculate urban ecological efficiency using panel data from 254 prefecture-level cities between 2007 and 2020. The methods of kernel density estimation and spatial Markov chains are applied to the spatial analysis of urban ecological efficiency. The spatial analyses examine the spatiotemporal patterns and dynamic evolving trends of urban ecological efficiency. Additionally, the multiperiod difference-in-differences method is used to assess the impact of the LCCP policy on urban ecological efficiency. On this basis, we apply the method of mechanism analysis to discuss the influence mechanism. Results: The results indicate that urban ecological efficiency gradually forms a continuous spatial clustering pattern, although there is a widening tendency in the absolute differences. In the subsequent transfer process, the spatial factor plays a highly significant role. Benchmark regressions and auxiliary robustness tests demonstrate that the LCCP policy is effective in improving urban ecological efficiency. Further analysis shows that the effect of LCCP policies on ecological efficiency can be transmitted by promoting green technology innovation as well as facilitating a low-carbon transition in the energy mix. The above findings indicate the presence of the "Green Innovation Effect" and the "Decoupling Effect". Discussion: These discoveries enhance the theoretical framework of urban ecology and offer valuable insights for other regions seeking to implement low-carbon urban governance. [ABSTRACT FROM AUTHOR]

Accurate and reliable price forecasting of agricultural products is significant for promoting the production and distribution of agricultural products, optimizing resource allocation and improving market efficiency. Owing to the nonstationary feature in agricultural price, based on decomposition integration and kernel density estimation (KDE), this paper proposes a hybrid model for agricultural price forecasting that can quantify the uncertainty of potential forecasts by converting traditional point forecasts into interval forecasts. Firstly, the price sequence is decomposed through variational modal decomposition (VMD) determined by energy entropy (EE); secondly, K-means clustering is used to reconstruct the intrinsic modal functions (IMFs) into low-frequency components and high-frequency components, forecasted by different methods. In addition, adaptive kernel density estimation (AKDE) is established through dynamic window and whale optimization algorithm (WOA), which is used to construct prediction interval with the residual signal obtained by VMD. Finally, to validate the superiority of the proposed model, comparative experiments with three different datasets are conducted. The results show that prediction performance of the proposed model is better than other models in both point forecasts and interval forecasts, and it can provide more accurate uncertainty information to agricultural participants. [ABSTRACT FROM AUTHOR]

Coordinating the development of the economy and environment has always been a tough challenge. The digital village construction in China offers unprecedented historical opportunities for rural areas to propel coordinated development and catch up with their urban counterparts. This paper used a coupling coordination degree model, exploratory spatial data analysis, and an obstacle degree model to investigate the coupling coordination of digital village construction, economic growth, and the environmental protection system (DEES) in rural China from 2015 to 2021. The results indicate: (1) The coupling coordination degree of DEES increases annually, yet a significant spatial imbalance among regions persists. (2) There is a positive spatial correlation in the coupling coordination of DEES, with the clustering trend gradually strengthening and subsequently weakening. (3) The most consistent obstacles are economic growth and per capita income, while obstacle factors vary among provinces. The results suggest that governments should increase investment in rural digital infrastructure, promote the synergistic efforts of the digital economy and green development, and drive the digitization of rural areas. Meanwhile, each province should use its own resources effectively, to make up for any shortcomings, thereby promoting coupling coordinated development in rural areas. [ABSTRACT FROM AUTHOR]

The Yellow River Basin plays a crucial role as both an ecological barrier and an economic development area in China. However, achieving synergistic development between the environment and industry in the coastal provinces remains challenging. Conducting in-depth studies on the synergy between these two aspects can provide valuable guidance for the development of each province. This paper employs the Topsis method to assess the development level of the industrial and environmental systems in the Yellow River Basin. Additionally, it utilizes the Harken model to analyze the synergy between the environment and industry in the coastal provinces of the Yellow River Basin from 2011 to 2021. Furthermore, the overall distribution of the level of synergistic development between the industry and environment in the Yellow River Basin is analyzed using kernel density estimation. The empirical findings reveal the following: 1. The development level of both environmental and industrial systems in each province of the Yellow River Basin has exhibited a consistent increase over the study period. 2. The average synergy between environment and industry in the Yellow River Basin has shown a declining trend from 2011 to 2021. In 2021, the synergy level follows the pattern of "upstream

This paper introduces an approach to select the bandwidth or smoothing parameter in multiresolution (MR) density estimation and nonparametric density estimation. It is based on the evolution of the second, third and fourth central moments and the shape of the estimated densities for different bandwidths and resolution levels. The proposed method has been applied to density estimation by means of multiresolution densities as well as kernel density estimation (MRDE and KDE respectively). The results of the simulations and the empirical application demonstrate that the level of resolution resulting from the moments method performs better with multimodal densities than the Bayesian Information Criterion (BIC) for multiresolution densities estimation and the plug-in for kernel densities estimation. [ABSTRACT FROM AUTHOR]

Water supply continuity is a crucial indicator of service efficiency. Multiple South Asian cities have Intermittent Water Supply (IWS). Water supply equity in cities depends on the spatial patterns of intermittency. The present study used Kernel Density Estimation (KDE) to investigate the spatial pattern of water supply intermittency for 3071 localities of Hyderabad, India. Further, the relationship between population density and supply continuity has been examined in 146 wards of Hyderabad using Spatial Autocorrelation and Geographically Weighted Regression (GWR). Spatial Autocorrelation defines two distinct clusters of central wards for high continuity (12 wards) and population density (19 wards). However, the ward clusters are different for continuity and density, with only three central wards (2.06% of all wards) being common in both the high-high clusters. The results highlight that the water supply continuity in Hyderabad has minimal consideration for population density, resulting in a spatially inequitable water supply. [ABSTRACT FROM AUTHOR]

Kernel density estimation of circular data has recently received considerable attention for its ability to model and analyse distributions on unit circles and other periodic domains. Our aim is to contribute to the literature on data-driven bandwidth selectors in circular kernel density estimation. We propose a novel circular-specific method that is based on a crossvalidation procedure with a von Mises density used as a kernel function. Using simulated data as well as real-world circular datasets, we evaluate and validate the proposed method and compare it with the existing methods. [ABSTRACT FROM AUTHOR]

This paper comprehensively utilizes the entropy-TOPSIS method, Lyapunov index, and kernel density estimation to measure the spatiotemporal evolution characteristics of regional economic resilience in 52 African countries (regions) from 2008 to 2019. It also examines the spatial network characteristics of regional economic resilience in each country (region) through gravity models and social network analysis. The findings reveal that: (1) Although the resilience of African regional economies fluctuates, it generally shows an improving trend. Traditional economic powers and regional giants such as Libya, Nigeria, South Africa, Egypt, Morocco, and Tunisia demonstrate outstanding performance in economic resilience. (2) In terms of scale resilience, the countries along the North African Mediterranean coast exhibit particularly prominent advantages. However, the overall performance of Africa in fiscal resilience and openness resilience tends to be weak. Industrial resilience is influenced by colonial legacies and tends to stabilize. (3) The differences in economic resilience values and the fluctuation trajectories of economic resilience levels converge. North African economies exhibit resilience far higher than the mean and other regions, while East, West, and Central Africa consistently perform below the mean in the long term. Southern Africa's gap from the mean is relatively small, leading to a stalemate. The fluctuation amplitude of differences within each region varies. (4) The overall level of resilience in African regional economies has steadily improved, displaying a trend of polarization. There is evident spatial polarization in West Africa, with Southern Africa demonstrating a trend of multipolarity transitioning towards bipolarity. Conversely, North Africa strengthens its features of bipolar differentiation, while East and Central Africa exhibit tendencies towards multipolarity. (5) Despite some fluctuations in the spatial network of regional economic resilience around 2016, connections among African countries have become increasingly tight, gradually forming three major spatial correlation network clusters: the North African Mediterranean coast, the West–Central African Pan-Gulf of Guinea region, and the East–South African Rift Valley region. Nigeria holds a prominent position as a regional core. Zambia, Cameroon, and the Central African Republic have played certain regional core roles at different times. Nigeria and South Africa also demonstrate significant intermediary roles, while Zambia, Cameroon, and Burkina Faso act as bridges in different periods of network connections. Based on the characteristics of spatial correlation networks, African regions gradually form four major cohesive subgroups and eight sub-subgroups. [ABSTRACT FROM AUTHOR]

Building on the observation of gaps in current research, this study provides a comprehensive analysis of the spatial patterns of heritage sites along the Silk Road, focusing on how historical trade routes shaped what are now recognized as heritage sites. Using data from the United Nations Educational, Scientific and Cultural Organization (UNESCO) World Heritage List, the research examines heritage sites across Eurasia and North Africa, with a specific emphasis on the Silk Road corridors. This study employs a spatiotemporal approach, categorizing sites into northern overland routes and southern maritime routes to highlight regional variations in network development. The key findings of this study reveal the significant influence of historical trade routes on the development of settlements, cities, and cultural landmarks along the Silk Road. These findings identify clear trends in the Silk Road network's evolution over time, illustrating a shift in its spatial focus across different historical periods. Initially, the network was centered in the eastern Mediterranean during the Classical Period. In the medieval period, this focus expanded to include a dual core area in both the eastern Mediterranean and Central Asia. By the late Medieval period, the network had shifted again, with a new core emerging in Europe. This chronological and spatial analysis allows for a detailed examination of the Silk Road network's heritage landscape evolution. The study underscores the interconnectedness of heritage sites across these regions, contributing to a deeper understanding of how landscape connectivity and trade network dynamics evolved over time. Furthermore, by identifying patterns of network development and shifts in centrality and density, this research offers valuable insights for the conservation and management of heritage landscapes. These findings are particularly crucial for preserving the historical and cultural integrity of Silk Road heritage sites. [ABSTRACT FROM AUTHOR]

The prevailing environmental, social and governance (ESG) framework is currently based on micro‐ESG indicators. Research on national ESG is often limited to theory building and policy analysis. Based on previous scholars, this paper constructs a national ESG index framework consisting of 39 indices and updates the national ESG indices for 121 countries worldwide from 1990 to 2021 using the entropy weight method, aiming to provide a set of instrumental indices that capture the status and evolution of national ESG performance. The research findings are as follows: First, the Gini coefficient shows that the gap between national ESG performance has gradually widened over time. Second, the kernel density distribution suggests that global ESG performance is on the rise. High‐income countries are placing greater emphasis on ESG growth. Third, the results of the Markov transformation matrix suggest that there is a "club convergence" in ESG performance across countries. [ABSTRACT FROM AUTHOR]

Forest fires are a major natural calamity that inflict substantial harm on forest resources and the socio-economic landscape. The eastern region of China is particularly susceptible to frequent forest fires, characterized by high population density and vibrant economic activities. Precise forecasting in this area is essential for devising effective prevention strategies. This research utilizes a blend of kernel density analysis, autocorrelation analysis, and the standard deviation ellipse method, augmented by geographic information systems (GISs) and deep-learning techniques, to develop an accurate prediction system for forest-fire occurrences. The deep-learning model incorporates data on meteorological conditions, topography, vegetation, infrastructure, and socio-cultural factors to produce monthly forecasts and assessments. This approach enables the identification of spatial patterns and temporal trends in fire occurrences, enhancing both the precision and breadth of the predictions. The results show that global and local autocorrelation analyses reveal high-incidence areas mainly concentrated in Guangdong, Fujian, and Zhejiang provinces, with cities like Jiangmen exhibiting distinct concentration characteristics and a varied spatial distribution of fire occurrences. Kernel density analysis further pinpoints high-density fire zones primarily in Meizhou, Qingyuan, and Jiangmen in Guangdong Province, and Dongfang City in Hainan Province. Standard deviation ellipse and centroid shift analysis indicate a significant northward shift in the fire-occurrence centroid over the past 20 years, with an expanding spatial distribution range, decreasing flattening, and relatively stable fire-occurrence direction. The model performs effectively on the validation set, achieving an accuracy of 80.6%, an F1 score of 81.6%, and an AUC of 88.2%, demonstrating its practical applicability. Moreover, monthly fire zoning analysis reveals that high-incidence areas in spring and winter are mainly concentrated in Guangdong, Fujian, Zhejiang, and Hainan, while autumn shows widespread medium-incidence areas, and summer presents lower fire occurrences in most regions. These findings illustrate the influence of seasonal climate variations on fire occurrences and highlight the necessity for enhanced fire monitoring and prevention measures tailored to different seasons. [ABSTRACT FROM AUTHOR]

As it is an important industrial base in China, it is of great significance to improve the industrial carbon emission efficiency in the western region to promote the low-carbon sustainable development of the region. This paper selects the input–output panel data of 11 provinces in western China from 2010 to 2021, and adopts the three-stage DEA model to measure the industrial carbon emission efficiency in western China under a non-traditional geographic division at the overall and regional levels and analyze its influencing factors. The Dagum Gini coefficient, its decomposition method, and the kernel density estimation method are used to analyze the regional differences and dynamic evolution process of industrial carbon emission efficiency in the western region. The results of the study show that (1) after removing environmental and random factors, the industrial carbon emission efficiency in western China has been improved, but there are inter-regional differences, characterized by "the third region > the second region > the first region"; (2) the levels of green development, shared development, innovative development, and coordinated development have a positive impact on the improvement of industrial carbon emission efficiency in western China, while the level of industrialization has a relatively smaller influence, and economic development, government support, open development level, and energy consumption structure have not yet played a significant role; (3) the spatial differences in the efficiency of industrial carbon emissions in western China have generally increased during the sample period, with inter-regional differences being the main source; and (4) the industrial carbon emission efficiency in western China is characterized by overall improvements in time and space but with stage differences and multi-polarization of regional differences. This study has a certain reference value for improving industrial carbon emission efficiency in western China. [ABSTRACT FROM AUTHOR]

To address the problem of the small failure probability of highly reliable structures such as rockfill dams, marine platforms and super high-rise buildings, in this paper, a method to estimate the extreme value distribution of the earth and rock dam response under nonstationary random seismic excitation is presented, and the small failure probability based on the SGLD model is analysed. First, a preliminary estimation of the extreme value distribution based on the kernel density estimation is made and then utilized as the raw data. Second, the main body of the extreme value distribution is obtained by combining the least squares method and a fitting process based on the SGLD model. Finally, the tail of the extreme value distribution is acquired by fitting to a quadratic equation. By using an example of earth-rock dam engineering, a comparison between the direct kernel density estimation method and the method proposed in this paper is performed. The results showed that the proposed method is closer to the extreme value distribution and probability of exceedance curves calculated by Monte Carlo simulation, which verified its effectiveness and accuracy. [ABSTRACT FROM AUTHOR]

Abstract Traffic density is a crucial indicator for evaluating the level of service, as it directly reflects the degree of road congestion and driving comfort. However, accurately predicting real-time traffic density has been a significant challenge in Intelligent Transportation Systems (ITS) due to the nonlinear and spatial-temporal dynamic complexity of traffic density. In this paper, we propose a novel Model-enhanced Spatial-Temporal Attention Network (MSTAN), which constructs a spatial-temporal traffic kernel density model using the Kernel Density Estimation (KDE) method to process the spatiotemporal data and calculate the probabilities of various spatiotemporal events. These probabilities are input into the attention mechanism, enabling the model to recognize the inherent connection between dynamic and distant events. Through this fusion, the network can deeply learn and analyze the spatial-temporal properties of traffic features. Furthermore, this paper utilizes the attention mechanism to dynamically model spatial-temporal dependencies, capturing real-time traffic conditions and density, and constructs a spatial-temporal attention module for learning. To validate the performance of the proposed MSTAN model, experiments are conducted on two public datasets of California highways (PeMS04 and PeMS08). The experimental results demonstrate that the MSTAN model outperforms existing state-of-the-art baseline models in terms of prediction accuracy, thus proving the effectiveness of the model both theoretically and practically.

Abstract Object Promoting the accessibility and equity of healthcare services, as well as enhancing service capacity, are crucial for building a sound healthcare system. Particularly in the past two years of the normalized COVID-19 situation, this issue has garnered widespread attention in the academic community. This study aims to investigate and analyze the characteristics and trends of the spatial-temporal evolution of healthcare service supply levels in China. It also seeks to explore the influencing factors and pathways for development, with the goal of optimizing the allocation of healthcare resources. Methods This article uses the entropy weight TOPSIS method combined with Dagum Gini coefficient and Kernel density to evaluate the supply level of healthcare services in 31 provinces and cities in China from 2012 to 2020, and explores its development and spatial pattern characteristics. Then, through Moran index, panel regression model and spatial econometric testing, the spatial correlation problem and its influencing factors are further analyzed, and targeted policy recommendations are proposed based on it, laying the foundation for further promoting the balanced development of healthcare service supply capacity. Results (1) Healthcare services supply levels in various provinces and cities in China have significantly increased, with a shift in spatial distribution from ‘higher in the east and lower in the west’ to ‘convergence between east and west, with lower levels in the central regions.’ (2) Relative differences among regions are narrowing annually, primarily due to interactions between the four regions rather than within each region, with expanding impact of overlapping regions. (3) Absolute differences among regions are also decreasing, moving towards uniformity with a contraction of extension and a restraint on the trend towards multipolarization. (4) Spatial correlation between adjacent regions is weakening, eventually becoming non-significant, with fading spatial effects. (5) The correlation between local economic development, population factors, institutional arrangements, and the current state of supply is significant, and the research design and conclusions remain robust even after thorough consideration of spatial effects. The study explores the development pathways based on the objective existence of regional development and the controllable government actions. Conclusion The overall level of healthcare service supply in China has improved, but regional differences still exist. The objective level of regional development and the subjective behavior of local governments have a significant impact on the supply of healthcare services. Therefore, it is recommended that each region adapt to local conditions, identify its own strengths and weaknesses, coordinate resource supply and demand, consider the impact of key factors, and optimize the allocation of healthcare development resources.

Abstract Exploring the interaction and coupling effects within the population‒land‒water‒industry (PLWI) system is conducive to promoting high-quality regional sustainable development. Taking the lower Yellow River during the period from 2000 to 2020 as a research sample, this study used the entropy weight TOPSIS method, the coupling coordination degree (CCD) model and kernel density estimation to synthetically evaluate the CCD of the PLWI system. The GeoDetector model was applied to explore the factors influencing the CCD of the PLWI system considering the nonlinear relationship. The major results can be summarized as follows: (1) From 2000 to 2020, the comprehensive development index (CDI) of the population, land, water and industry subsystems followed a gradual upward trend in the lower Yellow River, increasing by 0.293, 0.033, 0.111 and 0.369, respectively. However, the CDI of the land subsystem varied greatly between regions. Some cities, such as Jinan, Jining and Binzhou, experienced large declines in the CDI of the land subsystem, from 0.433, 0.534 and 0.572 to 0.358, 0.481 and 0.522, respectively. (2) The CCD of the PLWI system in the lower Yellow River showed an upward trend, increasing from 0.481 to 0.678, and became more concentrated during 2000–2020. Most of the region transitioned from near disorder to primary coordination. (3) Factors such as number of health technicians per 10,000 people, average salary, number of college students per 10,000 people, per capita GDP and per capita education expenditure were critical to the coordinated development of the PLWI system, the explanatory powers were 0.644, 0.639, 0.610, 0.498 and 0.455, respectively. Finally, this study proposed three policy recommendations to improve coupling coordination in the lower Yellow River Basin: Improving population quality, promoting green technology and rational land planning.

Abstract Background This study aims to explore the development status of the supply level of professional public health resources in Beijing Municipality, analyze the areal differences and spatial distribution characteristics of the supply level in 16 districts, and provide a scientific basis for promoting the balanced development of the supply level of professional public health resources in each district of Beijing Municipality. Methods Based on panel data from Statistical Yearbook of Health Work in Beijing Municipality and Health and Family Planning Work in Beijing Municipality from 2014 to 2022. Using the entropy method to measure the supply level of professional public health resources in Beijing, employing the Dagum Gini coefficient and Kernel density estimation method to analyze the spatial differentiation characteristics and dynamic evolution process of the supply level, and using heat maps to display the spatial distribution of the supply level in various districts of Beijing. Results The Dagum Gini coefficient of the supply level of professional public health resources in Beijing Municipality decreased continuously from 0.3419 in 2014 to 0.29736 in 2020, then gradually increased, showing a trend of initially decreasing and then increasing overall spatial differences. The spatial differences mainly stem from differences between areas. The kernel density curve shows that the supply level of professional public health resources in Beijing Municipality gradually increased, slightly decreased after 2021, and did not form a situation of two or multi-level differentiation. Conclusion From 2014 to 2022, the supply level of professional public health resources in Beijing Municipality showed an overall upward trend, but attention should be paid to the decline after 2021; spatial differences initially decreased and then increased, and the differences between areas is the main source of the overall difference in Beijing. Therefore, the Beijing Municipal Government should focus on narrowing the differences between areas, determine the allocation and management of public health resources based on the actual situation of core areas, promote coordinated development within and outside areas, and thus enhance the supply level of professional public health resources.

Background: Training load is typically described in terms of internal and external load. Investigating the coupling of internal and external training load is relevant to many sports. Here, continuous kernel-density estimation (KDE) may be a valuable tool to capture and visualize this coupling. Aim: Using training load data in speed skating, we evaluated how well bivariate KDE plots describe the coupling of internal and external load and differentiate between specific training sessions, compared to training impulse scores or intensity distribution into training zones. Methods: On-ice training sessions of 18 young (sub)elite speed skaters were monitored for velocity and heart rate during 2 consecutive seasons. Training session types were obtained from the coach's training scheme, including endurance, interval, tempo, and sprint sessions. Differences in training load between session types were assessed using Kruskal–Wallis or Kolmogorov–Smirnov tests for training impulse and KDE scores, respectively. Results: Training impulse scores were not different between training session types, except for extensive endurance sessions. However, all training session types differed when comparing KDEs for heart rate and velocity (both P <.001). In addition, 2D KDE plots of heart rate and velocity provide detailed insights into the (subtle differences in) coupling of internal and external training load that could not be obtained by 2D plots using training zones. Conclusion: 2D KDE plots provide a valuable tool to visualize and inform coaches on the (subtle differences in) coupling of internal and external training load for training sessions. This will help coaches design better training schemes aiming at desired training adaptations. [ABSTRACT FROM AUTHOR]

The primary causes leading to a high false warning rate in Lane Departure Warning (LDW) systems are the inaccuracy in departure trajectory prediction and inadequate consideration in determining the departure threshold. This study proposes a personalized LDW algorithm based on Non-stationary Crossformer and kernel density estimation. Non-stationary Crossformer is used to predict the future departure trajectory. The model adequately considers the cross-dimension de-pendency, preserving the departure trajectory's non-stationary characteristics while capturing variable information across different time scales. Then the kernel density estimation (KDE) is used to establish a personalized departure threshold for each driver, considering the variance of risk tolerance for departure among drivers. Leveraged by the impact of each driver's historical departure area, the KDE divides the historical area into two regions and determines the threshold. Vali-dation is carried out using Shanghai natural driving data collected from 10 drivers. The results show that, compared with baseline modes, Non-stationary Crossformer can accurately predict departure trajectory when there are significant trajectory changes, and the KDE can better deter-mine the personalized departure threshold. The proposed LDW algorithm reduces the false warning rate to 2.4 %, which is 2.4 %-24 % lower than Baseline models. The algorithm would con-tribute to improving the LDW in the future.

Traffic density is a crucial indicator for evaluating the level of service, as it directly reflects the degree of road congestion and driving comfort. However, accurately predicting real-time traffic density has been a significant challenge in Intelligent Transportation Systems (ITS) due to the nonlinear and spatial-temporal dynamic complexity of traffic density. In this paper, we propose a novel Model-enhanced Spatial-Temporal Attention Network (MSTAN), which constructs a spatial-temporal traffic kernel density model using the Kernel Density Estimation (KDE) method to process the spatiotemporal data and calculate the probabilities of various spatiotemporal events. These probabilities are input into the attention mechanism, enabling the model to recognize the inherent connection between dynamic and distant events. Through this fusion, the network can deeply learn and analyze the spatial-temporal properties of traffic features. Furthermore, this paper utilizes the attention mechanism to dynamically model spatial-temporal dependencies, capturing real-time traffic conditions and density, and constructs a spatial-temporal attention module for learning. To validate the performance of the proposed MSTAN model, experiments are conducted on two public datasets of California highways (PeMS04 and PeMS08). The experimental results demonstrate that the MSTAN model outperforms existing state-of-the-art baseline models in terms of prediction accuracy, thus proving the effectiveness of the model both theoretically and practically. [ABSTRACT FROM AUTHOR]

In the traditional interference of stress strength model, it is generally supposed that the strength variable and the stress variable are independent. However, in many engineering applications strength variable and the stress variable are dependent. To solve the situation where the strength variable and the stress variable are dependent, in this paper the dependent between strength and stress is considered, the correlation between strength and stress is described by Farlie–Gumbel–Morgenstern copula function. Three estimate methods (maximum likelihood estimation, Bayes estimator, kernel density estimation) are used to estimate the reliability for δ = P (X < Y) under first failure progressively unified hybrid censoring samples. Also, bootstrap confidence interval is constructed using percentile bootstrap method. The effectiveness of the proposed estimation method is demonstrated by numerical simulation. Finally, the practicability of the proposed estimation method is verified by data of breakdown time of an insulating fluid. [ABSTRACT FROM AUTHOR]

ObjectivesDistributed photovoltaic power prediction is of great significance for the operation and scheduling of photovoltaic power plants. Point prediction methods are difficult to comprehensively describe the uncertainty of distributed photovoltaic power. This article proposed a distributed photovoltaic power interval prediction model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and sparrow search algorithm optimized least squares support vector machine (SSA-LSSVM).MethodsFirstly, the photovoltaic sequence was broken down into multimodal components through CEEMDAN, and then the high-frequency non-stationary components obtained from the first decomposition were decomposed twice. Secondly, sample entropy (SE) was used to reconstruct all components into trend and oscillation components. Then, the point prediction values of the two components were obtained through SSA-LSSVM. Finally, the probability density estimation was performed on the point prediction error of the oscillation component, and the stacked point prediction value was used to obtain the overall prediction interval result.ResultsThe interval prediction model proposed in this paper has higher interval coverage and narrower average interval width.ConclusionsAdding secondary modal decomposition to distributed photovoltaic power data processing and combining sample entropy to reconstruct its sub-sequences can effectively reduce the complexity of the original prediction components and improve the accuracy of model prediction.

ObjectiveTo understand the degree of coordination and the changing trends of Healthy China construction and high-quality economic development in different provincial-level administrative divisions (PLADs) of China, so as to promote the synergy of the two systemic developments. MethodsFrom China Statistical Yearbook, China Health Statistical Yearbook, and China National Environmental Monitoring Centre, we extracted relevant panel data of 31 PLADs in Chinese mainland from 2012 to 2021. A coupling coordination degree model of Healthy China construction and high-quality economic development was constructed for PLADs. Kernel density estimation and spatial Markov prediction model were used to explore the spatial-temporal dynamic evolution characteristics of coupling coordination degree in each PLAD and predict the evolution trend. ResultsFrom 2012 to 2021, the average coordination index of the 31 PLADs was 0.510, with 3 PLADs having the index of intermediate and primary coordination, 10 having the index of just about coordination, and 18 having the index of mildly unbalanced coordination. From the perspective of spatial evolution, there was an obvious "gradient effect" within the economic development region-specific coupling coordination indexes, showing an east-central-west step distribution and a descending spatial distribution pattern for the eastern region (coordination index of 0.582), the national average (0.510), the central region (0.480), and the western region (0.463). In terms of temporal evolution, the region-specific coupling coordination indices continued to increase, with the central region showing the largest increase (0.120), followed by the eastern (0.116) and western (0.110) regions. ConclusionHealthy China construction and high-quality economic development in China shows a positive trend, and the regional coordination level is limited by the lag of high-quality economic development. There is significant regional heterogeneity in the coordination level of each region. Considering the spatial effect, adjacent PLADs with high and relatively high levels will have a radiating and driving effect on PALDs with low and relatively low levels, and there is a positive spillover effect.

In order to solve the problems of system peaking and consumption caused by the integration of large-scale new energy sources into the grid, this paper analyzes the source-load-storage peaking capacity and its complementarities, so as to tap the system's peaking capacity and promote new energy consumption. Firstly, taking into account the peaking pricing and compensation for ancillary services for thermal power, energy storage and electric vehicles, the peaking cost sharing and compensation mechanism is analyzed and a deep peaking model is established. Secondly, considering the uncertainty and correlation of wind power, a typical wind power sequence based on kernel density estimation and Frank Copula function is generated and a step-type demand response model is established to realize the graded compensation of the response amount. Then, it is possible to improve the response enthusiasm of demand-side users. Finally, with the objective of minimizing the total operating cost, a joint source-load-storage peak-peaking optimization model considering uncertainty and pricing compensation is constructed, and the improved IEEE 30-node system is used as an example for analysis. The results show that the proposed model can increase the system's peak-shaving capability and renewable energy consumption level by utilizing the flexible peak-shaving potential of resources on the three sides of source, load, and storage.

Abstract The inequality in CO2 emissions from agricultural energy consumption is a major challenge for coordinating low-carbon agricultural development across regions in China. However, the evolutionary characteristics and driving factors of inequality in China’s agricultural energy-related CO2 emissions are poorly understood. In response, the Kaya–Theil model was adopted to examine the three potential factors influencing CO2 emission inequality in China’s agricultural energy consumption. The results revealed that, from 1997 to 2021, agricultural energy-related CO2 emissions per capita showed a significant upward trend, with prominent polarization and right-tailing phenomena. Overall, the inequality was on a downward trend, with the Theil index falling from 0.4109 in 1997 to 0.1957 in 2021. Meanwhile, the decomposition of the national inequality revealed that the within-group inequality declined from 0.3991 to 0.1634, which was greater than between-group inequality, based on zoning the 28 provinces into three grain production functional areas. As for the three kaya factors, the energy intensity contributed the most to the overall inequality, followed by the agricultural economic development and CO2 emission intensity. Based on these results, this study provided some potential strategies to reduce agricultural-related CO2 emissions.

ABSTRACT The case study area is a small but typical prehistoric landscape in the Kras Plateau on the north coast of the central Mediterranean. The Late Bronze and Iron Age Kras Plateau was an emblematic Mediterranean archaeological landscape dotted with numerous hillforts. Since the mid‐20th century, the landscape had been overgrown with some of the most archaeology‐hostile vegetation, severely impeding landscape archaeology until archaeological LiDAR revealed thousands of archaeological features that attest to a carefully constructed and managed agro‐pastoral landscape. However, these discoveries were hampered by insecure chronology typical of any LiDAR guided analysis. This case study meticulously documented two prehistoric hillforts and a previously unknown agro‐pastoral landscape with hundreds of archaeological features. The focus of the article, however, was on establishing a more precise and objective dating method. We proposed a multiproxy method to date the archaeological landscape. It combines relative dating using remote sensing data and historical maps; dating based on historical context; relative stratigraphic dating; indirect dating based on associated archaeological finds; and dating by association. Particularly, we focused on the association‐based dating of archaeological features. We proposed a method based on the concept of taskscapes that relies on kernel density estimation. Using these methods, we objectively demonstrated that agro‐pastoral landscape features documented with archaeological LiDAR were contemporaneous with Late Bronze Age and Iron Age hillforts and have no connection to the post‐medieval landscape. The latter has important methodological implications for the prehistoric archaeology of Mediterranean karst landscapes, where backdating post‐Medieval landscapes is a common practise. [ABSTRACT FROM AUTHOR]

Accurately predicting wind speeds is of great significance in various engineering applications, such as the operation of high-speed trains. Machine learning models are effective in this field. However, existing studies generally provide deterministic predictions and utilize decomposition techniques in advance to enhance predictive performance, which may encounter data leakage and fail to capture the stochastic nature of wind data. This work proposes an advanced framework for the prediction and early warning of wind speeds by combining the optimized gated recurrent unit (GRU) and adaptive kernel density estimator (AKDE). Firstly, 12 samples (26,280 points each) were collected from an extensive open database. Three representative metaheuristic algorithms were then employed to optimize the parameters of diverse models, including extreme learning machines, a transformer model, and recurrent networks. The results yielded an optimal selection using the GRU and the crested porcupine optimizer. Afterwards, by using the AKDE, the joint probability density and cumulative distribution function of wind predictions and related predicting errors could be obtained. It was then applicable to calculate the conditional probability that actual wind speed exceeds the critical value, thereby providing probabilistic-based predictions in a multilevel manner. A comparison of the predictive performance of various methods and accuracy of subsequent decisions validated the proposed framework. [ABSTRACT FROM AUTHOR]

Road traffic accidents pose significant challenges to sustainable urban safety and intelligent transportation management. The effective hazard identification of crash hotspots is crucial in implementing targeted safety measures. A severity-weighted system was adopted to quantify crash hazard levels. Using 1059 valid crash records of the City of London, the spatial correlations of crash points were first examined via average nearest neighbor analysis. Then, the optimal KDE bandwidth was determined via ArcGIS's automatic extraction method, multi-distance spatial cluster analysis, and incremental spatial autocorrelation (ISA) analysis. The predictive accuracy index (PAI) was used to evaluate the accuracy of KDE results at various bandwidths. The results revealed a clustered spatial distribution of crash points. The optimized KDE bandwidth obtained via ISA analysis was 134 m, and the yielded PAI was 4.381, indicating better predictive accuracies and balanced hotspot distributions and reflecting both local concentrations and the overall continuity of crash hazard hotspots. Applying this bandwidth to the validation data allowed the successful identification of most high-risk areas and potential crash hazard hotspots attributed to traffic environmental factors; this method exhibits reliability, accuracy, and robustness over medium to long time scales. This workflow can serve as an analytical template for assisting planners in improving the identification accuracy of hazard hotspots, thereby reducing crash occurrences, actively promoting sustainable traffic safety development, and providing valuable insights for targeted crash prevention and intelligent traffic safety management in urban areas. [ABSTRACT FROM AUTHOR]

The performance of electrical connectors can be significantly impacted by periodic variations in contact resistance caused by vibrational stress. Intermittent faults resulting from such stress are characterized by their random and fleeting nature, making it difficult to study and replicate them. This paper proposes a novel method for reproducing intermittent faults in electrical connectors. To implement this method, intermittent fault data are first collected from electrical connectors subjected to different vibration loads. Next, a statistical distribution model is constructed using kernel density estimation (KDE). Based on this model, a fault injector is designed to simulate intermittent faults under varying vibration loads. The simulated faults are then compared to real-world intermittent fault signals in a controlled environment to validate the accuracy of the method. The results demonstrate that the proposed method effectively reproduces intermittent faults in electrical connectors under varying vibration conditions. This approach can be used to better understand the behavior of connectors under vibrational stress and to develop more effective testing and fault diagnosis methods. [ABSTRACT FROM AUTHOR]

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