Kernel Density Research Articles

Spatial Distribution Characteristics and Sustainable Inheritance Strategies of National Traditional Fine Arts Intangible Cultural Heritage in China

A total of 417 intangible cultural heritage objects (ICHOs) are intertwined with traditional Chinese art, showcasing China’s rich historical heritage and distinctive creative allure. However, ICHOs currently grapple with a significant succession quandary due to modernization endeavors and the impacts of globalization. Hence, this study scrutinizes the spatial distribution of ICHO projects and inheritors and examines methodologies for inheriting these 417 ICHOs, employing approaches such as the closest neighbor index, Moran’s I index, kernel density estimation, geographic concentration analysis, and imbalance index assessment. Research indicates that ICHOs exhibit a spatial aggregation pattern, yet there is no substantial spatial correlation observed in their distribution. The national distribution highlights two core density zones: Beijing and Shanghai. In China, ICHO programs display uneven distribution across various types, levels, regions, and cultural zones. Regarding the ethnic composition of projects and inheritors, ICHO projects predominantly focus on the Han ethnic group, with a scattering of minority representations. Minority ICHO projects and inheritors are concentrated in central and eastern areas, while Han ICHO projects and inheritors are concentrated in central and western regions. Among the 148 ICHO projects lacking inheritors, 203 have one inheritor, and only 66 have several inheritors. Shanghai serves as the core density zone for 269 ICHO projects with inheritors, while Beijing holds that status for 148 ICHO projects without inheritors. Out of the 148 ICHO projects lacking inheritors, 115 belong to the Han ethnic group, with 33 split among 16 different ethnic minorities, and 2 have no inheritors. These disparities underscore the uneven distribution of ICHO projects and the critical issue of inheritance. This study identified education, tourism, digital communication, incentivizing inheritors, and international cooperation as potential strategies for preserving ICHA. Moreover, a sustainable inheritance pathway integrating government, education, tourism, and media is imperative for the effective perpetuation of ICHOs’ legacy.

The coupling coordination relationship between sports industry agglomeration and economic resilience in the Yangtze River Delta region.

With a rapidly growing sports industry worldwide, one may argue that sports industry agglomeration can play a crucial role in the economy of the sports industry. In particularly, the coupling linkage between sports industry agglomeration and economic resilience can be leveraged to promote both economic quality and efficiency. Based on data on three provinces and one city in the Yangtze River Delta region during the 2011-2020 period, this study uses the entropy-weighted TOPSIS method, coupling coordination degree model, and relative development models to explore the coupling coordination relationship between sports industry agglomeration and economic resilience in this region. The results show that: (1) Sports industry agglomeration shows an overall increasing albeit fluctuating trend with inter-provincial differences. (2) Economic resilience has steadily increased, while the economic resilience kernel density curve generally shows a "dual peaks" trend. (3) The coupling coordination between sports industry agglomeration and economic resilience remains in a fluctuating, albeit coordinated state. These findings are relevant for the integration and high-quality development of the sports industry in the Yangtze River Delta region.

The Spatial Pattern of Polluting Enterprises and the Effects of Local Regulation in the Guanzhong Plain Urban Agglomeration

In the context of actively undertaking the transfer of domestic and foreign industries in the central and western regions of China, local regulatory behaviors influence the spatial distribution of polluting enterprises (PEs). This study examined the Guanzhong Plain urban agglomeration (GPUA), the largest urban agglomeration in the northwest region of China and one of the main regions that undertakes industrial transfer, using kernel density estimation and geographically and temporally weighted regression to explore the spatial pattern characteristics and evolution of PEs and reveal the effects of local regulatory behaviors, including environmental regulation (ER) and local protection (LP). The results indicate that (1) The distribution of the PEs tended towards energy and mineral resources and agglomerated along the development axes, aligning with the strategic positioning of the major function-oriented zones. Agglomerated areas gradually concentrate in key development zones. Major agricultural production zones exit high-density areas, and those adjacent to high-density areas often become secondary agglomeration core areas. Key ecological functional zones do not form high-density areas. (2) Both ER and LP have a positive impact on the distribution of PEs, and the dominant influence shifts from ER to LP. Counties with strict ER have increased the tendency of PEs to exhibit a certain layout due to better pollution treatment facilities and more mature pollution control technologies. The “pollution haven effect” has not yet formed within the GPUA. (3) The role of LP was more prominent in key development zones and major agricultural production zones, whereas the role of ER was more evident in key ecological functional zones. (4) RE and LP have mutually reinforcing effects on the distribution of PEs; the “innovation compensation effect” gradually manifests, but an increase in ER leads to a decrease in regional industrial clustering. This study provides a reference value for understanding the impact of government regulation on the distribution of PEs in underdeveloped areas.

Peaglet: A user-friendly probabilistic Kernel density estimation of intracranial cortical and subcortical stimulation sites

BackgroundData on human brain function obtained with direct electrical stimulation (DES) in neurosurgical patients have been recently integrated and combined with modern neuroimaging techniques, allowing a connectome-based approach fed by intraoperative DES data. Within this framework is crucial to develop reliable methods for spatial localization of DES-derived information to be integrated within the neuroimaging workflow. New methodTo this aim, we applied the Kernel Density Estimation for modelling the distribution of DES sites from different patients into the MNI space. The algorithm has been embedded in a MATLAB-based User Interface, Peaglet. It allows an accurate probabilistic weighted and unweighted estimation of DES sites location both at cortical level, by using shortest path calculation along the brain 3D geometric topology, and subcortical level, by using a volume-based approach. ResultsWe applied Peaglet to investigate spatial estimation of cortical and subcortical stimulation sites provided by recent brain tumour studies. The resulting NIfTI maps have been anatomically investigated with neuroimaging open-source tools. Comparison with existing methodsPeaglet processes differently cortical and subcortical data following their distinguishing geometrical features, increasing anatomical specificity of DES-related results and their reliability within neuroimaging environments. ConclusionsPeaglet provides a robust probabilistic estimation of the cortical and subcortical distribution of DES sites going beyond a region of interest approach, respecting cortical and subcortical intrinsic geometrical features. Results can be easily integrated within the neuroimaging workflow to drive connectomic analysis.

Enhancing Broiler Weight Estimation through Gaussian Kernel Density Estimation Modeling

The management of individual weights in broiler farming is not only crucial for increasing farm income but also directly linked to the revenue growth of integrated broiler companies, necessitating prompt resolution. This paper proposes a model to estimate daily average broiler weights using time and weight data collected through scales. In the proposed model, a method of self-adjusting weights in the bandwidth calculation formula is employed, and the daily average weight representative value is estimated using KDE. The focus of this study is to contribute to the individual weight management of broilers by intensively researching daily fluctuations in average broiler weight. To address this, weight and time data are collected and preprocessed through scales. The Gaussian kernel density estimation model proposed in this paper aims to estimate the representative value of the daily average weight of a single broiler using statistical estimation methods, allowing for self-adjustment of bandwidth values. When applied to the dataset collected through scales, the proposed Gaussian kernel density estimation model with self-adjustable bandwidth values confirmed that the estimated daily weight did not deviate beyond the error range of ±50 g compared with the actual measured values. The next step of this study is to systematically understand the impact of the broiler environment on weight for sustainable management strategies for broiler demand, derive optimal rearing conditions for each farm by combining location and weight data, and develop a model for predicting daily average weight values. The ultimate goal is to develop an artificial intelligence model suitable for weight management systems by utilizing the estimated daily average weight of a single broiler even in the presence of error data collected from multiple weight measurements, enabling more efficient automatic measurement of broiler weight and supporting both farms and broiler demand.

Uncovering the progressive failure process of primary coal-rock mass specimens: Insights from energy evolution, acoustic emission crack patterns, and visual characterization

The failure and instability of deep coal-rock mass structures, which are influenced by mining disturbances, are major contributors to disasters like rock bursts. This study involved conducting indoor experiments using uniaxial loading test and acoustic emission (AE) experiments on indigenous primary coal-rock mass (PCRM). We utilize chaotic bifurcation based on strain energy to characterize the progressive damage stages of PCRM, demonstrating the chaotic characteristics of energy evolution during the gradual destruction process. The degree of damage resulting from PCRM failure was evaluated by jointly applying P-wave velocity tomography and AE event localization. Additionally, the classification of crack patterns in PCRM was conducted using the RA-AF correlation analysis method. The segmentation boundaries of crack classification patterns were then redefined using the Gaussian mixture model (GMM) algorithm. The research findings indicate that the chaotic bifurcation model of strain energy classifies stress into four stages: the stable region (Ⅰ), the metastable region (Ⅱ), the bifurcation region (Ⅲ), and the chaotic region (Ⅳ). The presence of a low-velocity zone in P-waves may indicate crack accumulation and damage within the system. Additionally, areas with abnormal P-wave velocities exhibit numerous AE events. The crack classification patterns of the four stages undergo an evolution from predominantly tensile cracks to a mixture of tensile and shear cracks, and the GMM algorithm successfully identifies the optimal separation path for crack classification boundaries. We propose an adaptive kernel density estimation (AKDE) algorithm to quantify the spatial distribution of AE events, thus offering a visual representation of the damage patterns at various stages.

The Association between Rainfall and Taxi Travel Activities: A Case Study from Wuhan, China

Rainfall has a significant impact on urban population mobility, posing great challenges to traffic management and urban planning. An understanding of this influence from multiple perspectives is urgently needed. In this study, we devised a multiscale comparative research framework to explore the spatiotemporal effects of rainfall on taxi travel patterns, aiming to provide a new perspective on the investigation of rainfall’s impact on urban human mobility. More specifically, at the macroscopic scale, we computed taxi travel indicators across the entire study area and used kernel density estimates to observe the spatiotemporal distribution patterns influenced by rainfall. Subsequently, complex traffic networks were constructed by considering urban road intersections as nodes and combined with visualization methods to understand changes in taxi travel patterns visually at the microscopic level. We selected Wuhan City, a typical urban area in southern China with frequent rainfall, as the study area and used meteorological data along with a large volume of taxi spatiotemporal trajectory data for investigation. Results indicated a 4.16% decrease in weekly travel volume due to rainfall, with a 3.96% decrease on workdays and a 4.64% decrease on weekends. However, nighttime rainfall between 19:00 and 22:00 on weekdays increased the demand for taxi travel. Furthermore, the impact of rainfall on weekends exceeded that on workdays, restricting people’s mobility and leisure activities, resulting in reduced travel to recreational tourist spots and commercial pedestrian streets. Rainfall altered residents’ travel preferences to some extent, with more residents choosing taxis during rainy weather, which led to decreased transportation efficiency and increased traffic congestion. These findings contribute to a deeper understanding of the complex relationship between population mobility patterns and the urban ecological environment, providing valuable insights for planning resident travel and taxi dispatching under adverse weather conditions.

Spatial distribution characteristics and influencing factors of traditional villages based on geodetector: Jiarong Tibetan in Western Sichuan, China.

Jiarong Tibetan is a regional group with distinctive regional characteristics and possess precious traditional village resources. Studying the spatial distribution and influencing factors of traditional villages is of vital significance for the protection and renewal of villages and the revitalization of cultural heritage. Nevertheless, due to the fact that the Jiarong Tibetan inhabited area has not been clearly defined for a long time, there is a lack of holistic discussion on the distribution features and driving mechanisms of traditional villages in this region. In order to fill this research gap, the paper is the first to break away from the existing county administrative divisions to define the study area. Moreover, the analysis is carried out by using the nearest neighbor index, kernel density, GoeDa and Geodetector, etc. for traditional villages at national-level and provincial-level. The results show that the spatial distribution of the traditional villages of Jiarong Tibetan is characterized by typical aggregation, with the core intensive area in Danba County and the sub-core intensive area in the central and northern parts. The results of factor detection show that the spatial distribution pattern of the traditional villages is the consequence of the synergistic effect of multiple factors, and the interaction effect is significantly enhanced. The economic level and climatic conditions play a controlling role, and population, elevation, intangible cultural heritage and rivers also have notable effects. The findings of study can offer scientific guidance and suggestions for the inheritance and development of traditional villages in Jiarong Tibetan settlement area.

A Water Shortage Risk Assessment Model Based on Kernel Density Estimation and Copulas

Accurate assessment and prediction of water shortage risk are essential prerequisites for the rational allocation and risk management of water resources. However, previous water shortage risk assessment models based on copulas have strict requirements for data distribution, making them unsuitable for extreme conditions such as insufficient data volume and indeterminate distribution shapes. These limitations restrict the applicability of the models and result in lower evaluation accuracy. To address these issues, this paper proposes a water shortage risk assessment model based on kernel density estimation (KDE) and copula functions. This approach not only enhances the robustness and stability of the model but also improves its prediction accuracy. The methodology involves initially utilizing kernel density estimation to quantify the random uncertainties in water supply and demand based on historical statistical data, thereby calculating their respective marginal probability distributions. Subsequently, copula functions are employed to quantify the coupled interdependence between water supply and demand based on these marginal probability distributions, thereby computing the joint probability distribution. Ultimately, the water shortage risk is evaluated based on potential loss rates and occurrence probabilities. This proposed model is applied to assess the water shortage risk of the Yuxi water receiving area in the Central Yunnan Water Diversion Project, and compared with existing models through experimental contrasts. The experimental results demonstrate that the model exhibits evident advantages in terms of robustness, stability, and evaluation accuracy, with a rejection rate of 0 for the null hypothesis of edge probability fitting and a smaller deviation in joint probability fitting compared to the most outstanding model in the field. These findings indicate that the model presented in this paper is capable of adapting to non-ideal scenarios and extreme climatic conditions for water shortage risk assessment, providing reliable prediction outcomes even under extreme circumstances. Therefore, it can serve as a valuable reference and source of inspiration for related engineering applications and technical research.

Stability Evaluation of Slope Based on Global Sensitivity Analysis

The uncertainty of parameters will have a significant impact on slope stability, where sensitivity analysis is a commonly used method in uncertainty research. However, traditional sensitivity analysis method costs much computation time. When calculating the sensitivity index of one parameter, all other parameters are taken as fixed values, and the uncertainty of all parameters cannot be considered simultaneously. Therefore, the variance-based and the moment-independent global sensitivity analysis (GSA) methods are both introduced to determine the influence of geotechnical parameters on slope stability in this study. To solve the importance index of GSA, the least angle regression algorithm, the kernel density estimation, and orthogonal polynomial estimation methods are developed to obtain variance-based importance index and the moment-independent importance index, respectively. The proposed methods allow all variables to change simultaneously within their variation range and have high computational efficiency. The results are in good at with those obtained by the variance-based Monte Carlo simulation method, which is considered as the exact solution forobtaining the importance index. The influence of the correlation between the shear strength parameters (c and φ) on the importance index is also studied, which indicates that the negative correlation will have a great impact on the importance index, which in turn affects the safety assessment of slope. Three engineering cases have been studied for engineering application, and the compared results indicate that the impact of the geotechnical parameters uncertainty on the safety factor (Fs) and failure probability (pf) are different. Therefore, the approaches based on GSA which can integrate the Fs with pf will be a promising approach for slope stability evaluation.

Where Are Business Incubators Built? County-Level Spatial Distribution and Rationales Based on the Big Data of Chinese Yangtze River Delta Region

Business incubators (BIs) in China have predominantly exhibited a government-led characteristic, recently broadening their spatial and temporal scope and extending reach to the county level. Regarding the inadequacies of county-level analysis scale, this study leverages Points of Interest (POI) big data to overcome them. To comprehend the governmental rationale in the construction of BIs, we examine the evolution dynamics of BIs in conjunction with policies. An economic geography framework is developed, conceptualizing BIs as quasi-public goods and productive services, and incorporating considerations of county-level fiscal operations and industrial structures. Focusing on the Yangtze River Delta (YRD) region as a case study, our findings reveal that over 98% of County Administrative Units (CAUs) have built BIs. Using kernel density estimation and Moran’s I, the spatial patterns of CAUs are identified. The CAUs are further classified into three categories of economic levels using the k-means algorithm, uncovering differentiated relationships between industry, finance, and their respective BI. Additionally, we analyze the density relationship between BIs and other facilities at a micro-level, showcasing various site selection rationales. The discussions highlight that while BIs tend to align with wealthier areas and advanced industries, affluent CAUs offer location advantages on BIs, whereas less wealthy CAUs prioritize quantity for political achievements. This paper concludes with recommendations about aligning BIs based on conditions and outlooks on future research.

Spatial utilization of historical topographic map and its application in land reconstruction of ancient Chinese urban land use

The historical topographic map preserves rich geographic information and can provide direct assistance for the reconstruction of various geographic elements. Based on the historical data of cities throughout the Qing Dynasty, the land use scale data of cities across the country was obtained using GIS and urban perimeter conversion models. This study combines city information and city circumference records from the historical maps and archives of the late Qing Dynasty to quantitatively reconstruct the use patterns of ancient China’s urban land at a spatial resolution of 1° × 1°. Uncertainty analysis of the reconstruction results was conducted using modern remote sensing image data as the validation data set. The results showed the following. (1) During the late Qing Dynasty, the total area of urban land in the various provinces and regions was 1456.015 km2. The maximum value was 208.691 km2 in Beijing-Tianjin-Hebei, the minimum value was 1.713 km2 in Qinghai, and the average value was 56.001 km2. (2) The results of grid reconstruction show that among the 398 grids with urban land distribution, the maximum value is 64.099 km2/grid, the minimum value is 0.013 km2/grid, and the average value is 3.658 km2/grid. (3) Of all the grids with urban land, the urban land grid to the west of the Hu Line accounts for 12.5% and the east to 87.5%. (4) During the late Qing Dynasty, urban land use in China was primarily concentrated in agriculturally developed areas such as the North China Plain, the Central Plains, Jiangnan, and the Sichuan-Chongqing region. (6) The results of a kernel density estimation showed that there were obviously three core areas of urban land agglomeration in China during the late Qing Dynasty: the North China Plain-Central Plains, the Jiangsu-Shanghai-Zhejiang-Anhui area, and the Sichuan-Chongqing urban core area. This study provides basic data for urban land use during historical periods and provides a basis for the quantitative reconstruction of relevant urban land data for historical archives.

Study on spatial distribution, regional differences and dynamic evolution of rural financial risk in China.

Based on panel data from 2009 to 2021, covering 30 provinces in China, we have been constructed the Rural Financial Risk Index using the objective entropy weighting method to study rural financial risk in China systematically from the perspective of spatial distribution. Specifically, we discuss the spatial distribution, regional differences and dynamic evolution of rural financial risk across Chinese four different regions divided into the Northeast, East, Central and West. It's found that Local government debt and Land transfer income are the two primary determinants influencing the level of rural financial risk in China. Furthermore, we conclude the ranking value of rural financial risk across four regions that the central exhibits the highest level, followed by the West, the East, and finally the Northeast, where the reasons for such ranking results as follows. Firstly, although the highest level of risk among provinces in the West is equivalent to that in the Central, there exists a smaller minimum rural financial risk in the former compared to the latter. Then, it should be noted that there's a low-low agglomeration of rural financial risk in the Northeast, while it demonstrates a high-high agglomeration in the Central according to the Moran Index test analysis. Again, there's a declining trend in rural financial risk disparity within the region and an upward trend is observed when comparing different regions (except the East vs West), especially increase largely between the Northeast and Central in past two years after analyzing the decomposition of Dagum Gini coefficient. Moreover, we study the absolute differences and dynamic evolution in different four regions through three-dimensional diagram of kernel density estimation, and it's found that the change of rural financial risk in four regions moved to the right as a whole, while the tail distribution remains inconspicuous. The absolute difference is diminishing in the Northeast, and the two-level differentiation characteristics tend to weaken as a whole in the Central, with a disordered wave peak height observed in both the East and West. Finally, the article presents pertinent policy implications but limitations according to the research findings.

Progress in Digital Climate Governance in China: Statistical Measurement, Regional Differences, and Dynamic Evolution

The capacity for climate governance is crucial for sustainable advancement, with data elements being a pivotal production factor in contemporary governance. This study examines the trajectory and strategy of digital transformation in climate governance, creating a three-dimensional dataset encapsulating 11 primary and 36 secondary indicators to facilitate the assessment of digital climate governance. Employing spatiotemporal analysis and coupling coordination models, this study evaluates the digitalization levels in climate governance across 30 regions in China, examining how to progress digital integration from governmental and market perspectives. Findings reveal a consistent improvement in China’s regional digital climate governance, bolstering economic and social progress. Nonetheless, regional disparities and developmental lags persist, with convergence analysis indicating a divergence trend in provincial climate governance capabilities. Moreover, kernel density and Markov chain analyses suggest an ongoing evolution in regional digital climate governance efforts, aiming at achieving a higher development plateau. The study emphasizes the dual role of government and market dynamics in boosting digital governance levels, deducing from two-stage regression that effective government-market interplay is vital for elevating governance quality and fostering new productive forces, recommending an integrated governance mechanism for optimal synergy.

Early Holocene inundation of Doggerland and its impact on hunter-gatherers: An inundation model and dates-as-data approach

Sea-level rise was a key factor changing environments during the Early Holocene in Northwest Europe. It affected Mesolithic hunter-gatherer communities by inundating large areas in the current North Sea, commonly referred to as Doggerland. In this paper we present novel inundation models for the southern North Sea providing visualisations of lateral inundation driven by sea-level rise and relate it to frequency analysis of radiocarbon dates from archaeological sites. These improve on previous studies that relied on bathymetric data, which includes post-inundation overprints of Holocene sedimentation and erosion, and thus significantly underestimates the timing of inundation in some areas.We constructed a paleoDEM (a composite elevation grid of the top of the Pleistocene) for the eastern part of the southern North Sea; and sea level surfaces that combine relative sea-level curves from glacio-isostatic adjustment models optimised for Britain and southern Scandinavia respectively. We corrected our paleoDEMs for tectonic background basin subsidence, and in the inundation modelling account for pre-compaction elevation of peat in coastal areas. We evaluated the impact of these model components on our results and describe the possible inundation history of Doggerland. We suggest earlier inundation than predicted by previous models, showing significant area loss around 10.5–10 ka cal BP.Palaeogeographic changes are compared with archaeological radiocarbon data using a dates-as-data approach. Composite Kernel Density Estimate and permutation tested Summed Probability Distributions are used as a proxy for the visibility, nature and intensity of human activity. Results indicate key periods of growth and decline recorded in the dataset, as well as regional differences in growth rate, some correlating with inundation phases. Chiefly, we find elevated growth rates around 10.5–10 ka in northwest Germany and the Netherlands, contemporaneous with the abovementioned phase of extensive area loss, and moreover, with changes in culture and practices on Early Mesolithic sites.However, the spatiotemporal distribution of archaeological data is significantly influenced by accessibility and preservation of sediments of a certain age. We discuss the importance of inundation modelling and sediment data in understanding how landscape taphonomy affects archaeological patterning, especially in dates-as-data approaches.

Spatial distribution, influencing factors and innovative development countermeasures of farmer cooperatives in Hunan Province, China

China’s rural reform is reliant on farmers' cooperatives as a key organization vehicle. It plays an important role in promoting rural revitalization. Such as, realizing the organic connection between small farmers and agricultural modernization. This study used the nearest neighbor index and kernel density estimation to analyze the spatial distribution and structural characteristics of farmer cooperatives in Hunan Province. It revealed the spatial differentiation law of cooperatives. Also using geographical detectors to figure out the main factors that affect the spatial distribution. The results show that: ① Hunan Farmers Cooperatives show significant spatial agglomeration. The overall spatial distribution shows the spatial characteristics of "one core, one circle, and multiple points". And the spatial distribution pattern of "large agglomeration, finger-shaped radiation distribution". Among them, the distribution of provincial demonstration cooperatives is relatively balanced. ② Cooperatives in six types of industries, such as planting, forestry, and feeding, showed agglomeration distribution. Different industrial cooperatives spread out in different ways, such as in an anti-"L" shape, a dual-core shape, or a one-center area. ③ The 24 influence factors selected from the five dimensions of the natural environment, social economic basis, production basis, output capacity, and facility basis have high, general, and weak influence on cooperatives' spatial distribution. The development and distribution of cooperatives mainly depend on resource endowment, social and economic development level, and market dependence. The spatial distribution heterogeneity of different professional cooperatives is mainly affected by slope, per capita disposable income of rural residents, road density, and other factors. ④ The progression of farmer cooperatives in Hunan Province should prioritize high-quality development, emphasizing the need for innovative approaches and transformative strategies within rural industrial organizations. It is imperative to optimize the spatial distribution of cooperatives, strategically contributing to the establishment of a novel framework for modern agricultural development in Hunan Province. Additionally, there is a critical emphasis on spearheading collaborative initiatives among cooperatives of varied industrial types, aimed at fostering the integrated development of rural primary, secondary, and tertiary industries.

Subtropical mangroves poleward shift to the Yangtze Estuary under different carbon emission scenarios

Mangroves are crucial plant communities in the coastal intertidal zones, providing various ecological services and supporting biodiversity. To identify the primary factors affecting the poleward shift of subtropical mangroves and the northernmost extent of subtropical mangroves under various carbon emission scenarios, the MaxEnt model was utilized with presence data of subtropical mangrove and environmental data under different carbon emission scenarios. Kandelia obovata, Avicennia marina, and Aegiceras corniculatum in China were included for modeling, utilizing 22 bioclimate variables to predict mangrove distributions, and kernel density analysis was further employed to identify conservation and afforestation hotspots for mangroves. The results indicated that the number of days below 10 °C per year is the major climatic factor shaping the distribution of subtropical native mangroves, while precipitation exerts a relatively lower contribution. Under the low and medium emissions scenarios (SSP126 and SSP245), the northern boundary of the distribution of K. obovata remains stable, while A. marina and A. corniculatum are projected to shift northward to Fuzhou, Fujian Province (26.1°N) from 24.9°N by 2100. Under higher emissions scenarios (SSP370 and SSP585), the suitable habitat of K. obovata is expected to shift north to the mouth of the Yangtze River (31.9°N) from 26.8°N, and A. marina and A. corniculatum are projected to move northward to the coastal area of Ningde, Fujian Province (26.8°N) by 2100. In conclusion, with the increase in carbon emissions, there is a significant northward migration observed in subtropical native mangroves (p < 0.01), and suitable areas for restoration in China have been identified, with the northernmost region located in Zhejiang Province. The spatial and temporal patterns of range change predicted in this study provide valuable information for conservation and afforestation strategies for these ecologically important species.

International comparison of China's digitalization level and its enlightenment.

The global digital wave has flourished in recent years, and the digital technology revolution has emerged. Digitalization plays an undeniable role in promoting modern economic and social development in multiple aspects such as economy, society, innovation, public services and sustainable development, China's digitalization application is also developing rapidly. In order to better measure the current situation of China's digitalization development level, this paper constructs a comprehensive evaluation index system of digitalization development from four dimensions of talents in the digital field, digital infrastructure construction, digitalization innovation ability and international competitiveness, and tests the index system. The entropy method is used to measure the level of digitalization development between China and the United States, the United Kingdom, France and other major developed countries in the world, and the coefficient of variation method, kernel density estimation and Dagum Gini coefficient method are used to analyze the temporal and spatial characteristics and regional differences of digitalization development level of seven countries. This paper makes a comparative analysis between China and major developed countries from the historical perspective of the evolution of the techno-economic paradigm. With a view to summarizing and exploring from it and drawing on the advanced experiences accumulated by the developed countries over a long period of time, so as to provide China with useful insights and bases for further enhancing its digitalization development level.

Study on the distribution of average wind speeds at a mountainous bridge site for structural durability design

Conventional wind speed distribution methods (e.g., Rayleigh distribution and Weibull distribution) may not adequately capture complex characteristics of wind fields in mountainous areas. To address this problem, this study proposes a semi-parametric mix method for modeling the distribution of average wind speeds based on the combination of nonparametric Kernel Density Estimation (KDE) and Generalized Pareto Distribution (GPD). In the proposed method, KDE focuses on capturing the distribution in the main part of average wind speeds, while GPD aims at performing the distribution in terms of those in the extreme part. The segment point (i.e., the threshold) between KDE and GPD distributions is determined based on the combination of conditional mean excesses criterion and empirical rule. Meanwhile, the selection of modeling parameters should ensure that the mix distribution model is continuous and differentiable at the identified threshold point. Then, the commonly-used conditional probability model is further introduced to describe the wind direction distribution. Finally, a case study based on the measured 10-min average wind speeds at a mountainous bridge site is employed to demonstrate the effectiveness of the proposed method. The results indicate that: (1) the distribution of omnidirectional average wind speeds in the mountainous bridge site exhibits an obviously single-peak characteristic, while those considering wind directionality present a certain bimodal characteristic; (2) the proposed method can effectively describe wind speed distributions with different statistical characteristics, and the fitting accuracy outperforms the frequently-employed Weibull distribution model.

A window into the forest: post‐release behaviour of rehabilitated Bornean sun bears Helarctos malayanus euryspilus in Sabah, Malaysia

As the number of threatened wildlife species increases globally, captive rehabilitation and release of wild caught animals plays an increasingly significant role in conservation efforts, particularly where poaching threatens wild populations. Sun bear Helarctos malayanus populations are decreasing globally, with habitat loss and fragmentation and poaching as key threats to the species. This study aimed to assess post‐release behaviour of rehabilitated sun bears (n = 12) using GPS‐collar data to inform ongoing efforts to rehabilitate wild caught bears. We assessed spatial patterns of movement using kernel density estimates and utilisation distributions. We utilised an information theory (IT) and generalised linear modelling approach to assess temporal patterns of sun bear movement (n = 9). Post‐release home range estimates (mean = 56.68 km2, range:1.9–202.4) and behaviour varied widely between bears, with no significant difference in home range size between males (n = 3) and females (n = 6). Home ranges of most bears stabilised within the first month. Bears that were rehabilitated and released together did not spend a significant amount of time together following release. Bears were most active during daytime hours and most established a post‐release home range encompassing the riverside. The ultimate success of the releases is unknown, due to collar malfunction and damage, and the inaccessibility of the study terrain. We find that future releases of sun bears would benefit from soft‐release and in situ rehabilitation techniques. This study provides important insights into the complexity of bear reintroductions informing future rehabilitation and releases for threatened bear species.