Kernel Density Research Articles

Accelerated reduction in China's cropland fires against the background of policy enhancement

Cropland fires contribute significantly to the emission of air pollutants, with implications for the atmospheric environment, public health, and climate change. However, the development of cropland fires in response to straw burning policy interventions has not been fully explored. With the Moderate Resolution Imaging Spectroradiometer Collection 6 (MODIS C6) active fire products and China land-cover data (CLCD), counts and fire radiative power (FRP) of cropland fire spots were extracted in China from 2003 to 2022, followed by analyses of their spatiotemporal patterns and evolutionary characteristics at the provincial, regional, national, and grid (0.25° × 0.25°) levels and at monthly to annual scales via trend analysis, kernel density analysis, and geographic distribution analysis. The key findings include: (1) National policies banning straw burning reversed the changes in the quantity and intensity of China's cropland fires in 2013, with the frequency and FRP in 2022 declining by 58% and 62% compared with the 2014 peak. (2) Cropland fires were highly prevalent in Northeast and North China (NEC and NC), with significant regional differences, particularly in spring, summer and autumn. (3) A notable lag effect of burning ban policies was seen in Northeast China (NEC), where the increasing trends of the frequency and intensity of cropland fires were not reversed until 2017, followed by declines in frequency and FRP of 67% and 62%, respectively during 2017–2022. (4) Cropland fires showed an increasing concentration trend but with enormous regional differences, with an increasing trend of a “high-high” concentration pattern in NEC and a decreasing trend in NC. This study contributes to understanding the effectiveness of national policies that ban straw burning and support cropland fire management and policy-making.

Chance-Constrained Optimal Design of PV-Based Microgrids under Grid Blackout Uncertainties

A grid blackout is an intractable problem with serious economic consequences in many developing countries. Although it has been proven that microgrids (MGs) are capable of solving this problem, the uncertainties regarding when and for how long blackouts occur lead to extreme difficulties in the design and operation of the related MGs. This paper addresses the optimal design problem of the MGs considering the uncertainties of the blackout starting time and duration utilizing the kernel density estimator method. Additionally, uncertainties in solar irradiance and ambient temperature are also considered. For that, chance-constrained optimization is employed to design residential and industrial PV-based MGs. The proposed approach aims to minimize the expected value of the levelized cost of energy (LCOE), where the restriction of the annual total loss of power supply (TLPS) is addressed as a chance constraint. The results show that blackout uncertainties have a considerable effect on calculating the size of the MG’s components, especially the battery bank size. Additionally, it is proven that considering the uncertainties of the input parameters leads to an accurate estimation for the LCOE and increases the MG reliability level.

Comparing landscape characteristics to understand Malayan tapir (Tapirus indicus) conflicts: A case study in Negeri Sembilan

The IUCN Red List has listed the Malayan tapir (Tapirus indicus) as an endangered species in 2016, owing to its declining population trend. Human-caused forest degradation and encroachment on wildlife habitats drive this species to roam out of its natural range, resulting in human-tapir conflict (HTC). This study executes kernel density analysis on 78 HTC points recorded in Negeri Sembilan from 2013 to 2018 to determine the high and low HTC zones, and investigates the influence of five landscape variables on the occurrence of HTC. Values of five landscape variables in high and low HTC zones were extracted, and the significance of occurrence between these two zones was tested using an independent sample t-test. In terms of spatial distribution, Kuala Pilah and Jelebu covered high-conflict zones, while the lowest conflict zone was in Rembau. The t-test indicated that elevation (t = –3.551, p < 0.05) and slope (t = –2.012, p < 0.05) were significant between high and low conflict zones. Distance to the forest (t = 1.424, p > 0.05), distance to water (t = –0.106, p > 0.05), and distance to urban areas (t = –1.536, p > 0.05) were not significant for HTC. HTC tends to occur in low topographic areas, while tapirs outside of their natural habitat forage in close proximity to the forest and water bodies, but restrict their movement near human settlements. Hot Spot Analysis (Getis-Ord Gi*) on forest patches revealed an overlaping region between high HTC zones and low forest-patch contiguity, indicating that forest fragmentation may promote tapir conflicts. Hence, by systematically recording the conflict distribution in Negeri Sembilan, many actions could be taken into consideration to mitigate HTC in this state.

How do taxi drivers expose to fine particulate matter (PM2.5) in a Chinese megacity: a rapid assessment incorporating with satellite-derived information and urban mobility data

BackgroundTaxi drivers in a Chinese megacity are frequently exposed to traffic-related particulate matter (PM2.5) due to their job nature, busy road traffic, and urban density. A robust method to quantify dynamic population exposure to PM2.5 among taxi drivers is important for occupational risk prevention, however, it is limited by data availability.MethodsThis study proposed a rapid assessment of dynamic exposure to PM2.5 among drivers based on satellite-derived information, air quality data from monitoring stations, and GPS-based taxi trajectory data. An empirical study was conducted in Wuhan, China, to examine spatial and temporal variability of dynamic exposure and compare whether drivers’ exposure exceeded the World Health Organization (WHO) and China air quality guideline thresholds. Kernel density estimation was conducted to further explore the relationship between dynamic exposure and taxi drivers’ activities.ResultsThe taxi drivers’ weekday and weekend 24-h PM2.5 exposure was 83.60 μg/m3 and 55.62 μg/m3 respectively, 3.4 and 2.2 times than the WHO’s recommended level of 25 µg/m3. Specifically, drivers with high PM2.5 exposure had a higher average trip distance and smaller activity areas. Although major transportation interchanges/terminals were the common activity hotspots for both taxi drivers with high and low exposure, activity hotspots of drivers with high exposure were mainly located in busy riverside commercial areas within historic and central districts bounded by the “Inner Ring Road”, while hotspots of drivers with low exposure were new commercial areas in the extended urbanized area bounded by the “Third Ring Road”.ConclusionThese findings emphasized the need for air quality management and community planning to mitigate the potential health risks of taxi drivers.

Climbing mechanisms as a central trait to understand the ecology of lianas across the tropics

AbstractAimsLianas are a central component of tropical forests. However, how the type of climbing mechanisms is related to the functional and taxonomic diversity of lianas across the tropics, remains largely unresolved. Here, we tested two main hypotheses: (i) the functional diversity of lianas differs with climbing mechanism (active and passive) and (ii) the association between taxonomic diversity with contemporary climate, paleoclimate, forest structure and phylogeny differ between climbing mechanisms.LocationTropical forests.Time PeriodPresent.Major Taxa StudiedTerrestrial plants.MethodsWe assembled functional traits and the type of climbing mechanism for 702 liana species and used the World Checklist of Vascular Plants (WCVP v.2.0) to standardize species names, map geographical distribution and estimate taxonomic richness. We used kernel density n‐dimensional hypervolume to estimate the functional diversity of each type of climbing mechanism. We compared the environmental response of taxonomic richness of each type of climbing mechanism, active and passive, to the response of overall liana species richness. We assessed the magnitude and direction of the environmental response considering variables of climate, soil fertility and forest structure.ResultsWe found that active climbing exhibits a higher functional richness than passive climbing. Richness patterns of active and passive climbing mechanisms were mainly driven by contemporary climate, paleoclimate and phylogenetic relatedness. More importantly, paleoclimate was negatively associated with active climbing and positively associated with passive climbing.Main ConclusionsOur study highlights differences in functional diversity (richness, dispersion, evenness and originality) between active and passive climbing species, likely reflecting their distinct ecological strategies for resource use, stress tolerance and dispersal. Integrating taxonomic and functional diversity metrics with information about the type of climbing mechanism provides deeper insights into the ecology and response of lianas to climate change.

Velocity Dispersion of the Open Cluster NGC 2571 by Radial Velocities and Proper Motions

We use a kernel density estimator method to evaluate the stellar velocity dispersion in the open cluster NGC 2571. We derive the 3D velocity dispersion using both proper motions as extracted from Gaia Data Release 3 and single-epoch radial velocities as obtained with the instrument FLAMES at ESO's Very Large Telescope. The mean-square velocity along the line of sight is found to be larger than the one in the tangential direction by a factor of 6–8. We argue that the most likely explanation for such an occurrence is the presence of a significant quantity of unresolved binary and multiple stars in the radial velocity sample. Special attention should be paid to single-line spectroscopic binaries (SB1) since in this case we observe the spectral lines of the primary component only, and therefore the derived radial velocity is not the velocity of the binary system center of mass. To investigate this scenario, we performed numerical experiments varying the fractional abundance of SB1 in the observed sample. These experiments show that the increase of the mean-square radial velocity depends on the fractional abundance of SB1 to a power in the range [0.39, 0.45]. We used the 3D velocity dispersion obtained by the dispersions in the tangential directions and the assumption that the radial velocity dispersion is the same as a tangential one to estimate the virial cluster mass and the cluster mass, taking into account the gravitational field of the Galaxy and the nonstationarity of the cluster. These estimates are 650 ± 30 M ⊙ and 310 ± 80 M ⊙, respectively, in substantial agreement with the photometric cluster mass.

Dynamic geothermal resource assessment: Integrating reservoir simulation and Gaussian Kernel Density Estimation under geological uncertainties

This paper presents a dynamic geothermal resource assessment method that integrates reservoir simulation and Gaussian kernel density estimation (KDE). This approach addresses geological uncertainties by employing reservoir simulation techniques to model the fluid and heat flow under the condition of permeability heterogeneity. Incorporating probabilistic resource assessment through Gaussian KDE, the study quantifies uncertainties, estimating the probability density function (PDF) of ensemble results under conditions like thermal breakthrough thresholds, fixed reservoir lifespans, and target energy production. The demonstrations of assessment start with a simple homogeneous model. The results show that larger doublet well distances result in extended lifespan, higher final production well temperatures, and increased energy production. Brugge reservoir emphasizes the impact of heterogeneity and uncertainty on production outcomes, especially at smaller doublet well distances. Assessment of fluvial Egg model reveals that drilling in fluvial channels causes rapid thermal breakthrough. This result indicates that, to optimize reservoir performance, it is recommended to refrain from drilling doublet wells within high-permeability fluvial channels. Furthermore, it is worthy of mention that the Gaussian kernel is not always favored for KDE, particularly in scenarios involving non-Gaussian distribution ensembles. The proposed method, which integrates reservoir simulation and Gaussian KDE, enhances understanding of geological uncertainties and the intricate nature of geothermal reservoirs, facilitating more reliable and accurate resource assessments.

Interactive visual query of density maps on latent space via flow‐based models

AbstractVisual querying of spatiotemporal data has become a dominant mode in the field of visual analytics. Previous studies have utilized well‐designed data structures to speed up the querying of spatiotemporal data. However, reducing storage overhead while improving the querying efficiency of data distribution remains a significant challenge. We propose a flow‐based neural representation method for efficient visual querying. First, we transform spatiotemporal data into density maps through kernel density estimation. Then, we leverage the data‐driven modeling capabilities of a flow‐based neural network to achieve a highly latent representation of the data. Various computations and queries can be performed on the latent representation to improve querying efficiency. Our experiments demonstrate that our approach achieves competitive results in visually querying spatiotemporal data in terms of storage overhead and real‐time interaction efficiency.

Inventory, Dynamic Evolution, and Scenario Projections of Agricultural Carbon Emissions in Shandong Province, China

The reduction in agricultural carbon emissions (ACEs) in Shandong Province is essential to China’s carbon peak and carbon neutrality objectives. In this regard, we constructed an ACE inventory for Shandong Province at a resolution of 1 km × 1 km, integrating the emission factor method with geographic information system (GIS) technology. Building upon this, we explored the dynamic evolution patterns of ACEs using kernel density estimation and conditional probability density estimation. Additionally, long short-term memory networks were trained to predict ACEs under various scenarios. The results showed that: (1) ACEs in Shandong Province exhibited two stages of change, i.e., “rise and decline”. Notably, 64.39% of emissions originated from the planting industry. The distribution of emissions was closely correlated with regional agricultural production modes. Specifically, CO2 emissions were predominantly distributed in crop cultivation areas, while CH4 and N2O emissions were primarily distributed in livestock breeding areas. The uncertainty of the emission inventory ranged from −12.04% to 10.74%, mainly caused by emission factors. (2) The ACE intensity of various cities in Shandong Province is decreasing, indicating a decoupling between ACEs and agricultural economic growth. Furthermore, the emission disparities among different cities are diminishing, although significant spatial non-equilibrium still persists. (3) From 2022 to 2030, the ACEs in Shandong Province will show a continuous downward trend. By 2030, the projected values under the baseline scenario, low-carbon scenario I, and low-carbon scenario II will be 6301.74 × 104 tons, 5980.67 × 104 tons, and 5850.56 × 104 tons. The low-carbon scenario reveals greater potential for ACE reduction while achieving efficient rural economic development and urbanization simultaneously. This study not only advances the methodology of the ACE inventory but also provides quantitative references and scientific bases for promoting low-carbon, efficient, and sustainable regional agriculture.

A spatial analysis of risk factors associated with road collisions in Ciudad Juarez, Mexico and using a geographically weighted regression approach

This study examined changes in the spatial patterns and determinants of road collisions in Ciudad Juarez, Mexico in 2019 and 2020, encompassing the initial period of COVID-19 mobility restrictions. Kernel density estimation and local indicators of spatial association were used to compare collision distributions and identify significant clusters between years. A geographically weighted negative binomial regression model then generated local coefficients to analyze how demographic, socioeconomic, land use, and road network factors influenced collision probability spatially. Results show collisions decreased 13.18% in 2020 but clustered differently, validating restrictions' impact. Population aged 15–64 and industrial land uses significantly increased risk spatially, whereas commercial uses decreased. Lower socioeconomic conditions also correlated with higher risk. Younger populations presented varying collision likelihoods intra-urbanly. This research thus emphasizes how local contexts shape risk factors’ effects and informs data-driven safety policies accounting for place-specific issues. By adopting an explicitly spatial modeling approach, localized risk patterns were uncovered not detectable through traditional methods.

Ecological risk assessment and spatial–temporal differentiation of soil and water resources in the Hefei metropolitan area

There are important ways to solve the ecological risk problems of regional water resources and soil resources, and to promote the benign development of soil and water resources, involving scientific evaluation of the ecological risk of soil and water resources in Hefei metropolitan area, clarifying the intrinsic evolution law of ecological risk and identifying the characteristics of spatial and temporal variations. Based on the conceptual model of “ST-QS-RR”, the evaluation indicator system is constructed, the CRITIC method is used to assign weights, and the TOPSIS method, kernel density method, markov chain and resistance model are used to measure and analyse the spatial and temporal characteristics of ecological risk of soil and water resources, and to explore the main factors that cause ecological risk of soil and water resources. The results of the study show that: (1) Hefei metropolitan area and its cities show a steady decline and the characteristics of “high in the north and low in the south, high in the west and low in the east”. (2) Most of the subsystems in the Hefei metropolitan area and the cities show a decreasing trend, with its resistance factors mainly concentrated in the QS system. (3) There is club convergence in Hefei metropolitan area. When the type of adjacent domain is higher, the change of risk type is more sensitive.

Anomaly detection of wind turbine based on norm-linear-ConvNeXt-TCN

The supervisory control and data acquisition (SCADA) system of wind turbines continuously collects a large amount of monitoring data during their operation. These data contain abundant information about the operating status of the turbine components. Utilizing this information makes it feasible to provide early warnings and predict the health status of the wind turbine. However, due to the strong coupling between the various components of the wind turbine, the data exhibits complex spatiotemporal relationships, multiple state parameters, strong non-linearity, and noise interference, which brings great difficulty to anomaly detection of the wind turbine. This paper proposes a new method for detecting abnormal operating conditions of wind turbines, based on a cleverly designed multi-layer linear residual module and the improved temporal convolutional network (TCN) with a new norm-linear-ConvNeXt architecture (NLC-TCN). Initially, the NLC-TCN deep learning reconstruction model is trained with historical data of normal behavior to extract the spatiotemporal features of state parameters under normal operational conditions. Subsequently, the condition score of the unit is determined by calculating the average normalized root mean square error between the reconstructed data and actual data. The streaming peaks-over-threshold real-time calculation of the anomaly warning threshold, based on extreme value theory, is then used for preliminary fault monitoring. Moreover, by shielding the fault alarm for low wind speeds and implementing a continuous delay perception mechanism, issues related to wind speed fluctuations and internal and external interference are addressed, enabling early warning for faulty units. Finally, the effectiveness and reliability of the proposed method are validated through comparative experiments using actual offshore wind farm SCADA data. The performance of the proposed method surpasses that of other compared methods. Additionally, the results of the proposed method were evaluated using the uniform manifold approximation and projection dimensionality reduction technique and kernel density estimation.

Analysis of regional differences and evolution features for waterway transport efficiency in the Yangtze River Economic Belt considering undesired outputs

Waterway transport plays an increasingly important role in pursuing the sustainable development of the transportation industry and promoting regional economic development. Understanding the regional differences and evolution features of waterway transport efficiency (WTE) is a requisite for formulating waterway transport development policies. However, many studies of transport efficiency mainly focus on road, railway, and air transportation. Few studies have conducted an in-depth analysis of the WTE in the Yangtze River Economic Belt (YREB) from the perspective of considering undesired output. Adopting the Super slack-based measure (Super-SBM) model, Dagum Gini coefficient, and kernel density estimation as an analysis tool, this study explored the WTE’s region disparities and dynamic evolution features based on the data from 2011 to 2020 in YREB. The analysis results represent that: (1) there represented a fluctuating and slowly upward trend of waterway transport efficiency during the study period. (2) The midstream region has the highest waterway transport efficiency, followed by the upstream region, and last is the downstream region. (3) The regional disparities in waterway transport efficiency exist in YREB, and the primary source of these differences is the inter-regional difference. And (4) the polarization phenomenon and agglomeration characteristics of waterway transport efficiency in YREB are gradually weakened and decreased, respectively. In the end, this article summarized the implications of this study to improve the waterway transport efficiency and service capabilities in YREB.

A novel framework for high resolution air quality index prediction with interpretable artificial intelligence and uncertainties estimation

Accurate air quality index (AQI) prediction is essential in environmental monitoring and management. Given that previous studies neglect the importance of uncertainty estimation and the necessity of constraining the output during prediction, we proposed a new hybrid model, namely TMSSICX, to forecast the AQI of multiple cities. Firstly, time-varying filtered based empirical mode decomposition (TVFEMD) was adopted to decompose the AQI sequence into multiple internal mode functions (IMF) components. Secondly, multi-scale fuzzy entropy (MFE) was applied to evaluate the complexity of each IMF component and clustered them into high and low-frequency portions. In addition, the high-frequency portion was secondarily decomposed by successive variational mode decomposition (SVMD) to reduce volatility. Then, six air pollutant concentrations, namely CO, SO2, PM2.5, PM10, O3, and NO2, were used as inputs. The secondary decomposition and preliminary portion were employed as the outputs for the bidirectional long short-term memory network optimized by the snake optimization algorithm (SOABiLSTM) and improved Catboost (ICatboost), respectively. Furthermore, extreme gradient boosting (XGBoost) was applied to ensemble each predicted sub-model to acquire the consequence. Ultimately, we introduced adaptive kernel density estimation (AKDE) for interval estimation. The empirical outcome indicated the TMSSICX model achieved the best performance among the other 23 models across all datasets. Moreover, implementing the XGBoost to ensemble each predicted sub-model led to an 8.73%, 8.94%, and 0.19% reduction in RMSE, compared to SVM. Additionally, by utilizing SHapley Additive exPlanations (SHAP) to assess the impact of the six pollutant concentrations on AQI, the results reveal that PM2.5 and PM10 had the most notable positive effects on the long-term trend of AQI. We hope this model can provide guidance for air quality management.

Design Strategies to Improve Metro Transit Station Walking Environments: Five Stations in Chongqing, China

While transit-oriented development (TOD) has been widely adopted in urban design alongside the expansion of urban metro transit, the creation of pedestrian-friendly environments has often been overlooked during implementation. This has resulted in a lower walking advantage around metro transit stations. To address this issue and encourage walking and public transport use in metro transit station areas, this study undertook a quantitative comparative analysis of the pedestrian environment in five Chongqing metro transit station areas. The analysis focused on three key dimensions: “comprehensive evaluation”, “basic scale”, and “structural quality”. The comprehensive evaluation considered factors such as the pedestrian catchment area ratio, POI kernel density distribution, and crowd agglomeration. The basic scale dimension comprised floor area ratio, building density, pedestrian road density, and the quantity of station entrances and exits. Finally, structural quality factors included land use type mixing degree, POI function mixing degree, intersection connectivity, median street length, pedestrian route directness, and green view index. Based on these analyses, this study proposes a series of pedestrian environment design strategies including land use and transportation. The strategies for land use advocate for “developing compact and diverse land use”, “strengthening attraction of station center”, “positioning large projects on the edge”, “restricting private transportation capabilities”. The strategies for transportation consist of “increasing pedestrian road density”, “traffic calming organization”, “subdivision of road types”, and “three-dimensional pedestrian traffic system”. These strategies aim to create a more humanized and environmentally friendly pedestrian environment, proactively rise to the challenge of climate change, thereby cultivating sustainable urban development.

Spatiotemporal effect and influencing factors of the total factor energy efficiency: Evidence from the urban agglomerations in China

Amidst the ongoing global energy quandary, the imperative to augment energy efficiency emerges as a strategic response to counteract energy profligacy and propel sustainable development initiatives. This study aims to assist policymakers in gaining a more accurate understanding of the temporal and spatial complexity and dominant variables of total factor energy efficiency at the urban agglomeration level, thereby providing empirical insights for promoting a comprehensive improvement of the existing energy development framework. Based on the dynamic DEA model, this article utilizes an improved common frontier dynamic super-efficiency SBM model to conduct a quantitative analysis of total factor energy efficiency across 19 urban agglomerations in China. The influence factors of total factor energy efficiency are analyzed using spatial kernel density and geographical detectors at both temporal and factor levels. The research findings indicate that: (1) Total factor energy efficiency primarily fluctuates at a mid to low level, with a general decline to below 0.5 after 2018; (2) Significant regional heterogeneity is evident among Chinese urban agglomerations, displaying a “high in the northwest, low in the southeast” regional characteristic; (3) With time lag, the negative spatial spillover effect among regions further intensifies, becoming heterogeneous when the total factor energy efficiency of local urban agglomerations reaches 1.5 or above; (4) The singular and interactive effects of research funding become the main influencing factors of regional differentiation, and the integration of multiple factors is a key driver for enhancing total factor energy efficiency in the new era.

The spatiotemporal effects on ecosystem services supply-demand from industrial transformation and upgrading: Promotion or inhibition?

Traditional industrial development models have already caused an imbalance in ecosystem services supply and demand (ESSD). As a response strategy, industrial transformation and upgrading (ITU) are increasingly playing a vital role in regulating the relationship among ESSD. However, research on the interplay and impact of these two aspects remains insufficient. Therefore, a comprehensive analysis of the complex effects of ITU on ESSD is essential for industrial planning and environmental management. We developed a theoretical framework to interpret how ITU impacts ESSD, from the perspective of the natural-social-economic composite ecosystem. Using the Yangtze River Economic Belt (YREB) as an example, we employed the Dynamic Spatial Durbin model (DSDM) to reveal the spatiotemporal effects of ITU on ESSD for four distinct types. The results indicated that from 2010 to 2021, the total supply of ecosystem services in the YREB exceeded the demand. The kernel density curves of ESSD ratios related to food production, carbon sequestration, water conservation, and soil conservation all show a trend of peak shifting to the bottom right over time. Additionally, pronounced spatial heterogeneity was observed in the ESSD ratios, with a notable clustering trend over time. Furthermore, the analysis of spatiotemporal effects decomposition revealed that ITU had a detrimental influence on the equilibrium between the supply and demand of food production, with this adverse effect intensifying and manifesting a stronger spatial spillover as time progressed. Positive effects were observed in the supply and demand for carbon sequestration and water conservation, both demonstrating significant spatial spillover effects. Steady negative spatiotemporal effects were noted for soil conservation supply and demand. Finally, we clarified the principal pathways through which ITU impacts ESSD. It offers policy recommendations for industrial development and ecosystem stewardship to mitigate the ESSD imbalance.

CAPITALIZATION EFFECTS OF RIVERS IN URBAN HOUSING SUBMARKETS – A CASE STUDY OF THE YANGTZE RIVER

The study aims to investigate the heterogeneity of the Yangtze River’s impact on housing prices, using the data of 12,325 residential transactions within 8 kilometers of the Yangtze River in Wuhan, based on submarkets divided according to geographical location and buyer groups. The kernel density plots reveal that properties near the Yangtze River have the highest price and the lowest density, while properties further away from the river exhibit the opposite trend. Then the Spatial Generalized Additive Model and the Spatial Quantile Generalized Additive Model show the following results, respectively: (1) The Yangtze River has an influence range of roughly 5 kilometers on adjacent dwellings, with an average impact of 0.035%. However, within the chosen geographical interval, the impact rises from 1.582% to 2.072%. (2) The Yangtze River has the greatest impact on middle-priced houses, followed by high-priced houses, and the least impact on low-priced houses. (3) The Spatial Generalized Additive Model and the Spatial Quantile Generalized Additive Model have been proven to be effective at capturing spatial and temporal impacts on data. In conclusion, this article advises that the government should pay more attention to non-central locations with limited natural resources.

Movement and Home Range of Amur Soft-Shell Turtle (Pelodiscus maackii) in the Ussuri River, Heilongjiang Province, China.

Comprehensively understanding the spatial ecology and habitat preferences of endangered species is essential for population restoration and conservation. We investigated the home range and movement of the endangered Amur soft-shell turtle (Pelodiscus maackii) in the Ussuri River, Heilongjiang Province, Northeastern China. The study involved tracking 19 Amur soft-shell turtles from late June to mid-October, 2022, resulting in complete and partial home range size data for eight subadults and two adults, respectively. The primary analysis focused on eight subadults, and the models that best described daily movement were identified. We also explored the potential factors influencing home range size. The mean movement rate ranged from 39.18 ± 20.04 m/day to 72.45 ± 29.36 m/day and was positively correlated with the linear home range and water temperature. The most enlightening estimation of home range was derived from a 95% kernel density estimate, utilizing likelihood cross-validation smoothing while adhering to constraints delineated by the river boundaries. The average size of the home range was determined to be 1.02 hectares and displayed no correlation with body size. Subadults tended to establish well-defined home ranges over time, whereas defining home ranges for adults proved challenging. This research addresses a gap regarding the ecology of the Amur soft-shell turtle and provides a foundation for future conservation plans.

Morphology of Graphite Agglomerates Obtained by Spherical Agglomeration via Propagation-Based X-Ray Microtomography

The aim of this work was to develop a novel method for studying the 3D morphology of agglomerates obtained by spherical agglomeration. It has been found, that the combination of shock-freezing the samples in a mixture of ethanol and dry ice followed by an X-ray microtomography measurement leads to useful results. Hereby, the image quality for low absorbing material like the used graphite was enhanced by propagation-based X-ray microtomography, which results in phase contrast images. We also discuss our 3D image post-processing routine, which is used to determine the morphology parameters sphericity, fractal dimension and packing density. Furthermore, a two-dimensional kernel density estimation is used to calculate the joint probability density of agglomerate size and the morphology parameter. In future, this method will be used to determine the morphological behaviour of agglomerates during the different phases of spherical agglomeration.