Spatial Distribution Characteristics Research Articles

Spatial distribution characteristics and influencing factors of tourism resources based on point of interest data.

The agglomeration and dispersion of tourist attractions in space greatly affect the development of regional tourism resources and the consumption choice of tourism market. At present, the research on the spatial distribution characteristics of tourist attractions and their influencing factors mainly adopts induction and investigation, and there is a lack of effective statistical models for the research on the spatial distribution of tourist attractions and their influencing factors in some historical and cultural ancient cities. This paper uses Internet technology to obtain the spatial distribution data of tourist attractions in Shaoxing city, and uses mean nearest neighbor analysis, nuclear density analysis, imbalance index analysis, standard deviation ellipse and other spatial statistical analysis techniques and geographical detector methods to study the spatial distribution characteristics and influencing factors of tourist attractions in Shaoxing City. This paper studied the distribution characteristics of tourist attractions in Shaoxing city, such as spatial aggregation, distribution equilibrium and spatial orientation, and applied geographical detector to study the influencing factors of the spatial distribution of scenic spots. It was concluded that the spatial distribution pattern of scenic spots was affected by various factors such as natural environment, social environment and economic environment. The explanatory power of two-factor interaction is obviously stronger than that of single factor. The research results provide scientific basis for the planning, layout and development of tourist attractions in Shaoxing and its similar cities, and then promote the high-quality development of tourism in Shaoxing and its similar historical and cultural ancient cities.

An Evaluation of Ecosystem Quality and Its Response to Aridity on the Qinghai–Tibet Plateau

Exploring the response of spatial and temporal characteristics of ecological quality change to aridity on the Qinghai–Tibet Plateau (QTP) can provide valuable information for regional ecological protection, water resource management, and climate change adaptation. In this study, we constructed the Remote Sensing Ecological Index (RSEI) and Standardized Precipitation Evapotranspiration Index (SPEI) based on the Google Earth Engine (GEE) platform with regional characteristics and completely analyzed the spatial and temporal variations of aridity and ecological quality on the QTP in the years 2000, 2005, 2010, 2015, and 2020. Additionally, we explored the responses of ecological quality to aridity indices at six different time scales. The Mann–Kendall test, correlation analysis, and significance test were used to study the spatial and temporal distribution characteristics of meteorological aridity at different time scales on the QTP and their impacts on the quality of the ecological environment. The results show that the ecological environmental quality of the QTP has a clear spatial distribution pattern. The ecological environment quality is significantly better in the south-east, while the Qaidam Basin and the west have lower ecological environment quality indices, but the overall trend of environmental quality is getting better. The Aridity Index of the QTP shows a differentiated spatial and temporal distribution pattern, with higher Aridity Indexes in the north-eastern and south-western parts of the plateau and lower Aridity Indexes in the central part of the plateau at shorter time scales. Monthly, seasonal, and annual-scale SPEI values showed an increasing trend. There is a correlation between aridity conditions and ecological quality on the QTP. The areas with significant positive correlation between the RSEI and SPEI in the study area were mainly concentrated in the south-eastern, south-western, and northern parts of the QTP, where the ecological quality of the environment is more seriously affected by meteorological aridity.

Risk Assessment of Community-Scale High-Temperature and Rainstorm Waterlogging Disasters: A Case Study of the Dongsi Community in Beijing

With the advancement of urbanization and acceleration of global warming, extreme weather and climate events are becoming increasingly frequent and severe, and climate risk continues to rise. Each community is irreplaceable and important in coping with extreme climate risk and improving urban resilience. In this study, the Dongsi Community in the functional core area of Beijing was explored, and the risk assessment of high temperatures and rainstorm waterlogging was implemented at the community scale. Local navigation observations were integrated into a theoretical framework for traditional disaster risk assessment. The risk assessment indicator system for community-scale high-temperature and rainstorm waterlogging disasters was established and improved from a microscopic perspective (a total of 22 indicators were selected from the three dimensions of hazard, exposure, and vulnerability). Geographic Information Systems (GIS) technology was used to integrate geographic information, meteorological, planning, municipal, socioeconomic and other multisource information layers, thus enabling more detailed spatial distribution characteristics of the hazard, exposure, vulnerability, and risk levels of community-scale high temperatures and rainstorm waterlogging to be obtained. The results revealed that the high-risk area and slightly high-risk area of high-temperature disasters accounted for 13.5% and 15.1%, respectively. The high-risk area and slightly high-risk area of rainstorm waterlogging disasters accounted for 9.8% and 31.6%, respectively. The high-risk areas common to high temperatures and waterlogging accounted for 3.9%. In general, the risk of high-temperature and rainstorm waterlogging disasters at the community scale showed obvious spatial imbalances; that is, the risk in the area around the middle section of Dongsi Santiao was the lowest, while a degree of high temperatures or rainstorm waterlogging was found in other areas. In particular, the risk of high-temperature and rainstorm waterlogging disasters along Dongsi North Street, the surrounding areas of Dongsi Liutiao, and some areas along the Dongsi Jiutiao route was relatively high. These spatial differences were affected to a greater extent by land cover (buildings, vegetation, etc.) and population density within the community. This study is a useful exploration of climate risk research for resilient community construction, and provides scientific support for the planning of climate-adaptive communities, as well as the proposal of overall adaptation goals, action frameworks, and specific planning strategies at the community level.

Dark field photon scattering state measurements from bumps on a gold nanofilm

Abstract A dark and bright field (BF) imaging technique explored plasmonic phenomena arising from bumps on a gold nanofilm coated on a silica substrate. The study employs dark field (DF) polarization indirect microscopic imaging to investigate the scattering photon state and reveal the spatial distribution characteristics with distinct multipolar features, contrasting with those observed in the BF imaging configuration. Computational simulations utilizing the finite-difference time-domain method were conducted to understand this behaviour further and consider the experimental setup’s impact. The observations of varying multipolar scattering features with changes in the incident angle of the DF illumination suggest that the excitation of plasmonic effects differs for light beams incident at different angles.

Spatial and Temporal Evolution Characteristics of the Ecosystem Service Value along the Beijing–Hangzhou Grand Canal

The study of the spatiotemporal evolution characteristics of ecosystem service values (ESVs) is an important basis for the coordinated development of the regional nature, economy, and society and the optimization of the ecological environment. The ecological zone is an important component of the Beijing–Hangzhou Grand Canal cultural belt. Ecosystem services are a concrete manifestation of land use structure and function. A thorough study of the value of ecosystem services in areas along the Beijing–Hangzhou Grand Canal is important for promoting the long-term and stable sustainable development of the regional economy. Based on a revised equivalent factor table, this study selected land use data from 1991, 2006, and 2021 to analyze the temporal and spatial evolution characteristics of ESVs along the Beijing–Hangzhou Grand Canal. The results show that (1) the ESVs along the Grand Canal first increased and then decreased from 1991 to 2021. The reason for this is the change in land use along the Beijing–Hangzhou Grand Canal. Specifically, the conversion of land use types from farmland to water areas contributed to the increase in the value of ecosystem services, while the conversion of farmland and grassland into construction land led to a decrease in the service value of the region. (2) the value of individual ecosystem services along the Beijing–Hangzhou Grand Canal from 1991 to 2021 varied greatly. The ESV provided by hydrological regulation was the largest and the ESV provided by maintenance nutrients was the smallest. (3) the areas along the Beijing–Hangzhou Grand Canal exhibited a specific pattern in terms of the value of ecosystem services, with the regions centered in Beijing and Tianjin showing relatively low values, while the middle section of the Grand Canal demonstrated relatively high ESV. According to the spatial and temporal distribution characteristics and the leading factor for the changes in ESVs, appropriate policies can be formulated in respective regions to implement ecological protection and land use planning, thereby providing a reference for the adaptation and restoration strategies of the ecosystem along the Grand Canal.

Spatial and temporal analysis and forecasting of TB reported incidence in western China

ObjectiveTuberculosis (TB) remains an important public health concern in western China. This study aimed to explore and analyze the spatial and temporal distribution characteristics of TB reported incidence in 12 provinces and municipalities in western China and to construct the optimal models for prediction, which would provide a reference for the prevention and control of TB and the optimization of related health policies.MethodsWe collected monthly data on TB reported incidence in 12 provinces and municipalities in western China and used ArcGIS software to analyze the spatial and temporal distribution characteristics of TB reported incidence. We applied the seasonal index method for the seasonal analysis of TB reported incidence and then established the SARIMA and Holt-Winters models for TB reported incidence in 12 provinces and municipalities in western China.ResultsThe reported incidence of TB in 12 provinces and municipalities in western China showed apparent spatial clustering characteristics, and Moran’s I was greater than 0 (p < 0.05) over 8 years during the reporting period. Among them, Tibet was the hotspot for TB incidence in 12 provinces and municipalities in western China. The reported incidence of TB in 12 provinces and municipalities in western China from 2004 to 2018 showed clear seasonal characteristics, with seasonal indices greater than 100% in both the first and second quarters. The optimal models constructed for TB reported incidence in 12 provinces and municipalities in western China all passed white noise test (p > 0.05).ConclusionsAs a hotspot of reported TB incidence, Tibet should continue to strengthen government leadership and policy support, explore TB intervention strategies and causes. The optimal prediction models we developed for reported TB incidence in 12 provinces and municipalities in western China were different.

Analysis of the spatio-temporal distribution of Chinese professional football clubs and influential factors based on empirical evidence of clubs

The frequent relocation of professional football clubs is a phenomenon unique to Chinese professional football, which has long attracted extensive attention from policy makers and scholars. Using data on spatial geography and competitive levels between 1994 and 2021, this research explores the spatial distribution and migration characteristics of top professional football clubs in China, in which GIS spatial analysis and exploratory spatial data analysis (ESDA) were used to analyze the influence of the regional economic, demographic, and developmental levels of the sports industry and institutional supply on the distribution and migration of clubs. The results indicate the following: (1) the overall spatial distribution of professional football clubs in China shows the pattern of “more in the east and less in the west”, and the degree of club concentration gradually increases over time; (2) the migration frequency and downward migration of professional football clubs in China gradually decreased during the period of C League and CSL, and the overall situation tends to be stable; and (3) in the early stages of development of professional leagues, changes in ownership and local policy supply had a significant impact on the distribution and migration of professional clubs. As time progressed, the top-down design of institutional arrangements changed, and the regional economic, demographic, and developmental levels of the sports industry gradually became the essential determinants of the distribution and migration of professional football clubs. In the future, Chinese professional football clubs should focus on rationalizing the advantages of the regional economy, demography, and sports industry as well as reducing their reliance on short-term resources, such as corporate investment and policy benefits.

Spatiotemporal evolution and driving factors analysis of fractional vegetation coverage in the arid region of northwest China

The arid region of northwest China (ARNC) is the most ecologically fragile region in China, and is characterized by harsh natural conditions, severe soil erosion, and poor soil fertility. Understanding long-term vegetation changes in this region is critical for effective environmental monitoring and climate change adaptation. Fractional vegetation coverage (FVC) is a key parameter for characterizing the ecological conditions of the ARNC. However, the reliance on low-resolution FVC and NDVI data due to the lack of medium-resolution data has limited our understanding of the environmental dynamics in this region. Therefore, this study addressed this gap by utilizing Landsat data to generate FVC data, enabling a detailed investigation of the spatial-temporal variations and driving factors of vegetation in the ARNC from 2000 to 2020. The results indicated the following: (1) The FVC was generally low, with an average of 0.191. The FVC was greater in the northwest and lower in the southeast in terms of spatial distribution features. The trend of FVC change in ARNC showed significant spatial variability, with degradation outweighing improvement. (2) The coefficient of variation of FVC was 0.377, indicating significant temporal fluctuations, with more stable conditions in the northwest than in the southeast. (3) The spatial differentiation of the FVC in ARNC was primarily driven by land cover types, evapotranspiration, and precipitation, with explanatory powers exceeding 30 % each. This study is significant because it provides a comprehensive understanding of vegetation dynamics in one of China's most vulnerable regions, offering critical insights for ecological restoration, desertification control, and sustainable development. The findings underscore the importance of targeted ecological governance to address the challenges posed by environmental degradation in the ARNC.

Disaster mechanism of large-diameter shield tunnel segments under multi-source load coupling: A case study

The deflection squeezing of the shield shell and the eccentric jack thrust significantly impact the segment dislocation and damage. Based on summarizing the derivative relationship between engineering factors and disasters, this study established a 3D numerical calculation model under the multi-source load coupling. Then, this study explored the displacement and dislocation distribution of segments, as well as their derivative relationship with bolt failure. Furthermore, this study analyzed the spatial distribution and evolution characteristics of segment damage. Further revealing the disaster mechanism and proposing the engineering treatment measures. The research results indicate that as the deflection angle increases, the vertical relative compression deformation of the segment that is detaching from the shield tail (segment-DFST) becomes more significant. The plastic strain of the internal bolts inside the two segments in direct contact with the shield shell is mainly related to the radial and longitudinal dislocation. The increase in the deflection angle will lead to a more significant increase in the adjacent dislocation on both sides and the internal dislocation. The plastic strain of the internal bolts of the segment that is completely in the shield shell (segment-CSS) is more influenced by joint opening and longitudinal dislocation under the lower eccentric compression (LEC) state. The plastic strain of the internal bolts in segment-DFST is more influenced by radial dislocation and joint opening under the LEC state. The tension damage of the segments mainly occurs on both sides of the circumferential joint and the lower part of the segment-CSS. The compression damage of the segments is mainly distributed at the top and bottom of the segment-CSS. The eccentric distribution of jack thrust has the most significant impact on the block damage of the lower part of the segment-CSS. With the increase of the deflection angle, the compression damage and distribution range of the blocks at the bottom of the segment-DFST increase sharply. The compression damage and distribution range of the segment-CSS gradually spread from the upper and lower parts to the middle of the segment. The increase in deflection angle will promote the influence of the thrust in LEC state on the compression damage of the lower block of the segment-CSS, and on the tension damage of the upper block of the segment- DFST.

G[formula omitted]BFNN: Generalized geodesic basis function neural network

Real-world data is typically distributed on low-dimensional manifolds embedded in high-dimensional Euclidean spaces. Accurately extracting spatial distribution features on general manifolds that reflect the intrinsic characteristics of data is crucial for effective feature representation. Therefore, we propose a generalized geodesic basis function neural network (G2BFNN) architecture. The generalized geodesic basis functions (G2BF) are defined based on generalized geodesic distances. The generalized geodesic distance metric (G2DM) is obtained by learning the manifold structure. To implement this architecture, a specific G2BFNN, named discriminative local preserving projection-based G2BFNN (DLPP-G2BFNN) is proposed. DLPP-G2BFNN mainly contains two modules, namely the manifold structure learning module (MSLM) and the network mapping module (NMM). In the MSLM module, a supervised adjacency graph matrix is constructed to constrain the learning of the manifold structure. This enables the learned features in the embedding subspace to maintain the manifold structure while enhancing the discriminability. The features and G2DM learned in the MSLM are fed into the NMM. Through the G2BF in the NMM, the spatial distribution features on manifold are obtained. Finally, the output of the network is obtained through the fully connected layer. Compared with the local response neural network based on Euclidean distance, the proposed network can reveal more essential spatial structure characteristics of the data. Meanwhile, the proposed G2BFNN is a generalized network architecture that can be combined with any manifold learning method, showcasing high scalability. The experimental results demonstrate that the proposed DLPP-G2BFNN outperforms existing methods by utilizing fewer kernels while achieving higher recognition performance.

Spatiotemporal Variations in MODIS EVI and MODIS LAI and the Responses to Meteorological Drought across Different Slope Conditions in Karst Mountain Regions

Based on monthly MODIS EVI and LAI data from 2001 to 2020, combined with the Standardized Precipitation Evapotranspiration Index (SPEI), this study employs Theil–Sen trend analysis, Mann–Kendall (MK) test, Hurst index analysis, and correlation analysis to comparatively analyze the overall vegetation trends, spatial distribution characteristics, and future trends of different vegetation types in Guizhou Province under varying slope conditions. The study also explores the response of vegetation to SPEI at different time scales across different slopes. The results indicate the following: (1) From 2001 to 2020, the average values of EVI (0.34%/a) and LAI (1.4%/a) during the growing season exhibited an increasing trend, with the improved vegetation areas primarily concentrated in the western region of Guizhou, while the degradation areas were mainly located in the central and eastern regions. (2) Under different slope conditions, EVI generally showed slight improvement, while LAI exhibited significant improvement, with dry-lands experiencing the largest changes. Future trends indicate continuous improvement, but the proportion of vegetation improvement area decreases with increasing slope. When the slope is less than 5°, the proportion of vegetation improvement area is the highest. (3) The positive correlation between EVI, LAI, and SPEI at different time scales is stronger than the negative correlation, with the strongest correlations observed when the slope is less than 5°. When the slope exceeds 35°, the relationship between vegetation and drought response is almost unaffected by the slope. These findings provide a scientific basis for vegetation growth monitoring and the study of climate change and vegetation interactions in Guizhou Province.

Quantifying species biases among multidata sources on illegal wildlife trade and its implications for conservation.

Unsustainable wildlife consumption and illegal wildlife trade (IWT) threaten biodiversity worldwide. Although publicly accessible data sets are increasingly used to generate insights into IWT, little is known about their potential bias. We compared three typical and temporally corresponding data sets (4204 court verdicts, 926 seizure news reports, and 219 bird market surveys) on traded birds native to China and evaluated their possible species biases. Specifically, we evaluated bias and completeness of sampling for species richness, phylogeny, conservation status, spatial distribution, and life-history characteristics among the three data sets when determining patterns of illegal trade. Court verdicts contained the largest species richness. In bird market surveys and seizure news reports, phylogenetic clustering was greater than that in court verdicts, where songbird species (i.e., Passeriformes) were detected in higher proportions in market surveys. The seizure news data set contained the highest proportion of species of high conservation priority but the lowest species coverage. Across the country, all data sets consistently reported relatively high species richness in south and southwest regions, but markets revealed a northern geographic bias. The species composition in court verdicts and markets also exhibited distinct geographical patterns. There was significant ecological trait bias when we modeled whether a bird species is traded in the market. Our regression model suggested that species with small body masses, large geographical ranges, and a preference for anthropogenic habitats and those that are not nationally protected were more likely to be traded illegally. The species biases we found emphasize the need to know the constraints of each data set so that they can optimally inform strategies to combat IWT.

Numerical and experimental study on the aerodynamic noise characteristics of 600-km/h high-speed maglev trains

Abstract High-speed maglev trains represent a new mode of transportation; however, their 600-km/h traveling speed causes serious aerodynamic noise problems, with very little research on the aerodynamic noise of such trains. To clarify the aerodynamic noise source and radiation characteristics of high-speed maglev trains, both large eddy simulations and wind tunnel experiments with a 1:8 scale three-train model were conducted to obtain the aerodynamic noise source spectrum and spatial distribution characteristics of the maglev train at different speeds; the contribution of the dipole noise source was also analyzed. The results revealed that the train shape has a significant impact on the frequency distribution of the far-field aerodynamic noise below 1 kHz, with the highest sound power concentrated on the train head, especially in the areas around the track joint and streamlined sections. The surface dipole sound source energy increases linearly with speed and is mainly distributed at the bottom of the train head and tail train body. A short-streamlined train exhibited the highest ratio, especially in the trailing edge area, up to 1.42×1011 dB. The far-field sound pressure level of the short-streamlined maglev train is significantly higher, with a maximum difference of about 10 dB(A). The distribution pattern of far-field sound pressure level is influenced by the train type, reflected in the tail train and wake area. Different frequency intervals have different relationships between the acoustic pressure level and frequency; long-streamlined models may enhance broadband features in the sound field distribution of high-speed maglev trains. These findings could be used to guide the aero-acoustic design of maglev trains with a designed speed of 600 km/h.

Long-term grazing changed the spatial distributions of dominant species in typical steppe of Inner Mongolia

Dominant species occupy a pivotal role in plant community, influencing the structure and function of the ecosystem. The spatial distributions of dominant species can react to the effect of different grazing intensities, thereby reflecting their tolerance and adaptive strategies toward grazing. In this study, geostatistical methods were mainly used to study the spatial distribution characteristics of Stipa krylovii Roshev. and Leymus chinensis (Trin.) Tzvel. species at two interval scales (quadrat size 5 m × 5 m, 10 m × 10 m) and two treatments (free grazing, FG, 1.66 sheep·ha− 1·a− 1; control, CK, 0 sheep·ha− 1·a− 1) in typical steppe of Inner Mongolia. A systematic sampling method was used in each 100 m × 100 m representative sample plots to obtain the height, coverage, and density of all species in the community. The results showed that grazing altered the concentrated distribution of S. krylovii and the spatial mosaic distribution pattern of S. krylovii and L. chinensis while having no effect on the spatial clumped distribution of L. chinensis. It also found that the spatial distributions of dominant species are primarily affected by structural factors, and random factors such as long-term grazing led to a transition of S. krylovii from a concentrated distribution to a small patchy random pattern should not be overlooked. Our findings suggest that long-term grazing alters the spatial distribution pattern of dominant species and that adaptive strategies may be the key for maintaining the dominant role of structural factors.

The dynamic evolution characteristics of PM2.5 concentrations and health risk assessment during typical forest fires in China

In the context of climate change, increasingly frequent wildfire events are exacerbating the air quality crisis in China and have become a significant source of atmospheric PM2.5. This study selected 12 major forest wildfire events in China from 2018 to 2023. Using atmospheric pollutant and component concentration data, as well as meteorological data, the study employed MK trend analysis, forward trajectory simulation, potential source contribution function (PSCF), and health risk indices to investigate the evolution patterns of PM2.5 concentrations before and after each wildfire event. The study explored the driving role of meteorological factors in this evolution process and quantitatively analyzed the spatial distribution characteristics of health risks in surrounding cities post-wildfire. The results indicated that: 1) PM2.5 levels changed significantly before and after each wildfire event, although the proportion of components remained relatively stable. 2) PM2.5 concentrations exhibited varying characteristics within different buffer zones of the wildfire areas. In the forest wildfire events in Foshan, Guangdong Province, and Jinzhong, Shanxi Province, the average PM2.5 concentrations within the 50 km buffer zones reached approximately 90 μg/m3, about 4–5 times the multi-year average for these areas. 3) Wildfire emissions were mainly influenced by meteorological and topographical factors. Wind field dependence diagrams showed that PM2.5 tended to accumulate in all wind directions, particularly at lower wind speeds of 0–5 m/s 4) Potential source analysis revealed that PM2.5 emissions from wildfires posed significant risks to surrounding areas. Multiple events had high-risk areas (WPSCF ≥0.8), and among the cities in these high-risk areas, the health risk value (ΔM) for Ya'an City reached an extremely high 85.1. This study provides a theoretical basis for policymakers to develop locally tailored wildfire management policies, ensuring the protection of the public's right to breathe clean air.

Global feature capture and spatially-aware neural networks for predicting CO2-flooding performance in heterogeneous low-permeable reservoirs

CO2-flooding can enhance crude oil recovery rates by reducing the viscosity of crude oil, improving the mobility ratio between crude oil and water, and leveraging the swelling effects of crude oil components. Furthermore, it facilitates the sequestration of greenhouse gases, leading to the realization of carbon capture, utilization, and storage (CCUS), which holds significant strategic importance for mitigating climate change, safeguarding ecological environments, and achieving sustainable development. Consequently, predicting CO2-flooding performance in heterogeneous low-permeability reservoirs holds crucial significance in sustainable energy production. However, the drawbacks of numerical simulation techniques, such as extensive computational resource consumption and prolonged simulation durations, have prompted extensive research into the utilization of artificial intelligence technologies. Prevalent approaches often treat the problem as a time series issue, which proves inadequate for reservoirs lacking historical production data. Furthermore, this methodology is unable to fully exploit the complex implicit relationships between various factors and the prediction target. Our study proposes a novel method for predicting the performance, which stands apart from previous approaches by not treating the problem as a time series issue, eliminating the need for historical production data for each prediction, and can predict various production variables such as gas production, oil production, water production, etc. in a universal manner. We combine enhanced MultiScale Non-local Neural Networks with ConvNet to handle sparse well network matrices with injection-production parameters. Additionally, we employ MultiScale convolutional networks with spatial attention mechanism to address formation permeability fields with spatial distribution features. Experimental results demonstrate the superiority of our approach in accuracy, achieving a prediction accuracy of over 97%. This research provides an innovative and flexible method, making significant contributions to the intelligentization and sustainable development of the petroleum industry, aligned with global efforts to both reduce carbon emissions and neutralize the existing ones.

Long-term gully dynamics over cropland in the black soil area of China based on systematic sampling

Understanding the large-scale spatial distribution characteristics of gully development dynamics, particularly over long periods, can help in accurately identifying areas with severe gully erosion and is thus crucial for targeted gully prevention and rehabilitation efforts. This study aimed to investigate the long-term dynamics of permanent gullies on cropland in the Songnen typical black soil region (SBR), which is the most important commercial grain production area in China, covering an area of 212,000 km2. For this purpose, 998 sampling units were selected using the systematic sampling method. Based on Corona KH-4B images from 1970 and Google images from 2018, all gullies within each sampling unit were visually interpreted. In the past 50 years, the number of permanent gullies on cropland in SBR increased by 24.55 %, but the average linear density of gullies in the cropland sampling unit decreased from 0.47 to 0.45 km·km−2 because the average lengths of gullies decreased from 285.90 m to 233.15 m. While 50.50 % of gullies found in 1970 disappeared from the images of 2018, more gullies formed and were widespread in the east part of the study area characterized by a topography of rolling hills. In particular, 66.70 % of gullies were active, including all newly formed gullies and 16.28 % of long-standing gullies (LSGs), and the average gully retreat rate of LSGs was 0.53 m·yr−1, with active LSGs grew at a rate of 3.26 m·yr−1 on average, indicating the severity of gully erosion and limited effectiveness of efforts made to control gully erosion in the black soil region of China. The threat of gully erosion is more serious in the eastern part of SBR, with 44.69 % of cropland suffering gully erosion and 66.14 % of the gullies being active. Moreover, the trend of increased gully erosion in the centre and the west requires further attention. The findings highlight the need for studies on more effective and targeted measures for gully control and their wide application in order to ensure the sustainable utilization of the valuable black soil resources.

Changing seroprevalence of schistosomiasis japonica in China from 1982 to 2020: A systematic review and spatial analysis.

Schistosomiasis is a global public health issue. In China, while the seroprevalence of Schistosomiasis japonica has currently reduced to a relatively low level, risk of infection still exists in certain areas. However, there has been a lack of comprehensive research on the long-term trends of national seroprevalence, changes across age groups, and characteristics in spatial distribution, which is crucial for effectively targeting interventions and achieving the goal of eliminating schistosomiasis by 2030. Our study aimed to address this gap by analyzing the long-term trends of Schistosomiasis japonica seroprevalence in China from 1982 to 2020 based on the data from diverse sources spanning a period of 39 years. Seroprevalence data were collected from literature databases and national schistosomiasis surveillance system. Meta-analysis was conducted to estimate the seroprevalence. Joinpoint model was used to identify changing trend and inflection point. Inverse distance weighted interpolation was used to determine the spatial distribution of seroprevalence. The seroprevalence decreased from 34.8% in 1982 to 2.4% in 2020 in China. Before 2006, the seroprevalence was higher in the middle age group, and a pattern of increasing with age was observed afterwards. The areas with high seroprevalence existed in Dongting Lake, Poyang Lake, Jianghan Plain, the Anhui branch of the Yangtze River and some localized mountainous regions in Sichuan and Yunnan provinces. There was a significant decline in the seroprevalence of Schistosomiasis japonica from 1982 to 2020 in China. Nevertheless, schistosomiasis has not been eradicated; thus, implementing precise and personalized monitoring measures is crucial for the elimination of schistosomiasis, especially in endemic areas and with a particular focus on the elderly.

A novel histopathological feature of spatial tumor-stroma distribution predicts lung squamous cell carcinoma prognosis.

We used a mathematical approach to investigate the quantitative spatial profile of cancer cells and stromain lung squamous cell carcinoma tissues and its clinical relevance. The study enrolled 132 patients with 3-5 cm peripheral lung squamous cell carcinoma, resected at the National Cancer Center Hospital East. We utilized machine learning to segment cancer cells and stroma on cytokeratin AE1/3 immunohistochemistry images. Subsequently, a spatial form of Shannon's entropy was employed to precisely quantify the spatial distribution of cancer cells and stroma. This quantification index was defined as the spatial tumor-stroma distribution index (STSDI). The patients were classified as STSDI-low and -high groups for clinicopathological comparison. The STSDI showed no significant association with baseline clinicopathological features, including sex, age, pathological stage, and lymphovascular invasion. However, the STSDI-low group had significantly shorter recurrence-free survival (5-years RFS: 49.5% vs. 76.2%, p < 0.001) and disease-specific survival (5-years DSS: 53.6% vs. 81.5%, p < 0.001) than the STSDI-high group. In contrast, the application of Shannon's entropy without spatial consideration showed no correlation with patient outcomes. Moreover, low STSDI was an independent unfavorable predictor of tumor recurrence and disease-specific death (RFS; HR = 2.668, p < 0.005; DSS; HR = 3.057, p < 0.005), alongside the pathological stage. Further analysis showed a correlation between low STSDI and destructive growth patterns of cancer cells within tumors, potentially explaining the aggressive nature of STSDI-low tumors. In this study, we presented a novel approach for histological analysis of cancer tissues that revealed the prognostic significance of spatial tumor-stroma distribution in lung squamous cell carcinoma.

Research and analysis of urban-rural residential carbon emissions in China

Residential carbon emissions are an important component of anthropogenic carbon emissions. a significant increase in residential carbon emissions has become a reality under the global urbanization process. In this context, this paper built a feature combination value model based on NPP-VIIRS nighttime light remote sensing data, and divided urban-rural areas through breakpoint analysis method and reference comparison method. Then, explored the characteristics and differences of residential carbon emissions and per capita residential carbon emissions in nine different levels of cities in 2019 from the perspective of urban-rural areas. The results indicate that the residential carbon emissions and per capita residential carbon emissions shows the spatial distribution characteristics of first tier cities>second tier cities>third tier cities. Among them, the residential carbon emissions in Beijing, Guangzhou, Nanjing, Taiyuan show a distribution pattern of urban>urban-rural fringe>rural. The residential carbon emissions in Shijiazhuang, Wuxi, Xiangyang, Zunyi, Huai’an show a distribution pattern of rural>urban-rural fringe>urban. The per capita residential carbon emissions of urban areas are relatively low, while the per capita residential carbon emissions of rural areas are relatively high, show a distribution pattern of rural>urban-rural fringe >urban. The results can help the Chinese government balance the needs of urban-rural development in different levels of cities, so as to formulate targeted carbon emission reduction policies and achieve low-carbon goals.