Spatial-temporal Distribution Characteristics Research Articles

Study on the evolution of heterogeneous double-cavity induced by near-wall and the fluctuation characteristics of load field

It is well known that the collapse of heterogeneous multi-cavity near the wall will induce the fluctuation of the load field. To address this problem, the Lattice Boltzmann Method (LBM) is applied to model the three-phase coupling between gas-liquid-solid. The objective is to investigate the evolution of heterogeneous double bubbles and the spatial-temporal distribution characteristics of wall loads induced near the wall. In this study, the pseudopotential Multi-Relaxation-Time Lattice Boltzmann Model (MRT-LBM) and the Carnahan-Starling Equation of State (C-S-EOS) with an extended format for the external force term are used. The effects of the distance of the bubble to the wall, the pressure differences between the inside and outside of the bubble, and the relative size of the bubble on the dynamic evolution and the load distribution characteristics of heterogeneous multi-bubbles near the wall are investigated in order to determine the influence of these factors. Under a two-dimensional pressure field, the collapse process of double cavitation bubbles is visualized. Through the flow field, the morphological changes of the cavitation bubble collapse near the wall are also described. Various parameters are found to have an influence on the evolution of double cavitation bubbles near the wall and the resulting load field. The study employs the Lattice Boltzmann Method and the Potential Model for the analysis of the heterogeneous bubble collapses in the near wall region.

Assessing the spatial-temporal impacts of underlying surfaces on 3D thermal environment: A field study based on UAV vertical measurements

The composition of underlying surfaces has a significant impact on the thermal environment within urban canopies. Current research predominantly focuses on surface temperature (Ts) and near-surface air temperature (Ta) at the pedestrian scale. However, studies on the Spatial-temporal distribution characteristics of the three-dimensional (3D) thermal environment within urban canopies remain scarce. This study selected 7 typical underlying surfaces and investigated the Spatial-temporal distribution characteristics of Ts and vertical Ta from 1.5 to 200 m by using unmanned aerial vehicle (UAV) and fixed measurement techniques. The results indicated that the largest differences in 3D thermal environment characteristics across different underlying surfaces occurred at 14:00, with the smallest at 05:30. Each underlying surface exhibited distinct day-night variations in their impacts on the 3D thermal environment, with the water body showing significant day-night difference. The dense forest positively regulated the urban thermal environment, but at night, it slightly adversely effected the thermal environment at the pedestrian scale. The bare soil had the second most adverse effect on the 3D thermal environment during the day, following the impervious pavement; thus, exposure of the land should be minimized in urban environments. There was a significant positive correlation between Ts and Ta at different heights (P < 0.05). At 05:30, 14:00, and 21:00, the heights of vertical influence of Ts on Ta were 2 m, 50 m, and 32 m, respectively. These results provide important references for urban environmental managers to mitigate the 3D urban heat island (3D UHI).

Assessment of Ecological Quality and Analysis of Influencing Factors in Coal-Bearing Hilly Areas of Northern China: An Exploration of Human Mining and Natural Topography

The Changhe Basin is located in the earth–rock mountainous area in southeastern Shanxi, China, and represents a characteristic northern coal-bearing hilly area. The terrain is complex, and the area is rich in coal mines. It plays an indispensable role in maintaining ecological balance and sustainable development in North China. To investigate the changes in ecological quality in the Changhe Basin, as well as the impact of human mining activities and natural topography on ecological quality, this study constructs the Remote Sensing Ecological Index (RSEI) based on Landsat remote sensing images from 2001, 2008, 2015, and 2022, undertaking an analysis of the spatial–temporal distribution characteristics of the ecological quality and its changing trends over the past 20 years. Additionally, spatial autocorrelation distribution features are revealed using Moran’s I. The exploration extends to examining the relationship between mining activities and the surrounding ecological quality. Subsequently, we study the relationship between Topographic Wetness Index (TWI) and RSEI. The results indicate the following: (1) On the temporal scale, the average proportion of RSEIs categorized as excellent and good from 2001 to 2022 is 46.78%. Types showcasing stable ecological conditions average 52.49%. The level of overall ecological quality of the basin has remained consistently high. On the spatial scale, the western part of the Changhe River, particularly in mountainous areas, exhibits higher ecological quality. Poorer areas concentrate in Chuandi Town in the southwestern part, and are significantly impacted by mining activities. The eastern region manifests areas undergoing either rapid or gradual degradation. (2) The four-phase Moran index results reveal a robust positive correlation in the spatial distribution of ecological quality within the basin. High–High and Low–Low clusters dominate, while High–Low and Low–High distributions are scattered. (3) Mining activities exert a discernible impact on the surrounding ecological quality. As the distance from the buffer zone outside the mining area increases, RSEI gradually decreases. The impact level exhibits an initial increase and subsequent decrease from 2001 to 2022.

A dataset of remote sensing coastline changes of Guangxi, China, during 1990–2020

As an important gateway at the intersection of the 'Belt and Road' initiative, Guangxi is situated along the Beibu Gulf to the south endowed with abundant coastal port infrastructure and marine biological resources. The remote sensing monitoring of the location change and type transformation along its coastline provides valuable scientific data support for the sustainable development and management of the coastal zone. The study uses Landsat TM/OLI data spanningz five time periods from 1990 to 2020 to delineate the coastline of Guangxi. We use ArcGIS 10.2 to visually interpret the coastline location, dividing the coastline into two primary types: artificial and natural, with six secondary categories. This dataset is characterized by long time series, high accuracy, comprehensive coastline types and a focus on coastline trends before and after 2015 (the first year of sustainable development goals). The dataset can be applied to the study of spatial-temporal distribution characteristics of coastlines and the evolution of land-sea patterns.

Bi-Level Planning of Electric Vehicle Charging Stations Considering Spatial–Temporal Distribution Characteristics of Charging Loads in Uncertain Environments

With the increase in the number of distributed energy resources (DERs) and electric vehicles (EVs), it is particularly important to solve the problem of EV charging station siting and capacity determination under the distribution network considering a large proportion of DERs. This paper proposes a bi-level planning model for EV charging stations that takes into account the characteristics of the spatial–temporal distribution of charging loads under an uncertain environment. First, the Origin–Destination (OD) matrix analysis method and the real-time Dijkstra dynamic path search algorithm are introduced and combined with the Larin Hypercube Sampling (LHS) method to establish the EV charging load prediction model considering the spatial and temporal distribution characteristics. Second, the upper objective function with the objective of minimizing the cost of EV charging station planning and user charging behavior is constructed, while the lower objective function with the objective of minimizing the cost of distribution network operation and carbon emission cost considering the uncertainty of wind power and photovoltaics is constructed. The constraints of the lower-layer objective function are transformed into the upper-layer objective function through Karush–Kuhn–Tucker (KKT) conditions, the optimal location and capacity of charging stations are finally determined, and the model of EV charging station siting and capacity determination is established. Finally, the validity of the model was verified by planning the coupled IEEE 33-node distribution network with the traffic road map of a city in southeastern South Dakota, USA.

Estimating high spatio-temporal resolution XCO2 using spatial features deep fusion model

The high temporal-spatial resolution estimation of XCO2 data is foundational for precision quantification of carbon dioxide sources and sinks at a regional scale. This study proposed an advanced XCO2 data estimation method, using spatial features deep fusion. Leveraging convolutional neural network (CNN) principles, SpatialFusionNet—a module was designed to amalgamate geographical features within a defined spatial range. This module captures and integrates the spatial characteristics of meteorological and surface environmental factors, enhancing its application to the XCO2 estimation model. Building upon machine learning methods including Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and Deep Neural Network (DNN), combined with the SpatialFusionNet module, the spatial features deep fusion models were constructed utilizing relationships among OCO-2 satellite XCO2 trajectory monitoring data in China, Copernicus Atmospheric Monitoring Service (CAMS) XCO2 reanalysis data, and meteorological factors, surface vegetation, and meteorological factors. Model performance improvements were significant, with SVM, DNN, and XGBoost showing respective RMSE reductions of 1.297 ppm, 0.480 ppm, and 0.200 ppm in ten-fold cross-validation based on OCO-2 trajectory samples. In data validation with TCCON Hefei station, the correlation between inversion data and ground-based data reached 0.85, affirming the method's high accuracy. Employing the spatial feature extraction module combined with DNN, the 2015 XCO2 annual spatial distribution of China, analyzing temporal-spatial distribution characteristics in China was generated. The DNN model, combining the SpatialFusionNet module, significantly contributes to the estimation of high temporal-spatial resolution XCO2 datasets, facilitating fine-scale quantification of regional carbon cycling.

Spatiotemporal changes of orchards in the ancient canal of the Yellow River using Landsat data

As an important part of the Yellow River Basin, the ancient canal of the Yellow River plays a great role in promoting ecological protection and high-quality development in the Yellow River Basin. There is a need to explore the distribution of the available resources to optimally manage and utilize them more effectively. By analyzing multi-temporal characteristic of the remote sensing images, the spatial temporal distribution characteristics of orchards in the ancient canal of Yellow River were investigated. Time series of orchards were derived from the Landsat images over a nearly 30-year period at approximately 5-year intervals since 1990. And the spatial distribution characteristics were qualified basing on the spatial overlay analysis methods. The results show that from 1990 to 2020, the orchard area in the ancient canal of the Yellow River increased rapidly in the early, then slowed down after 2000; the area of orchards in 2020 was about 1268.30 km2. Dangshan County, Feng County and Xiao County were the major areas planting orchards; 86.84 % of the orchards distributed in flat terrain with an elevation of 21–60 m, 88.50 % distributing on a slope of less than 4° and 91.12 % of the orchards distributed within the distance of more than 280 km from the coastline; Moran's I index of orchard planting density was greater than 0 for all four spatial scales and was increasing when downscaling, which showed significant spatial positive correlation. These findings provide helpful information for the research in promoting ecological protection and high-quality development of the ancient canal of the Yellow River Basin.

Environmental Influences on Illex argentinus Trawling Grounds in the Southwest Atlantic High Seas

To understand the spatial temporal distribution characteristics of Illex argentinus caught by trawl fishing vessels in the Southwestern Atlantic Ocean and their relationship with key marine environmental factors, this study analyzed the temporal and spatial changes in the fishing ground center of trawl vessels at the ten-day scale from December 2019 to May 2022, combining Chinese trawl fishing log data marine environmental data with satellite remote sensing marine environmental data. Utilizing the Maxent model, ten-day intervals were used as the temporal scale, and ten marine environmental factors, including sea surface temperature, sea surface height, sea surface salinity, chlorophyll concentration, temperature at 50 m and 100 m depth, and the meridional and zonal velocities of ocean currents were quantitatively analyzed to explore the correlation between the spatial distribution of catch and environmental factors. The study reveals that the trawl fishing grounds for Illex argentinus are divided into southern and northern grounds. The southern grounds first appear near 45°20′ S in December, gradually moving southeastward in February and March. The northern grounds do not appear until April, near 42° S in the high seas. On the ten-day time scale, the central fishing grounds of Illex argentinus show significant spatial variability but minor interannual differences. The Maxent model results indicate that sea surface temperature and chlorophyll a concentration are the key environmental factors influencing the spatial and temporal variability of the high seas trawl fishing grounds for most of the time, with high environmental contribution rates during the fishing season. While the range of suitable habitats with an HSI &gt; 0.6 identified by the Maxent model varies significantly between years, a pattern is observed where the range expands at the start and end of the fishing season and contracts during the peak fishing season. This suggests that a more concentrated range of suitable habitats is conducive to accurate predictions of trawl fishing grounds, enabling efficient fishing operations.

Eulerian–Lagrangian multiscale numerical analysis of multimodal partial shedding dynamics

The objective of this paper is to investigate the multimodal partial shedding dynamics from a multiscale perspective of cloud cavitating flows under two distinct cavity shedding mechanisms, namely the re-entrant jet mechanism and the shock wave propagation mechanism. A two-way Eulerian–Lagrangian coupling algorithm is applied to capture the multiscale vapor topologies from microbubble to large-scale cavities. The large-scale cavity evolution is solved through large eddy simulations (LES) with the volume of fraction (VOF) method in Eulerian frame. The sub-grid microbubbles are tracked in Lagrangian frame based on the discrete bubble model (DBM) method. The predictions agree well with experimental observation of the periodical cavity evolution and microbubble dynamics under both the re-entrant jet mechanism and shock wave mechanism around a NACA66 hydrofoil. The numerical simulation provides detailed analysis of the cavitating turbulent flow on the microbubble behavior with emphasis on the spatial-temporal distribution characteristics of microbubbles. The results show that the number and mean size of microbubbles in the cavitation region increase gradually with the growth of attached sheet cavity, development of re-entrant jet and collapse of largescale cavity for both cavitation patterns. Meanwhile, microbubbles are mainly distributed on the largescale interfaces where have high value of vorticity and turbulent kinetic energy under the effect of re-entrant jet and vortex structures. And the probability density functions (PDFs) of microbubble exhibit gamma distributions with a dominant peak at approximately 50 μm for both shedding mechanisms. However, the shock wave formation and propagation process only occurs in the final stage of cavitating flow under shock wave mechanism causing the condensation of vapor and the decrease of the number and mean size of microbubbles. Moreover, the microbubbles are uniformly distributed along the streamwise and vertical directions behind shock wave front.

Spatial-temporal distribution characteristics of pulmonary tuberculosis in eastern China from 2011 to 2021.

China is still among the 30 high-burden tuberculosis (TB) countries in the world. Few studies have described the spatial epidemiological characteristics of pulmonary TB (PTB) in Jiangsu Province. The registered incidence data of PTB patients in 95 counties of Jiangsu Province from 2011 to 2021 were collected from the Tuberculosis Management Information System. Three-dimensional spatial trends, spatial autocorrelation, and spatial-temporal scan analysis were conducted to explore the spatial clustering pattern of PTB. From 2011 to 2021, a total of 347,495 newly diagnosed PTB cases were registered. The registered incidence rate of PTB decreased from 49.78/100,000 in 2011 to 26.49/100,000 in 2021, exhibiting a steady downward trend (χ2=414.22, P<0.001). The average annual registered incidence rate of PTB was higher in the central and northern regions. Moran's I indices of the registered incidence of PTB were all >0 (P<0.05) except in 2016, indicating a positive spatial correlation overall. Local autocorrelation analysis showed that 'high-high' clusters were mainly distributed in northern Jiangsu, and 'low-low' clusters were mainly concentrated in southern Jiangsu. The results of this study assist in identifying settings and locations of high TB risk and inform policy-making for PTB control and prevention.

Water, Salt, and Ion Transport and Its Response to Water-Saving Irrigation in the Hetao Irrigation District Based on the SWAT-Salt Model

Soil salinization is one of the main hazards affecting the sustainable development of agriculture in the Hetao Irrigation District (HID) of Inner Mongolia. To grasp the water and salt transport patterns and spatial–temporal distribution characteristics of the HID at the regional scale, the improved Soil and Water Assessment Tool with a salinity module (SWAT-Salt) model was used to establish the distributed water and salt transport model for the watershed in this study. The results demonstrated that the modified model could more accurately represent the process of water and salt changes in the HID. The coefficient of determination (R2) in the simulation of streamflow and discharge salt loading was 0.83 and 0.86, respectively, and the Nash–Sutcliffe efficiency (NSE) was 0.80 and 0.74, respectively. Based on this, different hydrological processes (surface runoff, lateral flow, groundwater, soil seepage) as well as spatial–temporal distribution characteristics of water salinity in groundwater and soil were analyzed in the HID. Differences in groundwater and soil salinity in different land uses and soil types were also compared. Of these, surface runoff and lateral flow salt discharge loading are concentrated in the southwestern portion of the basin, while groundwater salt discharge loading is concentrated in the eastern as well as southwestern portions of the basin. The salt discharge loading from groundwater accounts for about 98.7% of the total salt discharge loading from all hydrological pathways and is the major contributing part of salt discharge from the irrigation area. Soil salinity increases gradually from west to east. Groundwater salinity (2946 mg/L) and soil water electrical conductivity (0.309 dS/m) were minimized in the cropland. Meanwhile, rational allocation of irrigation water can appropriately increase the amount of salt discharge loading. In conclusion, the model could provide a reference for the investigation of soil salinization and water–salt management measures in irrigation areas.

Photodetector array for measuring the spatial–temporal distribution characteristics of electric field in oil–pressboard insulation based on the Kerr electro‐optic effect

AbstractAccurate measurement and effective analysis of the space electric field and charge characteristics within the oil–pressboard insulation structure of converter transformer are essential for achieving precise insulation structure design. Presently, most electric field measurement methods relying on the Kerr electro‐optical effect are limited to single‐point measurements. Given the complexity of converter transformer insulation structures, including their large scale and the use of various materials, existing technologies and findings struggle to provide comprehensive electric field observations within the oil–pressboard region. After leveraging the Kerr electro‐optic effect, a high‐precision 32‐unit photodetector array has been developed to achieve regional measurements of spatial‐temporal electric field in oil–pressboard insulation. The array boasts a measurement spatial resolution of 1.4 mm2 and a sensitivity of 0.15 kV/mm, ensuring measurement accuracy exceeding 96.50%. Through practical measurements of the spatial electric field distribution within the oil–pressboard insulation under direct current voltage, the non‐uniform distribution of the electric field is effectively captured in oil. Notably, the maximum deviation in field strength at different positions within the transformer oil can reach 18.5%. Moreover, as the applied voltage increases, the unevenness coefficient of the field strength gradually rises, peaking at 1.19, which signifies a progressive expansion in the area affected by electric field distortion. By conducting tests to assess the dispersion of volume resistivity in samples from various positions within large sheets of pressboard, the variation in volume resistivity of materials is posited as one of the contributing factors to the non‐uniform distribution of electric field within the oil. The detailed accomplishments aim to provide essential technical and theoretical support for optimising insulation structure design of converter transformers.

Ecological characteristics and seasonal changes of macrozoobenthos in the waters of Miaodao Archipelago, China

To understand the macrozoobenthic community composition and spatial-temporal distribution characteristics of macrobenthos in the waters of Miaodao Archipelago, Yantai, Shandong and its response to habitat changes, we conducted surveys of macrobenthos and environmental elements in the waters of Miaodao Islands in May (spring), August (summer), and October (autumn) in 2022. Results showed that a total of 127 macrozoobenthic species were recorded, with Mollusca and Annelida (Polychaeta) as the dominant taxa, consisting of 47 and 45 species, respectively. The key dominant species included Sternaspis chinensis, Glycinde bonhourei, Moerella hilaris, and Amphioplus (Lymanella) japonicus. The average annual density and biomass of macrozoobenthos were 190 ind·m-2 and 28.69 g·m-2, respectively. There was no significant seasonal differences in density and biomass. The Shannon diversity index (H), evenness index (J), and richness index (D) averaged 3.10, 0.90, and 2.40, respectively. Cluster analysis results showed low similarity coefficients of community among the three seasons, suggesting a distinct distribution pattern. Factors such as bottom seawater temperature, chlorophyll a, nutrient, sediment grain size, and organic matter content could significantly influence the structure and diversity of macrozoobenthic community. Compared with historical research data, the Changdao National Wetland Nature Reserve and the implementation of enclosure aquaculture have led to notable changes in the dominant species of macrobenthos. Specifically, there was a noticeable decline in both density and H, and an increase in biomass and J. Additionally, body size of benthic fauna was transitioning from small to big.

Spatial-temporal distribution characteristics of etiologically positive pulmonary tuberculosis in Jiangsu Province from 2011 to 2021

Objective: To analyze the spatial-temporal distribution of etiologically positive pulmonary tuberculosis (PTB) at the county (city, district) unit in Jiangsu Province from 2011 to 2021 to provide evidence for the implementation and adjustment of prevention and control strategies of PTB in Jiangsu Province. Methods: The registration data of etiologically positive PTB patients in Jiangsu Province from 2011 to 2021 were collected from the Tuberculosis Management Information System in the China Information System of Disease Control and Prevention. Data on the permanent population were from the statistical yearbook of each county (city, district) in Jiangsu Province. Geoda 1.18.0 software was used to analyze the global and local spatial autocorrelation and explore the spatial clustering. SaTScan 10.1 software was used to analyze the spatial-temporal clusters, and ArcGIS 10.7 software was used to visualize the spatial-temporal clusters. Results: A total of 128 240 etiological positive PTB cases were registered in Jiangsu Province from 2011 to 2021, with an average annual registration rate of 13.99/100 000. The registration rate showed an overall upward trend (trend χ2=63.49, P<0.001) after 2017, and the etiologically positive rate showed an overall upward trend (trend χ2=3 710.86, P<0.001). The annual Moran's I values ranged from 0.107 to 0.343, which showed a spatial clustering distribution. The results of local spatial autocorrelation analysis showed that there were "high-high" clustering areas in Jiangsu Province each year, showing a dynamic distribution, and most of the areas were distributed in the central and southern regions of Jiangsu Province, with the largest number (7) in 2015 and the smallest number (1) in 2011. A total of 4 spatial-temporal clustering areas were explored by spatial-temporal scanning analysis (all P<0.001), among which the first-level clustering area covered 3 counties (cities, districts), namely Changshu, Taicang, and Xiangcheng District of Suzhou, and the clustering time was from 2011 to 2015. The secondary clustering areas covered 24 counties (cities, districts), mainly covering Jiangsu's central and northern regions, such as Huai'an, Suqian, and Yancheng. The third-level clustering areas covered 26 counties (cities, districts); the fourth-level clustering area was the Gaochun District of Nanjing, with the clustering period from 2017 to 2021. Conclusions: From 2011 to 2021, the etiologically positive PTB registration rate at the county (city, district) level in Jiangsu Province had obvious spatial-temporal clustering characteristics. The clustering areas included the northern areas with relatively backward economies and the southern areas with better economic development. Multiple measures should be taken to prevent and control PTB according to the specific situation in different regions.

New insights on the spatial and temporal distribution characteristics of Chinese marine environmental quality and its driving factors from 2003 to 2021

As land and sea integration continues to accelerate, it will inevitably have an effect on the marine ecological environment. However, the study on spatial and temporal distribution characteristics of Chinese marine environmental quality and its driving factors was lacking. This paper employs Regression Analysis, Spearman Correlation Analysis, Canonical Correlation Analysis, Boston Consulting Group Matrix and Environmental Kuznets Curve to examine the spatial–temporal distribution characteristics and correlation factors of Chinese jurisdictional sea areas environmental quality. The environmental quality of the waters under China's jurisdiction improved on average from 2003 to 2021, the area of sea with excellent water quality increased by 7.21 × 104 km2. The spatial distribution showed “South superior, North inferior” trend. The accumulated red tide area is 2.01 × 105 km2, and the green tide has the average maximum area of distribution is 3.13 × 104 km2. The marine dumping area's volume of waste has a three stages of slow rise (2003–2007), steady (2008–2014) and rapid rise (2015–2021). The average percentage of seawater bathing areas with good water quality is 82.10 %. Urban Environmental Infrastructure Investment and Urban Drainage Construction Investments are positively correlated with excellent water quality and negatively correlated with light-sewage water quality. The typical correlation for the group “Environmental Quality/Environmental Governance” is 0.911, and the strongest correlation is between excellent water quality and urban drainage construction investment. The typical correlation coefficient for the “Environmental Quality/Socioeconomic” group is 0.960, and the strongest correlation is between excellent water quality and per capita GDP. The Gross Ocean Product, Per Capita GDP, Proportion of Non-agricultural Industries, Year-end Resident Population, and excellent water quality are positively correlated whereas light-sewage water quality is negatively correlated. Liaoning, Tianjin, Shandong and Guangdong shift from high-pollution-low-investment to low-pollution-low-investment model. Shanghai, Zhejiang, Fujian and Hainan maintain high-pollution-low-investment model, Hainan maintains high-pollution-high-investment model. Jiangsu maintains low-pollution-low-investment model. Hebei and Guangxi shift from low-pollution-low-investment to low-pollution-high-investment model. Liaoning, Hebei, Tianjin, Shandong, Guangxi and Shanghai show inverted “N” shape, Jiangsu shows positive “N” shape, Zhejiang and Fujian show monotonic decline shape, Guangdong and Hainan show positive “U” shape. The results could provide valuable suggestions for coastal provinces to accomplish economic development and environmental improvement, which to prevent the old way of “pollution first and treatment later”.

Temporal-spatial distribution characteristics and associated socioeconomic factors of visiting frequency for rural patients with hypertension in Fujian Province, Southeast China

BackgroundRegular follow-up and medication can effectively reduce the risk of adverse outcomes for patients with hypertension. This study aimed to explore the temporal-spatial distribution characteristics and associated socioeconomic factors of visiting frequency for rural patients with hypertension in Fujian province from 2011 to 2016.MethodsThe medical records of patients with hypertension were abstracted from the database of New Rural Cooperative Medical Scheme. Geographically and temporally weighted regression model was used to analyze the associations between percentage of patients whose visiting frequency ≥ 4 times within a year and seven socioeconomic factors at the county level.ResultsThe visiting rate of rural patients with hypertension was 0.79%, 1.27%, 1.87%, 2.29%, 2.78%, 3.43% over the six-year study period, respectively. The percentage of patients whose visiting frequency ≥ 4 times within a year gradually increased over time and the percentage ranged from 61 to 80% in a half of the counties by 2016. In general, there was positive association between Gross Domestic Product per capita and the percentage of patients whose visiting frequency ≥ 4 times within a year. The percentage of female patients, percentage of patients who aged ≥ 60 years, percentage of low-income patients, carbon emission intensity, percentage of savings and number of health technicians per 10,000 persons were negatively correlated with the percentage of patients whose visiting frequency ≥ 4 times within a year in most of counties of Fujian Province. In the sensitivity analysis, the percentage of outpatients whose visiting frequency ≥ 4 times within a year was higher than that of all patients. There was positive association between percentage of outpatients who aged ≥ 60 years and the percentage of outpatients whose visiting frequency ≥ 4 times.ConclusionsThe visiting rate and the visiting frequency within a year for rural patients with hypertension in Fujian province need to be improved. Female and elderly patients should be the focus of health management. Effectively implementing the family doctor services, providing several kinds of free antihypertensive drugs, improving energy utilization efficiency and reasonably allocating the health resources may be the effective strategies to improve the follow-up compliance of patients.

Analysis of the Spatial-Temporal Distribution Characteristics of Climate and Its Impact on Winter Wheat Production in Shanxi Province, China, 1964-2018.

The possible influence of global climate changes on agricultural production is becoming increasingly significant, necessitating greater attention to improving agricultural production in response to temperature rises and precipitation variability. As one of the main winter wheat-producing areas in China, the temporal and spatial distribution characteristics of precipitation, accumulated temperature, and actual yield and climatic yield of winter wheat during the growing period in Shanxi Province were analysed in detail. With the utilisation of daily meteorological data collected from 12 meteorological stations in Shanxi Province in 1964-2018, our study analysed the change in winter wheat yield with climate change using GIS combined with wavelet analysis. The results show the following: (1) Accumulated temperature and precipitation are the two most important limiting factors among the main physical factors that impact yield. Based on the analysis of the ArcGIS geographical detector, the correlation between the actual yield of winter wheat and the precipitation during the growth period was the highest, reaching 0.469, and the meteorological yield and accumulated temperature during this period also reached its peak value of 0.376. (2) The regions with more suitable precipitation and accumulated temperature during the growth period of winter wheat in the study area had relatively high actual winter wheat yields. Overall, the average actual yield of the entire region showed a significant increasing trend over time, with an upward trend of 47.827 kg ha-1 yr-1. (3) The variation coefficient of winter wheat climatic yield was relatively stable in 2008-2018. In particular, there were many years of continuous reduction in winter wheat yields prior to 2006. Thereafter, the impact of climate change on winter wheat yields became smaller. This study expands our understanding of the complex interactions between climate variables and crop yield but also provides practical recommendations for enhancing agricultural practices in this region.

Spatial–temporal distribution characteristics of Harpadon nehereus in the Yangtze River Estuary and its relationship with environmental factors

To investigate the spatial–temporal distribution characteristics of Harpadon nehereus in the Yangtze River Estuary (YRE) and its relationship with environmental factors, this study used the data from resource and environmental surveys conducted in the YRE and adjacent waters during August (summer) and November (autumn), 2017–2022. Generalized additive models (GAM) were employed to analyze the relationships between the relative resources of H. nehereus and environmental factors and to predict the spatial–temporal distribution of H. nehereus resources in 2022. Our results revealed that the best model deviance explained in summer and autumn was 64.89% and 49.90%, with average effect sizes of 0.75 and 0.70, respectively, for cross-validated regression slopes. Water temperature and salinity were identified as the key environmental factors influencing the relative resources of H. nehereus in the YRE. Overall, there were notable seasonal differences in the relationship between the relative resources of H. nehereus and environmental factors. The relative resources of H. nehereus in the YRE were higher in the summer than in autumn. In summer, both water temperature and salinity exhibited multi-wave nonlinear relationships with the relative resources of H. nehereus, while in autumn, the relative resources of H. nehereus showed a positive linear relationship with water temperature and a non-linear relationship with salinity. Additionally, the predicted and observed values of the relative resources of H. nehereus in 2022 showed similar spatial distribution patterns. The relative resources of H. nehereus was higher in the northern branch than in the southern branch and the offshore regions compared to the near-estuary regions. Altogether, our study provides a scientific basis for conservation management and sustainable utilization of H. nehereus resources in the YRE, thereby contributing to the restoration and management of fishery resources in the region.

Data-driven-based distributionally robust optimization approach for a virtual power plant considering the responsiveness of electric vehicles and Ladder-type carbon trading

To encourage the utilization of decentralized renewable energy systems, a data-driven-based distributionally robust optimization (DRO) model is proposed for a virtual power plant (VPP) considering the responsiveness of electric vehicles (EVs) and a ladder-type carbon trading mechanism (LT-CTM). An EV virtual energy storage (EV-VES) calculation model for dispatchable power is formulated to facilitate the centralized scheduling of EVs, considering responsiveness and spatial–temporal distribution characteristics. Additionally, multiple kinds of energy storage systems and flexible loads are considered in the VPP. Furthermore, to handle multiple uncertainties for both the renewable energy and load, Latin hypercube sampling is applied to generate the renewable energy scenarios, and K-means clustering is used to obtain typical scenes. Then the DRO model is used to solve the dispatch problem. An LT-CTM is leveraged to reduce carbon emissions. The column and constraint generation algorithm is then employed to identify an optimal operational benefit solution. Finally, a multi-parameter coupling analysis of ladder-type carbon trading is used for the environmental protection and economy of the VPP. The results confirm the efficacy of the proposed day-ahead scheduling model, offering a theoretical reference for operators when making scheduling decisions.

Research on spatial-temporal distribution characteristics and influencing factors of national cultural relics protection units in Shanxi Province

This study takes 530 national cultural relics protection units in Shanxi Province as the research object, and uses mathematical statistics and GIS spatial analysis methods to explore the spatial-temporal distribution characteristics and influencing factors of national cultural relics protection units in Shanxi Province. The results show that there are differences in the number of national cultural relics protection units in Shanxi Province during different historical periods, showing a trend of first increasing and then decreasing. The formation of various types of national cultural relics protection units has a relatively concentrated historical period. The distribution center of national cultural relics protection units in Shanxi Province has changed with historical periods, showing a migration characteristic in the direction of south northeast southeast south southeast northwest northeast. The national cultural relics protection units in Shanxi Province are in a clustered distribution state, and the clustering characteristics of different types of national security units are also different. The gradient distribution characteristics of national cultural relics protection units in different administrative cities are obvious. The spatial and temporal distribution of national cultural relics protection units in Shanxi Province is influenced by factors such as topography, river systems, and transportation.