Patna is among the cities high populated at risk of ecological and environmental deterioration due to a variety of human activities,such as poor land cover management.One of the most crucial elements of a successful l...Patna is among the cities high populated at risk of ecological and environmental deterioration due to a variety of human activities,such as poor land cover management.One of the most crucial elements of a successful land resource management plan is the evaluation of Land Use Land Cover(LULC).Over the past 20 years,our planet’s land cover resources have undergone substantial changes due to rapid development.The Land Use Land Cover(LULC)categories of the Patna Urban Agglomeration(PUA),including water bodies,agricultural land,barren land,built-up areas,and vegetation,were identified using Geographic Information System(GIS)techniques.Three multi-temporal images were analyzed and classified through supervised classification using the maximum likelihood method.By comparing three separately created LULC categorized maps from 1990 and 2024,temporal changes were analyzed.In order to update land cover or manage natural resources,it is vital to use change detection as a tool to identify changes in LULC over time in PUA,Patna between 1990,2010 and 2024.According to their respective Kappa coefficients,the accuracy rates for 1990,2010 and 2024 LULC are 91.66 and 94.93,respectively.An accuracy evaluation was conducted to determine the correctness of the classification system and to determine the efficacy of the LULC classification maps.One hundred reference test pixels were identified.There have been found significant changes in the LULC were built up area has increased doubled in last thirty-four years of timeline.展开更多
Interfacial interactions between rough mineral particles have garnered considerable attention as they directly determine particle agglomeration and floatability.This study comprehensively investigates the agglomeratio...Interfacial interactions between rough mineral particles have garnered considerable attention as they directly determine particle agglomeration and floatability.This study comprehensively investigates the agglomeration characteristics of siderite particles after argon(Ar)plasma surface modification through settling tests,flocs size measurements,and fractal dimension calculations.Ar plasma surface modification promotes the agglomeration of siderite particles,as evidenced by increased floc size and density.The agglomeration mechanism induced by Ar plasma surface modification is evaluated using a theoretical model combining the surface element integration(SEI)approach,differential geometry,and the composite Simpson's rule.Changes in surface roughness,wettability,and charge are considered in this model.Compared to the unpretreated siderite particles,the energy barrier for interaction of the 30-min Ar plasma-pretreated siderite particles decreases from 2.3×10-^(17)J to 1.6×10^(-17)J.This reduction provides strong evidence for the agglomeration behavior of siderite particles.Furthermore,flotation experiments confirm that Ar plasma surface modification is conducive to the aggregation flotation of siderite.These findings offer crucial insights into particle aggregation and dispersion behaviors,with notable application in mineral flotation.展开更多
The continuous growth of urban agglomerations in China has increased their complexity as well as vulnerability. In this context, urban resilience is critical for the healthy and sustainable development of urban agglom...The continuous growth of urban agglomerations in China has increased their complexity as well as vulnerability. In this context, urban resilience is critical for the healthy and sustainable development of urban agglomerations. Focusing on the Beijing-Tianjin-Hebei(BTH) urban agglomeration, this study constructs an urban resilience evaluation system based on four subsystems: economy, society, infrastructure, and ecology. It uses the entropy method to measure the urban resilience of the BTH urban agglomeration from 2000 to 2018.Theil index, standard deviation ellipse, and gray prediction model GM(1,1) methods are used to examine the spatio-temporal evolution and dynamic simulation of urban resilience in this urban agglomeration. Our results show that the comprehensive evaluation index for urban resilience in the BTH urban agglomeration followed a steady upward trend from 2000 to 2018,with an average annual growth rate of 6.72%. There are significant differences in each subsystem’s contribution to urban resilience;overall, economic resilience is the main factor affecting urban resilience, with an average annual growth rate of 8.06%. Spatial differences in urban resilience in the BTH urban agglomeration have decreased from 2000 to 2018, showing the typical characteristic of being greater in the central core area and lower in the surrounding non-core areas. The level of urban resilience in the BTH urban agglomeration is forecast to continue increasing over the next ten years. However, there are still considerable differences between the cities. Policy factors will play a positive role in promoting the resilience level. Based on the evaluation results, corresponding policy recommendations are put forwar to provide scientific data support and a theoretical basis for the resilience construction of the BTH urban agglomeration.展开更多
Research on the carbon budget and zoning for carbon compensation in major functional zones(MFZs)is important for formulating strategies for low-carbon development for each functional zone,promoting the collaborative g...Research on the carbon budget and zoning for carbon compensation in major functional zones(MFZs)is important for formulating strategies for low-carbon development for each functional zone,promoting the collaborative governance of the regional ecological environment,and achieving high-quality development.Such work can also contribute to achieving peak emissions and carbon neutrality.This paper constructs a theoretical framework for the carbon budget and carbon compensation from the perspective of the MFZ,uses 157 county-level units of the Beijing-Tianjin-Hebei urban agglomeration(BTHUA)as the study area,and introduces the concentration index,normalized revealed comparative advantage index,and Self Organizing Mapping-K-means(SOM-K-means)model to examine spatio-temporal variations in the carbon budget and carbon compensation zoning for the BTHUA from the perspective of MFZs.The authors propose a scheme for the spatial minimization of carbon emissions as oriented by low-carbon development.The results show that:(1)From 2000 to 2017,the carbon budget exhibited an upward trend of volatility,its centralization index was higher than the“warning line”of 0.4,and large regional differences in it were noted on the whole.(2)There were significant regional differences in the carbon budget,and carbon emissions exhibited a core-periphery spatial pattern,with a high-value center at Beijing-Tianjin-Tangshan that gradually decreased as it moved outward.However,the spatial pattern of carbon absorption tended to be stable,showing an inverted“U-shaped”pattern.It was high in the east,north,and west,and was low in the middle and the south.(3)The carbon budget was consistent with the strategic positioning of the MFZ,and the optimized development zone and key development zone were the main pressure-bearing areas for carbon emissions,while the key ecological functional zone was the dominant zone of carbon absorption.The difference in the centralization index of carbon absorption among the functional zones was smaller than that in the centralization index of carbon emissions.(4)There were 53 payment areas,64 balanced areas,and 40 obtaining areas in the study area.Nine types of carbon compensation zones were finally formed in light of the strategic objectives of the MFZ,and directions and strategies for low-carbon development are proposed for each type.(5)It is important to strengthen research on the carbon balance and horizontal carbon compensation at a microscopic scale,enrich the theoretical framework of regional carbon compensation,integrate it into the carbon trading market,and explore diversified paths for achieving peak emissions and carbon neutrality.展开更多
The rapid expansion of China’s urban agglomerations in recent decades has resulted in over-occupied ecological spaces and increased ecological pressure that are restricting healthy regional development.This paper exa...The rapid expansion of China’s urban agglomerations in recent decades has resulted in over-occupied ecological spaces and increased ecological pressure that are restricting healthy regional development.This paper examines the structure and characteristics of distribution of“production-living-ecological”spaces in five mega-urban agglomerations in China:Beijing-Tianjin-Hebei(BTH),the Yangtze River Delta(YRD),Guangdong-Hong Kong-Macao Greater Bay Area(GBA),Chengdu-Chongqing(CY),and the middle reaches of the Yangtze River(MYR).We analyze spatial and temporal variations in the ecological spaces and factors influencing them from 1990 to 2020,and examine the comprehensive ecological carrying capacity and status of ecological spaces in the past 30 years based on the available water resources,regulation of water and air quality,and leisure and recreation.The results show the following:(1)Urban agglomerations in different stages of formation and development represent varying area ratios of“ecological-production-living”spaces.The modes of expansion and evolution of the living spaces are dominated by multi-center combinations as well as the spatial structure of ecological spaces,including barrier,compact,discrete,and fully enveloping spaces.(2)From 1990 to 2020,the area occupied by living spaces in urban agglomerations continued to increase significantly while that of spaces for ecological production decreased.Except in the GBA,ecological spaces have exhibited a trend of increase in area,especially in the past 10 years.The area ratios and spatio-temporal variations in the“production-living-ecological”spaces indicate that the main functions of production and ecological spaces in mega-urban agglomerations have shifted from supply to regulation and culture,and reflect the transition from rapid urbanization to sustainable urbanization in China.(3)The comprehensive ecological carrying capacities of 78.6%,73.1%,54.5%,56.3%,and 25.8%of cities in BTH,YRD,GBA,CY and MYR are severely overburdened.Water supply and the regulation of water quality are the main factors restricting the ecological carrying capacity of BTH and YRD while leisure and recreation services have hindered the capacities of GBA and CY.Policymakers thus need to pay attention to the conservation and rational layout of ecological spaces to reduce the ecological pressure in urban agglomerations.The work here can provide a scientific basis for the green and sustainable development of urban agglomerations as well as the optimized configuration of“production-living-ecological”spaces.展开更多
It is crucial to investigate the urban agglomerations spatio-temporal evolution patterns and driving factors for analyzing the urban spatial structure-functional division and promoting the coordinated development of u...It is crucial to investigate the urban agglomerations spatio-temporal evolution patterns and driving factors for analyzing the urban spatial structure-functional division and promoting the coordinated development of urban agglomerations.In this study,a novel vegetation-building-nighttime light-adjusted index(VBNAI)was established for rapid and effective mapping of urban construction land(UCL)in Central Plains Urban Agglomeration(CPUA),China during 2000–2020 based on Google Earth Engine(GEE)platform.Compared with traditional indices,VBNAI can significantly decrease the blooming effect,Normalized Difference Vegetation Index(NDVI)saturation,and soil background of nighttime light data.In addition,the urban expansion indices and standard deviation ellipse model were synthetically adopted to analyze the spatio-temporal evolution pattern of urban expansion.The gravity model and the geographically weighted regression model were employed to determine the spatial interaction forces and drivers of urban expansion,respectively.The results showed that the VBNAI index has obvious advantages in efficiency and accuracy to extract UCL with the overall accuracy of more than 91%.The UCL of CPUA had increased by 4489.84 km2 during 2000–2020 with the gravity center moving towards southeast continuously.From 2000 to 2010,the urban expansion was in a‘center-hinterland’pattern which had benefit from the favorable effect of the traffic shaft belt.During 2010–2020,the urban network structure had basically established.Urban expansion had been influenced by a variety of socio-economic and demographic factors,and the impact degree varied from region to region.This study could provide scientific references for facilitating the intensive utilization of urban resources and optimizing the spatial development pattern of urban agglomeration.展开更多
Under the background of collaborative innovation,innovation spillovers at urban agglomeration(UA)level is an important issue but rarely discussed.Using balanced panel data of innovation activities in China’s five UAs...Under the background of collaborative innovation,innovation spillovers at urban agglomeration(UA)level is an important issue but rarely discussed.Using balanced panel data of innovation activities in China’s five UAs from 2003 to 2016,this paper shows spatio-temporal evolution process of their technological innovation capacity with discussing polarization-diffusion patterns,and simultaneously examines driving factors of that evolution processes.We report three main findings:1)there is a high degree of concentration in technological innovation capacity distribution within all China’s five UAs,linked by economic and innovation collaboration.2)Innovation capacity increase of China’s five UAs is driven by government’s investment in science and technology(S&T)to a large extent,followed by influences of infrastructure facilities construction,human capital for S&T,as well as transformation of industrial structure,with great disparities emerged between UAs in our sample period.3)While the intra-region innovation gaps are reducing in Yangtze River Delta Urban Agglomeration(YRD)with obvious innovation diffusion,urban agglomeration in middle reaches of the Yangtze River(MYR)is still dominated by innovation polarized growth.Differences in polarization-diffusion patterns between these two UAs may be explained by opposite returns of industrial structure and internet infrastructure.Our findings contribute to more effective policy making in promoting innovation development when reducing regional inequality through innovation diffusion.展开更多
Under the background of new infrastructure,the Yellow River Basin’s superior growth cannot be separated originating with the synergistic effect of scientific and technological inventiveness and ecological civilizatio...Under the background of new infrastructure,the Yellow River Basin’s superior growth cannot be separated originating with the synergistic effect of scientific and technological inventiveness and ecological civilization construction.In light of the coupling coordination analysis of the coordination effect of provincial high-tech industry agglomeration and resource carrying capacity in the Yellow River Basin from 2009 to 2021,The evolution of the geographical and temporal pattern of development was investigated using the Moran index and kernel density estimation.The results show that the agglomeration of high-tech industries in the Yellow River Basin presents a development trend of seek improvement in stability,and there is a good coupling and coordination throughout the progression of scientific and technological innovation and the loading capacity of the resource,from the viewpoint of a time series.From the perspective of spatial pattern distribution,the whole basin aims at the lower reaches,accelerates the optimization of digital industry and promotes Yellow River Basin development of superior quality through innovation support and increase of input,and based on policy guidance.展开更多
Due to water conflicts and allocation in the Lancang-Mekong River Basin(LMRB),the spatio-temporal differentiation of total water resources and the natural-human influence need to be clarified.This work investigated LM...Due to water conflicts and allocation in the Lancang-Mekong River Basin(LMRB),the spatio-temporal differentiation of total water resources and the natural-human influence need to be clarified.This work investigated LMRB's terrestrial water storage anomaly(TWSA)and its spatio-temporal dynamics during 2002–2020.Considering the effects of natural factors and human activities,the respective contributions of climate variability and human activities to terrestrial water storage change(TWSC)were separated.Results showed that:(1)LMRB's TWSA decreased by 0.3158 cm/a.(2)TWSA showed a gradual increase in distribution from southwest of MRB to middle LMRB and from northeast of LRB to middle LMRB.TWSA positively changed in Myanmar while slightly changed in Laos and China.It negatively changed in Vietnam,Thailand and Cambodia.(3)TWSA components decreased in a descending order of soil moisture,groundwater and precipitation.(4)Natural factors had a substantial and spatial differentiated influence on TWSA over the LMRB.(5)Climate variability contributed 79%of TWSC in the LMRB while human activities contributed 21%with an increasing impact after 2008.The TWSC of upstream basin countries was found to be controlled by climate variability while Vietnam and Cambodia's TWSC has been controlled by human activities since 2012.展开更多
Majority of carbon emissions originate from fossil energy consumption,thus necessitating calculation and monitoring of carbon emissions from energy consumption.In this study,we utilized energy consumption data from Si...Majority of carbon emissions originate from fossil energy consumption,thus necessitating calculation and monitoring of carbon emissions from energy consumption.In this study,we utilized energy consumption data from Sichuan Province and Chongqing Municipality for the years 2000 to 2019 to estimate their statistical carbon emissions.We then employed nighttime light data to downscale and infer the spatial distribution of carbon emissions at the county level within the Chengdu-Chongqing urban agglomeration.Furthermore,we analyzed the spatial pattern of carbon emissions at the county level using the coefficient of variation and spatial autocorrelation,and we used the Geographically and Temporally Weighted Regression(GTWR)model to analyze the influencing factors of carbon emissions at this scale.The results of this study are as follows:(1)from 2000 to 2019,the overall carbon emissions in the Chengdu-Chongqing urban agglomeration showed an increasing trend followed by a decrease,with an average annual growth rate of 4.24%.However,in recent years,it has stabilized,and 2012 was the peak year for carbon emissions in the Chengdu-Chongqing urban agglomeration;(2)carbon emissions exhibited significant spatial clustering,with high-high clustering observed in the core urban areas of Chengdu and Chongqing and low-low clustering in the southern counties of the Chengdu-Chongqing urban agglomeration;(3)factors such as GDP,population(Pop),urbanization rate(Ur),and industrialization structure(Ic)all showed a significant influence on carbon emissions;(4)the spatial heterogeneity of each influencing factor was evident.展开更多
The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to u...The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.展开更多
This study focuses on urgent research on restoring and enhancing carbon storage capacity in the Beibu Gulf Urban Agglomer-ation of China,a key area in the‘Belt and Road’Initiative,which aligns with carbon peaking an...This study focuses on urgent research on restoring and enhancing carbon storage capacity in the Beibu Gulf Urban Agglomer-ation of China,a key area in the‘Belt and Road’Initiative,which aligns with carbon peaking and neutrality goals.This research ana-lyzes the spatial characteristics of carbon metabolism from 2000 to 2020 and uses models to identify stable carbon sink areas,positive carbon flow corridors,and carbon sequestration nodes.The goal is to construct a carbon metabolism spatial security pattern(CMSSP)and propose territorial ecological restoration strategies under different development demand scenarios.The results show the following:1)in 2020,the study area’s carbon sink decreased by 8.29×10^(4) t C/yr compared with that in 2010 and by 10.83×10^(4) t C/yr compared with that in 2000.High-carbon sinks were found mainly in mountainous areas,whereas low-carbon sinks are concentrated in urban con-struction land,rural residential areas,and land margins.2)From 2000 to 2020,the spatial security pattern of carbon metabolism tended to be‘high in the middle of the east and west and low in the gulf.’In 2000,2010,and 2020,16 stable carbon sinks were identified.The carbon energy flow density in Guangxi was greater than that in Guangdong and Hainan,with positive carbon flow corridors located primarily in Guangxi and Guangdong.The number of carbon sequestration nodes remained stable at approximately 15,mainly in Guangxi and Hainan.3)Scenario simulations revealed that under the Nature-based mild restoration scenario,the carbon sink rate will reach 611.85×10^(4) t C/yr by 2030 and increase to 612.45×10^(4) t C/yr by 2060,with stable carbon sinks increasing to 18.In the restora-tion scenario based on Anti-globalization,the carbon sink will decrease from 610.24×10^(4) t C/yr in 2030 to 605.19×10^(4) t C/yr in 2060,with the disappearance of some positive carbon flow corridors and stable carbon sinks.Under the Human-based sustainable restoration scenario,the carbon sink area will decrease from 607.00×10^(4) t C/yr in 2030 to 596.39×10^(4) t C/yr in 2060,with carbon sink areas frag-menting and positive carbon flow corridors becoming less dense.4)On the basis of the current and predicted CMSSPs,this study ex-plores spatial ecological restoration strategies for high-carbon storage areas in bay urban agglomerations at four levels:the land control region,urban agglomeration structure system,carbon sink structure and bay structure control region.展开更多
Objective To investigate the spatiotemporal patterns and socioeconomic factors influencing the incidence of tuberculosis(TB)in the Guangdong Province between 2010 and 2019.Method Spatial and temporal variations in TB ...Objective To investigate the spatiotemporal patterns and socioeconomic factors influencing the incidence of tuberculosis(TB)in the Guangdong Province between 2010 and 2019.Method Spatial and temporal variations in TB incidence were mapped using heat maps and hierarchical clustering.Socioenvironmental influencing factors were evaluated using a Bayesian spatiotemporal conditional autoregressive(ST-CAR)model.Results Annual incidence of TB in Guangdong decreased from 91.85/100,000 in 2010 to 53.06/100,000in 2019.Spatial hotspots were found in northeastern Guangdong,particularly in Heyuan,Shanwei,and Shantou,while Shenzhen,Dongguan,and Foshan had the lowest rates in the Pearl River Delta.The STCAR model showed that the TB risk was lower with higher per capita Gross Domestic Product(GDP)[Relative Risk(RR),0.91;95%Confidence Interval(CI):0.86–0.98],more the ratio of licensed physicians and physician(RR,0.94;95%CI:0.90-0.98),and higher per capita public expenditure(RR,0.94;95%CI:0.90–0.97),with a marginal effect of population density(RR,0.86;95%CI:0.86–1.00).Conclusion The incidence of TB in Guangdong varies spatially and temporally.Areas with poor economic conditions and insufficient healthcare resources are at an increased risk of TB infection.Strategies focusing on equitable health resource distribution and economic development are the key to TB control.展开更多
Farmland transfer is an important land policy for reducing agricultural fragmentation and improving land use efficiency.Many studies have investigated the driving forces of farmland transfer at the farmers’scale.Howe...Farmland transfer is an important land policy for reducing agricultural fragmentation and improving land use efficiency.Many studies have investigated the driving forces of farmland transfer at the farmers’scale.However,the overall spatial distribution and driving mechanisms of farmland transfer at the county scale has been less quantified.In this study,we evaluated farmland transfer and its spatial pattern in Central Yunnan Urban Agglomeration(CYUA)of China by using statistical data at the county scale in 2020.A so-cial-ecological indicator system,comprising natural endowment,social indicators,economic indicators,and landscape patterns,was es-tablished to explore the relationship between farmland transfer and its driving factors.Additionally,a heuristic structural equation mod-el(SEM)was employed to disentangle direct and indirect drivers of farmland transfer.The results indicated that significant spatial clusters of farmland transfer,with high transfer rates concentrated in highly urbanized areas and low transfer rates prevalent in tradition-al ethnic minority regions.Farmland transfer is primarily driven by soil quality,landscape patterns,terrain,and social-economic rurality.Specifically,higher soil quality and improved landscape connectivity facilitate farmland transfer directly,while gentler slopes promote farmland transfer indirectly by supporting better educational opportunities and fewer minority population.Improving rural vocational training and optimizing landscape patterns through land consolidation and redistribution are important in the mountainous areas.This study can provide valuable analytical framework for farmland management for other mountainous regions.展开更多
This study examines the effects of rapid land use changes in India,with a specific focus on Sonipat District in Haryana—a region undergoing significant urban expansion.Over the past two decades,rural landscapes in So...This study examines the effects of rapid land use changes in India,with a specific focus on Sonipat District in Haryana—a region undergoing significant urban expansion.Over the past two decades,rural landscapes in Sonipat have undergone notable transformation,as open spaces and agricultural lands are increasingly converted into residential colonies,commercial hubs,and industrial zones.While such changes reflect economic development and urban growth,they also raise critical concerns about sustainability,especially in terms of food security,groundwater depletion,and environmental degradation.The study examines land use changes between 2000 and 2024 using remote sensing techniques and spatial analysis.It further incorporates secondary data and insights from community-level interactions to assess the socio-economic and ecological impacts of this transformation.The findings indicate rising land fragmentation,loss of agricultural livelihoods,pressure on civic infrastructure,and increasing pollution—factors that threaten long-term regional sustainability.The study underscores the urgent need to reconcile urban development with environmental and social sustainability.By offering a detailed case study of Sonipat,this research contributes to the broader discourse on India’s urbanisation pathways.It aims to provide policymakers,planners,and researchers with evidence-based recommendations to manage land transitions more responsibly,promoting urban growth models that ensure ecological integrity,equitable development,and long-term resilience.展开更多
As Deepfake technology continues to evolve,the distinction between real and fake content becomes increasingly blurred.Most existing Deepfake video detectionmethods rely on single-frame facial image features,which limi...As Deepfake technology continues to evolve,the distinction between real and fake content becomes increasingly blurred.Most existing Deepfake video detectionmethods rely on single-frame facial image features,which limits their ability to capture temporal differences between frames.Current methods also exhibit limited generalization capabilities,struggling to detect content generated by unknown forgery algorithms.Moreover,the diversity and complexity of forgery techniques introduced by Artificial Intelligence Generated Content(AIGC)present significant challenges for traditional detection frameworks,whichmust balance high detection accuracy with robust performance.To address these challenges,we propose a novel Deepfake detection framework that combines a two-stream convolutional network with a Vision Transformer(ViT)module to enhance spatio-temporal feature representation.The ViT model extracts spatial features from the forged video,while the 3D convolutional network captures temporal features.The 3D convolution enables cross-frame feature extraction,allowing the model to detect subtle facial changes between frames.The confidence scores from both the ViT and 3D convolution submodels are fused at the decision layer,enabling themodel to effectively handle unknown forgery techniques.Focusing on Deepfake videos and GAN-generated images,the proposed approach is evaluated on two widely used public face forgery datasets.Compared to existing state-of-theartmethods,it achieves higher detection accuracy and better generalization performance,offering a robust solution for deepfake detection in real-world scenarios.展开更多
The formation of large-sized inclusions cluster severely impacts the continuous casting process and product quality of titanium-containing steel.Thermodynamic calculations were initially conducted to predict the forma...The formation of large-sized inclusions cluster severely impacts the continuous casting process and product quality of titanium-containing steel.Thermodynamic calculations were initially conducted to predict the formation of various complex oxide inclusions,namely Al_(2)O_(3),TiO_(x)and Al-Ti-O.Based on that,laboratory-scale experiments were designed to prepare samples with a single type of inclusions.Then,the scanning electron microscope-energy dispersive spectrometer was used for quantitative characterization.Subsequently,the agglomeration behavior of inclusions in Fe-Al-Ti-O melt was observed in situ by high-temperature confocal laser scanning microscopy.Furthermore,a quantitative analysis of the agglomeration characteristics of the various inclusions was conducted based on the attractive forces in accordance with Newton's second law and the capillary forces as described by the Kralchevsky-Paunov model.The results indicate that the size of Al_(2)O_(3)inclusions is larger than that of TiO_(x)and Al-Ti-O,but the number density of TiO_(x)is the highest.Based on the in situ observation and the theoretical calculation,Al_(2)O_(3),TiO_(x)and Al-Ti-O inclusions can all agglomerate into large-sized clusters without segregation,but the agglomeration tendency of Al_(2)O_(3)and TiO_(x)is stronger than that of Al-Ti-O.The attractive force between Al_(2)O_(3)inclusions’pair is the largest,ranging from 2.26×10^(-15)to 6.12×10^(-14)N,followed by TiO_(x)(7.13×10^(-16)to 3.56×10^(-14)N)and Al-Ti-O(1.16×10^(-17)to 3.77×10^(-16)N).展开更多
Sandfly fever is a viral infectious disease transmitted by sand flies that is widely prevalent in tropical and subtropical regions.Previous studies on its infection mechanism,immune response and diagnosis and treatmen...Sandfly fever is a viral infectious disease transmitted by sand flies that is widely prevalent in tropical and subtropical regions.Previous studies on its infection mechanism,immune response and diagnosis and treatment methods were lack of systematic.This study applied spatio-temporal omics technology to comprehensively explain the dynamic changes of immunity in the incubation period,exacerbation period,peak period and recovery period of Sandfl y fever,and integrated with diff erent coping strategies.To provide new research ideas for its overall research.展开更多
Agriculture holds a pivotal position in the economic fabric of every nation,yet concerns about agricultural carbon emission intensity(ACI)have become a major hurdle to achieving global economic sustainability.Focusing...Agriculture holds a pivotal position in the economic fabric of every nation,yet concerns about agricultural carbon emission intensity(ACI)have become a major hurdle to achieving global economic sustainability.Focusing on 31 provincial-level regions in China,this study uses the Exploratory Spatio-temporal Data Analysis(ESTDA)and Panel Quantile Regression(PQR)model to analyze the spatio-temporal interaction characteristics and influencing factors of ACI in China from 2004 to 2023.The findings are as follows:(1)ACI showed an overall downward trend,and the spatial distribution pattern was characterized by“high in the western region and low along the southeastern coast”.Although the overall disparity tended to converge,some high-carbon-intensity regions exhibited extreme trends.ACI displayed clear spatial directionality,with the spatial center shifting steadily toward the northeast.(2)Regions in the northwest,northeast,and central-south parts exhibited strong local spatial structural dynamics,and the local spatial dependence of ACI in each region showed a nonlinear trend.Generally speaking,the spatial association pattern demonstrated a certain degree of inertia in spatial transfer,reflecting strong path dependence or spatial lock-in characteristics.(3)Optimization of industrial structure and improvement in agricultural mechanization will increase ACI,while economic development can effectively reduce it.The impact of urbanization on ACI exhibits a nonlinear pattern.The coordinated development of economic growth and urbanization significantly reduces ACI,with a stronger emission reduction observed in regions with low ACI.The optimization of industrial structure,when combined with urbanization and environmental regulation,contributes to significant emission reductions particularly in high-ACI areas.Similarly,the synergy between agricultural mechanization and urbanization effectively lowers emissions in low-ACI regions,though this effect diminishes in areas with higher ACI.展开更多
Sloping farmland,particularly in mountainous and hilly areas,constitutes a significant component of regional farmland resources.An investigation into the spatio-temporal pattern of sloping farmland and its influencing...Sloping farmland,particularly in mountainous and hilly areas,constitutes a significant component of regional farmland resources.An investigation into the spatio-temporal pattern of sloping farmland and its influencing factors in China is imperative for the efficient utilization of farmland and the optimization of land space.We used land use transfer matrix,geographically weighted regression model and geographical detector to conduct this study.Results showed that sloping farmland in China firstly decreased and then increased from 2000 to 2020.The proportion of sloping farmland decreased radially outward from Sichuan basin to the surrounding areas.Change rates of sloping farmland with different slopes varied and the slope with 6°-15°underwent the fastest changes.The influencing factors of farmland at various slope degrees were different.For sloping farmland below 15°,land use intensity and elevation had the greatest contribution.For sloping farmland between 15°and 25°,elevation,land use intensity,and population density were the main influencing factors.Sloping farmland above 25°was mostly affected by natural factors.This study can provide scientific basis for rational development and protection of sloping farmland.展开更多
文摘Patna is among the cities high populated at risk of ecological and environmental deterioration due to a variety of human activities,such as poor land cover management.One of the most crucial elements of a successful land resource management plan is the evaluation of Land Use Land Cover(LULC).Over the past 20 years,our planet’s land cover resources have undergone substantial changes due to rapid development.The Land Use Land Cover(LULC)categories of the Patna Urban Agglomeration(PUA),including water bodies,agricultural land,barren land,built-up areas,and vegetation,were identified using Geographic Information System(GIS)techniques.Three multi-temporal images were analyzed and classified through supervised classification using the maximum likelihood method.By comparing three separately created LULC categorized maps from 1990 and 2024,temporal changes were analyzed.In order to update land cover or manage natural resources,it is vital to use change detection as a tool to identify changes in LULC over time in PUA,Patna between 1990,2010 and 2024.According to their respective Kappa coefficients,the accuracy rates for 1990,2010 and 2024 LULC are 91.66 and 94.93,respectively.An accuracy evaluation was conducted to determine the correctness of the classification system and to determine the efficacy of the LULC classification maps.One hundred reference test pixels were identified.There have been found significant changes in the LULC were built up area has increased doubled in last thirty-four years of timeline.
基金financially supported by the National Natural Science Foundation of China(No.52204284)the China Postdoctoral Science Foundation(No.2025MD784125)+2 种基金the Natural Science Foundation of Shaanxi Province,China(No.2024JC-YBQN-0365)the Shaanxi Province Postdoctoral Science Foundation,China(No.2025BSHSDZZ363)Outstanding Youth Science Fund of Xi’an University of Science and Technology,China(No.202308)。
文摘Interfacial interactions between rough mineral particles have garnered considerable attention as they directly determine particle agglomeration and floatability.This study comprehensively investigates the agglomeration characteristics of siderite particles after argon(Ar)plasma surface modification through settling tests,flocs size measurements,and fractal dimension calculations.Ar plasma surface modification promotes the agglomeration of siderite particles,as evidenced by increased floc size and density.The agglomeration mechanism induced by Ar plasma surface modification is evaluated using a theoretical model combining the surface element integration(SEI)approach,differential geometry,and the composite Simpson's rule.Changes in surface roughness,wettability,and charge are considered in this model.Compared to the unpretreated siderite particles,the energy barrier for interaction of the 30-min Ar plasma-pretreated siderite particles decreases from 2.3×10-^(17)J to 1.6×10^(-17)J.This reduction provides strong evidence for the agglomeration behavior of siderite particles.Furthermore,flotation experiments confirm that Ar plasma surface modification is conducive to the aggregation flotation of siderite.These findings offer crucial insights into particle aggregation and dispersion behaviors,with notable application in mineral flotation.
基金Innovation Research Group Project of National Natural Science Foundation of China,No.42121001。
文摘The continuous growth of urban agglomerations in China has increased their complexity as well as vulnerability. In this context, urban resilience is critical for the healthy and sustainable development of urban agglomerations. Focusing on the Beijing-Tianjin-Hebei(BTH) urban agglomeration, this study constructs an urban resilience evaluation system based on four subsystems: economy, society, infrastructure, and ecology. It uses the entropy method to measure the urban resilience of the BTH urban agglomeration from 2000 to 2018.Theil index, standard deviation ellipse, and gray prediction model GM(1,1) methods are used to examine the spatio-temporal evolution and dynamic simulation of urban resilience in this urban agglomeration. Our results show that the comprehensive evaluation index for urban resilience in the BTH urban agglomeration followed a steady upward trend from 2000 to 2018,with an average annual growth rate of 6.72%. There are significant differences in each subsystem’s contribution to urban resilience;overall, economic resilience is the main factor affecting urban resilience, with an average annual growth rate of 8.06%. Spatial differences in urban resilience in the BTH urban agglomeration have decreased from 2000 to 2018, showing the typical characteristic of being greater in the central core area and lower in the surrounding non-core areas. The level of urban resilience in the BTH urban agglomeration is forecast to continue increasing over the next ten years. However, there are still considerable differences between the cities. Policy factors will play a positive role in promoting the resilience level. Based on the evaluation results, corresponding policy recommendations are put forwar to provide scientific data support and a theoretical basis for the resilience construction of the BTH urban agglomeration.
基金National Natural Science Foundation of China(42121001)National Natural Science Foundation of China(42130712)+1 种基金National Natural Science Foundation of China(42022007)Youth Innovation Promotion Association,CAS(2018069)。
文摘Research on the carbon budget and zoning for carbon compensation in major functional zones(MFZs)is important for formulating strategies for low-carbon development for each functional zone,promoting the collaborative governance of the regional ecological environment,and achieving high-quality development.Such work can also contribute to achieving peak emissions and carbon neutrality.This paper constructs a theoretical framework for the carbon budget and carbon compensation from the perspective of the MFZ,uses 157 county-level units of the Beijing-Tianjin-Hebei urban agglomeration(BTHUA)as the study area,and introduces the concentration index,normalized revealed comparative advantage index,and Self Organizing Mapping-K-means(SOM-K-means)model to examine spatio-temporal variations in the carbon budget and carbon compensation zoning for the BTHUA from the perspective of MFZs.The authors propose a scheme for the spatial minimization of carbon emissions as oriented by low-carbon development.The results show that:(1)From 2000 to 2017,the carbon budget exhibited an upward trend of volatility,its centralization index was higher than the“warning line”of 0.4,and large regional differences in it were noted on the whole.(2)There were significant regional differences in the carbon budget,and carbon emissions exhibited a core-periphery spatial pattern,with a high-value center at Beijing-Tianjin-Tangshan that gradually decreased as it moved outward.However,the spatial pattern of carbon absorption tended to be stable,showing an inverted“U-shaped”pattern.It was high in the east,north,and west,and was low in the middle and the south.(3)The carbon budget was consistent with the strategic positioning of the MFZ,and the optimized development zone and key development zone were the main pressure-bearing areas for carbon emissions,while the key ecological functional zone was the dominant zone of carbon absorption.The difference in the centralization index of carbon absorption among the functional zones was smaller than that in the centralization index of carbon emissions.(4)There were 53 payment areas,64 balanced areas,and 40 obtaining areas in the study area.Nine types of carbon compensation zones were finally formed in light of the strategic objectives of the MFZ,and directions and strategies for low-carbon development are proposed for each type.(5)It is important to strengthen research on the carbon balance and horizontal carbon compensation at a microscopic scale,enrich the theoretical framework of regional carbon compensation,integrate it into the carbon trading market,and explore diversified paths for achieving peak emissions and carbon neutrality.
基金The Strategic Priority Research Program of the Chinese Academy of Sciences,No.XDA20010202,No.XDA20010302。
文摘The rapid expansion of China’s urban agglomerations in recent decades has resulted in over-occupied ecological spaces and increased ecological pressure that are restricting healthy regional development.This paper examines the structure and characteristics of distribution of“production-living-ecological”spaces in five mega-urban agglomerations in China:Beijing-Tianjin-Hebei(BTH),the Yangtze River Delta(YRD),Guangdong-Hong Kong-Macao Greater Bay Area(GBA),Chengdu-Chongqing(CY),and the middle reaches of the Yangtze River(MYR).We analyze spatial and temporal variations in the ecological spaces and factors influencing them from 1990 to 2020,and examine the comprehensive ecological carrying capacity and status of ecological spaces in the past 30 years based on the available water resources,regulation of water and air quality,and leisure and recreation.The results show the following:(1)Urban agglomerations in different stages of formation and development represent varying area ratios of“ecological-production-living”spaces.The modes of expansion and evolution of the living spaces are dominated by multi-center combinations as well as the spatial structure of ecological spaces,including barrier,compact,discrete,and fully enveloping spaces.(2)From 1990 to 2020,the area occupied by living spaces in urban agglomerations continued to increase significantly while that of spaces for ecological production decreased.Except in the GBA,ecological spaces have exhibited a trend of increase in area,especially in the past 10 years.The area ratios and spatio-temporal variations in the“production-living-ecological”spaces indicate that the main functions of production and ecological spaces in mega-urban agglomerations have shifted from supply to regulation and culture,and reflect the transition from rapid urbanization to sustainable urbanization in China.(3)The comprehensive ecological carrying capacities of 78.6%,73.1%,54.5%,56.3%,and 25.8%of cities in BTH,YRD,GBA,CY and MYR are severely overburdened.Water supply and the regulation of water quality are the main factors restricting the ecological carrying capacity of BTH and YRD while leisure and recreation services have hindered the capacities of GBA and CY.Policymakers thus need to pay attention to the conservation and rational layout of ecological spaces to reduce the ecological pressure in urban agglomerations.The work here can provide a scientific basis for the green and sustainable development of urban agglomerations as well as the optimized configuration of“production-living-ecological”spaces.
基金Under the auspices of Social Science and Humanity on Young Fund of the Ministry of Education of China(No.21YJCZH100)the Scientific Research Project on Outstanding Young of the Fujian Agriculture and Forestry University(No.XJQ201920)+1 种基金the Science and Technology Innovation Special Fund Project of Fujian Agriculture and Forestry University(No.CXZX2021032)the Forestry Peak Discipline Construction Project of Fujian Agriculture and Forestry University(No.72202200205)。
文摘It is crucial to investigate the urban agglomerations spatio-temporal evolution patterns and driving factors for analyzing the urban spatial structure-functional division and promoting the coordinated development of urban agglomerations.In this study,a novel vegetation-building-nighttime light-adjusted index(VBNAI)was established for rapid and effective mapping of urban construction land(UCL)in Central Plains Urban Agglomeration(CPUA),China during 2000–2020 based on Google Earth Engine(GEE)platform.Compared with traditional indices,VBNAI can significantly decrease the blooming effect,Normalized Difference Vegetation Index(NDVI)saturation,and soil background of nighttime light data.In addition,the urban expansion indices and standard deviation ellipse model were synthetically adopted to analyze the spatio-temporal evolution pattern of urban expansion.The gravity model and the geographically weighted regression model were employed to determine the spatial interaction forces and drivers of urban expansion,respectively.The results showed that the VBNAI index has obvious advantages in efficiency and accuracy to extract UCL with the overall accuracy of more than 91%.The UCL of CPUA had increased by 4489.84 km2 during 2000–2020 with the gravity center moving towards southeast continuously.From 2000 to 2010,the urban expansion was in a‘center-hinterland’pattern which had benefit from the favorable effect of the traffic shaft belt.During 2010–2020,the urban network structure had basically established.Urban expansion had been influenced by a variety of socio-economic and demographic factors,and the impact degree varied from region to region.This study could provide scientific references for facilitating the intensive utilization of urban resources and optimizing the spatial development pattern of urban agglomeration.
文摘Under the background of collaborative innovation,innovation spillovers at urban agglomeration(UA)level is an important issue but rarely discussed.Using balanced panel data of innovation activities in China’s five UAs from 2003 to 2016,this paper shows spatio-temporal evolution process of their technological innovation capacity with discussing polarization-diffusion patterns,and simultaneously examines driving factors of that evolution processes.We report three main findings:1)there is a high degree of concentration in technological innovation capacity distribution within all China’s five UAs,linked by economic and innovation collaboration.2)Innovation capacity increase of China’s five UAs is driven by government’s investment in science and technology(S&T)to a large extent,followed by influences of infrastructure facilities construction,human capital for S&T,as well as transformation of industrial structure,with great disparities emerged between UAs in our sample period.3)While the intra-region innovation gaps are reducing in Yangtze River Delta Urban Agglomeration(YRD)with obvious innovation diffusion,urban agglomeration in middle reaches of the Yangtze River(MYR)is still dominated by innovation polarized growth.Differences in polarization-diffusion patterns between these two UAs may be explained by opposite returns of industrial structure and internet infrastructure.Our findings contribute to more effective policy making in promoting innovation development when reducing regional inequality through innovation diffusion.
基金supported by the 2021 Research and Practice Project of Higher Education Teaching Reform in Henan Province(Grant No.2021SJGLX072Y).
文摘Under the background of new infrastructure,the Yellow River Basin’s superior growth cannot be separated originating with the synergistic effect of scientific and technological inventiveness and ecological civilization construction.In light of the coupling coordination analysis of the coordination effect of provincial high-tech industry agglomeration and resource carrying capacity in the Yellow River Basin from 2009 to 2021,The evolution of the geographical and temporal pattern of development was investigated using the Moran index and kernel density estimation.The results show that the agglomeration of high-tech industries in the Yellow River Basin presents a development trend of seek improvement in stability,and there is a good coupling and coordination throughout the progression of scientific and technological innovation and the loading capacity of the resource,from the viewpoint of a time series.From the perspective of spatial pattern distribution,the whole basin aims at the lower reaches,accelerates the optimization of digital industry and promotes Yellow River Basin development of superior quality through innovation support and increase of input,and based on policy guidance.
基金National Natural Science Foundation of China,No.42161006Yunnan Fundamental Research Projects No.202201AT070094,No.202301BF070001-004+1 种基金Special Project for High-level Talents of Yunnan Province for Young Top Talents,No.C6213001159European Research Council(ERC)Starting-Grant STORIES,No.101040939。
文摘Due to water conflicts and allocation in the Lancang-Mekong River Basin(LMRB),the spatio-temporal differentiation of total water resources and the natural-human influence need to be clarified.This work investigated LMRB's terrestrial water storage anomaly(TWSA)and its spatio-temporal dynamics during 2002–2020.Considering the effects of natural factors and human activities,the respective contributions of climate variability and human activities to terrestrial water storage change(TWSC)were separated.Results showed that:(1)LMRB's TWSA decreased by 0.3158 cm/a.(2)TWSA showed a gradual increase in distribution from southwest of MRB to middle LMRB and from northeast of LRB to middle LMRB.TWSA positively changed in Myanmar while slightly changed in Laos and China.It negatively changed in Vietnam,Thailand and Cambodia.(3)TWSA components decreased in a descending order of soil moisture,groundwater and precipitation.(4)Natural factors had a substantial and spatial differentiated influence on TWSA over the LMRB.(5)Climate variability contributed 79%of TWSC in the LMRB while human activities contributed 21%with an increasing impact after 2008.The TWSC of upstream basin countries was found to be controlled by climate variability while Vietnam and Cambodia's TWSC has been controlled by human activities since 2012.
基金supported by the Humanities and Social Sciences Project of the Ministry of Education of the Peoples Republic(No.21YJCZH099)the National Natural Science Foundation of China(Nos.41401089 and 41741014)the Science and Technology Project of Sichuan Province(No.2023NSFSC1979).
文摘Majority of carbon emissions originate from fossil energy consumption,thus necessitating calculation and monitoring of carbon emissions from energy consumption.In this study,we utilized energy consumption data from Sichuan Province and Chongqing Municipality for the years 2000 to 2019 to estimate their statistical carbon emissions.We then employed nighttime light data to downscale and infer the spatial distribution of carbon emissions at the county level within the Chengdu-Chongqing urban agglomeration.Furthermore,we analyzed the spatial pattern of carbon emissions at the county level using the coefficient of variation and spatial autocorrelation,and we used the Geographically and Temporally Weighted Regression(GTWR)model to analyze the influencing factors of carbon emissions at this scale.The results of this study are as follows:(1)from 2000 to 2019,the overall carbon emissions in the Chengdu-Chongqing urban agglomeration showed an increasing trend followed by a decrease,with an average annual growth rate of 4.24%.However,in recent years,it has stabilized,and 2012 was the peak year for carbon emissions in the Chengdu-Chongqing urban agglomeration;(2)carbon emissions exhibited significant spatial clustering,with high-high clustering observed in the core urban areas of Chengdu and Chongqing and low-low clustering in the southern counties of the Chengdu-Chongqing urban agglomeration;(3)factors such as GDP,population(Pop),urbanization rate(Ur),and industrialization structure(Ic)all showed a significant influence on carbon emissions;(4)the spatial heterogeneity of each influencing factor was evident.
文摘The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.
基金Under the auspices of the National Natural Science Foundation of China(No.52268008)。
文摘This study focuses on urgent research on restoring and enhancing carbon storage capacity in the Beibu Gulf Urban Agglomer-ation of China,a key area in the‘Belt and Road’Initiative,which aligns with carbon peaking and neutrality goals.This research ana-lyzes the spatial characteristics of carbon metabolism from 2000 to 2020 and uses models to identify stable carbon sink areas,positive carbon flow corridors,and carbon sequestration nodes.The goal is to construct a carbon metabolism spatial security pattern(CMSSP)and propose territorial ecological restoration strategies under different development demand scenarios.The results show the following:1)in 2020,the study area’s carbon sink decreased by 8.29×10^(4) t C/yr compared with that in 2010 and by 10.83×10^(4) t C/yr compared with that in 2000.High-carbon sinks were found mainly in mountainous areas,whereas low-carbon sinks are concentrated in urban con-struction land,rural residential areas,and land margins.2)From 2000 to 2020,the spatial security pattern of carbon metabolism tended to be‘high in the middle of the east and west and low in the gulf.’In 2000,2010,and 2020,16 stable carbon sinks were identified.The carbon energy flow density in Guangxi was greater than that in Guangdong and Hainan,with positive carbon flow corridors located primarily in Guangxi and Guangdong.The number of carbon sequestration nodes remained stable at approximately 15,mainly in Guangxi and Hainan.3)Scenario simulations revealed that under the Nature-based mild restoration scenario,the carbon sink rate will reach 611.85×10^(4) t C/yr by 2030 and increase to 612.45×10^(4) t C/yr by 2060,with stable carbon sinks increasing to 18.In the restora-tion scenario based on Anti-globalization,the carbon sink will decrease from 610.24×10^(4) t C/yr in 2030 to 605.19×10^(4) t C/yr in 2060,with the disappearance of some positive carbon flow corridors and stable carbon sinks.Under the Human-based sustainable restoration scenario,the carbon sink area will decrease from 607.00×10^(4) t C/yr in 2030 to 596.39×10^(4) t C/yr in 2060,with carbon sink areas frag-menting and positive carbon flow corridors becoming less dense.4)On the basis of the current and predicted CMSSPs,this study ex-plores spatial ecological restoration strategies for high-carbon storage areas in bay urban agglomerations at four levels:the land control region,urban agglomeration structure system,carbon sink structure and bay structure control region.
基金supported by the Guangdong Provincial Clinical Research Center for Tuberculosis(No.2020B1111170014)。
文摘Objective To investigate the spatiotemporal patterns and socioeconomic factors influencing the incidence of tuberculosis(TB)in the Guangdong Province between 2010 and 2019.Method Spatial and temporal variations in TB incidence were mapped using heat maps and hierarchical clustering.Socioenvironmental influencing factors were evaluated using a Bayesian spatiotemporal conditional autoregressive(ST-CAR)model.Results Annual incidence of TB in Guangdong decreased from 91.85/100,000 in 2010 to 53.06/100,000in 2019.Spatial hotspots were found in northeastern Guangdong,particularly in Heyuan,Shanwei,and Shantou,while Shenzhen,Dongguan,and Foshan had the lowest rates in the Pearl River Delta.The STCAR model showed that the TB risk was lower with higher per capita Gross Domestic Product(GDP)[Relative Risk(RR),0.91;95%Confidence Interval(CI):0.86–0.98],more the ratio of licensed physicians and physician(RR,0.94;95%CI:0.90-0.98),and higher per capita public expenditure(RR,0.94;95%CI:0.90–0.97),with a marginal effect of population density(RR,0.86;95%CI:0.86–1.00).Conclusion The incidence of TB in Guangdong varies spatially and temporally.Areas with poor economic conditions and insufficient healthcare resources are at an increased risk of TB infection.Strategies focusing on equitable health resource distribution and economic development are the key to TB control.
基金Under the auspices of Yunnan Provincial Science and Technology Project at Southwest United Graduate School(No.202302AO370012)National Natural Science Foundation of China(No.42201290)+1 种基金Talent Introduction Fund of Yunnan University(No.CZ22623101)the Fourteenth Program of Research Innovation Fund for Graduate Students of Yunnan University(No.KC-22221099)。
文摘Farmland transfer is an important land policy for reducing agricultural fragmentation and improving land use efficiency.Many studies have investigated the driving forces of farmland transfer at the farmers’scale.However,the overall spatial distribution and driving mechanisms of farmland transfer at the county scale has been less quantified.In this study,we evaluated farmland transfer and its spatial pattern in Central Yunnan Urban Agglomeration(CYUA)of China by using statistical data at the county scale in 2020.A so-cial-ecological indicator system,comprising natural endowment,social indicators,economic indicators,and landscape patterns,was es-tablished to explore the relationship between farmland transfer and its driving factors.Additionally,a heuristic structural equation mod-el(SEM)was employed to disentangle direct and indirect drivers of farmland transfer.The results indicated that significant spatial clusters of farmland transfer,with high transfer rates concentrated in highly urbanized areas and low transfer rates prevalent in tradition-al ethnic minority regions.Farmland transfer is primarily driven by soil quality,landscape patterns,terrain,and social-economic rurality.Specifically,higher soil quality and improved landscape connectivity facilitate farmland transfer directly,while gentler slopes promote farmland transfer indirectly by supporting better educational opportunities and fewer minority population.Improving rural vocational training and optimizing landscape patterns through land consolidation and redistribution are important in the mountainous areas.This study can provide valuable analytical framework for farmland management for other mountainous regions.
文摘This study examines the effects of rapid land use changes in India,with a specific focus on Sonipat District in Haryana—a region undergoing significant urban expansion.Over the past two decades,rural landscapes in Sonipat have undergone notable transformation,as open spaces and agricultural lands are increasingly converted into residential colonies,commercial hubs,and industrial zones.While such changes reflect economic development and urban growth,they also raise critical concerns about sustainability,especially in terms of food security,groundwater depletion,and environmental degradation.The study examines land use changes between 2000 and 2024 using remote sensing techniques and spatial analysis.It further incorporates secondary data and insights from community-level interactions to assess the socio-economic and ecological impacts of this transformation.The findings indicate rising land fragmentation,loss of agricultural livelihoods,pressure on civic infrastructure,and increasing pollution—factors that threaten long-term regional sustainability.The study underscores the urgent need to reconcile urban development with environmental and social sustainability.By offering a detailed case study of Sonipat,this research contributes to the broader discourse on India’s urbanisation pathways.It aims to provide policymakers,planners,and researchers with evidence-based recommendations to manage land transitions more responsibly,promoting urban growth models that ensure ecological integrity,equitable development,and long-term resilience.
基金supported by National Natural Science Foundation of China(Nos.62477026,62177029,61807020)Humanities and Social Sciences Research Program of the Ministry of Education of China(No.23YJAZH047)the Startup Foundation for Introducing Talent of Nanjing University of Posts and Communications under Grant NY222034.
文摘As Deepfake technology continues to evolve,the distinction between real and fake content becomes increasingly blurred.Most existing Deepfake video detectionmethods rely on single-frame facial image features,which limits their ability to capture temporal differences between frames.Current methods also exhibit limited generalization capabilities,struggling to detect content generated by unknown forgery algorithms.Moreover,the diversity and complexity of forgery techniques introduced by Artificial Intelligence Generated Content(AIGC)present significant challenges for traditional detection frameworks,whichmust balance high detection accuracy with robust performance.To address these challenges,we propose a novel Deepfake detection framework that combines a two-stream convolutional network with a Vision Transformer(ViT)module to enhance spatio-temporal feature representation.The ViT model extracts spatial features from the forged video,while the 3D convolutional network captures temporal features.The 3D convolution enables cross-frame feature extraction,allowing the model to detect subtle facial changes between frames.The confidence scores from both the ViT and 3D convolution submodels are fused at the decision layer,enabling themodel to effectively handle unknown forgery techniques.Focusing on Deepfake videos and GAN-generated images,the proposed approach is evaluated on two widely used public face forgery datasets.Compared to existing state-of-theartmethods,it achieves higher detection accuracy and better generalization performance,offering a robust solution for deepfake detection in real-world scenarios.
基金support of Postdoctoral Fellowship Program of CPSF(GZC20230393)Natural Science Foundation of Liaoning Province in China(2023-BSBA-112)Fundamental Research Funds for the Central Universities(N2425032).
文摘The formation of large-sized inclusions cluster severely impacts the continuous casting process and product quality of titanium-containing steel.Thermodynamic calculations were initially conducted to predict the formation of various complex oxide inclusions,namely Al_(2)O_(3),TiO_(x)and Al-Ti-O.Based on that,laboratory-scale experiments were designed to prepare samples with a single type of inclusions.Then,the scanning electron microscope-energy dispersive spectrometer was used for quantitative characterization.Subsequently,the agglomeration behavior of inclusions in Fe-Al-Ti-O melt was observed in situ by high-temperature confocal laser scanning microscopy.Furthermore,a quantitative analysis of the agglomeration characteristics of the various inclusions was conducted based on the attractive forces in accordance with Newton's second law and the capillary forces as described by the Kralchevsky-Paunov model.The results indicate that the size of Al_(2)O_(3)inclusions is larger than that of TiO_(x)and Al-Ti-O,but the number density of TiO_(x)is the highest.Based on the in situ observation and the theoretical calculation,Al_(2)O_(3),TiO_(x)and Al-Ti-O inclusions can all agglomerate into large-sized clusters without segregation,but the agglomeration tendency of Al_(2)O_(3)and TiO_(x)is stronger than that of Al-Ti-O.The attractive force between Al_(2)O_(3)inclusions’pair is the largest,ranging from 2.26×10^(-15)to 6.12×10^(-14)N,followed by TiO_(x)(7.13×10^(-16)to 3.56×10^(-14)N)and Al-Ti-O(1.16×10^(-17)to 3.77×10^(-16)N).
基金College Students Innovation and Entrepreneurship Training Program(X202511049398)College Students Innovation and Entrepreneurship Training Program(X202511049201)+1 种基金College Students Innovation and Entrepreneurship Training Program(X202511258005S)University-Level Research Funding Program of Hainan Science and Technology Vocational University(HKKY2024-87)。
文摘Sandfly fever is a viral infectious disease transmitted by sand flies that is widely prevalent in tropical and subtropical regions.Previous studies on its infection mechanism,immune response and diagnosis and treatment methods were lack of systematic.This study applied spatio-temporal omics technology to comprehensively explain the dynamic changes of immunity in the incubation period,exacerbation period,peak period and recovery period of Sandfl y fever,and integrated with diff erent coping strategies.To provide new research ideas for its overall research.
基金National Natural Science Foundation of China,No.42230106,No.42171250State Key Laboratory of Earth Surface Processes and Resource Ecology,No.2022-ZD-04。
文摘Agriculture holds a pivotal position in the economic fabric of every nation,yet concerns about agricultural carbon emission intensity(ACI)have become a major hurdle to achieving global economic sustainability.Focusing on 31 provincial-level regions in China,this study uses the Exploratory Spatio-temporal Data Analysis(ESTDA)and Panel Quantile Regression(PQR)model to analyze the spatio-temporal interaction characteristics and influencing factors of ACI in China from 2004 to 2023.The findings are as follows:(1)ACI showed an overall downward trend,and the spatial distribution pattern was characterized by“high in the western region and low along the southeastern coast”.Although the overall disparity tended to converge,some high-carbon-intensity regions exhibited extreme trends.ACI displayed clear spatial directionality,with the spatial center shifting steadily toward the northeast.(2)Regions in the northwest,northeast,and central-south parts exhibited strong local spatial structural dynamics,and the local spatial dependence of ACI in each region showed a nonlinear trend.Generally speaking,the spatial association pattern demonstrated a certain degree of inertia in spatial transfer,reflecting strong path dependence or spatial lock-in characteristics.(3)Optimization of industrial structure and improvement in agricultural mechanization will increase ACI,while economic development can effectively reduce it.The impact of urbanization on ACI exhibits a nonlinear pattern.The coordinated development of economic growth and urbanization significantly reduces ACI,with a stronger emission reduction observed in regions with low ACI.The optimization of industrial structure,when combined with urbanization and environmental regulation,contributes to significant emission reductions particularly in high-ACI areas.Similarly,the synergy between agricultural mechanization and urbanization effectively lowers emissions in low-ACI regions,though this effect diminishes in areas with higher ACI.
基金supported by the Key Laboratory of Natural Resources Monitoring and Supervision in Southern Hilly Region,Ministry of Natural Resources(NRMSSHR2023Y02)Yunnan Key Laboratory of Plateau Geographic Processes and Environmental Changes,Faculty of Geography,Yunnan Normal University(PGPEC2304)China Scholarship Council。
文摘Sloping farmland,particularly in mountainous and hilly areas,constitutes a significant component of regional farmland resources.An investigation into the spatio-temporal pattern of sloping farmland and its influencing factors in China is imperative for the efficient utilization of farmland and the optimization of land space.We used land use transfer matrix,geographically weighted regression model and geographical detector to conduct this study.Results showed that sloping farmland in China firstly decreased and then increased from 2000 to 2020.The proportion of sloping farmland decreased radially outward from Sichuan basin to the surrounding areas.Change rates of sloping farmland with different slopes varied and the slope with 6°-15°underwent the fastest changes.The influencing factors of farmland at various slope degrees were different.For sloping farmland below 15°,land use intensity and elevation had the greatest contribution.For sloping farmland between 15°and 25°,elevation,land use intensity,and population density were the main influencing factors.Sloping farmland above 25°was mostly affected by natural factors.This study can provide scientific basis for rational development and protection of sloping farmland.
