Desertification is a global crucial ecological and environmental issue,and China is among the countries most seriously affected by desertification.In recent decades,numerous independent studies on desertification dyna...Desertification is a global crucial ecological and environmental issue,and China is among the countries most seriously affected by desertification.In recent decades,numerous independent studies on desertification dynamics have been carried out using remote sensing technology,but there has been a lack of systematic research on desertification trends in China.This study employed the meta-analysis to integrate the findings of 140 published research cases and examined the dynamics of desertification in the eight major deserts,four major sandy lands,and their surrounding areas in China from 1970 to 2019,with a comparative analysis of differences between the eastern(including the Mu Us Sandy Land,the Otindag Sandy Land,the Hulunbuir Sandy Land,the Horqin Sandy Land,and the Hobq Desert)and western(including the Taklimakan Desert,the Gurbantunggut Desert,the Kumtagh Desert,the Ulan Buh Desert,the Qaidam Basin Desert,the Badain Jaran Desert,and the Tengger Desert)regions.The results revealed that from 1970 to 2019,desertification first expanded and then reversed in the whole region.Specifically,desertification expanded from 1980 to 1999 and reversed after 2000.The desertification trend exhibited distinct spatio-temporal variations between the eastern and western regions.From 1970 to 2019,the western region experienced relatively minor changes in desertified land area compared to the eastern region.In the context of global climate change,beneficial climatic conditions and ecological construction projects played a crucial role in reversing desertification.These findings provide valuable insights for understanding the development patterns of desertification in the most representative deserts and sandy lands in China and formulating effective desertification control strategies.展开更多
Environmental degradation linked to land occupation and use, such as climate change and anthropogenic activities, has led to the modification of the landscape units of the Kadzel sub-watershed. The objective of this s...Environmental degradation linked to land occupation and use, such as climate change and anthropogenic activities, has led to the modification of the landscape units of the Kadzel sub-watershed. The objective of this study is to analyze the dynamics of land use units in the Kadzel area in Diffa between 1992 and 2022 and to propose a future scenario for sustainable environmental management. The approach used relies on remote sensing and geographic information systems to analyze the dynamics of land use units. Additionally, the Markov Cellular Automata (CA) model was used to predict future land use. The land cover maps were produced from a supervised classification by maximum likelihood based on the true and false color compositions of bands 4/3/2 (TM5), 3/2/1 (ETM+) and 7/5/4 (8 OLI). Ten occupation classes were discriminated. Between 1992 and 2022, there was a decrease in the areas of irrigated crops (4.91% and 2.88%), of shrubby tree steppes (14.31% and 9.48%), field-fallow complexes (22.23% and 10.52%), and degraded areas. Grassy steppes (25.76% and 13.32%). However, this reduction has been beneficial for wastelands, urban areas and bodies of water. Based on predictive modeling, it is predicted that by 2052, urban areas, fallow field complexes and bare soils will constitute the main types of housing units. The regressive trend in natural resources appears to continue into the future with current land use practices.展开更多
Exploring the spatio-temporal dynamics of poverty is important for research on sustainable poverty reduction in China. Based on the perspective of development geography, this paper proposes a panel vector autoregressi...Exploring the spatio-temporal dynamics of poverty is important for research on sustainable poverty reduction in China. Based on the perspective of development geography, this paper proposes a panel vector autoregressive(PVAR) model that combines the human development approach with the global indicator framework for Sustainable Development Goals(SDGs) to identify the poverty-causing and the poverty-reducing factors in China. The aim is to measure the multidimensional poverty index(MPI) of China’s provinces from 2007 to 2017, and use the exploratory spatio-temporal data analysis(ESTDA) method to reveal the characteristics of the spatio-temporal dynamics of multidimensional poverty. The results show the following:(1) The poverty-causing factors in China include the high social gross dependency ratio and crop-to-disaster ratio, and the poverty-reducing factors include the high per capita GDP, per capita social security expenditure, per capita public health expenditure, number of hospitals per 10,000 people, rate of participation in the new rural cooperative medical scheme, vegetation coverage, per capita education expenditure, number of universities, per capita research and development(R&D) expenditure, and funding per capita for cultural undertakings.(2) From 2007 to 2017, provincial income poverty(IP), health poverty(HP), cultural poverty(CP), and multidimensional poverty have been significantly reduced in China, and the overall national poverty has dropped by 5.67% annually. there is a differentiation in poverty along different dimensions in certain provinces.(3) During the study period, the local spatial pattern of multidimensional poverty between provinces showed strong spatial dynamics, and a trend of increase from the eastern to the central and western regions was noted. The MPI among provinces exhibited a strong spatial dependence over time to form a pattern of decrease from northwestern and northeastern China to the surrounding areas.(4) The spatio-temporal networks of multidimensional poverty in adjacent provinces were mainly negatively correlated, with only Shaanxi and Henan, Shaanxi and Ningxia, Qinghai and Gansu, Hubei and Anhui, Sichuan and Guizhou, and Hainan and Guangdong forming spatially strong cooperative poverty reduction relationships. These results have important reference value for the implementation of China’s poverty alleviation strategy.展开更多
It is known that the exploitation of opencast coal mines has seriously damaged the environments in the semi-arid areas.Vegetation status can reliably reflect the ecological degeneration and restoration in the opencast...It is known that the exploitation of opencast coal mines has seriously damaged the environments in the semi-arid areas.Vegetation status can reliably reflect the ecological degeneration and restoration in the opencast mining areas in the semi-arid areas.Long-time series MODIS NDVI data are widely used to simulate the vegetation cover to reflect the disturbance and restoration of local ecosystems.In this study, both qualitative(linear regression method and coefficient of variation(CoV)) and quantitative(spatial buffer analysis, and change amplitude and the rate of change in the average NDVI) analyses were conducted to analyze the spatio-temporal dynamics of vegetation during 2000–2017 in Jungar Banner of Inner Mongolia Autonomous Region, China, at the large(Jungar Banner and three mine groups) and small(three types of functional areas: opencast coal mining excavation areas, reclamation areas and natural areas) scales.The results show that the rates of change in the average NDVI in the reclamation areas(20%–60%) and opencast coal mining excavation areas(10%–20%) were considerably higher than that in the natural areas(<7%).The vegetation in the reclamation areas experienced a trend of increase(3–5 a after reclamation)-decrease(the sixth year of reclamation)-stability.The vegetation in Jungar Banner has a spatial heterogeneity under the influences of mining and reclamation activities.The ratio of vegetation improvement area to vegetation degradation area in the west, southwest and east mine groups during 2000–2017 was 8:1, 20:1 and 33:1, respectively.The regions with the high CoV of NDVI above 0.45 were mainly distributed around the opencast coal mining excavation areas, and the regions with the CoV of NDVI above 0.25 were mostly located in areas with low(28.8%) and medium-low(10.2%) vegetation cover.The average disturbance distances of mining activities on vegetation in the three mine groups(west, southwest and east) were 800, 800 and 1000 m, respectively.The greater the scale of mining, the farther the disturbance distances of mining activities on vegetation.We conclude that vegetation reclamation will certainly compensate for the negative impacts of opencast coal mining activities on vegetation.Sufficient attention should be paid to the proportional allocation of plant species(herbs and shrubs) in the reclamation areas, and the restored vegetation in these areas needs to be protected for more than 6 a.Then, as the repair time increased, the vegetation condition of the reclamation areas would exceed that of the natural areas.展开更多
Understanding the dynamics of urbanization is essential to the sustainable development of cities. Meanwhile the analysis of urban development can also provide scientifically and effective information for decision-maki...Understanding the dynamics of urbanization is essential to the sustainable development of cities. Meanwhile the analysis of urban development can also provide scientifically and effective information for decision-making. With the long-term Defense Meteorological Satellite Program’s Operational Linescan System(DMSP/OLS) nighttime light images, a pixel level assessment of urbanization of China from 1992 to 2013 was conducted in this study, and the spatio-temporal dynamics and future trends of urban development were fully detected. The results showed that the urbanization and urban dynamics of China experienced drastic fluctuations from 1992 to 2013, especially for those in the coastal and metropolitan areas. From a regional perspective, it was found that the urban dynamics and increasing trends in North Coast China, East Coast China and South Coast China were much more stable and significant than that in other regions. Moreover, with the sustainability estimating of nighttime light dynamics, the regional agglomeration trends of urban regions were also detected. The light intensity in nearly 50% of lighted pixels may continuously decrease in the future, indicating a severe situation of urbanization within these regions. In this study, The results revealed in this study can provided a new insight in long time urbanization detecting and is thus beneficial to the better understanding of trends and dynamics of urban development.展开更多
Mapping crop distribution with remote sensing data is of great importance for agricultural production, food security and agricultural sustainability. Winter rape is an important oil crop, which plays an important role...Mapping crop distribution with remote sensing data is of great importance for agricultural production, food security and agricultural sustainability. Winter rape is an important oil crop, which plays an important role in the cooking oil market of China. The Jianghan Plain and Dongting Lake Plain (JPDLP) are major agricultural production areas in China. Essential changes in winter rape distribution have taken place in this area during the 21st century. However, the pattern of these changes remains unknown. In this study, the spatial and temporal dynamics of winter rape from 2000 to 2017 on the JPDLP were analyzed. An artificial neural network (ANN)-based classification method was proposed to map fractional winter rape distribution by fusing moderate resolution imaging spectrometer (MODIS) data and high-resolution imagery. The results are as follows:(1) The total winter rape acreages on the JPDLP dropped significantly, especially on the Jianghan Plain with a decline of about 45% during 2000 and 2017.(2) The winter rape abundance keeps changing with about 20–30% croplands changing their abundance drastically in every two consecutive observation years.(3) The winter rape has obvious regional differentiation for the trend of its change at the county level, and the decreasing trend was observed more strongly in the traditionally dominant agricultural counties.展开更多
Understanding crop patterns and their changes on regional scale is a critical re- quirement for projecting agro-ecosystem dynamics. However, tools and methods for mapping the distribution of crop area and yield are st...Understanding crop patterns and their changes on regional scale is a critical re- quirement for projecting agro-ecosystem dynamics. However, tools and methods for mapping the distribution of crop area and yield are still lacking. Based on the cross-entropy theory, a spatial production allocation model （SPAM） has been developed for presenting spa- tio-temporal dynamics of maize cropping system in Northeast China during 1980-2010. The simulated results indicated that （1） maize sown area expanded northwards to 48~N before 2000, after that the increased sown area mainly occurred in the central and southern parts of Northeast China. Meanwhile, maize also expanded eastwards to 127°E and lower elevation （less than 100 m） as well as higher elevation （mainly distributed between 200 m and 350 m）; （2） maize yield has been greatly promoted for most planted area of Northeast China, espe- cially in the planted zone between 42°N and 48°N, while the yield increase was relatively homogeneous without obvious longitudinal variations for whole region; （3） maize planting density increased gradually to a moderately high level over the investigated period, which reflected the trend of aggregation of maize cultivation driven by market demand.展开更多
We evaluated the dynamics of land use in the Bouba Ndjidda National Park (BNNP) and adjacent areas, in northern Cameroon. Using a maximum likelihood supervised classification of satellite images from 1990 to 2016, cou...We evaluated the dynamics of land use in the Bouba Ndjidda National Park (BNNP) and adjacent areas, in northern Cameroon. Using a maximum likelihood supervised classification of satellite images from 1990 to 2016, coupled with field and a socio-economic survey, we performed a robust land-use classification. Between 1990 and 2016, the area included eight classes of land use, with the largest in 1990 being the woody savannah (42.9%) followed by the gallery forest (20.2%) and the clear forest (16.3%). Between 1990 and 1999, the gallery forest lost 64.8% of its area mostly to the benefit of woody savannahs. Between 1999 and 2016, the largest loss of area was that of the clear forest, which decreased generally by 43.2% in favor of woody savannah. Rates of increase of crop field areas were 59.6% and 78.8% respectively for the periods of 1990 to 1999 and 1999 to 2016 to the detriment of woody savannahs. We attribute the changes in land use observed mainly to the increasing human population and associated agriculture, overgrazing, fuelwood harvesting and bush fires. The exploitation of non-timber forest products and climatic factors may also have changed the vegetation cover. We recommend the implementation of farming techniques with low impact on the environment such as agroforestry.展开更多
Swidden agriculture is an age-old, widespread but controversial farming practice in Montane Mainland Southeast Asia （MMSEA）. In the uplands of northern Laos, swidden ag- riculture has remained a predominant human-do...Swidden agriculture is an age-old, widespread but controversial farming practice in Montane Mainland Southeast Asia （MMSEA）. In the uplands of northern Laos, swidden ag- riculture has remained a predominant human-dominated land-use type for centuries. However swidden system has undergone dramatic transformations since the mid-1990s. Debates on changes in swidden cultivation are linked to globally critical issues, such as land use/cover changes （LUCC）, biodiversity loss and environmental degradation. Since the implementation of Reducing Emissions from Deforestation and Forest Degradation （REDD）, much attention has been paid nationally and internationally to swidden agriculture in the tropics. However, knowledge of the explicitly spatial characteristics of swidden agriculture and the conse- quences of these transitions at macroscopic scale is surprisingly scarce. In this study, the intensity of swidden use and fallow forest recovery in northern Laos in 1990, 2002, and 2011 were delineated by means of Landsat Thematic Mapper （TM） and Enhanced Thematic Mapper plus （ETM＋） imagery （30 m） using a decision tree classification approach, followed by an analysis of the spatio-temporal changes in swidden agriculture. Next, annual successive TM/ETM＋ images during 2000-2010 were used to delineate the dynamics of the burning and cropping phase. Subsequently, the burned pixels identified in 2000 were compared respectively with their counterparts in the following years （2001-2011） to investigate temporal trends, land-use frequency, and the swidden cycle using time-series Landsat-based Normalized Difference Vegetation Index （NDVI） data. Finally, as the swidden cycle changed from 1 to 11 years, the fallow vegetation recovery process was studied. The results showed that： （1） from 1990 to 2011, the area of swidden agriculture increased by 54.98%, from 1.54× 10^5 ha to 2.38×10^5 ha in northern Laos. The increased swidden cultivation area was mainly distributed in Luang Prabang and southern Bokeo, whereas the decreased parts were mainly found in Phongsali; （2） swidden agriculture increased mainly at elevations of 500-800 m, 300-500 m, and 800-1000 m and on slopes of 10°-20° and 200-30°. Over 80% of swidden fields were transformed from forests; （3） during 2000-2011, the frequency of swidden use in northern Laos was about two or three times. The interval between two successive utilization of a swidden ranged from one to seven years. Comparison of swidden cycles and the related proportions of swidden farming in 2000, 2003, and 2007 revealed that swidden cycles in most areas were shortened; and （4） there was a significant correlation （0.97） between fallow vegetation recovery and the swidden cycle. The NDVI of regenerated vegetation could approach the average level of forest when the swidden cycle reached 10 years.展开更多
Permafrost is one of the key components of terrestrial ecosystem in cold regions. In the context of climate change, few studies have investigated resilience of social ecological system(SER) from the perspective of per...Permafrost is one of the key components of terrestrial ecosystem in cold regions. In the context of climate change, few studies have investigated resilience of social ecological system(SER) from the perspective of permafrost that restricts the hydrothermal condition of alpine grassland ecosystem. In this paper, based on the structural dynamics, we developed the numerical model for the SER in the permafrost regions of the source of Yangtze and Yellow Rivers, analyzed the spatial-temporal characteristics and sensitivity of the SER, and estimated the effect of permafrost change on the SER. The results indicate that: 1) the SER has an increasing trend, especially after 1997, which is the joint effect of precipitation, temperature, NPP and ecological conservation projects; 2) the SER shows the spatial feature of high in southeast and low in northwest,which is consistent with the variation trends of high southeast and low northwest for the precipitation, temperature and NPP, and low southeast and high northwest for the altitude; 3) the high sensitive regions of SER to the permafrost change have gradually transited from the island distribution to zonal and planar distribution since 1980, moreover, the sensitive degree has gradually reduced; relatively, the sensitivity has high value in the north and south, and low value in the south and east; 4) the thickness of permafrost active layer shows a highly negative correlation with the SER. The contribution rate of permafrost change to the SER is-4.3%, that is, once the thickness of permafrost active layer increases 1 unit, the SER would decrease 0.04 units.展开更多
This article uses TM images in 1999 and 2006 in Dahua County,selects the driving factors having great impact on urban land use change,and conducts data processing using GIS software.It then uses CLUE-S model to simula...This article uses TM images in 1999 and 2006 in Dahua County,selects the driving factors having great impact on urban land use change,and conducts data processing using GIS software.It then uses CLUE-S model to simulate land use change pattern in 2006,and uses land use map in 2006 to test the simulation results.The results show that the simulation achieves good effect,indicating that we can use CLUE-S model to simulate the future urban land use change in karst areas,to provide scientific decision-making support for sustainable development of land use.展开更多
Optical coherence tomography angiography(OCTA)has emerged as an advanced in vivo im-aging modality,which is widely used for the clinic ophthalmology and neuroscience research in the rodent brain cortex among others.Ba...Optical coherence tomography angiography(OCTA)has emerged as an advanced in vivo im-aging modality,which is widely used for the clinic ophthalmology and neuroscience research in the rodent brain cortex among others.Based on the high numerical aperture(NA)probing lens and the motion-corrected algorithms,a high-resolution imaging technique called OCT micro-angiography is applied to resolve the small blood capillary vessels ranging from 5μm to 10μm in diameter.As OCT-based techniques are recently evolving further from the structural imaging of capillaries toward spatio-temporal dynamic imaging of blood flow in capillaries,here we present a review on the latest techniques for the dynamic flow imaging.Studies on capillary blood flow using these techniques will help us better understand the roles of capillary blood flow for normal functioning of the brain as well as how it malfunctions in diseases.展开更多
The Changbai Mountains and the Appalachian Mountains have similar spatial contexts.The elevation,latitude,and moisture gradients of both mountain ranges offer regional insight for investigating the vegetation dynamics...The Changbai Mountains and the Appalachian Mountains have similar spatial contexts.The elevation,latitude,and moisture gradients of both mountain ranges offer regional insight for investigating the vegetation dynamics in eastern Eurasia and eastern North America.We determined and compared the spatial patterns and temporal trends in the normalized difference vegetation index(NDVI)in the Changbai Mountains and the Appalachian Mountains using time series data from the Global Inventory Modeling and Mapping Studies 3^(rd) generation dataset from 1982 to 2013.The spatial pattern of NDVI in the Changbai Mountains exhibited fragmentation,whereas NDVI in the Appalachian Mountains decreased from south to north.The vegetation dynamics in the Changbai Mountains had an insignificant trend at the regional scale,whereas the dynamics in the Appalachian Mountains had a significant increasing trend.NDVI increased in 55% of the area of the Changbai Mountains and in 95% of the area of the Appalachian Mountains.The peak NDVI occurred one month later in the Changbai Mountains than in the Appalachian Mountains.The results revealed a significant increase in NDVI in autumn in both mountain ranges.The climatic trend in the Changbai Mountains included warming and decreased precipitation,and whereas that in the Appalachian Mountains included significant warming and increased precipitation.Positive and negative correlations existed between NDVI and temperature and precipitation,respectively,in both mountain ranges.Particularly,the spring temperature and NDVI exhibited a significant positive correlation in both mountain ranges.The results of this study suggest that human actives caused the differences in the spatial patterns of NDVI and that various characteristics of climate change and intensity of human actives dominated the differences in the NDVI trends between the Changbai Mountains and the Appalachian Mountains.Additionally,the vegetation dynamics of both mountain ranges were not identical to those in previous broader-scale studies.展开更多
The present work is related to the numerical investigation of the spatio-temporal susceptible-latent-breaking out-recovered(SLBR)epidemic model.It describes the computer virus dynamics with vertical transmission via t...The present work is related to the numerical investigation of the spatio-temporal susceptible-latent-breaking out-recovered(SLBR)epidemic model.It describes the computer virus dynamics with vertical transmission via the internet.In these types of dynamics models,the absolute values of the state variables are the fundamental requirement that must be fulfilled by the numerical design.By taking into account this key property,the positivity preserving algorithm is designed to solve the underlying SLBR system.Since,the state variables associated with the phenomenon,represent the computer nodes,so they must take in absolute.Moreover,the continuous system(SLBR)acquires two steady states i.e.,the virus-free state and the virus existence state.The stability of the numerical design,at the equilibrium points,portrays an exceptional aspect about the propagation of the virus.The designed discretization algorithm sustains the stability of both the steady states.The computer simulations also endorse that the proposed discretization algorithm retains all the traits of the continuous SLBR model with spatial content.The stability and consistency of the proposed algorithm are verified,mathematically.All the facts are also ascertained by numerical simulations.展开更多
Understanding regional carbon emissions from human activities,particularly their spatio-temporal patterns,is essential for implementing decarbonization strategies and cultivating a low-carbon economy.This study develo...Understanding regional carbon emissions from human activities,particularly their spatio-temporal patterns,is essential for implementing decarbonization strategies and cultivating a low-carbon economy.This study develops a spatial visualization model to estimate carbon emissions in Southeast Asia using calibrated nighttime light data,with DMSP-OLS(Defense Meteorological Satellite Program Operational Linescan System)and NPP-VIIRS(National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite)standardized through polynomial regression and machine learning to ensure consistency.Emissions in Southeast Asia increased by 2.51 times from 1992 to 2022,shifting from gradual to rapid growth.Validation against Open-source Data Inventory for Anthropogenic CO2(ODIAC)and Emissions Database for Global Atmospheric Research(EDGAR)shows strong agreement in high-emission urban areas but discrepancies in low-emission rural regions due to data sparsity and satellite sensor limits.Spatial analysis reveals that major Southeast Asian cities and their peripheries exhibit robust,sustained growth,while rural,less-developed areas show slower trends,highlighting persistent urbanrural disparities.These urban regions demonstrate a“circular economy advantage”,where per-unit-area carbon emissions steadily rise in economically advantageous zones.Despite high model accuracy,uncertainties persist due to variations in regional economic activities and the limitations of satellite-based emission proxies.Forecasts suggest elevated emission levels in major cities will continue,while changes in other areas remain relatively minimal.Consequently,achieving a low-carbon economy in Southeast Asia requires a top-down approach,emphasizing infrastructure enhancement,resource and energy optimization,and fostering a sustainable,circular socio-economic system.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
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
Predicting the progression from Mild Cognitive Impairment(MCI)to Alzheimer's Disease(AD)is a critical challenge for enabling early intervention and improving patient outcomes.While longitudinal multi-modal neuroim...Predicting the progression from Mild Cognitive Impairment(MCI)to Alzheimer's Disease(AD)is a critical challenge for enabling early intervention and improving patient outcomes.While longitudinal multi-modal neuroimaging data holds immense potential for capturing the spatio-temporal dynamics of disease progression,its effective analysis is hampered by significant challenges:temporal heterogeneity(irregularly sampled scans),multi-modal misalignment,and the propensity of deep learning models to learn spurious,noncausal correlations.We propose CASCADE-Net,a novel end-to-end pipeline for robust and interpretable MCI-to-AD progression prediction.Our architecture introduces a Dynamic Temporal Alignment Module that employs a Neural Ordinary Differential Equation(Neural ODE)to model the continuous,underlying progression of pathology from irregularly sampled scans,effectively mapping heterogeneous patient data to a unified latent timeline.This aligned,noise-reduced spatio-temporal data is then processed by a predictive model featuring a novel Causal Spatial Attention mechanism.This mechanism not only identifies the critical brain regions and their evolution predictive of conversion but also incorporates a counterfactual constraint during training.This constraint ensures the learned features are causally linked to AD pathology by encouraging invariance to non-causal,confounder-based changes.Extensive experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset demonstrate that CASCADE-Net significantly outperforms state-of-the-art sequential models in prognostic accuracy.Furthermore,our model provides highly interpretable,causally-grounded attention maps,offering valuable insights into the disease progression process and fostering greater clinical trust.展开更多
This paper introduces techniques in Gaussian process regression model for spatiotemporal data collected from complex systems.This study focuses on extracting local structures and then constructing surrogate models bas...This paper introduces techniques in Gaussian process regression model for spatiotemporal data collected from complex systems.This study focuses on extracting local structures and then constructing surrogate models based on Gaussian process assumptions.The proposed Dynamic Gaussian Process Regression(DGPR)consists of a sequence of local surrogate models related to each other.In DGPR,the time-based spatial clustering is carried out to divide the systems into sub-spatio-temporal parts whose interior has similar variation patterns,where the temporal information is used as the prior information for training the spatial-surrogate model.The DGPR is robust and especially suitable for the loosely coupled model structure,also allowing for parallel computation.The numerical results of the test function show the effectiveness of DGPR.Furthermore,the shock tube problem is successfully approximated under different phenomenon complexity.展开更多
Appropriately characterising the mixed space-time relations of the contagion process caused by hybrid space and time factors remains the primary challenge in COVID-19 forecasting.However,in previous deep learning mode...Appropriately characterising the mixed space-time relations of the contagion process caused by hybrid space and time factors remains the primary challenge in COVID-19 forecasting.However,in previous deep learning models for epidemic forecasting,spatial and temporal variations are captured separately.A unified model is developed to cover all spatio-temporal relations.However,this measure is insufficient for modelling the complex spatio-temporal relations of infectious disease transmission.A dynamic adaptive spatio-temporal graph network(DASTGN)is proposed based on attention mechanisms to improve prediction accuracy.In DASTGN,complex spatio-temporal relations are depicted by adaptively fusing the mixed space-time effects and dynamic space-time dependency structure.This dual-scale model considers the time-specific,space-specific,and direct effects of the propagation process at the fine-grained level.Furthermore,the model characterises impacts from various space-time neighbour blocks under time-varying interventions at the coarse-grained level.The performance comparisons on the three COVID-19 datasets reveal that DASTGN achieves state-of-the-art results with a maximum improvement of 17.092%in the root mean-square error and 11.563%in the mean absolute error.Experimental results indicate that the mechanisms of designing DASTGN can effectively detect some spreading characteristics of COVID-19.The spatio-temporal weight matrices learned in each proposed module reveal diffusion patterns in various scenarios.In conclusion,DASTGN has successfully captured the dynamic spatio-temporal variations of COVID-19,and considering multiple dynamic space-time relationships is essential in epidemic forecasting.展开更多
基金supported by the State Key Research and Development Program of China(2023YFF1305304)the Open Bidding for Selecting the Best Candidates Project of Inner Mongolia Autonomous Region(2024JBGS0020).
文摘Desertification is a global crucial ecological and environmental issue,and China is among the countries most seriously affected by desertification.In recent decades,numerous independent studies on desertification dynamics have been carried out using remote sensing technology,but there has been a lack of systematic research on desertification trends in China.This study employed the meta-analysis to integrate the findings of 140 published research cases and examined the dynamics of desertification in the eight major deserts,four major sandy lands,and their surrounding areas in China from 1970 to 2019,with a comparative analysis of differences between the eastern(including the Mu Us Sandy Land,the Otindag Sandy Land,the Hulunbuir Sandy Land,the Horqin Sandy Land,and the Hobq Desert)and western(including the Taklimakan Desert,the Gurbantunggut Desert,the Kumtagh Desert,the Ulan Buh Desert,the Qaidam Basin Desert,the Badain Jaran Desert,and the Tengger Desert)regions.The results revealed that from 1970 to 2019,desertification first expanded and then reversed in the whole region.Specifically,desertification expanded from 1980 to 1999 and reversed after 2000.The desertification trend exhibited distinct spatio-temporal variations between the eastern and western regions.From 1970 to 2019,the western region experienced relatively minor changes in desertified land area compared to the eastern region.In the context of global climate change,beneficial climatic conditions and ecological construction projects played a crucial role in reversing desertification.These findings provide valuable insights for understanding the development patterns of desertification in the most representative deserts and sandy lands in China and formulating effective desertification control strategies.
文摘Environmental degradation linked to land occupation and use, such as climate change and anthropogenic activities, has led to the modification of the landscape units of the Kadzel sub-watershed. The objective of this study is to analyze the dynamics of land use units in the Kadzel area in Diffa between 1992 and 2022 and to propose a future scenario for sustainable environmental management. The approach used relies on remote sensing and geographic information systems to analyze the dynamics of land use units. Additionally, the Markov Cellular Automata (CA) model was used to predict future land use. The land cover maps were produced from a supervised classification by maximum likelihood based on the true and false color compositions of bands 4/3/2 (TM5), 3/2/1 (ETM+) and 7/5/4 (8 OLI). Ten occupation classes were discriminated. Between 1992 and 2022, there was a decrease in the areas of irrigated crops (4.91% and 2.88%), of shrubby tree steppes (14.31% and 9.48%), field-fallow complexes (22.23% and 10.52%), and degraded areas. Grassy steppes (25.76% and 13.32%). However, this reduction has been beneficial for wastelands, urban areas and bodies of water. Based on predictive modeling, it is predicted that by 2052, urban areas, fallow field complexes and bare soils will constitute the main types of housing units. The regressive trend in natural resources appears to continue into the future with current land use practices.
基金National Natural Science Foundation of China,No.71974070, No.41501593National Key R&D Project,No.2016YFA0602500Humanities and Social Sciences Foundation of Ministry of Education of China,No.19YJCZH068。
文摘Exploring the spatio-temporal dynamics of poverty is important for research on sustainable poverty reduction in China. Based on the perspective of development geography, this paper proposes a panel vector autoregressive(PVAR) model that combines the human development approach with the global indicator framework for Sustainable Development Goals(SDGs) to identify the poverty-causing and the poverty-reducing factors in China. The aim is to measure the multidimensional poverty index(MPI) of China’s provinces from 2007 to 2017, and use the exploratory spatio-temporal data analysis(ESTDA) method to reveal the characteristics of the spatio-temporal dynamics of multidimensional poverty. The results show the following:(1) The poverty-causing factors in China include the high social gross dependency ratio and crop-to-disaster ratio, and the poverty-reducing factors include the high per capita GDP, per capita social security expenditure, per capita public health expenditure, number of hospitals per 10,000 people, rate of participation in the new rural cooperative medical scheme, vegetation coverage, per capita education expenditure, number of universities, per capita research and development(R&D) expenditure, and funding per capita for cultural undertakings.(2) From 2007 to 2017, provincial income poverty(IP), health poverty(HP), cultural poverty(CP), and multidimensional poverty have been significantly reduced in China, and the overall national poverty has dropped by 5.67% annually. there is a differentiation in poverty along different dimensions in certain provinces.(3) During the study period, the local spatial pattern of multidimensional poverty between provinces showed strong spatial dynamics, and a trend of increase from the eastern to the central and western regions was noted. The MPI among provinces exhibited a strong spatial dependence over time to form a pattern of decrease from northwestern and northeastern China to the surrounding areas.(4) The spatio-temporal networks of multidimensional poverty in adjacent provinces were mainly negatively correlated, with only Shaanxi and Henan, Shaanxi and Ningxia, Qinghai and Gansu, Hubei and Anhui, Sichuan and Guizhou, and Hainan and Guangdong forming spatially strong cooperative poverty reduction relationships. These results have important reference value for the implementation of China’s poverty alleviation strategy.
基金supported by the National Key Research and Development Program of China (2016YFC0501107)the Project of Ordos Science and Technology Program (2017006)the Special Project of Science and Technology Basic Work of Ministry of Science and Technology of China (2014FY110800)
文摘It is known that the exploitation of opencast coal mines has seriously damaged the environments in the semi-arid areas.Vegetation status can reliably reflect the ecological degeneration and restoration in the opencast mining areas in the semi-arid areas.Long-time series MODIS NDVI data are widely used to simulate the vegetation cover to reflect the disturbance and restoration of local ecosystems.In this study, both qualitative(linear regression method and coefficient of variation(CoV)) and quantitative(spatial buffer analysis, and change amplitude and the rate of change in the average NDVI) analyses were conducted to analyze the spatio-temporal dynamics of vegetation during 2000–2017 in Jungar Banner of Inner Mongolia Autonomous Region, China, at the large(Jungar Banner and three mine groups) and small(three types of functional areas: opencast coal mining excavation areas, reclamation areas and natural areas) scales.The results show that the rates of change in the average NDVI in the reclamation areas(20%–60%) and opencast coal mining excavation areas(10%–20%) were considerably higher than that in the natural areas(<7%).The vegetation in the reclamation areas experienced a trend of increase(3–5 a after reclamation)-decrease(the sixth year of reclamation)-stability.The vegetation in Jungar Banner has a spatial heterogeneity under the influences of mining and reclamation activities.The ratio of vegetation improvement area to vegetation degradation area in the west, southwest and east mine groups during 2000–2017 was 8:1, 20:1 and 33:1, respectively.The regions with the high CoV of NDVI above 0.45 were mainly distributed around the opencast coal mining excavation areas, and the regions with the CoV of NDVI above 0.25 were mostly located in areas with low(28.8%) and medium-low(10.2%) vegetation cover.The average disturbance distances of mining activities on vegetation in the three mine groups(west, southwest and east) were 800, 800 and 1000 m, respectively.The greater the scale of mining, the farther the disturbance distances of mining activities on vegetation.We conclude that vegetation reclamation will certainly compensate for the negative impacts of opencast coal mining activities on vegetation.Sufficient attention should be paid to the proportional allocation of plant species(herbs and shrubs) in the reclamation areas, and the restored vegetation in these areas needs to be protected for more than 6 a.Then, as the repair time increased, the vegetation condition of the reclamation areas would exceed that of the natural areas.
基金Under the auspices of State Scholarship Fund of China Scholarship Council(No.201706320300)。
文摘Understanding the dynamics of urbanization is essential to the sustainable development of cities. Meanwhile the analysis of urban development can also provide scientifically and effective information for decision-making. With the long-term Defense Meteorological Satellite Program’s Operational Linescan System(DMSP/OLS) nighttime light images, a pixel level assessment of urbanization of China from 1992 to 2013 was conducted in this study, and the spatio-temporal dynamics and future trends of urban development were fully detected. The results showed that the urbanization and urban dynamics of China experienced drastic fluctuations from 1992 to 2013, especially for those in the coastal and metropolitan areas. From a regional perspective, it was found that the urban dynamics and increasing trends in North Coast China, East Coast China and South Coast China were much more stable and significant than that in other regions. Moreover, with the sustainability estimating of nighttime light dynamics, the regional agglomeration trends of urban regions were also detected. The light intensity in nearly 50% of lighted pixels may continuously decrease in the future, indicating a severe situation of urbanization within these regions. In this study, The results revealed in this study can provided a new insight in long time urbanization detecting and is thus beneficial to the better understanding of trends and dynamics of urban development.
基金supported by the Natural Science Foundation of Hubei Province, China (2017CFB434)the National Natural Science Foundation of China (41506208 and 61501200)the Basic Research Funds for Yellow River Institute of Hydraulic Research, China (HKYJBYW-2016-06)
文摘Mapping crop distribution with remote sensing data is of great importance for agricultural production, food security and agricultural sustainability. Winter rape is an important oil crop, which plays an important role in the cooking oil market of China. The Jianghan Plain and Dongting Lake Plain (JPDLP) are major agricultural production areas in China. Essential changes in winter rape distribution have taken place in this area during the 21st century. However, the pattern of these changes remains unknown. In this study, the spatial and temporal dynamics of winter rape from 2000 to 2017 on the JPDLP were analyzed. An artificial neural network (ANN)-based classification method was proposed to map fractional winter rape distribution by fusing moderate resolution imaging spectrometer (MODIS) data and high-resolution imagery. The results are as follows:(1) The total winter rape acreages on the JPDLP dropped significantly, especially on the Jianghan Plain with a decline of about 45% during 2000 and 2017.(2) The winter rape abundance keeps changing with about 20–30% croplands changing their abundance drastically in every two consecutive observation years.(3) The winter rape has obvious regional differentiation for the trend of its change at the county level, and the decreasing trend was observed more strongly in the traditionally dominant agricultural counties.
基金Foundation： National Natural Science Foundation of China, No.41171328, No.41201184, No.41101537 National Basic Program of China, No.2010CB951502
文摘Understanding crop patterns and their changes on regional scale is a critical re- quirement for projecting agro-ecosystem dynamics. However, tools and methods for mapping the distribution of crop area and yield are still lacking. Based on the cross-entropy theory, a spatial production allocation model （SPAM） has been developed for presenting spa- tio-temporal dynamics of maize cropping system in Northeast China during 1980-2010. The simulated results indicated that （1） maize sown area expanded northwards to 48~N before 2000, after that the increased sown area mainly occurred in the central and southern parts of Northeast China. Meanwhile, maize also expanded eastwards to 127°E and lower elevation （less than 100 m） as well as higher elevation （mainly distributed between 200 m and 350 m）; （2） maize yield has been greatly promoted for most planted area of Northeast China, espe- cially in the planted zone between 42°N and 48°N, while the yield increase was relatively homogeneous without obvious longitudinal variations for whole region; （3） maize planting density increased gradually to a moderately high level over the investigated period, which reflected the trend of aggregation of maize cultivation driven by market demand.
文摘We evaluated the dynamics of land use in the Bouba Ndjidda National Park (BNNP) and adjacent areas, in northern Cameroon. Using a maximum likelihood supervised classification of satellite images from 1990 to 2016, coupled with field and a socio-economic survey, we performed a robust land-use classification. Between 1990 and 2016, the area included eight classes of land use, with the largest in 1990 being the woody savannah (42.9%) followed by the gallery forest (20.2%) and the clear forest (16.3%). Between 1990 and 1999, the gallery forest lost 64.8% of its area mostly to the benefit of woody savannahs. Between 1999 and 2016, the largest loss of area was that of the clear forest, which decreased generally by 43.2% in favor of woody savannah. Rates of increase of crop field areas were 59.6% and 78.8% respectively for the periods of 1990 to 1999 and 1999 to 2016 to the detriment of woody savannahs. We attribute the changes in land use observed mainly to the increasing human population and associated agriculture, overgrazing, fuelwood harvesting and bush fires. The exploitation of non-timber forest products and climatic factors may also have changed the vegetation cover. We recommend the implementation of farming techniques with low impact on the environment such as agroforestry.
基金National Natural Science Foundation of China, No.41301090, No.41271117 Key Program for Strategic Science and Technology, Chinese Academy of Sciences, No.2014SJCB006
文摘Swidden agriculture is an age-old, widespread but controversial farming practice in Montane Mainland Southeast Asia （MMSEA）. In the uplands of northern Laos, swidden ag- riculture has remained a predominant human-dominated land-use type for centuries. However swidden system has undergone dramatic transformations since the mid-1990s. Debates on changes in swidden cultivation are linked to globally critical issues, such as land use/cover changes （LUCC）, biodiversity loss and environmental degradation. Since the implementation of Reducing Emissions from Deforestation and Forest Degradation （REDD）, much attention has been paid nationally and internationally to swidden agriculture in the tropics. However, knowledge of the explicitly spatial characteristics of swidden agriculture and the conse- quences of these transitions at macroscopic scale is surprisingly scarce. In this study, the intensity of swidden use and fallow forest recovery in northern Laos in 1990, 2002, and 2011 were delineated by means of Landsat Thematic Mapper （TM） and Enhanced Thematic Mapper plus （ETM＋） imagery （30 m） using a decision tree classification approach, followed by an analysis of the spatio-temporal changes in swidden agriculture. Next, annual successive TM/ETM＋ images during 2000-2010 were used to delineate the dynamics of the burning and cropping phase. Subsequently, the burned pixels identified in 2000 were compared respectively with their counterparts in the following years （2001-2011） to investigate temporal trends, land-use frequency, and the swidden cycle using time-series Landsat-based Normalized Difference Vegetation Index （NDVI） data. Finally, as the swidden cycle changed from 1 to 11 years, the fallow vegetation recovery process was studied. The results showed that： （1） from 1990 to 2011, the area of swidden agriculture increased by 54.98%, from 1.54× 10^5 ha to 2.38×10^5 ha in northern Laos. The increased swidden cultivation area was mainly distributed in Luang Prabang and southern Bokeo, whereas the decreased parts were mainly found in Phongsali; （2） swidden agriculture increased mainly at elevations of 500-800 m, 300-500 m, and 800-1000 m and on slopes of 10°-20° and 200-30°. Over 80% of swidden fields were transformed from forests; （3） during 2000-2011, the frequency of swidden use in northern Laos was about two or three times. The interval between two successive utilization of a swidden ranged from one to seven years. Comparison of swidden cycles and the related proportions of swidden farming in 2000, 2003, and 2007 revealed that swidden cycles in most areas were shortened; and （4） there was a significant correlation （0.97） between fallow vegetation recovery and the swidden cycle. The NDVI of regenerated vegetation could approach the average level of forest when the swidden cycle reached 10 years.
基金supported by grants from the National Natural Science Foundation of China (Grant No. 41571523, and Grant No. 41661144038)the National Basic Research Program of China(Grant No. 2013CBA01808)the National Key Technology R&D Program of the Ministry of Science and Technology of China (Grant No. 2014BAC05B01)
文摘Permafrost is one of the key components of terrestrial ecosystem in cold regions. In the context of climate change, few studies have investigated resilience of social ecological system(SER) from the perspective of permafrost that restricts the hydrothermal condition of alpine grassland ecosystem. In this paper, based on the structural dynamics, we developed the numerical model for the SER in the permafrost regions of the source of Yangtze and Yellow Rivers, analyzed the spatial-temporal characteristics and sensitivity of the SER, and estimated the effect of permafrost change on the SER. The results indicate that: 1) the SER has an increasing trend, especially after 1997, which is the joint effect of precipitation, temperature, NPP and ecological conservation projects; 2) the SER shows the spatial feature of high in southeast and low in northwest,which is consistent with the variation trends of high southeast and low northwest for the precipitation, temperature and NPP, and low southeast and high northwest for the altitude; 3) the high sensitive regions of SER to the permafrost change have gradually transited from the island distribution to zonal and planar distribution since 1980, moreover, the sensitive degree has gradually reduced; relatively, the sensitivity has high value in the north and south, and low value in the south and east; 4) the thickness of permafrost active layer shows a highly negative correlation with the SER. The contribution rate of permafrost change to the SER is-4.3%, that is, once the thickness of permafrost active layer increases 1 unit, the SER would decrease 0.04 units.
文摘This article uses TM images in 1999 and 2006 in Dahua County,selects the driving factors having great impact on urban land use change,and conducts data processing using GIS software.It then uses CLUE-S model to simulate land use change pattern in 2006,and uses land use map in 2006 to test the simulation results.The results show that the simulation achieves good effect,indicating that we can use CLUE-S model to simulate the future urban land use change in karst areas,to provide scientific decision-making support for sustainable development of land use.
基金National Institute of Biomedical Imaging and Bioengineering(R00EB014879)Natural Science Foundation of Jiangsu Province(Grant no.BK20190697)and Natural Science Foundation of China(61901222).
文摘Optical coherence tomography angiography(OCTA)has emerged as an advanced in vivo im-aging modality,which is widely used for the clinic ophthalmology and neuroscience research in the rodent brain cortex among others.Based on the high numerical aperture(NA)probing lens and the motion-corrected algorithms,a high-resolution imaging technique called OCT micro-angiography is applied to resolve the small blood capillary vessels ranging from 5μm to 10μm in diameter.As OCT-based techniques are recently evolving further from the structural imaging of capillaries toward spatio-temporal dynamic imaging of blood flow in capillaries,here we present a review on the latest techniques for the dynamic flow imaging.Studies on capillary blood flow using these techniques will help us better understand the roles of capillary blood flow for normal functioning of the brain as well as how it malfunctions in diseases.
基金supported by the National Natural Science Foundation of China (Grant No. 41601438 and 41571078)the Fundamental Research Funds for the Central Universities (Grant No.2412016KJ026)the Foundation of the Education Department of Jilin Province in the 13~(th) Five-Year project (Grant No. JJKH20170916KJ)
文摘The Changbai Mountains and the Appalachian Mountains have similar spatial contexts.The elevation,latitude,and moisture gradients of both mountain ranges offer regional insight for investigating the vegetation dynamics in eastern Eurasia and eastern North America.We determined and compared the spatial patterns and temporal trends in the normalized difference vegetation index(NDVI)in the Changbai Mountains and the Appalachian Mountains using time series data from the Global Inventory Modeling and Mapping Studies 3^(rd) generation dataset from 1982 to 2013.The spatial pattern of NDVI in the Changbai Mountains exhibited fragmentation,whereas NDVI in the Appalachian Mountains decreased from south to north.The vegetation dynamics in the Changbai Mountains had an insignificant trend at the regional scale,whereas the dynamics in the Appalachian Mountains had a significant increasing trend.NDVI increased in 55% of the area of the Changbai Mountains and in 95% of the area of the Appalachian Mountains.The peak NDVI occurred one month later in the Changbai Mountains than in the Appalachian Mountains.The results revealed a significant increase in NDVI in autumn in both mountain ranges.The climatic trend in the Changbai Mountains included warming and decreased precipitation,and whereas that in the Appalachian Mountains included significant warming and increased precipitation.Positive and negative correlations existed between NDVI and temperature and precipitation,respectively,in both mountain ranges.Particularly,the spring temperature and NDVI exhibited a significant positive correlation in both mountain ranges.The results of this study suggest that human actives caused the differences in the spatial patterns of NDVI and that various characteristics of climate change and intensity of human actives dominated the differences in the NDVI trends between the Changbai Mountains and the Appalachian Mountains.Additionally,the vegetation dynamics of both mountain ranges were not identical to those in previous broader-scale studies.
文摘The present work is related to the numerical investigation of the spatio-temporal susceptible-latent-breaking out-recovered(SLBR)epidemic model.It describes the computer virus dynamics with vertical transmission via the internet.In these types of dynamics models,the absolute values of the state variables are the fundamental requirement that must be fulfilled by the numerical design.By taking into account this key property,the positivity preserving algorithm is designed to solve the underlying SLBR system.Since,the state variables associated with the phenomenon,represent the computer nodes,so they must take in absolute.Moreover,the continuous system(SLBR)acquires two steady states i.e.,the virus-free state and the virus existence state.The stability of the numerical design,at the equilibrium points,portrays an exceptional aspect about the propagation of the virus.The designed discretization algorithm sustains the stability of both the steady states.The computer simulations also endorse that the proposed discretization algorithm retains all the traits of the continuous SLBR model with spatial content.The stability and consistency of the proposed algorithm are verified,mathematically.All the facts are also ascertained by numerical simulations.
基金supported by the 2024 Open Project of Collaborative Innovation Center for Emissions Trading System Co-constructed by the Province and Ministry(24CICETS-YB013).
文摘Understanding regional carbon emissions from human activities,particularly their spatio-temporal patterns,is essential for implementing decarbonization strategies and cultivating a low-carbon economy.This study develops a spatial visualization model to estimate carbon emissions in Southeast Asia using calibrated nighttime light data,with DMSP-OLS(Defense Meteorological Satellite Program Operational Linescan System)and NPP-VIIRS(National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite)standardized through polynomial regression and machine learning to ensure consistency.Emissions in Southeast Asia increased by 2.51 times from 1992 to 2022,shifting from gradual to rapid growth.Validation against Open-source Data Inventory for Anthropogenic CO2(ODIAC)and Emissions Database for Global Atmospheric Research(EDGAR)shows strong agreement in high-emission urban areas but discrepancies in low-emission rural regions due to data sparsity and satellite sensor limits.Spatial analysis reveals that major Southeast Asian cities and their peripheries exhibit robust,sustained growth,while rural,less-developed areas show slower trends,highlighting persistent urbanrural disparities.These urban regions demonstrate a“circular economy advantage”,where per-unit-area carbon emissions steadily rise in economically advantageous zones.Despite high model accuracy,uncertainties persist due to variations in regional economic activities and the limitations of satellite-based emission proxies.Forecasts suggest elevated emission levels in major cities will continue,while changes in other areas remain relatively minimal.Consequently,achieving a low-carbon economy in Southeast Asia requires a top-down approach,emphasizing infrastructure enhancement,resource and energy optimization,and fostering a sustainable,circular socio-economic system.
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.
文摘Predicting the progression from Mild Cognitive Impairment(MCI)to Alzheimer's Disease(AD)is a critical challenge for enabling early intervention and improving patient outcomes.While longitudinal multi-modal neuroimaging data holds immense potential for capturing the spatio-temporal dynamics of disease progression,its effective analysis is hampered by significant challenges:temporal heterogeneity(irregularly sampled scans),multi-modal misalignment,and the propensity of deep learning models to learn spurious,noncausal correlations.We propose CASCADE-Net,a novel end-to-end pipeline for robust and interpretable MCI-to-AD progression prediction.Our architecture introduces a Dynamic Temporal Alignment Module that employs a Neural Ordinary Differential Equation(Neural ODE)to model the continuous,underlying progression of pathology from irregularly sampled scans,effectively mapping heterogeneous patient data to a unified latent timeline.This aligned,noise-reduced spatio-temporal data is then processed by a predictive model featuring a novel Causal Spatial Attention mechanism.This mechanism not only identifies the critical brain regions and their evolution predictive of conversion but also incorporates a counterfactual constraint during training.This constraint ensures the learned features are causally linked to AD pathology by encouraging invariance to non-causal,confounder-based changes.Extensive experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset demonstrate that CASCADE-Net significantly outperforms state-of-the-art sequential models in prognostic accuracy.Furthermore,our model provides highly interpretable,causally-grounded attention maps,offering valuable insights into the disease progression process and fostering greater clinical trust.
基金co-supported by the National Natural Science Foundation of China(No.12101608)the NSAF(No.U2230208)the Hunan Provincial Innovation Foundation for Postgraduate,China(No.CX20220034).
文摘This paper introduces techniques in Gaussian process regression model for spatiotemporal data collected from complex systems.This study focuses on extracting local structures and then constructing surrogate models based on Gaussian process assumptions.The proposed Dynamic Gaussian Process Regression(DGPR)consists of a sequence of local surrogate models related to each other.In DGPR,the time-based spatial clustering is carried out to divide the systems into sub-spatio-temporal parts whose interior has similar variation patterns,where the temporal information is used as the prior information for training the spatial-surrogate model.The DGPR is robust and especially suitable for the loosely coupled model structure,also allowing for parallel computation.The numerical results of the test function show the effectiveness of DGPR.Furthermore,the shock tube problem is successfully approximated under different phenomenon complexity.
基金Youth Innovation Promotion Association CAS,Grant/Award Number:2021103Strategic Priority Research Program of Chinese Academy of Sciences,Grant/Award Number:XDC02060500。
文摘Appropriately characterising the mixed space-time relations of the contagion process caused by hybrid space and time factors remains the primary challenge in COVID-19 forecasting.However,in previous deep learning models for epidemic forecasting,spatial and temporal variations are captured separately.A unified model is developed to cover all spatio-temporal relations.However,this measure is insufficient for modelling the complex spatio-temporal relations of infectious disease transmission.A dynamic adaptive spatio-temporal graph network(DASTGN)is proposed based on attention mechanisms to improve prediction accuracy.In DASTGN,complex spatio-temporal relations are depicted by adaptively fusing the mixed space-time effects and dynamic space-time dependency structure.This dual-scale model considers the time-specific,space-specific,and direct effects of the propagation process at the fine-grained level.Furthermore,the model characterises impacts from various space-time neighbour blocks under time-varying interventions at the coarse-grained level.The performance comparisons on the three COVID-19 datasets reveal that DASTGN achieves state-of-the-art results with a maximum improvement of 17.092%in the root mean-square error and 11.563%in the mean absolute error.Experimental results indicate that the mechanisms of designing DASTGN can effectively detect some spreading characteristics of COVID-19.The spatio-temporal weight matrices learned in each proposed module reveal diffusion patterns in various scenarios.In conclusion,DASTGN has successfully captured the dynamic spatio-temporal variations of COVID-19,and considering multiple dynamic space-time relationships is essential in epidemic forecasting.
