China' Mainland has a poor distribution of meteorological stations.Existing models’estimation accuracy for creating high-resolution surfaces of meteorological data is restricted for air temperature,and low for re...China' Mainland has a poor distribution of meteorological stations.Existing models’estimation accuracy for creating high-resolution surfaces of meteorological data is restricted for air temperature,and low for relative humidity and wind speed(few studies reported).This study compared the typical generalized additive model(GAM)and autoencoder-based residual neural network(hereafter,residual network for short)in terms of predicting three meteorological parameters,namely air temperature,relative humidity,and wind speed,using data from 824 monitoring stations across China’s mainland in 2015.The performance of the two models was assessed using a 10-fold cross-validation procedure.The air temperature models employ basic variables such as latitude,longitude,elevation,and the day of the year.The relative humidity models employ air temperature and ozone concentration as covariates,while the wind speed models use wind speed coarse-resolution reanalysis data as covariates,in addition to the fundamental variables.Spatial coordinates represent spatial variation,while the time index of the day captures time variation in our spatiotemporal models.In comparison to GAM,the residual network considerably improved prediction accuracy:on average,the coefficient of variation(CV)R2 of the three meteorological parameters rose by 0.21,CV root-mean square(RMSE)fell by 37%,and the relative humidity model improved the most.The accuracy of relative humidity models was considerably improved once the monthly index was included,demonstrating that varied amounts of temporal variables are crucial for relative humidity models.We also spoke about the benefits and drawbacks of using coarse resolution reanalysis data and closest neighbor values as variables.In comparison to classic GAMs,this study indicates that the residual network model may considerably increase the accuracy of national high spatial(1 km)and temporal(daily)resolution meteorological data.Our findings have implications for high-resolution and high-accuracy meteorological parameter mapping in China.展开更多
In integrating geo-spatial datasets, sometimes layers are unable to perfectly overlay each other. In most cases, the cause of misalignment is the cartographic variation of objects forming features in the datasets. Eit...In integrating geo-spatial datasets, sometimes layers are unable to perfectly overlay each other. In most cases, the cause of misalignment is the cartographic variation of objects forming features in the datasets. Either this could be due to actual changes on ground, collection, or storage approaches used leading to overlapping or openings between features. In this paper, we present an alignment method that uses adjustment algorithms to update the geometry of features within a dataset or complementary adjacent datasets so that they can align to achieve perfect integration. The method identifies every unique spatial instance in datasets and their spatial points that define all their geometry;the differences are compared and used to compute the alignment parameters. This provides a uniform geo-spatial features’ alignment taking into consideration changes in the different datasets being integrated without affecting the topology and attributes.展开更多
Through analyzing theprinciple of data sharing in the data-base system,this paper discusses theprinciple and method for integratingand sharing GIS data by data engine,introduces a way to achieve the highintegration an...Through analyzing theprinciple of data sharing in the data-base system,this paper discusses theprinciple and method for integratingand sharing GIS data by data engine,introduces a way to achieve the highintegration and sharing of GIS data on the basis of VCT in VC^(++),and pro-vides the method for uniting VCT intoRDBMS in order to implement a spa-tial database with object-oriented datamodel.展开更多
The paper presents a set of techniques of digital watermarking by which copyright and user rights messages are hidden into geo-spatial graphics data,as well as techniques of compressing and encrypting the watermarked ...The paper presents a set of techniques of digital watermarking by which copyright and user rights messages are hidden into geo-spatial graphics data,as well as techniques of compressing and encrypting the watermarked geo-spatial graphics data.The technology aims at tracing and resisting the illegal distribution and duplication of the geo-spatial graphics data product,so as to effectively protect the data producer＇s rights as well as to facilitate the secure sharing of geo-spatial graphics data.So far in the CIS field throughout the world,few researches have been made on digital watermarking.The research is a novel exploration both in the field of security management of geo-spatial graphics data and in the applications of digital watermarking technique.An application software employing the proposed technology has been developed.A number of experimental tests on the 1：500,000 digital bathymetric chart of the South China Sea and 1：10,000 digital topographic map of Jiangsu Province have been conducted to verify the feasibility of the proposed technology.展开更多
The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities...The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities and obstacles.The huge and diversified nature of these datasets cannot always be managed using traditional data analysis methods.As a consequence,deep learning has emerged as a strong tool for analysing numerous omics data due to its ability to handle complex and non-linear relationships.This paper explores the fundamental concepts of deep learning and how they are used in multi-omics medical data mining.We demonstrate how autoencoders,variational autoencoders,multimodal models,attention mechanisms,transformers,and graph neural networks enable pattern analysis and recognition across all omics data.Deep learning has been found to be effective in illness classification,biomarker identification,gene network learning,and therapeutic efficacy prediction.We also consider critical problems like as data quality,model explainability,whether findings can be repeated,and computational power requirements.We now consider future elements of combining omics with clinical and imaging data,explainable AI,federated learning,and real-time diagnostics.Overall,this study emphasises the need of collaborating across disciplines to advance deep learning-based multi-omics research for precision medicine and comprehending complicated disorders.展开更多
High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging ...High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging foundation models and multimodal learning frameworks are enabling scalable and transferable representations of cellular states,while advances in interpretability and real-world data integration are bridging the gap between discovery and clinical application.This paper outlines a concise roadmap for AI-driven,transcriptome-centered multi-omics integration in precision medicine(Figure 1).展开更多
Gastrointestinal tumors require personalized treatment strategies due to their heterogeneity and complexity.Multimodal artificial intelligence(AI)addresses this challenge by integrating diverse data sources-including ...Gastrointestinal tumors require personalized treatment strategies due to their heterogeneity and complexity.Multimodal artificial intelligence(AI)addresses this challenge by integrating diverse data sources-including computed tomography(CT),magnetic resonance imaging(MRI),endoscopic imaging,and genomic profiles-to enable intelligent decision-making for individualized therapy.This approach leverages AI algorithms to fuse imaging,endoscopic,and omics data,facilitating comprehensive characterization of tumor biology,prediction of treatment response,and optimization of therapeutic strategies.By combining CT and MRI for structural assessment,endoscopic data for real-time visual inspection,and genomic information for molecular profiling,multimodal AI enhances the accuracy of patient stratification and treatment personalization.The clinical implementation of this technology demonstrates potential for improving patient outcomes,advancing precision oncology,and supporting individualized care in gastrointestinal cancers.Ultimately,multimodal AI serves as a transformative tool in oncology,bridging data integration with clinical application to effectively tailor therapies.展开更多
Concerning about the rapid urban growth in recent China, this study takes Beijing as a case and puts forward that urban sprawl can be measured from spatial configuration, urban growth efficiency and external impacts, ...Concerning about the rapid urban growth in recent China, this study takes Beijing as a case and puts forward that urban sprawl can be measured from spatial configuration, urban growth efficiency and external impacts, and then develops a geo-spatial indices system for measuring sprawl, a total of 13 indicators. In order to calculate these indices, different sources data are selected, including land use maps, former land use planning, land price and floor-area-ratio samples, digitized map of the highways and city centers, population and GDP statistical data, etc. Various GIS spatial analysis methods are used to spatialize these indices into 100mx100m cells. Besides, an integrated urban sprawl index is calculated by weight sum of these 13 indices. The application result indicates that geo-spatial indices system can capture most of the typical features and interior differentia of urban sprawl. Construction land in Beijing has kept fast growing with large amount, low efficiency and disordered spatial configuration, indicating a typical sprawling tendency. The following specific sprawl features are identified by each indicator： （1） typical spatial configuration of sprawling： obvious fragmentation and irregularity of landscape due to unsuccessful enforcement of land use planning, unadvisable pattern of typical discontinuous development, strip development and leapfrog development; （2） low efficiency of sprawl： low development density, low population density and economic output in newly developed area; and （3） negative impacts on agriculture, environment and city life. According to the integrated sprawl index, the sprawling amount in the northern part is larger than that in the southern, but the sprawling extent is in converse case; most sprawling area include the marginal area of the near suburbs and the area between highways, etc. Four sprawling patterns are identified： randomly expansion at urban fringe, strip development along or between highways, scattered development of industrial land, leapfrog development of urban residence and industrial area.展开更多
The process of sharing of data has become easier than ever with the advancement of cloud computing and software tools.However,big challenges remain such as efficient handling of big geospatial data,supporting and shar...The process of sharing of data has become easier than ever with the advancement of cloud computing and software tools.However,big challenges remain such as efficient handling of big geospatial data,supporting and sharing of crowd sourced/citizen science data,integration with semantic heterogeneity,and inclusion of agile processes for continuous improvement of geospatial technology.This paper discusses the new frontiers regarding these challenges and the related work performed by the Open Geospatial Consortium,the world leading organization focused on developing open geospatial standards that“geo-enable”the Web,wireless,and location-based services and mainstream IT.展开更多
The enhancement of computing power,the maturity of learning algorithms,and the richness of application scenarios make Artificial Intelligence(AI)solution increasingly attractive when solving Geo-spatial Information Sc...The enhancement of computing power,the maturity of learning algorithms,and the richness of application scenarios make Artificial Intelligence(AI)solution increasingly attractive when solving Geo-spatial Information Science(GSIS)problems.These include image matching,image target detection,change detection,image retrieval,and for generating data models of various types.This paper discusses the connection and synthesis between AI and GSIS in block adjustment,image search and discovery in big databases,automatic change detection,and detection of abnormalities,demonstrating that AI can integrate GSIS.Moreover,the concept of Earth Observation Brain and Smart Geo-spatial Service(SGSS)is introduced in the end,and it is expected to promote the development of GSIS into broadening applications.展开更多
Tourism is a rapidly growing investment point in Sri Lanka, where huge investment is takeing place. Even though the investment is very massive, the planning, development, and marketing are key components of success in...Tourism is a rapidly growing investment point in Sri Lanka, where huge investment is takeing place. Even though the investment is very massive, the planning, development, and marketing are key components of success in tourism zone enhancement. The main objective of this study was to implement a geo-spatial information system for development of tourism in Kandy district. Primary data collection methods i.e. questionnaire survey, interviews, focus group interviews, and observations were employed for data collection. Google maps with Google API standards which are specially designed for developers and computer programmers were used for implementation of the system. System requirements were identified by interviewing tourists and observations made on tourist sites. Proximity analysis, spatial joint, and network analysis with Google direction application program interface （API） and Google place API were used to analyze data. The study highlights the potential tourist attractions and the accessibility and other required details through a web output. Issues and challenges faced by travelers are mainly lack of specific location information, public transport schedules, and reliable tourist attraction information. Online geo-spatial information system created in this study provides a guide for tourists to fred the destination routes, the service areas, and all necessary details on particular destinations.展开更多
Regarding the special potential of ports located on international coastlines such as Makoran Sea (Iran) for goods and human smuggling, national level of coastline security is very important. They can play a significan...Regarding the special potential of ports located on international coastlines such as Makoran Sea (Iran) for goods and human smuggling, national level of coastline security is very important. They can play a significant role in the development of power and security. Based on military reviews and analyses, police location and monitoring field view in the coastlines are strategic issues in modern security development. This research proposes a tool for development of coastal roads and coastal walking routes in the deployment of police. The main focuses are monitoring field view and accessibility to the strategic coastline. GIS tool plays an essential role in producing important security maps. Chabahar Port in Iran, as the most important port of Makoran Sea, has been selected as the study area, regarding its strategic role in the national economy and security. Research method focused on these major axes: successful establishment of police stations in shoreline for increasing monitoring and coastal security and suitable patrol of patrol police car in the coastal roads. This study adopts a scientific approach to the analysis of the present and future development in urban and security planning in coastal towns in the national and regional levels.展开更多
Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced tran...Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.展开更多
The Intelligent Internet of Things(IIoT)involves real-world things that communicate or interact with each other through networking technologies by collecting data from these“things”and using intelligent approaches,s...The Intelligent Internet of Things(IIoT)involves real-world things that communicate or interact with each other through networking technologies by collecting data from these“things”and using intelligent approaches,such as Artificial Intelligence(AI)and machine learning,to make accurate decisions.Data science is the science of dealing with data and its relationships through intelligent approaches.Most state-of-the-art research focuses independently on either data science or IIoT,rather than exploring their integration.Therefore,to address the gap,this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT(IIoT)system by classifying the existing IoT-based data science techniques and presenting a summary of various characteristics.The paper analyzes the data science or big data security and privacy features,including network architecture,data protection,and continuous monitoring of data,which face challenges in various IoT-based systems.Extensive insights into IoT data security,privacy,and challenges are visualized in the context of data science for IoT.In addition,this study reveals the current opportunities to enhance data science and IoT market development.The current gap and challenges faced in the integration of data science and IoT are comprehensively presented,followed by the future outlook and possible solutions.展开更多
Viral infectious diseases,characterized by their intricate nature and wide-ranging diversity,pose substantial challenges in the domain of data management.The vast volume of data generated by these diseases,spanning fr...Viral infectious diseases,characterized by their intricate nature and wide-ranging diversity,pose substantial challenges in the domain of data management.The vast volume of data generated by these diseases,spanning from the molecular mechanisms within cells to large-scale epidemiological patterns,has surpassed the capabilities of traditional analytical methods.In the era of artificial intelligence(AI)and big data,there is an urgent necessity for the optimization of these analytical methods to more effectively handle and utilize the information.Despite the rapid accumulation of data associated with viral infections,the lack of a comprehensive framework for integrating,selecting,and analyzing these datasets has left numerous researchers uncertain about which data to select,how to access it,and how to utilize it most effectively in their research.This review endeavors to fill these gaps by exploring the multifaceted nature of viral infectious diseases and summarizing relevant data across multiple levels,from the molecular details of pathogens to broad epidemiological trends.The scope extends from the micro-scale to the macro-scale,encompassing pathogens,hosts,and vectors.In addition to data summarization,this review thoroughly investigates various dataset sources.It also traces the historical evolution of data collection in the field of viral infectious diseases,highlighting the progress achieved over time.Simultaneously,it evaluates the current limitations that impede data utilization.Furthermore,we propose strategies to surmount these challenges,focusing on the development and application of advanced computational techniques,AI-driven models,and enhanced data integration practices.By providing a comprehensive synthesis of existing knowledge,this review is designed to guide future research and contribute to more informed approaches in the surveillance,prevention,and control of viral infectious diseases,particularly within the context of the expanding big-data landscape.展开更多
Air pollution in China covers a large area with complex sources and formation mechanisms,making it a unique place to conduct air pollution and atmospheric chemistry research.The National Natural Science Foundation of ...Air pollution in China covers a large area with complex sources and formation mechanisms,making it a unique place to conduct air pollution and atmospheric chemistry research.The National Natural Science Foundation of China’s Major Research Plan entitled“Fundamental Researches on the Formation and Response Mechanism of the Air Pollution Complex in China”(or the Plan)has funded 76 research projects to explore the causes of air pollution in China,and the key processes of air pollution in atmospheric physics and atmospheric chemistry.In order to summarize the abundant data from the Plan and exhibit the long-term impacts domestically and internationally,an integration project is responsible for collecting the various types of data generated by the 76 projects of the Plan.This project has classified and integrated these data,forming eight categories containing 258 datasets and 15 technical reports in total.The integration project has led to the successful establishment of the China Air Pollution Data Center(CAPDC)platform,providing storage,retrieval,and download services for the eight categories.This platform has distinct features including data visualization,related project information querying,and bilingual services in both English and Chinese,which allows for rapid searching and downloading of data and provides a solid foundation of data and support for future related research.Air pollution control in China,especially in the past decade,is undeniably a global exemplar,and this data center is the first in China to focus on research into the country’s air pollution complex.展开更多
Imputation of missing data has long been an important topic and an essential application for intelligent transportation systems(ITS)in the real world.As a state-of-the-art generative model,the diffusion model has prov...Imputation of missing data has long been an important topic and an essential application for intelligent transportation systems(ITS)in the real world.As a state-of-the-art generative model,the diffusion model has proven highly successful in image generation,speech generation,time series modelling etc.and now opens a new avenue for traffic data imputation.In this paper,we propose a conditional diffusion model,called the implicit-explicit diffusion model,for traffic data imputation.This model exploits both the implicit and explicit feature of the data simultaneously.More specifically,we design two types of feature extraction modules,one to capture the implicit dependencies hidden in the raw data at multiple time scales and the other to obtain the long-term temporal dependencies of the time series.This approach not only inherits the advantages of the diffusion model for estimating missing data,but also takes into account the multiscale correlation inherent in traffic data.To illustrate the performance of the model,extensive experiments are conducted on three real-world time series datasets using different missing rates.The experimental results demonstrate that the model improves imputation accuracy and generalization capability.展开更多
文摘China' Mainland has a poor distribution of meteorological stations.Existing models’estimation accuracy for creating high-resolution surfaces of meteorological data is restricted for air temperature,and low for relative humidity and wind speed(few studies reported).This study compared the typical generalized additive model(GAM)and autoencoder-based residual neural network(hereafter,residual network for short)in terms of predicting three meteorological parameters,namely air temperature,relative humidity,and wind speed,using data from 824 monitoring stations across China’s mainland in 2015.The performance of the two models was assessed using a 10-fold cross-validation procedure.The air temperature models employ basic variables such as latitude,longitude,elevation,and the day of the year.The relative humidity models employ air temperature and ozone concentration as covariates,while the wind speed models use wind speed coarse-resolution reanalysis data as covariates,in addition to the fundamental variables.Spatial coordinates represent spatial variation,while the time index of the day captures time variation in our spatiotemporal models.In comparison to GAM,the residual network considerably improved prediction accuracy:on average,the coefficient of variation(CV)R2 of the three meteorological parameters rose by 0.21,CV root-mean square(RMSE)fell by 37%,and the relative humidity model improved the most.The accuracy of relative humidity models was considerably improved once the monthly index was included,demonstrating that varied amounts of temporal variables are crucial for relative humidity models.We also spoke about the benefits and drawbacks of using coarse resolution reanalysis data and closest neighbor values as variables.In comparison to classic GAMs,this study indicates that the residual network model may considerably increase the accuracy of national high spatial(1 km)and temporal(daily)resolution meteorological data.Our findings have implications for high-resolution and high-accuracy meteorological parameter mapping in China.
文摘In integrating geo-spatial datasets, sometimes layers are unable to perfectly overlay each other. In most cases, the cause of misalignment is the cartographic variation of objects forming features in the datasets. Either this could be due to actual changes on ground, collection, or storage approaches used leading to overlapping or openings between features. In this paper, we present an alignment method that uses adjustment algorithms to update the geometry of features within a dataset or complementary adjacent datasets so that they can align to achieve perfect integration. The method identifies every unique spatial instance in datasets and their spatial points that define all their geometry;the differences are compared and used to compute the alignment parameters. This provides a uniform geo-spatial features’ alignment taking into consideration changes in the different datasets being integrated without affecting the topology and attributes.
文摘Through analyzing theprinciple of data sharing in the data-base system,this paper discusses theprinciple and method for integratingand sharing GIS data by data engine,introduces a way to achieve the highintegration and sharing of GIS data on the basis of VCT in VC^(++),and pro-vides the method for uniting VCT intoRDBMS in order to implement a spa-tial database with object-oriented datamodel.
基金Under the auspices of Jiangsu Provincial Science and Technology Fundation of Surveying and Mapping (No. 200416 )
文摘The paper presents a set of techniques of digital watermarking by which copyright and user rights messages are hidden into geo-spatial graphics data,as well as techniques of compressing and encrypting the watermarked geo-spatial graphics data.The technology aims at tracing and resisting the illegal distribution and duplication of the geo-spatial graphics data product,so as to effectively protect the data producer＇s rights as well as to facilitate the secure sharing of geo-spatial graphics data.So far in the CIS field throughout the world,few researches have been made on digital watermarking.The research is a novel exploration both in the field of security management of geo-spatial graphics data and in the applications of digital watermarking technique.An application software employing the proposed technology has been developed.A number of experimental tests on the 1：500,000 digital bathymetric chart of the South China Sea and 1：10,000 digital topographic map of Jiangsu Province have been conducted to verify the feasibility of the proposed technology.
文摘The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities and obstacles.The huge and diversified nature of these datasets cannot always be managed using traditional data analysis methods.As a consequence,deep learning has emerged as a strong tool for analysing numerous omics data due to its ability to handle complex and non-linear relationships.This paper explores the fundamental concepts of deep learning and how they are used in multi-omics medical data mining.We demonstrate how autoencoders,variational autoencoders,multimodal models,attention mechanisms,transformers,and graph neural networks enable pattern analysis and recognition across all omics data.Deep learning has been found to be effective in illness classification,biomarker identification,gene network learning,and therapeutic efficacy prediction.We also consider critical problems like as data quality,model explainability,whether findings can be repeated,and computational power requirements.We now consider future elements of combining omics with clinical and imaging data,explainable AI,federated learning,and real-time diagnostics.Overall,this study emphasises the need of collaborating across disciplines to advance deep learning-based multi-omics research for precision medicine and comprehending complicated disorders.
文摘High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging foundation models and multimodal learning frameworks are enabling scalable and transferable representations of cellular states,while advances in interpretability and real-world data integration are bridging the gap between discovery and clinical application.This paper outlines a concise roadmap for AI-driven,transcriptome-centered multi-omics integration in precision medicine(Figure 1).
基金Supported by Xuhui District Health Commission,No.SHXH202214.
文摘Gastrointestinal tumors require personalized treatment strategies due to their heterogeneity and complexity.Multimodal artificial intelligence(AI)addresses this challenge by integrating diverse data sources-including computed tomography(CT),magnetic resonance imaging(MRI),endoscopic imaging,and genomic profiles-to enable intelligent decision-making for individualized therapy.This approach leverages AI algorithms to fuse imaging,endoscopic,and omics data,facilitating comprehensive characterization of tumor biology,prediction of treatment response,and optimization of therapeutic strategies.By combining CT and MRI for structural assessment,endoscopic data for real-time visual inspection,and genomic information for molecular profiling,multimodal AI enhances the accuracy of patient stratification and treatment personalization.The clinical implementation of this technology demonstrates potential for improving patient outcomes,advancing precision oncology,and supporting individualized care in gastrointestinal cancers.Ultimately,multimodal AI serves as a transformative tool in oncology,bridging data integration with clinical application to effectively tailor therapies.
基金National Natural Science Foundation of China, No.40571056 Sustentation Fund on Doctoral Thesis from Beijing Science and Technology Committee, No.ZZ0608
文摘Concerning about the rapid urban growth in recent China, this study takes Beijing as a case and puts forward that urban sprawl can be measured from spatial configuration, urban growth efficiency and external impacts, and then develops a geo-spatial indices system for measuring sprawl, a total of 13 indicators. In order to calculate these indices, different sources data are selected, including land use maps, former land use planning, land price and floor-area-ratio samples, digitized map of the highways and city centers, population and GDP statistical data, etc. Various GIS spatial analysis methods are used to spatialize these indices into 100mx100m cells. Besides, an integrated urban sprawl index is calculated by weight sum of these 13 indices. The application result indicates that geo-spatial indices system can capture most of the typical features and interior differentia of urban sprawl. Construction land in Beijing has kept fast growing with large amount, low efficiency and disordered spatial configuration, indicating a typical sprawling tendency. The following specific sprawl features are identified by each indicator： （1） typical spatial configuration of sprawling： obvious fragmentation and irregularity of landscape due to unsuccessful enforcement of land use planning, unadvisable pattern of typical discontinuous development, strip development and leapfrog development; （2） low efficiency of sprawl： low development density, low population density and economic output in newly developed area; and （3） negative impacts on agriculture, environment and city life. According to the integrated sprawl index, the sprawling amount in the northern part is larger than that in the southern, but the sprawling extent is in converse case; most sprawling area include the marginal area of the near suburbs and the area between highways, etc. Four sprawling patterns are identified： randomly expansion at urban fringe, strip development along or between highways, scattered development of industrial land, leapfrog development of urban residence and industrial area.
文摘The process of sharing of data has become easier than ever with the advancement of cloud computing and software tools.However,big challenges remain such as efficient handling of big geospatial data,supporting and sharing of crowd sourced/citizen science data,integration with semantic heterogeneity,and inclusion of agile processes for continuous improvement of geospatial technology.This paper discusses the new frontiers regarding these challenges and the related work performed by the Open Geospatial Consortium,the world leading organization focused on developing open geospatial standards that“geo-enable”the Web,wireless,and location-based services and mainstream IT.
基金This work was supported in part by the National key R and D plan on strategic international scientific and technological innovation cooperation special project[grant number 2016YFE0202300]the National Natural Science Foundation of China[grant number 61671332,41771452,51708426,41890820,41771454]+1 种基金the Natural Science Fund of Hubei Province in China[grant number 2018CFA007]the Independent Research Projects of Wuhan University[grant number 2042018kf0250].
文摘The enhancement of computing power,the maturity of learning algorithms,and the richness of application scenarios make Artificial Intelligence(AI)solution increasingly attractive when solving Geo-spatial Information Science(GSIS)problems.These include image matching,image target detection,change detection,image retrieval,and for generating data models of various types.This paper discusses the connection and synthesis between AI and GSIS in block adjustment,image search and discovery in big databases,automatic change detection,and detection of abnormalities,demonstrating that AI can integrate GSIS.Moreover,the concept of Earth Observation Brain and Smart Geo-spatial Service(SGSS)is introduced in the end,and it is expected to promote the development of GSIS into broadening applications.
文摘Tourism is a rapidly growing investment point in Sri Lanka, where huge investment is takeing place. Even though the investment is very massive, the planning, development, and marketing are key components of success in tourism zone enhancement. The main objective of this study was to implement a geo-spatial information system for development of tourism in Kandy district. Primary data collection methods i.e. questionnaire survey, interviews, focus group interviews, and observations were employed for data collection. Google maps with Google API standards which are specially designed for developers and computer programmers were used for implementation of the system. System requirements were identified by interviewing tourists and observations made on tourist sites. Proximity analysis, spatial joint, and network analysis with Google direction application program interface （API） and Google place API were used to analyze data. The study highlights the potential tourist attractions and the accessibility and other required details through a web output. Issues and challenges faced by travelers are mainly lack of specific location information, public transport schedules, and reliable tourist attraction information. Online geo-spatial information system created in this study provides a guide for tourists to fred the destination routes, the service areas, and all necessary details on particular destinations.
文摘Regarding the special potential of ports located on international coastlines such as Makoran Sea (Iran) for goods and human smuggling, national level of coastline security is very important. They can play a significant role in the development of power and security. Based on military reviews and analyses, police location and monitoring field view in the coastlines are strategic issues in modern security development. This research proposes a tool for development of coastal roads and coastal walking routes in the deployment of police. The main focuses are monitoring field view and accessibility to the strategic coastline. GIS tool plays an essential role in producing important security maps. Chabahar Port in Iran, as the most important port of Makoran Sea, has been selected as the study area, regarding its strategic role in the national economy and security. Research method focused on these major axes: successful establishment of police stations in shoreline for increasing monitoring and coastal security and suitable patrol of patrol police car in the coastal roads. This study adopts a scientific approach to the analysis of the present and future development in urban and security planning in coastal towns in the national and regional levels.
基金research was funded by Science and Technology Project of State Grid Corporation of China under grant number 5200-202319382A-2-3-XG.
文摘Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.
基金supported in part by the National Natural Science Foundation of China under Grant 62371181in part by the Changzhou Science and Technology International Cooperation Program under Grant CZ20230029+1 种基金supported by a National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(2021R1A2B5B02087169)supported under the framework of international cooperation program managed by the National Research Foundation of Korea(2022K2A9A1A01098051)。
文摘The Intelligent Internet of Things(IIoT)involves real-world things that communicate or interact with each other through networking technologies by collecting data from these“things”and using intelligent approaches,such as Artificial Intelligence(AI)and machine learning,to make accurate decisions.Data science is the science of dealing with data and its relationships through intelligent approaches.Most state-of-the-art research focuses independently on either data science or IIoT,rather than exploring their integration.Therefore,to address the gap,this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT(IIoT)system by classifying the existing IoT-based data science techniques and presenting a summary of various characteristics.The paper analyzes the data science or big data security and privacy features,including network architecture,data protection,and continuous monitoring of data,which face challenges in various IoT-based systems.Extensive insights into IoT data security,privacy,and challenges are visualized in the context of data science for IoT.In addition,this study reveals the current opportunities to enhance data science and IoT market development.The current gap and challenges faced in the integration of data science and IoT are comprehensively presented,followed by the future outlook and possible solutions.
基金supported by the National Natural Science Foundation of China(32370703)the CAMS Innovation Fund for Medical Sciences(CIFMS)(2022-I2M-1-021,2021-I2M-1-061)the Major Project of Guangzhou National Labora-tory(GZNL2024A01015).
文摘Viral infectious diseases,characterized by their intricate nature and wide-ranging diversity,pose substantial challenges in the domain of data management.The vast volume of data generated by these diseases,spanning from the molecular mechanisms within cells to large-scale epidemiological patterns,has surpassed the capabilities of traditional analytical methods.In the era of artificial intelligence(AI)and big data,there is an urgent necessity for the optimization of these analytical methods to more effectively handle and utilize the information.Despite the rapid accumulation of data associated with viral infections,the lack of a comprehensive framework for integrating,selecting,and analyzing these datasets has left numerous researchers uncertain about which data to select,how to access it,and how to utilize it most effectively in their research.This review endeavors to fill these gaps by exploring the multifaceted nature of viral infectious diseases and summarizing relevant data across multiple levels,from the molecular details of pathogens to broad epidemiological trends.The scope extends from the micro-scale to the macro-scale,encompassing pathogens,hosts,and vectors.In addition to data summarization,this review thoroughly investigates various dataset sources.It also traces the historical evolution of data collection in the field of viral infectious diseases,highlighting the progress achieved over time.Simultaneously,it evaluates the current limitations that impede data utilization.Furthermore,we propose strategies to surmount these challenges,focusing on the development and application of advanced computational techniques,AI-driven models,and enhanced data integration practices.By providing a comprehensive synthesis of existing knowledge,this review is designed to guide future research and contribute to more informed approaches in the surveillance,prevention,and control of viral infectious diseases,particularly within the context of the expanding big-data landscape.
基金supported by the National Natural Science Foundation of China(Grant No.92044303)。
文摘Air pollution in China covers a large area with complex sources and formation mechanisms,making it a unique place to conduct air pollution and atmospheric chemistry research.The National Natural Science Foundation of China’s Major Research Plan entitled“Fundamental Researches on the Formation and Response Mechanism of the Air Pollution Complex in China”(or the Plan)has funded 76 research projects to explore the causes of air pollution in China,and the key processes of air pollution in atmospheric physics and atmospheric chemistry.In order to summarize the abundant data from the Plan and exhibit the long-term impacts domestically and internationally,an integration project is responsible for collecting the various types of data generated by the 76 projects of the Plan.This project has classified and integrated these data,forming eight categories containing 258 datasets and 15 technical reports in total.The integration project has led to the successful establishment of the China Air Pollution Data Center(CAPDC)platform,providing storage,retrieval,and download services for the eight categories.This platform has distinct features including data visualization,related project information querying,and bilingual services in both English and Chinese,which allows for rapid searching and downloading of data and provides a solid foundation of data and support for future related research.Air pollution control in China,especially in the past decade,is undeniably a global exemplar,and this data center is the first in China to focus on research into the country’s air pollution complex.
基金partially supported by the National Natural Science Foundation of China(62271485)the SDHS Science and Technology Project(HS2023B044)
文摘Imputation of missing data has long been an important topic and an essential application for intelligent transportation systems(ITS)in the real world.As a state-of-the-art generative model,the diffusion model has proven highly successful in image generation,speech generation,time series modelling etc.and now opens a new avenue for traffic data imputation.In this paper,we propose a conditional diffusion model,called the implicit-explicit diffusion model,for traffic data imputation.This model exploits both the implicit and explicit feature of the data simultaneously.More specifically,we design two types of feature extraction modules,one to capture the implicit dependencies hidden in the raw data at multiple time scales and the other to obtain the long-term temporal dependencies of the time series.This approach not only inherits the advantages of the diffusion model for estimating missing data,but also takes into account the multiscale correlation inherent in traffic data.To illustrate the performance of the model,extensive experiments are conducted on three real-world time series datasets using different missing rates.The experimental results demonstrate that the model improves imputation accuracy and generalization capability.
