Translation is a crucial step in gene expression.Over the past decade,the development and application of ribosome profiling(Ribo-seq)have significantly advanced our understanding of translational regulation in vivo.Ho...Translation is a crucial step in gene expression.Over the past decade,the development and application of ribosome profiling(Ribo-seq)have significantly advanced our understanding of translational regulation in vivo.However,the analysis and visualization of Ribo-seq data remain challenging.Despite the availability of various analytical pipelines,improvements in comprehensiveness,accuracy,and user-friendliness are still necessary.In this study,we develop RiboParser/RiboShiny,a robust framework for analyzing and visualizing Ribo-seq data.Building on published methods,we optimize ribosome structure-based and start/stopbased models to improve the accuracy and stability of P-site detection,even in species with a high proportion of leaderless transcripts.Leveraging these improvements,RiboParser offers comprehensive analyses,including quality control,gene-level analysis,codon-level analysis,and the analysis of Ribo-seq variants.Meanwhile,RiboShiny provides a user-friendly and adaptable platform for data visualization,facilitating deeper insights into the translational landscape.Furthermore,the integration of standardized genome annotation renders our platform universally applicable to various organisms with sequenced genomes.This framework has the potential to significantly improve the precision and efficiency of Ribo-seq data interpretation,thereby deepening our understanding of translational regulation.展开更多
Aviation data analysis can help airlines to understand passenger needs,so as to provide passengers with more sophisticated and better services.How to explore the implicit message and analyze contained features from la...Aviation data analysis can help airlines to understand passenger needs,so as to provide passengers with more sophisticated and better services.How to explore the implicit message and analyze contained features from large amounts of data has become an important issue in the civil aviation passenger data analysis process.The uncertainty analysis and visualization methods of data record and property measurement are offered in this paper,based on the visual analysis and uncertainty measure theory combined with parallel coordinates,radar chart,histogram,pixel chart and good interaction.At the same time,the data source expression clearly shows the uncertainty and hidden information as an information base for passengers’service展开更多
As industrial production progresses toward digitalization,massive amounts of data have been collected,transmitted,and stored,with characteristics of large-scale,high-dimensional,heterogeneous,and spatiotemporal dynami...As industrial production progresses toward digitalization,massive amounts of data have been collected,transmitted,and stored,with characteristics of large-scale,high-dimensional,heterogeneous,and spatiotemporal dynamics.The high complexity of industrial big data poses challenges for the practical decision-making of domain experts,leading to ever-increasing needs for integrating computational intelligence with human perception into traditional data analysis.Industrial big data visualization integrates theoretical methods and practical technologies from multiple disciplines,including data mining,information visualization,computer graphics,and human-computer interaction,providing a highly effective manner for understanding and exploring the complex industrial processes.This review summarizes the state-of-the-art approaches,characterizes them with six visualization methods,and categorizes them based on analytical tasks and applications.Furthermore,key research challenges and potential future directions are identified.展开更多
Geochemical survey data are essential across Earth Science disciplines but are often affected by noise,which can obscure important geological signals and compromise subsequent prediction and interpretation.Quantifying...Geochemical survey data are essential across Earth Science disciplines but are often affected by noise,which can obscure important geological signals and compromise subsequent prediction and interpretation.Quantifying prediction uncertainty is hence crucial for robust geoscientific decision-making.This study proposes a novel deep learning framework,the Spatially Constrained Variational Autoencoder(SC-VAE),for denoising geochemical survey data with integrated uncertainty quantification.The SC-VAE incorporates spatial regularization,which enforces spatial coherence by modeling inter-sample relationships directly within the latent space.The performance of the SC-VAE was systematically evaluated against a standard Variational Autoencoder(VAE)using geochemical data from the gold polymetallic district in the northwestern part of Sichuan Province,China.Both models were optimized using Bayesian optimization,with objective functions specifically designed to maintain essential geostatistical characteristics.Evaluation metrics include variogram analysis,quantitative measures of spatial interpolation accuracy,visual assessment of denoised maps,and statistical analysis of data distributions,as well as decomposition of uncertainties.Results show that the SC-VAE achieves superior noise suppression and better preservation of spatial structure compared to the standard VAE,as demonstrated by a significant reduction in the variogram nugget effect and an increased partial sill.The SC-VAE produces denoised maps with clearer anomaly delineation and more regularized data distributions,effectively mitigating outliers and reducing kurtosis.Additionally,it delivers improved interpolation accuracy and spatially explicit uncertainty estimates,facilitating more reliable and interpretable assessments of prediction confidence.The SC-VAE framework thus provides a robust,geostatistically informed solution for enhancing the quality and interpretability of geochemical data,with broad applicability in mineral exploration,environmental geochemistry,and other Earth Science domains.展开更多
Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation.Modern seismological research produces vast volumes of heterogeneous data from sei...Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation.Modern seismological research produces vast volumes of heterogeneous data from seismic networks,satellite observations,and geospatial repositories,creating the need for scalable infrastructures capable of integrating and analyzing such data to support intelligent decision-making.Data warehousing technologies provide a robust foundation for this purpose;however,existing earthquake-oriented data warehouses remain limited,often relying on simplified schemas,domain-specific analytics,or cataloguing efforts.This paper presents the design and implementation of a spatio-temporal data warehouse for seismic activity.The framework integrates spatial and temporal dimensions in a unified schema and introduces a novel array-based approach for managing many-to-many relationships between facts and dimensions without intermediate bridge tables.A comparative evaluation against a conventional bridge-table schema demonstrates that the array-based design improves fact-centric query performance,while the bridge-table schema remains advantageous for dimension-centric queries.To reconcile these trade-offs,a hybrid schema is proposed that retains both representations,ensuring balanced efficiency across heterogeneous workloads.The proposed framework demonstrates how spatio-temporal data warehousing can address schema complexity,improve query performance,and support multidimensional visualization.In doing so,it provides a foundation for integrating seismic analysis into broader big data-driven intelligent decision systems for disaster resilience,risk mitigation,and emergency management.展开更多
Software-defined,data-intensive cyber-physical systems and software-defined networks of atmospheric observers are evolving rapidly due to the rapid expansion of sensing diversity,the volume of streaming data,and the d...Software-defined,data-intensive cyber-physical systems and software-defined networks of atmospheric observers are evolving rapidly due to the rapid expansion of sensing diversity,the volume of streaming data,and the demand for low-latency,decision-relevant products.Simultaneously,artificial intelligence(AI)and the continuously evolving state of computing are making it possible to create end-to-end architecture fostering the migrations of the presumably single algorithm to combined intelligent ingestion,quality control,and multi-modal fusion,uncertainty-related retrieval,and scalable service delivery at the edge-to-cloud-high-performance computing(HPC)environment.This overview summarizes AI-based models of future atmospheric observation networks within a single,consolidated taxonomy based on deployment topology,learning and update modes,connectivity to physical models and data assimilation,level of autonomy(passive to adaptive sensing),and model of governance.Next,we consider recurring architectural themes,such as edge intelligence and streaming provenance and machine learning operations(MLOps)/model operations(ModelOps)to continue evaluation and safely update,and we scrutinize integration gateways with physical models,like data-assimilation-oriented outputs,hybrid/physics-informed designs,and simulation of observing systems using digital twins.Lastly,we address evaluation and readiness aspects that are not limited to predictive skill,but also involve calibrated uncertainty,nonstationary and extreme robustness,system latency and reliability,interoperability,security,and demonstrated downstream influence on analyses and forecasts.Through bringing together the cross-cutting issues and prospects,this review provides a road map with respect to trustworthy,interoperable,and sustainable observation infrastructures in which code and climate science will co-evolve.展开更多
Indeed,a range of systems in the environment requires timely,spatially explicit,and credible information to support its environmental decision-making,but no one observing system can give the complete and reliable meas...Indeed,a range of systems in the environment requires timely,spatially explicit,and credible information to support its environmental decision-making,but no one observing system can give the complete and reliable measures of the Earth system across scales.This review summarizes how the realization of the Compute the Planet is underway in the form of machine learning,remote sensing,and sensor data fusion to generate decision-ready environmental insights.We use the application-first approach,which considers remote sensing,in situ and Internet of Things(IoT)sensing,and physics-based models as complementary streams of evidence with similar strengths and failures.We look critically at how an integrated system can convert heterogeneous observations to action products across three high impact application areas:atmosphere and air quality,water–land–ecosystem dynamics,and hazards.Rapid-response situational awareness,ecosystem condition metrics,drought and flood indicators,exposure maps,and hazard/extreme indicators are key products.The integrated systems to environment interface in three high impact application areas:atmosphere and air quality,water-land-ecosystem dynamics,and hazard Examine Our operational requirements can often determine real-life value such as latency,time stability,smooth degradation in the presence of missing or degraded inputs,and calibrated uncertainty usable in thresholdbased decisions.These pitfalls are common across fields:mismatch in the scale between a point sensor and a gridded product,objectives on proxies in remotely sensed measurements,domain shift in the extremes and changing baselines,and evaluation aspects,which overestimate generalization because of spatiotemporal autocorrelation.Based on these lessons,we present cross-domain proposals for strong validation,uncertainty quantification,provenance,and versioning,as well as fair performance evaluation.We conclude that the next era of environmental intelligence will see a reduction in average accuracy improvement and an increase in terms of robustness,transparency,and operational responsibility,thus allowing the integrated environmental intelligence system to be deployed,which may be relied on to monitor human health,resource allocation,and survival in a more climate-adapted world.展开更多
The development of remote sensing has seen the creation of a global measurement infrastructure of sustainable development due to growing multipolar archives,rising revisit frequency,and the availability of cloud-acces...The development of remote sensing has seen the creation of a global measurement infrastructure of sustainable development due to growing multipolar archives,rising revisit frequency,and the availability of cloud-accessible platforms of Earth observation.This review summarizes how remote sensing big data is being organized into decision-grade sustainability intelligence,the new approaches to analytics,and how Sustainable Development Goals(SDGs)-oriented application pathways inter-relate action pathways that bridge observations with action.The terminologies like new data ecosystem,data readiness and interoperability,changing economics of scalable computation,and detailing the functions of diversity of modalities(optical,Synthetic Aperture Radar—SAR,thermal,Light Detection and Ranging—LiDAR,hyperspectral)have been defined.These themes of analytics,which are transforming the practice of operational analytics,are then condensed:foundations and self-supervised learning of transferable representations,multi-modal fusion to gap fill and richer inference,spatiotemporal intelligence to trend of early warning,physics-aware hybrid methods to enhance robustness and meaning under non-stationary conditions.Across the climate risk,food systems,water resources,sustainable cities,ecosystems and biodiversity,energy transitions,and health exposure pathways,the roles of Earth Observation(EO)products as direct measures and proxies,and concepts of validating,semantic comparability,and communicating uncertainties play a key role in EO products becoming credible when faced with high-stakes deployment decisions.Lastly,we chart world ways of implementation via monitoring services,early warning systems,and systems of multiple regimes,and previously underline cross-cutting priorities,scalable structures in validation,performance,so that domains of shift,agreeable governance,and Dual-use risk safeguards,and sustainable lifecycle support of EO services.These priorities form a realistic set of priorities on the alignment of remote sensing innovation with quantifiable SDGs progress.展开更多
Hue-Saturation-Intensity （HSI） color model, a psychologically appealing color model, was employed to visualize uncertainty represented by relative prediction error based on the case of spatial prediction of pH of to...Hue-Saturation-Intensity （HSI） color model, a psychologically appealing color model, was employed to visualize uncertainty represented by relative prediction error based on the case of spatial prediction of pH of topsoil in the peri-urban Beijing. A two-dimensional legend was designed to accompany the visualization-vertical axis （hues） for visualizing the predicted values and horizontal axis （whiteness） for visualizing the prediction error. Moreover, different ways of visualizing uncertainty were briefly reviewed in this paper. This case study indicated that visualization of both predictions and prediction uncertainty offered a possibility to enhance visual exploration of the data uncertainty and to compare different prediction methods or predictions of totally different variables. The whitish region of the visualization map can be simply interpreted as unsatisfactory prediction results, where may need additional samples or more suitable prediction models for a better prediction results.展开更多
The control system of Hefei Light Source II(HLS-Ⅱ) is a distributed system based on the experimental physics and industrial control system(EPICS). It is necessary to maintain the central configuration files for the e...The control system of Hefei Light Source II(HLS-Ⅱ) is a distributed system based on the experimental physics and industrial control system(EPICS). It is necessary to maintain the central configuration files for the existing archiving system. When the process variables in the control system are added, removed, or updated, the configuration files must be manually modified to maintain consistency with the control system. This paper presents a new method for data archiving, which realizes the automatic configuration of the archiving parameters. The system uses microservice architecture to integrate the EPICS Archiver Appliance and Rec Sync. In this way, the system can collect all the archived meta-configuration from the distributed input/output controllers and enter them into the EPICS Archiver Appliance automatically. Furthermore, we also developed a web-based GUI to provide automatic visualization of real-time and historical data. At present,this system is under commissioning at HLS-Ⅱ. The results indicate that the new archiving system is reliable and convenient to operate. The operation mode without maintenance is valuable for large-scale scientific facilities.展开更多
A visualization tool was developed through a web browser based on Java applets embedded into HTML pages, in order to provide a world access to the EAST experimental data. It can display data from various trees in diff...A visualization tool was developed through a web browser based on Java applets embedded into HTML pages, in order to provide a world access to the EAST experimental data. It can display data from various trees in different servers in a single panel. With WebScope, it is easier to make a comparison between different data sources and perform a simple calculation over different data sources.展开更多
Monitoring data are often used to identify stormwater runoff characteristics and in stormwater runoff modelling without consideration of their inherent uncertainties. Integrated with discrete sample analysis and error...Monitoring data are often used to identify stormwater runoff characteristics and in stormwater runoff modelling without consideration of their inherent uncertainties. Integrated with discrete sample analysis and error propagation analysis, this study attempted to quantify the uncertainties of discrete chemical oxygen demand （COD）, total suspended solids （TSS） concentration, stormwater flowrate, stormwater event volumes, COD event mean concentration （EMC）, and COD event loads in terms of flow measurement, sample collection, storage and laboratory analysis. The results showed that the uncertainties due to sample collection, storage and laboratory analysis of COD from stormwater runoff are 13.99%, 19.48% and 12.28%. Meanwhile, flow measurement uncertainty was 12.82%, and the sample collection uncertainty of TSS from stormwater runoff was 31.63%. Based on the law of propagation of uncertainties, the uncertainties regarding event flow volume, COD EMC and COD event loads were quantified as 7.03%, 10.26% and 18.47%.展开更多
With long-term marine surveys and research,and especially with the development of new marine environment monitoring technologies,prodigious amounts of complex marine environmental data are generated,and continuously i...With long-term marine surveys and research,and especially with the development of new marine environment monitoring technologies,prodigious amounts of complex marine environmental data are generated,and continuously increase rapidly.Features of these data include massive volume,widespread distribution,multiple-sources,heterogeneous,multi-dimensional and dynamic in structure and time.The present study recommends an integrative visualization solution for these data,to enhance the visual display of data and data archives,and to develop a joint use of these data distributed among different organizations or communities.This study also analyses the web services technologies and defines the concept of the marine information gird,then focuses on the spatiotemporal visualization method and proposes a process-oriented spatiotemporal visualization method.We discuss how marine environmental data can be organized based on the spatiotemporal visualization method,and how organized data are represented for use with web services and stored in a reusable fashion.In addition,we provide an original visualization architecture that is integrative and based on the explored technologies.In the end,we propose a prototype system of marine environmental data of the South China Sea for visualizations of Argo floats,sea surface temperature fields,sea current fields,salinity,in-situ investigation data,and ocean stations.An integration visualization architecture is illustrated on the prototype system,which highlights the process-oriented temporal visualization method and demonstrates the benefit of the architecture and the methods described in this study.展开更多
Simulation and interpretation of marine controlled-source electromagnetic(CSEM) data often approximate the transmitter source as an ideal horizontal electric dipole(HED) and assume that the receivers are located on a ...Simulation and interpretation of marine controlled-source electromagnetic(CSEM) data often approximate the transmitter source as an ideal horizontal electric dipole(HED) and assume that the receivers are located on a flat seabed.Actually,however,the transmitter dipole source will be rotated,tilted and deviated from the survey profile due to ocean currents.And free-fall receivers may be also rotated to some arbitrary horizontal orientation and located on sloping seafloor.In this paper,we investigate the effects of uncertainties in the transmitter tilt,transmitter rotation and transmitter deviation from the survey profile as well as in the receiver's location and orientation on marine CSEM data.The model study shows that the uncertainties of all position and orientation parameters of both the transmitter and receivers can propagate into observed data uncertainties,but to a different extent.In interpreting marine data,field data uncertainties caused by the position and orientation uncertainties of both the transmitter and receivers need to be taken into account.展开更多
Cyber security has been thrust into the limelight in the modern technological era because of an array of attacks often bypassing tmtrained intrusion detection systems （IDSs）. Therefore, greater attention has been di...Cyber security has been thrust into the limelight in the modern technological era because of an array of attacks often bypassing tmtrained intrusion detection systems （IDSs）. Therefore, greater attention has been directed on being able deciphering better methods for identifying attack types to train IDSs more effectively. Keycyber-attack insights exist in big data; however, an efficient approach is required to determine strong attack types to train IDSs to become more effective in key areas. Despite the rising growth in IDS research, there is a lack of studies involving big data visualization, which is key. The KDD99 data set has served as a strong benchmark since 1999; therefore, we utilized this data set in our experiment. In this study, we utilized hash algorithm, a weight table, and sampling method to deal with the inherent problems caused by analyzing big data; volume, variety, and velocity. By utilizing a visualization algorithm, we were able to gain insights into the KDD99 data set with a clear iden- tification of ＂normal＂ clusters and described distinct clusters of effective attacks.展开更多
A GIS for ocean applications called ＂the Xiamen Atmospheric and Oceanographic Data Management and Display System （AODMDS）＂ has been designed and developed. The system is based on ArcObjects （AO）, a component-bas...A GIS for ocean applications called ＂the Xiamen Atmospheric and Oceanographic Data Management and Display System （AODMDS）＂ has been designed and developed. The system is based on ArcObjects （AO）, a component-based GIS de- velopment tool. The paper discusses in detail the storage and organization of the atmospheric and oceanographic data, the strategy and methods for the visualization and mapping of oceanographic and atmospheric data, and the implementation of the methods in AODMDS. It also discusses some advanced display control techniques that expand the functions of ArcObjects One of the techniques is ＂gradient-fill-style color-map control,＂ which provides a feasible color-rich display control for all types of raster maps. As a stand-alone desktop GIS system built on AO, AODMDS provides effective data management and powerful mapping and visualization functions for atmospheric and oceanographic data.展开更多
Graphical methods are used for construction.Data analysis and visualization are an important area of applications of big data.At the same time,visual analysis is also an important method for big data analysis.Data vis...Graphical methods are used for construction.Data analysis and visualization are an important area of applications of big data.At the same time,visual analysis is also an important method for big data analysis.Data visualization refers to data that is presented in a visual form,such as a chart or map,to help people understand the meaning of the data.Data visualization helps people extract meaning from data quickly and easily.Visualization can be used to fully demonstrate the patterns,trends,and dependencies of your data,which can be found in other displays.Big data visualization analysis combines the advantages of computers,which can be static or interactive,interactive analysis methods and interactive technologies,which can directly help people and effectively understand the information behind big data.It is indispensable in the era of big data visualization,and it can be very intuitive if used properly.Graphical analysis also found that valuable information becomes a powerful tool in complex data relationships,and it represents a significant business opportunity.With the rise of big data,important technologies suitable for dealing with complex relationships have emerged.Graphics come in a variety of shapes and sizes for a variety of business problems.Graphic analysis is first in the visualization.The step is to get the right data and answer the goal.In short,to choose the right method,you must understand each relative strengths and weaknesses and understand the data.Key steps to get data:target;collect;clean;connect.展开更多
The availability and quantity of remotely sensed and terrestrial geospatial data sets are on the rise.Historically,these data sets have been analyzed and quarried on 2D desktop computers;however,immersive technologies...The availability and quantity of remotely sensed and terrestrial geospatial data sets are on the rise.Historically,these data sets have been analyzed and quarried on 2D desktop computers;however,immersive technologies and specifically immersive virtual reality(iVR)allow for the integration,visualization,analysis,and exploration of these 3D geospatial data sets.iVR can deliver remote and large-scale geospatial data sets to the laboratory,providing embodied experiences of field sites across the earth and beyond.We describe a workflow for the ingestion of geospatial data sets and the development of an iVR workbench,and present the application of these for an experience of Iceland’s Thrihnukar volcano where we:(1)combined satellite imagery with terrain elevation data to create a basic reconstruction of the physical site;(2)used terrestrial LiDAR data to provide a geo-referenced point cloud model of the magmatic-volcanic system,as well as the LiDAR intensity values for the identification of rock types;and(3)used Structure-from-Motion(SfM)to construct a photorealistic point cloud of the inside volcano.The workbench provides tools for the direct manipulation of the georeferenced data sets,including scaling,rotation,and translation,and a suite of geometric measurement tools,including length,area,and volume.Future developments will be inspired by an ongoing user study that formally evaluates the workbench’s mature components in the context of fieldwork and analyses activities.展开更多
基金supported by the National Key Research and Development Program of China(2022YFA0912100)the National Natural Science Foundation of China(32270098 and 32470073)+1 种基金the Fundamental Research Funds for the Central Universities(2662024JC015)the National Key Laboratory of Agricultural Microbiology(AML2024D02)to Z.Z.
文摘Translation is a crucial step in gene expression.Over the past decade,the development and application of ribosome profiling(Ribo-seq)have significantly advanced our understanding of translational regulation in vivo.However,the analysis and visualization of Ribo-seq data remain challenging.Despite the availability of various analytical pipelines,improvements in comprehensiveness,accuracy,and user-friendliness are still necessary.In this study,we develop RiboParser/RiboShiny,a robust framework for analyzing and visualizing Ribo-seq data.Building on published methods,we optimize ribosome structure-based and start/stopbased models to improve the accuracy and stability of P-site detection,even in species with a high proportion of leaderless transcripts.Leveraging these improvements,RiboParser offers comprehensive analyses,including quality control,gene-level analysis,codon-level analysis,and the analysis of Ribo-seq variants.Meanwhile,RiboShiny provides a user-friendly and adaptable platform for data visualization,facilitating deeper insights into the translational landscape.Furthermore,the integration of standardized genome annotation renders our platform universally applicable to various organisms with sequenced genomes.This framework has the potential to significantly improve the precision and efficiency of Ribo-seq data interpretation,thereby deepening our understanding of translational regulation.
文摘Aviation data analysis can help airlines to understand passenger needs,so as to provide passengers with more sophisticated and better services.How to explore the implicit message and analyze contained features from large amounts of data has become an important issue in the civil aviation passenger data analysis process.The uncertainty analysis and visualization methods of data record and property measurement are offered in this paper,based on the visual analysis and uncertainty measure theory combined with parallel coordinates,radar chart,histogram,pixel chart and good interaction.At the same time,the data source expression clearly shows the uncertainty and hidden information as an information base for passengers’service
基金supported in part by the National Key Research and Development Plan Project(2022YFB3304700)in part by the Xinliao Talent Program of Liaoning Province(XLYC2202002).
文摘As industrial production progresses toward digitalization,massive amounts of data have been collected,transmitted,and stored,with characteristics of large-scale,high-dimensional,heterogeneous,and spatiotemporal dynamics.The high complexity of industrial big data poses challenges for the practical decision-making of domain experts,leading to ever-increasing needs for integrating computational intelligence with human perception into traditional data analysis.Industrial big data visualization integrates theoretical methods and practical technologies from multiple disciplines,including data mining,information visualization,computer graphics,and human-computer interaction,providing a highly effective manner for understanding and exploring the complex industrial processes.This review summarizes the state-of-the-art approaches,characterizes them with six visualization methods,and categorizes them based on analytical tasks and applications.Furthermore,key research challenges and potential future directions are identified.
基金supported by the National Natural Science Foundation of China(Nos.42530801,42425208)the Natural Science Foundation of Hubei Province(China)(No.2023AFA001)+1 种基金the MOST Special Fund from State Key Laboratory of Geological Processes and Mineral Resources,China University of Geosciences(No.MSFGPMR2025-401)the China Scholarship Council(No.202306410181)。
文摘Geochemical survey data are essential across Earth Science disciplines but are often affected by noise,which can obscure important geological signals and compromise subsequent prediction and interpretation.Quantifying prediction uncertainty is hence crucial for robust geoscientific decision-making.This study proposes a novel deep learning framework,the Spatially Constrained Variational Autoencoder(SC-VAE),for denoising geochemical survey data with integrated uncertainty quantification.The SC-VAE incorporates spatial regularization,which enforces spatial coherence by modeling inter-sample relationships directly within the latent space.The performance of the SC-VAE was systematically evaluated against a standard Variational Autoencoder(VAE)using geochemical data from the gold polymetallic district in the northwestern part of Sichuan Province,China.Both models were optimized using Bayesian optimization,with objective functions specifically designed to maintain essential geostatistical characteristics.Evaluation metrics include variogram analysis,quantitative measures of spatial interpolation accuracy,visual assessment of denoised maps,and statistical analysis of data distributions,as well as decomposition of uncertainties.Results show that the SC-VAE achieves superior noise suppression and better preservation of spatial structure compared to the standard VAE,as demonstrated by a significant reduction in the variogram nugget effect and an increased partial sill.The SC-VAE produces denoised maps with clearer anomaly delineation and more regularized data distributions,effectively mitigating outliers and reducing kurtosis.Additionally,it delivers improved interpolation accuracy and spatially explicit uncertainty estimates,facilitating more reliable and interpretable assessments of prediction confidence.The SC-VAE framework thus provides a robust,geostatistically informed solution for enhancing the quality and interpretability of geochemical data,with broad applicability in mineral exploration,environmental geochemistry,and other Earth Science domains.
文摘Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation.Modern seismological research produces vast volumes of heterogeneous data from seismic networks,satellite observations,and geospatial repositories,creating the need for scalable infrastructures capable of integrating and analyzing such data to support intelligent decision-making.Data warehousing technologies provide a robust foundation for this purpose;however,existing earthquake-oriented data warehouses remain limited,often relying on simplified schemas,domain-specific analytics,or cataloguing efforts.This paper presents the design and implementation of a spatio-temporal data warehouse for seismic activity.The framework integrates spatial and temporal dimensions in a unified schema and introduces a novel array-based approach for managing many-to-many relationships between facts and dimensions without intermediate bridge tables.A comparative evaluation against a conventional bridge-table schema demonstrates that the array-based design improves fact-centric query performance,while the bridge-table schema remains advantageous for dimension-centric queries.To reconcile these trade-offs,a hybrid schema is proposed that retains both representations,ensuring balanced efficiency across heterogeneous workloads.The proposed framework demonstrates how spatio-temporal data warehousing can address schema complexity,improve query performance,and support multidimensional visualization.In doing so,it provides a foundation for integrating seismic analysis into broader big data-driven intelligent decision systems for disaster resilience,risk mitigation,and emergency management.
文摘Software-defined,data-intensive cyber-physical systems and software-defined networks of atmospheric observers are evolving rapidly due to the rapid expansion of sensing diversity,the volume of streaming data,and the demand for low-latency,decision-relevant products.Simultaneously,artificial intelligence(AI)and the continuously evolving state of computing are making it possible to create end-to-end architecture fostering the migrations of the presumably single algorithm to combined intelligent ingestion,quality control,and multi-modal fusion,uncertainty-related retrieval,and scalable service delivery at the edge-to-cloud-high-performance computing(HPC)environment.This overview summarizes AI-based models of future atmospheric observation networks within a single,consolidated taxonomy based on deployment topology,learning and update modes,connectivity to physical models and data assimilation,level of autonomy(passive to adaptive sensing),and model of governance.Next,we consider recurring architectural themes,such as edge intelligence and streaming provenance and machine learning operations(MLOps)/model operations(ModelOps)to continue evaluation and safely update,and we scrutinize integration gateways with physical models,like data-assimilation-oriented outputs,hybrid/physics-informed designs,and simulation of observing systems using digital twins.Lastly,we address evaluation and readiness aspects that are not limited to predictive skill,but also involve calibrated uncertainty,nonstationary and extreme robustness,system latency and reliability,interoperability,security,and demonstrated downstream influence on analyses and forecasts.Through bringing together the cross-cutting issues and prospects,this review provides a road map with respect to trustworthy,interoperable,and sustainable observation infrastructures in which code and climate science will co-evolve.
文摘Indeed,a range of systems in the environment requires timely,spatially explicit,and credible information to support its environmental decision-making,but no one observing system can give the complete and reliable measures of the Earth system across scales.This review summarizes how the realization of the Compute the Planet is underway in the form of machine learning,remote sensing,and sensor data fusion to generate decision-ready environmental insights.We use the application-first approach,which considers remote sensing,in situ and Internet of Things(IoT)sensing,and physics-based models as complementary streams of evidence with similar strengths and failures.We look critically at how an integrated system can convert heterogeneous observations to action products across three high impact application areas:atmosphere and air quality,water–land–ecosystem dynamics,and hazards.Rapid-response situational awareness,ecosystem condition metrics,drought and flood indicators,exposure maps,and hazard/extreme indicators are key products.The integrated systems to environment interface in three high impact application areas:atmosphere and air quality,water-land-ecosystem dynamics,and hazard Examine Our operational requirements can often determine real-life value such as latency,time stability,smooth degradation in the presence of missing or degraded inputs,and calibrated uncertainty usable in thresholdbased decisions.These pitfalls are common across fields:mismatch in the scale between a point sensor and a gridded product,objectives on proxies in remotely sensed measurements,domain shift in the extremes and changing baselines,and evaluation aspects,which overestimate generalization because of spatiotemporal autocorrelation.Based on these lessons,we present cross-domain proposals for strong validation,uncertainty quantification,provenance,and versioning,as well as fair performance evaluation.We conclude that the next era of environmental intelligence will see a reduction in average accuracy improvement and an increase in terms of robustness,transparency,and operational responsibility,thus allowing the integrated environmental intelligence system to be deployed,which may be relied on to monitor human health,resource allocation,and survival in a more climate-adapted world.
文摘The development of remote sensing has seen the creation of a global measurement infrastructure of sustainable development due to growing multipolar archives,rising revisit frequency,and the availability of cloud-accessible platforms of Earth observation.This review summarizes how remote sensing big data is being organized into decision-grade sustainability intelligence,the new approaches to analytics,and how Sustainable Development Goals(SDGs)-oriented application pathways inter-relate action pathways that bridge observations with action.The terminologies like new data ecosystem,data readiness and interoperability,changing economics of scalable computation,and detailing the functions of diversity of modalities(optical,Synthetic Aperture Radar—SAR,thermal,Light Detection and Ranging—LiDAR,hyperspectral)have been defined.These themes of analytics,which are transforming the practice of operational analytics,are then condensed:foundations and self-supervised learning of transferable representations,multi-modal fusion to gap fill and richer inference,spatiotemporal intelligence to trend of early warning,physics-aware hybrid methods to enhance robustness and meaning under non-stationary conditions.Across the climate risk,food systems,water resources,sustainable cities,ecosystems and biodiversity,energy transitions,and health exposure pathways,the roles of Earth Observation(EO)products as direct measures and proxies,and concepts of validating,semantic comparability,and communicating uncertainties play a key role in EO products becoming credible when faced with high-stakes deployment decisions.Lastly,we chart world ways of implementation via monitoring services,early warning systems,and systems of multiple regimes,and previously underline cross-cutting priorities,scalable structures in validation,performance,so that domains of shift,agreeable governance,and Dual-use risk safeguards,and sustainable lifecycle support of EO services.These priorities form a realistic set of priorities on the alignment of remote sensing innovation with quantifiable SDGs progress.
基金Under the auspices of Knowledge Innovation Frontier Project of Institute of Soil Science,Chinese Academy of Sciences(No.ISSASIP0716 )the National Nature Science Foundation of China ( No.40701070,40571065)
文摘Hue-Saturation-Intensity （HSI） color model, a psychologically appealing color model, was employed to visualize uncertainty represented by relative prediction error based on the case of spatial prediction of pH of topsoil in the peri-urban Beijing. A two-dimensional legend was designed to accompany the visualization-vertical axis （hues） for visualizing the predicted values and horizontal axis （whiteness） for visualizing the prediction error. Moreover, different ways of visualizing uncertainty were briefly reviewed in this paper. This case study indicated that visualization of both predictions and prediction uncertainty offered a possibility to enhance visual exploration of the data uncertainty and to compare different prediction methods or predictions of totally different variables. The whitish region of the visualization map can be simply interpreted as unsatisfactory prediction results, where may need additional samples or more suitable prediction models for a better prediction results.
基金supported by the National Natural Science Foundation of China(No.11375186)
文摘The control system of Hefei Light Source II(HLS-Ⅱ) is a distributed system based on the experimental physics and industrial control system(EPICS). It is necessary to maintain the central configuration files for the existing archiving system. When the process variables in the control system are added, removed, or updated, the configuration files must be manually modified to maintain consistency with the control system. This paper presents a new method for data archiving, which realizes the automatic configuration of the archiving parameters. The system uses microservice architecture to integrate the EPICS Archiver Appliance and Rec Sync. In this way, the system can collect all the archived meta-configuration from the distributed input/output controllers and enter them into the EPICS Archiver Appliance automatically. Furthermore, we also developed a web-based GUI to provide automatic visualization of real-time and historical data. At present,this system is under commissioning at HLS-Ⅱ. The results indicate that the new archiving system is reliable and convenient to operate. The operation mode without maintenance is valuable for large-scale scientific facilities.
基金supported by National Natural Science Foundation of China (No.10835009)Chinese Academy of Sciences for the Key Project of Knowledge Innovation Program (No.KJCX3.SYW.N4)Chinese Ministry of Sciences for the 973 project (No.2009GB103000)
文摘A visualization tool was developed through a web browser based on Java applets embedded into HTML pages, in order to provide a world access to the EAST experimental data. It can display data from various trees in different servers in a single panel. With WebScope, it is easier to make a comparison between different data sources and perform a simple calculation over different data sources.
基金supported by the National Natural Science Foundation of China(No.50778098)the Youth Project of Fujian Provincial Department of Science&Technology(No.2007F3093)
文摘Monitoring data are often used to identify stormwater runoff characteristics and in stormwater runoff modelling without consideration of their inherent uncertainties. Integrated with discrete sample analysis and error propagation analysis, this study attempted to quantify the uncertainties of discrete chemical oxygen demand （COD）, total suspended solids （TSS） concentration, stormwater flowrate, stormwater event volumes, COD event mean concentration （EMC）, and COD event loads in terms of flow measurement, sample collection, storage and laboratory analysis. The results showed that the uncertainties due to sample collection, storage and laboratory analysis of COD from stormwater runoff are 13.99%, 19.48% and 12.28%. Meanwhile, flow measurement uncertainty was 12.82%, and the sample collection uncertainty of TSS from stormwater runoff was 31.63%. Based on the law of propagation of uncertainties, the uncertainties regarding event flow volume, COD EMC and COD event loads were quantified as 7.03%, 10.26% and 18.47%.
基金Supported by the Knowledge Innovation Program of the Chinese Academy of Sciences (No.KZCX1-YW-12-04)the National High Technology Research and Development Program of China (863 Program) (Nos.2009AA12Z148,2007AA092202)Support for this study was provided by the Institute of Geographical Sciences and the Natural Resources Research,Chinese Academy of Science (IGSNRR,CAS) and the Institute of Oceanology, CAS
文摘With long-term marine surveys and research,and especially with the development of new marine environment monitoring technologies,prodigious amounts of complex marine environmental data are generated,and continuously increase rapidly.Features of these data include massive volume,widespread distribution,multiple-sources,heterogeneous,multi-dimensional and dynamic in structure and time.The present study recommends an integrative visualization solution for these data,to enhance the visual display of data and data archives,and to develop a joint use of these data distributed among different organizations or communities.This study also analyses the web services technologies and defines the concept of the marine information gird,then focuses on the spatiotemporal visualization method and proposes a process-oriented spatiotemporal visualization method.We discuss how marine environmental data can be organized based on the spatiotemporal visualization method,and how organized data are represented for use with web services and stored in a reusable fashion.In addition,we provide an original visualization architecture that is integrative and based on the explored technologies.In the end,we propose a prototype system of marine environmental data of the South China Sea for visualizations of Argo floats,sea surface temperature fields,sea current fields,salinity,in-situ investigation data,and ocean stations.An integration visualization architecture is illustrated on the prototype system,which highlights the process-oriented temporal visualization method and demonstrates the benefit of the architecture and the methods described in this study.
基金funded by the National Natural Science Foundation of China (41130420)the State High-Tech Development Plan of China (2012AA09A20101)
文摘Simulation and interpretation of marine controlled-source electromagnetic(CSEM) data often approximate the transmitter source as an ideal horizontal electric dipole(HED) and assume that the receivers are located on a flat seabed.Actually,however,the transmitter dipole source will be rotated,tilted and deviated from the survey profile due to ocean currents.And free-fall receivers may be also rotated to some arbitrary horizontal orientation and located on sloping seafloor.In this paper,we investigate the effects of uncertainties in the transmitter tilt,transmitter rotation and transmitter deviation from the survey profile as well as in the receiver's location and orientation on marine CSEM data.The model study shows that the uncertainties of all position and orientation parameters of both the transmitter and receivers can propagate into observed data uncertainties,but to a different extent.In interpreting marine data,field data uncertainties caused by the position and orientation uncertainties of both the transmitter and receivers need to be taken into account.
文摘Cyber security has been thrust into the limelight in the modern technological era because of an array of attacks often bypassing tmtrained intrusion detection systems （IDSs）. Therefore, greater attention has been directed on being able deciphering better methods for identifying attack types to train IDSs more effectively. Keycyber-attack insights exist in big data; however, an efficient approach is required to determine strong attack types to train IDSs to become more effective in key areas. Despite the rising growth in IDS research, there is a lack of studies involving big data visualization, which is key. The KDD99 data set has served as a strong benchmark since 1999; therefore, we utilized this data set in our experiment. In this study, we utilized hash algorithm, a weight table, and sampling method to deal with the inherent problems caused by analyzing big data; volume, variety, and velocity. By utilizing a visualization algorithm, we were able to gain insights into the KDD99 data set with a clear iden- tification of ＂normal＂ clusters and described distinct clusters of effective attacks.
文摘A GIS for ocean applications called ＂the Xiamen Atmospheric and Oceanographic Data Management and Display System （AODMDS）＂ has been designed and developed. The system is based on ArcObjects （AO）, a component-based GIS de- velopment tool. The paper discusses in detail the storage and organization of the atmospheric and oceanographic data, the strategy and methods for the visualization and mapping of oceanographic and atmospheric data, and the implementation of the methods in AODMDS. It also discusses some advanced display control techniques that expand the functions of ArcObjects One of the techniques is ＂gradient-fill-style color-map control,＂ which provides a feasible color-rich display control for all types of raster maps. As a stand-alone desktop GIS system built on AO, AODMDS provides effective data management and powerful mapping and visualization functions for atmospheric and oceanographic data.
基金This research work is supported by Hunan Provincial Education Science 13th Five Year Plan(Grant No.XJK016BXX001)Social Science Foundation of Hunan Province(Grant No.17YBA049)+2 种基金Hunan Provincial Natural Science Foundation of China(Grant No.2017JJ2016)National Students’platform for innovation and entrepreneurship training(Grant No.201811532010)The work is also supported by Open foundation for University Innovation Platform from Hunan Province,China(Grand No.16K013)and the 2011 Collaborative Innovation Center of Big Data for Financial and Economical Asset Development and Utility in Universities of Hunan Province.We also thank the anonymous reviewers for their valuable comments and insightful suggestions.
文摘Graphical methods are used for construction.Data analysis and visualization are an important area of applications of big data.At the same time,visual analysis is also an important method for big data analysis.Data visualization refers to data that is presented in a visual form,such as a chart or map,to help people understand the meaning of the data.Data visualization helps people extract meaning from data quickly and easily.Visualization can be used to fully demonstrate the patterns,trends,and dependencies of your data,which can be found in other displays.Big data visualization analysis combines the advantages of computers,which can be static or interactive,interactive analysis methods and interactive technologies,which can directly help people and effectively understand the information behind big data.It is indispensable in the era of big data visualization,and it can be very intuitive if used properly.Graphical analysis also found that valuable information becomes a powerful tool in complex data relationships,and it represents a significant business opportunity.With the rise of big data,important technologies suitable for dealing with complex relationships have emerged.Graphics come in a variety of shapes and sizes for a variety of business problems.Graphic analysis is first in the visualization.The step is to get the right data and answer the goal.In short,to choose the right method,you must understand each relative strengths and weaknesses and understand the data.Key steps to get data:target;collect;clean;connect.
基金This work was supported by the National Science Foundation[grant numbers 1526520 to AK and 0711456 to PL].
文摘The availability and quantity of remotely sensed and terrestrial geospatial data sets are on the rise.Historically,these data sets have been analyzed and quarried on 2D desktop computers;however,immersive technologies and specifically immersive virtual reality(iVR)allow for the integration,visualization,analysis,and exploration of these 3D geospatial data sets.iVR can deliver remote and large-scale geospatial data sets to the laboratory,providing embodied experiences of field sites across the earth and beyond.We describe a workflow for the ingestion of geospatial data sets and the development of an iVR workbench,and present the application of these for an experience of Iceland’s Thrihnukar volcano where we:(1)combined satellite imagery with terrain elevation data to create a basic reconstruction of the physical site;(2)used terrestrial LiDAR data to provide a geo-referenced point cloud model of the magmatic-volcanic system,as well as the LiDAR intensity values for the identification of rock types;and(3)used Structure-from-Motion(SfM)to construct a photorealistic point cloud of the inside volcano.The workbench provides tools for the direct manipulation of the georeferenced data sets,including scaling,rotation,and translation,and a suite of geometric measurement tools,including length,area,and volume.Future developments will be inspired by an ongoing user study that formally evaluates the workbench’s mature components in the context of fieldwork and analyses activities.
