0 INTRODUCTION Turkey is located at the intersection of the Eurasian,Anatolian,Arabian,and African tectonic plates.Due to the ongoing northward compression from the Arabian Plate,the Anatolian Plate is pushed westward...0 INTRODUCTION Turkey is located at the intersection of the Eurasian,Anatolian,Arabian,and African tectonic plates.Due to the ongoing northward compression from the Arabian Plate,the Anatolian Plate is pushed westward in a tectonic escape mechanism,leading to the formation of the North Anatolian fault zone(NAFZ)and the East Anatolian fault zone(EAFZ)(e.g.,Bayrak et al.,2015;Duman and Emre,2013;Reilinger et al.,2006).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
This study used Corona KH-4A and Advanced Land Observing Satellite (ALOS) PRISM images to generate digital terrain models (DTMs) of the distal part of Imja Glacier,where a few supraglacial ponds (～0.07 km 2) expanded...This study used Corona KH-4A and Advanced Land Observing Satellite (ALOS) PRISM images to generate digital terrain models (DTMs) of the distal part of Imja Glacier,where a few supraglacial ponds (～0.07 km 2) expanded into the large Imja Glacier Lake (Imja Tsho,～0.91 km 2) between 1964 and 2006.DTMs and subsequently derived topographical maps with contour intervals of 1 m were created from the high-resolution images (Corona in 1964 and ALOS in 2006) in the Leica Photogrammetric Suite (LPS) platform.The DTMs and topographic maps provided excellent representation of the elevation and micro-topography of the glacier surface,such as its supra-glacial ponds/lake,surface depressions,and moraine ridges,with an error of about +/-4 m (maximum).The DTMs produced from the Corona and ALOS PRISM images are suitable for use in studies of the surface change of glaciers.The topographical maps produced from the Corona data (1964) showed that part of the dead ice in the down-glacier area was even higher than the top of the lateral moraine ridges,while the glacier surface in the up-glacier area was noticeably lower than the moraine crests.This suggests more extensive melting of glacier ice in the up-glacier area before 1964.The average lowering of the glacier surface from 1964 to 2006 was 16.9 m for the dead-ice area west of the lake and 47.4 m for the glacier surface east of the lake;between 1964 and 2002,the lake surface lowered by 82.3 m.These figures represent average lowering rates of 0.4,1.1,and 2.2 m/year for the respective areas.展开更多
To understand the relationship between turbid water and coral damage caused by the heavy rain disaster at the end of October 2010 in Amami-Oshima, Kagoshima Prefecture, Japan, turbid water and coral damage distributio...To understand the relationship between turbid water and coral damage caused by the heavy rain disaster at the end of October 2010 in Amami-Oshima, Kagoshima Prefecture, Japan, turbid water and coral damage distribution monitoring was attempted using satellite imagery and a diving survey immediately after the disaster. ALOS AVNIR-2 images (spatial resolution: 10 m) on October 6 (before the disaster), October 24, October 30, and October 31 (after the disaster) were obtained as satellite data in 2010. The red-silt deposition index (RSI) map based on the method by Nadaoka and Tamura (1992) was also created. Moreover, a diving survey was conducted via the spot check method on December 18, 2010. As a result, comparison between the high turbidity (RSI) areas estimated using AVNIR-2 data and the coral damage areas judging from the field survey was considered relatively light. It is shown that satellite data such as AVNIR-2 can be a powerful tool to monitor damage distribution of coral reefs after heavy rain.展开更多
Agricultural land has a strategic function as the primary food provider for the people of Indonesia. Various methods of agricultural production estimation, particularly food crops, provide different information. It ca...Agricultural land has a strategic function as the primary food provider for the people of Indonesia. Various methods of agricultural production estimation, particularly food crops, provide different information. It can be a source of error in decision making. Satellite data, provides information periodically, wide coverage area, can be used as a source of information on the condition of agricultural lands and even remote areas. The advantages of SAR data that does not depend on sunlight and can penetrate of clouds and fog can fill the lack of optical data. ALOS PALSAR data has been used for analysis and ALOS AVNIR-2 is for checking of land cover visually, with acquisition date on 10 May 2007. Sampling of each rice crop growth period used several of rice field conditions in each period, on one scene data. Results showed a possibility to use soil moisture conditions derived from ALOS PALSAR for estimating rice planting area. On a scatter diagram between backscatter of ALOS PALSAR and near infrared of ALOS AVNIR-2 showed a specific pattern for each growing period of paddy. The results of the analysis produce distribution maps of the rice planting area Subang area, West Java Province. However, validation of the method used remains to be done. Remote sensing results of this study are expected to provide better information and can contribute in the planning of higher quality agricultural land.展开更多
Based on the continuation of our past study, the present analysis is conducted to examine recent effects of the urbanization process occurring over the entire district of Rome. Overlays of ALOS AVNIR-2 and SENTINEL-2A...Based on the continuation of our past study, the present analysis is conducted to examine recent effects of the urbanization process occurring over the entire district of Rome. Overlays of ALOS AVNIR-2 and SENTINEL-2A satellite images, collected over a 6 years period, were validated via Geographic Information System (GIS) techniques, in a particular procedure applied to urban land and agricultural transformations. The use of Copernicus SENTINEL-2A imagery has improved the previous results on urban processes, by reducing the uncertainty of the discrimination of land cover classes and facilitating the photo-interpretation. Statistical analysis was performed via the Urban Area Profile index in order to quantify the sprawl phenomenon, by defining several landscape metrics. This work, to be enriched in the future by means of complementary information available from Copernicus radar sensors, like the one onboard Sentinel-1, completes the series of observations on land use published by the Italian National Institute for Environmental Protection and Research, which stopped back in 2008.展开更多
Above Ground Biomass is one of the six pools identified in the inventory of forest resources and estimation of greenhouse gas emissions and sinks from the forestry sector. The pool varies by management practices in di...Above Ground Biomass is one of the six pools identified in the inventory of forest resources and estimation of greenhouse gas emissions and sinks from the forestry sector. The pool varies by management practices in different agro-ecological or agro-climatic zones in forests. The quantification of above ground biomass (AGB) hence carbon sequestration in forests has been very difficult due to the immense costs required. This research was done to estimate AGB using ALOS PALSAR L band data (HH, HV polarisation) acquired in 2009 in relation with ground measurements data in Kericho and Aberdares ranges in Kenya. Tree data information was obtained from ground measurement of DBH and tree heights in 100 circular plots of 15 m radius, by use of random sampling technique. ALOS PALSAR image is advantageous for its active microwave sensor using L-band frequency to achieve cloud free imageries, and the ability of long wavelength cross-polarization to estimate AGB accurately for tropical forests. The variations result between Natural and plantation forest for measured and estimated biomass in Kericho HV band regression value was 0.880 and HH band was 0.520. In Aberdare ranges HV regression value of 0.708 and HH band regression value of 0.511 for measured and estimated biomass respectively. The variations can be explained by the influence of different management regimes induced human disturbances, forest stand age, density, species composition, and trees diameter distribution. However, further research is required to investigate how strong these factors affect relationship between AGB and Alos Palsar backscatters.展开更多
Many supervised classification algorithms have been proposed, however, they are rarely evaluated for specific application. This research examines the performance of machine learning classifiers support vector machine ...Many supervised classification algorithms have been proposed, however, they are rarely evaluated for specific application. This research examines the performance of machine learning classifiers support vector machine (SVM), neural network (NN), Random Forest (RF) against maximum classifier (MLC) (traditional supervised classifier) in forest resources and land cover categorization, based on combination of Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) and Landsat Thematic Mapper (TM) data, in Northern Tanzania. Various data categories based on Landsat TM surface reflectance, ALOS PALSAR backscattering and their derivatives were generated for various classification scenarios. Then a separate and joint processing of Landsat and ALOS PALSAR data were executed using SVM, NN, RF and ML classifiers. The overall classification accuracy (OA), kappa coefficient (KC) and F1 score index values were computed. The result proves the robustness of SVM and RF in classification of forest resource and land cover using mere Landsat data and integration of Landsat and PALSAR (average OA = 92% and F1 = 0.7 to 1). A two sample t-statistics was utilized to evaluate the performance of the classifiers using different data categories. SVM and RF indicate there is no significance difference at 5% significance level. SVM and RF show a significant difference when compared to NN and ML. Generally, the study suggests that parametric classifiers indicate better performance compared to parametric classifier.展开更多
The potential of ALOS-2 SAR data for the estimation of tropical forest structural characteristics was assessed in Vietnam by collecting forest inventory data. The effect of polarization and seasonality of the SAR data...The potential of ALOS-2 SAR data for the estimation of tropical forest structural characteristics was assessed in Vietnam by collecting forest inventory data. The effect of polarization and seasonality of the SAR data on the estimation of forest biomass was analyzed. The combination of HH, HV, and HH/HV polarizations using multiple linear regression did not improve the estimation of biomass compared to using the HV channel independently, as the HH and HH/HV variables were not statistically significant. The dry season HV backscattering intensity was highly sensitive to the biomass compared to the rainy season backscattering intensity. The SAR data acquired in the rainy season with humid and wet canopies was not very sensitive to the biomass. The strong dependence of the biomass estimates with the season of SAR data acquisition confirmed that the choice of right season SAR data is very important for improving the satellite based estimates of the biomass. The validation results showed that the dry season HV polarization could explain 54% variation of the biomass.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
Rice paddy mapping with optical remote sensing is challenging in Bangladesh due to the heterogeneous cropping pattern, fragmented field size and cloud </span><span style="font-family:Verdana;">co...Rice paddy mapping with optical remote sensing is challenging in Bangladesh due to the heterogeneous cropping pattern, fragmented field size and cloud </span><span style="font-family:Verdana;">cover during the growing period. The high-resolution Synthetic Aperture</span><span style="font-family:Verdana;"> Radar (SAR) sensor is the potential alternate to mapping rice area in Bangla</span><span style="font-family:Verdana;">desh. The L-band SAR sensor onboard Advanced Land Observing Satellit</span><span style="font-family:Verdana;">e (</span><span style="font-family:Verdana;">ALOS) acquires multi-polarization and multi-temporal images are </span><span style="font-family:Verdana;">a very useful tool for rice area mapping. In this study, we used ALOS-2 ScanSAR dual (HH</span></span><span style="font-family:""> </span><span style="font-family:Verdana;">+</span><span style="font-family:""> </span><span style="font-family:Verdana;">HV) polarized time series data in the study area. We used orthorectification and slope corrected backscatter (sigma-naught) images and median filtering (3 × 3) window for image processing. The unsupervised classification with the k-means++ algorithm is used for initial clustering (20 categories) of images over the study area. The GPS location of rice paddy field with cropping pattern over study area uses for classifying the different rice-growing season from the k-means clustering data. The result is compared with the moderate resolution imaging spectroradiometer (MODIS) based rice area and national statistical agricultural yearbook statistics. The results show that, based on the MODIS based rice map, the rice fields can be mapped with a conditional Kappa value of 0.68 and at user’s and producer’s accuracies of 86% and 90%, respectively. The large commission error primarily came from confusion between wet season Aus rice and others crop, Aus-Amon and Boro-Aus-Amon cropping pattern because of their similar backscatter amplitudes and temporal similarities in the rice growing season. The relatively high rice mapping accuracy in this study indicates that the ALOS/PALSAR-2 data could provide useful information in rice cropping management in subtropical regions such Bangladesh.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
As a new type of production factor in healthcare,healthcare data elements have been rapidly integrated into various health production processes,such as clinical assistance,health management,biological testing,and oper...As a new type of production factor in healthcare,healthcare data elements have been rapidly integrated into various health production processes,such as clinical assistance,health management,biological testing,and operation and supervision[1,2].Healthcare data elements include biolog.ical and clinical data that are related to disease,environ-mental health data that are associated with life,and operational and healthcare management data that are related to healthcare activities(Figure 1).Activities such as the construction of a data value assessment system,the devel-opment of a data circulation and sharing platform,and the authorization of data compliance and operation products support the strong growth momentum of the market for health care data elements in China[3].展开更多
As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy protection.Current research emphasizes data security and use...As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy protection.Current research emphasizes data security and user privacy concerns within smart grids.However,existing methods struggle with efficiency and security when processing large-scale data.Balancing efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent challenge.This paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data modalities.The approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user privacy.It also explores the application of Boneh Lynn Shacham(BLS)signatures for user authentication.The proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.展开更多
基金supported by the National Natural Science Foundation of China(Nos.42174023,42304037)the National Key Research and Development Program(No.2022YFB3903602)+1 种基金the Natural Science Foundation of Hunan Province(No.2024JJ3031)the Frontier Cross Research Project of Central South University(No.2023QYJC006)。
文摘0 INTRODUCTION Turkey is located at the intersection of the Eurasian,Anatolian,Arabian,and African tectonic plates.Due to the ongoing northward compression from the Arabian Plate,the Anatolian Plate is pushed westward in a tectonic escape mechanism,leading to the formation of the North Anatolian fault zone(NAFZ)and the East Anatolian fault zone(EAFZ)(e.g.,Bayrak et al.,2015;Duman and Emre,2013;Reilinger et al.,2006).
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the Japan Science Society (Sasagawa Research Fund)the Global Centre of Excellence (GCOE) programme by JSPS for partial funding
文摘This study used Corona KH-4A and Advanced Land Observing Satellite (ALOS) PRISM images to generate digital terrain models (DTMs) of the distal part of Imja Glacier,where a few supraglacial ponds (～0.07 km 2) expanded into the large Imja Glacier Lake (Imja Tsho,～0.91 km 2) between 1964 and 2006.DTMs and subsequently derived topographical maps with contour intervals of 1 m were created from the high-resolution images (Corona in 1964 and ALOS in 2006) in the Leica Photogrammetric Suite (LPS) platform.The DTMs and topographic maps provided excellent representation of the elevation and micro-topography of the glacier surface,such as its supra-glacial ponds/lake,surface depressions,and moraine ridges,with an error of about +/-4 m (maximum).The DTMs produced from the Corona and ALOS PRISM images are suitable for use in studies of the surface change of glaciers.The topographical maps produced from the Corona data (1964) showed that part of the dead ice in the down-glacier area was even higher than the top of the lateral moraine ridges,while the glacier surface in the up-glacier area was noticeably lower than the moraine crests.This suggests more extensive melting of glacier ice in the up-glacier area before 1964.The average lowering of the glacier surface from 1964 to 2006 was 16.9 m for the dead-ice area west of the lake and 47.4 m for the glacier surface east of the lake;between 1964 and 2002,the lake surface lowered by 82.3 m.These figures represent average lowering rates of 0.4,1.1,and 2.2 m/year for the respective areas.
文摘To understand the relationship between turbid water and coral damage caused by the heavy rain disaster at the end of October 2010 in Amami-Oshima, Kagoshima Prefecture, Japan, turbid water and coral damage distribution monitoring was attempted using satellite imagery and a diving survey immediately after the disaster. ALOS AVNIR-2 images (spatial resolution: 10 m) on October 6 (before the disaster), October 24, October 30, and October 31 (after the disaster) were obtained as satellite data in 2010. The red-silt deposition index (RSI) map based on the method by Nadaoka and Tamura (1992) was also created. Moreover, a diving survey was conducted via the spot check method on December 18, 2010. As a result, comparison between the high turbidity (RSI) areas estimated using AVNIR-2 data and the coral damage areas judging from the field survey was considered relatively light. It is shown that satellite data such as AVNIR-2 can be a powerful tool to monitor damage distribution of coral reefs after heavy rain.
文摘Agricultural land has a strategic function as the primary food provider for the people of Indonesia. Various methods of agricultural production estimation, particularly food crops, provide different information. It can be a source of error in decision making. Satellite data, provides information periodically, wide coverage area, can be used as a source of information on the condition of agricultural lands and even remote areas. The advantages of SAR data that does not depend on sunlight and can penetrate of clouds and fog can fill the lack of optical data. ALOS PALSAR data has been used for analysis and ALOS AVNIR-2 is for checking of land cover visually, with acquisition date on 10 May 2007. Sampling of each rice crop growth period used several of rice field conditions in each period, on one scene data. Results showed a possibility to use soil moisture conditions derived from ALOS PALSAR for estimating rice planting area. On a scatter diagram between backscatter of ALOS PALSAR and near infrared of ALOS AVNIR-2 showed a specific pattern for each growing period of paddy. The results of the analysis produce distribution maps of the rice planting area Subang area, West Java Province. However, validation of the method used remains to be done. Remote sensing results of this study are expected to provide better information and can contribute in the planning of higher quality agricultural land.
文摘Based on the continuation of our past study, the present analysis is conducted to examine recent effects of the urbanization process occurring over the entire district of Rome. Overlays of ALOS AVNIR-2 and SENTINEL-2A satellite images, collected over a 6 years period, were validated via Geographic Information System (GIS) techniques, in a particular procedure applied to urban land and agricultural transformations. The use of Copernicus SENTINEL-2A imagery has improved the previous results on urban processes, by reducing the uncertainty of the discrimination of land cover classes and facilitating the photo-interpretation. Statistical analysis was performed via the Urban Area Profile index in order to quantify the sprawl phenomenon, by defining several landscape metrics. This work, to be enriched in the future by means of complementary information available from Copernicus radar sensors, like the one onboard Sentinel-1, completes the series of observations on land use published by the Italian National Institute for Environmental Protection and Research, which stopped back in 2008.
文摘Above Ground Biomass is one of the six pools identified in the inventory of forest resources and estimation of greenhouse gas emissions and sinks from the forestry sector. The pool varies by management practices in different agro-ecological or agro-climatic zones in forests. The quantification of above ground biomass (AGB) hence carbon sequestration in forests has been very difficult due to the immense costs required. This research was done to estimate AGB using ALOS PALSAR L band data (HH, HV polarisation) acquired in 2009 in relation with ground measurements data in Kericho and Aberdares ranges in Kenya. Tree data information was obtained from ground measurement of DBH and tree heights in 100 circular plots of 15 m radius, by use of random sampling technique. ALOS PALSAR image is advantageous for its active microwave sensor using L-band frequency to achieve cloud free imageries, and the ability of long wavelength cross-polarization to estimate AGB accurately for tropical forests. The variations result between Natural and plantation forest for measured and estimated biomass in Kericho HV band regression value was 0.880 and HH band was 0.520. In Aberdare ranges HV regression value of 0.708 and HH band regression value of 0.511 for measured and estimated biomass respectively. The variations can be explained by the influence of different management regimes induced human disturbances, forest stand age, density, species composition, and trees diameter distribution. However, further research is required to investigate how strong these factors affect relationship between AGB and Alos Palsar backscatters.
文摘Many supervised classification algorithms have been proposed, however, they are rarely evaluated for specific application. This research examines the performance of machine learning classifiers support vector machine (SVM), neural network (NN), Random Forest (RF) against maximum classifier (MLC) (traditional supervised classifier) in forest resources and land cover categorization, based on combination of Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) and Landsat Thematic Mapper (TM) data, in Northern Tanzania. Various data categories based on Landsat TM surface reflectance, ALOS PALSAR backscattering and their derivatives were generated for various classification scenarios. Then a separate and joint processing of Landsat and ALOS PALSAR data were executed using SVM, NN, RF and ML classifiers. The overall classification accuracy (OA), kappa coefficient (KC) and F1 score index values were computed. The result proves the robustness of SVM and RF in classification of forest resource and land cover using mere Landsat data and integration of Landsat and PALSAR (average OA = 92% and F1 = 0.7 to 1). A two sample t-statistics was utilized to evaluate the performance of the classifiers using different data categories. SVM and RF indicate there is no significance difference at 5% significance level. SVM and RF show a significant difference when compared to NN and ML. Generally, the study suggests that parametric classifiers indicate better performance compared to parametric classifier.
文摘The potential of ALOS-2 SAR data for the estimation of tropical forest structural characteristics was assessed in Vietnam by collecting forest inventory data. The effect of polarization and seasonality of the SAR data on the estimation of forest biomass was analyzed. The combination of HH, HV, and HH/HV polarizations using multiple linear regression did not improve the estimation of biomass compared to using the HV channel independently, as the HH and HH/HV variables were not statistically significant. The dry season HV backscattering intensity was highly sensitive to the biomass compared to the rainy season backscattering intensity. The SAR data acquired in the rainy season with humid and wet canopies was not very sensitive to the biomass. The strong dependence of the biomass estimates with the season of SAR data acquisition confirmed that the choice of right season SAR data is very important for improving the satellite based estimates of the biomass. The validation results showed that the dry season HV polarization could explain 54% variation of the biomass.
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.
文摘Rice paddy mapping with optical remote sensing is challenging in Bangladesh due to the heterogeneous cropping pattern, fragmented field size and cloud </span><span style="font-family:Verdana;">cover during the growing period. The high-resolution Synthetic Aperture</span><span style="font-family:Verdana;"> Radar (SAR) sensor is the potential alternate to mapping rice area in Bangla</span><span style="font-family:Verdana;">desh. The L-band SAR sensor onboard Advanced Land Observing Satellit</span><span style="font-family:Verdana;">e (</span><span style="font-family:Verdana;">ALOS) acquires multi-polarization and multi-temporal images are </span><span style="font-family:Verdana;">a very useful tool for rice area mapping. In this study, we used ALOS-2 ScanSAR dual (HH</span></span><span style="font-family:""> </span><span style="font-family:Verdana;">+</span><span style="font-family:""> </span><span style="font-family:Verdana;">HV) polarized time series data in the study area. We used orthorectification and slope corrected backscatter (sigma-naught) images and median filtering (3 × 3) window for image processing. The unsupervised classification with the k-means++ algorithm is used for initial clustering (20 categories) of images over the study area. The GPS location of rice paddy field with cropping pattern over study area uses for classifying the different rice-growing season from the k-means clustering data. The result is compared with the moderate resolution imaging spectroradiometer (MODIS) based rice area and national statistical agricultural yearbook statistics. The results show that, based on the MODIS based rice map, the rice fields can be mapped with a conditional Kappa value of 0.68 and at user’s and producer’s accuracies of 86% and 90%, respectively. The large commission error primarily came from confusion between wet season Aus rice and others crop, Aus-Amon and Boro-Aus-Amon cropping pattern because of their similar backscatter amplitudes and temporal similarities in the rice growing season. The relatively high rice mapping accuracy in this study indicates that the ALOS/PALSAR-2 data could provide useful information in rice cropping management in subtropical regions such Bangladesh.
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.
基金supported by National Natural Science Foundation of China(Grants 72474022,71974011,72174022,71972012,71874009)"BIT think tank"Promotion Plan of Science and Technology Innovation Program of Beijing Institute of Technology(Grants 2024CX14017,2023CX13029).
文摘As a new type of production factor in healthcare,healthcare data elements have been rapidly integrated into various health production processes,such as clinical assistance,health management,biological testing,and operation and supervision[1,2].Healthcare data elements include biolog.ical and clinical data that are related to disease,environ-mental health data that are associated with life,and operational and healthcare management data that are related to healthcare activities(Figure 1).Activities such as the construction of a data value assessment system,the devel-opment of a data circulation and sharing platform,and the authorization of data compliance and operation products support the strong growth momentum of the market for health care data elements in China[3].
基金supported by the National Key R&D Program of China(No.2023YFB2703700)the National Natural Science Foundation of China(Nos.U21A20465,62302457,62402444,62172292)+4 种基金the Fundamental Research Funds of Zhejiang Sci-Tech University(Nos.23222092-Y,22222266-Y)the Program for Leading Innovative Research Team of Zhejiang Province(No.2023R01001)the Zhejiang Provincial Natural Science Foundation of China(Nos.LQ24F020008,LQ24F020012)the Foundation of State Key Laboratory of Public Big Data(No.[2022]417)the“Pioneer”and“Leading Goose”R&D Program of Zhejiang(No.2023C01119).
文摘As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy protection.Current research emphasizes data security and user privacy concerns within smart grids.However,existing methods struggle with efficiency and security when processing large-scale data.Balancing efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent challenge.This paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data modalities.The approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user privacy.It also explores the application of Boneh Lynn Shacham(BLS)signatures for user authentication.The proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.
