Climate change affects distribution and persistence of species. However, forecasting species＇ re-sponses to these changes requires long-term data series that are often lacking in ecological studies.We used 15 years o...Climate change affects distribution and persistence of species. However, forecasting species＇ re-sponses to these changes requires long-term data series that are often lacking in ecological studies.We used 15 years of small mammal trapping data collected between 1978 and 2015 in 3 areas atDoSana National Park （southwest Spain） to （i） describe changes in species composition and （ii） test theassociation between local climate conditions and size of small mammal populations. Overall, 5 specieswere captured： wood mouse Apodemus sylvaticus, algerian mouse Mus spretus, greater white-toothed shrew Crocidura russula, garden dormouse Eliomys quercinus, and black rat Rattus rattus. Thetemporal pattern in the proportion of captures of each species suggests that the small mammal diver-sity declined with time. Although the larger species （e.g., E. quercinus）, better adapted to colder cli-mate, have disappeared from our trapping records, M. spretus, a small species inhabiting southwestEurope and the Mediterranean coast of Africa, currently is almost the only trapped species. We used 2-level hierarchical models to separate changes in abundance from changes in probability of captureusing records of A. sylvaticus in all 3 areas and of Mo spretus in 1. We found that heavy rainfall and lowtemperatures were positively related to abundance of A. sylvaticus, and that the number of extremelyhot days was negatively related to abundance of M. spretus. Despite other mechanisms are likely to beinvolved, our findings support the importance of climate for the distribution and persistence of thesespecies and raise conservation concerns about potential cascading effects in the Donana ecosystem.展开更多
Long-term navigation ability based on consumer-level wearable inertial sensors plays an essential role towards various emerging fields, for instance, smart healthcare, emergency rescue, soldier positioning et al. The ...Long-term navigation ability based on consumer-level wearable inertial sensors plays an essential role towards various emerging fields, for instance, smart healthcare, emergency rescue, soldier positioning et al. The performance of existing long-term navigation algorithm is limited by the cumulative error of inertial sensors, disturbed local magnetic field, and complex motion modes of the pedestrian. This paper develops a robust data and physical model dual-driven based trajectory estimation(DPDD-TE) framework, which can be applied for long-term navigation tasks. A Bi-directional Long Short-Term Memory(Bi-LSTM) based quasi-static magnetic field(QSMF) detection algorithm is developed for extracting useful magnetic observation for heading calibration, and another Bi-LSTM is adopted for walking speed estimation by considering hybrid human motion information under a specific time period. In addition, a data and physical model dual-driven based multi-source fusion model is proposed to integrate basic INS mechanization and multi-level constraint and observations for maintaining accuracy under long-term navigation tasks, and enhanced by the magnetic and trajectory features assisted loop detection algorithm. Real-world experiments indicate that the proposed DPDD-TE outperforms than existing algorithms, and final estimated heading and positioning accuracy indexes reaches 5° and less than 2 m under the time period of 30 min, respectively.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
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
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
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
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
Polyurethane-fluorinated polysiloxane(PU-^(F)PDMS)with high-strength,high-bonding and low surface en-ergy is synthesized as the matrix,and the PU-^(F)PDMS/MCs/Ag marine anti-fouling coating on the sur-face of imitatio...Polyurethane-fluorinated polysiloxane(PU-^(F)PDMS)with high-strength,high-bonding and low surface en-ergy is synthesized as the matrix,and the PU-^(F)PDMS/MCs/Ag marine anti-fouling coating on the sur-face of imitation crab shells is constructed by assembling butenolide@1,1-stilbene-modified hydrolyzed polyglycidyl methacrylate/graphene oxide microcapsules(Bu@PGMAm/GO MCs)with compact multi-shell structure and Ag nanoparticles(AgNPs)step by step on the PU-^(F)PDMS matrix.The PU-^(F)PDMS/MCs/Ag bionic anti-fouling coatings achieve long-term and stable anti-fouling effect under the combination of robust low-surface-energy PU-^(F)PDMS matrix,steady-state sustained release of butenolide encapsulated by the compact multi-shell,bionic surface formed by the microcapsules and AgNPs,and the release of Ag^(+).The shear strength,tensile strength,and elongation at break of the PU-^(F)PDMS/MCs/Ag are 3.53 MPa,6.7 MPa,and 192.83%,respectively.Its static contact angle and sliding angle are 161.8°and 3.6°,respectively.The antibacterial rate of PU-^(F)PDMS/MCs/Ag against Escherichia coli,Staphylococcus aureus,and Candida albicans can reach 100%.Compared with glass blank,PU,PU-^(F)PDMS,PU-^(F)PDMS/Ag,and PU-^(F)PDMS/MCs,both the adhesion number and coverage percentage of chlorella adhere to PU-^(F)PDMS/MCs/Ag are the minimum values,which are 600 cell mm^(-2) and 1.53%,respectively.After 6 months of marine field test,the primer blank,PU,PU-^(F)PDMS all show different degrees of attachment by shellfish,spirorbis,al-gae and other biofouling,while the PU-^(F)PDMS/MCs/Ag coating is still not covered with biofouling,while the PU-^(F)PDMS/MCs/Ag coatings still exhibit little attachment of marine fouling.The PU-^(F)PDMS/MCs/Ag bionic anti-fouling coatings are expected to be widely used in the fields of anti-fouling,anti-icing,anti-fogging,drag reduction,self-cleaning,and antibacterial.展开更多
Objective:Triple-negative breast cancer(TNBC)is a highly aggressive subtype that lacks targeted therapies,leading to a poorer prognosis.However,some patients achieve long-term recurrence-free survival(RFS),offering va...Objective:Triple-negative breast cancer(TNBC)is a highly aggressive subtype that lacks targeted therapies,leading to a poorer prognosis.However,some patients achieve long-term recurrence-free survival(RFS),offering valuable insights into tumor biology and potential treatment strategies.Methods:We conducted a comprehensive multi-omics analysis of 132 patients with American Joint Committee on Cancer(AJCC)stage III TNBC,comprising 36 long-term survivors(RFS≥8 years),62 moderate-term survivors(RFS:3-8 years),and 34 short-term survivors(RFS<3 years).Analyses investigated clinicopathological factors,whole-exome sequencing,germline mutations,copy number alterations(CNAs),RNA sequences,and metabolomic profiles.Results:Long-term survivors exhibited fewer metastatic regional lymph nodes,along with tumors showing reduced stromal fibrosis and lower Ki67 index.Molecularly,these tumors exhibited multiple alterations in genes related to homologous recombination repair,with higher frequencies of germline mutations and somatic CNAs.Additionally,tumors from long-term survivors demonstrated significant downregulation of the RTK-RAS signaling pathway.Metabolomic profiling revealed decreased levels of lipids and carbohydrate,particularly those involved in glycerophospholipid,fructose,and mannose metabolism,in long-term survival group.Multivariate Cox analysis identified fibrosis[hazard ratio(HR):12.70,95%confidence interval(95%CI):2.19-73.54,P=0.005]and RAC1copy number loss/deletion(HR:0.22,95%CI:0.06-0.83,P=0.026)as independent predictors of RFS.Higher fructose/mannose metabolism was associated with worse overall survival(HR:1.30,95%CI:1.01-1.68,P=0.045).Our findings emphasize the association between biological determinants and prolonged survival in patients with TNBC.Conclusions:Our study systematically identified the key molecular and metabolic features associated with prolonged survival in AJCC stage III TNBC,suggesting potential therapeutic targets to improve patient outcomes.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
Earth’s internal core and crustal magnetic fields,as measured by geomagnetic satellites like MSS-1(Macao Science Satellite-1)and Swarm,are vital for understanding core dynamics and tectonic evolution.To model these i...Earth’s internal core and crustal magnetic fields,as measured by geomagnetic satellites like MSS-1(Macao Science Satellite-1)and Swarm,are vital for understanding core dynamics and tectonic evolution.To model these internal magnetic fields accurately,data selection based on specific criteria is often employed to minimize the influence of rapidly changing current systems in the ionosphere and magnetosphere.However,the quantitative impact of various data selection criteria on internal geomagnetic field modeling is not well understood.This study aims to address this issue and provide a reference for constructing and applying geomagnetic field models.First,we collect the latest MSS-1 and Swarm satellite magnetic data and summarize widely used data selection criteria in geomagnetic field modeling.Second,we briefly describe the method to co-estimate the core,crustal,and large-scale magnetospheric fields using satellite magnetic data.Finally,we conduct a series of field modeling experiments with different data selection criteria to quantitatively estimate their influence.Our numerical experiments confirm that without selecting data from dark regions and geomagnetically quiet times,the resulting internal field differences at the Earth’s surface can range from tens to hundreds of nanotesla(nT).Additionally,we find that the uncertainties introduced into field models by different data selection criteria are significantly larger than the measurement accuracy of modern geomagnetic satellites.These uncertainties should be considered when utilizing constructed magnetic field models for scientific research and applications.展开更多
Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subse...Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subsets via hierarchical clustering,but objective methods to determine the appropriate classification granularity are missing.We recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within clusters.Here we present the corresponding protocol to classify cellular datasets by combining datadriven unsupervised hierarchical clustering with statistical testing.These general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values,including molecula r,physiological,and anatomical datasets.We demonstrate the protocol using cellular data from the Janelia MouseLight project to chara cterize morphological aspects of neurons.展开更多
The accelerated advancement of the Internet of Things(IoT)has generated substantial data,including sensitive and private information.Consequently,it is imperative to guarantee the security of data sharing.While facili...The accelerated advancement of the Internet of Things(IoT)has generated substantial data,including sensitive and private information.Consequently,it is imperative to guarantee the security of data sharing.While facilitating fine-grained access control,Ciphertext Policy Attribute-Based Encryption(CP-ABE)can effectively ensure the confidentiality of shared data.Nevertheless,the conventional centralized CP-ABE scheme is plagued by the issues of keymisuse,key escrow,and large computation,which will result in security risks.This paper suggests a lightweight IoT data security sharing scheme that integrates blockchain technology and CP-ABE to address the abovementioned issues.The integrity and traceability of shared data are guaranteed by the use of blockchain technology to store and verify access transactions.The encryption and decryption operations of the CP-ABE algorithm have been implemented using elliptic curve scalarmultiplication to accommodate lightweight IoT devices,as opposed to themore arithmetic bilinear pairing found in the traditional CP-ABE algorithm.Additionally,a portion of the computation is delegated to the edge nodes to alleviate the computational burden on users.A distributed key management method is proposed to address the issues of key escrow andmisuse.Thismethod employs the edge blockchain to facilitate the storage and distribution of attribute private keys.Meanwhile,data security sharing is enhanced by combining off-chain and on-chain ciphertext storage.The security and performance analysis indicates that the proposed scheme is more efficient and secure.展开更多
调查和分析元数据标准在健康科学数据中的应用现状,有助于为我国健康科学数据描述中元数据标准的选择、健康科学数据平台的建设提供参考。通过网络调研法对科学数据仓储注册系统(registry of research data repositories,re3data)中的...调查和分析元数据标准在健康科学数据中的应用现状,有助于为我国健康科学数据描述中元数据标准的选择、健康科学数据平台的建设提供参考。通过网络调研法对科学数据仓储注册系统(registry of research data repositories,re3data)中的健康科学数据管理平台进行调研,梳理所应用的元数据标准,分析典型元数据标准在平台中的应用情况,并归纳其在健康科学数据描述中的适用性。re3data中各健康科学数据平台共使用14种元数据标准,其中DC、DataCite、DDI、仓储自建元数据标准的使用最为广泛,多数平台组合使用多种元数据标准。各类元数据标准可分为通用型、社会科学型、自建型3类,分别适用于描述健康科学数据通用属性、社会科学研究产生的健康科学数据、特色和专业性强及政府开放的健康科学数据。展开更多
Kawasaki disease(KD)is a significant pediatric vasculitis known for its potential to cause severe coronary artery complications.Despite the effectiveness of initial treatments,such as intravenous immunoglobulin,KD pat...Kawasaki disease(KD)is a significant pediatric vasculitis known for its potential to cause severe coronary artery complications.Despite the effectiveness of initial treatments,such as intravenous immunoglobulin,KD patients can experience long-term cardiovascular issues,as evidenced by a recent case report of an adult who suffered a ST-segment elevation myocardial infarction due to previous KD in the World Journal of Clinical Cases.This editorial emphasizes the critical need for long-term management and regular surveillance to prevent such complications.By drawing on recent research and case studies,we advocate for a structured approach to follow-up care that includes routine cardiac evaluations and preventive measures.展开更多
Open networks and heterogeneous services in the Internet of Vehicles(IoV)can lead to security and privacy challenges.One key requirement for such systems is the preservation of user privacy,ensuring a seamless experie...Open networks and heterogeneous services in the Internet of Vehicles(IoV)can lead to security and privacy challenges.One key requirement for such systems is the preservation of user privacy,ensuring a seamless experience in driving,navigation,and communication.These privacy needs are influenced by various factors,such as data collected at different intervals,trip durations,and user interactions.To address this,the paper proposes a Support Vector Machine(SVM)model designed to process large amounts of aggregated data and recommend privacy preserving measures.The model analyzes data based on user demands and interactions with service providers or neighboring infrastructure.It aims to minimize privacy risks while ensuring service continuity and sustainability.The SVMmodel helps validate the system’s reliability by creating a hyperplane that distinguishes between maximum and minimum privacy recommendations.The results demonstrate the effectiveness of the proposed SVM model in enhancing both privacy and service performance.展开更多
This article explores the design of a wireless fire alarm system supported by advanced data fusion technology.It includes discussions on the basic design ideas of the wireless fire alarm system,hardware design analysi...This article explores the design of a wireless fire alarm system supported by advanced data fusion technology.It includes discussions on the basic design ideas of the wireless fire alarm system,hardware design analysis,software design analysis,and simulation analysis,all supported by data fusion technology.Hopefully,this analysis can provide some reference for the rational application of data fusion technology to meet the actual design and application requirements of the system.展开更多
We combine gradient data from the Macao Science Satellite-1(MSS-1),CHAllenging Minisatellite Payload(CHAMP),Swarm-A,and Swarm-C satellites to develop a 110-degree lithospheric magnetic field model.We then comprehensiv...We combine gradient data from the Macao Science Satellite-1(MSS-1),CHAllenging Minisatellite Payload(CHAMP),Swarm-A,and Swarm-C satellites to develop a 110-degree lithospheric magnetic field model.We then comprehensively evaluate the performance of the model by power spectral comparisons,correlation analyses,sensitivity matrix assessments,and comparisons with existing lithospheric field models.Results showed that using near east–west gradient data from MSS-1 significantly enhances the model correlation in the spherical harmonic degree(N) range of 45–60 while also mitigating the decline in correlation at higher degrees(N > 60).Furthermore,the unique orbital characteristics of MSS-1 enable its gradient data to provide substantial contributions to modeling in the mid-to low-latitude regions.With continued data acquisition from MSS-1 and further optimization of data processing methods,the performance of the model is expected to improve.展开更多
The security of the seed industry is crucial for ensuring national food security.Currently,developed countries in Europe and America,along with international seed industry giants,have entered the Breeding 4.0 era.This...The security of the seed industry is crucial for ensuring national food security.Currently,developed countries in Europe and America,along with international seed industry giants,have entered the Breeding 4.0 era.This era integrates biotechnology,artificial intelligence(AI),and big data information technology.In contrast,China is still in a transition period between stages 2.0 and 3.0,which primarily relies on conventional selection and molecular breeding.In the context of increasingly complex international situations,accurately identifying core issues in China's seed industry innovation and seizing the frontier of international seed technology are strategically important.These efforts are essential for ensuring food security and revitalizing the seed industry.This paper systematically analyzes the characteristics of crop breeding data from artificial selection to intelligent design breeding.It explores the applications and development trends of AI and big data in modern crop breeding from several key perspectives.These include highthroughput phenotype acquisition and analysis,multiomics big data database and management system construction,AI-based multiomics integrated analysis,and the development of intelligent breeding software tools based on biological big data and AI technology.Based on an in-depth analysis of the current status and challenges of China's seed industry technology development,we propose strategic goals and key tasks for China's new generation of AI and big data-driven intelligent design breeding.These suggestions aim to accelerate the development of an intelligent-driven crop breeding engineering system that features large-scale gene mining,efficient gene manipulation,engineered variety design,and systematized biobreeding.This study provides a theoretical basis and practical guidance for the development of China's seed industry technology.展开更多
文摘Climate change affects distribution and persistence of species. However, forecasting species＇ re-sponses to these changes requires long-term data series that are often lacking in ecological studies.We used 15 years of small mammal trapping data collected between 1978 and 2015 in 3 areas atDoSana National Park （southwest Spain） to （i） describe changes in species composition and （ii） test theassociation between local climate conditions and size of small mammal populations. Overall, 5 specieswere captured： wood mouse Apodemus sylvaticus, algerian mouse Mus spretus, greater white-toothed shrew Crocidura russula, garden dormouse Eliomys quercinus, and black rat Rattus rattus. Thetemporal pattern in the proportion of captures of each species suggests that the small mammal diver-sity declined with time. Although the larger species （e.g., E. quercinus）, better adapted to colder cli-mate, have disappeared from our trapping records, M. spretus, a small species inhabiting southwestEurope and the Mediterranean coast of Africa, currently is almost the only trapped species. We used 2-level hierarchical models to separate changes in abundance from changes in probability of captureusing records of A. sylvaticus in all 3 areas and of Mo spretus in 1. We found that heavy rainfall and lowtemperatures were positively related to abundance of A. sylvaticus, and that the number of extremelyhot days was negatively related to abundance of M. spretus. Despite other mechanisms are likely to beinvolved, our findings support the importance of climate for the distribution and persistence of thesespecies and raise conservation concerns about potential cascading effects in the Donana ecosystem.
文摘Long-term navigation ability based on consumer-level wearable inertial sensors plays an essential role towards various emerging fields, for instance, smart healthcare, emergency rescue, soldier positioning et al. The performance of existing long-term navigation algorithm is limited by the cumulative error of inertial sensors, disturbed local magnetic field, and complex motion modes of the pedestrian. This paper develops a robust data and physical model dual-driven based trajectory estimation(DPDD-TE) framework, which can be applied for long-term navigation tasks. A Bi-directional Long Short-Term Memory(Bi-LSTM) based quasi-static magnetic field(QSMF) detection algorithm is developed for extracting useful magnetic observation for heading calibration, and another Bi-LSTM is adopted for walking speed estimation by considering hybrid human motion information under a specific time period. In addition, a data and physical model dual-driven based multi-source fusion model is proposed to integrate basic INS mechanization and multi-level constraint and observations for maintaining accuracy under long-term navigation tasks, and enhanced by the magnetic and trajectory features assisted loop detection algorithm. Real-world experiments indicate that the proposed DPDD-TE outperforms than existing algorithms, and final estimated heading and positioning accuracy indexes reaches 5° and less than 2 m under the time period of 30 min, respectively.
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 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 in part by the National Natural Science Foundation of China under Grant 62371181in part by the Changzhou Science and Technology International Cooperation Program under Grant CZ20230029+1 种基金supported by a National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(2021R1A2B5B02087169)supported under the framework of international cooperation program managed by the National Research Foundation of Korea(2022K2A9A1A01098051)。
文摘The Intelligent Internet of Things(IIoT)involves real-world things that communicate or interact with each other through networking technologies by collecting data from these“things”and using intelligent approaches,such as Artificial Intelligence(AI)and machine learning,to make accurate decisions.Data science is the science of dealing with data and its relationships through intelligent approaches.Most state-of-the-art research focuses independently on either data science or IIoT,rather than exploring their integration.Therefore,to address the gap,this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT(IIoT)system by classifying the existing IoT-based data science techniques and presenting a summary of various characteristics.The paper analyzes the data science or big data security and privacy features,including network architecture,data protection,and continuous monitoring of data,which face challenges in various IoT-based systems.Extensive insights into IoT data security,privacy,and challenges are visualized in the context of data science for IoT.In addition,this study reveals the current opportunities to enhance data science and IoT market development.The current gap and challenges faced in the integration of data science and IoT are comprehensively presented,followed by the future outlook and possible solutions.
基金supported by the National Natural Science Foundation of China(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 Natural Science Foundation of China(Nos.52003148 and 52261045)the State Key Laboratory of Marine Resource Utilization in South China Sea,Hainan University(No.MRUKF2021023)+3 种基金the Key Research and Development Project of Shaanxi Province(No.2023-YBGY-475)the Key Scientific Research Project of Education Department of Shaanxi Province(No.22JS003)the Industrialization Project of the State Key Laboratory of Biological Resources and Ecological Environment(Cultivation)of Qinba Region(No.SXC-2310)the key cultivation project funds of Shaanxi University of Technology(No.SLGKYXM2201).
文摘Polyurethane-fluorinated polysiloxane(PU-^(F)PDMS)with high-strength,high-bonding and low surface en-ergy is synthesized as the matrix,and the PU-^(F)PDMS/MCs/Ag marine anti-fouling coating on the sur-face of imitation crab shells is constructed by assembling butenolide@1,1-stilbene-modified hydrolyzed polyglycidyl methacrylate/graphene oxide microcapsules(Bu@PGMAm/GO MCs)with compact multi-shell structure and Ag nanoparticles(AgNPs)step by step on the PU-^(F)PDMS matrix.The PU-^(F)PDMS/MCs/Ag bionic anti-fouling coatings achieve long-term and stable anti-fouling effect under the combination of robust low-surface-energy PU-^(F)PDMS matrix,steady-state sustained release of butenolide encapsulated by the compact multi-shell,bionic surface formed by the microcapsules and AgNPs,and the release of Ag^(+).The shear strength,tensile strength,and elongation at break of the PU-^(F)PDMS/MCs/Ag are 3.53 MPa,6.7 MPa,and 192.83%,respectively.Its static contact angle and sliding angle are 161.8°and 3.6°,respectively.The antibacterial rate of PU-^(F)PDMS/MCs/Ag against Escherichia coli,Staphylococcus aureus,and Candida albicans can reach 100%.Compared with glass blank,PU,PU-^(F)PDMS,PU-^(F)PDMS/Ag,and PU-^(F)PDMS/MCs,both the adhesion number and coverage percentage of chlorella adhere to PU-^(F)PDMS/MCs/Ag are the minimum values,which are 600 cell mm^(-2) and 1.53%,respectively.After 6 months of marine field test,the primer blank,PU,PU-^(F)PDMS all show different degrees of attachment by shellfish,spirorbis,al-gae and other biofouling,while the PU-^(F)PDMS/MCs/Ag coating is still not covered with biofouling,while the PU-^(F)PDMS/MCs/Ag coatings still exhibit little attachment of marine fouling.The PU-^(F)PDMS/MCs/Ag bionic anti-fouling coatings are expected to be widely used in the fields of anti-fouling,anti-icing,anti-fogging,drag reduction,self-cleaning,and antibacterial.
基金supported by grants from the Medical Engineering Jiont Fund of the Fudan University(No.IDH2310117)。
文摘Objective:Triple-negative breast cancer(TNBC)is a highly aggressive subtype that lacks targeted therapies,leading to a poorer prognosis.However,some patients achieve long-term recurrence-free survival(RFS),offering valuable insights into tumor biology and potential treatment strategies.Methods:We conducted a comprehensive multi-omics analysis of 132 patients with American Joint Committee on Cancer(AJCC)stage III TNBC,comprising 36 long-term survivors(RFS≥8 years),62 moderate-term survivors(RFS:3-8 years),and 34 short-term survivors(RFS<3 years).Analyses investigated clinicopathological factors,whole-exome sequencing,germline mutations,copy number alterations(CNAs),RNA sequences,and metabolomic profiles.Results:Long-term survivors exhibited fewer metastatic regional lymph nodes,along with tumors showing reduced stromal fibrosis and lower Ki67 index.Molecularly,these tumors exhibited multiple alterations in genes related to homologous recombination repair,with higher frequencies of germline mutations and somatic CNAs.Additionally,tumors from long-term survivors demonstrated significant downregulation of the RTK-RAS signaling pathway.Metabolomic profiling revealed decreased levels of lipids and carbohydrate,particularly those involved in glycerophospholipid,fructose,and mannose metabolism,in long-term survival group.Multivariate Cox analysis identified fibrosis[hazard ratio(HR):12.70,95%confidence interval(95%CI):2.19-73.54,P=0.005]and RAC1copy number loss/deletion(HR:0.22,95%CI:0.06-0.83,P=0.026)as independent predictors of RFS.Higher fructose/mannose metabolism was associated with worse overall survival(HR:1.30,95%CI:1.01-1.68,P=0.045).Our findings emphasize the association between biological determinants and prolonged survival in patients with TNBC.Conclusions:Our study systematically identified the key molecular and metabolic features associated with prolonged survival in AJCC stage III TNBC,suggesting potential therapeutic targets to improve patient outcomes.
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.
基金supported by the National Natural Science Foundation of China(42250101)the Macao Foundation。
文摘Earth’s internal core and crustal magnetic fields,as measured by geomagnetic satellites like MSS-1(Macao Science Satellite-1)and Swarm,are vital for understanding core dynamics and tectonic evolution.To model these internal magnetic fields accurately,data selection based on specific criteria is often employed to minimize the influence of rapidly changing current systems in the ionosphere and magnetosphere.However,the quantitative impact of various data selection criteria on internal geomagnetic field modeling is not well understood.This study aims to address this issue and provide a reference for constructing and applying geomagnetic field models.First,we collect the latest MSS-1 and Swarm satellite magnetic data and summarize widely used data selection criteria in geomagnetic field modeling.Second,we briefly describe the method to co-estimate the core,crustal,and large-scale magnetospheric fields using satellite magnetic data.Finally,we conduct a series of field modeling experiments with different data selection criteria to quantitatively estimate their influence.Our numerical experiments confirm that without selecting data from dark regions and geomagnetically quiet times,the resulting internal field differences at the Earth’s surface can range from tens to hundreds of nanotesla(nT).Additionally,we find that the uncertainties introduced into field models by different data selection criteria are significantly larger than the measurement accuracy of modern geomagnetic satellites.These uncertainties should be considered when utilizing constructed magnetic field models for scientific research and applications.
基金supported in part by NIH grants R01NS39600,U01MH114829RF1MH128693(to GAA)。
文摘Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subsets via hierarchical clustering,but objective methods to determine the appropriate classification granularity are missing.We recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within clusters.Here we present the corresponding protocol to classify cellular datasets by combining datadriven unsupervised hierarchical clustering with statistical testing.These general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values,including molecula r,physiological,and anatomical datasets.We demonstrate the protocol using cellular data from the Janelia MouseLight project to chara cterize morphological aspects of neurons.
文摘The accelerated advancement of the Internet of Things(IoT)has generated substantial data,including sensitive and private information.Consequently,it is imperative to guarantee the security of data sharing.While facilitating fine-grained access control,Ciphertext Policy Attribute-Based Encryption(CP-ABE)can effectively ensure the confidentiality of shared data.Nevertheless,the conventional centralized CP-ABE scheme is plagued by the issues of keymisuse,key escrow,and large computation,which will result in security risks.This paper suggests a lightweight IoT data security sharing scheme that integrates blockchain technology and CP-ABE to address the abovementioned issues.The integrity and traceability of shared data are guaranteed by the use of blockchain technology to store and verify access transactions.The encryption and decryption operations of the CP-ABE algorithm have been implemented using elliptic curve scalarmultiplication to accommodate lightweight IoT devices,as opposed to themore arithmetic bilinear pairing found in the traditional CP-ABE algorithm.Additionally,a portion of the computation is delegated to the edge nodes to alleviate the computational burden on users.A distributed key management method is proposed to address the issues of key escrow andmisuse.Thismethod employs the edge blockchain to facilitate the storage and distribution of attribute private keys.Meanwhile,data security sharing is enhanced by combining off-chain and on-chain ciphertext storage.The security and performance analysis indicates that the proposed scheme is more efficient and secure.
文摘调查和分析元数据标准在健康科学数据中的应用现状,有助于为我国健康科学数据描述中元数据标准的选择、健康科学数据平台的建设提供参考。通过网络调研法对科学数据仓储注册系统(registry of research data repositories,re3data)中的健康科学数据管理平台进行调研,梳理所应用的元数据标准,分析典型元数据标准在平台中的应用情况,并归纳其在健康科学数据描述中的适用性。re3data中各健康科学数据平台共使用14种元数据标准,其中DC、DataCite、DDI、仓储自建元数据标准的使用最为广泛,多数平台组合使用多种元数据标准。各类元数据标准可分为通用型、社会科学型、自建型3类,分别适用于描述健康科学数据通用属性、社会科学研究产生的健康科学数据、特色和专业性强及政府开放的健康科学数据。
文摘Kawasaki disease(KD)is a significant pediatric vasculitis known for its potential to cause severe coronary artery complications.Despite the effectiveness of initial treatments,such as intravenous immunoglobulin,KD patients can experience long-term cardiovascular issues,as evidenced by a recent case report of an adult who suffered a ST-segment elevation myocardial infarction due to previous KD in the World Journal of Clinical Cases.This editorial emphasizes the critical need for long-term management and regular surveillance to prevent such complications.By drawing on recent research and case studies,we advocate for a structured approach to follow-up care that includes routine cardiac evaluations and preventive measures.
基金supported by the Deanship of Graduate Studies and Scientific Research at University of Bisha for funding this research through the promising program under grant number(UB-Promising-33-1445).
文摘Open networks and heterogeneous services in the Internet of Vehicles(IoV)can lead to security and privacy challenges.One key requirement for such systems is the preservation of user privacy,ensuring a seamless experience in driving,navigation,and communication.These privacy needs are influenced by various factors,such as data collected at different intervals,trip durations,and user interactions.To address this,the paper proposes a Support Vector Machine(SVM)model designed to process large amounts of aggregated data and recommend privacy preserving measures.The model analyzes data based on user demands and interactions with service providers or neighboring infrastructure.It aims to minimize privacy risks while ensuring service continuity and sustainability.The SVMmodel helps validate the system’s reliability by creating a hyperplane that distinguishes between maximum and minimum privacy recommendations.The results demonstrate the effectiveness of the proposed SVM model in enhancing both privacy and service performance.
基金Chongqing Engineering University Undergraduate Innovation and Entrepreneurship Training Program Project:Wireless Fire Automatic Alarm System(Project No.:CXCY2024017)Chongqing Municipal Education Commission Science and Technology Research Project:Development and Research of Chongqing Wireless Fire Automatic Alarm System(Project No.:KJQN202401906)。
文摘This article explores the design of a wireless fire alarm system supported by advanced data fusion technology.It includes discussions on the basic design ideas of the wireless fire alarm system,hardware design analysis,software design analysis,and simulation analysis,all supported by data fusion technology.Hopefully,this analysis can provide some reference for the rational application of data fusion technology to meet the actual design and application requirements of the system.
基金the support of the National Natural Science Foundation of China (Nos. 42250103, 41974073, and 41404053)the Macao Foundation and the preresearch project of Civil Aerospace Technologies (Nos. D020308 and D020303)funded by China’s National Space Administration, the Specialized Research Fund for State Key Laboratories。
文摘We combine gradient data from the Macao Science Satellite-1(MSS-1),CHAllenging Minisatellite Payload(CHAMP),Swarm-A,and Swarm-C satellites to develop a 110-degree lithospheric magnetic field model.We then comprehensively evaluate the performance of the model by power spectral comparisons,correlation analyses,sensitivity matrix assessments,and comparisons with existing lithospheric field models.Results showed that using near east–west gradient data from MSS-1 significantly enhances the model correlation in the spherical harmonic degree(N) range of 45–60 while also mitigating the decline in correlation at higher degrees(N > 60).Furthermore,the unique orbital characteristics of MSS-1 enable its gradient data to provide substantial contributions to modeling in the mid-to low-latitude regions.With continued data acquisition from MSS-1 and further optimization of data processing methods,the performance of the model is expected to improve.
基金partially supported by the Construction of Collaborative Innovation Center of Beijing Academy of Agricultural and Forestry Sciences(KJCX20240406)the Beijing Natural Science Foundation(JQ24037)+1 种基金the National Natural Science Foundation of China(32330075)the Earmarked Fund for China Agriculture Research System(CARS-02 and CARS-54)。
文摘The security of the seed industry is crucial for ensuring national food security.Currently,developed countries in Europe and America,along with international seed industry giants,have entered the Breeding 4.0 era.This era integrates biotechnology,artificial intelligence(AI),and big data information technology.In contrast,China is still in a transition period between stages 2.0 and 3.0,which primarily relies on conventional selection and molecular breeding.In the context of increasingly complex international situations,accurately identifying core issues in China's seed industry innovation and seizing the frontier of international seed technology are strategically important.These efforts are essential for ensuring food security and revitalizing the seed industry.This paper systematically analyzes the characteristics of crop breeding data from artificial selection to intelligent design breeding.It explores the applications and development trends of AI and big data in modern crop breeding from several key perspectives.These include highthroughput phenotype acquisition and analysis,multiomics big data database and management system construction,AI-based multiomics integrated analysis,and the development of intelligent breeding software tools based on biological big data and AI technology.Based on an in-depth analysis of the current status and challenges of China's seed industry technology development,we propose strategic goals and key tasks for China's new generation of AI and big data-driven intelligent design breeding.These suggestions aim to accelerate the development of an intelligent-driven crop breeding engineering system that features large-scale gene mining,efficient gene manipulation,engineered variety design,and systematized biobreeding.This study provides a theoretical basis and practical guidance for the development of China's seed industry technology.
