Habitats with different features such as soil depth and soil/rock conditions can provide favorable environments for species with different requirements, while anthropogenic disturbances normally exert additional effec...Habitats with different features such as soil depth and soil/rock conditions can provide favorable environments for species with different requirements, while anthropogenic disturbances normally exert additional effects on species composition. However, specific studies have rarely been conducted in the degraded karst regions of Southwest China despite the high heterogeneity of karst habitats and past human disturbances. In this study, woody species richness and composition on rocky outcrops on a typical karst hillslope were investigated and compared with those of nearby matrices on shallow and rocky soil. Our results showed that matrix vegetation was more diverse in genera and species than vegetation on rocky outcrops. This might relate to the contrasting substrate features and different disturbance histories of these two habitats. Unlike the significant effect of slope on species richness of the matrix vegetation, rocky outcrops exhibited no significant differences between upper and lower slope positions, largely because their microhabitats were similar in different slope positions. Although the study area has been reforested naturally for about 30 years, woody species of the matrix vegetation were still dominated by pioneer shrub species. Rocky outcrops were dominated by late-successional tree species, which was primarily related to their isolated features and resistance to certain disturbances. Most of these late-successional species were not habitat endemics, indicating the possibility for their encroachment into surrounding the matrix. From this aspect, further studies will be necessary to identify and address the limiting factors for the encroachment of these late-successional species into the surrounding environment.展开更多
The widespread use of the Internet of Things(IoTs)and the rapid development of artificial intelligence technologies have enabled applications to cross commercial and industrial band settings.Within such systems,all pa...The widespread use of the Internet of Things(IoTs)and the rapid development of artificial intelligence technologies have enabled applications to cross commercial and industrial band settings.Within such systems,all participants related to commercial and industrial systems must communicate and generate data.However,due to the small storage capacities of IoT devices,they are required to store and transfer the generated data to third-party entity called“cloud”,which creates one single point to store their data.However,as the number of participants increases,the size of generated data also increases.Therefore,such a centralized mechanism for data collection and exchange between participants is likely to face numerous challenges in terms of security,privacy,and performance.To address these challenges,Federated Learning(FL)has been proposed as a reasonable decentralizing approach,in which clients no longer need to transfer and store real data in the central server.Instead,they only share updated training models that are trained over their private datasets.At the same time,FL enables clients in distributed systems to share their machine learning models collaboratively without their training data,thus reducing data privacy and security challeges.However,slow model training and the execution of additional unnecessary communication rounds may hinder FL applications from operating properly in a distributed system.Furthermore,these unnecessary communication rounds make the system vulnerable to security and privacy issues,because irrelevant model updates are sent between clients and servers.Thus,in this work,we propose an algorithm for fully homomorphic encryption called Cheon-Kim-Kim-Song(CKKS)to encrypt model parameters for their local information privacy-preserving function.The proposed solution uses the impetus term to speed up model convergence during the model training process.Furthermore,it establishes a secure communication channel between IoT devices and the server.We also use a lightweight secure transport protocol to mitigate the communication overhead,thereby improving communication security and efficiency with low communication latency between client and server.展开更多
Naturally occurred precore(PC,G1896A)and/or basal core promoter(BCP,A1762T/G1764A)mutations are prevalent in chronic HBV-infected patients,especially those under HBeAg-negative status.However,the replicative capacity ...Naturally occurred precore(PC,G1896A)and/or basal core promoter(BCP,A1762T/G1764A)mutations are prevalent in chronic HBV-infected patients,especially those under HBeAg-negative status.However,the replicative capacity of HBV with PC/BCP mutations remains ambiguous.Herein,meta-analysis showed that,only under HBeAg-negative status,the serum HBV DNA load in patients with PC mutation was 7.41-fold higher than those without the mutation.Both PC mutation alone and BCPþPC mutations promoted HBV replication in cell and hydrodynamic injection mouse models.In human hepatocyte chimeric mouse model,BCPþPC mutations led to elevated replicative capacity and intrahepatic core protein accumulation.Mechanistically,preC RNA harboring PC mutation could serve as mRNA to express core and P proteins,and such pgRNA-like function favored the maintenance of cccDNA pool under HBeAg-negative status.Additionally,BCPþPC mutations induced more extensive and severe human hepatocyte damage as well as activated endoplasmic reticulum stress and TNF signaling pathway in livers of chimeric mice.This study indicates that HBeAg-negative patients should be monitored on HBV mutations regularly and are expected to receive early antiviral treatment to prevent disease progression.展开更多
Heterogeneous karst surfaces exerted scaling effects whereby specific runoff decrease with increasing area.The existing RUSLE-L equations are limited by the default implicit assumption that the surface-runoff intensit...Heterogeneous karst surfaces exerted scaling effects whereby specific runoff decrease with increasing area.The existing RUSLE-L equations are limited by the default implicit assumption that the surface-runoff intensity is constant at any slope length.The objective of this study was to modify the L-equation by establishing the functional relationship between surface-runoff intensity and karst slope length,and to evaluate its predictive capability at different resolution DEMs.Transfer grid layers were generated based on the area rate of surface karstification and considered the runoff transmission percentage at the exposed karst fractures or conduits to be zero.Using the multiple flow direction algorithm united with the transfer grid(MFDTG),the flow accumulation of each grid cell was simulated to estimate the average surface-runoff intensity over different slope lengths.The effectiveness of MFDTG algorithm was validated by runoff plot data in Southwestern China.The simulated results in a typical peak-cluster depression basin with an area rate of surface karstification of 6.5%showed that the relationship between surface-runoff intensity and slope length was a negative power function.Estimated by the proposed modified L-equation((al_(x)^((b+1))/22.13)^(m)),the L-factor averages of the study basin ranged from 0.35 to 0.41 at 1,5,25 and 90 m resolutions respectively.This study indicated that the modified L-equation enables an improved prediction of the much smaller L-factor and the use of any resolution DEMs on karst landscapes.Particular attention should be given to the variation of surface-runoff intensity with slope length when predicting L-factor on hillslopes with runoff scale effect.展开更多
With the rapid development of information technologies,industrial Internet has become more open,and security issues have become more challenging.The endogenous security mechanism can achieve the autonomous immune mech...With the rapid development of information technologies,industrial Internet has become more open,and security issues have become more challenging.The endogenous security mechanism can achieve the autonomous immune mechanism without prior knowledge.However,endogenous security lacks a scientific and formal definition in industrial Internet.Therefore,firstly we give a formal definition of endogenous security in industrial Internet and propose a new industrial Internet endogenous security architecture with cost analysis.Secondly,the endogenous security innovation mechanism is clearly defined.Thirdly,an improved clone selection algorithm based on federated learning is proposed.Then,we analyze the threat model of the industrial Internet identity authentication scenario,and propose cross-domain authentication mechanism based on endogenous key and zero-knowledge proof.We conduct identity authentication experiments based on two types of blockchains and compare their experimental results.Based on the experimental analysis,Ethereum alliance blockchain can be used to provide the identity resolution services on the industrial Internet.Internet of Things Application(IOTA)public blockchain can be used for data aggregation analysis of Internet of Things(IoT)edge nodes.Finally,we propose three core challenges and solutions of endogenous security in industrial Internet and give future development directions.展开更多
As an advantageous technique and service,the blockchain has shown great development and application prospects.However,its security has also met great challenges,and many security vulnerabilities and attack issues in b...As an advantageous technique and service,the blockchain has shown great development and application prospects.However,its security has also met great challenges,and many security vulnerabilities and attack issues in blockchain-based services have emerged.Recently,security issues of blockchain have attracted extensive attention.However,there is still a lack of blockchain security research from a full-stack architecture perspective,as well as representative quantitative experimental reproduction and analysis.We aim to provide a security architecture to solve security risks in blockchain services from a full-stack architecture perspective.Meanwhile,we propose a formal definition of the full-stack security architecture for blockchain-based services,and we also propose a formal expression of security issues and defense solutions from a full-stack security perspective.We use ConCert to conduct a smart contract formal verification experiment by property-based testing.The security vulnerabilities of blockchain services in the Common Vulnerabilities and Exposures(CVE)and China Nation Vulnerability Database(CNVD)are selected and enumerated.Additionally,three real contract-layer real attack events are reproduced by an experimental approach.Using Alibaba's blockchain services and Identity Mixer in Hyperledger Fabric as a case study,the security problems and defense techniques are analyzed and researched.At last,the future research directions are proposed.展开更多
Deep learning frameworks promote the development of artificial intelligence and demonstrate considerable potential in numerous applications.However,the security issues of deep learning frameworks are among the main ri...Deep learning frameworks promote the development of artificial intelligence and demonstrate considerable potential in numerous applications.However,the security issues of deep learning frameworks are among the main risks preventing the wide application of it.Attacks on deep learning frameworks by malicious internal or external attackers would exert substantial effects on society and life.We start with a description of the framework of deep learning algorithms and a detailed analysis of attacks and vulnerabilities in them.We propose a highly comprehensive classification approach for security issues and defensive approaches in deep learning frameworks and connect different attacks to corresponding defensive approaches.Moreover,we analyze a case of the physical-world use of deep learning security issues.In addition,we discuss future directions and open issues in deep learning frameworks.We hope that our research will inspire future developments and draw attention from academic and industrial domains to the security of deep learning frameworks.展开更多
基金supported by the National Key Research and Development Program of China(2016YFC0502402)the National Natural Science Foundation of China(31570428)+1 种基金the Young Scholars of Western China,Chinese Academy of Sciences(for Y.N.)the Youth Innovation Promotion Association of the Chinese Academy of Sciences(2018397)
文摘Habitats with different features such as soil depth and soil/rock conditions can provide favorable environments for species with different requirements, while anthropogenic disturbances normally exert additional effects on species composition. However, specific studies have rarely been conducted in the degraded karst regions of Southwest China despite the high heterogeneity of karst habitats and past human disturbances. In this study, woody species richness and composition on rocky outcrops on a typical karst hillslope were investigated and compared with those of nearby matrices on shallow and rocky soil. Our results showed that matrix vegetation was more diverse in genera and species than vegetation on rocky outcrops. This might relate to the contrasting substrate features and different disturbance histories of these two habitats. Unlike the significant effect of slope on species richness of the matrix vegetation, rocky outcrops exhibited no significant differences between upper and lower slope positions, largely because their microhabitats were similar in different slope positions. Although the study area has been reforested naturally for about 30 years, woody species of the matrix vegetation were still dominated by pioneer shrub species. Rocky outcrops were dominated by late-successional tree species, which was primarily related to their isolated features and resistance to certain disturbances. Most of these late-successional species were not habitat endemics, indicating the possibility for their encroachment into surrounding the matrix. From this aspect, further studies will be necessary to identify and address the limiting factors for the encroachment of these late-successional species into the surrounding environment.
基金supported by the National Key Research and Development Program of China(No.2018YFB0803403)the Fundamental Research Funds for the Central Universities(Nos.FRF-AT-20-11 and FRF-AT-19-009Z)from the Ministry of Education of China.
文摘The widespread use of the Internet of Things(IoTs)and the rapid development of artificial intelligence technologies have enabled applications to cross commercial and industrial band settings.Within such systems,all participants related to commercial and industrial systems must communicate and generate data.However,due to the small storage capacities of IoT devices,they are required to store and transfer the generated data to third-party entity called“cloud”,which creates one single point to store their data.However,as the number of participants increases,the size of generated data also increases.Therefore,such a centralized mechanism for data collection and exchange between participants is likely to face numerous challenges in terms of security,privacy,and performance.To address these challenges,Federated Learning(FL)has been proposed as a reasonable decentralizing approach,in which clients no longer need to transfer and store real data in the central server.Instead,they only share updated training models that are trained over their private datasets.At the same time,FL enables clients in distributed systems to share their machine learning models collaboratively without their training data,thus reducing data privacy and security challeges.However,slow model training and the execution of additional unnecessary communication rounds may hinder FL applications from operating properly in a distributed system.Furthermore,these unnecessary communication rounds make the system vulnerable to security and privacy issues,because irrelevant model updates are sent between clients and servers.Thus,in this work,we propose an algorithm for fully homomorphic encryption called Cheon-Kim-Kim-Song(CKKS)to encrypt model parameters for their local information privacy-preserving function.The proposed solution uses the impetus term to speed up model convergence during the model training process.Furthermore,it establishes a secure communication channel between IoT devices and the server.We also use a lightweight secure transport protocol to mitigate the communication overhead,thereby improving communication security and efficiency with low communication latency between client and server.
基金supported by National Key R&D Program of China(2023YFC2306800)National Natural Science Foundation of China(No.82072280 and No.82272315)Beijing Municipal Natural Science Foundation(No.7212063 and No.7222108).
文摘Naturally occurred precore(PC,G1896A)and/or basal core promoter(BCP,A1762T/G1764A)mutations are prevalent in chronic HBV-infected patients,especially those under HBeAg-negative status.However,the replicative capacity of HBV with PC/BCP mutations remains ambiguous.Herein,meta-analysis showed that,only under HBeAg-negative status,the serum HBV DNA load in patients with PC mutation was 7.41-fold higher than those without the mutation.Both PC mutation alone and BCPþPC mutations promoted HBV replication in cell and hydrodynamic injection mouse models.In human hepatocyte chimeric mouse model,BCPþPC mutations led to elevated replicative capacity and intrahepatic core protein accumulation.Mechanistically,preC RNA harboring PC mutation could serve as mRNA to express core and P proteins,and such pgRNA-like function favored the maintenance of cccDNA pool under HBeAg-negative status.Additionally,BCPþPC mutations induced more extensive and severe human hepatocyte damage as well as activated endoplasmic reticulum stress and TNF signaling pathway in livers of chimeric mice.This study indicates that HBeAg-negative patients should be monitored on HBV mutations regularly and are expected to receive early antiviral treatment to prevent disease progression.
基金funded by the National Key Research and Development Program of China(2022YFF1300701)National Natural Science Foundation of China(41807074,41930652,U20A2048,42171134,U21A20189).
文摘Heterogeneous karst surfaces exerted scaling effects whereby specific runoff decrease with increasing area.The existing RUSLE-L equations are limited by the default implicit assumption that the surface-runoff intensity is constant at any slope length.The objective of this study was to modify the L-equation by establishing the functional relationship between surface-runoff intensity and karst slope length,and to evaluate its predictive capability at different resolution DEMs.Transfer grid layers were generated based on the area rate of surface karstification and considered the runoff transmission percentage at the exposed karst fractures or conduits to be zero.Using the multiple flow direction algorithm united with the transfer grid(MFDTG),the flow accumulation of each grid cell was simulated to estimate the average surface-runoff intensity over different slope lengths.The effectiveness of MFDTG algorithm was validated by runoff plot data in Southwestern China.The simulated results in a typical peak-cluster depression basin with an area rate of surface karstification of 6.5%showed that the relationship between surface-runoff intensity and slope length was a negative power function.Estimated by the proposed modified L-equation((al_(x)^((b+1))/22.13)^(m)),the L-factor averages of the study basin ranged from 0.35 to 0.41 at 1,5,25 and 90 m resolutions respectively.This study indicated that the modified L-equation enables an improved prediction of the much smaller L-factor and the use of any resolution DEMs on karst landscapes.Particular attention should be given to the variation of surface-runoff intensity with slope length when predicting L-factor on hillslopes with runoff scale effect.
基金supported by the National Key Research and Development Program of China(No.2018YFB0803403)Fundamental Research Funds for the Central Universities(Nos.FRF-AT-19-009Z and FRF-AT-20-11)from the Ministry of Education of China.
文摘With the rapid development of information technologies,industrial Internet has become more open,and security issues have become more challenging.The endogenous security mechanism can achieve the autonomous immune mechanism without prior knowledge.However,endogenous security lacks a scientific and formal definition in industrial Internet.Therefore,firstly we give a formal definition of endogenous security in industrial Internet and propose a new industrial Internet endogenous security architecture with cost analysis.Secondly,the endogenous security innovation mechanism is clearly defined.Thirdly,an improved clone selection algorithm based on federated learning is proposed.Then,we analyze the threat model of the industrial Internet identity authentication scenario,and propose cross-domain authentication mechanism based on endogenous key and zero-knowledge proof.We conduct identity authentication experiments based on two types of blockchains and compare their experimental results.Based on the experimental analysis,Ethereum alliance blockchain can be used to provide the identity resolution services on the industrial Internet.Internet of Things Application(IOTA)public blockchain can be used for data aggregation analysis of Internet of Things(IoT)edge nodes.Finally,we propose three core challenges and solutions of endogenous security in industrial Internet and give future development directions.
基金supported by the National Key Research and Devel-opment Program of China(2018YFB0803403)Fundamental Research Funds for the Central Universities(FRF-AT-20-11)from the Ministry of Education of China。
文摘As an advantageous technique and service,the blockchain has shown great development and application prospects.However,its security has also met great challenges,and many security vulnerabilities and attack issues in blockchain-based services have emerged.Recently,security issues of blockchain have attracted extensive attention.However,there is still a lack of blockchain security research from a full-stack architecture perspective,as well as representative quantitative experimental reproduction and analysis.We aim to provide a security architecture to solve security risks in blockchain services from a full-stack architecture perspective.Meanwhile,we propose a formal definition of the full-stack security architecture for blockchain-based services,and we also propose a formal expression of security issues and defense solutions from a full-stack security perspective.We use ConCert to conduct a smart contract formal verification experiment by property-based testing.The security vulnerabilities of blockchain services in the Common Vulnerabilities and Exposures(CVE)and China Nation Vulnerability Database(CNVD)are selected and enumerated.Additionally,three real contract-layer real attack events are reproduced by an experimental approach.Using Alibaba's blockchain services and Identity Mixer in Hyperledger Fabric as a case study,the security problems and defense techniques are analyzed and researched.At last,the future research directions are proposed.
基金supported by the National Key Research and Development Program of China(No.2018YFB0803403)Fundamental Research Funds for the Central Universities(Nos.FRF-AT-19-009Z and FRF-BD-19-012A)National Social Science Fund of China(No.18BGJ071)。
文摘Deep learning frameworks promote the development of artificial intelligence and demonstrate considerable potential in numerous applications.However,the security issues of deep learning frameworks are among the main risks preventing the wide application of it.Attacks on deep learning frameworks by malicious internal or external attackers would exert substantial effects on society and life.We start with a description of the framework of deep learning algorithms and a detailed analysis of attacks and vulnerabilities in them.We propose a highly comprehensive classification approach for security issues and defensive approaches in deep learning frameworks and connect different attacks to corresponding defensive approaches.Moreover,we analyze a case of the physical-world use of deep learning security issues.In addition,we discuss future directions and open issues in deep learning frameworks.We hope that our research will inspire future developments and draw attention from academic and industrial domains to the security of deep learning frameworks.
