Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance,high mobility and strong destructive power.Numerical methods have been widely used to pred...Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance,high mobility and strong destructive power.Numerical methods have been widely used to predict the landslide runout but a fundamental problem remained is how to determine the reliable numerical parameters.This study proposes a framework to predict the runout of potential landslides through multi-source data collaboration and numerical analysis of historical landslide events.Specifically,for the historical landslide cases,the landslide-induced seismic signal,geophysical surveys,and possible in-situ drone/phone videos(multi-source data collaboration)can validate the numerical results in terms of landslide dynamics and deposit features and help calibrate the numerical(rheological)parameters.Subsequently,the calibrated numerical parameters can be used to numerically predict the runout of potential landslides in the region with a similar geological setting to the recorded events.Application of the runout prediction approach to the 2020 Jiashanying landslide in Guizhou,China gives reasonable results in comparison to the field observations.The numerical parameters are determined from the multi-source data collaboration analysis of a historical case in the region(2019 Shuicheng landslide).The proposed framework for landslide runout prediction can be of great utility for landslide risk assessment and disaster reduction in mountainous regions worldwide.展开更多
IoT devices rely on authentication mechanisms to render secure message exchange.During data transmission,scalability,data integrity,and processing time have been considered challenging aspects for a system constituted...IoT devices rely on authentication mechanisms to render secure message exchange.During data transmission,scalability,data integrity,and processing time have been considered challenging aspects for a system constituted by IoT devices.The application of physical unclonable functions(PUFs)ensures secure data transmission among the internet of things(IoT)devices in a simplified network with an efficient time-stamped agreement.This paper proposes a secure,lightweight,cost-efficient reinforcement machine learning framework(SLCR-MLF)to achieve decentralization and security,thus enabling scalability,data integrity,and optimized processing time in IoT devices.PUF has been integrated into SLCR-MLF to improve the security of the cluster head node in the IoT platform during transmission by providing the authentication service for device-to-device communication.An IoT network gathers information of interest from multiple cluster members selected by the proposed framework.In addition,the software-defined secured(SDS)technique is integrated with SLCR-MLF to improve data integrity and optimize processing time in the IoT platform.Simulation analysis shows that the proposed framework outperforms conventional methods regarding the network’s lifetime,energy,secured data retrieval rate,and performance ratio.By enabling the proposed framework,number of residual nodes is reduced to 16%,energy consumption is reduced by up to 50%,almost 30%improvement in data retrieval rate,and network lifetime is improved by up to 1000 msec.展开更多
Crohn’s Disease (CD) is a chronic recurrent inflammatory bowel disease with unknown etiology, most common in the terminal ileum and adjacent colon. In the past 30 years, the global prevalence of CD has continued to r...Crohn’s Disease (CD) is a chronic recurrent inflammatory bowel disease with unknown etiology, most common in the terminal ileum and adjacent colon. In the past 30 years, the global prevalence of CD has continued to rise. Although the etiology is unknown, studies have shown that it is closely related to geographical environment, microfold cell damage, intestinal flora changes, epithelial barrier damage, immune dysfunction and low. The main clinical manifestations of CD are abdominal pain, chronic diarrhea, fatigue, fever and weight loss. At present, 5-aminosalicylic acid, antibiotics, glucocorticoids, immunosuppressants and biological agents are mainly used for treatment, but the drug effect is limited and the effect is not good. Recent studies have shown that Mesenchymal Stem Cells (MSC) can down-regulate immune response and promote tissue healing, which has certain safety and effectiveness in the treatment of moderate and severe CD, and has broad application prospects, but also has certain limitations. This paper summarizes the application and efficacy of related therapies in CD, providing a reference for clinicians.展开更多
In this paper, an efficient methodology for synthesizing the indirect work exchange networks(WEN) considering isothermal process and adiabatic process respectively based on transshipment model is first proposed. In co...In this paper, an efficient methodology for synthesizing the indirect work exchange networks(WEN) considering isothermal process and adiabatic process respectively based on transshipment model is first proposed. In contrast with superstructure method, the transshipment model is easier to obtain the minimum utility consumption taken as the objective function and more convenient for us to attain the optimal network configuration for further minimizing the number of units. Different from division of temperature intervals in heat exchange networks,different pressure intervals are gained according to the maximum compression/expansion ratio in consideration of operating principles of indirect work exchangers and the characteristics of no pressure constraints for stream matches. The presented approach for WEN synthesis is a linear programming model applied to the isothermal process, but for indirect work exchange networks with adiabatic process, a nonlinear programming model needs establishing. Additionally, temperatures should be regarded as decision variables limited to the range between inlet and outlet temperatures in each sub-network. The constructed transshipment model can be solved first to get the minimum utility consumption and further to determine the minimum number of units by merging the adjacent pressure intervals on the basis of the proposed merging methods, which is proved to be effective through exergy analysis at the level of units structures. Finally, two cases are calculated to confirm it is dramatically feasible and effective that the optimal WEN configuration can be gained by the proposed method.展开更多
基金supported by the National Natural Science Foundation of China(41977215)。
文摘Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance,high mobility and strong destructive power.Numerical methods have been widely used to predict the landslide runout but a fundamental problem remained is how to determine the reliable numerical parameters.This study proposes a framework to predict the runout of potential landslides through multi-source data collaboration and numerical analysis of historical landslide events.Specifically,for the historical landslide cases,the landslide-induced seismic signal,geophysical surveys,and possible in-situ drone/phone videos(multi-source data collaboration)can validate the numerical results in terms of landslide dynamics and deposit features and help calibrate the numerical(rheological)parameters.Subsequently,the calibrated numerical parameters can be used to numerically predict the runout of potential landslides in the region with a similar geological setting to the recorded events.Application of the runout prediction approach to the 2020 Jiashanying landslide in Guizhou,China gives reasonable results in comparison to the field observations.The numerical parameters are determined from the multi-source data collaboration analysis of a historical case in the region(2019 Shuicheng landslide).The proposed framework for landslide runout prediction can be of great utility for landslide risk assessment and disaster reduction in mountainous regions worldwide.
文摘IoT devices rely on authentication mechanisms to render secure message exchange.During data transmission,scalability,data integrity,and processing time have been considered challenging aspects for a system constituted by IoT devices.The application of physical unclonable functions(PUFs)ensures secure data transmission among the internet of things(IoT)devices in a simplified network with an efficient time-stamped agreement.This paper proposes a secure,lightweight,cost-efficient reinforcement machine learning framework(SLCR-MLF)to achieve decentralization and security,thus enabling scalability,data integrity,and optimized processing time in IoT devices.PUF has been integrated into SLCR-MLF to improve the security of the cluster head node in the IoT platform during transmission by providing the authentication service for device-to-device communication.An IoT network gathers information of interest from multiple cluster members selected by the proposed framework.In addition,the software-defined secured(SDS)technique is integrated with SLCR-MLF to improve data integrity and optimize processing time in the IoT platform.Simulation analysis shows that the proposed framework outperforms conventional methods regarding the network’s lifetime,energy,secured data retrieval rate,and performance ratio.By enabling the proposed framework,number of residual nodes is reduced to 16%,energy consumption is reduced by up to 50%,almost 30%improvement in data retrieval rate,and network lifetime is improved by up to 1000 msec.
文摘Crohn’s Disease (CD) is a chronic recurrent inflammatory bowel disease with unknown etiology, most common in the terminal ileum and adjacent colon. In the past 30 years, the global prevalence of CD has continued to rise. Although the etiology is unknown, studies have shown that it is closely related to geographical environment, microfold cell damage, intestinal flora changes, epithelial barrier damage, immune dysfunction and low. The main clinical manifestations of CD are abdominal pain, chronic diarrhea, fatigue, fever and weight loss. At present, 5-aminosalicylic acid, antibiotics, glucocorticoids, immunosuppressants and biological agents are mainly used for treatment, but the drug effect is limited and the effect is not good. Recent studies have shown that Mesenchymal Stem Cells (MSC) can down-regulate immune response and promote tissue healing, which has certain safety and effectiveness in the treatment of moderate and severe CD, and has broad application prospects, but also has certain limitations. This paper summarizes the application and efficacy of related therapies in CD, providing a reference for clinicians.
基金Supported by the National Natural Science Foundation of China(21576036 and 21776035)
文摘In this paper, an efficient methodology for synthesizing the indirect work exchange networks(WEN) considering isothermal process and adiabatic process respectively based on transshipment model is first proposed. In contrast with superstructure method, the transshipment model is easier to obtain the minimum utility consumption taken as the objective function and more convenient for us to attain the optimal network configuration for further minimizing the number of units. Different from division of temperature intervals in heat exchange networks,different pressure intervals are gained according to the maximum compression/expansion ratio in consideration of operating principles of indirect work exchangers and the characteristics of no pressure constraints for stream matches. The presented approach for WEN synthesis is a linear programming model applied to the isothermal process, but for indirect work exchange networks with adiabatic process, a nonlinear programming model needs establishing. Additionally, temperatures should be regarded as decision variables limited to the range between inlet and outlet temperatures in each sub-network. The constructed transshipment model can be solved first to get the minimum utility consumption and further to determine the minimum number of units by merging the adjacent pressure intervals on the basis of the proposed merging methods, which is proved to be effective through exergy analysis at the level of units structures. Finally, two cases are calculated to confirm it is dramatically feasible and effective that the optimal WEN configuration can be gained by the proposed method.
