It is essential to precisely predict the crack growth,especially the near-threshold regime crack growth under different stress ratios,for most engineering structures consume their fatigue lives in this regime under ra...It is essential to precisely predict the crack growth,especially the near-threshold regime crack growth under different stress ratios,for most engineering structures consume their fatigue lives in this regime under random loading.In this paper,an improved unique curve model is proposed based on the unique curve model,and the determination of the shape exponents of this model is provided.The crack growth rate curves of some materials taken from the literature are evaluated using the improved model,and the results indicate that the improved model can accurately predict the crack growth rate in the nearthreshold and Paris regimes.The improved unique curve model can solve the problems about the shape exponents determination and weak ability around the near-threshold regime meet in the unique curve model.In addition,the shape exponents in the improved model at negative stress ratios are discussed,which can directly adopt that in the unique curve model.展开更多
Internet of things(IoT)has become more popular due to the development and potential of smart technology aspects.Security concerns against IoT infrastructure,applications,and devices have grown along with the need for ...Internet of things(IoT)has become more popular due to the development and potential of smart technology aspects.Security concerns against IoT infrastructure,applications,and devices have grown along with the need for IoT technologies.Enhanced system security protocols are difficult due to the diverse capabilities of IoT devices and the dynamic,ever-changing environment,and simply applying basic security requirements is dangerous.Therefore,this proposed work designs a malware detection and prevention approach for secure data transmission among IoT gadgets.The malware detection approach is designed with the aid of a deep learning approach.The initial process is identifying attack nodes from normal nodes through a trust value using contextual features.After discovering attack nodes,these are considered for predicting different kinds of attacks present in the network,while some preprocessing and feature extraction strategies are applied for effective classification.The Deep LSTM classifier is applied for this malware detection approach.Once completed malware detection,prevention is performed with the help of the Improved Elliptic Curve Cryptography(IECC)algorithm.A hybrid MA-BW optimization is adopted for selecting the optimal key during transmission.Python 3.8 software is used to test the performance of the proposed approach,and several existing techniques are considered to evaluate its performance.The proposed approach obtained 95%of accuracy,5%of error value and 92%of precision.In addition,the improved ECC algorithm is also compared with some existing algorithm which takes 6.02 s of execution time.Compared to the other methods,the proposed approach provides better security to IoT gadgets during data transmission.展开更多
Quantitative assessments of the impacts of climate change and anthropogenic activities on runoff help us to better understand the mechanisms of hydrological processes.This study analyzed the dynamics of mountainous ru...Quantitative assessments of the impacts of climate change and anthropogenic activities on runoff help us to better understand the mechanisms of hydrological processes.This study analyzed the dynamics of mountainous runoff in the upper reaches of the Shiyang River Basin(USRB)and its sub-catchments,and quantified the impacts of climate change and human activities on runoff using the improved double mass curve(IDMC)method,which comprehensively considers the effects of precipitation and evapotranspiration on runoff,instead of only considering precipitation as before.The results indicated that the annual runoff depth in the USRB showed a slightly increased trend from 1961 to 2018,and sub-catchments were increased in the west and decreased in the east.The seasonal distribution pattern of runoff depth in the USRB and its eight sub-catchments all showed the largest in summer,followed by autumn and spring,and the smallest in winter with an increasing trend.Quantitative assessment results using the IDMC method showed that the runoff change in the USRB is more significantly affected by climate change,however,considerable differences are evident in sub-catchments.This study further developed and improved the method of runoff attribution analysis conducted at watershed scale,and these results will contribute to the ecological protection and sustainable utilization of water resources in the USRB and similar regions.展开更多
文摘It is essential to precisely predict the crack growth,especially the near-threshold regime crack growth under different stress ratios,for most engineering structures consume their fatigue lives in this regime under random loading.In this paper,an improved unique curve model is proposed based on the unique curve model,and the determination of the shape exponents of this model is provided.The crack growth rate curves of some materials taken from the literature are evaluated using the improved model,and the results indicate that the improved model can accurately predict the crack growth rate in the nearthreshold and Paris regimes.The improved unique curve model can solve the problems about the shape exponents determination and weak ability around the near-threshold regime meet in the unique curve model.In addition,the shape exponents in the improved model at negative stress ratios are discussed,which can directly adopt that in the unique curve model.
文摘Internet of things(IoT)has become more popular due to the development and potential of smart technology aspects.Security concerns against IoT infrastructure,applications,and devices have grown along with the need for IoT technologies.Enhanced system security protocols are difficult due to the diverse capabilities of IoT devices and the dynamic,ever-changing environment,and simply applying basic security requirements is dangerous.Therefore,this proposed work designs a malware detection and prevention approach for secure data transmission among IoT gadgets.The malware detection approach is designed with the aid of a deep learning approach.The initial process is identifying attack nodes from normal nodes through a trust value using contextual features.After discovering attack nodes,these are considered for predicting different kinds of attacks present in the network,while some preprocessing and feature extraction strategies are applied for effective classification.The Deep LSTM classifier is applied for this malware detection approach.Once completed malware detection,prevention is performed with the help of the Improved Elliptic Curve Cryptography(IECC)algorithm.A hybrid MA-BW optimization is adopted for selecting the optimal key during transmission.Python 3.8 software is used to test the performance of the proposed approach,and several existing techniques are considered to evaluate its performance.The proposed approach obtained 95%of accuracy,5%of error value and 92%of precision.In addition,the improved ECC algorithm is also compared with some existing algorithm which takes 6.02 s of execution time.Compared to the other methods,the proposed approach provides better security to IoT gadgets during data transmission.
基金National Natural Science Foundation of China,No.42361005,No.41861034,No.41661040,No.32060373。
文摘Quantitative assessments of the impacts of climate change and anthropogenic activities on runoff help us to better understand the mechanisms of hydrological processes.This study analyzed the dynamics of mountainous runoff in the upper reaches of the Shiyang River Basin(USRB)and its sub-catchments,and quantified the impacts of climate change and human activities on runoff using the improved double mass curve(IDMC)method,which comprehensively considers the effects of precipitation and evapotranspiration on runoff,instead of only considering precipitation as before.The results indicated that the annual runoff depth in the USRB showed a slightly increased trend from 1961 to 2018,and sub-catchments were increased in the west and decreased in the east.The seasonal distribution pattern of runoff depth in the USRB and its eight sub-catchments all showed the largest in summer,followed by autumn and spring,and the smallest in winter with an increasing trend.Quantitative assessment results using the IDMC method showed that the runoff change in the USRB is more significantly affected by climate change,however,considerable differences are evident in sub-catchments.This study further developed and improved the method of runoff attribution analysis conducted at watershed scale,and these results will contribute to the ecological protection and sustainable utilization of water resources in the USRB and similar regions.
