During long-term operation,the performance of obstacles would be changed due to the material accumulating upslope the obstacle.However,the effects of retained material on impact,overflow and landing dynamics of granul...During long-term operation,the performance of obstacles would be changed due to the material accumulating upslope the obstacle.However,the effects of retained material on impact,overflow and landing dynamics of granular flow have not yet been elucidated.To address this gap,physical flume tests and discrete element simulations are conducted considering a range of normalized deposition height h0/H from 0 to 1,where h0 and H represent the deposition height and obstacle height,respectively.An analytical model is modified to evaluate the flow velocity and flow depth after interacting with the retained materials,which further serve to calculate the peak impact force on the obstacle.Notably,the computed impact forces successfully predict the experimental results when a≥25°.In addition,the results indicate that a higher h0/H leads to a lower dynamic impact force,a greater landing distance L,and a larger landing coefficient Cr,where Cr is the ratio of slope-parallel component of landing velocity to flow velocity just before landing.Compared to the existing overflow model,the measured landing distance L is underestimated by up to 30%,and therefore it is insufficient for obstacle design when there is retained material.Moreover,the recommended Cr in current design practice is found to be nonconservative for estimating the landing velocity of geophysical flow.This study provides insightful scientific basis for designing obstacles with deposition.展开更多
Team Formation(TF)is considered one of the most significant problems in computer science and optimization.TF is defined as forming the best team of experts in a social network to complete a task with least cost.Many r...Team Formation(TF)is considered one of the most significant problems in computer science and optimization.TF is defined as forming the best team of experts in a social network to complete a task with least cost.Many real-world problems,such as task assignment,vehicle routing,nurse scheduling,resource allocation,and airline crew scheduling,are based on the TF problem.TF has been shown to be a Nondeterministic Polynomial time(NP)problem,and high-dimensional problem with several local optima that can be solved using efficient approximation algorithms.This paper proposes two improved swarm-based algorithms for solving team formation problem.The first algorithm,entitled Hybrid Heap-Based Optimizer with Simulated Annealing Algorithm(HBOSA),uses a single crossover operator to improve the performance of a standard heap-based optimizer(HBO)algorithm.It also employs the simulated annealing(SA)approach to improve model convergence and avoid local minima trapping.The second algorithm is the Chaotic Heap-based Optimizer Algorithm(CHBO).CHBO aids in the discovery of new solutions in the search space by directing particles to different regions of the search space.During HBO’s optimization process,a logistic chaotic map is used.The performance of the two proposed algorithms(HBOSA)and(CHBO)is evaluated using thirteen benchmark functions and tested in solving the TF problem with varying number of experts and skills.Furthermore,the proposed algorithms were compared to well-known optimization algorithms such as the Heap-Based Optimizer(HBO),Developed Simulated Annealing(DSA),Particle SwarmOptimization(PSO),GreyWolfOptimization(GWO),and Genetic Algorithm(GA).Finally,the proposed algorithms were applied to a real-world benchmark dataset known as the Internet Movie Database(IMDB).The simulation results revealed that the proposed algorithms outperformed the compared algorithms in terms of efficiency and performance,with fast convergence to the global minimum.展开更多
Different abnormalities are commonly encountered in computer network systems.These types of abnormalities can lead to critical data losses or unauthorized access in the systems.Buffer overflow anomaly is a prominent i...Different abnormalities are commonly encountered in computer network systems.These types of abnormalities can lead to critical data losses or unauthorized access in the systems.Buffer overflow anomaly is a prominent issue among these abnormalities,posing a serious threat to network security.The primary objective of this study is to identify the potential risks of buffer overflow that can be caused by functions frequently used in the PHP programming language and to provide solutions to minimize these risks.Static code analyzers are used to detect security vulnerabilities,among which SonarQube stands out with its extensive library,flexible customization options,and reliability in the industry.In this context,a customized rule set aimed at automatically detecting buffer overflows has been developed on the SonarQube platform.The memoization optimization technique used while creating the customized rule set enhances the speed and efficiency of the code analysis process.As a result,the code analysis process is not repeatedly run for code snippets that have been analyzed before,significantly reducing processing time and resource utilization.In this study,a memoization-based rule set was utilized to detect critical security vulnerabilities that could lead to buffer overflow in source codes written in the PHP programming language.Thus,the analysis process is not repeatedly run for code snippets that have been analyzed before,leading to a significant reduction in processing time and resource utilization.In a case study conducted to assess the effectiveness of this method,a significant decrease in the source code analysis time was observed.展开更多
基金funded by the National Natural Science Foundation of China(Grant Nos.42120104002,41941019)the Research Grants Council of the Hong Kong Special Administrative Region,China(Grant No.AoE/E-603/18).
文摘During long-term operation,the performance of obstacles would be changed due to the material accumulating upslope the obstacle.However,the effects of retained material on impact,overflow and landing dynamics of granular flow have not yet been elucidated.To address this gap,physical flume tests and discrete element simulations are conducted considering a range of normalized deposition height h0/H from 0 to 1,where h0 and H represent the deposition height and obstacle height,respectively.An analytical model is modified to evaluate the flow velocity and flow depth after interacting with the retained materials,which further serve to calculate the peak impact force on the obstacle.Notably,the computed impact forces successfully predict the experimental results when a≥25°.In addition,the results indicate that a higher h0/H leads to a lower dynamic impact force,a greater landing distance L,and a larger landing coefficient Cr,where Cr is the ratio of slope-parallel component of landing velocity to flow velocity just before landing.Compared to the existing overflow model,the measured landing distance L is underestimated by up to 30%,and therefore it is insufficient for obstacle design when there is retained material.Moreover,the recommended Cr in current design practice is found to be nonconservative for estimating the landing velocity of geophysical flow.This study provides insightful scientific basis for designing obstacles with deposition.
文摘Team Formation(TF)is considered one of the most significant problems in computer science and optimization.TF is defined as forming the best team of experts in a social network to complete a task with least cost.Many real-world problems,such as task assignment,vehicle routing,nurse scheduling,resource allocation,and airline crew scheduling,are based on the TF problem.TF has been shown to be a Nondeterministic Polynomial time(NP)problem,and high-dimensional problem with several local optima that can be solved using efficient approximation algorithms.This paper proposes two improved swarm-based algorithms for solving team formation problem.The first algorithm,entitled Hybrid Heap-Based Optimizer with Simulated Annealing Algorithm(HBOSA),uses a single crossover operator to improve the performance of a standard heap-based optimizer(HBO)algorithm.It also employs the simulated annealing(SA)approach to improve model convergence and avoid local minima trapping.The second algorithm is the Chaotic Heap-based Optimizer Algorithm(CHBO).CHBO aids in the discovery of new solutions in the search space by directing particles to different regions of the search space.During HBO’s optimization process,a logistic chaotic map is used.The performance of the two proposed algorithms(HBOSA)and(CHBO)is evaluated using thirteen benchmark functions and tested in solving the TF problem with varying number of experts and skills.Furthermore,the proposed algorithms were compared to well-known optimization algorithms such as the Heap-Based Optimizer(HBO),Developed Simulated Annealing(DSA),Particle SwarmOptimization(PSO),GreyWolfOptimization(GWO),and Genetic Algorithm(GA).Finally,the proposed algorithms were applied to a real-world benchmark dataset known as the Internet Movie Database(IMDB).The simulation results revealed that the proposed algorithms outperformed the compared algorithms in terms of efficiency and performance,with fast convergence to the global minimum.
文摘Different abnormalities are commonly encountered in computer network systems.These types of abnormalities can lead to critical data losses or unauthorized access in the systems.Buffer overflow anomaly is a prominent issue among these abnormalities,posing a serious threat to network security.The primary objective of this study is to identify the potential risks of buffer overflow that can be caused by functions frequently used in the PHP programming language and to provide solutions to minimize these risks.Static code analyzers are used to detect security vulnerabilities,among which SonarQube stands out with its extensive library,flexible customization options,and reliability in the industry.In this context,a customized rule set aimed at automatically detecting buffer overflows has been developed on the SonarQube platform.The memoization optimization technique used while creating the customized rule set enhances the speed and efficiency of the code analysis process.As a result,the code analysis process is not repeatedly run for code snippets that have been analyzed before,significantly reducing processing time and resource utilization.In this study,a memoization-based rule set was utilized to detect critical security vulnerabilities that could lead to buffer overflow in source codes written in the PHP programming language.Thus,the analysis process is not repeatedly run for code snippets that have been analyzed before,leading to a significant reduction in processing time and resource utilization.In a case study conducted to assess the effectiveness of this method,a significant decrease in the source code analysis time was observed.
