Defining and measuring resilience using a unified framework has been a topic of intense research.This article presents a perspective on how resilience could be quantitatively assessed through a set of indices.It start...Defining and measuring resilience using a unified framework has been a topic of intense research.This article presents a perspective on how resilience could be quantitatively assessed through a set of indices.It starts with a brief explanation of resilience in the context of supply chain and a quick summary of existing quantitative measures of resilience.It then discusses how resilience could be quantified in a constructive manner so that the resulting metrics are representative of the performance throughout the system's life cycle.In particular,it is proposed that resilience should be evaluated according to different time periods,i.e.before,during and after a disruption has occurred.Four dimensions of resilience,namely reliability,robustness,recovery and reconfigurability,can then be used to make up a set of indices for resilience.For numerical illustration,these indices are computed based on recovery data arising from Hurricane Sandy in October 2012.Finally,it is postulated that resilience will be the performance metric that complements productivity and sustainability as the third pillar for measuring success of organizations,and in turn,that of sovereign countries in their quests for developing smart cities.展开更多
目的以脆性指数为工具指标评估心血管Meta分析的稳健性。方法检索PubMed、EMbase及Web of Science数据库,收集2018—2022年心血管领域Meta分析相关文献,计算脆性指数;通过Spearman相关性分析探索脆性指数与样本量、总事件数、效应值及...目的以脆性指数为工具指标评估心血管Meta分析的稳健性。方法检索PubMed、EMbase及Web of Science数据库,收集2018—2022年心血管领域Meta分析相关文献,计算脆性指数;通过Spearman相关性分析探索脆性指数与样本量、总事件数、效应值及效应值置信区间宽度的关系。结果共纳入29篇文献的212个Meta分析,中位脆性指数11(5,25),中位样本量10301(3384,48330)例,中位事件数360(129,1309)个。多数Meta分析选择相对危险度作为效应指标(179/212),选择Mantel-Haenszel方法(102/212)和随机效应模型(153/212);脆性指数与样本量(rs=0.56,P<0.05)和总事件数(rs=0.61,P<0.05)呈正相关关系,与效应值置信区间宽度呈负相关关系(rs=−0.52,P<0.05),与效应值大小的相关性无统计学意义。结论发表在高影响力综合性期刊与专业心血管期刊上的心血管Meta分析的脆性指数普遍较低,稳健性不高。在医学研究中可增加对脆性指数的报告,辅助说明P值。展开更多
This research extensively evaluates three leading mathematical software packages: Python, MATLAB, and Scilab, in the context of solving nonlinear systems of equations with five unknown variables. The study’s core obj...This research extensively evaluates three leading mathematical software packages: Python, MATLAB, and Scilab, in the context of solving nonlinear systems of equations with five unknown variables. The study’s core objectives include comparing software performance using standardized benchmarks, employing key performance metrics for quantitative assessment, and examining the influence of varying hardware specifications on software efficiency across HP ProBook, HP EliteBook, Dell Inspiron, and Dell Latitude laptops. Results from this investigation reveal insights into the capabilities of these software tools in diverse computing environments. On the HP ProBook, Python consistently outperforms MATLAB in terms of computational time. Python also exhibits a lower robustness index for problems 3 and 5 but matches or surpasses MATLAB for problem 1, for some initial guess values. In contrast, on the HP EliteBook, MATLAB consistently exhibits shorter computational times than Python across all benchmark problems. However, Python maintains a lower robustness index for most problems, except for problem 3, where MATLAB performs better. A notable challenge is Python’s failure to converge for problem 4 with certain initial guess values, while MATLAB succeeds in producing results. Analysis on the Dell Inspiron reveals a split in strengths. Python demonstrates superior computational efficiency for some problems, while MATLAB excels in handling others. This pattern extends to the robustness index, with Python showing lower values for some problems, and MATLAB achieving the lowest indices for other problems. In conclusion, this research offers valuable insights into the comparative performance of Python, MATLAB, and Scilab in solving nonlinear systems of equations. It underscores the importance of considering both software and hardware specifications in real-world applications. The choice between Python and MATLAB can yield distinct advantages depending on the specific problem and computational environment, providing guidance for 展开更多
To optimize the placement of soft open points(SOPs)in active distribution networks(ADNs),many aspects should be considered,including the adjustment of transmission power,integration of distributed generations(DGs),coo...To optimize the placement of soft open points(SOPs)in active distribution networks(ADNs),many aspects should be considered,including the adjustment of transmission power,integration of distributed generations(DGs),coordination with conventional control methods,and maintenance of economic costs.To address this multi-objective planning problem,this study proposes a multi-stage coordinated robust optimization model for the SOP allocation in ADNs with photovoltaic(PV).First,two robust technical indices based on a robustness index are proposed to evaluate the operation conditions and robust optimality of the solutions.Second,the proposed coordinated allocation model aims to optimize the total cost,robust voltage offset index,robust utilization index,and voltage collapse proximity index.Third,the optimization methods of the multiand single-objective models are coordinated to solve the proposed multi-stage problem.Finally,the proposed model is implemented on an IEEE 33-node distribution system to verify its effectiveness.Numerical results show that the proposed index can better reveal voltage offset conditions as well as the SOP utilization,and the proposed model outperforms conventional ones in terms of robustness of placement plans and total cost.展开更多
Protecting the security of sensitive information has become a matter of great concern to everyone. Data hiding technique solves the problem to some extent, but still, some shortcomings remain for researching. To impro...Protecting the security of sensitive information has become a matter of great concern to everyone. Data hiding technique solves the problem to some extent, but still, some shortcomings remain for researching. To improve the capability of hiding huge data file in disk with high efficiency. In this paper, we propose a novel approach called CryptFS, which is achieved by utilizing the file access mechanism and modifying the cluster chain structure to hide data. CryptFS can quickly hide data file with G bytes size in less than 0.1s. The time used for hiding and recovering data is irrelevant to the size of data file, and the reliability of the hidden file is high, which will not be overlaid by new created file and disk defragment.展开更多
基金This work is supported by the National Research Foundation,Prime Minister's Office,Singapore under its Campus for Research Excellence and Technological Enterprise(CREATE)program on Future Resilient Systems phase 2(FRS2).
文摘Defining and measuring resilience using a unified framework has been a topic of intense research.This article presents a perspective on how resilience could be quantitatively assessed through a set of indices.It starts with a brief explanation of resilience in the context of supply chain and a quick summary of existing quantitative measures of resilience.It then discusses how resilience could be quantified in a constructive manner so that the resulting metrics are representative of the performance throughout the system's life cycle.In particular,it is proposed that resilience should be evaluated according to different time periods,i.e.before,during and after a disruption has occurred.Four dimensions of resilience,namely reliability,robustness,recovery and reconfigurability,can then be used to make up a set of indices for resilience.For numerical illustration,these indices are computed based on recovery data arising from Hurricane Sandy in October 2012.Finally,it is postulated that resilience will be the performance metric that complements productivity and sustainability as the third pillar for measuring success of organizations,and in turn,that of sovereign countries in their quests for developing smart cities.
文摘目的以脆性指数为工具指标评估心血管Meta分析的稳健性。方法检索PubMed、EMbase及Web of Science数据库,收集2018—2022年心血管领域Meta分析相关文献,计算脆性指数;通过Spearman相关性分析探索脆性指数与样本量、总事件数、效应值及效应值置信区间宽度的关系。结果共纳入29篇文献的212个Meta分析,中位脆性指数11(5,25),中位样本量10301(3384,48330)例,中位事件数360(129,1309)个。多数Meta分析选择相对危险度作为效应指标(179/212),选择Mantel-Haenszel方法(102/212)和随机效应模型(153/212);脆性指数与样本量(rs=0.56,P<0.05)和总事件数(rs=0.61,P<0.05)呈正相关关系,与效应值置信区间宽度呈负相关关系(rs=−0.52,P<0.05),与效应值大小的相关性无统计学意义。结论发表在高影响力综合性期刊与专业心血管期刊上的心血管Meta分析的脆性指数普遍较低,稳健性不高。在医学研究中可增加对脆性指数的报告,辅助说明P值。
文摘This research extensively evaluates three leading mathematical software packages: Python, MATLAB, and Scilab, in the context of solving nonlinear systems of equations with five unknown variables. The study’s core objectives include comparing software performance using standardized benchmarks, employing key performance metrics for quantitative assessment, and examining the influence of varying hardware specifications on software efficiency across HP ProBook, HP EliteBook, Dell Inspiron, and Dell Latitude laptops. Results from this investigation reveal insights into the capabilities of these software tools in diverse computing environments. On the HP ProBook, Python consistently outperforms MATLAB in terms of computational time. Python also exhibits a lower robustness index for problems 3 and 5 but matches or surpasses MATLAB for problem 1, for some initial guess values. In contrast, on the HP EliteBook, MATLAB consistently exhibits shorter computational times than Python across all benchmark problems. However, Python maintains a lower robustness index for most problems, except for problem 3, where MATLAB performs better. A notable challenge is Python’s failure to converge for problem 4 with certain initial guess values, while MATLAB succeeds in producing results. Analysis on the Dell Inspiron reveals a split in strengths. Python demonstrates superior computational efficiency for some problems, while MATLAB excels in handling others. This pattern extends to the robustness index, with Python showing lower values for some problems, and MATLAB achieving the lowest indices for other problems. In conclusion, this research offers valuable insights into the comparative performance of Python, MATLAB, and Scilab in solving nonlinear systems of equations. It underscores the importance of considering both software and hardware specifications in real-world applications. The choice between Python and MATLAB can yield distinct advantages depending on the specific problem and computational environment, providing guidance for
基金supported in part by the National Natural Science Foundation of China(General Program)(No.52077017)the International Postdoctoral Exchange Fellowship Program(Talent-Introduction Program)(No.YJ20210337)。
文摘To optimize the placement of soft open points(SOPs)in active distribution networks(ADNs),many aspects should be considered,including the adjustment of transmission power,integration of distributed generations(DGs),coordination with conventional control methods,and maintenance of economic costs.To address this multi-objective planning problem,this study proposes a multi-stage coordinated robust optimization model for the SOP allocation in ADNs with photovoltaic(PV).First,two robust technical indices based on a robustness index are proposed to evaluate the operation conditions and robust optimality of the solutions.Second,the proposed coordinated allocation model aims to optimize the total cost,robust voltage offset index,robust utilization index,and voltage collapse proximity index.Third,the optimization methods of the multiand single-objective models are coordinated to solve the proposed multi-stage problem.Finally,the proposed model is implemented on an IEEE 33-node distribution system to verify its effectiveness.Numerical results show that the proposed index can better reveal voltage offset conditions as well as the SOP utilization,and the proposed model outperforms conventional ones in terms of robustness of placement plans and total cost.
基金Supported by the National High Technology Research and Development Program of China (863 Program) (2009AA01Z434)the "Core Electronic Devices, High_End General Chip, and Fundamental Software" Major Project (2013JH00103)
文摘Protecting the security of sensitive information has become a matter of great concern to everyone. Data hiding technique solves the problem to some extent, but still, some shortcomings remain for researching. To improve the capability of hiding huge data file in disk with high efficiency. In this paper, we propose a novel approach called CryptFS, which is achieved by utilizing the file access mechanism and modifying the cluster chain structure to hide data. CryptFS can quickly hide data file with G bytes size in less than 0.1s. The time used for hiding and recovering data is irrelevant to the size of data file, and the reliability of the hidden file is high, which will not be overlaid by new created file and disk defragment.
