Global warming may result in increased polar amplification,but future temperature changes under different climate change scenarios have not been systematically investigated over Antarctica.An index of Antarctic amplif...Global warming may result in increased polar amplification,but future temperature changes under different climate change scenarios have not been systematically investigated over Antarctica.An index of Antarctic amplification(AnA)is defined,and the annual and seasonal variations of Antarctic mean temperature are examined from projections of the Coupled Model Intercomparison Project Phase 6(CMIP6)under scenarios SSP119,SSP126,SSP245,SSP370 and SSP585.AnA occurs under all scenarios,and is strongest in the austral summer and autumn,with an AnA index greater than 1.40.Although the warming over Antarctica accelerates with increased anthropogenic forcing,the magnitude of AnA is greatest in SSP126 instead of in SSP585,which may be affected by strong ocean heat uptake in high forcing scenario.Moreover,future AnA shows seasonal difference and regional difference.AnA is most conspicuous in the East Antarctic sector,with the amplification occurring under all scenarios and in all seasons,especially in austral summer when the AnA index is greater than 1.50,and the weakest signal appears in austral winter.Differently,the AnA over West Antarctica is strongest in austral autumn.Under SSP585,the temperature increase over the Antarctic Peninsula exceeds 0.5℃when the global average warming increases from 1.5℃to 2.0℃above preindustrial levels,except in the austral summer,and the AnA index in this region is strong in the austral autumn and winter.The projections suggest that the warming rate under different scenarios might make a large difference to the future AnA.展开更多
Software reliability for business applications is becoming a topic of interest in the IT community. An effective method to validate and understand defect behaviour in a software application is Fault Injection. Fault i...Software reliability for business applications is becoming a topic of interest in the IT community. An effective method to validate and understand defect behaviour in a software application is Fault Injection. Fault injection involves the deliberate insertion of faults or errors into software in order to determine its response and to study its behaviour. Fault Injection Modeling has demonstrated to be an effective method for study and analysis of defect response, validating fault-tolerant systems, and understanding systems behaviour in the presence of injected faults. The objectives of this study are to measure and analyze defect leakage;Amplification Index (AI) of errors and examine “Domino” effect of defects leaked into subsequent Software Development Life Cycle phases in a business application. The approach endeavour to demonstrate the phasewise impact of leaked defects, through causal analysis and quantitative analysis of defects leakage and amplification index patterns in system built using technology variants (C#, VB 6.0, Java).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 researchers and practitioners in selecting tools for their unique challenges.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.42276260,41671073)the 2021 technical support talent project of the Chinese Academy of Sciences。
文摘Global warming may result in increased polar amplification,but future temperature changes under different climate change scenarios have not been systematically investigated over Antarctica.An index of Antarctic amplification(AnA)is defined,and the annual and seasonal variations of Antarctic mean temperature are examined from projections of the Coupled Model Intercomparison Project Phase 6(CMIP6)under scenarios SSP119,SSP126,SSP245,SSP370 and SSP585.AnA occurs under all scenarios,and is strongest in the austral summer and autumn,with an AnA index greater than 1.40.Although the warming over Antarctica accelerates with increased anthropogenic forcing,the magnitude of AnA is greatest in SSP126 instead of in SSP585,which may be affected by strong ocean heat uptake in high forcing scenario.Moreover,future AnA shows seasonal difference and regional difference.AnA is most conspicuous in the East Antarctic sector,with the amplification occurring under all scenarios and in all seasons,especially in austral summer when the AnA index is greater than 1.50,and the weakest signal appears in austral winter.Differently,the AnA over West Antarctica is strongest in austral autumn.Under SSP585,the temperature increase over the Antarctic Peninsula exceeds 0.5℃when the global average warming increases from 1.5℃to 2.0℃above preindustrial levels,except in the austral summer,and the AnA index in this region is strong in the austral autumn and winter.The projections suggest that the warming rate under different scenarios might make a large difference to the future AnA.
文摘Software reliability for business applications is becoming a topic of interest in the IT community. An effective method to validate and understand defect behaviour in a software application is Fault Injection. Fault injection involves the deliberate insertion of faults or errors into software in order to determine its response and to study its behaviour. Fault Injection Modeling has demonstrated to be an effective method for study and analysis of defect response, validating fault-tolerant systems, and understanding systems behaviour in the presence of injected faults. The objectives of this study are to measure and analyze defect leakage;Amplification Index (AI) of errors and examine “Domino” effect of defects leaked into subsequent Software Development Life Cycle phases in a business application. The approach endeavour to demonstrate the phasewise impact of leaked defects, through causal analysis and quantitative analysis of defects leakage and amplification index patterns in system built using technology variants (C#, VB 6.0, Java).
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 researchers and practitioners in selecting tools for their unique challenges.
