硬盘的S．M．A.R.T．(Self-Monitoring Analysis and Reporting Technology,即“自检测分析和报告技术”)可控制硬盘的访问。在启用S．M.A.R.T的状态下,当计算机启动时,BIOS可从硬盘接收分析信息，并根据这一信息决定是不是向用户发送...硬盘的S．M．A.R.T．(Self-Monitoring Analysis and Reporting Technology,即“自检测分析和报告技术”)可控制硬盘的访问。在启用S．M.A.R.T的状态下,当计算机启动时,BIOS可从硬盘接收分析信息，并根据这一信息决定是不是向用户发送硬盘可能在未来某时失败的警告。但在某些情况下，这一技术却会让电脑无法启动。前段时间，笔者就被S.M．A.R．T技术撞了一下腰！展开更多
S.M.A.R.T是英文Self-Monitoring，Analysis and Reporting Technology的缩写，即自检、分析和报告技术。顾名思义，这是用来对硬盘自身进行监测，并向用户提供分析结果的一项自诊断技术。自ATA-3以后，S.M.A.R.T技术已经成为IDE硬盘必...S.M.A.R.T是英文Self-Monitoring，Analysis and Reporting Technology的缩写，即自检、分析和报告技术。顾名思义，这是用来对硬盘自身进行监测，并向用户提供分析结果的一项自诊断技术。自ATA-3以后，S.M.A.R.T技术已经成为IDE硬盘必备的规范。现在，无论是IDE产品还是SCSI产品，几乎所有市售硬盘都支持S.M.A.R.T功能，但要使用这个功能，必须具备相应的BIOS和专门的应用程序。展开更多
财政科技支出是政府激励微观经济主体创新的重要政策手段。选取A省规模以上工业企业2005-2020年的时间序列数据,从创新研发投入和创新成果产出两个角度,运用简单线性回归模型评估财政科技支出对企业创新的影响效应,运用向量自回归模型(V...财政科技支出是政府激励微观经济主体创新的重要政策手段。选取A省规模以上工业企业2005-2020年的时间序列数据,从创新研发投入和创新成果产出两个角度,运用简单线性回归模型评估财政科技支出对企业创新的影响效应,运用向量自回归模型(Vector autoregressive model,以下简称VAR模型)分析二者的动态关联程度。结果表明,财政科技支出能够驱动企业增加研究与开发(Research And Development,以下简称R&D)经费支出和新产品销售收入。VAR模型显示财政科技支出对于企业R&D经费支出的方差贡献度更高,但其对R&D经费支出的激励作用弱于新产品销售收入。通过探讨财政科技支出对企业创新的影响路径,发现财政科技支出可以通过改善企业创新环境来促进企业创新,且财政科技支出的结构关乎其对企业创新的影响效应。本文据此提出,将科技支出作为财政支出的重点加以保障,扩大覆盖对象和范围并进一步调整结构,在创新链的不同阶段找准财税科技政策差异化的着力点,夯实企业的创新主体地位并优化企业的创新环境,配合其他政策协同发力。展开更多
The Owen’s T function is presented in four new ways, one of them as a series similar to the Euler’s arctangent series divided by 2π, which is its majorant series. All possibilities enable numerically stable ...The Owen’s T function is presented in four new ways, one of them as a series similar to the Euler’s arctangent series divided by 2π, which is its majorant series. All possibilities enable numerically stable and fast convergent computation of the bivariate normal integral with simple recursion. When tested computation on a random sample of one million parameter triplets with uniformly distributed components and using double precision arithmetic, the maximum absolute error was 3.45 × 10<sup>-</sup><sup>16</sup>. In additional testing, focusing on cases with correlation coefficients close to one in absolute value, when the computation may be very sensitive to small rounding errors, the accuracy was retained. In rare potentially critical cases, a simple adjustment to the computation procedure was performed—one potentially critical computation was replaced with two equivalent non-critical ones. All new series are suitable for vector and high-precision computation, assuming they are supplemented with appropriate efficient and accurate computation of the arctangent and standard normal cumulative distribution functions. They are implemented by the R package Phi2rho, available on CRAN. Its functions allow vector arguments and are ready to work with the Rmpfr package, which enables the use of arbitrary precision instead of double precision numbers. A special test with up to 1024-bit precision computation is also presented.展开更多
文摘硬盘的S．M．A.R.T．(Self-Monitoring Analysis and Reporting Technology,即“自检测分析和报告技术”)可控制硬盘的访问。在启用S．M.A.R.T的状态下,当计算机启动时,BIOS可从硬盘接收分析信息，并根据这一信息决定是不是向用户发送硬盘可能在未来某时失败的警告。但在某些情况下，这一技术却会让电脑无法启动。前段时间，笔者就被S.M．A.R．T技术撞了一下腰！
文摘S.M.A.R.T是英文Self-Monitoring，Analysis and Reporting Technology的缩写，即自检、分析和报告技术。顾名思义，这是用来对硬盘自身进行监测，并向用户提供分析结果的一项自诊断技术。自ATA-3以后，S.M.A.R.T技术已经成为IDE硬盘必备的规范。现在，无论是IDE产品还是SCSI产品，几乎所有市售硬盘都支持S.M.A.R.T功能，但要使用这个功能，必须具备相应的BIOS和专门的应用程序。
文摘财政科技支出是政府激励微观经济主体创新的重要政策手段。选取A省规模以上工业企业2005-2020年的时间序列数据,从创新研发投入和创新成果产出两个角度,运用简单线性回归模型评估财政科技支出对企业创新的影响效应,运用向量自回归模型(Vector autoregressive model,以下简称VAR模型)分析二者的动态关联程度。结果表明,财政科技支出能够驱动企业增加研究与开发(Research And Development,以下简称R&D)经费支出和新产品销售收入。VAR模型显示财政科技支出对于企业R&D经费支出的方差贡献度更高,但其对R&D经费支出的激励作用弱于新产品销售收入。通过探讨财政科技支出对企业创新的影响路径,发现财政科技支出可以通过改善企业创新环境来促进企业创新,且财政科技支出的结构关乎其对企业创新的影响效应。本文据此提出,将科技支出作为财政支出的重点加以保障,扩大覆盖对象和范围并进一步调整结构,在创新链的不同阶段找准财税科技政策差异化的着力点,夯实企业的创新主体地位并优化企业的创新环境,配合其他政策协同发力。
文摘The Owen’s T function is presented in four new ways, one of them as a series similar to the Euler’s arctangent series divided by 2π, which is its majorant series. All possibilities enable numerically stable and fast convergent computation of the bivariate normal integral with simple recursion. When tested computation on a random sample of one million parameter triplets with uniformly distributed components and using double precision arithmetic, the maximum absolute error was 3.45 × 10<sup>-</sup><sup>16</sup>. In additional testing, focusing on cases with correlation coefficients close to one in absolute value, when the computation may be very sensitive to small rounding errors, the accuracy was retained. In rare potentially critical cases, a simple adjustment to the computation procedure was performed—one potentially critical computation was replaced with two equivalent non-critical ones. All new series are suitable for vector and high-precision computation, assuming they are supplemented with appropriate efficient and accurate computation of the arctangent and standard normal cumulative distribution functions. They are implemented by the R package Phi2rho, available on CRAN. Its functions allow vector arguments and are ready to work with the Rmpfr package, which enables the use of arbitrary precision instead of double precision numbers. A special test with up to 1024-bit precision computation is also presented.
