Radiation hardness characterization of low gain avalanche detector prototypes for the high granularity timing detector

The high granularity timing detector (HGTD) is a crucial component of the ATLAS phase II upgrade to cope with the extremely high pile-up (the average number of interactions per bunch crossing can be as high as 200). With the precise timing information (σ_t～30 ps) of the tracks, the track-to-vertex association can be performed in the “4-D” space. The Low Gain Avalanche Detector (LGAD) technology is chosen for the sensors, which can provide the required timing resolution and good signal-to-noise ratio. Hamamatsu Photonics K.K. (HPK) has produced the LGAD with thicknesses of 35 μm and 50 μm. The University of Science and Technology of China(USTC) has also developed and produced 50 μm LGADs prototypes with the Institute of Microelectronics (IME) of Chinese Academy of Sciences. To evaluate the irradiation hardness, the sensors are irradiated with the neutron at the JSI reactor facility and tested at USTC. The irradiation effects on both the gain layer and the bulk are characterized by I-V and C-V measurements at room temperature (20 ℃) or ?30 ℃. The breakdown voltages and depletion voltages are extracted and presented as a function of the fluences. The final fitting of the acceptor removal model yielded the c-factor of 3.06×10?16 cm?2, 3.89×10?16 cm?2 and 4.12×10?16 cm?2 for the HPK-1.2, HPK-3.2 and USTC-1.1-W8, respectively, showing that the HPK-1.2 sensors have the most irradiation resistant gain layer. A novel analysis method is used to further exploit the data to get the relationship between the c-factor and initial doping density.     

Measuring Environmental Performance of Provincial Thermal Power Plants in China: A Malmquist DEA Approach with Fixed-Sum Undesirable Outputs

With the development of China’s economy, environmental pollution has become cumulatively serious. The primary source of environmental pollution is thermal power generation, which has attracted the attention of governments and academia in recent years. To effectively reduce environmental pollution, research should study how to constrain the undesirable output of thermal power plants, that is, to limit the total undesirable output of the plants to a certain fixed sum. However, few studies have suggested that these undesirable outputs should be fixed-sum outputs. Moreover, no previous research publication about thermal power plants has analyzed their environmental performance changes. To address these gaps, a novel Malmquist-DEA approach is proposed for evaluate the environmental performance of thermal power plants in this paper. This approach generalizes the equilibrium efficient frontier DEA model with fixed-sum undesirable outputs and incorporates the model into the Malmquist productivity index (MPI). The authors apply this approach to the analysis of provincial thermal power plant environmental performance in China and analyze such plants’ trends based on panel data from 2011 to 2014. The empirical research shows that the environmental performance of regional thermal power plants was positively affected by efficiency change and negatively affected by technical change. Finally, the authors provide policy suggestions based on our findings.

     

Au—Si界面的室温反应

本文利用XPS研究了在超高真空条件下形成的Au—Si(113)界面初始阶段的室温反应,测量了Si2p、2S和Au4f光电子发射峰的强度和能量位置随Au复盖量的变化。所有的结果都表明,与Au—Si(111)和Au—Si(100)系统一样,存在一个发生界面室温反应的临界Au厚度～5ML,从而推断,这个现象可能是Au—Si界面形成过程的普遍特性。根据我们的实验结果,还讨论了Au—Si界面形成的可能模型。     

利玛窦与西方数学的传播

利玛窦构建了良好的知识结构,具有很深的数学功底;他注意理论与实际的结合,学以致用,为他到中国传播西方的数学奠定了坚实的基础;他以数学赢取民心支持,在中国不仅传播了欧氏几何,而且还传播了大量非欧几何等数学知识;他一方面激活了正在走向衰落的中国传统数学,开创了中西数学文化融合的新纪元,另一方面他也激励了一大批中国数学家的成长、成才,推动了中国近现代数学的萌芽发展.     

A pair of two－component regulatory genes ecrA1／A2 in S. coelicolor

Two-component genes are kinds of genetic elements involved in regulation of antibiotic production in Streptomyces coelicolor. DNA microarray analysis revealed that ecrA1/A2, which mapped at distant sites from red locus and encode respectively the kinase and regulator, expressed coordinately with genes of Red specific biosynthetic pathway, ecrA1 and ecrA2 gene-disruptive mutants were constructed using homogenotisation by reciprocal double crossover. Fermentation data showed that the undecylprodigiosin (Red) level of production was lower than that of wild-type strain. However, the change of the actinorhodin (Act) production level was not significant compared with wild type. Thus, these experiment results confirmed that the two-component system ecrA 1/A2 was positive regulatory element for red gene cluster.     

突发事件快速评估模型

突发事件发生初期,进行突发事件的快速综合评估,有利于及时、准确地把握事件的态势,以满足应急决策的时效性要求.为解决突发事件数据量较大,数据更新较快时,过于复杂的运算过程和计算软件缺乏以及已有软件兼容性低而难以满足应急决策的时效性要求的问题,本文简化了Simos过程和PROMETHEEⅡ方法,构建了突发事件快速评估模型,即SSPPM(Simple Simos'Procedure and PROMETHEEⅡModel).为了验证新模型的有效性,本文选取了两个数据进行实验.实验结果表明,在对评估速度的要求大于对精度的要求的突发事件控制阶段,新模型能快速综合多位应急决策者的意见,快速获取权重,简化评估过程,提高突发事件综合评估效率.     

电力二次系统安全防护综合仿真试验床研究

分析比较了现有的信息与网络安全仿真模型,指出各自模型的适用性和局限性.在以往主要针对局部对象进行安全仿真的研究基础上,结合电力二次系统整体安全防护需求,综合运用8种仿真技术和方案,设计实现了基于4个仿真层次,17个种类仿真试验的信息安全综合仿真试验床,实现针对电力信息系统的大型、分布式、联动式的综合信息安全仿真.使用证明,试验床能够为电力信息系统安全防护工程设计和实施提供多方位的仿真测试和评估依据.     

Learning multivariate time series causal graphs based on conditional mutual information

Detection and clarification of cause-effect relationships among variables is an important problem in time series analysis.This paper provides a method that employs both mutual information and conditional mutual information to identify the causal structure of multivariate time series causal graphical models.A three-step procedure is developed to learn the contemporaneous and the lagged causal relationships of time series causal graphs.Contrary to conventional constraint-based algorithm, the proposed algorithm does not involve any special kinds of distribution and is nonparametric.These properties are especially appealing for inference of time series causal graphs when the prior knowledge about the data model is not available.Simulations and case analysis demonstrate the effectiveness of the method.     

LEARNING CAUSAL GRAPHS OF NONLINEAR STRUCTURAL VECTOR AUTOREGRESSIVE MODEL USING INFORMATION THEORY CRITERIA

Detection and clarification of cause-effect relationships among variables is an important problem in time series analysis. Traditional causality inference methods have a salient limitation that the model must be linear and with Gaussian noise. Although additive model regression can effectively infer the nonlinear causal relationships of additive nonlinear time series, it suffers from the limitation that contemporaneous causal relationships of variables must be linear and not always valid to test conditional independence relations. This paper provides a nonparametric method that employs both mutual information and conditional mutual information to identify causal structure of a class of nonlinear time series models, which extends the additive nonlinear times series to nonlinear structural vector autoregressive models. An algorithm is developed to learn the contemporaneous and the lagged causal relationships of variables. Simulations demonstrate the effectiveness of the nroosed method.     

An M-Objective Penalty Function Algorithm Under Big Penalty Parameters

Some classical penalty function algorithms may not always be convergent under big penalty parameters in Matlab software, which makes them impossible to find out an optimal solution to constrained optimization problems. In this paper, a novel penalty function (called M-objective penalty function) with one penalty parameter added to both objective and constrained functions of inequality constrained optimization problems is proposed. Based on the M-objective penalty function, an algorithm is developed to solve an optimal solution to the inequality constrained optimization problems, with its convergence proved under some conditions. Furthermore, numerical results show that the proposed algorithm has a much better convergence than the classical penalty function algorithms under big penalty parameters, and is efficient in choosing a penalty parameter in a large range in Matlab software.