Constructing and evaluating core inflation measures from component‐level inflation data

This paper undertakes a comprehensive examination of 10 measures of core inflation and evaluates which measure produces the best forecast of headline inflation out‐of‐sample. We use the Personal Consumption Expenditure Price Index as our measure of inflation. We use two sets of components (17 and 50) of the Personal Consumption Expenditure Price Index to construct these core inflation measures and evaluate these measures at the three time horizons (6, 12 and 24 months) most relevant for monetary policy decisions. The best measure of core inflation for both sets of components and over all time horizons uses weights based on the first principal component of the disaggregated (component‐level) prices. Interestingly, the results vary by the number of components used; when more components are used the weights based on the persistence of each component is statistically equivalent to the weights generated by the first principal component. However, those forecasts using the persistence of 50 components are statistically worse than those generated using the first principal component of 17 components. The statistical superiority of the principal component method is due to the fact that it extracts (in the first principal component) the common source of variation in the component level prices that accurately describes trend inflation over the next 6–24 months.     

Forecasting in long horizons using smoothed direct forecast

This paper constructs a forecast method that obtains long‐horizon forecasts with improved performance through modification of the direct forecast approach. Direct forecasts are more robust to model misspecification compared to iterated forecasts, which makes them preferable in long horizons. However, direct forecast estimates tend to have jagged shapes across horizons. Our forecast method aims to “smooth out” erratic estimates across horizons while maintaining the robust aspect of direct forecasts through ridge regression, which is a restricted regression on the first differences of regression coefficients. The forecasts are compared to the conventional iterated and direct forecasts in two empirical applications: real oil prices and US macroeconomic series. In both applications, our method shows improvement over direct forecasts.     

植入式硅神经微电极的发展

 神经科学和神经工程研究需要研究大脑神经元的电活动情况，以了解大脑产生、传输和处理信息的机制。植入式神经微电极作为一种传感器件，是时间分辨率最高的神经电活动传感手段之一。介绍了国内外几种主要的植入式硅基神经微电极的结构特点、制备方法和性能特点。分析表明，未来通过不断结构优化和改性修饰，特别是在高通量的神经记录方面，通过与同样基于硅材料的电路的集成，硅神经微电极能够进一步提高生物相容性，解决大规模的电极通道体内外传输与连接问题，实现对神经元的在体大规模长时间记录。     

基于ESI和InCites的学科发展预测研究--以中国地质大学为例

基于汤姆路透公司的ESI和InCites数据库，分析了中国地质大学各学科的特征，探讨了高被引论文、热门论文、顶尖论文与学科发展的关系，并预测后续最有可能入围ESI世界前1％的学科。     

基于交通信号灯信息的混合动力汽车节能预测控制方法

针对传统混合动力汽车控制方法不考虑已知道路交通信号灯信息对车辆能量管理影响的问题,提出了基于交通信号灯信息的混合动力汽车节能预测控制智能优化策略。通过建立混合动力汽车系统的简化模型,并采用连续广义最小残量方法求解模型预测控制问题。运用MATLAB/Simulink进行仿真,仿真结果验证了交通信号灯信息模型的有效性,以及所设计的模型预测控制算法大幅度提高混合动力汽车的燃油经济性的能力和实时控制性能。研究结果表明所提出的控制策略可以实现车辆行驶轨迹的优化控制,显著提高了车辆的燃油经济性,并满足系统的实时最优控制要求。     

金属间化合物(Mg,Zn)_(12)Ce与(Mg,Zn)_(11)Ce的研究

对300℃时Mg-Zn-Ce系富镁侧化合物的成分特征、结构和相平衡关系进行了研究.结果表明,Mg-Zn-Ce富镁角存在一个Mg12Ce的二元置换固溶体(Mg,Zn)12Ce,还存在一个三元线性化合物(Mg,Zn)11Ce(τ相).(Mg,Zn)12Ce中Zn的范围为0~7.3%(原子分数),其晶体结构为体心四方晶格.三元线性化合物τ相含Zn为8.5%~43.5%(原子分数),其晶体结构为C底心正交晶格.(Mg,Zn)12Ce和τ相均与α(Mg)存在稳定的两相平衡,且在Mg-Zn-Ce系富镁角还存在三相平衡Mg+(Mg,Zn)12Ce+τ.     

一个基于Exchange的企业办公自动化系统的实现

介绍了一个基于Exchange的企业办公自动化系统的实现，其中包括系统目标、开发平台和工具的选择、系统的功能模块、关键技术以及系统的安全机制。     

基于套接字关于HTTP的研究

本文详细阐述了套接字(SOCKET)和HTTP的工作原理，并以此为依据简化HTTP服务器程序，从而提出隐藏网络底层复杂的结构和协议的方法。     

基于B/S模式的水利水电工程造价管理系统研究

提出了基于B／S模式的水利水电工程造价管理系统，对B／S的体系结构、系统的总体结构设计、功能及功能设计进行了阐述，并说明了系统实现的关键技术．     

MySQL安全机制及应对措施探讨

探讨作为一种最主要的网络数据库系统MySQL的基于授权表的安全机制，以及如何在应用中就文件系统保护、口令保护、权限表设置和运行选项的采用来提高安全性能的问题。