积分复位控制三相功率因数校正

介绍了一种工作于连续导电模式的三相功率因数校正电路，对其控制方程进行详细的推导，并用积分复位控制电路进行实现。积分复位控制电路由-可复位积分器，-RS触发器和-比较器组成控制电路的核心器件，结构十分简单。积分复位控制三相功率因数校正具有下述特点：常频控制，这有利于滤波器的设计；控制电路简单，不需要乘法器；控制思路清晰，控制效果好。仿真结果证明了理论分析的正确性。     

山东省经济发展与能源消费关系研究——基于灰色关联理论

在对山东省经济增长与能源消费之间关系进行定性分析的基础上,构建了可定量评价的灰色关联体系,计算结果证实:山东省经济发展过度依赖煤炭资源,但煤炭总体利用率不高,目前电力、石油和天然气的使用量正在逐步增加。为解决经济增长与能源消费之间的不协调问题提出了加快产业结构调整、完善能源消费结构、实施天然气产业发展、学习国外先进技术和管理经验等措施。     

无侧移失稳模式下框架的稳定方程及框架柱的计算长度系数

对无侧移框架结构进行了弹性稳定分析，得到了理想状态下无侧移框架结构的稳定方程，并对r=0.6-1.0的理想情况下框架柱的计算长度系数计算的结果表明：在r=1.0时计算值与规范值符合非常好，虽然方程的成立依赖于较为理想的假定条件，不适于对实际结构进行计算，但仍可做为实际稳定分析的参考。     

Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors

In this paper, we assess the predictive content of latent economic policy uncertainty and data surprise factors for forecasting and nowcasting gross domestic product (GDP) using factor-type econometric models. Our analysis focuses on five emerging market economies: Brazil, Indonesia, Mexico, South Africa, and Turkey; and we carry out a forecasting horse race in which predictions from various different models are compared. These models may (or may not) contain latent uncertainty and surprise factors constructed using both local and global economic datasets. The set of models that we examine in our experiments includes both simple benchmark linear econometric models as well as dynamic factor models that are estimated using a variety of frequentist and Bayesian data shrinkage methods based on the least absolute shrinkage operator (LASSO). We find that the inclusion of our new uncertainty and surprise factors leads to superior predictions of GDP growth, particularly when these latent factors are constructed using Bayesian variants of the LASSO. Overall, our findings point to the importance of spillover effects from global uncertainty and data surprises, when predicting GDP growth in emerging market economies.     

Does a lot help a lot? Forecasting stock returns with pooling strategies in a data‐rich environment

A variety of recent studies provide a skeptical view on the predictability of stock returns. Empirical evidence shows that most prediction models suffer from a loss of information, model uncertainty, and structural instability by relying on low‐dimensional information sets. In this study, we evaluate the predictive ability of various lately refined forecasting strategies, which handle these issues by incorporating information from many potential predictor variables simultaneously. We investigate whether forecasting strategies that (i) combine information and (ii) combine individual forecasts are useful to predict US stock returns, that is, the market excess return, size, value, and the momentum premium. Our results show that methods combining information have remarkable in‐sample predictive ability. However, the out‐of‐sample performance suffers from highly volatile forecast errors. Forecast combinations face a better bias–efficiency trade‐off, yielding a consistently superior forecast performance for the market excess return and the size premium even after the 1970s.     

Revisiting Targeted Factors

This paper proposes new methods for ‘targeting’ factors estimated from a big dataset. We suggest that forecasts of economic variables can be improved by tuning factor estimates: (i) so that they are both more relevant for a specific target variable; and (ii) so that variables with considerable idiosyncratic noise are down‐weighted prior to factor estimation. Existing targeted factor methodologies are limited to estimating the factors with only one of these two objectives in mind. We therefore combine these ideas by providing new weighted principal components analysis (PCA) procedures and a targeted generalized PCA (TGPCA) procedure. These methods offer a flexible combination of both types of targeting that is new to the literature. We illustrate this empirically by forecasting a range of US macroeconomic variables, finding that our combined approach yields important improvements over competing methods, consistently surviving elimination in the model confidence set procedure. Copyright © 2016 John Wiley & Sons, Ltd.     

Real‐Time Forecasts of Inflation: The Role of Financial Variables

We present a mixed‐frequency model for daily forecasts of euro area inflation. The model combines a monthly index of core inflation with daily data from financial markets; estimates are carried out with the MIDAS regression approach. The forecasting ability of the model in real time is compared with that of standard VARs and of daily quotes of economic derivatives on euro area inflation. We find that the inclusion of daily variables helps to reduce forecast errors with respect to models that consider only monthly variables. The mixed‐frequency model also displays superior predictive performance with respect to forecasts solely based on economic derivatives. Copyright © 2012 John Wiley & Sons, Ltd.     

恶性肿瘤中IGFBP-rP1的抑癌基因作用机制及其可能的临床实用价值

胰岛素样生长因子结合蛋白相关蛋白1(IGFBP-rP1)是近年来恶性肿瘤的研究热点.本文主要综述IGFBP-rP1在恶性肿瘤中的抑癌基因作用机制及可能的临床实用价值.IGFBP-rP1在恶性肿瘤中的作用广泛涉及细胞的增殖、衰老、凋亡、分化、血管生成等多方面,研究指出IGFBP-rP1可缩短细胞增殖周期并影响非停泊性生长从而抑制增殖,降低致瘤能力;调节BRAF-MEKERK信号通路及pRB、HSP60等相关蛋白的表达从而影响衰老及凋亡;主要通过IGF依赖方式抑制血管生成;而且IGFBP-rP1表达下降跟肿瘤细胞分化程度降低有关.研究显示IGFBP-rP1有一定的临床实用价值,如其表达量跟恶性肿瘤的进展相关,低表达提示某些化疗药物抵抗,可提示预后.而在恶性肿瘤中特异性地上调IGFBP-rP1,可抑制肿瘤增殖及血管生成、诱导细胞衰老凋亡、提高肿瘤分化程度及化疗敏感性,具有治疗意义,但研究者们还在努力探究,争取早日找到一种临床有效的靶向IGFBP-rP1的基因治疗方法.     

基于非等量采样的伪码同步技术研究

针对数字通信系统中伪随机噪声(pseudo-random noise, PN)码同步技术的同步精度受限于采样间隔的问题,结合可变群时延的高精度同步思想,利用采样点位置偏移量的变化,推导出了非等量采样(non-commensurate sampling, NCS)下的PN码相关函数解析表达式,提出了基于精度因子的NCS率选取准则,通过精度因子可快速判断出NCS后PN码的同步精度下限。在此基础上,分析了码序列周期、接收机前端滤波器等附加因素对PN码同步精度的影响。对NCS率选取准则的研究,为优化数字通信系统设计提供了理论指导,达到以低硬件消耗实现高精度同步的目的。