板式换热器中蒸汽凝结换热特性

在板式换热器的蒸汽凝结换热试验台上进行了实验研究,获取蒸汽完全凝结和部分凝结两种典型工况下的换热和压降特性．且就板式换热器中蒸汽凝结过程的换热进行分析和处理,得到了一个在板片槽道中蒸汽凝结换热系数关联式．并推荐用于板式换热器的蒸汽凝结换热系数的计算．     

Google Trends and the forecasting performance of exchange rate models

In this paper, we use Google Trends data for exchange rate forecasting in the context of a broad literature review that ties the exchange rate movements with macroeconomic fundamentals. The sample covers 11 OECD countries’ exchange rates for the period from January 2004 to June 2014. In out‐of‐sample forecasting of monthly returns on exchange rates, our findings indicate that the Google Trends search query data do a better job than the structural models in predicting the true direction of changes in nominal exchange rates. We also observed that Google Trends‐based forecasts are better at picking up the direction of the changes in the monthly nominal exchange rates after the Great Recession era (2008–2009). Based on the Clark and West inference procedure of equal predictive accuracy testing, we found that the relative performance of Google Trends‐based exchange rate predictions against the null of a random walk model is no worse than the purchasing power parity model. On the other hand, although the monetary model fundamentals could beat the random walk null only in one out of 11 currency pairs, with Google Trends predictors we found evidence of better performance for five currency pairs. We believe that these findings necessitate further research in this area to investigate the extravalue one can get from Google search query data.     

Price–Dividend Ratios and Stock Price Predictability

A long‐standing puzzle to financial economists is the difficulty of outperforming the benchmark random walk model in out‐of‐sample contests. Using data from the USA over the period of 1872–2007, this paper re‐examines the out‐of‐sample predictability of real stock prices based on price–dividend (PD) ratios. The current research focuses on the significance of the time‐varying mean and nonlinear dynamics of PD ratios in the empirical analysis. Empirical results support the proposed nonlinear model of the PD ratio and the stationarity of the trend‐adjusted PD ratio. Furthermore, this paper rejects the non‐predictability hypothesis of stock prices statistically based on in‐ and out‐of‐sample tests and economically based on the criteria of expected real return per unit of risk. Copyright © 2011 John Wiley & Sons, Ltd.     

Forecasting Ability of a Periodic Component Extracted from Large‐Cap Index Time Series

We develop a method to extract periodic variations in a time series that are hidden in large non‐periodic and stochastic variations. This method relies on folding the time series many times and allows direct visualization of a hidden periodic component without resorting to any fitting procedure. Applying this method to several large‐cap stock time series in Europe, Japan and the USA yields a component with periodicity of 1 year. Out‐of‐sample tests on these large‐cap time series indicate that this periodic component is able to forecast long‐term (decade) behavior for large‐cap time series. Copyright © 2016 John Wiley & Sons, Ltd.     

A Comparison of the Forecasting Ability of Immediate Price Impact Models

As a consequence of recent technological advances and the proliferation of algorithmic and high‐frequency trading, the cost of trading in financial markets has irrevocably changed. One important change, known as price impact, relates to how trading affects prices. Price impact represents the largest cost associated with trading. Forecasting price impact is very important as it can provide estimates of trading profits after costs and also suggest optimal execution strategies. Although several models have recently been developed which may forecast the immediate price impact of individual trades, limited work has been done to compare their relative performance. We provide a comprehensive performance evaluation of these models and test for statistically significant outperformance amongst candidate models using out‐of‐sample forecasts. We find that normalizing price impact by its average value significantly enhances the performance of traditional non‐normalized models as the normalization factor captures some of the dynamics of price impact. Copyright © 2016 John Wiley & Sons, Ltd.     

FGM圆板考虑面内振动的动态响应

为研究考虑面内振动时FGM圆板的动态响应,首先基于经典板理论,同时考虑横向振动和面内振动,并利用Hamilton原理推导出轴对称情况下功能梯度材料圆薄板线性自由振动的控制方程。采用打靶法对无量纲控制方程进行数值求解,并将方程退化为一般均质板,求解其固有频率,得到了与相关文献非常接近的结果。对具体问题的数值结果进行对比分析,结果表明,面内振动会影响FGM圆板振动频率大小,忽略面内振动的影响会过高估计FGM板的频率。     

Modeling and Forecasting the Yield Curve by an Extended Nelson‐Siegel Class of Models: A Quantile Autoregression Approach

This paper compares the in‐sample fitting and the out‐of‐sample forecasting performances of four distinct Nelson–Siegel class models: Nelson–Siegel, Bliss, Svensson, and a five‐factor model we propose in order to enhance the fitting flexibility. The introduction of the fifth factor resulted in superior adjustment to the data. For the forecasting exercise the paper contrasts the performances of the term structure models in association with the following econometric methods: quantile autoregression evaluated at the median, VAR, AR, and a random walk. As a pattern, the quantile procedure delivered the best results for longer forecasting horizons. Copyright © 2011 John Wiley & Sons, Ltd.     

铜铝层状金属的研发与应用

铜铝层状复合金属板（clad metal sheet,CMS）材料集成了铜与铝的优良性能。其综合导电率高于铝合金,密度小于铜,界面为冶金结合层,可以有效避免导电状态下铜铝双金属搭接部位的腐蚀问题。在动力电池、储能电站、光伏发电、电力、电子、通讯器件、LED照明、建筑装饰面板、热沉材料等领域具有广泛的应用。介绍了铜铝CMS材料的主要产品形式,展示并展望了铜铝CMS材料的主要应用场景,提出了生产环节与研究机构值得关注的研究方向,概括了该材料大规模产业化与应用过程需要注意的若干问题。     

基于MCMC方法的虚拟试验贝叶斯校准研究

虚拟试验利用计算模型研究复杂物理过程,并预测其性能.计算模型输入参数中通常包含部分固定但未知参数,可利用计算模型结果和少量有限的试验数据校准未知参数,并研究存在建模不确定性时虚拟试验的预测问题.提出了一种贝叶斯统计方法,采用高斯过程为仿真计算模型以及模型不确定性建模,利用Markov chain蒙特卡罗抽样方法计算校准参数和仿真模型预测后验分布.设计测试算例演示所提出方法的高效性.     

基于新的采样更新方法的粒子滤波算法

以往的粒子滤波采用由初始先验概率密度产生一组粒子,然后通过重要性密度函数去更新粒子,但会产生粒子退化的问题,因此引入了各种各样的重采样算法,但这样做又产生了粒子多样性丧失的问题。针对粒子滤波的粒子退化现象,提出基于新的采样更新方法的粒子滤波算法,新方法从滤波值和滤波误差协方差矩阵上产生粒子。仿真试验表明,新方法在非线性非高斯情况下要远远好于EKF。