Treed Avalanche Forecasting: Mitigating Avalanche Danger Utilizing Bayesian Additive Regression Trees

Little Cottonwood Canyon Highway is a dead‐end, two‐lane road leading to Utah's Alta and Snowbird ski resorts. It is the only road access to these resorts and is heavily traveled during the ski season. Professional avalanche forecasters monitor this road throughout the ski season in order to make road closure decisions in the face of avalanche danger. Forecasters at the Utah Department of Transportation (UDOT) avalanche guard station at Alta have maintained an extensive daily winter database on explanatory variables relating to avalanche prediction. Whether or not an avalanche crosses the road is modeled in this paper via Bayesian additive tree methods. Utilizing daily winter data from 1995 to 2011, results show that using Bayesian tree analysis outperforms traditional statistical methods in terms of realized misclassification costs that take into consideration asymmetric losses arising from two types of error. Closing the road when an avalanche does not occur is an error harmful to resort owners, and not closing the road when one does may result in injury or death. Copyright © 2016 John Wiley & Sons, Ltd.     

Dynamic Model Averaging and CPI Inflation Forecasts: A Comparison between the Euro Area and the United States

The paper forecasts consumer price inflation in the euro area (EA) and in the USA between 1980:Q1 and 2012:Q4 based on a large set of predictors, with dynamic model averaging (DMA) and dynamic model selection (DMS). DMA/DMS allows not solely for coefficients to change over time, but also for changes in the entire forecasting model over time. DMA/DMS provides on average the best inflation forecasts with regard to alternative approaches (such as the random walk). DMS outperforms DMA. These results are robust for different sample periods and for various forecast horizons. The paper highlights common features between the USA and the EA. First, two groups of predictors forecast inflation: temporary fundamentals that have a frequent impact on inflation but only for short time periods; and persistent fundamentals whose switches are less frequent over time. Second, the importance of some variables (particularly international food commodity prices, house prices and oil prices) as predictors for consumer price index inflation increases when such variables experience large shocks. The paper also shows that significant differences prevail in the forecasting models between the USA and the EA. Such differences can be explained by the structure of these respective economies. Copyright © 2015 John Wiley & Sons, Ltd.     

基于SHPB试验的高速铁路CRTSⅡ型CA砂浆动态性能

采用分离式霍普金森压杆(Split Hopkinson Pressure Bar,SHPB)试验研究了高速铁路CRTS II型水泥乳化沥青砂浆(CA砂浆)的动态力学性能,并建立了CRTS II型CA砂浆的动态本构关系模型.结果表明:随着应变率的增大,CRTS II型CA砂浆峰值强度逐渐增加,但增加速率随应变率的增大而减小,当应变率从44.17增加至54.79 s-1和从54.79增加至108.47 s-1时,峰值强度分别增加了初始峰值强度的52.28%和7.5%,弹性模量随应变率的变化规律性较差;应变率越大,破坏时的贯通裂纹越多,碎裂程度越大;CRTS II型CA砂浆的比能量吸收随着应变率的增大而增大.所建立的动态本构模型拟合曲线与试验曲线具有较好的一致性.     

Forecast Combination and Bayesian Model Averaging: A Prior Sensitivity Analysis

In this study we evaluate the forecast performance of model‐averaged forecasts based on the predictive likelihood carrying out a prior sensitivity analysis regarding Zellner's g prior. The main results are fourfold. First, the predictive likelihood does always better than the traditionally employed ‘marginal’ likelihood in settings where the true model is not part of the model space. Secondly, forecast accuracy as measured by the root mean square error (RMSE) is maximized for the median probability model. On the other hand, model averaging excels in predicting direction of changes. Lastly, g should be set according to Laud and Ibrahim (1995: Predictive model selection. Journal of the Royal Statistical Society B 57 : 247–262) with a hold‐out sample size of 25% to minimize the RMSE (median model) and 75% to optimize direction of change forecasts (model averaging). We finally apply the aforementioned recommendations to forecast the monthly industrial production output of six countries, beating for almost all countries the AR(1) benchmark model. Copyright © 2011 John Wiley & Sons, Ltd.     

基于Beta过程的高分辨ISAR成像

对于高信噪比、完整回波、目标平稳运动等理想观测环境,现有成像技术已经较为成熟,可以获得聚焦良好的高分辨逆合成孔径雷达(inverse synthetic aperture radar, ISAR)像。但在实际中的方位回波缺损与低信噪比观测情况下,随机相位误差等因素会降低现有成像算法的性能甚至使其失效。本文首先建立了ISAR稀疏观测模型,并基于稀疏贝叶斯学习理论,通过引入Beta过程非参数先验构建层级概率模型,进而交替利用Gibbs采样及最大似然方法对ISAR像及随机相位误差进行估计。实验结果表明,所提方法在低信噪比、回波缺损等复杂观测环境下能够获得聚焦良好的ISAR图像。     

多变量过程的边界调整策略

针对存在设置偏差且生产阶段有限的多变量过程,研究了调整费用恒定情形下的使过程质量损失最小的边界调整策略.在建立过程状态空间方程的基础上,利用贝叶斯推断和动态规划给出了调整策略中随生产阶段变化的边界.通过算例解释了边界调整策略的实现方法,并通过仿真将调整策略与其他两种质量控制策略进行了比较分析,仿真结果表明调整策略能够更好的减少过程的总体质量损失.     

胰岛素信号通路PI3K／Akt对海马神经元β-淀粉样前体蛋白裂解酶1mRNA水平的影晌

目的通过胰岛素和磷脂酰肌醇-3激酶（P13K）抑制剂渥曼青霉素（wortmannin）对P13K／丝氨酸苏氨酸蛋白激酶（P13K／Akt）信号通路的激活和抑制作用,观察P13K／Akt信号通路对海马神经元β-淀粉样前体蛋白裂解酶1（BACEl）mRNA水平表达的影响。方法20只sD大鼠随机分为空白对照组、假手术组、胰岛素组和渥曼青霉素组,海马立体定向注射胰岛素和P13K抑制剂渥曼青霉素。逆转录一聚合酶链反应（RT-PCR）检测P13K／Akt信号传导下游蛋白Akt以及BACEImRNA水平。结果注射胰岛素的海马P13K信号通路下游信号分子：AktmRNA表达上调（分别较空白和阴性对照组P=0．047,P=0．002）,而BACElmRNA表达下调（分别较空白和阴性对照组P=0．004,P=0．01）。渥曼青霉素组的P13K下游信号分子AktmRNA表达明显被抑制（分别较空白和阴性对照组P=0．002,P=0．039）,同时BACEImRNA的表达较对照组上调（分别较空白和阴性对照组P=0．039,P=0．018）。结论胰岛素信号通路P13K／AM可以调节BACEl的转录水平参与阿尔茨海默病的发病机制。     

基于人眼状态识别的驾驶员疲劳检测方法研究

提出一种基于眼睛状态识别的驾驶员疲劳状态检测的方法。首先通过建立肤色模型分割出人脸区域,再利用搜索连通域的方法实现眼睛区域的定位;然后计算出眼睛区域的垂直积分投影曲线,提取并比较曲线的特征参数,从而识别眼睛的状态;最后以眼睛闭合时间比率为测量标准实现对驾驶员疲劳程度的检测。实验表明,该方法简单、有效、实时性较好,疲劳检测的正确率较高。     

外源性H2S通过调节β-位淀粉样前体蛋白裂解酶1表达对PC12细胞APP／Aβ代谢的影响

目的观察外源性硫化氢对嗜铬细胞瘤细胞β-位淀粉样前体蛋白裂解酶1（BACE1）的调节作用,进而探讨其对淀粉样前体蛋白／β-位淀粉样蛋白代谢途径的影响。方法用硫氢化钠作外源性H2s供体,实验设空白对照组、NaHs50μmol／L组、NaHS100μmol／L组和NariS200μmol／L组,按分组浓度处理PC12细胞24h后,RT-PCR和Western blot法检测细胞内BACE1 mRNA及蛋白表达,并用Western blot法继而检测APP代谢过程中关键蛋白APP、C99、C83表达变化,EusA法检测细胞培养液中AB40和AB42水平。结果NaHS在实验浓度范围内从基因与蛋白两个水平上呈剂量依赖性下调BACE1表达,并下调C99、Ap40和Ap42蛋白表达,上调C83蛋白,各NaHS组分别与对照组比较,差别均有统计学意义（P〈0．05）,而对APP蛋白表达没有影响,各组间比较差别无显著性（P〉0．05）。结论外源性H2s具有通过调节PC12细胞BACE1表达下调APP／Aβ代谢的作用。     

Testing for the usefulness of forecasts

Ashley (Journal of Forecasting 1983; 2 (3): 211–223) proposes a criterion (known as Ashley's index) to judge whether the external macroeconomic variables are well forecast to serve as explanatory variables in forecasting models, which is crucial for policy makers. In this article, we try to extend Ashley's work by providing three testing procedures, including a ratio‐based test, a difference‐based test, and the Bayesian approach. The Bayesian approach has the advantage of allowing the flexibility of adapting all possible information content within a decision‐making environment such as the change of variable's definition due to the evolving system of national accounts. We demonstrate the proposed methods by applying six macroeconomic forecasts in the Survey of Professional Forecasters. Researchers or practitioners can thus formally test whether the external information is helpful. Copyright © 2010 John Wiley & Sons, Ltd.