土壤风蚀模型研究进展

土壤风蚀模型是世界土壤风力侵蚀学科的前沿领域和土壤风力侵蚀过程定量研究的有效手段.根据土壤风蚀模型的空间尺度,按田阃尺度模型、区域尺度模型和大陆尺度模型三个层次对国外较有影响的土壤风蚀模型进行了总结,对国内风蚀模型的进展进行了综述,并对未来我国风蚀模型研究的发展趋势进行了讨论.     

Multivariate Forecasting with BVARs and DSGE Models

In this paper I assess the ability of Bayesian vector autoregressions (BVARs) and dynamic stochastic general equilibrium (DSGE) models of different size to forecast comovements of major macroeconomic series in the euro area. Both approaches are compared to unrestricted VARs in terms of multivariate point and density forecast accuracy measures as well as event probabilities. The evidence suggests that BVARs and DSGE models produce accurate multivariate forecasts even for larger datasets. I also detect that BVARs are well calibrated for most events, while DSGE models are poorly calibrated for some. In sum, I conclude that both are useful tools to achieve parameter dimension reduction. Copyright © 2016 John Wiley & Sons, Ltd.     

土壤系统分类中的基层分类研究进展

本文介绍了ST(soil taxonomy)制的土壤基层分类研究的发展与现状，并在此基础上，对我国土壤基层分类的研究方法与重点作较为深入的探讨。     

The Volatility and Density Prediction Performance of Alternative GARCH Models

This study compares the volatility and density prediction performance of alternative GARCH models with different conditional distribution specifications. The conditional residuals are specified as normal, skewedHyphen;t or compound Poisson (jump) distribution based upon a nonlinear and asymmetric GARCH (NGARCH) model framework. The empirical results for the S&P 500 and FTSE 100 index returns suggest that the jump model outperforms all other models in terms of both volatility forecasting and density prediction. Nevertheless, the superiority of the nonHyphen;normal models is not always significant and diminished during the sample period on those occasions when volatility experiences an obvious structural change. Copyright © 2011 John Wiley & Sons, Ltd.     

土壤退化研究的进展及展望

本文论述了土壤退化的涵义、现状、研究进展,并对今后的发展趋势作了展望。     

Fruiting ofCoprinus congregatus. Interacting effect of radiant flux density and temperature

Summary Illumination during the photoinhibited phase of the development ofCoprinus congregatus reduced fruiting. This inhibitory effect of light was dependent on the radiant flux density and on temperature. Radiant fluxes far lower than the flux provided by moonlight elicited photoresponses.     

Mechanisms and consequences of intestinal dysbiosis

The composition of the gut microbiota is in constant flow under the influence of factors such as the diet, ingested drugs, the intestinal mucosa, the immune system, and the microbiota itself. Natural variations in the gut microbiota can deteriorate to a state of dysbiosis when stress conditions rapidly decrease microbial diversity and promote the expansion of specific bacterial taxa. The mechanisms underlying intestinal dysbiosis often remain unclear given that combinations of natural variations and stress factors mediate cascades of destabilizing events. Oxidative stress, bacteriophages induction and the secretion of bacterial toxins can trigger rapid shifts among intestinal microbial groups thereby yielding dysbiosis. A multitude of diseases including inflammatory bowel diseases but also metabolic disorders such as obesity and diabetes type II are associated with intestinal dysbiosis. The characterization of the changes leading to intestinal dysbiosis and the identification of the microbial taxa contributing to pathological effects are essential prerequisites to better understand the impact of the microbiota on health and disease.     

学习和记忆的神经基础

学习和记忆是脑是基本的高级功能,是我们每时每刻都在经历着的事情。学习和记忆过程中脑内发生了什么？在过去的２０多年中,有关学习和记忆的神经机制研究取得了重大进展。     

Estimating and Forecasting Large Panels of Volatilities with Approximate Dynamic Factor Models

We introduce an approximate dynamic factor model for modeling and forecasting large panels of realized volatilities. Since the model is estimated by means of principal components and low‐dimensional maximum likelihood, it does not suffer from the curse of dimensionality. We apply the model to a panel of 90 daily realized volatilities pertaining to S&P 100 from January 2001 to December 2008. Results show that our model is able to capture the stylized facts of panels of volatilities (comovements, clustering, long memory, dynamic volatility, skewness and heavy tails), and that it performs fairly well in forecasting, in particular in periods of turmoil, in which it outperforms standard univariate benchmarks. Copyright © 2015 John Wiley & Sons, Ltd.