Structural Changes of Water Molecules Upon the Reduction of Quinones in The Reaction Center from Rhodobactery Sphaeroides

1 Results The photosynthetic bacterial reaction center (RC) is a membrane protein complex.The RC is composed of three protein subunits and redox components such as bacteriochlorophylls, bacteriopheophytins,and quinones.The RC performs the photochemical electron transfer from the bacteriochlorophyll dimer through a series of electron donor and acceptor molecules to a secondary quinone,QB.QB accepts electrons from a primary quinone,QA,in two sequential electron transfer reactions.The second electron trans...     

Forecasting volatility by means of threshold models

The aim of this paper is to compare the forecasting performance of competing threshold models, in order to capture the asymmetric effect in the volatility. We focus on examining the relative out‐of‐sample forecasting ability of the SETAR‐Threshold GARCH (SETAR‐TGARCH) and the SETAR‐Threshold Stochastic Volatility (SETAR‐THSV) models compared to the GARCH model and Stochastic Volatility (SV) model. However, the main problem in evaluating the predictive ability of volatility models is that the ‘true’ underlying volatility process is not observable and thus a proxy must be defined for the unobservable volatility. For the class of nonlinear state space models (SETAR‐THSV and SV), a modified version of the SIR algorithm has been used to estimate the unknown parameters. The forecasting performance of competing models has been compared for two return time series: IBEX 35 and S&P 500. We explore whether the increase in the complexity of the model implies that its forecasting ability improves. Copyright © 2007 John Wiley & Sons, Ltd.     

葡萄酒月度价格指标的季节ARIMA时间序列分析

以时间序列分析为工具,研究了法国葡萄酒价格月度指标,使用基于周期图的Fisher检验证实该数据指标具有潜在周期,最后发现(1,0,0)×(1,0,0)12模型能较好地拟合数据.在此基础上给出相应的预测.     

Perceptual learning of dot pattern

Dot pattern is a type of pattern defined by specific spatial relationship among some dots. As a compensation for concrete and meaningful visual stimuli, meaningless dot pattern can be used as stimuli in learning tasks. We performed an experiment for exploring the perceptual learning process of dot pattern against random-dot background. Participants were required to learn two types of dot patterns (9-dot and 11-dot). They were assigned to two groups: participants in Group 1 learned 9-dot pattern first and 11-dot pattern later, while those in Group 2 learned 11-dot pattern first and 9-dot pattern later. The results showed that participants could acquire the spatial relationship of dot pattern through perceptual learning in relatively short learning time. In comparison with 9-dot pattern, learning time of 11-dot pattern was slightly longer and its accuracy rate lower, but there was significant positive transfer effect from 11-dot pattern learning to 9-dot learning.