The goal of the article is to definite that sleep with sufficient time and depth is a high quality sleep regardless of whether the sleep time changes or not. The author had conducted an experiment about sleep time. During the experiment, the author changed participants’ main sleep time four times. The result of the survey shows that participants take a few days to adapt to the new condition after their main sleep times have changed. It would not affect the recovery of the energy of the people even though the time point of their main sleep changes.
Since October 1, 2010, a GPS receiver is put into operation at Tokai (Japan) in an experiment on Neutrino Physics (T2K). A significant variation of the altitude was detected from the beginning of March 2011, so that it has made worthwhile to investigate the possibility that such variations could be correlated to the Tohoku earthquake. In order to investigate in details this possibility, we analyzed the GPS data collected during 2011 by GEONet the GPS Earth Observation Network (GEONET). GEONET is the GPS network of Japan and consists of 1240 permanent stations. Preliminary results of the analysis seemed to show ten days before the earthquake, some possible anomalous behaviors of the stations. These anomalous behaviors were particularly relevant for stations of the network near the epicentral area. While co-seismic and post-seismic variations are widely expected, the anomalies recorded about ten days before the earthquake could be seriously considered among short-term precursors of the earthquake. In order to confirm this possibility, more detailed studies have been performed. In particular, GEONET currently makes available only daily solutions of the stations coordinates. On the contrary, it is very important to improve the time resolution just to understand the features of the anomalies till the last hours before the Earthquake. For this reason, we have performed an analysis to evaluate the coordinates and movement on hourly basis so improving the time resolution.
In this paper, time series modelling is examined with a special application to modelling inflation data in Tanzania. In particular the theory of univariate non linear time series analysis is explored and applied to the inflation data spanning from January 1997 to December 2010. Time series models namely, the autoregressive conditional heteroscedastic (ARCH) (with their extensions to the generalized autoregressive conditional heteroscedasticity ARCH (GARCH)) models are fitted to the data. The stages in the model building namely, identification, estimation and checking have been explored and applied to the data. The best fitting model is selected based on how well the model captures the stochastic variation in the data (goodness of fit). The goodness of fit is assessed through the Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC) and minimum standard error (MSE). Based on minimum AIC and BIC values, the best fit GARCH models tend to be GARCH(1,1) and GARCH(1,2). After estimation of the parameters of selected models, a series of diagnostic and forecast accuracy test are performed. Having satisfied with all the model assumptions, GARCH(1,1) model is found to be the best model for forecasting. Based on the selected model, twelve months inflation rates of Tanzania are forecasted in sample period (that is from January 2010 to December 2010). From the results, it is observed that the forecasted series are close to the actual data series.