extract: physical mobility and exercise are necessary for people health. for patient with mobility limitation this issue has more importance because these exercises help them to return to normal function more effectively. since the individual planning for each patient is not time effective, so we could plan group exercises and perform properly in orthopedic wards. the important goals of this pl...

This paper asks what low-income countries can expect from growth in terms of happiness. It interprets the set of available international evidence pertaining to the relationship between income growth and subjective well-being. Conforming to the Easterlin paradox, higher income always correlates with higher happiness, except in one case: whether national income growth yields higher well-being is ...

There exists no signiÐcant correlation between the Homestake neutrino data up to run 133 and the monthly sunspot number, according to a test that is based on certain optimality properties for this type of problem. It is argued that priorly reported highly signiÐcant results for segments of the data are due to a statistical fallacy : the usual methods for evaluating the signiÐcance of common tes...

Time series and their methods of analysis are important subjects in statistics. Most of time series have a linear behavior and can be modelled by linear ARIMA models. However, some of realized time series have a nonlinear behavior and for modelling them one needs nonlinear models. For this, many good parametric nonlinear models such as bilinear model, exponential autoregressive model, threshold...

The most important impacts of climate change relate to temperature and precipitation. Precipitation is particularly important, because changes in precipitation patterns may lead to floods or droughts in different areas. Also, precipitation is a major factor in agriculture and in recent years interest has increased in learning about precipitation variability for periods of months to annual and s...

in this paper, we propose a new residual analysis method using fourier series transform into fuzzy time series model for improving the forecasting performance. this hybrid model takes advantage of the high predictable power of fuzzy time series model and fourier series transform to fit the estimated residuals into frequency spectra, select the low-frequency terms, filter out high-frequency term...

In this paper we apply the method of inferred causation for macroeconomic analysis. First we introduce briefly the theory of inferred causation developed by Pearl and Verma (1991). We apply this method to the identification of structural vector autoregression (SVAR) models. In an example of monetary policy analysis we demonstrate how causal information embedded in the data can be used to identi...

Several models have been developed to capture the dynamics of the conditional correlations between time series of financial returns, but few studies have investigated the determinants of the correlation dynamics. A common opinion is that the market volatility is a major determinant of the correlations. We extend some models to capture explicitly the dependence of the correlations on the volatil...