The main objective of the research was evaluation of the effects of breakfast on attention and short-term memory of 8-years old school children. The study design was randomized controlled trial (RCT) which is done in a cross-over frame. 38 school children divided into two groups randomly and group one was given a standard breakfast (as 20% of RDA for energy in this age) at 8:00 AM. Before and one hour after breakfast pre-and post test of arithmetic and vocabulary tests were performed. At phase 2 of study (after two days), by crossing over the groups new tests were given and results analysed. All individual characteristics such as anthropometric, socio-economic, parents and teacher’s opinion about children activity and school performances, and school average scores were obtained. Results indicate that both attention ability and short-term memory scores of subjects have elevated at least 2 scores (P<0.05) in breakfast group, but in other group memory test failed, although decrement was not significant. Because of similarity of both groups, we suggest that the promotion of scores is due to consumption of breakfast.

The paper investigates the relationship between inflation and inflation uncertainty using the Iranian data over the period 1959:03 – 2005:12. GARCH models are used to examine this relationship. Granger methods are employed to provide statistical evidence for the relationship between average inflation and inflation uncertainty. Threshold GARCH (TGARCH) models are considered to investigate asymmetry in the conditional variance of inflation. The Component GARCH (CGARCH) models are employed to decompose inflation uncertainty into a short-run and a Long-run component by permitting transitory deviations of the conditional volatility around a time-varying trend. This model examines the presence of the Long memory in the conditional variance of inflation.The findings show that increased inflation raises inflation uncertainty confirming the theoretical predictions made by Friedman. Furthermore, the findings of bi-directional causality support the Cukierman and Meltzer model. Using the standard TGARCH models, the presence of asymmetry is found in the conditional variance of annualized inflation, and finally the evidence of Long memory exists in the conditional variance of annualized inflation.

There are two approaches for simulating memory as well as learning in artificial intelligence; the functionalistic approach and the cognitive approach. The necessary condition to put the second approach into account is to provide a model of brain activity that contains a quite good congruence with observational facts such as mistakes and forgotten experiences. Given that human memory has a solid core that includes the components of our identity, our family and our hometown, the major and determinative events of our lives, and the countless repeated and accepted facts of our culture, the more we go to the peripheral spots the data becomes flimsier and more easily exposed to oblivion. It was essential to propose a model in which the topographical differences are quite distinguishable. In our proposed model, we have translated this topographical situation into quantities, which are attributed to the nodes. The result is an edge-weighted graph with mass-based values on the nodes which demonstrates the importance of each atomic proposition, as a truth, for an intelligent being. Furthermore, it dynamically develops and modifies, and in successive phases, it changes the mass of the nodes and weight of the edges depending on gathered inputs from the environment.

In recent years a new type of tradable assets appeared, generically known as cryptocurrencies. Some of them are widespread and global. This paper examines the volatility of cryptocurrencies, with particular attention to their potential Long memory properties. Three different Long-memory methods (R/S analysis, fractional integration and fractional GARCH extensions) are used to analyze it in the case of the four main cryptocurrencies (BitCoin, Ethereum, LiteCoin and Ripple) over the sample period January 2013–,November 2019. Our results are twofold. First, R/S method is prone to detect Long memory whereas the findings of ARFIMA and GARCH type models indicate that in the case of two examined cryptocurrencies (BitCoin and Ethereum), Long memory exist (there is a positive correlation between its past and future values). Such predictability represents evidence of market inefficiency in their markets: trend trading strategies can be used to generate abnormal profits in these markets. Although our findings show that returns of Litecoin and Ripple don’, t have a significant Long memory.