The appropriate investment and decision making about taking of correct long and short position needs proved strategies. In this research, pairs trading have been studied and a new non-parametric approach proposed based on Renko and Kagi construction which are two Japanese charting indicators. The proposed approach exploits information about the Variability of spread process and a constant long-run mean dose not find for spread process but trade towards it like other methods of pairs trading and the only needed assumption is remaining constant of Statistical properties of the spread process volatility.In this research, profitability of proposed method have been proved theoretically mean-reverting process with stochastic volatility, then pairs trading have been performed based on this approach on selective data of Tehran stock exchange. The results of implementation show that used strategy obtain 52.91% per return in stock pair of KHTRAC and KHTOGHA, 33.645 per return in stock pair of KHMOHAREKEH and KHODRO for appropriate selection of H.

Observations show that the global surface air temperature has risen by 0.6 during th 20th century. Global warming causes extreme events and bad weather in the near term. The loss of seasonal distinctions, larger fluctuation in intra-day temperature and large fluctuations in precipitations, and increasing of frequency and magnitude of the extreme events may be due to global warming. World Meteorological Organization (WMO), Intergovernmental Panel of Climate Change (IPCC) and the other responsible groups began to develop guidelines related to detection of climate change by applying Statistical methods.The purpose of this paper is to present a few simple and complex Statistical models staking into account the effects of climatic change. In addition, the result of a few related projects which evaluated climate Variability by Statistical methods in the country has been presented. It has been shown, there are a number of changes including trend, jump, unusual fluctuation, changes in distribution of elements and also changes in extreme indices including heat waves, diurnal temperature range, growing length season, ext.

Climatic and regional conditions have provided a good environment for agricultural development, especially in the Caspian Sea provinces. The purpose of this study is to identify the effect of climate variables on spatial analysis of grain yield and Grain cultivation capability in the southern coast of the Caspian Sea. Then, using important functions of spatial statistics including moron index and hot spots analysis of spatial patterns of rice, wheat and barley yields on the southern coast of the Caspian Sea during the Statistical period of 2000-2016 were investigated. According to the results of Moran index analysis, it was determined that the yield of rice, wheat and barley were positive and close to one in the study period, indicating the clustering of the spatial distribution of the yield of the products under study. Also, based on the results of Local Indicators of Spatial Association and the analysis of hot spots, high value values or positive spatial correlation of rice yield were mainly found in Mazandaran province and high values or positive spatial correlation of wheat yield in southern parts of Golestan province and limited parts From Mazandaran Province. For yield of barley, the most areas with high positive spontaneous autocorrelation (hot spots) are mainly confined to Babolsar and Joybar cities of Mazandaran province at confidence level of 99%, Babol in 95% confidence level and Aliabad city Located in Golestan province at a confidence level of 90%. Low value of wheat and barley yields were found in the eastern and western parts of Gilan province at 99, 95 and 90 percent confidence levels.

Nano scale Statistical Variability which arises from discreteness of charge and granularity of matter has become one of major concerns in digital design particularly in sub-50nm technology nodes. Device intrinsic parameters such as the threshold voltage and drive current will be influenced by random dopants, line edge roughness and gate grain granularity which in turn results in variation of SRAM cell performance. Therefore, providing an accurate Statistical model is one of the key issues among SRAM design community. Since both 6T and 8T SRAMs are widely used in the industry as standard cells, this article analyzes sensitivity of static noise margin (SNM) in response to Statistical variations in 6- and 8-transistor cells. The results show that though 8T cells need more transistors and thus consume more area on the wafer compared with 6T cells, they are more stable and thus are better candidates for Variability aware design in future technology nodes.

Seasonal variation in water quality of Anchar Lake has been evaluated, using two multivariate Statistical techniques, namely Principal Component Analysis (PCA) and Cluster Analysis (CA). Water quality data, collected during four seasons, have been analyzed for 13 parameters and ANOVA has shown that pH (F3=10.86, P<0.05), temperature (F3=65, P<0.05), electrical conductivity (F3=32.72, P<0.05), Calcium (F3=36.84, P<0.05), Magnesium (F3=16.52, P<0.05), nitrate-nitrogen (F3=48.06, P<0.05), ammonical nitrogen (F3=198.75, P<0.05), and dissolved oxygen (F3=4.96, P<0.05) varied by season, whereas the substantial variations of sodium (F2=7.18, P<0.05), phosphate-phosphorous (F2=25.31, P<0.05), biological oxygen demand (F2=11.02, P<0.05), and chemical oxygen demand (F2=37.73, P<0.05) were based on different sites. CA has grouped the three sampling sites throughout the four seasons into three clusters of similar water quality as relatively Less-Polluted (LP), Medium-Polluted (MP), and Highly-Polluted (HP). In addition, PCA has been applied on the extract to recognize the factors, responsible for water quality variations in four seasons of the year, resulting in four principal components for winter, summer, and autumn, five ones for spring, accounting for 79.58%, 89.07%, 83.34%, and 93.13% of total variance respectively. Thus the factors, responsible for water quality variation, are mainly related to domestic wastewaters, seasonal variation, agricultural runoff, and catchment geology. These results will help decision-makers better understand the influence of various factors on water quality and manage pollution/eutrophication adaptively in Anchar Lake.

In this article, an attempt has been made to estimate the amount of sound transmission loss in a flat oval channel by applying the approach of Statistical energy analysis. Correct estimation of sound transmission loss in an air conditioning channel is of great importance due to the harmful effects of noise pollution in the environment on human health. Simulation with the Statistical energy analysis method is a powerful approach to estimate sound and vibration in problems in which we deal with complex and multi-part systems; is considered. In this method, first, a system is divided into several subsystems, and then by writing a matrix equation that includes the energy exchanges between subsystems and energy loss coefficients; It is investigated from the perspective of vibration and sound estimation.On average, the model presented in this research is able to estimate the sound transmission loss in different dimensions of the air conditioning channels according to the experimental results in the accuracy range of ± 2.5 dB. Considering that it seems that the results obtained from modeling with this method are in good agreement with the experimental data; The results of this research can be used as an efficient approach to estimate noise in oval shaped channels stretched in different lengths.

With shrinking transistor dimensions into nano meter scale, electrical parameters of transistors become more sensitive against Statistical or random variations. Moreover, accurate estimation of these variations using “atomistic simulators” is time consuming and not a cost effective approach. In this paper for the first time, analytical models have been used to study the impacts of Statistical Variability of fabrication process on propagation delay time in a 35 nm CMOS NAND gate. With selecting appropriate set from analytical model’s parameters, the impact of Statistical Variability on the propagation delay time have been modeled and extended. Moreover, target analytical model has been benchmarked against Statistical Variability of fabrication process. The results obtained from extension of this model have been compared with the accurate atomistic simulations. It is observed that by applying different sets of parameters the maximum error of propagation delay time reaches to 8.7% against accurate atomistic simulations but by applying our proposed approach, Standard Deviation (SD) error of propagation delay is estimated to 2.4%. Also the SD error of propagation delay reaches to 9.9% when normal regenerated parameters have been used. Eventually using proposed algorithm which encompasses regenerated Gaussian parameters while taking the correlation factor into account, the SD error decreases to 1.6%.