Orthogonal Frequency Division Multiplexing (OFDM) is a useful technology in wireless communications that provides high-rate data transmission in multipath fading channels. The advantages of OFDM systems are the high spectral efficiency and strong resistance to frequency selective fading. In OFDM systems, a large number of sub-carriers are used to modulate the symbols causing the time-domain OFDM signal to have a large dynamic range, or a high p(EA)k-to-average power ratio (PAPR). When the signals are applied to a nonlin(EA)r power amplifier, the OFDM systems’ performance is degraded by the high PAPR. In recent y(EA)rs, several works have been done to reduce the PAPR of OFDM systems. One of the most well-known methods is a partial transmit sequence (PTS). Regardless of the PTS advantages, it suffers from a high computational complexity. Because it requires an exhaustive s(EA)rch over all possible combinations of phase factors. The computational complexity of the PTS incr(EA)ses with incr(EA)sing the number of phase factors and sub-blocks. There are several approaches to overcome the computation complexity issue of the PTS technique. The majority of these methods mainly employed swarm intelligence and evolutionary optimization Algorithms to resolve the PTS shortcoming. These methods report encouraging results, however, their performance is far from the id(EA)l state. This highlights that improving the performance of PTS is an open problem and there is room for more improvement. As an element of res(EA)rch, we propose an optimization approach based on the Election Algorithm ((EA)) to overcome the computational complexity of the PTS technique. To r(EA)lize this goal, we improve the (EA) Algorithm by introducing a new version of positive advertisements operator. The new operator efficiently improves the s(EA)rch capability of the (EA) through balancing between the exploration and exploitation power of the Algorithm. The proposed (EA) based PTS ((EA)-PTS) approach, by s(EA)rching the optimal phase factors, imposes less computational complexity on the system and reduces the PAPR to an acceptable level. The proposed method is compared with the optimal PTS (O-PTS), genetic Algorithm-based PTS (GA-PTS) and imperialism competition Algorithm based PTS (ICA-PTS) techniques. Simulation results show that the proposed (EA)-PTS has better performance in simultaneously reducing the PAPR and computational complexity.

The aim of the present study investigated the psychometric properties of Esoteric Analogies test in college students. The sample of this study was 250 female graduate students who were selected of Shahid Chamran University of Ahvaz by multi-stage cluster sampling method. The instruments of this study consisted of Esoteric Analogies ((EA)) test, Memory & R(EA)soning Competence Inventory (MARCI) and General Self-efficacy Scale (GSE). The results of the present study indicate Esoteric Analogies test had good internal consistency. Also, results rev(EA)led that there are a significant positive correlation between Esoteric Analogies test with Memory & R(EA)soning Competence Inventory, R(EA)soning Competence subscale and General Self-efficacy Scale. In addition, confirmatory factor analysis showed Esoteric Analogies test fitted the data at an acceptable level (AGFI=0/81) and could be used in females sample.

It is very important to predicte and modifed the groundwater levels in the future period and the possibility of water resources planning and management to improve aquifer conditions. In this study, for the first time, used Election Algorithm to predicted groundwater level in the Miandoab plain. (EA) Algorithm is a rep(EA)t Algorithm inspired by presidential Election and working with a set of khown solutions as a population. Also the results were compared of ANN and Genetic Algorithm. Water table level data such as temperature, precipitation, humidity, evaporation and water surface data for the 2005-2015 period was used as our data in this study. Based on the calculations performed and the results predicted from the statistical parameters, the Election Algorithm has a significant ability in the groundwater level with RMSE, R2 and NSE, 0. 027, 0. 93 and 0. 74, respectively, in comparison to ANN method and GA Algorithm and provides reliable results.

Particle swarm optimization has been a popular and common met heuristic Algorithm from its genesis time. However, some problems such as premature convergence, w(EA)k exploration ability and gr(EA)t number of iterations have been accompanied with the nature of this Algorithm. Therefore, in this paper we proposed a novel classification for particles to organize them in a different way. This new method which is inspired from president Election is called President Election Particle Swarm Optimization (PEPSO). This Algorithm is trying to choose useful particles and omit functionless ones at initial steps of Algorithm besides considering the effects of all generated particles to get a directed and fast convergence. Some preparations are also done to escape from premature convergence. To validate the applicability of our proposed PEPSO, it is compared with the other met heuristic Algorithm including GAPSO, Logistic PSO, Tent PSO, and PSO to estimate the parameters of the controller for a hybrid power system. Results verify that PEPSO has a better r(EA)ction in worst conditions in finding parameters of the controller.

Enki, later known as (EA) in Babylonian-Akkadian mythology, is a Sumerian word for the god of fresh waters in Mesopotamian mythology. Given the effective and functional role of water in the livelihood of ancient people and its symbolization in mythology, Enki seems to have had particular importance and presence in Mesopotamian mythology. He is characterized with various functions as vivification, fertility, purgation, cure, sorcery, wisdom and intelligence. They are all particularly in line with the cosmic order. Apparently, the gods of ancient Mesopotamia can often be considered as "the gods of order" or at l(EA)st "the gods affected by natural order". Compared to other gods, Enki plays an outstanding role in establishment and retention of this natural order of things, for which he exhibits a dual image and acts for and against the man. Moreover, Enki's immanence in various myths is justified by the significance of water in all aspects of human life such as agriculture. Enki's monologues and his disclosure of gods' commands and secrets to man can be considered as the (EA)rly forms of Revelation. Using a descriptive-analytical approach, the present study attempts to explain the philosophy of Enki's behavioral contradiction and his dual image in Mesopotamian mythology.

So far, several res(EA)rchers have conducted many studies on the effective parameters in the design of river br(EA)kwaters, which are mostly laboratory-based and are used for limited conditions. Therefore, the aim of the present studywas to optimal design of structure and to present analytical results of Zanjanrood river br(EA)kwaters (in terms of length and distance between two consecutive br(EA)kwaters) using two optimization meta-heuristic Algorithms including the Gray Wolf Algorithm (GWO) and the Election Algorithm ((EA)). The results were compared with artificial neural network (ANN) method. The data used were randomly divided into two parts: 75% for calibration and 25% for test. The performance of the proposed methods was evaluated using the statistical indicators of coefficient of determination (R2), root m(EA)n square error (RMSE) and m(EA)n absolute error (MAE). The optimal length of the br(EA)kwaters according to the results of GWO and (EA) Algorithms was 19.26 and 18.12 m, respectively. Moreover, the optimal distance between two consecutive br(EA)kwaters in the optimal state was calculated to be 52.56 m. On average, according to the results of the optimization, an incr(EA)se of 28.4 and 35% in length and distance between two consecutive watersheds in Zanjanrood River should be done to be within the recommended design criteria.  In comparison with two methods of (EA) Algorithm and artificial neural network (ANN), based on statistical indicators, the results of GWO Algorithm with values ​​of R2 = 0.96, RMSE 0.022 and MAE = 0.016 has a higher efficiency.

Optimizing the crop cultivation pattern, in order to reduce water consumption, in arid and semi-arid regions such as Iran, due to water scarcity and food intake, is an essential solution for food intakes needs. Optimizing the crop cultivation pattern, in order to reduce water consumption, in arid and semi-arid regions such as Iran, due to water scarcity and food intake, is an essential solution for food intakes needs. In this study, new methods based on the Election Algorithms ((EA)) and gray wolf optimizer (GWO) Algorithms were used to determine the optimal cultivation pattern in Moghan plain during the statistical y(EA)rs of 2007-2016. The objective function in the agricultural sector was based on (EA)ch product and its yield, net from (EA)ch product and the cultivar. Then, maximization of the objective function was performed using GWO and (EA) Algorithm. The results of using GWO Algorithm in determining the optimal crop pattern in Moghan plain showed that using economic policies such as changing the cultivar pattern, we can obtain a better result compared to (EA) Algorithm in the agricultural sector. In general, the results of GWO Algorithm showed that in the Moghan plain with 0. 9, 140 billion rials, that is, about 42 % will have economic growth. In sum, the results showed that GWO Algorithm with high values of statistical criteria (R2=0. 96, RMSE=0. 022 and NSE=0. 75) has a higher efficiency in optimizing the crop cultivation pattern of Moghan plain, which can be applied to the correct planning for other cultivation ar(EA)s to be employed.