r. moeini

Improving the lifestyle of city residents is conditional on benefiting from high-quality urban infrastructure to satisfy daily demands. The urban water supply network is one of the most basic urban infrastructures, and its optimal design and service are essential during the planning period. Therefore, it is important to determine the actual amount of consumption and predict it for the future. For this purpose, in this research, a method based on artificial intelligence, i.e., genetic programming (GP), as well as Pearson's correlation coefficient data mining method, is proposed. The data mining method is applied here for the database, including daily data on temperature, precipitation, humidity, and the amount of daily water produced in Najafabad city (presenting the total water consumption) from the beginning of 2014 to the end of 2018, and the best set of input data vectors is selected. The selected data are used as input data vectors for the proposed. The obtained results are compared with the results of models based on artificial neural network (ANN). To investigate the performance of the models, R², RMSE, and NSE statistical indices are calculated. A comparison of the results indicates the acceptable performance of the proposed models based on the GP. In other words, the values of RMSE, NSE, R², and MAPE statistical indices for training data in the best GP model are equal to 3262.59 MCM, 0.80, 0.80, and 5.38%, respectively, and for test data equal to 3507.68 MCM, 0.78, 0.78, and 6.67%.

In this paper, a hybrid model is proposed to solve a reservoir operation optimization problem considering uncertain inflow conditions. In this model, the artificial neural network (ANN) and improved particle swarm optimization (IPSO) algorithm are used for inflow forecasting and reservoir operation optimization problem, respectively. The proposed IPSO is developed after applying some useful modifications to the original form of particle swarm optimization (PSO) algorithm. The modifications are proposed in order to reposition the infeasible particles. Two different conditions are considered in order to show the effect of inflow forecasting on reservoir operation optimization problem using ANN. It is worth noting that the ANN model is a powerful data driven model that can be used for real time inflow forecasting. In this research, in the first case, the actual measured inflow values are considered as input data to solve reservoir operation optimization problem. In the second case, the ANN model is used to forecast inflows while considering the effects of previous months inflows on the target month inflow. After determining the inflow, the reservoir operation optimization problem is solved using the forecasted inflows. In addition, in the proposed hybrid model, two different formulations are suggested to solve the optimization problem considering water release and reservoir storage volume as decision variables of the problem. The simple and the hydropower operation problems of Dez dam reservoir are solved for forecasted (5 year) time period considering all formulations and cases and the results are presented and compared with other available results. The results indicate the ability of ANN model to forecast the inflow of the Dez dam with acceptable accuracy. In addition, the improved particle swarm optimization algorithm shows to be an effective algorithm to solve reservoir operation optimization problem in which the results of first formulation is better than the second one.

In this research, artificial honey bee colony algorithm, is used to solve single reservoir operation optimization problem. For this purpose, improved artificial bee colony algorithm is proposed using some modification in the basic algorithm. The simple and hydropower operation problems of Dez reservoir over 5- and 20-year time periods are solved using the proposed algorithm and the outputs are compared with the other available research results. In order to solve these problems, two different formulations are proposed in which the water release and storage volumes are considered as decision variables in the first and second formulations, respectively. If the first formulation of the improved artificial bee colony algorithm is used to solve the simple reservoir operation over 5 and 20 years, the objective function values are improved %9.94 and %55.266 than basic artificial bee colony algorithm, respectively. If the second formulation is used to solve simple reservoir operation over 5 and 20 years, the objective function values are improved %14.63 and %7.18 than basic artificial bee colony algorithm, respectively. In addition, if the first formulation of improved artificial bee colony algorithm is used to solve hydropower reservoir operation over 5 and 20 years, the objective function values are improved %7.76 and %26.47 than basic artificial bee colony algorithm, respectively. If the second formulation is used to solve hydropower reservoir operation over 5 and 20 years, the objective function values are improved %3.79 and %25.49 than basic artificial bee colony algorithm, respectively. Finally, comparison of the results shows that using the improved artificial bee colony algorithm leads to better results with low computational costs.

In Persian medicine (PM), Achillea millefolium L. has a wide range of applications in the treatment of several diseases.

The aim of this study is to introduce the viewpoints of PM scholars about this herb in the field of female diseases and to compare them with the recent studies.

In this review study, therapeutic applications of Achillea millefolium in the field of female diseases were collected from credible Persian medical sources from 4th to 14th lunar centuries then current scientific databases such as PubMed and Google scholar were searched by using key world Achillea millefolium for collecting the findings of clinical, experimental and in vitro studies about this herb in the field of obstetrics and gynecology to compared to the PM scholars' viewpoints.

In PM, Achillea millefolium is recommended for treatment various female reproductive diseases such as menstrual disorders, facilitating labor, reducing adhesions of cervix, controlling uterine infections. In recent studies, therapeutic effects of this herb on ovarian cyst, menstrual cycle regulation and dysmenorrhea have been reported. Also, in several studies, the antibacterial and antifungal effects of this plant on controlling infections have been investigated; as well the extract of this plant has been effective in inhibiting the proliferation of ovarian adenocarcinoma cells.

Considering the extensive uses of Achillea millefolium reflected in PM and recent studies, it seems that this plant can be used in many fields of female diseases to provide ideas for research and therapy.

In this paper, the optimal solution of a single reservoir system operation optimization problem is determined using new Meta heuristic algorithm. Generally, various methods have been proposed to solve this problem. These methods are classified as: 1) Linear Programming (LP) 2) Non-Linear Programming (NLP) 3) Dynamic Programming (DP) and 4) Meta heuristic algorithms. Most recently, Meta heuristic algorithms, because of intelligent performance of them, are more useful method to solve optimization problem. Meta heuristic algorithms such as Genetic Algorithm (GA), Honey Bee Mating algorithm (HBMO), Ant Colony Optimization algorithm (ACO) and Particle Swarm Optimization algorithm (PSO) are new classification of optimization methods in which they are usually proposed based on the swarm behavior of social insects and real phenomena. Gravitational search algorithm is one of these newest algorithms that is based on the Newton's law of gravity. In the Gravitational search algorithm, a collection of masses is considered as searcher agents, in which these masses interact with each other based on the Newton's law of gravity and motion. In this paper, the simple and hydropower reservoir operation optimization problems of Dez dam have been solved for 5 and 20 operation periods proposing two different formulations. In the first formulation, the water releases from the reservoir and in the second formulation the reservoir storage volumes are taken as decision variables of the problem. The results are presented and compared with each other and with other available results. Comparison of the result with other existing results indicates better
performance of the gravitational search algorithm to solve reservoir operation optimization problem. Furthermore, while both proposed formulations show good performance to solve this problem, the first formulation is shown to produce better results with the same computational effort and to be less sensitive to the randomly generated initial guess presented by the scaled standard.

In recent years, hydraulic models are widely used to simulate hydraulic performance of water distribution systems. They play a significant role in evaluation and operational management of these systems. Calibration is necessary for improvement of model function and reduction of the uncertainties during different consumption scenarios in the system. At the moment just a few commercial models are capable of calibration calculation besides the hydraulic performance evaluation. However, the popular and freely available model, EPANET can not be calibrated. In this paper, an optimization procedure is proposed using Genetic Algorithm (GA) for calibration of the hydraulic simulator. For this problem, the pipe roughness (Hazen Williams) coefficients, nodal demands and pipe diameters are considered as decision variables of the optimization problem. The procedure is implemented for four different consumption scenarios: normal, minimum, maximum and fire flow. Finally, to evaluate the proposed methodology, a test network at different consumption scenarios is calibrated and the results are compared. Comparison of the results shows that the best result with minimum error has been obtained when the fire consumption with considering both variables of Hazen-William coefficient and nodal demand was selected.