Background and aim: Sensory deficits after stroke are common with prevalence rates variously reported to be 11% to 85%. In spite of the fact that Sensory deficits can decrease the quality of upper limb's movements and result in impairments in fine motor manipulation of objects, grasp and manipulative skills and finally decrease the patient's quality of life but commonly overlooked in stroke rehabilitation. The aim of this study was to investigate the effects of sensory retraining on upper limb recovery.Materials and methods: In this study we studied 5 chronic stroke patients were participated using a Single-system (A-B) design. During the baseline phase patient' s function and motor impairment of upper limb were measured by Fugl-Meyer and Motoricity Index and their hand dexterity were measured by Box and Block test on three days intervals. When Patients reached to a stable pattern of changes, their treatment program began. In this phase they received sensory retraining intervention for 6 weeks. Based on sensory retraining principles and according to each patient's abilities, intervention began with detection and localization of constant and moving touch and followed by higher discriminatory tasks, such as graphestesia. Then results of evaluations of two phases were analyzed.Results: Four patients showed significant improvements in upper limb function. The data showed significant improvements in motor impairments and hand dexterity in all of them.Conclusion: Regarding of significant recovery of upper limb function, motor impairment and hand dexterity of patients, sensory retraining can be considered as a practical method in rehabilitation of stroke patients. However further research with larger sample size is needed.

In this paper, the optimal solution of a Single reservoir system operation optimization problem is determined using new Meta heuristic algorithm. Generally, various methods have been proposed to solve this problem. These methods are classified as: 1) Linear Programming (LP) 2) Non-Linear Programming (NLP) 3) Dynamic Programming (DP) and 4) Meta heuristic algorithms. Most recently, Meta heuristic algorithms, because of intelligent performance of them, are more useful method to solve optimization problem. Meta heuristic algorithms such as Genetic Algorithm (GA), Honey Bee Mating algorithm (HBMO), Ant Colony Optimization algorithm (ACO) and Particle Swarm Optimization algorithm (PSO) are new classification of optimization methods in which they are usually proposed based on the swarm behavior of social insects and real phenomena. Gravitational search algorithm is one of these newest algorithms that is based on the Newton's law of gravity. In the Gravitational search algorithm, a collection of masses is considered as searcher agents, in which these masses interact with each other based on the Newton's law of gravity and motion. In this paper, the simple and hydropower reservoir operation optimization problems of Dez dam have been solved for 5 and 20 operation periods proposing two different formulations. In the first formulation, the water releases from the reservoir and in the second formulation the reservoir storage volumes are taken as decision variables of the problem. The results are presented and compared with each other and with other available results. Comparison of the result with other existing results indicates better performance of the gravitational search algorithm to solve reservoir operation optimization problem. Furthermore, while both proposed formulations show good performance to solve this problem, the first formulation is shown to produce better results with the same computational effort and to be less sensitive to the randomly generated initial guess presented by the scaled standard.

Background and Objectives: Nowadays, water scarcity is a major challenge for our country, Iran. Therefore, storage and optimal operation of limited resources, including water stored in dams' reservoirs, is one of the issues of interest for researchers in the field of water resources. In this paper, optimization of Single-reservoir operation problem is solved by using one of the newest heuristic algorithms, named Charged system Search algorithm. Generally, this algorithm is based on the electrostatics laws to determine the quantity of resultant force. Kaveh and Talatahari (2010a) proposed this algorithm for the first time and examined its capabilities for solving engineering problems and sample functions. Results showed that the algorithm has good performance. Therefore, its use for solving engineering optimization problems is recommended. However, a review of literature shows that using of this algorithm is very limited in the field of water resources engineering. Materials and Methods: In this paper, the simple and hydropower operation of Dez reservoir, over 5 and 20 years of operation period are solved using the proposed algorithm. In order to solve these problems, two different formulations are proposed considering water release or storage volume as decision variables of the problem in the first and second formulations, respectively and the results are compared to other available methods. Results: Comparison of the results shows the capability of the proposed algorithm, in which the results of first formulation are better than the second one’ s. In other words, the results of first formulation for solving simple operation problem over 5 and 20 years are reduced by 11. 29% and 16. 69% in comparison with the results of second formulation and also the results are improved by 20. 06% and 37. 66% using the first formulation for solving hydropower problem. Furthermore, the results of the proposed algorithm for solving simple operation problem over 5 and 20 years are reduced by 33. 64% and 74. 97% in comparison with the results of Particle Swarm Optimization algorithm and also the results of using the proposed algorithm for solving hydropower problem are improved by 6. 53% and 41. 48%. In addition, the results of the proposed algorithm for solving simple operation problems over 5 and 20 years are reduced by 7. 79% and 35. 59% in comparison with the results of Genetic Algorithm and also the results of using proposed algorithm for solving hydropower problem are improved by 11. 32% and 67. 43%. Conclusion: Investigating these results with the results obtained by using other existing algorithms indicates a better performance of the Charged system Search algorithm for solving the reservoir operation optimization problem. According to these results, the use of this algorithm is recommended for solving other problems in the field of water engineering.

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.

Optimum operating of reservoir dams is one of the most important challenges in the water resources management. In fact, integrated water resource management is necessary for an optimum and efficient operating of these resources. In this research the performance of differential evolution algorithm is compared with the improved harmony search algorithm and nonlinear programming method in optimized operating of the Eleviyan Single reservoir dam on Soofi-chay River which is one of the most important dams in the Urmia lake basin. Considering the vital importance of the ecological flow to prevent the death of Urmia Lake, an optimization model is developed to determine the minimum required ecological flow of the Soofi-chay River, besides to the water flow needs such as the municipal, industrial demands, and also minimizing the water shortage for agricultural targets. The reservoir parameters including reliability, vulnerability and sustainability indicators are used to evaluate the performance of the studied algorithms in an optimal utilization of the reservoir. The results showed that using the differential evolution algorithm gives a higher-performance compared to the other methods employed in this study in efficient operation of the Eleviyan dam system.

system dynamics, a feedback - based object - oriented simulation approach, not only represents complex dynamic systemic systems in a realistic way but also allows the involvement of end users in model development to increase their confidence in modeling process. The increased speed of model development, the possibility of group model development, the effective communication of model results, and the trust developed in the model due to user participation are the main strengths of this approach. The ease of model modification in response to changes in the system and the ability to perform sensitivity analysis make this approach more attractive compared with systems analysis techniques for modeling water management systems. In this study, a system dynamics model was developed for the Zayandehrud basin in central Iran. This model contains river basin, dam reservoir, plains, irrigation systems, and groundwater. Current operation rule is conjunctive use of ground and surface water. Allocation factor for each irrigation system is computed based on the feedback from groundwater storage in its zone. Deficit water is extracted from groundwater. The results show that applying better rules can not only satisfy all demands such as Gaw khuni swamp environmental demand, but it can also prevent groundwater level drawdown in future.

In this study, the objective function of minimizing the total power of the difference between the demand of agriculture and release has been used to solve the problem of optimizing the operation of the Amirkabir reservoir. The purpose of this study was to evaluate the performance of Single-objective versions of algorithms such as multi-verse optimizer and genetic algorithm, as well as the performance of a combination of these two algorithms (MVGA). The results of the study of meta-heuristic algorithms indicated that among the multi-verse, genetic algorithm and MVGA algorithm, the MVGA algorithm similar to GA, has a lower number of iterations with objective function values of 24.29 and 24.22, respectively, better than the MVO algorithm with objective function values 29.14. The results of this study showed that to increase the efficiency of one algorithm, it can be combined with another algorithm. In this study, the combination of a genetic algorithm with multi-world algorithm has improved the performance of the multi-world algorithm by 16.64%.

Occurring periods of drought in the past decade, increasing growth of population, and limiting surface water resources necessitate the proper management of the reservoirs of dams. Operation of reservoirs is influenced by a lot of goals which many of them are incompatible with each other. The inflows to the reservoir and the storage volumes are uncertain which increase complexity in the operation of the reservoir. The main challenge is to find the best situation of the reservoir release and optimize hydro systems. Various optimization methods have been introduced for the operation of the reservoir. But some of these methods have disadvantages that use of them is not possible in all conditions. Bozorg Haddad (2005) used Honey Bees Mating optimization for solving design problems and the Ant Colony algorithm was also used to exploit a four-reservoir system in a discrete space that was able to optimize the problem with greater accuracy and less computing time than the genetic algorithm (Jalali et al. 2007). Mousavi et al. (2017) used the Harmony Search Algorithm for the optimization of water powerhouse storage projects and reported satisfactory results. Harmony Search algorithm was presented by Geem et al. for the first time in 2000. In this research Harmony Search Algorithm (HSA) is evaluated to determine the optimal operation of multi-reservoir systems. Then in order to evaluate the ability of the algorithm to solve real problems, the optimal operation of Dez Dam reservoir in Khuzestan province, Iran, has been considered for the period of 10 years (1990-1992) with 120 months. In the Single-reservoir of Dez Dam, the goal is to provide of agricultural demand of downstream or to determine the optimal monthly release for 10 years operation. The optimum value was obtained by using a linear programming model (Lingo). Lingo model can solve nonlinear problems and provides the global optimum in some cases such as the intended problem where the objective function is convex. Therefore, the solutions obtained from the HSA model were compared with the outcomes of Lingo software. A new heuristic algorithm derived from an artificial phenomenon found in musical performance, namely the process of searching for the better harmony, has been introduced. Music harmony is a combination of sounds considered pleasing from an aesthetic point of view. Harmony in nature is a special relationship between several sound waves that have different frequencies. Musical performances seek the best state (fantastic harmony) determined by aesthetic estimation, as the optimization algorithms seek the best state (global optimum-minimum cost or maximum benefit or efficiency) determined by objective function evaluation. Aesthetic estimation can be found by the set of the sounds played by joined instruments, just as objective function evaluation which is obtained by the set of the values produced by component variables. The sounds for better aesthetic estimation can be improved through several practices; just as the values for better objective function, evaluation can be improved by several iterations. The new algorithm is named Harmony Search (HS) and the steps in the procedure of HS are as follows: Step 1) Initialize a Harmony Memory (HM); Step 2) Improvise a new harmony from HM; Step 3) If the new harmony is better than the least harmony in HM, include the new harmony in HM, and exclude the minimum harmony from HM; Step 4) If stopping criteria are not satisfied, go to Step 2. Harmony Memory Considering Rate (HMCR ranges from 0 to 1. If a uniformly generated value between 0-1 occurs above the current value of the HMCR, then HS finds notes randomly within the possible playable range without considering HM. For improving solutions and escaping local optima, another option may be introduced. This option mimics the pitch adjustment of each instrument for tuning the ensemble. For computation, the pitch adjustment mechanism is devised as shifting to neighboring values within a range of possible values. A Pitch Adjusting Rate (PAR) of 0. 10 means that the algorithm chooses a neighboring value with 10% probability (an upper value with 5% or lower value with 5%. In the present study, first HSA was used for the optimization of a four-reservoir system. The objective function was calculated as308. 2915 and 308. 2900 using Lingo software and HSA, respectively, which had a different of 0. 0005 percent with the global optimum. After the success of HSA in solving the four-reservoir system, a ten-reservoir system was considered. This time, the objective function was calculated as1194. 4 and 1193. 1 by using Lingo software and HSA, respectively, which had a different of 0. 1 percent with the global optimum. In the Single-reservoir problem of Dez Dam, the value of global optimum of the objective function was calculated as 1. 9188 and 1. 944 by using Lingo and HSA, respectively, which had the different of 1. 31% with the global optimum. So, it can be concluded that this algorithm has the ability to solve optimization problems of the real system.