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Background: A design storm is a theoretical storm event based on rainfall intensities associated with frequency of occurrence and having a set duration. Estimating design storm via rainfall intensity–duration–frequency (IDF) curves is important for hydrological planning of urban areas.
Material and Methods: The impact of changes in rainfall intensity–duration–frequency (IDF) curves on flood properties in an urban area of Zanjan city was investigated, using Storm Water Management Model (SWMM). For the IDF curve generation, Sherman and Ghahreman-Abkhezr methods were compared.
Results: According to results, the estimated rainfall depth and, consequently the peak runoff rate for different return periods had decreased in the recent years, except for 2-year return period. Decrease in peak runoff rate was 30, 39, 41 and 42 percent for 5-10-20 and 50-year return periods, respectively. Based on the results, for peak runoff evaluated in 50-year return period using Sherman and Ghahreman-Abkhezr hyetograph, percent of flood that occurred before the peak runoff were 27 and 22 percent, respectively.
Discussion and Conclusion: Design rainfall hyetograph showed that Sherman method gave larger rainfall intensity compared to Ghahreman-Abkhezr method. Estimated peak and total runoff volume follow trend of rainfall intensity. As Ghahreman-Abkhezr method use longer and newer rainfall data for creating IDF curves, we can conclude that climate change cause change in rainfall characteristics. The runoff modeling show that main urban drainage system had enough transfer capacity against the flood condition, but survey information indicated several inundations in some flat areas, curbs and gutters. Inappropriate design and obstruction of the runoff paths via urban garbage and sediments are some parameters that could lead to such local inundation.

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Aims: Increasing the aquatic consumption, developing aquaculture, and the need for aquatic food production will make unclear the availability to fish oil in the future. The aim of this study was to investigate the effect of replacement of fish oil by grape seed oil on growth indices and protease enzymes activity in Rainbow Trout.
Materials & Methods: This study was conducted on 450 Rainbow Trout during a 60-day period. In this study, control diet (A) containing 100% fish oil and 25% (B), 50% (C), 75% (D), and 100% (E) grape seed oil were used instead of fish oil. The data were analyzed by Graph pad prism and SPSS 20 software, using one-way ANOVA test.
Findings: The highest final weight and weight gain was in diet C and the lowest was in D, having a significant difference. Specific Growth Rate (SGR) and Protein Efficiency Ratio (PER) had no significant difference. The highest and lowest feed conversion ratio (FCR) was observed in diets E and C, respectively, with a significant difference. The highest fat efficiency was in diet C. The highest feed efficiency was in diets C and D and the lowest was in E, and the diets were not significantly different. The most activity of pepsin and trypsin was observed in pyloric additions in diet C and in intestine in C and D. The optimal amount of fish oil replacement was satisfied by grape seed oil 50% (diet C).
Conclusion: The diet containing 50% fish oil and 50% grape seed oil is effective in improving the growth indices and activity of pepsin and trypsin enzymes in Rainbow Trout.

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Dynamic model identification and state variables estimation from the corrupted measurement data have been attracted much research efforts during the recent years. In this way, Kalman and H-infinity filters have been increasingly used to estimate the parameters individually. In this paper, a mixed kalman-H_∞ filter is designed in an innovative approach using a multi-objective optimization method. It is desired to simultaneously employ the advantages of both filters to minimize both the root-mean squared errors and the upper bounds limit of estimation errors associated with Kalman and H-infinity filters, respectively. Some Pareto optimum design points are presented for two case studies from which trade-off optimum design points can be simply selected.

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These days overhead crane is widely used in different industries such as automobile companies, harbor, navigation and also transportation of tools in storerooms. Most of models which is done through industrial dynamic systems include some vitiated parameter with noise and disturbance which overhead crane model is not also an exception. Disturbance in system can be due to its model or measuring tool. Kalman filter is a practical method in order to recognize the model and also filtration of disordered data. By the note of that overhead crane is a nonlinear model, asymmetric sigma-point Kalman filter improved by genetic algorithm (GA-ASKF) is intended to estimate system parameters. One of common ways in controlling overhead crane parameters is using controlling force, Bang-Bang. By the way, function of Bang-Bang controller depends on controlling force switched times. In this paper, beside using this controller, its switched times is found by using genetic algorithm for noisy system. The design aim is to achieve the target point in minimum time with minimum error. Also by considering Bang-Bang controller entrance part, the article is compared situation of the system in different mass relativeness. Simulation results shows improved performance of the GA-ASKF algorithm to determine the switching time of controller and also achieving the target point in minimum time.

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In this paper, an intelligent powerful control scheme is presented for a lower-limb rehabilitation robot. The focus of this study is on maintaining patient safety, focusing on the concept of assist as needed to improve the efficacy of robotic rehabilitation exercises and intelligent controller behavior. The proposed control scheme is consists of force field control and fuzzy logic control. Gravity compensation, friction forces, and interaction torque have been considered to the dynamic model of the system. The force field control method creates a virtual wall along the desired trajectory in the sagittal plane that can guide the patient's gait. Force field control parameters are selected using the fuzzy logic control rules o improve the concept of assist as needed for the rehabilitation robot in order to make a freedom of action for the patient. Therefore, the fuzzy logic control algorithm was proposed to improve the behavioral quality of the rehabilitation robot depending on the patient's ability in the gait process. In this regard, the proposed control scheme has been implemented for the lower-limb rehabilitation robot system. Simulation results show the efficiency of the proposed controller to improve the quality of motorized gait training.