In this paper, OPTIMAL bridge management system MODELS have been presented. These optimization MODELS are capable of allocating limited resources to the bridge preservation schemes in order to establish the OPTIMAL time of completing the activities. Bridge-based activities are divided into two main groups: repair projects, and maintenance activities and both MODELS are presented in this paper. Particular attention has been made to optimize the management of the two system activities. The dynamic programming approach was utilized to formulate and analyze the two MODELS. The developed MODELS are found to be more accurate and faster than the previous ones.

We present a new method for representation of multiresolution graphic MODELS. The considered MODELS are curves, images and surfaces. The method is based on a discrete approach to the theory of wavelets. Multiresolution has various applications to computer graphics and pattern recognition, such as image compression and smoothing, graphics model processing, and scanning transformations. We concentrate on the Chaikin"s subdivision method for generating smooth MODELS. This method makes use of an OPTIMAL projection of a multiresolution model onto a space having a lower multiresolution level. The optimization is made locally and discretely and results in biorthogonal wavelets. We demonstrate the effectiveness of the decomposition and reconstruction processes of our method through various examples.

An attempt has been made in this paper, to develop MODELS for bridge repair and maintenance activities in which the assignment of resources has taken place in an optimized procedure, and the resulted benefits in the roads network are maximized. One of the major characteristics of the developed MODELS is the ability of multi-period optimization. By integration of internal and external MODELS, a new prioritization approach has been developed. Using the new MODELS, the loss of benefit in the whole bridge network would be minimized. The optimization network MODELS are able, not only to determine the appropriate time for implementation of repair and maintenance activities, but also for allocation of limited resources to the bridge projects.

In this paper, a novel risk-based, two-objective (technical and economical) OPTIMAL reactive power dispatch method in a wind-integrated power system is proposed which is more consistent with operational criteria.  The technical objective includes the minimization of the new voltage instability risk index. The economical objective includes cost minimization of reactive power generation and active power loss. The proposed voltage instability risk employs a hybrid possibilistic (Delphi-Fuzzy)-probabilistic approach that takes into consideration the operator’s experience, the wind speed and demand forecast uncertainties when quantifying the risk index. The decision variables are the reactive power resources of the system. To solve the problem, the modified multi-objective particle swarm optimization algorithm with sine and cosine acceleration coefficients is utilized. The method is implemented on the modified IEEE 30-bus system. The proposed method is compared with those in the previously published literature, and the results confirm that the proposed risk index is better at estimating the voltage instability risk of the system, especially in cases with severe impact and low probability. In addition, according to the simulation results compared to typical security-based planning, the proposed risk-based planning may increase the security and economy of the system due to better utilization of system resources.

So far, several researchers have conducted many studies on the effective parameters in the design of river breakwaters, 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 breakwaters (in terms of length and distance between two consecutive breakwaters) 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 mean square error (RMSE) and mean absolute error (MAE). The OPTIMAL length of the breakwaters according to the results of GWO and EA algorithms was 19.26 and 18.12 m, respectively. Moreover, the OPTIMAL distance between two consecutive breakwaters in the OPTIMAL state was calculated to be 52.56 m. On average, according to the results of the optimization, an increase 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.