Competition law is a newcomer to the legal system recently. A sound understanding of competition policy can provide us with sufficient bases to apply a fundamental and normative view of the issues of competition law. The difference in supervision and regulation determines how the market functions and in order to understand this difference one must understand competition policy. Competition policy may be based on governmental support for national production and industry or on a non-interventional and regulatory posture. Moreover, supervision, based on the principle of non-intervention in the market mechanism, is rooted in liberal ideas; however, regulation, whether as a rule or an exception, is based on the assertion that the market has been ineffective in attaining its goals. Therefore, the government will resort to interventions to regulate inefficiencies.  This paper aims to analyze Supervisory Authority in Implementing Competitive Policy by employing the description method. In this article the author tries to first delineate competition policy, its related requirements and imposed deviations to the market. Then, by defining the supervisory entity and clarifying its distinction from the regulatory institutions, the author considers the characteristics of an appropriate supervisory entity conducting a comparative study of this issue in Iran and the U.S.A. This form of Competition policy because of its applicable experiences which have been well described by recent scholarship is considered suitable for the native system.

The purpose of the current research was to investigate the obstacles to the implementation of school-based management in Iran's educational system based on the interpretive structural modeling approach. The research method was a modeling study. The statistical population of the research was specifically the academic staff members of the university, 25 of whom were purposefully selected from the desired cases. The data collection tool was a researcher-made self-interactive questionnaire. In order to measure and evaluate the validity of the questionnaire, the criterion of content validity was used. The content validity of the questionnaire was 0.96. The data analysis was done with interpretive structural modeling method. The results of the research showed that 9 factors play a role as barriers to school-oriented management. Also, the results showed that financial obstacles (the cost of school-oriented management), socio-cultural obstacles and political obstacles (political focus on schools by the central government) as key independent variables have high influence power and low dependence. Also, the results showed that the lack of belief in the professional competence of school administrators, the lack of infrastructure facilities, the emphasis of upper management on the supervision and control of schools, and the lack of cooperation of local communities with school-based management as the linking variables with the greatest power of influence and the greatest power of dependence. and the centralism in the education system of the country and the lack of legal powers to school managers and staff as dependent variables have high dependency power and low influence power.

Weighted Uniform Simulation (WUS) is recently presented as one of the efficient simulation methods to obtain structural failure probability and most probable point (MPP). This method requires initial assumptions of failure probability to obtain results. Besides, it has the problem of variation in results when it conducted with few samples. In the present study three strategies have been presented that efficiently enhanced capabilities of WUS. To this aim, a progressively expanding intervals strategy proposed to eliminate the requirement to initial assumptions in WUS, while low-discrepancy samples simultaneously employed to reduce variations in failure probabilities. Moreover, to improve the accuracy of MPP, a new simple local search method proposed and combined with the simulation that strengthened the method to obtain more accurate MPP. The capabilities of proposed strategies investigated by solving several structural reliability problems and obtained results compared with traditional WUS and common reliability methods. Results show that proposed strategies efficiently improved the capabilities of conventional WUS.

The Hasofer-Lind and Rackwitz-Fiessler (HLRF) algorithm, which is based on the first-order reliability method (FORM), is widely used to estimate failure probability, reliability index, and design point in structural reliability analysis. However, due to the high nonlinearity of the limit state surface, the HLRF algorithm can be unstable. To address this issue, this paper proposes an optimization method to locate and estimate the design point in the standard normal space and calculate the corresponding failure probability. The reliability problem is solved using sequential least squares programming (SLSQP) to improve accuracy, robustness, and efficiency. SLSQP replaces the quadratic programming problem with a linear least-squares problem, using a stable LDL factorization of the Hessian of the Lagrangian equation. The initial optimization problem is converted into a minimum distance optimization problem with a lower bound constraint. To eliminate linearization errors, the probability expectation method with rotation directions space is employed. The proposed algorithm is demonstrated in several benchmark numerical examples with both explicit and implicit limit state functions. Its fast convergence rate is a notable feature of the proposed algorithm, which enhances its competitiveness in structural reliability analysis.

The computational burdens and more accurate approximations for the estimation of the failure probability are the main concerns in the structural reliability analyses. The Monte Carlo simulation (MCS) method can simply provide an accurate estimation for the failure probability, but it is a time-consuming method for complex reliability engineering problems with a low failure probability and may efficiently approximate the failure probability. In this paper, the efficiency of MCS for the computations of the performance function is improved using a random-weighted method known as the random-weighted MCS (RWMC) method. By using the weighted exponential function, the weights of random data points generated by MCS are adjusted by selecting the random point in the design space. The convergence performances including the computational burdens for evaluating the limit sate function and the accuracy of failure probabilities of RWMC are compared with MCS by using several nonlinear and complex mathematical and structural problems with normal and no-normal random variables. The results indicate that the proposed RWMC method can estimate the accurate results with the less computational burdens, about 100 to 1000 times faster than MCS.

In this paper, a new method is proposed for fuzzy structural reliability analysis; it considers epistemic uncertainty arising from the statistical ambiguity of random variables. The proposed method, namely, fuzzy dynamic-directional stability transformation method, includes two iterative loops. An internal algorithm performs the reliability analysis using the dynamicdirectional stability transformation method and an external algorithm performs the fuzzy analysis by applying the alpha-cut level optimization method based on the genetic algorithm. Implementation of the proposed method, which solves some nonlinear performance functions, indicates the efficiency and robustness of the dynamic-directional stability transformation method, as compared to other first order reliability methods.

In this paper epistemic uncertainty of random variables in the reliability analysis of Adjusted Stability Transformation Method has been modeled and fuzzy structural reliability index has been determined using GA. In the reliability analysis process, a dynamic adaptive approach based on stability transformation method has been applied which the control coefficient is self-adapted at each iteration. Moreover in order to investigate the importance of epistemic uncertainty in the fuzzy structural reliability index, sensitivity analysis using Entropy-Shannon has been done. Survey results of three structural examples indicate that the proposed reliability method provides stable results with suitable computational efficiency. The sensitivity of failure probability to epistemic uncertainty has shown a considerable effect on the structural reliability in some problems.

In most studies done in the field of design of structures against explosion, uncertainties in explosive loading are not directly considered. By considering these uncertainties and using reliability analysis, more accurate and reasonable estimation of structural safety and probability of failure can be reached. In this paper, using Mont-Carlo simulation method and considering uncertainties associated with blast loading, the probability of failure of steel columns under different blast loads is calculated. Then, the minimum required protective distance is determined to prevent significant damage of structure. The single degree of freedom and finite element simulation methods are utilized for structural analysis. Finally, safe protective distances are proposed as 4. 5, 9. 5, 12 and 14 meters for charge weights of 30, 200, 400 and 600 kg TNT, respectively.