Buildings in high seismic regions are prone to severe damage and collapse during earthquakes due to large lateral deformations. The use of superelastic shape memory alloys (SMAs) as reinforcements in concrete structures is gradually gaining interest among researchers. In this study, the effect of SMAs as reinforcement in concrete structures is analytically investigated for 3, 6 and 8-story reinforced concrete (RC) buildings. Each building has fi ve bays in both directions with the same bay length of 5m. For each concrete building, three different reinforcement details are considered: (1) steel reinforcement (Steel) only, (2) SMA bar used in the plastic hinge region of the beams and steel bar in other regions (SteelSMA), and (3), beams fully reinforced with SMA bar (SMA) and steel bar in other regions. For each case, columns are reinforced with the steel bar. Incremental Dynamic Analyses (IDA) are performed using ten different ground motion records to determine the seismic performance of Steel, Steel-SMA and SMA RC buildings using OPENSEES program. Results obtained from the analyses indicate that the value of Sa in SteelSMA frames are higher than SMA frames, and its recovery capacity is almost similar with SMA frames. However, the SMAs materials are expensive, and the use of Steel-SMA frames can be reasonably effective in seismic zones. The comparison between frames with various reinforcement’ s details shows that Sa of 3-story frames with various reinforcements are almost identical. But, in 6-and 8-story frames, Sa of Steel frames are higher than others. In other words, frames with SMA bars in the all length or plastic hinge region of the beam have reached the same level of seismic demand under lower spectral acceleration which can be resulted from the decreased stiffness caused by SMA bars. Furthermore, the results indicate that Structural behaviour factor in 3 and 6 story buildings with different types of reinforcement is not much change, but this change is perceptible in the 8-story frame. In the case of residual drift in all cases, the use of shape memory alloys will reduce these drifts.

In statistics it is often assumed that sample observations are independent. But sometimes in practice, observations are somehow dependent on each other. Spatiotemporal data are dependent data which their correlation is due to their spatiotemporal locations. Spatiotemporal models arise whenever data are collected across both time and space. Therefore such models have to be analyzed in terms of their spatial and temporal structure. Usually a spatiotemporal random field {Z(s, t) : (s, t) Î D x T} is used for modeling the spatiotemporal data, where D Ì Rd, d ³ 1 is a space region and T Í R is a time region. One of the fundamental subjects in analyzing such data is prediction. In spatial statistics, assuming that the spatiotemporal random field Z(s,.t) is stationary with finite variance at all coordinates (s, t) Î D x T, and spatiotemporal covariance function C(h, u) = cov (Z(s, t)j Z(s + h, t + u)) exists, the unknown value of the random field at a given location (s0, t0) is usually predicted with kriging as the best linear unbiased predictor. In practice, the spatiotemporal covariance function is unknown and a positive definite function should be fitted to the estimates of the covariance function. To ensure that a valid spatiotemporal covariance model is fitted to the data, one usually considers a parametric family whose members are known to be separable positive definite functions. A separable spatiotemporal covariance function might decompose into sum or product of a purely spatial and a purely temporal covariance function. In this paper the product-sum model introduced by De Iaco et al. (2001) is used to determine the spatiotemporal correlation of the data.In some applied problems, in addition to the values of an attribute of interest Z(0, 0), some additional information is available in each sample location, so the precision of prediction would be improved by their implementation. In this paper, to exploit this additional information in kriging, two techniques for spatiotemporal kriging of temperature are compared. The first technique, spatiotemporal ordinary kriging, is the simplest of the two, and uses only information about temperature. The second technique, spatiotemporal kriging with external drift, uses also the relationship between temperature and height to aid the interpolation. It is shown that the behavior of the temperature predictions is physically more realistic when using spatiotemporal kriging with external drift. The implementation of spatiotemporal kriging with external drift, then, is illustrated in a real problem, consisting of maximum and minimum temperature of 6 provinces in Iran.

Statistical Shape Models are used to interpret shapes. They include mean and variance of corresponding points of training shapes. One of the most important challenges in building statistical shape models is to establish correct correspondences among landmarks in a training set. In this paper, the non-rigid CPD (Coherent Point drift) method is used to find correct correspondences among points. This method uses both Deterministic Annealing and a non-rigid scheme to register two shapes simultaneously. Then, the statistical shape model is built using a rigid transformation. The proposed method is evaluated using Compactness, Generalization ability and Specificity measures. The built model is compared to models created using the ICP (Iterative Closest Point), TPS-RPM (Thin Plate Spline – Robust Point Matching) and MDL (Minimum Descreption Length) methods by these metrics. The results show that the proposed method performs like the MDL regarding Specificity measure (0.21±0.06). The Compactness and Generalization ability measures of the proposed method are very similar to those for the MDL method. The run-time of our proposed method is about 68 seconds which is faster than non-rigid TPS-RPM and MDL approaches (390 and 3600 seconds respectively). Our results are superior to the ICP and TPS-RPM algorithms.

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.