Community detection is one of the emerging and well-known topics in the area of data mining and social network analysis, which has wide variety applications in discovering communities in real-world networks such as biological networks, internet WEBlogs, scientific and research WEBsites, etc. WEB community detection can especially help admins assign the optimal bandwidth to the WEBsites of their own networks. Most of WEB community detection approaches only use the network topology to discover the WEB communities. However, the results of the most recent researches show that traditional community detection methods have to be substantially modified to consider WEB attributes as well as network topology. Therefore, in this paper, a mathematical PROGRAMMING approach is developed for community detection in attributed internet networks by simultaneously considering both network topology and node attributes. In this approach, first, similarities of WEB pages are calculated using node attributes and a desired similarity measure and are considered as the weight of the corresponding edges. Then, communities of the resulted weighted network will be detected by the proposed mathematical model. To validate and prove the efficiency, it is hypothesized that the detected communities of the proposed approach have a better quality than that of previous models. Experimental results demonstrate that the proposed approach has the ability to significantly improve the quality of detected WEB communities, when the model uses the Jaccard index. However, the results of other hypotheses indicate that the correct selection of similarity measure has a significant impact on the quality of the detected communities.

Learning to rank (L2R) has emerged as a promising approach in handling the existing challenges of WEB search engines. However, there are major drawbacks with the present learning to rank techniques. Current L2R algorithms do not take into account to the search behavior of the users embedded in their search sessions’ logs. On the other hand, machine-learning as a data-intensive process requires a large volume of data about users’ queries as well as WEB documents. This situation has made the usage of L2R techniques questionable in the real-world applications. Recently, by the use of the click-through data model and based on the generation of click-through features, a novel approach is proposed, named as MGP-Rank. Using the layered genetic-PROGRAMMING model, MGP-Rank has achieved noticeable performance on the ranking of the English WEB content. In this study, with respect to the specific characteristics of the Persian language, some suitable scenarios are presented for the generation of the click-through features. In this way, a customized version of the MGP-Rank is proposed of the Persian WEB retrieval. The evaluation results of this algorithm on the dotIR dataset, indicate its considerable improvement in comparison with major ranking methods. The improvement of the performance is particularly more noticeable in the top part of the search results lists, which are most frequently visited by the WEB users.

One of the issues of reliable performance in the power grid is the existence of electromechanical oscillations between interconnected generators. The number of generators participating in each electromechanical oscillation mode and the frequency oscillation depends on the structure and function of the power grid. In this paper, to improve the transient nature of the network and damping electromechanical fluctuations, a decentralized robust adaptive control method based on dynamic PROGRAMMING has been used to design a stabilizing power system and a complementary static var compensator (SVC) controller. By applying a single line to ground fault in the network, the robustness of the designed control systems is demonstrated. Also, the simulation results of the method used in this paper are compared with controllers whose parameters are adjusted using the PSO algorithm. The simulation results show the superiority of the decentralized robust adaptive control method based on dynamic PROGRAMMING for the stabilizing design of the power system and the complementary SVC controller. The performance of the control method is tested using the IEEE 16-machine, 68-bus, 5-area is verified with time domain simulation.

Distributing geographical information on the Internet is an enforcing factor for information providers. Internet allows all levels of society to access geographical information, and provides a media for processing geo-related information with no location restrictions. WEB-based GIS is evolved from different WEB maps and client-server architecture to distributed ones. As such, Internet reshapes all functions of information systems including: gathering, storing, retrieving, analyzing, and visualizing data. The high cost of GIS system, the release of system specific databases, and the enormous software developer efforts on upgrading the system are fading with the introduction of WEB-based GIS Moreover, disseminating spatial information on the Internet improves the decision-making processes.This paper examines the current WEB GIS technologies with emphasis on architectures. Eight state-of–the-art WEB GIS products from leading GIS companies have been scientifically assessed. This paper also proposes a WEB GIS development strategies starting from requirement analysis, and ending in GIS use and maintenance. Disseminating Iranian Road information on the Internet has been presented as a successful implementation of the strategy.It is concluded that the Internet based information delivery adds the margin of profitability of the Transportation and Terminal Organization. The proposed strategy has been tested successfully. Civilian can get to their destinations faster.

Computational grids have provided the usage of computational distributed resources for computation-intensive applications. The development of programs that use these capabilities is one of the challenging issues for grid computing. In this article, an effort has been made in order to solve this problem by presenting mobile-agent-based parallel PROGRAMMING on the grid. The presentation of this model, which has been materialized by extending Alchemi™ grid infrastructure, adding agent properties and navigational commands that let the user to develop his/her program by using agents’ mobility and communication between them. In order to evaluate the system, algorithm of matrix multiplication as well as algorithm of finding the convex hull of a series of points has been implemented in the mentioned system.

In mammals, the intrauterine condition has an important role in the development of fetal physiological systems in later life. Suboptimal maternal environment can alter the regulatory pathways that determine the normal development of the fetus in utero, which in post-natal life may render the individual more susceptible to cardiovascular or metabolic adult-life diseases. Changes in the intrauterine availability of nutrients, oxygen and hormones can change the fetal tissue developmental regulatory planning, which occurs genomically and non-genomically and can cause permanent structural and functional changes in the systems, leading to diseases in early years of life and those that particularly become overt in adulthood. In this review we take a brief look at the main elements which program the fetal system development and consequently induce a crucial impact on the cardiovascular, nervous and hormonal systems in adulthood.

In this study, we discussed a fuzzy PROGRAMMING approach to bi-level linear PROGRAMMING problems and their application. Bi-level linear PROGRAMMING is characterized as mathematical PROGRAMMING to solve decentralized problems with two decision-makers in the hierarchal organization. They become more important for the contemporary decentralized organization where each unit seeks to optimize its own objective. In addition to this, we have considered Bi-Level Linear PROGRAMMING (BLPP) and applied the Fuzzy Mathematical PROGRAMMING (FMP) approach to get the solution of the system. We have suggested the FMP method for the minimization of the objectives in terms of the linear membership functions. FMP is a supervised search procedure (supervised by the upper Decision Maker (DM)). The upper-level decision-maker provides the preferred values of decision variables under his control (to enable the lower level DM to search for his optimum in a wider feasible space) and the bounds of his objective function (to direct the lower level DM to search for his solutions in the right direction).

There are many cases that a nonlinear fractional PROGRAMMING, generated as a result of studying fractional stochastic PROGRAMMING, must be solved. Sometimes an approximate solution may be sufficient enough to start a new process of calculations. To this end, this author introduces a new linear approximation technique for solving a fractional chance constrained PROGRAMMING (CCP) problem. After introducing the problem, the equivalent deterministic form of the fractional nonlinear PROGRAMMING problem is developed. To solve the problem, a fuzzy goal PROGRAMMING model of the equivalent deterministic form of the fractional chance constrained PROGRAMMING is provided and then the process of defuzzification and linearization of the problem is started. A sample test problem is solved for presentation purposes.