Online social networks (OSNs) provide services targeting multifarious types of users in order to attract and retain them. For this purpose, developing new services according to user preferences has recently been under focused by various researchers. Most of present studies focus only on extracting the behavioral patterns of users, and neglect users’ interactions, which is the main part of the social activities in OSNs. To cope with this issue, this paper proposes a new methodology to bring both dimensions of data, the extracted behavioral patterns of users and their social interactions, in order to reach a better analysis. Moreover, the idea provides a basis for considering other dimensions efficiently. In order to evaluate the performance of the methodology, this paper performs a case study, and conducts a set of experiments on the computer-generated datasets. The results indicates the great performance of the methodology.

This paper considers a portfolio selection problem with normally distributed returns and different rates for borrowing and lending. The primary concern is to determine the amount of investment in different planning horizons when the rate of borrowing is greater than the rate of lending. Chance constrained programming as an appropriate tool for addressing intrinsic uncertainty in portfolio selection problem is used. To solve this nonlinear programming, Genetic Algorithm is utilized. Numerical experiments are performed and the results are analyzed to present the performance of the proposed methodology.

Small and Medium Enterprises (SME’s), and Entrepreneurial businesses (EB) play an important role in tourism since tourism has increasingly become the largest economic sector. Entrepreneurships known as a mean of economic development and policymakers are looking for creating a competitive and dynamic entrepreneurial economy. The aim of this study is to present a model for entrepreneurship tourism policy by using entrepreneurship development framework, which interacts with economic development. To this purpose, entrepreneurship policy dimensions are weighted and prioritized by tourism entrepreneurs, tourism policymakers and experts using decision-making trial and evaluation laboratory (DEMATEL) technique and Analytical Network Process (ANP) technique. First, DEMATEL technique is used to identify complex relationships, and to form a network relationship map. Then, ANP technique is used to calculate the influential weights of policy criteria. Results show that policymakers for entrepreneurship development in tourism need to consider basic requirements factors. In addition, they should improve efficiency enhancer criteria as well as innovation driver criteria. Also, the relationship among dimensions and their influences on each other are illustrated. Using the capabilities of this method, we can determine the casual relationships between dimensions and criteria and identify priorities for policy.

Dynamic pricing is a kind of pricing strategy in which the price of products varies based on present demand value. So far, several research works have been reported for using neural network for pricing, such as predicting demand and modeling the customer's choices. However, less work has been performed on using them for optimizing pricing policies. In this project, we try to explain the way of combining neural network and evolutionary algorithms to optimize pricing policies. We create a neural network on the basis of demand model and benefit from evolutionary algorithms for optimizing the resulted model. This has got two privileges: First, necessary flexibilities are created by using neural network to model different demand scenarios that is occurred with different products and services, and second, using evolutionary algorithms provides us with the ability of solving complicated models. Wavelet neural network has been used and the resulted pricing policy has been compared with other demand models that are widely used. The results show that the suggested model match up well under different scenarios and presents a better pricing policy than other suggested models.

Powder mixed electro discharge machining (PMEDM) is a hybrid machining process where electrically conductive powder is suspended into a dielectric medium, for enhancing the material removal as well as the surface finish. In this investigation, electro discharge machining (EDM) has been performed for the machining of AISI 304 stainless steel by using the tungsten carbide electrode, when silicon carbide (SiC) powder is suspended into kerosene dielectric medium. Peak current, pulse on time, gap voltage, duty cycle and powder concentration are considered as process parameter while the surface roughness (Ra) is the only response. The effect of significant process parameters on the response has been studied. A regression analysis has been performed to describe the correlation of data between the machining parameter, and the response. Microstructural analysis has been done for the PMEDMed surface. The result shows that peak current is the most influential parameter for surface roughness. Surface roughness decreases with the increase of powder concentration.

Nowadays efficiency measurement is considered as one of the most important methods for performance assessment of the organizations. Assessment of academic education and research system is a vital factor for education and research promotion and also is a panoramic mirror for education and research activities. The aim of this research is to assess the efficiency measurement of Shiraz university colleges over the period 2009 – 2014. Data Envelopment Analysis (DEA) as one of the most important methods of efficiency measurement has two limitations: First, it calculates cross-sectional efficiency values and second, it may consider many units as an efficient unit. Window Data Envelopment Analysis (WDEA) is used for eliminating the first limitation and similarly double frontier analysis is used to overcome the second limitation. The results show that proposed WDEA method with double frontier in comparison with traditional analysis, provides more accurate results.

In the present research work, four different multi response optimization techniques, viz. multiple response signal-to-noise (MRSN) ratio, weighted signal-to-noise (WSN) ratio, Grey relational analysis (GRA) and VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje in Serbian) methods have been used to optimize the electro-discharge machining (EDM) performance characteristics such as material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR) simultaneously. Experiments have been planned on a D2 steel specimen based on L9 orthogonal array. Experimental results are analyzed using the standard procedure. The optimum level combinations of input process parameters such as voltage, current, pulse-on-time and pulse-off-time, and percentage contributions of each process parameter using ANOVA technique have been determined. Different correlations have been developed between the various input process parameters and output performance characteristics. Finally, the optimum performances of these four methods are compared and the results show that WSN ratio method is the best multiresponse optimization technique for this process. From the analysis, it is also found that the current has the maximum effect on the overall performance of EDM operation as compared to other process parameters.

Developing innovation, based on knowledge and technology, as a driving force of the economy, is necessary for survival and is required in having strong interactions within the globalized world of business. Innovation and technology development require an intertwined network of organizational interactions between public and private sector. The activities and interactions of these firms are the reasons for innovation development in the framework of innovation systems. Following strategies is of crucial necessity and importance in industries such as aerospace and remotely-piloted helicopters (RPH) with their complex characteristics, costly and time-consuming processes. Understanding the business environment and identifying the success factors is a significant step towards adopting innovative strategies and planning for technology development. The aim of this article is to evaluate the key success factors in technological innovation development of remotely-piloted helicopters (RPH) industry. The methodology used in this article is Best-Worst method which is considered as one of the most prominent and effective MCDM methods. Based on a case study and by reviewing the extant and relevant literature, the key success factors of technological innovation development of remotely-piloted helicopters (RPH) industry in Iran were identified. Then by applying the “Best-Worst” method and the experts’ opinions, the key success factors were analyzed and prioritized. Finally, some suggestions are made by considering the results of the study.

Reliability issues are most important types of optimization problems and they are used in communication, transportation and electrical systems. This paper presents two mathematical models to solve the k-out-of-n redundancy problem where there are two objectives: maximization of reliability and minimization of cost subject to two constraints. Constraints are associated with weight and volume. In addition, strategy of redundancy is intended and ready to go cold and the components of the systems are also identical, because the model is to solve the complex models of the genetic algorithm (GA) and simulated annealing (SA). The proposed study uses NSGAII and MOPSO to solve the proposed studies and compare them using TOPSIS method.