The use of elastic elements in actuators can be effective in improving their performance. The present paper examines the different layouts of the elastic element in actuators, choosing the appropriate design, and then designing a series elastic actuator for use in an active knee orthosis. For this purpose, the design elements of actuators were extracted using the knee data for walking. In the next step, considering the effect of the spring stiffness on the energy consumption and actuator’s peak power, its optimum amount for gait was calculated. Afterwards, the effect of adding the spring has been shown at an optimal value. Since the human knee in a gait cycle involves position and force control, designing an efficient controller to track the force and position of the knee during the gait cycle is the next section of this study. Due to the specific application of this actuator, its volume and size are important for the user. For this reason, it is necessary to design and construct an actuator with a suitable size and weight, with good output. For this purpose, a lightweight and low volume actuator was fabricated.

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.

Credit risk consists of probability of non-return, which may be in the form of bankruptcy or a decrease in financial and credit situation of the lessee. The variables are extracted from the Central Bank. In this study the independent variables are measured with six factors that are called external factors. The external factors are size of leasing, ownership interest rate, foreign exchange, inflation, and Gross Domestic Product (GDP). The present study uses related observations from 31 leasing companies from 2008 to 2013 to find out the determinants of the credit risk. The combined evidences suggest that internal factors such as upfront prepayment, credit insurance contract, security deposits, time and period contract, collateral and guarantees, contract amount, as well as external factors such as interest rate, inflation, foreign exchange, Gross Domestic Product infrastructure, and credit risk are determinants in the policy-making process involving the industrial leasing. Furthermore, the empirical results indicate the size of leasing and ownership are not the significant determinants of credit risk. The results of this dissertation provide several implications for policy-makers in the leasing industry. Policy-makers will be better off employing different procedures for leasing activities in the leasing industry.

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.

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.

In this paper, we design a revenue sharing contract to coordinate pricing and inventory control decisions in a serial supply chain consisting of one supplier, one manufacturer and one retailer. We assume that the retailer faces Poisson demand and his unsatisfied demands will be lost. The retailer applies one-for-one period policy in which he constantly places an order for one unit of product to the manufacturer in a predetermined time interval which results in a deterministic demand for the manufacturer. Solution procedures are developed to find the equilibrium in the Vendor Managed Inventory (VMI) program with a revenue sharing contract, in which no party is willing to deviate from for the sake of maximizing his own profit. Furthermore, we formulate the total profit of the system and obtain the optimal retail price and order cycle for the retailer and the inventory policy of manufacturer which maximizes the supply chain’s total profit and at the end we will highlight the revenue sharing contract performance.

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.

In the existing literature, there are a huge number of studies focused on p-hub median problems and inventing heuristic or metaheuristic algorithms for solving them. But such analogous body of literature does not exist for its counterpart problem; p-hub center problem. In fact, since p-hub center has been lately introduced and has a particular objective function, minimizing the maximum cost between origin-destination nodes, there are few studies investigating the problem and the challenges for solving it. In this study, after presenting a complete definition of the uncapacitated multiple allocation p-hub center problem (UMApHCP) two well-known metaheuristic algorithms are proposed to solve the problem for small scale and large scale standard data sets. These two algorithms are one single solution-based algorithm, Simulated Annealing (SA), and one population-based metaheuristic, Genetic Algorithm (GA). Because of the particular nature of the problem, Dijkstra’s algorithm has been incorporated in the fitness function calculation part of the proposed methods. The numerical results of running the GA and SA for standard test problems show that for smaller scale test problems, single solution-based SA shows greater performance versus GA but for larger scales of data sets the GA generally yield more desirable solutions.

A fast and facile one-pot procedure for the preparation of symmetric 5-Aryloyl-1,9-dimethyl-5,9-dihydro-2H-pyrano[2,3-d:6,5-d']dipyrimidine-2,4,6,8(1H,3H,7H)-tetraone derivatives by two-component reaction of N-methylbarbituric acid and arylglyoxalmonohydrates catalyzed by DABCO in ethanol at 50 ºC is described. This protocol has the advantages of environmental friendless, very simple operation, high regio- and chemoselectivity and moderate to excellent yields.

This paper presents an empirical investigation to determine factors influencing on content marketing in banking industry. The study designs a questionnaire consists of 40 questions in Likert scale and distributes it among 550 randomly selected regular customers of Bank Mellat in city of Tehran, Iran and 400 properly filled questionnaires are collected. Cronbach alphas for all components of the survey are well above desirable level. Using principle component analysis with Varimax rotation, the study has determined six factors influencing the most on content marketing including organization, details, having new ideas, quality, sensitivity and power while the last component contains only two subcomponents and is removed from the study.