Search

Background A medical device is any instrument, apparatus, implement, machine, appliance, software, material, which is intended material, to be utilized, either alone or in combination, for medical purpose. These devices should work precisely and the maintenance program of them has also a key role to achieve this goal. Many of the maintenance programs have not considered important functional parameters such as equipment type, risk factors, and expert opinion. Objective The purpose of this study is to present a novel fuzzy method for medical device risk assessment. The obtained values for risk could be used to prioritize maintenance operations by considering allocation budget. Material and methods This experimental study aims to make a new application of Ordered-Weighted Average operator in aggregation of different parameters for calculating Risk Priority Number. This model is a fuzzy multi-criteria decision making approach based on risk maintenance framework for medical device prioritization. Results A limited budget is one of the barrier in medical centers. The suggested framework presents a simple and reliable method to choose the best maintenance strategy for each kind of medical device by considering budget limitation. Based on obtained results from numerical model, defibrillators and surgical suction have respectively the highest and the lowest priority in mentioned example. Conclusion Risk prioritization of medical devices is valuable because the medical centers can prioritize maintenance operations and thereby to establish preference of maintenance strategy. Implementation of our proposed maintenance program has many effective results in medical center budgets.

The Smart Grid is defined as a large-scale electrical grid that can amalgamate the actions of whole users connected to it — generators, consumers and those that do both. Now, the technologies should lie on the abstract mathematics firmly to model and control the operations. Game theory is a mathematical tool to model systems with multi-agents (rational decision-makers). Game theory models the behavior of independent and rational agents which they want to maximize their profits. To this end, we model the behavior of agents and the possible behaviors profit. In the new system, users are able to generate the required energy from renewable energy sources such as solar panels, electric generators, and so forth. Hence, in these complex systems, we would make the use of the mathematical tools to model the interactions and features between the entities. In this paper, three basic questions will be answered which I) Why cooperation between agents is valuable. II) Which forms of cooperation are forming and III) Why and how game theory can model the cooperation. In the following, we survey a number of Game theory-based applications especially cooperative game theory to solve relevant problems in Smart Grid. Smart grid mainly is divided into three branches including Distributed electricity generation (Micro-grid), Demand Side Management (DSM), and Communications that we will describe the applications of game theory in Micro-grid in detail in the following.

Recommender Systems (RS) are turned into remarkable tools in electronics commerce (e-commerce) in a way that they effectively find items which are suitable for user's interests. Techniques such as collaborative filtering and content-based filtering are designed for RS. One of the novel methods to recommend appropriate items is using the Ordered Weighted Averaging (OWA) operators to fuzzify the output of RS [1]. OWA is one of the decision-making methods capable of considering the priorities and mental evaluations of a decision-maker. Furthermore it has the ability to assess the measure of orness and include the computation in final decision. This article aims at presenting methods that have been proposed to combine RS and OWA operators and also at proposing the implementation and development of these two methods in future.

A Grid-Federation environment is composed of a collection of autonomous and selfish distributed cluster resource managers. These selfish managers participate in Grid-Federation to share their resources. Market models could be used to motivate the self-interested participants to share their resources. In this paper, firstly, a market for resource exchange in grid federation environment is established. Then, in order that the market reaches a Walrasian equilibrium, a computationally tractable mechanism is proposed. A Walrasian equilibrium problem consists of finding a set of prices and allocations of resources in such a way that the cluster resource managers could maximize their utilities and the market clears. Market clears when the resource supply equals to the demand. We show that in a Walrasian equilibrium, the Grid Federation market reaches an efficient resource allocation.

A knowledge grid is an intelligent interconnection environment, built on top of a computational grid, to facilitate the creation of virtual organizations. An important feature of a virtual environment is its support for collaborative decision-making. A major difficulty with current approaches is that they cannot easily handle environments where decision makers are added or removed dynamically. In this article, a new approach to alter the number of decision makers dynamically is suggested. The amount of decision accuracy made by each decision maker, for a given subject, is determined subjectively considering the other decision makers' opinions. The effect of decisions made by each decision maker varies gradually considering its past decisions. Assuming each decision maker provides a fuzzy answer set in response to each decision problem, an operator for fusing of the decision makers' decision sets is suggested. The aim of the fusion is improvement of the decision quality. The fusing operator provides a fuzzy answer set that is a function of the accuracy possibility of each decision maker and its fuzzy answer set.

Smart grids are integrating advanced technologies into power grids. The new structure has a profound effect on the entire system. Now, power networks can deploy computing technologies to achieve new level of performance and efficiency. Game theory is a mathematical tool for modeling distributed multi-agent systems. There are game theoretic approaches for studying and solving smart grid issues. Important issues have been studied with game theory include demand side management, load shifting and dynamic energy price. Different authors have used different types of games and payoff functions. In this paper, we categorize most important issues and subjects in both game theory and smart grid. Also, we have a comprehensive review of articles which propose game theoretic approaches in the field of smart grid.

Service time especially for tasks with predefined deadline is an important criterion in evaluation of a grid resource manager. This parameter is affected directly by the failure of connection links and computational resources. In this paper a new approach to estimate expectation of execution time and probability of successful execution of a task considering both permanent and transient failures is proposed. Also in current works in order to increase the probability of successful execution and consequently to decrease the execution time, the use of parallel resources is suggested. Nevertheless due to the cost constraint the use of parallel resources is not recommended. In this paper the use of active parallel systems to increase the reliability of services in a grid environment is suggested. An active parallel system has lower impact on reducing the execution time, but is more economical.

Credibility of knowledge grid members who act as a cooperative decision making community, affects the degree of accuracy of the decisions made. Apparently, decisions made by a decision maker should be affected by the degree of the decision maker's credibility. The problem is how to estimate decision makers' credibility within a knowledge grid environment, specially, those environments in which the number of decision makers is altered dynamically. In this article, a new approach to estimate the credibility of decision makers based upon the opinion of the other members of decision makers' community within a dynamic knowledge grid environment is proposed.

Designing an efficient Distributed Database System (DDBS) is considered as one of the most challenging problems because of multiple interdependent factors which are affecting its performance. Allocation and fragmentation are two processes which their efficiency and correctness influence the performance of DDBS. Therefore, efficient data fragmentation and allocation of fragments across the network sites are considered as an important research area in distributed database design. This paper presents an approach which simultaneously fragments data vertically and allocates the fragments to appropriate sites across the network. Bond Energy Algorithm (BEA) is applied with a better affinity measure that improves the generated clusters of attributes. The algorithm simultaneously generates clusters of attributes, calculates the cost of allocating each cluster to each site and allocates each cluster to the most appropriate site. Results show more efficient clustering and allocation which gives better performance. Keywords: Bond Energy Algorithm, Distributed database system, Data allocation and fragmentation, Clustering