With the increasing use of particle accelerators in various fields, the construction of accelerators has developed. In these accelerators, measurement instruments that measure important and versatile quantities are very important. Due to the role of electric potential in particle acceleration, accurate measurement of electric potential at any time in accelerators is essential. In this research, design, fabrication and testing of a non-destructive electric potential measuring device called generating voltmeter (GVM) has been done. This instrument uses the principle of capacitance changes to measure voltage. Also, the effect of various parameters such as the number of rotor blades, stator thickness and diameter on the instrument performance has been investigated. Finally, a suitable GVM has been selected for use in the dynamitron electrostatic accelerator at the Nuclear Science and Technology Research Institute.

The present ideology of neo-classical economic paradigm to follow their own interests and are destroying the ethical basis and its consequences, ethical virtuous society is low. Lean thinking judgment based on knowledge of human capital to "do the right thing" is not merely to "do it right". Doing the right things with just the ethical value management by humans as creatures is possible. Therefore, attention to the concept of ethical capital to create value beyond the existing mission and values will be doubled. In the present article aims to identify ethical capital, and reviewed different views on this issue.

This paper considers the generation of some interpretable fuzzy rules for assigning an amino acid sequence into the appropriate protein super families. Since the main objective of this classifier is the interpretability of rules, we have used the distribution of amino acids in the sequences of proteins as features. These features are the occurrence probabilities of six exchange groups in the sequences. To generate the fuzzy rules, we have used some modified versions of a common approach. The generated rules are simple and understandable, especially for biologists. To evaluate our fuzzy classifiers, we have used four protein super families from UniProt database. Experimental results show the comprehensibility of generated fuzzy rules with comparable classification accuracy.

The main goal of this research is generating a model for Bank Mellat corporate identity. Mixed method is used in this research. In qualitative part, is used grounded theory and interview and participate participation observation was main data collection method. Conceptual model indicates the main identity of bank is Superiority that originate from bank heritage. This identity cause to bank move toward Excellence in different banking aspect as strategy. Also, contextual and intervening factor are influencing on strategy. Finally, bank identity is visible in touch point with a customer. In quantitative part, structural equation modeling and Lisrel software are used to test Bank Mellat corporate identity. Results indicate that the model fits with data, but intervening factor has a negative effect on strategy in Bank Mellat corporate identity model.

In 1986 S. Sattolo introduced a simple algorithm for uniform random generation of cyclic permutations on a fixed number of symbols. Recently, H. Prodinger analysed two important random variables associated with the algorithm, and found their mean and variance. H. Mahmoud extended Prodinger’s analysis by finding limit laws for the same two random variables.The present article, starting from the definition of the algorithm, is completely self-contained. After giving a simple new proof of correctness, we generalize the abovementioned probabilistic results by determining the “grand” probability generating functions of the random variables. The focus throughout is on using standard methods that give a unified approach, and open the door to further study.

In all systems simulation, random variates are considered as a main factor and based of simulation heart. Actually, randomization is inducted by random variates in the simulation. Due to the importance of such a problem, a new method for generation of random variates from continuous distributions is presented in this paper. The proposed algorithm, called uniform fractional part (UFP) is simpler and more efficient compared with other methods of random variates generation. Despite useful consequences, this algorithm has several shortcomings such as 1) being approximate, 2) not accessibility of the inverse of cumulative density function (CDF) for all distributions in order to determine the cut-off points and 3) truncating the tails of infinite distributions, which all of the aforementioned shortcomings reduce the precision and speed of the algorithm. The main goal of this research is proposing the improved version of this algorithm (IUFP) through recognizing its deficiencies.