This paper discusses a FUZZY variance ANALYSIS model with interaction between explanatory variables using Tanakas model. Based on Tanakas model, a linear programming model has been formulated for measuring the value of response factors. This model measures both values of the response factors, as well as their interactions.

Purpose: The flourishing of political parties and currents is one of the signs of the degree of development in societies. In contemporary Iran and since the formation of the constitutional movement, political currents have gone through many ups and downs and it can be said that political currents in Iran after the Islamic Revolution have not yet reached the stage of institutionalization and stability. A characteristic feature of political currents in Iran is the divergence and division among political currents in recent decades, and this can be one of the reasons for the instability and cross-sectional and seasonal activity of political parties in Iran. Therefore, the necessity of leading research seeks to answer the question of how factors and divergence among political currents in Iran after the victory of the Islamic Revolution can be analyzed? And what are the scenarios for the advancement of political currents in Iran?Method: To answer this question, the method of causal-layer ANALYSIS, which is one of the qualitative methods in futures research, has been used.Findings: The research findings indicate that this divergence is due to a wide range of reasons from the level of causal systems (from the institutionalization of power to the formation of parties as elitist initiatives), worldview and discourse (from charismatic political authority to culture). Subsidiary-follower politics to myth-metaphor (Iranian individualism to belief in a strong state-weak society) can be analyzed.Conclusions: Three scenarios for the future of Iranian political currents can be considered: integration of currents as the security valve of the political system, the collapse of political currents in the traditional form, integration and consolidation in new social movements (virtualized parties).

Background & Aim: Knowledge Management (KM) is widely known as a critical issue in offices, factories and organizations. The present study intends to predict the success or failure of KM implementation in the automotive industry. Methods & Materials: we have tried to analyze and predict the degree to which how successfully we can implement KM in the automotive industry using FUZZY inference method (FIS). In this regard, after data collection, and employed FIS software to analyze our results. Results: As our results show, the projected level for the implementation of KM in Iran Khodro was about 58%. Given that this study was conducted in five different, but related parts in Iran Khodro as well as these five sectors were less similar in terms of structure, individual and usage of technology, we should not expect similar results about the rate of implementing knowledge management. Conclusion: Our results would be important and interesting, because they will provide the basis for how successfully establish KM in the automotive industry and similar organizations in order to improve the efficiency and productivity in organizations.

The goal of this paper is to show how the concept of FUZZY logic can be used to establish a degree to which an investment project belongs to a class of risk. Also, the probability of the FUZZY event is presented and is applied to calculate the probability of the FUZZY event "the project X is a good investment". This process has to enable the decision maker to compare several alternative investments from the FUZZY logic perspective and, in this way, allows him to include the uncertainty that comes with the problem in reality. Moreover, by experiments with the proposed FUZZY model the user can obtain new knowledge for investment risk ANALYSIS.

FUZZY analytical hierarchy process (FUZZY-AHP) technique as a knowledge-driven approach relies on the geologist’ s input to weight the importance of each evidence layer is applied for prospectivity modeling of Au mineralization within the Takhte-soleyman district. Firstly, in current study, all of the criteria and sub-criteria which are gained from exploratory data (geochemistry, remote sensing, geology and tectonic) are weighted by using AHP and thereafter are transformed into [0-1] range based on the FUZZY logic. All fuzzified layers according to the calculated weights were synthesized and prospectivity modeling of Au mineralization based on the FUZZY-AHP technique was generated. In this study, it was used another FUZZY method based on the FUZZY operators (OR and GAMMA) that called “ FUZZY-GAMMA” to generate the map of promising areas of Au mineralization in the study area. Finally, in order to evaluate the ability of the applied methods in delineating reliable promising areas, locations of Au occurrences in the study area were used. Consequently, the results demonstrate the more acceptable outcomes for FUZZY-AHP model by comparison with FUZZY-GAMMA model.

Recognition and understanding the genetic control of traits, combining ability and genetic structure are directly related to the success of breeding programs. For this purpose, a 7 × 7 one-way diallel design was conducted in a randomized complete block design with three replications. The measured traits were included plant height, height to the first capsule, number of days to 50% and 90% of flowering, number of capsules per plant, number of seeds per capsule, number of days to physiological ripening, number of branches, leaves number and length, 1000-seeds weight, capsule weight, length and width, chlorophyll a, b and total chlorophyll, biological andeconomic yields, harvest index, oil and protein percentage. ANALYSIS of variance showed that there was a significant difference between genotypes and diallel ANALYSIS showed that the additive variance of all traits and dominant variance of all traits except height to the first fruit-bearing capsule were significant. The oltan cultivar was the best and Ardestan genotype was the worst genotype in terms of general combining ability. Sabzevar×TS-3 and Sirjan×Fars were the best hybrids in most traits. The general heritability was between 0.90 to 0.96 for biologic yield and number of branches, respectively and narrow heritability was between 0.36 to 0.91 for the number of branches and harvest index, respectively. The ANALYSIS of variance by Hayman method confirmed the results of Griffing ANALYSIS.