Foresight has recently emerged as one of the most attractive and practical fields of study, while being used to draw up a preferable future and formulate appropriate strategies for achieving predetermined goals. The present research aimed at providing a framework for foresight with a primary focus on the role of a cognitive approach and its combination with the concept of fuzzy cognitive map in the environments of uncertainty and ambiguity. The proposed framework consisted of the 3 phases: pre-foresight, foresight, and post-foresight. The main stage (foresight) focused on the role of imagination and intuition in drawing the future in the experts’ minds and depicting their perceptions above perceptions in the form of a fuzzy cognitive map influenced by variables related to the subject under study in order to determine a preferable future. The use of a Z-number concept and integrating it with fuzzy cognitive maps in the foresight-oriented decision-making space, which was mainly saturated with uncertainty and ambiguity, was one of the main strengths of the proposed framework in the current investigation. The present paper focused primarily on the evolution of expert’s knowledge with regard to the topic of foresight. The role of Z-number in various processes, from data collection to illustration, analysis, and aggregation of cognitive maps, was considered for gaining knowledge and understanding into the nature of future. Moreover, an ultimate objective was realized through identifying, aggregating, and selecting the variables from each expert’s perspective and then the relationship between each variable was determined in the main stage of foresight. Finally, the proposed framework was presented and explicated in the form of a case study, which revealed satisfactory results.

Regarding the scarcity of appropriate recorded earthquakes, and the ever-increasing use of dynamic time history analyses for more accurate calculation of structures response, the simulation of artificially produced records necessary. In this study, accelerograms are simulated from the response or design spectrum by using generalized regression neural networks. In the training phase the response spectrum is used as the input for the simulating network, and the corresponding accelerogram as the output. Accelerograms achieved from some recorded earthquakes of Iran are used for training the neural network. The appropriate accuracy, and high speed of training are the properties of the network. After training the network, accelerogram corresponding to the design spectrum of Iranian code of practice for seismic resistance design of buildings is generated. Similar procedures can be carried out for design spectrum of other cods to achieve the corresponding records.

Quality of services in airline industry plays an important role in market penetration and customer retention. In this paper, we present a factor analysis to find important factors in Iranian Airline industry. The study designs a questionnaire consist of 35 questions and distribute it among 200 customers who regularly use services from 16 different airlines and they are investigated based on the implementation of factor analysis. The results of our survey determines seven important factors including physical features of the environment, Kettering, Pre-flight passenger services, Ability to respond, Reliability, Passenger service flight and Virtual Passenger Services. The paper discusses that improving these seven factors can significantly improve service quality in this sector.

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