AbstractPurpose: The velocity of change in the surrounding world has forced them to identify those factors which impact their change capacity. The present study was conducted to identify and prioritize influential factors on organizational change capacity.Methodology: In terms of purpose, this was explorative and applied research. Its population included ten familiar experts with research subjects in a research and training center. By reviewing the literature on change capacity, relevant, influential factors were identified, and they were concurred by the fuzzy Delphi technique, and then they were ranked. After recognizing influential factors, a pair comparisons questionnaire was distributed among ten experts, and after gathering the questionnaires, effecting factors were ranked using the Expert Choice11 Software package and AHP technique.Findings: Results show that organizational culture, structure, and style of leadership were the main factors that impacted change capacity.Conclusion: The influential factors can be divided into three categories: environmental, content, and structural elements. Environmental factors include environment and innovation; content factors include team working, intellectual capital, leadership, political behavior, and human resource management; Structural elements include strategy, structure, organizational policies, information technology, knowledge management, and technology. Value: This is the first time in the literature that the influential variables affecting the change capacity were recognized and categorized in a conceptual classification that has not been seen in the previous research.

Ten polycyclic aromatic hydrocarbons associated with suspended particulate matter in the ambient air were collected at two sampling sites: urban and suburban residential areas of Yokohama, Japan from 1999 to 2005. The concentrations of target compounds were determined using gas chromatography-mass spectrometry. The total concentrations were higher in urban residential area than suburban residential area. They ranged from 0.31 to 6.16 ng/m3 with a mean of 2.07 ng/m3 and 0.33 to 2.87 ng/m3 with a mean of 1.02 ng/m3 in the urban and suburban residential areas, respectively. The predominant compounds determined both in the urban and suburban residential areas were benzo [b] fluoranthene, benzo [ghi] perylene, indeno [1, 2, 3-cd] pyrene and benzo [e] pyrene. The concentrations of 5- and 6-ring polycyclic aromatic hydrocarbons were higher in the urban than the suburban residential area, accounting for 77.3 % and 72.1% of total polycyclic aromatic hydrocarbons, respectively. Source identification was performed by principal component analysis. Two principal components were identified for each study area and these accounted for 95.9% and 83.3% of total variance, respectively. The results revealed that stationary source and vehicle emissions were the major pollution sources in the studied areas. The impact of emission regulations for automobiles in large Japanese cities, which were enacted during 2002 and 2003, was also studied. Based on the average total concentration, emission reduction was 47.8% and 18.6% in urban residential and suburban residential areas, respectively.

One of the main parameters of road management system is the process of prioritizing repair and maintenance alternatives and allocating resources for their implementation. In this research, after a comprehensive overview of prioritizing and allocation approaches, the Analytical Hierarchy Process (AHP) was utilized to develop a series of road repair and maintenance models. By using the AHP, it was possible to develop some of the basic data to build the parameters of models. The paired comparison tables, which were completed by groups of experts, were used in the prioritization and resource allocation models. The main sub models developed for a road section are for pavement, infrastructure and safety devices. Moreover; the infrastructure model includes, bridges, tunnels and retaining walls. Using the developed models and prioritization of alternatives and resource allocation can be preformed conveniently. The models were evaluated by comparison of models" results with the manual prioritization of four provinces by a group of experts and managers and the results were proved to be satisfactory.

This study aimed to identify the most influential factors in deforestation using multi-criteria decision-making method in a part of northern Zagros forests in Iran with a total area of 9177 hectares. Identifying the most important factors affecting deforestation, these factors were classified into three main criteria: human factors, natural factors and physiographic factors. By establishing hierarchical structure and performing pairwise comparisons, we determined the weight and importance of the main criteria and the sub-criteria. The final weight of each of the ten sub-criteria was extracted by combining the opinions of experts. After preparing the maps related to each of the sub-criteria, these maps were converted into standardized scale maps using the linear scale conversion method. In the final step, with the overlapping and integration of all sub-criteria maps, the zoning map of areas susceptible to deforestation was prepared in four groups with low risk, medium risk, high risk and very high risk. According to the results, 3. 25% of the territory was located in very high-risk, 55. 92% in high-risk, 40. 45% in moderate-risk and 0. 38% in low-risk zone. Accuracy assessment was done by comparing the deforestation risk zoning map with real deforestation map of the study area. The results showed that 77. 81% of the areas that has deforested in this period was located in highrisk and very high-risk zones. This amount of accuracy supported the efficiency of Multi Criteria Decision Making Method in deforestation zoning. Similar studies confirm the effectiveness of multi-criteria decision analysis systems and the presentation of GIS-based models in deforestation risk zoning.

Mineral Resources which can be regarded as a best potential opportunity to sustainable development of the country. Consequently appropriate investment in exploitation of these reserves can achieve high value added to the most industries. Therefore, it is necessary to recognize the priority of minerals for future investments. In this paper, the Analytical Hierarchy Process, in which a complex problem is modeled in a hierarchical structure showing the relationships of the goal, objectives (criteria) and alternatives, is used to recognize high priority 33 minerals for future investment with regards to the 6 criteria and 29 sub-criteria. For this purpose, on the bases of interview with mineral sector experts weights of criteria are calculated and then the piority of each mineral is determined for investments. The results show that copper, Iron, Phosphate, lead and zinc, coal, gold have high priority for future investments.