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The Competitive Intelligence is the energy resulting from the systematic process of collection, review, and analysis of the information of environment, competitors, customers, suppliers, and market trends. It should be mentioned that Iran includes multiple subcultures. Now the major question that arises is whether we can try to promote competitive intelligence in an environment consisting of subcultures, without benefiting from cultural intelligence and compatibility with different cultures, or not? This research seeks to design a developed & organized framework in regards to the concepts of “Cultural Intelligence” and “Competitive Intelligence” in connection with each other and in Iranian knowledge based corporations; It also tries to take action towards development of borders of the knowledge available in this area. This research is of an applied type, with surveying nature. The required data has been collected through a premeditated sampling, from Iranian knowledge-based corporations, being active in Information Technology. Structural Equation method and SmartPLS software were used for data analysis. The results indicate that the Cultural Intelligence affect the Competitive Intelligence of Iranian Knowledge-Based Corporations.

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In this study, impact of work climate and social network size on the volunteers’ motivation were investigated through the application of self-determination theory. This research type is applied and research method is Descriptive. In this study Active volunteers of Tehran Red Crescent Society were selected as Statistical population. Sample size was estimated 146 using Cochran Formula. Two stage Cluster sampling method used for sampling. For gathering data standard questionnaires were used. For testing hypotheses, Structural Equation Modeling (SEM) using LISREL software were employed. Results showed that the work climate has positive effect on volunteers autonomous and controlled motivation while the needs satisfaction acts as a mediator. Also the results showed that the social network size has positive effect on the volunteers’ autonomous motivation and negative effect on the volunteers’ controlled motivation and the needs satisfaction acts as a mediator of both impacts.

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The evolution of financial data shows a high degree of volatility of the series, coupled with increasing difficulties of forecasting financial variables. Some alternative forecasting methods, based on the literature review, have been developed, which can be particularly useful in the analysis of financial time series. Despite of the numerous time series forecasting models, the accuracy of time series forecasting is fundamental to many decision processes. Selecting an efficient technique in unique situations is very difficult task for forecasters. Many researchers have integrated linear and nonlinear methods in order to yield more accurate results. In practice, it is difficult to determine the time series under study are generated from a linear or nonlinear underlying process while many aspects of economic behavior may not be pure linear or nonlinear. Although both ARIMA and Artificial Neural Networks (ANNs) models have the flexibility in modeling a variety of problems, none of which is universally the best model used indiscriminately in every forecasting situation. In this paper, based on the foundations of ARIMA and ANNs models, a hybrid method is proposed to forecast exchange rate. Empirical results indicate that integrating linear and nonlinear ARIMA and Artificial Neural Networks (ANNs) models can be an effective way to improve forecasting accuracy achieved by either of the above linear and nonlinear models used separately.

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Backgrounds: The global spread of carbapenemase-producing Enterobacteriaceae represents a public health concern. This study aimed to investigate the prevalence of carbapenem resistance and the presence of some oxacillinase types and class 1-3 integrons among Enterobacter clinical isolates from an Iranian inpatient population.
Materials & Methods: Ninety Enterobacter isolates from hospitalized patients were diagnosed by microbiological methods. Antibiogram pattern was also determined. The presence of class 1-3 integrons and four types of oxacillinase genes was assessed using PCR.
Findings: Among 90 Enterobacter isolates, the most common species was E. aerogenes, (45.6%), followed by E. cloacae (30%). The highest resistance rate was against ampicillin (96.7%). Multi-drug resistance (MDR) was substantial (93%). Carbapenemase-producers were detected in 96% of carbapenem-resistant isolates by mCIM test. The frequency of evaluated genes was as follows: intI1 = 50 (55.6%), intI2 =12 (13.3%), bla_oxa-1 =6 (6.7%), bla_oxa-2 =5 (5.6%), bla_oxa-10 =18 (20%), and bla_oxa-48 =18 (20%).
Conclusion: Determinants of class 1 integron along with OXA-10 and OXA-48 like carbapememases are responsible for relatively considerable carbapenem resistance among isolates. This is the first report about the presence of OXA-10 and OXA-48-producing Enterobacter spp. in Iran, indicating that the prevalence of oxacillinases in the country might be on the rise.    

 

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Backgrounds: Diabetic patients are at risk of developing serious foot infections with methicillin-resistant Staphylococcus aureus (MRSA) strains, which are associated with high morbidity and mortality. This study aimed to investigate the frequency of different prophage types and virulence factors among MRSA strains isolated from patients with diabetic foot infections (DFIs) in a referral hospital in Tehran, Iran during 2019 and 2020.
Materials & Methods: A total of 238 S. aureus isolates were collected and confirmed using specific primers. The presence of staphylococcal enterotoxins (sea-seq) and hlb, sak, eta, etb, and tsst-1 genes among MRSA isolates was tested using separate polymerase chain reaction (PCR) assays. Also, multiplex PCR was employed for prophage typing of MRSA isolates.
Findings: A total of 73 (31%) isolates were confirmed as MRSA, among which four prophage types and 13 different prophage patterns were identified, and prophage type SGF and prophage pattern 7 consisting of SGB, SGF, SGFa, and SGFb types were the dominant ones. Also, 11 enterotoxin-encoding genes and four virulence factor genes were detected among the isolates. All MRSA isolates were positive for sea, sek, seq, and hlb genes. Moreover, out of 12 different enterotoxin patterns, most MRSA isolates were classified into enterotoxin pattern 1, harboring three enterotoxin genes (sea, sek, and seq).
Conclusion: This study results indicated the presence of different prophage types and virulence factor genes among MRSA strains isolated from DFI patients, which enable them to produce a variety of diseases.

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The purpose of this research is developing a model for competencies of strategic management and organization consultants. The research method is qualitative and in accordance with the emerging Glaser approach of grounded theory. 21 strategy consultants participated in this research who have been selected through purposeful sampling and snowball approach. Semi-structured interviews were used for data collection and MAXQDA version 10 software and triple coding were used for data analysis. The results indicated that the competencies of strategy consultants, includes personality competencies, general management competencies, general consultancy competencies, and specialized competences of strategic management and management consulting. These competencies are necessary for consultants to be successful. At the lowest level are personality competencies. These are the competencies that are useful to success in any field of activity, but they are vital for strategy consultants. Managerial and consulting competencies are at the second level of the pyramid that are essential for any managers and consultants. At the highest level of the pyramid, there are two categories of strategic management and management consulting competencies. Strategic management competencies are common to all who are involved in formulating, implementing or controlling organizational strategies. Management consulting competencies are also common in all strategic management consultants who deal with top-level managers in an organization.

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Khorasan Province is one of the most important provinces of Iran, especially as regards agricultural products. The prediction of crop yield with available data has important effects on socio-economic and political decisions at the regional scale. This study shows the ability of Artificial Neural Network (ANN) technology and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for the prediction of dryland wheat (Triticum aestivum) yield, based on the available daily weather and yearly agricultural data. The study area is located in Khorasan Province, north-east of Iran which has different climate zones. Evapotranspiration, temperature (max, min, and dew temperature), precipitation, net radiation, and daily average relative humidity for twenty-two years at nine synoptic stations were the weather data used. The potential of ANN and Multi-Layered Preceptron (MLP) methods were examined to predict wheat yield. ANFIS and MLP models were compared by statistical test indices. Based on these results, ANFIS model consistently produced more accurate statistical indices (R2= 0.67, RMSE= 151.9 kg ha-1, MAE= 130.7 kg ha-1), when temperature (max, min, and dew temperature) data were used as independent variables for prediction of dryland wheat yield.

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Saffron is one of the most valuable agricultural and medicinal plants of the world and has a special place in Iran's export of products. Presently, Iran is the world's largest producer and exporter of saffron and more than 93/7% of the world production belongs to Iran. However, despite the long history of saffron cultivation and its value-added in comparison to many of the other crops in the country, a lower share of new technologies is assigned to it, and its production is mainly based on local knowledge. This study aimed to develop and evaluate the performance of Adaptive Neuro-Fuzzy Inference System model (ANFIS) in calculating the yield of saffron using meteorological data from 20 synoptic stations in the province, including evapotranspiration, temperature (maximum, minimum), the mean relative humidity, and rainfall. To this end, by using software Wingamma, data and parameters were analyzed and the best combinations of inputs to the model were determined. In order to assess the models, statistical parameters of correlation coefficient, the mean absolute error, and mean square error were used to predict the performance of the plant. ANFIS model was most effective when the data of total minimum temperature, precipitation, evapotranspiration, and relative humidity of autumn were used as independent variables for forecasting yield (R²= 0.5627, RMSE= 2.051 kg ha^-1, and MAE = 1.7274 kg ha^-1) .

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Researchers have focused mainly on the importance and determinability of the front end phase in the continuation of collaboration and renewal of the collaborative processes of the organizations involved, but an exploration of the factors to be taken into consideration at the front has been overlooked. Accordingly, this Paper aims at delineation of a framework for inter organizational cooperation in the front-end phase. Interpretive Structural modeling and structural equation modeling are used to analyze the data. The statistical population of the study consists of 9 experts in the Iranian public and private sectors as well as university professors in the Interpretive Structural Modeling section. For the structural equation modeling, the sample consists of 415 employees and managers of the organizations which have been involved in different Inter organizational projects. It is indicated that top management commitment, communication, motivation, shared vision, inter relationships, consistent goals, cooperation culture, trust, joint planning, power distribution, conflict management, creation of control and monitoring systems, creation of information systems, As well as Political and economic uncertainty affect the effectiveness of inter organizational cooperation. It is also demonstrated that the effectiveness of inter organizational cooperation contributes to the value creation and business success.