Purpose -Knowledge sharing (KS) of employees has numerous benefits for organizations. Therefore, the aim of this study is to provide a model for KS in research centers (RCs) that can facilitate the employee's knowledge sharing behavior (KSB). Design/methodology/approach -Based on the extensive literature review, a valid instrument was adopted to collect the required data set on KS, KSB and intention to KS, and finally 317 complete questionnaires were collected from Iranian research centers. The structural equation modeling (SEM) was used to assess the measurement model and to test the research hypotheses. Findings -The findings show that intrinsic and extrinsic motivational factors and intention to methods of KS play an important role in KSB. In other words, simultaneous supply of motivational factors and KS methods interesting for employees lead to their KSB. The SEM confirmed the research model and showed a good fit of it. Practical implications -The implication emanating from this study is that the employees' KSB in RCs as a significant part depends on simultaneous supplying of motivational factors (especially intrinsic motivational factors) and methods of KS that are interesting for employees. Originality/value -What distinguishes this study from other studies in KS domain could be implied in two subjects. First, the presented model is simple and prepared of the introduced factors, which will lead to KSB. Second, this study was conducted in diverse research fields such as electrical and electronics, telecommunications, materials, chemistry, biotechnology, information technology, management and industrial engineering, computer network security, mechanical and manufacturing. The research model was derived from the collected data of these areas that is unique in this domain.

Introduction: Public urban parks are accessible to everyone in a city. The time people need to reach a public park influences the accessibility of the park. Parks far away from home are less accessible because of time to spend to reach them than those in the neighborhood. Good green urban infrastructure aims to provide different types of parks (by size and structure) to all urban dwellers. The network of parks should allow all inhabitants to have access to parks within close proximity to their residences.

Introduction: In recent years, models of land-use change and urban growth have become important tools for city planners, economists, ecologists, and resource managers. In most models, future land-use changes are forecasted based on past development pattern and expansion to periphery. While today, metropolitan areas employ smart-growth strategies. The main objectives in this study are according to the smart-growth infill. In this approach, transmission of incompatible land uses to the outside of the city boundary, redevelopment, improvement, and renovation of urban old district and worn-out texture and reuse of abandoned land to new urban development are considered. In fact, the objective is the using of the infill development pattern to modeling approach for simulating urban future development using potentials inside the city. Methods: This paper presents a Land Transformation Model of urban land-use change based on an artificial neural network and a geographical information system. For developing this approach, future development of Tabriz city based on past development trend and infill development pattern is modeled.

Introduction: Due to the health effects caused by airborne pollutants in urban areas, forecasting of air quality parameters is one of the most important topics of air quality research. During recent years, statistical models based on artificial neural networks (ANNs) have been increasingly applied and evaluated for forecasting of air quality. Methods: The development of ANN and multiple linear regressions (MLRs) has been applied to short-term prediction of the NO 2 and NO x concentrations as a function of meteorological conditions. The optimum structure of ANN was determined by a trial and error method. We used hourly NO x and NO 2 concentrations and metrological parameters, automatic monitoring network during October and November 2012 for two monitoring sites (Abrasan and Farmandari sites) in Tabriz, Iran. Results: Designing of the network architecture is based on the approximation theory of Kolmogorov, and the structure of ANN with 30 neurons had the best performance. ANN trained by scaled-conjugate-gradient (trainscg) training algorithm has implemented to model. It also demonstrates that MLP neural networks offer several advantages over linear MLR models. The results show that the correlation coefficient (R 2 ) values are 0.92 and 0/94 for NO 2 and NO x concentrations, respectively. But in MLR model, R 2 values were 0.41 and 0.44 for NO 2 and NO x concentrations, respectively. Conclusions: This work shows that MLP neural networks can accurately model the relationship between local meteorological data and NO 2 and NO x concentrations in an urban environment compared to linear models.

Lake Urmia (LU) is considered as the largest salt water lake in Iran and has severe restrictions on water resources and becoming a salt lake increasingly. The LU drought will Couse ecological, health, social and economic problems. Land-use change and the increasing of salt areas evaluated in this work using satellite imagery. We evaluated the present situation and changes of the lake area in the past and further changes until 2025. The results indicated that from 1987 to 2000, the process of change has slowed down and less than 2% of the lake’s water area was reduced, and from 2000 to 2010, these shrinking processes were faster and more than 28% of the lake water area disappeared. The intensity of the shrinking from 2010 to 2014 is very severe. Using the Land Transformation Model, the continuation of the changes was modeled until 2025. The results of the modeling indicate the conversion of the water lake to salt lake in this period, and in the north part, the shallow waters occupy 0.7% of the total lake area. The result shows that climate change was not the significant factors for drying up of the lake but human factors such as building dams to store water for irrigation, increasing groundwater use by established deeper wells for agricultural irrigation were the important factors for drying. With changing of management of the waters leading to the lake and the transfer of new water resources to the lake between 2014 and 2016, the area of the lake increased to a double. It was evident that by proper planning and managing of water resources, the lake’s restoration can be achieved.