فهرست مطالب ziba batvandi

The order matrix method is an appropriate approach for basin balance evaluation. Recognition of the geometry and morphology of a basin, especially river morphometry and disequilibrium, can help with effective management and conservation of rivers. The purpose of this research is to determine geological formations and factors of equilibrium in the study area and to analyze the relationship between the forms and pattern of the drainage network in the Seymareh river basin using the order matrix model. In ArcGIS10.5, ranking of the streams in the study area and the scale were calculated, using Global Mapper, a 1:100000 geological map, a 1:50000 topographic map, and the DEM map of the area. There was no perfect balance between the number of lines of the same name that were interconnected on the two sides of the study area. The length of the drainage network in the left section of the study area is incremental in relation with the length of the right channel due to the effect of erosion on tectonic activity. Also, hierarchichal order in the basin is low, indicative of tectonic activity. The tectonics in the basin and dissimilarity of rocks in terms of resistance are the main causes of disequilibrium and inequality, leading to a longer lag time and a form of disorder and mismatch between the form and processes in the basin.

The main purpose of satellite image processing is preparing thematic and efficient maps, so choosing appropriate classification algorithm has important role in this case. In parametric methods such as maximum likelihood main problem is their dependence on the statistical distribution of input data. Artificial neural network is nonparametric classification method which is not dependent on any particular distribution and extract desired functions from within data. This study aimed to compare the efficiency of neural network and maximum likelihood to classify land cover Using Landsat Satellite Images. Determine classes and samples to classify land cover Using field operations, topographic maps, aerial photographs and maps were made and using the above information four classes vegetation cover, building, water and outdoor were selected. After applying two algorithms, the neural network and maximum likelihood on the Landsat 8 satellite image with OLI sensors, land cover map of the arvand coastal area was prepared. Multi-layer perceptron network neural network structure consists of three input neurons, seven intermediate neurons, and four output neurons. For network training, a back propagation algorithm has been used. with Kappa coefficient, the accuracy of the classification methods was evaluated. Based on the results, Artificial neural network method with kappa coefficient of 0.92 in comparison to maximum probability algorithm with kappa coefficient of 0.79 has a better performance in providing land cover map of the arvand coastal area which is due to Neural network is nonparametric and nonlinear.

The deployment of value-added services can be very effective in achieving a vision in order to facilitate the strategic decisions of ports. This research is based on empirical studies with a critical and descriptive realistic approach using case studies from Iranian ports. It aims to explore the potential contribution of value added services in ports as a logistical strategy for increasing competition. The lack of research on the provision of value-added services in marine logistics and its potential, as a competitive advantage is analysed from the point of view of port users. The results indicate that value-added services that are easily accessible and presented, are the provision of transportation services, warehousing services, fresh water supply services, refrigerated services, and product assembly sites. Based on the assumptions made in this article, the provision of value-added services has contributed to attract and keep port users (customers), and the conclusion is made on the validity of this claim.

The outbreak of diseases is a limiting factor in fish farms. A large number of plants are being used in traditional medicine for the treatment and control of diseases. One of such plants is ginger (Zingiber officinale). In this study antimicrobial effects of alcoholic extracts of ginger on six important positive and negative gram bacteria including Vibrio alginolyticus, Vibrio parahemolyticus, Micrococcus luteus, Staphylococcus epidermicus, Bacillus cereus, Stereptococcus zoo epidermicus were analyzed. Extraction was conducted on rhizomes of Ginger using 80% ethanol in laboratory. In order to assess antimicrobial effects of the ginger extract on the studied bacterial species, diffusion test on agar media using disk method on studied bacteria was conducted. Minimum Inhibitory Concentration (MIC) and Minimum Bactericidal Concentration (MBC) were determined by serial dilution assay using Liquid culture in 96 wells microplates. Results indicated that ginger alcoholic extracts have inhibitory effects on the bacterial growth and is lethal for all bacterial strains. Also ginger alcoholic extract has the most and the least growth inhibitory and lethal affects on Vibrio alginolyticus and Vibrio parahemolyticus, respectively.