فهرست مطالب میلاد بهرامی

Phytoremediation is a sustainable and inexpensive technology and has become popular as an alternative solution to traditional wastewater treatment methods. The aim of this study was to use fish farm effluent as a culture medium for the Azolla and also to improve the effluent. For this purpose, the initial amount of 60 g of Azolla was dvided in two treatments with three replications, including Azolla in the wastewater of Beluga sturgeon farm (SW) and Azolla in the water of Anzali wetland (AL) which were grown in-vitro for 30 days. Sampling of water was performed to evaluate some water quality parameters at 10, 20 and 30 days. At the end of the experiment, the plant growth performance, fatty acid profile, and approximate composition were measured. The results showed that maximum amount of biomass (295.22 g) and plant width (9.64 mm) were observed in SW treatment (p <0.05). Also, the results of fatty acid composition showed that saturated fatty acids in SW treatment had a significant increase compared to AL treatment. However, unsaturated fatty acids in SW treatment showed a significant decrease compared to AL (p <0.05). In addition, a significant downward trend in the water quality parameters (phosphate, nitrate, and nitrite) was observed in SW treatment on day 30. The highest and lowest contents of fat and moisture were observed in SW treatment (p <0.05). Therefore, the production of Azolla in Beluga farm effluent can be an effective and cost-effective method due to the rapid growth rate, high ability to absorb the amount of phosphorus and nitrogen in water, as well high nutritional values (protein and lipid contents) and high diversity of fatty acids.

To compare the central corneal thickness measured through Pentacam, OCTCasia2, and Orbscan IIz in normal individuals. In this cross sectional comparative study, the researcher investigated the eyes of 70 normal candidates (140 eyes) with the mean age of 31.42 ± 7.70, referring to Dr. Sedaghat eye clinic for refractive surgery. For each person, Central Corneal Thickness (CCT) was measured, using 3 devices including Pentacam, OCTCasia2 and Orbscan. The data were collected and analyzed. The Pearson Correlation Coefficient was employed to assess correlation of 3 devices with each other. Bland-Altman plot was also used to evaluate the agreement between devices. The mean CCT measured through Orbscan, Pentacam, and OCTCasia2 was reported to be 535.06±35.11 µm, 525.21±31.60 µm, and 511.06±30.87 µm, respectively. The Pearson Correlation Coefficient between Pentacam-OCTCasia2, OCTCasia2-Orbscan, Pentacam-Orbscan were r=0.955; P<0.001, r=0.913; P<0.001, r=0.941; P<0.001, respectively. There was a very good correlation and agreement between devices, but none of the devices had the ability to replace each other.

Earthquake as the most devastating natural disaster in urban areas causes huge physical and human damages worldwide. One way to assist reducing the impact of the earthquake on people and infrastructures is to produce a reliable seismic vulnerability map. The physical seismic vulnerability of a region as a multi criteria problem is concerned with seismic intensity, land slope, the number of building floors, building age and quality. Among the most important sources of uncertainty in determining the vulnerability of each urban statistical unit, is the uncertainty related to the conflicts in expert opinions concerning the level of severity of the seismic vulnerability. The main objective of this paper is to manage uncertainty considering different vulnerability classes allocated by the experts in integration of the concerned parameters. In this model, to reduce the uncertainty in the decision making process related to the expert opinions on allocating a seismic physical vulnerability class to each urban statistical unit, interval mathematics, genetic algorithm and granular computing methods are used. The physical seismic vulnerability map has been produced for Tehran on the basis of activation of North Tehran fault. Among 3174 urban statistical units, 150 randomly selected samples have been selected by 5 experts in related geoscience fields. The experts are asked to fill a questionnaire for allocating the physical seismic vulnerability of the samples. Due to the disaggregation in the experts’ knowledge on the physical seismic vulnerability of each statistical unit, their opinions have been integrated using interval mathematics. For the conflict resolution among the experts, genetic algorithm is used. Granular computing has been applied to manage the uncertainty caused by the large amount of information achieved from the parameters affecting the physical vulnerability to assess the seismic physical vulnerability. The relations among the input parameters and the vulnerability classes are presented in a decision table. The rules with a minimum conflict from the decision table are extracted. The vulnerability classes have been sorted from 1 to 5 considering 1 as the least vulnerable class and 5 as the most vulnerable class. According to the results, most of the statistical units in Tehran fall within interval class vulnerabilities of [3 4] and [5 4]. To compare the similarity between the results of the model and those of the previous research by Khamespnah in the same study area, who used an integrated model of granular computing and rough set theory, Spearman rank correlation coefficient was employed. The value of this coefficient was 0.47 that shows some similarities between the results. The accuracy of 76% was achieved in this research using Kappa index verifying the importance of managing uncertainty using interval mathematics.