Over the last years, artificial intelligence models have been widely and successfully applied in many fields. In the present study, the efficiency of adaptive neuro-fuzzy inference system (ANFIS), SVM (Support Vector Machine) and Least Squares Support Vector Machines (LS-SVM) have been investigated to estimate the sediment concentration in four gauging stations, namely Jangaldeh, Nodeh, Arazkoosh, and Gazaghly along the Gorganrood River in Golestan province, Iran. The models were defined based on the five different combinations of the river flow and precipitation using time lags from 0 to 2 previous days. The results showed that the LS-SVM model with simplex search procedure had a better performance than the grid search method. Meanwhile, the results obtained from ANFIS model which estimated sediment concentration in Jangaldeh, Nodeh, Arazkoose and Ghazaghli stations with MEF Error of 5. 3, 13. 4, 4. 8 and 2. 8 percent, respectively, suggested a higher performance than other models. Overall, at all stations except Gazaghly, considering the antecedent flow with two-day time lag as the input data of the model increased the error magnitudes. Furthermore, the rainfall of the same day and one-day time lag could only enhance the efficiency of the model at Arazkooseh station.

Background and Aim: Considering the importance of the design of framework in the stress distribution and probability of creating initial cracks in connectors region, proper design of framework is considered as a requirement. For this purpose, in order to examine the stress distribution in two designs of straight and curved framework, comparison was made using FEM method.Materials and Methods: Research using a descriptive method of FEM was performed. A laboratory prototype was produced and was scanned in a digital form, using CMM equipment by the laboratory and a super spot from the geometry of the part was created. The super spot was transformed to a 3D solid model by utilizing Catia software. The above model was divided into a high number small elements, using Meshing method, by Abaqus software to analyze the finite elements. Afterwards, by Boundary Condition definition, the part was ready to be analyzed. The mechanical properties of Zirconia was entered into the software in the form of a table. Reviewing stress distribution in the bridge specially with the stress on connectors region was performed. Three vertical loading models for each part were applied in central and distal Fosa and analyzed separately.Results: In this study, the findings were described in the form of Von mises stress in Mpa scale. The stress in the curved model was more than the Straight model. As a result, the stress distribution is more desirable.Conclusion: According to the results of this study, the straight model showed a more desirable stress distribution compared with the curved model. However, it should be noted that these results were obtained in F.E. environment.

The main objective of this study is to examine the relationship between inflation, unemployment and economic growth in terms of the Phillips, Okun and Aggregate supply curves. In this regard, we use nonlinear smooth transition autoregressive methods during the period from 1380 to 1393. The results show that A) Based on the Terasvirta Test, the Phillips curve follows the nonlinear logistic model; the Okun curve follows the exponential nonlinear model and the aggregate supply follows the linear model. B) The Phillips model follows two regimes; in the down regime, the relationship between inflation and unemployment is positive and in the up regime, this is a negative relationship. C) The Okun model follows two regimes that, in down regime, has a negative relationship between economic growth and unemployment; but in up regime, the relationship between economic growth and unemployment is positive. D) The relationship between economic growth and inflation is positive.

Elliptic curve cryptosystems (ECC) are new generations of public key cryptosystems that have a smaller key size for the same level of security. The exponentiation on elliptic curve is the most important operation in ECC, so when the ECC is put into practice, the major problem is how to enhance the speed of the exponentiation. It is thus of great interest to develop algorithms for exponentiation, which allow e cient implementations of ECC. In this paper, we improve e cient algorithm for exponentiation on elliptic curves de ned over Fp in terms of a ne coordinates. The algorithm computes 2n2(2n1P +Q) directly from random points P and Q on an elliptic curve, without computing the intermediate points. Moreover, we apply the algorithm to exponentiation on elliptic curves with width{w Mutual Opposite Form (wMOF) and analyze their computational complexity. This algorithm can speed up the wMOF exponentiation of elliptic curves of size 160{bit about (21. 7%) as a result of its implementation with respect to a ne coordinates.

Unemployment-inflation trade-off is known as Philips curve. This relationship was first defined for England economy by Professor Philips since 1958. Much development has been submitted by different economic schools about this innovation.In this paper, apart from dealing with the New-Keynesians Philips curve, the curve mentioned is estimated for Iranian economy. In addition, we are going to compare the result of this estimation with the result of other studies. The result shows that, in comparison with other standard models, New-Keynesians Philips curve is more corresponding with economic condition of Iran.Considering this matter, unemployment-inflation trade-off exists both in long-run and short-run, however, this relationship is weaker in the long-run and politicians can influence inflation and unemployment rate in the short-run and long-run by demand management policies, but this influence is weaker in the long-run.

Background and purpose: Enterococci as a group of bacteria infecting raw foods and dairy products can contaminate different food products. The purpose of this study was to isolate strains of enterococci based on the Real time PCR method using melting curve analysis in food samples.Materials and methods: In this experimental study, 510 samples were collected from which 138 different samples (chicken, meat, milk, and cheese) containing different strains of enterococci were investigated. Then, identification of Enterococcus species was performed by targeting specific sites and specific primers were designed according to Real time PCR-based melting curve analysis.Results: Based on melting curve analysis by Real Time PCR, the Enterococcus species identified were as follows: E.faecalis in 84 isolates (60.86%), E.faecium in 48 (34.78%), E.gallinarum in 1 (0.7%), E.avium in 4 (2.8%), and E.Caselli flavus in 1 isolate (0.7%). The most frequent isolates were detected in 29 samples of chicken meat (51.44%) and red meat (n=21, 24.63%). Considering the results of sequencing as a Gold a standard test, the sensitivity and specificity of phenotypic methods for E.faecalis, E.faecium, E.gallinarum, E.avium, and E.Caselli flavus were 94.78% and 90.74%, 89.13 % and 97.77 %, 50% and 98.52%, 66.66% and 98.52%, and 50% and 98.56%, respectively. A significant relationship was observed between the sample and distribution of Enterococcus species (P≤0.05).Conclusion: Due to extensive viability error in identification of Enterococcus species isolated from food by phenotypic methods, using a rapid and sensitive method is necessary.

To obtain soil-moisture characteristic curve experimentally is time-consuming and usually subject to considerable errors. So, many investigators have tried to predict soil-moisture characteristic curve by different models. One of these models predicts soil moisture characteristic curve based on soil particle size distribution and bulk density. In this model, soil particle size distribution curve is divided into a number of segments, each with a specific particle radius and cumulative particle mass greater than that of the radius. Using these data, soil-moisture characteristic curve was estimated. In this model, a scale factor, a , is used which may be considered as a constant, or obtained by logistic or linear procedures. The average values of a for clay, silty clay, sandy loam, two loam soils, and two silty clay loam soils were 1.159, 1.229, 1.494, 1.391, 1.393, 1.253 and 1.254, respectively. For most conditions, soil particle size distribution curve is not available, but only the percentages of clay, silt, and sand could be obtained using soil textural data, which is not enough to draw a precise soil particle size distribution curve. In this situation, a precise soil particle size distribution curve must be initially developed on the basis of which the soil moisture characteristic curve can be predicted. In this study, using soil textural data of seven different soils, soil moisture characteristic curve of each was estimated. In these estimations, logistic and linear methods were used to obtain the a value. Then, the results were compared with those of measured soil moisture characteristic curve. For estimation of soil particle size distribution curve, two extreme values for soil particle radius, 125 and 999 mm, were used.The results indicated that using particle radius of 999 mm is more appropriate. On the other hand, it was found that for clay, silty clay, and sitly clay loam texture, it is more appropriate to employ a linear equation to determine a for estimating soil-moisture characteristic curve while the logistic equation can be more appropriately used for loam and sand loam textures.