Velocity curve studies of spectroscopic binary stars V380 Cygni, V401 Cyg, V523 Cas, V373 Cas and V2388 Oph

Using measured radial velocity data of five double lined spectroscopic binary systems V380 Cygni, V401 Cyg, V523 Cas, V373 Cas and V2388 Oph, we find corresponding orbital and spectroscopic elements via the method introduced by Karami & Mohebi (2007) and Karami & Teimoorinia (2007). Our numerical results are in good agreement wit. those obtained by others using more traditional methods.     

Velocity curve analysis of the spectroscopic binary stars RZ Cas,CC Cas,HS Her,HD 93917, V921 Her and Y Cygni by the artificial neural networks

We use an Artificial Neural Network (ANN) to derive the orbital parameters ofspectroscopic binary stars. Using measured radial velocity data of six double‐lined spectroscopic binary systems RZ Cas, CC Cas, HS Her, HD 93917, V921 Her and Y Cygni, we find corresponding orbital and spectroscopic elements. Our numerical results are in good agreement with those obtained by others using more traditional methods (© 2009 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Further Study on the Kinetics of Bioremediation of Soils Contaminated with the Herbicide 2,4‐Dichlorophenoxy Acetic Acid

The bioremediation of soil contaminated with the herbicide 2,4‐dichlorophenoxy acetic acid has been studied applying the bacterium Ralstonia eutropha. The effects of temperature, pH, and moisture content of soils on the rate of degradation of this herbicide have been investigated. The Taguchi experimental design method was applied to determine the relative impacts of the pertinent system variables. According to this approach, four series of experiments were performed under various operating conditions. In all four series the herbicide was degraded efficiently, however, the highest rate was observed at a temperature of 35°C, pH 6, and at 80% moisture content of the soil. A kinetic model for the biodegradation of the herbicide was developed for this particular conditions based on the Monod type growth equation and autocatalytic behavior of the decomposition reaction. A close correlation has been observed between the experimental data and those predicted from the model. The degree of agreement was between 85–95%.     

Discrimination of Different Erosion Levels of Porphyry Cu Deposits using ASTER Image Processing in Eastern Iran: a Case Study in the Maherabad, Shadan, and Chah Shaljami Areas

The Lut block, eastern Iran, is one of the most extensive Cenozoic magmatic rocks, that show suitable targets for porphyry Cu-Au and high-sulfidation epithermal Au related to porphyry Cu-Au mineralization. In this study, Advanced Spaceborne Thermal Emission and Reflection Radiometer （ASTER） was used to identify different erosion levels of three porphyry Cu deposits, including Maherabad, Shadan, and Chah Shaljami, located in the Lut block volcanic-plutonic belt. Alteration minerals, including kaolinite, dickite, alunite, jarosite, epidote, chlorite, montmorillonite, muscovite, biotite, orthoclase, gypsum, and quartz, are selected to map different alteration zones and erosion levels. Distributions of end-members are mapped by using the SAM and MTMF in VNIR and SWIR of ASTER bands and the results are evaluated against the field studies. For some end-members, the results of SAM processing are more reliable compared to the MTMF because the latter looses field spectra. The use of angle threshold in the SAM, and MF-score and infeasibility value in the MTMF or low abundance of some end-members, and finally comparison of output images of spectral processing show good correlation with alteration maps. Differentiation and explanation of various erosion levels of porphyry Cu deposits are done successfully by using the ASTER sensor data.     

Probabilistic reservoir operation using Bayesian stochastic model and support vector machine

Available water resources are often not sufficient or too polluted to satisfy the needs of all water users. Therefore, allocating water to meet water demands with better quality is a major challenge in reservoir operation. In this paper, a methodology to develop operating strategies for water release from a reservoir with acceptable quality and quantity is presented. The proposed model includes a genetic algorithm (GA)-based optimization model linked with a reservoir water quality simulation model. The objective function of the optimization model is based on the Nash bargaining theory to maximize the reliability of supplying the downstream demands with acceptable quality, maintaining a high reservoir storage level, and preventing quality degradation of the reservoir. In order to reduce the run time of the GA-based optimization model, the main optimization model is divided into a stochastic and a deterministic optimization model for reservoir operation considering water quality issues.The operating policies resulted from the reservoir operation model with the water quantity objective are used to determine the released water ranges (permissible lower and upper bounds of release policies) during the planning horizon. Then, certain values of release and the optimal releases from each reservoir outlet are determined utilizing the optimization model with water quality objectives. The support vector machine (SVM) model is used to generate the operating rules for the selective withdrawal from the reservoir for real-time operation. The results show that the SVM model can be effectively used in determining water release from the reservoir. Finally, the copula function was used to estimate the joint probability of supplying the water demand with desirable quality as an evaluation index of the system reliability. The proposed method was applied to the Satarkhan reservoir in the north-western part of Iran. The results of the proposed models are compared with the alternative models. The results show that the proposed models could be used as effective tools in reservoir operation.     

Shift changes and monotonic trends in autocorrelated temperature series over Iran

Temperature data from 29 synoptic stations in Iran for a period of 40?years (1966–2005) were analyzed to test for the existence of monotonic trends and shift changes in the annual, seasonal, and monthly mean air temperature series using the Mann–Kendall and Mann–Whitney tests. The influences of significant lag-1 serial correlation were eliminated from data by the trend-free pre-whitening method prior to the trend analysis. The magnitude of the temperature trends was derived from the Theil–Sen’s slope estimator. It was found that annual mean air temperature increased at 25 out of the 29 stations, of which 17 stations showed significant monotonic trends. The magnitude of the annual mean air temperature trends averagely was (+)0.224°C per decade. Most of the stations with the significant positive monotonic trends had a significant upward shift change. The analysis indicated that the change point year of the significant upward shift changes was 1972 for the whole stations except the coastal ones. Moreover, the strongest monotonic increasing trends and upward shift changes were observed in summer especially in August and September. The spatial analysis of the mean air temperature trends revealed the highest numbers of significant monotonic trends in the big cities of Iran. These findings provide more insights for better understanding of regional temperature behavior in the study area.     

Application of a new non-linear least squares velocity curve analysis technique for spectroscopic binary stars

Using measured radial velocity data of nine double lined spectroscopic binary systems NSV 223, AB And, V2082 Cyg, HS Her, V918 Her, BV Dra, BW Dra, V2357 Oph, and YZ Cas, we find corresponding orbital and spectroscopic elements via the method introduced by Karami and Mohebi (Chin. J. Astron. Astrophys. 7:558, 2007a) and Karami and Teimoorinia (Astrophys. Space Sci. 311:435, 2007). Our numerical results are in good agreement with those obtained by others using more traditional methods.     

Evaluation of Carcinogenic Risk Due to Accidental Ingestion of PAHs in Contaminated Soils

Polycyclic aromatic hydrocarbons (PAHs) are types of hazardous contaminants, which their ingestion could cause severe consequences on human health. Leakages from storage tanks, underground pipelines, and evaporation ponds are the main sources of soil and groundwater contaminations at the Tehran Oil Refinery area (TOR site), located in south of Tehran, Iran. In this study, soil samples were collected from different locations at and adjacent to a polluted stream in the south of the refinery. The samples were analyzed for two hazardous PAH compounds, namely benzo[a]anthracene and acenaphthene. The clean up levels due to the accidental ingestion of contaminated soils at the site were also investigated in accordance to the U.S.EPA guidelines. Comparing the soil analysis results indicated that the benzo[a]anthracene concentrations in the samples varied from 53 to 299 mg/kg, which were higher than the clean up level of 1.17 mg/kg. Thus, soil remediation is required for this contaminant. The acenaphthene analysis results denoted that the average concentration of this contaminant was below the clean up level of 116.67 mg/kg, indicating that no treatment for this contaminant is necessary at the TOR site. Also, because the slope of the ground extends to the south of the stream, which stimulates the migration of the contaminants in this direction due to advection and dispersion mechanisms, the average of benzo[a]anthracene concentrations in south samples was higher than north samples (i.e., C_avg(S) = 160 ppm, C_avg(N) = 113 ppm). Various treatment techniques such as thermal desorption, soil vapor extraction (SVE), and solidification/stabilization (S/S) were investigated for this site. Due to moderate to high plasticity and relatively low permeability of the soil and low volatility of benzo[a]anthracene, however S/S method is recommended as a practical approach for the remediation of the soil at the site.