Adaptive neuro-fuzzy selection of the optimal parameters of protective spur dike

This study proposes a new approach for determining optimum dimensions of protective spur dike to mitigate scour amount around existing spur dikes. The main objective of this article was to predict the most optimum values of the protective spur dikes to reach the best performance. To predict the protective spur dike parameters for scour controlling around spur dikes, this paper constructed a process which selects the optimal protective spur dike parameters in regard to actual length of the protective spur dike, actual length of the main spur dikes, distance between the protective spur dike and the first spur dike, angle between protective spur dike and flow direction, flow intensity and median size of bed sediments with adaptive neuro-fuzzy (ANFIS) method. To build a protective spur dike with the best features, it is desirable to select and analyze factors that are truly relevant or the most influential to the spur dike. This procedure is typically called variable selection, and it corresponds to finding a subset of the full set of recorded variables that exhibits good predictive abilities. In this study, architecture for modeling complex systems in function approximation and regression was used, based on using ANFIS. Variable searching using the ANFIS network was performed to determine how the five factors affect the protective spur dike. Experimental model of the protective spur dike was used to generate training and checking data for the ANFIS network.     

f(T) modified teleparallel gravity as an alternative for holographic and new agegraphic dark energy models

In the present work, we reconstruct different f (T)-gravity models corresponding to the original and entropy-corrected versions of the holographic and new agegraphic dark energy models. We also obtain the equation of state parameters of the corresponding f(T)-gravity models. We conclude that the original holographic and new agegraphic f (T)-gravity models behave like the phantom or quintessence model, whereas in the entropy-corrected models, the equation of state parameter can justify the transition from the quintessence state to the phantom regime as indicated by the recent observations.     

Third Order Effect of Rotation on Stellar Oscillations of a B Star

We aim at investigating the effect of rotation up to the third order in the angular velocity of a star on the p and g modes, based on the formalism developed by Soufi et al. Our ultimate goal is the study of oscillations of β Cephei stars which are often rapidly rotating stars. Our results show that the third-order perturbation formalism presented by Soufi et al. should be corrected for some missing terms and some misprints in the equations. As a first step in our study of β Cephei stars, we quantify by numerical calculations the effect of rotation on the oscillation frequencies of a uniformly rotating zero-age main-sequence star with 12 M<,??>. For an equatorial velocity of 100km s-1, it is found that the second-and third-order corrections for (l, m)=(2, 2), for instance, are of the order of 0.01% of the frequency for radial order n=6 and reaches up to 0.5% for n=14.     

Velocity curve analysis of the spectroscopic binary stars PV Pup,HD 141929, EE Cet and V921 Her by nonlinear regression

We use the method introduced by Karami & Mohebi (2007), and Karami & Teimoorinia (2007) which enable us to derive the orbital parameters of the spectroscopic binary stars by the nonlinear least squares of observed vs. curve fitting (o-c). Using the measured experimental data for radial velocities of the four double-lined spectroscopic binary systems PV Pup, HD 141929, EE Cet and V921 Her, we find both the orbital and the combined spectroscopic elements of these systems. Our numerical results are in good agreement with those obtained using the method of Lehmann-Filhés.     

Entropy-corrected new agegraphic dark energy in Hořava-Lifshitz cosmology

We study the entropy-corrected version of the new agegraphic dark energy (NADE) model and dark matter in a spatially non-flat Universe and in the framework of Hořava-Lifshitz cosmology. For the two cases containing noninteracting and interacting entropy-corrected NADE (ECNADE) models, we derive the exact differential equation that determines the evolution of the ECNADE density parameter. Also the deceleration parameter is obtained. Furthermore, using a parametrization of the equation of state parameter of the ECNADE model as ω _Λ(z)=ω ₀+ω ₁ z, we obtain both ω ₀ and ω ₁. We find that in the presence of interaction, the equation of state parameter ω ₀ of this model can cross the phantom divide line which is compatible with the observation.     

Velocity curve analysis of the spectroscopic binary stars V2082 Cyg,V918 Her,BW Dra,V2357 Oph,YZ Cas and V380 Cygni by the Artificial Neural Networks

We use an Artificial Neural Network (ANN) to derive the orbital parameters of spectroscopic binary stars. Using measured radial velocity data of six double-lined spectroscopic binary systems V2082 Cyg, V918 Her, BW Dra, V2357 Oph, YZ Cas and V380 Cygni, we find corresponding orbital and spectroscopic elements. Our numerical results are in good agreement with those obtained by others using more traditional methods.     

Accumulation of heavy metals in soil and uptake by plant species with phytoremediation potential

Contamination of heavy metals represents one of the most pressing threats to water and soil resources, as well as human health. Phytoremediation can be potentially used to remediate metal contaminated sites. In this study, concentrations of copper, zinc, iron, and magnesium accumulated by native plant species were determined in field conditions of Hame Kasi iron and copper mine in the central part of Iran in Hamadan province. The results showed that metal accumulation by plants differed among species and tissue bodies. Species grown in substrata with elevated metals contained significantly higher metals in plants. Metals accumulated by plants were mostly distributed in root tissues, suggesting that an exclusion strategy for metal tolerance exists widely amongst them. The mentioned species could accumulate relatively higher metal concentrations far above the toxic concentration in the plant shoots. With high translocation factor, metal concentration ratio of plant shoots to roots indicates internal detoxification metal tolerance mechanism; thus, they have potential for phytoextraction. The factors affecting metal accumulation by plant species including metal concentrations, pH, electrical conductivity, and nutrient status in substrata were measured. Mostly, concentrations of zinc and copper in both aboveground and underground tissues of the plants were significantly, positively related to their total in substrata, while iron, zinc, and copper were negatively correlated to soil phosphorus.     

Changes in nutritional values induced by butachlor in juvenile diploid and triploid <Emphasis Type="Italic">Clarias gariepinus</Emphasis>

There is a paucity of information about the effects of environmental stressors on nutritional values in fish. This study investigated the effects of the organochlorine pesticide, butachlor, on key indicators of nutritional value in both diploid and triploid African catfish (Clarias gariepinus). Amino acids (AA), fatty acids (FA), and the proximate composition (protein, lipid, moisture, and ash content) in the white muscle of full-sibling juvenile fish were measured following a 21-day exposure to graded butachlor concentrations [mean measured: 26.3, 48.4, and 66.1 µg/L]. No significant differences in AAs, FAs, protein, lipid, moisture, or ash content were observed between unexposed diploid and triploid fish. In diploids, exposure to butachlor significantly altered the concentrations of some of the AAs and FAs, and protein content, when compared to the control group. In triploid fish, however, butachlor treatments had no effect on the AA or proximate composition, but significantly changed the concentration of two individual FAs in the muscle. Butachlor treatments showed fewer changes in the nutritional values of triploid fish. This research is the first to study the nutritional values in any polyploid animals following the exposure to a contaminant.     

Bat algorithm for dam–reservoir operation

Optimizing reservoir operation rule is considered as a complex engineering problem which requires an efficient algorithm to solve. During the past decade, several optimization algorithms have been applied to solve complex engineering problems, which water resource decision-makers can employ to optimize reservoir operation. This study investigates one of the new optimization algorithms, namely, Bat Algorithm (BA). The BA is incorporated with different rule curves, including first-, second-, and third-order rule curves. Two case studies, Aydoughmoush dam and Karoun 4 dam in Iran, are considered to evaluate the performance of the algorithm. The main purpose of the Aydoughmoush dam is to supply water for irrigation. Hence, the objective function for the optimization model is to minimize irrigation deficit. On the other hand, Karoun 4 dam is designed for hydropower generation. Three different evaluation indices, namely, reliability, resilience, and vulnerability were considered to examine the performance of the algorithm. Results showed that the bat algorithm with third-order rule curve converged to the minimum objective function for both case studies and achieved the highest values of reliability index and resiliency index and the lowest value of the vulnerability index. Hence, the bat algorithm with third-order rule curve can be considered as an appropriate optimization model for reservoir operation.     

Prediction of river flow using hybrid neuro-fuzzy models

The complex nature of hydrological phenomena, like rainfall and river flow, causes some limitations for some admired soft computing models in order to predict the phenomenon. Evolutionary algorithms (EA) are novel methods that used to cover the weaknesses of the classic training algorithms, such as trapping in local optima, poor performance in networks with large parameters, over-fitting, and etc. In this study, some evolutionary algorithms, including genetic algorithm (GA), ant colony optimization for continuous domain (ACO_R), and particle swarm optimization (PSO), have been used to train adaptive neuro-fuzzy inference system (ANFIS) in order to predict river flow. For this purpose, classic and hybrid ANFIS models were trained using river flow data obtained from upstream stations to predict 1-, 3-, 5-, and 7-day ahead river flow of downstream station. The best inputs were selected using correlation coefficient and a sensitivity analysis test (cosine amplitude). The results showed that PSO improved the performance of classic ANFIS in all the periods such that the averages of coefficient of determination, R², root mean square error, RMSE (m³/s), mean absolute relative error, MARE, and Nash-Sutcliffe efficiency coefficient (NSE) were improved up to 0.19, 0.30, 43.8, and 0.13%, respectively. Classic ANFIS was only capable to predict river flow in 1-day ahead while EA improved this ability to 5-day ahead. Cosine amplitude method was recognized as an appropriate sensitivity analysis method in order to select the best inputs.