Showing papers by "University of Tabriz published in 2009"

The Ascomycota Tree of Life: A Phylum-wide Phylogeny Clarifies the Origin and Evolution of Fundamental Reproductive and Ecological Traits

Abstract: We present a 6-gene, 420-species maximum-likelihood phylogeny of Ascomycota, the largest phylum of Fungi. This analysis is the most taxonomically complete to date with species sampled from all 15 currently circumscribed classes. A number of superclass-level nodes that have previously evaded resolution and were unnamed in classifications of the Fungi are resolved for the first time. Based on the 6-gene phylogeny we conducted a phylogenetic informativeness analysis of all 6 genes and a series of ancestral character state reconstructions that focused on morphology of sporocarps, ascus dehiscence, and evolution of nutritional modes and ecologies. A gene-by-gene assessment of phylogenetic informativeness yielded higher levels of informativeness for protein genes (RPB1, RPB2, and TEF1) as compared with the ribosomal genes, which have been the standard bearer in fungal systematics. Our reconstruction of sporocarp characters is consistent with 2 origins for multicellular sexual reproductive structures in Ascomycota, once in the common ancestor of Pezizomycotina and once in the common ancestor of Neolectomycetes. This first report of dual origins of ascomycete sporocarps highlights the complicated nature of assessing homology of morphological traits across Fungi. Furthermore, ancestral reconstruction supports an open sporocarp with an exposed hymenium (apothecium) as the primitive morphology for Pezizomycotina with multiple derivations of the partially (perithecia) or completely enclosed (cleistothecia) sporocarps. Ascus dehiscence is most informative at the class level within Pezizomycotina with most superclass nodes reconstructed equivocally. Character-state reconstructions support a terrestrial, saprobic ecology as ancestral. In contrast to previous studies, these analyses support multiple origins of lichenization events with the loss of lichenization as less frequent and limited to terminal, closely related species.

A Multivariate ANN-Wavelet Approach for Rainfall–Runoff Modeling

Abstract: Without a doubt the first step in any water resources management is the rainfall–runoff modeling over the watershed. However considering high stochastic property of the process, many models are being still developed in order to define such a complex phenomenon in the field of hydrologic engineering. Recently Artificial Neural Network (ANN) as a non-linear inter-extrapolator is extensively used by hydrologists for rainfall–runoff modeling as well as other fields of hydrology. In the current research, the wavelet analysis was linked to the ANN concept for modeling Ligvanchai watershed rainfall–runoff process at Tabriz, Iran. For this purpose the main time series of two variables, rainfall and runoff, were decomposed to some multi-frequently time series by wavelet theory, then these time series were imposed as input data to the ANN to predict the runoff discharge 1 day ahead. The obtained results show the proposed model can predict both short and long term runoff discharges because of using multi-scale time series of rainfall and runoff data as the ANN input layer.

Analysis of heat conduction in a disk brake system

Abstract: In this paper, the governing heat equations for the disk and the pad are extracted in the form of transient heat equations with heat generation that is dependant to time and space. In the derivation of the heat equations, parameters such as the duration of braking, vehicle velocity, geometries and the dimensions of the brake components, materials of the disk brake rotor and the pad and contact pressure distribution have been taken into account. The problem is solved analytically using Green’s function approach. It is concluded that the heat generated due to friction between the disk and the pad should be ideally dissipated to the environment to avoid decreasing the friction coefficient between the disk and the pad and to avoid the temperature rise of various brake components and brake fluid vaporization due to excessive heating.

Dispersive liquid-liquid microextraction using extraction solvent lighter than water.

Abstract: For the first time a dispersive liquid-liquid microextraction method on the basis of an extraction solvent lighter than water was presented in this study. Three organophosphorus pesticides (OPPs) were selected as model compounds and the proposed method was carried out for their preconcentration from water samples. In this extraction method, a mixture of cyclohexane (extraction solvent) and acetone (disperser) is rapidly injected into the aqueous sample in a special vessel (see experimental section) by syringe. Thereby, a cloudy solution is formed. In this step, the OPPs are extracted into the fine droplets of cyclohexane dispersed into aqueous phase. After centrifuging the fine droplets of cyclohexane are collected on the upper of the extraction vessel. The upper phase (0.40 microL) is injected into the gas chromatograph (GC) for separation. Analytes were detected by a flame ionization detector (FID) (for high concentrations) or MS (for low concentrations). Some important parameters, such as the kind of extraction and dispersive solvents and volume of them, extraction time, temperature, and salt amount were investigated. Under the optimum conditions, the enrichment factors (EFs) ranged from 100 to 150 and extraction recoveries varied between 68 and 105%, both of which are relatively high over those of published methods. The linear ranges were wide (10-100 000 microg/L for GC-FID and 0.01-1 microg/L for GC-MS) and LODs were low (3-4 microg/L for GC-FID and 0.003 microg/L for GC-MS). The RSDs for 100.0 microg/L of each OPP in water were in the range of 5.3-7.8% (n = 5).

A novel ACO-GA hybrid algorithm for feature selection in protein function prediction

Abstract: Protein function prediction is an important problem in functional genomics. Typically, protein sequences are represented by feature vectors. A major problem of protein datasets that increase the complexity of classification models is their large number of features. Feature selection (FS) techniques are used to deal with this high dimensional space of features. In this paper, we propose a novel feature selection algorithm that combines genetic algorithms (GA) and ant colony optimization (ACO) for faster and better search capability. The hybrid algorithm makes use of advantages of both ACO and GA methods. Proposed algorithm is easily implemented and because of use of a simple classifier in that, its computational complexity is very low. The performance of proposed algorithm is compared to the performance of two prominent population-based algorithms, ACO and genetic algorithms. Experimentation is carried out using two challenging biological datasets, involving the hierarchical functional classification of GPCRs and enzymes. The criteria used for comparison are maximizing predictive accuracy, and finding the smallest subset of features. The results of experiments indicate the superiority of proposed algorithm.

A novel technique for selecting mother wavelet function using an intelli gent fault diagnosis system

Abstract: This paper presents an optimized gear fault identification system using genetic algorithm (GA) to investigate the type of gear failures of a complex gearbox system using artificial neural networks (ANNs) with a well-designed structure suited for practical implementations due to its short training duration and high accuracy. For this purpose, slight-worn, medium-worn, and broken-tooth of a spur gear of the gearbox system were selected as the faults. In fault simulating, two very similar models of worn gear have been considered with partial difference for evaluating the preciseness of the proposed algorithm. Moreover, the processing of vibration signals has become much more difficult because a full-of-oil complex gearbox system has been considered to record raw vibration signals. Raw vibration signals were segmented into the signals recorded during one complete revolution of the input shaft using tachometer information and then synchronized using piecewise cubic hermite interpolation to construct the sample signals with the same length. Next, standard deviation of wavelet packet coefficients of the vibration signals considered as the feature vector for training purposes of the ANN. To ameliorate the algorithm, GA was exploited to optimize the algorithm so as to determine the best values for ''mother wavelet function'', ''decomposition level of the signals by means of wavelet analysis'', and ''number of neurons in hidden layer'' resulted in a high-speed, meticulous two-layer ANN with a small-sized structure. This technique has been eliminated the drawbacks of the type of mother function for fault classification purpose not only in machine condition monitoring, but also in other related areas. The small-sized proposed network has improved the stability and reliability of the system for practical purposes.

Harmonic Minimization in Multilevel Inverters Using Modified Species-Based Particle Swarm Optimization

Abstract: Harmonic minimization in multilevel inverters is a complex optimization problem that involves nonlinear transcendental equations having multiple local minima. In this paper, a solution to the harmonic minimization problem using a novel particle swarm optimization (PSO) approach based on species-based PSO (SPSO) is presented. The original SPSO is modified, which increased the robustness of algorithm to find global optimum of the search space. The proposed method is able to find the optimum switching angles when their number is increased, while it is not possible to determine them using either conventional iterative techniques or resultant theory method. Theoretical results are verified by experiments and simulations for an 11-level H-bridge inverter. Results show that the proposed method effectively minimizes a large number of specific harmonics, and the output voltage results in very low total harmonic distortion and switching frequency.

Phase Diagrams for the Aqueous Two-Phase Ternary System Containing the Ionic Liquid 1-Butyl-3-methylimidazolium Bromide and Tri-potassium Citrate at T ) (278.15, 298.15, and 318.15) K

Abstract: Liquid−liquid equilibria (LLE) for the aqueous 1-butyl-3-methylimidazolium bromide ([C4mim]Br) + tri-potassium citrate system have been experimentally determined at T = (278.15, 298.15, and 318.15) K. An empirical nonlinear expression developed by Merchuk was used for reproducing the experimental binodal data. In this work, the three fitting parameters of the Merchuk equation were obtained with the temperature dependence expressed in the linear form with (T − T0) K as a variable. The effect of temperature on the phase-forming ability in the investigated system has also been studied on the basis of a salting-out coefficient obtained from fitting the tie-line data to a Setschenow-type equation for each temperature. The values of the salting-out coefficients may reflect the extent of the preferential hydration of the ionic liquid. Furthermore, the kosmotropicity of tri-potassium citrate for the exclusion of [C4mim][Br] in an aqueous solution has been examined in comparison with the previously studied kosmotr...

Biological activity of quercetin-3-O-glucoside, a known plant flavonoid

Abstract: Cytotoxic, phytotoxic, antimicrobial and antioxidant effects of quercetin 3- O -glucoside (Q3G) iso- lated by HPLC from aerial parts of Prangos ferulaceae was studied by MTT assay, lettuce germination assay, disk diffusion and DPPH method. Our results showed that Q3G exhibits high antioxidant effect with RC 50 of 22 µ g/mL, it has low cytotoxicity and no antibacterial effects. Q3G exhibits high phytotoxic effect with IC 50 value of 282.7 µ g/ml, as well. It is assumed that Q3G does not play a defense role in plants and it may act as an allelopatic agent.