Effects of bed-load movement on flow resistance over bed forms

The effect of bed-load transport on flow resistance of alluvial channels with undulated bed was experimentally investigated. The experiments were carried out in a tilting flume 250mm wide and 12.5m long with glass-sides of rectangular cross-section and artificial dune shaped floor that was made from Plexi-glass. Steady flow of clear as against sediment-laden water with different flow depths and velocities were studied in the experiments with a fine sand (d ₅₀ = 0.5mm). The results indicate that the transport of fine particles (d ₅₀ = 0.5mm) can decrease the friction factor by 22% and 24% respectively for smooth and rough beds. Increasing the bed-load size (d ₅₀ = 2.84 mm) can decrease the friction factor by 32% and 39% respectively for smooth and rough beds. The decrease in flow resistance is due to filling up of the troughs of dunes. This separation zone is responsible for increasing the flow resistance. On the upstream side of dunes condition is similar to plane bed. Presence of bed-load causes to increase the shear velocity and hence increasing flow resistance. But decreasing in flow resistance is more and it causes to decrease the total flow resistance. Grains saturated the troughs in the bed topography, effectively helping in smoothening of bed irregularities.     

The Addiction-Stroop test: Theoretical considerations and procedural recommendations.

Decisions about using addictive substances are influenced by distractions by addiction-related stimuli, of which the user might be unaware. The addiction-Stroop task is a paradigm used to assess this distraction. The empirical evidence for the addiction-Stroop effect is critically reviewed, and meta-analyses of alcohol-related and smoking-related studies are presented. Studies finding the strongest effects were those in which participants had strong current concerns about an addictive substance or such concerns were highlighted through experimental manipulations, especially those depriving participants of the substance. Theories to account for addiction-related attentional bias are discussed, of which the motivational theory of current concerns appears to provide the most complete account of the phenomenon. Recommendations are made for maximizing the precision of the addiction-Stroop test in future research. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Investigating the effect of nanoparticles on the rutting behaviour of hot-mix asphalt

Rutting is considered as one of the major damages in asphalt mixtures. In this study, different types of nanoparticles such as TiO₂, Al₂O₃, Fe₂O₃ and ZnO in different percentages were added to the base asphalt binder in order to decrease the rutting potential of hot-mix asphalt (HMA). In the first step, asphalt binder tests for characteristics such as penetration grade, ductility, softening point and viscosity were performed on the asphalt binder modified by the nanoparticles. Then, after preparing HMA samples, the static creep test was done at two stress levels at a specific temperature. Results of this study showed that using the nanoparticles improved the behavioural properties of the asphalt binder and decreased rutting in asphalt mix samples. Furthermore, scanning electron microscope images taken from the asphalt binder samples modified by the nanoparticles demonstrated that these nanoparticles were properly distributed in the asphalt binder space and had a positive effect on the rutting performance of the asphalt mixes.     

A NEW DISTRIBUTED JOB SCHEDULING ALGORITHM FOR GRID SYSTEMS

Job scheduling is one of the key issues in the design of grid environments. The performance of the grid system severely degrades if a method does not exist to efficiently schedule the user jobs. In this article, a fully distributed, learning automata–based job scheduling algorithm is proposed for grid environments. The proposed method is composed of two types of procedures: in the first, a procedure is run at the grid nodes and in the second, the procedure is run at the schedulers. The proposed algorithm synchronizes the performance of the schedulers by the learning automata that select their actions using the pseudo-random number generators with the same seed. In this method, the grid computational capacity that is allocated to each scheduler is proportional to its workload. To show the efficiency of the proposed method, several simulation experiments were conducted under different grid scenarios. The obtained results show that the proposed algorithm outperforms several well-known methods in terms of makespan, flow time, and load balancing.     

Using class-based feature selection for the classification of hyperspectral data

The rapid advances in hyperspectral sensing technology have made it possible to collect remote-sensing data in hundreds of bands. However, the data-analysis methods that have been successfully applied to multispectral data are often limited in achieving satisfactory results for hyperspectral data. The major problem is the high dimensionality, which deteriorates the classification due to the Hughes Phenomenon. In order to avoid this problem, a large number of algorithms have been proposed, so far, for feature reduction. Based on the concept of multiple classifiers, we propose a new schema for the feature selection procedure. In this framework, instead of using feature selection for whole classes, we adopt feature selection for each class separately. Thus different subsets of features are selected at the first step. Once the feature subsets are selected, a Bayesian classifier is trained on each of these feature subsets. Finally, a combination mechanism is used to combine the outputs of these classifiers. Experiments are carried out on an Airborne Visible/Infrared Imaging Spectroradiometer (AVIRIS) data set. Encouraging results have been obtained in terms of classification accuracy, suggesting the effectiveness of the proposed algorithms.     

Robustification of input redundant feedback systems using robust actuator weighting in the control allocation problem

In this article, a new methodology for robust actuator weighting in the control allocation (CA) problem of input redundant feedback systems is addressed. The methodology is based on the control structural properties of the plant which were previously used for control configuration selection. Robust performance (RP) measures including H _∞ norm and structured singular value of the closed-loop system are used in this article. The capability of the approach is proven with application to lateral dynamics control of the vehicle over-actuated with front and rear steering systems. Employing the RP measures, it is concluded that the vehicle feedback control with front steering angles gives superior RP properties in comparison with the feedback loop of the rear steering angles. Based on these results, the penalty weightings in the cost function of the CA unit are determined. Simulation results based on nonlinear seven degrees of freedom vehicle handling model show that the selection of penalty weightings in the CA unit based on the RP properties of the control inputs (front and rear steering angles) improves the RP of the closed-loop.     

Morphology, rheology and dynamic mechanical properties of PP/EVA/clay nanocomposites

This paper reports on morphology, rheology and dynamic mechanical properties of polypropylene (PP)/ethylene vinyl acetate (EVA) copolymer/clay nanocomposite system prepared via a single step melt compounding process using a twin screw micro-compounder. Scanning electron microscopic (SEM) investigations revealed that the dispersed phase droplet size was reduced with incorporation of an organo-modified montmorillonite (OMMT). This reduction was more significant in presence of a maleated PP (PP-g-MAH) used as compatibilizer. Phase inversion in the compatibilized blends caused a further decrease in PP droplet size. The OMMT gallery spacing was higher in nanocomposites with EVA as matrix which could be attributed to higher tendency of OMMT nanoparticles towards EVA rather than PP. This enhanced tendency was confirmed by rheological analysis too. Transmission electron microscopy (TEM) results also showed that the majority of OMMT nanoparticles were localized on the interface and within EVA droplets. According to dynamic mechanical analysis, the compatibilized nanocomposites showed higher storage and loss moduli due to better dispersion of OMMT layers. The modulus enhancement of nanocomposites as a function of OMMT volume fraction was modeled by Halpin-Tsai’s-Nielsen expression of modulus for nanocomposites. The results of modeling suggested that the aspect ratio of the intercalated OMMT, in the form of Einstein coefficient (K _E), plays a determining role in the modulus enhancement of nanocomposites.     

A skewed unscented Kalman filter

State estimation of nonlinear systems is a challenging task, especially when the Gaussian approximation fails. The unscented Kalman filter was proposed to deal with state estimation of nonlinear systems. We modify the traditional unscented Kalman filter to capture the third-order moment (skewness) of the state vector. Methods are also proposed to reduce the computation time of the suggested approach, and showing that the proposed algorithm is as fast as the unscented Kalman filter. Simulation results confirm that the method is better than, or at least as good as, the unscented Kalman filter.     

A new and accurate fault location algorithm for combined transmission lines using Adaptive Network-Based Fuzzy Inference System

This paper presents a new and accurate algorithm for locating faults in a combined overhead transmission line with underground power cable using Adaptive Network-Based Fuzzy Inference System (ANFIS). The proposed method uses 10 ANFIS networks and consists of 3 stages, including fault type classification, faulty section detection and exact fault location. In the first part, an ANFIS is used to determine the fault type, applying four inputs, i.e., fundamental component of three phase currents and zero sequence current. Another ANFIS network is used to detect the faulty section, whether the fault is on the overhead line or on the underground cable. Other eight ANFIS networks are utilized to pinpoint the faults (two for each fault type). Four inputs, i.e., the dc component of the current, fundamental frequency of the voltage and current and the angle between them, are used to train the neuro-fuzzy inference systems in order to accurately locate the faults on each part of the combined line. The proposed method is evaluated under different fault conditions such as different fault locations, different fault inception angles and different fault resistances. Simulation results confirm that the proposed method can be used as an efficient means for accurate fault location on the combined transmission lines.     

Chemical vapor deposition of poly(3‐alkylthiophene) nanoparticles on fabric: Chemical and electrochemical characterization

Chemical vapor deposition of poly(3‐methylthiophene) and poly (3‐hexylthiophene) as conductive polymers on the surface of polyester fabrics was successfully obtained. Fourier transform infrared spectroscopy confirmed the formation of polymers on surface of fabrics (the fingerprint of polythiophenes, υ 600–1500 cm^?1). The uniformity of deposition and nanoparticles (average size of 60 nm) were proved with scanning electron microscopy. Electrochemical impedance spectroscopy showed that P3HT‐coated samples offer higher conductivity in compared to P3MT‐coated samples. The impedance modulus of P3HT‐coated samples was lowered nine times to that of row materials and reached to c8000 Ω. The samples have also shown electrochromic properties under electrical current, changing its color from yellowish green at 0 V to dark green at +12 V for poly (3‐hexylthiophene) samples and from brown at 0 V to red at +12 V for poly(3‐methylthiophene)‐coated fabrics (V = 0 V, λ = 450 nm; V = 12 V, λ = 650 nm). © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014 , 131, 40673.