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.     

Solution of diffusion equation in deformable spheroids

The time-dependent diffusion of neutrons in a spheroid as a function of the focal distance has been studied. The solution is based on an orthogonal basis and an extrapolation distanced related boundary condition for the spheroidal geometry. It has been shown that spheres and disks are two limiting cases for the spheroids, for which there is a smooth transition for the systems properties between these two limits. Furthermore, it is demonstrated that a slight deformation from a sphere does not affect the fundamental mode properties, to the first order. The calculations for both multiplying and non-multiplying media have been undertaken, showing good agreement with direct Monte Carlo simulations.     

Rapid Separating and Enrichment of 4,4′-Dimethylsterols of Vegetable Oils by Solid-Phase Extraction

Phytosterols are separated into three classes: 4-desmethylsterols, 4-monomethylsterols and 4,4′-dimethylsterols. 4,4′-Dimethylsterols are used to detect vegetable oil adulteration and some compounds from this class can have anti-inflammatory and anticancer properties. There are methods such as thin layer chromatography (TLC) and solid phase extraction (SPE) used to separate phytosterol classes from each other. However, in some cases, separation of all three classes is not required. In addition, TLC has some drawbacks such as low recovery and it is time consuming. An SPE method has previously been used, but it was necessary to use high volume of solvents with this method to avoid coelution of phytosterol classes. In this study, an SPE (silica, 1 g) method was developed to separate and enrich only 4,4′-dimethylsterols from unsaponifiables of vegetable oil samples using 25 mL n-hexane and diethyl ether (95:5, v:v). This method was applied to hazelnut and olive oils and results were compared with those of TLC and the previously developed SPE method. Recovery of 4,4′-dimethylsterols was two times higher with the new SPE method compared with the TLC method. The newly developed SPE method generally gave a similar recovery compared with the previously developed SPE method. Moreover, the SPE method developed in this study has the advantage of using a 3.5 times lower volume of solvent than previously developed SPE methods. Because the newly developed SPE method has a single step requiring a low volume of solvents, it is rapid and simple, and can easily be used to detect olive oil adulteration with hazelnut oil and to analyze and quantify effective nutritional compounds in the 4,4′-dimethylsterols class.     

Equilibrium,kinetic and thermodynamic study of removal of reactive orange 12 on platinum nanoparticle loaded on activated carbon as novel adsorbent

The proposed research describes the synthesis and characterization of platinum nanoparticles loaded on activated carbon (Pt-NP-AC) and its efficient application as novel adsorbent for efficient removal of reactive orange 12 (RO-12). The influences of effective parameters following the optimization of variables on removal percentages, their value was set as 0.015 g Pt-NP-AC, pH 1, contact time of 13 min. At optimum values of all variables at 25 and 50 mgL⁻¹ of RO-12 enthalpy (ΔH⁰) and entropy (ΔS⁰) changes was found to be 59.89 and 225.076, respectively, which negative value of ΔG⁰ shows a spontaneous nature, and the positive values of ΔH⁰ and ΔS⁰ indicate the endothermic nature and adsorption organized of dye molecule on the adsorbent surface. Experimental data was fitted to different kinetic models including first-order, pseudo-second-order, Elovich and intra-particle diffusion models, and it was seen that the adsorption process follows pseudo-second-order model in consideration to intra-particle diffusion mechanism. At optimum values of all variables, the adsorption process follows the second-order kinetic and Langmuir isotherm model with adsorption capacity 285.143 mg g⁻¹ at room temperature.