Assessing and interpreting personality change and continuity in patients treated for major depression.

Structural, mean- and individual-level, differential, and ipsative personality continuity were examined in 599 patients treated for major depression assigned to 1 of 6 forms of a 6-month pharmaco-psychotherapy program. Covariation among traits from the Five Factor model remained invariant across treatment, and patients described themselves as slightly more extraverted, open to experience, agreeable and conscientious, and substantially more emotional stable after treatment. Trait changes were only to a small extent explained by changes in depression severity. There was evidence for differential, individual-level, and ipsative stability, with stable personality profiles in terms of shape and to a lesser extent in terms of scatter and elevation. Traits remain relatively stable, except for emotional stability, despite the depressive state and the psychopharmacological interventions. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Modal Kalman filtering based impedance relaying

An algorithm is presented for computer distance relaying. It is based on modal Kalman filtering (MKF) to estimate fundamental phasors, MKF exploits all the measurement information available from a three-phase line and is founded on enhanced models of the electromagnetic transients noise. To support the modeling of noise for different faults, a study of nontrivial electrical networks with accurate electromagnetic transients modeling is made for 400 kV transmission lines. MKF fits the behavior of the different modal noise for the Clarke phasors, but it is invariant for each type of fault, assuring robustness and minimum error variance. To computer distance, Clarke αB phasors are transformed to +/- symmetrical components, and then a well-known expression is used to computer the positive impedance. Statistical analysis if presented to assess observation time versus estimation accuracy for the different types of line faults     

A study of mixtures of poly(ethylene oxide) with polystyrene or poly(styrene-co-acrylic acid) by inverse gas chromatography and viscosimetry

Summary Systems of poly(ethylene oxide)/polystyrene and of poly(ethylene oxide)/poly(styrene-co-acrylic acid) of different ratios were studied by inverse gas chromatography using benzene and n-decane as molecule probes and by viscosimetry in tetrahydrofuran. The transition temperatures as shown in the retention diagrams and the interaction parameters as determined by inverse gas chromatography and by viscosimetry showed that whereas poly(ethylene oxide) is incompatible with polystyrene, the introduction of amounts of acrylic acid groups into polystyrene chains by free radical copolymerization led to compatible systems of the acidic copolymer with the poly(ethylene oxide).     

Dogs (Canis familiaris) learn their owners via observation in a manipulation task.

Eighty-seven pet dogs (Canis familiaris) were involved in an experiment in which they had to solve a task to obtain a ball. After witnessing a full demonstration by their owner (10 times pushing the handle of the box, which released a ball), most dogs preferred to touch the handle sooner and more frequently in comparison with other parts of the box, and they used the handle to get the ball. In contrast dogs in 3 control groups developed their own respective methods. The lack of emergence of the ball and playing after the demonstration did not affect the learning performance strongly. This suggests that in dogs the outcome of a demonstration plays only a restricted role in the manifestation of social learning. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Nitrogen dioxide effect in the reduction of nitric oxide by propane in oxidizing atmosphere

The role of nitrogen dioxide in the selective reduction of NO by propane over a Cu-MFI zeolite is investigated. NO₂ and NO reductions were carried out under similar conditions of reaction. In the presence of oxygen, the reduction of NO by C₃H₈ does not differ significantly from that of NO₂. In the absence of oxygen, the reduction of NO₂ by propane occurs with a partial decomposition of the nitric dioxide molecule. Such a decomposition leads to the formation of oxygen, which is responsible for the increase in catalytic activity by comparison with the same reaction performed with NO. NO₂ formed and released in the gas phase during the reduction of NO by propane in the presence of oxygen does not play a predominant role in the catalytic process.     

Electrophilic chlorination of methane over superacidic sulfated zirconia

Catalytic chlorination of methane was studied over SO ₄ ^2– /ZrO₂, Pt/SO ₄ ^2– /ZrO₂, and Fe/Mn/SO ₄ ^2– /ZrO₂ solid superacid catalysts. The reactions were carried out in a continuous flow reactor under atmospheric pressure, at temperatures below 240°C, with a gaseous hourly space velocity of 1000 ml/g h and a methane to chlorine ratio of 4 to 1. At 200°C with 30% chlorine converted the selectivity in methyl chloride exceeds 90%. At more elevated temperatures, the selectivity decreases but stays above 80% in methyl chloride at 225°C using the sulfated zirconia catalysts. The selectivity can be enhanced by adding platinum to sulfated zirconia catalysts. An iron and manganese-doped catalyst exhibited excellent selectivities at somewhat lower conversions. Methyl chloride is obtained at 235°C in selectivities greater than 85%. No chloroform or carbon tetrachloride is formed. The electrophilic insertion involves electron-deficient metal-coordinated chlorine into the methane C-H bond.Catalysis by solid superacids, 29. For part 28 see ref. [14].     

A General Reliable Quadratic Form: An Extension of Affine Arithmetic

In this article, a new extension of affine arithmetic is introduced. This technique is based on a quadratic form named general quadratic form. We focus here on the computation of reliable bounds of a function over a hypercube by using this new tool. Some properties of first quadratic functions and then polynomial ones are reported. In order to show the efficiency of such a method, ten polynomial global optimization problems are presented and solved by using an interval branch-and-bound based algorithm. The work of the first author was also supported by the Laboratoire de Mathématiques Appliquées CNRS–FRE 2570, Université de Pau et des Pays de l'Adour, France, and by the Laboratoire d'Electrotechnique et d'Electronique Industrielle CNRS–UMR5828, Group EM³, INPT–ENSEEIHT.     

Tracking control and robustness analysis for planar vertical takeoff and landing aircraft under bounded feedbacks

We study feedback tracking problems for the planar vertical takeoff and landing (PVTOL) aircraft dynamics, which is a benchmark model in aerospace engineering. We provide a survey of the literature on the model. Then we construct new feedback stabilizers for the PVTOL tracking dynamics. The novelty of our work is in the boundedness of our feedback controllers and their applicability to cases where the velocity measurements may not be available, coupled with the uniform global asymptotic stability and uniform local exponential stability of the closed‐loop tracking dynamics and the input‐to‐state stable performance of the closed‐loop tracking dynamics with respect to actuator errors. Our proofs are based on a new bounded backstepping result. We illustrate our work in a tracking problem along a circle. Copyright © 2011 John Wiley & Sons, Ltd.     

Network regression with predictive clustering trees

Network data describe entities represented by nodes, which may be connected with (related to) each other by edges. Many network datasets are characterized by a form of autocorrelation, where the value of a variable at a given node depends on the values of variables at the nodes it is connected with. This phenomenon is a direct violation of the assumption that data are independently and identically distributed. At the same time, it offers an unique opportunity to improve the performance of predictive models on network data, as inferences about one entity can be used to improve inferences about related entities. Regression inference in network data is a challenging task. While many approaches for network classification exist, there are very few approaches for network regression. In this paper, we propose a data mining algorithm, called NCLUS, that explicitly considers autocorrelation when building regression models from network data. The algorithm is based on the concept of predictive clustering trees (PCTs) that can be used for clustering, prediction and multi-target prediction, including multi-target regression and multi-target classification. We evaluate our approach on several real world problems of network regression, coming from the areas of social and spatial networks. Empirical results show that our algorithm performs better than PCTs learned by completely disregarding network information, as well as PCTs that are tailored for spatial data, but do not take autocorrelation into account, and a variety of other existing approaches.     

EASEA: specification and execution of evolutionary algorithms on GPGPU

EASEA is a framework designed to help non-expert programmers to optimize their problems by evolutionary computation. It allows to generate code targeted for standard CPU architectures, GPGPU-equipped machines as well as distributed memory clusters. In this paper, EASEA is presented by its underlying algorithms and by some example problems. Achievable speedups are also shown onto different NVIDIA GPGPUs cards for different optimization algorithm families.