Descriptive/Inferential Cognitive Processes and Evaluative Cognitive Processes: Relationships Among Each Other and with Emotional Distress

We aimed to delineate key constructs from two forms of cognitive-behavioral therapy: cognitive therapy and rational-emotive behavior therapy. Furthermore, we aimed to investigate the interrelations among each other and with emotional distress. The key constructs of the underlying theories of these therapies (i.e., descriptive/inferential beliefs, evaluative beliefs) are often treated together as distorted cognitions and included as such in various scales. We used a cross-sectional design. Seventy-four undergraduate students (mean age = 24.68) completed measures of automatic thoughts and emotional distress. Three therapists trained in cognitive-behavioral therapy divided automatic thoughts into descriptive/inferential beliefs and evaluative beliefs by consensus. Correlation and mediation analyses were performed. These constructs showed medium to high associations to each other and to distress. The relationship between descriptive/inferential beliefs and distress was mediated by evaluative beliefs. Descriptive and inferential cognitions may not produce emotions without first being appraised in terms of personal relevance.

Adherence to a lifestyle physical activity program: Trends over time and associations with processes of change and physical activity

Research on lifestyle physical activity interventions suggests that they help individuals meet the new recommendations for physical activity made by the Centers for Disease Control and Prevention (CDC) and the American College of Sports Medicine (ACSM). The purpose of this research was to describe the rates of adherence to two lifestyle physical activity intervention arms and to examine the association between adherence and outcome variables, using data from Project PRIME, a lifestyle physical activity intervention based on the transtheoretical model and conducted by the Cooper Institute of Aerobics Research, Dallas, Texas. Participants were 250 sedentary healthy adults, aged 35 to 70 years, primarily non-Hispanic White, and in the contemplation and preparation stages of readiness to change. They were randomized to a group (PRIME G) or a mail- and telephone-delivered condition (PRIME C). Adherence measures included attending class (PRIME G), completing a monthly telephone call with a health educator (PRIME C), and completing homework assignments and self-monitoring minutes of moderate- to vigorous physical activity (both groups). In the first results paper, adherence over time and between conditions was examined: Attendance in group, completing the monthly telephone call, and homework completion decreased over time, and participants in PRIME G were more likely to complete homework than those in PRIME C. Paper 2 aimed to determine whether the adherence measures predicted achievement of the CDC/ACSM physical activity guideline. In separate models for the two conditions, a latent variable measuring adherence was found to predict achievement of the guideline. Paper 3 examined the association between adherence measures and the transtheoretical model's processes of change within each condition. For both, participants who completed at least two thirds of the homework assignments improved their use of the processes of change more than those who completed less than that amount. These results suggest that encouraging adherence to a lifestyle physical activity intervention, at least among already motivated volunteers, may increase the likelihood of beneficial changes in the outcomes. ^

Clay mineralogy and multi-element chemistry of surface sediments on the Aiberian-Arctic Shelf

Clay mineral and bulk chemical (Si, Al, K, Mg, Sr, La, Ce, Nd) analyses of terrigenous surface sediments on the Siberian-Arctic shelf indicate that there are five regions with distinct, or endmember, sedimentary compositions. The formation of these geochemical endmembers is controlled by sediment provenance and grain size sorting. (1) The shale endmember (Al, K and REE rich sediment) is eroded from fine-grained marine sedimentary rocks of the Verkhoyansk Mountains and Kolyma-Omolon superterrain, and discharged to the shelf by the Lena, Yana, Indigirka and Kolyma Rivers. (2) The basalt endmember (Mg rich) originates from NE Siberia's Okhotsk-Chukotsk volcanic belt and Bering Strait inflow, and is prevalent in Chukchi Sea Sediments. Concentrations of the volcanically derived clay mineral smectite are elevated in Chukchi fine-fraction sediments, corroborating the conclusion that Chukchi sediments are volcanic in origin. (3) The mature sandstone endmember (Si rich) is found proximal to Wrangel Island and sections of the Chukchi Sea's Siberian coast and is derived from the sedimentary Chukotka terrain that comprises these landmasses. (4) The immature sandstone endmember (Sr rich) is abundant in the New Siberian Island region and reflects inputs from sedimentary rocks that comprise the islands. (5) The immature sandstone endmember is also prevalent in the western Laptev Sea, where it is eroded from sedimentary deposits blanketing the Siberian platform that are compositionally similar to those on the New Siberian Islands. Western Laptev can be distinguished from New Siberian Island region sediments by their comparatively elevated smectite concentrations and the presence of the basalt endmember, which indicate Siberian platform flood basalts are also a source of western Laptev sediments. In certain locations grain size sorting noticeably affects shelf sediment chemistry. (1) Erosion of fines by currents and sediment ice rafting contributes to the formation of the coarse-grained sandstone endmembers. (2) Bathymetrically controlled grain size sorting, in which fines preferentially accumulate offshore in deeper, less energetic water, helps distribute the fine-grained shale and basalt endmembers. An important implication of these results is that the observed sedimentary geochemical endmembers provide new markers of sediment provenance, which can be used to track sediment transport, ice-rafted debris dispersal or the movement of particle-reactive contaminants.

Evaluation of a Change Detection Methodology by Means of Binary Thresholding Algorithms and Informational Fusion Processes

Landcover is subject to continuous changes on a wide variety of temporal and spatial scales. Those changes produce significant effects in human and natural activities. Maintaining an updated spatial database with the occurred changes allows a better monitoring of the Earth?s resources and management of the environment. Change detection (CD) techniques using images from different sensors, such as satellite imagery, aerial photographs, etc., have proven to be suitable and secure data sources from which updated information can be extracted efficiently, so that changes can also be inventoried and monitored. In this paper, a multisource CD methodology for multiresolution datasets is applied. First, different change indices are processed, then different thresholding algorithms for change/no_change are applied to these indices in order to better estimate the statistical parameters of these categories, finally the indices are integrated into a change detection multisource fusion process, which allows generating a single CD result from several combination of indices. This methodology has been applied to datasets with different spectral and spatial resolution properties. Then, the obtained results are evaluated by means of a quality control analysis, as well as with complementary graphical representations. The suggested methodology has also been proved efficiently for identifying the change detection index with the higher contribution.

Regularized logistic regression and multi-objective variable selection for classifying MEG data

This paper addresses the question of maximizing classifier accuracy for classifying task-related mental activity from Magnetoencelophalography (MEG) data. We propose the use of different sources of information and introduce an automatic channel selection procedure. To determine an informative set of channels, our approach combines a variety of machine learning algorithms: feature subset selection methods, classifiers based on regularized logistic regression, information fusion, and multiobjective optimization based on probabilistic modeling of the search space. The experimental results show that our proposal is able to improve classification accuracy compared to approaches whose classifiers use only one type of MEG information or for which the set of channels is fixed a priori.

Regularized model learning in EDAs for continuous and multi-objective optimization

Probabilistic modeling is the de�ning characteristic of estimation of distribution algorithms (EDAs) which determines their behavior and performance in optimization. Regularization is a well-known statistical technique used for obtaining an improved model by reducing the generalization error of estimation, especially in high-dimensional problems. `1-regularization is a type of this technique with the appealing variable selection property which results in sparse model estimations. In this thesis, we study the use of regularization techniques for model learning in EDAs. Several methods for regularized model estimation in continuous domains based on a Gaussian distribution assumption are presented, and analyzed from di�erent aspects when used for optimization in a high-dimensional setting, where the population size of EDA has a logarithmic scale with respect to the number of variables. The optimization results obtained for a number of continuous problems with an increasing number of variables show that the proposed EDA based on regularized model estimation performs a more robust optimization, and is able to achieve signi�cantly better results for larger dimensions than other Gaussian-based EDAs. We also propose a method for learning a marginally factorized Gaussian Markov random �eld model using regularization techniques and a clustering algorithm. The experimental results show notable optimization performance on continuous additively decomposable problems when using this model estimation method. Our study also covers multi-objective optimization and we propose joint probabilistic modeling of variables and objectives in EDAs based on Bayesian networks, speci�cally models inspired from multi-dimensional Bayesian network classi�ers. It is shown that with this approach to modeling, two new types of relationships are encoded in the estimated models in addition to the variable relationships captured in other EDAs: objectivevariable and objective-objective relationships. An extensive experimental study shows the e�ectiveness of this approach for multi- and many-objective optimization. With the proposed joint variable-objective modeling, in addition to the Pareto set approximation, the algorithm is also able to obtain an estimation of the multi-objective problem structure. Finally, the study of multi-objective optimization based on joint probabilistic modeling is extended to noisy domains, where the noise in objective values is represented by intervals. A new version of the Pareto dominance relation for ordering the solutions in these problems, namely �-degree Pareto dominance, is introduced and its properties are analyzed. We show that the ranking methods based on this dominance relation can result in competitive performance of EDAs with respect to the quality of the approximated Pareto sets. This dominance relation is then used together with a method for joint probabilistic modeling based on `1-regularization for multi-objective feature subset selection in classi�cation, where six di�erent measures of accuracy are considered as objectives with interval values. The individual assessment of the proposed joint probabilistic modeling and solution ranking methods on datasets with small-medium dimensionality, when using two di�erent Bayesian classi�ers, shows that comparable or better Pareto sets of feature subsets are approximated in comparison to standard methods.

Experiences of spanish public administration in requirements management and acquisition management processes

This paper shows the main contributions of the 1st Symposium on Improvement Process Models and Software Quality of Public Administrations. The obtained results expose the need to promote the implementation of Software Maturity Models and show possible advantages of its application in software processes of Public Administrations. Specifically, it was analyzed the current status in two process areas: Requirements Management and Subcontracting Management.

Gear dynamics monitoring using discrete wavelet transformation and multi-layer perceptron neural networks

This paper presents a multi-stage algorithm for the dynamic condition monitoring of a gear. The algorithm provides information referred to the gear status (fault or normal condition) and estimates the mesh stiffness per shaft revolution in case that any abnormality is detected. In the first stage, the analysis of coefficients generated through discrete wavelet transformation (DWT) is proposed as a fault detection and localization tool. The second stage consists in establishing the mesh stiffness reduction associated with local failures by applying a supervised learning mode and coupled with analytical models. To do this, a multi-layer perceptron neural network has been configured using as input features statistical parameters sensitive to torsional stiffness decrease and derived from wavelet transforms of the response signal. The proposed method is applied to the gear condition monitoring and results show that it can update the mesh dynamic properties of the gear on line.

Design and Multi-Objective Optimization of EMI Input Filters

An EMI filter design procedure for power converters is proposed. Based on a given noise spectrum, information about the converter noise source impedance and design constraints, the design space of the input filter is defined. The design is based on component databases and detailed models of the filter components, including high frequency parasitics, losses, weight, volume, etc.. The design space is mapped onto a performance space in which different filter implementations are evaluated and compared. A multi-objective optimization approach is used to obtain optimal designs w.r.t. a given performance function.