Modelling commuting time in the US: Bootstrapping techniques to avoid overfitting

The research on commuting has emerged in recent decades, but the issue of whether the empirical techniques used are appropriate has not been analysed. Thus, results from prior research could be based on non-accurate models, leading to misleading conclusions. We apply an algorithmic approach based on bootstrapping, variable selection, and mean absolute prediction errors, which is designed to avoid overfitting. Using the American Time Use Survey, we find that models with a reduced set of explanatory variables have similar accuracy to standard econometric models. Our results shed light on the importance of determining whether models can be overfitted.     

Viscosity virtual sensor to control combustion in fossil fuel power plants

Thermo-electrical power plants utilize fossil fuel oil to transform the calorific power of fuel into electric power. An optimal combustion in the boiler requires the fuel oil to be in its best conditions. One of fuel's most important properties to consider is viscosity. Viscosity has influence on the optimal combustion between fuel and air. Hardware viscosity meters for fuel oils are expensive and unreliable to operate in power plant conditions. Chemical laboratory measures viscosity accurately with special apparatus, but they cannot be used in a real time process. This paper describes the development of a virtual sensor that estimates fuel oil viscosity in the combustion process of a power plant. A virtual sensor or soft sensor is a computer program that estimates the value of a certain variable based on related measurements and a model of the process where the variable participates. In this project, a probabilistic model is constructed using automatic learning algorithms with historical data and experts' advice. The learning and validation experiments are described and discussed. The virtual sensor is installed in the Tuxpan Power Plant in Veracruz, Mexico.     

Iberian universities: a characterisation from ESI rankings

The access to bibliographic and citation databases allows to evaluate scientific performance, and provides useful means of general characterisation. In this paper we investigate the clustering of Iberian universities, resulting from the similarity in the number and specific nature of the scientific disciplines given by the Essential Science Indicators database. A further refining of the analysis, as provided by PCA, clearly reveals the relationship between the universities and the scientific disciplines in the main groups. Similarity between universities is not dictated only by the number of areas in the ranking, but also stems from the nature of the ranked scientific areas and the specific combination in each university.     

Perfluoropolyether polymers by UV curing: design,synthesis and characterization

Perfluoropolyether (PFPE) structures can be functionalized with acrylic groups using appropriate hydrogenated acrylic monomers: the macromers obtained are highly reactive under UV irradiation, and fluorinated polymers can be obtained. In the first part of this work is described the synthesis of new PFPE (meth)acrylic oligomers by extending OH‐terminated fluorinated chains with urethane groups and reactive acrylic functions. The photopolymerization reaction of each product is then reported followed by the characterisation of the main thermal, mechanical and surface properties of UV‐cured coatings. The polymers have good thermal resistance and fair mechanical and chemical resistance. More interestingly they show very low refractive index and low surface tension. For these latter properties the products can be advantageously used in highly demanding applications such as photonic devices and nano‐patterning. Copyright © 2011 Society of Chemical Industry     

Modeling the leadership – project performance relation: radial basis function,Gaussian and Kriging methods as alternatives to linear regression

The purpose of this paper is to analyze alternative forecasting methods that produce results at least similar to or better than linear regression (MLR) that can be used in the modeling of social systems. While organizations may be considered as typically non-linear systems, the common feature of most models found in literature continues to be the use of linear regression techniques. From a case study, advanced statistical methods of Gaussian and Kriging are evaluated, as well as an artificial intelligence (AI) tool, the radial basis function (RBF). The results show the best performance of the suggested methods compared to MLR, especially RBF, because of its uniform prediction behavior throughout all ranges of evaluation. These techniques, although somewhat unconventional in social systems modeling, present a potential contribution in increasing the accuracy and precision of the predictions allowing a more accurate assessment of the impact of certain strategies on the project performance to be made before the allocation of material, human and financial resources.     

Long‐Term Structural Evolution of an Intercalated Layered Semiconductor

Intercalated molecules can dramatically modify the electronic band structure of layered semiconductors, significantly altering the optical properties of the material. In the layered monochalcogenide Gallium Telluride (GaTe), exposure to air induces a nearly 1 eV reduction of its band gap due to the interlayer diffusion and chemisorption of oxygen. The effect of oxygen chemisorption at the Te‐terminated surfaces on the structure of GaTe, however, is much less known. To better understand the structure–property relationship of intercalated GaTe, a systematic, long‐term, X‐ray diffraction study has been performed on GaTe exfoliated crystals exposed to ambient conditions. It is observed that the structural changes are not limited to a previously observed short‐term increase in lattice expansion. Over the course of months and even years after exfoliation, the oxygen adsorbates continue to modify the structure of GaTe, inducing significant disorder and grain reorientation. It is estimated that approximately one out of every two grains is slightly displaced by the intercalating oxygen, demonstrating a significant increase in grain mosaicity, while still maintaining the original {?2 0 1} out‐of‐plane texture. Correlating these structural transformations to observed changes in electrical and optical properties will enable capitalization of the use of adsorbates to engineer novel properties in these layered materials.     

Automatic word naming recognition for an on-line aphasia treatment system

One of the most common effects among aphasia patients is the difficulty to recall names or words. Typically, word retrieval problems can be treated through word naming therapeutic exercises. In fact, the frequency and the intensity of speech therapy are key factors in the recovery of lost communication functionalities. In this sense, speech and language technology can have a relevant contribution in the development of automatic therapy methods. In this work, we present an on-line system designed to behave as a virtual therapist incorporating automatic speech recognition technology that permits aphasia patients to perform word naming training exercises. We focus on the study of the automatic word naming detector module and on its utility for both global evaluation and treatment. For that purpose, a database consisting of word naming therapy sessions of aphasic Portuguese native speakers has been collected. In spite of the different patient characteristics and speech quality conditions of the collected data, encouraging results have been obtained thanks to a calibration method that makes use of the patients’ word naming ability to automatically adapt to the patients’ speech particularities.     

End-user adaptable technologies for rehabilitation: a systematic literature review

Universal Access in the Information Society - Studies on end-user adaptable applications for rehabilitation aim to offer exercises to patients based on their therapeutic goals and individual...