The importance of target audiences in the design of training actions

This paper describes the process of definition, conceptualization and implementation of a business course addressed for logistic and industrial managers. This course was designed using a blended methodology, with training in classroom, visits to enterprises and self- study, supported by an eLearning platform. The aim of this work is to create an opportunity to reflect about the decisions and strategies implemented and point future developments

Hydrogen storage in the form of metal hydrides

Reversible reactions between hydrogen and such materials as iron/titanium and magnesium/ nickel alloy may provide a means for storing hydrogen fuel. A demonstration model of an iron/titanium hydride storage bed is described. Hydrogen from the hydride storage bed powers a converted gasoline electric generator

Informed citizen and empowered citizen in health: results from an European survey

Background: The knowledge about the relationship between health-related activities on the Internet (i.e. informed citizens) and individuals? control over their own experiences of health or illness (i.e. empowered citizens) is valuable but scarce. In this paper, we investigate the correlation between four ways of using the Internet for information on health or illness and citizens attitudes and behaviours toward health professionals and health systems and establish the profile of empowered eHealth citizens in Europe. Methods: Data was collected during April and May 2007 (N = 7022), through computer-assisted telephone interviews (CATI). Respondents from Denmark, Germany, Greece, Latvia, Norway, Poland and Portugal participated in the survey. The profiles were generated using logistic regressions and are based on: a) socio-demographic and health information, b) the level of use of health-related online services, c) the level of use of the Internet to get health information to decide whether to consult a health professional, prepare for a medical appointment and assess its outcome, and d) the impact of online health information on citizens? attitudes and behavior towards health professionals and health systems. Results: Citizens using the Internet to decide whether to consult a health professional or to get a second opinion are likely to be frequent visitors of health sites, active participants of online health forums and recurrent buyers of medicines and other health related products online, while only infrequent epatients, visiting doctors they have never met face-to-face. Participation in online health communities seems to be related with more inquisitive and autonomous patients. Conclusions: The profiles of empowered eHealth citizens in Europe are situational and country dependent. The number of Europeans using the Internet to get health information to help them deal with a consultation is raising and having access to online health information seems to be associated with growing number of inquisitive and self-reliant patients. Doctors are increasingly likely to experience consultations with knowledgeable and empowered patients, who will challenge them in various ways

Maximum Entanglement in Squeezed Boson and Fermion States

A class of squeezed boson and fermion states is studied with particular emphasis on the nature of entanglement. We first investigate the case of bosons, considering two-mode squeezed states. Then we construct the fermion version to show that such states are maximum entangled, for both bosons and fermions. To achieve these results, we demonstrate some relations involving squeezed boson states. The generalization to the case of fermions is made by using Grassmann variables.Comment: 4 page

On the automated learning of air pollution prediction models from data collected by mobile sensor networks

This paper addresses the problem of automated learning of air pollution predictive models that were trained using information gathered by a set of mobile low-cost sensors. Concretely, fast to compute machine learning methods (Decision Trees and Support Vector Machines) were used to build regression models that predict air pollution levels for a given location. The models were trained using the data collected by the OpenSense project, in particular, number of particulate matter, particle diameter, and lung deposited surface area (LDSA). We examined two different sets of attributes: one based on a geographical description of the location under analysis (e.g. distribution of households and roads), and another based on a time series of past air pollution observations in that location. Overall, we have found out that past measures lead to better pollution predictions. The best R2 score was 0.751 obtained with the model that predicts LDSA and was trained with the data set with time series attributes, while the worst R2 was 0.009 obtained with the geographical data set to predict number of particles. The performance of the best model is on par with similar air pollution systems. Moreover it can be used in a production system that requires frequent updates.info:eu-repo/semantics/acceptedVersio