No abstract

No abstract

The goal of this paper is to present a competence assessment method in project management that is based on participants' performance and value creation. It seeks to close an existing gap in competence assessment in higher education. The proposed method relies on information and communication technology (ICT) tools and combines Project Management Information System (PMIS) tools with a survey system. This permits it to be implemented in an Internet-based simulation game. The system enforces the assessment of individual competences by a set of performance indicators and value stream analyses. A specific case study is presented as a way to validate the principles on which the proposed method is based. It has been proven to be helpful in a complex learning environment that involves two different universities and levels of degrees (undergraduate and master). When the method has been implemented, it is possible to collect detailed information regarding competence at a process for each participant. This increases the transparency of the work carried out, as well as enabling the specific design of educational programs for specific competence learning paths. Based on the experience that has been acquired, a specific recommendation is made concerning the feedback value provided for practitioners, students and teachers.

Estimating the atmospheric parameters of M-type stars has been a difficult task due to the lack of simple diagnostics in the stellar spectra. We aim at uncovering good sets of predictive features of stellar atmospheric parameters (T eff , log (g), [M/H]) in spectra of M-type stars. We define two types of potential features (equivalent widths and integrated flux ratios) able to explain the atmospheric physical parameters. We search the space of feature sets using a genetic algorithm that evaluates solutions by their prediction performance in the framework of the BT-Settl library of stellar spectra. Thereafter, we construct eight regression models using different machine-learning techniques and compare their performances with those obtained using the classical χ 2 approach and independent component analysis (ICA) coefficients. Finally, we validate the various alternatives using two sets of real spectra from the NASA Infrared Telescope Facility (IRTF) and Dwarf Archives collections. We find that the crossvalidation errors are poor measures of the performance of regression models in the context of physical parameter prediction in M-type stars. For R ∼ 2000 spectra with signal-to-noise ratios typical of the IRTF and Dwarf Archives, feature selection with genetic algorithms or alternative techniques produces only marginal advantages with respect to representation spaces that are unconstrained in wavelength (full spectrum or ICA). We make available the atmospheric parameters for the two collections of observed spectra as online material.

No abstract

No abstract

No abstract

This paper proposes a framework for an Air Quality Decision Support System (AQDSS), and as a proof of concept, develops an Internet of Things (IoT) application based on this framework. This application was assessed by means of a case study in the City of Madrid. We employed different sensors and combined outdoor and indoor data with spatiotemporal activity patterns to estimate the Personal Air Pollution Exposure (PAPE) of an individual. This pilot case study presents evidence that PAPE can be estimated by employing indoor air quality monitors and e-beacon technology that have not previously been used in similar studies and have the advantages of being low-cost and unobtrusive to the individual. In future work, our IoT application can be extended to include prediction models, enabling dynamic feedback about PAPE risks. Furthermore, PAPE data from this type of application could be useful for air quality policy development as well as in epidemiological studies that explore the effects of air pollution on certain diseases.