Regulació motivacional, satisfacció i rendiment acadèmic dels estudiants de psicologia

Aquest estudi aplica la Teoria de l‟Autodeterminació (Deci i Ryan, 1985, 2000, 2002) i el constructe motivacional d‟Engagement (Schaufeli, Martínez, Marqués, Salanova i Bakker, 2002; Schaufeli, Salanova, González-Roma i Bakker, 2002) per investigar els efectes de la regulació motivacional dels estudiants, l‟Engagement, la Competència percebuda i la Vinculació sobre les Expectatives Acadèmiques i Laborals, la Satisfacció i el Rendiment acadèmic. Els resultats revelen que: (1) els 214 estudiants de Psicologia de la Universitat de Girona que varen participar en la recerca mostren alts nivells d‟autonomia (Regulació Identificada i Intrínseca), però nivells moderats d‟Engagement (Vigor i Absorció, i una mica més alts de Dedicació); (2) les intercorrelacions entre totes les variables motivacionals considerades confirmen les prediccions de la Teoria de l‟Autodeterminació i del model motivacional de l‟Engagement; (3) la Competència percebuda prediu les Expectatives Acadèmiques, mentre que la Motivació Intrínseca i l‟Engagement prediuen les Expectatives Laborals; (4) l‟Amotivació i el Cinisme (en sentit negatiu) i la Motivació Intrínseca i l‟Engagement (en positiu) prediuen la Satisfacció amb la carrera; i (5) l‟Amotivació (en negatiu) i la Competència percebuda (en positiu) prediuen el Rendiment acadèmic

SQualTrack: A Tool for Robust Fault Detection

One of the techniques used to detect faults in dynamic systems is analytical redundancy. An important difficulty in applying this technique to real systems is dealing with the uncertainties associated with the system itself and with the measurements. In this paper, this uncertainty is taken into account by the use of intervals for the parameters of the model and for the measurements. The method that is proposed in this paper checks the consistency between the system's behavior, obtained from the measurements, and the model's behavior; if they are inconsistent, then there is a fault. The problem of detecting faults is stated as a quantified real constraint satisfaction problem, which can be solved using the modal interval analysis (MIA). MIA is used because it provides powerful tools to extend the calculations over real functions to intervals. To improve the results of the detection of the faults, the simultaneous use of several sliding time windows is proposed. The result of implementing this method is semiqualitative tracking (SQualTrack), a fault-detection tool that is robust in the sense that it does not generate false alarms, i.e., if there are false alarms, they indicate either that the interval model does not represent the system adequately or that the interval measurements do not represent the true values of the variables adequately. SQualTrack is currently being used to detect faults in real processes. Some of these applications using real data have been developed within the European project advanced decision support system for chemical/petrochemical manufacturing processes and are also described in this paper

Quantified set inversion with applications to control

This paper describes a new reliable method, based on modal interval analysis (MIA) and set inversion (SI) techniques, for the characterization of solution sets defined by quantified constraints satisfaction problems (QCSP) over continuous domains. The presented methodology, called quantified set inversion (QSI), can be used over a wide range of engineering problems involving uncertain nonlinear models. Finally, an application on parameter identification is presented