A. Manuela Gonçalves

Cognitive Appraisal as a Mediator in the Relationship Between Stress and Burnout

The relation between job stressors and burnout is well established in the literature. However, the mechanisms behind this relationship are still not clear. This study has the main goal of analysing the mediating role of cognitive appraisal in the relation between occupational stress and burnout. Cognitive appraisals comprise primary appraisals, which are of the significance of a situation to the individual, and secondary appraisals, which involve evaluating the individuals ability to cope with stressors. To test the relationship between appraisals, stress and burnout, structural equation modelling was used in a sample of academic teaching staff (N = 333) working at a public university in Portugal. The participants completed a survey with measures that included their level of stress, cognitive appraisals of aspects of their work, and the Maslach Burnout Inventory–Educators Survey. The results indicated perceptions of distinct sources of stress on their work activity and a relation between stress, cognitive appraisal and burnout. Most importantly, the results showed that primary and secondary cognitive appraisals partially mediated the relationship between occupational stress and burnout at work, making the relationship between these variables a promising underlying mechanism for explaining responses to work-related stress.

Predicting seasonal and hydro-meteorological impact in environmental variables modelling via Kalman filtering

This study focuses on the potential improvement of environmental variables modelling by using linear state-space models, as an improvement of the linear regression model, and by incorporating a constructed hydro-meteorological covariate. The Kalman filter predictors allow to obtain accurate predictions of calibration factors for both seasonal and hydro-meteorological components. This methodology can be used to analyze the water quality behaviour by minimizing the effect of the hydrological conditions. This idea is illustrated based on a rather extended data set relative to the River Ave basin (Portugal) that consists mainly of monthly measurements of dissolved oxygen concentration in a network of water quality monitoring sites. The hydro-meteorological factor is constructed for each monitoring site based on monthly precipitation estimates obtained by means of a rain gauge network associated with stochastic interpolation (kriging). A linear state-space model is fitted for each homogeneous group (obtained by clustering techniques) of water monitoring sites. The adjustment of linear state-space models is performed by using distribution-free estimators developed in a separate section.

Irrigation planning: an optimal control approach

We aim to plan the water usage in the irrigation of a given farmland keeping, at same time, the field cultivation in a good state of preservation. This problem is modeled and tackled as an optimal control problem: minimize the water flow (control) so that the extent water amount in the soil (trajectory) fulfills the cultivation water requirements. To estimate rainfall, we consider two models: one based on the average monthly rainfall of the last 10 years in Lisbon area and another which considers the best linear combination of average monthly rainfall from the last 10 years and the amount of rainfall in the previous month. We study the behavior of the solutions under different weather scenarios and we compare the solutions obtained using our model of rainfall with solutions obtained having a prior knowledge of the rainfall.

A statistical model for the demand of undergraduate engineering courses in Portugal : a first study of Bologna Process impact

The observed changes in higher education, at least in the past 12 years, have changed the way students choose the academic program which they wish to attend in higher education, particularly in engineering. In this study, we used regression analysis applied to count data by means a negative binomial model for the access to higher education engeneering programs. The data used consider the academic year 2010/2011 given by Portuguese Ministry of Education. The statistical model obtained presents a statistical significance for the explanatory variables applied.

Application of Change‐Point Detection to a Structural Component of Water Quality Variables

In this study were developed methodologies in statistical time series models, such as multivariate state‐space models, to be applied to water quality variables in a river basin. In the modelling process is considered a latent variable that allows incorporating a structural component, such as seasonality, in a dynamic way. A change‐point detection method is applied to the structural component in order to identify possible changes in the water quality variables in consideration.

Optimal Control Applied to an Irrigation Planning Problem

We propose a mathematical model to study the water usage for the irrigation of given farmland to guarantee that the field crop is kept in a good state of preservation. This problem is formulated as an optimal control problem. The lack of analytic solution leads us to turn to numerical methods to solve the problem numerically. We then apply necessary conditions of optimality to validate the numerical solution. To deal with the high degree of unpredictability of water inflow due to weather, we further propose a replan strategy and we implement it.

Irrigation planning: replanning and numerical solution

A model to optimize the water use in the irrigation of a farm field via optimal control (water flow) that take into account the evapotranspiration, rainfall, and losses by filtration and runoff was developed in [1]. Here we improve the previous model to take into account real data of rainfall. Model predictive control is applied to replan. We test and compare different nonlinear constrained optimization techniques for solving the nonlinear constrained optimization problem that arises from the discretization of the proposed optimal control problem. Furthermore, we test different time discretization steps.

Combining Statistical Methodologies in Water Quality Monitoring in a Hydrological Basin - Space and Time Approaches

Fundacao para a Ciencia e a Tecnologia (FCT) - Est-C/MAT/UI0013/2011, PEstOE/MAT/UI0209/2011,

A new perspective of students allocation satisfaction in engineering courses in Portugal

This work aims at describing and characterizing students allocation satisfaction in the Portuguese public higher education system through the students’ point of view, namely in the academic engineering programs. A complementary ratiois defined that allows the comparison with the ratio provided by the Portuguese Education Ministry through the Institutions’ point of view. Mann-Whitney and Kruskal-Wallis tests were performed. Through cluster analysis it was possible to identify and characterize the new ratio and to make a comparison with the existing one.

Forecasting time series combining Holt-Winters and bootstrap approaches

Exponential smoothing methods are the most used in time series modeling and forecasting, due to their versatility and the vast model option they integrate. Also, within the computing statistical area, Bootstrap methodology is widely applied in statistical inference concerning time series. Therefore, this study’s main objective is to analyse Holt-Winters exponential smoothing method’s performance associated to Bootstrap methodology, as an alternative procedure for modeling and forecasting in time series. The Bootstrap methodology combined with Holt-Winters methodology is applied to a study case on an environmental time series concerning a surface water quality variable, Dissolved Oxygen (DO). The proposed procedure allows to obtaining better point forecasts and interval forecasts with less amplitude than those obtained by means of the usual methods.