Abstract

Abstract
Innovation plays a key role in meeting the challenges of the future, but despite the unprecedented investment in innovation, Portugal has seen a decline in the various indicators that assess the country's performance. This study aims to answer questions about the state of innovation in Portugal, based on the relevant global and European innovation indicators, comparing the country's performance with that of Ireland, Belgium, and the Czech Republic. Using secondary data collected from the reports of the last four years, explanatory research was conducted based on statistical and graphical methods in order to establish causal relationships. The areas where the main changes have taken place are presented, highlighting the aspects in which Portugal stands out for superior or poor performance, providing a benchmark for the definition of policies to foster innovation in Portugal. The results demonstrate that institutions, business sophistication, and knowledge and technology score negatively, while creativity stands out as a strength. Environmental sustainability, firms' investment in innovation, and the impact of innovation on sales are aspects that Portugal needs to improve; human capital and the attractiveness of the R & D system deserve positive remarks. It is fundamental to understand how Portugal is preparing for the future and what the country can learn from others. This study is limited by the specific period in analysis, which could affect causal relationships, and the historical perspective could provide guidelines to the understanding of the relative position of the country. This study contributes new perspectives and knowledge about the state of innovation in Portugal, providing clues to entrepreneurs, policy makers, and the scientific community.

Abstract
Predictive Maintenance applications are increasingly complex, with interactions between many components. Black-box models are popular approaches due to their predictive accuracy and are based on deep-learning techniques. This paper presents an architecture that uses an online rule learning algorithm to explain when the black-box model predicts rare events. The system can present global explanations that model the black-box model and local explanations that describe why the black-box model predicts a failure. We evaluate the proposed system using four real-world public transport data sets, presenting illustrative examples of explanations.

Abstract
Censored data are frequently found in diverse fields including environmental monitoring, medicine, economics and social sciences. Censoring occurs when observations are available only for a restricted range, e.g., due to a detection limit. Ignoring censoring produces biased estimates and unreliable statistical inference. The aim of this work is to contribute to the modelling of time series of counts under censoring using convolution closed infinitely divisible (CCID) models. The emphasis is on estimation and inference problems, using Bayesian approaches with Approximate Bayesian Computation (ABC) and Gibbs sampler with Data Augmentation (GDA) algorithms.

Abstract
There are several compelling reasons for exploring the ocean, for instance, the potential for accessing valuable resources, such as energy and minerals; establishing sovereignty; and addressing environmental issues. As a result, the scientific community has increasingly focused on the use of autonomous underwater vehicles (AUVs) for ocean exploration. Recent research has demonstrated that buoyancy change modules can greatly enhance the energy efficiency of these vehicles. However, the literature is scarce regarding the dynamic models of the vertical motion of buoyancy change modules. It is therefore difficult to develop adequate depth controllers, as this is a very complex task to perform in situ. The focus of this paper is to develop simplified linear models for a buoyancy change module that was previously designed by the authors. These models are experimentally identified and used to fine-tune depth controllers. Experimental results demonstrate that the controllers perform well, achieving a virtual zero steady-state error with satisfactory dynamic characteristics.

Abstract