Abstract
O Mosteiro da Batalha é uma relevante obra da arquitectura gótica, resultou do cumprimento da promessa feita por D. João I, após vitória na batalha de Aljubarrota. O mosteiro esteve na posse dos dominicanos até à extinção das ordens religiosas, sendo actualmente dependência da DGPC e Património da Humanidade pela UNESCO. Recentemente a Universidade Aveiro caracterizou estrutural do monumento. Entre as técnicas testadas considera-se aqui o potencial espontâneo, anteriormente aplicados na Igreja da Graça em Santarém (Martinho et. al, 2014). Seleccionou-se uma parede com eflorescências na qual se sobrepôs uma grelha de eléctrodos (12×5) espaçados de 0.5m. O mapeamento do potencial espontâneo foi realizado tendo como referência um eléctrodo sobre eflorescências. Concluiu-se que as zonas da parede com sais estão ao mesmo potencial, enquanto as zonas onde as eflorescências não ocorrem apresentam um potencial mais elevado. A composição mineralógica dos sais foi estudada por Difracção de Raios X.

Abstract
Policymakers have an important role in enabling eco-innovation. To assess the effectivity of these interventions, it is necessary to characterize policies, namely the level of policy stringency. The present study contributes to extant empirical literature by performing a cross-country assessment of the impact of policy stringency on the outcomes (rather than the inputs) of the eco-innovation process. Contrasting with extant evidence, results fail to evidence the relevance of policy stringency for eco-innovation performance. Notwithstanding, policy stringency emerged indirectly as a potential critical determinant. Indeed, the possibility to save costs is often driven by policy instruments that punish pollution intensive firms.

Abstract
Research concerning mining data from learning management systems have been consistently been appearing in the literature. However, in many situations there is not a clear path on the data mining procedures that lead to solid conclusions. Therefore, many studies result in ad-hoc conclusions with insufficient generalization capabilities. In this article, we describe a methodology and report our findings in an experiment which one online course which involved more than 150 students. We used the Moodle LMS during the period of one academic semester, collecting all the interactions between the students and the system. These data scales up to more than 33K records of interactions where we applied data mining tools following the procedure for data extraction, cleaning, feature identification and preparation. We then proceeded to the creation of automatic learning models based on decision trees, we assessed the models and validate the results by assessing the accuracy of the predictions using traditional metrics and draw our conclusions on the validity of the process and possible alternatives1.

Abstract
Abstract This paper describes a methodology for diabetic retinopathy detection from eye fundus images using a generalization of the bag-of-visual-words (BoVW) method. We formulate the BoVW as two neural networks that can be trained jointly. Unlike the BoVW, our model is able to learn how to perform feature extraction, feature encoding, and classification guided by the classification error. The model achieves 0.97 area under the curve (AUC) on the DR2 dataset while the standard BoVW approach achieves 0.94 AUC. Also, it performs at the same level of the state-of-the-art on the Messidor dataset with 0.90 AUC.

Abstract
Tabling is a powerful implementation technique that improves the declarativeness and expressiveness of traditional Prolog systems in dealing with recursion and redundant computations. It can be viewed as a natural tool to implement dynamic programming problems, where a general recursive strategy divides a problem in simple sub-problems that are often the same. When tabling is combined with multithreading, we have the best of both worlds, since we can exploit the combination of higher declarative semantics with higher procedural control. However, at the engine level, such combination for dynamic programming problems is very difficult to exploit in order to achieve execution scalability as we increase the number of running threads. In this work, we focus on two well-known dynamic programming problems, the Knapsack and the Longest Common Subsequence problems, and we discuss how we were able to scale their execution by using the multithreaded tabling engine of the Yap Prolog system. To the best of our knowledge, this is the first work showing a Prolog system to be able to scale the execution of multithreaded dynamic programming problems. Our experiments also show that our system can achieve comparable or even better speedup results than other parallel implementations of the same problems.

Abstract
The Arteriolar-to-Venular Ratio (AVR) is a popular dimensionless measure which allows the assessment of patients' condition for the early diagnosis of different diseases, including hypertension and diabetic retinopathy. This paper presents two new approaches for AVR computation in retinal photographs which include a sequence of automated processing steps: vessel segmentation, caliber measurement, optic disc segmentation, artery/vein classification, region of interest delineation, and AVR calculation. Both approaches have been tested on the INSPIRE-AVR dataset, and compared with a ground-truth provided by two medical specialists. The obtained results demonstrate the reliability of the fully automatic approach which provides AVR ratios very similar to at least one of the observers. Furthermore, the semi-automatic approach, which includes the manual modification of the artery/vein classification if needed, allows to significantly reduce the error to a level below the human error.