Abstract
Transthyretin-associated Familial Amyloid Polyneuropathy (TTR-FAP) is a chronic fatal disease with a high incidence in Portugal. It is therefore relevant to provide professionals and citizens with a tool that enables a detailed geographical and territorial study. For this reason, we have developed an web based application that brings together techniques applied to spatial data that allow the study of the historical progression and growth of cases in patients' residential areas and areas of origin as well as an epidemic forecast. The tool enables the exploration of geographical longitudinal data at national, district and county levels. High density regions and periods can be visually identified according to parameters selected by the user. The visual evaluation of the data and its comparison across different time spans of the disease era can have an impact on more informed decision making by those working with patients to improve their quality of life, treatment or follow-up. The tool is available online for data exploration and its code is available on GitHub for adaptation to other geospatial scenarios.

Abstract
The urban development model used in the past has proved unsustainable. Due to this fact, cities such as Rio de Janeiro, with high population density, mainly regarding precarious settlements, need immediate practical actions to become more sustainable. The insufficiency of planning and investments contributed to the city occupying the 8th position of the most vulnerable cities in the world, triggering the primary motivation of this research study. Therefore, the leading research focused on exploring how sustainable social housing (SH) could contribute to Rio de Janeiro becoming more sustainable. Literature review, document analysis and semi-structured interviews were carried out to identify sustainable SH in Rio de Janeiro that can serve as a benchmark and listen to the opinion of engineers or architects regarding the importance of sustainable SH for the population and the city. Thus, in consonant with structural changes in thinking, production, and living, it was possible to identify the main contributions of sustainable SH to the city.

Abstract
The scientific community has been exploring new concepts as a result of the usage of optical fibers as absolute measurement sensors. While cross-sensitivity is a common issue with optical fiber sensors, this issue has been mitigated by simultaneous measurement techniques. But when it comes to absolute measurements, these methods have some limitations. The white light interferometer, which offers a superb solution for a range of applications, especially for absolute temperature measurement, is one of the most often used methods for absolute measurements.

Abstract
In a clinical context, physicians usually take into account information from more than one data modality when making decisions regarding cancer diagnosis and treatment planning. Artificial intelligence-based methods should mimic the clinical method and take into consideration different sources of data that allow a more comprehensive analysis of the patient and, as a consequence, a more accurate diagnosis. Lung cancer evaluation, in particular, can benefit from this approach since this pathology presents high mortality rates due to its late diagnosis. However, many related works make use of a single data source, namely imaging data. Therefore, this work aims to study the prediction of lung cancer when using more than one data modality. The National Lung Screening Trial dataset that contains data from different sources, specifically, computed tomography (CT) scans and clinical data, was used for the study, the development and comparison of single-modality and multimodality models, that may explore the predictive capability of these two types of data to their full potential. A ResNet18 network was trained to classify 3D CT nodule regions of interest (ROI), whereas a random forest algorithm was used to classify the clinical data, with the former achieving an area under the ROC curve (AUC) of 0.7897 and the latter 0.5241. Regarding the multimodality approaches, three strategies, based on intermediate and late fusion, were implemented to combine the information from the 3D CT nodule ROIs and the clinical data. From those, the best model-a fully connected layer that receives as input a combination of clinical data and deep imaging features, given by a ResNet18 inference model-presented an AUC of 0.8021. Lung cancer is a complex disease, characterized by a multitude of biological and physiological phenomena and influenced by multiple factors. It is thus imperative that the models are capable of responding to that need. The results obtained showed that the combination of different types may have the potential to produce more comprehensive analyses of the disease by the models.

Abstract
Abstract This study aims to assess the lecturers’ opinions about the use of e-learning tools to support distance and blended learning in higher education in Portugal, evidently reinforced by the COVID-19 pandemic. This research was based on a qualitative methodology, specifically, a focus group with professors from five higher education institutions from different geographical areas in Portugal. The obtained results were analysed along four main dimensions: (1) the level of knowledge of e-learning tools, (2) the reasons for using or (3) not using them, and, finally, (4) the opinion of lecturers on the student assessment process using these tools. The results showed that in addition to the concerns with smooth running classes and the appropriate delivery of the syllabus, the lecturers considered the transition to the e-learning context to have been easy. They noted a high level of literacy in the used tools, believed in the continued use of e-learning in the post-pandemic context, indicated several advantages for those involved in the e-learning context and a majority of limitations related to the time required for the adoption of more tools; and, finally, underlined the student assessment issue, which was pointed out as the most sensitive topic in the whole e-learning context. The study informed on the lecturers’ perspective on e-learning and the used tools and provided insight into their perceived usefulness and benefits for lecturers and students. An especially strong concern was verified on the part of lecturers to optimise e-learning tools to provide better knowledge delivery to students.

Abstract
Greenland ice core records display abrupt transitions, designated as Dansgaard-Oeschger (DO) events, characterised by episodes of rapid warming (typically decades) followed by a slower cooling. The identification of abrupt transitions is hindered by the typical low resolution and small size of paleoclimate records, and their significant temporal variability. Furthermore, the amplitude and duration of the DO events varies substantially along the last glacial period, which further hinders the objective identification of abrupt transitions from ice core records Automatic, purely data-driven methods, have the potential to foster the identification of abrupt transitions in palaeoclimate time series in an objective way, complementing the traditional identification of transitions by visual inspection of the time series.In this study we apply an algorithmic time series method, the Matrix Profile approach, to the analysis of the NGRIP Greenland ice core record, focusing on:- the ability of the method to retrieve in an automatic way abrupt transitions, by comparing the anomalies identified by the matrix profile method with the expert-based identification of DO events;- the characterisation of DO events, by classifying DO events in terms of shape and identifying events with similar warming/cooling temporal patternThe results for the NGRIP time series show that the matrix profile approach struggles to retrieve all the abrupt transitions that are identified by experts as DO events, the main limitation arising from the diversity in length of DO events and the method’s dependence on fixed-size sub-sequences within the time series. However, the matrix profile method is able to characterise the similarity of shape patterns between DO events in an objective and consistent way.