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.

Abstract
Over these past five years, significant breakthroughs, led by Transformers and large language models, have been made in understanding natural language text. However, the ability to capture contextual nuances in longer texts is still an elusive goal, let alone the understanding of consistent fine-grained narrative structures in text. These unsolved challenges and the interest in the community are at the basis of the sixth edition of Text2Story workshop to be held in Dublin on April 2nd, 2023 in conjunction with the 45th European Conference on Information Retrieval (ECIR'23). In its sixth edition, we aim to bring to the forefront the challenges involved in understanding the structure of narratives and in incorporating their representation in well-established models, as well as in modern architectures (e.g., transformers) which are now common and form the backbone of almost every IR and NLP application. It is hoped that the workshop will provide a common forum to consolidate the multi-disciplinary efforts and foster discussions to identify the wide-ranging issues related to the narrative extraction and generation task. Text2Story includes sessions devoted to full research papers, work-in-progress, demos and dissemination papers, keynote talks and space for an informal discussion of the methods, of the challenges and of the future of this research area.

Abstract
This study seeks to answer the following research question: "What factors can explain the number of patent filing requests made by residents in Brazil at patent offices in Brazil, the United States, Europe, and triadic patent families?". The methods used in this research are quantitative, using big data from private and public investments in Science and Technology, and about patent deposit numbers in Brazil from 2000 to 2017. A model of linear regression was performed and explains how these investments in Science and Technology influence patent deposit numbers. The results of this research study point towards the importance of universities, up and beyond the traditional training and education aspect of university activity. The importance of public and private innovation investments is also shown to be important. This study shows that the patent registrations in the different regions under analysis are affected by different factors. There is thus no single formula towards the creation of innovation output and governments would do well to continue to invest in higher education while also investing in public research and development activities. Additionally, and not least important, private entities should be continually encouraged to make innovation investments and favourable government policies need to thus exist for this to happen. Finally, the low numbers regarding patent filings in Brazil may be linked to institutional deficiencies in the country. Patent breaches may be difficult to punish, and the judicial system may be slow and untrustworthy, compared to the United States and to Europe-leading to diminished patent registrations in Brazil. A set of implications and recommendations for policy derived from this study and will be strategic for policymakers.

Abstract
Tourism business models have used several technologies in their development, such as Virtual Reality (VR). Previous studies show that VR allows tourism organizations to promote new types of relationships between tourists and destinations, to enhance the appeal and memorability of tourist experiences and to diversify consumption patterns, which could also be interesting for dealing with sustainability issues, such as seasonal demand of destinations or activities in wine tourism. Thus, we propose a conceptual model to analyze the influence of memorable tourism experiences on wine tourists' future intentions after a VR experience, providing additional details on the research methodology to empirically test the conceptual model. Innovation in business models with VR to promote new relationships with destinations or activities and diversify tourists' consumption patterns could be interesting to address seasonal activities, such as the grape harvest or grape-treading, which are not continuously available for tourist observation/ participation, despite their high appeal. On the other hand, the results could contribute to wine and other kinds of tourism, conditioned by mobility issues such as restrictions on movements or personal interaction, due to health crises or personal constraints, increasing these tourism experiences' accessibility also in times of unavailability. © 2023, Universidade de Aveiro. All rights reserved.