Abstract
This study aimed to identify dietary patterns (DPs) and their associations with sociodemographic factors and diet quality in Portuguese adults and the elderly. Cross-sectional data were obtained from the National Food, Nutrition and Physical Activity Survey (2015-2016), with two non-consecutive dietary 24 h recalls. Food items were classified according to the NOVA system and its proportion (in grams) in the total daily diet was considered to identify DPs by latent class analysis, using age and sex as concomitant variables. Multinomial logistic and linear regressions were performed to test associations of DPs with sociodemographic characteristics and diet quality, respectively. Three DPs were identified: "Traditional " (higher vegetables, fish, olive oil, breads, beer and wine intake), "Unhealthy " (higher pasta, sugar-sweetened beverages, confectionery and sausages intake) and "Diet concerns " (lower intake of cereals, red meat, sugar-sweetened and alcoholic beverages). "Unhealthy " was associated with being younger and lower intake of dietary fiber and vitamins and the highest free sugars and ultra-processed foods (UPF). "Diet concerns " was associated with being female and a more favorable nutrient profile, but both DPs presented a higher contribution of UPF than the "Traditional " DP. These findings should be considered for the design of food-based interventions and public policies for these age groups in Portugal.

Abstract
Many archival collections have been recently digitized and made available to a wide public. The contained documents however tend to have limited attractiveness for ordinary users, since content may appear obsolete and uninteresting. Archival document collections can become more attractive for users if suitable content can be recommended to them. The purpose of this research is to propose a new research direction of Archival Content Suggestion to discover interesting content from long-term document archives that preserve information on society history and heritage. To realize this objective, we propose two unsupervised approaches for automatically discovering interesting sentences from news article archives. Our methods detect interesting content by comparing the information written in the past with one created in the present to make use of a surprise effect. Experiments on New York Times corpus show that our approaches effectively retrieve interesting content. © 2021, Springer Nature Switzerland AG.

Abstract
Our society generates massive amounts of digital data, significant portion of which is being archived and made accessible to the public for the current and future use. In addition, historical born-analog documents are being increasingly digitized and included in document archives which are available online. Professionals who use document archives tend to know what they wish to search for. Yet, if the results are to be useful and attractive for ordinary users they need to contain content which is interesting and familiar. However, the state-of-the-art retrieval methods for document archives basically apply same techniques as search engines for synchronic document collections. In this paper, we introduce a novel concept of estimating the relation of archival documents to the present times, called contemporary relevance. Contemporary relevance can be used for improving access to archival document collections so that users have higher probability of finding interesting or useful content. We then propose an effective method for computing contemporary relevance degrees of news articles using Learning to Rank with a range of diverse features, and we successfully test it on the New York Times Annotated document collection. Our proposal offers a novel paradigm of information access to archival document collections by incorporating the context of contemporary time.

Abstract
Crowdsourced data streams are continuous flows of data generated at high rate by users, also known as the crowd. These data streams are popular and extremely valuable in several domains. This is the case of tourism, where crowdsourcing platforms rely on tourist and business inputs to provide tailored recommendations to future tourists in real time. The continuous, open and non-curated nature of the crowd-originated data requires robust data stream mining techniques for on-line profiling, recommendation and evaluation. The sought techniques need, not only, to continuously improve profiles and learn models, but also be transparent, overcome biases, prioritise preferences, and master huge data volumes; all in real time. This article surveys the state-of-art in this field, and identifies future research opportunities. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
In the area of Information and Communication Technologies, in addition to the problem of engagement, students often have difficulties in learning subjects related to modeling and programming. The reasons for these difficulties are well known and described in the literature, pointing to difficulties in abstraction and logic thinking. Knowing that the value of flexible and personalized learning, teachers are changing the way they teach, using different active learning methodologies, such as flipped classroom, project-based learning, and peer instruction. This paper describes an experiment conducted to improve the learning experiences of the students enrolled in the Computer Science bachelor's degree course, attending three curricular units: Information Systems Development, Data Structures, and Web Languages and Technologies. The approach followed by the teachers used project-based learning as an active learning methodology. This methodology allows us to achieve four main objectives: (i) improve student engagement; (ii) improve learning outcomes achievement (iii) increase the course success rate and (iv) allow students to experience the need for the software development lifecycle, feeling that software engineering is not a block-based process but depending on previous activity, often leads to the need to go back in the process. The results obtained with the use of the active methodology were well accepted by the students and allowed both teachers and students to reach the objectives set.

Abstract
In the last two years, the world has gone through an unprecedented change in the most diverse dimensions (social, economic, and even political), leading that society had to adapt very quickly to the contingencies imposed by COVID-19. All organizations (independent of their area of activity) had to adjust their processes to respond, efficiently and effectively, to these constraints. In this context, companies with concerns in internationalization (those that are already internationalized and those in an internationalization process) have had to resort to technologies to support the change in their modus operandi. The digital transformation (until now had an essential role in the transformation of organizations, but which was in a relatively slow implementation process) started to perform, in an accelerated way, the base of work for the heads of the organizations to be able to respond to these challenges. In this context, the transformation of the business model, supported by digital technology, has been documented as one of the strategies used to respond to disruptive environmental changes, particularly technologies that help companies identify new business practices. This study aims to find evidence of the importance of integrating and influencing technological innovations in the practice of international business strategy before and during COVID-19 pandemic. The results show the influence of the digitalization on the business strategies.