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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.