Abstract
Background: Digital clinical tools are a new technology that can be used in the screening or diagnosis of obstructive sleep apnea (OSA), notwithstanding the crucial role of polysomnography, the gold standard.Objective: This study aimed to identify, gather, and analyze the most accurate digital tools and smartphone-based health platforms used for OSA screening or diagnosis in the adult population. Methods: We performed a comprehensive literature search of PubMed, Scopus, and Web of Science databases for studies evaluating the validity of digital tools in OSA screening or diagnosis until November 2022. The risk of bias was assessed using the Joanna Briggs Institute critical appraisal tool for diagnostic test accuracy studies. The sensitivity, specificity, and area under the curve (AUC) were used as discrimination measures.Results: We retrieved 1714 articles, 41 (2.39%) of which were included in the study. From these 41 articles, we found 7 (17%) smartphone-based tools, 10 (24%) wearables, 11 (27%) bed or mattress sensors, 5 (12%) nasal airflow devices, and 8 (20%) other sensors that did not fit the previous categories. Only 8 (20%) of the 41 studies performed external validation of the developed tool. Of these, the highest reported values for AUC, sensitivity, and specificity were 0.99, 96%, and 92%, respectively, for a clinical cutoff of apnea-hypopnea index (AHI)& GE;30. These values correspond to a noncontact audio recorder that records sleep sounds, which are then analyzed by a deep learning technique that automatically detects sleep apnea events, calculates the AHI, and identifies OSA. Looking at the studies that only internally validated their models, the work that reported the highest accuracy measures showed AUC, sensitivity, and specificity values of 1.00, 100%, and 96%, respectively, for a clinical cutoff AHI & GE;30. It uses the Sonomat-a foam mattress that, aside from recording breath sounds, has pressure sensors that generate voltage when deformed, thus detecting respiratory movements, and uses it to classify OSA events.Conclusions: These clinical tools presented promising results with high discrimination measures (best results reached AUC>0.99). However, there is still a need for quality studies comparing the developed tools with the gold standard and validating them in external populations and other environments before they can be used in clinical settings.

Abstract
Recent societal changes have brought new challenges to contemporary organisations, e.g., how to properly manage the work-family/family-work dyad and, thus, promote adequate task performance. This paper aimed to study the relationship between conflict (work-family and family-work) and task performance, and whether this relationship was moderated by well-being. Thus, the following hypotheses were formulated: (1) conflict (work-family and family-work) is negatively associated with task performance; (2) conflict (work-family and family-work) is negatively associated with well-being; (3) well-being is positively associated with task performance; and (4) well-being moderates the relationship between conflict (work-family and family-work) and task performance. A total of 596 subjects participated in this study, all employed in Portuguese organisations. The results underlined that only family-work conflict was negatively and significantly associated with task performance. Work-family conflict established a negative and significant relationship with well-being. Well-being was positively and significantly associated with performance and moderated the relationship between conflict (work-family and family-work) and task performance. These results show that organisations should provide employees with situations that promote their well-being, especially in Portugal, where a relationship culture exists (rather than task culture, which is predominant in the USA and Canada, for example) which means that additional and considerable time must be dedicated to personal and family matters for people to fit in and be accepted harmoniously.

Abstract
The purpose of this research is to demonstrate how retailers have used the digital transformation to mitigate the negative effects of COVID-19. As this research aims to understand a real-life phenomenon for which there is very limited knowledge, we created the opportunity to empirically explore the digital transformation in the retail industry during COVID-19 pandemic. In general terms, the research follows a qualitative, descriptive, and exploratory case study design. The results have shown that retailers should focus on technological innovations, adapt their business models, manage their distribution channels, and strengthen their customer-centric strategy. Moreover, it is necessary to emphasize that while smart retail is gradually standing out in the sector, there are also some factors that have not been overcome, such as lack of digital culture, training, and digital leadership. Despite these identified difficulties, the adoption of a digital strategy will allow a differentiating, safe and secure shopping experience, which today is one of the decisive factors for the survival of companies. The COVID-19 pandemic had social and economic effects in all industries - retail was no exception. In turn, the digital technologies already used by companies began to contribute to retailers being able to respond more quickly to customer needs, having been fundamental in fighting the COVID-19 pandemic. To the best of the authors' knowledge, this research is one of the first to explore this topic, bringing new contributions to theory and managerial practice.

Abstract
A Bloom Filter is a probabilistic data structure designed to check, rapidly and memory-efficiently, whether an element is present in a set. It has been vastly used in various computing areas and several variants, allowing deletions, dynamic sets and working with sliding windows, have surfaced over the years. When summarizing data streams, it becomes relevant to identify the more recent elements in the stream. However, most of the sliding window schemes consider the most recent items of a data stream without considering time as a factor. While this allows, e.g., storing the most recent 10000 elements, it does not easily translate into storing elements received in the last 60 seconds, unless the insertion rate is stable and known in advance. In this paper, we present the Time-limited Bloom Filter, a new BF-based approach that can save information of a given time period and correctly identify it as present when queried, while also being able to retire data when it becomes stale. The approach supports variable insertion rates while striving to keep a target false positive rate. We also make available a reference implementation of the data structure as a Redis module.

Abstract

Abstract
Aim There has been an increase in the development of technologies that can deliver personalised dietary advice. Devising healthy, sustainable dietary plans will mean taking into consideration extrinsic factors such as individual social circumstances. The aim of this study was to identify societal groups more or less receptive to and likely to engage with personalised nutrition initiatives. Sample and methods Volunteers were recruited via a social research agency from within the UK. The resultant sample (N = 1061) was 49% female, aged 18-65 years. Results MANOVA (Tukey HSD applied) indicated that females and younger people (aged 18-29 years) had more favourable attitudes and were more likely to intend to adopt personalised nutrition. There were no differences in attitude toward or intention to adopt personalised nutrition between different education levels, income brackets or occupational groups. Conclusion These results imply that females and younger people may be most likely to adopt personalised nutrition in the future. Initiatives to promote healthy eating should target males and older people.