Abstract
(1) Background: The repercussions of work environments were widely studied before the pandemic. However, there are still many difficulties to be discovered considering the impact generated by it. Thus, this study aimed to analyse the impact of COVID-19 on nursing practice environments and nurses' job satisfaction. (2) Methods: A correlational study was conducted in a hospital in northern Portugal, with the participation of 416 registered nurses. Data were collected in June 2021 through questionnaires. The study was approved by the Institutional Ethics Committee. (3) Results: COVID-19 had a favourable impact on the structure component of the practice environments; the process component decreased compared to the pre-pandemic period; the outcome component remained moderately favourable to the quality of care. Nurses were not very satisfied or not at all satisfied with their valuation and remuneration; moderately satisfied with the leadership and staffing; and satisfied with the organisation and resources, co-workers and valuation by patients and families. In more favourable environments, nurses' job satisfactions were higher. (4) Conclusions: Identifying the dimensions with the best and worst scores allowed the institution's managers to concentrate efforts on where improvements were needed, thus preparing professional contexts for the recovery of care activities.

Abstract
MUSIC OFTEN EVOKES A REGULAR BEAT AND A pleasurable sensation of wanting to move to that beat called groove. Recent studies show that a rhythmic pattern's ability to evoke groove increases at moderate levels of syncopation, essentially, when some notes occur earlier than expected. We present two studies that investigate that effect of syncopation in more realistic polyphonic music examples. First, listeners rated their urge to move to music excerpts transcribed from funk and rock songs, and to algorithmically transformed versions of these excerpts: 1) with the original syncopation removed, and 2) with various levels of pseudorandom syncopation introduced. While the original excerpts were rated higher than the de-syncopated, the algorithmic syncopation was not as successful in evoking groove. Consequently, a moderate level of syncopation increases groove, but only for certain syncopation patterns. The second study provides detailed comparisons of the original and transformed rhythmic structures that revealed key differences between them in: 1) the distribution of syncopation across instruments and metrical positions, 2) the counter-meter figures formed by the syncopating notes, and 3) the number of pickup notes. On this basis, we form four concrete hypotheses about the function of syncopation in groove, to be tested in future experiments.

Abstract
This chapter describes a software architecture for processing big-data analytics considering the complete compute continuum, from the edge to the cloud. The new generation of smart systems requires processing a vast amount of diverse information from distributed data sources. The software architecture presented in this chapter addresses two main challenges. On the one hand, a new elasticity concept enables smart systems to satisfy the performance requirements of extreme-scale analytics workloads. By extending the elasticity concept (known at cloud side) across the compute continuum in a fog computing environment, combined with the usage of advanced heterogeneous hardware architectures at the edge side, the capabilities of the extreme-scale analytics can significantly increase, integrating both responsive data-in-motion and latent data-at-rest analytics into a single solution. On the other hand, the software architecture also focuses on the fulfilment of the non-functional properties inherited from smart systems, such as real-time, energy-efficiency, communication quality and security, that are of paramount importance for many application domains such as smart cities, smart mobility and smart manufacturing. © The Author(s) 2022. All rights reserved.

Abstract
The Mid-Infrared ELT Imager and Spectrograph (METIS) is one of the first generation science instruments on ESO's 39m Extremely Large Telescope (ELT). METIS will provide diffraction-limited imaging and medium resolution slit-spectroscopy from 3 - 13 microns (L, M, and N bands), as well as high resolution (R similar to 100,000) integral field spectroscopy from 2.9 - 5.3 microns. Both imaging and IFU spectroscopy can be combined with coronagraphic techniques. After passing its preliminary design review (PDR) in May 2019, and the final design review (FDR) of its optical system in June 2021, METIS is now preparing for the FDR of its entire system in the fall of 2022, while the procurements of many optical components have already started. First light at the telescope is expected in 2028, after a comprehensive assembly integration and test phase. In this paper we focus mainly on the various design aspects, and present a status update on the final optical and mechanical design of METIS. We describe the conceptual setup of METIS, its key functional components, and the resulting observing modes. Last but not least, we present the expected scientific performance, in terms of sensitivity, adaptive optics, and high contrast imaging performance.

Abstract
E-learning tools are gaining increasing relevance as facilitators in the task of learning how to program. This is mainly a result of the pandemic situation and consequent lockdown in several countries, which forced distance learning. Instant and relevant feedback to students, particularly if coupled with gamification, plays a pivotal role in this process and has already been demonstrated as an effective solution in this regard. However, teachers still struggle with the lack of tools that can adequately support the creation and management of online gamified programming courses. Until now, there was no software platform that would be simultaneously open-source and general-purpose (i.e., not integrated with a specific course on a specific programming language) while featuring a meaningful selection of gamification components. Such a solution has been developed as a part of the Framework for Gamified Programming Education (FGPE) project. In this paper, we present its two front-end components: FGPE AuthorKit and FGPE PLE, explain how they can be used by teachers to prepare and manage gamified programming courses, and report the results of the usability evaluation by the teachers using the platform in their classes.

Abstract
One of the major barriers for the retailers is to understand the consumption elasticity they can expect from their contracted demand response (DR) clients. The current trend of DR products provided by retailers are not consumer-specific, which poses additional barriers for the active engagement of consumers in these programs. The elasticity of consumers' demand behavior varies from individual to individual. The utility will benefit from knowing more accurately how changes in its prices will modify the consumption pattern of its clients. This work proposes a functional model for the consumption elasticity of the DR contracted consumers. The model aims to determine the load adjustment the DR consumers can provide to the retailers or utilities for different price levels. The proposed model uses a Bayesian probabilistic approach to identify the actual load adjustment an individual contracted client can provide for different price levels it can experience. The developed framework provides the retailers or utilities with a tool to obtain crucial information on how an individual consumer will respond to different price levels. This approach is able to quantify the likelihood with which the consumer reacts to a DR signal and identify the actual load adjustment an individual contracted DR client provides for different price levels they can experience. This information can be used to maximize the control and reliability of the services the retailer or utility can offer to the System Operators. (c) 2021 Published by Elsevier B.V.