Abstract

The increasing popularity of attention mechanisms in deep learning algorithms for computer vision and natural language processing made these models attractive to other research domains. In healthcare, there is a strong need for tools that may improve the routines of the clinicians and the patients. Naturally, the use of attention-based algorithms for medical applications occurred smoothly. However, being healthcare a domain that depends on high-stake decisions, the scientific community must ponder if these high-performing algorithms fit the needs of medical applications. With this motto, this paper extensively reviews the use of attention mechanisms in machine learning (including Transformers) for several medical applications. This work distinguishes itself from its predecessors by proposing a critical analysis of the claims and potentialities of attention mechanisms presented in the literature through an experimental case study on medical image classification with three different use cases. These experiments focus on the integrating process of attention mechanisms into established deep learning architectures, the analysis of their predictive power, and a visual assessment of their saliency maps generated by post-hoc explanation methods. This paper concludes with a critical analysis of the claims and potentialities presented in the literature about attention mechanisms and proposes future research lines in medical applications that may benefit from these frameworks.

Abstract
The region West of Estarreja is topographically flat and, from the geological point of view, characterized by Quaternary and Cretaceous formations that lie discordantly over Pre-Cambrian schists. The topography of this contact is poorly known and, therefore, is investigated here. Data from drilling, electrical resistivity profiles and gravity mapping, over a 30 km2 area bounded on the Eastern side by the “Norte” Railway line and on the Western by the Ria de Aveiro that separates Murtosa from Torreira, are jointly interpreted to produce a robust model for the topography of the bedrock. Hence, NW-SE alignments, inferred by previous works, are clearly identified. Bedrock depressions are also proposed and agree with previous geophysical models from areas to the South of the region herein investigated. © 2022 LNEG – Laboratório Nacional de Energia e Geologia IP.

Abstract
The quality of the digital experiences delivered by engineers and their business success depends on empowering developers and operators with an effective method for continuously assessing a system's health, diagnosing possible issues, and recovering from service outages. In other words, monitoring is essential to ensure the quality of an application. However, monitoring best practices may not be apparent to everyone and, most of the time, are not sufficiently explained or documented to be learned quickly and communicated effectively. Therefore, practices usually lack formalisation and a standard structure that would make all of them easy to communicate and share among practitioners. To tackle this issue, this paper describes three proactive monitoring practices as design patterns: Liveness Endpoint, Readiness Endpoint and Synthetic Testing. Design patterns provide enough structure and detail to be easily reused by practitioners and have space to accommodate different needs and quirks depending on the usage context. The proposed patterns are based on existing literature and tools, stemming from industry best practices that are further detailed and adapted to design patterns. Relations to existing monitoring patterns are also analysed to point the reader to more patterns that complement the ones proposed in this work.

Abstract
<p>Radon and its progeny are well-documented sources of natural radioactivity which can be used as benchmarks for testing a novel ionisation detector. The miniaturised ionisation detector was deployed aboard the NRP Sagres on a SAIL mission in July 2021 which travelled between the Açores and Lisbon in the North Atlantic Ocean. On its voyage, the detector profiled natural background radiation and in-directly detected cosmic ray muons, providing both spectroscopic energy discrimination and count rate data. The detector was simultaneously run with a NaI(Tl) gamma ray counter and other meteorological instruments.</p><p>The small form factor and low-power detector, composed of a 1x1x0.8 cm<sup>3 </sup>CsI(Tl) microscintillator coupled to a PiN photodiode, was able to identify gamma peaks from Bi-214 and K-40, having been calibrated using laboratory gamma sources up to 1.3 MeV. This research aims to investigate the performance of the ionisation detector and behaviour of discrete gamma energies over the duration of the voyage. Additionally, we will show a comparison of the CsI(Tl) based ionisation detector against the gamma ray counter which features a larger NaI(Tl) scintillator.</p>

Abstract

Abstract
Current developments in heat pumps, supported by innovative business models, are driving several industry sectors to take a proactive role in future district heating and cooling networks in cities. For instance, supermarkets and data centers have been assessing the reuse of waste heat as an extra source for the district heating network, which would offset the additional investment in heat pumps. This innovative business model requires complete deregulation of the district heating market to allow industrial heat producers to provide waste heat as an additional source in the district heating network. This work proposes the application of innovative market designs for district heating networks, inspired by new practices seen in the electricity sector. More precisely, pool and Peer-to-Peer (P2P) market designs are addressed, comparing centralized and decentralized market proposals. An illustrative case of a Nordic district heating network is used to assess the performance of each market design, as well as the potential revenue that different heat producers can obtain by participating in the market. An important conclusion of this work is that the proposed market designs are in line with the new trends, encouraging the inclusion of new excess heat recovery players in district heating networks.