Abstract
O presente estudo tem o objetivo de demonstrar a aplicação dos indicadores de Desenvolvimento Sustentável sugeridos pela OCDE (Organização para Cooperação e Desenvolvimento Econômico) para Museus. Esta aplicação tem o intuito de demonstrar como é possível reconhecer o nível de sustentabilidade dos Museus. Para isso foram realizadas pesquisas bibliográficas que evidenciam a importância de se organizar tais parâmetros, com base nas características do País em que o Museu está instalado e que demonstram a efetiva contribuição destas organizações no que se refere aos objetivos do Desenvolvimento Sustentável. Neste estudo preliminar, foram realizadas recolha de dados em dois museus da Região Centro de Portugal. A recolha de dados teve como referência Indicadores de Sustentabilidade sugeridos e publicados pela OCDE  em 2018. Estes parâmetros foram a base da recolha de dados, que resultou em uma análise do nível de sustentabilidade desses dois museus, seguindo as referências OCDE. O resultado demonstrou uma efetiva possibilidade de medição dos níveis de sustentabilidade dos museus e mostra também, a potencialidade dos museus para cooperação nos objetivos delineados pela Agenda 2030 do Desenvolvimento Sustentável, elaborado pela Organização das Nações Unidas.

Abstract

Abstract
With the growing research in the field of optical clearing and the various applications of this technique that have been recently developed, more than 1000 agents have been tested in various tissues in the past two decades to evaluate their clearing potential. To optimize the clearing treatments, knowledge on the dispersions and absorption spectra of the agents is necessary. We have gathered experimental and literature data to show that the absorption bands of typical clearing agents are located in the deep ultraviolet range, where the refractive index is significantly high. The desired characteristics for the clearing agents are presented, and their classification in three major groups is indicated. Solutions containing mixtures of optical clearing agents (OCAs) and diluted solutions are also important for certain applications, such as the enhancement of agent delivery or the evaluation of agent diffusion properties. Such applications are referred, and some examples are presented. A simple method to prepare diluted solutions of clearing agents is also described. © 2019, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
This paper presents the application of collaborative reinforcement learning models to enable the distributed learning of energy contracts negotiation strategies. The learning model combines the learning process on the best negotiation strategies to apply against each opponent, in each context, from multiple learning sources. The diverse learning sources are the learning processes of several agents, which learn the same problem under different perspectives. By combining the different independent learning processes, it is possible to gather the diverse knowledge and reach a final decision on the most suitable negotiation strategy to be applied. The reinforcement learning process is based on the application of the Q-Learning algorithm; and the continuous combination of the different learning results applies and compares several collaborative learning algorithms, namely BEST-Q, Average (AVE)-Q; Particle Swarm Optimization (PSO)-Q, and Weighted Strategy Sharing (WSS)-Q. Results show that the collaborative learning process enables players’ to correctly identify the negotiation strategy to apply in each moment, context and against each opponent. © Springer Nature Switzerland AG 2019.

Abstract
Air exposures occurring in indoor swimming pools are an important public health issue due to their popularity and regular use by the general population, including vulnerable groups such as children and elderly people. More comprehensive information on indoor air quality (IAQ) in swimming pools is thus needed in order to understand health risks, establish appropriate protective limits and provide evidence-based opportunities for improvement of IAQ in these facilities. In this context, twenty public indoor swimming pools located in the Northern Region of Portugal were examined in two sampling campaigns: January-March and May-July 2018. For each campaign, a comprehensive set of environmental parameters was monitored during the entire period of the facilities' operating hours of a weekday, both indoors and outdoors. In addition, four air (1-h samplings) and water samples were collected. Findings show that comfort conditions, ultrafine particles number concentrations and exposure to substances in the indoor air (concentration and composition) is likely to vary greatly from one public indoor swimming pool to another. Trihalomethanes (THM) and dichloroacetonitrile were the predominant disinfection by-products identified in the indoor air but other potentially hazardous volatile organic compounds, such as limonene, 1,2,4-trimethylbenzene, 2,2,4,4,6,8,8-heptamethylnonane, 2- and 3-methylbutanenitrile, acetophenone, benzonitrile, and isobutyronitrile were found to have relevant putative emission sources in the environment of the swimming pools analyzed. Furthermore, indicators of poor ventilation conditions (namely carbon dioxide, relative humidity and existence of signs of condensation in windows) and some water-related parameters (THM levels, conductivity and salinity) were found to be determining factors of the measured airborne THM concentrations that appeared to significantly potentiate the exposure. In summary, this work provides evidence for the need to establish adequate standards for the comprehensive evaluation of IAQ in public swimming pools, in order to guide further development of evidence-based prevention/remediation strategies for promoting healthy environments in swimming pools.

Abstract
Motivation: Network alignment (NA) finds conserved regions between two networks. NA methods optimize node conservation (NC) and edge conservation. Dynamic graphlet degree vectors are a state-of-the-art dynamic NC measure, used within the fastest and most accurate NA method for temporal networks: DynaWAVE. Here, we use graphlet-orbit transitions (GoTs), a different graphlet-based measure of temporal node similarity, as a new dynamic NC measure within DynaWAVE, resulting in GoT-WAVE. Results: On synthetic networks, GoT-WAVE improves DynaWAVE's accuracy by 30% and speed by 64%. On real networks, when optimizing only dynamic NC, the methods are complementary. Furthermore, only GoT-WAVE supports directed edges. Hence, GoT-WAVE is a promising new temporal NA algorithm, which efficiently optimizes dynamic NC. We provide a user-friendly user interface and source code for GoT-WAVE.