Abstract
Regional museums are relatively recent museum structures that emerged in the late 19th century after universal exhibitions. They are museums specifically dedicated to the representation of a given population in a specific territorial context, highlighting the fundamental traits that characterize the nature and essence of that community, differentiating it from others. In northern Portugal, law no. 125/97, created the Douro Museum, a territory museum that represents the natural and cultural heritage of the demarcated Douro region, the first demarcated and regulated region of the world, in 1756, by Marques de Pombal, extending over an area of of 250,000 ha, between Barqueiros and Barca d’Alva along the Douro River and its tributaries. The museum has a “polynuclear structure distributed throughout the Douro region, based in Peso da Régua” (art. 2), serving as an element for mobilizing tourists, mainly through its main temporary exhibitions, videos, etc. In an information society, characterized by the empowerment of citizens with regard to their ability to independently obtain information and, in the process, to leave their footprint, it is crucial to understand and anticipate their interests. In this way, the supply and responsiveness of tourism agents and regional actors will be increased, making them better able to decide for an offer better suited to the real interests of visitors and even enable to influence them. This article aims to know the profile of tourists/consumers through their online behavior, trying to understand what kind of information they are looking for, which keywords are most used and searched using the fundamentals of Data Analytics and using the Google Trends tool. Moreover, this study enables to better understand the connection between online search interests and the reality of the Douro Museum visitants. This approach is nowadays a major contribute to bridge the gap between visitors needs/interests and tourism player’s strategies definition, making Data Analytics a fundamental tool to enable decision support systems. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Abstract
This study presents the optimal model of the coordinated flexible energy and self-healing management (C-FE&SH-M) in the active distribution network (ADN) including renewable energy sources (RESs), electric vehicles (EVs) and demand response program (DRP).The flexible energy management (FEM) is extracted using coordination between the RESs, EVs and DRP. The self-healing method (SHM) is related to multi-agent system-based restoration process (MAS-based RP) that finds the optimal restoration pattern at the fault condition according to the different zone agents (ZAs) distributing along with the network. This method minimizes the difference between energy cost and flexibility benefit related to the FEM part and difference between the number of switching operation and priority loads restored based on the SHM part. Also, this problem subjects to power flow equations, RESs and active loads constraints, restoration process formulation and system operation limits. Stochastic programming is used to model the uncertainty of loads, energy prices, RESs and EVs. Hereupon, the suggested strategy is implemented on the 33-bus radial distribution network and it is solved by the crow search algorithm (CSA). Ultimately, the obtained results imply the high flexibility and security of the operation, incorporating the proposed strategy, and delineate the optimal restoration scheme for the ADN.

Abstract
Distributed power sources will become increasingly ubiquitous in the near future. In this power production paradigm, photovoltaic conversion systems will play a fundamental role due to the growing tendency of energy price, and an opposed trend for the photovoltaic panels. This will lead to increased pressure for the installation of this particular renewable energy source in home buildings. In particular, on-grid photovoltaic systems where the generated power can be injected directly to the main power grid. This strategy requires the use of DC-AC inverters whose output is synchronized, in phase, with the main grid voltage. In order to provide steady output in the presence of load disturbances, the inverter must work in closed-loop. This work presents a new way to design an inverter controller by resorting to the CDM design technique. The obtained results suggest that the controller achieved with this method, although simpler than other methods, leads to an acceptable and robust closed-loop response. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

Abstract
We present a scoping review of biosensors appropriation as control structures in interactive music systems (IMSs). Technical and artistic dimensions promoted by transdisciplinary approaches, ranging from biomedicine to musical performance and interaction design fields, support a taxonomy for biosensor-driven IMSs. A broad catalog of 70 biosensor-driven IMSs, ranging in publication dates from 1965 to 2019, was compiled and categorized according to the proposed taxonomy. From the catalog data, we extrapolated representative historical trends, notably to critically verify our working hypothesis that biosensing technologies are expanding the array of control structures within IMSs. Observed data show that our hypothesis is consistent with the historical evolution of the biosensor-driven IMSs. From our findings, we advance future challenges for novel means of control across humans and machines that should ultimately transform the agents involved in interactive music creation to form new corporalities in extended performative settings.

Abstract
The growing demand for everyday data insights drives the pursuit of more sophisticated infrastructures and artificial intelligence algorithms. When combined with the growing number of interconnected devices, this originates concerns about scalability and privacy. The main problem is that devices can detect the environment and generate large volumes of possibly identifiable data. Public cloud-based technologies have been proposed as a solution, due to their high availability and low entry costs. However, there are growing concerns regarding data privacy, especially with the introduction of the new General Data Protection Regulation, due to the inherent lack of control caused by using off-premise computational resources on which public cloud belongs. Users have no control over the data uploaded to such services as the cloud, which increases the uncontrolled distribution of information to third parties. This work aims to provide a modular approach that uses cloud-of-clouds to store persistent data and reduce upfront costs while allowing information to remain private and under users' control. In addition to storage, this work also extends focus on usability modules that enable data sharing. Any user can securely share and analyze/compute the uploaded data using private computing without revealing private data. This private computation can be training machine learning (ML) models. To achieve this, we use a combination of state-of-the-art technologies, such as MultiParty Computation (MPC) and K-anonymization to produce a complete system with intrinsic privacy properties.

Abstract
Transmission Expansion Planning (TEP) aims at identifying a list of new assets to be installed on the transmission grid to meet the long-term forecasted demand while ensuring a safe supply over the entire planning horizon. As TEP is a Mixed Integer Non-Linear Problem (MINLP) with a huge search space, in the last years several modern heuristic algorithms were proposed to deal with its challenging characteristics. In this way, this paper describes and evaluates the impact and implementation of four operators that can be easily incorporated in any evolutionary algorithm, namely: Neighborhood Search for Local Improvement (NSLI), Diversity Control (DC), Elitist Reproduction (ER) and Boundary Local Search (BLS). The impact of these operators is assessed and discussed over a hundred simulations using a traditional Genetic Algorithm (GA) and a well-known test system, the RTS 24-bus. Regarding the results, the NSLI and the BLS operator considerably improved the GA performance in solving the TEP problem regarding both the final value of the objective function and the diversity of solutions.