Abstract
Nowadays, power distribution systems face some challenges related to power losses minimization and voltage stability improvement along the networks. These challenges can also be a big opportunity to develop smarter and more efficient distribution networks, ensuring the continuity of service and the power quality supplied to consumers. At the same time, there are several international standards that regulate the power quality levels required for distribution networks.This paper addresses deeply these issues and provide a solution to solve both of these problems using Distributed Generators (DG) on optimal locations of the grid. The proposed method will analyze the injection of both real and reactive power in a regulated IEEE-69 bus system. In the first stage, the voltage and loss sensitivity of the load flow analysis is calculated using a MatLab algorithm. In a second stage, the methodology uses the voltage stability index (VSI) to obtain the optimal location of the DGs to ensure the best results of both, power loss and voltage stability for the grid. The obtained results show the good effectiveness of the proposed method.

Abstract
Demonstration of a fiber-integrated non-volatile reconfigurable metasurface providing high-contrast group delay dispersion switching functionality is reported, which may be engineered to operate at wavelengths across the near-infrared (telecoms) band. Light-induced amorphous-crystalline phase switching in a chalcogenide (germanium antimony telluride) metasurface, only a fraction of a wavelength thick, fabricated on the end-facet of a single-mode optical fiber, enables intensity and phase modulation of the guided wave at metasurface designated bands. Such devices present a range of opportunities in fiberized remotely programmable phase/intensity multiplexing and dynamic dispersion compensation for emerging telecommunications and data storage/processing applications, including in photonic neural network and neuromorphic computing architectures.

Abstract
The operation of isolated power systems with 100% converter-based generation requires the integration of battery energy storage systems (BESS) using grid-forming-type power converters. Under these operating conditions, load dynamics influences the network frequency and voltage following large voltage disturbances. In this sense, the inclusion of induction motor (IM) load models is required to be properly considered in BESS power converter sizing. Thus, this paper presents an extensive sensitivity analysis, demonstrating how load modeling affects the BESS power converter capacity when adopting conventional control strategies while aiming to assure the successful recovery of all IM loads following a network fault. Furthermore, this work highlights that generators with converter interfaces can actively contribute to mitigate the negative impacts resulting from IM loads following a network fault. Thereby, two distinct control strategies are proposed to be integrated in the power electronic interfaces of the available converter-based generators: one to be adopted in grid-following converters and another one suitable for grid-forming converters. The proposed control strategies provide an important contribution to consolidating insular grid codes, aiming to achieve operational scenarios accommodating 100% penetration of converter-based generation with a significative percentage of the IM load composition without resorting to a significative increase in BESS power converter sizing.

Abstract
Enhancing performance is crucial when developing applications for high-performance and embedded computing. It requires sophisticated techniques and in-depth knowledge of the application domain and target architecture. Typically, developers prioritize the application's functional requirements over extra-functional requirements. Thus, a large part of the optimization effort is shifted to performance engineers, who rely on manual effort, alongside many analysis and optimization tools that need integration. This paper focuses on memoization, which caches results of pure computations and retrieves them if a function is called with repeating arguments. We propose a methodology for allowing developers and performance engineers to apply memoization straightforwardly by automating code analysis, code transformations, and memoization-specific profiling. It helps developers with no optimization expertise to quickly set up memoization and, simultaneously, it provides performance engineers with highly customizable analysis and memoization. We provide a concrete implementation supported by a DSL, a source-to-source compiler, and a memoization framework. We evaluate the methodology and framework with publicly available benchmarks. We show how one can analyze applications to select functions with performance improvement potential, which the experiments reveal might be challenging to find, and improve some applications with minimal effort.

Abstract
Today, games play active roles in diverse stages of the treatment of various mental illnesses. Despite this favourable context, their use as tools in the treatment of anorexia nervosa is still quite unexplored. We are developing playful and gamified tools that can be used as instruments in the prevention, treatment and monitoring of this complex disease. The objective of this paper is to present the initial process of design and development of a card-based game called How do I feel when… Its purpose consists of stimulating empathy among family members of patients with anorexia nervosa. Play sessions are to be mediated by health professionals and are supposed to occur in the context of support and therapeutic groups for family members of people suffering from eating disorders or in clinical consultations. Preliminary tests and expert reviews showed that the game is effective in establishing free association of subjects in players’ reasonings, and in inciting the externalisation and sharing of players’ feelings. It also demonstrated that, because players usually read the cards in accordance to their life experience, the game is able to circumscribe and personalise the subjects to be discussed. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Faced with the emergence of the Covid-19 pandemic, and to better understand and contain the disease's spread, health organisations increased the collaboration with other organisations sharing health data with data scientists and researchers. Data analysis assists such organisations in providing information that could help in decision-making processes. For this purpose, both national and regional health authorities provided health data for further processing and analysis. Shared data must comply with existing data protection and privacy regulations. Therefore, a robust de-identification procedure must be used, and a re-identification risk analysis should also be performed. De-identified data embodies state-of-the-art approaches in Data Protection by Design and Default because it requires the protection of direct and indirect identifiers (not just direct). This article highlights the importance of assessing re-identification risk before data disclosure by analysing a data set of individuals infected by Covid-19 that was made available for research purposes. We stress that it is highly important to make this data available for research purposes and that this process should be based on the state of the art methods in Data Protection by Design and by Default. Our main goal is to consider different re-identification risk analysis scenarios since the information on the intruder side is unknown. Our conclusions show that there is a risk of identity disclosure for all of the studied scenarios. For one, in particular, we proceed to an example of a re-identification attack. The outcome of such an attack reveals that it is possible to identify individuals with no much effort.