Abstract
The deployment of smart metering technologies in the low voltage (LV) grid created conditions for the application of data-driven monitoring and control functions. However, data privacy regulation and consumers' aversion to data sharing may compromise data exchange between utility and customers. This work presents a data-driven method, based on smart meter data, to estimate linear sensitivity factors for three-phase unbalanced LV grids, which combines a privacy-preserving protocol and varying coefficients linear regression. The proposed method enables centralized and peer-to-peer learning of the sensitivity factors. Potential applications for the sensitivity factors are demonstrated by solving voltage violations or computing operating envelopes in a LV grid without resorting to its network topology or electrical parameters.

Abstract
Bidirectional electric vehicle charging stations (EVSE) offer new business models for private users and companies such as Demand Response. Even if first standards for smart charging (ISO 15118, OCPP) are established, no commercial turnkey solution for the integration of a vehicle to grid (V2G) charging station into a smart prosumer household exists yet. This study shows a possible concept for the integration of a V2G charger for a vehicle to home (V2H) use case. A smart controller for a prosumer household is developed allowing the interconnection of different types of electrical equipment like a V2G charger, a photovoltaic system for electricity generation, a heat pump for heating. Therefore, different interfaces such as Modbus TCP, Modbus RTU, OCPP, HTTP are used. An algorithm is developed to charge the vehicle at low electricity prices or at times of overproduction of the PV system respective to discharge the car at high electricity prices or times of no PV production. The modular concept allows realizing the solution as a cloud-based service which can be applied to energy communities. © 2022 IEEE.

Abstract
Our lives have been increasingly filled with technologies that use Artificial Intelligence (AI), whether at home, in public spaces, in social organizations, or in services. Like other technologies, adopting this emerging technology also requires society's attention to the challenges that may arise from it. The media brought to the public some unexpected results from using these technologies, for example, the unfairness case in the COMPAS system. It became more evident that these technologies can have unintended consequences. In particular, in the public interest domain, these unintended consequences and their origin are a challenge for public policies, governance, and responsible AI. This work aims to identify the technological and ethical risks in data-driven decision systems based on AI and conduct a diagnosis of these risks and their perception. To do that, we use a triangulation of methods. In the first stage, a search on Web of Science has been performed. We consider all the 412 papers. The second stage corresponds to a analysis of experts. The papers have been classified according to the relevance to the topic by the experts. In the third stage, we use the survey method and include risk insights from stage two in our questions. We found 24 concerns which arise from the perspective of the ethical and technological risk perspective. The perception of participants regarding the level of concern they have with the risks of a data-driven system based on AI is high than their perception of society's concern. Fairness is considered the risk whose perception is more severe. Fairness, Bias, Accountability, Interpretability, and Explainability are considered the most relevant concepts for a responsible AI. Consequently, also the most relevant for responsible governance of AI.

Abstract
Depression is a mental disease that affects over 264 million people worldwide and is responsible for causing great suffering, work dysfunction, faulty education, family relationships and can lead to suicide. Depression stigma prevents over half of the people who suffer from major depression from seeking professional help. Stigma mostly results from a deficient understanding of the mental disease. Research indicates that first-hand experiences of the perceptions of an individual diagnosed with a mental disorder in a simulated virtual reality environment can increase empathy and positive attitudes towards the individual. Interactive VR experiences have been described as a human-computer interface that enables users to immerse themselves in a computer generated, multi-dimensional environment. This project aims at examining the impact of a VR-assisted experience on reducing stigma and increase empathy towards individuals with depression. Following a methodology based on well-established results from psychology about common depression misunderstandings, we present a VR experience that simulates some of the most common difficulties encountered by people diagnosed with depression.

Abstract
Objective. In this work, an analytical framework for the multiscale analysis of multivariate Gaussian processes is presented, whereby the computation of Partial Information Decomposition measures is achieved accounting for the simultaneous presence of short-term dynamics and long-range correlations. Approach. We consider physiological time series mapping the activity of the cardiac, vascular and respiratory systems in the field of Network Physiology. In this context, the multiscale representation of transfer entropy within the network of interactions among Systolic arterial pressure (S), respiration (R) and heart period (H), as well as the decomposition into unique, redundant and synergistic contributions, is obtained using a Vector AutoRegressive Fractionally Integrated (VARFI) framework for Gaussian processes. This novel approach allows to quantify the directed information flow accounting for the simultaneous presence of short-term dynamics and long-range correlations among the analyzed processes. Additionally, it provides analytical expressions for the computation of the information measures, by exploiting the theory of state space models. The approach is first illustrated in simulated VARFI processes and then applied to H, S and R time series measured in healthy subjects monitored at rest and during mental and postural stress. Main Results. We demonstrate the ability of the VARFI modeling approach to account for the coexistence of short-term and long-range correlations in the study of multivariate processes. Physiologically, we show that postural stress induces larger redundant and synergistic effects from S and R to H at short time scales, while mental stress induces larger information transfer from S to H at longer time scales, thus evidencing the different nature of the two stressors. Significance. The proposed methodology allows to extract useful information about the dependence of the information transfer on the balance between short-term and long-range correlations in coupled dynamical systems, which cannot be observed using standard methods that do not consider long-range correlations.

Abstract
This paper explores which variables are more significant in municipal executive recommendation by citizens. We estimated the influence of public dimensions, such as municipe loyalty, municipe satisfaction, and municipe perceived value in municipal executive recommendation by citizens. Then, we tried to understand if the citizen's opinions influenced the evaluation of the municipal executive recommendation. The parishes of the municipality of Valongo were selected and analyzed, namely the parishes of Alfena, Campo e Sobrado, Valongo, and Ermesinde, and a total of 998 questionnaires were collected. Data were collected in November 2020 in the different parishes under study. It was concluded that all studied dimensions were statistically significant in the final structural estimated model. The structural results point to municipe loyalty and municipe satisfaction dimensions having a direct, positive, and statistically significant influence on municipal executive recommendation. On the other side, the municipe perceived value dimension has a direct positive but not statistically significant influence on municipal executive recommendation. This study showed that a loyal and satisfied citizen recommends the continuity of the municipal executive in the city's political leadership in which he or she lives. Therefore, for the municipal executive administration, it is fundamental to know which dimensions the society considers most important in order to be able to remain in the management of the shared destinies of a city. In this sense, political decisions throughout the mandates can be directed, on the one hand, to the satisfaction and loyalty of the citizens and, on the other hand, to the balanced management of the destinies of this type of public entity.