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.

Abstract
The preservation and sustainability of the marine ecosystem could benefit from the surge of new technologies to design autonomous vehicles. These underwater robots operate in a complex environment where the loss of human lives is highly probable. Consequently, a considerable percentage of the ocean remains unexplored due to the complexities of the underwater environment. Robotics can be a solution to overcome these limitations. However, training these complex systems is challenging and resource expensive. Human-in-the-loop input is essential in accelerating the training process by teaching the robots how to perform in specific scenarios and validate the simulated environment. This work presents a case study that simulates the dynamics of a Remotely Operated Vehicle in an underwater environment and uses imitation learning to train the vehicle to navigate autonomously toward a target. It was possible to measure and observe the similarity between the expert and the autonomous trajectories generated by the ROV. However, the imitation learning performance cannot surpass the expert, considering the time and the number of successes in finding the target. © ESM 2022. All rights reserved.

Abstract
Demand response (DR) is an important technique to explore the demand-side flexibility. The wide deployment of smart meters makes it possible to quantify the baseline load. As an intermediate agent, demand response aggregator needs to obtain the aggregated baseline load (ABL) for the DR event. Previous studies about the household level estimation focus on the estimation method. However, for ABL estimation, customer division is an important issue. A major limitation is the mismatch between the objectives of segmentation and estimation. Therefore, this paper proposes a new closed-loop method for estimating the ABL, which utilizes the contextual bandit with policy gradient to link the segmentation with the estimation. As such, the ABL estimation accuracy can guide the segmentation to divide the customers. The segmentation and estimation optimize collaboratively to improve the ABL estimation accuracy. An ensemble method for combining network's weights during the training process is proposed. Moreover, a pre- and post-event adjustment method is developed to further improve the estimation accuracy. Comprehensive comparisons demonstrate the proposed method can achieve the best estimation performance with regard to the MAPE and RMSE. It improves the estimation accuracy by 7% in terms of MAPE, and 11% in terms of RMSE.

Abstract
The aim of this paper is to provide an analysis that will be necessary for the development of a future digital marketing plan for CubiCasa OY with the main goal to attract new customers in the United States market. Specifically, the following topics are addressed during the analysis: external and internal examination of CubiCasa in the United States market to assess the current company’s position in the market; performing empirical research to figure out the most important aspects for real estate photographers and businesses. It was followed a qualitative research method with semi-structured surveys sent by Google forms to eight customers who use floor plan creation tools and operate in the US. The findings will help to analyze and make an appropriate decision when adapting it to a digital marketing plan and help to improve customer reach and brand awareness among the United States floor plan makers and companies specializing in real estate business. Copyright © 2022. Adomas ZAIKAUSKAS, Ricardo Fontes CORREIA and Carlos R. CUNHA.