Abstract
Parkinson's disease (PD) is a neurodegenerative disorder that impairs people's mobility. Due to its erratic nature and complexity, the progression of the disease differs from person to person, making it difficult to keep track of the patient's progress. These factors, together with the limited number of annual clinical appointments, create the need to have a tool that can help patients and healthcare professionals better manage Parkinson's outside of the clinical environment. PDapp strives to address this need combining mHealth features with the capabilities of the iHandU appcessory, a novel and seamless wearable device designed to measure wrist rigidity, bradykinesia (slow movement), and tremor, thus enabling continuous effective follow-up, while connecting patients and clinicians remotely. The PDapp system is comprised of a mobile application where patients can manage their medication, self-perform various symptom tests, and maintain clinicians informed of relevant events; a specialized web dashboard for clinicians to monitor all their patient's history and recent events; and a cloud database that exhibits existing data in real-time. The first prototype integrates all these components and provides a promising proof-of-concept that, with a few additions, can be a system that brings value to Parkinson's management. This application design and functionalities were developed jointly with clinicians, addressing their problems and needs. The collected feedback was very positive stating that its usability and simplicity is completely suitable for patients to use. PDapp will introduce a complete and innovative methodology to follow-up PD patient's disease progression and support clinicians during appointments and patients at home, guiding medication adjustment for better disease management. This system is intended as one more step to the PD mHealth ecosystem, improving follow-up and disease therapy yet reducing clinicians' workload.

Abstract
Collaborative robots are being increasingly used by manufacturing companies due to their potential to help companies cope with market volatility. Before introducing this technology, companies face the decision phase where they determine the investment feasibility. Decision models for cobot adoption can assist decision-makers in this task, but they require previous identification of decision criteria. Since existing literature overlooked this issue, this study aims to provide a list of decision criteria that can be considered in the cobot adoption decision process. These criteria were identified by a literature review of the benefits, advantages, and disadvantages of cobot adoption. Results show that flexibility, competitiveness, ergonomics, quality, safety, space, mobility, ease of programming, technical features, human-robot collaboration, and productivity are important aspects to consider when deciding whether to invest in cobots. The findings of this study provide a better understanding of the decision process for cobot adoption by listing decision criteria along with some indicators, which is an important input for the design of a decision-making process.

Abstract
The broadcast of audio-video sports content is a field with increasingly larger audiences demanding higher quality content and involvement. This growth creates the necessity to develop more content to engage the users and keep this trend. Otherwise, it may stall or even diminish. Therefore, enhancing the user experience, engagement, and involvement during live sports event broadcasts is of utmost importance. This paper proposes a solution to extract event’s information from video, resorting to Computer Vision techniques and Deep Learning algorithms. More specifically, the project encompassed the definition and implementation of field registration, object detection and tracking tasks. Focusing on football sports events, a novel dataset combining several video sources was created and used for analysis and metadata extraction. In particular, the proposed solution can detect and track players with acceptable precision using state-of-the-art methods, like YOLOv5 and DeepSORT. Furthermore, resorting to unsupervised learning techniques, the system provides team segmentation based on the colour of the players’ kits. A series of visual representations regarding the players’ movements on the field enables broadcast enrichment and increased user experience. The presented solution is framed in the H2020 DataCloud project and will be deployed in a cloud environment simplifying its access and utilisation. Copyright © 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

Abstract
This paper presents a multi-stage day-ahead and real-time optimization algorithm for scheduling of system's energy resources in the normal and external shock operational conditions. The main contribution of this paper is that the model considers the non-utility electricity generation facilities capacity withholding opportunities in the optimal scheduling of system resources. The real-time simulation of external shock impacts is another contribution of this paper that the algorithm simulates the sectionalizing of the system into multi-microgrids to increase the resiliency of the system. The optimization process is categorized into two stages that compromise normal and contingent operational conditions. Further, the normal operational scheduling problem is decomposed into three steps. At the first step, the optimal day-ahead scheduling of system resources and the switching of normally opened switches are determined. At the second step, the optimal real-time market scheduling is performed and the switching of normally closed switches is optimized. At the third step, different extreme shock scenarios are simulated in the real-time horizon and the effectiveness of sectionalizing the system into multi-micro grids are assessed. Finally, at the contingent operational conditions, the optimal topology of the system and scheduling of energy resources are determined. The proposed method was successfully assessed for the 33-bus and 123-bus test systems. The algorithm were reduced the expected cost of the worst-case contingencies for the 33-bus and 123-bus systems by about 97.89% and 88.11%, respectively. Further, the average and maximum values of the 123-bus system capacity-withholding index for real-time conditions reduced by about 67.40% and 71.05%, respectively. © 2021 Elsevier Ltd

Abstract
The fast-charging units have become a more efficient and attractive option recently for reducing the challenges due to the long charging time of electric vehicles (EVs). To evaluate the impacts of the EV fast charging stations (EVFCS) on the power grid and also to assess their contributions to the system operation through the vehicle-to-grid (V2G) technology, two control methods, namely, sliding mode control (SMC) and fuzzy logic control (FLC), are developed in this study for a DC microgrid including EVFCS and distributed generation sources. In these methods, the EV battery is used as a DC source of a distribution static compensator (D-STATCOM) with the objective of mitigating the voltage sag in the microgrid. Various simulations are conducted in MATLAB Simulink/SimPowerSystems environment in order to examine the effectiveness of the proposed control approaches in terms of ensuring the stability and improving the dynamic performance of the EVFCS. The results show that considerable improvements can be achieved, especially in the case of using the SMC method.

Abstract
In an earlier work we described and applied a methodology to find an adequate distribution for Nearly Gaussian (NG) random variables. In this work, we compare two different methods, m1 and m2 to estimate a power transform parameter for NG random variables. The m1 method is heuristic and based on sample kurtosis. Herein, we describe and apply it using a new reduced data set. The second method m2 is based on the maximization of a pseudo-log-likelihood function. As an application, we compare the performance of each method using high power statistical tests for the null hypothesis of normality. The data we use are the daily errors in the forecasts of maximum and minimum temperatures in the city of Porto. We show that the high kurtosis of the original data is due to high correlation among data. We also found that although consistent with normality the data is better fitted by distributions of the power normal (PN) family than by the normal distribution. Regarding the comparison of the two parameter estimation methods we found that the m1 provides higher p-values for the observed statistics tests except for the Shapiro-Wilk test.