Abstract
Increased levels of renewable generation and electric vehicles require grid operators to adapt their assets to ensure that they can maintain safe and reliable grid operations. This paper presents a methodology and a business case to determine the size and location of multiple storage options with respect of traditional wires grid upgrade. The work is focused on the analysis of a business case of a DSO that owns a distribution network and exploit the possibility to install energy storage to defer network infrastructure upgrade caused by peak power flow that exceed the existing capacity or give rise to voltage quality problems. The proposed method is validated by simulations considering a real distribution network in the northern Portugal in three different scenarios. The results show that installing energy storage is still more expensive than traditional wires upgrade.

Abstract
The power distribution grid is centrally managed concerning the requirements of the end-users, however, with the appearance of smart grids, new technologies arc arising. Therefore, distributed energy resources, mainly, renewables, energy storage systems, electric mobility, and power quality are viewed as encouraging contributions for improving power management. In these circumstances, this paper presents a power electronics perspective for the power distribution grid, considering innovative features, and including a power quality perception. Throughout the paper are presented relevant concepts for a concrete realization of a smart grid, supported by the integration of power electronics devices as the interface of the mentioned technologies. Aiming to support the innovative power electronics systems for interfacing the mentioned technologies in smart grids, a set of developed power electronics equipment was developed and, along with the paper, are shown and described, supporting the most important contributions of this paper.

Abstract
This report describes a preliminary study that took place during the second semester of the school year 2019-2020, where suddenly classes had to be held online due to COVID 19 pandemic. Kahoot! a gamified application was used in some of the problem-solving classes of an undergraduate physics course of the integrated masters of the Electrical and Computers Engineering program. The quizzes applied covered rigid body dynamics and thermodynamics, both contents included in the syllabus of the course. The study was planned prior to the pandemic and the necessary adjustments of teaching online altered the goals. A simple analysis of the data obtained with the Kahoot! quizzes is performed and the results are discussed in the context of the positive and negative effects of going online.

Abstract
Sensors based on polarization are suitable for application in power grids due to their excellent characteristics, such as high electrical insulation, non-magnetic saturation, oil-free, no risk of explosive failures, and high bandwidth. Utility companies are incorporating new technologies that are driving the evolution of electrical systems. Thus, it is interesting to evaluate the possibility of using polarization sensors in a network configuration. In this work, we present an experimental study of a current and voltage polarization sensor network applied to a medium voltage distribution grid. The current sensor is based on the Faraday effect, and the voltage sensor uses the Pockels effect. Both sensors use a 90° polarization degree between the two output ports to compensate for the various impairments on the measurements by applying the difference-over-sum. The network uses a DWDM topology centered at the 1550 nm range, and both current and voltage sensors in this work used this spectral band. We evaluated the sensor node in terms of accuracy according to IEC standard 61869-10 and IEC standard 61869-11. Considering that an important application of this sensor network is in the aerial cable of medium voltage networks, sensor node accuracy was also estimated in the presence of cable vibration. The calculated power budget of the proposed network indicates that reaching ten nodes of current and voltage sensors in a 10 km optical link is possible, which is enough for a medium urban voltage distribution network.

Abstract
Software to analyse very large sets of experimental data often relies on a pipeline of irregular computational tasks with decisions to remove irrelevant data from further processing. A user-centred framework was designed and deployed, HEP-Frame, which aids domain experts to develop applications for scientific data analyses and to monitor and control their efficient execution. The key feature of HEP-Frame is the performance portability of the code across different heterogeneous platforms, due to a novel adaptive multi-layer scheduler, seamlessly integrated into the tool, an approach not available in competing frameworks. The multi-layer scheduler transparently allocates parallel data/tasks across the available heteroge-neous resources, dynamically balances threads among data input and computational tasks, adaptively reorders in run-time the parallel execution of the pipeline stages for each data stream, respecting data dependencies, and efficiently manages the execution of library functions in accelerators. Each layer implements a specific scheduling strategy: one balances the execution of the computational stages of the pipeline, distributing the execution of the stages of the same or different dataset elements among the available computing threads; another controls the order of the pipeline stages execution, so that most data is filtered out earlier and later stages execute the computationally heavy tasks; yet another adaptively balances the automatically created threads among data input and the computational tasks, taking into account the requirements of each application. Simulated data analyses from sensors in the ATLAS Experiment at CERN evaluated the scheduler efficiency, on dual multicore Xeon servers with and without accelerators, and on servers with the many-core Intel KNL. Experimental results show significant improved performance of these data analyses due to HEP-Frame features and the codes scaled well on multiple servers. Results also show the improved HEP-Frame scheduler performance over the key competitor, the HEFT list scheduler. The best overall performance improvement over a real fine tuned sequential data analysis was impressive in both homogeneous and heterogeneous multicore servers and in many-core servers: 81x faster in the homogeneous 24+24 core Skylake server, 86x faster in the heterogeneous 12+12 core Ivy Bridge server with the Kepler GPU, and 252x faster in the 64-core KNL server. Program summary Program Title: HEP-Frame CPC Library link to program files: https://doi.org/10.17632/m2jwxshtfz.1 Licencing provisions: GPLv3 Programming language: C++. Supplementary material: The current HEP-Frame public release available at https://bitbucket.org/ ampereira/hep-frame/wiki/Home . Nature of problem: Scientific data analysis applications are often developed to process large amounts of data obtained through experimental measurements or Monte Carlo simulations, aiming to identify patterns in the data or to test and/or validate theories. These large inputs are usually processed by a pipeline of computational tasks that may filter out irrelevant data (a task and its filter is addressed as a proposition in this communication), preventing it from being processed by subsequent tasks in the pipeline. This data filtering, coupled with the fact that propositions may have different computational intensities, contribute to the irregularity of the pipeline execution. This can lead to scientific data analyses I/O-, memory-, or compute-bound performance limitations, depending on the implemented algorithms and input data. To allow scientists to process more data with more accurate results their code and data structures should be optimized for the computing resources they can access. Since the main goal of most scientists is to obtain results relevant to their scientific fields, often within strict deadlines, optimizing the performance of their applications is very time consuming and is usually overlooked. Scientists require a software framework to aid the design and development of efficient applications and to control their parallel execution on distinct computing platforms. Solution method: This work proposes HEP-Frame, a framework to aid the development and efficient execution of pipelined scientific analysis applications on homogeneous and heterogeneous servers. HEP-Frame is a user-centred framework to aid scientists to develop applications to analyse data from a large number of dataset elements, with a flexible pipeline of propositions. It not only stresses the interface to domain experts so that code is more robust and is developed faster, but it also aims high-performance portability across different types of parallel computing platforms and desirable sustainability features. This framework aims to provide efficient parallel code execution without requiring user expertise in parallel computing. Frameworks to aid the design and deployment of scientific code usually fall into two categories: (i) resource-centred, closer to the computing platforms, where execution efficiency and performance portability are the main goals, but forces developers to adapt their code to strict framework con-straints; (ii) user-centred, which stresses the interface to domain experts to improve their code development speed and robustness, aiming to provide desirable sustainability features but disregarding the execution performance. There are also a set of frameworks that merge these two categories (Liu et al., 2015 [1]; Deelman et al., 2015 [2]) for scientific computing. While they do not have steep learning curves, concessions have to be made to their ease of use to allow for their broader scope of targeted applications. HEP-Frame attempts to merge this gap, placing itself between a fully user-or resource-centred framework, so that users develop code quickly and do not have to worry about the computational efficiency of the code It handles (i) by ensuring efficient execution of applications according to their computational requirements and the available resources on the server through a multi-layer scheduler, while (ii) is addressed by automatically generating code skeletons and transparently managing the data structure and automating repetitive tasks. Additional comments: An early stage proof-of-concept was published in a conference proceedings (Pereira et al., 2015). However, the HEP-Frame version presented in this communication only shares a very small portion of the code related to the skeleton generation (less than 5% of the overall code), while the rest of the user interface, multi-layer scheduler, and parallelization strategies were completely redesigned and re-implemented.

Abstract
Abstract The increased adoption of IoT and analytical devices, telecommunication systems, smart sensors, and diverse information systems have brought significant impacts to Industry 4.0. To respond to this rapid technological evolvement is essential to ensure a proper articulation of HEI (Higher Education Institutions) with industry, to better prepare graduates with the appropriate competencies and technological capabilities within the digital transition of the industry. This paper aims to present a research project developed in digital transformation amongst HEI students, in the Alto-Minho region disclosing the trigger points to increase the digital adoption and embedding of Industry 4.0 paradigm. The obtained results enabled to conclude an apparent low level of digital literacy, a lack of vision and awareness about the technology potentialities throughout industry activities, besides its use almost restricted to leisure and as study support, as well as lack on cybersecurity domain-specific competencies.