Abstract
The use of robots is widely spread across the industry. It is paramount that the robot end-effector tracks a pre-defined trajectory with the lowest energy loss. To contribute to the solution of this problem, the robot trajectory is defined using a tracking parameter which is optimised using the Matlab (R) fminunc function and the Particle Swam Optimisation algorithm. This approach was tested for a case study with the energy loss being reduced in approximately 96.15%.

Abstract

Abstract
Breast cancer is currently one of the main causes of death and tumoral diseases in women. Even if early diagnosis processes have evolved in the last years thanks to the popularization of mammogram tests, nowadays, it is still a challenge to have available reliable diagnosis systems that are exempt of variability in their interpretation. To this end, in this work, the design and development of an intelligent clinical decision support system to be used in the preventive diagnosis of breast cancer is presented, aiming both to improve the accuracy in the evaluation and to reduce its uncertainty. Through the integration of expert systems (based on Mamdani-type fuzzy-logic inference engines) deployed in cascade, exploratory factorial analysis, data augmentation approaches, and classification algorithms such as k-neighbors and bagged trees, the system is able to learn and to interpret the patient's medical-healthcare data, generating an alert level associated to the danger she has of suffering from cancer. For the system's initial performance tests, a software implementation of it has been built that was used in the diagnosis of a series of patients contained into a 130-cases database provided by the School of Medicine and Public Health of the University of Wisconsin-Madison, which has been also used to create the knowledge base. The obtained results, characterized as areas under the ROC curves of 0.95-0.97 and high success rates, highlight the huge diagnosis and preventive potential of the developed system, and they allow forecasting, even when a detailed and contrasted validation is still pending, its relevance and applicability within the clinical field.

Abstract
In recent years several efforts have being made in bringing smart network connectivity to the Renewable Energy Plant (REP) environment. On the other hand, REP is extending in scale from specialized points where the energy provider acts as a supplier to home REP (self-energy producers). This enables new important features such as: process automation, monitoring, control and optimizations. On the other hand, and in particular during and after the Covid19 pandemics the cybersecurity menace is a massive concern. The digital literacy of a worker of such an infrastructure is relevant to the correct implementation of adequate security policies. This article describes the threats and challenges on the field and conducts an enquire for perceiving the awareness of Automation and Mechanics Engineering students for this relevant problem, as future player in the field. © 2022, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.

Abstract
This paper addresses the problem of Volt-Var optimization for conservation voltage reduction (CVR) implementation in medium voltage electric distribution systems (EDS) with high penetration levels of renewable energy sources (RES)-based distributed generation (DG). The proposed strategy seeks to coordinate the power dispatch of aggregated electric vehicles (EVs) for EDS voltage control taking into account technical characteristics and the driving patterns of individual EVs. The strategy is for the day-ahead operation scheduling, where decisions are made based on predictions of RES-based DG power production, conventional load consumption and EV driving patterns. Forecast errors are taken into account through a two-stage stochastic programming formulation, where probability density functions are used to describe the uncertainties of predicted parameters. Simulations were carried out on a 33-bus test system and results showed energy savings of up to 3% when EVs participate in voltage control.

Abstract
The competency-based learning approach arose from the Bologna signed declaration. However, a competency definition has never been easy and has been evolving and adapted over time, from indicators and learning goals to learning outcomes, which were formulated in terms of competencies. Meanwhile, the competency concept becomes discussed by pears, in particular, associating knowledge, skills, and human dispositions or attitudes into the competency definition. This information will be an essential update to the previous approaches and certainly contribute to achieving more accurate and reliable competencies information for employers and higher education institutions (HEI). This paper aims to reinforce the relevance of these concepts and suggest how each construct of “knowledge, skills and human dispositions” could be approached to formulate a competency. In addition, due to accelerated digital transformation, an example of a digital competency defined by the DigComp 2.0 framework, with proposed information regarding each of the three constructs, will be presented to consolidate this challenge. As future work, it is intended to analyze the eight different levels, and competency profiles defined by the European Qualification Framework (EQF) and assign a profile to each defined competency. In the end, it is expected altogether to contribute to achieving a competency roadmap definition.