Abstract
The uncertainty and variability as a consequence of modern utilization of wind power in the electrical system besides unpredicted contingencies of the system components can impose crucial challenges on the Independent System Operator's (ISO) performance. In such a situation, increasing operational flexibility is the main way to cover wind power unpredictability and to enable secure operation of the power system. To this end, this paper proposes a flexible security-constrained program to schedule supply-side and demand-side via an optimal pricing and incentive scheme. The considered demand response (DR) programs include time of use (TOU), real-time pricing (RTP), critical peak pricing (CPP), as well as emergency demand response program (EDRP). The study aims to find the most effective DR scheme among a set of DR programs to improve the efficiency of electricity markets while guaranteeing the security and environmental restrictions through minimization of two objective functions, the ISO's operational cost and pollutant emissions from generation units.

Abstract

Abstract
The evolution of knowledge, where intellectual development and adaptive skills are required, has rendered the traditional rigid curricula and teaching methodologies outdated. Education and training must be acquired throughout life, hence the need to change the teaching-learning processes. The Bologna Process introduced new paradigms that resulted in curricula revisions, the reorganization of teaching-learning processes, the structuring of courses and curricular units. It shifted the teaching process to a new form that enhances and assesses the acquisition of learning skills based on the work performed by the students. It is student-centered teaching, where it is essential to define the intended levels of knowledge, how the students achieve them, and if they reach the necessary skills in each of those levels. Each student needs to be motivated to learn and to participate in the activities, which presupposes meaningful activities to arouse the interest in developing them. The purpose of this paper is to describe a design project, its implementation, orientation and effectiveness in problem-based learning (PBL). Another purpose is the establishment of a relationship between the Bologna process and the implicit implications in the structure of teaching and training in lighting projects. This paper presents a proposal of the use of Information and Communication Technologies (ICT) using DIALux evo software. It is part of the learning methodology, supported by computer simulation for lighting projects, in the Electrical Installation and Lighting Engineering Master's Degree in Service Engineering of Construction, 1st semester of the 1st year, in the Department of Electrical Engineering of the Institute of Engineering of Coimbra (ISEC). PBL combined with Flipped Classroom is used as a pedagogical strategy to enable higher education students to approach problems, in groups and with the supervision of a teacher, in order to acquire specialized knowledge. The implementation of the PBL method aimed at bringing education and learning closer to the professional reality of the future engineers. © 2018 IEEE.

Abstract
Robotic vehicles are already deployed around the globe as a preferred tool for ocean sampling, from surface coastal waters down to the deepest remote locations. They are mainly used to perform routine measurement tasks, with obvious benefits in terms of space and time density, as well as accuracy in the localization of measurements. As the underlying technology matures, new features are being introduced and validated in operational scenarios, enabling new paradigms in ocean observation. This chapter describes the latest developments in marine robotics, presented in different stages of maturity, and sheds light on upcoming features available to the scientific community.

Abstract
In the increasingly competitive fashion retail industry, companies are constantly adopting strategies focused on adjusting the products characteristics to closely satisfy customers' requirements and preferences. Although the lifecycles of fashion products are very short, the definition of inventory and purchasing strategies can be supported by the large amounts of historical data which are collected and stored in companies' databases. This study explores the use of a deep learning approach to forecast sales in fashion industry, predicting the sales of new individual products in future seasons. This study aims to support a fashion retail company in its purchasing operations and consequently the dataset under analysis is a real dataset provided by this company. The models were developed considering a wide and diverse set of variables, namely products' physical characteristics and the opinion of domain experts. Furthermore, this study compares the sales predictions obtained with the deep learning approach with those obtained with a set of shallow techniques, i.e. Decision Trees, Random Forest, Support Vector Regression, Artificial Neural Networks and Linear Regression. The model employing deep learning was found to have good performance to predict sales in fashion retail market, however for part of the evaluation metrics considered, it does not perform significantly better than some of the shallow techniques, namely Random Forest.

Abstract
The use of cutting-edge technologies such as wearable devices to control reactive audiovisual systems are rarely applied in more conventional stage performances, such as opera performances. This work reports a cross-disciplinary approach for the research and development of the WMT Sensor-Glove, a data-glove used in an opera performance to control audiovisual elements on stage through gestural movements. A system architecture of the interaction between the wireless wearable device and the different audiovisual systems is presented, taking advantage of the Open Sound Control (OSC) protocol. The developed wearable system was used as audiovisual controller in "As sete mulheres de Jeremias Epicentro", a portuguese opera by Quarteto Contratempus, which was premiered in September 2017.