Abstract
Object detection in the traffic domain has faced growing relevance through the years in developing autonomous driving mechanisms. As with vehicles, pedestrians face a very dynamic context, and identifying relevant objects from a pedestrian perspective presents many challenges. Improving the detection of some objects, such as crosswalks, is very relevant in this regard. This paper presents a technique that applies a computer vision approach to automatically generate datasets for training YOLO-based deep learning algorithms. An initial precision of 0.82 achieved with the generated dataset, which is increased to 0.84 after manually removing incorrect annotations. Results show that our approach leverages the dataset building process by reducing the manual workload needed. The approach could be used for training other object detection models used in traffic scenarios.

Abstract
The distribution network operation problem (DNOP) is an optimization problem in which the objective function is the total operation cost of the distribution company (Disco), to be minimized considering the technical constraints of the network. In the presence of distributed energy resources (DERs) and microgrids (MGs), new decision makers, including MG and DER operators or managing entities, are emerging and are changing the decision-making framework for distribution systems. To describe the cooperation and competition between the Disco, MG and DER operators, different frameworks and models have been proposed in the literature. Moreover, different computational techniques and metaheuristic algorithms have been used to solve the optimal operation problems. Hence, this paper considers DNOP as one of the timely problems under study and of major interest for future research, presenting a comprehensive review on the decision-making frameworks referring to DNOP in the presence of DERs and MGs, as a new contribution to earlier studies. The focus is set on the comparison among different frameworks characterized by increasingly higher level of participation of the DER managers to the distribution system operation, offering a complementary view with respect to available reviews on similar topics based on technical aspects of the DER connection and integration in MGs and distribution networks, which is noteworthy.

Abstract
We propose a method for color dehazing with four main characteristics: it does not introduce color artifacts, it does not depend on inverting any physical equation, it is based on models of visual perception, and it is fast, potentially real time. Our method converts the original input image to the HSV color space and works in the saturation and value domains by: (1) reducing the value component via a global constrained histogram flattening; (2) modifying the saturation component in consistency with the previous reduced value; and (3) performing a local contrast enhancement in the value component. Results show that our method competes with the state-of-the-art when dealing with standard hazy images, and outperforms it when dealing with challenging haze cases. Furthermore, our method is able to dehaze a FullHD image on a GPU in 90 ms.

Abstract
Energy arbitrage have monetary benefits for privately owned battery energy storage systems, such as the battery of an electric vehicle or residential batteries. However, the life cycle and degradation cost of the battery storage should be taken into consideration and can decrease obtained income in the long-term. This paper proposes an optimization framework to derive optimal bidding and offering curves for lead-acid battery storage participate in a stepwise energy market. The objective is to maximize the profit comes from participating in energy arbitrage action, while the life cycle of the battery is considered by objective function and constraints. Due to the small capacity of the considered storage unit, it can be assumed that this unit is a pricetaker participant, which its actions cannot influence the market prices. Hence, the energy prices are modeled as uncertain parameters using stochastic programming approach. The second order stochastic dominance constraints are as risk management method.

Abstract
Introduction: In 2015 the Directorate-General for Health of Portugal published new standards (DGS 001/2015) for the registration of cesarean section indicators. The existing scenario was the lack of data, influencing the quality of indicators and analyses on them. The use of a single computer tool was encouraged to register and compare indicators between hospitals with special attention to the Robson Classification as it employs basic information of pregnancy to classify all deliveries in 10 groups. The selected tool was Obscare software. Aim: Describe the scenario on data quality by analyzing the completeness of obstetric records from 2016 to 2018 of the variables used in Robson's classification collected by the Obscare tool. Methods: The completeness is evaluated using a number of missing values. The lower the completeness, the higher the number of missing values. Also, we perform the imputation of data based on basic concepts and analyzed the participation of this data in the indication of the type of delivery to be performed according to classification suggested by DGS 001/2015. Results: From 2016 to 2018. 5922 number of pregnancies resulted in 5922 of Robson Classifications. The variables with lower completeness were related to previous cesarean section (77%) and previous pregnancies (43%). After imputation, it fell to 3.9% and 0.56%, respectively causing 4.6% of discarded data from the total. Discussion: There is a significant amount of missing data in basic variables used to study the classification of delivery type. We believe that encouraging data completion with the possibility of comparing data between hospitals should be a priority in the health area.

Abstract
Teaching based on challenges and competitions is one of the most exciting and promising methods for students. In this paper, a competition of the Portuguese Robotics Open is addressed and a solution is proposed. The Robot@Factory Lite is a new challenge and accepts participants from secondary schools (Rookie) and universities. The concepts of simulation, hardware-in-the-loop and timed finite state machine are presented and validated in the real robot prototype. The aim of this paper is to disseminate the developed solution in order to attract more students to STEM educational program.