Abstract
Path planning for mobile robotics in unknown environments or with moving obstacles requires re-planning paths based on information gathered from the surroundings. Moving obstacles and real time constraints require fast computing to navigate and make decisions in a mobile robot. This paper addresses an optimization approach to compute, with real time constraints, the optimal path for a mobile robot based on a dynamically simplified A* search algorithm with a commitment on the available time.

Abstract
Robotics is a growing industry with applications in numerous markets, including retail, transportation, manufacturing, and even as personal assistants. Consumers have evolved to expect more from the buying experience, and retailers are looking at technology to keep consumers engaged. There are currently many interesting initiatives that explore how robots can be used in retail. In today's highly competitive business climate, being able to attract, serve, and satisfy more customers is a key to success. A happy customer is more likely to be a loyal one, who comes back and often to the store. It is our belief that smart robots will play a significant role in physical retail in the future. One successful example is wGO, a robotic shopping assistant developed by Follow-Inspiration. The wGO is an autonomous and self-driven shopping cart, designed to follow people with reduced mobility in commercial environments. With the Retail Robot, the user can control the shopping cart without the need to push it. This brings numerous advantages and a higher level of comfort since the user does not need to worry about carrying the groceries or pushing the shopping cart. The wGO operates under a vision-guided approach based on user-following with no need for any external device. Its integrated architecture of control, navigation, perception, planning, and awareness is designed to enable the robot to successfully perform personal assistance while the user is shopping. This paper presents the wGOs functionalities and requirements to enable the robot to successfully perform personal assistance while the user is shopping in a safe way. It also presents the details about the robot's behaviour, hardware and software technical characteristics. Experiments conducted in real scenarios were very encouraging and a high user satisfaction was observed. Based on these results, some conclusions and guidelines towards the future full deployment of the wGO in commercial environments are drawn.

Abstract
The success of the Emergency Plan depends on the ability of its occupants to respond. For this reason, it is fundamental to develop an appropriate training strategy for each organization. This pilot study aimed to understand the influence of specific training program on the emergency response. This study included a total of twenty-two workers of a company. The workers were divided into three emergency response teams with four elements and one another group with ten elements. The emergency response team had specific training actions with theoretical and practical contents. Finally, all workers participated in an activity called emergency scenarios, where a moment of brainstorming was provided for the solve each scenario. The classifications obtained in different assessments moments (M1: after training and M2: after three weeks of training) revealed that knowledge had been acquired by participants. Additionally, it was verified that teams, with specific training, presented better results in their specific scenario. The emergency response training may have better results if it enhances teamwork and the involvement of all stakeholders. © Springer Nature Switzerland AG 2019.

Abstract
Transportation modes prediction is a fundamental task for decision making in smart cities and traffic management systems. Traffic policies based on trajectory mining can save money and time for authorities and the public. It may reduce the fuel consumption, commute time, and more pleasant moments for residents and tourists. Since the number of features that may be used to predict a user transportation mode can be substantial, finding a subset of features that maximizes a performance measure is worth investigating. In this work, we explore a wrapper and an information retrieval methods to find the best subset of trajectory features for a transportation mode dataset. Our results were compared with two related papers that applied deep learning methods. The results showed that our work achieved better performance. Furthermore, two types of cross-validation approaches were investigated, and the performance results show that the random cross-validation method may provide overestimated results. © 2019 Copyright held by the owner/author(s).

Abstract
Distance Education (DE) still have some challenges to be considered similar to the face-to-face mode of instruction regarding the quality of learning, including the lack in promoting the sense of presence. This research investigates whether a differentiated media support, complementary to the traditional Virtual Learning Environment (VLE), composed by the integration of 3D Virtual Worlds (3DVW) and Conversational Agents, in the role of a Virtual Companions, can promote the student’s sense of presence in order to contribute with the learning process in DE. A quasi-experiment pilot study was conducted with 36 students enrolled in the Financial Management discipline from a DE formal course. A 3DVW was developed in the light of the pedagogical model of Experiential Learning, in the form of a role-play simulation. The results reveal that although the students positively evaluated the experience in the 3DVW, it did not stimulate the sense of presence as expected. However, better performance rates were diagnosed for students who had the help of the Virtual Companion. © 2019, Springer Nature Switzerland AG.

Abstract
Security and quality of supply continue to be major concern of power system operators. Thus, the expansion of transmission grids is certainly one of the major drivers to achieve this goal. In this scope, this paper presents a three-stage approach to solve the multi-year Transmission Expansion Planning (TEP) problem. This approach uses heuristic algorithms coupled with the Harmony Search (HS) metaheuristic and the Branch & Bound (B&B) algorithm. This hybrid method (HS-B&B) aims at finding the optimal multi-stage investment plan avoiding load shedding over the planning horizon. In this work, the AC-Optimal Power Flow (AC-OPF) is used to model the network as a way to consider the real operation conditions of the system. The method was validated using the Garver and the IEEE RTS 24 bus systems. Results demonstrate the reduction of computational effort without compromising the quality of the TEP.