Abstract
The welfare and safety of the employees of an enterprise is a great concern and priority in a responsible and successful organization. The identification of patterns of work-related accidents is important to reduce and prevent further mishaps and injuries. To improve the safety of the work environment, accidents related data must be analyzed to identify the possible risk factors and their effects on the type of accident and its level of severity. Thus, data related to workplace accidents in fishmonger stores were collected from a Portuguese retail company where it was analyzed with statistical, clustering, and classification techniques to identify potential underlying correlation and patterns between the data, and in this way, collecting important information to prevent future accident or lesions.

Abstract
Weather conditions have a major impact on citizens’ daily mobility. Depending on weather conditions trips may be delayed, demand may be changed as well as the modal shift. These variations have a major impact on the use and operation of public transport, particularly in transport systems that operate close to capacity. However, the influence of weather conditions on transport demand is difficult to predict and quantify. For this purpose, an artificial neural network model – the Multilayer Perceptron – is used as a regression model to estimate the demand of urban public transport buses based on weather conditions. Transit bus ridership and weather conditions were collected along a year from a medium-size European metropolitan area (Oporto, Portugal) and linked under the assumption that individuals choose the travel mode based on the weather conditions that are observed during the departure hour, the hour before and two hours before. The transit ridership data were also labelled according to the hour, day of the week, month, and whether there was a strike and/or holiday or not. The results demonstrate that it is possible to predict the demand of public transport buses using the weather conditions observed two hours before with low error for the entire network (MAE = 143 and RMSE = 322). The use of weather conditions allow to decreases the error of the prediction by ~8% for the entire network. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Achieving persistent and reliable autonomy for mobile robots in challenging field mission scenarios is a long-time quest for the Robotics research community. Deep learning-based LIDAR odometry is attracting increasing research interest as a technological solution for the robot navigation problem and showing great potential for the task. In this work, an examination of the benefits of leveraging learning-based encoding representations of real-world data is provided. In addition, a broad perspective of emergent Deep Learning robust techniques to track motion and estimate scene structure for real-world applications is the focus of a deeper analysis and comprehensive comparison. Furthermore, existing Deep Learning approaches and techniques for point cloud odometry tasks are explored, and the main technological solutions are compared and discussed. Open challenges are also laid out for the reader, hopefully offering guidance to future researchers in their quest to apply deep learning to complex 3D non-matrix data to tackle localization and robot navigation problems.

Abstract
Assessment is an important part of the educational process, playing a crucial role in student learning. The increase in the number of students in higher education has placed extreme pressure on assessment practices, often leading to a teacher having hundreds of assignments to correct, not only giving feedback too late, but also low quality feedback, as it is humanly impossible to correct all these assessments by giving quality feedback in such a short time. Due to the social confinement caused by the pandemic of COVID-19, there was the need to change the evaluation method initially associated with a thin exam, to a continuous evaluation method based on multiple weekly assignments. In order to deal with this situation, we developed AOCO, the first automatic correction tool for the ARMv8 AArch64 assembly language. This work presents the AOCO tool, as well as the results of the evaluation of a first use with students.

Abstract
<p>The current SARS-CoV-2 pandemic has been affecting all sectors worldwide, and efforts have been targeting the enhancement of people’s health and labour conditions of collaborators belonging to healthcare institutions. The recent vaccines emerging against covid-19 are seen as a solution to address the problem that has already killed up to two million people. The preparation of the Pfizer-BioNTech covid-19 vaccine requires a specific manipulation before its administration. A correct homogenization with saline solution is needed and, therefore, a manual process with a predefined protocol should be accomplished. This action can endanger the operators’ ergonomics due to the repetitive movement of the process. This paper proposes a low-cost prototype incorporating an arduino based embedded system actuating a servomotor to perform an autonomous vials’ homogenization allowing to redirect these healthcare workers to other tasks. Moreover, a contactless start order process was implemented to avoid contact with the operator and, consequently, the contamination. The prototype was successfully tested and recognised, and is being applied during the preparation of the covid-19 vaccines at the hospital pharmacy of <em>Centro Hospitalar de Vila Nova de Gaia/Espinho</em>, <em>E.P.E.</em>, Portugal. It can be easily replicated since the source files to assemble it are provided by the authors.</p>

Abstract
Automated guided vehicles (AGV) represent a key element in industries’ intralogistics and the use of AGV fleets bring multiple advantages. Nevertheless, coordinating a fleet of AGV is already a complex task but when exposed to delays in the trajectory and communication faults it can represent a threat, compromising the safety, productivity and efficiency of these systems. Concerning this matter, trajectory planning algorithms allied with supervisory systems have been studied and developed. This article aims to, based on work developed previously, implement and test a Multi AGV Supervisory System on real robots and analyse how the system responds to the dynamic of a real environment, analysing its intervention, what influences it and how the execution time is affected.