Abstract
Training in a virtual environment can augment the current methods of professional's training, preparing them better for possible situations in the field of work while taking advantage of Virtual Reality (VR) benefits. This paper proposes a cost-effective immersive VR platform designed in real-context usage, consisting of an authoring tool that permits the creation and manipulation of training courses and the execution of these courses in an immersive environment. Accomplishing a good training experience in an immersive simulation requires an equilibrium between the simulator performance and the virtual world aesthetics quality. Thus, in addition to presenting the development of the proposed training platform based on Unity technologies, this paper describes an objective performance evaluation of a virtual training scene using the different render pipelines and across immersive and non-immersive setups. Results confirmed the platform's viability and revealed that the rendering pipeline should be defined according to the display device used.

Abstract
Low-cost smartphone-compatible portable ophthalmoscopes can capture visuals of the patient's retina to screen several ophthalmological diseases like glaucoma. The images captured have lower quality and resolution than standard retinography devices but enough for glaucoma screening. Small videos are captured to improve the chance of inspecting the eye properly; however, those videos may not always have enough quality for screening glaucoma, and the patient needs to repeat the inspection later. In this paper, a method for automatic assessment of the quality of videos captured using the D-Eye lens is proposed and evaluated with a personal dataset with 539 videos. Based on two methods developed for retina localization on the images/frames, the Circle Hough Transform method with a precision of 78,12% and the YOLOv7 method with a precision of 99,78%, the quality assessment method automatically decides on the quality of the video by measuring the number of frames of good-quality in each video, according to the chosen threshold. © 2024 Elsevier B.V.. All rights reserved.

Abstract
Traditional revenue management systems are built under the assumption of independent demand per fare. The fare adjustment theory is a methodology to adjust fares that allows for the continued use of optimization algorithms and seat inventory control methods, even with the shift toward dependent demand. Since accurate demand forecasts are a key input to this methodology, it is reasonable to assume that for a scenario with uncertainties it may deliver suboptimal performance. Particularly, during and after COVID-19, airlines faced striking challenges in demand forecasting. This study demonstrates, firstly, the theoretical dominance of the fare adjustment theory under perfect conditions. Secondly, it lacks robustness to forecast errors. A Monte Carlo simulation replicating a revenue management system under mild assumptions indicates that a forecast error of ±20% can potentially prompt a necessity to adjust the margin employed in the fare adjustment theory by -10%. Moreover, a tree-based machine learning model highlights the forecast error as the predominant factor, with bias playing an even more pivotal role than variance. An out-of-sample study indicates that the predictive model steadily outperforms the fare adjustment theory. © The Author(s), under exclusive licence to Springer Nature Limited 2024.

Abstract
In the Fourth Industrial Revolution era, commonly known as Industry 4.0, the manufacturing industry is undergoing a profound transformation driven by the convergence of technological advancements. Industry 4.0 technologies are revolutionising how products are manufactured, from design to production to delivery. These technologies, such as collaborative robotics, digital twins, IoT, and data analytics, enable manufacturers to improve efficiency, productivity, and quality. As Industry 4.0 continues to evolve, the demand for skilled engineers who can effectively design, implement, and manage these sophisticated systems is growing rapidly. Future mechanical engineers must be prepared to navigate this complex and data-driven manufacturing landscape. To address this need, the Faculty of Engineering at the University of Porto developed a new course titled Smart Factories, specifically designed to equip master's students with the knowledge and skills necessary to thrive in the factories of the future. This course utilises an innovative, active experimental learning methodology with industry collaborations and a comprehensive curriculum to foster the development of the multidisciplinary skills necessary to excel in this rapidly evolving field. Through this comprehensive and innovative approach, the Smart Factories course aims to prepare future mechanical engineers to become leaders in smart manufacturing, driving innovation and shaping future factories.

Abstract
Unmanned Aerial Vehicles (UAVs) are suited as cost-effective and adaptable platforms for carrying Wi-Fi Access Points (APs) and cellular Base Stations (BSs). Implementing aerial networks in disaster management scenarios and crowded areas can effectively enhance Quality of Service (QoS). Maintaining Line-of-Sight (LoS) in such environments, especially at higher frequencies, is crucial for ensuring reliable communication networks with high capacity, particularly in environments with obstacles. The main contribution of this paper is a traffic- and obstacle-aware UAV positioning algorithm named Reinforcement Learning-based Traffic and Obstacle-aware Positioning Algorithm (RLTOPA), for such environments. RLTOPA determines the optimal position of the UAV by considering the positions of ground users, the coordinates of obstacles, and the traffic demands of users. This positioning aims to maximize QoS in terms of throughput by ensuring optimal LoS between ground users and the UAV. The network performance of the proposed solution, characterized in terms of mean delay and throughput, was evaluated using the ns-3 simulator. The results show up to 95% improvement in aggregate throughput and 71% in delay without compromising fairness.

Abstract
Nowadays ransomware attacks have become one of the main problems organizations face. The threat of ransomware attacks, with their capacity to paralyze entire organizations, creates the need to develop a ransomware recovery utility function to help further prepare for the impact of such attacks and enhance the organization's knowledge and perception of risk. This work proposes a ransomware recovery utility function that aims to estimate the impact of a ransomware attack measured in manpower hours till recovery and taking into account different devices and different scenarios.