Abstract
Virtual reality (VR) for training helps minimize risks and costs by allowing more frequent and varied use of experiential training experiences, leading to active and improved learning. However, creating VR training experiences is costly and time-consuming, requiring software development experts. Additionally, current authoring tools are desktop-oriented, which detaches the process of creating the immersive experience from experiencing it in a situated context. This paper presents the development of an immersive authoring tool designed to create immersive virtual environments that can be used to train operatives. The authoring tool can record and replay animations of each action the user performed that can later be used to instruct other users how the task should be performed. Participants were divided into two groups, and the proposed authoring tool was evaluated using usability, satisfaction, presence and cybersickness. Between groups, Independent T-tests revealed that there were no significant differences between expert and non-expert groups in any of the studied variables. Also, the results showed that the authoring tool had high usability and satisfaction, average presence, and low probability of cybersickness symptoms.

Abstract
Cybersickness refers to the negative symptoms caused by exposure to a Virtual Reality (VR) experience. The literature is consensual that cybersickness is a key factor in an experience, as the non-existence of cybersickness provides an optimal virtual experience. Thus, it is of utmost importance to evaluate cybersickness when assessing VR applications to understand the impact of this factor on the user experience and, ultimately, on the VR application viability. However, there is a lack of Portuguese instruments to evaluate this variable. To tackle this, this aimed to translate and validate the Simulator Sickness Questionnaire (SSQ) to Portuguese so it can be used with the Portuguese population and maintain its psychometric properties. The new instrument was validated using a sample of 603 Portuguese subjects aged between 16 and 79. Based on the observed results, the obtained theoretical model shows that the Portuguese version of the SSQ is valid for properly evaluating cybersickness in VR experiences with Portuguese samples.

Abstract
Over the years, various immersive virtual training environments (iVTEs) have been developed, allowing companies to start transitioning to Virtual Reality (VR) technologies to train their personnel. This transition forces companies to start using game engines as a foundation to develop such iVTEs, which also requires a multidisciplinary team. When developing such training environments, challenges on how to present tasks to users arise. The way these tasks are presented can dictate the efficacy of the VR training application. This paper presents three different task presentation methodologies (avatar animation, videos, and instruction manual) and assesses them using 36 participants, divided into those three groups. Usability, sense of presence, satisfaction, cybersickness, and technology acceptance variables were studied and results indicated that only the total number of actions performed had differences between groups where the instruction manual reported the higher number of actions (usability) when compared to the other conditions. Therefore it was concluded that the instruction manual proved to be where users kept losing focus and making more actions. It was also concluded that all conditions had a similar sense of presence, satisfaction, cybersickness, and acceptance scores.

Abstract
The Internet -of -Things (IoT) has created a complex environment where hardware and software interact in complex ways. Despite being a prime candidate for applying well -established software engineering practices, IoT has not seen the same level of adoption as other areas, such as cloud development. This discrepancy is even more evident in the case of edge devices, where programming and managing applications can be challenging due to their heterogeneous nature and dependence on specific toolchains and languages. However, the emergence of WebAssembly as a viable solution for running high-level languages on some devices presents an opportunity to streamline development practices, such as DevOps. In this paper, we present WASMICO - a firmware and command -line utility that allows for the execution and management of application lifecycles in microcontrollers. Our solution has been benchmarked against other state-of-the-art tools, demonstrating its feasibility, novel features, and empirical evidence that it outperforms some of the most widely used solutions for running high-level code on these devices. Overall, our work aims to promote the use of wellestablished software engineering practices in the IoT domain, helping to bridge the gap between cloud and edge development.

Abstract
Modern agriculture faces important challenges for feeding a fast-growing planet's population in a sustainable way. One of the most important challenges faced by agriculture is the increasing destruction caused by pests to important crops. It is very important to control and manage pests in order to reduce the losses they cause. However, pest detection and monitoring are very resources consuming tasks. The recent development of computer vision-based technology has made it possible to automatize pest detection efficiently. In Mediterranean olive groves, the olive fly (Bactrocera oleae Rossi) is considered the key-pest of the crop. This paper presents olive fly detection using the lightweight YOLO-based model for versions 7 and 8, respectively, YOLOv7-tiny and YOLOv8n. The proposed object detection models were trained, validated, and tested using two different image datasets collected in various locations of Portugal and Greece. The images are constituted by sticky yellow trap photos and by McPhail trap photos with olive fly exemplars. The performance of the models was evaluated using precision, recall, and mAP.95. The YOLOV7-tiny model best performance is 88.3% of precision, 85% of Recall, 90% of mAP.50, and 53% of mAP.95. The YOLOV8n model best performance is 85% of precision, 85% of Recall, 90% mAP.50, and 55% of mAP.50 YOLO8n model achieved worst results than YOLOv7-tiny for a dataset without negative images (images without olive fly exemplars). Aiming at installing an experimental prototype in the olive grove, the YOLOv8n model was implemented in a Ubuntu Server 23.04 Raspberry PI 3 microcomputer.

Abstract
AbstractSeveral approaches have been proposed to obtain successful models to solve complex next-item recommendation problem in non-prohibitive computational time, such as by using heuristics, designing architectures, and applying information filtering techniques. In the current technological scenario of artificial intelligence, sequential recommender systems have been gaining attention and they are a highly demanding research area, especially using deep learning in their development. Our research focuses on an efficient and practical model for managing sequential session-based recommendations of specific products for users using the wine and movie domains as case studies. Through an innovative recommender model called X-Model4Rec – eXtensible Model for Recommendation, we explore the user's dynamic taste profile using architectures with transformer and multi-head attention mechanisms to solve the next-item recommendation problem. The performance of the proposed model is compared to that of classical and baseline recommender models on two real-world datasets of wines and movies, and the results are better for most of the evaluation metrics.