Abstract
Forest soil ripping is a practice that involves revolving the soil in a forest area to prepare it for planting or sowing operations. Advanced sensing systems may help in this kind of forestry operation to assure ideal ripping depth and intensity, as these are important aspects that have potential to minimise the environmental impact of forest soil ripping. In this work, a cost-effective contactless system - capable of detecting and mapping soil ripping depth in real-time - was developed and tested in laboratory and in a realistic forest scenario. The proposed system integrates two single-point LiDARs and a GNSS sensor. To evaluate the system, ground-truth data was manually collected on the field during the operation of the machine with a ripping implement. The proposed solution was tested in real conditions, and the results showed that the ripping depth was estimated with minimal error. The accuracy and mapping ripping depth ability of the low-cost sensor justify their use to support improved soil preparation with machines or robots toward sustainable forest industry.

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.

Abstract
A significant concern that is widely discussed in the field of Higher Education is declining student participation. In several institutions, attendance is optional, allowing students to attend lectures at their convenience. This study proposes the integration of Artificial Intelligence and Gamification to improve student engagement and attendance rates. The initiative combines advanced technological strategies with conventional educational methodologies to enhance the lecture experience. The initiative is significant as formal lectures often witness dwindling student interest and frequent absenteeism, undermining the educational process and student's future career prospects. This combination has the potential to revolutionise Higher Education by providing a more interactive and engaging learning experience. While gamification has positively impacted learning in various contexts, integration with Artificial Intelligence is a game-changer, paving the way for a modernised educational experience. This innovative exploration of the AI-gamification blend sets the stage for future research and the implementation of updated academic strategies, ultimately addressing student engagement and attendance. This position paper presents the bases and foundations for understanding the problem of student attendance and engagement and the role of AI and gamification in Higher Education in alleviating it.;Uma inquietação relevante e extensamente discutida no domínio do Ensino Superior é a diminuição da participação dos estudantes. Em diversas instituições, a assiduidade é facultativa, permitindo aos estudantes a frequência às aulas segundo a sua disponibilidade. Este estudo propõe a integração da Inteligência Artificial e da Gamification como meios para melhorar o envolvimento e as taxas de assiduidade dos estudantes. A iniciativa em causa articula estratégias tecnologicamente avançadas com metodologias educativas convencionais no intuito de enriquecer a experiência de ensino. Tal iniciativa assume importância dada à constante diminuição do interesse dos estudantes e a assiduidade irregular nas aulas formais, fatores que afetam negativamente o processo de ensino e as perspetivas de carreira dos estudantes. Esta combinação ostenta o potencial de revolucionar o Ensino Superior, proporcionando uma experiência de aprendizagem mais interativa e envolvente. Embora a Gamification já tenha impactado positivamente o processo de aprendizagem em diversos contextos, a sua integração com a Inteligência Artificial surge como um elemento transformador, abrindo caminho para uma experiência educacional modernizada. Esta investigação inovadora que combina a IA e Gamification prepara as bases para investigações futuras e a implementação de estratégias académicas aprimoradas, concentrando-se principalmente no envolvimento e na assiduidade dos estudantes. Este artigo de posicionamento apresenta as bases e os fundamentos necessários para a compreensão do problema da frequência e envolvimento dos estudantes no Ensino Superior, assim como o papel da IA e da Gamification na sua mitigação.

Abstract
This paper explores the intricate interplay between perceived challenge and narrative immersion within role-playing game (RPG) video games, motivated by the escalating influence of game difficulty on player choices. A quantitative methodology was employed, utilizing three specific questionnaires for data collection on player habits and experiences, perceived challenge, and narrative immersion. The study consisted of two interconnected stages: an initial research phase to identify and understand player habits, followed by an in-person intervention involving the playing of three distinct RPG video games. During this intervention, selected players engaged with the chosen RPG video games separately, and after each session, responded to two surveys assessing narrative immersion and perceived challenge. The study concludes that a meticulous adjustment of perceived challenge by video game studios moderately influences narrative immersion, reinforcing the enduring prominence of the RPG genre as a distinctive choice in narrative.

Abstract
Industry 4.0 has been changing and improving the manufacturing processes. To embrace these changes, factories must keep up to date with all the new emerging technologies. In the automotive industry, the growing demand for customization and constant car model changes leads to an inevitable grow of complexity of the final product quality inspection process. In the project INDTECH 4.0, smart technologies are being explored in an automotive factory assembly line to automate the vehicle quality control, which still relies on human inspection based on paper conformity checklists. This paper proposes an automated inspection process based on computer vision to assist operators in the conformity assessment of informative labels affixed inside the engine compartment of the car. Two of the most recent object detection algorithms: YOLOv5 and YOLOX are evaluated for the identification of labels in the images. Our results show high mean average precision on both algorithms (98%), which overall, tells us that both algorithms showed good performances and have potential to be implemented in the shop floor to support the vehicle quality control.

Abstract
Stand-level forest tree species perception and identification are needed for monitoring-related operations, being crucial for better biodiversity and inventory management in forested areas. This paper contributes to this knowledge domain by researching tree trunk types multispectral perception at stand-level. YOLOv5 and YOLOv8 - Convolutional Neural Networks specialized at object detection and segmentation - were trained to detect and segment two tree trunk genus (pine and eucalyptus) using datasets collected in a forest region in Portugal. The dataset comprises only two categories, which correspond to the two tree genus. The datasets were manually annotated for object detection and segmentation with RGB and RGB-NIR images, and are publicly available. The Small variant of YOLOv8 was the best model at detection and segmentation tasks, achieving an F1 measure above 87% and 62%, respectively. The findings of this study suggest that the use of extended spectra, including Visible and Near Infrared, produces superior results. The trained models can be integrated into forest tractors and robots to monitor forest genus across different spectra. This can assist forest managers in controlling their forest stands.