Abstract
Tourism business models have used several technologies in their development, such as Virtual Reality (VR). Previous studies show that VR allows tourism organizations to promote new types of relationships between tourists and destinations, to enhance the appeal and memorability of tourist experiences and to diversify consumption patterns, which could also be interesting for dealing with sustainability issues, such as seasonal demand of destinations or activities in wine tourism. Thus, we propose a conceptual model to analyze the influence of memorable tourism experiences on wine tourists' future intentions after a VR experience, providing additional details on the research methodology to empirically test the conceptual model. Innovation in business models with VR to promote new relationships with destinations or activities and diversify tourists' consumption patterns could be interesting to address seasonal activities, such as the grape harvest or grape-treading, which are not continuously available for tourist observation/ participation, despite their high appeal. On the other hand, the results could contribute to wine and other kinds of tourism, conditioned by mobility issues such as restrictions on movements or personal interaction, due to health crises or personal constraints, increasing these tourism experiences' accessibility also in times of unavailability. © 2023, Universidade de Aveiro. All rights reserved.

Abstract
Performing experiments with Internet-of-Things edge devices is not always a trivial task, as large physical testbeds or complex simulators are often needed, leading to low reproducibility and several difficulties in crafting complex scenarios and tweaking parameters. Most available simulators try to simulate as close to reality as possible. While we agree that this kind of high-fidelity simulation might be necessary for some scenarios, we argue that a low-fidelity easy-to-change simulator may be a good solution when rapid prototyping orchestration strategies and algorithms. In this work, we introduce SIMoT, a low-fidelity orchestrator simulator created to achieve shorter feedback loops when testing different orchestration strategies for task allocation in edge devices. We then transferred the simulator-validated algorithms to both physical and virtual testbeds, where it was possible to assert that the simulator results correlate strongly with the observations on those testbeds.

Abstract
In the current technological scenario of artificial intelligence growth, especially using machine learning, large datasets are necessary. Recommender systems appear with increasing frequency with different techniques for information filtering. Few large wine datasets are available for use with wine recommender systems. This work presents X-Wines, a new and consistent wine dataset containing 100,000 instances and 21 million real evaluations carried out by users. Data were collected on the open Web in 2022 and pre-processed for wider free use. They refer to the scale 1-5 ratings carried out over a period of 10 years (2012-2021) for wines produced in 62 different countries. A demonstration of some applications using X-Wines in the scope of recommender systems with deep learning algorithms is also presented.

Abstract
The study of the evacuation of buildings in emergency fire situations has deserved the attention of researchers for decades, particularly regarding the real-time guiding of occupants in their way to exit the building. However, finding solutions to guide the occupants evacuating a building requires a thorough knowledge of that domain. Using ontological models to model the knowledge of a domain allows the understanding of that domain to be shared. This paper presents an ontological model that pretends to reinforce and deepen knowledge of the domain under study and help develop solutions and systems capable of guiding the occupants during a building evacuation. The ontology was developed following the METHONTOLOGY methodology, and for implementation, the Protege tool was used. The ontological model was successfully submitted to a thorough evaluation process and is publicly available on the Web.

Abstract
Building evacuation simulation allows for a better assessment of fire safety conditions in existing buildings, which is why it is of interest to develop an easyto-use-Web platform that helps fire safety technicians in this assessment. To achieve this goal, the geometric and physical representation of the building and installed fire safety devices are necessary, as well as the modelling of occupant movement. Although these are widely studied areas, in this paper, we present two new model approaches, either for the physical and geometric representation of a building or for the occupant's movement simulation, during a building evacuation process. To test both models, we develop a multi-agentWeb simulator platform. The tests carried out show the suitability of the model approaches herein presented.

Abstract
Fires in large buildings can have tragic consequences, including the loss of human lives. Despite the advancements in building construction and fire safety technologies, the unpredictable nature of fires, particularly in large buildings, remains an enormous challenge. Acknowledging the paramount importance of prioritising human safety, the academic community has been focusing consistently on enhancing the efficiency of building evacuation. While previous studies have integrated evacuation simulation models, aiding in aspects such as the design of evacuation routes and emergency signalling, modelling human behaviour during a fire emergency remains challenging due to cognitive complexities. Moreover, behavioural differences from country to country add another layer of complexity, hindering the creation of a universal behaviour model. Instead of centring on modelling the occupant behaviour, this paper proposes an innovative approach aimed at enhancing the occupants' behaviour predictability by providing real-time information to the occupants regarding the most suitable evacuation routes. The proposed models use a building's environmental conditions to generate contextual information, aiding in developing solutions to make the occupants' behaviour more predictable by providing them with real-time information on the most appropriate and efficient evacuation routes at each moment, guiding the occupants to safety during a fire emergency. The models were incorporated into a context-aware recommender system for testing purposes. The simulation results indicate that such a system, coupled with hazard and congestion models, positively influences the occupants' behaviour, fostering faster adaptation to the environmental conditions and ultimately enhancing the efficiency of building evacuations.