Abstract
Climate change affects global temperature and precipitation patterns. These effects, in turn, influence the intensity and, in some cases, the frequency of extreme environmental events, such as forest fires, hurricanes, heat waves, floods, droughts, and storms. In general, these events can be particularly conducive to the appearance of plant pests and diseases. The availability of models and a data collection system is crucial to manage pests and diseases in sustainable agricultural ecosystems. Agricultural ecosystems are known to be complex, multivariable, and unpredictable. It is important to anticipate crop pests and diseases in order to improve its control in a more ecological and economical way (e.g., precision in the use of pesticides). The development of an intelligent monitoring and management platform for the prevention of pests and diseases in olive groves at Trás-os- Montes region will be very beneficial. This platform must: a) integrate data from multiple data sources such as sensory data (e.g., temperature), biological observations (e.g., insect counts), georeferenced data (e.g., altitude) or digital images (e.g., plant images); b) systematize these data into a regional repository; c) provide relevant forecasts for pest and diseases. Convolutional Neural Networks (CNNs) can be a valuable tool for the identification and classification of images acquired by Internet of Things (IoT).

Abstract
Considering the growing volume of information and services available on the web, it has become essential to provide websites and applications with tools, such as recommender systems, capable of helping users to obtain the information and services appropriate to their interests. Due to the complexity of web adaptation and the ability of multi-agent systems to deal with complex problems, the use of multi-agent approaches in recommender systems has been increasing. In the present work, we make a thorough review of the use of multi-agent-based recommender systems. The review shows the diversity of applications of multi-agent systems in recommender systems, namely on what concerns the diversity of domains, different types of approaches and contribution to the performance improvement of the recommender systems.

Abstract
Recommender systems form a class of Artificial Intelligence systems that aim to recommend relevant items to the users. Due to their utility, it has gained attention in several applications domains and is high demanded for research. In order to obtain successful models in the recommendation problem in non-prohibitive computational time, different heuristics, architectures and information filtering techniques are studied with different datasets. More recently, machine learning, especially through the use of deep learning, has driven growth and expanded the sequential recommender systems development. This research focuses on models for managing sequential recommendation supported by session-based recommendation. This paper presents the characterization in the specific theme and the state-of-the-art towards study object of the thesis: the adaptive recommendation to mitigate the information overload in online environments.

Abstract
Guiding the building occupants under fire emergency to a safe place is an open research problem. Finding solutions to address the problem requires a perfect knowledge of the fire building evacuation domain. The use of ontologies to model knowledge of a domain allows a common and shared understanding of that domain, between people and heterogeneous systems. This paper presents an ontology that aims to build a knowledge model to better understand the referred domain and to help develop more capable building evacuation solutions and systems. The herein proposed ontology considers the different variables and actors involved in the fire building evacuation process. We followed the Methontology methodology for its developing, and we present all the development steps, from the specification to its implementation with the Protégé tool.

Abstract
The evacuation of buildings in case of fire is a sensitive issue for civil society that also motivates the academic community to develop and study solutions to improve the efficiency of evacuating these spaces. The study of human behavior in fire emergencies has been one of the areas that have deserved the attention of researchers. However, this modeling of human behavior is difficult and complex because it depends on factors that are difficult to know and that vary from country to country. In this paper, a paradigm shift is proposed which, instead of focusing on modeling the behavior of occupants, focuses on conditioning this behavior by providing real-time information on the most efficient evacuation routes. Making this information available to occupants is possible with a solution that takes advantage of the growing use of the IoT (Internet of Things) in buildings to help occupants adapt to the environment. Supported by the IoT, multi-agent recommender systems can help users to adapt to the environment and provide the occupants with the most efficient evacuation routes. This paradigm shift is achieved through a context-based multi-agent recommender system based on contextual data obtained from IoT devices, which recommends the most efficient evacuation routes at any given time. The obtained results suggest that the proposed solution can improve the efficiency of evacuating buildings in the event of a fire; for a scenario with two hundred people following the system recommendations, the time they take to reach a safe place decreases by 17.7%.

Abstract
Virtual Reality (VR) can infiuence users' perception of a given location through experiences in immersive environments. In the tourism context, the use of this technology is crucial in the promotion of products and destinations by improving the perception of tourism content and generating impactful information. However, it is difficult to find comprehensive reviews of studies on VR in tourism. To overcome such limitation, this study is a desk-based, descriptive and retrospective research that combines bibliometric analysis techniques to 37 papers from the Web of Science and Scopus databases, between 1999 and 2020. We aim to provide an overview of the scientific production in the tourism sector associated with VR, identify empirical infiuences of the conceptual framework and suggest new paths. The results allow us to conclude that the use of VR for promotional purposes in tourism is infrequent. The most recurrent studies present software proposals for VR and reviews about technological concepts, marketing and destination image. There is little empirical evidence about the implications and applications of VR. Therefore, we consider imperative more research that explore the applicability of VR in tourism promotion. © 2022, Universidade de Aveiro. All rights reserved.