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
Physical activity is movement of the body or part of the body to make muscles more active and to lose the energy from the body. Regular physical activity in the daily routine is very important to maintain good physical and mental health. It can be performed at home, a rehabilitation center, gym, etc., with a regular monitoring system. How long and which physical activity is essential for specific people is very important to know because it depends on age, sex, time, people that have specific diseases, etc. Therefore, it is essential to monitor physical activity either at a physical activity center or even at home. Physiological parameter monitoring using contact sensor technology has been practiced for a long time, however, it has a lot of limitations. In the last decades, a lot of inexpensive and accurate non-contact sensors became available on the market that can be used for vital sign monitoring. In this study, the existing research studies related to the non-contact and video-based technologies for various physiological parameters during exercise are reviewed. It covers mainly Heart Rate, Respiratory Rate, Heart Rate Variability, Blood Pressure, etc., using various technologies including PPG, Video analysis using deep learning, etc. This article covers all the technologies using non-contact methods to detect any of the physiological parameters and discusses how technology has been extended over the years. The paper presents some introductory parts of the corresponding topic and state of art review in that area.

Abstract
The process of sectorization aims at dividing a dataset into smaller sectors according to certain criteria, such as equilibrium and compactness. Sectorization problems appear in several different contexts, such as political districting, sales territory design, healthcare districting problems and waste collection, to name a few. Solution methods vary from application to application, either being exact, heuristics or a combination of both. In this paper, we propose two quadratic integer programming models to obtain a sectorization: one with compactness as the main criterion and equilibrium constraints, and the other considering equilibrium as the objective and compactness bounded in the constraints. These two models are also compared to ascertain the relationship between the criteria.

Abstract
Worldwide organic food consumption has registered a consistent rise in recent years. Despite the relevant body of literature on the topic, it is necessary to further understand the antecedents of purchase intention. This article aims to identify the factors that influence the consumer's intention to purchase organic food. It extends the theory of planned behavior model by including environmental concerns, health concerns, and perceived quality as determinants of attitude toward organic food products. Additionally, it considers the effect of product availability on consumers' perceived behavioral control. This article includes a quantitative study that was conducted in Portugal in 2020 (n = 206). Structural equation modeling was used to test the proposed set of research hypotheses. In line with extant literature, this study confirmed that attitude toward organic food is the main determinant of purchase intention. Additionally, it demonstrates that health concerns and perceived quality have a significant impact on attitude toward organic food. The impact of environmental concerns on attitude was not confirmed by this study. Based on these findings, it is recommended that managers stress health benefits and quality of organic food in order to foster positive attitudes and consequently leverage purchase intention.

Abstract
The fifth generation of mobile communications networks (5G) emerges with the potential to customize the technical parameters of the same physical infrastructure for each application, service, or user, which can compromise the fundamentals that made the Internet the leading platform for dissemi-nating information and a transnational instrument of collaboration of indi-viduals and institutions. In this scenario, the present study intends to ana-lyze this new technological standard, its influence on the informational flow of the Internet, and evaluate the role of information policy for the gover-nance of the multiple interests that permeate the digital ecosystem.

Abstract
The increasing use of geospatial information from satellites and unmanned aerial vehicles (UAVs) has been contributing to significant growth in the availability of instruments and methodologies for data acquisition and analysis. For better management of vineyards (and most crops), it is crucial to access the spatial-temporal variability. This knowledge throughout the vegetative cycle of any crop is crucial for more efficient management, but in the specific case of viticulture, this knowledge is even more relevant. Some research studies have been carried out in recent years, exploiting the advantage of satellite and UAV data, used individually or in combination, for crop management purposes. However, only a few studies explore the multi-temporal use of these two types of data, isolated or synergistically. This research aims to clearly identify the most suitable data and strategies to be adopted in specific stages of the vineyard phenological cycle. Sentinel-2 data from two vineyard plots, located in the Douro Demarcated Region (Portugal), are compared with UAV multispectral data under three distinct conditions: considering the whole vineyard plot; considering only the grapevine canopy; and considering inter-row areas (excluding all grapevine vegetation). The results show that data from both platforms are able to describe the vineyards' variability throughout the vegetative growth but at different levels of detail. Sentinel-2 data can be used to map vineyard soil variability, whilst the higher spatial resolution of UAV-based data allows diverse types of applications. In conclusion, it should be noted that, depending on the intended use, each type of data, individually, is capable of providing important information for vineyard management.