Abstract
Olive oil production is a noteworthy economic activity in multiple places worldwide. Due to environmental degradation and lack of resources with population growth, there is a global tendency for more sustainable and efficient practices, driving the implementation of more responsible agricultural and industrial systems. This paper aims to develop an intelligent system architecture focused on optimizing the production of olive oil, improving product quality, reducing operational waste, and maximizing the efficient use of natural resources. Through the use of Industrial Internet of Things (IIoT) technologies, the proposed solution aims to monitor and control the parameters of olive oil production automatically. In addition, the study addresses sensors already used in the market and existing systems to compare and seek improvements. The proposed architecture contains three layers: device, edge, and cloud computing layer, which are integrated and enable the implementation of a scalable and complete solution that allows real-time visualization and control of the production process.

Abstract
MathE (mathe.pixel-online.org) is an e-learning platform for higher education developed and implemented by a consortium of seven institutional partners from five European countries. The aim of the project is to enhance the quality of teaching and improve pedagogies and assessment methods by facilitating the identification of students’ gaps in Math, providing appropriate digital tools and promoting self-evaluation with immediate feedback. The Polytechnic Institute of Bragança (IPB), in Portugal, is one of the consortium members: sixteen of its teachers collaborate in the development of this platform, being responsible for thirteen of the topics/subtopics in which the platform is structured. Such topics cover a wide range of contents, from linear transformations to integration, from graph theory to probabilities. The articulation of the topics of the MathE collection corresponds to the canonic mathematics content of engineering, business and education degrees. The MathE platform is organized into three main sections: Student´s Assessment, MathE Library and Community of Practice. So far, IPB has already developed a collection of around 800 questions for the student´s assessment section and is currently developing the MathE Library. More than 350 students from IPB are using the MathE platform; some offered as volunteers, whose role is testing the behavior of the platform as well as looking for bugs and other details that require improvement, while others are already using the platform in their study. The feedback received up until now is quite encouraging.

Abstract

Abstract
This paper presents the development process of the mobile App D.R.E.A.M., Design-thinking to Reach-out, Embrace and Acknowledge Mental health, which is a tool for self-assessment and self-care in promoting the mental health of higher education students. In Portugal, the program for promoting Mental Health in higher education advocates the development and use of digital tools, such as apps and/or social networks and platforms, aimed at promoting wellbeing and with the potential for use to be more accessible to higher education students. The objective of this app is to promote the mental health and wellbeing of higher education students. Design Thinking was used as the methodology for building the app, which was developed using a combination of low-code/no-code tools, Flutter/Dart coding, and Google's Firebase capabilities and database functionalities. In the first semester of the 2023/2024 academic year, 484 students downloaded the app, and 22 emails were received for psychological consultations. A dynamic update of the app is required, with modules on time management and study organization, structured physical activity programs, development of socio-entrepreneurial skills, and vocational area.

Abstract
The extraction of phenolic compounds from eucalyptus leaves was optimized using heat and ultrasound-assisted techniques, and the bioactive potential of the resulting extract was assessed. The independent variables, including time (t), solvent concentration (S), and temperature (T) or power (P), were incorporated into a five- level central composite design combined with Response Surface Methodology. Phenolic content was determined by HPLC-DAD-ESI/MS and used as response criteria. The developed models were successfully fitted to the experimental data to identify the optimal extraction conditions. Heat-assisted extraction proved to be the most efficient method for phenolic recovery, yielding 27 +/- 2 mg/g extract under optimal conditions (120 min, 76.5 degrees C, and 25 % ethanol, v/v). The extracts exhibited a high concentration of phenolic glycoside derivatives, including gallotannin, quercetin, and isorhamnetin. These findings suggest that the extracts hold promise as natural additives in food technology, owing to their moderate antimicrobial activity and strong antioxidant properties.

Abstract
The energy policy of the European Union stresses the need for sustainable energy consumption, improvements in energy efficiency and lower fossil fuel dependence in a decoupling strategy from unstable democracies. Transportation still represents a sector largely dependent on fossil fuels, which come with several negative impacts. Measuring and assessing the sustainability of the transport sector becomes necessary. This study aims to assess the sustainability performance of the transport sector across 28 European countries over a four-year period, aligned with the policy agenda outlined in strategic documents. The methodological approach involves applying Benefit-of-the-Doubt (BoD) models, comparing aversion that uses transformation methods for anti- isotonic sub-indicators with a variant that directly incorporates these sub-indicators as reverse indicators. In general, the European countries have improved the sustainability performance of their transport sector during the time span analyzed according to the results of both models. For the inefficient units, two improvement strategies are presented based on the profiles identified on the benchmarks from both models, which can be alternative stages to achieve the robust best practices of the benchmarks.