Abstract
This paper advocates for the integration of system identification in graduate-level control system courses using accessible theoretical tools. Emphasising real-world applications, particularly in Remotely Operated Vehicle (ROV), the study proposes ROV as educational platforms for teaching control principles. As a concrete example, the paper presents a graduation course project focusing on designing a depth control system for an ROV, where students derive the model from experimental data. This practical application not only enhances the students skills in system identification but also prepares them for challenges in controlling complex systems in both academic and industrial settings.

Abstract
Deep learning networks, more specifically convolutional neural networks, have shown a notable distinction when it comes to computer vision problems. Their versatility spans various domains, where they are applied for tasks such as classification and regression, contingent primarily on the availability of a representative dataset. This work explores the feasibility of employing this approach in the domain of agriculture, particularly within the context of olive growing. The objective is to enhance and facilitate cultivar identification techniques by using images of olive tree leaves. To achieve this, a comparative analysis involving ten distinct convolutional networks (VGG16, VGG19, ResNet50, ResNet152-V2, Inception V3, Inception ResNetV2, XCeption, MobileNet, MobileNetV2, EfficientNetB7) was conducted, all initiated with transfer learning as a common starting point. Also, the impact of adjusting network hyperparameters and structural elements was explored. For the training and evaluation of the networks, a dedicated dataset was created and made available, consisting of approximately 4200 images from the four most representative categories of the region. The findings of this study provide compelling evidence that the majority of the examined methods offer a robust foundation for cultivar identification, ensuring a high level of accuracy. Notably, the first nine methods consistently attain accuracy rates surpassing 95%, with the top three methods achieving an impressive 98% accuracy (ResNet50, EfficientNetB7). In practical terms, out of approximately 2016 images, 1976 were accurately classified. These results signify a substantial advancement in olive cultivar identification through computer vision techniques.

Abstract
Choreographic languages describe possible sequences of interactions among a set of agents. Typical models are based on languages or automata over sending and receiving actions. Pomsets provide a more compact alternative by using a partial order to explicitly represent causality and concurrency between these actions. However, pomsets offer no representation of choices, thus a set of pomsets is required to represent branching behaviour. For example, if an agent Alice can send one of two possible messages to Bob three times, one would need a set of 2 x 2 x 2 distinct pomsets to represent all possible branches of Alice's behaviour. This paper proposes an extension of pomsets, named branching pomsets, with a branching structure that can represent Alice's behaviour using 2 + 2 + 2 ordered actions. We compare the expressiveness of branching pomsets with that of several forms of event structures from the literature. We encode choreographies as branching pomsets and show that the pomset semantics of the encoded choreographies are bisimilar to their operational semantics. Furthermore, we define well-formedness conditions on branching pomsets, inspired by multiparty session types, and we prove that the well-formedness of a branching pomset is a sufficient condition for the realisability of the represented com-munication protocol. Finally, we present a prototype tool that implements our theory of branching pomsets, focusing on its applications to choreographies. (c) 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons .org /licenses /by /4 .0/).

Abstract
There is no doubt that tourism benefits Portugal's economy. The area should be able to better target the market for its travel and tourist products and increase its performance with the use of a marketing strategy, especially in a field where global competition is always increasing. This digital marketing plan seeks to analyze the strengths and limitations of the Portuguese region in its target markets, as well as the most current advancements in the global travel and tourism sector. In this work, it is conducted an analysis of both micro and macro environments of Casa Pereirinha. In addition, it was created a marketing mix based on the 7P's. Furthermore, the work provides the prospective avenues for product growth and marketing following the markets' stated growth objectives. This project comprises creating a comprehensive digital marketing strategy for Casa Pereirinha as a significant example of the region's tourism business, and the primary purpose is to conserve its past, while developing a new strategy.

Abstract
Sustainable agri-food systems seek to deliver food affordably and sustainably, without compromising the economic, social, and environmental bases for current and coming generations. Food-energy systems integrating renewable energy sources contribute towards this sustainability, and new solutions are being proposed in the literature or implemented in real facilities. This work reviews the existing literature on the integration of renewable energy, cross-sector energy efficiency and flexibility approaches, circular economy, digital solutions, and energy communities' (EC) structures within the agri-food sector. It proposes a formal classification of the main solutions found and describes the associated Business Models (BMs) to support their actual development cost-effectively. The main roles, actors and value propositions are reviewed, and a case example of an EC to be developed in Portugal in the Tools4AgriEnergy project is also described. The EC is based on floating PV panels to power water distribution pumps and share the surplus with local agri-food industries.

Abstract
Entrepreneurs’ decision-making in sustainable open innovation practices holds significant relevance in fostering environmentally conscious and socially responsible business strategies. Sustainable decision-making not only aligns with ethical principles but also addresses pressing global challenges such as climate change and resource depletion. This study aims to characterize the decision-making role played by entrepreneurs in the context of the open innovation paradigm and to understand the factors that influence entrepreneurial performance. A quantitative methodology supported by Partial Least Squares Structural Equation Modeling was adopted, considering a sample of 407 startups incubated in science and technology parks. After that, a mixed-methods approach was employed to explore the differences between sectors of activity, in which 4 ventures were involved. The results confirm that of the 9 hypotheses formulated in the relationship between the constructs, only innovation novelty is not significant for sustainable open innovation management processes, unlike innovation openness. This study offers theoretical and practical implications for startups that intend to use open innovation networks to integrate environmental and social considerations into the core of business strategies.