Abstract
In the field of intelligent autonomous robots, integrating optimization techniques with classical control theory methods for mobile robot control is an increasingly prominent area of research. The combination enhances robots' ability to perform their tasks more efficiently, reliably, and safely. This paper addresses the development of a path and motion planning framework for omnidirectional robots, leveraging B-Splines and Trajectory Tracking with Model Predictive Control. The proposed framework is evaluated through software-in-the-loop tests using two distinct dynamical models and sets of hyperparameters. Final validation is conducted by implementing the framework within a ROS environment and performing field tests on a robotic platform. The results demonstrate that the robot can reliably track trajectories at its actuation limits, and the proposed framework enables the robot to increase its velocity up to 50% when compared to a PID path-following controller.

Abstract
Digital transformation has been taking place for several decades in different sectors of activity and is contributing significantly to mitigating the environmental impacts of those sectors. Various digital solutions are related to energy consumption and production, which is crucial to ensure continuous decarbonisation. Most of them are targeted to be used by general consumers. Therefore, it is essential to consider consumers' attitudes towards those solutions and their adoption behaviour to ensure a broad diffusion of them. This study uses the Technology Acceptance Model to understand the adoption of energy-related digital solutions in Europe. We conclude that the perceived usefulness of the solutions is more relevant in attitude formation than the perceived ease of use. Moreover, attitude highly influences adoption behaviour, as reported in the literature. Finally, these relations seem to be highly influenced by the belief that, by adopting digital solutions, consumers contribute to a better balance between energy supply and demand.

Abstract
Archaeological discoveries can benefit enormously from linked open data (LOD) technologies since, as new objects are discovered, data about them can be placed in the LOD cloud and instantly accessible to third parties. This article presents a framework developed to publish LOD on arrowheads from the Chalcolithic and Early/Middle Bronze Age chronologies (2800/2900 BC to 1500 BC) found in the last 25 years of excavations on an archaeological site in Portugal. These arrowheads were kept in boxes, hidden from the possibility of being studied and viewed by interested parties. The framework encompasses a metadata application profile (MAP) and tools to be used with this MAP, such as a namespace, two metadata schemas and eight vocabulary coding schemes. The MAP domain model was developed with the support of the scientific literature about this type of arrowheads, and the team integrated two archaeologists. This framework was created with the design philosophy of maximising data interoperability, so terms from the CIDOC CRM conceptual models and other vocabularies widely used in the LOD cloud were used. The MAP was tested using a set of seven arrowheads, which proved, in the first instance, the viability of the developed MAP. The team plans to test the model in future work with arrowheads of other excavations.

Abstract
Water is essential at various stages of winemaking, from irrigation in the vineyard to cleaning equipment and facilities, controlling fermentation temperatures, and diluting grape juice if necessary. Additionally, water is used for sanitation purposes to ensure the quality and safety of the final product. This article provides an overview of the existing knowledge regarding the use of water in wineries throughout the winemaking process, water consumption values, effluent treatment, efficient use of water measures, and water reuse. Different assessment methods, including Water Footprint (WF) and Life Cycle Assessment(LCA), provide varied insights into water use impacts, emphasizing the importance of standardized methodologies for accurate assessment and sustainable practices. This research showed that the characterization of the vinification processes of each type of wine is fundamental for further analysis on the environmental impact of winemaking regarding water use. It was also observed that WF is affected by factors like climate, irrigation needs, and cleaning procedures. Thus, efficient water management in all the stages of wine production is crucial to reduce the overall WF. Water efficiency measures may involve the modification of the production processes, reusing and recycling water and the implementation of cleaner production practices and technological innovations, such as automated fermentation systems that reduce water needs. Furthermore, waste management in wineries emphasizes the importance of sustainable practices and technological innovations to mitigate environmental impacts and enhance resource efficiency.

Abstract
Digitalization has begun as a transformative force within the energy sector, reforming traditional practices and paving the way for enhanced operational efficiency and sustainability. Enabled by key technologies such as smart meters, digitalization embodies a paradigm shift in energy management. Nonetheless, it is crucial to recognize that these enabling technologies are only the catalysts and not the end goal. This paper presents a comprehensive overview of digital services and products in the energy sector, with a specific focus on emerging technologies like AI and Connected Data Spaces. The objective of this review paper is to assess the maturity and adoption levels of these digital solutions, seeking to draw insights into the factors influencing their varying levels of success. This maturity and adoption assessment was carried out by applying a Fuzzy logic approach which allowed us to compensate for the lack of detailed information in current literature. By analyzing the reasons behind high maturity-low adoption and vice-versa, this study seeks to cast light on the dynamics shaping the digital transformation of the energy sector.

Abstract
Association Rule Mining (ARM) is a field of data mining (DM) that attempts to identify correlations among database items. It has been applied in various domains to discover patterns, provide insight into different topics, and build understandable, descriptive, and predictive models. On the one hand, Enterprise Architecture (EA) is a coherent set of principles, methods, and models suitable for designing organizational structures. It uses viewpoints derived from EA models to express different concerns about a company and its IT landscape, such as organizational hierarchies, processes, services, applications, and data. EA mining is the use of DM techniques to obtain EA models. This paper presents a literature review to identify the newest and most cited ARM algorithms and techniques suitable for EA mining that focus on automating the creation of EA models from existent data in application systems and services. It systematically identifies and maps fourteen candidate algorithms into four categories useful for EA mining: (i) General Frequent Pattern Mining, (ii) High Utility Pattern Mining, (iii) Parallel Pattern Mining, and (iv) Distribute Pattern Mining. Based on that, it discusses some possibilities and presents an exemplification with a prototype hypothesizing an ARM application for EA mining.