Abstract
This work presents a bio-inspired Autonomous UnderwaterVehicle (AUV) concept called Raya that enables high manoeuvrability required for close-range inspection and intervention tasks, while fostering endurance for long-range operations by enabling efficient navigation. The AUV has an estimated terminal velocity of 0.82 m/s in an optimal environment, and a capacity to acquire visual data and sonar measurements in all directions. Raya was designed with the potential to incorporate an electric manipulator arm of 6 degrees of freedom (DoF) for free-floating underwater intervention. Smart and biologically inspired principles applied to morphology and a strategic thruster configuration assure that Raya is capable of manoeuvring in all 6 DoFs even when equipped with a manipulator with a 5 kg payload. Extensive experiments were conducted using simulation tools and real-life environments to validate Raya's requirements and functionalities. The stresses and displacements of the rigid bodies were analysed using finite element analysis (FEA), and an estimation of the terminal forward velocity was achieved using a dynamic model. To assess the accuracy of the perception system, a reconstruction task took place in an indoor pool, resulting in a 3D reconstruction with average length, width, and depth errors below1. 5%. The deployment of Raya in theATLANTIS Coastal Testbed and Porto de Leixões allowed the validation of the propulsion system and the gathering of valuable 2D and 3D data, thus proving the suitability of the vehicle for operation and maintenance (O&M) activities of underwater structures. © 2013 IEEE.

Abstract
With the recent advancements in the development of hybrid offshore parks and the expected large-scale implementation of them in the near future, it becomes paramount to investigate proper energy management strategies to improve the integrability of these parks into the power systems. This paper addresses a multiobjective energy management approach using a hybrid energy storage system comprising batteries and hydrogen/fuel-cell systems applied to multi-source wind-wave and wind-solar offshore parks to maximize the delivered energy while minimizing the variations of the power output. To find the solution of the optimization problem defined for energy management, a strategy is proposed based on the examination of a set of weighting factors to form the Pareto front while the problem associated with each of them is assessed in a mixed-integer linear programming framework. Subsequently, fuzzy decision making is applied to select the final solution among the ones existing in the Pareto front. The studies are implemented in different locations considering scenarios for electrical system limitation and the place of the storage units. According to the results, applying the proposed multiobjective framework successfully addresses the enhancement of energy delivery and the decrease in power output fluctuations in the hybrid offshore parks across all scenarios of electrical system limitation and combinational storage locations. Based on the results, in addition to the increase in delivered energy, a decrease in power variations by around 40 % up to over 80 % is observed in the studied cases.

Abstract
Platforms to support novices learning to program are often accompanied by automated next-step hints that guide them towards correct solutions. Many of those approaches are data-driven, building on historical data to generate higher quality hints. Formal specifications are increasingly relevant in software engineering activities, but very little support exists to help novices while learning. Alloy is a formal specification language often used in courses on formal software development methods, and a platform-Alloy4Fun-has been proposed to support autonomous learning. While non-data-driven specification repair techniques have been proposed for Alloy that could be leveraged to generate next-step hints, no data-driven hint generation approach has been proposed so far. This paper presents the first data-driven hint generation technique for Alloy and its implementation as an extension to Alloy4Fun, being based on the data collected by that platform. This historical data is processed into graphs that capture past students' progress while solving specification challenges. Hint generation can be customized with policies that take into consideration diverse factors, such as the popularity of paths in those graphs successfully traversed by previous students. Our evaluation shows that the performance of this new technique is competitive with non-data-driven repair techniques. To assess the quality of the hints, and help select the most appropriate hint generation policy, we conducted a survey with experienced Alloy instructors.

Abstract
The use of machine learning in decision-making has become increasingly pervasive across various fields, from healthcare to finance, enabling systems to learn from data and improve their performance over time. The transformative impact of these new technologies warrants several considerations that demand the development of modern solutions through responsible artificial intelligence-the incorporation of ethical principles into the creation and deployment of AI systems. Fairness is one such principle, ensuring that machine learning algorithms do not produce biased outcomes or discriminate against any group of the population with respect to sensitive attributes, such as race or gender. In this context, visualization techniques can help identify data imbalances and disparities in model performance across different demographic groups. However, there is a lack of guidance towards clear and effective representations that support entry-level users in fairness analysis, particularly when considering that the approaches to fairness visualization can vary significantly. In this regard, the goal of this work is to present a comprehensive analysis of current tools directed at visualizing and examining group fairness in machine learning, with a focus on both data and binary classification model outcomes. These visualization tools are reviewed and discussed, concluding with the proposition of a focused set of visualization guidelines directed towards improving the comprehensibility of fairness visualizations.

Abstract
The need for more flexible and robust models to reason about systems in the presence of conflicting information is becoming more and more relevant in different contexts. This has prompted the introduction of paraconsistent transition systems, where transitions are characterized by two pairs of weights: one representing the evidence that the transition effectively occurs and the other its absence. Such a pair of weights can express scenarios of vagueness and inconsistency. . This paper establishes a foundation for a compositional and structured specification approach of paraconsistent transition systems, framed as paraconsistent institution. . The proposed methodology follows the stepwise implementation process outlined by Sannella and Tarlecki.

Abstract
Directed Energy Deposition (DED) is a free-form metal additive manufacturing process characterized as toolless, flexible, and energy-efficient compared to traditional processes. However, it is a complex system with a highly dynamic nature that presents challenges for modeling and optimization due to its multiphysics and multiscale characteristics. Additionally, multiple factors such as different machine setups and materials require extensive testing through single-track depositions, which can be time and resource-intensive. Single-track experiments are the foundation for establishing optimal initial parameters and comprehensively characterizing bead geometry, ensuring the accuracy and efficiency of computer-aided design and process quality validation. We digitized a DED setup using the Robot Operating System (ROS 2) and employed a thermal camera for real-time monitoring and evaluation to streamline the experimentation process. With the laser power and velocity as inputs, we optimized the dimensions and stability of the melt pool and evaluated different objective functions and approaches using a Response Surface Model (RSM). The three-objective approach achieved better rewards in all iterations and, when implemented in areal setup, allowed to reduce the number of experiments and shorten setup time. Our approach can minimize waste, increase the quality and reliability of DED, and enhance and simplify human-process interaction by leveraging the collaboration between human knowledge and model predictions.