Abstract
This work presents a bio-inspired Autonomous Underwater Vehicle (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 below 1. 5%. The deployment of Raya in the ATLANTIS Coastal Testbed and Porto de Leix & otilde;es 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.

Abstract
As industrial product development becomes increasingly complex and knowledge-intensive, the integration of Artificial Intelligence (AI) agents into design workflows offers great potential to improve efficiency and decision making. However, the opacity of current AI reasoning processes remains a major obstacle for adoption in engineering domains. This position paper explores the need for Explainable AI (XAI) within agentic design systems, proposing a conceptual architecture where agents, powered by Large Language Models (LLMs), not only perform domain-specific tasks, but also generate human-readable justifications for their decisions. Unlike black-box systems, these agents are designed to promote transparency, trust, and traceability, all of which are critical in high-stakes industrial contexts. Building upon the foundation of the Agentic Approach to Product Design, we outline how roles such as requirement analysis, material selection, and specification interpretation can be reimagined with explainability at their core. This work advocates for a shift towards interpretable, auditable AI assistants, capable of supporting collaborative engineering processes. An illustrative scenario is used to exemplify the practical value and challenges of agents supported by XAI. Future research directions are highlighted, including evaluation metrics for explainability and potential integrations into existing agent orchestration platforms such as CrewAI. As a conceptual position paper, this work aims to stimulate the development of explainable multi-agent design systems and guide future empirical validation in industrial contexts. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
Additive Layer Manufacturing, particularly Fused Deposition Modelling, faces significant batch loss risks during production. The traditional Concurrent Printing Mode produces all parts simultaneously (layer-by-layer, bottom-to-top), efficiently using printing space but risking complete batch failure if problems occur. In contrast, Sequential Printing Mode produces one part at a time, reducing the risk of total batch loss but utilising printing space less efficiently. In this work, we propose an algorithm that, given a set of parts, performs the nesting of the parts for Concurrent Printing Mode, and for the first time, for the Sequential Printing Mode. A no-fit polygon based approach is used to handle geometry between pairs of parts by using multiple horizontal 2D layer projections of 3D parts, to ensure non-overlapping constraints and prevent machine-part collisions. A Greedy Randomized Adaptive Search Procedure is proposed, tested and benchmarked against a commercial software, using a new set of real-world instances. The approach shows the ability to find high-quality solutions. The approach significantly reduces the number of batches, minimises waste, reduces manufacturing time, and promotes parts quality.

Abstract
The increasing penetration of electric vehicles will be accompanied for a wide deployment of charging infrastructures. Large charging demand brings formidable challenges to existing power networks, driving them near to their operational limits. In this regard, it becomes pivotal developing novel energy management strategies for active distribution networks that take into account the strategic behaviour of parking lots. This paper focuses on this issue, developing a novel energy management tool for distribution networks encompassing distributed generators and parking lots. The new proposal casts as a tri-level game equilibrium framework where the profit maximization of lots is implicitly considered, thus ensuring that network-level decisions do not detract the profit of parking owners. The original tri-level model is reduced into a tractable single-level mixed-integer-linear programming by combining equivalent primal-dual and first-order optimality conditions of the distribution network and parking operational models. This way, the model can be solved using off-the-shelf solvers, with superiority against other approaches like metaheuristics. The developed model is validated in well-known 33-, and 85-bus radial distribution systems. Results show that, even under unfavourable conditions with limited distributed generation, charging demand is maximized, thus preserving the interests of parking owners. Moreover, the model is further validated through a number of simulations, showing its effectiveness. Finally, it is demonstrated that the developed tool scales well with the size of the system, easing its implementation in real-life applications.

Abstract
With technological advancements, much data is being captured by sensors, smartphones, wearable devices, and so forth. These vast datasets are stored in data centres and utilized to forge data-driven models for the condition monitoring of infrastructures and systems through future data mining tasks. However, these datasets often surpass the processing capabilities of traditional information systems and methodologies due to their significant size. Additionally, not all samples within these datasets contribute valuable information during the model training phase, leading to inefficiencies. The processing and training of Machine Learning algorithms become time-consuming, and storing all the data demands excessive space, contributing to the Big Data challenge. In this paper, we propose two novel techniques to reduce large time-series datasets into more compact versions without undermining the predictive performance of the resulting models. These methods also aim to decrease the time required for training the models and the storage space needed for the condensed datasets. We evaluated our techniques on five public datasets, employing three Machine Learning algorithms: Holt-Winters, SARIMA, and LSTM. The outcomes indicate that for most of the datasets examined, our techniques maintain, and in several instances enhance, the forecasting accuracy of the models. Moreover, we significantly reduced the time required to train the Machine Learning algorithms employed.

Abstract
Optical resonant structures, such as circular disks and optical microbubble resonators (OMBRs), are crucial for highresolution chemical and biochemical sensing. Both can be integrated into microfluidic systems: resonant disks can be fabricated within microfluidic channels, while OMBRs use thin silica capillary walls to confine fluid samples in a hollowcore cavity. Optical modes are typically excited using tapered optical fibers, which offer efficiency but lack robustness for functional devices. This work presents two femtosecond laser-written waveguide designs for exciting whispering gallery modes (WGMs) in these resonant structures. For resonant disks, suspended waveguides are fabricated tangentially between the microfluidic channel walls. For OMBRs, integrated waveguides are written on fused silica substrates to excite resonant modes. Both configurations provide stable and robust optical sensing solutions. The OMBR platform achieved a sensitivity of 45 nm/RIU with a resolution of 4.4x10(-5) RIU, while monolithically integrated disks reached 80 nm/RIU with a resolution of 7.0x10(-4) RIU. In both cases, the Q-factor exceeded 10(4) across the measurement range. These results confirm that femtosecond laser-written waveguides can efficiently excite resonant modes, offering promising platforms for chemical and biochemical sensing applications.