Cidete (Spain)

Abstract. Rip currents are hazardous, fast-moving seaward flows and remain one of the leading causes of rescues and drownings on surf beaches, yet their automated detection remains a significant challenge due to their amorphous, dynamic morphology and the environmental complexity of the surf zone. This study introduces a novel platform-agnostic deep learning–based framework for automated rip current detection from beach imaging platforms, integrating three core contributions: a diverse new dataset, a rigorous architectural benchmark, and a deployable operational tool. We first present RipAID, a comprehensive dataset enriched with multi-platform imagery and multiple viewing angles to ensure scale-invariant learning. Building on this resource, a systematic evaluation of state-of-the-art architectures demonstrates that geometric fidelity is critical; specifically Oriented Bounding Boxes (OBB) significantly outperform standard axis-aligned methods. Our optimized YOLOv11n-OBB model achieves robust performance (mAP50: 0.927), with inference speeds from 2.4 to 60 FPS on hardware ranging from edge devices to GPU workstations. To bridge the gap between research and practice, and ensure that the results are reusable and reproducible, the framework and model weights have been released as an open-source, containerized module (socib-rip-currents-detection), providing the coastal safety community with a scalable, ready-to-use and standardized tool for continuous, automated rip current monitoring.

N6-methyladenosine (m6A) RNA modifications and reovirus infections have recently been implicated in the development of celiac disease. While viral infections are known to alter the RNA modification machinery of the host, their interplay in autoimmune tissue damage remains unexplored. In this study we employed an in vitro model combining a viral mimic with gliadin peptides to simulate celiac disease conditions, alongside the analysis of serum and intestinal biopsies from controls and patients to investigate the link between m6A methylation and viral infections in inducing autoimmune inflammation. We found elevated anti-reovirus reactivity in patients, increased antiviral gene expression and enhanced m6A levels. Notably, the expression of IRF7 was synergistically induced by the combined exposure to the viral mimic and gliadin peptides, and this is mediated through m6A methylation in its coding region and interaction with the reader protein YTHDC2. Furthermore, the reduction of m6A through METTL3 silencing or simvastatin treatment reduced IRF7 mRNA methylation and downstream proinflammatory gene expression both in vitro and ex vivo. These findings highlight m6A methylation as a modulator of antiviral responses and a potential therapeutic target in autoimmune disorders.

This paper introduces a simple and efficient method for generating Goppa polynomials used in post-quantum cryptography based on any variant of the McEliece algorithm. The approach demonstrates that such polynomials can be constructed more rapidly by multiplying several low-degree polynomials that satisfy specific properties. It is also proven that employing these polynomials does not compromise the code’s error-correcting capability or overall security. The proposed method is especially advantageous when high-order Goppa polynomials are required. As a proof of concept, we present an application for user identification that combines cryptography and iris biometrics. In this system, encrypted versions of iris templates are securely stored. Using the homomorphic property of McEliece, recognition can be performed within the encrypted domain, ensuring that biometric data remains confidential throughout the entire process.

This paper presents an innovative methodology to filter out redundant images initially selected to form seabed photo mosaics. Underwater robot missions often capture hundreds or thousands of images, especially in large or long-duration surveys. These images typically contain overlapping areas that must be carefully removed to prevent redundant information, questionable pixel aggregations, unrealistic color blending, and excessive computational load during mosaic construction. Experiments using datasets collected by an Autonomous Underwater Vehicle in ecologically significant marine habitats demonstrate substantial reductions in visual data without losing essential information, significantly optimizing computational efforts and reducing considerably processing times.

Acoustic telemetry is a key tool in marine ecology, yet traditional methods remain limited in efficiency and costeffectiveness. This study evaluates the feasibility of integrating bidirectional acoustic tags (BTAG) with ultra-short baseline (USBL) systems to improve aquatic animal tracking. Applying time of flight (ToF) and time difference of arrival (TDOA) calculations reduces infrastructure requirements while enhancing spatial resolution. Experimental validation, including simulations and controlled test tank experiments, demonstrates that a USBL system can successfully decode BTAG signals with a bearing accuracy of up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\pm 5^{\mathrm{e}}$</tex>. The findings indicate that this methodology enables more precise and cost-effective underwater tracking, benefiting ecological monitoring and conservation efforts.

Abstract This study quantifies the relationship between acoustic emission (AE) and stiffness loss in the fused-filament microstructure of additively manufactured objects, considering the effect of tensile force direction as strain increases. We correlate the continuous decrease in tangent Young’s modulus with the rate of detected AE signals using uniaxial tensile tests. The results reveal a highly significant correlation between the number of detected acoustic emission signals and the decrease in tangent Young’s modulus, demonstrating that the detected acoustic emission events can be employed to numerically predict the reduction in stiffness with increasing strain.

For a Henselian valued field [Formula: see text] we establish a complete parallelism between the arithmetic properties of irreducible polynomials [Formula: see text], encoded by their Okutsu frames, and the valuation-theoretic properties of their induced valuations [Formula: see text] on [Formula: see text], encoded by their Mac Lane–Vaquié chains. This parallelism was only known for defectless irreducible polynomials.

Coastal cities, due to their geographic location, are particularly exposed to hydro-meteorological and climate-related natural hazards. The EU-funded Horizon 2020 project SCORE (Smart Control of the Climate Resilience in European Coastal Cities), within its various activities, aims to provide a better understanding of how to mitigate and manage the effects of extreme events, particularly floods, in European coastal cities. Achieving this objective requires adequate knowledge about the probabilities and potential consequences of flood events based on a probabilistic risk assessment framework encompassing models of flood hazard for different climate scenarios, exposed elements, and vulnerability.In this context, the present work describes the methodology and presents the results of quantitative risk assessments developed for fluvial and coastal flooding for three of SCORE&amp;#8217;s coastal city living labs (CCLLs): Massa (Italy), Oarsoaldea (Spain) and Vilanova i la Geltr&amp;#250; (Spain). The risk assessments cover four types of exposed elements, i.e., population, buildings, roads, and railways, and a number of flood scenarios, both in terms of different climate conditions and considering the absence or presence of ecosystem-based approaches (EBAs) for the mitigation of fluvial flood hazard. This allows understanding both the impact that climate change is expected to have on flood risk in these CCLLs, and the influence that specific EBAs can have in reducing fluvial flood risk from a baseline to an improved infrastructural condition (i.e., residual risk).The results of the assessments provide invaluable information to support flood risk management activities, such as gridded maps of losses for each hazard scenario and type of exposed element, maps of estimated average annual losses (AAL), and aggregate loss metrics at urban scale. In addition, they serve as input for subsequent tasks of the SCORE project, such as the development of cost-benefit analyses of specific EBA solutions and the development of financial resilience strategies for the flood risk management of the three CCLLs.

This review investigates the application of acoustic emission (AE) techniques in analyzing the mechanical properties and damage prediction of human bone. AE is a non-invasive and non-destructive evaluation method that captures the elastic waves released during microstructural deformations under stress, providing critical insights into bone behavior and failure mechanisms. By analyzing 57 studies, this review synthesizes findings on AE signal characteristics, experimental configurations, and their correlations with bone's mechanical parameters such as yield strength, elastic modulus, and micro-damage evolution. This article highlights AE's potential in early damage detection, differentiation of failure modes, and predictive modeling using stochastic and percolation theories. These models facilitate the prediction of fracture risk and mechanical failure without inducing irreversible damage. This review addresses the strengths and limitations of AE techniques and outlines future directions in biomechanical research.