We consider a family of linearly elastic and elliptic membrane shells, all sharing the same middle surface, with thickness 2ε, clamped along their entire lateral face, which upon deformation may enter in frictional contact with a moving foundation along its lower face. As a result of friction, material might be removed from the interface, thus causing wear. In this paper, we focus in the numerical analysis of our model and provide a priori error estimates for displacements, stress and wear fields. We have implemented the fully discrete scheme with the FreeFEM++ programming environment, by using an intrinsic (basis free) formulation, reduction of order, penalization and Newton method. We carried on a series of numerical simulations to demonstrate the capabilities of our code, providing experimental evidence of our theoretical results regarding the convergence with respect to the thickness parameter ε and mesh size h. As a practical application, we have also included the results of a simulation of the wear that undergoes the tyre of a vehicle braking on a road.
The marine environment is a rich source of bioactive compounds with a wide range of applications. In this study, 65 extracts from sponges, ascidians, and a gorgonian collected from the Yucatán Peninsula, Mexico, were evaluated for their modulatory effects on the growth of four microalgal species. Some extracts were found to exhibit pronounced algicidal activity, while others promoted growth. Bioassay-guided fractionation of the extract from Haliclona (Rhizoniera) curacaoensis led to the identification of arenosclerins A and C as potent algicidal agents, active across all tested microalgae. In contrast, the extract from Halichondria melanadocia enhanced microalgal growth, and chemical analysis revealed the presence of medelamine A and B as putative growth-promoting compounds. This study provides a new report on microalgae-modulating activities of marine sponge metabolites. The dual activity observed, both inhibitory and stimulatory, not only contributes to our understanding of marine compound diversity but also underscores the potential of these marine-derived compounds for sustainable environmental management practices.
In the era of big data, selecting representative samples has become essential to mitigate overfitting, noise, and high computational cost in machine learning. This study systematically reviews the evolution of instance selection (IS) methods, highlighting the growing importance of instance hardness (IH) as a guiding criterion to improve training efficiency and model robustness. Through a comprehensive search in Scopus and Web of Science, fifty-five studies were identified and analyzed following strict inclusion and exclusion criteria. The reviewed works were classified according to their underlying rationale–error-based, geometric, heuristic, or explainability-driven–revealing that IH principles intersect these categories as a transversal perspective on data quality. Most studies focus on enhancing predictive accuracy (56
Digital Elevation Models (DEMs) are essential for a wide range of geospatial applications, including urban planning, environmental monitoring, and disaster management. Conventional methods for DEM generation, such as LiDAR and radar, are accurate but costly and constrained by regulatory and logistical challenges. This study explores the use of deep learning techniques for DEM generation from single satellite images, focusing on the integration of advanced attention mechanisms and novel normalization strategies. A custom dataset, featuring Sentinel‑2 and Landsat 9 imagery paired with high-resolution DEMs of the Iberian Peninsula, was developed and made publicly available to support transparency and reproducibility. Two normalization approaches were evaluated: Global normalization, which preserves global elevation relationships, and Global normalization with Shift, which emphasizes local terrain features. Additionally, state-of-the-art architectures, including U‑Net, Pix2Pix, and DRPAN, were adapted with attention mechanisms such as the Global Attention Mechanism (GAM) and gradient-based loss terms. The results indicate that the proposed GAM, when combined with Shift normalization, produces Mean Absolute Error (MAE) values of 65 m when evaluated under the corresponding local terrain reconstruction objective. While the method does not match the accuracy of traditional sensors, it offers a promising, scalable, and cost-effective alternative for DEM generation in contexts with limited data or resources, where relative elevation accuracy is sufficient.
Clearance is an important phenomenon in mechanisms that stems from manufacturing imperfections and wear and tear. Undetected clearance can compromise machine operations, negatively impacting its performance, and cause premature damage that requires maintenance actions. Monitoring clearance growth is useful to improve maintenance plans and helps reduce the number of unwanted interruptions during machine operation. In this work, a prediction method that targets the determination of the clearance size based on minimal, raw sensor data and machine learning is proposed. The data in this study are generated using multibody dynamics simulations based on planar mechanisms and used to train and compare several types of neural networks in terms of their ability to assess clearance size. Results show that clearance parameters can be reliably estimated with appropriate combinations of sensor locations and type of neural network. The developed method offers reliable clearance detection based on measurements that can be obtained from physical systems and used to monitor the state of their clearance defects.
