Abstract
The integration of precise landing capabilities into unmanned aerial vehicles (UAVs) is crucial for enabling autonomous operations, particularly in challenging environments such as the offshore scenarios. This work proposes a heterogeneous perception system that incorporates a multimodal fiducial marker, designed to improve the accuracy and robustness of autonomous landing of UAVs in both daytime and nighttime operations. This work presents ViTAL-TAPE, a visual transformer-based model, that enhance the detection reliability of the landing target and overcomes the changes in the illumination conditions and viewpoint positions, where traditional methods fail. VITAL-TAPE is an end-to-end model that combines multimodal perceptual information, including photometric and radiometric data, to detect landing targets defined by a fiducial marker with 6 degrees-of-freedom. Extensive experiments have proved the ability of VITAL-TAPE to detect fiducial markers with an error of 0.01 m. Moreover, experiments using the RAVEN UAV, designed to endure the challenging weather conditions of offshore scenarios, demonstrated that the autonomous landing technology proposed in this work achieved an accuracy up to 0.1 m. This research also presents the first successful autonomous operation of a UAV in a commercial offshore wind farm with floating foundations installed in the Atlantic Ocean. These experiments showcased the system's accuracy, resilience and robustness, resulting in a precise landing technology that extends mission capabilities of UAVs, enabling autonomous and Beyond Visual Line of Sight offshore operations.

Abstract

Abstract
Deep learning techniques have demonstrated significant potential in computer-assisted diagnosis based on medical imaging. However, their integration into clinical workflows remains limited, largely due to concerns about interpretability. To address this challenge, we propose Efficient-Proto-Caps, a lightweight and inherently interpretable model that combines capsule networks with prototype learning for lung nodule characterization. Additionally, an innovative Davies-Bouldin Index with multiple centroids per cluster is employed as a loss function to promote clustering of lung nodule visual attribute representations. When evaluated on the LIDC-IDRI dataset, the most widely recognized benchmark for lung cancer prediction, our model achieved an overall accuracy of 89.7 % in predicting lung nodule malignancy and associated visual attributes. This performance is statistically comparable to that of the baseline model, while utilizing a backbone with only approximately 2 % of the parameters of the baseline model's backbone. State-of-the-art models achieved better performance in lung nodule malignancy prediction; however, our approach relies on multiclass malignancy predictions and provides a decision rationale aligned with globally accepted clinical guidelines. These results underscore the potential of our approach, as the integration of lightweight and less complex designs into accurate and inherently interpretable models represents a significant advancement toward more transparent and clinically viable computer-assisted diagnostic systems. Furthermore, these findings highlight the model's potential for broader applicability, extending beyond medicine to other domains where final classifications are grounded in concept-based or example-based attributes.

Abstract

In modern, high-speed work settings, the significance of mental health disorders is increasingly acknowledged as a pressing health issue, with potential adverse consequences for organizations, including reduced productivity and increased absenteeism. Over the past few years, various mental health management solutions, such as biofeedback applications, have surfaced as promising avenues to improve employees' mental well-being.

Abstract
In many e-commerce platforms user communities share product information in the form of reviews and ratings to help other consumers to make their choices. This study develops a new theoretical framework generating a bipartite network of products sold by Amazon.com in the category musical instruments, by linking products through the reviews. We analyze product rating and perceived helpfulness of online customer reviews and the relationship between the centrality of reviews, product rating and the helpfulness of reviews using Clustering, regression trees, and random forests algorithms to, respectively, classify and find patterns in 2214 reviews. Results demonstrate: (1) that a high number of reviews do not imply a high product rating; (2) when reviews are helpful for consumer decision-making we observe an increase on the number of reviews; (3) a clear positive relationship between product rating and helpfulness of the reviews; and (4) a weak relationship between the centrality measures (betweenness and eigenvector) giving the importance of the product in the network, and the quality measures (product rating and helpfulness of reviews) regarding musical instruments. These results suggest that products may be central to the network, although with low ratings and with reviews providing little helpfulness to consumers. The findings in this study provide several important contributions for e-commerce businesses' improvement of the review service management to support customers' experiences and online customers' decision-making.

Abstract
Gastric cancer (GC) remains a significant global health issue, and convolutional neural networks (CNNs) have shown their high potential for detecting precancerous gastrointestinal (GI) conditions on endoscopic images [1] [2]. Despite the need for high resolution to capture the complexity of GI tissue patterns, the impact of endoscopic image resolution on the performance of these models remains underexplored. This study investigates how different image resolutions affect CNNs classification of intestinal metaplasia (IM) using two datasets with different resolutions and imaging modalities. Our results reveal that the often adopted input resolution of 224×224 pixels does not provide optimal performance for detecting IM, even when using transfer learning from networks pre-trained on images with this resolution. Higher resolutions, such as 512×512, consistently outperform 224 × 224, with notable improvements in F1-scores (e.g., InceptionV3: 94.46% at 512 × 512 vs. 91.49% at 224 × 224). Additionally, our findings indicate that model performance is constrained by the original image quality, underscoring the critical importance of maintaining the higher original image resolutions and quality provided by endoscopes during clinical exams, for the purposes of training and testing CNNs for gastric cancer management.Clinical Relevance- This research highlights the importance of image quality, particularly when endoscopes capture lower-resolution images. Understanding how image resolution impacts diagnostic accuracy can guide clinicians in improving imaging techniques and employing Artificial Intelligence-driven tools effectively for more accurate GC detection and better patient outcomes.