Abstract
High-frequency radio networks, including those operating in the millimeter-wave bands, are sensible to Line-of-Sight (LoS) obstructions. Computer Vision (CV) algorithms can be leveraged to improve network performance by processing and interpreting visual data, enabling obstacle avoidance and ensuring LoS signal propagation. We propose a vision-aided Radio Access Network (RAN) based on the O-RAN architecture and capable of perceiving the surrounding environment. The vision-aided RAN consists of a gNodeB (gNB) equipped with a video camera that employs CV techniques to extract critical environmental information. An xApp is used to collect and process metrics from the RAN and receive data from a Vision Module (VM). This enhances the RAN's ability to perceive its surroundings, leading to better connectivity in challenging environments. © 2025 IFIP.

Abstract
The link between epicardial adipose tissue (EAT) and cardiovascular risk is well established, with EAT volume being strongly associated with inflammation, coronary artery disease (CAD) risk, and mortality. However, its EAT quantification is hindered by the time-consuming nature of manual EAT segmentation in cardiac computed tomography (CT). 300 non-contrast cardiac CT scans were collected and the pericardium was manually delineated. In a subset of this data (N = 30), manual delineation was repeated by the same operator and by a second operator. Two automatic methods were then used for pericardial segmentation: a commercially available tool, Siemens Cardiac Risk Assessment (CRA) software; and a deep learning solution based on a U-Net architecture trained exclusively with external public datasets (CardiacFat and OSIC). EAT segmentations were obtained through thresholding to [- 150,- 50] Hounsfield units. Pericardial and EAT segmentation performance was evaluated considering the segmentations by the first operator as reference. Statistical significance of differences for all metrics and segmentation methods was tested through Student t-tests. Pericardial segmentation intra-/interobserver variability was excellent, with the U-Net outperforming Siemens CRA (p < 0.0001). The intra- and interobserver agreement for EAT segmentation was lower with Dice Scores (DSC) of 0.862 and 0.775 respectively, while the U-Net and Siemens CRA obtained DSCs of 0.723 and 0.679 respectively. EAT volume quantification showed that the agreement between a human observer and the U-Net was better than that of two human observers (p = 0.0141), with a Pearson Correlation Coefficient (PCC) of 0.896 and a bias of - 2.83 cm(3) (below the interobserver bias of 9.05 cm3). The lower performances of EAT segmentation highlight the difficulty in segmenting this structure. For both pericardial and EAT segmentation, the deep learning method outperformed the commercial solution. While the segmentation performance of the U-Net solution was below interobserver variability, EAT volume quantification performance was competitive with human readers, motivating future use of these tools. Clinical trial number: NCT03280433, registered retrospectively on 2017-09-08.

Abstract

Abstract
A comprehensive understanding of the mechanisms underlying bone fatigue failure is crucial for advancing treatment strategies. In this regard, this study presents a novel approach to quantify crack propagation in cortical bone tissue through fatigue testing under mode I loading. To closely replicate real bone damage mechanisms, pre-cracked bone samples were subjected to cyclic loading. A compliance-based beam method and cubic B-spline interpolation method were employed to accurately extract fatigue coefficients and reduce experimental noise, yielding refined modified Paris law coefficients. A cohesive zone model for high-cycle fatigue was used to simulate crack propagation, capturing the nonlinear material response by means of the cohesive zone length, mimicking the non-negligible fracture process zone. The goal is to validate the followed experimental procedure. This study offers valuable insights into the fatigue and fracture mechanisms in cortical bone, providing a more accurate and realistic framework for characterizing fatigue life compared to previous methodologies. Coefficients produced from the cohesive model may be readily integrated into simulation tools commonly used in many areas of engineering, allowing biomechanical experts to create more robust designs that simulate actual world conditions for application in implants and orthopaedic structures.

Abstract

Abstract
Non-pharmaceutical interventions (NPIs), such as lockdowns, travel restrictions, and social distancing mandates, play a critical role in controlling the spread of infectious diseases by shaping human mobility patterns. Using COVID-19 as a case study, this research investigates the relationships between NPIs, mobility, and the effective reproduction number (R-t) across 13 European countries. We employ XGBoost regression models to estimate missing mobility data from NPIs and missing R(t )values from mobility, achieving high accuracy. Additionally, using clustering techniques, we uncover national distinctions in social compliance. Northern European countries demonstrate higher adherence to NPIs than Southern Europe, which exhibits more variability in response to restrictions. These differences highlight the influence of cultural and social norms on public health outcomes. In general, our analysis reveals a strong correlation between NPIs and mobility reductions, highlighting the immediate impact of restrictions on population movement. However, the relationship between mobility and R(t )is weaker and more nuanced, reflecting the time delays involved, as changes in mobility take time to influence transmission rates. These results underscore the interdependence of restrictions, mobility, and disease spread while demonstrating the potential for data-driven approaches to guide policy decisions. Our approach offers valuable insights for optimizing public health strategies and tailoring interventions to diverse cultural contexts during future health crises.