http://hdl.handle.net/10498/6777 2026-11-06T15:56:01Z http://hdl.handle.net/10498/40546 An efficient framework to assess the predictive value of 4D ultrasound-based wall motion indices for abdominal aortic aneurysm rupture risk stratification Schönborn, Manuel; Hegner, Achim Maximilian Claus; Derwich, Wojciech; Oikonomou, Kyriakos; Huß, Armin; Gámez López, Antonio Juan; Wittek, Andreas; Blase, Christopher Clinical assessment of abdominal aortic aneurysm (AAA) rupture risk relies on diameter criteria, which are often insufficient. Validating non-invasive biomarkers against experimental ground truth is a crucial prerequisite before conducting large-scale clinical trials. We hypothesize that Wall Motion Indices (WMI), derived from 4D ultrasound (4D-US) strain imaging, capture the functionally compromised mechanical state of the aneurysm wall. This study introduces a validation framework using patient-specific, experimental ground truth metrics. For nine AAA patients, the Normalized Experimental Rupture Potential (NERP) was established. The NERP, a normalized ratio of physiological loading to failure capacity, combined finite element analysis (FEA)-estimated wall tension with ex vivo rupture strength from harvested tissue. To ensure strict data separation, patient-specific ground truths (NERP and rupture strength) were correlated with pre-operative WMI exclusively at the documented harvest location. While maximum diameter showed no significant association with NERP or rupture strength, specific WMI quantifying kinematic anisotropy and local strain concentrations demonstrated notable correlations. Strain ratios, identifying local hotspots relative to background deformation, correlated with different structural integrity metrics. Principal strain ratio correlated significantly with intrinsic ex vivo rupture strength (r=−0.711,p=0.032), whereas the macroscopic anatomical strain ratio correlated with the computationally derived NERP (r=0.833,p=0.005). This study demonstrates an efficient validation framework for biomechanical biomarkers in small cohorts. The findings suggest that disparity-based WMI are promising, non-invasive indicators of AAA wall integrity that warrant further investigation. Statement of Significance This study addresses the critical need for reliable rupture risk assessment in abdominal aortic aneurysms (AAA), moving beyond simple diameter measurements. We introduce a multi-modal validation framework that serves as a crucial, mechanistically-informed precursor to large-scale clinical trials. By correlating non-invasive, 4D ultrasound-based wall motion biomarkers with patient-specific, experimental “ground truth” data (tissue strength and stress) from the exact same anatomical location, this approach establishes the necessary biomechanical plausibility of predictive metrics before clinical scaling. Our findings reveal that while traditional clinical parameters fail to predict wall integrity, disparity indices quantifying the mechanical disorganization (anisotropy of heterogeneity) of the aneurysm wall correlate strongly with structural vulnerability. This work establishes an efficient tool for evaluating biomechanical markers in small cohorts and highlights strain disparity-based Wall Motion Indices as promising candidates for a more refined, patient-specific risk stratification. 2026-05-15T00:00:00Z http://hdl.handle.net/10498/40500 RADRISK Inspector: a mobile application for shielding design and safety distance calculation in industrial gamma radiography Suffo Pino, Miguel; Brea-Piñero, D.; Molina Pérez, J. F.; Mota Macías, José Miguel Industrial gamma radiography is a widely used non-destructive testing (NDT) technique, but it involves significant radiological risks that require precise planning of shielding and safety distances. Current practices rely heavily on in-situ radiometric measurements, which are time-consuming and may increase unnecessary operator exposure during setup and verification stages. This work presents RADRISK Inspector (RRI), a cross-platform mobile application that enables real-time radiological safety planning through physics-based calculations. The system estimates minimum safety distances, required shielding thickness, and risk zoning based on radionuclide activity, exposure parameters, and material attenuation properties. In addition, RRI integrates interactive zoning maps, exposure timers, and automated dosimetric reporting compliant with international standards (IAEA, CSN), supporting full digitalization of radiological risk management workflows. The application was preliminarily verified under real industrial conditions during pipeline weld inspections using an Ir-192 source. In this field case, RRI estimated an operator safety distance of 66 m, compared with 22 m obtained from in-situ dose-rate measurements, indicating conservative behaviour consistent with its safety-first planning approach. Accordingly, RRI is not intended to replace in-situ radiometric verification or high-fidelity radiation transport modelling, but to support conservative pre-inspection planning, risk zoning and traceable professional reporting in industrial gamma radiography. RRI represents a novel and beneficial application of radioactivity in modern industry, enabling proactive radiological protection, reducing unnecessary exposure, and improving operational efficiency in gamma radiography. Its compatibility with ATEX-certified devices further supports deployment in hazardous environments. This work supports a shift in radiographic NDT from reactive measurement-based practices to predictive, physics-based safety planning. 2026-01-01T00:00:00Z http://hdl.handle.net/10498/40396 Cold Metal Transfer-Based Wire Arc Additive Manufacturing of Al–Si Alloys: Technology Principles, Process Control, Material Behaviour and Defect Formation Rodríguez García, Gabriela; Salguero Gómez, Jorge; Batista Ponce, Moisés; González Rovira, Leandro; Sol Illana, Irene del Wire Arc Additive Manufacturing (WAAM) has gained attention as a metal additive manufacturing process producing complex large-scale components with high deposition rates and lower costs. Cold Metal Transfer (CMT) offers reduced heat input and enhanced control of metal transfer, making it suitable for aluminium. This review analyses CMT-based WAAM with a focus on Al–Si alloys, providing a synthesis for this material system and establishing a structured comparison of representative studies on process fundamentals, arc mode variants, and key processing parameters. The influence of electrical and kinematic parameters and thermal management on process and geometrical stability, microstructural evolution, defect formation, and mechanical behaviour is discussed. Process behaviour is governed by the temporal distribution of heat input within the CMT cycle and thermal history. Control of heat input can reduce porosity, microstructural heterogeneity, and geometric instability, while advanced CMT modes can improve process stability and material efficiency under appropriate process configurations. Mechanical performance depends on the interaction between process parameters, microstructure, and defects, leading to variability and anisotropy. Despite progress, challenges related to process repeatability, narrow processing windows, defect susceptibility, and predictive capability remain. Future research should focus on parameter optimization, integrated modelling, real-time control, and WAAM-specific alloys to enable reliable industrial implementation. 2026-01-01T00:00:00Z http://hdl.handle.net/10498/40097 Artificial Intelligence for Detecting Surface Alteration Phenomena in Stone-Built Heritage: The Case of the ‘Unfinished Church’ of Venosa Buldo, Michele; Fatiguso, Fabio; Cabrera Revuelta, Elena; Verdoscia, Cesare Point clouds and 3D models have become essential not only for the digitisation process but also for the non-invasive assessment of deterioration and potential decay mapping in Cultural Heritage, particularly in the built environment and architectural landmarks. These resources facilitate precise digital inspections and enable a comprehensive analysis of the morphological and material properties of heritage assets, in strict alignment with conservation principles. Recent advancements in Artificial Intelligence have further refined 3D data and image processing, introducing sophisticated techniques for segmentation and classification through both supervised and unsupervised learning paradigms. Building upon these breakthroughs, this study explores the semi-automatic identification of surface alterations in the stone masonry of the south-east façade of the ‘Unfinished Church’, which is part of the Most Holy Trinity Complex in Venosa (Southern Italy). The mapping process started with the photogrammetric point cloud, employing RGB colour-detection techniques, followed by the implementation of two Machine Learning algorithms (Fast Random Forest and K-Nearest Neighbours) to examine the UV texture of the polygonal model. Comparative analyses, both quantitative and qualitative, were conducted to assess the effectiveness of these methods in identifying and classifying alterations, highlighting their potential to support preservation efforts and guide future maintenance strategies. 2025-04-01T00:00:00Z