Fluid-structure interaction for functional assessment of ascending aortic aneurysms: a biomechanical-based approach toward clinical practice

Publicações

Clinical prediction and spatial statistical analysis of ascending thoracic aortic aneurysm structure

Publication . Rodriguez, Katalina; Carvalho, Alda; Valente, Rodrigo; Xavier, José; Tomás, António

This study presents an analysis of data from patients with ascending thoracic aortic aneurysms (ATAAs). Two databases of 87 patients were available: one containing clinical variables and the other consisting of measurements of the maximum diameter taken along the ascending aorta. For the clinical database, both a supervised and an unsupervised learning method were applied to explore patterns within the data. On the other hand, for the ascending aorta dataset, experimental variograms were calculated, from which key parameters of interest were extracted. These parameters were then analyzed over time to assess temporal patterns. This analysis aimed to assess the emergence of similar patterns or behaviour in patients with aneurysms of comparable sizes. Based on the analyses conducted, the clinical variables with the greatest importance in surgical decision-making were identified, while the spatial statistical analysis revealed patterns that may be related to elasticity, stiffness, or deformations of the aorta

2026-01-09Artigo científico

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Clustering of wind speed time series as a tool for wind farm diagnosis

Publication . Martins, Ana; Vaz, Daniel; Silva, Tiago; Cardoso, Margarida; Carvalho, Alda

In several industrial fields, environmental and operational data are acquired with numerous purposes, potentially generating a huge quantity of data containing valuable information for management actions. This work proposes a methodology for clustering time series based on the K-medoids algorithm using a convex combination of different time series correlation metrics, the COMB distance. The multidimensional scaling procedure is used to enhance the visualization of the clustering results, and a matrix plot display is proposed as an efficient visualization tool to interpret the COMB distance components. This is a general-purpose methodology that is intended to ease time series interpretation; however, due to the relevance of the field, this study explores the clustering of time series judiciously collected from data of a wind farm located on a complex terrain. Using the COMB distance for wind speed time bands, clustering exposes operational similarities and dissimilarities among neighboring turbines which are influenced by the turbines’ relative positions and terrain features and regarding the direction of oncoming wind. In a significant number of cases, clustering does not coincide with the natural geographic grouping of the turbines. A novel representation of the contributing distances—the COMB distance matrix plot—provides a quick way to compare pairs of time bands (turbines) regarding various features.

2024Artigo científico

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Palavras-chave

Advanced computational modelling ,Ascending aortic aneurysm ,Hemodynamic,Biological tissue mechanical behaviour, Engineering and technology

Entidade financiadora

Fundação para a Ciência e a Tecnologia, I.P.

Programa de financiamento

Concurso de Projetos IC&DT em Todos os Domínios Científicos

Número da atribuição

PTDC/EMD-EMD/1230/2021