405 research outputs found

    4D FLOW CMR in congenital heart disease

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    This thesis showed that the use of a cloud-based reconstruction applicationwith advanced eddy currents correction, integrated with interactiveimaging evaluation tools allowed for remote visualization and interpretationof 4D flow data and that was sufficient for gross visualizationof aortic valve regurgitation. Further, this thesis demonstrated that bulkflow and pulmonary regurgitation can be accurately quantified using 4Dflow imaging analyzed. Peak systolic velocity over the pulmonary valvemay be underestimated. However, the measurement of peak systolicvelocity can be optimized if measured at the level of highest velocity inthe pulmonary artery. Also correlated against invasive measurements (inan animal model), this thesis shows that aorta flow and pulmonary flowcan be accurately and simultaneously measured by 4D flow MRI.When applied in clinical practice, 4D flow has extra advantages, of beingable to visualize flow pattern, vorticity and to predict aortic growth. InASD patients it can measure shunt volume directly following the septumframe by frame. In Fontan patients in can visualize better than standardMRI the Fontan circuit and it can measure flow at multiple points alongthe Fontan circuit. We observed in our Fontan population that shunt lesionswere very common, most of the time via veno-venous collaterals.Further using advanced computations, we showed that WSS angle wasthe only independent predictor of aortic growth in BAV patients. We alsoshowed the feasibility of GLS analysis on 4D flow MRI and presented anintegrative approach in which flow and functional data are acquired inone sequence.From the technical point of view, 4D flow MRI has proved to complementthe traditional components of the standard cardiac MR exams, enablingin-depth insights into hemodynamics. At this moment it proved its addedvalue, but in most of the cases it is not able yet to replace the standardexam. This is still due to long scanning times and relatively longpost-processing times.<br/

    Easy-to-implement hp-adaptivity for non-elliptic goal-oriented problems

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    The FEM has become a foundational numerical technique in computational mechanics and civil engineering since its inception by Courant in 1943 Courant1943. Originating from the Ritz method and variational calculus, the FEM was primarily employed to derive solutions for vibrational systems. A distinctive strength of the FEM is its capability to represent mathematical models through the weak variational formulation of PDE, facilitating computational feasibility even in intricate geometries. However, the search for accuracy often imposes a significant computational task. In the FEM, adaptive methods have emerged to balance the accuracy of solutions with computational costs. The hh-adaptive FEM designs more efficient meshes by reducing the mesh size hh locally while keeping the polynomial order of approximation pp fixed (usually p=1,2p=1,2). An alternative approach to the hh-adaptive FEM is the pp-adaptive FEM, which locally enriches the polynomial space pp while keeping the mesh size hh constant. By dynamically adapting hh and pp, the hphp-adaptive FEM achieves exponential convergence rates. Adaptivity is crucial for obtaining accurate solutions. However, the traditional focus on global norms, such as L2L^2 or H1H^1, might only sometimes serve the requirements of specific applications. In engineering, controlling errors in specific domains related to a particular QoI is often more critical than focusing on the overall solution. That motivated the development of GOA strategies. In this dissertation, we develop automatic GO hphp-adaptive algorithms tailored for non-elliptic problems. These algorithms shine in terms of robustness and simplicity in their implementation, attributes that make them especially suitable for industrial applications. A key advantage of our methodologies is that they do not require computing reference solutions on globally refined grids. Nevertheless, our approach is limited to anisotropic pp and isotropic hh refinements. We conduct multiple tests to validate our algorithms. We probe the convergence behavior of our GO hh- and pp-adaptive algorithms using Helmholtz and convection-diffusion equations in one-dimensional scenarios. We test our GO hphp-adaptive algorithms on Poisson, Helmholtz, and convection-diffusion equations in two dimensions. We use a Helmholtz-like scenario for three-dimensional cases to highlight the adaptability of our GO algorithms. We also create efficient ways to build large databases ideal for training DNN using hphp MAGO FEM. As a result, we efficiently generate large databases, possibly containing hundreds of thousands of synthetic datasets or measurements

    A Tale of Two Approaches: Comparing Top-Down and Bottom-Up Strategies for Analyzing and Visualizing High-Dimensional Data

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    The proliferation of high-throughput and sensory technologies in various fields has led to a considerable increase in data volume, complexity, and diversity. Traditional data storage, analysis, and visualization methods are struggling to keep pace with the growth of modern data sets, necessitating innovative approaches to overcome the challenges of managing, analyzing, and visualizing data across various disciplines. One such approach is utilizing novel storage media, such as deoxyribonucleic acid~(DNA), which presents efficient, stable, compact, and energy-saving storage option. Researchers are exploring the potential use of DNA as a storage medium for long-term storage of significant cultural and scientific materials. In addition to novel storage media, scientists are also focussing on developing new techniques that can integrate multiple data modalities and leverage machine learning algorithms to identify complex relationships and patterns in vast data sets. These newly-developed data management and analysis approaches have the potential to unlock previously unknown insights into various phenomena and to facilitate more effective translation of basic research findings to practical and clinical applications. Addressing these challenges necessitates different problem-solving approaches. Researchers are developing novel tools and techniques that require different viewpoints. Top-down and bottom-up approaches are essential techniques that offer valuable perspectives for managing, analyzing, and visualizing complex high-dimensional multi-modal data sets. This cumulative dissertation explores the challenges associated with handling such data and highlights top-down, bottom-up, and integrated approaches that are being developed to manage, analyze, and visualize this data. The work is conceptualized in two parts, each reflecting the two problem-solving approaches and their uses in published studies. The proposed work showcases the importance of understanding both approaches, the steps of reasoning about the problem within them, and their concretization and application in various domains

    Proceedings XXIII Congresso SIAMOC 2023

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    Il congresso annuale della Società Italiana di Analisi del Movimento in Clinica (SIAMOC), giunto quest’anno alla sua ventitreesima edizione, approda nuovamente a Roma. Il congresso SIAMOC, come ogni anno, è l’occasione per tutti i professionisti che operano nell’ambito dell’analisi del movimento di incontrarsi, presentare i risultati delle proprie ricerche e rimanere aggiornati sulle più recenti innovazioni riguardanti le procedure e le tecnologie per l’analisi del movimento nella pratica clinica. Il congresso SIAMOC 2023 di Roma si propone l’obiettivo di fornire ulteriore impulso ad una già eccellente attività di ricerca italiana nel settore dell’analisi del movimento e di conferirle ulteriore respiro ed impatto internazionale. Oltre ai qualificanti temi tradizionali che riguardano la ricerca di base e applicata in ambito clinico e sportivo, il congresso SIAMOC 2023 intende approfondire ulteriori tematiche di particolare interesse scientifico e di impatto sulla società. Tra questi temi anche quello dell’inserimento lavorativo di persone affette da disabilità anche grazie alla diffusione esponenziale in ambito clinico-occupazionale delle tecnologie robotiche collaborative e quello della protesica innovativa a supporto delle persone con amputazione. Verrà infine affrontato il tema dei nuovi algoritmi di intelligenza artificiale per l’ottimizzazione della classificazione in tempo reale dei pattern motori nei vari campi di applicazione

    Value Creation with Extended Reality Technologies - A Methodological Approach for Holistic Deployments

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    Mit zunehmender Rechenkapazität und Übertragungsleistung von Informationstechnologien wächst die Anzahl möglicher Anwendungs-szenarien für Extended Reality (XR)-Technologien in Unternehmen. XR-Technologien sind Hardwaresysteme, Softwaretools und Methoden zur Erstellung von Inhalten, um Virtual Reality, Augmented Reality und Mixed Reality zu erzeugen. Mit der Möglichkeit, Nutzern Inhalte auf immersive, interaktive und intelligente Weise zu vermitteln, können XR-Technologien die Produktivität in Unternehmen steigern und Wachstumschancen eröffnen. Obwohl XR-Anwendungen in der Industrie seit mehr als 25 Jahren wissenschaftlich erforscht werden, gelten nach wie vor als unausgereift. Die Hauptgründe dafür sind die zugrundeliegende Komplexität, die Fokussierung der Forschung auf die Untersuchung spezifische Anwendungsszenarien, die unzu-reichende Wirtschaftlichkeit von Einsatzszenarien und das Fehlen von geeigneten Implementierungsmodellen für XR-Technologien. Grundsätzlich wird der Mehrwert von Technologien durch deren Integration in die Wertschöpfungsarchitektur von Geschäftsmodellen freigesetzt. Daher wird in dieser Arbeit eine Methodik für den Einsatz von XR-Technologien in der Wertschöpfung vorgestellt. Das Hauptziel der Methodik ist es, die Identifikation geeigneter Einsatzszenarien zu ermöglichen und mit einem strukturierten Ablauf die Komplexität der Umsetzung zu beherrschen. Um eine ganzheitliche Anwendbarkeit zu ermöglichen, basiert die Methodik auf einem branchen- und ge-schäftsprozessunabhängigen Wertschöpfungsreferenzmodell. Dar-über hinaus bezieht sie sich auf eine ganzheitliche Morphologie von XR-Technologien und folgt einer iterativen Einführungssequenz. Das Wertschöpfungsmodell wird durch ein vorliegendes Potential, eine Wertschöpfungskette, ein Wertschöpfungsnetzwerk, physische und digitale Ressourcen sowie einen durch den Einsatz von XR-Technologien realisierten Mehrwert repräsentiert. XR-Technologien werden durch eine morphologische Struktur mit Anwendungsmerk-malen und erforderlichen technologischen Ressourcen repräsentiert. Die Umsetzung erfolgt in einer iterativen Sequenz, die für den zu-grundeliegenden Kontext anwendbare Methoden der agilen Soft-wareentwicklung beschreibt und relevante Stakeholder berücksich-tigt. Der Schwerpunkt der Methodik liegt auf einem systematischen Ansatz, der universell anwendbar ist und den Endnutzer und das Ökosystem der betrachteten Wertschöpfung berücksichtigt. Um die Methodik zu validieren, wird der Einsatz von XR-Technologien in zwei industriellen Anwendungsfällen unter realen wirtschaftlichen Bedingungen durchgeführt. Die Anwendungsfälle stammen aus unterschiedlichen Branchen, mit unterschiedlichen XR-Technologiemerkmalen sowie unterschiedlichen Formen von Wert-schöpfungsketten, um die universelle Anwendbarkeit der Methodik zu demonstrieren und relevante Herausforderungen bei der Durch-führung eines XR-Technologieeinsatzes aufzuzeigen. Mit Hilfe der vorgestellten Methodik können Unternehmen XR-Technologien zielgerichtet in ihrer Wertschöpfung einsetzen. Sie ermöglicht eine detaillierte Planung der Umsetzung, eine fundierte Auswahl von Anwendungsszenarien, die Bewertung möglicher Her-ausforderungen und Hindernisse sowie die gezielte Einbindung der relevanten Stakeholder. Im Ergebnis wird die Wertschöpfung mit wirtschaftlichem Mehrwert durch XR-Technologien optimiert

    Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review

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    The prevalence of cardiovascular diseases is increasing around the world. However, the technology is evolving and can be monitored with low-cost sensors anywhere at any time. This subject is being researched, and different methods can automatically identify these diseases, helping patients and healthcare professionals with the treatments. This paper presents a systematic review of disease identification, classification, and recognition with ECG sensors. The review was focused on studies published between 2017 and 2022 in different scientific databases, including PubMed Central, Springer, Elsevier, Multidisciplinary Digital Publishing Institute (MDPI), IEEE Xplore, and Frontiers. It results in the quantitative and qualitative analysis of 103 scientific papers. The study demonstrated that different datasets are available online with data related to various diseases. Several ML/DP-based models were identified in the research, where Convolutional Neural Network and Support Vector Machine were the most applied algorithms. This review can allow us to identify the techniques that can be used in a system that promotes the patient’s autonomy.N/

    Fabrication of Vanadium Dioxide Thin Films and their Structural, Optical and Electrical Characterization for Optoelectronic Applications

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    Vanadium dioxide (VO2) is a transition metal oxide that is well known for its metal-to-insulator phase transition (MIT). One of the most common forms of VO2 that has been generally studied is the thin film form. VO2 thin films are considered a strong candidate in various new-generation optical, electronic, and optoelectronic (photonic) applications. From the technology perspective, the fabrication of single-crystal VO2 thin films appears to be challenging. Up to now, research on the preparation of VO2 thin films has focused on employing different material fabrication techniques to produce high-quality VO2 thin films. The stoichiometry and quality of VO2 thin films strongly depend on the fabrication process. There is still a need to study the production of near-single-crystal, high-quality VO2 thin films and their structural, optical and electrical characterization. Secondly, the metal-to-insulator phase transition phenomenon in VO2 is a topical research field. The percolation theory has introduced some rigor in explaining the phase transition. This dissertation focuses on two aspects of research on VO2 thin films. The first aspect focuses on studying the effect of specific deposition parameters such as substrate biasing and substrate temperature on the quality of VO2 thin films. Also, the synthesis of high-quality VO2 thin films prepared on single-crystal silicon, quartz and sapphire substrates is investigated. The films are examined using various analysis techniques including Raman spectroscopy, scanning electron microscopy (SEM), x-ray diffraction (XRD), x-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM) and energy-dispersive x-ray spectroscopy (EDS). The optical constants, namely the refractive index (n) and the extinction coefficient (K), and the optical bandgap (Eg) of the films are extracted using the Swanepoel and Manifacier techniques. The second aspect of this dissertation covers the application of percolation theory on the phase transition in VO2 thin films. Accordingly, the topology of conducting clusters during the IMT and MIT is investigated by means of optical and electrical switching in a high-quality VO2 thin film. Additionally, self-heating-induced electrical and optical switching in VO2 thin films prepared on sapphire substrates under constant applied current pulses has been studied. The difference in the two switching dynamics is explained by a simple model based on the percolation theory

    COVID-19 Booster Vaccine Acceptance in Ethnic Minority Individuals in the United Kingdom: a mixed-methods study using Protection Motivation Theory

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    Background: Uptake of the COVID-19 booster vaccine among ethnic minority individuals has been lower than in the general population. However, there is little research examining the psychosocial factors that contribute to COVID-19 booster vaccine hesitancy in this population.Aim: Our study aimed to determine which factors predicted COVID-19 vaccination intention in minority ethnic individuals in Middlesbrough, using Protection Motivation Theory (PMT) and COVID-19 conspiracy beliefs, in addition to demographic variables.Method: We used a mixed-methods approach. Quantitative data were collected using an online survey. Qualitative data were collected using semi-structured interviews. 64 minority ethnic individuals (33 females, 31 males; mage = 31.06, SD = 8.36) completed the survey assessing PMT constructs, COVID-19conspiracy beliefs and demographic factors. 42.2% had received the booster vaccine, 57.6% had not. 16 survey respondents were interviewed online to gain further insight into factors affecting booster vaccineacceptance.Results: Multiple regression analysis showed that perceived susceptibility to COVID-19 was a significant predictor of booster vaccination intention, with higher perceived susceptibility being associated with higher intention to get the booster. Additionally, COVID-19 conspiracy beliefs significantly predictedintention to get the booster vaccine, with higher conspiracy beliefs being associated with lower intention to get the booster dose. Thematic analysis of the interview data showed that barriers to COVID-19 booster vaccination included time constraints and a perceived lack of practical support in the event ofexperiencing side effects. Furthermore, there was a lack of confidence in the vaccine, with individuals seeing it as lacking sufficient research. Participants also spoke of medical mistrust due to historical events involving medical experimentation on minority ethnic individuals.Conclusion: PMT and conspiracy beliefs predict COVID-19 booster vaccination in minority ethnic individuals. To help increase vaccine uptake, community leaders need to be involved in addressing people’s concerns, misassumptions, and lack of confidence in COVID-19 vaccination
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