Network-Based Approach for Modeling and Analyzing Coronary Angiography

Ravandi, Babak; Ravandi, Arash

doi:10.1007/978-3-030-40943-2_15

Cited by 3 publications

(2 citation statements)

References 31 publications

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“…weaving [11,12,13,14,15]. Each technique has characteristics more suitable for a certain application and each technique exhibits differences in terms of mechanic properties, behaviour and fracture mechanism.…”

Section: Introductionmentioning

confidence: 99%

AE analysis of mode II interlaminarfracture toughness of 3D reinforced CFRP

Oliveira

Bortoluzzi

Barbosa

et al. 2023

Preprint

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The industrial application of composite materials is increasing, showing its viability to replace other materials have been gradually growing. Particularly, composite materials reinforced with carbon fibres have a good ratio of resistance/weight, being an advantage for many applications since Carbon Fibre Reinforced Polymers (CFRP) allows reducing weight and keeping structural resistance. Resistance to delamination damage is a key factor for composite materials, since it deals with material safety during service life. Fibre orientation in composite materials is designed to reinforce resistance in specific directions of severe solicitation. Nonetheless, out-of-plane loadings are inherent solicitations that occurs in service life and fibre orientation are not intended to reinforce thought thickness direction, making out-of-plane loadings a critical factor. Reinforcing composite materials through thickness, or 3D, has the potential to increase the material out-of-plane resistance and thus permitting a higher accuracy levels in terms of structural design. There are different ways to apply 3D reinforcement on composite materials, like Z-pinning, Stitching and Tufting. Non-destructive testing (NDT) are efficient methods of reassuring safety of components, NDT allows detection of damage and flaws in early stages and monitoring of a component. NDT methods are particular useful for composite materials for avoiding parts containing flaws, indicating repairs to increase lifespan and for lifespan estimation. Acoustic emission is one technique of NDT (non-destructive testing) that allows monitoring of damage evolution in real time, since it detects elastic waves emitted by energy release caused by changes in internal structure, for NDT purposes it means damage. This paper monitors and evaluates the signs generated during the mode II interlaminar fracture toughness test, end-notched flexure (ENF), monitored by acoustic emission comparing the effect of different though thickness reinforcements and a non-reinforced sample. The testing procedure and damage development are analysed regarding acoustic emission features and its behaviour. The results show that acoustic emission has a strong relationship with stages of testing, and samples resistance induced by the reinforcement. The data acquired from the ENF tests in synergy with acoustic emission provided damage characterization and differentiation, reaffirmed by fractography analysis, especially in terms of final stage of damage and 3D reinforcement effects on it.

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Section: Introductionmentioning

confidence: 99%

AE analysis of mode II interlaminarfracture toughness of 3D reinforced CFRP

Oliveira

Bortoluzzi

Barbosa

et al. 2023

Preprint

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“…The EHR records from 14.3 million cardiovascular disease (CVD) patients were collected for network construction. Although several previous studies had constructed disease networks on CVDs 1, [13][14][15][16] , most of them focused only on undirected associations among diseases, such as whether they share common genes. In our study, we leverage the time stamps of diagnoses to build progCDN with directed edges based on temporal associations.…”

Section: Introductionmentioning

confidence: 99%

Disease Network Delineates the Disease Progression Profile of Cardiovascular Diseases

Tang¹,

Yu²,

Ng³

et al. 2020

Preprint

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As Electronic Health Records (EHR) data accumulated explosively in recent years, the tremendous amount of patient clinical data provided opportunities to discover real world evidence. In this study, a graphical disease network, named progressive cardiovascular disease network (progCDN), was built based on EHR data from 14.3 million patients 1 to delineate the progression profiles of cardiovascular diseases (CVD). The network depicted the dominant diseases in CVD development, such as the heart failure and coronary arteriosclerosis. Novel progression relationships were also discovered, such as the progression path from long QT syndrome to major depression. In addition, three age-group progCDNs identified a series of age-associated disease progression paths and important successor diseases with age bias. Furthermore, we extracted a list of salient features to build a series of disease risk models based on the progression pairs in the disease network. The progCDN network can be further used to validate or explore novel disease relationships in real world data. Features with sufficient abundance and high correlation can be widely applied to train disease risk models when using EHR data.

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