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Finite element analysis is a well-established computational tool which can be used for the analysis of soft tissue mechanics. Due to the structural complexity of the leaflet tissue of the heart valve, the currently available finite element models do not adequately represent the leaflet tissue. A method of addressing this issue is to implement computationally expensive finite element models, characterized by precise constitutive models including high-order and high-density mesh techniques. In this study, we introduce a novel numerical technique that enhances the results obtained from coarse mesh finite element models to provide accuracy comparable to that of fine mesh finite element models while maintaining a relatively low computational cost. Introduced in this study is a method by which the computational expense required to solve linear and nonlinear constitutive models, commonly used in heart valve mechanics simulations, is reduced while continuing to account for large and infinitesimal deformations. This continuum model is developed based on the least square algorithm procedure coupled with the finite difference method adhering to the assumption that the components of the strain tensor are available at all nodes of the finite element mesh model. The suggested numerical technique is easy to implement, practically efficient, and requires less computational time compared to currently available commercial finite element packages such as ANSYS and/or ABAQUS.

Alternative fuel sources, such as hydrogen-enriched natural gas (HENG), are highly sought after by governments globally for lowering carbon emissions. Consequently, the recognition of hydrogen as a valuable zero-emission energy carrier has increased, resulting in many countries attempting to enrich natural gas with hydrogen; however, there are rising concerns over the safe use, storage, and transport of H2 due to its characteristics such as flammability, combustion, and explosivity at low concentrations (4 vol%), requiring highly sensitive and selective sensors for safety monitoring. Microfluidic-based metal–oxide–semiconducting (MOS) gas sensors are strong tools for detecting lower levels of natural gas elements; however, their working mechanism results in a lack of real-time analysis techniques to identify the exact concentration of the present gases. Current advanced machine learning models, such as deep learning, require large datasets for training. Moreover, such models perform poorly in data distribution shifts such as instrumental variation. To address this problem, we proposed a Sparse Autoencoder-based Transfer Learning (SAE-TL) framework for estimating the hydrogen gas concentration in HENG mixtures using limited datasets from a 3D printed microfluidic detector coupled with two commercial MOS sensors. Our framework detects concentrations of simulated HENG based on time-series data collected from a cost-effective microfluidic-based detector. This modular gas detector houses metal–oxide–semiconducting (MOS) gas sensors in a microchannel with coated walls, which provides selectivity based on the diffusion pace of different gases. We achieve a dominant performance with the SAE-TL framework compared to typical ML models (94% R-squared). The framework is implementable in real-world applications for fast adaptation of the predictive models to new types of MOS sensor responses.

In order to develop surgical skills required for cardiac surgeries, such as with surgeries of all types, years of practice and experience is required. Young cardiac surgeons need to improve technical proficiency in order to enrich the quality of care provided and to ensure patient safety. Realistic synthetic platforms and models are common resources for teaching and enhancing practical skills for both inexperienced as well as senior medical students, however are found particularly useful for young surgeons in training. Appropriate and available educational platforms can play an increasingly vital role in the training progression for young trainees in the area of cardiothoracic surgery. For example, the Coronary Artery Bypass Graft (CABG) surgery encompasses an extensive range of pathologic anatomies and surgical performances. In this chapter, we present original, synthetic, biomimetic models that allows for the accurate practice of surgeries such as the CABG surgery and aortic valve implantation. The prototype uses a polyvinyl alcohol (PVA) cryogel specifically designed to mimic the geometric properties of vasculature and the tissue of heart valve leaflets. The proposed models both visually resemble and feel like human tissue in addition to possessing relatively consistent mechanical properties. The technology and platform proposed have the potential for application in all cardiovascularrelated reconstructive surgeries.

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