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openalexOpen Science Framework2026-07-26Cited by 2

The Sovereign FEA Computational Protocol: Digital Twin Validation of Acoustic Capsid Cleavage (Adenovirus Baseline)

D.J. Chapman

This protocol establishes a deterministic, multi-physics digital twin framework for validating the targeted acoustic cleavage of viral nucleocapsids within an extracorporeal shunt. Utilizing a non-enveloped Adenovirus baseline, the methodology bridges discrete atomistic data and macroscopic continuum elastodynamics. It programmatically translates AlphaFold 3 atomic coordinates into a volumetric continuum mesh, coarse-grained via Elastic Network Models (ENMs). The simulation architecture integrates GPU-accelerated pseudo-spectral time-domain (PSTD) acoustic solvers with decoupled Representative Volume Element (RVE) sub-modeling to prevent matrix gridlock. To eliminate arbitrary computational parameterization, the protocol rigorously anchors its solid mechanics and maximum principal strain failure criteria in empirical Atomic Force Microscopy (AFM) data using virtual Hertzian contact mechanics and explicit element deletion algorithms. Furthermore, the framework maps localized fluid-structure interactions utilizing the Keller-Miksis cavitation equations and free-stream Navier-Stokes momentum, bounded by modified Pennes bioheat thermodynamics to ensure strict clinical safety limits. Please, download PDF to mitigate any coding issues on OSF.

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openalexOpen Science Framework2026-07-26

Machine Learning-Enhanced Echocardiography for the Detection of Coronary Artery Disease: A Scoping Review Protocol

Wagner Rios-García, Erick Barrientos-Ventura, Victoria E. Butrón-Verástegui, Daniela E. Oriundo-Arbizu, Kehit A. Velasquez-Taipe, Abigail D. Via-y-Rada-Torres, et al.

Coronary artery disease (CAD) remains a leading cause of morbidity and mortality worldwide. Echocardiography is widely available and provides real-time structural and functional assessment, but diagnostic accuracy is limited by operator dependency. Machine learning (ML) and deep…

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openalexOpen Science Framework2026-07-26

Personalized Forecasting and Just-in-Time Interventions for Repetitive Negative Thinking: A Proof-of-Concept Study

Ohad Hadar, Gal Lazarus

This research project examines whether person-specific prediction models can improve the timing and effectiveness of just-in-time adaptive interventions for rumination. This proof-of-concept study integrates intensive ecological momentary assessment, idiographic machine-learning…

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