Physics‑Informed Neural Networks (PINNs) offer a promising way to estimate blood‑flow behavior in arteries, especially near the vessel wall where traditional methods struggle. In this study, students tested how sensor density and placement affect PINN accuracy in modeling carotid artery flow and found that accuracy improves up to a moderate sensor density and depends strongly on how close sensors are placed to the arterial wall. Applying the optimized configuration to a patient‑specific carotid model produced velocity predictions closely matching computational fluid dynamics results, highlighting PINNs’ potential for future personalized cardiovascular assessment.
The explored the potential of an epitope-based vaccine for Rocio virus using computational methods and M13 bacteriophage technology. Researchers identified 30 promising B- and T-cell epitopes and designed vaccine candidates using both phage display and E. coli expression systems.
This study explores the interaction between precocene II and trichocethecene 3-O-acetyltransferase using molecular docking simulations. Computational analysis identified several potential binding sites on the enzyme surface and predicted favorable ligand-protein interactions involving key residues. These findings provide insight into how precocene II may interact with this enzyme and demonstrate the use of computational approaches to explore potential antifungal mechanisms.
Elevated acetylcholinesterase (AChE) activity contributes to cognitive decline and neurodegenerative diseases such as Alzheimer’s, motivating the search for more effective inhibitors with better bioavailability. This study used computational methods to design novel, non-toxic AChE inhibitors.
Here, recognizing the important role of bacterial biofilms in many life-threatening chronic infections, the authors investigated the effectiveness of a combination treatment on biofilms composed of up to three different common species within the lungs of cystic fibrosis patients with computational analysis. They found that a triple cocktail therapy targeting three different signaling pathways has significant potential as both a treatment and prophylaxis.
This study's goal was to identify the Mach numbers for which electrostatic drag and heat transfer manipulation would be most applicable inside the stratosphere. The experiments were conducted using computational fluid dynamics software. The study demonstrated that, on average, higher Mach speeds resulted in a considerably higher potential decrease in density. The study highlights that further research on the surface charge method is warranted to explore higher hypersonic speeds within the stratosphere.
Here, recognizing that brain aneurysms pose a severe threat, often misdiagnosed and leading to high mortality, particularly in younger individuals, the authors explored a novel computer-aided engineering approach. They used magnetic resonance angiography images and computational fluid dynamics, to improve aneurysm detection and risk assessment, aiming for more personalized treatment.
Plant diseases can cause up to 50% crop yield loss for the popular tomato plant. A mobile device-based method to identify diseases from photos of symptomatic leaves via computer vision can be more effective due to its convenience and accessibility. To enable a practical mobile solution, a “shallow” convolutional neural networks (CNNs) with few layers, and thus low computational requirement but with high accuracy similar to the deep CNNs is needed. In this work, we explored if such a model was possible.
Engineered bacteria that degrade oil are currently being considered as a safe option for the treatment of oil spills. For this approach to be successful, the bacteria must effectively express oil-degrading genes they uptake as part of an external genoming vehicle called a "plasmid". Using a computational approach, the authors investigate plasmid-bacterium compatibility to find pairs that ensure high levels of gene expression.
Vibrato, defined as a rapid and subtle oscillation in pitch, is a technique that is commonly used by musicians to add expression and colour to notes. However, on stringed instruments, there are certain notes (open string notes) on which it is impossible to perform the technique. Without vibrato, they can sound angular and unpleasant, especially when juxtaposed against other notes played with vibrato. String players therefore use an alternative to achieve the same vibrato effect on the open string — a technique referred to as “open string vibrato”. While the technique is widely used, it is unknown how much of a physical effect it has on the sound waves produced, if any at all. The purpose of this study is to analyse open string vibrato using a statistical approach to provide evidence to characterize the physical effect of the technique, and then compare it to normal vibrato. We hypothesised that it would have a noticeable and measurable effect on the sound waves produced because of the technique’s widespread usage. To test this, notes, with and without either open string vibrato or normal vibrato, were recorded on the violin. We analyzed the audio recordings using a computational and statistical approach. The results of the study partially agreed with our hypothesis: while the technique has an observable physical effect on the sound waves, the effect is weaker than expected. We concluded that open string vibrato does work, but has quite a subtle effect, and thus should only be used when there is no other option.
