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Rheology

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Modeling consolidation of soft clay by developing a fractional differential constitutive model in conjunction with an intelligent displacement inversion method.

PloS one
Studying the constitutive relation of soft clays is of critical importance for fundamentally understanding their complex consolidation behavior. This study proposes a fractional differential constitutive model in conjunction with an intelligent displ...

Development and Validation of a Deep Learning System for Sound-based Prediction of Urinary Flow.

European urology focus
BACKGROUND: Uroflowmetry remains an important tool for the assessment of patients with lower urinary tract symptoms (LUTS), but accuracy can be limited by within-subject variation of urinary flow rates. Voiding acoustics appear to correlate well with...

Cell deformability heterogeneity recognition by unsupervised machine learning from in-flow motion parameters.

Lab on a chip
Cell deformability is a well-established marker of cell states for diagnostic purposes. However, the measurement of a wide range of different deformability levels is still challenging, especially in cancer, where a large heterogeneity of rheological/...

Artificial intelligence velocimetry reveals in vivo flow rates, pressure gradients, and shear stresses in murine perivascular flows.

Proceedings of the National Academy of Sciences of the United States of America
Quantifying the flow of cerebrospinal fluid (CSF) is crucial for understanding brain waste clearance and nutrient delivery, as well as edema in pathological conditions such as stroke. However, existing in vivo techniques are limited to sparse velocit...

Self-assembled pH-stable gellan/κ-carrageenan bigel: Rheological studies and viscosity prediction by neural network.

International journal of biological macromolecules
The current study focused on analysing and predicting the effect of physicochemical parameters on the rheological properties of the novel polysaccharide-based bigel. This is the first study to report a bigel fabricated entirely from polysaccharides a...

Ultra-fast ultrasound blood flow velocimetry for carotid artery with deep learning.

Artificial intelligence in medicine
Accurate measurement of blood flow velocity is important for the prevention and early diagnosis of atherosclerosis. However, due to the uncertainty of parameter settings, the autocorrelation velocimetry methods based on clutter filtering are prone to...

Refining the rheological characteristics of high drug loading ointment via SDS and machine learning.

PloS one
This paper presents an optimized preparation process for external ointment using the Definitive Screening Design (DSD) method. The ointment is a Traditional Chinese Medicine (TCM) formula developed by Professor WYH, a renowned TCM practitioner in Jia...

Mapping Extracellular Space Features of Viral Encephalitis to Evaluate the Proficiency of Anti-Viral Drugs.

Advanced materials (Deerfield Beach, Fla.)
The extracellular space (ECS) is an important barrier against viral attack on brain cells, and dynamic changes in ECS microstructure characteristics are closely related to the progression of viral encephalitis in the brain and the efficacy of antivir...

Artificial intelligence-based speckle featurization and localization for ultrasound speckle tracking velocimetry.

Ultrasonics
Deep learning-based super-resolution ultrasound (DL-SRU) framework has been successful in improving spatial resolution and measuring the velocity field information of a blood flows by localizing and tracking speckle signals of red blood cells (RBCs) ...

Artificial intelligence-based prediction of the rheological properties of hydrocolloids for plant-based meat analogues.

Journal of the science of food and agriculture
BACKGROUND: Methylcellulose has been applied as a primary binding agent to control the quality attributes of plant-based meat analogues. H owever, a great deal of effort has been made to search for hydrocolloids to replace methylcellulose because of ...