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Stress, Mechanical

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Computational Analysis of Mechanical Interactions between a Soft Robotic Device and a Skin-Muscle Phantom for Mechanotherapy.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Studying the soft robot-tissue mechanical interaction in muscle stimulation devices poses a significant challenge due to the complex behavior of the materials involved. To advance this field, this paper models computationally three types of soft elas...

Optimization of grinding parameters in robotic-assisted preparation of cracked teeth based on fracture mechanics: FEA and experiment.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: If left untreated, cracked teeth can lead to tooth loss, of which the incidence is 70%. Dental preparation is an effective treatment, but it is difficult to meet the clinical requirements when traditionally prepared by dent...

Machine learning reveals correlations between brain age and mechanics.

Acta biomaterialia
Our brain undergoes significant micro- and macroscopic changes throughout its life cycle. It is therefore crucial to understand the effect of aging on the mechanical properties of the brain in order to develop accurate personalized simulations and di...

Design and application of ISSA-BP neural network model for predicting soft tissue relaxation force.

Acta of bioengineering and biomechanics
: Accurate biomechanical modeling is crucial for enhancing the realism of virtual surgical training. This study addressed the computational cost and complexity associated with traditional viscoelastic models by incorporating neural network algorithms...

[Optimization of centrifugal artificial heart pump blade parameters based on back propagation neural network and grey wolf optimization algorithm].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
The impeller, as a key component of artificial heart pumps, experiences high shear stress due to its rapid rotation, which may lead to hemolysis. To enhance the hemolytic performance of artificial heart pumps and identify the optimal combination of b...

Prediction of stress-strain behavior of rock materials under biaxial compression using a deep learning approach.

PloS one
Deep learning has significantly advanced in predicting stress-strain curves. However, due to the complex mechanical properties of rock materials, existing deep learning methods have the problem of insufficient accuracy in predicting the stress-strain...

MechBERT: Language Models for Extracting Chemical and Property Relationships about Mechanical Stress and Strain.

Journal of chemical information and modeling
Language models are transforming materials-aware natural-language processing by enabling the extraction of dynamic, context-rich information from unstructured text, thus, moving beyond the limitations of traditional information-extraction methods. Mo...

Simultaneous Isotropic Omnidirectional Hypersensitive Strain Sensing and Deep Learning-Assisted Direction Recognition in a Biomimetic Stretchable Device.

Advanced materials (Deerfield Beach, Fla.)
Omnidirectional strain sensing and direction recognition ability are features of the human tactile sense, essential to address the intricate and dynamic requirements of real-world applications. Most of the current strain sensors work by converting un...

Constitutive neural networks for main pulmonary arteries: discovering the undiscovered.

Biomechanics and modeling in mechanobiology
Accurate modeling of cardiovascular tissues is crucial for understanding and predicting their behavior in various physiological and pathological conditions. In this study, we specifically focus on the pulmonary artery in the context of the Ross proce...

Towards fast and reliable estimations of 3D pressure, velocity and wall shear stress in aortic blood flow: CFD-based machine learning approach.

Computers in biology and medicine
In this work, we developed deep neural networks for the fast and comprehensive estimation of the most salient features of aortic blood flow. These features include velocity magnitude and direction, 3D pressure, and wall shear stress. Starting from 40...