In therapeutic ultrasound, the presence of shock waves can be significant due to the use of high intensity beams, as well as due to shock formation during inertial cavitation. Although modeling of such strongly nonlinear waves can be carried out using spectral methods, such calculations are typically considered impractical, since accurate calculations often require hundreds or even thousands of harmonics to be considered, leading to prohibitive computational times. Instead, time-domain algorithms which generally utilize Godunov-type finite-difference schemes are commonly used. Although these time domain methods can accurately model steep shock wave fronts, unlike spectral methods they are inherently unsuitable for modeling realistic tissue dispersion relations. Motivated by the need for a more general model, the use of Gegenbauer reconstructions as a postprocess tool to resolve the band-limitations of the spectral methods are investigated. The present work focuses on eliminating the Gibbs phenomenon when representing a steep wave front using a limited number of harmonics. Both plane wave and axisymmetric 2D transducer problems will be presented to characterize the proposed method.
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http://dx.doi.org/10.1121/1.3621485 | DOI Listing |
J Neuroinflammation
January 2025
Department of Ophthalmology, Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, 21231, USA.
Background: The retinal degenerative diseases retinitis pigmentosa (RP) and atrophic age- related macular degeneration (AMD) are characterized by vision loss from photoreceptor (PR) degeneration. Unfortunately, current treatments for these diseases are limited at best. Genetic and other preclinical evidence suggest a relationship between retinal degeneration and inflammation.
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January 2025
Department of Ophthalmology, Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, 2630 Sugitani, Toyama, 930-0194, Japan.
To assess retinal pigment epithelium (RPE) tears in eyes which underwent pars plana vitrectomy (PPV) for submacular hemorrhage (SMH) secondary to age-related macular degeneration and to investigate the prognostic factors of visual outcomes. This study was a retrospective, observational case series that included 24 eyes of 24 patients who underwent PPV with subretinal tissue plasminogen activator and air for SMH. RPE tears were investigated using spectral-domain or swept-source optical coherence tomography images with raster scan, combined confocal scanning laser ophthalmoscope near-infrared images and color fundus photographs.
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January 2025
School of Information Engineering, Tianjin University of Commerce, Tianjin, China.
Deep learning is a double-edged sword. The powerful feature learning ability of deep models can effectively improve classification accuracy. Still, when the training samples for each class are limited, it will not only face the problem of overfitting but also significantly affect the classification result.
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January 2025
Rashpetco Company, Cairo, Egypt.
This study presents a comprehensive workflow to detect low seismic amplitude gas fields in hydrocarbon exploration projects, focusing on the West Delta Deep Marine (WDDM) concession, offshore Egypt. The workflow integrates seismic spectral decomposition and machine learning algorithms to identify subtle anomalies, including low seismic amplitude gas sand and background amplitude water sand. Spectral decomposition helps delineate the fairway boundaries and structural features, while Amplitude Versus Offset (AVO) analysis is used to validate gas sand anomalies.
View Article and Find Full Text PDFNeural Netw
January 2025
Department of Mathematics, Southern Methodist University, Dallas, 75275, TX, USA. Electronic address:
In this paper, we derive diffusion equation models in the spectral domain to study the evolution of the training error of two-layer multiscale deep neural networks (MscaleDNN) (Cai and Xu, 2019; Liu et al., 2020), which is designed to reduce the spectral bias of fully connected deep neural networks in approximating oscillatory functions. The diffusion models are obtained from the spectral form of the error equation of the MscaleDNN, derived with a neural tangent kernel approach and gradient descent training and a sine activation function, assuming a vanishing learning rate and infinite network width and domain size.
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