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Noise reduction in brain magnetic resonance imaging using adaptive wavelet thresholding based on linear prediction factor.
Front Neurosci
January 2025
Graduate Program in Electrical Engineering, Federal University of Pará - UFPA, Belém, Brazil.
Introduction: Wavelet thresholding techniques are crucial in mitigating noise in data communication and storage systems. In image processing, particularly in medical imaging like MRI, noise reduction is vital for improving visual quality and accurate analysis. While existing methods offer noise reduction, they often suffer from limitations like edge and texture loss, poor smoothness, and the need for manual parameter tuning.
View Article and Find Full Text PDFModeling effects of climate change on crop phenology and yield of wheat-maize cropping system and exploring sustainable solutions.
J Sci Food Agric
January 2025
Center of Research and Innovation, Asia International University, Bukhara, Uzbekistan.
Background: Wheat-maize cropping systems in semi-arid regions are expected to be affected by climate change in the future, which is alarming for global food security, environmental sustainability and socioeconomic development. Therefore, management practices like optimized plant geometry and fertilization need to be explored to counter these expected threats. To do this, the APSIM model was calibrated using 5-year data (from 2017/2018 to 2022) regarding yield, biomass, plant height, emergence, anthesis and crop maturity of wheat and maize from farmer fields.
View Article and Find Full Text PDFXIS-PM: A daily spatiotemporal machine-learning model for PM in the contiguous United States.
Environ Res
January 2025
Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA; The Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva, Israel.
Air-pollution monitoring is sparse across most of the United States, so geostatistical models are important for reconstructing concentrations of fine particulate air pollution (PM) for use in health studies. We present XGBoost-IDW Synthesis (XIS), a daily high-resolution PM machine-learning model covering the contiguous US from 2003 through 2023. XIS uses aerosol optical depth from satellites and a parsimonious set of additional predictors to make predictions at arbitrary points, capturing near-roadway gradients and allowing the estimation of address-level exposures.
View Article and Find Full Text PDFControlling the energies of the single-rotor large wind turbine system using a new controller.
Sci Rep
January 2025
Department of Electrical Engineering, College of Engineering, King Khalid University, P.O. Box 394, Abha, 61421, KSA, Saudi Arabia.
In wind energy generation systems, ensuring high energy quality is critical but is often compromised due to the limited performance and durability of conventional regulators. To address this, this work presents a novel controller for managing the machine-side inverter of a single-rotor large wind turbine system using an induction machine-type generator. The proposed controller is designed using proportional, integral, and derivative error-based mechanisms, which fundamentally differ from traditional proportional-integral (PI) regulators.
View Article and Find Full Text PDFImproving MRI turbulence quantification by addressing the measurement errors caused by the derivatives of the turbulent velocity field - Sequence development and in-vitro validation.
Magn Reson Imaging
January 2025
Institute of Fluid Mechanics, University of Rostock, Rostock, Germany.
Purpose: To improve the current method for MRI turbulence quantification which is the intravoxel phase dispersion (IVPD) method. Turbulence is commonly characterized by the Reynolds stress tensor (RST) which describes the velocity covariance matrix. A major source for systematic errors in MRI is the sequence's sensitivity to the variance of the derivatives of velocity, such as the acceleration variance, which can lead to a substantial measurement bias.
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