The accuracy of parton-shower simulations is often a limiting factor in the interpretation of data from high-energy colliders. We present the first formulation of parton showers with accuracy 1 order beyond state-of-the-art next-to-leading logarithms, for classes of observables that are dominantly sensitive to low-energy (soft) emissions, specifically nonglobal observables and subjet multiplicities. This represents a major step toward general next-to-next-to-leading logarithmic accuracy for parton showers.
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http://dx.doi.org/10.1103/PhysRevLett.131.161906 | DOI Listing |
Molecules
December 2024
School of Pharmacy, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Shanghai 201203, China.
In this study, we developed a colloidal gold immunochromatographic strip (CGIS) method that used the matrix-matched calibration curves of contamination ratio models to quantitatively determine the total aflatoxin in five herbal medicines. This approach addresses issues related to false results and poor accuracy associated with conventional methods. The CGIS was analyzed using a Vertu touch reader, and the matrix-matched calibration was established based on the absorbance ratios of the T and C lines, as well as the logarithmic values of the total aflatoxin concentrations.
View Article and Find Full Text PDFJ Vis
January 2025
Department of Psychology, University of Washington, Seattle, WA, USA.
The population receptive field (pRF) method, which measures the region in visual space that elicits a blood-oxygen-level-dependent (BOLD) signal in a voxel in retinotopic cortex, is a powerful tool for investigating the functional organization of human visual cortex with fMRI (Dumoulin & Wandell, 2008). However, recent work has shown that pRF estimates for early retinotopic visual areas can be biased and unreliable, especially for voxels representing the fovea. Here, we show that a log-bar stimulus that is logarithmically warped along the eccentricity dimension produces more reliable estimates of pRF size and location than the traditional moving bar stimulus.
View Article and Find Full Text PDFSci Rep
December 2024
Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt.
The rice plant is one of the most significant crops in the world, and it suffers from various diseases. The traditional methods for rice disease detection are complex and time-consuming, mainly depending on the expert's experience. The explosive growth in image processing, computer vision, and deep learning techniques provides effective and innovative agriculture solutions for automatically detecting and classifying these diseases.
View Article and Find Full Text PDFJMIR Biomed Eng
December 2024
School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, United States.
Background: Cell concentration in body fluid is an important factor for clinical diagnosis. The traditional method involves clinicians manually counting cells under microscopes, which is labor-intensive. Automated cell concentration estimation can be achieved using flow cytometers; however, their high cost limits accessibility.
View Article and Find Full Text PDFSyst Rev
December 2024
School of Medicine, Academic Unit of Translational Medical Sciences, University of Nottingham, Nottingham, UK.
Background: One of the most challenging aspects of treating patients facing primary ovarian insufficiency, especially those eligible for controlled ovarian hyperstimulation (COH), is the assessment of ovarian function and response to stimulatory protocols in terms of the number of oocytes retrieved. The lack of consistency between studies regarding the best parameter for response evaluation necessitates a comprehensive statistical analysis of the most commonly utilized ovarian reserve markers (ORM). This systematic review and meta-analysis aims to establish the optimal metric for assessing ovarian reserve among COH candidates.
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