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http://dx.doi.org/10.1200/JCO.2005.09.045 | DOI Listing |
Radiother Oncol
January 2025
Radiotherapy Centre, National Institute of Oncology, Budapest, Hungary; Department of Oncology, Semmelweis University, Budapest, Hungary.
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.
View Article and Find Full Text PDFSci Total Environ
January 2025
Department of Earth Resources & Environmental Engineering, Hanyang University, Seoul 04763, Republic of Korea. Electronic address:
Concentrated animal feeding operation facility in modern livestock industry is pointed out as a point site causing environmental pollution due to massive generation of manure. While livestock manure is conventionally treated through biological processes, composting and anaerobic digestion, these practices pose difficulties in achieving efficient carbon utilization. To address this, this study suggests a pyrolytic valorization of livestock manure, with a focus on enhancing syngas production.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Electrical and Electronic Engineering, Wuhan Polytechnic University, Wuhan 430048, China.
Tea bud localization detection not only ensures tea quality, improves picking efficiency, and advances intelligent harvesting, but also fosters tea industry upgrades and enhances economic benefits. To solve the problem of the high computational complexity of deep learning detection models, we developed the Tea Bud DSCF-YOLOv8n (TBF-YOLOv8n)lightweight detection model. Improvement of the Cross Stage Partial Bottleneck Module with Two Convolutions(C2f) module via efficient Distributed Shift Convolution (DSConv) yields the C2f module with DSConv(DSCf)module, which reduces the model's size.
View Article and Find Full Text PDFMicromachines (Basel)
January 2025
Research Center for Novel Computing Sensing and Intelligent Processing, Zhejiang Lab, Hangzhou 311100, China.
General matrix multiplication (GEMM) in machine learning involves massive computation and data movement, which restricts its deployment on resource-constrained devices. Although data reuse can reduce data movement during GEMM processing, current approaches fail to fully exploit its potential. This work introduces a sparse GEMM accelerator with a weight-and-output stationary (WOS) dataflow and a distributed buffer architecture.
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