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Silicon-based hybrid photon-counting pixel detectors have become the standard for diffraction experiments of all types at low and moderate X-ray energies. More recently, hybrid pixel detectors with high- materials have become available, opening up the benefits of this technology for high-energy diffraction experiments. However, detection layers made of high- materials are less perfect than those made of silicon, so care must be taken to correct the data in order to remove systematic errors in detector response introduced by inhomogeneities in the detection layer, in addition to the variation of the response of the electronics. In this paper we discuss the steps necessary to obtain the best-quality powder diffraction data from these detectors, and demonstrate that these data are significantly superior to those acquired with other high-energy detector technologies.
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http://dx.doi.org/10.1107/S1600576724010033 | DOI Listing |
Front Plant Sci
September 2025
College of Big Data, Yunnan Agricultural University, Kunming, China.
Introduction: Accurate identification of cherry maturity and precise detection of harvestable cherry contours are essential for the development of cherry-picking robots. However, occlusion, lighting variation, and blurriness in natural orchard environments present significant challenges for real-time semantic segmentation.
Methods: To address these issues, we propose a machine vision approach based on the PIDNet real-time semantic segmentation framework.
Neural Netw
September 2025
organization=Chongqing Key Laboratory of Computer Network and Communication Technology, School of Computer Science and Technology (National Exemplary Software School), Chongqing University of Posts and Telecommunications, city=Chongqing, postcode=400065, country=China. Electronic address: tianh519@1
Image deblurring and compression-artifact removal are both ill-posed inverse problems in low-level vision tasks. So far, although numerous image deblurring and compression-artifact removal methods have been proposed respectively, the research for explicit handling blur and compression-artifact coexisting degradation image (BCDI) is rare. In the BCDI, image contents will be damaged more seriously, especially for edges and texture details.
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August 2025
Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming, China.
Introduction: Rice is an important food crop but is susceptible to diseases. However, currently available spot segmentation models have high computational overhead and are difficult to deploy in field environments.
Methods: To address these limitations, a lightweight rice leaf spot segmentation model (MV3L-MSDE-PGFF-CA-DeepLabv3+, MMPC-DeepLabv3+) was developed for three common rice leaf diseases: rice blast, brown spot and bacterial leaf blight.
Front Vet Sci
August 2025
Unitat mixta d'investigació IRTA-UAB en Sanitat Animal, Centre de Recerca en Sanitat Animal (CReSA), Campus de la Universitat Autònoma de Barcelona (UAB), Bellaterra, Spain.
Introduction: Detection of porcine circovirus 2 (PCV2) in lymphoid tissues is essential for diagnostic and research purposes. hybridisation (ISH) enables the localisation of viral genomes in tissue sections but is traditionally assessed visually, which may introduce subjectivity.
Methods: This study developed an automated pixel classifier to quantify the PCV2 genome using RNAscope ISH technology.
Med Phys
September 2025
Heidelberg Institute for Radiation Oncology (HIRO), National Center for Research in Radiation Oncology (NCRO), Heidelberg, Germany.
Background: As advanced treatment plans increasingly include optimizing both dose and linear energy transfer (LET), there is a growing demand for tools to measure LET in clinical settings. Although various detection systems have been investigated in this pursuit, the scarcity of detectors capable of providing per-ion data for a fast and streamlined verification of LET distributions remains an issue. Silicon pixel detector technology bridges this gap by enabling rapid tracking of single-ion energy deposition.
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