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Purpose: The JSMP01 dosimetry protocol had adopted the provisional Co calibration coefficient [Formula: see text], namely, the product of exposure calibration coefficient N and conversion coefficient k . After that, the absorbed dose to water D standard was established, and the JSMP12 protocol adopted the [Formula: see text] calibration. In this study, the influence of the calibration shift on the measurement of D among users was analyzed.
Materials And Methods: The intercomparison of the D using an ionization chamber was annually performed by visiting related hospitals. Intercomparison results before and after the calibration shift were analyzed, the deviation of D among users was re-evaluated, and the cause of deviation was estimated.
Results: As a result, the stability of LINAC, calibration of the thermometer and barometer, and collection method of ion recombination were confirmed. The statistical significance of standard deviation of D was not observed, but that of difference of D among users was observed between N and [Formula: see text] calibration.
Conclusion: Uncertainty due to chamber-to-chamber variation was reduced by the calibration shift, consequently reducing the uncertainty among users regarding D . The result also pointed out uncertainty might be reduced by accurate and detailed instructions on the setup of an ionization chamber.
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http://dx.doi.org/10.1007/s11604-017-0644-9 | DOI Listing |
ACS Nano
September 2025
State Key Lab of New Ceramic Materials, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China.
SnSe is a layered semiconductor with intrinsically low thermal conductivity, making it a promising candidate for thermoelectric and thermal management applications. However, detailed measurements of the intrinsic thermal conductivity of SnSe nanosheets grown by chemical vapor deposition (CVD) remain scarce. Here, monocrystalline SnSe nanosheets were synthesized by CVD, with systematic investigation of thickness-dependent in-plane thermal conductivity.
View Article and Find Full Text PDFPLoS One
September 2025
Department of design fundamentals, Faculty of Mechanical Engineering, Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam.
The slider-crank mechanism (SCM) is fundamental to various mechanical systems. However, optimizing its dynamic performance remains a pressing challenge due to excessive torque, joint reactions, and energy consumption. This study introduces two key innovations to address these challenges: (1) the integration of springs into SCM to optimize dynamic performance and (2) a novel hybrid optimization approach combining the Conjugate Direction with Orthogonal Shift (CDOS) method and Parameter Space Investigation (PSI).
View Article and Find Full Text PDFIEEE Trans Image Process
September 2025
3D imaging based on phase-shifting structured light is widely used in industrial measurement due to its non-contact nature. However, it typically requires a large number of additional images (multi-frequency heterodyne (M-FH) method) or introduces intensity features that compromise accuracy (space domain modulation phase-shifting (SDM-PS) method) for phase unwrapping, and it remains sensitive to motion. To overcome these issues, this article proposes a nonlinear phase coding-based stereo phase unwrapping (NPC-SPU) method that requires no additional patterns while maintaining measurement accuracy.
View Article and Find Full Text PDFIEEE Trans Neural Netw Learn Syst
September 2025
In this article, we first tackle a more realistic domain adaptation (DA) setting: source-free blending-target DA (SF-BTDA), where we cannot access to source-domain data while facing mixed multiple target domains without any domain labels in prior. Compared to existing DA scenarios, SF-BTDA generally faces the coexistence of different label shifts in different targets, along with noisy target pseudolabels generated from the source model. In this article, we propose a new method called evidential graph contrastive alignment (EGCA) to decouple the blending-target domain and alleviate the effect of noisy target pseudolabels.
View Article and Find Full Text PDFStat Biosci
August 2024
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
Large-scale genomics data combined with Electronic Health Records (EHRs) illuminate the path towards personalized disease management and enhanced medical interventions. However, the absence of "gold standard" disease labels makes the development of machine learning models a challenging task. Additionally, imbalances in demographic representation within datasets compromise the development of unbiased healthcare solutions.
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