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    Estimation of the systematic error constituents in the calculations of the critical systems by the Monte Carlo method by V. V. Galchenko, V. I. Makodym

    Published 2009-09-01
    “…Analysis of factors which influence on systematic error formation in the calculations of the critical systems by the Monte Carlo method for the MCNP and SCALE computer codes is executed. It has been shown that non-linearity of iteration process of calculation and uncertainty in initial neutron sources distributing effect on a systematic error. …”
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    Rapid Path Planning Algorithm for Percutaneous Rigid Needle Biopsy Based on Optical Illumination Principles by Jian Liu, Shuai Kang, Juan Ren, Dongxia Zhang, Bing Niu, Kai Xu

    Published 2025-03-01
    “…Timely trajectory planning is essential, as it relies on preoperative CT imaging. Prolonged processing times increase the risk of patient movement, rendering the planned path invalid. …”
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    Benchmarking Big Data Systems: Performance and Decision-Making Implications in Emerging Technologies by Leonidas Theodorakopoulos, Aristeidis Karras, Alexandra Theodoropoulou, Georgios Kampiotis

    Published 2024-11-01
    “…In this study, we benchmark another two widely utilized graph processing systems, Apache Spark GraphX and Apache Fink, concerning the key performance criterion by means of response time, scalability, and computational complexity. …”
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    First study of the RuPatient health information system with optical character recognition of medical records based on machine learning by A. A. Komkov, V. P. Mazaev, S. V. Ryazanova, D. N. Samochatov, E. V. Koshkina, E. V. Bushueva, O. M. Drapkina

    Published 2022-01-01
    “…The number of recognized words was 549±174,9 vs 522,4±215,6 (p=0,5), critical errors in words — 2,1±1,6 vs 4,4±2,8 (p<0,001), non-critical errors — 10,3±4,3 vs 5,6±3,3 (p<0,001) for RuPatient HIS and optical character recognition application for a personal computer, respectively.Conclusion. The developed RuPatient HIS, which includes a module for recognizing medical records and entering data into the corresponding fields, significantly increases the document management efficiency with high quality of optical character recognition based on neural network technologies and the automation of filling process.…”
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