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Light propagation in turbid mediums such as atmosphere, fluids, and biological tissues is a challenging problem which necessitates accurate simulation techniques to account for the effects of multiple scattering. The Monte Carlo method has long established itself as a gold standard and is widely adopted for simulating light transport, however, its computationally intensive nature often requires significant processing power and energy consumption. In this paper a novel, open source Monte Carlo algorithm is introduced which is specifically designed for use with energy-efficient processors, effectively addressing those challenges, while maintaining the accuracy/compatibility and outperforming existing solutions. The proposed implementation optimizes photon transport simulations by exploiting the unique capabilities of Apple's low-power, high-performance M-family of chips. The developed method has been implemented in an open-source software package, enabling seamless adaptation of developed algorithms for specific applications. The accuracy and performance are validated using comprehensive comparison with existing solvers commonly used for biomedical imaging. The results demonstrate that the new algorithm achieves comparable accuracy levels to those of existing techniques while significantly reducing computational time and energy consumption.
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http://dx.doi.org/10.1364/OE.496516 | DOI Listing |
J Healthc Sci Humanit
January 2024
Formerly Associate Professor of Epidemiology and Risk Analysis, Department of Pathobiology/Department of Graduate Public Health, College of Veterinary Medicine, Tuskegee University, Phone: (334) 524-1988, Email:
The COVID-19 pandemic is a highly infectious disease of paramount public health importance. COVID-19 is mainly transmitted via human-to-human contact. This could be through self-inoculation resulting from failure to observe proper hand hygiene and infection control practices.
View Article and Find Full Text PDFMed Phys
September 2025
Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, China.
Background: Dual-energy computed tomography (DECT) enhances material differentiation by leveraging energy-dependent attenuation properties particularly for carbon ion therapy. Accurate estimation of tissue elemental composition via DECT can improve quantification of physical and biological doses.
Objective: This study proposed a novel machine-learning-based DECT (ML-DECT) method to predict the physical density and mass ratios of H, C, N, O, P, and Ca.
Med Phys
September 2025
Medical Physics Unit, Department of Oncology, Faculty of Medicine, McGill University, Montréal, Québec, Canada.
Background: Se ( 120 days, 215 keV) offers advantages over Ir ( 74 days, 360 keV) as a high dose rate brachytherapy source due to its lower gamma energy and longer half-life. Despite its widespread use in industrial gamma radiography, a Se brachytherapy source has yet to be manufactured.
Purpose: A novel Se-based source design with a vanadium diselenide core, titled the SeCure source, was proposed.
Solid State Nucl Magn Reson
August 2025
Department of Chemistry & Biochemistry, Florida State University, Tallahassee, FL, 32306, USA; National High Magnetic Field Laboratory, Tallahassee, FL, 32310, USA. Electronic address:
Quadrupolar NMR crystallography guided crystal structure prediction (QNMRX-CSP) is a method for determining the crystal structures of organic solids. To date, our two previous QNMRX-CSP studies have relied upon on Cl solid-state NMR (SSNMR) spectroscopy, powder X-ray diffraction (PXRD), Monte-Carlo simulated annealing (MC-SA), and dispersion-corrected density functional theory (DFT-D2∗) calculations for the determination of crystal structures for organic HCl salts with known crystal structures, in order to benchmark the method and subject it to blind tests. Herein, we apply QNMRX-CSP for the de novo crystal structure determination of L-alaninamide HCl (L-Ala-NH), for which no crystal structure has been reported, using Cl SSNMR and PXRD data for structural prediction and refinement, along with C and N SSNMR data for subsequent structural validation.
View Article and Find Full Text PDFEcotoxicol Environ Saf
September 2025
Key Laboratory of Karst Georesources and Environment (Guizhou University), Ministry of Education, College of Resources and Environmental Engineering, Guizhou University, Guiyang 550025, China.
Despite global phase-out initiatives, legacy polychlorinated biphenyls (PCBs) remobilize in marine ecosystems as secondary emission sources, posing ecotoxicological and human health risks emerge through cross-trophic dietary exposure pathways. This study aimed to systematically examined the distribution, trophic transfer properties, and health risks of PCBs in six fish and eight invertebrate species from the Beibu Gulf in southern China, by stable isotope analysis, hierarchical cluster analysis, and Monte Carlo simulation. The ΣPCBs concentrations ranged from 0.
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