A Monte Carlo model has been developed for optical coherence tomography (OCT). A geometrical optics implementation of the OCT probe with low-coherence interferometric detection was combined with three-dimensional stochastic Monte Carlo modelling of photon propagation in the homogeneous sample medium. Optical properties of the sample were selected to simulate intralipid and blood, representing moderately (g = 0.7) and highly (g = 0.99) anisotropic scattering respectively. For shallow optical depths in simulated intralipid (<3 scattering mean free path (mfp) units), the number of detected backscattered photons followed the extinction-single-backscatter model, and OCT was found to detect only minimally scattered photons. Within this depth range the backscatter positions of detected photons corresponded well with the nominal focus position of the probe. For propagation to deeper positions in intralipid, localization of backscattering was quickly lost due to detection of stray photons, and the number of detected photons remained constant with increasing depth in the non-absorbing medium. For strongly forward-directed scattering in simulated blood, the number of detected photons approached the extinction-single-backscatter model only for very shallow depths (<2 mfp units). However, backscattering positions for detected photons correlated well with the nominal focus position of the probe even for optical depths greater than 40 mfp units.
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http://dx.doi.org/10.1088/0031-9155/43/10/024 | DOI Listing |
Integr Environ Assess Manag
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Institute of Environmental Toxicology, Western Washington University, Bellingham, Washington, USA.
Traditional ecological and human health risk assessment often relies on deterministic frameworks that preclude the presence of variability or uncertainty among input parameters characterizing exposure, effects, and risk. To promote increased realism and generate more robust risk management decisions, probabilistic risk assessment (PRA) has been introduced as a foundational grouping of techniques that seeks to broadly characterize variability among its components. While multiple methods exist (e.
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Technology Institute, University of Passo Fundo, Passo Fundo, RS, Brazil.
Food waste offers a potential source for bioethanol production, but productivity depends on the chemical composition of the raw materials and the processes involved. However, assessment of the environmental sustainability of these processes is often absent and can be carried out using the Life Cycle Assessment (LCA) methodology. This study aimed to perform an LCA on bioethanol production from mixtures of different wastes, including tubers, fruits, and processed foods, focusing on the gate-to-gate phase.
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Institut Curie, Université PSL, CNRS UMR3347, Inserm U1021, Signalisation Radiobiologie et Cancer, Orsay, France.
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Ionizing and Non-Ionizing Radiation Protection Research Center (INIRPRC), Shiraz University of Medical Sciences, Shiraz, Iran.
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