AIMC Topic: Monte Carlo Method

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Evaluation of coseismic landslide susceptibility by combining Newmark model and XGBoost algorithm.

PloS one
Coseismic landslides are among the most perilous geological disasters in hilly places after earthquakes. Precise assessment of coseismic landslide susceptibility is crucial for forecasting the effects of landslides and alleviating subsequent tragedie...

Last vertex splitting: a new retroactive Monte Carlo splitting technique applied to LINAC out-of-field dose computation.

Physics in medicine and biology
We propose a new variance reduction technique called last vertex splitting (LVS) designed to reduce computation time in Monte Carlo (MC) simulations for particles traversing high-attenuating media, such as the collimator and other beam-limiting devic...

Machine learning-based source apportionment and source-oriented probabilistic ecological risk assessment of heavy metals in urban green spaces.

Ecotoxicology and environmental safety
Global urbanization has significantly contributed to soil contamination by heavy metals (HMs), posing serious ecological risks, particularly within urban green spaces (UGS). This study focused on UGS soils in Lanzhou, a major river-valley city in Chi...

Video-based pupillometry using Fourier Mellin image correlation.

Scientific reports
We introduce a novel method for evaluating the pupil light reflex (PLR) response using digital video recordings. Expensive, specialized devices are replacing traditional penlight tests in emergency and neurotrauma departments, but they are not widely...

ComptoNet: a Compton-map guided deep learning framework for multi-scatter estimation in multi-source stationary CT.

Physics in medicine and biology
Multi-source stationary computed tomography (MSS-CT) offers significant advantages in medical and industrial applications due to its gantryless scan architecture and capability of simultaneous multi-source emission. However, the lack of anti-scatter ...

Metaverse-based deep learning framework for coronary artery stenosis classification using Monte Carlo Dropout-based ResNet-152.

Computers in biology and medicine
Metaverse offers an immersive healthcare platform that combines virtual reality (VR) and artificial intelligence (AI), providing a new approach to medical diagnostics. However, difficulties such as inadequate spatial resolution, uncertainty managemen...

AI-enhanced patient-specific dosimetry in I-131 planar imaging with a single oblique view.

Scientific reports
This study aims to enhance the dosimetry accuracy in I planar imaging by utilizing a single oblique view and Monte Carlo (MC) validated dose point kernels (DPKs) alongside the integration of artificial intelligence (AI) for accurate dose prediction w...

optiGAN: a deep learning-based alternative to optical photon tracking in Python-based GATE (10+).

Physics in medicine and biology
To accelerate optical photon transport simulations in the GATE medical physics framework using a generative adversarial network (GAN), while ensuring high modeling accuracy. Traditionally, detailed optical Monte Carlo methods have been the gold stand...

Edaphic and meteorological parameters as determinants of radon exhalation and its environmental implication in Peruvian agroecosystems.

Scientific reports
Radon exhalation is a natural process by which atoms of the radioactive gas radon diffuse in the soil and then exhale to an indoor and/or outdoor environment. High radon concentration levels, possibly from high radon exhalation rate levels, can gener...

Enhancing image quality in fast neutron-based range verification of proton therapy using a deep learning-based prior in LM-MAP-EM reconstruction.

Physics in medicine and biology
This study investigates the use of list-mode (LM) maximum(MAP) expectation maximization (EM) incorporating prior information predicted by a convolutional neural network for image reconstruction in fast neutron (FN)-based proton therapy range verifica...