Institut de recherche en informatique de Toulouse, Université de Toulouse, CNRS, Toulouse 31062, France.
Published: January 2024
AI Article Synopsis
Article Abstract
The Earth's radiative cooling is a key driver of climate. Determining how it is affected by greenhouse gas concentration is a core question in climate-change sciences. Due to the complexity of radiative transfer processes, current practices to estimate this cooling require the development and use of a suite of radiative transfer models whose accuracy diminishes as we move from local, instantaneous estimates to global estimates over the whole globe and over long periods of time (decades). Here, we show that recent advances in nonlinear Monte Carlo methods allow a paradigm shift: a completely unbiased estimate of the Earth's infrared cooling to space can be produced using a single model, integrating the most refined spectroscopic models of molecular gas energy transitions over a global scale and over years, all at a very low computational cost (a few seconds).
Purpose: Individual-level state-transition microsimulations (iSTMs) have proliferated for economic evaluations in place of cohort state transition models (cSTMs). Probabilistic economic evaluations quantify decision uncertainty and value of information (VOI). Previous studies show that iSTMs provide unbiased estimates of expected incremental net monetary benefits (EINMB), but statistical properties of iSTM-produced estimates of decision uncertainty and VOI remain uncharacterized.
Multivariate longitudinal ordinal and continuous data exist in many scientific fields. However, it is a rigorous task to jointly analyse them due to the complicated correlated structures of those mixed data and the lack of a multivariate distribution. The multivariate probit model, assuming there is a multivariate normal latent variable for each multivariate ordinal data, becomes a natural modeling choice for longitudinal ordinal data especially for jointly analysing with longitudinal continuous data.
Heavy metals, nitrate, and nitrite pose significant risks to public health and have raised substantial concern worldwide. This study aimed to investigate the content of nitrate, nitrite, and heavy metals, including Ba, Be, Ca, Fe, K, Li, Mg, Mn, Mo, and Na, in 30 bottled water brands in winter and summer in Tehran, Iran. Heavy metal contents in the samples were analyzed using Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES), and nitrate and nitrite contents were analyzed using Ion Chromatography (IC).
Purpose: To investigate sensitivity of contributing factors to heterogeneity corrections in ocular brachytherapy using iodine-125 (I) Collaborative Ocular Melanoma Study (COMS) plaques.
Material And Methods: Using egs_brachy, Monte Carlo (MC) simulations were performed for I COMS plaques (model: IsoAid IAI-125A). Homogeneous dose (D) was estimated under the American Association of Physicists in Medicine Task Group-43 assumptions, with 85 Gy prescribed to a 5 mm depth.
Non-invasive glucose monitoring is crucial for diabetes management. This study explores the use of photoacoustic (PA) signals based on optical rotation estimation at multiple depths for detection of glucose concentrations. Experiments were performed with glucose samples mixed in bovine serum albumin with different polarization incidences-vertical (V), 45° linear (P), and right circular (R) polarization.
