Patient Photograph Association With Radiologist Recommendations for Additional Imaging.

Objective: Assess whether display of a patient photograph in the electronic health record (EHR) alongside head and neck CT or MRI radiology examinations is associated with recommendations for additional imaging (RAI) and whether self-reported race modifies that association.

Methods: This multi-institution health care system retrospective observational study from June 1, 2021 to May 31, 2022 included all patients with a head/neck CT or MRI report. We investigated association of photograph with RAIs using mixed-effects models adjusting for age, sex, complexity score, race, and area deprivation index while conditioning on patient and radiologist.

Machine learning based quantification of fuel-air equivalence ratio and pressure from laser-induced plasma spectroscopy.

In this study, we demonstrate successful development of a predictive model that detects both the fuel-air equivalence ratio (ϕ) and local pressure prior to plasma formation via machine-learning from the laser-induced plasma spectra; the resulting model enables measurement of a wide range of fuel concentrations and pressures. The process of model acquisition is composed of three steps: (i) normalization of the spectra, (ii) feature extraction and selection, and (iii) training of an artificial neural network (ANN) with feature scores and the corresponding labels. In detail, the spectra were first normalized by the total emission intensity; then principal component analysis (PCA) or independent component analysis (ICA) was carried out for feature extraction and selection.

Combined use of TDLAS and LIBS for reconstruction of temperature and concentration fields.

A new technique is developed for reconstructing the temperature and species-concentration fields by employing tunable diode laser absorption spectroscopy (TDLAS) and laser-induced breakdown spectroscopy (LIBS) on axisymmetric combustion fields. For two-line thermometry, the uncertainties in linestrengths of the absorption lines may cause systematic errors in temperature and species concentration estimations. Thus, the radial profiles of water vapor concentration are obtained first using the LIBS, assuming that the combustion is complete; then, the radial temperature profiles are estimated from the radial profiles of absorption coefficient, as reconstructed from the absorbance profiles obtained using the TDLAS.

Latent Class Analysis for Multiple Discrete Latent Variables: A Study on the Association Between Violent Behavior Drug-Using Behaviors.

This paper proposes a new type of latent class analysis, joint latent class analysis (JLCA), which provides a set of principles for the systematic identification of the subsets of joint patterns for multiple discrete latent variables. Inferences about the parameters are obtained by a hybrid method of EM and Newton-Raphson algorithms. We apply JLCA in an investigation of adolescent violent behavior and drug-using behaviors.