Charge exchange spectroscopy using spatial heterodyne spectrometer in the large helical device

In this study, the use of a spatial heterodyne spectrometer (SHS) to measure the toroidal flow velocity (Vf) and the ion temperature (????????6+) of the C6+ impurity ion by charge exchange spectroscopy was explored. The instrumental width (IW) of the SHS (aperture size = 16.77 mm2, etendue = 2.9867 mm2sr) was extrapolated to be 0.09 nm, which is half of the 0.17 nm IW extrapolated for a conventionally used dispersive spectrometer (DS) (aperture size = 2.6 mm2, etendue = 0.2605 mm2sr). The resulting Vf and ????????6+ measurements were found to be in good agreement with those measured using the DS

Machine learning-based prediction of the electron energy distribution function and electron density of argon plasma from the optical emission spectra

Arellano F.J., Kusaba M., Wu S., et al. Journal of Vacuum Science and Technology A 42, 053001 (2024) https://doi.org/10.1116/6.0003731.Optical emission spectroscopy (OES) is a highly valuable tool for plasma characterization due to its nonintrusive and versatile nature. The intensities of the emission lines contain information about the parameters of the underlying plasma-electron density n e and temperature or, more generally, the electron energy distribution function (EEDF). This study aims to obtain the EEDF and n e from the OES data of argon plasma with machine learning (ML) techniques. Two different models, i.e., the Kernel Regression for Functional Data (KRFD) and an artificial neural network (ANN), are used to predict the normalized EEDF and Random Forest (RF) regression is used to predict n e . The ML models are trained with computed plasma data obtained from Particle-in-Cell/Monte Carlo Collision simulations coupled with a collisional-radiative model. All three ML models developed in this study are found to predict with high accuracy what they are trained to predict when the simulated test OES data are used as the input data. When the experimentally measured OES data are used as the input data, the ANN-based model predicts the normalized EEDF with reasonable accuracy under the discharge conditions where the simulation data are known to agree well with the corresponding experimental data. However, the capabilities of the KRFD and RF models to predict the EEDF and n e from experimental OES data are found to be rather limited, reflecting the need for further improvement of the robustness of these models

Knowledge regarding diseases caused by improper waste management among selected residents of Barangay Sampaloc IV, Dasmarinas City, Cavite.

Demographic profiles that were used in the study are age and educational attainment. The descriptive method of research was employed. Researchers used a self-made instrument composed of 20 statements answerable by true or false to investigate their knowledge regarding the diseases caused by improper waste management. Respondents were determined using quota sampling. Using Slovin’s Formula, researchers had a sample of 393 respondents aged 18 years old and above. The data was treated using frequency, percentage, mean, standard deviation, Chi-square, Duncan’s multiple range tests and ANOVA. Based on the results, 1) the profile showed that majority of the respondents are aged 18-35 years old and most of them reached high school level; 2) the knowledge of the respondents on diseases caused by improper waste management is good; 3) among the variables included significant findings in the knowledge of the respondents was only indicated when grouped according to educational attainment. Those who have reached college and high school level have a better knowledge than those who does not have any formal education