The accuracy of determining arsenic and lead using the optical technique Slurry–Total Reflection X-ray Fluorescence (Slurry-TXRF) was significantly enhanced through the application of a machine learning method, aimed at improving the ecological risk assessment of agricultural soils. The overlapping of the arsenic Kα signal at 10.55 keV with the lead Lα signal at 10.54 keV due to the relatively low resolution of TXRF could compromise the determination of lead. However, by applying a Partial Least Squares (PLS) machine learning algorithm, we mitigated interference variations, resulting in improved selectivity and accuracy. Specifically, the average percentage error was reduced from 15.6% to 9.4% for arsenic (RMSEP improved from 5.6 mg kg⁻¹ to 3.3 mg kg⁻¹) and from 18.9% to 6.8% for lead (RMSEP improved from 12.3 mg kg⁻¹ to 5.03 mg kg⁻¹) compared to the previous univariable model. This enhanced predictive accuracy, within the set of samples concentration range, is attributable to the efficiency of the multivariate calibration first-order advantage in quantifying the presence of interferents. The evaluation of X-ray fluorescence emission signals for 26 different synthetic calibration mixtures confirmed these improvements, overcoming spectral interferences. Additionally, the application of these models enabled the quantification of arsenic and lead in soils from a viticultural subregion of Chile, facilitating the estimation of ecological risk indices in a fast and reliable manner. The results indicate that the contamination level of these soils with arsenic and lead ranges from moderate to considerable.

Context . Astronomical observations at millimeter and submillimeter wavelengths strongly depend on the amount of precipitable water vapor (PWV) in the atmosphere, which directly affects the sky transparency and decreases the signal-to-noise ratio of the signals received by radio telescopes. Aims . Predictions of PWV at different forecasting horizons are crucial to supporting the telescope operations, engineering planning, scheduling and observational efficiency of radio observatories installed in the Chajnantor area in northern Chile. Methods . We developed and validated a long short-term memory (LSTM) deep-learning-based model to predict PWV at forecasting horizons of 12, 24, 36, and 48 hours using historical data from two 183 GHz radiometers and a weather station in the Chajnantor area. Results . We find that the LSTM method is able to predict PWV in the 12- and 24-hour forecasting horizons with a mean absolute percentage error of ~22% compared to ~36% for the traditional Global Forecast System method used by the Atacama Pathfinder Experiment, and the root mean square error is reduced by ~50%. Conclusions . We present a first application of deep learning techniques for preliminary predictions of PWV in the Chajnantor area. The prediction performance shows significant improvements compared to traditional methods in 12- and 24-hour time windows. We also propose strategies to improve our method on shorter (36 hour) forecasting timescales.

INPLASY202640048 Impacts of the use of Generative Artificial Intelligence in doctoral programmes: A systematic review based on knowledge and mobilisation management Keywords: Generative Artificial …