LIBS and Raman spectroscopy in tandem with machine learning for interrogating weatherization of lithium hydride.

Lithium compounds such as lithium hydride ( ) and anhydrous lithium hydroxide ( ) have various applications in industry but are highly reactive when exposed to moisture and . These reactions create new molecular compounds that degrade applications. Environmental conditions such as temperature and moisture are examples of environmental conditions that are of interest for these reactions.

Machine learning in analytical spectroscopy for nuclear diagnostics [Invited].

Analytical spectroscopy methods have shown many possible uses for nuclear material diagnostics and measurements in recent studies. In particular, the application potential for various atomic spectroscopy techniques is uniquely diverse and generates interest across a wide range of nuclear science areas. Over the last decade, techniques such as laser-induced breakdown spectroscopy, Raman spectroscopy, and x-ray fluorescence spectroscopy have yielded considerable improvements in the diagnostic analysis of nuclear materials, especially with machine learning implementations.

Development of advanced machine learning models for analysis of plutonium surrogate optical emission spectra.

This work investigates and applies machine learning paradigms seldom seen in analytical spectroscopy for quantification of gallium in cerium matrices via processing of laser-plasma spectra. Ensemble regressions, support vector machine regressions, Gaussian kernel regressions, and artificial neural network techniques are trained and tested on cerium-gallium pellet spectra. A thorough hyperparameter optimization experiment is conducted initially to determine the best design features for each model.

Rapid quantitative analysis of trace elements in plutonium alloys using a handheld laser-induced breakdown spectroscopy (LIBS) device coupled with chemometrics and machine learning.

We present the first reported quantification of trace elements in plutonium via a portable laser-induced breakdown spectroscopy (LIBS) device and demonstrate the use of chemometric analysis to enhance the handheld device's sensitivity and precision. Quantification of trace elements such as iron and nickel in plutonium metal via LIBS is a challenging problem due to the complex nature of the plutonium optical emission spectra. While rapid analysis of plutonium alloys has been demonstrated using portable LIBS devices, such as the SciAps Z300, their detection limits for trace elements are severely constrained by their achievable pulse power and length, light collection optics, and detectors.

Optimising aerial military medical evacuation dispatching decisions via operations research techniques.

Senior military leaders and medical practitioners continuously seek new ways to improve the performance and organisation of deployed medical evacuation (MEDEVAC) systems to minimise mortality rates of combat casualties. The objective of this paper is to highlight how recent research in the fields of operations research and machine learning can be leveraged to better inform the implementation and modification of current and future MEDEVAC tactics, techniques and procedures for combat operations in a deployed environment. More specifically, this paper discusses state-of-the-art techniques that optimise the management of MEDEVAC assets prior to and during combat operations.