Analysis of Extended X-Ray Absorption Fine Structure (EXAFS) Data Using Artificial Intelligence Techniques

We have addressed the issue of improper and unreliable analysis of materials characterization data by developing an artificial intelligence based methodology that can reliably and more efficiently analyze experimental results from extended X-ray absorption fine structure (EXAFS) measurements. Such methods help address growing reproducibility problems that are slowing research progress, discouraging the quest for research excellence, and inhibiting effective technology transfer and manufacturing innovation. We have developed a machine learning system for automated analysis of EXAFS spectroscopy measurements and demonstrated its effectiveness on measurements collected at powerful, third generation synchrotron radiation facilities. Specifically, the system uses a genetic algorithm to efficiently find sets of structural parameters that lead to high quality fits of the experimental spectra. A human analyst suggests a set of chemical compounds potentially present in the sample, used as theoretical standards. The algorithm then searches the large multidimensional space of combinations of these materials to determine the set of structural paths using the theoretical standards that best reproduces the experimental data. The algorithm further calculates a goodness of fit value from the suggested standards that can be used to identify the chemical moieties present in the measured sample

Using risk model judgements to better understand perceptions of synergistic risks

Numerous scientific studies show that risk factors can interact to synergistically increase the likelihood of certain adverse and life-threatening outcomes. Yet, the extent to which individuals know that specific risk factor combinations present ‘synergistic risks’ is unclear and little is known about the determinants of such knowledge. This is largely because epistemological progress concerning this topic has been frustrated by a reliance on metrics that have latterly been judged to be of questionable validity. To address this issue, this paper presents two studies that assess an alternative approach (i.e., risk model judgements) which requires respondents to judge the risk for a factor combination relative to, rather than in isolation from, the risk attributable to each constituent factor. Results from both studies indicate that risk model judgements overcome the drawbacks of traditional metrics. More importantly, the results provide epistemological insights into what can determine whether an individual understands that a factor combination presents a synergistic risk; these determinants include experiential and intuitive insights into the effects of combining specific risk factors, domain-specific judgemental experience and exposure to effective learning opportunities. These findings can be utilized in interven- tions aimed at helping individuals to make better decisions concerning multiple risk factors