Search

This work presents the design and testing of a seeder based on laser ablation capable of introducing gallium particles into a gaseous flow at elevated pressures typical of that found in practical combustion devices. The ability to seed such a flow with gallium particles is required to apply Ga-TLAF, a spatially and temporally resolved thermometry imaging technique well suited to harsh combustion environments. The design criteria for this gallium particle seeder are first listed and all the necessary details required to understand and replicate it are then provided. Next, the efficiency of gallium ablation is verified as a function of the ablation laser's fluence and repetition rate and of the pressure using gallium laser induced fluorescence and laser scattering measurements at ∼403 nm. For the conventional 355-nm nanosecond, Nd:YAG laser used for ablation in this study, data show that the quantity of gallium seeded into the flow can be conveniently modulated by varying the fluence of the ablation laser and/or its repetition rate. Data also show that the efficiency of ablation is marginally better for solid gallium than for liquid gallium, but that ablation of liquid gallium should be preferred to avoid a loss of ablation efficiency after some time. The temperature of the liquid gallium feedstock is found to be unimportant. SEM, EDX, and SPMS analyses show that laser ablation yields pure gallium particles with a characteristic size ranging at least from 50 nm to 10 μm and that the size distribution is insensitive to the pressure and to the ablation laser's fluence. Also important for future applications of Ga-TLAF in high-pressure flames, data show that the gallium LIF intensity recorded at room temperature or in the hot products of a flame is not significantly affected by pressure.

Background: Though there has been considerable effort to implement machine learning (ML) methods for health care, clinical implementation has lagged. Incorporating explainable machine learning (XML) methods through the development of a decision support tool using a design thinking approach is expected to lead to greater uptake of such tools., Objective: This work aimed to explore how constant engagement of clinician end users can address the lack of adoption of ML tools in clinical contexts due to their lack of transparency and address challenges related to presenting explainability in a decision support interface., Methods: We used a design thinking approach augmented with additional theoretical frameworks to provide more robust approaches to different phases of design. In particular, in the problem definition phase, we incorporated the nonadoption, abandonment, scale-up, spread, and sustainability of technology in health care (NASSS) framework to assess these aspects in a health care network. This process helped focus on the development of a prognostic tool that predicted the likelihood of admission to an intensive care ward based on disease severity in chest x-ray images. In the ideate, prototype, and test phases, we incorporated a metric framework to assess physician trust in artificial intelligence (AI) tools. This allowed us to compare physicians' assessments of the domain representation, action ability, and consistency of the tool., Results: Physicians found the design of the prototype elegant, and domain appropriate representation of data was displayed in the tool. They appreciated the simplified explainability overlay, which only displayed the most predictive patches that cumulatively explained 90% of the final admission risk score. Finally, in terms of consistency, physicians unanimously appreciated the capacity to compare multiple x-ray images in the same view. They also appreciated the ability to toggle the explainability overlay so that both options made it easier for them to assess how consistently the tool was identifying elements of the x-ray image they felt would contribute to overall disease severity., Conclusions: The adopted approach is situated in an evolving space concerned with incorporating XML or AI technologies into health care software. We addressed the alignment of AI as it relates to clinician trust, describing an approach to wire framing and prototyping, which incorporates the use of a theoretical framework for trust in the design process itself. Moreover, we proposed that alignment of AI is dependent upon integration of end users throughout the larger design process. Our work shows the importance and value of engaging end users prior to tool development. We believe that the described approach is a unique and valuable contribution that outlines a direction for ML experts, user experience designers, and clinician end users on how to collaborate in the creation of trustworthy and usable XML-based clinical decision support tools., (©Michael Shulha, Jordan Hovdebo, Vinita D’Souza, Francis Thibault, Rola Harmouche. Originally published in JMIR Formative Research (https://formative.jmir.org), 16.04.2024.)