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Generative AI (genAI) tools, such as ChatGPT or Copilot, are advertised to improve developer productivity and are being integrated into software development. However, misaligned trust, skepticism, and usability concerns can impede the adoption of such tools. Research also indicates that AI can be exclusionary, failing to support diverse users adequately. One such aspect of diversity is cognitive diversity -- variations in users' cognitive styles -- that leads to divergence in perspectives and interaction styles. When an individual's cognitive style is unsupported, it creates barriers to technology adoption. Therefore, to understand how to effectively integrate genAI tools into software development, it is first important to model what factors affect developers' trust and intentions to adopt genAI tools in practice? We developed a theoretically grounded statistical model to (1) identify factors that influence developers' trust in genAI tools and (2) examine the relationship between developers' trust, cognitive styles, and their intentions to use these tools in their work. We surveyed software developers (N=238) at two major global tech organizations: GitHub Inc. and Microsoft; and employed Partial Least Squares-Structural Equation Modeling (PLS-SEM) to evaluate our model. Our findings reveal that genAI's system/output quality, functional value, and goal maintenance significantly influence developers' trust in these tools. Furthermore, developers' trust and cognitive styles influence their intentions to use these tools in their work. We offer practical suggestions for designing genAI tools for effective use and inclusive user experience. 

The transport and deposition of firebrand particles is an important fire spread mechanism in wildland fires. These particles can be transported by wind over large distances and can ignite secondary fires upon landing. The transport of firebrands by wind is a complex, multiscale process that is largely controlled by interactions between the firebrand particles and the atmospheric wind. To account for the complex temporal evolution of atmospheric turbulence over large scales, the use of large-eddy simulation (LES) techniques is necessary. However, filtering of subgrid-scale (SGS) turbulence in LES hinders the accuracy of particle transport models. In this work, we employ a Lagrangian SGS model in an LES framework to investigate the effects of small-scale turbulence on the transport of mass- and size-changing firebrand particles. The impact of SGS turbulence was analyzed by comparing landing and trajectory statistics for firebrand and regular (fixed size and mass) particles under different Stokes numbers. It was found that the presence of SGS turbulence modifies the particle transport behavior, which is characterized by smaller spanwise dispersions but larger travel distances along the streamwise direction compared with particles under no SGS turbulence. As expected, the enhanced velocity field produced by the SGS model has larger influence on the statistics of firebrand particles compared with regular particles due to the time-evolving reduction in particle mass and size induced by pyrolysis.