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1. Microscopic modeling of attention-based movement behaviors

   https://doi.org/10.1016/j.trc.2024.104583  

   Li, Danrui; Schwartz, Mathew; Sohn, Samuel S; Yoon, Sejong; Pavlovic, Vladimir; Kapadia, Mubbasir (May 2024, Transportation Research Part C: Emerging Technologies)
2. Connectome-Based Predictive Modeling of Creativity Anxiety

   https://doi.org/10.1016/j.neuroimage.2020.117469  

   Ren, Zhiting; Daker, Richard J; Shi, Liang; Sun, Jiangzhou; Beaty, Roger E; Wu, Xinran; Chen, Qunlin; Yang, Wenjing; Lyons, Ian M; Green, Adam E; et al (January 2021, NeuroImage)

   While a recent upsurge in the application of neuroimaging methods to creative cognition has yielded encouraging progress toward understanding the neural underpinnings of creativity, the neural basis of barriers to creativity are as yet unexplored. Here, we report the first investigation into the neural correlates of one such recently identified barrier to creativity: anxiety specific to creative thinking, or creativity anxiety (Daker et al., 2019). Wee mployed a machine-learning technique for exploring relations between functional connectivity and behavior (connectome-based predictive modeling; CPM) to investigate the functional connections underlying creativity anxiety. Using whole-brain resting-state functional connectivity data, we identified a network of connections or “edges” that predicted individual differences in creativity anxiety, largely comprising connections within and between regions of the executive and default networks and the limbic system. We then found that the edges related to creativity anxiety identified in one sample generalize to predict creativity anxiety in an independent sample. We additionally found evidence that the network of edges related to creativity anxiety were largely distinct from those found in previous work to be related to divergent creative ability (Beaty et al., 2018). In addition to being the first work on the neural correlates of creativity anxiety, this research also included the development of a new Chinese-language version of the Creativity Anxiety Scale, and demonstrated that key behavioral findings from the initial work on creativity anxiety are replicable across cultures and languages. 

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3. Navier-Stokes-based modeling of the clarinet

   Giordano, N.; Thacker, J. W. (January 2019, Proceedings of the International Symposium on Musical and Room Acoustics, Detmold, Germany)

   Results are presented from a modeling study of the clarinet in which the air flow through the instrument is calculated using the Navier-Stokes equations. The reed is modeled as an Euler-Bernoulli beam with damping whose motion is driven by the pressure in the mouthpiece. Damping of the reed due to its contact with the lip is studied and shown to be crucial to achieve oscillations in which the reed vibrates at the lowest resonant frequency of the instrument, producing sound at that frequency. This finding is consistent with previous studies in which a clarinet is excited with an artificial blowing machine. 

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4. Kinematic Modeling of Origami-based Forcep Design

   Abulon, D. J.; McCarthy, J. M. (July 2019, Proceedings of the ASME 2019 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference)

   In the design of practical grasping tools such as forceps or grippers, it may be desirable to create a compact, lightweight, and easily manufactured tool. Origami inspired designs can help simplify gripper manufacturing to a single planar sheet of material while still allowing for deployment and actuation. Inflatable structures can reduce weight and be compacted. This paper explores the design of an inflatable, deployable, action origami inspired gripper through the development of a predictive model, prototype fabrication, and preliminary design assessments. 

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5. ChromGene: gene-based modeling of epigenomic data

   https://doi.org/10.1186/s13059-023-03041-5  

   Jaroszewicz, Artur; Ernst, Jason (September 2023, Genome Biology)

   Abstract Various computational approaches have been developed to annotate epigenomes on a per-position basis by modeling combinatorial and spatial patterns within epigenomic data. However, such annotations are less suitable for gene-based analyses. We present ChromGene, a method based on a mixture of learned hidden Markov models, to annotate genes based on multiple epigenomic maps across the gene body and flanks. We provide ChromGene assignments for over 100 cell and tissue types. We characterize the mixture components in terms of gene expression, constraint, and other gene annotations. The ChromGene method and annotations will provide a useful resource for gene-based epigenomic analyses. 

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