Contributors: Xiao, Sa, Lee, Aaron, Rokem, Ariel
... These are the learned pytorch weights for these transformations
A randomized controlled trial of a long-term professional mentoring program for children at risk: Outcomes across the first 5 years
Contributors: Eddy, J. Mark, Martinez, Charles R., Jr., Grossman, Jean Baldwin, Cearley, Jennifer J., Wheeler, Alice C., Rempel, Jeff S., Foney, Dana, Burraston, Bert O., Harachi, Tracy W., Haggerty, Kevin P.
... This is a post-peer-review, pre-copyedit version of an article published in Prevention Science. The final authenticated version is available online at: http://dx.doi.org/10.1007/s11121-017-0795-z
Spatial Analysis of Accessible Seating Area on the Next Generation Passenger Rail Cars using 3-D Modeling and Digital Human Modeling
Contributors: Hunter-Zaworski, Katharine, Tabattanon, K.
... Data sets can be found on the PacTrans Dataverse here: https://doi.org/10.7910/DVN/SMQU1D
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Contributors: Salazar, Amy M., Brown, Eric C., Monahan, Kathryn C., Catalano, Richard F.
... © 2015 Z. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). https://www.sciencedirect.com/science/article/pii/S0149718915001329?via%3Dihub
Contributors: Salazar, Amy M., Haggerty, Kevin P., de Haan, Benjamin, Catalano, Richard F., Vann, Terri, Vinson, Jean, Lansing, Michaele
... ©American Psychological Association, 2016. This paper is not the copy of record and may not exactly replicate the authoritative document published in the APA journal. Please do not copy or cite without author's permission. The final article is available at: https://doi.org/10.1037/ort0000078."
Contributors: Haggerty, Kevin P., Barton, Vaughnetta J., Catalano, Richard F., Spearmon, Margaret L., Margaret L., Edith C., Reese, Raymonda C., Uehara, Edwina S.
... © 2017 by the Society for Social Work and Research https://www.journals.uchicago.edu/doi/abs/10.1086/690561
Contributors: Harrop, Erin, Catalano, Richard F.
... © 2016 Elsevier Inc. https://www.clinicalkey.com/#!/content/journal/1-s2.0-S1056499316300281
Towards a General Method for Constructing Manufacturability Design Rules for an Additive Manufacturing Process
Contributors: Weiss, Benjamin M, Hamel, Joshua M, Storti, Duane W, Ganter, Mark A
... Additive manufacturing (AM) presents a unique set of manufacturability constraints, among the most important of which are the smallest producible feature size and the maximum overhang angle before support structures are required. In this work, an approach is presented which includes both a parameterization strategy for small features, and a subsequent iterative experiment for realizing minimum feature size design rules as functions of feature shape and orientation. This approach was designed to be applicable to a wide variety of AM processes, and was applied to an example machine in the material extrusion AM process category for demonstration purposes. This case study involved a thorough experimental evaluation to explore the tradeoffs between the number of oriented shapes evaluated and the predictive quality of the resulting design rules, and the results produced found that minimum feature size can vary by as much as 10x over the set of considered oriented shapes for the AM system studied. Compared to existing design rules in the literature, using the proposed approach made it possible to increase the design space for the AM system considered by providing lower minimum feature sizes when possible, by incorporating more accurate overhang angle constraints into the minimum feature size definition, and by detecting un-manufacturable features that existing design rules would have incorrectly allowed.
Contributors: Weiss, Benjamin M, Hamel, Joshua M, Ganter, Mark A, Storti, Duane W
... The topology optimization (TO) of structures to be produced using additive manufacturing (AM) is explored using a data-driven constraint function that predicts the minimum producible size of small features in different shapes and orientations. This shape- and orientation-dependent manufacturing constraint, derived from experimental data, is implemented within a TO framework using a modified version of the Moving Morphable Components (MMC) approach. Because the analytic constraint function is fully differentiable, gradient-based optimization can be used. The MMC approach is extended in this work to include a “bootstrapping” step, which provides initial component layouts to the MMC algorithm based on intermediate Solid Isotropic Material with Penalization (SIMP) topology optimization results. This “bootstrapping” approach improves convergence compared to reference MMC implementations. Results from two compliance design optimization example problems demonstrate the successful integration of the manufacturability constraint in the MMC approach, and the optimal designs produced show minor changes in topology and shape compared to designs produced using fixed-radius filters in the traditional SIMP approach. The use of this data-driven manufacturability constraint makes it possible to take better advantage of the achievable complexity in additive manufacturing processes, while resulting in typical penalties to the design objective function of around only 2% when compared to the unconstrained case.
Thermal Weakening, Convergent Flow, and Vertical Heat Transport in the Northeast Greenland Ice Stream Shear Margins - Supporting Data
Contributors: Holschuh, Nicholas, Lilien, David, Christianson, Knut
... This archive contains radar data and model output referenced in the paper "Thermal Weakening, Convergent Flow, and Vertical Heat Transport in the Northeast Greenland Ice Stream Shear Margins", published in the journal Geophysical Research Letters in 2019.