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1. pystablemotifs: Python library for attractor identification and control in Boolean networks

   https://doi.org/10.1093/bioinformatics/btab825  

   Rozum, Jordan C.; Deritei, Dávid; Park, Kyu Hyong; Gómez Tejeda Zañudo, Jorge; Albert, Réka; Cowen, ed., Lenore (December 2021, Bioinformatics)

   Abstract Summarypystablemotifs is a Python 3 library for analyzing Boolean networks. Its non-heuristic and exhaustive attractor identification algorithm was previously presented in Rozum et al. (2021). Here, we illustrate its performance improvements over similar methods and discuss how it uses outputs of the attractor identification process to drive a system to one of its attractors from any initial state. We implement six attractor control algorithms, five of which are new in this work. By design, these algorithms can return different control strategies, allowing for synergistic use. We also give a brief overview of the other tools implemented in pystablemotifs. Availability and implementationThe source code is on GitHub at https://github.com/jcrozum/pystablemotifs/. Supplementary informationSupplementary data are available at Bioinformatics online. 

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2. General route to design polymer molecular weight distributions through flow chemistry

   https://doi.org/10.1038/s41467-020-16874-6  

   Walsh, Dylan_J; Schinski, Devin_A; Schneider, Robert_A; Guironnet, Damien (June 2020, Nature Communications)

   Abstract The properties of a polymer are known to be intrinsically related to its molecular weight distribution (MWD); however, previous methodologies of MWD control do not use a design and result in arbitrary shaped MWDs. Here we report a precise design to synthesis protocol for producing a targeted MWD design with a simple to use, and chemistry agnostic computer-controlled tubular flow reactor. To support the development of this protocol, we constructed general reactor design rules by combining fluid mechanical principles, polymerization kinetics, and experiments. The ring opening polymerization of lactide, the anionic polymerization of styrene, and the ring opening metathesis polymerization are used as model polymerizations to develop the reactor design rules and synthesize MWD profiles. The derivation of a mathematical model enables the quantitative prediction of the experimental results, and this model provides a tool to explore the limits of any MWD design protocol. 

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3. GAN-DUF: Hierarchical Deep Generative Models for Design Under Free-Form Geometric Uncertainty

   https://doi.org/10.1115/1.4055898  

   Chen, Wei; Lee, Doksoo; Balogun, Oluwaseyi; Chen, Wei (January 2023, Journal of Mechanical Design)

   Abstract Deep generative models have demonstrated effectiveness in learning compact and expressive design representations that significantly improve geometric design optimization. However, these models do not consider the uncertainty introduced by manufacturing or fabrication. The past work that quantifies such uncertainty often makes simplifying assumptions on geometric variations, while the “real-world,” “free-form” uncertainty and its impact on design performance are difficult to quantify due to the high dimensionality. To address this issue, we propose a generative adversarial network-based design under uncertainty framework (GAN-DUF), which contains a deep generative model that simultaneously learns a compact representation of nominal (ideal) designs and the conditional distribution of fabricated designs given any nominal design. This opens up new possibilities of (1) building a universal uncertainty quantification model compatible with both shape and topological designs, (2) modeling free-form geometric uncertainties without the need to make any assumptions on the distribution of geometric variability, and (3) allowing fast prediction of uncertainties for new nominal designs. We can combine the proposed deep generative model with robust design optimization or reliability-based design optimization for design under uncertainty. We demonstrated the framework on two real-world engineering design examples and showed its capability of finding the solution that possesses better performance after fabrication. 

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4. Hierarchical Deep Generative Models for Design Under Free-Form Geometric Uncertainty

   https://doi.org/10.1115/DETC2022-89707  

   Chen, Wei; Lee, Doksoo; Balogun, Oluwaseyi; Chen, Wei (August 2022, American Society of Mechanical Engineers)

   Abstract Deep generative models have demonstrated effectiveness in learning compact and expressive design representations that significantly improve geometric design optimization. However, these models do not consider the uncertainty introduced by manufacturing or fabrication. Past work that quantifies such uncertainty often makes simplifying assumptions on geometric variations, while the “real-world”, “free-form” uncertainty and its impact on design performance are difficult to quantify due to the high dimensionality. To address this issue, we propose a Generative Adversarial Network-based Design under Uncertainty Framework (GAN-DUF), which contains a deep generative model that simultaneously learns a compact representation of nominal (ideal) designs and the conditional distribution of fabricated designs given any nominal design. This opens up new possibilities of 1) building a universal uncertainty quantification model compatible with both shape and topological designs, 2) modeling free-form geometric uncertainties without the need to make any assumptions on the distribution of geometric variability, and 3) allowing fast prediction of uncertainties for new nominal designs. We can combine the proposed deep generative model with robust design optimization or reliability-based design optimization for design under uncertainty. We demonstrated the framework on two real-world engineering design examples and showed its capability of finding the solution that possesses better performances after fabrication. 

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5. Investigating Information: A Qualitative Analysis of Expert Designers’ Information Representation and Structuring Behaviors

   https://doi.org/10.1115/DETC2019-98362  

   Damen, Nicole B.; Toh, Christine A. (August 2019, International Design Engineering Technical Conferences and Computers and Information in Engineering Conference)

   Abstract Information organization and utilization are integral to the design and development of creative ideas. However, navigating this often complex information space can be challenging, even for experienced designers. Therefore, deep analysis of how expert designers utilize and organize information is needed to provide qualitative insights into their information organization strategies. To address this, four professionals in the software design and development field were recruited for individual 3-hour design sessions. They were asked to generate ideas for a design challenge (reducing distraction-based pedestrian accidents) using information sheets specifically developed to contain different types of information, as identified by prior work. Results reveal individual differences in information approach and categorization, although these were motivated by similar underlying patterns of evaluating the relevance of information for its ability to inform the project constraints, resources or (user) requirements. Designer experience and use of design processes and knowledge transfer tools enhanced their ability to turn information into insights. 

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