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1. Diversified caregiver input to upgrade the Young Children’s Participation and Environment Measure for equitable pediatric re/habilitation practice

   https://doi.org/10.1186/s41687-023-00627-2  

   Villegas, Vivian C. ; Bosak, Dianna L. ; Salgado, Zurisadai ; Phoenix, Michelle ; Parde, Natalie ; Teplicky, Rachel ; Khetani, Mary A. ; High Value Early Intervention Research Group ; Kuznicki, L. ; Pedrow, A. ; et al ( August 2023 , Journal of Patient-Reported Outcomes)

   Practitioner and family experiences of pediatric re/habilitation can be inequitable. The Young Children’s Participation and Environment Measure (YC-PEM) is an evidence-based and promising electronic patient-reported outcome measure that was designed with and for caregivers for research and practice. This study examined historically minoritized caregivers’ responses to revised YC-PEM content modifications and their perspectives on core intelligent virtual agent functionality needed to improve its reach for equitable service design.

   Caregivers were recruited during a routine early intervention (EI) service visit and met five inclusion criteria: (1) were 18 + years old; (2) identified as the parent or legal guardian of a child 0–3 years old enrolled in EI services for 3 + months; (3) read, wrote, and spoke English; (4) had Internet and telephone access; and (5) identified as a parent or legal guardian of a Black, non-Hispanic child or as publicly insured. Three rounds of semi-structured cognitive interviews (55–90 min each) used videoconferencing to gather caregiver feedback on their responses to select content modifications while completing YC-PEM, and their ideas for core intelligent virtual agent functionality. Interviews were transcribed verbatim, cross-checked for accuracy, and deductively and inductively content analyzed by multiple staff in three rounds.

   Eight Black, non-Hispanic caregivers from a single urban EI catchment and with diverse income levels (Mdn = $15,001–20,000) were enrolled, with children (M = 21.2 months,SD = 7.73) enrolled in EI. Caregivers proposed three ways to improve comprehension (clarify item wording, remove or simplify terms, add item examples). Environmental item edits prompted caregivers to share how they relate and respond to experiences with interpersonal and institutional discrimination impacting participation. Caregivers characterized three core functions of a virtual agent to strengthen YC-PEM navigation (read question aloud, visual and verbal prompts, more examples and/or definitions).

   Results indicate four ways that YC-PEM content will be modified to strengthen how providers screen for unmet participation needs and determinants to design pediatric re/habilitation services that are responsive to family priorities. Results also motivate the need for user-centered design of an intelligent virtual agent to strengthen user navigation, prior to undertaking a community-based pragmatic trial of its implementation for equitable practice.

    

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2. Situating intuition in engineering practice

   https://doi.org/10.1002/jee.20521  

   Miskioğlu, Elif Eda ; Aaron, Caitlyn ; Bolton, Caroline ; Martin, Kaela M. ; Roth, Madeline ; Kavale, Sanjeev M. ; Carberry, Adam R. ( March 2023 , Journal of Engineering Education)

   A defining characteristic of expertise is the use of intuition to navigate tasks. The construct of intuition and its importance is well‐studied in other disciplines, but little is known about how it translates to engineering. Existing literature on intuition does not clearly define the construct and its relationship to problem solving, which creates a substantial gap in our understanding of intuition and its applicability to engineering.

   This study's purpose is to better understand the relationship between expertise, decision‐making, and intuition from the perspective of engineering practitioners. We additionally seek to define engineering intuition from this same perspective.

   Seventeen semi‐structured interviews were conducted with engineering practitioners with at least 6 years of experience. Sensitizing concepts tied to models of expertise development and dual process cognition were used to guide the study's design and data analysis. Iterative qualitative analysis culminating in code mapping supported the development of a definition and theory of engineering intuition.

   This study's result is an emergent definition of engineering intuition and a complementary framework called Leveraging Intuition Toward Engineering Solutions (LITES). LITES uniquely situates intuition as part of the problem‐solving process among experienced engineering practitioners and describes how practitioners use their intuition.

   This work advances knowledge of the relationship between intuition and expertise in engineering education by providing a definition of engineering intuition and a framework describing intuition's role in engineering problem solving. This contribution furthers efforts to equip current and future engineers with the necessary skills to navigate existing and upcoming societal challenges.

    

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3. Design Optimization of an Ethanol Heavy-Duty Engine Using Design of Experiments and Bayesian Optimization

   https://doi.org/10.1115/ICEF2022-90257  

   Tekgul, Bulut ; Liu, I-Han ; Vittal, Manohar ; Schanz, Robert ; Blumreiter, Julie ; Johnson, Bernard H. ; Magnotti, Gina M. ( November 2022 , ASME 2022 ICE Forward Conference)

   Diesel-fueled engines still hold a large market share in the medium and heavy-duty transportation sector. However, the increase in fossil fuel prices and the strict emission regulations are leading engine manufacturers to seek cleaner alternatives without a compromise in performance. Alcohol-based fuels, such as ethanol, offer a promising alternative to diesel fuel in meeting regulatory demands. Ethanol provides cleaner combustion and lower levels of soot due to its chemical properties, in particular its lower level of carbon content. In addition, the stoichiometric operating conditions of alcohol fueled engines enable the mitigation of NOx emissions in aftertreatment stage. With the promise of retrofitting diesel engines to run on ethanol to reduce emissions, the thermal efficiency of these engines remains the primary optimization target. In order to find the optimal ethanol-fueled engine design that maximizes the thermal efficiency, a large design space needs to be investigated using engineering tools.

   In this study, previous research by the authors on optimizing the design of a single-cylinder ethanol-fueled engine was extended to explore the design space for a heavy-duty multi-cylinder engine configuration. A heavy-duty engine setup with multiple operating conditions at different engine speeds and loads were considered. A design optimization analysis was performed to identify the potential designs that maximize the indicated thermal efficiency in an ethanol-fueled compression ignition engine. First, a computational fluid dynamics (CFD) model of the engine was validated using experimental data for four drive cycle points. Using a design of experiments (DoE) approach and a parameterized piston bowl geometry, the model was then exercised to explore the relationship among geometric features of the piston bowl and spray targeting angle and indicated thermal efficiency across all tested operating conditions. After evaluating 165 candidate designs, a piston bowl geometry was identified that yielded an increase between 1.3 to 2.2 percentage points in indicated thermal efficiency for all tested conditions, while satisfying the operational design constraints for peak pressure and maximum pressure rise rate. The increased performance was attributed to enhanced mixing that led to the formation of a more homogeneous distribution of in-cylinder temperature and equivalence ratio, higher combustion temperatures, and shorter combustion duration. Finally, a Bayesian optimization (BOpt) analysis was employed to find the optimal piston bowl geometry with a fixed spray injector angle for one of the operating conditions. Using BOpt, a piston candidate was identified that resulted in a 1.9 percentage point increase in thermal efficiency from the baseline design, yet only required 65% of the design samples investigated using the DoE approach.

    

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4. iCFN: an efficient exact algorithm for multistate protein design

   https://doi.org/10.1093/bioinformatics/bty564  

   Karimi, Mostafa ; Shen, Yang ( September 2018 , Bioinformatics)

   Multistate protein design addresses real-world challenges, such as multi-specificity design and backbone flexibility, by considering both positive and negative protein states with an ensemble of substates for each. It also presents an enormous challenge to exact algorithms that guarantee the optimal solutions and enable a direct test of mechanistic hypotheses behind models. However, efficient exact algorithms are lacking for multistate protein design.

   We have developed an efficient exact algorithm called interconnected cost function networks (iCFN) for multistate protein design. Its generic formulation allows for a wide array of applications such as stability, affinity and specificity designs while addressing concerns such as global flexibility of protein backbones. iCFN treats each substate design as a weighted constraint satisfaction problem (WCSP) modeled through a CFN; and it solves the coupled WCSPs using novel bounds and a depth-first branch-and-bound search over a tree structure of sequences, substates, and conformations. When iCFN is applied to specificity design of a T-cell receptor, a problem of unprecedented size to exact methods, it drastically reduces search space and running time to make the problem tractable. Moreover, iCFN generates experimentally-agreeing receptor designs with improved accuracy compared with state-of-the-art methods, highlights the importance of modeling backbone flexibility in protein design, and reveals molecular mechanisms underlying binding specificity.

   https://shen-lab.github.io/software/iCFN

   Supplementary data are available at Bioinformatics online.

    

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5. 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)

   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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