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1. Machine‐learning‐based optimization framework to support recovery‐based design

   https://doi.org/10.1002/eqe.3860  

   Issa, Omar; Silva‐Lopez, Rodrigo; Baker, Jack W.; Burton, Henry V. (March 2023, Earthquake Engineering & Structural Dynamics)

   Abstract Recovery‐based design links building‐level engineering and broader community resilience objectives. However, the relationship between above‐code engineering improvements and recovery performance is highly nonlinear and varies on a building‐ and site‐specific basis, presenting a challenge to both individual owners and code developers. In addition, downtime simulations are computationally expensive and hinder exploration of the full design space. In this paper, we present an optimization framework to identify optimal above‐code design improvements to achieve building‐specific recovery objectives. We supplement the optimization with a workflow to develop surrogate models that (i) rapidly estimate recovery performance under a range of user‐defined improvements, and (ii) enable complex and informative optimization techniques that can be repeated for different stakeholder priorities. We explore the implementation of the framework using a case study office building, with a 50th percentile baseline functional recovery time of 155 days at the 475‐year ground‐motion return period. To optimally achieve a target recovery time of 21 days, we find that nonstructural component enhancements are required, and that increasing structural strength (through increase of the importance factor) can be detrimental. However, for less ambitious target recovery times, we find that the use of larger importance factors eliminates the need for nonstructural component improvements. Such results demonstrate that the relative efficacy of a given recovery‐based design strategy will depend strongly on the design criteria set by the user. 

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2. Improvements to Target-Based 3D LiDAR to Camera Calibration

   https://doi.org/10.1109/ACCESS.2020.3010734  

   Huang, Jiunn-Kai; Grizzle, Jessy W. (January 2020, IEEE Access)
3. Transitioning to Green Discovery‐Based Catalysis

   https://doi.org/10.1002/chem.202404602  

   Waterman, Rory (January 2025, Chemistry – A European Journal)

   Abstract Pressure is mounting to minimize the carbon footprint of chemical industry while increasing its sustainability. An argument is made that working from Green Chemistry principles during discovery‐based catalysis results in effective chemistry and circumvents a need to “rediscover” chemical reactivity under sustainable conditions. Examples of comparative success in selected examples of hydrophosphination catalysis in various degrees of development are provided to support two main ideas: 1) Starting from more sustainable practices in chemical discovery is inertia in methodology that should be overcome, and 2) substantial challenges remain in catalysis for which sustainable solutions would positively impact other areas of chemistry. Examples of successes, even in the face of the challenges noted, are presented herein as indications that even as a starting point, sustainability can meet short‐ and long‐term needs. These ideas indicate critical but simple strategies for fundamental research to be impactful in the sustainability of the chemical industry broadly. 

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4. Effectiveness of code contribution: from patch-based to pull-request-based tools

   https://doi.org/10.1145/2950290.2950364  

   Zhu, Jiaxin; Zhou, Minghui; Mockus, Audris (January 2016, Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering)

   Code contributions in Free/Libre and Open Source Software projects are controlled to maintain high-quality of software. Alternatives to patch-based code contribution tools such as mailing lists and issue trackers have been developed with the pull request systems being the most visible and widely available on GitHub. Is the code contribution process more effective with pull request systems? To answer that, we quantify the effectiveness via the rates contributions are accepted and ignored, via the time until the first response and final resolution and via the numbers of contributions. To control for the latent variables, our study includes a project that migrated from an issue tracker to the GitHub pull request system and a comparison between projects using mailing lists and pull request systems. Our results show pull request systems to be associated with reduced review times and larger numbers of contributions. However, not all the comparisons indicate substantially better accept or ignore rates in pull request systems. These variations may be most simply explained by the differences in contribution practices the projects employ and may be less affected by the type of tool. Our results clarify the importance of understanding the role of tools in effective management of the broad network of potential contributors and may lead to strategies and practices making the code contribution more satisfying and efficient from both contributors' and maintainers' perspectives. 

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5. A Modular Simulation-based MBSE Approach Applied to a Cloud-based System.

   Booth, T; Ghosh, S (July 2024, Proceedings of the 34th Annual INCOSE International Symposium)