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  4. The availability of quantitative text analysis methods has provided new waysof analyzing literature in a manner that was not available in thepre-information era. Here we apply comprehensive machine learning analysis tothe work of William Shakespeare. The analysis shows clear changes in the styleof writing over time, with the most significant changes in the sentence length,frequency of adjectives and adverbs, and the sentiments expressed in the text.Applying machine learning to make a stylometric prediction of the year of theplay shows a Pearson correlation of 0.71 between the actual and predicted year,indicating that Shakespeare's writing style as reflected by the quantitativemeasurements changed over time. Additionally, it shows that the stylometrics ofsome of the plays is more similar to plays written either before or after theyear they were written. For instance, Romeo and Juliet is dated 1596, but ismore similar in stylometrics to plays written by Shakespeare after 1600. Thesource code for the analysis is available for free download.

     
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    Free, publicly-accessible full text available July 13, 2024
  5. Community resilience is a compelling problem that brings together many disciplines of study. Too often researchers wait until the end of research projects to disseminate findings, and may not include any intentional efforts toward technology translation. Convergence, and particularly the technology transfer aspects of convergence, should be a central goal for resilience research. This paper presents a theory of change proposing community engagement as the intervention needed for realizing actual community resilience. Three illustrative examples simultaneously demonstrate the need for the intervention and are used to provide guidance to researchers interested in learning how to engage. The first example illustrates investigator-driven research via post-hurricane reconnaissance coupled with experimental testing in a wind laboratory. The first example exemplifies technology transfer through regulatory changes. The second example illustrates community-based research via a post-tornado reconnaissance study, and exemplifies technology transfer through industry and outreach publications and public media. The third example illustrates community-driven research that developed a local climate plan, and incorporated the co-production of knowledge. The research translated throughout the project due to the community engaged approach leading to immediate adoption of the final research outcomes. Findings from this paper can be used to help other researchers determine the level of community involvement and navigate technology transfer options based on the goals and context of their own research.

     
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  8. Strong hurricane winds often cause severe infrastructure damage and pose social and economic consequences in coastal communities. In the context of community resilience planning, estimating such impacts can facilitate developing more risk-informed mitigation plans in the community of interest. This study presents a new framework for synthetically simulating scenario-hurricane winds using a parametric wind field model for predicting community-level building damage, direct economic loss, and social consequences. The proposed synthetic scenario approach uses historical hurricane data and adjusts its original trajectory to create synthetic change scenarios and estimates peak gust wind speed at the location of each building. In this research, a stochastic damage simulation algorithm is applied to assess the buildings’ physical damage. The algorithm assigns a damage level to each building using the corresponding damage-based fragility functions, predicted maximum gust speed at the building’s location, and a randomly generated number. The monetary loss to the building inventory due to its physical damage is determined using FEMA’s direct loss ratios and buildings’ replacement costs considering uncertainty. To assess the social impacts of the physical damage exposure, three likely post-disaster social disruptions are measured, including household dislocation, employment disruption, and school closures. The framework is demonstrated by its application to the hurricane-prone community of Onslow County, North Carolina. The novel contribution of the developed framework, aside from the introduced approach for spatial predicting hurricane-induced wind hazards, is its ability to illuminate some aspects of the social consequences of substantial physical damages to the building inventory in a coastal community due to the hurricane-induced winds. These advancements enable community planners and decision-makers to make more risk-informed decisions for improving coastal community resilience.

     
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