NRT-DESE: Preparing Resilient and Operationally Adaptive Communities through an Interdisciplinary, Venture-based Education (PROACTIVE)

NRT-DESE：通过跨学科、基于风险的教育（主动）打造有弹性和适应性强的社区

• 批准号: 1633608
• 负责人: Christopher Kitchens
• 金额: $ 298.99万
• 依托单位: Clemson University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-15 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1633608&HistoricalAwards=false
• 关键词: NRT; DESE; Preparing; Resilient; Operationally

This National Science Foundation Research Traineeship (NRT) award to Clemson University will respond to the urgent need for professionals capable of crossing disciplinary boundaries to assess technological and societal risks, to communicate those risks to decision makers, and to devise strategies that improve community resilience to natural or man-made disasters. The last five decades have seen a sharp increase in the frequency and impact of natural hazards and a significantly higher risk of man-made disasters, particularly since 9/11. Predicting and mitigating such extreme events is difficult due to interactions among infrastructure systems with complex, poorly understood feedback loops. Society needs professionals who can conceptualize complex systems where physical, cyber, and human infrastructure systems converge and who can transform this conceptual understanding into reliable computational models that are validated by data. Moreover, these professionals must be equipped with skills to effectively communicate with their peers in other disciplines and with decision and policy makers to ensure cohesion between science and policy. This NRT award will address these challenges through curriculum development, transformation in graduate education, and research with societal impact. The project anticipates training fifty-two (52) MS and PhD students, including twenty-six (26) funded trainees, from a variety of science and engineering disciplines related to model- and data-enabled infrastructure resiliency. This project envisions a new paradigm of graduate education conducive to training STEM professionals who are transdisciplinary system thinkers capable of crossing disciplinary boundaries and working in a dynamic network of continuously learning individuals and evolving knowledge. It represents a transformation in graduate education through the creation of collaborative research communities with strong peer-learning aspects, resulting in a local scientific community that enables students to learn the ?business? of science (networking, collaboration, communication, etc.). The NRT award will promote an agile, adaptive curriculum structure responsive to the changing needs of students through the development of a modular, personalized training program. It will enhance students? ability to apply academic research to complex, real-world problems with an awareness of societal impacts via a uniquely integrated research, training, and outreach program that studies infrastructure vulnerabilities that disproportionately affect low-income regions. Developed within a logic framework and with a thorough, research-driven evaluation plan, this training program will be reproducible on a larger scale. Student and faculty teams will conduct research in three core model engineering and data science areas: (1) integrating models to models, (2) incorporating data into models, and (3) communicating model predictions to decision makers. Their work in each of these areas will allow them to highlight key model/data science issues, understand how these issues translate to societal impacts caused by vulnerabilities in infrastructure systems, and develop solutions to mitigate damage caused by potential infrastructure vulnerabilities. The research on infrastructure resiliency will result in new approaches for modeling and analyzing coupled systems, enabling scientists and decision makers to come together to better understand interdependent infrastructure systems and their uncertainties.The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The Traineeship Track is dedicated to effective training of STEM graduate students in high priority interdisciplinary research areas, through the comprehensive traineeship model that is innovative, evidence-based, and aligned with changing workforce and research needs.

这个国家科学基金会研究培训（NRT）奖给克莱姆森大学将响应对能够跨越学科界限的专业人员的迫切需求，以评估技术和社会风险，将这些风险传达给决策者，并制定提高社区对自然或人为灾害的应变能力的战略。在过去50年中，自然灾害的频率和影响急剧增加，人为灾害的风险显著增加，特别是自9/11以来。由于基础设施系统之间的相互作用，以及对反馈回路的复杂性和知之甚少，预测和缓解此类极端事件是困难的。社会需要能够将物理、网络和人类基础设施系统融合在一起的复杂系统概念化的专业人士，他们可以将这种概念性理解转化为可靠的计算模型，并通过数据进行验证。此外，这些专业人员必须具备与其他学科同行以及决策者进行有效沟通的技能，以确保科学与政策之间的一致性。该NRT奖将通过课程开发，研究生教育转型以及具有社会影响的研究来应对这些挑战。该项目预计将培训五十二（52）名硕士和博士生，包括二十六（26）名受资助的学员，他们来自与模型和数据支持的基础设施弹性相关的各种科学和工程学科。该项目设想了一种新的研究生教育范式，有利于培养STEM专业人员，他们是跨学科的系统思想家，能够跨越学科界限，并在不断学习的个人和不断发展的知识的动态网络中工作。它代表了研究生教育的转型，通过创建具有强大的同行学习方面的合作研究社区，从而在当地的科学界，使学生能够学习？生意？科学（网络，协作，通信等）。NRT奖将通过开发模块化，个性化的培训计划，促进敏捷，自适应的课程结构，以满足学生不断变化的需求。它会提高学生的？能够将学术研究应用于复杂的现实问题，并通过独特的综合研究，培训和推广计划了解社会影响，该计划研究不成比例地影响低收入地区的基础设施脆弱性。在一个逻辑框架内开发，并有一个全面的，研究驱动的评估计划，这个培训计划将在更大的规模上重现。学生和教师团队将在三个核心模型工程和数据科学领域进行研究：（1）将模型集成到模型中，（2）将数据整合到模型中，以及（3）将模型预测传达给决策者。他们在每个领域的工作将使他们能够突出关键的模型/数据科学问题，了解这些问题如何转化为基础设施系统脆弱性造成的社会影响，并开发解决方案以减轻潜在基础设施脆弱性造成的损害。对基础设施弹性的研究将为耦合系统的建模和分析带来新的方法，使科学家和决策者能够走到一起，更好地了解相互依赖的基础设施系统及其不确定性。NSF研究培训计划（NRT）旨在鼓励为STEM研究生教育培训开发和实施大胆的、新的潜在变革性模型。该培训轨道致力于在高优先级的跨学科研究领域的STEM研究生的有效培训，通过全面的培训模式，是创新的，以证据为基础，并与不断变化的劳动力和研究需求保持一致。

项目成果

Comparison of statistical inversion with iteratively regularized Gauss Newton method for image reconstruction in electrical impedance tomography

• DOI: 10.1016/j.amc.2019.03.063
• 发表时间: 2019-10-01
• 期刊: APPLIED MATHEMATICS AND COMPUTATION
• 影响因子: 4
• 作者: Ahmad, Sanwar;Strauss, Thilo;Khan, Taufiquar
• 通讯作者: Khan, Taufiquar

Productive and Performant Generic Lossy Data Compression with LibPressio

使用 LibPressio 进行高效且高性能的通用有损数据压缩

• DOI: 10.1109/drbsd754563.2021.00005
• 发表时间: 2021
• 期刊: 2021 7th International Workshop on Data Analysis and Reduction for Big Scientific Data (DRBSD-7
• 作者: Underwood, Robert;Malvoso, Victoriana;Calhoun, Jon C.;Di, Sheng;Cappello, Franck
• 通讯作者: Cappello, Franck

‘A Finite Element Model of a UHPC Beam Reinforced with HSS Bars

• DOI: 10.1109/icmsao.2019.8880414
• 发表时间: 2019-04
• 期刊: 2019 8th International Conference on Modeling Simulation and Applied Optimization (ICMSAO)
• 作者: Omar R. Abuodeh;F. Abed

Stalk Bending Strength is Strongly Associated with Maize Stalk Lodging Incidence Across Multiple Environments

• DOI: 10.1016/j.fcr.2020.107737
• 发表时间: 2020-04-01
• 期刊: FIELD CROPS RESEARCH
• 影响因子: 5.8
• 作者: Sekhon, Rajandeep S.;Joyner, Chase N.;Robertson, Daniel J.
• 通讯作者: Robertson, Daniel J.

Broadband Access in South Carolina and the Advent of COVID-19

南卡罗来纳州的宽带接入和 COVID-19 的出现

• DOI: 10.13140/rg.2.2.20274
• 发表时间: 2021
• 期刊: CEDC Summit
• 作者: Bisbee, Cora;Ennis, Nami;Van Evera, Kyler;Arnold, Jacob;Leopard, Bailey;Klasing, Chris;Vaughn, David
• 通讯作者: Vaughn, David