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.

美国国家科学基金会向克莱姆森大学颁发的研究实习生 (NRT) 奖将满足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，向决策者传达这些风险，并制定提高社区适应能力的策略自然或人为的灾难。过去五年，自然灾害的频率和影响急剧增加，人为灾害的风险显着增加，特别是自 9/11 以来。由于基础设施系统之间存在复杂且难以理解的反馈循环，因此预测和缓解此类极端事件非常困难。社会需要专业人员能够概念化物理、网络和人类基础设施系统融合的复杂系统，并且能够将这种概念性理解转化为经数据验证的可靠计算模型。此外，这些专业人员必须具备与其他学科的同行以及决策者和政策制定者有效沟通的技能，以确保科学与政策之间的凝聚力。该 NRT 奖项将通过课程开发、研究生教育转型以及具有社会影响力的研究来应对这些挑战。该项目预计培训五十二 (52) 名硕士和博士生，其中包括二十六 (26) 名受资助的受训人员，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考者，能够跨越学科界限，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变，通过创建具有强大同行学习方面的合作研究社区，形成了一个使学生能够学习“商业”的当地科学社区。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，促进灵活、适应性强的课程结构，以满足学生不断变化的需求。会提高学生吗？通过独特的综合研究、培训和外展计划，研究对低收入地区造成不成比例影响的基础设施脆弱性，能够将学术研究应用于复杂的现实问题，并意识到社会影响。该培训计划是在逻辑框架内制定的，并有一个全面的、以研究为驱动的评估计划，可以在更大范围内重复实施。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) 将数据合并到模型中，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漏洞造成的社会影响，并开发解决方案来减轻潜在基础设施漏洞造成的损害。关于基础设施弹性的研究将带来对耦合系统进行建模和分析的新方法，使科学家和决策者能够聚集在一起，更好地了解相互依赖的基础设施系统及其不确定性。 NSF 研究实习生 (NRT) 计划旨在鼓励开发和实施大胆的、具有潜在变革性的 STEM 研究生教育培训新模式。培训课程致力于通过创新、循证且符合不断变化的劳动力和研究需求的综合培训模式，对高度优先的跨学科研究领域的 STEM 研究生进行有效培训。

项目成果

Classification of Tea Aromas Using Multi-Nanoparticle Based Chemiresistor Arrays

使用基于多纳米粒子的化学电阻阵列对茶香气进行分类

• DOI: 10.3390/s19112547
• 发表时间: 2019-06-01
• 期刊: Sensors (Basel, Switzerland)
• 作者: Tuo Gao;Yongchen Wang;Chengwu Zhang;Zachariah A. Pittman;Ale;ra M. Oliveira;ra;Kan Fu;Jing Zhao;R. Srivastava;B. Willis
• 通讯作者: B. Willis

Improving the Interpretability of Physics-Based Bias in Material Models

提高材料模型中基于物理的偏差的可解释性

• DOI: 10.1115/vvs2020-8816
• 发表时间: 2020-05
• 期刊: ASME 2020 Verification and Validation Symposium
• 作者: Chodora, Evan;Flynn, Garrison;Tippetts, Trevor;Unal, Cetin
• 通讯作者: Unal, Cetin

Ultraclean hybrid poplar lignins via liquid–liquid fractionation using ethanol–water solutions

使用乙醇-水溶液通过液-液分馏获得超净混合杨木质素

• DOI: 10.1557/s43579-021-00090-4
• 发表时间: 2021-10
• 期刊: MRS Communications
• 影响因子: 1.9
• 作者: Tindall, Graham;Lynn, Bronson;Fitzgerald, Carter;Valladares, Lucas;Pittman, Zachariah;Bécsy;Hodge, David;Thies, Mark
• 通讯作者: Thies, Mark

The flexural behavior of bolting and bonding Aluminum Alloy plates to RC beams

铝合金板与 RC 梁螺栓连接和粘接的弯曲性能

• DOI: 10.1016/j.prostr.2019.08.052
• 发表时间: 2024-09-13
• 期刊: Procedia Structural Integrity
• 作者: Omar R. Abuodeh;Jamal A. Abdalla;R. Hawileh
• 通讯作者: R. Hawileh

Clustered Latent Dirichlet Allocation for Scientific Discovery

• DOI: 10.1109/bigdata47090.2019.9005964
• 发表时间: 2019-12-01
• 期刊: 2019 IEEE International Conference on Big Data (Big Data)
• 作者: Christopher Gropp;Ale;er Herzog;er;Ilya Safro;P. W. Wilson;A. Apon
• 通讯作者: A. Apon