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  1. Abstract

    Systems‐level approaches are required for addressing the world's major challenges at the food–energy–water nexus. Taking on complex issues, such as rising food insecurity, malnutrition, and food waste, concomitant with unprecedented levels of stress on environmental systems, will necessitate that future scholars and decision makers be prepared through transdisciplinary student training. However, in higher education, students tend to be siloed within their discipline. In this study, we present a case for the development of transdisciplinary graduate student training based on an inter‐institutional and fully remote group of graduate students who assembled during the COVID‐19 pandemic to address the issue of food waste. We use our wide‐ranging disciplinary backgrounds, high‐performance transdisciplinary team training, and stakeholder feedback to develop and conduct a weeklong social media campaign to share educational resources for reducing household food waste. This work offers valuable lessons learned through the student's lens to those seeking to create or improve future transdisciplinary training methods for tackling food waste and other global grand challenges. Key insights from this process include the importance of accountability and open communication when conducting collaborative teamwork, the utility of various mobile and online tools for effectively facilitating remote group work, and the vital role of transdisciplinarity in devising creative solutions.

     
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  2. Abstract

    Due to climate change and rapid urbanization, Urban Heat Island (UHI), featuring significantly higher temperature in metropolitan areas than surrounding areas, has caused negative impacts on urban communities. Temporal granularity is often limited in UHI studies based on satellite remote sensing data that typically has multi-day frequency coverage of a particular urban area. This low temporal frequency has restricted the development of models for predicting UHI. To resolve this limitation, this study has developed a cyber-based geographic information science and systems (cyberGIS) framework encompassing multiple machine learning models for predicting UHI with high-frequency urban sensor network data combined with remote sensing data focused on Chicago, Illinois, from 2018 to 2020. Enabled by rapid advances in urban sensor network technologies and high-performance computing, this framework is designed to predict UHI in Chicago with fine spatiotemporal granularity based on environmental data collected with the Array of Things (AoT) urban sensor network and Landsat-8 remote sensing imagery. Our computational experiments revealed that a random forest regression (RFR) model outperforms other models with the prediction accuracy of 0.45 degree Celsius in 2020 and 0.8 degree Celsius in 2018 and 2019 with mean absolute error as the evaluation metric. Humidity, distance to geographic center, and PM2.5concentration are identified as important factors contributing to the model performance. Furthermore, we estimate UHI in Chicago with 10-min temporal frequency and 1-km spatial resolution on the hottest day in 2018. It is demonstrated that the RFR model can accurately predict UHI at fine spatiotemporal scales with high-frequency urban sensor network data integrated with satellite remote sensing data.

     
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  3. Understanding urban heat exposure dynamics is critical for public health, urban management, and climate change resilience. Near real-time analysis of urban heat enables quick decision-making and timely resource allocation, thereby enhancing the well-being of urban residents, especially during heatwaves or electricity shortages. To serve this purpose, we develop a cyberGIS framework to analyze and visualize human sentiments of heat exposure dynamically based on near real-time location-based social media (LBSM) data. Large volumes and low-cost LBSM data, together with a content analysis algorithm based on natural language processing are used effectively to generate near real-time heat exposure maps from human sentiments on social media at both city and national scales with km spatial resolution and census tract spatial unit. We conducted a case study to visualize and analyze human sentiments of heat exposure in Chicago and the United States in September 2021. Enabled with high-performance computing, dynamic visualization of heat exposure is achieved with fine spatiotemporal scales while heat exposure detected from social media data can be used to understand heat exposure from a human perspective and allow timely responses to extreme heat. 
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    Free, publicly-accessible full text available July 2, 2025
  4. The communities of Puerto Rico are highly vulnerable to climate change as the archipelago has experienced a multitude of compounding crises and extreme weather events in recent years. To address these issues, the research, analysis, and design of grand challenge solutions for disaster-prone regions like Puerto Rico can utilize collaborative transdisciplinary efforts. Local non-governmental and community-based organizations have a pivotal role in the reconstruction processes and the building of community and environmental resilience in underserved communities. This paper contributes an empirical case study of an online transdisciplinary collaboration between a group of academics and a Puerto Rican non-governmental organization, Caras con Causa. From participant observation, it includes a document analysis of meeting notes with cohort members who were involved in a collaborative National Science Foundation Project, The INFEWS-ER: A Virtual Resource Center Enabling Graduate Innovations at the Nexus of Food, Energy, and Water Systems, with Caras con Causa between October 2020 and April 2021. Caras con Causa focuses on uplifting Puerto Ricans by creating and administering environmental, educational, economic, and community programs, highlighting disaster relief and resilience to help Puerto Rican food, energy, and water systems. Eight key discussion themes emerged from the document analysis: team organization, collaboration with Caras con Causa, deliverables, team contributions, context understanding, participation outcomes, technology setup, and lessons learned. We analyze each of the emerging themes to explain how academics may use transdisciplinary skill sets in addition to standard disciplinary-based approaches or techniques to enhance the institutional capacity of a non-governmental organization doing community resilience work to benefit local food, energy, and water systems. While the learned lessons in this non-governmental organization-academic collaboration may be context-specific, we provide insights that may be generalizable to collaborations in comparable transdisciplinary settings.

     
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  5. Wicked problems are inherent in food–energy–water systems (FEWS) due to the complexity and interconnectedness of these systems, and addressing these challenges necessitates the involvement of the diverse stakeholders in FEWS. However, successful stakeholder engagement requires a strong understanding of the relationships between stakeholders and the specific wicked problem. To better account for these relationships, we adapted a means, motive, and opportunity (MMO) framework to develop a method of stakeholder analysis that evaluates the agency of stakeholders related to a wicked problem in FEWS. This method involves two key components: (1) identification of a challenge at the FEWS nexus and (2) evaluation of stakeholder agency related to the challenge using the dimensions of MMO. This approach provides a method for understanding the characteristics of stakeholders in FEWS and provides information that could be used to inform stakeholder engagement in efforts to address wicked problems at the FEWS nexus. In this article, we present the stakeholder analysis method and describe an example application of the MMO method by examining stakeholder agency related to the adoption of improved swine waste management technology in North Carolina, USA. 
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