Pennsylvania State Univ University Park

NSF Awards · FY 2024 · 2024-07

Both developed and developing countries utilize tourism to protect biodiverse spaces while bolstering the economy. However, balance between tourism development, environmental conservation, and community benefits is hard to achieve. It is necessary to research the challenges and opportunities to accomplish such balance to inform future tourism governance. Research should particularly focus on islands where land is limited and isolated, there is high endemism, and economies are heavily reliant on tourism. To address this need, this doctoral dissertation study examines social and environmental implications of tourism governance in an island setting. The project trains a graduate student in methods of scientific data collection and analysis and builds capacity for the future conduct of scientific research in this setting. The data and findings are being disseminated to improve the public’s understanding of science and the scientific method, through colloquia, guest, and other communicative platforms outside academia. This research applies common pool resource (CPR) governance theory to tourism in a novel way to help understand governance more thoroughly. Although historically used to explain natural resources, tourists can also be considered a CPR. CPRs are characterized by exclusion and subtraction. Resource users are nearly impossible to exclude, and one user’s gain is another’s loss. In tourism, tourists can be considered the resource, and those employed by tourism considered the users. Through the application of CPR governance to tourism, the study’s research questions consider formal and informal tourism management strategies, power dynamics among stakeholders, differing property rights regimes, and environmental impacts in and out of designated protected areas. The focus of the research in a major UNESCO World Heritage Site proves an ideal location to study socioecological balance and tourism governance, as the site’s famous wildlife drives the local economy through ecotourism. This ethnographic study utilizes 100 semi-structured interviews of stakeholders to capture community perspectives of tourism governance. Data analysis includes deductive and inductive coding of collected materials. The results engage with and further develop CPR governance theory by considering tourists as a mobile resource and users as those dependent on tourism, Additionally, results inform decision-makers on how to better address the balance between tourism, conservation, and community well-being, especially in island settings. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

A major barrier to research on plant function and to crop improvement is a limitation in methods available for genetically manipulating plants. Techniques currently used include the culturing of plant tissues, the insertion or editing of DNA, and the recovery of whole plants, but each of these steps poses challenges to the degree that they work reliably in relatively few species, and even then may not work well in all varieties of the species. To fill this gap, we propose that the parasitic plant dodder (Cuscuta species) can be used to deliver gene editing molecules to a wide range of plants. Dodder plants live by attaching themselves to the stems of host plants and forming connections to withdraw water and nutrients. The organs that form the connections are called haustoria, and function somewhat similar to the way a mosquito taps into a vein to feed, and dodder is able to transmit a variety of large molecules, including proteins and RNAs, to their hosts. Another key feature of dodder is its ability to connect to an unusually wide range of host species, including the most important broadleaf crops. We will evaluate the ability of dodder to mobilize genome editing molecules into its hosts, with the goal of producing gene-edited seeds. Success in this activity would establish a novel vehicle for genetic modification of plants that is relatively simple, rapid, and broadly applicable. The project will explore multiple possibilities for transferring Cas9 and single guide RNA (sgRNA) between dodder and hosts. Among the possibilities are the movement of these molecules from an easily transformed host, such as Arabidopsis, to result in transformed dodder, or the reverse from stably transformed dodder to result in a transformed host. Given success with these, we will explore the ability for dodder to serve as a bridge between a Cas9-sgRNA expressing donor host and a target host (e.g., tomato). The project has three major aims to achieve these outcomes, including: 1) the stable transformation of dodder (C. campestris) to be used in host gene editing, 2) the development of a dodder protoplast system for rapid screening of gene editing constructs, and 3) the development of a dodder-mediated gene editing system. Preliminary results indicate that Agrobacterium-mediated transformation of dodder is possible, and the procedure will be optimized to enable generation of multiple lines bearing various transgene constructs. Other considerations include identifying promotors for the appropriate expression of gene constructs and optimized targeting for systemic trafficking of Cas9/sgRNA in the parasite-host system. For the outreach goal of this project, PIs will develop a program for refugee students to help them participate in project-related activities and develop their understanding of plant science at the University of Missouri. In summary, the project will leverage the intrinsically engaging topics of plant parasitism, RNA mobility, and genome editing to attract these students to plant science. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-06

The broader impact of this I-Corps project is the development of a chemical-free and zero waste recycling method for printed circuit boards and other waste streams, including solar panels, wind turbines, and batteries. Currently, the surge in digitalization has led to a staggering amount of electronic waste (e-waste), with approximately 53.6 million metric tons discarded annually, and only 17.4% of which is collected and recycled. This technology addresses e-waste by using a sustainable process for recycling. The goal is to recover base and precious metals from printed circuit boards and other urban waste streams to reduce the need for mining and its associated environmental impact. This approach may minimize material and energy consumption and may create new revenue streams through the recovery of valuable materials from post-consumer electronic waste. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of a process for the selective and efficient physical separation of components in e-waste. Current recycling methods, marked by high energy consumption, environmental pollution, and low recovery of valuable materials, typically involve energy-intensive size reduction, hydrometallurgical process. This process departs from current methods by leveraging inherent material properties and differences in malleability and size among e-waste components. This technology efficiently liberates components without the need for intensive energy use. It further capitalizes on physical separation techniques, exploiting differences in size, density, and magnetic susceptibility to isolate high-grade, saleable elements and other products, including base metals (such as copper and aluminum), precious metals (such as gold, silver, and palladium), ferrous metals (such as iron, cobalt, nickel, and manganese), silicon, and plastics. High-purity metals were obtained when the process was tested with a char-metal mixture from e-waste thermolysis. Embracing circular economy principles, this chemical-free and zero-waste approach not only maximizes resource recovery and product quality but also reduces the carbon footprint, offering a sustainable and economically viable alternative to current recycling methods. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-06

The broader impact of this I-Corps project is the development of a technology for non-invasive assessment and monitoring of neurocognitive disorders, especially Alzheimer’s disease. The prevalence of Alzheimer’s is set to surge, potentially overwhelming current healthcare practices, due to increasing life expectancy and the static number of neurology practitioners. Current diagnoses are largely based on clinical interviews and questionnaires, which are subjective and can lead to recalls and interviewer biases. This technology is a non-invasive technique to screen patients with Alzheimer’s disease or other dementia-inducing neurocognitive disorders. These tools are designed to help physicians and clinicians rapidly diagnose patients. The technology may improve patient outcomes, enhance the quality of healthcare delivery, improve patient and family comfort, and reduce financial burdens. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of sensor-based artificial intelligence (AI) to analyze speech patterns that can be used to identify early signs of cognitive decline. Integrated into an easily accessible web platform, one model identifies speech pattern differences to assess dementia risk, providing a probability metric. The other model automates the AD8 Dementia Screening scoring, a screening test used to detect early cognitive changes, enhancing efficiency in diagnosis. Both models, utilizing deep learning and natural language processing methods. Preliminary validations affirm the models' accuracy, with full real-world testing underway to ensure reliability. By guiding physicians in the diagnosis and treatment planning process, this technology may facilitate timely and effective medical interventions to streamline dementia detection. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-06

This research project focuses on enhancing the way vital information is delivered to smart mobile devices—such as smartphones and tablets. With the advancement of technology, there is a growing necessity for these devices to receive various types of information (like images, videos, and texts) instantly and effectively. One promising approach to achieving this is through the use of Geospatial Digital Twins (GDT), which are digital models of physical environments. GDTs are becoming increasingly important as they allow for real-time updates and interactions, making them invaluable for various applications such as monitoring, maintenance, and emergency response. Traditionally, data for GDTs has been collected through automated systems like distributed sensor devices, satellites and drones. However, these methods have limitations, especially when it comes to updating data quickly and covering hard-to-reach areas. To overcome these challenges, this project will develop a novel approach that involves the community through “human-in-the-loop” strategies. This means using crowd-sourced data, where people provide real-time updates to digital models. This method not only promises to enhance the accuracy and timeliness of the information but also to allow discovery of new information. The project has the potential to revolutionize how we interact with and understand our physical world, potentially making this work a cornerstone for further scientific and educational advancements. The project will also play an important role in education, integrating research findings into university curricula and offering unique learning opportunities for students, including students from underrepresented groups. The goal of this project is to establish an intellectual foundation for building a real-time crowd-sourced GDT. To achieve this goal, we will work toward a fundamental understanding of crowd-sourced multi-modal information collection and processing to account for the underlying human incentives and human-machine integration, which underpin the foundation of crowd-sourced GDT. In this project, we will investigate the design of crowd-sourced GDT to ensure timely, truthful, and unbiased imagery data collection from the crowd. Our efforts will be organized around four tightly integrated research thrusts: 1) ensuring crowd-sourced data freshness for a GDT; 2) integrating crowd-sourced data for real-time GDT updates; 3) guaranteeing truthful reporting in crowd-sourced data collection; and 4) mitigating self-reinforcing bias in crowd-sourced GDT updates. Collectively, this project will result in new tools for optimization and control that directly contribute to real-time crowd-sourced GDTs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-06

In federated learning, machine learning models are trained using the data of individual users without sharing the data outside of the original secure system. The paradigm is popular, in part because of the promise it holds for protecting privacy of data while still using data from multiple sources to create a powerful central model. Algorithms for federated learning require expensive computations for deriving large sets of hyperparameters (the variables that control learning) that control their operation. Recently, Adaptive Federated Learning (AFL) is being used, which offers improved training performance and reduced effort for identifying the hyperparameters. Despite these improvements, AFL is still in its early stages and suffers from issues in both communication of large number of hyperparameters and training efficiency. Moreover, the worst-case efficiency of adaptive federated learning also remain largely understudied from a theoretical perspective. The broad goal of this project is to fill this research gap by: (1) providing a provably efficient approach to adaptive federated learning, and (2) building practical adaptive federated learning systems for real-world machine learning settings such as the Internet of Things (IoT) and healthcare. This project will offer a novel perspective for developing practical and theoretically-sound efficient adaptive federated learning. The core idea of this project is the integration of adaptive federated system design with the perspective of nonconvex optimization, which provides a systematic way to deliver intuitive solutions with theoretical insights/guarantees. Specifically, this project consist of three aims: (i) improving the communication efficiency of adaptive federated learning with new algorithm designs and rigorous convergence guarantees; (ii) improving the training efficiency of adaptive federated learning under heterogeneous data distributions with rigorous convergence guarantees; (iii) building and evaluating the practical adaptive federated learning systems in real-world applications such as IoT and healthcare. Taken together, this project provides new approaches and insights into efficient adaptive federated learning systems design and will promote advances in the fields of machine learning, data mining, and related application field. It will also help develop and culture graduate and undergraduate students with machine learning and federated optimization techniques, recruit undergraduates from diverse backgrounds into research activities, interest K-12 school students for a broader education in computer science and make the proposed research accessible to a broader audience. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-05

CO2 is a critical feedstock for our future decarbonized energy and chemical industries. Chemical conversion of CO2 into useful fuels and chemicals, even in the presence of catalyst materials that effectively accelerate chemical reactions, requires high temperatures and/or pressures to counteract its inherent stability. A promising alternative is to use nonthermal plasma together with catalysts to circumvent conventional heat management and pressure requirements. Nonthermal plasma is composed of highly energetic electrons that can interact with molecules and activate strong bonds in stable molecules such as CO2. Plasma-enhanced catalysis processes are complex and involve coupled interactions across states of matter and length scales that remain poorly understood. This project promotes the progress of science and advances the field by disentangling and generating fundamental insights into the plasma-gas, gas-solid, and plasma-solid interactions that determine process performance (i.e., the amounts and types of products generated from the amount of CO2 and H2 consumed). The insights from this work will be incorporated into coursework at the undergraduate and graduate levels at Penn State across three different departments. The PIs will also develop modules for computational and experimental outreach activities at the middle-school and college levels. This project aims to dissect the coupled plasma-catalyst-molecule interactions that underpin plasma-enhanced catalysis, focusing on CO2 hydrogenation over FeCo bimetallic catalysts using dielectric barrier discharge (DBD) packed bed reactors. The specific goals are to (1) elucidate the relationship between plasma characteristics and the configuration/composition of packed catalyst beds, (2) understand how plasma characteristics influence gas-phase radical reactions in empty reactors and in packed beds of oxides, and (3) clarify the nature of catalyst active sites and their role in plasma-catalyst synergy. The interdisciplinary approach combines experimental methods from plasma science and heterogeneous catalysis together with theory (ReaxFF and eReaxFF based atomistic-scale simulations) to decouple plasma-catalyst/solid-molecule interactions across length scales. This work will provide fundamental understanding of the plasma-catalyst, catalyst-plasma interactions crucial for observed synergies, with specific insights for CO2 hydrogenation that are generalizable to plasma-enhanced catalysis. If successful, this work will accelerate development of plasma-based technologies as an alternative to conventional thermal processes, uniquely suited for our future electrified power grid. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.