This project is centered around the creation and validation of an innovative digital framework to foster an ethical manufacturing ecosystem where machine ethics, including legality, integrity, and accountability, is mandated throughout the product design-fabrication-service life cycle. In the era of Industry 4.0, manufacturing systems are enhanced with advanced technologies and interconnectivity. While this evolution significantly expands the functionalities and accessibility of manufacturing machinery, it also introduces unprecedented ethical challenges in manufacturing regulation, such as malicious use of customizable fabrication (e.g., counterfeit goods and 3D printed weapons). This project pioneers the exploration of ethical Industry 4.0 and anchors principles of legality, integrity, and accountability as fundamental components in the digital manufacturing ecosystem. Specifically, the team designs and implements a set of computational methods and digital tools to provide ethical assurances spanning each stage of the design-fabrication-service life cycle from design, to fabrication, to service. By leveraging existing research platforms and local manufacturing partners, the ethical framework can be integrated and validated within real-world manufacturing testbeds. This project has multi-dimensional educational and societal impacts. The project can necessitate the inclusion of ethics and regulation compliance promotion within Industry 4.0. Moreover, the project also contributes significantly to the education of the future manufacturing workforce, promotes public awareness of manufacturing ethics, and brings benefits to the US manufacturing sector and general society. This research project explores principles of ethics as new functional components and constructs a digital framework that tackles technical obstacles in ethical Industry 4.0, including intellectual property (IP) protection, data security and privacy, regulation, and compliance. The team investigates and develops an ethical cyber layer, incorporating a set of computational algorithms, architecture, and software, to enhance the legality, integrity, and accountability of the digital manufacturing system. The technical thrusts in this project are threefold: (1) an efficient and robust ethical design checking mechanism using nonlinear matching algorithms for complex 3D digital models imported to manufacturing machines to avoid IP theft and malicious design; (2) ethical fabrication monitoring tools for manufacturing machines via secure computation with high privacy needs, limited annotated samples, and diverse operation conditions; and (3) ethical product tracking to analyze product texture fingerprint and assist the forensics of source machine identification with illegal and non-integrity usage. The validated resources and tools, including advanced models, algorithms, software and test benches, and educational materials, will be publicly available.
Funder: National Science Foundation
PI: Hongyue Sun, Franklin College of Arts and Sciences, Department of Statistics
Dynamic binary translation (DBT), used to translate executable code from one instruction set architecture to another, supports a range of essential applications, including architectural simulators, hardware emulators, runtime optimizers, program analysis tools, software testing platforms, and more. However, existing DBT systems have not kept up with the rapid evolution of computer software and hardware. For instance, existing DBT systems suffer from significant execution inefficiencies due to the poor quality of the translated binary code. As a consequence, it becomes increasingly challenging to adopt DBT for many important applications, especially those in emerging domains, e.g., machine learning and graph processing. The goal of this project is to modernize DBT systems through a series of novel innovations. A key insight is that the translation between different computer instruction sets –usually represented in assembly language– is similar to the translation between different natural languages. Inspired by this insight, this project will invent novel translation approaches to produce high-quality binary code. The innovations developed by this project will improve the performance efficiency of DBT systems when running binary code. Moreover, they will enhance the capability of DBT systems to support important applications. More efficient, more capable, and more powerful DBT systems, as modernized by this project, will not only benefit current DBT applications, but also enable DBT applications in new frontiers, producing long-lasting impacts. The innovative technologies developed in this project will provide insights for the research community to push forward the DBT field. Additionally, this project will assemble a set of well-organized teaching and mentoring activities to promote research experiences for undergraduates and create a more diverse and inclusive educational environment.
Funder: National Science Foundation
PI: Wenwen Wang, School of Computing
This project seeks to understand how plants control the synthesis of cellulose, a critically important polysaccharide that governs plant growth and development. Cellulose is an important component of food, fiber, textiles, and fuel for human and forage animals. While many of the enzymes that participate in cellulose synthesis in land plants have been identified, it is still unclear how plants make the decision to produce more or less cellulose. The Wallace lab will use advanced genetic, biochemical, and cell biological techniques to elucidate how cellulose biosynthesis is controlled during normal plant development and in response to changing environmental conditions, such as heat, drought, salt stress, and limiting nutrients. This information will be utilized to engineer plants with increased cellulose contents that could be used as biomass feedstocks for the synthesis of sustainable value-added products or as more efficient feedstocks for forage animals. By understanding how cellulose biosynthesis is controlled, the investigator also expects to provide a foundation for understanding how this fundamental feature of plant cell biology is linked to plant growth and development. Additionally, this project will support the research training of graduate, undergraduate, and high school students from diverse and under-represented backgrounds. This project will also support the course development of a quantitative mass spectrometry module for undergraduate students as well as public outreach through local agricultural events and web-based videos. The long-term goal is to understand how plant cellulose biosynthesis is controlled by post-translational phosphorylation, and to utilize this knowledge to increase cellulose output under changing environmental conditions. The central hypothesis guiding this research effort is that CESA (Cellulose synthase A) subunits and other CSC (Cellulose Synthase Complex) components are phosphorylated by protein kinases involved in the brassinosteroid signaling cascade, and these phosphorylation events regulate CSC velocity or subcellular localization. Specific Aim 1 employs biochemical and genetic methods to identify brassinosteroid-regulated protein kinases that phosphorylate and regulate components of the CSC and to understand how these regulatory events control plant growth and development. Specific Aim 2 utilizes advanced quantitative proteomic methods to investigate how changing environmental conditions lead to alterations in CSC phosphorylation. Specific Aim 3 focuses on determining how phosphorylation of CESA subunits influences the in vitro enzymatic activity of cellulose biosynthesis. The unifying goal of this work is to develop a holistic understanding of how post-translational phosphorylation regulates that activity of the CSC both in vitro and in vivo.
Funder: National Science Foundation
PI: Ian Wallace, Franklin College of Arts and Sciences, Department of Biochemistry and Molecular Biology
Long term soil carbon preservation in coastal marshes starts with organic matter deposition and depends on the hydrological and biogeochemical environment that affects subsequent transformations and fate. We posit that the intensity of hydrological oscillations, due to tides and plant transpiration and that affect redox conditions, influence organic matter transformations following deposition and set the trajectory for long-term soil carbon storage or loss. In this proposal, we will characterize mechanisms controlling initial soil carbon transformations (Aim 1), determine how flow modifies the fate of recent root exudates (Aim 2), and assess the physicochemical properties of organic matter buried for 10s-to-100s of years (Aim 3). For Aim 1, we will conduct four, complementary laboratory-based flow-through reactor experiments that probe the mechanistic underpinnings of interacting biotic and abiotic controls on carbon transformations. Aim 2 tests how these mechanisms operate under different flow conditions by applying a 13CO2 label to marsh grasses and tracing it belowground through soil microbes and particles, during spring and neap tides. Aim 3 uses a depth-for-time substitution approach to describe the compositional changes in soil organic carbon that are associated with long-term preservation and affected by surface and groundwater flows. For all three Aims, we will focus on marsh creekbanks and interiors, which represent two ends of a hydraulic gradient with different oscillations in tidal flushing and redox conditions. The overarching hypothesis is that oscillating redox conditions will promote decomposition of root carbon and organic matter associated with minerals whereas more stable and reducing conditions will enhance preservation, particularly of compounds produced by microbes or associated with soil particles. Results from this proposal and collaborations with CMarsh PIs on these Aims will produce a synthetic understanding of how oscillating water flows affect the short-term carbon transformations (minutes-weeks-months) that contribute to long-term preservation (decades-centuries).
Funder: Simons Foundation
PI: Amanda Spivak, Franklin College of Arts and Sciences, Department of Marine Sciences
Obstructive sleep apnea (OSA) is a major health problem and can lead to or increase the risk of cardiovascular disease, stroke, metabolic disease, daytime sleepiness, workplace errors, traffic accidents and death, if it is left undetected. Worldwide it is estimated one billion people, one in seven adults, have OSA. As sleep occurs primarily in the bedroom, monitoring sleep quality at home, instead of in sleep labs, would significantly advance the self-management, and potentially the clinical management, of OSA and other sleep disorders. Thereafter, an approach that noninvasively monitors sleep quality at home would have significant societal and health benefits. This project brings together leading researchers from informatics and health disciplines to create a contactless sensor system for OSA monitoring and characterization, which integrates advanced Artificial Intelligence (Al) and Data Science (OS) into smart sensors and home care. The key research challenges are to convert the information-rich sensor signals to clinically meaningful vital signs and behavioral patterns that are linked with OSA. This project addresses three primary themes of the SCH solicitation: Automating Health, Transformative Data Science, and Effective Usability. The proposed research makes fundamental contributions to computer, data and biomedical science and engineering and will create the first contactless Internet of Things (loT) system for real-time and engagement-free sleep apnea monitoring and characterization. The main technological innovation is a set of novel stream data Al/OS for sleep events and vitals monitoring: a robust signal quality control and segmentation process based on a moving-sum statistic and recursive binary segmentation; a novel factor auto-regressive recurrent neural network framework to characterize key sleep events; a new approach of monitoring vital signs and their variations based on an innovative panel data model and the structural changes in regression coefficients; and a flexible and distributional robust feature assessment method to enable out-of-distribution (000) generalization. The proposed interdisciplinary research takes a coordinated approach that balances theory with evidence-based analysis and systematic advances. The project will conduct empirical validation of new concepts through research prototypes, ranging from specific components to entire systems, and lead to new fundamental insights and effective usability.
Funder: National Institutes of Health
PI: Wenzhan Song, College of Engineering
Demand for radio spectrum space is growing quickly, spurred by the explosion of emerging technologies such as the Internet of Things (IoT), Unmanned Aircraft Systems (UASs), and 5G networks. Unfortunately, the growth of active wireless systems often increases radio frequency (RF) interference (RFI) in science observations. As it stands, very little of the RF spectrum is dedicated to science, and the small amount of spectrum available can fall victim to neighboring RFI or re-allocation for commercial use in the wake of the growing demand for bandwidth in commercial applications. This project focuses on changing the paradigm of remote sensing methods and developing next generation technologies and ideas that are more spectrum efficient, more effective, and meet the challenges of present and future spectrum congestion. In particular, the project will recycle existing RF communication and navigation signals to enable new remote sensing methodologies at these commercially protected bands for scientific use in a myriad of practical solutions for precision agriculture, forestry, water conservation. This project will demonstrate new, low-cost sensing technologies in practical settings and contribute to the agriculture economy. The developed technology aims to usher in a host of precision irrigation for agricultural applications in the nation and worldwide with emphasis in economically distressed areas and developing countries. The complementary educational goals of the Principal Investigator (PI) are to generate a greater awareness and understanding among students, the public, and farmers about the amazing world of microwave remote sensing and its utility for non-intrusive tracking of the world’s most precious resource: water in plants and soil. The project will support the PI’s efforts to broaden the participation of today’s diverse students, including underrepresented minority groups, in STEM education though activities such as new mobile apps, drones, games, and fun facts. This project will construct fundamental microwave remote sensing science, a disruptive sensing framework, and integrated ubiquitous platforms that are non-intrusive, widely accessible, and automated to improve water utilization. This goal will be realized by offering at least three specific new contributions: (1) generating fundamental knowledge needed for a paradigm shift towards microwave bands in UAS-based precision agriculture, (2) designing an integrated/connected RF testbed for evaluating the new paradigm, and (3) integrating smartphones into low-cost drones for broader adaptation. These objectives will be achieved by conducting advanced electromagnetic modeling and simulations, physics-aware machine-learning-based soil moisture retrievals, and field validation. Specifically, this work will generate the scientific basis for accurate water monitoring of root-zone soil moisture observations by recycling low-frequency emissions in microwave spectrum from small drones. Exploring the low-frequency microwave spectrum for remote sensing from drones is unprecedented because no existing small drone instrument is capable of remote sensing at such low frequencies in microwave spectrum. This project will fill in the necessary scientific basis to evaluate the approach’s feasibility and develop the foundation for the algorithms to support such a paradigm. This work will be important for developing the requirements for water utilization in irrigated and rainfed farming and creating algorithms for the new paradigm of RF-assisted UAS-based precision agriculture.
Funder: National Science Foundation
PI: Mehmet Kurum, College of Engineering
Additive manufacturing, or 3D printing, is the process of joining materials to make objects from 3D model data, usually layer upon layer, instead of conventional manufacturing technologies with subtractive features and a longer lead time. As a revolutionary technology, additive manufacturing significantly improves logistics, quickly enables new products and increases material readiness, critical to bringing manufacturing back to the U.S. However, there are many challenges to 3D printing. For example, one significant difficulty stemming from most 3D printing principles is precisely controlling structural orders (e.g., patterned dots, lines, pillars) when manufacturing multiple materials at small scales (e.g., nanomanufacturing in the semiconductor industry). This Faculty Early Career Development (CAREER) award will support the research needed to develop a new additive manufacturing method that can precisely process a diversity of materials. The new manufacturing platform will enable layer-by-layer nanomaterial deposition at desired locations with optional polymers or nanoparticles. The multidisciplinary study includes research in polymer science, nanoparticle synthesis, and interfacial engineering. As a result, the newly-enabled composites could have broad applications in sensors, actuators, soft robotics, supercapacitors, batteries, and regenerative medicine. By involving female and underrepresented minority students in teaching, research, and international collaborations, this project will enhance their education and their representation in an important workforce. Current 3D printing methods rely heavily on external fields (e.g., electrical, magnetic, and acoustic assistance) to precisely place nanoparticles at desired locations and control their long-range orders. However, these 3D printing platforms mandate nanoparticles to be field-interactive, and they have manufacturing limitations when highly concentrated nanoparticles form agglomerations in colloids. This research will advance fundamental knowledge of a new 3D printing method, Multiphase Direct Ink Writing (MDIW), to improve additive manufacturing precision and efficiency. MDIW will enable the deposition of submicron-scale structures without the constraints on part size and build speeds that are typically present in nanoscale additive manufacturing. In addition, this research involves studying the fundamentals of polymer science and nanoparticle engineering to generate new knowledge concerning a 3D printing method for directed nanoparticle assembly. Specifically, the research team will develop a new nanomanufacturing mechanism with layering capabilities, synthesize nanoparticles of controlled dimensions and with desired surface features, and form patterned surfaces with desired profiles by manipulating polymer-nanoparticle interactions to create submicron hierarchical structures. The heterogeneous microstructures generated in the nanocomposites will possess desirable nanoparticle distributions and orientations with controlled packing density, enabling the demonstration of rapidly-prototyped multifunctional sensors.
Funder: National Science Foundation
PI: Kenan Song, College of Engineering
The Farm Bill has been an important tool for conservation for almost 40 years; however, many of our important game species and associated non-game species that inhabit grasslands, savannas, marginal farmland, and woodlands continue to decline. New conservation models need to be explored that minimize the need for tax-payer-funded subsidies in the long-term and maximize the availability of wildlife populations so that they can be enjoyed by sportsmen and sportswomen. This project will test and demonstrate different approaches to engaging private landowners in conservation. We will measure how key wildlife populations—northern bobwhites, white-tailed deer, and wild turkeys—respond to management at large spatial scales. Furthermore, to address problems related to the lack of motivation to restore and manage wildlife habitat on private working lands we will measure the co-production of ecosystem services to landowners and society such that those services can be financially enumerated. At the end of the five years, a possible new paradigm for adaptively managing private lands could emerge leading to innovative and exciting ways to restore wildlife populations and engage hunters with the land.
Funder: Georgia Department of Natural Resources
PI: James Martin, Warnell School of Forestry and Natural Resources
Neuroinflammation plays a critical role in both the onset and progression of traumatic brain injury (TBI); however, most therapies are unable to address the multifaceted aspects. Following TBI, microglia become activated and produce inflammatory cytokines (including TNF-α and IL-6) that damage blood brain barrier (BBB), tight junctions, and lead to infiltration of peripheral immune cells such as neutrophils, monocytes, and T cells. Identification of new immunomodulatory strategies that target microglia and promote a neuroprotective environment is critical for treating this devastating disease. Mesenchymal stromal cells (MSCs) are a promising therapy for regenerative medicine applications due to their immunomodulatory function, which is mediated by secreted extracellular vesicles (MSC-EVs) that possess distinct surface composition and intravesicular cargo. Our group has demonstrated the ability of MSC-EVs to modulate cell-types involved in neuroinflammation such as microglia, T cells and pericytes. MSC-EVs are a promising therapeutic for TBI because they can i) have comparable immunomodulatory function to parent MSCs, ii) cross the BBB, and iii) address safety concerns associated with MSC delivery (i.e. tumorigenesis and thrombosis). Further, the targeting capabilities (mediated by surface signals) and MSC-EV cargo can be engineered through priming (preconditioning) of MSCs with different microenvironmental cues such as cytokines Interferon-gamma and Tumor necrosis factor alpha. However, there is a gap in knowledge over the role and mechanisms of MSC-EV modulation of microglia in the context of TBI and whether this effect can be enhanced through priming. The proposed work seeks to elucidate the mechanisms by which MSC-EVs modulate microglia with a specific focus on MSC-EV mitochondrial transfer. Our central hypothesis is that MSC-EVs produced from cytokine-primed MSCs will have greater functionality through modulation of microglia towards a more neuroprotective phenotype (e.g. reduced production of inflammatory cytokines and reactive oxygen species) and that this effect is mediated by MSC-EV derived mitochondrial transfer in vitro and in vivo. We will test this hypothesis in the following aims: 1) Define the mechanisms of mitochondrial transfer from MSC-EV on microglia metabolism, and 2) Assess MSC-EV therapeutic efficacy in a porcine TBI model. Successful completion of the proposed work will create a novel, tunable approach for targeting the brain’s immune system and treating TBI through a better understanding of MSC-EV mechanisms of action.
Funder: National Institutes of Health
PI: Ross Marklein, College of Engineering
The University of Georgia research program, titled, “Advanced Geospatial Analytics and Electromagnetic Shielding Technologies for Enhanced Sustainability,” addresses new technological capabilities and novel mechanisms that reside at the nexus of sustainability and autonomy in order to enable multi-domain operations (MDO) in a broad range of challenging environments and applications. Leveraging technology advancements in geospatial intelligence and advanced shielding materials will provide multi-layered protection to ensure operational success with high levels of sustainability. The proposed fundamental research will help advance multi-agent autonomous sensing, sensor fusion, and sensor robustness for MDO. An emphasis will be placed on developing solutions for edge devices that can operate in harsh and GPS-denied environments. The research is centered on the development of a robust terrain awareness system (3D) that fuses diverse spatial data streams and leverages advances in deep neural networks (DNNs), simultaneous localization and mapping (SLAM), and heterogeneous computing. Further enhancement of terrain awareness systems will be investigated with a semantic segmentation algorithm combining SLAM and deep semantic segmentation networks to improve image registration performance. Semantic information can narrow the location search range, thereby improving system stability across various scenes. Uncertainty in terrain awareness will be addressed by quantifying uncertainty in digital terrain models, building terrain awareness algorithms that consider uncertainty in terrain data, and optimizing the algorithms on edge devices. The work also seeks to achieve real-time, high-precision 3D mapping by fusing heterogeneous sensing sources collected by UAVs, UGVs, and satellites, and by achieving real-time map construction on resource-constrained edge devices. In order to further assure that edge devices can operate in harsh environments, this work will also deliver research in novel approaches to address electromagnetic threat and protection. The research is based on innovative multidimensional composites containing low-dimensional filler particles, i.e., assemblies of 1D and 2D nanomaterials, within polymer matrices. The work plan begins with a focus on the low-dimensional nanomaterials: initial selection of ~201D and 2D compositions based on their properties in bulk and nanostructured forms; their preparation and modification on laboratory scale by top-down synthetic methods; their characterization to determine composition, phase, and dimensions; and evaluation of their shielding effectiveness (SE) over a broad frequency range of approximately 300 kHz to 12 GHz (GFD). The anticipated outcome is a set of 5-8 low-dimensional nanomaterials with promising EMI shielding properties, each with gram-scale synthetic protocols, confirmation through materials characterization techniques, and suitable stability.
Funder: U.S. Department of Army
PI: Deepak Mishra, Franklin College of Arts and Sciences, Department of Geography