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Application of Polymer Nanocomposites in the Design of Prosthetic Sockets That Feature Auxetic Meta-Structures
Get PDFThis research evaluates the response of prosthetic sockets constructed using polymer nanocomposites and incorporating auxetic meta-structures. The prosthetic sockets feature lightweight meta-structures between the inner and outer walls of the socket. Three different prosthetic designs featuring chiral, reentrant hexagon, and honeycomb meta-structures are evaluated in this study. The prosthetic sockets were designed in SolidWorks and compared using finite element analysis. The polymer nanocomposites used in this study include polypropylene and ultra-high molecular weight polyethylene containing uniformly distributed inclusions of either titanium dioxide, zinc oxide, or graphene nanoplatelets. Our results show a favorable pressure profile at the interface between the prosthetic socket and residual limb for sockets featuring chiral meta-structures. The auxetic chiral prosthetic socket, across all materials, demonstrated a 44.6% reduction in average interfacial pressure compared to the nonauxetic hexagon socket. The addition of nanoparticles led to less favorable contact pressure profiles but effectively reduced deformation, indicating potential for patient-specific customization based on individual stiffness and comfort requirements. The results indicate that auxetic structures, combined with the incorporation of appropriate nanoparticles, can aid in designing prosthetic sockets that minimize physical discomfort, discontinuation rates, and remove barriers to consistent prosthetic use
Chains of Knowledge: Slavery, Low-Wage Labor, and the Role of Intellectual Property in Antebellum Georgia (1789 to 1865)
Get PDFThis article investigates Georgia’s engagement with intellectual property from 1789 to 1865, with an epilogue to 1920, arguing that the state’s economic reliance on unfree labor systems profoundly shaped its approach to technological innovation. Drawing from a newly constructed, state-specific patent database, the study reveals that Georgia’s low patenting rates—examining the areas of in mining and metallurgy, textiles, and the establishment of the Confederate Patent Office during the American Civil War—was not indicative of technological stagnation, but rather evidence of a region where innovation was informal, experiential, and embedded in labor hierarchies. The article advances the argument that the absence of formal patent activity should be treated as historical evidence in itself, reflecting a distinct Southern model of development grounded in tacit knowledge and labor control. By reframing the historiography of invention away from technological determinism, this research offers a new interpretation of how intellectual property, labor, and regional economies intersected in antebellum Georgia
Life Cycle Assessment of the Cultivation of Lettuce in a Hydroponic System Using the Nutrient Film Technique
Get PDFHydroponic systems are an emerging method of soilless crop cultivation typically done inside a greenhouse where nutrients are delivered dissolved in water rather than being supplied from the soil. Potential benefits of hydroponic systems include reduced irrigation requirements, higher crop yield per area, and the ability to grow crops year-round in nontraditional areas such as urban settings, cold climates, and sites with contaminated soil. However, hydroponic systems tend to have high energy requirements for lighting and climate control, which can yield significant environmental impacts. To explore the environmental impacts of cultivating lettuce in a nutrient film technique (NFT) hydroponic system, a life cycle assessment (LCA) was conducted based on a case study of a greenhouse farming operation that agreed to provide data for the project. The functional unit was 1 year of production at the greenhouse, or 32603 kg of lettuce. The system boundary was from raw material extraction to when the finished product arrived at the distributor. OpenLCA was used along with the ecoinvent database for modeling. ReCiPe 2016 v1.03, midpoint (H), was the impact assessment method used. The results of the impact assessment for one year of lettuce production were 1.90 x 105 kg CO2 eq for climate change, 2.27 x 104 m2a crop eq for land use, 1800 m3 for water use, and 44 kg P eq for freshwater eutrophication. Electricity for lighting and climate control (Scope II), and natural gas for heating (Scope I) were the overwhelming contributors to climate change. Land use was mainly from the upstream impacts of cardboard production for packaging, followed by the direct land area that greenhouse facility occupies. Water use was 56% from water loss from the plant via transpiration. Upstream impact from electricity was the second highest contributor to water use. Freshwater eutrophication was driven by electricity. Coal is used in the electricity grid in the Southeast region of the United States, and the treatment of waste from coal mining has freshwater eutrophication impacts. When compared to traditional open-field growth of lettuce, the hydroponic system at the greenhouse facility had higher impacts in all four categories considered. Results of previous studies vary due to differing system boundaries, locations, and electricity grids. The results of the LCA for the greenhouse agree with some studies and disagree with others. The results confirm that electricity for climate control and lighting is a hot spot for the hydroponic cultivation of crops, and that using more renewable energy can decrease the environmental impacts of hydroponics. Further research should be done to refine the results and to be representative of greenhouses of other sizes and locations
VISOR-ZT: A Visibility, Simulation, and Operational Resilience Framework for Zero Trust Security in ROS 2
Get PDFRobotic systems are becoming more and more prevalent in modern society, with Robot Operating System 2 (ROS 2) being the dominant operating system for these implementations. Its popularity can be attributed to its design, which is purpose-built for distributed systems and asynchronous communications. However, ROS 2 security is static and therefore less capable of responding to contemporary threats and network behavior. This becomes a greater issue when considering its applications in the military and defense sectors, where security is of the highest importance. In recent years, the U.S. Department of Defense (DoD) has implemented zero trust (ZT) security based on seven core tenets explained by the National Institute of Standards and Technology (NIST). The absence of dynamic security in ROS 2 reveals a gap between this concept of ZT and the ROS 2 applications being deployed. Given the intricacies of distributed ROS 2 system security, to research solutions, there has to be a simulation environment where each component can be modeled. Current studies use several promising methods for ROS 2 implementations such as Docker, Kubernetes, tools to simulate network and system faults, and AI/ML algorithms. However, there is not a framework that integrates all of these methods. In this thesis, we develop a novel framework for modeling realistic distributed systems. We then use it to integrate dynamic security that combines ZT principles with ROS 2. The framework will utilize four core components: Docker for standardizing the application environment, Kubernetes for life cycle management and scaling of deployed units, Chaos Mesh to mimic network and system behavior, and an autoencoder for monitoring and anomaly detection. The framework offers visibility into the system, network, and application planes. It allows real-time simulation of attacks and anomalous activity. And it continuously logs data, enabling a human collaborator to analyze threats and deploy policies to isolate them, thus ensuring continuity for essential operations. To illustrate the capabilities of the framework, we present a case study to detect anomalous activity at various levels in real-time. Three experiments are conducted to analyze network anomalies, system anomalies, and application-level attacks, respectively. From these experiments, we demonstrate that our framework detects 100% of the anomalies on all three levels, producing detailed logs to verify each detection and convey the root cause. Even with detection efficiency, some of the logs were unclear, especially for stealthy and slow-rate attacks, demonstrating the need for more insightful autoencoder input features
A Generalizable and Privacy-Preserving Framework for Anomaly Detection in Heterogeneous IoT Environments
Get PDFWith the rapid growth of Internet of Things (IoT) devices across various sectors, detecting anomalies in such systems has become increasingly challenging. IoT environments produce diverse and evolving data streams, often lacking labeled examples, which limits the effectiveness of traditional machine learning models. These models typically require frequent retraining and struggle to adapt to new deployment conditions. This thesis proposes a flexible, privacy-aware framework for anomaly detection in multivariate time series data generated by heterogeneous IoT systems. The approach integrates a long short-term memory variational autoencoder (LSTM-VAE) with contrastive learning and adversarial adaptation, enabling the model to generalize across domains, even in few-shot or zero-shot situations where labeled target data is unavailable. A central contribution of this work is the use of domain-specific adapter layers that allow the model to adapt to new environments without needing access to the raw target data, thus preserving privacy. Additionally, the framework segments traffic based on destination IP addresses instead of fixed time windows, retaining communication context and enhancing the relevance of input sequences. The proposed method is evaluated using real-world IoT datasets from industrial, civilian, and military settings. Results demonstrate improved anomaly detection accuracy and strong cross-domain adaptability. Overall, this thesis introduces an effective, scalable, and privacy-respecting solution for securing dynamic IoT networks, capable of addressing the limitations of conventional methods and defending against emerging cyber threats
Robust Online Inertia Estimation in Power Systems Using Ambient Data in the Presence of Inverter-Based Resources
No full textThe increasing penetration of Inverter-Based Resources (IBRs) in power systems has significantly altered system dynamics, reducing the system\u27s effective rotational inertia and challenging frequency stability. Accurate online inertia estimation is essential for maintaining system reliability under these evolving conditions. This paper introduces a robust methodology for online inertia estimation using ambient data collected during normal system operation. The proposed method employs a state-space model to represent system dynamics and introduces synthetic step changes to simulate disturbances. By analyzing the frequency response and applying advanced signal processing techniques, the methodology estimates system inertia without requiring real large-scale disturbances. The approach is validated on the IEEE 39-bus systems under various load profiles. A detailed sensitivity analysis highlights the impact of window length, sampling rates, and noise on estimation accuracy, emphasizing the methodology’s applicability in renewable-dominated grids
Designing Self-Healable Aromatic Copolymers and Olefinic Composites
No full textSelf-healing polymers capable of recovering from mechanical damage are promising materials for advanced applications, especially those involving mechanical and/or physical fatigue. In these studies, we have developed techniques to achieve autonomous self-healing in commodity Styrene/n-butyl acrylate copolymers. The mechanism of self-healing in the designed polymers involves inter-and/or intrachain non-covalent interactions between π-cloud and polar linkages of acrylic nBA in random/preferentially alternating copolymers. A combination of spectroscopic tools, thermo-mechanical analysis, and molecular dynamics (MD) simulations has been used to elucidate the mechanism of self-healing. These studies further show the incorporation of dipolar C-F groups to understand the effect of having fluorinated aromatics on self-healing functionality. This dissertation also describes the effect of an interplay between dipolar and polar forces on the self-healing of novel polyionic liquids containing ionic species. Lastly, self-healing composites composed of commodity self-healing matrix and fiber reinforcements were developed, which can retain self-healing functionality for 25,000 damages and a temperature range of -196 to 85 °C
The Influence of Past Experiences on Middle School Band Directors’ Instructional Practices
Get PDFThis qualitative study investigated the influence of middle school band directors’ past experiences on their instructional decision-making and implementation of culturally relevant pedagogy (CRP). Embedded in Ladson-Billings’s (1995, 2014) CRP framework and Lortie’s (1975) apprenticeship of observation theory, this study explored the relationship between middle school band directors’ past experiences, their beliefs about CRP, and their instructional practices.
Music education in the United States has historically focused on Western European classical traditions that overlook the cultural diversity of today’s students and reinforce dominant pedagogical norms. Semi-structured interviews were conducted with six middle school band directors. Participants discussed their musical backgrounds, their beliefs about culturally relevant teaching, and how these beliefs and experiences interact to inform their instructional decisions. Data analysis used inductive and deductive coding through the lens of Brown-Jeffy and Cooper’s (2011) five principles of CRP.
Findings revealed that a combination of replicating and modifying past experiences shaped the directors’ instructional decisions. While all six band directors expressed a desire to be culturally relevant, performance expectations, community traditions, and their own educational backgrounds influenced their teaching practices. Student-teacher relationships emerged as a theme for effective implementation of CRP, but were often disconnected from instructional decisions and practices. Participants identified limitations in teacher preparation programs that hindered their ability to fully implement CRP