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Validation and Analysis of Exoplanetary Transit TOI594 4.01 Using Ground-based Observations
Get PDFThis study explores the realm of transiting exoplanets, focusing on the TESS mission. Using data from George Mason University, we calibrated the images in AstroImageJ using Dark and Flat frames to optimize image quality. We examined TOI5944-01, extracting a detailed light curve showing potential exoplanetary transits. AstroImageJ tools enabled initial analysis, with further modeling in Python's exoplanet package. Comparing our results with existing research offers fresh perspectives on exoplanet dimensions and orbits
Security Through Frequency Diversity in The 5G NR Standard
Get PDFThis thesis explores the use of pseudo-random frequency hopping for added security in the 5G New Radio specification. Frequency hopping makes it more difficult for an attacker to intercept, detect, or jam a wireless connection in a 5G network. Current 5G resource allocation options are examined, and the state-of-the-art literature regarding Orthogonal Frequency Division Multiple Access (OFDMA) frequency hopping under various channel conditions is reviewed. Computer simulations were conducted to compare the throughput performance of the frequency hopping technique vs. static resource allocation. It is shown that under certain channel conditions and power allocation schemes, the aggregate user throughput under frequency hopping is within 95% of that of static allocation, although less under more realistic power allocations, while the probabilities of intercept and detection is significantly reduced
Robot Motion Planning in Optimization Frameworks
Get PDFUsing motion planners as decision variables or objective functions in optimization frameworks is important in various fields such as robotics, computer graphics, and biology. By doing so, optimization methods can be used to maximize the motion planners' performance in such scenarios. However, developing robotic motion planners used as (part of) an evaluator in optimization frameworks is a challenging task because optimization methods typically call the evaluation function thousands of times and motion planning remains computationally expensive after decades of work. This high computational cost makes motion planners too expensive to be a useful evaluation function. This dissertation discusses the hypothesis that the motion planning problems in many optimization frameworks can be solved efficiently and approximately, and these approximate solutions still can provide a high-quality and discriminative evaluation of a design or behavior. I validate my hypothesis by studying a challenging problem called shepherding and a space design problem that considers Human-Robot interaction. For the shepherding problem, I develop learning frameworks that train the models effectively in the obstacle-filled environment via different planning information, such as a roadmap or a potential field. The models approximate the motion planners in shepherding problems and can be used to evaluate shepherding behavior in various environments. For the space design problem, I propose optimization frameworks of computational space design that consider human preference and different navigation constraints such as nonholonomic, task planning, and multi-agent motion planning. In these frameworks, motion planning is efficiently solved by reusing the solutions obtained from earlier iterations and a decentralized motion planner. The paths generated by the motion planner can be used as part of an evaluator for the space design and Human-Robot collaboration. I validate my results in different spaces such as bedrooms, offices, living rooms, and Human-Robot collaboration kitchens
ASSESSMENT OF BENTONITE-POLYMER GEOSYNTHETIC CLAY LINERS AS HYDRAULIC BARRIER
No full textThis work is embargoed by the author and will not be publicly available until December 2028.Geosynthetic clay liners (GCLs) are manufactured hydraulic barriers comprising of a thin layer (5-10 mm) of sodium bentonite (Na-B) sandwiched between two geotextiles. GCLs have been widely used in composite liner systems in waste containment facilities due to their low permeability, ease of installation, and relatively thin thickness. However, leachates from coal combustion products (CCP), mining waste (MW), and municipal solid waste incineration ash (MSWI) may have high ionic strength (I), predominantly polyvalent cations (RMD), and/or extreme pH condition, which can inhibit the osmotic swelling of Na-B and lead to high hydraulic conductivity of Na-B GCL (>10-10 m/s). Na-B GCLs have been modified by blending bentonite with polymer to improve its chemical compatibility, named as bentonite-polymer (B-P) GCLs with proprietary polymer(s).Tremendous research has been conducted to investigate the hydraulic conductivity of GCLs to saline solutions or leachates from solid waste disposal facilities. Tests are required to meet the termination criteria (e.g., hydraulic, and chemical equilibrium) per ASTM D6766 to reflect the long-term hydraulic conductivity of GCLs, which typically takes a very long time (e.g., months). Alternatively, swell index test (ASTM D5890) is used as a quick index test to evaluate the chemical compatibility of GCLs to leachates. The hydraulic conductivity of GCLs is inversely related to the swell index of Na-B GCLs, whereas the swell index and hydraulic conductivity of B–P GCLs are not well correlated as Na-B GCLs. Hence, the primary goal of this dissertation was to develop and evaluate new methods and alternative index tests to assess and predict hydraulic conductivity of B-P GCLs. First, this study evaluated the effect of effective stress on hydraulic conductivity of bentonite-polymer geosynthetic clay liners to coal combustion product leachates. Experiments were conducted on one conventional Na-B GCLs and other four GCLs contained bentonite-polymer with four synthetic coal combustion product (CCP) leachates (Ionic strength = 473 mM to 4676 mM). The beneficial effect of increasing effective confining stress on the reduction in hydraulic conductivity of B-P GCLs was affected by the leachate properties and polymer loading. This study developed machine learning (ML) models to predict and classify hydraulic conductivity of Na-B GCLs to leachates. The XGBoost model provided good fitting for both regression and classification tasks. The classification model can be used as a tool to screen qualified Na-B GCLs to specific leachates (e.g., ≤ 1 × 10-10 m/s), while the regression model can provide good estimation of hydraulic conductivity of Na-B GCLs. This study also developed machine learning (ML) models predict and classify the hydraulic conductivity of bentonite-polymer geosynthetic clay liners (B-P GCLs). The results indicated that XGBoost consistently outperformed other methods in all feature subsets for both regression and classification analyses. The GEP-developed equation was developed to predict B-P GCL hydraulic conductivity to leachates. Classification trees derived from this study can serve as simple tools to screen B-P GCLs for leachate management in solid waste disposal facilities. This study conducted and evaluated three index tests (e.g., swell index, SRC, and viscosity tests) to assess the hydraulic conductivity of Na-B and B-P GCLs. Comparisons were made between the hydraulic conductivities of the GCLs and each of the index tests and combination of index tests conducted. The result showed that the combination of SRC and viscosity tests can be used to classify the hydraulic conductivity of GCLs with higher accuracy (SRC ≥ 6.0 mL/1g or Viscosity ≥ 50 mPa.s). Finally, this study also discussed the factors that affect the long-term hydraulic conductivity of B-P GCLs, especially focusing on the effect of polymer elution. Experiments were conducted on one conventional Na-B GCLs and the other six GCLs contained bentonite-polymer with two synthetic coal combustion product (CCP) leachates (Ionic strength = 50 mM to 96 mM). Polymer elution, in addition to slow cation exchange process, adversely affects the long-term hydraulic conductivity of B-P GCLs. Service ability and life can be improved when GCLs were introduced into a higher polymer loading.2028-12-1
Dynamic Modulation Scaling Enabled Real Time Transmission Scheduling for Wireless Sensor Networks
Get PDFReal-time low-power wireless monitoring are increasingly being used in applications such as: Industrial Internet-of-Things, Smart City technologies, and critical infrastructure monitoring. Creating a deadline driven scheduling while considering energy management creates complex optimization problems. My research integrates energy saving mechanisms with real-time scheduling for time critical WSNs to save energy while meeting the desired quality of service requirements. I investigate and improve the energy consumption of real-time wireless sensor network (WSN) protocols utilized in industrial control systems. My contributions are in three distinct areas. First, I focus on single cluster real-time WSNs specifically on improving time-slotted superframe based techniques. The current wireless standards for Industrial Control Networks (ICNs) employ static slots for their superframe structure. I study the concept of dynamic readjustment of time-slots to minimize the overall energy consumption and combine real- time performance with novel energy conservation methods by describing a set of dynamic modulation scaling (DMS) based adaptive packet transmission scheduling algorithms that reclaim unused slot times. To support my reclaiming method in a wireless environment, I introduce a novel low- power listening technique called Hybrid Low-Power Listening (HLPL) protocol. I evaluate my algorithms using Castalia simulator against an oracle-based approach, and show that my dynamic slot reclaiming approach, coupled with HLPL, can introduce substantial power savings without sacrificing real-time support. In order to further expand applicability scope of my solution, my dissertation work considers non-deterministic workloads. Next, I take a deeper look into DMS and its effect on low power wireless communication. In recent years a number of studies have suggested that DMS techniques can reduce energy consumption in low-power wireless transmission technologies. These studies tend to rely on theoretical or simulation DMS models to predict network performance metrics. However, there is little, if any, work that is based upon empirically verified network performance outcomes using DMS. My dissertation fills that gap. First, by using GNU Radio and SDR hardware I show how to emulate DMS in low power wireless systems. Second, I measure the impact of varying Signal-to-Noise levels on throughput and delivery rates for different DMS control strategies. Third, I quantify the impact of distance by using DMS and finally, I measure the impact of different elevations between sender and receiver on network performance. My results provide an empirical basis for future work in this area. For the third part, I investigate transmission scheduling of multi-hop time critical WSNs. Previous work has shown that connection driven topology control has tremendous potential to decrease energy consumption and/or latency [1–5]. DMS changes transmission energy levels and has a direct impact on packet loss rate and propagation distance. However, current work does not provide any multi-cluster communication solutions which incorporate DMS into already managed transmission energy level control. I address this gap by first formulating Mixed Integer Nonlinear Formulation (MINLP) of DMS enabled transmission scheduling for deadline driven networks. Next, I present two polynomial time heuristics. I compare them against the optimal solution by integrating the empirical measurements obtained from my SDR tests and present how DMS can be applied to multi-hop WSNs to save substantial amount of energy
Deconversion as Conflict: The Moral Grammar of Latter-day Saints and Ex-Mormons
Get PDFThis thesis has been embargoed for 2 years. It will not be available until May 2023 at the earliest.In religious psychology, deconversion is often studied as an intrapersonal phenomenon, a shift from religious belief to disbelief. But deconversion is at least analogous to (if not coterminous with) social conflict in that both are complex, non-linear social phenomena characterized by destructive relational patterns and protracted social identities. Hence this thesis presents a theory of deconversion as conflict. This theory is informed both by original research and the literatures of religious psychology, peace and conflict studies, and narratology. Original research includes a root narrative analysis of a triangulated dataset—five focus group interviews and a small sample of representative texts from three conflict parties. This includes the Church of Jesus Christ of Latter-day Saints, its current members, and its former members. This analysis demonstrates stark differences in moral grammar that make it difficult for each party to understand the points of view of the other. Who one group sees as a hero, the other sees as a villain; and what one group sees as their primary method of overcoming abuse, the other sees as an abuse of power. These disparate moral systems influence each party to choose resolution strategies such as evangelism and apologetics that fracture their relationship and prevent reconciliation. These findings suggest: a) deconversion transforms the relationships between believers and the newly formed disbeliever, b) evangelism and apologetics are win-lose modes of interaction unfit for the purpose of conflict resolution, and c) a healed relationship between believers and disbelievers will not occur automatically but requires renegotiation.2023-05-1
Quantifying Cyber Risk and Energy on Digitalized Advanced Manufacturing
Get PDFManufacturers confront multifaceted operational procedures, intricate machinery, an extensive array of product categories, and the dynamic landscape of swiftly evolving technologies within the context of a fiercely competitive global market. Moreover, the proliferation of emerging and digital technologies in the context of globalization engenders heightened interconnectedness among disparate manufacturing and supply chain processes. This augmented digital interconnectivity and data utilization requires the deployment of advanced automated technologies for data acquisition and retention, consequently augmenting susceptibility to cyber threats. Among the side effects of a higher incidence of cyber-attacks in manufacturing are the delays in processing times for manufacturing, which negatively impact production performance and energy consumption. The primary research question addressed in this study relates to the feasibility of consistently acquiring the cyber risk impact associated with the integration of sophisticated digitalization technologies into advanced manufacturing processes. Additionally, the study aims to investigate the potential consequences of a cyber attack on energy consumption. The exploration of the study subject will be conducted through the proposal of a framework that aims to systematically and comprehensively quantify cyber risk. This will be achieved by employing sophisticated methodologies for cyber risk quantification and modeling, energy quantification models, and simulation assessments. The overarching objective of this framework is to conduct an in-depth analysis of data acquisition and storage technologies within the industrial sector, with a specific focus on investigating the ramifications of cyber threats on these technological facets. While the research domain encompasses diverse applications in advanced manufacturing, it is pertinent to note that, for the purposes of this doctoral thesis, the semiconductor manufacturing sector has been selected as a case study to exemplify the foundational concepts formulated
Infectious Pathogens in Occupational Diving: A Literature Review
Get PDFMaster's projectThis literature review discusses the current risks occupational divers face in terms of infectious pathogens in their work environment. Much of diving research accounts for oxygen toxicity or decompression sickness; leaving infectious pathogens largely untapped. Various bacteria, viruses, protozoans and microplastics are described in this review, along with their level of risk to divers. A literature review for the risk of pathogens in occupational diving has not been done since 2006 (1). This review hopes to be an updated resource that can uncover research and data gaps to ensure the health and safety of our divers
ANALYSIS OF ENDOGENOUS PEPTIDES FROM UNDER-RESEARCHED SPECIES
Get PDFCompared to proteomics studies of organisms with well-characterized proteomes, the study of endogenous peptides in organisms with limited sequence information poses unique challenges, including separating low abundance peptides from complex matrices while inhibiting proteolysis, optimizing chromatography, comprehensively fragmenting peptides via mass spectrometry, and identifying peptides without reference to a well-curated database. Here, a peptide analysis workflow was developed in stages, from detection of host defense peptide standards in the well-characterized human and mouse plasma matrices, to the analysis of plasma of a more exotic species (Alligator mississippiensis) and a different matrix (skin secretion) from the caecilian species Typhlonectes natans. Results suggest that peptide enrichment via solvent precipitation or solid phase extraction followed by LC-MS/MS analysis using a larger-bore polymeric column and EThcD fragmentation provides a reasonably robust sample preparation workflow. Experiments from model systems also demonstrate the value of combining bottom-up proteomics and peptidomics workflows and provide insight into parameter optimization for the use of data-independent acquisition in peptidomics studies. Finally, results indicate that PEAKS software performs well for de novo peptide identification but highlight the strengths and limitations of existing sequence analysis tools in inferring the function and activity of de novo peptides for under-researched species. Therefore, work toward analyzing patterns in de novo sequencing errors and comparison of sequence patterns between peptide datasets is presented, with potential future application to improving peptidome analysis for non-model organisms
A Framework for Testing and Evaluating Secure and Verifiable Computational Offloading in Edge Computing
Get PDFThroughout the world, 8.74 billion Internet of Things (IoT) devices have been deployed, ranging from household thermostats to sensors in remote areas. However, these IoT devices are resource-constrained, not only in computational speed, but often in available electrical power. Computational offloading can provide significant power and latency savings, but often exposes data and systems to security breaches. Researchers have proposed a plethora of protocols to address these security gaps. These published works focus solely on theoretical power and latency savings and do not include end-to-end implementation or data. Furthermore, few, if any, of these protocols have been fielded by either academic or commercial projects. This paper presents the results from the end-to-end implementation of an encryption offloading protocol. Latency and power data were collected to enable comparisons of security computations done solely on the IoT device and partially outsourced to a nearby device. Using the analysis of this data and lessons learned from the end-to-end implementation, the author of this research also created a generic software library to implement computational offloading to the edge. The new software library enabled the integration of a known, secure and verifiable computing technique into the encryption offloading protocol