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A Comprehensive Review of the Thermophysical Properties of Energetic Ionic Liquids
No full textEnergetic ionic liquids (EILs) have various industrial applications because they release chemically stored energy under certain conditions. They can avoid some environmental problems caused by traditionally used toxic fuels. EILs, which are environmentally friendly and safer, are emerging as an alternative source for hypergolic bipropellant fuels. This review focuses on the crucial thermophysical properties of the EILs. The properties of imidazolium and triazolium-based ionic liquids (ILs) are discussed here. The thermophysical properties addressed, such as glass transition temperature, viscosity, and thermal stability, are critical for designing EILs to meet the need for sustainable energy solutions. Imidazolium-based ILs have tunable physical properties making them ideal for use in energy storage while triazolium-based ILs have thermal stability and energetic potential. As a result, it is important to understand and compile thermophysical properties so they can help researchers synthesize tailored compounds with desirable characteristics, advancing their application in energy storage and propulsion technologies
A Quantitative Examination of the Relationships Between Student Race, Discipline Referrals, and the Assignment of Exclusionary Discipline in a Rapidly Diversifying Texas Public School District
No full textBackground: Racial disproportionality in student discipline is a historical, national, and statewide problem. Riverfront Public Schools (a pseudonym) is a suburban/urban district experiencing rapid enrollment growth and tremendous racial diversification. Given that there is no public record of an equity audit, paired with its larger than state average racial dissonance between teachers and students, ensuring equitable learning environments is essential. Analysis of disciplinary data is a requisite element of such action. Purpose: I examined RPS data to determine the relationships between students’ races and 1) the frequency with which they received discipline referrals, 2) the rates/duration to which they were assigned exclusionary disciplinary consequences, and 3) how patterns of racial disproportionality might differ across student and campus characteristics. Methods: Participants included all PreK-12th grade students enrolled at any RPS campus at any point during the 2023-2024 school year. Dependent variables included number of disciplinary referrals and days suspended (in and out of school). In contrast, independent variables accounted for student race and a host of individual and campus characteristics. Inferential techniques included equality of variance tests (i.e., Brown-Forsythe) and associated related generalized linear models (GLM) accounting for the influence of independent variables. Results/Findings: The Brown-Forsythe test results indicated statistically significant variances in discipline referrals and days of exclusionary discipline across race groups. Post-hoc tests generally indicated that Asian and White students received fewer referrals and exclusionary discipline than Latino and Black students. Although statistical significance was determined, practical significance was less apparent, as all measures of the Brown Forsythe test were quite small. R-squared measures of GLM indicated extremely poor data fit, suggesting that the combination of race and student characteristics does not explain a substantive portion of the variance in disciplinary referrals and consequences among RPS students. Conclusion: In closing, this study examines the disparities of disproportionately in discipline in RPS. While statistically significant results were indicated in the study, there are future research implications for RPS to conduct to understand the practical application of the study
Robustness Analysis and Automated Flutter Suppression Controller Design for Aeroelastic Systems
No full textThis dissertation presents novel methodologies for robustness analysis and controller design in nonlinear aeroelastic systems, particularly focusing on airfoil flutter analysis and suppression. The flutter analysis framework developed here utilizes a Support Vector Machine (SVM) to predict the flutter boundary—a critical stability threshold in aeroelastic systems—based on initial conditions and uncertain parameters such as stiffness. The proposed method improves computational efficiency by selectively simulating points near the flutter boundary, providing a more accurate estimation of the stability region compared to traditional techniques. On the control side, the dissertation introduces a linear robust controller for flutter suppression using a combination of Linear-Parameter-Varying (LPV) control and synthesis, optimized via Latin Hypercube Sampling (LHS) and Genetic Algorithm (GA). This hybrid approach addresses both the nonlinear dynamics of aeroelastic systems and the uncertainties in model parameters by ensuring robust time-domain performance metrics such as signal saturation and settling time. The adaptive nature of LHS-GA promotes the survival of the most robust controllers, ensuring that time-domain constraints are prioritized in controller synthesis. Simulation results validate the efficacy of both the SVM-based flutter modeling and the LHS-GA-based controller design. The proposed methods significantly reduce the computational effort required for flutter boundary prediction and enable effective flutter suppression across a wide range of operational conditions, including parameter uncertainties and nonlinearities. This work demonstrates the potential of machine learning techniques, such as SVM, to enhance aeroelastic stability analysis and control design, paving the way for more efficient and robust solutions in practical applications. Future directions include extending the methodologies to more complex dynamic systems and integrating the proposed controllers with real-time system identification and observer-based designs to handle even greater uncertainties and operational demands
Virtualized Hybrid Architecture and Post-Quantum Security in Future Smart Cars
No full textSmart vehicles data communications systems have evolved over the years. The number of sensors have grown considerately which has placed a demand for faster and secure data communication systems in the automobile environment. In this dissertation, three key contributions in the field of Controller Area Network Bus (CANBUS) architecture used in the automobiles. Firstly, the dissertation presented explores the possibility of introducing the Ethernet data communication technology into the existing CANBUS data communication technology thereby creating a hybrid environmental structure that operates in a virtual environment. In this hybrid virtualized architecture, the CANBUS and Ethernet data pathways are used together in such a way that allows for backward compatibility with older automobile systems while having increased data transfer speed by taking advantage of Ethernet technology speed with an added advantage of routing traffic to any of the two data paths depending on the data sizes and required speed all in isolated virtual environments. Second contribution is in the area of improving the security of the data as it is transferred across the hybrid data path to enhance the data security which has been a major issue in the automobile data communication. Distributed security architecture as data is transferred through the communication channels is proposed. The contributions also involve the use of tag-coded sensor, tag ID database and application of distributed encryption and decryption as data is transferred ensuring that there is no major increase in data transfer response time. The solution also ensures that the source and destination devices are authentic, retaining data integrity and confidentiality of data from source to destination. Third contribution involves the application of the post-quantum ASCON Lightweight Cryptography algorithm to authenticate and encrypt the data passing through the hybrid data platforms while hashing the sensor ID to ensure the original ID is not revealed. The application of ASCON to secure the data messages passing through both platforms is a great match for the limited memory and processor capacity of the devices used in smart vehicles. The response time introduced is minimal while making it post-quantum compatible
Calibration of Stalagmite δ¹⁸O for Paleoclimatic Interpretations in Cueva Ensueño, Hatillo, Puerto Rico
No full textStalagmites are invaluable archives of past climate conditions, as their oxygen isotopic composition (δ18Oc) closely mirrors the isotopic signature of the drip water (δ18Ow) from which they precipitate. This isotopic relationship is governed by the isotopic fractionation (18α), which is temperature dependent. In this research, we studied 18α within Cueva Ensueño, a cave situated in Puerto Rico’s northern karst region, as a step to calibrate δ18Oc for paleoclimate interpretations using its stalagmites. We collected drip waters, the temperature of which was measured in situ, and carbonate samples from ten selected stations within the cave. The sampling was done in two field seasons: one in December (a winter month) and another in May (a summer month). To help us better understand the isotopic variations in stalagmites, we farmed calcite at the same drip sampling locations during winter and during summer. Our findings reveal different 18α, with higher values for samples collected in May and lower values for samples collected in December. This variation may suggest a seasonality bias, but we noted that the bias was more driven by isotopic changes in the drip water than in the carbonates. The results from our watch glass experiments point to Rayleigh fractionation, with a heavy isotope enrichment away from the apex of the stalagmite. These effects can complicate the interpretation of paleoclimate signals. While comparing the drip water with the regional dataset, we observed a potential seasonal lag, such that summer drip waters were found to reflect winter signals, and vice versa. This phenomenon is likely attributable to processes such as epikarst mixing or “storage effects,” where water from different seasons is mixed or stored in the karst system before reaching the cave. Elemental analyses of the drip water also suggest that prior calcite precipitation (PCP) is likely enhanced during the dry winter months. The insights gained from this research can better guide us in studying stalagmites from Cueva Ensueño as effective paleoclimate proxies
Assessment of a Groundwater Potential Zone Using Geospatial Artificial Intelligence (Geo-AI), Remote Sensing (RS), and GIS Tools in Majerda Transboundary Basin (North Africa)
No full textGroundwater in northwest Tunisia plays a vital role in supporting the domestic, agriculture, industry, and tourism sectors. However, climate change and over-exploitation have led to significant degradation in groundwater quality and quantity. Traditional spatial analysis techniques such as Geographic Information Systems (GIS) and Remote Sensing (RS) are frequently used for assessing groundwater potential and water quality. Yet, these methods are limited by data availability. The integration of Geospatial Artificial Intelligence (Geo-AI) offers improved precision in groundwater potential zone (GWPZ) delineation. This study compares the effectiveness of the Analytical Hierarchy Process (AHP) and advanced Geo-AI techniques using deep learning to map GWPZ in the Majerda transboundary basin, shared between Tunisia and Algeria. By incorporating thematic layers such as rainfall, slope, drainage density, land use/land cover (LU/LC), lithology, and soil, a comprehensive analysis was conducted to assess groundwater recharge potential. The results revealed that both methods effectively delineated GWPZ; however, the Geo-AI approach demonstrated superior accuracy with a classification accuracy rate of approximately 92%, compared to 85% for the AHP method. This indicates that Geo-AI not only enhances the quality of groundwater potential assessments but also offers a reliable alternative to traditional methods. The findings underscore the importance of adopting innovative technologies in groundwater exploration efforts in this critical region, ultimately contributing to more effective and sustainable water resource management strategies
Design and Synthesis of a New Chiral DiRh Chiral Catalyst and Studies Towards Total Synthesis of Brazilide A
No full textThis dissertation encompasses three major projects. The first project focuses on the design and synthesis of a novel class of chiral bis-carboxylic acids, aimed at developing chiral rhodium(II) catalysts. The second project is dedicated to synthesizing the natural product brazilide A. The third project investigates C–C bond formation reactions involving allylic and benzylic alcohols through a catalytic silver system. Chiral rhodium(II) complexes featuring bidentate ligands are quite rare. The first chapter delves into the synthesis of new chiral bis-carboxylic acid ligands that can be employed in new chiral rhodium(II) catalysts. The second project is centered on the total synthesis of brazilide A, a natural product of the brazilin family. The ultimate objective is to perform structure-activity relationship (SAR) studies, which requires an efficient and flexible synthesis. This chapter outlines a linear synthetic pathway that utilizes commercially available starting materials to create the core structure containing all the necessary carbon atoms for brazilide A. The third chapter demonstrates that Ag(I) salts can effectively catalyze the substitution of allylic and benzylic alcohols to facilitate C–C bond formation. This approach tolerates atmospheric conditions, including oxygen and water, and has been recognized as one of the mildest Friedel-Crafts reactions. Finally, this methodology has been applied to the formal synthesis of echinosulfonic acid D
Liposomal Delivery of Sting Agonist for Intranasal Vaccines Against Respiratory Pathogens
No full textLower respiratory infections are one of the leading causes of morbidity and mortality globally. Children, adults over 60, and immunocompromised people are especially vulnerable to diseases caused by respiratory pathogens. Vaccination against pathogens is the most cost-effective and easiest way to prevent respiratory illnesses. Most licensed vaccines are injected through the parenteral route and provide sub-optimal protection at the mucosal routes of pathogen entry. Though mucosal vaccines are better at protecting mucosal sites, all approved mucosal vaccines are live-attenuated vaccines unsuitable for immunocompromised people. Protein subunit-based mucosal vaccines can reduce the risk of vaccine-induced adverse events in immunocompromised vaccinees. Then again, they usually require co-administration with a potent adjuvant. Development of subunit mucosal vaccines has been slow due to issues with adjuvant safety and immunogenicity. Stimulator of interferon genes (STING) agonists like 2’3’-cGAMP have recently been tested as a safe and potent adjuvant in parenteral and mucosal vaccines. However, safe and efficient vaccine platforms are needed to deliver adjuvants and antigens to the immune cells at the mucosal surfaces. In this report, we validate the immunogenicity and protective efficacy of NanoSTING, a cationic lipid-based cGAMP-encapsulating nanoparticle platform, against two respiratory pathogens of concern: Mycobacterium tuberculosis (Mtb) and respiratory syncytial virus (RSV). In the first part of this dissertation, we demonstrate the successful protective efficacy of a protein-based intranasal vaccine against tuberculosis. Mtb, an intracellular bacteria, is a major cause of lower respiratory tract infections leading to pulmonary tuberculosis. We formulated the vaccine by combining NanoSTING with the model fusion antigen H1. Our results show that NanoSTING effectively delays vaccine clearance from the nasal cavity, a beneficial effect that promotes antigen uptake by nasal antigen-presenting cells. Intranasal administration of this vaccine induced antigen-specific lung-homing T cells in mice. Importantly, two doses of the intranasal NanoSTING-H1 vaccine provided BCG-comparable protection against Mtb challenge, effectively protecting mice from severe disease and lung damage. In the second part of this dissertation, we have developed a prefusion protein-based intranasal vaccine against RSV. Prefusion proteins are known to elicit neutralizing antibodies against RSV. We demonstrated that vaccines formulated with NanoSTING and prefusion proteins preF1-s and preF2 elicit high prefusion protein-specific antibody titers in mice after a single dose. Two doses of intranasal NanoSTING-prefusion protein vaccines protect cotton rats against viral replication in the lung. NanoSTING-preF2 could also completely eliminate the virus from the nasal tissue four days after the viral challenge. These studies demonstrate the efficacy of NanoSTING as a promising mucosal adjuvant that warrants future pre-clinical and clinical studies
The Impact of Confirmation Bias and Memory on Consumers’ Interpretation of Foodservice Brand Messages
No full textArtificial intelligence (AI) algorithms enhance information customization, increasing selective exposure and making people more likely to encounter, recall, and trust content that aligns with their existing thoughts and beliefs. This preference leads to confirmation bias. This study examines how consumers’ confirmation bias and selective exposure-induced memory impact their interpretation of politically controversial brand messages. Study 1 results show that pro-GMO individuals prefer pro-GMO news articles, while anti-GMO individuals tend to prefer and remember articles that oppose GMOs. This highlights how selective exposure and confirmation bias impact memory related to existing attitudes. Study 2 results indicate that beliefs about GMOs significantly affect the evaluation of politically controversial messages from foodservice brands. Individuals opposing GMOs show higher purchase intentions, more positive attitudes toward the brand, and a greater likelihood to engage in e-WOM when a foodservice company emphasizes “all-natural” sourcing. Conversely, those with pro-GMO biases exhibit consistent reactions regardless of the message
Distance-Based Classification and Clustering of Smooth Surfaces
No full textThis dissertation addresses four primary research areas: (i) the study and enhancement of diffeomorphic interpolation between 3D surfaces, (ii) the application of distance-based classification method on two different datasets, (iii) the application of distance-based approach for automatic clustering and prototype selection of 3D surface dataset, and (iv) the development of segmentation and strain analysis technique of mitral valves using 2D images. Diffeomorphic interpolation between 3D shapes was improved by optimizing solver parameters tailored to our dataset and exploring the use of warm starts with initial guess shapes generated at a coarse resolution. The distance-based approach was applied to the Mitral Valves (MV) and Virtual Shapes (VS) classification problems, incorporating distances based on diffeomorphic registration, optimal transport, and the Hausdorff distance. The method was benchmarked against popular deep learning models such as VoxNet and PointNet, which are commonly used for 3D shape classification tasks. An additional use of the distance-based method involved automatic clustering and prototype selection. Multiple k-means algorithms were applied to dissimilarity representations of 3D shapes to divide the dataset into clusters. Selecting the most central cases within each cluster enabled systematic prototype selection, reducing the variance in distances between shapes and their prototypes. Accurate strain calculation of the MV is essential for medical analysis. To ensure precise strain measurement from a series of 2D cardiac images, consistent MV segmentation is required. A segmentation method was developed utilizing connected components, skeletonization, and a Random Forest (RF) classifier to achieve reliable MV segmentation. This dissertation incorporates sections from the article "Classification of Deformable Smooth Shapes Through Geodesic Flows of Diffeomorphisms" by Dabirian, Sultamuratov, Herring, El-Tallawi, Zoghbi, Mang, and Azencott