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From Syntheses to Applications of Cyclized Conjugated Molecules and Macromolecules
No full textThis dissertation delves into conjugated molecules and macromolecules featuring cyclized constitutional structures, including conjugated ladder molecules, polymers, and macrocycles. These materials offer unique optical and electronic properties due to their extended ��delocalization and strong intermolecular coupling, stemming from their rigid structures. The dissertation explores how these structures can be integrated into conjugated molecules and polymers to address challenges such as instability and limited state delocalization, paving the way for practical applications.
The introduction provides an overview of conductive organic molecules and discusses conjugated macrocycles and ladder molecules, along with the processing of conjugated ladder polymers.
Chapter II presents the synthesis of conjugated ladder polymers with isotopic substitutions, such as deuterium and carbon-13 labels. Deuterium labeling enhances neutron scattering contrast, aiding structural analysis, while carbon-13 labeling assists in defect quantification. Two such polymers are synthesized.
Chapter III introduces the synthesis of a ladder-type structure in polyaniline-inspired polymers. A low-defect conjugated ladder polymer is synthesized, achieving high conductivity (7 mS cm^���1 ) through oxidation and acid doping. It demonstrates exceptional stability against acids and UV irradiation, surpassing commercial standards, and excels in electrochromic devices and supercapacitors.
Chapter IV describes a self-doping ladder-type cyclohexadiene-1,4-diiminium-based system, offering stability and homogeneity, with a conductivity of 1��10^���3 S cm^���1 , surpassing traditional p-type molecules.
Chapter V discusses the synthesis and iodine doping of conjugated macrocycles with different side chains, forming single-crystal structures when doped with iodine, with conductivity ranging from 2.6��10^���3 to 0.65 S cm^���1 . The chapter also explores crystal packing and doping mechanisms.
The dissertation concludes with an outlook on future research into conjugated ladder molecules and cyclic macrocycles, showcasing how cyclized structures in conjugated molecules and macromolecules address challenges in organic electronics, revealing their potential as next-generation electronic materials
A Climate Smart Agricultural Productivity Assessment of Smallholder Women Farmers in Farming Associations in Nigeria: A Mixed Methods Study
No full textThe world is plagued with multiple planetary threats of rising food insecurity, COVID-19 pandemic, aggravated conflicts, shrinking natural resources and worsening climate crisis. Smallholder women farmers (SWFs) fare even worse in Africa than their male counterparts due to limiting opportunity structures stemming from gender biases and discriminating societal norms. This mixed methods research assessed the interaction of climate smart agricultural practices (CSAPs) and agricultural productivity among SWFs in Nigeria using the climate smart sustainable livelihoods framework.
My findings provide evidence on the levels of use of CSA among SWFs and their agricultural productivity demonstrated by their state of empowerment. The study employed a convergent mixed-method design, collecting data from SWFs through structured interviews (n=196) and focus group discussions (six groups), which informed the quantitative and qualitative strands of the study respectively. Participants were mutually exclusive of the two strands of study. Data was analyzed using descriptive and inferential statistics, rich thick description, thematic analysis, patterns, themes, and representative quotes. Findings from the quantitative strand converged with the qualitative strand in a joint display of data.
The study revealed the most preferred CSAP used by SWFs is mixed cropping while the least used in crop insurance. Participants livelihood dynamics were described, and significant associations are observed between Educational Level and Use of CSA, as well as between Empowerment Level and Use of CSA. Additionally, a significant amount of variance was explained in the Empowerment Level based on the Use of CSA and Educational Level (R�� = 0.288, F = 39.00, p = <.001). Furthermore, six overarching themes emerged from farmers livelihood context, dynamics, and outcomes. The themes reinforced the existential threat of climate change and vulnerability of SWFs which is exacerbated by a complex interplay of factors.
The results were triangulated with the sustainable livelihoods framework which used Amartya Sen���s capabilities theory on the robust concept of agricultural productivity, not only in terms of yield or income but in the real doings and capabilities of what a farmer is able to do and has done with their capabilities. I found that although participants demonstrated adequate intrinsic, instrumental, and collective agency, they face obstacles such as poor infrastructure, unfulfilled government promises, and limited access to assets and tools which underscores systemic and institutional challenges that counterproductively affect agricultural productivity and empowerment.
Overall, I found SWFs are moderate and high users of CSA, but their livelihood outcomes are exacerbated by the negative effects of climate change and institutional failures. This underscores the need for targeted interventions, including improved infrastructure, access to resources, and supportive policies to enhance the resilience and empowerment of these farmers in the face of ongoing challenges
Using X-Ray Computed Tomography to Quantify Pore Characteristics in a Shrink-Swell Clay
No full textShrink-swell soils are those which shrink when drying and swell when wetting. This creates cracks that may measure >10 cm in width and >1 m in depth when the soil is dry. These soils have low permeability when wet but allow rapid water movement through cracks when dry. Because of their dynamic nature, these soils can cause infrastructure damage, crush crop roots, and lead to unpredictable rates and concentrations of contaminant transport. Current numerical models are not able to accurately represent the dynamic pore characteristics, and often soil shrink-swell processes are not taken into consideration at all. To better model the potential impacts of dynamic pore networks in a shrink-swell soil, it is necessary to quantify changes in pore characteristics���size distribution, connectivity, and tortuosity���that accompany changes in soil water content. X-ray computed tomography (CT) scanning is a technology used to visualize the internal structure of an object and can be used to observe and quantify porosity in a soil sample. The goal of this project is to improve our understanding of dynamic porosity in shrink-swell soil, using X-ray CT scanning to quantify crack patterns and sizes in shrink-swell soils at multiple water contents. Three intact soil cores were saturated, scanned using Xray CT, then dried and scanned again. Dragonfly, ImageJ, and MATLAB software were used for image processing and analysis of structural and porosity changes within the cores. Our results show a higher number of pores with volumes >1 mm^3 and pores with lengths >5 mm in the dried cores compared to the saturated. A higher connectivity of these pores was observed in the dried cores compared to the saturated. The knowledge gained through this study regarding changes in porosity between saturated and dried cores will improve our understanding of shrink-swell soils, contributing to improvements in shrink-swell soil���s role in regulating hydrological processes
Role of Steroid Hormones and Their Neuroactive Metabolites in Pavlovian Fear Conditioning
No full textIn the United States, one in every three individuals will experience an anxiety disorder in their lifetime. Importantly, there are significant sex differences in the incidence of these disorders. Anxiety disorders are almost twice as prevalent in females than in males, particularly post-traumatic stress disorder (PTSD). This significant sex difference indicates a possible role for gonadal hormones in the development of these disorders. Indeed, progesterone, estrogen, and testosterone have been found to modulate fear and anxiety in clinical populations and in rodent research models. A better understanding of how hormones contribute to the development and maintenance of anxiety disorders is vital to the development of targeted, effective treatments. Here, we use a Pavlovian fear conditioning model in which rats are trained to associate a context and cue with an aversive footshock, which leads to conditioned responding in the absence of the footshock. Using this model, we explore the role of progesterone (PROG), its metabolite allopregnanolone (ALLO), and the testosterone metabolite 3a-androstanediol (3a-diol) on the acquisition, expression, and extinction of conditioned fear. Ovariectomized females were treated systemically with PROG whereas intact males received either ALLO or 3a-diol infusions into the bed nucleus of the stria terminalis (BNST), a sexually dimorphic brain structure shown to modulate anxiety and context-dependent fear. Our laboratory has previously shown that intra-BNST ALLO in male rats and estrous cycle phase in cycling female rats can confer state dependence to contextual fear such that retrieval of the fear memory is optimal when animals are tested in the same hormonal state experienced during conditioning. The results here demonstrate that although acute changes in systemic PROG of ovariectomized females can causes changes in conditioned fear expression, they do not mirror the state-dependent regulation observed in cycling females. In male rats, intra-BNST 3a-diol confers state dependence to contextual, but not cued, fear such that animals trained and tested in different hormonal states exhibit a state-dependent generalization decrement. Finally, we show that in male rats, intra-BNST ALLO can modulate extinction learning but not in a state-dependent manner. The work presented here further demonstrates that hormones and their neuroactive metabolites can influence different aspects of acquisition, expression, and extinction of conditioned fear and supports the consideration of interoceptive cues associated with hormonal state when developing treatments and interventions for anxiety- and trauma-related disorders
A Tide That Raises All the Boats: The Soviet Threat, NATO, and Marine Corps Innovation, 1969-1991
No full textAfter suffering the bitter defeat of the Vietnam War, the United States Marine Corps entered a nearly two-decade long period of reform and modernization. By the end of the Cold War in 1991, the service had markedly improved its training, equipment, doctrine, and proficiency for mid- and high-intensity conventional conflicts. Notably, the capabilities gained during the 1970s and 1980s remained in place well into the twenty-first century, marking this era as one comparable to the service���s most storied periods of innovation. The existing historical literature of the late-Cold War Marine Corps ascribed the service���s rise either to its military culture or crises in Southwest Asia. Such interpretations, however, overlook the impetus provided by the Soviet military threat and North Atlantic Treaty Organization (NATO) missions. An analysis of multi-archival and multi-service sources ��� including declassified war plans and studies, strategies and concepts, congressional testimony, exercise reports, student papers, journal articles, memoirs, oral histories, interviews with participants, commentary from NATO allies, and related secondary histories ��� demonstrates that the Soviet threat and NATO missions were salient driving influences on Marine Corps innovation during this period. This impetus, ever present, often unacknowledged, and occasionally even strenuously denied, provided the service with a unique azimuth of innovation, one qualitatively different from the id��e fixes of other militaries. The resulting service strategy served as a ���tide that raises all the boats,��� a focus on the severest test ��� a Soviet anti-amphibious defense in Europe ��� that simultaneously rehabilitated the Corps��� political relevance, modernized readiness, and inspired future concepts, all while still preserving flexibility for global employment across the spectrum of conflict. Accordingly, at Cold War���s end, observers praised the Corps as the ���most general-purpose force of the general-purpose forces,��� a service particularly well-suited for the uncertain new security environment. This dissertation advances historical interpretations of the Marine Corps and military innovation in the late-Cold War and demonstrates the effectiveness of threat-based strategies of innovation, providing a case study of value both to scholars of military innovation studies and strategic practitioners
AI and Machine Learning Using Wearables for Diabetes Care
No full textThe emergence of Internet of things (IoT) devices and technological advances have revolutionized healthcare, particularly the advent of AI-based wearables have enabled frequent monitoring of glucose levels in patients. This is critical for an incurable disease like diabetes which can only be managed through frequent monitoring of glucose values. Despite the progress made, making AI-based solutions more patient-centric remains a challenge. To address this, we present methods for efficient application of AI/ML solutions for improving diabetes care with an emphasis on patient needs.
This dissertation has two primary objectives: (1) To build robust machine learning models to improve diabetes care, and (2) develop alternatives to current glucose monitoring technologies to enhance the accessibility and reduce the intrusiveness. To achieve the Objective 1, we focus on developing machine learning algorithms for prediction of impending hypoglycemia risk in patients. A feature-based machine-learning model is built based on previous CGM values that gives real-time predictions for hypoglycemia risk, enabling patients to take intervening actions. We subsequently work on improving the quality of hypoglycemia predictive alerts in a real-world setting by focusing on predictive alerts that are based on sustained hypoglycemia. This drastically reduces instances of false alerts, a major deterrent for technology adoption among patients.
Machine learning models with robust performance rely on large corpus of data for training. However, healthcare data is sensitive, and its accessibility is restricted with many regulations in place. To this, we develop FedGlu, a machine learning model trained in a federated learning framework that simultaneously addresses the dual challenge of data availability and model performance. FedGlu also incorporates a customized loss function that improves the model���s predictive capabilities in the glycemic excursion regions.
CGM devices are valuable, but accessibility is limited as they are expensive and also available based only on prescriptions. In addition, they are invasive which can be painful for patients. In Objective 2, as an alternative to CGM devices, we extend the use of IoT based noninvasive wearables for glucose monitoring. For this, we evaluate hyperglycemia detection along with hypoglycemia detecting using ECG and accelerometer signals that are collected noninvasively. We comprehensively evaluate the proposed algorithms on people with and without diabetes and demonstrate the efficacy of the proposed approach. In closing, a summary of the contributions and directions of future work are presented
Sustainable Plastic Waste Recycling: Economics and Circularity Metrics
No full textOver the past few decades, the surge in plastic waste has created an urgent need for sustainable recycling methods to align with the Circular Economy (CE) goals. Process systems engineering (PSE) models have emerged as invaluable tools in plastic waste recycling, facilitating sustainability-driven problem-solving, optimizing process frameworks, and guiding the journey toward environmentally conscious circular solutions. This work proposes a robust mathematical model to optimize different recycling technologies, including pyrolysis, gasification, mechanical recycling, and incineration, striking a balance between economic feasibility and contribution to circular economy objectives. The model recognizes the potential of chemical recycling derivatives as versatile raw materials for various applications, including methanol synthesis, ammonia synthesis, hydrogen production, and more. It reveals the possibility of plastic waste yielding energy and valuable products through open- and closed-loop recycling pathways. A novel degree of circularity metric (DOCI) is enlisted to assess these pathways' contributions to the CE critically. It integrates various vital metrics, ensuring a holistic evaluation of each recycling route, encompassing material utilization, energy demand, water usage, waste generation, carbon footprint, economic viability, co-product utilization, recyclability, quality of the product, and technology maturity. An illustrative case study involving 20 scenarios for recycling plastic waste was evaluated and analyzed against incineration as a base case. The optimization results reveal that pyrolysis refinery technology offers promising avenues for producing sustainable fuels and olefins by tripling the base case profitability and more than 25% improvement in the total degree of circularity. Moreover, seeking the maximum possible circularity can be achieved by combining methanol synthesis and the pyrolysis refinery. This will provide an overall net profit of $29 M USD/y and a 44% enhancement of the DOCI of the base case. The flexible weight allocation for DOCI individual indicators in the optimization process emphasizes the significance of tailored solutions aligned with system-specific needs. Comparisons between plastic waste and conventional feedstocks are carried out, unveiling a product-dependent cost-effectiveness landscape. Capacity-level sensitivity analysis reveals that the optimal solutions consistently outperform the base case regarding circularity and profitability
Non-Intrusive Wearable Accelerometry for Early Detection and Monitoring of Chronic Diseases and Mental Health Issues
No full textChronic diseases and mental health issues account for a majority of deaths worldwide. Neuropsychiatric illnesses are a considerable component of this global health burden. Therefore, there is a need for early detection and continuous monitoring of neuropsychiatric illnesses, such as stress, Parkinson's disease, bipolar disorder, and hypoglycemia in patients with diabetes. Traditional monitoring methods, such as medical history, physical examinations, and laboratory tests, have several challenges due to their invasive nature, cost, and continuous monitoring issues.
Wearable technology offers a promising alternative to the traditional methods. Accelerometers, in particular, can provide a non-intrusive, cost-effective, and easy-to-integrate solution suitable for large-scale use. Acceleration data can be combined with machine learning models to provide insights into chronic diseases and mental health issues. Despite the benefits of accelerometer sensors, the use of acceleration data alone has remained unexplored, or it has been used with other physiological data such as heart rate in current literature.
In this dissertation, I study the potential of wearable accelerometer data to detect chronic diseases and mental disorders. In Chapter 1, I review the existing literature that used acceleration data to identify chronic diseases and mental disorders. In Chapter 2, I use wrist-worn acceleration data to detect hypoglycemia in individuals with type 1 diabetes. In Chapter 3, I examine the potential of acceleration data to detect stress in college students. In Chapters 4 and 5, I further extend my findings to stress detection using deep learning models, such as convolutional neural networks (CNNs) and Long Short-Term Memory (LSTM), in two populations: PTSD patients and Intensive Care Unit (ICU) nurses.
My first research (Chapter 2) shows the potential of wearable accelerometers in detecting hand tremors associated with hypoglycemia in diabetic patients. The ensemble of random forest, support vector machines, and K-nearest neighbor models achieved a precision of 81.5% and a recall of 78.6% for hypoglycemia detection. Furthermore, the proposed models for stress detection in college students (Chapter 3) achieved an accuracy of 72.45% for the general population and 78.11% for students with similar movement behaviors. In the PTSD study (Chapter 4), adding power spectral density to raw acceleration data significantly improved the accuracy of hyperarousal detection to 78.45%. Finally, the stress detection models in ICU nurses (Chapter 5) achieved an accuracy of 68.91% using a hybrid CNN-LSTM model.
In conclusion, this Ph.D. research shows the potential of wearable accelerometer data in detecting and monitoring chronic diseases and mental health disorders. The methodologies proposed in this dissertation can be deployed on wearable devices like smartwatches and provide interventions for managing chronic diseases and mental disorders
Enhancing Control Assessments and Risk Analysis in Batch Chemical Processes: A Focus on Safer Design Methodologies
No full textBatch and semi-batch processes involving critical chemical reactions pose significant risks to industrial safety and require rigorous control assessment and risk analysis. This study aims to survey current industry practices and methodologies for testing and assessing the controls in batch reactions. It identifies strengths and limitations in existing procedures and propose safer design methods to mitigate reactive hazards. In batch reactions, a diverse range of chemicals operates at dynamic parameters such as temperature and pressure. Assuming the worst case scenarios and improper evaluation of controls could lead to events with dire consequences. The study examines the typical tests conducted for batch reactions and identifies opportunities for optimization based on the outcomes. Layer of Protection Analysis is used to evaluate various preventive controls such as Inherently Safer Designs (ISDs), automated systems and relief sizing. Recognizing the pivotal role of critical controls such as quenching and relief sizing as the last line of safeguards in the batch processes, here we determine what are the different ways of performing quenching process to halt the reaction and the effective way of doing this operation. We also develop a model and calculate for effective quenching during worst-case scenarios, demonstrating how this proposed safer design method maximizes control functionality and minimizes risk. As relief is also considered in many cases where quenching is not practical, the relief sizing of reactive systems is quite complex and here we compare how drastic the change in relief size would be for a reactive system when compared to relief sizing performed in a conventional way. Another concern highlighted is relying solely on automated systems, which can lead to catastrophic consequences in case of process deviation. This research scrutinizes the conditions which are considered over conservatively by many of the process Industries, arriving at conclusion of adequacy in the Safety Systems and its impact on the availability of the Safety System on demand (PFD) using exSILentia tools. Overall, this study comprehensively explores industry practices in testing, standard controls and alternate safer designs along with conservative safety system settings
Structured 3��� UTRs Destabilize mRNAs in Plants
No full textRNA secondary structure (RSS) represents an intricate code that goes beyond the conventional genetic information, exerting regulatory roles in various biological processes, such as transcription, RNA processing, protein synthesis, and miRNA biogenesis. The 3��� untranslated regions (3��� UTRs) of mRNA emerge as critical orchestrators in gene regulation. Nevertheless, the specific roles of RSS within 3��� UTRs on gene expression remain a subject of inconsistency across diverse organisms and/or contexts.
In our study, a serendipitous discovery came to light: the primary substrate of miR159a (pri-miR159a), when inserted into a 3��� UTR, could promote mRNA accumulation remarkably. This enhanced expression was attributed to the premature polyadenylation of the transcript within the hybrid pri-miR159a-3��� UTR, resulting in a poorly structured 3��� UTR. Notably, RNA decay assays provided insights into the regulatory role of RSS within 3��� UTR. Poorly structured 3��� UTRs could promote mRNA stability, while highly structured 3��� UTRs led to mRNA destabilization both in vitro and in vivo. Furthermore, our exploration extended beyond reporter lines, as genome-wide DMS-MaPseq revealed a consistent inverse relationship between 3��� UTRs��� RSS and transcript accumulation across the entire transcriptome of not only Arabidopsis but also rice and human.
Mechanistically, transcripts with highly structured 3��� UTRs were found to be preferentially degraded by 3������5��� exoribonuclease SUPPRESSOR OF VARICOSE (SOV) and 5������3��� EXORIBONUCLEASE 4 (XRN4), resulting in decreased expression in Arabidopsis. Finally, our findings were underscored by the engineered different structured 3��� UTRs in an endogenous FLOWERING LOCUS T (FT) gene, yielding a demonstrable earlier flowering phenotype in Arabidopsis.
In summary, our study elucidates that highly structured 3��� UTRs tend to contribute to the reduced accumulation of harbored transcripts in Arabidopsis, a phenomenon that may extend to other organisms, including rice and mammals. Beyond its fundamental insights, our research introduces a pioneering strategy involving the engineering of 3��� UTRs��� RSS for the purpose of modulating plant traits in agricultural production and enhancing mRNA stability in biotechnology applications