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Due to inherent restrictions in human driving behavior and information access, freeway congestion and stop-and-go behavior are nearly unavoidable. The adverse impacts include increased safety risks, longer travel times, and excessive fuel consumption. Various techniques (e.g., Variable Speed Limit (VSL), which is also known as Dynamic Speed Harmonization (DSH)), have been proposed to dampen traffic oscillation and smooth traffic speed. However, the effectiveness of the VSL is related to the compliance rates of drivers. Fortunately, new opportunities are emerging with the development of Connected and Automated Vehicles (CAVs) that can completely comply with the control system. The objective of this study is to investigate the effects of coordinated speed control in mixed traffic flow involving Human-Driven Vehicles (HDVs) and CAVs on the freeway. Therefore, a control strategy based on Deep Reinforcement learning (DRL) is developed to better understand how CAVs can improve operational performance. To evaluate and quantify the impact, a comprehensive performance framework is formulated. A series of numerical experiments will be conducted under different market penetration rates (MPRs) under various simulated scenarios. The overall intent of this study is to inform practitioners about the potential interactions between MOEs in implementing specific control strategies in a CAV environment.
Nanotechnology has the potential to revolutionize various fields, addressing complex issues such as cancer treatment, waste remediation, and energy storage. To achieve this, precise engineering of nanocrystals at the atomic level is essential, going beyond mere control of size and shape. The unique properties of nanomaterials, which differ from their bulk counterparts, are influenced by surface chemistry, defects, and local structure. These characteristics are determined by the synthesis methods used, making a deep mechanistic understanding of these processes crucial for engineering nanoscale structures and properties.
To contribute to the rapidly evolving field of nanoscience, this dissertation focuses on the solution-based synthesis of vanadium oxide nanocrystals. Vanadium oxides are promising candidates for applications in catalysis, sensing, cathode materials for high-density lithium batteries, smart windows, neuromorphic computing, and optical switching. However, vanadium oxides exhibit multiple oxidation states (+2 to +5) and polymorphs. Consequently, the colloidal synthesis of high-quality vanadium oxide nanocrystals in a specific oxidation state and stoichiometry remains challenging.
This dissertation advances the synthesis of vanadium oxide nanocrystals, emphasizing the effects of synthetic parameters on their oxidation state and crystal structure. Key findings include the successful synthesis of anosovite V₃O₅ nanocrystals via a hot-injection method, marking the first colloidal synthesis of this rare phase from a readily available precursor. By adjusting vanadium precursor-to-alcohol-to-amine ratio, controlled reduction of vanadium was achieved to selectively synthesize V₃O₅ and V₂O₃ nanocrystals. The dissertation also presents an alcohol-mediated valence-state controlled synthesis method for selective preparation of pure corundum-structured V₂O₃ and anosovite V₃O₅ nanocrystals. Comprehensive characterization, including spectroscopic ellipsometry and diffuse reflectance spectroscopy, reveals unique optical properties deviating from bulk behavior, attributed to the nanoscale size effects. In addition, a heat-up method was developed for synthesizing VOx nanocrystals by thermal decomposition of vanadyl acetylacetonate, demonstrating the formation of vanadium monoxide nanocrystals. The reaction pathways for the formation of these nanocrystals via hot-injection method and heat-up methods were analyzed with ATR-FTIR spectroscopy. The findings will advance the fundamental understanding of vanadium oxide nanocrystal synthesis and pave the way for their application as advanced functional nanomaterials.
Edge computing-assisted artificial intelligence (edge-AI) has enabled a new paradigm of smart applications that have very stringent latency requirements, especially for applications on mobile devices (e.g., smartphones, wearable devices, and autonomous vehicles). However, the high mobility of users and instability in wireless networks decrease the overall Quality-of-Service (QoS) of an edge-AI application running on mobile devices with non-linear battery discharge properties. The objective of this research is to provide mobile AI applications with an energy-efficient wireless infrastructure to enhance the overall QoS.
This dissertation presents a comprehensive experimental study of mobile AI applications, including a novel performance analysis modeling framework and a Gaussian process regression-based general predictive energy model, focusing on computational resource utilization, delay, and energy consumption. To enhance mobile AI performance, this dissertation presents a novel periodic predictive AoI-based service aggregation method for high-mobility AI applications, which processes information updates according to their update cycles with satisfactory latency. Furthermore, an H.264 video encoding-based edge-AI system is proposed to overcome the challenges posed by unstable wireless networks. Finally, a novel deep reinforcement learning-based smart edge-AI system is proposed in this research, where the edge server provides smart and dynamic offloading and data processing decisions.
The invention of advanced Ni-containing concentrated solid solution alloys has significantly broadened the compositional space of alloy design. Unlike conventional alloys that typically consist of a principal solvent element with minor additions of various solute atoms, concentrated solid solution alloys involve multiple elements in equal or near-equal compositions. These concentrated solid solution alloys have exhibited remarkable properties such as exceptional toughness and superior radiation resistance. The exceptional performance of these concentrated solid solution alloys is generally attributed to specific intrinsic properties of concentrated solid solution alloys such as high entropy effect, severe lattice distortion and short ordering effect. However, being relatively new emerging materials, the theoretical understanding and experimental exploration of these alloys are still ongoing and not comprehensively understood.
This dissertation work will provide a systematic investigation on surface properties including mechanical properties and radiation damage of Ni-based concentrated solid solution alloys by using ex-situ and in-situ indentation techniques. The study explores the surface properties of a batch of Ni-containing concentrated solid solution alloys with addition of different 3d transition elements including binary NiCo, NiFe, Ni80Cr20, Ni80Mn20 and quaternary NiCoFeCr. First, initial investigations use ex-situ nanoindentation to obtain mechanical properties of five alloys including hardness, elastic modulus and strain rate sensitivity. A complete methodology is developed to acquire accurate property information directly from nanoindentation in a high throughput manner, which considers the indentation size effect and pile-up effect. Furthermore, the strengthening in Ni-concentrated solid solution alloys is attributed to being driven by solid solution strengthening induced by mismatch in atomic size. Notably, the intrinsic properties of alloying elements play a more critical role in strengthening than the number of alloying elements. Second, based on previous work, nanoindentation is further employed to evaluate the early-stage irradiation induced hardening in NiCo, NiFe and NiCoFeCr. It proposes using nanoindentation to detect early-stage irradiation-induced defects and hypothesizes that interactions between these defects and dislocations carried by deformation during indentation can quantify irradiation-induced defects. This approach successfully quantifies irradiation-induced hardening in NiCo, NiFe, and NiCoFeCr. Quantitative analysis reveals that irradiation-induced defects harden NiFe and NiCoFeCr, but no significant hardening is observed in NiCo. Additionally, irradiation-induced hardening is associated with the evolution of geometrically necessary dislocations and is interpreted by changes in the plastic zone during indentation. Finally, in-situ flat-punch indentation provides real-time observations of deformation behaviors, aiming to derive accurate stress-strain curves. A new protocol addresses thermal drift effects and contact issues, often neglected in measurements.
This comprehensive study on Ni-based concentrated solid solution alloys enhances understanding of their mechanical properties and irradiation resistance by using ex-situ and in-situ nano-mechanical techniques. Methodologies are developed for nanoindentations to acquire meaningful property information directly from surface in a high throughput manner. This systematic work offers valuable insights into the strengthening mechanisms and irradiation hardening mechanisms of Ni-based concentrated solid solution alloys. The robust experimental evidence supports that the exceptional properties of concentrated solid solution alloys are not solely determined by the number of elements but also determined by the intrinsic performance of alloying elements. These results provide new insights for alloying design strategy of concentrated solid solution alloys.
The invention of advanced Ni-containing concentrated solid solution alloys has significantly broadened the compositional space of alloy design. Unlike conventional alloys that typically consist of a principal solvent element with minor additions of various solute atoms, concentrated solid solution alloys involve multiple elements in equal or near-equal compositions. These concentrated solid solution alloys have exhibited remarkable properties such as exceptional toughness and superior radiation resistance. The exceptional performance of these concentrated solid solution alloys is generally attributed to specific intrinsic properties of concentrated solid solution alloys such as high entropy effect, severe lattice distortion and short ordering effect. However, being relatively new emerging materials, the theoretical understanding and experimental exploration of these alloys are still ongoing and not comprehensively understood.
This dissertation work will provide a systematic investigation on surface properties including mechanical properties and radiation damage of Ni-based concentrated solid solution alloys by using ex-situ and in-situ indentation techniques. The study explores the surface properties of a batch of Ni-containing concentrated solid solution alloys with addition of different 3d transition elements including binary NiCo, NiFe, Ni80Cr20, Ni80Mn20 and quaternary NiCoFeCr. First, initial investigations use ex-situ nanoindentation to obtain mechanical properties of five alloys including hardness, elastic modulus and strain rate sensitivity. A complete methodology is developed to acquire accurate property information directly from nanoindentation in a high throughput manner, which considers the indentation size effect and pile-up effect. Furthermore, the strengthening in Ni-concentrated solid solution alloys is attributed to being driven by solid solution strengthening induced by mismatch in atomic size. Notably, the intrinsic properties of alloying elements play a more critical role in strengthening than the number of alloying elements. Second, based on previous work, nanoindentation is further employed to evaluate the early-stage irradiation induced hardening in NiCo, NiFe and NiCoFeCr. It proposes using nanoindentation to detect early-stage irradiation-induced defects and hypothesizes that interactions between these defects and dislocations carried by deformation during indentation can quantify irradiation-induced defects. This approach successfully quantifies irradiation-induced hardening in NiCo, NiFe, and NiCoFeCr. Quantitative analysis reveals that irradiation-induced defects harden NiFe and NiCoFeCr, but no significant hardening is observed in NiCo. Additionally, irradiation-induced hardening is associated with the evolution of geometrically necessary dislocations and is interpreted by changes in the plastic zone during indentation. Finally, in-situ flat-punch indentation provides real-time observations of deformation behaviors, aiming to derive accurate stress-strain curves. A new protocol addresses thermal drift effects and contact issues, often neglected in measurements.
This comprehensive study on Ni-based concentrated solid solution alloys enhances understanding of their mechanical properties and irradiation resistance by using ex-situ and in-situ nano-mechanical techniques. Methodologies are developed for nanoindentations to acquire meaningful property information directly from surface in a high throughput manner. This systematic work offers valuable insights into the strengthening mechanisms and irradiation hardening mechanisms of Ni-based concentrated solid solution alloys. The robust experimental evidence supports that the exceptional properties of concentrated solid solution alloys are not solely determined by the number of elements but also determined by the intrinsic performance of alloying elements. These results provide new insights for alloying design strategy of concentrated solid solution alloys.
Approximately 30% of individuals with autism have complex communication needs (CCN). These individuals are unable to use vocal speech as their primary form of language and typically require support across several areas of communication such as comprehension, pragmatics, phonology, semantics, and syntax (Ganz et al., 2022; Reichle, 2019). Researchers have found that communication skills can greatly impact academic, behavioral, social, and postsecondary outcomes (Carter et al., 2012; Chiang, 2008; Matson et al., 2013; Park et al., 2012; Pillay & Bronlow, 2017). Fortunately, augmentative and alternative communication (AAC) has been effectively used to increase communication for individuals with intellectual and developmental disabilities (IDD; Crowe et al., 2022). Most often, individuals with autism are only taught to request using single words or short phrases using AAC devices (Ganz et al., 2017; Muharib et al., 2018; Tincani et al., 2020). Another way to expand communication through AAC is to teach sentence structure. Researchers have used an intervention package consisting of response prompting, sentence frames, and technology like AAC to teach students with autism and CCN to construct sentences (Pennington et al., 2021; Pennington, Flick, et al. 2018; Pennington, Foreman, et al. 2018; Pennington & Rockhold, 2018). Additionally, matrix training has been used as a generative framework to increase language for individuals with autism who use vocal speech (Frampton et al., 2016, 2019; Jimenez-Gomez et al., 2019; Kohler & Malott, 2014) and AAC (Marya et al., 2021; Naoi et al., 2006; Nigam et al., 2006; Tönsing et al., 2014). This study examined the effects of matrix training, response prompting, and sentence frames on sentence writing for four students, ages 10–18 with ASD and CCN in a specialized private school located in the southeastern United States. Three teachers, ages 23–46 served as the interventionists in the study. A series of A-B designs with modifications was used to examine the effects of the intervention package on the percentage of trained and untrained correct sentences, percentage of subject-verb combinations, and the percentage of correct word selections. Teachers presented photos of subject-verb combinations for students to write about using pre-programmed arrays with words and symbol supports on speech-generating devices. Overall, results indicated that across all interventions, there were no effects on the percentage of trained and untrained correct sentences and subject-verb combinations for all participants. Two students, however, increased their percentage of correct word selections. Overall, teachers found the intervention acceptable and beneficial for students in the classroom. Furthermore, three of four students preferred this writing intervention over their typical writing instruction in the classroom. Implications of this study provide several considerations for practitioners who would like to use matrix training to teach subject-verb combinations and/or sentence writing with students who have autism and CCN.
This dissertation explores the relationship between climate change, reproductive justice, and the prosperity of families, communities, and economies through a discussion utilizing a comprehensive literature review and the results of a quantitative study to examine the relationship between the adverse impacts of a changing climate and the cost of living, represented by increases in food prices, housing costs, and health care expenditures, and the associated impact on declining birth rates.
The first chapter builds the foundation for examining how climate change, reproductive justice, and societal prosperity interact by summarizing the climate change science literature that addresses the health and social harm disparities for low-income, communities of color, particularly women of color, and advocates for a reclaiming of bodily autonomy given the communities that are most impacted by climate change. The second chapter recalls the historical legacy of settler colonialism, especially the exploitation of Indigenous women, and examines how corporate expansion, consumerism, and white feminist ideologies create and maintain corporate colonialism and the oppression of nonwhite women through the disproportionate impacts of climate change, while calling for a reconstruction of feminism. The third chapter explores how a modern society measures prosperity through both financial and nonfinancial performance measures and introduces a new prosperity model based on a four-part test to promote, protect, and advance the health and long-term viability of families, communities, economies, and ecosystems through sustainable and responsible economic principles, means, and indicators of success. Implications and considerations for private industry and public policy are discussed, along with the need for additional research to better understand the new model’s effectiveness in predicting, determining, and protecting societal prosperity.
Cultivating problem-solving in highly motivated university students remains a persistent priority in higher education. These highly motivated students often enroll in honors programs to engage in small group discussions with their like-minded peers to enhance creative problem-solving skills; however, limited empirical research exists on the effectiveness of creative thinking interventions in creative problem-solving among introverted university honors students. This study focused on how the Six Thinking Hats method, a creative thinking tool designed to encourage individuals to think in parallel with those of others through six metaphoric Hats, increases creative problem-solving in introverted honors students.
A quantitative single-case multiple baseline design across four introverted university honors students was used to examine a functional relation between the Six Thinking Hats and creative problem-solving. The dependent variables were: (a) total number of Hats, (b) total number of topic-related participation units, (c) total number of creative ideas, and (d) total number of words per Hat. Results indicated a functional relation between the STH method and Hats (i.e., perspectives), but no functional relation existed for topic-related participation units, creative ideas, and words per Hat. The social validity data, confirmed through thematic analysis, revealed three themes regarding the STH method: (a) awareness of metacognition, (b) meaningfulness of the intervention, and (c) application to problem-solving situations. This study offers a first step in contributing to the small body of experimental research on the effectiveness of the Six Thinking Hats method in promoting multiple perspectives among undergraduate honors students.
Recent advancements in single-cell RNA sequencing have revolutionized our understanding of gene expression regulation under various biological contexts, providing higher resolution and system-level insights compared to traditional bulk RNA sequencing methods. In this dissertation, we utilize single cell RNA-seq (scRNA-seq) along with various statistical tools to unveil the stress response and developmental transcriptomic landscape of four model organisms. First, we sequenced yeast cells under three stress treatments (hypotonic condition, glucose starvation and amino acid starvation) using a full-length single-cell RNA-Seq method. We found that though single cells from the same treatment showed varying degrees of uniformity, technical noise and batch effects can confound results significantly. However, upon careful selection of samples to reduce technical artifacts and account for batch-effects, we were able to capture distinct transcriptomic signatures for different stress conditions as well as putative regulatory relationships between transcription factors and target genes.
Our results show that a full-length single-cell based transcriptomic analysis of the yeast may help paint a clearer picture of how the model organism responds to stress than do bulk cell population-based methods. Second, we present a transcriptomic level analysis into the oogenesis of C. elegans hermaphrodites. We dissected a hermaphrodite gonad into seven sections corresponding to the mitotic distal region, the pachytene, the diplotene, the early diakinesis region and the 3 most proximal oocytes, and deeply sequenced the transcriptome of each of them along with that of the fertilized egg using a single-cell RNA-seq protocol. We identified specific gene expression events as well as gene splicing events in finer detail along the oocyte germline and provided novel insights into underlying mechanisms of the oogenesis process. Furthermore, through careful review of relevant research literature coupled with patterns observed in our analysis, we attempt to delineate transcripts that may serve functions in the interaction between the germline and cells of the somatic gonad. These results expand our knowledge of the transcriptomic space of the C. elegans germline and lay a foundation on which future studies of the germline can be based upon. Lastly, we profiled mature oocytes and 1-cell zygotes of mice and rats to uncover elusive transcriptomic dynamics in the maternal to zygote transition. We confirm the existence of early gene expression in the mouse zygotic while revealing a similar chain of events occurring in the rat zygote. We observe an increase in nascent transcription in both species. Moreover, we find subtle but pervasive signals of differential splicing of genes related to key early zygotic activities occurring in both species. Meanwhile, we find distinct profiles of alternative polyadenylation between zygotes and oocytes in both species, specifically in genes related to major processes within the zygote. Finally, although a more dynamic transcriptomic landscape exists in the mice zygote, the rat zygote also displays similar transcriptomic features, suggesting that minor zygotic activation in rat occurs earlier than originally thought.
Cis-regulatory modules (CRMs) can function as enhancers and/or silencers to promote and repress, respectively, the transcription of their target genes in a spatiotemporal manner, thereby playing critical roles in virtually all biological processes. However, despite recent progresses, the understanding of CRMs’ precise locations, landscape and architecture in terms of transcription factor (TF) binding sites (TFBSs) in the genomes as well as their functional types (enhancer or silencer), states (active or inactive) and target genes in various cell/tissue types of organisms is still limited.
We have recently predicted comprehensive maps of CRMs and constituent TFBSs in the human and mouse genomes, enabling us to investigate the organization and architecture of the CRMs in both genomes. We reveal common rules of the organization and architecture of CRMs in the genomes. We conclude that the rules governing the organization and architecture of CRMs in the human and mouse genomes are highly conserved.
Moreover, until recently research has long been focused on enhancers, and much less is known about silencers. To fill the gap, we develop two logistic regression models for predicting the functional states of our previously predicted 1.2M CRMs as enhancers and silencers in any cell/tissue types using five epigenetic marks data. Applying the models to 56 human cell/tissue types with the required data available, we predict that 793,140 of the 1.2M CRMs are active as enhancers or/and silencers in at least one of these cell/tissue types, of which 14.8% and 28.6% of them only function as enhancers (enhancer-predominant) and silencers (silencer-predominant), respectively, while 10.6% functioned both as enhancers and silencers (dual functional). Thus, both dual functional CRMs and silencers might be more prevalent than previously assumed. Most dual functional CRMs function either as enhancers or silencers in different cell/tissue types (Type I), while some have dual functions regulating different genes in the same cell/tissue types (Type II). Different types of CRMs display different lengths and TFBS densities, reflecting the complexity of their functions.
Furthermore, identifying their target genes of predicted or experimentally validated CRMs remains a challenge due to the low quality of the predicted CRMs and the fact that CRMs often do not regulate their closest genes. To fill this gap, we developed a method — correlation and physical proximity (CAPP) to not only predict the CRMs’ target genes but also their functional types using only chromatin accessibility (CA) and RNA-seq data in a panel of cell/tissue types plus Hi-C data in a few cell types. Applying CAPP to a panel of 107 human cell/tissue types with CA and RNA-seq data available, we predict target genes for 20% of the 1.2M CRMs, of which 4.5% are predicted as both enhancers and silencers (dual functional CRMs), 95.2% as exclusive enhancers and 0.3% as exclusive silencers. Different types of CRMs as well as their target genes and regulatory links exhibit distinct properties. CAPP predicts more enhancer-gene and silencer-gene links with higher accuracy than state-of-the-art methods.
