Increasing numbers of mathematics teachers must be recruited in coming years, because of a growing student population, teacher attrition, calls for smaller class size, and the need to replace out-of-subject teachers. Recruitment can be made more effective and efficient, if better information on career paths is provided to decision makers. This study attempts to analyze the academic decisions which lead to the outcome "becoming a mathematics teacher". Four groups were compared and contrasted: mathematics teachers, science teachers, other teachers, and non-teachers. Science teachers were removed from the "other teachers" category because of their many similarities to mathematics teachers on the variables examined. The question of whether these groups differ in ways that could help predict the outcome of interest was examined using the NCES dataset Baccalaureate &Beyond:93/97, which provides thousands of variables on academic path, demographics, and labor market histories for over 8,000 individuals. It was analyzed using the NCES online analytic tool DAS to generate tables showing percentage distribution of the four groups on variables organized according to the concepts demographics, family environment, academic path, and academic achievement. Further examination was conducted by entering the variables into a discriminant analysis. Mathematics teachers were found to differ from teachers of other K-12 fields on all of the four conceptual categories. However, only a few such differences were statistically significant. More significant differences were observed when the analyses were conducted separately for women and men. The trend observed was that those who became mathematics teachers were more likely to have attended public high schools and to have first attended two-year colleges; to have lower GPAs, more mathematics credits, and midrange CEE scores; and to be female.

As designers began to pack multi-million transistors onto a single chip, their reliance on a global clocking signal to orchestrate the operations of the chip has started to face almost insurmountable difficulties. As a result, designers started to explore clockless circuits to avoid the global clocking problem. Recently, self-resetting circuits implemented in dynamic logic families have been proposed as viable clockless alternatives. While these circuits can produce excellent performances, they display serious limitations in terms of area cost and power consumption. A middle-of-the-road alternative, which can provide a good performance and avoid the limitations seen in dynamic self-resetting circuits, would be to implement self-resetting behavior in static circuits. This alternative has been introduced recently as Self-Resetting Stage Logic and used to propose three types of clockless pipelines. Experimental studies show that these pipelines have the potential to produce high throughputs with a minimum area overhead if a suitable synthesis methodology is available. This thesis proposes a novel synthesis methodology to design and verify clockless pipelines implemented in SRSL by taking advantage of the maturity of current CAD tools. This methodology formulates the synthesis problem as a combinatorial analytical problem for which a run-time efficient exact solution is difficult to derive. Consequently, a two-phase algorithm is proposed to synthesize these pipelines from gate netlists subject to user-specified constraints. The first phase is a heuristic based on the as-soon-as-possible scheduling strategy in which each gate of the netlist is assigned to a single pipeline stage without violating the period constraint of each pipeline stage. On the other hand, the second phase consists of a heuristic, based on the Kernighan-Lin partitioning strategy, to minimize the number of nets crossing each pair of adjacent pipeline stages. The objective of this optimization is to reduce the number of latches separating pipeline stages since these latches tend to occupy large areas. Experiments conducted on a prototype of the synthesis algorithm reveal that these self-resetting stage logic pipelines can easily reach throughputs higher than 1 GHz. Furthermore, these experiments reveal that the area overhead needed to implement the self-resetting circuitry of these pipelines can be easily amortized over the area of the logic embedded in the pipeline stages. In the overall, the synthesis methods developed for SRSL produce low area overhead pipelines for wide and deep gate netlists while it tends to produce high throughput pipelines for wide and shallow gate netlists. This shows that these pipelines are mostly suitable for coarse-grain datapaths.

Cal Poly is home to a solar farm designed to nominally generate 4.5 MW of electricity. The Gold Tree Solar Farm (GTSF) is currently the largest photovoltaic array in the California State University (CSU) system, and it was claimed to be able to produce approximately 11 GWh per year. These types of projections come from power generation models which have been developed to predict power production of these large solar fields. However, when it comes to near-term forecasting of power generation with variable sources such as wind and solar, there is definitely room for improvement. The two primary factors that could impact solar power generation are shading and the angle of the sun. The angle of the sun relative to GTSF’s panels can be analytically calculated using geometry. Shading due to cloud coverage, on the other hand, can be very difficult to map. Due to this, artificial neural networks (NN) have a lot of potential for accurate near-term cloud coverage forecasting. Much of the necessary training data (e.g. wind speeds, temperature, humidity, etc.) can be acquired from online sources, but the most important dataset needs to be captured at GTSF: sky images showing the exact location of the clouds over the solar field. Therefore, a new image capturing digital acquisition (DAQ) system has been implemented to gather the necessary training data for a goal of forecasting cloud coverage 15-30 minutes into the future.

Yes / Most previous studies on seabed liquefaction around offshore pipelines focused on investigating the wave-induced pore pressure variation in sandy seabed, while limited studies have been conducted for silty seabed. In this study, laboratory experiments are conducted to investigate wave-induced pore pressure within silty bed around the buried or partially/fully backfilled pipeline. Results show that residual pore pressure is the dominant factor that causes the liquefaction in silty soil. For buried pipeline, liquefaction first occurs at the pipeline bottom, then propagates upwards and downwards vertically. Comparing with the buried pipeline, the liquefaction potential is reduced when the pipeline is placed in a trench. To protect pipeline from liquefaction, backfill is recommended. Experiments show that the residual pore pressure significantly decreases as backfill depth increases. Fully backfilled pipeline is the best choice for silty seabed. Furthermore, backfill material with coarser particle size than native soil provides better protection for pipeline. In this study, there is no residual pore pressure around the pipeline periphery for three backfill soils (d50 = 0.15 mm; 0.3 mm; and 0.5 mm) tested. Results indicate that for the range of this experimental study, d50 = 0.15 mm is the best backfill material that provides the most protection for the underneath pipeline. / National Postdoctoral Program for Innovative Talents granted by China Postdoctoral Science Foundation (Grant No. BX20190105) and the Fundamental Research Funds for the Central Universities (Grant No. B200202062).

Automatic machine learning (AutoML) systems have been shown to perform better when they use metamodels trained offline. Existing offline metalearning approaches treat ML models as black boxes. However, modern ML models often compose multiple ML algorithms into ML pipelines. We expand previous metalearning work on estimating the performance and ranking of ML models by exploiting the metadata about which ML algorithms are used in a given pipeline. We propose a dynamically assembled neural network with the potential to model arbitrary DAG structures. We compare our proposed metamodel against reasonable baselines that exploit varying amounts of pipeline metadata, including metamodels used in existing AutoML systems. We observe that metamodels that fully exploit pipeline metadata are better estimators of pipeline performance. We also find that ranking pipelines based on dataset metafeature similarity outperforms ranking based on performance estimates.

This dissertation considers the incommensurable interests of people, fossil capital, federal energy politics, and place in Northern Canada during the 1970s. By the late 1960s, the insatiable North American appetite for fossil fuels had turned its attention toward the Arctic region. After the discovery of rich deposits in Prudhoe Bay, Alaska, in 1968, largescale energy projects were proposed to access and exploit these Arctic natural resources. Canada participated in this northern oil rush; an exploration of oil and gas in the Arctic regions was accelerated in the early 1970s. The next challenge involved transporting the oil and gas to southern markets. In 1974, the Canadian federal government initiated the Mackenzie Valley Pipeline Inquiry to investigate the social, environmental, and economic effects of the pipeline routes proposed by a consortium of American and Canadian oil companies through the Mackenzie River Valley in the Northwest Territories where it would connect with existing pipeline infrastructure in northern Alberta. The Inquiry’s report recommended against immediate construction, encouraging instead a ten-year moratorium. Inquiry commissioner Thomas Berger’s report rationalized the delay to make time for settling Indigenous land claims in the region and for taking conservation measures to protect some key areas in the Mackenzie River Valley. In this dissertation, I examine how the discussion around pipeline construction shaped the meaning of the North, self-determination, and cultural recognition. In this dissertation, I particularly focus on how Indigenous peoples asserted their claims by rejecting state-driven policies and the interests of fossil-fuel capitalism in the North. / Thesis / Doctor of Philosophy (PhD) / Through the close reading of the Mackenzie Valley Pipeline Inquiry hearings, I examine how the discussion around energy development shaped the 1970s’ contentious Canadian politics of nation and North. Central to this debate, I focus on how Indigenous peoples asserted their land claims by challenging and refusing the settler state policies and the interests of fossil-fuel capitalism in the western Northwest Territories in the 1970s. By using the Inquiry process, northern Indigenous peoples challenged the idea that the state had a legitimate authority to decide and control the future or destiny of a territory or peoples in its defined borders.

The aim of this thesis is to bridge the gap between the state-of-the-art bioinformatic tools and resources, currently at the forefront of epigenetic analysis, and their emerging applications to non-model species in the context of plant ecology. New, high-resolution research tools are presented; first in a specific sense, by providing new genomic resources for a selected non-model plant species, and also in a broader sense, by developing new software pipelines to streamline the analysis of bisulfite sequencing data, in a manner which is applicable to a wide range of non-model plant species. The selected species is the annual field pennycress, Thlaspi arvense, which belongs in the same lineage of the Brassicaceae as the closely-related model species, Arabidopsis thaliana, and yet does not benefit from such extensive genomic resources. It is one of three key species in a Europe-wide initiative to understand how epigenetic mechanisms contribute to natural variation, stress responses and long-term adaptation of plants. To this end, this thesis provides a high-quality, chromosome-level assembly for T. arvense, alongside a rich complement of feature annotations of particular relevance to the study of epigenetics. The genome assembly encompasses a hybrid approach, involving both PacBio continuous long reads and circular consensus sequences, alongside Hi-C sequencing, PCR-free Illumina sequencing and genetic maps. The result is a significant improvement in contiguity over the existing draft state from earlier studies. Much of the basis for building an understanding of epigenetic mechanisms in non-model species centres around the study of DNA methylation, and in particular the analysis of bisulfite sequencing data to bring methylation patterns into nucleotide-level resolution. In order to maintain a broad level of comparison between T. arvense and the other selected species under the same initiative, a suite of software pipelines which include mapping, the quantification of methylation values, differential methylation between groups, and epigenome-wide association studies, have also been developed. Furthermore, presented herein is a novel algorithm which can facilitate accurate variant calling from bisulfite sequencing data using conventional approaches, such as FreeBayes or Genome Analysis ToolKit (GATK), which until now was feasible only with specifically-adapted software. This enables researchers to obtain high-quality genetic variants, often essential for contextualising the results of epigenetic experiments, without the need for additional sequencing libraries alongside. Each of these aspects are thoroughly benchmarked, integrated to a robust workflow management system, and adhere to the principles of FAIR (Findability, Accessibility, Interoperability and Reusability). Finally, further consideration is given to the unique difficulties presented by population-scale data, and a number of concepts and ideas are explored in order to improve the feasibility of such analyses. In summary, this thesis introduces new high-resolution tools to facilitate the analysis of epigenetic mechanisms, specifically relating to DNA methylation, in non-model plant data. In addition, thorough benchmarking standards are applied, showcasing the range of technical considerations which are of principal importance when developing new pipelines and tools for the analysis of bisulfite sequencing data. The complete “Epidiverse Toolkit” is available at https://github.com/EpiDiverse and will continue to be updated and improved in the future.:ABSTRACT ACKNOWLEDGEMENTS 1 INTRODUCTION 1.1 ABOUT THIS WORK 1.2 BIOLOGICAL BACKGROUND 1.2.1 Epigenetics in plant ecology 1.2.2 DNA methylation 1.2.3 Maintenance of 5mC patterns in plants 1.2.4 Distribution of 5mC patterns in plants 1.3 TECHNICAL BACKGROUND 1.3.1 DNA sequencing 1.3.2 The case for a high-quality genome assembly 1.3.3 Sequence alignment for NGS 1.3.4 Variant calling approaches 2 BUILDING A SUITABLE REFERENCE GENOME 2.1 INTRODUCTION 2.2 MATERIALS AND METHODS 2.2.1 Seeds for the reference genome development 2.2.2 Sample collection, library preparation, and DNA sequencing 2.2.3 Contig assembly and initial scaffolding 2.2.4 Re-scaffolding 2.2.5 Comparative genomics 2.3 RESULTS 2.3.1 An improved reference genome sequence 2.3.2 Comparative genomics 2.4 DISCUSSION 3 FEATURE ANNOTATION FOR EPIGENOMICS 3.1 INTRODUCTION 3.2 MATERIALS AND METHODS 3.2.1 Tissue preparation for RNA sequencing 3.2.2 RNA extraction and sequencing 3.2.3 Transcriptome assembly 3.2.4 Genome annotation 3.2.5 Transposable element annotations 3.2.6 Small RNA annotations 3.2.7 Expression atlas 3.2.8 DNA methylation 3.3 RESULTS 3.3.1 Transcriptome assembly 3.3.2 Protein-coding genes 3.3.3 Non-coding loci 3.3.4 Transposable elements 3.3.5 Small RNA 3.3.6 Pseudogenes 3.3.7 Gene expression atlas 3.3.8 DNA Methylation 3.4 DISCUSSION 4 BISULFITE SEQUENCING METHODS 4.1 INTRODUCTION 4.2 PRINCIPLES OF BISULFITE SEQUENCING 4.3 EXPERIMENTAL DESIGN 4.4 LIBRARY PREPARATION 4.4.1 Whole Genome Bisulfite Sequencing (WGBS) 4.4.2 Reduced Representation Bisulfite Sequencing (RRBS) 4.4.3 Target capture bisulfite sequencing 4.5 BIOINFORMATIC ANALYSIS OF BISULFITE DATA 4.5.1 Quality Control 4.5.2 Read Alignment 4.5.3 Methylation Calling 4.6 ALTERNATIVE METHODS 5 FROM READ ALIGNMENT TO DNA METHYLATION ANALYSIS 5.1 INTRODUCTION 5.2 MATERIALS AND METHODS 5.2.1 Reference species 5.2.2 Natural accessions 5.2.3 Read simulation 5.2.4 Read alignment 5.2.5 Mapping rates 5.2.6 Precision-recall 5.2.7 Coverage deviation 5.2.8 DNA methylation analysis 5.3 RESULTS 5.4 DISCUSSION 5.5 A PIPELINE FOR WGBS ANALYSIS 6 THERE AND BACK AGAIN: INFERRING GENOMIC INFORMATION 6.1 INTRODUCTION 6.1.1 Implementing a new approach 6.2 MATERIALS AND METHODS 6.2.1 Validation datasets 6.2.2 Read processing and alignment 6.2.3 Variant calling 6.2.4 Benchmarking 6.3 RESULTS 6.4 DISCUSSION 6.5 A PIPELINE FOR SNP VARIANT ANALYSIS 7 POPULATION-LEVEL EPIGENOMICS 7.1 INTRODUCTION 7.2 CHALLENGES IN POPULATION-LEVEL EPIGENOMICS 7.3 DIFFERENTIAL METHYLATION 7.3.1 A pipeline for case/control DMRs 7.3.2 A pipeline for population-level DMRs 7.4 EPIGENOME-WIDE ASSOCIATION STUDIES (EWAS) 7.4.1 A pipeline for EWAS analysis 7.5 GENOTYPING-BY-SEQUENCING (EPIGBS) 7.5.1 Extending the epiGBS pipeline 7.6 POPULATION-LEVEL HAPLOTYPES 7.6.1 Extending the EpiDiverse/SNP pipeline 8 CONCLUSION APPENDICES A. SUPPLEMENT: BUILDING A SUITABLE REFERENCE GENOME B. SUPPLEMENT: FEATURE ANNOTATION FOR EPIGENOMICS C. SUPPLEMENT: FROM READ ALIGNMENT TO DNA METHYLATION ANALYSIS D. SUPPLEMENT: INFERRING GENOMIC INFORMATION BIBLIOGRAPHY

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This study focused on the self-efficacy and experiences described by a purposively sampled case (n = 8) of high school-aged underrepresented minority women (URMW) as they entered the medical career pipeline through their participation in a formal medical pipeline program. The study was framed by three theories: intersectionality, positionality, and self-efficacy. Research questions were analyzed qualitatively, using case study methods, and quantitatively, using a paired sample t-test. Study data revealed that participants came into the program with high levels of self-efficacy in several self-efficacy factors. Yet, participants in the pipeline program made significant improvements in their self-assertive efficacy. Analysis of other data revealed that students remained motivated and persisted in the pursuit of their aspirations in spite of challenges they encountered because of their ethnicities and gender. Also, students described a lack of engagement with science courses, indicated poor relationships with science instructors, and revealed inadequate understanding of important high science content that, along with ethnic and gendered factors, caused them to negatively position themselves in science. This study provides valuable information to K-12 science educators, medical education institutions, and policy makers concerned with extending science education and healthcare-related career opportunities to minority women.

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