VictorJuiz/thesis huggingface dataset:
Morphology, chemical composition, and defect evolution during synthesis and annealing of core/shell germanium/germanium-tin nanowires
Keywords: Materials Science and Engineering.

Nanowire structures provide a platform for fundamental research. Due to the inherent nano-scale diameter and large aspect ratio, nanowires demonstrate structure-property relationships that often differ from those of bulk materials, including dimensional confinement of electronic carriers and light, a large surface area to volume ratio, and size- and shape-dependent partitioning of mechanical strain and barriers to inelastic deformation. Herein Ge and core/shell Ge/GeSn nanowires were investigated with a range of techniques to investigate their synthesis and annealing properties. First, a technique using in-situ laser reflectometry was investigated for measuring the axial growth rate in chemical vapor deposition of assemblies of well-aligned vertical germanium nanowires grown epitaxially on single crystal substrates. Finite difference frequency domain optical simulations were performed to facilitate quantitative analysis and interpretation of the measured reflectivity data. The results showed an insensitivity of reflected intensity oscillation period to nanowire diameter and density within the range of experimental conditions investigated. Furthermore, we showed that the measured reflectivity decay can be used to determine the germanium nanowire nucleation time with good precision. The link between the optical emission and atomic structure of individual vertical Ge/GeSn core/shell nanowires was investigated using transmission electron microscopy (TEM) and cathodoluminescence (CL). Optical measurements of individually as-grown nanowires were performed using scanning electron microscopy (SEM) CL. Electron beam deposited fiducial markers were utilized to identify and track individual nanowires between CL measurements and focused ion beam preparation for TEM analysis. High angle annular dark field scanning TEM (HAADF STEM) was then performed as a probe into the origin of the non-homogeneous CL emission between the identified nanowires based on observable line defects. X-ray photoelectron spectroscopy was used to analyze results from in-situ and ex-situ annealing of core/shell Ge/GeSn nanowire arrays. The annealing characteristics of the germanium-tin surface and native oxide were investigated for tin contents in the range of 2 at% to 12 at% in both air-exposed and oxide-free nanowires. For air-exposed samples, we observed the presence of a tin-rich oxide that exhibits a composition dependent temperature for thermal decomposition via in-situ annealing. Utilizing nominally air-free transfer of freshly synthesized and rapid thermally annealed core/shell Ge/GeSn nanowires, we showed the protective effects of the surface oxide when comparing samples annealed under a rough vacuum environment compared to hydrogen annealing
Mothers' Experiences of Institutional Betrayal during Childbirth and Postpartum Mental Health Outcomes
Keywords: Postpartum mental health
Institutional Betrayal
Birth experience
Text
Article
Thesis

Purpose: Emerging research suggests that betrayal by healthcare institutions could explain how negative birth experiences may lead to postpartum mental health disorders. The current study is the first to examine the role of experiencing institutional betrayal during childbirth (failure of a health system to meet a mother’s expectations in a way that harms the mother) in the prediction of and causal pathways to poor postpartum mental health outcomes for mothers in the US.
Participants & Methods: Mothers who had given birth in the last 3 years in the US as of June 2021 were recruited online. Participants (n = 612; 72% White; 100% women; median age between 27 and 36 years) responded to Qualtrics survey questions about their birth experiences, including adverse medical events, negative birth experiences, and experiences of institutional betrayal.
Data Analysis: Multiple logistic regression analysis was used to examine if experiencing instances of institutional betrayal (i.e., having procedure that was more painful than expected, not being notified of test results, etc.) significantly predicted postpartum mental health condition. Hierarchical regressions were used to examine whether institutional betrayal is a moderating variable, and R’s mediate package was used to determine if institutional betrayal mediates the causal pathway between negative birth experiences and poor postpartum outcomes.
Results: 48% of respondents experienced at least once instance of Institutional Betrayal, and their average number of IB experiences was 1.9 (SD = 1.10, max=5). Mothers who experienced institutional betrayal compared to those who didn’t were more likely to have been diagnosed with any postpartum mental health condition (p < 0.001). Experiencing more institutional betrayal instances increased a mother’s odds of a postpartum mental condition by 1.54 times (p < 0.005). Institutional betrayal was not a moderating variable, but it was a mediator between negative birth experiences and poor postpartum mental health outcomes.
Discussion: These findings can inform interventions to prevent poor postpartum mental health outcomes at the level of the individual patient as well as at the level of the healthcare system or institution. Further research should focus on postpartum mental health and birth equity by developing an additional adaptation of the IB questionnaire for childbirth for mothers of color, recruit a sample of mothers of color, and conduct analyses into causal pathways to postpartum mental health disorders for that population.
Multifocus Convolutional Neural Networks for Short-term Solar PV Power Output Prediction
Keywords: Solar forecasting
Convolutional Neural Networks
Machine learning
Deep learning
Text
Thesis

Reliable forecasts for the power output from variable renewable energy generators like solar photovoltaic systems are important for balancing the load on real-time electricity markets and ensuring the reliability of the grid. However, solar PV power output is highly uncertain, with significant variations occurring over longer (daily or seasonally) and shorter (minutely) timescales due to weather conditions, especially cloud cover. Therefore, accurate models that can provide these predictions are necessary.
This thesis builds on existing work, the SUNSET model, that uses a convolutional neural network applied to the computer vision tasks of nowcasting and forecasting solar PV power output. Multifocus SUNSET Nowcast and Forecast model architectures are presented and compared to the baseline SUNSET models. The multifocus models utilize a two-stream approach: one branch ingests a 64 x 64 pixel downsampled full sky image and the other branch ingests a 64x64 pixel sun patch cropped from a 256 x 256 pixel resolution full sky image. Various locations of fusion within the model were performed in order to optimize for accuracy as well as for model size and parameter count.
It was found that the best Multifocus SUNEST Nowcast model outperformed the baseline model by 9.8% on the test set when considering images without border masking and by 14.8% when considering images with border masking. This large improvement comes from better performance on cloudy test days. Further, the best Multifocus SUNSET Nowcast model fused the two branches after the fully connected layers but before the regression. However, the multifocus model that fuses after the 1st convolution layer had a similar performance on the validation set with far fewer parameters. Thus, it may be worth utilizing the multifocus model that fuses after the 1st convolution layer instead if computational resources are limited. When considering the best Multifocus SUNSET Forecast model, with respect to the baseline model, only minor improvements in performance were noticed on the order of 0.01 kW measured as RMSE, relative to baseline RMSE of 2.62 kW. Thus, the Multifocus SUNSET Forecast model may not be the best choice for forecasting because it incurs additional computational costs compared to the baseline model without improving performance.
Multi-level resilience of small-scale fisheries in Baja California, Mexico
Keywords: Biology.

Social-ecological systems (SES) are those that couple the human and natural world through resource exploitation and utilization. They are intricate multi-layered systems that are defined by a complex network of interactions between the resource units, resource systems, governance systems and users within the SES, which can result in a variety of different outcomes. Global climate change is increasingly putting communities in these systems, and the resources they rely on at risk. As such, economically and ecologically important resources within SES are facing stress on two fronts, human extraction, and human induced climate change. Understanding these stressors individually and deciphering the interplay between the two is crucial for informed decision-making to promote resilience of a system and may be the key to the longevity of these SESs. In this dissertation, I explore how current fishing practices and future climate conditions interact across three components of the social-ecological system framework within the context of cooperative fisheries systems of Baja California, Mexico. First, I assess population structure and catch demographics of an economically important fishery resource unit, highlighting the occurrence of multiple recaptures of sub-legal individuals and potential need for bycatch reduction in the face of future climate scenarios. Expanding out, I then adapt a data-poor vulnerability assessment methodology to assess risk of fisheries species within a resource system to multiple climate change stressors. I provide considerations of drivers of risk to climate change between groups of species and highlight the need to account for compounding pressure of multiple climate stressors, multidimensional risk from fishing and climate, and spatial scale of application of the methodology when assessing results for management decisions. Finally, I survey resource users within a Baja fishing cooperative on their knowledge and access to information about climate change and find broad understanding of climate change, spotlight the importance of informal methods of knowledge acquisition, and provide recommendations on the utility of engaging stakeholders in information sharing through social media platforms. In studying multiple components of an SES, I argue co-consideration of fishing and climate impact understanding at every level is crucial for implementation of management schemes for future resilience
Multimodal, Self-Supervised Deep Learning-Based Estimation of Symptoms and Severity of Depression and Anxiety
Keywords: Computer Science
Machine learning
Deep learning (Machine learning)
Depression, Mental > Diagnosis
Anxiety disorders
Text
Thesis

Depression and anxiety are common mental health disorders, affecting 264 million and 284 million people worldwide, respectively. Although effective treatments for depression exist, healthcare providers in America diagnose only 47% of patients. Consequently, barriers to effective care include the lack of accurate, feasible screening and diagnosis tools. Currently, screening and diagnosis methods employ self-reported patient surveys, which are highly subjective, along with in-person clinical interviews to assess symptom severity, which are resource-intensive due to shortage of clinicians. In this work, we collect data from 110 patients before their regularly scheduled visit, either in-person or virtual, to the Stanford Family Medicine clinic. We collect ground truth PHQ-9 and GAD-7 scores, which are clinically validated instruments to screen for depression and anxiety, and record video, audio, and text transcriptions of patient responses to 11 questions. We train unimodal deep learning models that use audio, video, or text transcriptions of the conversations to detect the presence (binary classification) and severity (four-class classification) of depression and anxiety. We also train multimodal models that use all three modalities. Multimodal deep learning models detect the presence and severity of depression and anxiety with high balanced accuracy: 93.8% and 60.4% for depression, and 100% and 62.5% for anxiety. Additionally, we utilize a self-supervised, multi-modal pre-training scheme that jointly pre-trains audio, video, and text autoencoders to converge representations across the three modalities, followed by decoders that classify the patient into score buckets. Finally, we investigate model interpretability using saliency mapping to uncover specific behavioral markers, such as facial areas and emotionally valent words, that our models utilize in making estimations of depression or anxiety. Saliency mapping also illuminates specific questions out of the 11 total questions that provide the maximum signal to our text-based models. These results have both high-level technical and clinical significance. Technically, we suggest ways of conducting optimal representation learning in small data regimes by leveraging semantic alignment in data across several modalities. Clinically, we show the potential of an objective, automated tool for depression and anxiety screening that takes an average of three minutes per person to administer during casual conversations in clinical settings. Such a tool could be easily incorporated into current clinical workflows and facilitate low-cost, universal access to mental health care.
Multi-omic characterization of segmental graft dysfunction in liver transplant patients
Keywords: Chemistry.

Liver transplantation was pioneered in canine models in the 1950s and extended to humans in the 1960s. Since then, improvements in recipient-donor matching, organ allocation policies, and immunosuppression have made transplantation a viable intervention for patients with end-stage liver disease. Nowadays, the 5-year survival rate is 75%, with the majority of transplant failures occurring within 90 days after surgery. The success of liver transplantation is overshadowed by the significant organ shortage which motivated the inclusion of living donors. Within the first week after receiving a partial liver graft, patients could experience segmental graft dysfunction (SGD), a form of early allograft dysfunction (EAD). Although the majority of patients recover, an SGD diagnosis is still predictive of graft failure. This thesis focuses on determining the molecular signatures underpinning the development of SGD and developing predictive models of graft failure to identify high-risk patients
Multiomics reveals critical metabolites for colonization of salmonella in superspreaders hosts
Keywords: Microbiology and Immunology.

The molecular understanding of host-pathogen interactions in the GI tract of superspreader hosts is incomplete. In a mouse model of chronic, asymptomatic Salmonella enterica serovar Typhimurium (S. Tm) infection, we performed untargeted metabolomics on the feces of mice and found superspreader hosts possess distinct metabolic signatures compared to non-superspreaders, including differential levels of L-arabinose. RNA-seq on S. Tm in superspreader fecal samples showed increased expression of the L-arabinose catabolism pathway in vivo. By combining bacterial genetics and diet manipulation, we demonstrate that diet-derived L-arabinose provides S. Tm a competitive advantage in the GI tract. We show that the expansion of S. Tm in the GI tract requires a previously uncharacterized alpha-N-arabinofuranosidase that can liberate L-arabinose from dietary polysaccharides. Ultimately, we demonstrate that pathogen-liberated L-arabinose from the diet provides a competitive advantage to S. Tm in vivo. These findings propose L-arabinose as a critical driver of S. Tm expansion in the GI tracts of superspreader hosts
Multiscale solver for subsurface poromechanical problems
Keywords: Energy Resources Engineering.

Solution of linear systems is often the bottleneck in large-scale subsurface modeling, especially when tightly coupled multiphysics systems are considered. Iterative linear solvers that are employed critically depend on efficient and scalable preconditioners to achieve robust convergence. The objective of this work is the development of scalable and efficient multilevel multiscale preconditioners targeting problems of geomechanics, single-phase flow and poromechanics on general polyhedral grids in one unified framework. We have developed a flexible unstructured coarsening framework that constructs a multilevel hierarchy of topological grid representations suitable for both nodal (finite-element) and cell-centered (finite-volume) discretizations. At every level, interpolation and restriction operators are defined by computing numerical basis functions using the Multiscale Restriction-Smoothed Basis (MsRSB) method, which has been additionally extended with a matrix filtering strategy designed to guarantee convergence of the smoothing iterations for problems that do not produce an M-matrix. We also develop a multiscale preconditioner for coupled poromechanical problems based on a combination of pressure and displacement basis functions and a block-triangular smoothing operator. Robustness and algorithmic scalability of the method are verified using a series of two and three-dimensional test cases including single-physics and coupled problems. In addition, this work describes an optimized parallel implementation of the proposed multiscale methods, its main computational kernels and data structures for representing the mesh hierarchy and multiscale operators. We investigate the performance and scalability of the developed multiscale solver and compare it to a state-of-the-art algebraic multigrid solver using a series of structured and unstructured grid test problems. Good weak and strong scaling is observed on up to 16 shared memory cores and 32 distributed memory nodes for a range of problems from 1 million to 140 million grid cells
Multiwavelength Mitigation of Stellar Activity in Astrometric Planet Detection
Keywords: Physics
Astrometry
Extrasolar planets
Extrasolar planets > Detection
Solar activity
Sunspots
Stellar activity
Starspots
Text
Thesis

Astrometry, referring to the measurement of stellar positions in the sky, has long been a promising technique for exoplanet detection. As the Earth orbits the Sun, for example, its gravitational pull causes the Sun to wobble with an amplitude of 0.65 milli solar radii. Exoplanets induce analogous reflex motion in their host stars, which can serve as astrometric signals. At its theoretical limits, astrometry would allow for the detection of planets smaller than previously observed by current exoplanet search methods. However, stellar activity may make these theoretical limits unreachable. Magnetic activity in the photosphere of a Sun-like star induces variability of order 0.5 milli solar radii in the position of the photocenter. This astrometric “jitter” creates a fundamental astrophysical noise floor preventing detection of lower mass planets at a single wavelength.
In this thesis, we investigate mitigation of this fundamental astrometric noise for Sun-like stars using correlations across wavelengths. To do so, we perform planet injection-and-recovery tests in five different wavelength passbands using astrometry simulations calibrated from solar data. We treat this problem in the limit of an ideal telescope to isolate the noise floor set by stellar activity. For a true solar analog and a planet at 1 au semi-major axis, we find the 6σ detection limit to be 0.01 Earth masses in the best single passband. We then show that combining information from pairs of passbands with highly correlated jitter but less motion in the redder band enables higher precision measurements of the common signal from the planet. Using this method improves detectable planet masses at 1 au by up to a factor of 10 at some wavelengths, and it sets the new 6σ detection limit across all passbands to 0.005 Earth masses. Given these results, we recommend that future astrometry missions consider proceeding with two or more passbands to progress towards astrometric planet detection by reducing noise due to stellar activity.
Music, manuscripts, and missionaries in the early colonial Guatemalan Highlands
Keywords: Music.

The Q'anjob'al and Chuj Mayans of Guatemala's Cuchumatán mountains fiercely resisted the Spanish colonial presence. From the earliest contact in 1525 to the late seventeenth century, chroniclers reported "imminent risk" to Spanish interlopers. And yet, those same Mayan communities embraced a European-style Catholic music tradition, as choristers, scribes, and composers. Local maestros documented and exchanged their repertoires, eventually leaving behind more than fifty volumes of music manuscripts. The same people who set "demon spirits" upon priests sang songs such as "De la sagrada Maria" (Of the sacred Mary). What power did music have that arms and scripture lacked? How did this group of remote and resistant villages become Central America's most robust center of Catholic choral music-making of its time? My dissertation picks up these questions through an examination of this "Huehuetenango collection" of manuscripts, of which only sixteen now survive. This project is the first major scholarly work dedicated to the historical context of this collection. With newly catalogued works and song analyses, this study brings to light material of an entire region of interconnected communities, recording local colonial-era language use, religious hierarchies, and social networks. I argue that the Huehuetenango collection offers rare evidence of early colonial lived experiences of the Q'anjob'al and Chuj Maya people and the Indigenization of a musico-religious tradition
"My other job" : volunteer content moderation as platform labor
Keywords: Communication.

Volunteer content moderation has been a crucial feature of virtual discussion spaces throughout the history of the social internet. Over the last decade, global social media companies have built out the infrastructure for volunteers to administer their own groups and communities. The social media platform Facebook, for example, reported that as of 2020, 70 million volunteers moderate discussion groups regularly used by 1.8 billion people worldwide. Despite its broad use by nearly a quarter of humanity, Facebook's structural opacity has unfortunately limited external access to conduct rigorous research of these spaces. In this dissertation, I examine volunteer moderation on Facebook from the theoretical perspective of the sociology of labor, incorporating insights from management studies and the field of communication. This research uses ethnographic methods and is designed around 41 in-depth interviews with volunteer Facebook admins and moderators who moderate groups that vary in both size and topic. I also introduce a new method for use in digital ethnography that I call over-the-shoulder observation. Analysis of these interviews, field notes, and notes from over-the-shoulder observation yielded important insights into the dynamics of Facebook-hosted group moderation, including a framework for understanding the work of moderation teams, an exploration of volunteer moderation as a route to capital accumulation, and an application of labor process theory to the moderator-platform relationship. More broadly, this dissertation reveals how volunteer moderators' work is shaped by the economics of the emerging platform ecosystem. I argue that the scholarly moderation literature must move beyond framing moderation practices simply through the lenses of normative ethics and effective user experience, and rather scholars must recognize that broader social and economic subjectivities of these actors shape how they make and understand moderation decisions