VictorJuiz/thesis huggingface dataset:
Parents' views of failure as good or bad predict children's growth and fixed mindsets [electronic resource]
Keywords: Psychology.

Children's mindsets about intelligence (as fixed or malleable) robustly influence their motivation and learning. Yet, surprisingly, research has not linked parents' intelligence mindsets to their children's. We test the hypothesis that a different belief may be more visible to children (parents' failure mindsets) and therefore more prominent in shaping children's beliefs. Study 1 shows that parents can view failure as debilitating or enhancing, and these failure mindsets predict parenting practices and in turn children's intelligence mindsets. Study 2 probes more deeply into how parents display failure mindsets. Study 3 replicated these findings and additionally showed that parents who view failure as enhancing also reported more emotional responses to a similar scenario. Study 4 revealed that children can indeed accurately perceive their parents' failure but not intelligence mindsets. Study 5 showed that children's perceptions of their parents' failure mindsets also predicted their own intelligence mindsets. Finally, Study 6 showed a causal effect of parents' failure mindsets on responses to their child's hypothetical failure. Overall, parents who see failure as debilitating focus on children's performance and ability rather than learning, and this predicts whether their children believe that intelligence is fixed or malleable.
Part I: carbon monoxide reduction at gas diffusion electrodes ; Part II: development of a point-of-care device for blood ammonia measurement [electronic resource]
Keywords: Chemistry.

Part I: Carbon Monoxide Reduction at Gas Diffusion Electrodes: Anthropogenic climate change is one of the grand challenges facing humankind today. Although renewable energy sources are enjoying increasing adoption, issues with curtailment of excess energy, intermittency, and the present energy infrastructure's reliance on liquid fuels prevent their widespread deployment. Electrochemical carbon monoxide reduction to liquid fuel offers a promising pathway to bridge the gap between renewable energy sources and liquid fuels. Presently, only copper is known to efficiently perform these transformations, and results are confined to small-scale laboratory demonstrations owing to carbon monoxide's poor solubility in water. For the first time, gas diffusion electrodes based on copper catalysts have been shown to overcome this limitation, demonstrating that the underlying chemistry can scale favorably despite low solubility, and positioning carbon monoxide reduction as a transformative technology capable of hastening the deployment of renewable energy. Part II: Development of a Point-of-Care Device for Blood Ammonia Measurement: Hyperammonemia (elevated blood ammonia) is a life-threatening medical emergency that will result in permanent brain damage if untreated. Although a variety of effective treatment options exist, measurement of ammonia in the blood remains challenging as the only clinically-validated measurement is plagued by issues of significant interferences and sample instability. In this work, a handheld point-of-care instrument using an orthogonal detection scheme is developed and shown to be superior to the standard-of-care technology. The ultimate device can measure ammonia in 10 µL of a biological sample in less than 30 seconds. This handheld device opens the door to fingerstick blood ammonia measurements, thereby meeting the critical unmet need in the treatment of hyperammonemia.
Part I: development and applications of oligodeoxyfluorosides as metal ion sensor array; part II: fluorogenic darkzone dyes for in vitro and intracellular aldehyde labeling [electronic resource]
Keywords: Chemistry.

This thesis consists of two parts. Part I discusses the synthesis and applications of fluorescent DNA-based chemosensors on beads to detect and differentiate metal ions in water. Four fluorescent deoxyribosides were designed and synthesized as varied metal binding ligands. Combined with other deoxyribosides, a tetramer-length oligodeoxyfluoroside (ODF) library of 6561 members was constructed and screened for sequences responsive to metal ions. Screening of the ODF library yielded ODF sequences that respond to 37 metal ions within an hour. Seven strongly-responding sequences were selected as a sensor array and used to detect eight metal environmental contaminants in deionized water and in a contaminated California surface water sample at low micromolar range. Statistical analysis of the response patterns showed successful differentiation of the eight analytes in deionized water at concentrations as low as 100 nM in deionized water and surface water. The potential of the ODF metal ion sensor array was further explored by simultaneously discriminating 57 metals, including alkali, alkaline earth, post-transition, transition and lanthanide metals, with only nine sensors, demonstrating the ability of sensor array to detect and differentiate a large number of analytes with few sensors. As few as six ODF chemosensors can detect and differentiate 50 metals at 100 µM, and a blind test with 50 metals further confirmed the discriminating power of the ODFs. The ODF design described here demonstrates the potential of ODF sensor array as a convenient and inexpensive tool for environmental contaminant sensing. Part II of this thesis describes the design and development of a fluorogenic aldehyde labeling reagent using hydrazone exchange. This quenched hydrazone or DarkZone system adopts a previously unknown aldehyde dependent hydrazone exchange mechanism. Organocatalysts for hydrazone formation were found to accelerate DarkZone labeling, with at least two showing biocompatibility. The multi-color DarkZone dyes can label peptide in vitro and small aliphatic aldehydes and endogenously produced acetaldehyde in a cellular environment, demonstrating the versatility of the DarkZone dyes as a research and diagnostic tool.
Part I: development of oligodeoxyfluorosides printed on paper for sensing food spoilage in the vapor, phase II: development of oligodeoxyfluorosides as sensor array for water-soluble toxic anions and pesticides [electronic resource]
Keywords: Chemistry.

This thesis consists of two parts. In Part 1, DNA-based oligodeoxyfluoroside (ODF) dyes were developed to sense volatiles as they arise from food spoilage. In doing so, a new platform for fluorescence sensing was tested by inkjet printing of varied dyes on cellulose paper. ODFs are a class of short DNA-like oligomers with fluorophores replacing DNA bases. Six tetrameric ODFs of distinct sequences were printed on cotton paper using a commercial inkjet printer; printing methods and additives were evaluated for optimal fluorescence and response. ODFs were exposed via headspace volatiles to a variety of foods as they spoiled over a period of days. Fluorescence intensity and/or color changes during exposure to various foods were imaged under an epifluorescence microscope and the signals were quantified using digital values from red (R), blue (B), green (G), and luma (L) channels, creating "fingerprints" from the six sensor set and multiple color channels for each sensor. Using the digital data, some general trends were observed in food spoilage detection, such as different fluorescence changes according to food type, importance of spoiling conditions in fluorescence response due to influence from microbial activities, and the use of patterned response in differentiating spoiling foods and also distinguishing processes of spoilage in time course experiments. In Part 2, a new library of ODFs was synthesized for detection and differentiation of a set of target anions and pesticides in water. Detecting and quantifying anions or pesticides in water samples typically require complex instrumentation, adding cost and delaying analysis. These challenges were addressed by development of an optical molecular method to detect and discriminate a broad range of anionic and pesticides contaminants using a library of 1296 tetrameric-length ODFs composed of metal ligand and fluorescence modulating monomers on beads. These oligomers on beads were incubated with YIII or ZnII ions to provide affinity and responsiveness to anions and pesticides. An eight-ODF sensor set was able to differentiate seventeen anions at 250 μM in water from their patterns of response. Similarly, a ten-ODF sensor set was able to differentiate fourteen pesticides at 100 μM in water. Differential sensing through patterns of response was studied and used to develop applications where unknown concentrations or identity of samples were correctly predicted using statistical methods.
Partially correlated persistent scatterer theory and techniques for radar interferometry [electronic resource]
Keywords: Electrical Engineering.

Interferometric synthetic aperture radar (InSAR) is an effective technology for measuring temporal changes of the Earth's surface. By combining SAR echoes collected at varying times and orbit geometries, we can produce wide coverage images of crustal deformation fields with centimeter-level accuracy. However, conventional InSAR techniques often fail to recover a coherent deformation signal when the radar imaging geometry or surface scattering properties change significantly between radar passes. This phenomenon, known as decorrelation, produces a random phase term that obscures the deformation signal and reduces the amount of InSAR data suitable for time series analysis. We can overcome these limitations by exploiting a subset of intrinsically phase-stable pixels, the so-called persistent scatterers (PS). Identifying such pixels is a crucial component of this analysis, since phase unwrapping and subsequent deformation estimation on the spatially sparse PS network depends on both pixel selection accuracy and the network density. PS techniques have been shown to work well in urban areas with many strong, stable reflectors, but identifying an appropriate network of pixels in natural or vegetated terrain remains a challenge due to other spatiotemporally varying phase terms. In this dissertation, we present new theory and techniques for generalized PS analysis based on partially correlated persistent scatterers (PCPS): those that are non-ideal but stable enough for deformation time series measurement in largely decorrelated areas. We develop a new physically-based method for modeling spatiotemporal decorrelation, as well as a comprehensive statistical characterization of the resulting interferometric pixels. We show that these analytical results lay a theoretical foundation for PCPS algorithm development. Next, we introduce a more reliable PS selection technique that combines the full set of interferometric observations as a function of their acquisition intervals. The PCPS technique achieves a better trade-off between pixel selection accuracy and network density compared to other PS identification methods. Finally, we present examples of deformation measurements obtained using PCPS analysis. These results demonstrate that through improved statistical characterization, the PCPS technique attains reliable deformation measurements for a variety of wavelengths, terrain, and geophysical processes.
Particle acceleration in magnetized, relativistic outflows of astrophysical sources [electronic resource]
Keywords: Physics.

Many powerful and variable gamma-ray sources, including pulsar wind nebulae (particularly the Crab Nebula), active galactic nuclei and gamma-ray bursts, seem capable of accelerating particles to gamma-ray-emitting energies efficiently over very short time scales. These are likely due to rapid dissipation of electromagnetic energy in a highly magnetized, relativistic plasma. We term such a process as "magnetoluminescence". One possible scenario is that in the highly magnetized outflow of the prime mover, an ideal instability causes a tangled, high energy configuration to relax to a lower energy state over light crossing time scales; during the process extended E > B or E · B ≠ 0 regions can be formed and sustained as the particles are accelerated up to the radiation reaction limit, removing the electromagnetic energy in the form of gamma-ray emission. In order to test this conjecture, we devise simple models of magnetized, relativistic plasma configurations, which allow us to study in detail the macroscopic instability that leads to dramatic dissipation of electromagnetic energy. One class of examples are the so-called linear force-free equilibria within confining walls or 3D periodic boxes. Using analytical technique and MHD simulations, we find that many of the short wavelength configurations are unstable to ideal modes; the instability grows on Alfven wave crossing time scales (close to the light crossing time scale when the magnetization is high), and the system eventually relaxes to the longest wavelength state, or lowest energy state, as allowed by a conserved total helicity. We then used one of the lowest order unstable equilibria as a testbed to understand the generic features of particle acceleration and radiation in a relativistic, magnetized plasma, using radiative particle-in-cell (PIC) simulations. We find that the ideal instability forces a dynamic current layer formation and the highest energy particles are first accelerated by the parallel electric field in the current layers; fast variability can be produced by particle bunches ejected from the current layers. Meanwhile, we have been working closely with the observations, particularly the interpretation of multiwavelength data of the inner knot in the Crab Nebula, which was suspected to be the site of the gamma-ray flares. Though no convincing evidence has been found in that respect, we did a careful examination of emission models at the knot, which prompts us to reconsider the main particle acceleration mechanisms responsible for most of the emission (in the Optical/UV/X-ray wavebands) from the nebula. We think all the aforementioned results provide instructive steps along the way to learn about the basic properties of magnetized, relativistic plasmas and extreme particle acceleration. We also propose possible future directions in theoretical analysis, simulations, observations and laboratory astrophysics that may help us better understand these powerful engines in our universe.
Particle image velocimetry measurements of the wake of freely flying birds [electronic resource]
Keywords: Aeronautics and Astronautics.

This work focuses on better understanding the vortex wake, such as vortex break up and calculated weight support, of a slowly flying bird in undisturbed still air. The wake behind a pacific parrotlet flying at speeds between 1.5 and 2 m/s was measured in still air using stereo Particle Image Velocimetry (PIV). The bird was trained to fly between perches through the laser sheet while the bird was wearing laser safety goggles, specifically designed for the parrotlet. The measured wingtip vortices advected down and broke up relatively quickly, contradicting the frozen turbulence hypothesis typically assumed in wind tunnel flight experiments. The instantaneous lift was estimated based on the velocity field with the commonly used Kutta-Joukowski theorem, vortex ring model, and actuator disk model. The calculated lift was found to be sensitive to the applied model and different parameters in the models, including vortex span and time of measurement-rendering these accepted ways of calculating weight support inconsistent.
Passivation and integration of atomically-thin transition metal dichalcogenide electronics [electronic resource]
Keywords: Electrical Engineering.

The continued scaling and diversification of transistors, the bedrock of integrated electronics, requires novel material and design paradigms beyond the 10 nm technology node. Two-dimensional Transition Metal Dichalcogenide (TMD) nanosheets offer the potential for pristine, atomically-thin devices. Nonetheless, their development is hindered by issues of contact resistance, poor gate dielectric integration, and ambient degradation. In addition, few studies have focused on moderate-gap TMDs, comparable to or lower than bulk silicon at ~1.1 eV. This work introduces a methodology of air-free fabrication and VLSI-compatible dielectric passivation of TMDs, preserving intrinsic properties at the ultimate thickness limit. This is first applied to the Group-6 tellurides, WTe2 and MoTe2. The former, an anisotropic semimetal, is found to support current densities (~50 MA/cm2) higher than traditional metal interconnects, despite a low in-plane thermal conductivity. The latter, a moderate-gap ambipolar semiconductor, is subject to contact engineering for unipolar n-type transport with saturation current densities exceeding 400 µA/µm. High performance n-type transistors are demonstrated, both by exploiting Ag contacts which form interfacial compounds, and by suppressing extreme reactivity with low-work function metals via a one-atom-thick hexagonal boron nitride (h-BN) diffusion barrier. The final sections explore the novel 2D semiconductors HfSe2 and ZrSe2, first stabilized here in the few-layer limit despite extreme ambient sensitivity. Their decomposition is associated with oxidation into amorphous HfOx and ZrOx, high-K dielectrics of immediate technological relevance. Joint theoretical and spectroscopic studies establish indirect band gaps of ~0.9-1.2 eV persisting across monolayers to bulk films. Measured transistors achieve On/Off ratios exceeding 106, alongside respectable current densities and acute sensitivity to interfacial dielectrics, reproducing for the first time several key attributes of silicon in a 2D nanomaterial.
Patients as Consumers of Health Care: a Case Study of the Patient Experience in a Sample of the San Mateo County Low-income Population Receiving Care in an Emergency Department
Keywords:  Department of Anthropology
healthcare
Stanford University Department of Anthropology
Thesis

Patients’ perceptions of care have become increasingly significant in the evaluation of healthcare in the last decade in the United States. Hospitals now receive funding according to patients’ ratings of their hospital experience on the first national publicly reported survey, the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS). (Zusman, 2012) The HCAHPS does not ask for the patient income range, indicating that currently there is no way to look at the trends of patient experience for the low income population. Studies have shown that the low-income patient subgroup experiences unique challenges in the context of health care and that the emergency department (ED) acts as a social welfare institution for them. (Gordon) This study was designed to elucidate concerns that patients with low socio-economic statuses have about their health care experiences in the ED. Particularly, the goal of this research was to examine the cultural experience of the Stanford ED visit amongst low-income patients from San Mateo County.
The Stanford ED, a Level 1 pediatric and adult trauma center, is unique in that it treats a subpopulation with extreme wealth as well as one of extreme poverty. This study used an ethnographic approach that incorporated interviews and participant observation. Thirty responses that characterize the range of experiences in the ED were chosen for analysis.
In investigating themes that specifically relate to the perception of care of the low-income population in the ED, this study found that these patients placed greatest emphasis on waiting time, pain control, and communication with and attentiveness shown by the hospital staff. The data can be used to juxtapose the health care experiences of different subgroups to ultimately reduce disparities in health care services. In conclusion, the analysis of the interviews pointed out that the HCAHPS surveys gauge these patients’ experiences well because the concerns of the interviewees align with the majority of the concerns mentioned in the HCAHPS. However, the findings revealed a complex set of unique and important beliefs shared by the interviewed patient population, which indicates that the HCAHPS can add certain categories to reflect the patients’ experiences more accurately. By illuminating low-income patients’ ways of thinking about services provided, this study elucidated the factors that influence the perception of care of a subset of the ED patient population that has not been studied previously.
Pattern learning in smart homes and offices using motion sensor and mind wave data [electronic resource] : unsupervised approaches
Keywords: Electrical Engineering.

The general purpose of smart homes and smart offices is to provide people with personalized experiences according to their behaviors and intentions. The foundation of a smart home or office system involves the sensing, learning and understanding of human behaviors. Nowadays, a wide variety of sensors are available on the market that capture information regarding people's physical and mental activities in real-time. The key challenge is to extract useful information from this rich sensor data, in order to learn and recognize people's behavioral patterns. This dissertation focuses on learning and recognizing people's behavioral patterns. It explores people's physical and mental behaviors separately, and presents multiple unsupervised pattern learning approaches for extracting and recognizing these behaviors. The first half of the dissertation concentrates on learning people's movement patterns using motion sensors. Specifically, I first present an activation model that can be used to interpret motion sensor data as a sequence of people's movement events. Based on the activation model, I propose a trajectory-based pattern learning algorithm and develop a decomposition-based pattern learning method, both of which can be used to learn and extract a person's movement patterns from the unlabeled motion sensor data without human input. To validate both methods, the Aruba data set is used as a training set and its activity annotations are regarded as ground truth. The result shows high consistency between the output from both approaches and the activity annotations of the data set. The second half of the dissertation focuses on extracting people's mental workload patterns using MindWave sensors. In particular, I propose the Mental Motion State Model (MMSM) to extract the mental workload patterns. By applying the MMSM model on a MindWave sensor data set collected from 13 subjects during online project meetings in a global teamwork course, three mental motions states are discovered: mental deep recovery, mental light recovery, and mental running. Based on the MMSM model, the mental recovery curve model is introduced which shows objective insights about how people recover and increase their mental energy at different mental workload levels. Using the pattern learning methods proposed in this dissertation together, people's movement patterns and mental workload patterns can be extracted and learned from the motion sensor data and MindWave data without human supervision. Therefore, the work presented in this dissertation can serve as the foundation of an adaptive system, which is able to provide people with automatic assistances and feedback according to the observed behavior patterns.