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Description
Background
The transition from the home to college is a phase in which emerging adults shift toward more unhealthy eating and physical activity patterns, higher body mass indices, thus increasing risk of overweight/obesity. Currently, little is understood about how changing friendship networks shape weight gain behaviors. This paper describes the recruitment, data collection, and data analytic protocols for the SPARC (Social impact of Physical Activity and nutRition in College) study, a longitudinal examination of the mechanisms by which friends and friendship networks influence nutrition and physical activity behaviors and weight gain in the transition to college life.
Methods
The SPARC study aims to follow 1450 university freshmen from a large university over an academic year, collecting data on multiple aspects of friends and friendship networks. Integrating multiple types of data related to student lives, ecological momentary assessments (EMAs) are administered via a cell phone application, devilSPARC. EMAs collected in four 1-week periods (a total of 4 EMA waves) are integrated with linked data from web-based surveys and anthropometric measurements conducted at four times points (for a total of eight data collection periods including EMAs, separated by ~1 month). University databases will provide student card data, allowing integration of both time-dated data on food purchasing, use of physical activity venues, and geographical information system (GIS) locations of these activities relative to other students in their social networks.
Discussion
Findings are intended to guide the development of more effective interventions to enhance behaviors among college students that protect against weight gain during college.
ContributorsBruening, Meg (Author) / Ohri-Vachaspati, Punam (Author) / Brewis, Alexandra (Author) / Laska, Melissa (Author) / Todd, Michael (Author) / Hruschka, Daniel (Author) / Schaefer, David (Author) / Whisner, Corrie M (Author) / Dunton, Genevieve (Author)
Created2016-08-30
Description
Social norms are unwritten behavioral codes. They direct individual behaviors, facilitate interpersonal coordination and cooperation, and lead to variation among human populations. Understanding how norms are maintained and how they change is critical for understanding human evolutionary psychology, social organization, and cultural change. This dissertation uses a mathematical model and a field study to answer two questions: First, what factors determine the content and dynamics of a social norm? Second, how do people make decisions in a normative context? The mathematical model finds that contrary to the popular belief that even arbitrary or deleterious social norms can be maintained once established because deviants suffer coordination failures and social sanctions, norms with continuously varying options cannot be maintained by the pressure to do what others do. Instead, continuous norms evolve to the optimum determined by environmental pressure, individual preferences, or cognitive processes. Therefore, the content of norms across human societies may be less historically constrained than previously assumed. The field study shows that unlike what rational choice theory predicts, people in a small-scale subsistence society do not calculate the ecological and social payoffs of different behaviors in a normative context, even when they have the information to do so. Instead, they rely heavily on social information about what others do. This decision-making algorithm, together with mental categorization that ignores small deviations, and cognitive biases that favor the division prescribed by the norm, maintain an ecologically inefficient and widely disliked cooperative surplus division norm in a Derung village, Dizhengdang, in Yunnan, China.
ContributorsYan, Minhua (Author) / Boyd, Robert (Thesis advisor) / Mathew, Sarah (Thesis advisor) / Hruschka, Daniel (Committee member) / Arizona State University (Publisher)
Created2023
Description
A remarkable phenomenon in contemporary physics is quantum scarring in classically chaoticsystems, where the wave functions tend to concentrate on classical periodic orbits. Quantum
scarring has been studied for more than four decades, but the problem of efficiently detecting
quantum scars has remained to be challenging, relying mostly on human visualization of
wave function patterns. This paper develops a machine learning approach to detecting
quantum scars in an automated and highly efficient manner. In particular, this paper exploits Meta
learning. The first step is to construct a few-shot classification algorithm, under the
requirement that the one-shot classification accuracy be larger than 90%. Then propose a
scheme based on a combination of neural networks to improve the accuracy. This paper shows that
the machine learning scheme can find the correct quantum scars from thousands images of
wave functions, without any human intervention, regardless of the symmetry of the underlying
classical system. This will be the first application of Meta learning to quantum systems. Interacting spin networks are fundamental to quantum computing. Data-based tomography oftime-independent spin networks has been achieved, but an open challenge is to ascertain the
structures of time-dependent spin networks using time series measurements taken locally
from a small subset of the spins. Physically, the dynamical evolution of a spin network under
time-dependent driving or perturbation is described by the Heisenberg equation of motion.
Motivated by this basic fact, this paper articulates a physics-enhanced machine learning framework
whose core is Heisenberg neural networks. This paper demonstrates that, from local measurements, not only the local Hamiltonian can be recovered but the Hamiltonian reflecting the interacting structure of the whole system can
also be faithfully reconstructed. Using Heisenberg neural machine on spin networks of a
variety of structures. In the extreme case where measurements are taken from only one spin,
the achieved tomography fidelity values can reach about 90%. The developed machine
learning framework is applicable to any time-dependent systems whose quantum dynamical
evolution is governed by the Heisenberg equation of motion.
ContributorsHan, Chendi (Author) / Lai, Ying-Cheng (Thesis advisor) / Yu, Hongbin (Committee member) / Dasarathy, Gautam (Committee member) / Seo, Jae-Sun (Committee member) / Arizona State University (Publisher)
Created2022
Description
Contact tracing was deployed widely during the COVID-19 pandemic to attempt to stop the spread of SARS Co-V-2. This dissertation investigates the research on contact tracing from a scientometric perspective and looks qualitatively at how case investigators and contact tracers conducted public health practice during the pandemic. Through approaching the public health practice of contact tracing from both a broad, top-down angle, and an on the ground experiential approach, this dissertation provides insight into the issues facing contact tracing as a public health tool.
ContributorsWhite, Alexandra C. (Author) / Jehn, Megan (Thesis advisor) / Hruschka, Daniel (Committee member) / Gaughan, Monica (Committee member) / Arizona State University (Publisher)
Created2022
Description
Few-layer black phosphorous (FLBP) is one of the most important two-dimensional (2D) materials due to its strongly layer-dependent quantized bandstructure, which leads to wavelength-tunable optical and electrical properties. This thesis focuses on the preparation of stable, high-quality FLBP, the characterization of its optical properties, and device applications.Part I presents an approach to preparing high-quality, stable FLBP samples by combining O2 plasma etching, boron nitride (BN) sandwiching, and subsequent rapid thermal annealing (RTA). Such a strategy has successfully produced FLBP samples with a record-long lifetime, with 80% of photoluminescence (PL) intensity remaining after 7 months. The improved material quality of FLBP allows the establishment of a more definitive relationship between the layer number and PL energies.
Part II presents the study of oxygen incorporation in FLBP. The natural oxidation formed in the air environment is dominated by the formation of interstitial oxygen and dangling oxygen. By the real-time PL and Raman spectroscopy, it is found that continuous laser excitation breaks the bonds of interstitial oxygen, and free oxygen atoms can diffuse around or form dangling oxygen under low heat. RTA at 450 °C can turn the interstitial oxygen into dangling oxygen more thoroughly. Such oxygen-containing samples show similar optical properties to the pristine BP samples. The bandgap of such FLBP samples increases with the concentration of the incorporated oxygen.
Part III deals with the investigation of emission natures of the prepared samples. The power- and temperature-dependent measurements demonstrate that PL emissions are dominated by excitons and trions, with a combined percentage larger than 80% at room temperature. Such measurements allow the determination of trion and exciton binding energies of 2-, 3-, and 4-layer BP, with values around 33, 23, 15 meV for trions and 297, 276, 179 meV for excitons at 77K, respectively.
Part IV presents the initial exploration of device applications of such FLBP samples. The coupling between photonic crystal cavity (PCC) modes and FLBP's emission is realized by integrating the prepared sandwich structure onto 2D PCC. Electroluminescence has also been achieved by integrating such materials onto interdigital electrodes driven by alternating electric fields.
ContributorsLi, Dongying (Author) / Ning, Cun-Zheng (Thesis advisor) / Vasileska, Dragica (Committee member) / Lai, Ying-Cheng (Committee member) / Yu, Hongbin (Committee member) / Arizona State University (Publisher)
Created2022
Description
Predicting nonlinear dynamical systems has been a long-standing challenge in science. This field is currently witnessing a revolution with the advent of machine learning methods. Concurrently, the analysis of dynamics in various nonlinear complex systems continues to be crucial. Guided by these directions, I conduct the following studies. Predicting critical transitions and transient states in nonlinear dynamics is a complex problem. I developed a solution called parameter-aware reservoir computing, which uses machine learning to track how system dynamics change with a driving parameter. I show that the transition point can be accurately predicted while trained in a sustained functioning regime before the transition. Notably, it can also predict if the system will enter a transient state, the distribution of transient lifetimes, and their average before a final collapse, which are crucial for management. I introduce a machine-learning-based digital twin for monitoring and predicting the evolution of externally driven nonlinear dynamical systems, where reservoir computing is exploited. Extensive tests on various models, encompassing optics, ecology, and climate, verify the approach’s effectiveness. The digital twins can extrapolate unknown system dynamics, continually forecast and monitor under non-stationary external driving, infer hidden variables, adapt to different driving waveforms, and extrapolate bifurcation behaviors across varying system sizes. Integrating engineered gene circuits into host cells poses a significant challenge in synthetic biology due to circuit-host interactions, such as growth feedback. I conducted systematic studies on hundreds of circuit structures exhibiting various functionalities, and identified a comprehensive categorization of growth-induced failures. I discerned three dynamical mechanisms behind these circuit failures. Moreover, my comprehensive computations reveal a scaling law between the circuit robustness and the intensity of growth feedback. A class of circuits with optimal robustness is also identified. Chimera states, a phenomenon of symmetry-breaking in oscillator networks, traditionally have transient lifetimes that grow exponentially with system size. However, my research on high-dimensional oscillators leads to the discovery of ’short-lived’ chimera states. Their lifetime increases logarithmically with system size and decreases logarithmically with random perturbations, indicating a unique fragility. To understand these states, I use a transverse stability analysis supported by simulations.
ContributorsKong, Lingwei (Author) / Lai, Ying-Cheng (Thesis advisor) / Tian, Xiaojun (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Alkhateeb, Ahmed (Committee member) / Arizona State University (Publisher)
Created2023
Description
Latest estimates show that roughly 188 individuals in the United States die everyday due to an opioid-related overdose. This dissertation explores three avenues for
mitigating opioid use disorder (OUD) and the opioid epidemic in the United States (1.)
How can researchers and public health professionals identify areas most in need of
treatment for OUD in an easy-to-use and publicly accessible interface?; (2.) What do
practitioners see as opportunities for reducing barriers to treatment?; and (3.) Why do
differences in opioid mortality exist between demographic groups? To address question
one, I developed an interactive web-based to assist in identifying those counties with the
greatest unmet need of medically assisted treatment (MAT). To answer question two, I
conducted a study of stakeholders (medical providers, peer support specialists, public
health practitioners, etc.) in four New Mexico counties with high unmet need of MAT. to
identify cultural and structural barriers to MAT provision in underserved areas as well as
opportunities for improving access. To answer the third question. I conducted a
systematic review of peer-reviewed literature and government reports to identify how
previous research accounts for race/ethnic and sex disparities in opioid-related
mortality. While many opioid mortality studies show demographic differences, little is
known about why they exist. According to the findings of this systematic review, research
needs to go beyond identifying demographic differences in opioid-related mortality to
understand the reasons for those differences to reduce these inequities.
ContributorsDrake, Alexandria (Author) / Hruschka, Daniel (Thesis advisor) / Jehn, Megan (Committee member) / Scott, Mary Alice (Committee member) / Arizona State University (Publisher)
Created2023
Description
Previous work suggests that lower-income individuals are more likely to engage in mutual aid as a means to manage risk, giving rise to a psychology that is other-oriented, including an empathetic disposition and a proclivity to help people in need. While no study has directly investigated whether helping in times of need increases dispositional empathic concern over time, this assumption is deep-seated among social psychologists. Employing a two-year longitudinal survey of US adults (N = 915), I show that people who experience more needs report helping others when in need a greater number of times, in turn leading to a small but positive increase in their empathetic disposition. This study also identifies the types of needs that elicit empathic concern (i.e., those that arise from unpredictable sources of risk), and shows why cultivating an empathetic disposition is likely to pay off in the long run: those who provide help are more likely to receive help during future times of need. Moreover, this study identifies the types of targets for whom providing help might cultivate an empathetic disposition: those with whom people are likely to share lower interdependence. While previous theoretical frameworks posit that empathic concern selectively directs investment towards interdependent others, providing help to non-interdependent targets might allow people to build positive interdependence with prospective risk pooling partners. Cultivating an empathetic disposition and building interdependence with prospective risk pooling partners can allow people to manage needs that arise from unpredictable sources of risk.
ContributorsGuevara Beltran, Diego (Author) / Aktipis, Athena (Thesis advisor) / Hruschka, Daniel (Committee member) / Kenrick, Douglas (Committee member) / Shiota, Michelle (Committee member) / Arizona State University (Publisher)
Created2023
Description
A notable challenge when assembling synthetic gene circuits is that modularity often fails to function as intended. A crucial underlying reason for this modularity failure is the existence of competition for shared and limited gene expression resources. By designing a synthetic cascading bistable switches (Syn-CBS) circuit in a single strain with two coupled self-activation modules to achieve successive cell fate transitions, nonlinear resource competition within synthetic gene circuits is unveiled. However, in vivo it can be seen that the transition path was redirected with the activation of one switch always prevailing over that of the other, contradictory to coactivation theoretically expected. This behavior is a result of resource competition between genes and follows a ‘winner-takes-all’ rule, where the winner is determined by the relative connection strength between the two modules. Despite investigation demonstrating that resource competition between gene modules can significantly alter circuit deterministic behaviors, how resource competition contributes to gene expression noise and how this noise can be controlled is still an open issue of fundamental importance in systems biology and biological physics. By utilizing a two-gene circuit, the effects of resource competition on protein expression noise levels can be closely studied. A surprising double-edged role is discovered: the competition for these resources decreases noise while the constraint on resource availability adds its own term of noise into the system, denoted “resource competitive” noise. Noise reduction effects are then studied using orthogonal resources. Results indicate that orthogonal resources are a good strategy for eliminating the contribution of resource competition to gene expression noise.
Noise propagation through a cascading circuit has been considered without resource competition. It has been noted that the noise from upstream genes can be transmitted downstream. However, resource competition’s effects on this cascading noise have yet to be studied. When studied, it is found that resource competition can induce stochastic state
switching and perturb noise propagation. Orthogonal resources can remove some of the resource competitive behavior and allow for a system with less noise.
ContributorsGoetz, Hanah Elizabeth (Author) / Tian, Xiaojun (Thesis advisor) / Wang, Xiao (Committee member) / Lai, Ying-Cheng (Committee member) / Arizona State University (Publisher)
Created2022
Description
ABSTRACTWith the National Aeronautics and Space Administration (NASA) Psyche Mission, humans will soon have the first opportunity to explore a new kind of planetary body: one composed mostly of metal as opposed to stony minerals or ices. Identifying the composition of asteroids from Earth-based observations has been an ongoing challenge. Although optical reflectance spectra, radar, and orbital dynamics can constrain an asteroid’s mineralogy and bulk density, in many cases there is not a clear or precise match with analogous materials such as meteorites. Additionally, the surfaces of asteroids and other small, airless planetary bodies can be heavily modified over geologic time by exposure to the space environment. To accurately interpret remote sensing observations of metal-rich asteroids, it is therefore necessary to understand how the processes active on asteroid surfaces affect metallic materials. This dissertation represents a first step toward that understanding. In collaboration with many colleagues, I have performed laboratory experiments on iron meteorites to simulate solar wind ion irradiation, surface heating, micrometeoroid bombardment, and high-velocity impacts. Characterizing the meteorite surface’s physical and chemical properties before and after each experiment can constrain the effects of each process on a metal-rich surface in space. While additional work will be needed for a complete understanding, it is nevertheless possible to make some early predictions of what (16) Psyche’s surface regolith might look like when humans observe it up close. Moreover, the results of these experiments will inform future exploration beyond asteroid Psyche as humans attempt to understand how Earth’s celestial neighborhood came to be.
ContributorsChristoph, John Morgan M. (Author) / Elkins-Tanton, Linda (Thesis advisor) / Williams, David (Committee member) / Dukes, Catherine (Committee member) / Sharp, Thomas (Committee member) / Bell III, James (Committee member) / Arizona State University (Publisher)
Created2023