• Electroless Deposition of Cobalt by Coordination and Reduction of Palladium Catalyst by Surface Amines Groups

      Ng, Amy Sum-Yee (The University of Arizona., 2018)
      Solution-deposited palladium on amine-terminated self-assembled monolayers (SAMs) is a well-characterized catalyst and adhesion layer combination for electroless metallization of dielectric films. A reducing agent is typically added to the deposition bath or a sensitizer such as tin is co-deposited producing relatively thick Pd layers. Thinner Pd deposits would enable barrier seed layers for filling < 10 nm wide gaps in patterned dielectric films with metal. In this work, we eliminated the reducing agent from the deposition bath and worked at pH < 2 to deposit monomeric Pd(2+) species and show that the amine groups terminating the SAM reduce Pd(2+) to Pd(0). The amount of Pd deposited depended on the coverage of the two types of amines on the SAM. The adsorption of PdCl42- ions in solution on protonated amine groups (–NH3+) is well known. Our data suggest that the nonprotonated amine groups (–NH2), which coexist with –NH3+, chemically reduce the Pd(2+) ion to Pd metal by oxidizing to the amine radical cation (–NH2•+). Pd bonds to and covers the –NH2•+ groups in the process depositing around a monolayer of Pd from solution on the SAM-covered silicon oxide surface. The Pd layer served as a catalyst for solution deposition of cobalt films on the surface using a reducing agent under oxygen-free conditions. The cobalt deposited initially as islands that grew together into a closed film with good adhesion.
    • Postnatal Microcephaly in MASA/CRASH Syndrome: L1 Cellular Adhesion Molecule Loss Leads to Smaller, Fewer, and Directionally Challenged Neurites

      Gottman, Moriah (The University of Arizona., 2018)
      Postnatal microcephaly in MASA/CRASH (mental retardation, aphasia, shuffling gate, adducted thumbs) syndrome is caused by compromised neurite outgrowth, arborization, and directionality. Neurite outgrowth is lost due to a lack of L1-cellular adhesion molecule (L1CAM, or L1) and ankyrins B and G interaction. Neurite arborization is compromised due to a loss of L1CAM and ezrin-radixin-moesin (ERM) protein interaction. L1CAM and neuropilin-1(NP1) interaction leads to a loss of neurite directionality. How these neurite growth changes affect neurogenesis throughout the brain, and lead to microcephaly (MiC), has not yet been modeled. Future drug discovery projects aiming to halt the progression of postnatal MiC in MASA syndrome could narrow the scope of focus if modeling software parameters were adjusted to reflect phenotypic changes to neurites lacking L1 demonstrated in the literature.
    • Informatics for Trauma-Mediated Psychiatric Illness

      Gilchrist, Collin Andrew (The University of Arizona., 2018)
      Traumatic events such as direct or indirect exposure to serious injury or situations inciting strong fear response have been shown to result in mental disorders that are complex to diagnose, prognosticate, and treat. Neuropsychiatric sequelae following head injuries, including post concussive syndrome (PCS) and post-traumatic stress disorder (PTSD) are particularly challenging because of overlapping symptoms and the profound nature of the injury itself. This work aims to elucidate the similarities and differences between PCS and PTSD, two common sequelae following traumatic brain injury, using an emerging framework (Research Domain Criteria – RDoC) for understanding mental health disorders, and better characterize the mechanisms leading to psychiatric illness as a result of neurotrauma. The application of RDoC is demonstrated using analyses of clinical data from the Alzheimer's Disease Neuroinitiative. This analysis involves identification of subtle symptom variants for differentiating sequelae when clinical prognosis is uncertain based self-reported symptoms. In addition to specific self-reported symptoms, features in positron emission tomography and diffusion tensor imaging are also identified. These results are further supplemented by identification of brain structures associated with self-reported symptoms using imaging data. Overall, this project demonstrates the application of RDoC to better characterize PCS and PTSD, which would potentially allow for more effective treatment and management of these disorders.
    • A Study of Adversarial Attacks Against an LSTM Language Model and the Impact of Normalization in SNN

      Liang, Zhengzhong (The University of Arizona., 2018)
      Artificial Neural Networks (ANNs) have been used to many application-driven fields and have been shown to be quite successful, however, some aspects of ANNs are not well understood. One such area is learning an ANN in the presence of an adversary.In such a context, it is assumed that the attacker can manipulate the training (also referred to causative attack or poison) or testing data (also referred to exploratory attack) to disrupt its normal functionality. In turn, the defender aims at reducing the impact of such attacks as much as possible. The first part of this thesis focuses on causative attacks against an Long Short-Term Memory (LSTM) neural network in a language model. In causative attacks, it is assumed that the attacker can only change the training text in the language model. We study the behavior of the LSTM language model under different causative attacks and propose several simple measures that can reduce the impact of the attacks. Our results show that the poisoning ratio, the poisoning position and the generation of poisoned text can all influence the performance the LSTM language model. Furthermore, we show that proper use of dropout and gradient clipping can reduce the impact of poisoning the training data to some extent. We also contribute to understanding how to globally learn a Spiking Neural Network (SNN). SNNs are a type of ANN; however SNNs are much more biologically realistic than other ANNs. SNNs have not been widely adopted because of several critical issues of SNNs that are not well studied. One such effect is the training of SNNs and the encoding/decoding of signals in SNNs. In the second part of this thesis, we build an SNN based image classifier to study the encoding/decoding of signals and compare several learning rules for training an SNN. Results reveal that (i) classical STDP learning windows generally obtain the best performance using different decoding schemes; (ii) first-spike decoding has worse accuracy than count decoding classifier does when no normalization rules are applied, although first-spike decoding classifier consumes much less time than count decoding classifier; (iii) the performance of first-spike decoding classifier can be largely enhanced with proper use of normalization rules.
    • Demographics of Riparian Lizards in the Chiricahua Mountains in Relation to Water Availability and Emerging Aquatic Insects as a Potential Food Source

      McGee, Earyn Nycole (The University of Arizona., 2018)
      Severe drought driven by climate change and water use by humans are causing formerly perennial streams to flow intermittently, presenting an unprecedented level of disturbance. The loss of emerging aquatic insects as potential prey items could negatively impact riparian and terrestrial species, including lizards. Because lizards play important roles in riparian food webs (e.g. predators, nutrient cycling), it is crucial to understand the cascading effects of stream drying on lizard communities. We hypothesized that perennial streams provide aquatic subsidies to riparian lizards, reducing competition and opening niches. We predicted that lizard abundances would be greater, and that individuals within a species would grow larger and faster, along perennial streams compared to ephemeral streams. We studied three paired 100-meter perennial and ephemeral reaches with similar microhabitat but differing water availability in the Chiricahua Mountains of southeastern Arizona. We measured individual growth rates during a 2-month mark-recapture study of Yarrow’s spiny lizards (Sceloporus jarrovii), striped plateau lizards (Sceloporus virgatus), and ornate tree lizards (Urosaurus ornatus). We used emergence traps to quantify the availability of aquatic prey. Aquatic insects were collected in high abundances, suggesting a potential food source for lizards along perennial streams that may be unavailable along ephemeral streams. When considering mass at first capture, we found that S. jarrovii were larger at perennial versus ephemeral reaches. However, this pattern did not hold true for S. virgatus. Additionally, we failed to detect differences in abundances between paired perennial and ephemeral reaches for either S. jarrovii or S. virgatus. Low sample sizes prevented us from performing any statistical analyses for Urosaurus ornatus and on the mark-recapture data for S. jarrovii and S. virgatus. Although more research is needed to confirm these results, they indicate that emerging aquatic insects may be an important resource to riparian lizard species in arid environments. Future research should quantify trophic links between lizards and potential aquatic subsidies.
    • A Systematic Review of Recruitment and Retention Strategies Used in Dietary Randomized Controlled Interventions in Cancer Survivors

      Lavelle, Sarah Arline (The University of Arizona., 2018)
      Interpretation of results of dietary intervention trials in cancer survivors may be limited by insufficient recruitment or retention of study participants. This systematic review describes recruitment strategies, accrual of participants, and attrition (withdrawal) rates for dietary interventions conducted in breast, prostate, and colorectal cancer survivors. PubMed, CINAHL, Cochrane Central Register of Controlled Trials, Embase, PsychINFO, and Web of Science databases were searched. Eligible studies included national and international dietary randomized controlled trials (RCTs), with at least 12 weeks of intervention and 6 months of follow-up. Trials were required to include a CONSORT (CONsolidated Standards of Reporting Trials) diagram. Twenty cohorts were included: breast cancer (BC) survivors (n=11), prostate cancer (PC) (n=3), colorectal cancer (CRC) (n=1), and combined (n=5). Primary recruitment methods included health care providers (n=13) or cancer registries (n=9). Of studies that set a priori sample sizes, 12 met accrual targets and five did not. Attrition rates averaged 18.6% at 6 months, 16.3% at 12-13 months, and 20.3% at 2 years. Among completed studies (n=18), seven trials met a priori retention targets, three trials did not, one assessed feasibility, and seven trials did not provide a clearly defined retention goal. There were few trials in PC and CRC survivors. Missing CONSORT diagrams reduced the eligible studies. The majority of studies met recruitment goals (n=12). Overall, attrition rates averaged approximately 17.4%. Improved understanding of effective recruitment and retention strategies requires more diligent reporting. Qualitative research may allow for more systematic and detailed evaluation of challenges that contribute to insufficient recruitment and retention of cancer survivors in dietary intervention trials. Registration can be found at PROSPERO ref: CRD42018070396.
    • Deep Neural Networks for Modeling Sequential Prediction Tasks with Applications in Brain Tumors

      Guo, Jiashu (The University of Arizona., 2018)
      Gliomas are malignant brain tumors that are associated with high neurological morbidity and poor outcomes. Patients diagnosed with low-grade gliomas are typically followed by a sequence of measurements of the tumor size as they visit their medical physician. To optimize the timing of therapy of patients, the effective methodologies are needed to predict the future behavior of gliomas by modeling the mechanisms that mediate the characteristic features of gliomas. Machine learning is deployed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible, example applications include email filtering, optical character recognition (OCR), and computer vision. In the broad sweep of machine learning’s current worldly ambitions, healthcare applications seem to top the list for funding and press in the last three years. In this thesis, we demonstrate the promise of Long Short-Term Memory Neural Networks (LSTMs), and a 1-Dimensional Convolutional Neural Network (1-D CNN) to address two important clinical questions in low-grade gliomas: 1) classification and prediction of future behavior; and 2) early detection of dedifferentiation to a higher grade or more aggressive growth. The model of brain tumor growth was recently modeled to be a complex system of partial differential equations (PDEs). This system of PDE provides a significant amount of information about the grade of the tumor that cannot be easily measured from the tumor mass. For example, the PDEs have constants that are related to motility, angiogenesis and the mitotic rate; however, a single measurement of the brain tumor mass cannot easily be connected to these constants. This thesis presents a machine learning-based approach to estimate these parameters using a sequence of tumor mass measurements with an LSTM and a 1-D CNN network to solve the inverse problem of PDE parameter estimation. Experimental results show that through working with different architectures, for both of two models, accuracy increases as a function of the number of tumor measurements. This result shows that the LSTM and 1-D CNN provide better and better approximations to these PDE parameters as more measurements are made available (e.g., a tumor mass measurement every month). Moreover, the perplexity can be used for LSTMs to effectively detect a change in the tumor grade. These findings demonstrate the potential that machine learning and neural networks can provide the medical community for solving the inverse problem of PDE parameter estimation.
    • Failure Prediction of Structures Subjected to Random Vibrations

      Ballesteros, Luis Enrique (The University of Arizona., 2018)
      The purpose of this thesis is to study the failure prediction of structures subjected to random vibrations. First, the most relevant and fundamental theory in random vibrations is presented. A known method to efficiently calculate RMS von Mises stress in a random vibration environment is re-derived. Next, two practical case studies are presented: a cantilever beam and a rocket launcher payload subjected to random vibrations are analyzed using ANSYS. From this analysis, the expected frequency of the response and the root mean square (RMS) response for the displacement and stress are calculated. The statistical quantities obtained from the random vibration analysis, such as the expected frequency and RMS stress response, are used to determine the probability of the first-passage failure as well as the fatigue life.
    • Experimental Investigation of Boundary Layer Separation

      Balthazar, Michael Aeko (The University of Arizona., 2018)
      A study of boundary layer separation in wind tunnel, water tunnel, and free-flight conditions is reported on within these pages. Methods and results for building a sub-scale wing glove, installing active flow control, and testing in flight are presented. Water tunnel dye flow visualizations and particle image velocimetry of laminar separation bubbles are also presented.
    • Analytical Study on the Stability of Collector Members in Composite Steel Structures

      Lizarraga, Daniel (The University of Arizona., 2018)
      Seismic collectors transfer seismic forces to the primary vertical elements of the Seismic Force Resisting System (SFRS). In steel structures, collectors are typically elements in the floor framing system specially designed to carry these forces primarily though axial thrust. Steel collector members are designed as beam-columns under the compressive collector action. The controlling limit state in design may be strong axis beam-column behavior, weak axis buckling, torsional buckling or constrained-axis flange torsional buckling depending on the bracing condition providing by the floor slab or roof deck system attached to the collector. This paper presents an analytical study examining the stability behavior of steel seismic collectors under different structural configurations, including roof deck and composite slab. The orientation of the deck and slab is alternated between the parallel and deck perpendicular configurations, relative to the collector. The sizing of collector members, collector connections, and gravity system members are based on the design of a prototype structure in order to evaluate collectors under realistic design conditions. The compression strength of collector elements under these conditions is compared to design values per the American Institute of Steel Construction (AISC). Design considerations are provided for the stability design of steel seismic collectors.
    • Enabling Specialization for Dynamic Programming Languages

      Stephens, Jon (The University of Arizona., 2018)
      Scientists across many diverse fields, including medicine, astronomy and biology, often program to aid in the analysis of large datasets. Many of them prototype in dynamic programming languages due to their perceived convenience. While this may shorten the development time, the chosen language is often interpreted and therefore incurs a high runtime overhead, reducing scalability. Program specialization presents a promising method of decreasing the overhead without inconveniencing the user, but prior work cannot generically specialize interpreters. In this thesis, we take steps toward generic interpreter specialization by generically identifying specializable inputs. We do so by taking a checkpoint of the interpreter immediately before it begins executing the script. This captures much more state for specialization than prior work which should improve specialization's effectiveness. In addition, we show that checkpoints are practical and speculate on how specialization can improve interpreter performance.
    • Water Age in Residential Premise Plumbing

      Schück, Sasha (The University of Arizona., 2018)
      In most countries around the world, water is treated physically and chemically to a quality that is safe for human consumption. In spite of these efforts, every year people die as a consequence of drinking water-associated disease outbreaks. Legionella is arguably the deadliest pathogen in drinking water in the US and efforts are underway to reduce the likelihood of infecting potable water consumers. One of the primary factors to measure water quality degradation is water age. Water quality degrades with the time that the water sits in pipes. Over time, the residual disinfectant decays, disinfectant by products are created and the water becomes more susceptible to pathogen regrowth. This concern is not limited in the distribution systems but carries over to residential premise plumbing system. A key factor affecting water age in the premises is fixtures’ idle times. As a result, poorly designed plumbing layouts and intermittent usage patterns may lead to high residence times. In the present study, a methodology was developed to numerically quantify water age in residential premise plumbing systems. The scheme is composed of a hydraulic solver, EPANET with modifications, a demand stochastic simulator, SIMDEUM-UA, and a plumbing layout generator based on CAD models. This method was used to determine layout design practices that contribute to lower water ages. The layout is shown to have a significant impact on water age. Modified layouts reduced the water age metrics of absolute maximum age, mean maximum and mean water by up to 76%, 66% and 58%, respectively. A best practice is to connect the water closets at the end of the premise distribution branches. The effect of water heater types on residence times was also assessed. It was found that instant or on demand heater helps reduce water age across all layouts for all the metrics, at both the outlet and the point of connection of the fixture to the distribution system. To further decrease water age, auto-flushers were installed on certain nodes, as the USEPA (2016) recommends flushing the system at regular intervals, and further if combined with a flush of hot water at a temperature of at least 60 °C (140 °F), it would help sterilize the hot system between the heater and the flusher as recommended by the WHO (2007). Proposed methods to implement these so-called hot super-flushing were discussed for future research. However, none of the hydraulic approaches proposed here impact the “last foot” of pipe connecting plumbing fixtures with the premise distribution pipes. Lastly, when comparing the resulting pressures using the simulated demands against the peak demand estimates with flows from the plumbing code, code pressures are always lower than the simulated ones. This may indicate that the design method conservatively overestimates demands. Nonetheless, oversized pipes are detrimental for water age and should be avoided, as greater demands are required to flush the system.
    • Modeling and Optimization of Crop Production and Energy Generation for Economic Profit in an Organic Photovoltaics Integrated Greenhouse

      Okada, Kensaku (The University of Arizona., 2018)
      This study aimed to achieve the following two goals; first, developing an inclusive model which simulates solar irradiance to a tilted surface, electric energy generated by organic photovoltaic (OPV) modules on tilted greenhouse roof, light transmittance through multi-span greenhouse roof, solar irradiance available at the crop canopy level in the greenhouse, the crop (lettuce in this study) growth and yield, the energy consumed by greenhouse system for cooling and heating, cost and sales of electric energy and the crop. Finally, the model also determined the total economic profit achieved by the optimization program which computed the coverage ratio of OPV module, the period of shading curtain deployment for crop cultivation before and after summer period during which no crop cultivation occurred. The developed model also enables these analyses by adjusting the property of modeled PV modules, thus making it possible to study both organic (typically semi-transparent and flexible) and inorganic (opaque and solid) PV modules. To optimize the economic profit under the assumed environmental and economic conditions, MIDACO solver (http://www.midaco-solver.com/) was adopted. It can solve mixed integer non-linear programming (MINLP) problem by combining an extended evolutionary Ant Colony Optimization (ACO) algorithm with the Oracle Penalty Method for constrained handling (MIDACO-SOLVER, 2018, http://www.midaco-solver.com/index.php/download). All the simulation and optimization source code were written in Python 2.7. The optimization algorithm was licensed by MIDACO solver. The simulator can serve as a building block for further research about Agrivoltaics with new materials including OPV, and can be further enhanced with additional sub-simulation algorithms by other programmers and researchers interested in this field. The optimization results showed that, in Tucson, a semi-arid climate condition, the overall profitability could be increased by extending the cultivation of the lettuce into the summer, using both the shading curtain and OPV modules while decreasing high solar irradiance transmitted into the greenhouse and air temperature inside. The optimized OPV module coverage ratio was 58.0% when assuming its depreciation was completed, cell efficiency was 4.3%, visible light transmittance was 30%, the overall temperature coefficient was 0.02%, and the selling price of generated electricity was same as the purchase price, which was around 0.11 to 0.12 USD/kWh in Arizona. This indicated that the profit of lettuce cultivated in summer exceeded the cultivation cost in summer (labor cost and cooling cost) with a combination of PV module and shading curtain with a simple strategy that changes its deployment time each month according to the monthly average DLI: Thus, high-tech equipment for curtain control may not be required. At the optimal 58.0% OPV deployment rate, the amount of electricity generated per unit area basis was 47.7 kWh/m2 (satisfying 45.7% of the electricity consumption by greenhouse cooling, which was assumed to be the only factor consuming electricity) and the lettuce crop yield was 57.9 kg/m2, with an economic profit generated at 460.5 USD/m2. The simulation code developed also allows user or grower to evaluate alternative OPV coverage ratios and shade curtain deployments providing results on potential electricity generation, crop yields and economic profits. Although the profit made by electricity production with the current OPV film was much less than that of lettuce production, and further analyses should be conducted replacing various assumed values with available real data. The simulation result suggested some shading curtain and lettuce cultivation strategies in arid and semi-arid regions which had a potential to improve the profitability when OPVs integrated with a greenhouse system.
    • A Nanometric View into Strengthening Mechanisms of Iron Incorporation in Graphene-Based Nanocomposites

      Rand, Matthew (The University of Arizona., 2018)
      Recent advances in the ability to synthesize metal-ion coagulated graphene oxide (GO) colloidal dispersions have provided new avenues for fabrication of GO based thin films and membranes. Additionally, new fabrication techniques have recently emerged that enable the ability to intercalate and reduce metal halides in bulk graphite crystals, leading to metal-based graphite intercalated compounds (GICs). To this end, a fundamental study on the interplay between composition, atomic-scale structure and mechanical properties of metal-GO as well as metal-GIC composite materials was carried out employing molecular dynamics (MD) simulations. Specifically, the transition metal iron (Fe) was considered in this study; MD investigations reveal that Fe ions act as strong cross-linkers between individual GO sheets, increasing elastic modulus as well as tensile strength of the Fe-GO composite. Investigations of Fe intercalated GIC (Fe-GIC) showed interesting trends in its mechanical properties due to bond formation between the intercalated Fe atoms and the ‘sandwiching’ graphene sheets. In particular, with increasing iron concentration, there is strengthening in the out-of-plane direction, while reduction in the in-plane direction of the Fe-GIC lattice. While the Fe-C bonding ensures out-of-plane strengthening, it is equally detrimental to the strength of the in-plane C-C bonds within the graphene sheets. Valuable lessons learned from this work provide important insights into the design and development of GO and GIC composites for targeted mechanical and chemical applications.
    • Rising Temperature not alone in Influencing Arizona’s Snowpack: Precipitation Variability & its Impact on Water Resources

      Mennell, Joseph W. (The University of Arizona., 2018)
      Rising temperatures are widely regarded as the cause of declining snowpacks in the western United States. Variability in precipitation also poses risk to snowpacks but has not received attention in Arizona – where this research finds that variability in precipitation accounts for the majority of the decline in snowpack observed over the period 1981-2017. These findings are relevant in the Salt and Verde watersheds in central Arizona which provide approximately 40% of the water used in the Phoenix area annually, and may be used to better inform water management decisions in the area.
    • Reconstructing Snowpack Using Sierra Nevada Conifer Tree Rings In The Midst Of Changing Climate

      Lepley, Kai (The University of Arizona., 2018)
      Snowpack in the Sierra Nevada Mountains accounts for around one third of California’s water supply. Melting snow provides water into dry summer months characteristic of the region’s Mediterranean climate. As climate changes, understanding patterns of snowpack, snowmelt, and biological response is critical in this region of agricultural, recreational, and ecological value. Tree rings can be used as proxy records to inform scientists and resource managers of past climate variability where instrumental data are unavailable. Here we investigate relationships of tree rings of high-elevation conifer trees (Tsuga mertensiana, Abies magnifica, Abies concolor, Calocedrus decurrens, Juniperus occidentalis, and Pinus ponderosa) and regional climate indices with the goal of reconstructing April 1st snow-water equivalent (SWE) in the North Fork American River watershed of the Sierra Nevada Mountains. Chronologies are significantly positively correlated with April 1 SWE of the year prior to ring formation. Tsuga mertensiana ring growth is correlated negatively with April 1 SWE of the year of ring formation. Additionally, temporal trends in correlation between tree-ring chronologies and climate indices indicate strengthening tree-growth response to climate over time. We developed a skillful, nested reconstruction for April 1 SWE, 1661 – 2013. Variability of the reconstruction is within the envelope of 20th and 21st century variability; however, the 2015 record low snowpack is unprecedented in the tree-ring record, as in results from previous studies. We further explored the impacts of climate change on these conifers using seasonal correlation analysis to describe the change in climate signal evident in these tree-ring chronologies pre and post-climate change conditions. Significant rise in temperatures, reduced snowpack, and increased precipitation variability resulted in stronger climate signals in these tree rings since 1956. A subset of snow- sensitive, high-elevation conifers also exhibit signs of moderating environmental climate effects over multiple years. Future research should focus on integrating modern snow-sensor data into paleoclimate research and determining mechanistic linkages between climate and tree growth response.
    • Evaluating Training Procedures in a Naive Bayes Approach to Pathogenicity Prediction: The Case of the Family of Sodium Channel Proteins

      Li, Jing (The University of Arizona., 2018)
      PolyPhen-2 is a software that could help predict the pathogeneicity of mutations. We used it for prediction for sodium channel protein data after comparison with a few other prediction including Grantham Scores, SIFT, phyloP, PolyPhen-2, and CADD. But it’s still working with limited accu- racy. We are primarily concerned about is that our problem is trying to study the pathogenicity of sodium channel proteins and we are question- ing that if these softwares will help us predicting these mutation as well as the training set is based on the whole genome. So we tried to modify the software by training a data set from sodium channel protein mutations using naive Bayes algorithms. Then we compared the prediction results from our classifier and the prediction results of PolyPhen-2, then we could see that the classifier trained by our methods can make prediction results with significantly better accuracy.
    • Implementation and Verification of RCWA Model

      Gish Allouche, Genevieve (The University of Arizona., 2018)
      An RCWA Matlab simulation was developed to guide future designs of phase plates for VCSELs. We will discuss the verification process to validate our current model. Moreover, we will compare and contrast these results from the ones obtained from other software models, Polaris-M and OptiScan. A comparison between theoretical and experimental results will further test the validity of the model. Our second goal is to demonstrate experimentally Rayleigh anomalies, predicted by Harvey and Pfisterer’s non-paraxial scalar theory.
    • Intercultural Health and the Mapuche: Perceptions and Practice in Santiago, Chile

      Moretz, Hayley (The University of Arizona., 2018)
      Background: Intercultural health (IH), defined as the integration of western and indigenous medicine, is a public health approach that aims to reduce the divide between indigenous and biomedical health systems based on mutual respect and equal recognition of both knowledge systems. In Chile, IH has become a national strategy of indigenous health improvement through the Programa Especial de Salud y Pueblos Indígenas. With increasing Mapuche populations in urban centers, it is important to understand how these initiatives are conceptualized in urban settings. Methods: Through a qualitative assessment consisting of 10 in-depth, semi-structured interviews, this project sought to understand how IH is implemented in the La Florida municipality of the Metropolitan Region. Results: Results revealed that the current IH model is inadequate to meet the needs of the urban indigenous population. Constitutional recognition of the Mapuche people and culture was considered a prerequisite to restructure the IH model. Issues such as lack of funding, political favoritism, and a fundamental misunderstanding of Mapuche culture were seen as challenges to improving IH programs and indigenous health outcomes. Conclusion: Efforts to improve IH must take into account the indigenous concept of health and healthcare without forcing it into a biomedical model. A more comprehensive curriculum of indigenous healthcare and culture in general and medical education is critical to improve cross-cultural collaboration. An evaluation framework for funding mechanisms of IH at the regional level is needed to improve transparency and accountability among the Servicios de Salud, or Regional Health Departments, and indigenous associations. More research should be conducted in other urban areas with high indigenous populations to gather more representative data on IH implementation in the Metropolitan Region.
    • COMSOL Multiphysics Simulations of Wet Etching of High-Aspect-Ratio Structures with Surface Charge

      O'Connell, Ryan (The University of Arizona., 2018)
      In semiconductor manufacturing, the miniaturization of devices has always been a strong driving force in the industry. Making the individual transistors smaller makes it possible to increase the number of transistors on a given size chip, resulting in greater processing power and data storage capabilities. As the critical dimensions of today’s technology nodes approach the atomic scale, it is no longer feasible to obtain the same progression of increased transistor count through simple scaling. In the memory industry, new innovations are being incorporated into the manufacturing process, such as the verticalization of NAND memory to keep up with the demand for increased transistor density. With these innovations come new processing challenges, such as wet etching in extremely high-aspect-ratio (HAR) structures while maintaining top-to-bottom uniformity. The objective of this work was to create simulations in COMSOL Multiphysics that depict the time-dependent etching progress in HAR structures. A dynamic etching model was developed to predict the lateral recession profile of a HAR polysilicon trench bounded on the top and bottom by silicon dioxide (SiO2) etched by tetramethylammonium hydroxide. The model considered the diffusion of active etchant species, surface charging of polysilicon and SiO2, and reaction byproduct accumulation on the top-to-bottom etching profile. The results of the simulations suggest that the diffusion of reactant species into the trench could influence the top-to-bottom etch uniformity if the reaction order is greater than 1. The surface charging on polysilicon and SiO2 does not appear to affect the overall top-to-bottom uniformity but leads to a localized reduction in the etch rate in the corners of the trench where the polysilicon meets the SiO2. The secondary etching simulation was developed to model the wet etching of thin SiO2 films sandwiched between silicon layers, a structure that is often used to create cantilever microstructures. Etching models of SiO2 in hydrofluoric acid (HF) from the literature were utilized to provide boundary velocities in moving-boundary simulations of a dynamic etching process as a function of local concentrations of etchant species. The influence of the surface charge present on silicon in the Si/SiO2/Si stacked structures on the etch rate of SiO2 was modeled for varied thicknesses of SiO2. The etch rate of thin films of SiO2 was compared to the bulk etch rate without electrostatic interference. The simulations did not suggest that the SiO2 thickness in the stacked structure has a strong impact its etch rate because etch rate is dominated by neutral species.