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Impacts of pricipitation and temperature variability on rice production in Mitole Epa Chikwawa
No full textBackground: Rainfall and temperature variability are a threat to sustainable agricultural production in Malawi. Main crops which include rice are highly affected due to climate variability since this crop is grown during wet season. A study was therefore conducted in one EPA named Mitole in Chikwawa district in southern Malawi to determine the impacts of precipitation and temperature variability on rice production. Method: Secondary data of climate variables and rice data was used for a period of 16 years which was obtained from Department of Climate Change and Meteorological Services (DCCMS) in Blantyre and Mitole EPA in Chikwawa respectively. Before data analysis data quality control was done where outliers were manually corrected and also errors were corrected using homogeneity test in which single mass curve for each data were plotted. In data analyses; excel and R was used to do trend analysis. Mann Kendall test was used to test if there were significant trend of data or not. To determine the relationship between climate parameters and rice production correlation analysis tested. Regression analysis was also used to predict the results if the climate variables keep varying. Findings: The results showed that only minimum temperature had a negative significant trend and other variables had trends which were not significant. Correlation analysis showed non-significant relationship between climate parameters and rice production and also the results of regression analysis had non-significant relation therefore there was enough evidence to predict the results in future. Conclusion: Since the results did not provide enough evidence that climate variables specifically rainfall and temperature affect rice production in the area, it was recommended that more research must be done to discover the way farmers must follow to maximize the production. Novelty/Originality of this study: This study provides concrete data on the impacts of climate variability on rice production in southern Malawi, which farmers and policymakers can use to develop more effective agricultural adaptation strategies in the region.
Temperature and flow effects on mussel gaping behavior
No full textAnthropogenic induced thermal stress is a major driver of change, specifically within marine
coastal ecosystems. Mussel beds provide important structures and chemical functions to surround
marine ecosystem. Mussel behavior is known to be influenced by environmental factors, but less
has been explored about how behavior might influence their role in chemical manipulation. In
this study, we developed a system to monitor gaping distance of Mytilus (californianus and
trossulus) across time under different temperature and flow speed conditions. We documented
increases in Mytilus californianus gaping frequencies and decreases in Mytilus trossulus gaping
frequencies as water was warmed. Although Mytilus trossulus often inhabit low flow areas, there
was not a significant change observed in time spent open when placed in higher flow. Although
further replication is desired, temperature and flow are seen to affect gaping across species,
providing implications for marine warming created shifts
Modeling, Machine Learning, and Additive Printing for the Solar Cell Grid
No full textThesis (Ph.D.)--University of Washington, 2023The front electrode grid of a solar cell solves a simple problem: it must move current from the solar cell area to a sink. In doing so, it must minimize the resistive power losses incurred in transit while minimizing the size of the shadow it casts on the underlying photovoltaic material. The optimal geometry to achieve this may become complex depending on the material and scaling properties of the system. It may share morphological aspects with the natural systems that solve similar problems, such as leaves, veins, and waterways. The underpinnings of solar cell grid optimization are a set of well-known equations describing the principal sources of shadowing and resistive power loss. We challenged two assumptions that have been long entrenched in the field: first, that colinear line arrays are strictly optimal when designing solar cell grids. In fact, there is a class of isotropic grids that can outperform linear arrays in certain conditions. Second, that constant grid line height describes most grid applications. Instead, in the era of additive and printed electronics, many grids may be subject to virtuous scaling - the property that larger wires become more efficient conductors. Under virtuous scaling, complex ramified electrode structures become optimal. A dynamic graph approach was developed to model these electrode patterns, and a locally greedy approach to optimize them realized novel electrode patterns with ~1\% performance over standard linear grids. The continuous width variation inherent in these patterns presented a control challenge for an additive process attempting to print them. An electrohydrodynamic (EHD) inkjet printing approach was proposed; the powerful but difficult-to-control additive technique would be combined with machine learning (ML) model-based control. Rapid serial experiments on the EHD produced a large dataset of training samples. ML models were trained on this data to predict the jetting and deposited feature characteristics of the EHD as a function of the input waveform. Transfer learning was demonstrated between EHD task datasets using a follow-the-leader gated mixture-of-experts ensemble, and zero-shot models were trained for use in a model-based control algorithm. Model-based control of the EHD, paired with a single proportional tuning adjustment, was able to achieve ~5 micron error when printing lines and ~1 micron error when printing dots, both over at least one decade of feature sizes. This capability enabled EHD printing of micron-scale ramified solar cell electrodes with ML-produced recipes. These steps demonstrate a complete journey from concept to prototype, in which computational tools model, optimize, and ultimately manufacture a novel engineered electrode structure to improve solar cell performance
Gender minority stress and depressive symptoms among transfeminine and gender non-conforming individuals in China: mediating and moderating roles of coping
No full textThesis (Master's)--University of Washington, 2023Objectives: Transgender and gender non-conforming populations in China experience disproportionately higher burdens of mental health issues such as depression compared to their cisgender counterparts. The gender minority stress and resilience model further elaborates on the original minority stress theory in an effort to understand how various experiences such as gender-based violence, discrimination and rejection impact the mental health outcomes and physical well-being of this population. This study investigates the role of three coping factors: gender minority resilience, social support, and gender-affirming hormone use, and their association between gender minority stress and depression.
Methods:
A cross-sectional study in 2019 recruited and collected data on a total of 277 transfeminine and gender non-conforming individuals from 9 different cities in China. Gender minority stress and resilience scores were collected using the validated Gender Minority Stress and Resilience Measure, with 45 items for gender minority stress (GMS) and 13 items for gender minority resilience (GMR). Depression was measured using the Center for Epidemiologic Studies Depression Scale (CES-D 10) short form with 10 items, previously validated among populations in Hong Kong. Social support was measured using a total of 12 items, including questions on support from family members and friends in forms of emotional and practical support. In regard to gender affirmation care use, we initially investigated both surgical procedures and gender-affirming hormones. However, given that eligibility criteria for surgical procedure in China intentionally excludes individuals with major depressive disorders, we decided to focus on hormone use as a proxy to gender-affirming care use. We then conducted complete case analyses (N = 258) using structural equation models to examine the mediating role of social support on the effects of gender minority stress on depression, with both exposure and outcome modelled as latent variables. We conducted an exploratory moderation analysis with sum-of-scores of gender minority stress, resilience, and depression scores to test for interaction between gender minority stress and resilience on its relationship with depression. Finally, we explored whether gender-affirming hormone use mediates the relationship between depression and internalized transphobia, a subconstruct of gender minority stress previously found to be associated with gender-affirming hormone use using this study data. Model fit of initial models is reported, but model alterations and re-specification were not explored, thus results are provisional. For all of our models, we adjusted for the following confounders: age (continuous), gender identity (transfeminine & gender non-conforming) and income (3 level nominal categories with 3000 RMB (Chinese currency) per interval). For convenience of interpretation, coefficients included in this abstract are all path coefficients standardized by the variance of both observed and latent variables for structural equation models.
Results:
As noted, given that model fit was not taken into consideration, the findings of this thesis are provisional and require further analysis. For our sample demographics, most of the participants in the study identified as transfeminine (72.1%) and 30.1% were earning less than or equal to 460 USD (3000 RMB) per month. The average CES-D score was 11.8 (out of 30) and over half (62%) would be considered at risk for clinical depression given the CES-D 10 threshold. GMR score was found to be associated with overall depression score (β =-0.411, 95% CI: -0.782 - -0.023). However, we found that GMS has an equally negative impact on one’s depression score regardless of one’s GMR score (Interaction term β = 0.000, 95% CI: -0.006 – 0.006). In our initial model, social support was found to be negatively associated with both GMS and depression in our structural models. Social support also partially mediated the association between GMS and depression in this model (Indirect effect β = 0.082, 95% CI: 0.018 – 0.154, proportion mediated = 17%). We did not find evidence for any association between hormone use and depression in our starting model (β = -0.238, 95% CI: -0.499 – 0.180) nor did we find a mediating effect of hormone use on the association between internalized transphobia and depression (β = -0.005, 95% CI: -0.011 – 0.003).
Conclusion:
This study highlighted the potential role of social support as a mediator between GMS and depression among transfeminine and gender non-conforming individuals in China. Given the exploratory and cross-sectional nature of the study, we were unable to make inference on the association between hormone use and future mental health outcomes. Further efforts of confirmatory analysis using this data to find a better fitting model is needed to confirm those results. We also suggest that gender minority resilience measured by the GMSR measures (community connectedness and identity pride) need to be considered under the specific legal and social environment in China when thinking about future studies or interventions in those areas. Future studies might consider various expressions of resilience, in conjunction with environmental factors, to form a comprehensive support system in helping this population with coping. The findings of this thesis, as they currently stand, should not be utilized to inform any social or policy recommendations and decisions or guide medical and public health programming efforts for this population
Interpretation Errors: Extracting Functionality from Generative Models of Language by Understanding Them Better
No full textThesis (Ph.D.)--University of Washington, 2023The rise of large language models as the workhorse of NLP, and the continuous release of better models (OpenAI, 2023; Pichai, 2023; Schulman et al., 2022, inter alia) has created a strange situation: we have models that are more powerful language generators than ever before, but since we did not design them for a specific purpose we struggle to understand how they should be used or what their idiosyncracies are. This dissertation describes three empirical projects that sought to characterize the underlying behavior of language models and, importantly, to make them more reliable tools for generating and selecting text where this behavior does not match up with the tasks we would like models to complete. Each project attempts to understand what language models and accompanying inference methods currently optimize for, to characterize the gap between that and the true objective of a potential user, and to close it with some new inference method. An emergent theme through these works is that models are already doing what we trained them to do quite well—and it is often the experimenters and practitioners who misunderstand precisely what we trained models to do in the first place. We conclude with a conceptual analysis of how we should study generative models going forward—as models keep improving and new, unanticipated uses and misuses become ever more available. The first half of this dissertation concerns two works, Neural Text Degeneration and Surface Form Competition—two failure modes of generative models that occur when probability is viewed as equivalent to “correctness” in text generation and multiple choice scenarios, respectively. For these works we describe the resultant issues, and propose inference methods that largely alleviate them. The second half of this dissertation goes deeper into the question of how generative models of language capture the communicative goals that humans are optimizing: first with Learning to Write, operationalizing communicative goals into auxiliary search objectives for text decoding, and then with Generative Models as a Complex Systems Science, which presents a framework to think about the study of generative models as NLP shifts to analyzing systems that are often infeasible to replicate. How does a model that is predicting the distribution of next tokens understand—and fail to understand—the structure of an essay? This is precisely the kind of question we must face head-on in the new science of generative models
THE EFFECTS OF IRON SUPPLEMENTATION ON PRIMARY PRODUCERS IN THE WESTERN EQUATORIAL PACIFIC OCEAN
No full textThis study investigates the effect of iron (FeCl3) incubation on phytoplankton growth in the western Equatorial Pacific, an area known for its high-nutrient, low-chlorophyll (HNLC) conditions, which limit primary production. The El Niño-Southern Oscillation (ENSO) shifts between El Niño and La Niña, with neutral periods between them. During El Niño, nutrient availability is decreased, leading to a lack of primary production. Iron is an essential nutrient for primary production, and the western Equatorial Pacific is known for its low iron availability. Therefore, I hypothesize that FeCl3 incubation will increase phytoplankton growth. To test this hypothesis, I collected water from various stations in the western Equatorial Pacific and incubated it with FeCl3. I monitored phytoplankton growth and nutrient concentrations through chlorophyll measurements and size fractionation. My results showed that FeCl3 incubation increased phytoplankton growth, supporting my hypothesis. Furthermore, I used a dilute acid test to determine chlorophyll-a concentration in the stimulated chlorophyll colonies. My results suggest that iron is a limiting ingredient in the western equatorial Pacific, and bioavailable iron increased phytoplankton biomass. In conclusion, my study provides evidence that iron incubation can enhance phytoplankton growth in HNLC regions of the western Equatorial Pacific. My findings suggest that iron availability is critical in regulating primary production in these areas. Understanding the biogeochemical processes that control primary production in HNLC regions can provide essential insights into global climate change and biogeochemical cycling
Toolkit: Accessible Activity Instructions for Those with Cognitive Disabilities
No full textThesis (Master's)--University of Washington, 2023Accessibility is an essential piece in making museums more welcoming to all who visit. Having accommodations are helpful for those that use or need them but accessibility benefits everyone. It can be overwhelming for museum educators to not know where to begin to make their museum programs and activities more accessible for those with cognitive disabilities such as Autism, severe mental illness, and even dementia. This toolkit aims to share information and resources that will help museum educators adapt program and activity instructions to be more accessible for no additional monetary cost using supplies and resources readily available in museums
Estimating subnational health and demographic indicators using complex survey data
No full textThesis (Ph.D.)--University of Washington, 2023Subnational estimates of health and demographic indicators such as immunization coverage rates and child mortality rates are critical for identifying regional health disparities and guiding policy design. When population data on an outcome of interest are unavailable or incomplete, many countries gather information from a sample of the population using household surveys. These surveys are typically designed for producing national estimates of key indicators, but generally do not collect sufficient data to produce reliable subnational estimates using traditional direct estimation methods, especially when estimating the prevalence of rare events. In this setting, indirect methods that use statistical models to incorporate covariate information or smooth estimates across areas using random effects can be effective for generating more precise estimates. However, national statistical offices and policymakers commonly desire estimators that are robust to model misspecification, making careful selection of methods crucial for producing estimates that are acceptable for dissemination and decision making. In recent years, geostatistical models which treat quantities of interest as continuous spatial surfaces have become popular among global health researchers for mapping key health indicators, especially for low- and middle- income countries. These approaches often compensate for limited data availability by leveraging advances in spatial modeling and incorporating newly available covariate information derived from satellite imaging, but may fail to account for features of the complex surveys used to collect data, such as informative sampling or cluster effects, potentially leading to biased estimates. On the other hand, traditional small area estimation approaches common in the survey statistics literature are typically specified with careful consideration for survey design, but have historically been adopted in countries where high-quality census data on auxiliary covariates are available and may perform suboptimally in low data settings.
In this thesis, I propose a suite of methods for estimating subnational health and demographic indicators using complex survey data. First, I propose an area level model for demographic rates that jointly models the direct estimators and associated variance estimators and induces spatial smoothing of both means and variances. This method can be viewed as an extension of the Fay-Herriot model popular for small area estimation that is adapted for estimation of small area proportions. Second, I outline a smoothed model-assisted estimator for small area means that incorporates unit level covariate information and smoothing via random effects while accounting for the survey design via the use of survey weights. Finally, I describe a method for incorporating sampling weights when estimating unit level models in order to address the effects of clustering and informative sampling
Engineering Intravenous Therapies for Trauma
No full textThesis (Ph.D.)--University of Washington, 2023Trauma leading to severe hemorrhage and shock on average kills patients within 3 to 6 hours after injury. With average prehospital transport times reaching 1-6 hours in low- to middle-income countries, stopping the bleeding and reversing hemorrhagic shock is vital. This thesis aims to develop a “Bridge to Blood” that combines PolySTAT, an intravenous hemostat that crosslinks fibrin at the wound site, with a Low Volume Resuscitant (LVR) designed to refill the vascular space after severe hemorrhage. For PolySTAT, the main goals of this work have been to continue its translation through the optimization of its water solubility and synthesis method (Chapter 2), to determine its safety and efficacy in large animal models (Chapter 3), and to understand mechanistically how PolySTAT affects coagulation (Chapter 4). All of these chapters support the clinical translation of PolySTAT and gather the data necessary for an Investigational New Drug Application with the FDA. For the LVR, the goal of this work was to show proof of concept of how to engineer polymer chemistry, structure, and architecture to provide the desired oncotic effect in vivo and to avoid disruption of coagulation in in vitro assays (Chapter 5). This chapter has set the foundation for the lab to engineer new LVRs and complete the “Bridge to Blood”. Chapter 6 demonstrates how to leverage the flexible nature of the PolySTAT platform, and its ability to target fibrin networks in vivo, to target to and activate CAR T Cells in solid tumors as a potential treatment for cancer. The final chapter, Chapter 7, outlines future work to build on the PolySTAT and LVR platforms in hopes of overcoming challenges identified from the work completed in the previous chapters
Understanding Processing-Structure-Mechanical Property Relationships in Sustainable Biopolymer-based Composites Using Design of Experiments and Machine Learning
No full textThesis (Ph.D.)--University of Washington, 2023Nature uses hierarchy as a means to imbue materials with mechanical and physical properties that are greater than the sum of its parts. Molecules are combined into polymers which bundle together into fibers that make up ever-larger structural building blocks that comprise the organism. Wood exemplifies this concept, with cellulose as the main polymer backbone supported by biopolymers such as hemicellulose, pectin, and lignin. These components act as binders to toughen the tree against environmental forces. Moreover, the resulting composite exhibits functional features, such as the ability to transport nutrients throughout the structure.One of the most common approaches to making synthetic biocomposites has been through the extraction and use of select components from biomass. However, despite the use of the same materials used by nature, the properties observed in natural composites have proven difficult to replicate. As an alternative approach to making better biocomposites, there has been a research push in recent years toward exploring the use of the entire organism as a component. The recently coined term “biomatter” refers to such materials wherein the entire biological matter is used without removing any components, retaining native or minimally altered hierarchical nano- and microstructures. Biomatter materials offer a promising solution to address the need for sustainable composites; none of the components within the material go to waste or lead to waste from inefficient extraction processes, and the mechanical properties have already been optimized against stressors by the hierarchical design inherent to natural materials. In addition, by shifting away from petroleum feedstocks and harnessing abundant, renewable biological resources, we can fabricate materials that align with sustainability goals.
Another approach to making better biocomposites has been to combine natural materials not normally found together in nature. By leveraging general chemical and physical principles, best-in-class materials can be combined to rationally design composite materials. However, we currently lack the knowledge to construct composites that maximize the utility of materials with intricate and dissimilar components. The work presented in this thesis tackles this issue by employing three different strategies to rationally build composites with biomatter building blocks.
We started by selecting model materials of varying complexity, ranging from molecules to polymers to entire cells, as composite components. For the cell building blocks, we opted for microalgae, specifically Spirulina platensis (“spirulina”) and Chlorella sp. (“chlorella”). These algae offer several advantages, including their abundance in industries such as biofuel, pharmaceuticals, and nutraceuticals, ease of culturing and growth, absence of complex tissue-like structures, and a diverse polymer composition of proteins, short and long polysaccharides, phenolics, and small molecules, all of which serve as valuable components for constructing polymer-based composites. We selected bacterial cellulose (BC) fibers grown from bacteria-yeast cocultures as a model structural polymer. BC fibers possess a high molecular weight, a high degree of crystallinity, and exceptional mechanical properties, owing to their inherent hierarchical structure. Notably, BC fibers are the sole product of this culture system, eliminating the need for separation and extraction after harvesting. Lignin, an abundant phenolic polymer found in woody biomass and a byproduct of the pulp and paper-making industries, was chosen to serve as a binder in our composite systems. Lignin's rich aromatic backbone and diverse functional groups allow for secondary interactions with glucans, like cellulose, making it an appropriate choice as a binder. Lastly, we incorporated stearic acid as a small, functional molecule. Stearic acid is a native component of many types of biomass, and its amphiphilic structure enables it to function as a plasticizer and/or surfactant within the composite system.
The three strategies chosen to guide the development of multiscale biomatter composites were a traditional trial-and-error approach, design of experiments, and machine learning. Using the trial-and-error approach, we investigated the effects of three different post-processing methods and the influence of plant-extracted micro-crystalline cellulose fibers (CFs) on the structure and mechanical properties of a spirulina-based matrix. We aimed to establish methods by which a single biomatter component could be used to create multiscale objects. The smallest scale consisted of the polymeric composite units inherent in the cell walls of spirulina. The next scale consisted of individual cells of spirulina. The subsequent scale consisted of physically-confined particles of spirulina chains. The final scale was dictated by the macroscale-controllable geometry extruded via a 3D printer.
We employed a design of experiments approach to explore how processing conditions influence the ex-vivo incorporation of lignin into a BC matrix in hierarchical lignocellulose papers. We used heat, pressure, and pressing time as parameters in a hot-pressing process to facilitate a systematic understanding of the processing-property relationships. Our analysis revealed optimal processing temperature/pressure/time conditions for tuning mechanical and water-repellency properties via structural changes in the processed papers.
Finally, we developed machine learning models based on a Bayesian Optimization approach to guide the search for optimal compositions of ternary composites for improved mechanical properties. Chlorella, BC, stearic acid, and a water-ethanol solution were combined, processed, and cast into films to facilitate understanding of the relationship between composition and the resulting mechanical properties of the composites.
We conclude with a summary of the benefits and disadvantages of using traditional experimental approaches, statistical designs of experiments, and machine learning models to design and fabricate next-generation, sustainable biocomposites