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Welcome to the Masquerade
Get PDFIn 1843 St. Louis introduced legislation against cross-dressing. Until 1986 these laws were used as a way to legally punish people for being genderqueer and/or queer. In light of the current anti-LGBT legisltion in the state of Missouri, it\u27s important to remember where this ideology comes from, and how it was fought.
Mendel Sato Research Award 202
Lifecourse Patterns of Productive Engagement among Rural and Urban Older Adults
Get PDFMany older adults are engaged in productive activities that have important ramifications for health in later life. However, little is known about rural-urban patterns of productive engagement across the lifecourse. This dissertation used six waves (2008, 2010, 2012, 2014, 2016, 2018) of the nationally representative Health and Retirement Study to identify patterns of working, volunteering, and caregiving activities over a ten-year period using multichannel sequence analysis and cluster analysis. The antecedents of the patterns were studied using multinomial logistic regression, and the associations of the patterns with longstanding rural-urban disparities in cognitive functioning and self-rated health were studied using multiple linear regression and ordinal logistic regression, respectively. This study found conceptually meaningful patterns of productive engagement that varied by rural/urban residence and age groups. Furthermore, rural respondents had a significantly lower likelihood than urban respondents of being in the pattern of ‘increasing high-intensity volunteering’ and the pattern of ‘decreasing part-time working,’ after controlling for gender, age, education, marital status, race, religious affiliation, income, and number of diagnosed health problems. Finally, the patterns of ‘increasing high-intensity volunteering,’ ‘decreasing full-time working and low-intensity volunteering,’ and ‘decreasing full-time working and high-intensity volunteering’ were significantly associated with higher cognitive functioning scores in 2018; the pattern of ‘decreasing full-time working and low-intensity volunteering’ was significantly associated with higher self-rated health in 2018; and the pattern of ‘steady caregiving and decreasing volunteering and working’ was associated with lower self-reported health in 2018. These findings may inform programs and policies aimed at narrowing rural-urban health disparities and increasing the productive engagement of rural and urban older adults
How Incentives Help Us Do Hard Things First
Get PDFWhen facing tasks of differing difficulty, do people choose to tackle harder ones first or easier ones first? I show that the answer depends on how motivated they are to complete all of the tasks. I examine situations in which people must complete both a harder task and an easier one by the same deadline. When I incentivize people for completing both tasks by that deadline, they are more apt to complete the harder task before the easier task, compared to when they are not incentivized. This change results from two factors. First, the incentive leads people to care more about completing the tasks in an order that leads to success. Second, people believe that the difficult-first order is more likely to lead to success than is the easy-first order. Thus, when people are incentivized, they find successful task completion to be more important, and they switch to the difficult-first order, which they think is more likely to lead to success. I discuss implications of these findings both for individuals, who may be interested in more efficiently tackling difficult challenges, and also for managers and firms, who may be interested in influencing which tasks their employees and customers undertake first
Empirically Grounding Analytics (EGA) for Commodity Markets: Study of the Hog Production Industry
No full textThis dissertation is inspired by a real-world problem of dynamic planning decisions in a hog farming setting, involving transactions in both the contractual and open markets with stochastic inventories and prices of inputs and outputs. I utilize real datasets and various dynamic optimization techniques to address the following topics: (i) Optimal inventory strategies, (ii) optimal optimization methods, and (iii) the role of risk preferences in optimal dynamic decision-making. In the first chapter, we use approximate dynamic programming techniques to solve a problem in agriculture. Specifically, we study a terminal phase in the growth trajectory of farm hogs, which ensues before the dispatch of hogs into the pork supply chain for trading and processing. Every week, the farmer must evaluate the number of hogs primed for sale at this juncture and assess the prevailing market prices. This requires making informed decisions on which hogs to sell in the open market, which to retain for an additional week, and which to deliver to a meatpacker. The farmer is contractually obligated to supply a specified quantity of hogs weekly to the meatpacker, priced under a predetermined market index. If the delivery falls short, a penalty proportionate to the deficit based on a market index is imposed. Bio-security protocols prevent the farmer from buying hogs on the open market and selling them to the meatpacker. The farmer can, however, use the open market to sell hogs at current market prices. This problem constitutes a dynamic, multi-item inventory challenge with stochastic prices and quantities of input and outputs. The inputs comprise piglets aged one month, that require approximately 22 weeks to reach market maturity. Their numbers vary due to birth and mortality rate fluctuations. The outputs consist of market-ready hogs classified into two weight categories: regular-weight and underweight. Their numbers vary due to inconsistent weight gain influenced by exogenous factors, such as weather. These two types of hogs exhibit pricing and feeding differences. Regular-weight hogs command a higher market price than underweight hogs, yet underweight hogs can rapidly appreciate due to the accumulation of valuable lean weight with continued feeding. On the contrary, regular-weight hogs predominately gain fat when fed, which is not highly valued in the market. The optimal policy is a threshold policy with multiple, price-dependent thresholds. The computational complexity required to evaluate the thresholds is the biggest impediment to using the optimal policy as a decision-support tool. So, we utilize an approximate dynamic programming approach that exploits the optimal policy structure and produces a sharp heuristic that is easy to implement. We utilize the real dataset provided by the hog producer to calibrate the model. We reveal that the optimal policy with the heuristically estimated thresholds substantially improves the existing practice (around 25% on average). The success of the proposed model is attributed to recognizing the value of holding under-weight hogs and effectively hedging supply uncertainty and future prices -- an insight missed in the planning actions of the current practice. The second chapter examines the same dynamic planning decisions but proposes an industrial solution deployable as decision support within a farming environment. The objectives for using the dataset differ between these two papers. The first paper uses classical dynamic programming techniques according to Puterman (2014) to derive an optimal policy structure, utilizing the dataset to generate illustrative numerical examples with realistic parameters. In contrast, the second paper directly utilizes the rich dataset to develop an AI livestock management support agent. Currently, the hog producer implements a heuristic always fulfill\u27\u27 (AF) policy to manage marketable hogs. This policy ensures the meatpacking contract is met under all circumstances, with any excess hogs sold on the open market. If a shortage in the supply of regular-weight hogs arises, underweight hogs are used as a substitute at a reduced price. Our study implements a Deep Reinforcement Learning (DRL) approach to find an alternative to the AF policy. Our research is generalizable beyond the specific application context, and we contribute to the existing literature by extending the use of neural networks into a complex application with continuous and unbounded spaces, and modifying an existing DRL algorithm to accommodate operational constraints. We also show how one can use other machine learning techniques, including optimal classification trees, to interpret the DRL agent\u27s actions to optimize the farm\u27s performance. Our numerical experiments show that their DRL agent outperforms the current heuristic policy by 24.94 percent on average. Compared to an exact dynamic solution derived for a smaller, analytically tractable operating horizon, the DRL agent is less than 1 percent worse on average. In the third chapter, we investigate risk management strategies for a risk-averse hog production farm, focusing on both inventory allocations and hedging in the futures market. Applying both a dynamic programming approach and an empirically grounded DRL approach, we aim to maximize the firm\u27s mean-variance utility dynamically and delineate the optimal integrated policy of inventory allocation and hedging. We find that a farmer who dynamically maximizes the mean-variance utility will keep a smaller inventory than a farmer who doesn\u27t consider risk. However, they will keep a larger inventory than a farmer who doesn\u27t have the option to hedge. This happens for two reasons. First, previous research (Kouvelis et al. 2023a) has shown that it can be a good strategy for farms that don\u27t consider risk to hold onto some hogs for longer periods of time to manage uncertain prices, costs, and yields. A risk-averse farmer will want to hold fewer hogs because doing so introduces more risk from potential future price changes and uncertain yields. Second, hedging reduces the overall risk for the farm, which lets the risk-averse farmer hold more hogs than they would have without hedging. However, this benefit of hedging gets smaller as the farm holds more and more hogs. We show that the decision to hedge revenue or costs depends on both operational and financial characteristics. Revenue hedging is more advantageous when there is high volatility in selling prices, a high correlation with factor markets, and large sales volumes. Conversely, cost hedging is more valuable in the presence of high volatility in operational costs, a high correlation with the fodder market, and large inventory sizes. We also find that a more risk-averse farm is likely to hedge costs, whereas a less risk-averse farm finds greater value in hedging revenue. Lastly, hedging is more valuable when the volatility of factor markets is lower. As the volatility of these markets increases, hedging loses its value since the risks from financial markets begin to propagate to the farm, contributing more variance to cash flow. The hedging strategies are difficult to evaluate analytically because the farmer\u27s problem is inherently dynamic. For this reason, we obtain the exact hedging policy through deep reinforcement learning (DRL)
Development of a Wearable Short-Wave Infrared Photoplethysmography Device for Detection and Monitoring of Hemodilution During Postpartum Hemorrhage
Get PDFABSTRACT OF THE THESIS
A Thesis on the Development of a Wearable Short-Wave Infrared Photoplethysmography Device for Detection and Monitoring of Hemodilution During Postpartum Hemorrhage
by
Hannah Gruensfelder
Master of Science in Biomedical Engineering
Washington University in St. Louis, 2024
Professor Christine O’Brien, Chair
Postpartum hemorrhage (PPH), the leading cause of maternal death and morbidity, affects nearly 14 million people worldwide each year, disproportionally impacting racial minorities and people in low resource settings. A timely diagnosis of PPH is key in providing optimal patient care, as an estimated 90% of deaths due to PPH are preventable with early diagnosis and treatment. Early diagnosis is especially critical where there is limited access to blood transfusion and surgical care. There are few tools for diagnosing and monitoring PPH, and it is currently diagnosed by visually estimating blood loss and monitoring vital signs. In many clinical settings, visual estimations significantly underestimate blood loss and often fail to detect internally retained blood. During early hemorrhage, patients experience sudden changes in vascular equilibrium, and the body compensates to keep the heart rate and blood pressure stable despite moderate to large blood loss volumes. In young, healthy patients, such as those of child-bearing age, vital signs may only begin to fluctuate with large levels of blood loss and can therefore reliably identify only late stages of PPH, by which time surgical intervention or blood transfusions are required. In addition to detecting and diagnosing PPH early, it is also extremely important to monitor patients after beginning treatment for PPH. Current treatment options include medications, blood transfusions, and surgical interventions such as laparotomy and hysterectomy, which all can present significant risks to the patient. It is imperative that patients be closely monitored after introduction of treatment, however, patient monitoring is currently limited to in-patient hospital care using serial blood draws and vital sign monitoring devices.
One compensatory mechanism during PPH is hemodilution, in which water from the extravascular space is drawn into circulation to increase blood volume, resulting in a reduced hemoglobin concentration. If hemodilution is severe, it can compromise the patient\u27s ability to oxygenate their tissues and cause coagulopathies, creating a medical emergency. There is an urgent need for a tool that can detect PPH early and monitor its progression, along with the introduction of treatments.
Absorption spectroscopy, already used in many optical device designs, is a promising technique for identifying the best wavelengths for noninvasive monitoring. However, absorption spectroscopy has historically covered only the visible (VIS) to near-infrared (NIR) wavelength ranges. Thus, many optical devices use VIS and NIR components, which can cause inaccurate results in people with skin darkly pigmented by melanin. Melanin strongly absorbs light at shorter wavelengths but is a weaker absorber at short-wave infrared (SWIR) wavelengths, making the SWIR a better wavelength range for illuminating skin with high melanin content. However, because little is known about how biological tissue absorbs in the SWIR, characterizing common biological absorbers spanning the VIS to the SWIR wavelength ranges will provide the spectra necessary to create SWIR optical devices.
Here, we describe the design and development of a prototype wearable SWIR photoplethysmography (PPG) device to detect and monitor hemodilution during and after PPH. This low-cost wearable hemodilution sensor will provide continuous monitoring for early diagnosis of PPH and will prevent dangerous blood loss. Importantly, the use of SWIR LEDs and detectors will minimize the effects of melanin absorption, minimizing the racial disparities affecting other light-based diagnostics. This device has high potential to be successfully translated as an inexpensive, fully wireless, wearable watch, finger clip, or necklace that will be accessible to patients across the world, particularly where PPH mortality is the highest
Robustness of Trajectory Prediction Neural Network Models
Get PDFThe application of autonomous vehicles in real life relies on trajectory prediction models based on perception and observation of the surrounding scene. The deep neural network model has been widely proven to provide relatively stable and excellent performance in various scenarios. Many formal approaches are used as verification of the prediction results of DNN models, where Conformal Prediction is one which can provide statistical safety guarantee region for DNN models. However, so far, no research has shown that conformal prediction possesses satisfactory robustness in dealing with purposed adversarial attacks. In this paper, we propose an adversarial attack approach against trajectory prediction models that use conformal prediction to provide verification of deep neural network model prediction. While satisfying the assumption of conformal prediction, our approach could lead the deep neural network to generate erroneous results that following our expectations without manually introducing a specific-designed target. We also demonstrate in simulation experiments the horrible consequences that this erroneous result would have in real-life application scenarios. To our knowledge, this is the first adversarial attack model against deep neural networks equipped with conformal prediction
Numerical Simulations of Supersonic/ Hypersonic Flows in Compression Corners and a Hypersonic Flow Study of Atmospheric Entry of Mars Science Laboratory Capsule
Get PDFThis thesis consists of two related parts. The first part is the study of supersonic/hypersonic flow in compression corners. The compression corners are simple geometries but rich in flow-features that can be challenging for accurate prediction of their flow fields in high-speed compressible flow using the Reynold-Averaged Navier-Stokes (RANS) equations in conjunction with a turbulence model. At higher degrees of corner angles, there exists a shock-boundary layer interaction region which includes a significant recirculation zone in the corner. In this thesis, experimentally available test cases for compression corner at Mach 3, 8, and 11 at various corner angles are modeled as 2D planar geometries and are simulated using the ANSYS Fluent. Results are compared to the experimental studies for the same flow conditions from Settles et al. and Smits and Muck at Mach 2.85 and from Holden et al. at Mach 8 and Mach 11 for various corner angles. The Spalart-Allmaras (SA), SST k-ω, and Wray-Agarwal (WA) turbulence models are employed in the study. The surface static pressure, heat transfer rate, and separation bubble in the corner are compared between the simulations and the experiments. Generally, it was found that the SA model’s accuracy begins to falter at the higher corner angles (\u3e 20+ degrees). It was found that SST k-ω and WA models are suitable for these flow conditions, as they can reproduce the trends seen in the experimental static pressure measurements. Additionally, it was found that the WA model shows good behavior in generating the major recirculation region that is present in the corner for the higher corner angles (\u3e20+ degrees).
The second part of the thesis consists of a hypersonic flow study of atmospheric entry of Mars Science Laboratory (MSL) Capsule. The MSL spacecraft architecture constituted the design of the Curiosity and Perseverance rover missions. The heat shield of the MSL capsule used a tiled PICA (Phenolic Impregnated Carbon Ablator) TPS (Thermal Protection System), with gap-filler material applied in the space between the tiles. During atmospheric entry at high Mach numbers, the instrumentation of the MSL heatshield reported a sharp rise in the net heating rate of the heatshield, which indicated an earlier-than-expected onset of turbulent flow over the heatshield. A CFD investigation was conducted to determine if the protrusion of the gap-filler after a period of ablation could be a significant factor in the onset of turbulent flow over the heat shield. The case was run using the resources of NASA Ames Research Center’s Advanced Supercomputing Division and employed NASA’s DPLR hypersonic CFD code. The results obtained suggest that the gap-filler protrusion is a significant factor
Likelihood Ratio Test for Markov Switching Parameters
Get PDFI propose a likelihood ratio test for fixed unit root against time switching unit root models. Our test is different from the existing jump detection literature centered on the application of various forms of Augmented Dickey-Fuller tests. Our methodology involves the inclusion of random coefficients, which effectively capture both expansion and contraction behaviors. We show that the contiguous alternatives converge to the null hypothesis at the order of , where is the sample size. Our test is asymptotically optimal in the sense that it maximizes a weighted power function. We derive the asymptotic distribution of our test under the null and local alternatives
Essays in Macroeconomics and Financial Economics
Get PDFThis dissertation consists of three independent articles in the fields of Macreconomics and Financial Economics. Chapter one investigates the determinants of the demand for bonds of different maturities and the relationship with differences in idiosyncratic risk in an heterogeneous agent framework. Chapter two studies the effect of policy instability on the risk-return trade-off of different financial assets. Chapter three studies macroeconomic risk in an incomplete market economy. In the first chapter Heterogenous Liquidity Demand and the Term Structure of Interest Rates I study what determines differences in the demand for bonds of different maturities. I focus on the effect of differences in idiosyncratic risk. I find evidence that relates the demand for bonds of different maturities with earnings risk. To provide a rationale for these findings, I build a continuous-time, general equilibrium model with heterogeneous agents, two assets and incomplete markets. The model succesfully reproduces the fact that high idiosyncratic risk is associated with high demand for short-term assets, while low idiosyncratic risk is related to high demand for long-term assets. The second chapter Policy Instability and the Risk-Return Trade-Off , coauthored with Rodolfo Manuelli, we study what is the impact of large swings in economic policy on the risk-return trade-off faced by investors. We use data from Argentina---a country that has experienced frequent and very large regime changes---and find that the risk-return for individual assets and minimum variance portfolios are quite different across regimes. We then develop a dynamic model to understand optimal portfolios when investors are cognizant that regimes can change. We find that when portfolios are unrestricted, it is optimal for investors to take a large amount of risk. On the other hand, when portfolios are restricted to include only long positions, a real asset (real estate) dominates financial assets. The third chapter Incomplete Markets and Macroeconomic Risk analyze the equilibrium dynamics of asset prices, investment and risk premia in an incomplete financial market economy that is subject to aggregate risk shocks. It also addresses the question on how economic conditions endogenously affect risk in the economy. To this end, I use a continuous time macroeconomic model with financial frictions. The main findings of this article are that an exogenous increase in aggregate risk causes an increase in asset price volatility, an increase in risky asset returns through an increase in risk-premia, a decline in asset prices, investment and risk free interest rates. Moreover, the model presented here is able to reproduce counter-cyclical endogenous risk. This results also depend on how constrained are financial intermediaries. If financial intermediaries are constrained some of the results are amplified
The structural biology of gamma-4 CUP pili
No full textGram-negative bacteria can cause a variety of human infectious diseases, and urinary tract infections (UTIs) are a common disease type caused by these bacteria. Such infections predominantly affect women and are frequently recurrent. UTIs are becoming more difficult to treat due to increasing antibiotic resistance. Thus, in order to develop new anti-virulence strategies, we must gain greater insight into the virulence factors which contribute to uropathogenesis. Gram-negative bacteria use chaperone usher pathway (CUP) pili, tipped with adhesive proteins, called adhesins, to mediate host tissue adherence in particular and uropathogenesis in general. CUP pili are phylogenetically organized into distinct clades. The gamma-4 clade is composed of pili from a variety of Gram-negative genera, including Acinetobacter and uropathogenic Escherichia. A. baumannii makes use of two gamma-4 pili, Abp1 and Abp2, in combination to colonize the fibrinogen-coated catheter and bladder in a mouse model of A. baumannii catheter-associated urinary tract infection (CAUTI). These two pili contribute, to varying degrees, to A. baumannii biofilm formation. The tip adhesins of these pili, Abp1D and Abp2D, are able to bind fibrinogen, an important host protein in CAUTI pathogenesis, and collagen IV, a ubiquitous host xiii glycoprotein. Molecularly, they bind overlapping receptors. The crystal structures of Abp1D and Abp2D were solved and reveal highly structurally similar proteins. Their binding pockets contain a conserved anterior loop motif which is flexible and has the ability to open and close the pocket. This is regulated by an intramolecular interaction between residues on the underside of the anterior loop. Disruption of this interaction favors a closed pocket conformation in silico and increases the binding affinity of the protein. In uropathogenic E. coli, the gastrointestinal (GI) tract serves as a reservoir for UTIs, and uropathogenic E. coli uses CUP pili to mediate GI tract colonization. The Yeh pilus contributes to GI tract colonization fitness through the binding of GI tract luminal contents. In particular, the Yeh pilus adhesin, YehD, binds to pectin, a complex polysaccharide contained within plant material. The Yeh crystal structure reveals a receptor binding domain core and a novel alpha-helical flap motif that runs the length of the core. A hydrophobic region at the distal end of the flap contributes to holding it in a tucked conformation. Furthermore, a preliminary comparative study of the Yeh, S, Yqi, and F17-like pilus rods revealed CUP pilus rod quaternary structural diversity, with the gamma-4 Yeh and F17-like pili exhibiting a zigzag helical rod motif, demonstrating that this quaternary structure motif is present within classical CUP pili. This work has contributed to our understanding of the structural and functional diversity of gamma-4 CUP pili and their roles in Gram-negative infection and colonization