Theses and Dissertations at Montana State University (MSU)
Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/733
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Item Increasing recommended testing compliance for persons with type II diabetes in primary care(Montana State University - Bozeman, College of Nursing, 2024) Fleming, Brandi Lynn; Chairperson, Graduate Committee: Elizabeth A. Johnson; This is a manuscript style paper that includes co-authored chapters.Background: Type II diabetes affects one in 14 Montanans (Centers for Disease Control and Prevention (CDC), 2023). The CDC estimates annual direct and indirect costs of diabetes in Montana exceed $800 million (2023). Constraints persist when incorporating National Quality Forum measures and Healthy People 2030 objective guidance to address known challenges in managing Type II diabetes in a community setting due to minimal resources and lack of workflow appraisal. The rurality and radical weather patterns in Montana pose challenges for sustaining healthy diets and regular exercise. Purpose: The quality improvement project aims at generating consistent clinical decision support system (CDSS) electronic health record platform (EHR) reminders, streamlining workflow processes, and delaying Type II diabetes' concomitant conditions. Methods: A Plan-Do-Study-Act (PDSA) cycle employing Amazing Charts EHR to consistent clinical decision support system reminders, workflow process modification, and shared decision-making interventions. Purposive sampling included persons with Type II diabetes, 18-75 years, presenting for an annual visit type encounter. Interventions: Rule query preference entry and workflow process modification were monitored to a short-term goal benchmark of 90% for completion of recommended testing for persons with Type II diabetes. Data collection evaluated generation of CDSS reminders and annual completion of comprehensive foot examinations, urine microalbumin to creatinine ratio testing, and dilated eye examinations. Results: A total of six patients participated in the project, n = 5 met criteria for Type II diabetes diagnosis, n = 1 miscoded. The EHR generated CDSS reminders, and staff completed annual comprehensive foot examinations 83.33% of eligible encounters. Urine microalbumin testing was completed 66.63% of eligible encounters with n = 1 (16.33%) deferred testing until their annual visit. Strengths emerging from Strengths, Weakness, Opportunities, and Threats (SWOT) analysis included simple streamlined guidelines that promote teamwork. Conclusion: Consistent CDSS reminder facilitates recommendation completion, benefiting patients and providers. Although short term goals were not achieved at the 90% benchmark, the project is deemed clinically significant representing the homogeneity of Montanans. Future recommendations include participation in Merit-based Incentive Payment System (MIPS), extension of interventions for utilization of other chronic diseases, and integration of Current Procedural Terminology (CPT) codes for reimbursement for services.Item Quanitfying snow depth distributions and spatial variability in complex mountain terrain(Montana State University - Bozeman, College of Letters & Science, 2021) Miller, Zachary Stephen; Chairperson, Graduate Committee: Eric A. SprolesThe spatial variability of snow depth is a major source of uncertainty in avalanche and hydrologic forecasting. Identification of spatial and temporal patterns in snow depth is further complicated by the interactions of complex mountain topography and localized micro-meteorology. Recent studies have dramatically improved our understanding of snow depth spatial variability by utilizing increasingly accessible remote sensing technologies such as satellite imagery, terrestrial laser scanning, airborne laser scanning and uninhabited aerial systems (UAS) to map spatially continuous snow depths over a variety of spatiotemporal scales. However, much of this work focuses on relatively low-relief topographies or limited temporal frequencies. Our research presents a thorough evaluation of the evolution of snow depth spatial variability at the slope scale in steep complex mountain terrain (45.834 N, -110.935 E) using analysis from UAS imagery. We apply 13 spatially complete UAS-derived snow depth datasets collected throughout the course of the 2019/2020 winter to analyze spatial and temporal patterns of snow depth and snow depth change variability. Our results show greater spatial variability in steep complex mountain terrain than an adjacent mountain meadow both in the seasonal context and during individual meteorological periods. We analyze 2 cm horizontal resolution snow depth models by (i) comparing spatial patterns with coincident meteorological data, (ii) analysis of the temporal elevation specific patterns of snow depth, and (iii) a comprehensive multi-scalar evaluation of spatial variability. We quantify the unique spatial signature of four specific events: a major snow accumulation, a natural avalanche, a calm period, and a significant wind event. We find a non-linear relationship between elevation and snow depth, with upper elevations proving to be the most variable. We also verify that significant storm events result in the largest snow depth change variability throughout our study area, as compared to other meteorological events. The synthesis of these findings illustrate the dynamic spatial and temporal snow depth distribution patterns observed in complex mountain terrain during the course of a winter season. These findings are relevant to avalanche forecasters and researchers, snow hydrologists and local water resource managers, and downstream communities dependent on snow as a hydrologic reservoir.Item Extracting abstract spatio-temporal features of weather phenomena for autoencoder transfer learning(Montana State University - Bozeman, College of Engineering, 2020) McAllister, Richard Arthur; Chairperson, Graduate Committee: John SheppardIn this dissertation we develop ways to discover encodings within autoencoders that can be used to exchange information among neural network models. We begin by verifying that autoencoders can be used to make predictions in the meteorological domain, specifically for wind vector determination. We use unsupervised pre-training of stacked autoencoders to construct multilayer perceptrons to accomplish this task. We then discuss the role of our approach as an important step in positioning Empirical Weather Prediction as a viable alternative to Numerical Weather Prediction. We continue by exploring the spatial extensibility of the previously developed models, observing that different areas in the atmosphere may be influenced unique forces. We use stacked autoencoders to generalize across an area of the atmosphere, expanding the application of networks trained in one area to the surrounding areas. As a prelude to exploring transfer learning, we demonstrate that a stacked autoencoder is capable of capturing knowledge universal to these dataspaces. Following this we observe that in extremely large dataspaces, a single neural network covering that space may not be effective, and generating large numbers of deep neural networks is not feasible. Using functional data analysis and spatial statistics we analyze deep networks trained from stacked autoencoders in a spatiotemporal application area to determine the extent to which knowledge can be transferred to similar regions. Our results indicate high likelihood that spatial correlation can be exploited if it can be identified prior to training. We then observe that artificial neural networks, being essentially black-box processes, would benefit by having effective methods for preserving knowledge for successive generations of training. We develop an approach to preserving knowledge encoded in the hidden layers of several ANN's and collect this knowledge in networks that more effectively make predictions over subdivisions of the entire dataspace. We show that this method has an accuracy advantage over the single-network approach. We extend the previously developed methodology, adding a non-parametric method for determining transferrable encoded knowledge. We also analyze new datasets, focusing on the ability for models trained in this fashion to be transferred to operating on other storms.Item Atmospheric processes related to deep persistent slab avalanches in the western United States(Montana State University - Bozeman, College of Letters & Science, 2019) Schauer, Andrew Robert; Chairperson, Graduate Committee: Jordy HendrikxDeep persistent slab avalanches are a natural hazard that are particularly difficult to predict. These avalanches are capable of destroying infrastructure in mountain settings, and are generally unsurvivable by humans. Deep persistent slab avalanches are characterized by a thick (> 1 m) slab of cohesive snow overlaying a weak layer in the snowpack, which can fail due to overburden stress of the slab itself or to external triggers such as falling cornices, explosives, or a human. While formation of such snowpack structure is controlled by persistent weather patterns early in the winter, a snowpack exhibiting characteristics capable of producing a deep persistent slab avalanche may exist for weeks or months before a specific weather event such as a heavy precipitation or rapid warming pushes the weak layer to its breaking point. Mountain weather patterns are highly variable down to the local scale (1-10 m), but they are largely driven by atmospheric processes on the continental scale (1000 km). This work relates atmospheric circulation to deep persistent slab events at Mammoth, CA; Bridger Bowl, MT; and Jackson, WY. We classify 5,899 daily 500 millibar geopotential height maps into 20 synoptic types using Self-Organizing Maps. At each location, we examine the frequency of occurrence of each of the 20 types during November through January during major deep persistent slab seasons and compare those frequencies to seasons without deep persistent slab avalanches. We also consider the 72-hour time period preceding deep persistent slab avalanches at each location and identify synoptic types occurring frequently, as well as those rarely occurring prior to onset of activity. At each location, we find specific synoptic types that tend to occur at a higher rate during major deep persistent slab years, while minor years are characterized by different circulation patterns. We also find a small number of synoptic types dominating the 72-hour period prior to onset of deep slab activity. With this improved understanding of the atmospheric processes preceding deep persistent slab avalanches, we provide avalanche practitioners with an additional tool to better anticipate a difficult to predict natural hazard.Item Impacts of weather, habitat, and reproduction on the survival and productivity of wild turkeys in the northern Black Hills, South Dakota(Montana State University - Bozeman, College of Letters & Science, 2019) Yarnall, Michael James; Chairperson, Graduate Committee: Andrea Litt; Andrea R. Litt, Chad P. Lehman and Jay J. Rotella were co-authors of the article, 'Precipitation and reproductive effort combine to alter survival of wild turkey hens in the northern Black Hills, SD' submitted to the journal 'Journal of wildlife management' which is contained within this thesis.; Andrea R. Litt, Chad P. Lehman and Jay J. Rotella were co-authors of the article, 'Impacts of weather on reproductive productivity of wild turkeys in the northern Black Hills, SD' submitted to the journal 'Journal of wildlife management' which is contained within this thesis.The study of population ecology is motivated by a desire to understand variation in the factors that drive wildlife population dynamics. Robust vital rate estimates are crucial for effective wildlife conservation and management, particularly for at-risk or harvested species. In avian populations, the survival of females, nests, and young are important drivers of population growth, although the relative importance of each rate can differ among species. Annual and regional variation in vital rates within species is common; further, local climatic and habitat conditions may influence population dynamics. During 2016 - 2018, we used radio telemetry to study the impacts of weather and habitat conditions on the survival and productivity of Merriam's wild turkeys (Meleagris gallopavo merriami) in the northern Black Hills of South Dakota. Specifically, we quantified the impacts of 1) precipitation and reproductive effort on hen survival, 2) precipitation and habitat conditions on nest survival, and 3) precipitation and temperature on early poult survival. Precipitation reduced the survival of hens and nests, although the magnitude depended on the hen's incubation status or the vegetation characteristics at the nest site. Based on precipitation data from 2017, the estimated annual survival rate for a hen that did not incubate was 0.535 (SE = 0.038), whereas that and for a hen that incubated for 26 days was 0.436 (SE = 0.054). The probability that a nest would survive from initiation to hatching for a nest initiated by an adult hen on the median date of nest incubation in 2017 was estimated to be 0.432 (SE = 0.084). The estimated probability that a poult would survive from hatching to 4 weeks of age was 0.387 (SE = 0.061). Our results clearly demonstrate a negative cost of reproduction, as predicted by life-history theory, and show that hens and nests in this ecosystem are more vulnerable to predation during or immediately following rainfall, as predicted by the moisture-facilitated nest-predation hypothesis. Survival and productivity of turkeys was lower in our study area than in other portions of the Black Hills; we recommend that managers take steps to limit human-induced hen mortality of this important game species.Item Empirical assessment of a congestion and weather-responsive advisory variable speed limit system(Montana State University - Bozeman, College of Engineering, 2016) Siddiqui, Sohrab; Chairperson, Graduate Committee: Ahmed Al-KaisyTraffic congestion and safety along urban corridors have become major challenges for most highway agencies in the United States. Adverse weather conditions also present a considerable challenge, both in terms of safety and operations. All these problems along with the increasingly limited resources for infrastructure expansion have urged transportation agencies to investigate innovative traffic management approaches. One of these approaches is the use of Active Traffic Management (ATM) strategies. Within ATM, the practice of Variable Speed Limit (VSL) systems is well suited to improving safety and operations. These systems dynamically utilize real-time traffic and/or weather data to post appropriate speeds that are thought to improve safety and operations along a corridor. The overall aim of this thesis is to investigate the benefits of a recently installed advisory VSL system along OR-217 freeway in Portland, Oregon. This corridor is characterized by high traffic levels, severe congestion and unreliable travel times. The congestion of the freeway contribute to crash rates exceeding the statewide averages for this type of facility. Pacific Northwest's unpredictable climate presents another challenge that doubles the congestion and safety problems along the corridor. The effectiveness of this system was explored through an in-depth 'before and after' and 'on-and-off' analyses. The study was designed in a way that it encompasses both the safety and mobility benefits of the system. Besides, driver compliance with the system was also measured under different scenarios. The results indicated that the system had significant impacts on both mobility and safety. In terms of mobility it was found that system had lowered the average speeds along the corridor. The advisory VSL activation also resulted in reduced capacities. Safety assessment of the system suggested that, VSL has decreased crash rates and temporal and lateral variations of speed. Under certain scenarios, the system also decreased the longitudinal variations of speed. Further, it was also found that due to the advisory nature of the system, the majority of drivers do not comply with the system. However, VSL has resulted in reducing the percentage of aggressive drivers and have increased the number of drivers complying the static speed limit.Item Validation of a coupled weather and snowpack model across western Montana and its application as a tool for avalanche forecasting(Montana State University - Bozeman, College of Letters & Science, 2016) Van Peursem, Kyle Webb; Chairperson, Graduate Committee: Jordy HendrikxPredicting avalanche danger depends on knowledge of the existing snowpack structure and the current and forecasted weather conditions. In remote and data sparse areas this information can be difficult, if not impossible, to obtain, increasing the uncertainty and challenge of avalanche forecasting. In this study, we coupled the Weather Research and Forecasting (WRF) model with the snow cover model SNOWPACK to simulate the evolution of the snow structure for several mountainous locations throughout western Montana, USA during the 2014-2015 and 2015-2016 winter seasons. We then compared the model output to manual snow profiles and snow and avalanche observations to assess the quantitative and qualitative accuracy of several snowpack parameters (grain form, grain size, density, stratigraphy, etc.) during significant avalanche episodes. At our study sites, the WRF model tended to over-forecast precipitation and wind, which impacted the accuracy of the simulated snow depths and SWE throughout most of the study period. Despite this, the SNOWPACKWRF model chain managed to approximate the snowpack stratigraphy observed throughout the two seasons including early season faceted snow, the formation of various melt-freeze crusts, the spring transition to an isothermal snowpack, and the general snowpack structure during several significant avalanche events. Interestingly, the SNOWPACK-WRF simulation was statistically comparable in accuracy to a SNOWPACK simulation driven with locally observed weather data. Overall, the model chain showed potential as a useful tool for avalanche forecasting, but advances in numerical weather and avalanche models will be necessary for widespread acceptance and use in the snow and avalanche industry.Item The relationship between depression and suicide crisis calls and weather conditions(Montana State University - Bozeman, 1978) Roggia, Richard PeterItem Meteorological metrics associated with deep slab avalanches on persistent weak layers(Montana State University - Bozeman, College of Letters & Science, 2014) Marienthal, Alex Grayson; Chairperson, Graduate Committee: Jordy HendrikxSnow avalanches are a potentially fatal and highly destructive natural hazard. Snow slab avalanches occur in steep alpine terrain due to an unstable layered snowpack. When a consolidated layer of snow forms a slab above a weak layer of snow the slab may collapse and slide downhill due to gravitational and applied forces (e.g., the weight of a skier, explosive, or new snowfall). Persistent weak layers form in the snowpack due to strong vapor pressure gradients, and they can last for weeks to months as a slab builds above them. Avalanches on persistent weak layers become less frequent, yet are typically larger and more destructive the longer and deeper the layer is buried. Deep slab avalanches on persistent weak layers pose a difficult forecasting problem due to their low likelihood of occurrence and potentially high consequences. This thesis aims to identify meteorological metrics that are associated with deep slabs on persistent weak layers. We used univariate analysis, classification trees, and random forests to explore relationships between seasons with deep slabs and summaries of meteorological metrics over the beginning of the season during weak layer formation. We also looked at the relationship between days with these avalanches and summaries of meteorological metrics over the days prior to them. In addition, we reviewed a case study of a season that had multiple deep slabs on a persistent weak layer and a historic wet slab avalanche cycle on the same layer, at Bridger Bowl ski area. Seasons with deep slabs typically had relatively low precipitation throughout the early part of the season (i.e., November - January), and a snowpack in the beginning of the season that was sufficiently deep, but shallow enough for a weak layer to develop. Our results also showed warmer twenty-four hour temperatures and more precipitation over seven day prior to days with dry deep slabs, and extended periods of above freezing temperatures were seen prior to days with deep wet slabs. These results are in line with previous research and are suggestive of meteorological summaries that may be useful to forecast deep slab avalanches on persistent weak layers.Item Weathering Montana : the social meanings of extreme environments in the Big Sky(Montana State University - Bozeman, College of Letters & Science, 2002) Conradt, Kevin; Chairperson, Graduate Committee: Mary MurphyHistorians have largely ignored the influence of weather and climate on people. In Montana, this has certainly been the case. In a state where meteorological stability is ephemeral, society is consistently challenged by the extreme nature of Montana’s environment. In my thesis I argue that the term weather, which is a social construction, is flawed for assessing Montana’s meteorological instability because it relies on a methodology that sees temperature and precipitation in average or normal conditions. I also argue that the extreme nature of Montana’s environment has helped to shape the societal infrastructure of the state, which has in turn strengthened the Treasure State’s historical narrative. This nascent methodology requires a comprehensive understanding of meteorology from a state, regional, and global perspective. The combination of latitude, atmospheric circulation, land-water distribution, and topography act in concert to create the variability associated with Montana’s natural environment. From a societal perspective I have relied on a combination of primary and secondary source information to interpret the perceptions of people and their relationship to Montana’s natural environment. The human journey in Montana has historically been influenced by the severe nature of the state’s weather and climate. From Native Americans to Euro-Americans, evidence of societal development in Montana, especially in agrarian enterprises, indicates that the construction of place has been largely influenced by the meteorological variability of Montana’s natural environment. As long as Montana’s natural environment continues to be influenced by meteorological instability, people will continue to challenge themselves against an environment of extremes. My hope is that future scholars interpreting the bond between people and weather will help to strengthen the methodology linking human beings with their nature environment.