Scholarship & Research
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Item Effects of reactive balance training on joint health(Montana State University - Bozeman, College of Education, Health & Human Development, 2022) Peart, Dakota Paul; Chairperson, Graduate Committee: David GrahamFalls are a major cause of mortality and morbidity among older adults. One of the major risk factors of falls is impaired neuromotor function, which can be addressed through conventional exercise programs. While beneficial for many aspects of health, conventional training does not appear to greatly reduce the incidence of falls. More recently, reactive balance training (RBT) has emerged as a task-specific exercise that is highly effective and efficient at reducing subsequent fall rates. However, little is known about the tissue-level effects that this high-impact exercise may have on the joints of participants. Overtraining by performing RBT at excessive volumes or intensities could feasibly cause damage and degradation of joint-related tissues, potentially leading to discomfort and even post-traumatic osteoarthritis. Such processes are driven by mechanisms featuring general and tissue specific signaling molecules, and also yield tissue-specific breakdown products. To explore the possibility of joint damage resulting from reactive balance training, healthy middle-aged adults performed varying amounts of RBT, and the resulting signaling responses were observed. It was found that RBT does induce a prominent biochemical response, and the nature and magnitude of the response appears to be influenced by the volume and intensity of training performed.Item Cessna 182b windscreen material model development and full scale UAS to aircraft impact testing facility(Montana State University - Bozeman, College of Engineering, 2020) Arnold, Forrest Jacob; Chairperson, Graduate Committee: Douglas S. CairnsUnmanned Aircraft Systems (UAS) have become popular in the last decade. More than 1.5 million have been registered by the Federal Aviation Administration (FAA) since 2015. In order to understand the risk UAS pose to manned aircraft and make informed regulation decisions, the FAA has created air to air collision studies. As a part of the FAA general aviation air to air collision research, a Cessna 182 windscreen material model and a full scale impact testing facility were required. A Finite Element Crash Model of a Cessna 182 is in development as a part of the general aviation air to air collision research. The National Institute for Aviation Research at Wichita State University is managing development of the model. In support of that work, an LS-DYNA material model of the Poly(Methyl methacrylate) windscreen was developed. Results from tensile testing at multiple strain rates were used to develop material models using MAT_124 and MAT_187. A model of an impact tower was created to compare the material models to test results. The material models were tuned to better fit the impact tower test results. MAT_187 has more flexible material inputs, which allowed it to outperform MAT_124. A full scale impact testing facility was developed to support Finite Element model validation and direct testing of UAS to aircraft impact. A slingshot style launcher was designed and built to launch common quadcopter style UAS. Testing has shown that the launcher is capable of 120 knots with the accuracy required to repeatably hit the leading edge of a wing. Additionally, the launch site required a system for instrumented testing to compare experimental results with finite element results. A system was developed to allow flexible fixturing, impact speed and orientation measurement, and inclusion of load cells and strain gauges.Item An investigation modeling risk of wildlife-vehicle collisions in Montana, USA(Montana State University - Bozeman, College of Engineering, 2019) Bell, Matthew Andrew; Chairperson, Graduate Committee: Yiyi Wang and Damon Fick (co-chair)Road ecologists and transportation engineers have been exploring new methods to adapt to the environmental and motorist safety concerns involving wildlife-vehicle collisions. There are over one-million crashes with large-bodied wildlife every year in the U.S. that result in substantial property damage and personal injuries. Recent studies modeling these collisions identify where they cluster, and the landscape, road, and driver characteristics that influence the likelihood of a collision along short road corridors and small geographic regions. This research expands on current knowledge and attempts to model the risk of wildlife-vehicle collisions on a large geographic scale. This research investigates different analysis methods and creates predictive models that will estimate the risk of a wildlife-vehicle collision as drivers travel across multiple ecosystems. Different analysis units were created to extract two similar datasets that are modeled against two different response variables -- reported collisions and roadkill locations. Regularization is used to help with feature selection. Negative binomial regression models are built to predict risk. Random forest machine learning helps better understand the percent of variance explained by the variables in each model. A range of statistical measurements were taken to compare the non-nested models. The best performing model is applied to the seasonal division of data. Yearlong and seasonal risk is mapped onto the road network and color-coded to show the differences in risk on Montana's road network. The maps capture the changes in risk throughout the year, they generally match where wildlife-vehicle collisions actually happen, and even coincides with published work on the locations of collision hotspots in Montana. This research is the basis for future complex real-time risk-mapping models that can be integrated into smart technology and developed into on-board driver alert systems. With the advancements of autonomous vehicle, it is possible to incorporate real-time driving data into models that will analyze wildlife-vehicle collision risk based on vehicle location, season, time of day and driving habits. This can increase driver safety by informing them when they are traveling in areas where wildlife-vehicle collisions are more likely to happen, and can be especially helpful while driving on unfamiliar roads.Item Intelligent countermeasures in ungulate-vehicle collision mitigation(Montana State University - Bozeman, 2002) Farrell, Justin Edward; Chairperson, Graduate Committee: Lynn R. IrbyItem Label symbols and simulated poison bottle interaction among preschoolers(Montana State University - Bozeman, College of Letters & Science, 1971) Holt, Sonja BunkeItem Descriptive analysis of selected alignment factors of the lower extremity in relation to lower extremity trauma in athletic training(Montana State University - Bozeman, College of Education, Health & Human Development, 1976) Lilletvedt, Janice MarieItem An accident prediction model for highway-rail interfaces(Montana State University - Bozeman, College of Engineering, 2000) Austin, Ross DuaneItem An analysis of the Arizona high school athletic insurance program(Montana State University - Bozeman, College of Education, Health & Human Development, 1970) McCormick, Michael LorenItem Factors affecting bear and ungulate mortalities along the Canadian Pacific Railroad through Banff and Yoho national parks(Montana State University - Bozeman, College of Agriculture, 2011) Dorsey, Benjamin Paul; Chairperson, Graduate Committee: Lisa J. Rew.Railroads, roads and associated traffic have been shown to adversely affect ecosystems by killing wildlife and altering the landscape. Relatively little research has been conducted along railroads. Given the probable growth of railroads, it is imperative that we understand the impacts railroads exhibit on wildlife. In this thesis, I reviewed the documented impacts of railroads on wildlife then conducted analyses on data collected along the Canadian Pacific Railroad (CPR) through Banff and Yoho National Parks (~134 km). In the study area, over 1000 train strikes with 26 mammal species have been recorded between 1990 and 2010, which included 579 elk (Cervus elaphus), 185 deer (Odocoileus spp.) and 79 bears (Ursus spp.). The goal of this research was to provide an initial assessment of the factors affecting strikes with ungulates and bears along the CPR. To accomplish these goals, I studied four general factors that have been hypothesized to affect the rate and spatial distribution of strikes. These are: wildlife abundance, anthropogenic foods, and railroad design. I compared strike rates along three mile long rail segments to train spilled grain, train and railroad design variables. I developed an estimate of risk using line transect data so that I could determine if there was evidence for nonconstant strike risk. Statistical models were used to identify which factors best explained strike rates. I detected correlations between the density of train-spilled grain and bear foraging rates but not with bear strikes. I identified locations where corrective measures or mitigation solutions may be needed and identified railroad designs and landscape variables associated with those locations. Hotspots were identified for elk and deer but not bears. Relative abundance was generally correlated with strike rates. High risk locations, where more strikes occurred than were expected, were identified. Train speed limit and right-of-way width was positively associated with strikes for elk and/or deer. For bears, the number of structures (e.g. highway overpasses) and bridges were positively associated with strikes. These results were used to suggest management recommendations including train speed reductions, habitat modifications and railroad design alterations to reduce the risk of strikes.