Master of Science

thesisThis research explores the potential for an "intelligent" orthotic shoe sole to negate, or minimize, longitudinal slip by momentarily increasing friction force. The conceptual device takes the form of a rubberized shoe sole containing pockets of air that can be released via valves controlled by a microprocessor. During a slip event, the valves would be opened and the bladders would be collapsed by the weight of the user, which modulates contact and friction forces. The goal is to increase friction forces in this process, by creating an impact force between the user and ground surface, with the potential to achieve stiction and stop slip. The research first explores how design of the shoe sole can best compensate for longitudinal slip. Lumped parameter models of the system are developed to model bladder behavior and airflow through the system, which are then applied to optimize a prototype design. A friction model specific for two sliding surfaces is developed using basic coulombic friction equations. Simulations and experiments indicated flow rate was a limiting factor using existing valves, but experiments without valves confirmed that impact forces and friction forces can be increased by the system. The impulse created during impact creates a large spike in normal force, which translates into a spike in coulombic friction force that can be mathematically shown to reduce slipping velocity. The spike also causes an increase in Coefficient of Friction (COF) with the shoe and ground surface that, with surface specific testing, can be used to shift the sliding foot from a potentially dangerous kinetic COF range to a more stable static COF. Results of kinematic modeling are presented as well as empirical testing and future work

Traffic Operations Analysis of Merging Strategies for Vehicles in an Automated Electric Transportation System

Automated Electric Transportation (AET) is a concept of an emerging cooperative transportation system that combines recent advances in vehicle automation and electric power transfer. It is a network of vehicles that control themselves as they traverse from an origin to a destination while being electrically powered in motion – all without the use of connected wires. AET\u27s realization may provide unparalleled returns in the form of dramatic reductions in traffic-related air pollution, our nation’s dependence on foreign oil, traffic congestion, and roadway inefficiency. More importantly, it may also significantly improve transportation safety by dramatically reducing the number of transportation-related deaths and injuries each year as it directly addresses major current issues such as human error and adverse environmental conditions related to vehicle emissions. In this thesis, a logical strategy in transitioning from today’s current transportation system to a future automated and electric transportation system is identified. However, the chief purpose of this research is to evaluate the operational parameters where AET will be feasible from a transportation operations perspective. This evaluation was accomplished by performing lane capacity analyses for the mainline, as well as focusing on the merging logic employed at freeway interchange locations. In the past, merging operations have been known to degrade traffic flow due to the interruptions that merging vehicles introduce to the system. However, by analyzing gaps in the mainline traffic flow and coordinating vehicle movements through the use of the logic described in this thesis, mainline traffic operations can remain uninterrupted while still allowing acceptable volumes of merging vehicles to enter the freeway. A release-to-gap merging algorithm was developed and utilized in order to maximize the automated flow of traffic at or directly downstream of a freeway merge point by maximizing ramp flows without causing delay to mainline vehicles. Through these tasks, it is the hope of this research to aid in identifying the requirements and impending impacts of the implementation of this potentially life-altering technology

Complex Relationships between Competing Guilds along Large-Scale Environmental Gradients

Despite much research over the past 30 years there is still little general understanding of how the outcomes of interactions vary along environmental gradients, particularly at large geographic scales. A simple expectation is that decreasing environmental quality should reduce densities of competitors and hence the effects of competition should weaken in poorer environments. A counter-intuitive consequence is that associations between densities of competitors might change from negative to positive as environments decrease in quality. Here we test these predictions in a set of vascular plant communities where perennial species share space and resources with less competitive annuals. We surveyed nine grey dune communities annually for 5 years along a cross-European latitudinal gradient of habitat quality. We find that densities of annual and perennial species are negatively correlated at the high-quality end of the gradient, while at the low-quality end guild densities are uncorrelated or positively correlated, consistent with a weakening of competition linked to increasing environmental limitations. Our results suggest that even simple interactions can give rise to non-obvious changes in species associations along environmental gradients. They highlight that understanding the outcome of species interactions may require explicit characterization of their changing intensity with environmental quality, and that the factors limiting species’ co-distribution can vary along environmental gradients

Trait Evolution in Adaptive Radiations: Modeling and Measuring Interspecific Competition on Phylogenies

The incorporation of ecological processes into models of trait evolution is important for understanding past drivers of evolutionary change. Species interactions have long been thought to be key drivers of trait evolution. However, models for comparative data that account for interactions between species are lacking. One of the challenges is that such models are intractable and difficult to express analytically. Here we present phylogenetic models of trait evolution that include interspecific competition among chosen species. Competition is modeled as a tendency of sympatric species to evolve toward difference from one another, producing trait overdispersion and high phylogenetic signal. The model predicts elevated trait variance across species and a slowdown in evolutionary rate both across the clade and within each branch. The model also predicts a reduction in correlation between otherwise correlated traits. We use an approximate Bayesian computation approach to estimate model parameters. We find reasonable power to detect competition in sufficiently large (20+ species) trees compared with Brownian trait evolution and with Ornstein-Uhlenbeck and early burst models. We apply the model to examine the evolution of bill morphology of Darwin’s finches and find evidence that competition affects the evolution of bill length

Detecting Non-Brownian Trait Evolution in Adaptive Radiations

Many phylogenetic comparative methods that are currently widely used in the scientific literature assume a Brownian motion model for trait evolution, but the suitability of that model is rarely tested, and a number of important factors might affect whether this model is appropriate or not. For instance, we might expect evolutionary change in adaptive radiations to be driven by the availability of ecological niches. Such evolution has been shown to produce patterns of change that are different from those modelled by the Brownian process. We applied two tests for the assumption of Brownian motion that generally have high power to reject data generated under non-Brownian niche-filling models for the evolution of traits in adaptive radiations. As a case study, we used these tests to explore the evolution of feeding adaptations in two radiations of warblers. In one case, the patterns revealed do not accord with Brownian motion but show characteristics expected under certain niche-filling models

Testing the ability of Unmanned Aerial Systems and machine learning to map weeds at subfield scales: a test with the weed Alopecurus myosuroides (Huds).

BACKGROUND: It is important to map agricultural weed populations in order to improve management and maintain future food security. Advances in data collection and statistical methodology have created new opportunities to aid in the mapping of weed populations. We set out to apply these new methodologies (Unmanned Aerial Systems - UAS) and statistical techniques (Convolutional Neural Networks - CNN) for the mapping of black-grass, a highly impactful weed in wheat fields in the UK. We tested this by undertaking an extensive UAS and field-based mapping over the course of two years, in total collecting multispectral image data from 102 fields, with 76 providing informative data. We used these data to construct a Vegetation Index (VI), that we used to train a custom CNN model from scratch. We undertook a suite of data engineering techniques, such as balancing and cleaning to optimize performance of our metrics. We also investigate the transferability of the models from one field to another. RESULTS: The results show that our data collection methodology and implementation of CNN outperform pervious approaches in the literature. We show that data engineering to account for "artefacts" in the image data increases our metrics significantly. We are not able to identify any traits that are shared between fields that result in high scores from our novel leave one field our cross validation (LOFO-CV) tests. CONCLUSION: We conclude that this evaluation procedure is a better estimation of real-world predictive value when compared to past studies. We conclude that by engineering the image data set into discrete classes of data quality we increase the prediction accuracy from the baseline model by 5% to an AUC of 0.825. We find that the temporal effects studied here have no effect on our ability to model weed densities

Ecophysiological traits of grasses: resolving the effects of photosynthetic pathway and phylogeny

C4 photosynthesis is an important example of convergent evolution in plants, having arisen in eudicots, monocots and diatoms. Comparisons between such diverse groups are confounded by phylogenetic and ecological differences, so that only broad generalisations can be made about the role of C4 photosynthesis in
determining ecophysiological traits. However, 60% of C4 species occur in the grasses (Poaceae) and molecular phylogenetic techniques confirm that there are between 8 and 17 independent origins of C4 photosynthesis in the Poaceae. In a screening experiment, we compared leaf physiology and growth traits across several major
independent C3 & C4 groups within the Poaceae, asking 1) which traits differ consistently between photosynthetic
types and 2) which traits differ consistently between clades within each photosynthetic type

The Kolkata Paise Restaurant Problem and Resource Utilization

We study the dynamics of the "Kolkata Paise Restaurant problem". The problem is the following: In each period, N agents have to choose between N restaurants. Agents have a common ranking of the restaurants. Restaurants can only serve one customer. When more than one customer arrives at the same restaurant, one customer is chosen at random and is served; the others do not get the service. We first introduce the one-shot versions of the Kolkata Paise Restaurant problem which we call one-shot KPR games. We then study the dynamics of the Kolkata Paise Restaurant problem (which is a repeated game version of any given one shot KPR game) for large N. For statistical analysis, we explore the long time steady state behavior. In many such models with myopic agents we get under-utilization of resources, that is, we get a lower aggregate payoff compared to the social optimum. We study a number of myopic strategies, focusing on the average occupation fraction of restaurants.Comment: revtex4, 8 pages, 3 figs, accepted in Physica

Sex Allocation Patterns across Cooperatively Breeding Birds Do Not Support Predictions of the Repayment Hypothesis

The repayment hypothesis predicts that reproductive females in cooperative breeding systems overproduce the helping sex. Thanks to well-documented examples of this predicted sex ratio bias, repayment has been considered an important driver of variation in sex allocation patterns. Here we test this hypothesis using data on population brood sex ratios and facultative sex allocation from 28 cooperatively breeding bird species. We find that biased sex ratios of helpers do not correlate with production biases in brood sex ratios, contrary to predictions. We also test whether females facultatively produce the helping sex in response to a deficiency of help (i.e., when they have fewer or no helpers). Although this is observed in a few species, it is not a significant trend overall, with a mean effect size close to zero. We conclude that, surprisingly, repayment does not appear to be a widespread influence on sex ratios in cooperatively breeding birds. We discuss possible explanations for our results and encourage further examination of the repayment model