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Median Housing Prices in the Boston Area
Danny Jeffrey Fleming
This paper examines the relationship between housing prices and various demographic factors. In particular, the effect of several variables on the median price of houses in the greater Boston area was studied. The study uses independent variables such as crime rate, property tax rate, and house age. Stepwise regression analysis was used to determine which variables were most significant in predicting median house price. Variables such as crime rate, average number of rooms, and nitric oxide concentration were found to be statistically significant.
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Medical Image Denoising with Deep Convolutional Neural Networks
Zahangir Alom
In the last few years, Deep Leaning (DL) approaches are applied in different modalities of Bio-Medical imaging application including classification, segmentation, and detection tasks. In addition, DL based generative methods are also used for image denoising and restoration tasks. In particular, the generative models have applied for enhancement and restoration of Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) images and achieved state-of-the-art performance for noise cancelation and restoration. In this work, we apply different generative model including Generative Adversarial Network (GAN), and denoising convolutional auto-encoder for bio-medical image enhancement problem. The experiments are conducted on different publicly available datasets for MRI and CT images. The experimental result shows promising outputs which can be applied for different applications in the modalities of MRI and CT.
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Medical Image Segmentation using Deep Convolutional Neural Networks
Zahangir Alom, Ranga Burada
Deep learning (DL) has been evolved in many forms in recent years, with applications not only limited to the Computer Vision tasks, expanded towards Autonomous Driving, Medical Imaging, Bio-Medical Imaging including Digital Pathology Image Analysis (DPIA), and in many other forms. Deep Convolutional Neural Network (DCNN) methods such as LeNet, AlexNet, GoogleNet, VGGNet, ResidulaNet, DenseNet, and CapsuleNet within the DL has been very successful in object classification and detection problems on a very large scale publicly available data set. Due to the great success of these DCNN methods, researchers have explored these methods to other imaging areas such as medical imaging problems, where there is a greater need for automated computer algorithms to make the diagnosis quick and cost-efficient, specifically for image classification, segmentation, detection, registration, and medical image data processing. Several state of art methods that provided superior performance in medical image segmentation such as Fully Connect Networks (FCN), SegNet, DeepLabs, U-Net, V-Net, and R2U-Net have outperformed hand-crafted machine learning algorithms. These models have been tested on several medical imaging and DPIA data sets but have not been explored on multi-organ segmentation, so the primary goal of this proposal is to explore more on these state of art models and test on several publicly available multi-organ segmentation data sets. The quantitative and qualitative performance will be evaluated against existing models using different performance metrics including, Accuracy, Sensitivity, Specificity, F1-score, Receiver Operating Characteristics (ROC) curve, dice coefficient (DC), and Mean Squared Error (MSE).
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Medical Imaging to Patient Specific Additively Manufactured Implant
Monica M Yeager
As of 2018, there are only standard sized, cast molded hips available to patients requiring hip implants. However, everyone has a unique body, so a cast molded hip implant will fit people differently. Personalized additively manufactured, or 3D-printed, implants created from a patient’s own computerized tomography (CT) or magnetic resonance imaging (MRI) images may provide numerous benefits. A main benefit would be that the implant would provide a personalized fit for the patient, as it would be made from scans of his or her own body. With a personalized fit, there would be reduced surgery and recovery time. For people with a physiological abnormality, a customized solution may be printed for reconstructive surgery. Another benefit would be for surgeons who could 3D print their patient’s bone for pre-surgical planning, such as planning screw placements. As the baby boomer population ages, the commercial market for 3D-printed joint replacements is expected to soar over the next decade. The goal of the research conducted here is to demonstrate a prototype process for patient specific, additively manufactured hip implants made from Digital Imaging and Communications (DICOM) images of human femur bone, leading to additional research on imaging of trabecular bone for biomimetic lattice development.
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Mental-Health in School
Reid Coleman Eschallier, Kaitlin B Lewis
Anxiety has increased in students over the last few decades and has proven to affect their well-being and overall academic performance. One common factor that has increased stress-levels is the implementation of standardized testing. The pressure and weight that standardized tests put on students are detrimentally affecting their grades and overall health. In addition, certain demographics are being more negatively affected by standardized tests; therefore, putting specific groups at a disadvantage. As a result of increased anxiety, schools have started to address the need for programs that assess and assist students’ mental health. The first step many schools take is figuring out sources of anxiety for students. Educators and administrators have realized the importance of offering counseling for students as well as increasing awareness of mental health, as it has proven to lower anxiety levels. However, many obstacles still fall in the way of resources being available to all students, whether it be social pressures or outside factors that impact the school. As future educators, we are interested in researching why anxiety has increased in students in recent years, and how it can be addressed. We believe that teachers should help develop their students as people, not just focus on the student’s academic performance.
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Microbial Resistance to Traditionally Medicinal Plant Extracts
Emilee Kay Zoog
Microbial resistance to pharmaceutical antibiotics is a growing problem in healthcare and animal husbandry that has led to hundreds of deaths from bacterial infections that could once be cured with antibiotics. Scientists are currently studying these resistance mechanisms and formulating novel treatments for bacterial infections, but exhaustive research of the antimicrobial properties of many common plants has yet to be established. In this study, I tested five traditionally medicinal plants common to the Dayton area including wild hydrangea, black haw, dandelion, mayapple (roots), and red clover for antimicrobial action against several strains of the human pathogens Listeria monocytogenes, Staphylococcus aureus, and Escherichia coli, using disk diffusion assays in aerobic and anaerobic environments. Each extract had antimicrobial activity against at least most of the pathogens tested, but each pathogen reacted variably to each extract, though antimicrobial activity in both oxygen environments were comparable. This suggests that there may be active compounds in plants with antimicrobial properties, in which case said compounds should be isolated and tested further in order to better understand bacterial defense mechanisms in plants, and what, if any, benefit these antimicrobial properties could have for humans with bacterial infections.
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Micronutrients, hurricanes, and invasive species: insights into the micronutrient limitation and stress of an invasive ant along the coast of Texas
Maddie Rose Kurlandski, Ryan William Reihart
Human activities associated with climate change are rearranging the distribution of elements and species across the globe, but the consequences of these alterations remains unknown. Coastal ecosystems are likely at risk to an increase in the intensity and frequency of large tropical storms. These storms often deposit large amounts of micronutrients, which are less abundant in living tissue, and can affect the abundance and diversity of arthropods. Little is known, though, how additions of micronutrients can affect the success of consumers, especially invasive arthropods. To determine how changes in biogeochemistry affects litter arthropods, we utilized a factorial, fertilization experiment that manipulated macro- (N&P) and micronutrients (Ca, K, and Na; 16 treatments x 8 replicates = 128 plots), in 2016 and 2017, in large 30 m x 30 m plots in a coastal tallgrass prairie near Houston, TX. We collected litter arthropods using pitfall traps in 2017, and one year post-fertilization in 2018. Based on results from 2017, we conducted feeding trials, that manipulated the ratio of Ca:Na (by 10%, 25%, and 40%) in food, on an invasive ant, Nylanderia fulva in 2018. In 2017, N. fulva was the dominant litter arthropod across all treatments, and their abundance was limited by Ca, but tends to be suppressed by Na. In 2018, however, these effects disappeared as soil cations were likely leached from the soil, and abundance of N. fulva dropped 98%, likely due to Hurricane Harvey. Preliminary lab results show that manipulating the Ca:Na ratio in the food of N. fulva affects colony fitness, indicating that Na can reach toxic levels, suppressing colony size, while Ca ameliorates these toxic effects. These results indicate that changes in micronutrient availability may facilitate the success of an invasive species, and gives insight as to how human activities are altering coastal ecosystems.
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Modeling Chaotic Population Dynamics with Feedbacks
Christina Farwick
While generating a model for a particular system typically relies on the ability to predict the behavior of the system at some arbitrary time, deterministic chaos measures the diversion from predictability: more chaotic implies more disorder, less chaotic implies more predictable. This work will employ Lotka-Volterra equations to describe the dynamics of biological systems. The bifurcation point is the point at which the system goes from stable to unstable. Thus, the objective of this project is to modify the existing Lotka-Volterra model and create bifurcation diagrams. Previous work shows that population dynamics depend heavily on feedback with the environment. Feedback will therefore be introduced as a new variable, and it is expected that the updated model will be able to describe chaotic-dynamics with feedback included.
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Modeling of Nucleation-and-Growth in Macroscopic Systems Using Kolmogorov-Avrami-Johnson-Mehl (KAJM) Equation
Ming Gong
Many macroscopic (like lakes) and microscopic (like macromolecules) physical systems exhibit so-called nucleation phenomena, “collective growth” of patterns in the system. Nucleation could be illustrated as infinitesimal seeds of the stable phase from inside the unstable phase. The process of phase transitions, including continuous (second order) or discontinuous (first order), forms the nucleation. Moreover, the fact that the kinetics when the temperature is quenched from above to below the critical temperature is observed in continuous phase transitions. In reality, the formation of clouds, fog, rain, smoke from burning, ice crystals in the refrigerator, bubbles from soda and beer, etc. are all representatives of nucleation phenomena. Thus, nucleation is applicable everywhere from chemistry to climate science. The objectives of this work are to model nucleation and growth by applying Kolmogorov-Avrami-Johnson-Mehl (KAJM) equation based on the probability equation and to implement a computation algorithm to describe pattern growth.
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Modified Ramsey Numbers
Jack W Mccarthy
This presentation is a project for the course MTH 466: Graph Theory and Combinatorics. A graph is a mathematical objects that consists of two sets: A set of vertices and a set of edges. An edge joins two vertices and depicts a relationship between those vertices. This project will explore a modified Ramsey number, the rainbow Ramsey number RR(F) of a graph F, which is defined as the smallest positive integer n such that if each edge of a complete graph--a graph containing all possible edges between its vertices--is colored from any number of colors, then either an F with edges of only one color (monochromatic) or an F with edges with no repeated colors (rainbow) is produced.
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Modulation of Listeria monocytogenes Carbon Metabolism by Short Chain Fatty Acids
Diksha Bedi
Listeria monocytogenes, a bacterial pathogen, is associated with foodborne infections in humans. Listeria encounters short chain fatty acids (SCFAs) during its transit through the intestine but its metabolic responses to SCFAs are not fully understood. To determine how Listeria metabolism is affected by SCFAs, I performed basic microbiology assays, including monitoring optical density, determining acetoin production, and measuring culture pH levels. I also performed preliminary 13C-NMR assays to provide a more in-depth look into carbon metabolism in SCFA-treated Listeria. I found that propionate-supplemented Listeria produced significantly more acetoin compared to no supplemented controls. Because acetoin is a product of central carbon metabolism, my result suggests that Listeria is capable of changing its carbon metabolism in response to propionate. My preliminary 13C-NMR results have not revealed how carbon metabolism is altered by propionate and are under current investigation. Further investigation will provide more knowledge in the metabolic mechanism associated with Listeria responses to SCFAs during intestinal transit.
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Money Supply Growth and Inflation Trends Post 2008 Recession: A Closer Look at the PCE Inflation Index
Reed Thomas Aleck
After the 2008 recession, the Federal Reserve initiated an aggressive policy of monetary easing. In this study, I examine the relationship between money supply growth and inflation using Personal Consumption Expenditures (PCE-All) as my measure of inflation. I develop univariate regression models with M1, M2, and MZM as the independent variables and PCE-All as the dependent variable. I test the hypothesis that the slope coefficients are positive and statistically significant (T-Stats > 2). I also forecast 2018 PCE-All inflation rates to determine the forecasting accuracy of the models. My forecasts also take into account the root mean square forecasting error (RMSE).
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Monitoring change in fish and macroinvertebrate communities following low dam modification and kayak chute installation in the Great Miami River in downtown Dayton, Ohio - a heavily urbanized river channel.
John David Barnard, Samantha Jean Berkley, Maddi Elizabeth Conway, Audrey Allison Hayes, Emma Claire Hiltner, Madison Spooner Johnson, Suzanne L Lowes, Gretchen M Lozowski, Madeline Rebecca Norman, Emmett Justin Sheehan
From 2015-2017, the Miami Conservancy District and Five Rivers Metroparks completed a project to modify a low dam upstream of Monument Avenue into a kayak chute for recreational use. Low dams have negative impacts on river habitat by decreasing water velocity in the deep water impoundment behind the dam, destroying normal riffle-pool habitats, increasing sedimentation, and interfering with fish dispersion - among other things. Healthy physical habitat consists of alternating pools and riffles where sediments of sand, gravel, and cobble are kept exposed by fast-flowing water. The altered conditions created by the dams are detrimental to populations of fish and macroinvertebrates whose communities are negatively impacted by the altered physical conditions. In this project, we compare the current, post-modification conditions to the pre-modification conditions in terms of both the physical habitat and communities of fish and macroinvertebrates. Fish were sampled using electroshocking techniques and macroinvertebrates were sampled with Hester-Dendy artificial substrates, kick-nets, and sweep-nets. Samples were returned to the laboratory, processed, sorted, and the number and types of organisms were recorded. Collection of specimens has occurred between the years of 2017 and 2018.
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Monitoring the impact on biodiversity of a kayak chute constructed in a heavily urbanized section of the Great Miami River near Riverscape in in downtown Dayton, Ohio and an assessment of recreational safety based on levels of fecal coliform bacteria in the surrounding river water.
John David Barnard, Samantha Jean Berkley, Maddi Elizabeth Conway, Audrey Allison Hayes, Emma Claire Hiltner, Madison Spooner Johnson, Suzanne L Lowes, Madeline Rebecca Norman, Emmett Justin Sheehan
Our objective was to determine how aquatic life responded to the construction of a kayak chute in a heavily-urbanized corridor of the Great Miami River next to Riverscape in downtown Dayton, Ohio. We collected macroinvertebrate samples using sweep net, kick net, and artificial substrate sampling methods. Fish were sampled using electroshocking techniques. Macroinvertebrate samples were preserved in ethanol, sorted, identified, and counted in the lab. Fish were identified in the field and released. Data was also collected on levels of fecal coliform bacteria in the river near the kayak chute to assess recreational safety of kayakers using the feature.
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Motivation Indicators of Involved Commuter Students at the University of Dayton
Alysha K Rauen
The purpose of this qualitative, phenomenological study is to understand the motivations of commuter students in universities and colleges in the United States who get involved in co-curricular activities, such as recognized student organizations. This study will increase the already very minimal amount of research on commuter students on college campuses and will provide insight that has not been addressed. Understanding these motivations will help professionals better understand this population of students and be able to improve practices to better address their needs. Data was collected through in person interviews (n = 5) between the researcher and students who fit the criteria of being a commuter students and involved in at least one recognized student organization. Themes that emerged from the data were that commuter students are self-motivated to get involved and the distance of their commute does not affect their motivation.
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Moving towards tuning of ankle-foot orthoses: The influence of carbon and plastic AFOs for individuals with Multiple Sclerosis
Sarah Elizabeth Hollis
Mobility impairments are reported as the most debilitating symptoms for individuals with Multiple Sclerosis (MS). Fatigue, a major symptom of MS, further affects mobility. Ankle-foot orthoses (AFOs) are one potential solution to alleviate some of these mobility impairments; however, the effectiveness of AFOs for individuals with MS are currently inconclusive and have known downfalls. We took a comprehensive look at both carbon fiber and polypropylene AFOs to gain an understanding of the immediate effects of AFOs for individuals with MS. In collaboration with the University of Dayton’s Doctorate of Physical Therapy Program, data was collected for 10 participants on various balance, gait, and strength/fatigue assessments. Overall, no significant differences existed between the baseline, carbon, or plastic AFO conditions for any assessment outcome (p>0.05); however trends did arise within the static and dynamic balance task results. Many outcome parameters varied among participants, suggesting the importance of individual responses to AFOs and patient preferences in prescribing AFOs. The majority of participants preferred the carbon AFO. All AFOs were off-the-shelf with only slight adjustments to account for fit and alleviate any pain, AFO tuning is believed to help optimize the efficiency of AFOs by adjusting the angle of the shank during midstance and the stiffness of the footplate. The next step in this work is to investigate the effects of AFO tuning in collaboration with area clinical partners. A case study is currently underway to give insight and better understanding to the effects of AFO tuning.
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MPC for Optimized Energy Exchange Between Two Renewable-Energy Prosumers
Ibrahim Aldaouab
Renewable energy and information technologies are changing electrical energy distribution, favoring a move towards distributed production and trading between many buyers and sellers. There is new potential for trading between prosumers, entities which both consume and produce energy in small quantities. This work explores the optimization of energy trading between two prosumers, each of which consists of a load, renewable supply, and energy storage. The problem is described within a model predictive control (MPC) framework, which includes a single objective function to penalize undesirable behavior such as the use of energy from a utility company. MPC integrates future predictions of supply and demand into current dispatch decisions. The control system determines energy flows between each renewable supply and load, battery usage, and transfers between the two prosumers. At each time step, future predictions are used to create an optimized power dispatch strategy between the system prosumers, maximizing renewable energy use. Modeling results indicate that this coordinated energy sharing between a pair of prosumers can improve their overall renewable penetration. For one specific choice of prosumers (mixed residential-commercial) penetration is shown to increase from 71% to 84%.
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Multi-Dimensional Lung Segmentation using Deep Learning
Dhaval Dilip Kadia
The ultimate goal of science is a safer & healthier society and greater humanity. If the computer analyzes medical reports precisely, then enough time can be allotted to the individual patient; diagnosis can be accurate, time-efficient, cost-effective and labor saving. The objective of this research is performing 3-dimensional semantic lungs segmentation, by applying Deep Learning (DL) based methods on the sequence of Computed Tomography (CT) scan images. The motive is to design the 3-dimensional Neural Network architecture based on current 2-dimensional architecture, that is offering state-of-the-art performance, experimenting and evaluating it for improving its performance. The U-Net is a convolutional neural network that is a decent architecture for biomedical image segmentation, and applicable in volumetric segmentation. The proposed work will use the 3-dimensional patch in Recurrent Residual Convolution Neural Network (RRCNN) based U-Net (R2U-Net), applied on the sequence of CT scan images. These computational methods can replace the conventional methods, and overcome their limitations of time delays, the absence of a doctor, and unavailability of instruments. A large number of high-resolution CT images make numbers of slices, and some of the lesion features are not obvious, which leads the heavy work for doctors. The advantage of 3D imaging over 2D imaging is achieved by processing the higher dimensional data. 3D medical imaging can extract more features and surrounding information; that is helpful for the diagnosis. The output can be further helpful to recognize cancerous tumor with its volume inside the lungs. The proposed work will provide more opportunities to explore different modalities of medical imaging.
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Multi-Integrated Segmentation Approaches for Permafrost Lakes Observed in Satellite Images
Ming Gong
During the summer melt season, permafrost lakes in the Arctic display a complex geometry. Casual inspection of remote sensing images shows that the lake phase of Arctic landscape undergoes a transition where disconnected lakes evolve into much larger scale connected networks with complex boundaries. Spatiotemporal dynamics of lakes is crucial for the stability of the Arctic climate system. To understand how these features evolve over time, we propose to develop two integrated machine learning image segmentation techniques for lake pattern recognition. Classical machine learning methodologies for image segmentation require handcrafted features that are similar to our visual perception and simple classification strategies to provide accurate boundaries. Conversely, deep neural networks for image segmentation learn these features through different variations of gradient descent to create these boundaries as well. The specific objectives of this research are to implement a classical image segmentation architecture and a deep convolutional encoder-decoder architecture called SegNet and apply each architecture to Landsat satellite imagery obtained from Google Earth Engine in 2016. The study area covers Siberia (both Western and Eastern), Chukotka and Alaska. We compare deep learning segmentation with classical segmentation methodologies for segmenting permafrost lakes to determine the capabilities of each methodology and their effectiveness for lake segmentation in a variety of Landsat imageries.
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Music Therapists' Knowledge of and Attitudes Toward Sustainability: Instruments
Sarah Christine Whitehouse
Sustainability has become a common topic of conversation and serious concern in today’s society. The purpose of this project was to explore salient issues, attitudes, and practices in music therapy sustainability. Information was gathered through an in-depth review of the materials employed in the make and manufacturing of instruments commonly used in music therapy practice. In addition, a survey was sent to music therapy professionals with the MT-BC (Music Therapist – Board Certified) credential to ascertain their knowledge of and attitudes toward current issues in sustainability within the profession.
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Narratives of Lost Meaningfulness: When Contamination Sequences have Humanistic Themes
Joe Edward DeBrosse
Life stories' affective sequences indicate how people feel about the changes in their lives. Contamination is a common affective sequence in which the narrative begins with positive or neutral affect but declines to negative affect. While previous studies showed that contamination sequences predict a variety of poor well-being indices (e.g., McAdams, Reynolds, Lewis, Patten, & Bowman, 2001), whether contamination's accompanying themes—such as humanistic or materialistic concerns—change their predictive utility for well-being is unknown. Based on the low- and turning-point narratives of 211 participants, we examined whether contamination sequences with humanistic themes (e.g., a loss of meaning due to unemployment) differed in their relations to well-being compared to contaminated narratives without humanistic themes (e.g., a loss of prestige due to unemployment). We predicted that contamination sequences would interact with humanistic themes to predict significantly lower levels of well-being. Though the data showed a trend toward this interaction, contaminated humanistic narratives were exceptionally rare and the interaction was not statistically significant. In addition, we refined the standard measure of contamination sequences into three categories, finding that contamination sequences only predict well-being when they begin with positive, not neutral, affect. A third, new category, bad-to-worse contamination, predicted the lowest levels of well-being.
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Native and Non-Native English Teachers
Rowaida Hamad S Alhabis
Over the years, English has rapidly grown to the extent of outrivaling languages such as French which were previously prestigious. English’s accelerated development arises from the fact that it can be easily learned. Non-native speakers often opt to learn English as their second language. Consequently, this language has emerged as the preferred communication medium in most institutions, organizations and professional circles worldwide. Globally, everyone is striving to improve their competence in English. In their efforts, the majority often opt to enhance their expertise through the assistance of native speakers. Due to the enormous number of English Learners worldwide, it is obvious that most English teachers are non-native English speakers, and should not be looked down upon. According to David John Brining, non-native English teachers are faced by uncertainties when speaking the language they have to teach, and may therefore take on an aggressive attitude towards teaching the language (50). They become obsessed with the grammar and ignore minor but significant elements like linguistic appropriateness. However, he adds that non-native speakers are the best teachers since they can easily communicate with non-native students, as they have been through the process of learning English as a second language. In addition, Enric Llurda disagrees by pointing out that a lot of non-native English speakers have adopted English as their L1, and with the exemption of an accent, there is merely a distinction between them and native English teachers (118). The preference of native English speaking teachers is fuelled by the notion that they are inherently superior to their non-native counterparts. This presumption frames the focus of interest in this examination. The research specifically challenges the notion that native speakers are inherently superior teachers of English, compared to their non-native counterparts, through a detailed review of selected studies and an analysis of primary information collected from surveys. I hope to convince students that professionals who teach English as their second language are equivalently competent as their native colleagues.
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Natural Language Processing: A Look Into How Computers Understand Human Language
Brad Richard Sorg
The semantic interpretation of the human language is very complex and diverse making natural language processing an interesting task for researchers and engineers. Natural language processing is a subfield of machine learning focusing on enabling computers to understand and process human languages. Although computers do not have the same intuitive understanding of natural language like humans do, recent advances in machine learning have enabled computers to perform many useful things with natural language like text classification, language modeling, speech recognition, and question answering. Computers are able to accomplish these tasks by learning the deep contextual representations of words including both the syntax and semantics. Through the use of recurrent neural networks, long short-term memory units, temporal convolution networks, and different language embedding models, computers have made significant strides in their ability to interpret and understand human language. With large volumes of textual data available and the need to structure the unstructured data source that is human language, the area of natural language processing will continue to be of interest.
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New Natural Gas Site Locating in the Marcellus Shale Region PA
Ryan P Young
Natural gas is an important resource for many various reasons. In the current study, I aim to identify the best suited location for a new well using various factors and restraints. This resource is stored thousands of feet beneath the Earth’s surface, specifically in shale bearing layers. One shale unit in particular, the Middle Devonian Marcellus Formation, is of particular interest. It extends approximately six-hundred miles, covering large areas of the Appalachian Basin including Pennsylvania, West Virginia, Ohio, and New York. The area underlain by the Marcellus Formation is nearly 240,000 square kilometers (Kargbo et. al., 2010). However, most of the natural gas is located underneath Pennsylvanian land and therefore this state will be the focus of the study. Recent advancements in the extraction of this resource have led to an exponential increase in this industry. New techniques known as hydraulic fracturing and horizontal drilling have greatly influenced the efficiency of the process and therefore economic prosperity. Just in Pennsylvania alone, 2008 estimates show the creation of more than 29,000 jobs and $2.3 billion dollars in revenue (Kargbo et. al 2010). A previous study by Meng (2014) revealed significant landscape variables as driving mechanisms in well-site location. Higher elevation and wetlands were shown to be the most prone to natural gas sites while steeper slopes were correlated with lower probabilities. I will apply his findings and the use of GIS techiniques to identify the most suitable location for a new fracking site. For each of the variables, a suitability layer will be created. Once all of these layers are created, they will be combined in order to acquire an overall suitability score to determine the best suited location.
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Nitrate contaminant tracing in surface and groundwater in the Great Miami River Watershed: Environmental Isotope Approach
Rachel Kristine Buzeta
The global population has increased exponentially causing several challenges surrounding sustainability, including greater food production needs. To meet these demands and boost agricultural productivity, more efficient practices and fertilizers are used. Synthetic fertilizers and other nutrient sources have resulted in water quality degradation and pollution. Much of the Great Miami River Watershed’s streams and aquifers in southwestern Ohio are affected by nitrate contaminants originating from anthropogenic sources including synthetic and organic fertilizer used for agriculture, human wastes (domestic, industrial, and municipal wastes), and urbanization. High nitrate concentrations cause ecological disturbances across all trophic levels. Nitrate levels greater than 10 mg/L also pose a danger to human health, if the contaminant reaches drinking water sources. Water quality monitoring stations report nitrate concentrations in surface and groundwater, but a nitrate contaminant source has not been identified. Here we used isotope ratios of nitrogen (δ15N) and oxygen (δ18O) in nitrates to identify sources for surface and groundwater. Initially we fingerprinted the isotopic composition of the main nitrate contaminant sources in the watershed. Our results show a distinct low δ15N for commercial synthetic fertilizers (0.4±4‰) and high δ15N for animal and human waste (13.0±1.3‰). Further sampling along the Great Miami Mad, and Stillwater River provides insights into contaminant sources contributing to elevated nitrate levels in each river. In general, the δ15N from river samples collected during the low river flow lies within a range of human and animal waste, whereas δ15N values of groundwater suggest that the nitrates might have been derived from soil organic matter or synthetic fertilizers. This research provides a regional baseline for nitrate contaminant source tracing and helps to better inform state and local water quality and nutrient management planning.
The Brother Joseph W. Stander Symposium recognizes and celebrates academic excellence in undergraduate and graduate education. This annual event provides an opportunity for students from all disciplines to showcase their intellectual and artistic accomplishments. The Stander Symposium represents the Marianist tradition of education through community and is the principal campus-wide event in which faculty and students actualize our mission to be a "community of learners." This collection contains a sampling of the posters presented during the symposium in 2019.
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