Stander Symposium Projects

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  Aircraft Generator Design and Analysis

  David Gross

  Aircraft electrical power demands have been rapidly growing due to an increased amount of electrical load onboard aircraft. This increased load has come about as electrical sources for various aircraft subsystems, such as pumps, compressors and flight controls, replace mechanical sources. The main source of electrical power on an aircraft is a generator. The power demand on an aircraft is not constant, but rather dynamic, and the nature of these power demands causes increased temperatures and complex/dynamic loads, for which many contemporary generators are not designed. Because of the need for high amounts of reliable electrical power among future aircraft, future generators should be designed for reliability, stability, power density and long-term durability. The objective of this thesis project is to determine if generator sizing techniques (e.g. equations, assumptions, rule-of-thumb metrics) can be calculated to a reasonable accuracy for preliminary machine design optimization and analysis.

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  A Journey Towards Multiculturalism: Cultural Identity Development Among Chinese International Students

  Yuan Zhou

  This is a qualitative, narrative research study examining the stories told by six undergraduate Chinese international students about their transitional journey from a monocultural to multicultural identity. In the interviews, students were asked to reflect on their understanding of self, Chinese culture, and American culture. Students were also prompted to reflect on their past experiences in China leading up to their arrival to the United States as well as those while attending the University of Dayton. The commonalities in their narratives showed evidence of internal motivation to immerse themselves in American culture, in addition to the negative and positive encounters with both Chinese and American students on campus.

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  Alcohol Deprivation Effect: An Investigation of a Model of Alcohol Dependence and Relapse Behaviors in Male and Female Long Evans Rats

  Hanna Jane Peterson

  Humans that become addicted to alcohol and other drugs often relapse even long after becoming abstinent especially when they encounter stressors in life. Stress that a healthy person handles with coping mechanisms like exercise or talking to a friend, an addict may handle by resuming use of their drug of choice. People may also relapse if they re-enter an environment where they used the drug because of a learned association between that environment and the good feeling of the drug. In order to understand relapse behavior, a pre-clinical rodent model of relapse is used which models the important aspects of the human addiction and relapse condition. While a rodent model does not replicate every aspect of the human condition, it can model the aspects that are most important in addiction and relapse overall. The model used in this study is the alcohol deprivation effect (ADE) model. It has been found to model alcohol addiction and relapse in rats and can therefore allow for further understanding of relapse behavior as well as allow for testing of the effects of various variables like stress or therapeutic drugs on relapse behavior. However, before these further tests can be done, it must be clear that the model works in the Long Evans rats that are used in the lab. I am also interested in whether the ADE model yields similar results in male and female rats. This project will investigate the usefulness of the ADE model in Long Evans male and female rats by replicating a similar study done previously by Sinclair and Tiihonen (1988).

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  A Low Power High Throughput Architecture for Deep Network Training

  Yangjie Qi

  General purpose computing systems are used for a large variety of applications. Extensive supports for flexibility in these systems limit their energy efficiencies. Neural networks, including deep networks, are widely used for signal processing and pattern recognition applications. This poster presents a digital multicore on-chip learning architecture for deep neural networks. It has memories internal to each neural core to store synaptic weights. A variety of deep learning applications can be processed in this architecture. The system level area and power benefits of the specialized architecture are compared with an NVIDIA GEFORCE GTX 980Ti GPGPU. Our experimental evaluations show that the proposed architecture can provide significant area and energy efficiencies over GPGPUs for both training and inference.

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  Analysis of Environmental Effects of Chemical Deicers in the Southwestern Ohio

  Christopher Alexander Barrett

  In the modern age, virtually every sphere of society uses various manufactured chemicals. Factories use various lubricants to improve machine up time, sanitation crews use disinfectants to annihilate bacteria, and farmers use specialized fertilizer to improve crop growth. The use of these chemicals can be extremely helpful, and in some cases necessary to keep the wheels of modern society rolling. However, while these chemicals help, in some cases they can be very hazardous to individuals or the environment if not handled properly. This is the case with excessive use of chemical deicers that can runoff from roads into water and negatively affect freshwater wildlife. This project uses deicers as an example for the potential health and ecological effects that can be caused from the use of environmentally harmful chemicals, and attempts to highlight practices that minimize these risks.

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  Analyzing the Factors of Performance: Is There a More Precise Way for Trainers to Score an Individual's Form During Exercises?

  Amanda Nicole Delaney

  As part of a collaborative project, our overall research aim was to gain a better understanding of factors that contribute to successfully predicting performance in a variable environment. My research concentrated on adding objectivity for evaluating the effectiveness of training exercises and assessments that are performed and normally scored based on an expert’s rating. The Lock and Load exercise, which resembles the bird dog exercise but is done in a high plank position, was the focus of this study. Biomechanical marker data was recorded with an Xsens Awinda 17 sensor suit for comparison to the ratings of form assigned by the trainer. Analyzing the center of mass and maximum acceleration of the individual allowed for examination of how well the person was balanced, controlled, and in sync throughout the test. This data was then used to determine the accuracy of the form ratings given by the certified trainer involved in the project. Results suggest that other sensor-based outcomes may need to be incorporated in training exercises to provide a better picture that equates to the expert’s rating. Analysis of jerk, hip rotation, and coordination plots are the next steps in determining the relationship between the expert’s form rating and the true form.

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  Analyzing the Use of Pressure Sensing Shoe Insoles for Biomechanical Research

  Kevin Michael Nowacki

  The foot is an especially intricate piece of the already complex human body. In biomechanical research, gait is a popular research topic, but it is hard to demonstrate¬–or even understand–how the foot articulates. Motion capture is a common way to attempt to analyze how the foot works during gait. This method has shown major improvements in understanding the human foot, but has many limitations. There can be variation on where markers are placed, which lead to varying kinematics, which leads to inconsistent results. A pressure sensing insole could assist in understanding multi-segment foot kinematics. This would allow pressure mapping of the foot throughout the gait cycle. There are limited people and studies that have looked at pressure mapping of the foot through gait using insoles. In collaboration with a company called SensingTex out of Spain, I am experimenting with their product in development to analyze if it could be useful for future biomechanical research. This research will hopefully validate the device, and show it can accurately detect how people are distributing their weight across their foot. The ability to see how and where the pressure is being dispersed throughout the foot during gait can open a lot of doors and create many opportunities for other research moving forward. This research is a pilot study to analyze how the device works, and if it is a viable option for understanding multi-segment foot kinematics. Through initial testing, it was found that the sampling rate of the sensors is not fast enough to get an adequate number of samples during a foot strike. The next steps include increasing the sampling speed of the sensors, and continue testing to compare these data.

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  A Nationwide Study on the Impact of Racial Battle Fatigue on Black Student Affairs Professionals

  Beverly Auston Dines

  The purpose of this qualitative, online survey-based study is to explore racial battle fatigue and its impact on Black student affairs professionals across the United States. How do Black student affairs professionals describe racial battle fatigue and its impact on their professional lives? The findings enhance our understanding of the needs and opportunities for advocacy as it pertains to these professionals. The survey results are organized by theme and analyzed for trends and best practices.

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  And Give Me Support: How SUNY Institutions Address Employee Experiences of Burnout

  Conor Matthew Kutner

  The purpose of this content analysis study is to uncover how higher education institutions in the State University of New York (SUNY) system offer support to higher education professionals in their employment who experience burnout during the course of their work. With the high attrition and turnover rates of higher education professionals (Rosser & Javinar 2003; Tull, 2006), institutions do not seem to be meeting the needs of their employees. This study will utilize a content analysis approach to systematically review the publicly-available literature such as employee handbooks, human resource websites, and employee wellbeing services websites from SUNY system institutions (n = 10). This study seeks to identify a multi-dimensional method (Abbott & Baun, 2015) of addressing these issues and needs, of providing support and services to employees, and to offer recommendations on best practices in responding to those need-specific gaps in future research and progress.

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  An Efficient Leaf Recognition Based Approach for Plant Classification Using Machine Vision Strategy

  Redha Ali, Russell C Hardie

  The identification of plants is very important component of workflows in plant ecological research. Therefore, in this work, we are developing a novel automatic leaf recognition for plant classification. Unlike the existing techniques, our approach is efficient, fast, and simple. The proposed method is based on machine vision strategy that employs two main processing stages: 1) the Bag of Feature (BoF) approach, and 2) a decision-making model based on multiclass Support Vector Machine (SVM) classifier. The BoF is utilized for extracting the features from representative images. First, to provides excellent scale invariance break up the image into sub-regions by using speeded up robust features (SURF) detector. Then, compute the histogram of local detected features inside each sub-region. After that to create the visual words and to reduce the number of features, the K-means clustering approach is applied. The final sets of features are fed to a decision-making model based SVM classifier for automatic plant identification. Experimental results on several publicly available leaf datasets demonstrate that the effectiveness of the proposed method for plant classification compared to a set of state-of-the-art methods.

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  A New Method in Organophosphate Synthesis

  Emily Kathryn Granger, Kasia Maria Solomianko

  A variety of nucleophilic and Lewis acid catalysts were examined for use in promoting the synthesis of organophosphate triesters. All 8 organophosphate triesters are new compounds, reported here for the first time. MgSO4 was discovered as an inexpensive catalyst capable of improving the synthesis of a variety of aryl organophosphate triesters from the readily available and low cost precursor phosphorus oxychloride in a three-step, two-pot sequence. Yields for this method improve upon the uncatalyzed method by 8-36%. Although several chiral catalysts were tested, none were able to induce enantioselectivity in the reaction.

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  An Exploration of Children’s Executive Functioning and Maternal Emotion Regulation Skills: A Proposed Study

  Kirsten Lee Anderson

  Executive functioning (EF) skills, that is, skills involved in planning and problem solving, are imperative for children’s school readiness. According to a nationally representative survey of kindergarten teachers, EF skills are more important for success in kindergarten than purely “academic” skills such as knowing how to count to twenty, recite the alphabet, or recognize colors and shapes (Heaviside & Farris, 1993). Therefore, it is important to understand which factors relate to children’s early development of EF skills, especially for children growing up in poverty who are at risk for academic difficulties. Some research has suggested that there is intergenerational transmission of EF skills, specifically linking maternal EF and child EF, but this association has been limited to EF tasks measuring “cool” EF skills or those not involving emotion regulation (Kim, Shimomaeda, Giulano, & Skowron, 2018). The purpose of the proposed study is to examine if mothers’ self-reported “hot” EF, or emotion regulation skills, are significantly correlated with their child’s performance on their everyday EF skills behavior in both “cool” and “hot” EF skills domains. This study will examine data from 42 mother-child dyads enrolled in a parent education program across sites located in two high-poverty neighborhoods. At the time of enrollment, parents completed a self-report questionnaire of emotional regulation (Wong & Law, 2002) and reported on their child’s “hot” and “cool” EF skills as assessed by the Childhood Executive Functioning Inventory for Parents and Teachers (CHEXI; Thorell & Nyberg, 2008). Aggregate scores will be calculated for parent emotion regulation and the CHEXI for “hot” and “cool” items, and simple linear regression will be utilized to examine if there is a significant association between mothers’ reported emotion regulation skills and their children’s EF skills, controlling for covariates.

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  An Investigation into Bullying and Cyberbullying: The Effects of Anonymity and Form of Bullying on Severity of Victim Impact

  Ashley Marie Harrison

  Bullying has been a problem for many years, but due to the development of electronic communication, cyberbullying in particular has recently become a widespread problem (Boulton, Hardcastle, Down, Fowles, & Simmonds, 2014). Research suggests that cyberbullying may have a greater negative impact for victims than traditional bullying (Gilroy, 2013; Walker, Sockman, & Koehen, 2011). Previous research suggests that cyberbullying may cause impaired mental health and psychological distress and may increase risk factors for suicide among college students (Zalaquett & Chatters, 2014). Research also suggests that when the victims do not know the identity of a perpetrator, it decreases the perceived control the victim has over the bullying situation (Sticca & Parren, 2013). It was hypothesized that cyberbullying victimization would be associated with greater depression and anxiety than traditional bullying victimization alone, and that higher levels of perpetrator anonymity, reduced control, and increased frequency of victimization would explain, or mediate, this difference. Mediation analyses indicated that a reduction in perceived control significantly mediated the association (b = .09, 95% CI = .006 to .246) between cyberbullying and depression. Mediation analyses also revealed frequency of bullying to significantly mediate the association (b = -.08, 95% CI = -.204 to -.005) between cyberbullying and anxiety. These findings indicate that students who experience cyberbullying in addition to traditional bullying, compared to those only experiencing traditional bullying, experienced higher levels of anxiety and depression owing to a reduction in perceived control and an increase in frequency of victimization. These findings suggest that negative consequences of bullying in college may be mitigated by promoting awareness among students to increase their perceived control. Findings also suggest that risk for depression and anxiety in college may be mitigated by promoting bullying prevention programs to reduce frequency of cyberbullying victimization.

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  Applications of Gas Chromatography with Headspace Autosampler

  Paul Robert Maricocchi

  The identification and quantification of chemical compounds in a mixture aids in the analysis of a broad range of processes ranging from chemical separation processes to biological separations. In an effort to enhance interdisciplinary collaboration across Units and Departments within the University of Dayton, we seek to understand and develop Gas Chromatography (GC) and Headspace-GC (HS-GC) analytical methods for use both in the classroom and research. The overall goal of this research is to present a summary and explanation of the variables that are manipulated in a GC equipment for the development of characterization methods. Calibration curves will be used to quantify compounds from different samples. Specifically, the methods and calibrations will focus on: (1) analyzing ethanol content in aqueous and organic mixtures, which ultimately can be applied to chemical engineering unit operations such as distillation and fermentation; (2) characterizing the efficiency of liquid-liquid extraction processes, which will be characterized using HS-GC; and (3) a biological application towards characterizing SCFA (short chain fatty acids) content in Listeria metabolites present in mice feces. This is an interdisciplinary project scheduled for this coming summer with Dr. Sun of the Biology Department, along with Dr. Vasquez of the Chemical Engineering Department. Ultimately, this research will culminate in an Honors Thesis that will help to obtain various GC methods for a variety of processes.

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  Aquatic macroinvertebrate density in headwater streams with varying intensities of riparian Amur honeysuckle (Lonicera maackii) invasion

  Jenea Imani Adams, Eric Bradley Borth, Taylor Melissa Buskey, Julia I Chapman, Mitchell John Kukla, Corey Michael Kuminecz, Michelle Nicole Little, Sean David Mahoney, Meg Eileen Maloney, Joseph Michael Murphy, Taylor Marie Sparbanie

  Headwater streams in the midwestern United States are an important ecosystem because they are home to key macroinvertebrate species and serve as the starting point for freshwater river systems. According to the river continuum concept, any terrestrial inputs to headwater streams can influence biotic communities and abiotic conditions downstream. Amur honeysuckle (Lonicera maackii) is an invasive shrub species prominent in the midwestern U.S. whose allelopathic properties have proven detrimental to local species biodiversity. We hypothesized that increasing Amur honeysuckle density along headwater streams would alter the diversity of aquatic macroinvertebrate communities. Five sites located in Montgomery and Miami Counties, Ohio were chosen to represent varying Lonicera maackii density along the banks of headwater streams: one heavily invaded site, two moderately invaded sites, and two reference sites (little to no invasion). A 30-meter section of stream at each site was divided into five plots that were six-meters long and were used to sample aquatic macroinvertebrates for five consecutive seasons. A dip net was moved across each plot for 60 seconds, and macroinvertebrates were then separated from the collected debris, preserved in alcohol, and brought back to the lab where they were sorted into taxonomic groups. The total number of macroinvertebrates collected did not vary greatly among sites within each season. A preliminary assessment of community composition showed that there was a greater relative abundance of Diptera and Oligochaeta at the heavily-invaded site in fall and winter compared to the moderate and reference sites. The relative abundance of Trichoptera was lower at the heavy site than the moderate and reference sites in fall, spring, and summer. Further analyses are required to understand how community composition varies among the sites and how such differences relate to honeysuckle invasion and macroinvertebrate feeding mechanisms.

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  A Risk Analysis Framework for Evaluating On-Site Wastewater Treatment Systems

  Alexis Latise Wingfield

  The Beaver Water District in Lowell, AR provides clean, safe drinking water to more than 300,000 residents in Northwest Arkansas. As a water utility, the Beaver Water Department is concerned with the potential of on-site systems (septic tanks) contaminating Beaver Lake – the area’s primary source of drinking water. The purpose of this study is to develop a framework to assess the risk potential of on-site wastewater treatment systems in the Beaver Lake Source Water Protection Area. Through a multi-year collaboration, more than 2,400 septic tank permits across two counties have been located, digitized, and used to create a geodatabase. Information from the geodatabase including the geophysical characteristics and system design of each septic tank are considered when assessing a system’s risk of contaminating Beaver Lake. Our risk framework, initial risk assessment, and potential intervention areas are presented.

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  Asking the right questions: An indirect strategy for improving lie detection

  Elliot Duke Buccieri, Adriana Francesca Degenova, Lauren E Murphy, Margaret Frances Weingarz, Scott Anthony Wonderly

  Despite our ability, innate and learned, to perform many tasks, we are, on average, only 54% accurate at detecting lies; this rate of performance is only marginally better than a lucky guess (Bond & DePaulo, 2006). However, research by ten Brinke et al. (2016) suggests that people may be able to detect deception indirectly. Our team, used suggestions of future research from ten Brinke et al. (2016) to study indirect detection of deception by participants watching the video interview of a person suspected of lying. The first hypothesis for this research was that better deception detection would result from questions that primed implicit associations of dishonesty from an observer than from direct questioning of the observer about the dishonesty of a person in a video. A second hypothesis was that the type of question asked of an observer would produce more correct identifications of dishonesty when the questions (a) evoked biases about the person in the video (e.g., stereotypes that individuals in some professions are more or less honest than others), (b) expectations of behavior of the person in the video (e.g., would the person in the video cheat), and (c) probed about nonverbal characteristics indicating the dishonesty of the person in the video (e.g., fidgeting and looking around instead of at the interviewer). A control group of observers was directly questioned about whether the person in the video was lying. Data collection is in progress and our expectation is that indirect questioning will lead to more accurate deception detection than will direct questioning. We also expect that of the three types of indirect questions, those that evoke biases and expected behaviors will produce better deception detection than will questions about the personal characteristics of the person in the video.

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  A Smart Beta Portfolio Model for the SPDR Industrial Sector: An Empirical Analysis, 2009-2017

  Will Luis Perez

  Based on Rob Arnott’s foundational work on using stock fundamentals to weigh portfolios of stocks, I developed a smart beta portfolio weighting model for the top 20 stocks by market value in the SPDR Industrial Sector. The model uses a portfolio weighting factor based on the coefficient of variation (COV). In essence, a stock gets a higher weight if the 1/COV (the return-risk ratio) is higher compared to other stocks. A three year moving average of earnings per share is used to calculate the return/risk ratio for each stock. The return-risk ratios are updated yearly with actual portfolio returns generated for the years 2009-2017. The performance benchmark is the S&P 500 ETF (SPY).

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  A Smart Beta Portfolio Model fo the SPDR Healthcare Sector: An Empirical Analysis, 2009-2017

  Casey (Patrick) Casey Marsh

  In recent years a large number of Exchange Traded Funds (ETFs) have opted for fundamentals based portfolio stock weights rather than equal-weighted or market value weighted. Fundamentals-based weighting models are often referred to as smart beta models because they create stock betas more closely aligned with a stock's intrinsic value. In this study I developed a smart beta portfolio weighting model for the SPDR Healthcare Sector. I selected the top 20 Healthcare Sector stocks by market value as my test portfolio and based on a three year moving average of earnings per share I generated portfolio weights using the inverse coefficient of variation (1/Covariation). Since (1/Cov) is essentially a return-risk ratio, I gave higher weights to stocks with higher return-risk ratios. The portfolio weighting model is re-balanced annually. Portfolio Performance is calculated for the years 2009-2017 and the benchmark is the S&P 500 ETF (SPY).

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  Assessing Adequate Caloric, Macronutrient, and Micronutrient Intake in College Athletes of Various Sports

  Hannah M Boyer, Ashley Briana Marolo, Abby Louise Vichill

  Adequate micro- and macronutrient intakes are important for optimal athlete performance and recovery and decreased risk of injury. However, research suggests collegiate athletes often have suboptimal intakes of calories and nutrients such as calcium, vitamin D and iron. The purpose of this study was to examine the actual dietary intake to estimated need of calories and micro- and macronutrients of collegiate athletes across different sports at a Division I private university. Athletes from football, cross country, volleyball and track competed a three-day food record and 7-day physical activity log. Resting metabolic rate was determined through indirect calorimetry using a Medgem. Caloric need for each athlete was determined by adding the resting metabolic rate, calculated METS from exercise training, and physical activity and thermic effect of food factors. Macronutrient need for each athlete was calculated using determined grams/kg +/-10% based on the sport and compared to the calculated calories to ensure appropriate macronutrient distribution. Lastly, micronutrient intake was compared to the RDA for gender and age. The information from each three-day food record was entered into the Nutrient Database System for Research to obtain measures of each athletes’ calories and macro- and micronutrients consumed. Frequencies and Chi-square analyses were utilized to examine dietary adequacy across gender. Fourteen male and 8 female athletes participated in the study. All athletes were under the calculated need for carbohydrates with 31% not obtaining adequate fat and 36% under for protein. More than 50% were under the RDA for fiber, vitamin D and potassium. In terms of gender, girls were more likely than boys to be under for iron but over for fiber. In conclusion, nutrition education and careful dietary planning should be a focus within the college athletic arena to encourage adequate nutrient intake and optimal athletic performance.

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  Assessing Connecticut’s flood risk using multi-criterion analysis and its implication to environmental justice

  Julie Hays

  This project aims to achieve two objectives: 1) develop a flood risk map for the state of Connecticut using a multi-criterion approach based on geographic information system (GIS) techniques and compare the result with Connecticut’s Flood Insurance Rate Map (FIRM) and 2) investigate the issues of environmental injustice related to the direct financial impact caused by flood insurance policies. The flood risk map is generated using publicly available data and GIS tools. To account for the many elements which are involved with flooding, the Analytical Hierarchy Process method is used to assign a weighted numerical value to the parameters which contribute to flood vulnerability. The flood risk parameters chosen to be included in the study follow the FIGUSED method: flow accumulation (F), rainfall intensity (I), geology (G), land use (U), slope (S), elevation (E), and distance from drainage network (D). The net vulnerability of an area due to these parameters is referred to as the Flood Hazard Index value (FHI). Census data is then used to determine if socio-economically disadvantaged groups are at disproportionate risk. The variables that characterize such groups include race, level of income, and level of educational attainment. The significance of this study is to identify the possibility of minority communities being disproportionally affected by a federal policy regarding flood insurance, providing information for the equitable implementation of its polices.

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  Assessing Inundation Risk and Possible Race and Income Correlations

  Ryan M Matzuk

  This project has two objectives: (1) map areas vulnerable to inundation and (2) determine whether or not there is an racial or income disparity between the citizens of Harris County, TX at risk of inundation compared to those not at risk. Harris County is a relatively flat, near-coastal, highly urbanized, and low elevation area. This makes it highly susceptible to inundation issues from both sea level rise and high-precipitation storms such as hurricanes. Risk areas will be determined by utilizing Hurricane Harvey precipitation data to mimic a large, high-precipitation storm as well as by utilizing sea level rise projection data for the year 2100 to reveal inundation and land-loss from rising sea levels. This analysis will be performed using multiple digital elevation models (DEMs) from 2008 to precisely measure land-surface elevation of the study area. Hurricane Harvey precipitation data will be used in order to replicate precipitation conditions of large storms. The precipitation data will be used to create a map layer in order to visualize the areas at high risk of inundation. A similar map will also be created using sea level rise predictions for the year 2100. Census block data will be applied to the study area and analyzed in order to determine racial and mean income statistics for areas inside and outside of the high-risk floodplains. This study can provide critical information needed for future planning to address climate change as well as environmental justice issues.

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  A stable isotope approach to nitrate contaminant tracing in the Great Miami River Watershed

  Rachel Kristine Buzeta

  The global population has been increasing exponentially and has caused several challenges surrounding sustainability, including the need for greater food production. To meet these demands and boost agricultural productivity, mechanized, more efficient agricultural practices and chemical fertilizers are used. These chemicals have resulted in water pollution and water quality degradation. Much of the Great Miami River Watershed's streams and aquifers are impacted by excessive amounts of nutrients such as nitrate originating from anthropogenic sources including use of fertilizers for agriculture, human wastes (domestic, industrial, and municipal wastes), and urbanization. High nitrate concentrations can cause ecological disturbances and affect organisms across all trophic levels. It also poses a danger to human health (nitrate levels greater than 10 mg/l) if the contaminant reaches drinking water sources. Although a network of water quality monitoring stations report nitrate concentrations in surface and groundwater, contaminant source tracing has not been done. Here we used isotope fingerprinting techniques to trace sources of nitrates. Isotopes of nitrogen (δ15N) and oxygen (δ18O) are used to identify unique nitrate isotopic signature from different sources. Our results show distinct δ15N and δ18O isotopic signatures from different land use and sources such as agriculture, septic systems, and animal waste. Further analysis of boron isotopes (δ11B) is used to distinguish anthropogenic sources (synthetic fertilizer, wastewater) from natural sources (organic fertilizer). Preliminary data has shown that different nitrate sources have different ranges of δ15N, δ18O, and δ11B values. The collective data from our first-round of sampling suggests that the isotopic composition of these sources can be used to quantify contaminants in groundwater that comes from those sources. The outcome of this research could provide a regional baseline for nitrate contaminant tracing and help to inform state and local water quality management and public health policies related to nitrate resources.

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  A Survey of Tundra Lake Size Changes During Around 40-year Time Interval Observed in Historical Maps and Satellite Images

  Ming Gong

  Greenhouse gas emissions from tundra lakes are a significant positive feedback to the atmosphere in a changing climate as a pronounced growth of the numbers of tundra lake patterns has been observed in the Arctic region. Detailed knowledge of changes in tundra lakes size is potentially valuable in order to understand and accurately model the sources of greenhouse gas emissions. Therefore, we are using historical maps and satellite images with time interval around 40-year to show a study of tundra lake size changes. We have developed a novel algorithm framework that is employing three main processing stages: lake detection, lake segmentation, and lake size computation. In the first stage of the framework, there are two different approaches, one is for detecting the lakes on historical maps that is a color-based segmentation technique, and another one is for detecting the lakes from satellite images which is a decision-making model based on support vector machine classifier (SVM). The second stage of the algorithm is a region growing approach that is applied for the detected lakes from both historical maps and satellite images, to segment the actual lake size. The last stage is calculation the lake size which is applied for the final segmented lakes from both historical maps and satellite images. It is based on connected component analysis strategy, which calculates the lake size in terms of number of pixels. Experiments performed on changes in lake size over time in a set of lakes that were visually matched in both the historical map and the satellite imagery demonstrate that some lakes in our study region have increased in size over time, whereas others have decreased in size over time. Lake size change during this time interval can be up to half the size of the lake as recorded in the historical map.

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  Authentication Of the Internet of Things Devices Over ZigBee Networks

  James Patrick Althoff

  IOT, or the Internet of Things, is the inter-networking smart devices, buildings and other embedded systems to enable them to transfer data between them. This data can be used for various uses such as power management and home automation. Current projections of the Internet of Things predicate that the use of this technology will increase dramatically within the foreseeable future. Many of these devices are currently being implemented using protocol such as Bluetooth and ZigBee. ZigBee is a wireless communication protocol based on the IEEE 802.11.4 standard. ZigBee was created for low power devices, such as those that run on batteries, with the industrial settings being among the common implementation of ZigBee enabled devices. The project focuses on improving the ZigBee protocol, specifically in the authentication section of the protocol.