Stander Symposium Projects

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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.

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  Autonomous Self-Driving RC Car

  Kenneth Bester, Garrett M O'Grady

  With the age of automation coming to machines, self driving cars will turn from a fantasy to a reality in the next decade. These machines will utilize neural networks to learn from pilot drives to ultimately be able to drive completely autonomously on any road they are put on. This project demonstrates a small fraction of the technology that goes behind a self driving car with an implementation on a 1/16th scale RC car. Using the open source software ‘Donkey Car’ we were able to turn an RC car into a self driving car that get more intelligent every time it drives. Mounted above the car is a 3D printed roll cage which houses a fisheye lense camera (for image recognition) raspberry PI 3 Model B (for the neural networks) and a Servo Controller(to control the throttle and steering). As a demonstration during the presentation, the car will drive simultaneously around a track avoiding obstacles, following street laws and remaining between the lines of the road.

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  Bargain Brand Justice: Ohio's Indigent Defense Funding Model Makes Justice Inaccessible and Undermines the Sixth Amendment

  Ebony D Davenport

  In 1963, the Supreme Court of the United States ruled that the Sixth Amendment right to counsel shall not be denied to anyone due to their ability to pay in Gideon v. Wainright. The right to adequate legal representation is a noble one that sets this nation apart from other areas of the world. However, without the proper infrastructure, this right becomes less revolutionary and more decorative. When public defender offices are not properly funded, the most vulnerable among us are denied a fundamental right; justice becomes an experience exclusive to the elite; and the adversarial system is won not by the best advocate, but by the depth of her resources. The county-by-county funding model employed by the state of Ohio has the potential for causing disparities among counties because public defender offices are funded based on property tax revenue. This approach to funding creates a piecemeal system in which one’s access to justice depends largely on which county he is arrested. When the burden is shifted to the counties, they struggle to provide for public service like waste management, libraries, and schools. This strain on funding creates a Hunger Games-like situation wherein various public services compete for extra crumbs. Because Ohio’s current funding model for indigent defense prevents residents from equal access to justice, thus depriving them of a fundamental right to counsel, a new funding source that is independent from the general fund will allow for (1) counties to be fully staffed, (2) proper expert witnesses that can potentially strengthen a client’s defense, (3) reduce excessive caseloads which undermine quality representation, and (4) ensure that Gideon’s promise is upheld.

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  Beyond the Macro: exploring micronutrients in insect communities

  Kaitlin Marie Gawkins, Meg Elizabeth Gramza

  Macronutrients are widely considered to be an important limiting factor for insect herbivore populations in grassland ecosystems. However, a potential co-limitation by micronutrients has long been overlooked. We are studying the effect of soil micronutrients on herbivore community composition in coastal tallgrass prairies of Texas with a large-scale multi-nutrient, fertilization experiment manipulating Ca, K, and Na in concert with N and P. Initial results indicated that orthoptera abundance and diversity are co-limited by macronutrients and sodium. To determine if these effects arose through the herbivores feeding more heavily on plants with NP and Na added, we used in-lab choice trials with seven species of orthoptera from three feeding guilds with leaves from four plant species (2 grasses and 2 forbs) that were grown in treatments with either ambient soil nutrients or Na, NP, or Na plus NP added. We then determined how much each individual ate of each treatment leaf at the end of the 48 hour trial. With no preference and a choice of 4 leaves, each leaf should make up on average 25% of each individual’s total consumption. We compared each individuals feeding to these expected amounts and used pairwise t-tests to determine whether preferences existed. Pooling across all individuals, orthopterans chose the NP+Na leaves significantly more than any other leaf type. This finding suggests that orthopterans respond directly to leaf chemistry changes arising from our treatments and have important implications for management practices: orthopterans are considered major pests to agricultural systems and are controlled with billions of dollars of pesticides annually. However, because NP and Na in soils are both enhanced with current agricultural practices, our findings suggest that humans could actually be causing these insect herbivores to thrive and become abundant in agricultural systems by adding these limiting nutrients.

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  Beyond the Picture: Elaboration more than Description Enhances Memory and Appreciation for Paintings

  Jacob Severino Avendano, Abigail T Flower, Tessa Nicole Jatczak, Brad Charles Wolfred, Yu Zhao

  Aesthetic preferences involve many factors that determine how people respond to art. For example, the context surrounding photographic art (e.g., titles) influence its aesthetic appeal (Millis, 2001). The present study examines the effect of titles on aesthetic preferences for paintings. We hypothesize that (a) participants will show greater preference for paintings with elaborative titles compared to descriptive titles, and (b) participants will have better memory for paintings with elaborative titles compared to descriptive titles. Participants evaluated images of lesser-known Van Gogh paintings. Prior to the experiment, a sample of students was presented with these paintings along with elaborative and descriptive titles to verify that the titles were valid. In the experiment, all participants were exposed first to paintings without titles. Next, participants were exposed to the same paintings in a randomized order with titles. Titles were either elaborative (evoking a deeper thought process, such as “Water Sustains Life” for a painting of a bridge over a stream) or descriptive (naming objects in the painting, such as “Bridge over Water” for the same painting). Participants were asked questions assessing preferences: if they liked the painting, if they would buy the painting, and if they would hang the painting in their home. A control group also assessed the paintings twice, but without the titles present either time. All participants then completed tasks unrelated to the experiment to distract them from reviewing the paintings. Finally, participants viewed the original paintings intermingled with new Van Gogh paintings to test their memory for the original paintings. Preliminary results show that participants prefer and recall more of the paintings with elaborative titles compared to descriptive or no titles. These results support our hypotheses, suggesting that elaboration enhances one’s experience when viewing paintings, and elaboration also make the paintings more memorable.

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  Brain Machine Interface Software Application for Data Collection, Thought Analysis, and Robotic Arm Control

  Jason Demeter, Alexander Robert Jereb, Clayton T Kern, Brad Richard Sorg, Jamie Stanton

  The overall purpose of the ongoing Brain Machine Interface (BMI) project is to develop an electroencephalography (EEG) interface and a robotics control application which will further enable people with disabilities to achieve autonomy. The project consists of developing, building, and testing an end-to-end system to translate raw EEG data into actionable information. This can be used to control a robotic arm and for other research purposes. A BMI is a system that collects the brain’s electromagnetic signals by utilizing sensors, extracts meaningful signals from the data, classifies thoughts, and ultimately uses thoughts as an input to a computer system. The computer system then has the ability to control hardware and software, which for this project is a robotic arm. The team improved the robotic arm user interface, developed a graphical user interface (GUI) for thought recognition, and explored future research paths by partnering with local experts. To improve the usability of the robotic arm user interface, the team developed software that allows easier performance of useful activities, such as using a pen to play tic-tac-toe, playing piano, and picking up objects. The Insight headset by Emotiv was used by the team for data collection. The headset can stream real time EEG data and control signals, however the Emotiv software solution for data collection is closed and proprietary. To use the Vision Lab’s noise reduction and muscle signal removal algorithms, the team created a GUI to train the thought classification system and collect and process the data. EEG phoneme detection is a future research path that allows for thought to speech translation. The team investigated EEG phoneme detection by implementing algorithms which can identify phoneme sounds from audio recordings. Using these working algorithms, further research will implement phoneme detection using only EEG signals with no audio.

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  Business that Changed the World and Media Moguls

  Maryam Sulaiman Said SULAIMAN Al Ofi, Hassan Ali A Al Zainaddin, Fahad Y F A A Alduaij, Mahdi M E A H Alsaffar, Muhammad H H H M Alsarraf, Saleh Mohammed

  The presenters are students from the Intensive English Program. The posters they have created are representative of an end of term project for the level 3 Oral Communication and Listening/Note-taking course; an intermediate class for English language learning students at the intermediate proficiency level. The goal of this assignment is to give students an introduction to research and an opportunity to improve their speaking and presentation skills.

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  Caffeine Rush! Examining the Effects of Caffeine on Spatial Working Memory.

  Alexander N Lawriw

  Caffeine is well-known for its ability to make a person feel more attentive, more alert, and less fatigued, but could this popular stimulant also help improve a person’s memory, as well? Prior research suggests that caffeine might be a cognitive enhancer with participants showing improved performance on short-term memory tasks such as the recall of word lists (Arnold et al., 1987; Barraclough & Foreman, 1994; Rogers & Dernoncourt, 1998; Ryan et al., 2002; Schmitt, 2001a, b). However, conflicting results using similar tasks have called these findings into question. On top of this, the overall body of research concerning caffeine and memory has tended to focus solely on relatively simple assessments of newly established episodic memory, leaving a glaring gap in the literature when it comes to other types of memory (e.g., semantic memory). The present research aims to fill this gap by studying caffeine’s potential effects on spatial working memory, the temporary storage, maintenance, and manipulation of spatial information. In the experiment, participants were asked to complete levels of varying difficulty within a computerized version of the popular puzzle game, Rush Hour, after consuming either a 200 mg caffeine pill or a placebo. Rush Hour requires the player to move a designated red ‘target’ car to the exit of a 6 x 6 grid. Blocking the exit are other cars that can only be moved horizontally or vertically depending on the direction they are facing. Participants must use the spatial information of the grid layout in order to complete the levels as efficiently as possible. We hypothesize that those participants given caffeine will complete these levels quicker and with fewer errors than those given a placebo. However, this increase in performance may be limited on more difficult levels due to increased workload and ensuing stress.

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  Challenging the American Dream in Dayton, OH

  Abby Anne Pech

  The ideology of the American Dream has a negative impact on those who are low income by creating a false perception of what it takes for residents to reach their full potential. The American Dream emphasizes that everyone has the freedom and ability to succeed, economically and socially, through hard work and dedication, but fails to address the underlying barriers that stand in the way of this achievement. Drawing on narratives from the Facing Project in Dayton and social science literature, it is clear that non-merit factors such as social capital, cultural capital, and inheritance hinder the poor from achieving the American Dream. The goal of this poster is to highlight the historical impact of the American Dream on people in the Dayton area and emphasize how the upper class has an unfair advantage over the lower class. It will delve into the functional and conflict explanations of poverty and examine the issues surrounding the four ingredients needed to obtain the American Dream, which include talent, the right attitude, hard work, and moral character. In order to level the playing field in the Dayton area, I recommend a number of measures that could begin to foster equal opportunity to the American Dream.

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  Challenging the American Dream in Dayton, OH

  Abby Anne Pech

  The ideology of the American Dream has a negative impact on those who are low income by creating a false perception of what it takes for residents to reach their full potential. The American Dream emphasizes that everyone has the freedom and ability to suc­ceed, economically and socially, through hard work and dedication, but fails to address the underlying barriers that stand in the way of this achievement. Drawing on narratives from the Facing Project in Dayton and social science literature, it is clear that non-merit factors such as social capital, cultural capital, and inheritance hinder the poor from achieving the American Dream. The goal of this poster is to highlight the historical impact of the American Dream on people in the Dayton area and emphasize how the upper class has an unfair advantage over the lower class. It will delve into the functional and conflict explanations of poverty and examine the is­sues surrounding the four ingredients needed to obtain the American Dream, which include talent, the right attitude, hard work, and moral character. In order to level the playing field in the Dayton area, I recommend a number of measures that could begin to foster equal opportunity to the American Dream.