Stander Symposium Projects

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  Linkages In Mechanical Presses That Produce Substantial Dwell

  Matt Owen Deters, Zack James Jordan

  A mechanical press is a common manufacturing machine that is used to form and cut sheet metal. Presses can use mechanical, hydraulic, or pneumatic power to delivering a force over the stroke of the machine. The benefit of mechanical presses is the high stroking rate that can be achieved with low energy input while in use. Mechanical presses use a flywheel to store energy and a series of linkages to convert rotational motion into linear motion. Some forming operations like coining and squeezing benefit from a dwell at the bottom of the stroke, a position commonly referred to as bottom dead center (BDC). This research explores models for different mechanical systems that obtain this desired dwell. The designs presented include variations of a knuckle joint press mechanism and variations of a geared five bar with connecting rod and sliding output. Each of the mechanisms is being developed as a solid model and animation to assess the viability of the proposed designs.

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  Living with Loss: Resources Available to UD Students Grieving the Death of a Loved One

  Lexi Diane Brezovec, Henry A Kuechly, John R Moresea

  College students deal with a variety of stressors throughout their college experience. Several major stressors that arise on college campuses have been heavily researched and publicly addressed, while other stressors are ignored. One particularly prevalent overlooked stressor is the death of a family member or close friend of a student. Research has shown that anywhere from 22-30% of college students have experienced the loss of a close friend or family member within the past year. Additional research has shown that living with loss can negatively influence a students’ GPA and social life. Due to such a high percentage of impacted students, along with the impact on GPA and social life, we believe it is important to address this issue and what resources are available to University of Dayton (UD) students. Our research has primarily focused on the resources available at other universities as well as those resources available at UD. After conducting in person interviews and reviewing survey results, we have concluded that this topic is an issue that current UD students face. While many students are impacted by this issue, we discovered limited resources on UD’s campus to assist students in coping with this problem. Due to a lack of resources, our team will propose recommendations along with a brochure for students in order to aid in the grieving process.

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  Machine Learning for Cyberattack Detection

  Kayla D Chisholm

  Over the last two years, machine learning has become rapidly utilized in cybersecurity, rising from almost non-existent to currently over half of cybersecurity techniques utilized commercially. Machine learning is advancing at a rapid rate, and the application of new learning techniques to cybersecurity have yet to be investigated. The presented work discusses the use of novel machine learning algorithms for network intrusion detection. Typically, this type of network intrusion detection system operates based on a set of rules programmed to recognize known attacks and intrusion techniques. However, this detection method does little to prevent new, or ‘zero day’ attacks. On the other hand, a detection system that uses machine learning could analyze patterns in network data in real time to determine attack likelihood. To test the efficacy of machine learning and neural network algorithms for network security, we use publicly available data sets to ‘teach’ these neural systems what an attack may look like. After training, the system will be tested to determine how well it learned the features contained within the input data. Our results show accuracy and error rates of the algorithms that have been implemented.

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  Maternal Depression, Child Temperament, and Risk for Depression in Adolescence: A Test of the Differential Susceptibility Hypothesis

  Karina H Palermo

  This study examined the relationship between maternal depression, child temperament, and maternal parenting. Maternal depression has many effects on perceptions of and reactions to everyday interactions with the child (Belsky et al, 2007). Evidence has been found for depressed mothers reporting negative child behavior, while teachers report positive or neutral behavior in children of depressed mothers. Depression may skew a mother's understanding of her child's behavior. On the other hand, a child's difficult temperament may be especially challenging for a depressed mother. The depression, in combination with child difficult temperament, may cause changes in parenting styles that put the child at a greater risk for depression. The purpose of this study was to examine whether links between maternal depression and maternal parenting vary according to child difficult temperament. The identification of risk factors, such as difficult temperament or maternal depression, is important when trying to develop effective intervention strategies. It was predicted that maternal depression would be more strongly predictive of parenting deficits for mothers of children with a difficult temperament. This study used previously collected longitudinal data from a nationally representative sample of mothers and their offspring. Maternal depression before the birth of their child was measured using the CESD. Mothers reported on their children’s temperament between 1 and 23 months of age. Maternal parenting was assessed when children were between 3 and 5 years of age using a combination of an interview and observer ratings. Maternal depression showed a trend level association with decreased responsiveness (Beta = -.16, p = .076), and showed a significant association with increased harsh parenting (Beta = .15, p = .047) and decreased learning stimulation (Beta = -.14, p = .002). Fussy temperament did not moderate associations between maternal depression and parenting. These findings suggest that maternal depression is a risk factor for parenting characteristics that are associated with negative child outcomes, but that difficult temperament does not affect these associations.

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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.