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  Complications of Housing Arrangements in Dayton

  Dylan Anthony Penna-Powell

  American homeowners accumulate material wealth as they gain equity on their homes. A sociological look at how people are distributed around the city of Dayton according to their socioeconomic backgrounds reveals more than geographical details of social and economic inequality. This presentation explores the effects that disparities in housing arrangements have on closely related areas of interest such as education, access to affordable and nutritious foods, and individuals' likelihood of economic mobility. In addition, opportunities for reform through public education funding via local property tax dollars will be discussed. Reformation of a system in which the value of one's home is tightly linked to the quality of nearby public schools would provide greater opportunity for social and economic mobility to those in low-income communities.

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  Contrasting Patterns of Small and Large Glacial Lake Evolution in the Nepal Himalayas

  Katherine A Strattman

  The objective of this research is to assess annual ice velocities of three Himalayan glaciers in the Mount Everest region of Nepal. Glaciers worldwide are important indicators of climate change due to their tendencies of attaining equilibrium under changing climatic conditions. Imja, Lower Barun, and Hongu glaciers and their respective proglacial lakes have responded by retreat and growth, but at varying rates. Imja and Lower Barun Lakes have grown rapidly, but Hongu Glacier Lake has shown relatively slower growth. Despite the little accelerating growth of Hongu Glacier Lake, the moraine is composed of unconsolidated and unstable material, and poses the threat of an outburst flood due to less freeboard area. Therefore, it is important to monitor the development of all three lakes, as well as consider fluctuations of surface velocity. Using Landsat satellite imagery, I assessed the annual changes in surface ice velocity from 1992 to 2017. The yearly images were used as inputs to COSI-Corr, a co-registration and sub-pixel correlation software, to track changes on the glaciers surface. My results indicate short-term variations, despite all three glaciers’ location within the Mount Everest region, as well as similar long-term trends. Specifically, Imja, Lower Barun, and Hongu glaciers have shown long term trends of deceasing surface velocity, with varying rates of flow within each yearly pair. The three proglacial lakes have all grown at different rates as well, with Imja showing very rapid growth since the 1960s, Lower Barun showing deaccelerating growth, and Hongu showing very little growth. Understanding the dynamic nature of surface velocity can provide insight on overall glacier health, and may reveal how glaciers respond under rapid lake growth.

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  Convolutional Neural Network Based Multi-view Object Classification

  Zhiyuan Xie

  In recent years, neural networks have become more and more popular because of their outstanding performance in the object classification area. The convolutional neural network (CNN) is a deep learning, feed-forward neural network that has excellent performance in visual imagery analysis area. The idea of the connectivity pattern between neurons of the CNN came from the organization of the animal visual cortex. For human vision, different observational directions of objects can get different views. Human can easily recognize objects in different observational directions, but machines cannot achieve this easily. Therefore, multi-view object classification has been researched for many years. To solve this problem, we design an efficient CNN architecture to perform classification of the multi-view images of objects by appropriately choosing the number of layers, the sequence of layers cascading, and size of the filters. Then, we improve the classification performance by adding image enhancement techniques before CNN as a preprocessing stage. CNN extracts various significant features of the image. It is expected that an enhanced image helps to extract stronger features. The training and testing input images of the CNN are original images or enhanced images. The image enhancement is performed by nonlinear enhancement techniques such as multilevel windowed inverse sigmoid (MWIS) function based technique or a locally tuned sine nonlinearity (LTSN) technique. It is observed that the preprocessing by image enhancement provides improved performance in the cases of the smaller training set. Research work is in progress to modify the CNN architecture to see the impact of recognition performance for multi-view object classification. Advanced non-linear enhancement technologies might also be investigated to see the effectiveness in classification.

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  Cracking the Shell: An Investigation of Shell Repair in the Oyster, Crassostrea virginica

  Alyssa Ashley Outhwaite

  Mollusc shell formation has been an intriguing phenomenon for decades and current research efforts represent a paradigm shift in how oyster shell formation occurs. The older model of shell formation suggests a lack of cellular components as transport vectors for organic and mineral components. However, current research focuses on the potential role of oyster blood cells, hemocytes, in moving organic and mineral components to the shell formation front. A protein biomarker, the amino acid L-3,4-dihydroxyphenylalanine (L-DOPA), is unique to the proteins involved in insoluble organic matrix formation. Tracking the location and temporal occurrence of L-DOPA-containing proteins reveals the potential role of cells in shell repair. Three notch-repair experiments were conducted: a short term 36-hour notch-repair study, a mid-term 7-day notch-repair study, and a long term 8-week notch-repair study. At discrete time intervals, selected oyster compartments of hemocytes, mantle tissues, hemolymph, and nascent shell were sampled to determine the spatial and temporal distributions of the DOPA biomarker. Preliminary results show an increase in DOPA concentration in hemolymph from 0 to 48 hours. Conversely, hemocytes show a decrease in DOPA over time, with the greatest amount of DOPA present at 0 hours and a subsequent decrease over the course of repair. Additionally, nascent shell was analyzed during the 8-week study through the use of scanning electron microscopy (SEM). Analysis of the shell surface showed haphazard crystal formation under normal mineral deposition with crystals irregular in size, shape, and general placement. Newly formed shell from a notched specimen at 48 and 96 hours after notching; however, is characterized by directional and more uniformly shaped crystals. Together these results suggest that hemocytes are selectively shuttling and releasing protein resources to areas of shell repair and provide additional support for the cellular mediated shell formation model, where hemocytes play an active role in materials transport.

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  Creating a More Effective Explanation of Concussions for Intensive English Program Students

  Taylor Morgan Balk, Mark D Bugada, Caroline A Lynch, Olivia Marie Stanforth

  Low health literacy is a huge obstacle in communicating medical conditions and information to patients. An additional obstacle is created when physicians are communicating with international patients whose primary language is not English. The purpose of this project was to present written information on concussions to international students in the Level 2 Intensive English Program (IEP) at the University of Dayton in a format that they could read comfortably. For our methods, we went into the IEP classroom to assess the students’ knowledge of concussions. We then used the information the students provided along with an original fact sheet produced by the Center for Disease Control to create a brochure that would help the IEP students better understand concussions. We altered the original document’s wording, format, and content in order to display the information to the students so that they could better understand the material. Our document focused specifically on what a concussion is, its symptoms, and recovery tips. This final brochure corresponded with the IEP students’ reading level and allowed them to comprehend the information more clearly.

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  Cross Cultural Connections: A Phenomenological Study of Intercultural Learning from Intercultural Living.

  Megan Jacoby Woolf

  Higher education administrators and student affairs staff on some residential campuses have attempted to be more inclusive of international students and their U.S. peers by creating co-curricular learning communities addressing intercultural living. Intercultural residential communities, like Cross Cultural Connections (CCC) at the University of Dayton, serve to support the transition to intercultural living for first-year students. Through eight interviews of former CCC residents, this qualitative, phenomenological study explored how the community cultivates intercultural competence. According to the Refined Developmental Trajectory of Intercultural Maturity (Perez, Shim, King, & Baxter Magolda, 2015), former residents of the CCC expressed varying levels of advancements of their intercultural competence. This study assesses the longitudinal success of the CCC's learning outcomes and gives suggestions for similar intercultural residential communities.

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  Cross-Cultural Medical Interpretation: A Proposed Certificate for the University of Dayton

  Kevin Laurence Outwater

  The medical profession today is anchored with English as the primary language. Therefore, some individuals may struggle to communicate, causing the usage of interpreters to lower the burden for these patients. This interpretation model is going to address not only the primary skills of medical interpreters, but will also address languages and culture, in attempt to highlight meanings, and integrating other cultural notions. Since the demographics in the United States is changing, it is necessary to adapt these language and cultural changes to the medical field. Through this, I propose this interpretation model to be incorporated at the University of Dayton, becoming a medical interpretation certificate. This would follow the University’s belief in “Commitment to Community” by engaging with members of the Dayton area.

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  Crystallinity and Surface Morphology of Reactively Magnetron Sputtered Aluminum Scandium Nitride

  Rachel L Adams

  Aluminum nitride (AlN) is a low-loss piezoelectric that is commonly used in surface acoustic wave (SAW) and bulk acoustic wave (BAW) based filters for RF communication applications. It has previously been demonstrated that alloying AlN with scandium nitride (ScN) results in an increase of the piezoelectric coefficient up to five times greater than that of pure AlN without significantly increasing losses. This result could have significant impact on next-generation RF-filters. In order for this material to be incorporated into devices, a more thorough understanding of its growth, structural, electrical, and piezoelectric properties is needed. In this work, we investigate the role of deposition parameters during controllable-unbalanced reactive magnetron sputtering on the crystallinity, surface morphology, and composition of aluminum scandium nitride (AlxSc1-xN) on (0001)-oriented sapphire substrates. The conditions considered in this study include the sputter power, the nitrogen gas fraction, the gas ion flux to metal neutral flux ratio (ii/iMe) controlled by the coil current on an external electro-magnet, the substrate temperature, and the sputtering target Al-to-Sc ratio. X-ray photoelectron spectroscopy was used to determine the Al, Sc, and N concentrations in the films. X-ray diffraction of the films showed that the crystallinity was dependent upon ii/iMe and substrate temperature. The surface morphology, determined using atomic force microscopy, showed a similar dependence.

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  Data for our Students: How Three Large Public Universities Use Tech-Based Solutions to Foster and Track Student Success

  Ellen Elizabeth Marburger

  Using technological applications and databases for tracking student success in higher education is slowly becoming a necessity rather than a recommendation, especially at large campuses. This content analysis study sought to examine the breadth and depth of the use of these applications amongst three such universities in the midwest by analyzing and coding publicly available data around the themes of evaluation strategies, solutions, and communications. This study first examined the current commentary and research around this topic and defined student success indicators, identified three campuses, and finally compared the systems used, purposes for each, capabilities of each, and what gaps may still exist in the context of using such technology to assist in student success. Key findings suggest that this subset of the field is evolving, and widespread use and integration of these systems may be the next step for campuses and professionals, supplemented by future research.

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  Data Mining Approach for Estimating Residential Attic Thermal Resistance from Aerial Thermal Imagery, Utility Data, and Housing Data

  Salahaldin Faraj Alshatshati

  Conventional residential building energy auditing needed to identify opportunities for energy savings is expensive and time consuming. On-site energy audits require quantification of envelope U-values, air and duct leakage, and heating and cooling system efficiencies. There is a need to advance lower cost automated approaches, which could include aerial and drive-by thermal imaging at-scale in an effort to measure the building U-value. However, the thermal imaging approaches implemented to date, all based upon thermal-physical models of the envelopes, to estimate the U-values of walls require additional measurements and analysis prohibiting low-cost, at-scale implementation. This research focuses on interpreting aerial thermal images to estimate the U-value of roofs. A thermal-physics model of a ceiling is developed to show the difficulty in using the same approach used by others for walls, as new parameter estimates and thus more measurements would be required. A data-based methodology instead is posed. This approach integrates the inferred roof temperature measurement, historical utility data, and easily accessible or potentially easily accessible housing data. A Random Forest model is developed from a training subset of residences for which the ceiling U-values are known. This model is used to predict the roof U-values in a validation set of houses with unknown U-value. Demonstrated is an ability to estimate the attic/roof U-value with an R-squared value in the range of 0.96 using as few as 24 training houses. The implication of this research is significant, offering the possibility of auditing residences remotely at-scale via aerial and drive-by thermal imaging

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  Data Mining of Smart WiFi Thermostats to Develop Multiple Zonal Dynamic Energy and Comfort Models of a Residential Building

  Abdulrahman Mubarak Q Alanezi, Kefan Huang

  Smart WiFi thermostats have gained an increasing foothold in the residential building market. The data emerging from from these thermostats is transmitted to the cloud. Companies like Nest and Emerson Climate Technologies are attempting to use this data to add value to their customers. This overarching theme establishes the foundation for this research. This research seeks to utilize WiFi data from the Emerson Climate Technologies’ Helix test house to: develop a dynamic model to predict real time cooling demand and then apply this model to running ‘what-if’ thermostat scheduling scenarios with the ultimate goal of reducing energy use in the residence or responding to high demand events. The Helix residence, with two thermostat controlled zones for each floor, exists in a temperature/humidity controlled external environment, which can be controlled to simulate environmental conditions present in the hottest to coldest climates. A Design of Experiment approach was used to establish data needed for the model. The control variables in the experiments included: levels for the exterior environmental schedule and levels for the interior setpoint schedules for both zones. Simply, this data enabled data collection for constant or cyclical exterior environmental conditions and constant and scheduled interior setpoint conditions, not necessarily the same for each floor. From this data, a regression tree approach (Random Forest) was used to develop models to predict the room temperature as measured by each thermostat, as well as the cooling status for each zone. The models developed, when applied to validation data (e..g, data not employed in training the model) R2 values of greater than 0.95. Then, the models developed were utilized for various ‘What if’ scenarios. Two such scenarios were considered. The first looked at the possibility of using the model to estimate comfort in a demand response event, e.g., when the grid manager calls for demand reduction. In this case, the heat pump providing cooling would be powered off for some time. The second scenario sought to quantify the cooling savings from use of higher thermostat setpoint during simulated non-occupied periods and for different exterior temperature schedules. The ‘What if’ predictions are validated with experimental data, thus demonstrating the value of the data-driven, dynamic data solely from smart WiFi thermostat information.

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  Dayton and the Great Recession

  Nick Steven Lafrance

  Today, the city of Dayton, Ohio is still currently experiencing the side effects of the housing plummet in 2008, also known as the Great Recession. This poster will examine how, what, and why the Great Recession of 2008 happened. Meanwhile, keeping in mind that this is not the first time our country has experienced an economic fall. Drawing on social inequality literature, Facing Project Narratives, and national and local data, this poster will follow the effects the Great Recession has had on our local economy and more specifically the city of Dayton.

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  Dayton and the Opioid Epidemic: A Tragedy in the Making

  Courtney Rose Kemna

  Dayton has one of the highest drug overdose rates in the United States. This opioid epidemic can be linked to a number of broader socioeconomic challenges facing the area, including unemployment, the decline of manufacturing, and location. This poster will highlight information on this epidemic and its causes in the Dayton area. To do this, I will draw on social science concepts and theory as well as informal interviews with professionals who are engaged in the epidemic. In addition, I will provide an overview of current efforts in Dayton to address this issue (e.g. Community Overdose Action Team, Miracle Makers, and East End Community Services) and argue that long term solutions will require addressing the root causes of the epidemic.

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  Deep Neural Network Based on FPGA

  Shuo Zhang

  With the rapid proliferation of computing systems and the internet, the amount of data generated has been increasing exponentially. This includes data from mobile devices, where almost all information is now becoming computerized, and science experiments, where large simulations on supercomputers are increasingly becoming the norm. With this massive increase in data, a key issue is how we process and make sense of this data. This is called the “Big Data” challenge. Deep learning is a class of mathematical algorithms that is now heavily used for Big Data analytics. These algorithms are based on very large scale neural networks. One of the key challenges with deep learning is that it requires massive computing power. At present clusters of high performance graphics cards designed primarily for computing (known as GPGPUs) are used for these tasks. A key problem with clusters of GPGPUs, is that they consume large amounts of energy, thus making it difficult to scale existing massive computing systems to future Big Data volumes. The deep neural network designed by Parallel Cognitive Systems Laboratory is based on application specific integrated circuits (ASIC), which provides high performance at reasonably low power consumption. However, these are extremely expensive to fabricate. The Field Programmable Gate Array (FPGA) is a type of integrated circuit that can be reconfigured to implement a large range of arbitrary functions according to application requirements. FPGAs are much cheaper than ASIC and consume less power than CPU and GPU. The objective of this proposal is to develop deep learning network based on FPGA. I will optimize the whole design to make it more suitable for the deep learning. Several pattern recognition applications which use deep learning will be used to test and evaluate the design.

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  Dental Health Beliefs of Chinese College Student based on the Health Belief Model

  Peijun Hou

  Dental health problem is a serious and underexplored topic in China. This study used Health Belief Model to investigate the health belief of dental health promotion behavior among Chinese college students at University of Dayton. The results indicated that laziness is the biggest barrier for daily brushing behavior, while most of them also lack knowledge about flossing. The barriers to annual dental check-ups include expensive cost and disregarding the importance of oral health. The study also examined the preference of information seeking and scanning channel about dental health-related information. Most respondents come across information about health-related knowledge through social media and mass media, though they prefer to search such information through dentist and social media. Most participants think their dental health-related knowledge is moderate. One the most interesting findings is about self-efficacy, as most of Chinese students answered they have no idea when they were asked self-efficacy related questions such as “What can improve someone’s confidence on flossing daily?”. Consider the concept “self-efficacy” was built under Western culture and society, even though self-efficacy has been shown to be a strong predictor of performance with Western populations, whether self-efficacy can predict performance with non-Western populations is still not clear.

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  Depression, Suicidality, and Sleep Disturbances: A Literature Review

  Karla Leigh Borgerding

  Past research has shown that it is common for people with depression to report sleep disturbances. However, the relationship between sleep disturbances and suicide is not as well known. The current literature review looks at several articles relating to depression, suicidality, and sleep disturbances. Specifically, how sleep disturbances increase mood dysregulation which may leads to an increased amount of suicidal thoughts (Cukrowicz et al., 2006). Specific types of dreams (e.g. a nightmare) seem to be a prominent symptom of sleep disturbance that may result in greater mood dysregulation, and consequently, more severe and frequent suicidal ideation (Ağargϋn et al, 1998). In one study, women tended to report more frequent nightmares than men and were more likely to report suicidal ideation (Ağargϋn et al, 1998). From these articles, it is concluded that sleep disturbances are a possible risk factor for suicide among depressed populations. This could imply that therapists should incorporate sleep monitoring into their treatments for depressed and suicidal patients.

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  Design of a Body-Powered, Variable Strength and Conformable Shape Prosthetic Hand

  Zhipeng Jiang

  People with below elbow amputations often wear a body-powered prosthesis due to its affordable cost and the fact that it can improve their ability to do daily activities. While this technology is functional, it poses difficulties when trying to work with a range of different objects. The focus of this work is to improve its versatility by engineering both variable grip strength and changeable gripper shape capabilities into the device. Through mechanism design, the new prosthesis will have user-selected force options, thereby being able to hold objects of different masses. Additionally, exploring conformable-topology gripper designs will enable the ability to be able to grab items of diverse shapes and textures.

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  Design of an Enhanced Cellular Model for the Assessment and Tracking of Nanomaterials

  Maggie Elizabeth Jewett

  Due to their unique physicochemical properties nanomaterial (NM)-based technologies are growing exponentially in scope and economic importance. This surge is resulting in significant degrees of NM waste and increased rates of human exposure. This has created a vital need to fully understand the potential biological consequences of NM exposure, characterize resulting NM-biological interfaces, and determine subsequent toxicological effects. The long-term goal of this project is to design, optimize, and implement an enhanced microenvironment model (EMM) to bridge this in vitro – in vivo gap and evaluate NM characteristics, pharmacokinetic/deposition profiles, and induced biological responses under physiologically relevant conditions. To date efforts have focused on the generation of the EMM which uses a perfusion plate platform containing cellular compartments interconnected by dynamic fluid movement produced via a peristaltic pump. While the EMM system can be tailored to any target organ/tissue, this proposal is focused on the flow of NMs from lungs (A549; human alveolar epithelial) to liver (HepG2; human epithelial) to skin (HaCaT; human keratinocyte), as inhalation is a primary form of exposure and NMs have been shown to accumulate in the skin. Additionally, the human monocyte (U937) cell line will circulate through all compartments allowing for immune analysis. Once complete and optimized this EMM system will be one of the first non-microfluidic models to simultaneously incorporate physiological influences and multiple cellular compartments to improve relevance and promote in vivo-like behavior.

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  Design of a Self-Orienting Solar Array for Small Low-Earth Orbit Satellites

  Eric Matthew McGill

  As electronics have become increasingly smaller and more capable, small satellites called cubesats are deployed in missions that would have taken much larger spacecraft 30 years ago. To power these satellites while in orbit, a novel solar array design is proposed by which these small satellites may harvest energy. With the inspiration of a sunflower that autonomously faces the sun as it passes overhead, a solar array possessing similar characteristics is desirable. The proposed design could generate more energy during the craft's time in the sunlight by continuously adjusting to face the sun. More energy gathered corresponds to an enhancement of the capability of these cubesats due to the ability to accomplish missions with greater scope than those currently in use.

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  Determination of Stock Prices and Interest Rate's Behavioral Movement By Utilizing the Brownian Motion

  Matthew Scott Hooper

  Below is a look into the Brownian Motion, and how it is able to portray the erratic movement over time of stock prices and interest rates. Further, is a look into how different financial models such as the Ho-Lee Model and Vasicek Model are able to utilize this brownian motion in order to describe the movement of short term interest rates and thus can be used to carry out various financial valuations, such as bond option pricing and evaluating interest rate futures.

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  Determining Recovery Response to Slips on a Slip Trainer

  Stephen Thomas McFadden

  Common injuries in the elderly population often result from slipping or falling. A slip is a loss of balance which may lead to an injurious fall. To counter these falls, proactive balance training, which focuses on preventing slips from occurring through physical therapy and environmental modifications, has been tried with mixed results. Reactive balance training, which can increase how well an individual can recover once their balance is upset by a disruption, is a novel method to decrease injuries from falls. However, current reactive balance training is conducted in academic environments with highly expensive equipment. While this training has proved productive, a need has arisen for this reactive slip training to be helpful in clinical settings and provided at a reduced cost. This project is centered around work to design, build, and test a low-cost slip trainer to measure the recovery response of individuals in terms of a reactive step.

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  Determining the Role of Membrane Fatty Acid Composition in Antibiotic Resistance

  Andrew J Deak

  Bacterial infections that can no longer be treated by antibiotics because of bacterial mutations cause many infections and deaths each year. My research conducted aims to study how membrane fatty acid composition can affect bacterial susceptibility to antibiotics. Listeria monocytogenes, a gram-positive facultative anaerobe, is the bacterium that I am testing. Listeria has 80-90% branched-chain fatty acids (BCFAs) which allow membrane fluidity and sufficient protection against invaders. When Listeria is grown in the presence of butyrate, the BCFAs become straight-chain fatty acids (SCFAs) and make the once fluid membrane more rigid. We believe that this allows for easier antibiotic penetration of the phospholipid bilayer which lets the antibiotics affect cellular processes. By changing concentrations of butyrate I can therefore determine the minimum inhibitory concentrations of antibiotics for Listeria with different membrane fatty acid compositions. Moreover, as growth is a key factor in bacterial susceptibility to antibiotics. I also measure oxygen consumption rate in response to butyrate. Higher oxygen consumption rate is indicative of higher bacterial activity. Because oxygen consumption is carried out by protein complexes on the membrane, measuring oxygen consumption rate also reveal the effects of butyrate on cell membrane functionality.

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  Determining what environmental features affect waterbird diversity on the Great Miami River

  Hannah Marie Scharf

  The habitat of the Great Miami River is highly disturbed as it enters the city of Dayton, yet it still attracts many species of birds. These include Anseriformes, which are waterfowl, Charadriiformes, which contain shorebirds and gulls, Pelecaniformes, which contain herons, Suliformes which contain cormorants, and Podicipediformes, which are the grebes. This study seeks to determine which segments of the Great Miami River attract the most and the least numbers and species of waterbirds during the fall migration, and what environmental characteristics set these segments apart. To accomplish this, we divided a four mile segment of the Great Miami River into tenth of a mile intervals, spanning from the confluence of the Great Miami River with the Mad River downstream to the Tait Station low dam. For each interval we counted and identified every bird, in addition to recording environmental features of interest. After identifying the two best and the two worst intervals, we evaluated the habitat with the Qualitative Habitat Evaluation Index (QHEI) and plan to further investigate these areas with additional analyses. By identifying and characterizing these areas of the Great Miami River, sections of the river can be better managed to encourage greater species diversity and numbers of waterbirds.

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  Development and Actuation of a Shape-changing Rigid-body Human Foot Prototype

  Tanner Rolfe

  This project focuses on the actuation of a multi-segment rigid body foot prototype capable of matching the change in profile of a human foot during gait. Previous work has focused on the design of the prototype using methods of shape-changing kinematic synthesis. In order to actuate the prototype, a tendon-based actuation scheme was conceived and partially implemented. The current prototype includes a series of paired cables, each connected to a separate segment of the foot. Tension in the cables counteracts the force of torsional springs implemented at the joints keeping the segments positioned in a neutral configuration, allowing each segment to achieve appropriate plantar- and dorsiflexion to match gait-derived configurations. Current work focuses on implementing active elements to drive the cables, as well as refinement of joint stiffness to increase the functionality and biomechanical accuracy of the prototype.

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  Development of an Evidence-Based Strength Training Program for Individuals with Dementia Participating in Adult Day Services

  Jaclyn Helen Franz

  Falling and loss of mobility present serious risks for elderly adults, especially those with cognitive impairments such as dementia. These risks are shown to be significantly reduced when elderly adults participate in exercises focusing on strength and balance of sufficient intensity. Despite these potential benefits, many adult day programs do not incorporate exercise in a systematic and progressive fashion to achieve desirable improvements in function. The purpose of this project was to develop an evidence-based exercise program, later titled Simply Strong, for reducing fall risk and improving mobility in elderly adults with dementia participating in Goodwill Easter Seals adult day services. An extensive literature review of current research into the implementation and resulting outcomes of exercise for older adults with dementia was conducted. A supplementary survey of Goodwill Easter Seals program managers regarding barriers and needs was conducted. Barriers to providing such a program were identified through the survey and addressed in the creation of the program so that this program, Simply Strong, and other programs of a similar nature, have an increased likelihood of being utilized long-term. Based on the current literature, an evidence-based training program, titled Simply Strong, was developed to meet the needs of older adults with dementia and through the results of the staff survey was specifically tailored for individuals with dementia at Goodwill Easter Seals Adult Day Service. Staff members of Goodwill Easter Seals were instructed in providing the program so that the program remained self-sustaining after the conclusion of this project. Additionally, a training manual, an accompanying video, and an equipment cart to assist in the implementation of the program was fabricated and then donated to two Goodwill Easter Seals locations.