Grant Eifert, Rebekah Revadelo
Repairable adhesive elastomers are emerging materials employed in compelling applications such as soft robotics, biosensing, tissue regeneration, and wearable electronics. Facilitating adhesion requires strong interactions, while self-healing requires bond dynamicity. This contrast in desired bond characteristics presents a challenge in the design of healable adhesive elastomers. Furthermore, 3D printability of this novel class of materials has received limited attention, restricting the potential design space of as-built geometries. Here, we report a series of 3D-printable elastomeric materials with self-healing ability and adhesive properties. Repairability is obtained using Thiol-Michael dynamic crosslinkers incorporated into the polymer backbone, while adhesion is facilitated with acrylate monomers. The adhesive properties were tested by performing lap shear tests and measured across different lap materials and formulations of the tested material. We successfully 3D printed complex functional structures using a commercial digital light processing (DLP) printer. Shape-selective lifting of low surface energy Teflon objects is achieved using soft robotic actuators with designed geometries, wherein contour matching leads to increased adhesion and successful lifting capacity. The demonstrated utility of these adhesive elastomer materials provides unique capabilities to easily program soft robot functionality.
A Century of Disturbance and Dynamics During the Establishment of White Oak (Quercus alba) Dominance in Forests of Southeastern Ohio: Implications for Sustainable Forest Management
The current structure and composition of forest ecosystems throughout eastern North America has been determined greatly by historic natural disturbances and successional processes. The white oak tree (Quercus alba) has the largest range among tree species in eastern North America (the Atlantic to the Great Plains, the Great Lakes to the Gulf) and has historical dominance of the canopy. White oaks are a broadleaved tree species that form stands with conifers (Pinus spp.) and/or other broadleaved species (Carya spp., Fraxinus spp., Acer spp., Populus spp., etc.) The white oak group (Leucobalanus) was an important ecosystem service for Indigenous Peoples of eastern North America for hundreds of years and the European colonizers of the 18th and 19th centuries. Among Indigenous groups, oaks (including Q. macrocarpa and Q. rubra) were cultivated and managed for increased growing success. Acorns were used for flour and medicinal mixtures. Branches were used for basket weaving. Stems were used for cabins, canoes, and shelters. In addition to popular human uses, the white oak is of paramount importance for a variety of wildlife, including fungi and bacteria. Q. alba is known to be both historically and culturally important to forest ecosystems, however it is one of numerous species currently experiencing massive regeneration issues with its young offspring. Recruitment failure and a subsequent forest transition of substituting shade-tolerant maple species for oak species could give rise to a largescale shift in biodiversity, wildlife nutrition, and soil characteristics across the Eastern Deciduous Forest (EDF). In addition to a changing climate, the white oak, a paragon of EDF tree species, could quickly become much less secure in conservation status. We examined 65 cross section samples of white oak (n= 62), chestnut oak (n= 2), and shagbark hickory (n= 1) trees for their growth release history and fire scar history. Analyzing and comparing both periods of growth and fire throughout history gave insight into how the current forest came to be and the important factors necessary for successful oak regeneration. Results showed that growth releases and fire occurrence do not have a strong correlation. Only 2 samples exhibited a release within a time lag period of 3 years following a fire. There was a ~70-year period where a fire did not occur, but releases still happened frequently. However, a short period of fires within the 1920s were quickly followed by releases. Foresting the area in order to aid in the Civil War effort between 1850-1875 allowed numerous young oak samples to release, and a similar pattern was seen near WWII. We hypothesize that similar periodic thinning/cutting was done throughout the ~70-year period without fires for these releases to have occurred.
We present a compact Michelson interferometer-based Fourier transform spectrometer on a silicon photonic chip. In contrast to a conventional Mach Zehnder interferometer (MZI) designs demonstrated elsewhere, our design doubles the optical path difference between the two unbalanced arms of the interferometer thereby effectively doubling the spectral resolution while still maintaining the same geometric length in a MZI. Our design centered at 1550nm thus achieves ~0.8nm spectral resolution with a 40micron geometric path length difference between the two arms of the interferometer in contrast to ~1.6nm spectral resolution in the corresponding MZI. Devices have been fabricated and results will be presented.
Engaging in high-impact physical activities may not be suitable for individuals with disabilities and senior adults to maintain their fitness needs due to the risk of injury or exacerbating pre-existing conditions. Adaptive yoga provides a low-impact alternative that offers tailored exercises to different age groups and individuals with disabilities. However, post-pandemic travel can make attending yoga sessions challenging. To address this, the augmented-reality based yoga system provides a way to learn and practice yoga in an immersive mixed-reality environment at comfort of comfort of their home or preferred location. The application uses a database of yoga poses and a mixed-reality environment with a virtual instructor to guide users and allow them to interact with both virtual and real-world elements. The application provides different yoga routines based on user details, and live feedback with the help of a smartphone camera to correct and validate yoga poses. Moreover, the application tracks the user's progress and provides digital rewards to motivate them further. The user can also workout with family or friends represented by virtual avatars in a joint yoga session, promoting a sense of community and belonging. Adaptive yoga provides numerous health benefits, including strengthened bones, enhanced balance and flexibility, improved quality of sleep, and reduced stress, depression, and anxiety, promoting mental fitness and clarity for the elderly. The proposed application makes learning yoga engaging and enjoyable while providing various health benefits. Also, the application ensures exercises are practiced correctly and safely with the help of live feedback. The proposed application provides a convenient and accessible solution for post-pandemic times. The social aspect can help promote overall mental health and well-being, enhancing a sense of community and belonging. The proposed application has significant implications for promoting active and healthy aging.
For the 2022 Formula One (F1) season, F1 introduced a new set of technical regulations that reduce the complexity of the aerodynamic devices such as the spoiler, often called wings. The objective of this regulation change is to reduce the amount of turbulence produced allowing the cars to trail behind one another closer, making for easier overtaking and increasing the competitiveness of the sport. The present study evaluates and quantifies the aerodynamic performance of a 2022 F1 rear wing by using computational fluid dynamic (CFD) analyses. Both a study of a 2022 and 2021 specification rear wing is assessed to determine how the new technical regulations affect the turbulence in the wake of the car. The study is performed by taking cut planes in the fluid domain downstream of the rear wing model and integrating over the plane to determine the turbulence behind the wing. With this analysis, a comparison between the two rear wing specifications can be performed to determine the magnitude of impact the new technical regulations produce. From this, a conclusion can be made regarding the effectiveness of the 2022 F1 technical regulations, and whether the regulation change was justified.
A Factor Based Portfolio Weighing Model for the S&P 500 Health Care Sector (XLV): An Empirical Analysis of Portfolio Returns, 2009-2022
Nathan Jabaay, Kevin Cullen
In this study, I use firm revenue growth as my factor weight. I carry out two empirical tests: (1) my revenue growth factor based portfolio weighing model outperforms an equal weight portfolio model over the period 2009-2022; (2) firm revenue growth is a priced-in risk factor in the equity market. For my first test, I compare the long run cumulative returns for the revenue growth factor based portfolio weighing model to the returns for the equal weight portfolio, 2009-2022. For my second test, I determine if my portfolio weighing model generates excess returns over the broad market benchmark, the S&P 500 index, for the period 2009-2022. I use two investment strategies, a buy and hold strategy and an adjustable shares strategy, to generate the returns for my portfolio weighing model.
Recent experimental studies suggest that thin-film crystalline silicon is viable as a high-efficiency material for energy conversion in solar cells. A theoretical study on the optical properties of two-dimensional (2D) silicon thin films is needed to gain insights into the structure-property correlation of this material. In our project, we made Density Functional Theory calculations of multiple 2D silicon thin films of different structures. We first constructed their model structures from bulk silicon by cutting it along the (100), (110), and (111) low-Miller-index faces, varying the number of silicon atomic layers from two to eight layers, and exposing the bare surfaces or terminating them with hydrogen atoms. We then relaxed the atomic coordinates and in-plane lattice vectors of the 2D films. Next, we calculated the surface energies for the thin films with bare surfaces and modelled the octahedral crystal habit of silicon. From electronic structure calculations, we found that the 2D films with bare surfaces possess metallic in-gap states near the Fermi level, whereas hydrogen termination on the surfaces can render semiconducting thin films suitable for optical applications. Finally, we calculated the optical properties of the semiconducting thin films from the complex dielectric function at different levels of approximation, giving frequency-, face-index-, and thickness-dependent absorption coefficients. We found that the absorption coefficients increase with increasing thicknesses, asymptotically approaching that of the bulk structure. Among the three face indices studied, the (111) films have the lowest surface energy and achieve the highest absorption coefficients, making (111) the most favorable face index for thin-film silicon solar cell applications.
Jonathan Shepelak, Terrence Oliss, Michael Dimisa, Carson Karn, Benjamin Burkett, Ahmad S GH B Alazemi, Bader S M M M Alotaibi, Mohammad E M M Alazmi, Clara Schulze, Rachel Reid, Miguel Reyes, Makoro Kebe, William Uhlenbrock, Sean Coney, Andrew Koltas, Conner Kelley, David Hughes, Bobbi Gunn, Sheora Watkins, Charles Lynch, Michael Adedokun, Elizabeth Andreu, James Boldt, Bryon Helbling, Alexandra Cooney, Michael Cooney, Mia Huckleberry, Saad M S Z Alazemi, Anthony Rizkallah, Soud M S M Alazmi, Drew Gueterman, Piper Ashley, Alvaro Guerrero Valera, Ace Kinman
These presentations examine topics which shape contemporary African experience with a goal of introducing the audience to why and how Africa will shape events in the twenty-first century. The topic presented in include infectious diseases, Muammar Gadhafi, Human Trafficking, Blood Diamond, and civil conflict.
American Fracture, Digital Rage: Evaluating the Influence of Conspiratorial Rhetoric on the U.S. Catholic Community
It has been well documented that religious individuals and groups continue to play roles in both the complex global web of polarization, extremism, and terroristic violence and in the reciprocal forces of peacebuilding, justice work, and public reconciliation. My graduate thesis research examines the historical antecedents and current characteristics of digital spaces like #CatholicTwitter (and, in particular, the degree to which both conspiratorial and extremist rhetoric flow throughout them) to assess how such social media spaces both mediate the American Catholic experience and affect the civic health of the United States at large. This thesis speaks to all those interested in understanding, assessing, and combatting civic fragmentation, polarization, and extremism; enters into a growing scholarly conversation exploring the various ways Catholic individuals and institutions both affect and are affected by conspiratorial discourse, misinformation, and disinformation; and point toward potential reconciliatory paths forward. As a Catholic and Marianist institution, the University of Dayton "encourages its members to judge for themselves how institutions are performing their purposes (and) to expose deficiencies in their structures and operations..." through inquiry, dialogue, and praxis. In that spirit, I write in hopes of helping advance our campus community's communal mission to function as a university for the common good.
A Multi-Sector Portfolio Weighting Model with Firm Revenue Growth the Factor Weight: An Empirical Analysis of Portfolio Returns, 2009-2022
Daniel Montgomery, Vincent Rullo
In this study we conduct two empirical tests. First, we determine if the revenue growth factor weighted multi-sector portfolio outperforms an equal weight portfolio over the period 2009-2022. Second, we test to see if revenue growth is a “priced-in risk factor” by determining if the long term returns to our portfolio weighting model are in excess of the returns to the broad market index S&P 500.60 stocks from six S&P 500 sectors make up the portfolio. The six sectors are: (1) Consumer Discretionary, (2) Healthcare, (3) Industrials, (4) Information Technology, (5) Real Estate, and (6) Communication Services. Historically, these six sectors contribute most of the returns to the S&P 500. To generate our returns, we use two investment strategies: (1) Buy and Hold, and (2) Adjustable Shares.
Listeria monocytogenes is a prevalent food-borne pathogen, and a clear understanding of its pathogenesis can enhance our capability to treat infections. L. monocytogenes is ingested through contaminated foods, enters the intestinal lumen, and is able to spread throughout the rest of the body. The intracellular life cycle of L. monocytogenes requires the regulated expressions of a variety of virulence genes. We previously found that exposure to short chain fatty acids (SCFAs), fermentation byproducts present in the intestines, resulted in significant changes in L. monocytogenes pathogenesis. This research, divided into two major projects, aimed to understand the relationship between L. monocytogenes, its host, and the exposure to SCFAs. Project one evaluated the effect of prior anaerobic exposure of SCFAs, specifically propionate, on strain 07PF0776, a cardiotropic clinical isolate. Hemolytic assays were used to measure the activity of secreted LLO as an indication of bacterial virulence. This project also assessed intracellular growth and actin polymerization of L. monocytogenes in cardiac myoblast cells and macrophages. To further investigate the mechanism underlying L. monocytogenes response to SCFAs, project two explored the role of CodY, a transcription factor in response to levels of branched chain amino acids, in the opposing effects of propionate on LLO production. By comparing the culture supernatant LLO activities in strain 10403s and a mutant with a codY gene deletion (ΔcodY), I discovered that CodY was required for the increase in LLO production in response to anaerobic propionate exposure. Together, the results of these projects provide further evidence for the relationship between SCFA exposure and L. monocytogenes pathogenesis. Ultimately, these findings can be utilized to improve the understanding of L. monocytogenes and develop effective prevention and treatment methods.
The Movies Dataset, available on Kaggle, is a comprehensive dataset containing information on movies released between 1990 and 2017. In this project, we aim to analyze variables that have significant predictive power on the success of a movie and to build a recommendation system based on users' profiles.Firstly, we will explore the dataset and preprocess it to extract relevant information for our analysis. We will then perform exploratory data analysis to identify potential predictors of a movie's success, such as budget, genre, runtime, release date, and rating. We will use regression techniques to model the relationship between these predictors and a movie's success, as measured by box office revenue and user ratings. The results of our analysis will provide insights into the factors that contribute to a movie's commercial and critical success.In the second part of the project, we will develop a recommendation system that suggests movies to users based on their preferences. We will incorporate content-based filtering techniques, where we analyze the movie's features and recommend movies that are similar in genre, cast, or storyline to those the user has previously enjoyed. This can be a tricky problem as movie ratings are often times purely subjective and highly variable.Overall, this project will provide a comprehensive analysis of the movies dataset and a recommendation system that can help users discover new movies that match their preferences.
M M Shaifur Rahman
Deep learning (DL) is currently one of the most popular branch of Machine Learning and uses Deep Convolutional Neural Network (DCNN) architectures. It can transform medical diagnostics. DCNN predictions are significantly dependent on high-quality input data. However, large-scale images are challenging to operate with classical deep-learning architectures due to their vast memory and computational requirements. Currently, one of the popular approaches to deal with large-scale input images is to resize the large image to a smaller dimension which decays the performance of the overall system. Another popular approach to overcome large-scale image problems is to sequentially crop the high-resolution image into multiple smaller images to fit in the computation memory (GPU). In this work, we demonstrate a novel approach to training and inference in higher-resolution input images (e.g., 1024 x 1024) with DCNN. Our proposed architectures are constructed with state-of-the-art DCNN backbone models such as ResNet101, DenseNet-121 and EfficientNet. Finally, the models are evaluated using large-scale diabetic retinopathy datasets (e.g., Dataset for Diabetic Retinopathy, Kaggle 2019 BD). The experimental results are compared against existing deep learning methods and demonstrate significant improvements in accuracy.
The purpose of this study is to increase the understanding of power system resilience through pattern recognition of disaster-induced system disruption. This study consists of analyzing power system failure and recovery patterns in a post-extreme event environment to determine relevant pattern characteristics relating to power system resilience. Specifically, the methodology of this study consists of (1) collecting and processing data from power system failures induced by natural disasters categorized by power companies, states, counties, and natural disaster occurrence.; (2) developing failure and recovery curves for the collected data; (3) investigating and establishing statistical distribution models that correlate to the goodness of fit for plotted curves best characterizing the system behaviour for each extreme external occurrence; and (4) creating a quantitative algorithm for specifying the resilience of such engineered systems. The resultant algorithm will assist in answering questions about the resiliency of power systems. Since modern society relies extensively on power systems to survive, this increased insight into power system resilience will provide better situational awareness for stakeholders during future decision-making discussions regarding power system construction.
What keeps roller coasters safe? Why do you so rarely ever hear about roller coaster trains colliding? The answer is simple: blocking systems. A block is a section of track only one train may occupy at one time; at the end of each block is a method of stopping and holding a train reliably should the next block not be clear. Examples of this are chain lift hills, magnetic brakes paired with drive tires, or friction brakes that clamp onto a brake fin secured to the bottom of the train. For my project, I would like to design a simple logic system that utilizes proximity sensors to prevent trains from colliding with each other. Along with the sensors, I will be using other miniature versions of common roller coaster components and design a simple track layout for the trains to follow.
An anisotropic transfer matrix approach to profiled optical field propagation through hyperbolic metamaterials
We extend the transfer matrix method to study the propagation of beams and arbitrary profiled fields through anisotropic metamaterial slabs, and to demonstrate the negative refractive index property resulting in linear self-focusing of beams in hyperbolic metamaterials. Specifically, the transfer matrix method, commonly used to analyze bi-directional plane wave propagation, is developed to analyze beam propagation. By expressing a Gaussian beam as an angular spectrum of plane waves, an anisotropic transfer matrix, which is also obtained using the eigenvalues mentioned above, can be applied to calculate the beam spectrum at an arbitrary distance of propagation through a hyperbolic metamaterial. With given incident and emergent media, say, air, linear self-focusing within the metamaterial slab and subsequent reimaging in the emergent medium are numerically investigated for one transverse dimensional TM polarized Gaussian beam. Simulation results are compared with results from the unidirectional transfer function approach. The anisotropic transfer matrix method can be used to study beam transmission and reflection at the interfaces, and can be applied to analyze optical propagation through anisotropic metamaterial on uniaxial electro-optic substrates. The technique can be extended to arbitrary initial optical field profiles in one transverse dimension to assess the imaging quality of metamaterial slabs.
An Investigation of the Mechanics of an Ultra-Stretchable, Self-Healing, DLP 3D-Printed Hydrogel for Damage-Resistant Soft Robots
Joshua Michonski (presenter); other authors: Joseph Beckett, Carl Thrasher, Braeden Windham, Allyson Cox, Timothy Osborn, Anesia Auguste, Robert Lowe, and Christopher Crouse
Inspired by nature, soft robots composed of compliant (“soft”) materials are well-suited for uncertain, dynamic tasks requiring safe interaction between a robot and its environment. Vat photopolymerization (VP) additive manufacturing (AM) processes such as digital light processing (DLP) have disrupted traditional manufacturing of soft devices, enabling the fabrication of soft robotic components with unprecedented speed, resolution, and complexity. Concurrently, the rapid development of novel self-healing photo-curable soft materials for VP-based AM has paved the way for soft robots with embedded healing of damage (e.g., perforations, tears) induced, for instance, by an unintended interaction with a sharp object in their operating environment. At present, however, the mechanical behavior (deformation and fracture) of self-healing photo-curable soft materials (elastomers and hydrogels) used for next-generation soft robots is not well understood. To address this compelling research opportunity, this work focuses on the design and execution of a mechanical testing program to characterize BeckOHflex, a novel self-healing photo-curable hydrogel synthesized using off-the-shelf chemicals. The large-strain elasticity of BeckOHflex is investigated through quasi-static uniaxial tension testing. Both virgin and self-healed mechanical properties are shown to be commensurate or superior to the best-performing self-healing hydrogels in the literature. Further, a suite of demonstration prints produced on a commercial VP 3D printer highlight the material’s scalability and the ability to yield prints with complex form factors.
Satish Kumar Oad
A Novel, Efficient Approach for Determining the Post-Necking True Stress-Strain Response of Aerospace Metals
Yatik Rashmin Shah
To numerically simulate and predict the plastic deformation of aerospace metals and alloys during extreme impact events (e.g., turbine engine blade-out and rotor-burst events, bird strikes, and foreign object damage), accurate knowledge of the metal’s hardening behavior at large strains is requisite. Tensile tests on round cylindrical specimens are frequently used for this purpose, with the metal’s large-strain plasticity ultimately captured by a true stress vs. true plastic strain curve. During tensile testing, the strain field in the specimen gage section evolves from a nearly homogeneous profile prior to necking to a heterogeneous profile after the onset of necking. Concomitantly, the customary analytical relationships used to convert between engineering stress-strain and true stress-strain break down after necking, since the state of stress is no longer homogeneous or uniaxial after necking. Thus, a number of approaches have been proposed and employed to correct the post-necking hardening response. Although effective, these approaches are generally complex and/or computationally expensive, which can be particularly problematic for large experimental programs. In this talk, a novel and efficient post-necking correction method is proposed and benchmarked. Using the equivalent true strain history obtained from a digital image correlation virtual strain gage placed at the fracture location, an approximate first-order analytical approach is used to calculate the corresponding equivalent true stress. This true stress calculation is used to generate a simple post-necking hardening law, using linear interpolation between known true stress-strain states at necking and fracture. This approach is successfully benchmarked using experimental data from a suite of metals with different crystal structures and hardening behavior: Inconel 625, Inconel 718, 17-4 precipitation hardening (PH) stainless steel, and Ti-6Al-4V titanium alloy.
For this project, we reproduce the meshless method of lines numerical solution of coupled Drinfeld-Sokolov-Wilson system. This method uses radial basis functions (RBFs) for spatial collocation. Time integration of the resulting system of ODEs will be solved using fourth order of Runge-Kutta method. Accuracy will be compared with the results from other methods available in the literature.
Emma Blair, Casey Tirado, Rachel K. Young
Students in the CMM 357 Religious Rhetoric course were given the choice to choose a statue, building, space/place on campus that is marked as a religious text/artifact or propose a new object of visual rhetoric in order to analyze using the rhetorical theories from class. Thier analyses employ a variety of rhetorical theories and concepts discussed in class during the semester. Each group collaborated to answer the question: how does this artifact/text rhetorically communicate and construct religious identity at UD?
A Portfolio Weighting Model for the Consumer Discretionary sector with Revenue Growth as the Factor Weight: An Empirical Analysis of Portfolio Returns, 2009-2022
Nicholas Mulvihill and Hector Gutierrez
In this study my research objective is to answer two questions: (1) Does a revenue growth factor weighted portfolio of consumer discretionary stocks outperform an equal weight portfolio over the period 2009-2022. (2) Can the revenue growth factor weighted portfolio generate excess returns over the broad equity market measured by the S&P 500 index. In short, is revenue growth a priced-in risk factor. I use two investments strategies in my empirical analysis: (1) Buy and Hold and (2) Adjustable Shares.
A Portfolio Weighting Model for the Industrials Sector with Revenue Growth the Factor Weight: An Empirical Analysis of Portfolio Returns, 2009-2022
Thomas Letke, Kevin Cullen
In this study I test two hypotheses. 1: The revenue growth factor weighted portfolio model outperforms an equal weight portfolio model. 2: Firm revenue growth is a priced-in risk factor in the equity market. For the first portfolio, I compare the long-term cumulative returns for the revenue growth factor-based portfolio weighting model to the returns for the equal weight portfolio model, 2009-2022. For the second hypothesis, I determine the excess returns for my portfolio weighting model over the S&P 500 index, 2009-2022.
A Portfolio Weighting Model for the Information Technology Sector with Firm Revenue Growth the Factor Weight: An Empirical Analysis of Portfolio Returns, 2009-2022
Hayden Gray, Andrew Kohnen
In this study I run two types of portfolio return tests: (1) Determine if the returns for my revenue growth factor weighted portfolio are greater than an equal weight portfolio, 2009-2022. (2) Determine if the revenue growth factor weighted portfolio generates long term excess returns over the broad market index S&P 500 i.e. revenue growth is a priced-in risk factor. I use a buy and hold and an adjustable shares investment strategy to develop portfolio returns for the period 2009-2022.
A Portfolio Weighting Model for the Real Estate S&P500 Sector with Firm Revenue Growth the Factor Weight: An Empirical Analysis of Portfolio Returns, 2009-2022
Paul Waweru, Kathleen Hattrup
In this study we run two empirical tests: (1) The revenue growth factor weighted portfolio model has higher returns than an equal weight portfolio model. (2) Firm revenue growth is a priced-in risk factor in the equity market.For the first empirical test, we compare the long-run cumulative returns for the revenue growth factor-based portfolio weighting model to the cumulative returns for the equal weight portfolio, 2009-2022. For the second empirical test, we compare the cumulative excess returns for my portfolio weighting model over the S&P500 Index, 2009-2022.We use two investment strategies to generate my returns: (1) Buy & Hold, (2) Adjustable Shares.