Doctor of Philosophy, University of Toledo, 2022, Engineering

At the early stages of research on additive manufacturing (AM), most of the research efforts were spent on optimizing the process parameters to obtain practical parts with minimum to no defects, while the performance of such components remained a lesser priority. Although the physical properties are the key factor in manufacturing, the final thermo-mechanical properties are of importance when it comes to applications. From a metallurgical point of view, the thermo-mechanical properties of materials are linked to their microstructure. Thus, for altering or enhancing the mechanical performance of a component, it is necessary to tailor/alter the microstructure. On the other hand, the microstructure is directly influenced by the manufacturing process, thus it creates Process-Microstructure-Property-Performance (PMPP) linkage. Besides the capability of making the components with complex shapes, additive manufacturing also provides high flexibility to control the process compared to conventional manufacturing methods by leveraging a large number of parameters (i.e., heat source power, scan speed, etc.) that allow better control over the process and the microstructure. Thus, additive manufacturing not only is able to fabricate complex geometries that may not be possible with conventional methods but also empowers the enhancement of the material properties. It is well reported that crystallographic orientation plays a key role in the thermomechanical behavior of the single crystal NiTi-based SMAs. That notable effect has also been observed in strongly textured SMAs. Such a high dependency of NiTi-based SMAs on crystallographic texture highlights the importance of developing an approach to control the AM process and obtain the preferred crystallographic textures. On the other hand, despite the growing demand for SMAs rotary actuators, the existing literature focuses mostly on assessing the compression and tension behaviors of AM-fabricated NiTi SMAs, while the torsional beha (open full item for complete abstract)

Committee: Mohammad Elahinia (Committee Chair); Othmane Benafan (Committee Co-Chair); Behrang Poorganji (Committee Co-Chair); Meysam Haghshenas (Committee Member); Ala Qattawi (Committee Member) Subjects: Engineering; Materials Science; Mechanical Engineering

Master of Science in Mechanical Engineering (MSME), Wright State University, 2016, Mechanical Engineering

Ambient temperature dwell sensitivity is known to be deleterious to the fatigue response of near-alpha titanium alloys. Dwell fatigue refers to the presence of a sustained hold at peak stress as opposed to the continuous variation of normal cyclic fatigue loading. This reduction in failure life-times from dwell loading is attributed to early crack nucleation and faster crack propagation. The degradation is the result of plastic anisotropy on the microstructural scale along with tendency of titanium alloys to creep at low temperatures at stresses well below the 0.2% offset yield strength. Despite being the most widely used titanium alloy, Ti-6Al-4V has not been the subject of most dwell fatigue research. Generally, dwell sensitivity is microstructurally dependent and believed to only affect Ti-6Al-4V when severe crystallographic texture is present and under high peak stress loading. Recent studies, however, have suggested that small clusters of preferred crystal orientations, known as micro-textured regions (MTR), can have a significant effect on the dwell sensitivities in Ti-6Al-4V even without severe overall texture in the material. In this study, smooth-bar fatigue specimens were subjected to uniaxial fatigue at 20 Hz cyclic and 2-min dwell loading conditions under load-control at stresses representative of service conditions, until failure occurred. A reduction in specimen life-times by approximately a factor of three was observed under dwell conditions, which was less than for the highly susceptible near-a titanium alloys such as Ti-6Al-2Sn-4Zr-2Mo, where the dwell debit is often in excess of a factor of ten. Measurement of fatigue and dwell fatigue crack growth rates revealed a significant acceleration of the dwell crack growth rates in certain cases. Backscattered electron imaging and electron backscattered diffraction were utilized to quantify the interaction between the cracks and local microstructure. Though no correlation was found between crack growth (open full item for complete abstract)

Committee: Raghavan Srinivasan Ph.D. (Advisor); Adam Pilchak Ph.D. (Committee Member); Joy Gockel Ph.D. (Committee Member) Subjects: Materials Science; Mechanical Engineering

Doctor of Philosophy, The Ohio State University, 2010, Biomedical Engineering

Within medical science, pattern recognition is the basis for computer-aided diagnosis (CAD), which assists doctors (in particular radiologists) in the interpretation of medical images, whose quality and usefulness are constantly evolving. Modern magnetic resonance imaging (MRI) scans can provide information about subvoxel anatomical structures (e.g. microvessels) that may not be specifically resolvable within a given image set. Hence, subvoxel anatomical structures within a given image may not be readily apparent to the observer (i.e. radiologist); nevertheless, their presence may be detected through their statistical relationship with their surrounding voxels. Such statistical relationships can be characterized by texture features. The goal of this research dissertation is to investigate the feasibility of using anisotropic texture features for the identification of blood vessels that may not be specifically resolvable in the image datasets. Such features can be used as the basis for the feature extraction component of a complete pattern recognition system for the purpose of automatically identifying blood vessels in the human brain. The approach of this project is to apply 2D statistical texture features as inputs to a classifier (such as a neural network) to analyze MRI images. The specific aims of this dissertation are: a) to provide a set of texture features extracted from 7T MRI human brain phase images that demonstrate the ability to characterize the presence of underlying microvessel structures; b) to provide a classifier, in particular a neural network architecture that makes use of the extracted features; and c) to evaluate the performance of the feature-classifier combination. The results of this research demonstrated the feature-classifier combination exhibit reasonably well generalization across the testing data, and suggest it may be possible for a computer to discriminate hidden vessels not detectable by human observers.

Committee: Bradley Clymer PhD (Advisor); Petra Schmalbrock PhD (Committee Member); Donald Chakeres MD (Committee Member) Subjects: Biomedical Research

Master of Science (MS), Ohio University, 2004, Environmental Studies (Arts and Sciences)

The research constitutes the attempt to create new approach to optimization in the field of land use planning. It combines methodologies of remote sensing and landscape ecology, bringing together multi-spectral analysis of digital imagery and analysis of landscape texture. These powerful tools are used to classify and cluster the area of study to the best advantage that can be predicted in developed model. This means that the developed procedure can help to configure or redistribute the area and resources among land use types in a manner that allows maximization of output, which can be received from utilization of the resources. In contrast to the traditional land use assessment and optimization techniques used by USDA and FAO, this methodology does not use linear optimization for individual map unit. When running the optimization, developed model uses the idea of common effort and possibility to bring together all the necessary resources from different map units that can help to achieve the goal of a particular land utilization type. Based on those ideas, the algorithms of semi-lacunarity analysis and edge search were created and combined into one procedure of raster based heuristic land use optimization. Also, the structure of participating data types were designed for the need of proper input data storage and manipulations. The procedure was tested on the soil and terrain data obtained in Wayne National Forest (Ohio). The map of Optimized Land Use became the result of the research and testing. The model helped to exclude ineffective land uses and reassess the land to the effective ones, while keeping their distribution reasonably close to natural patterns of resource distribution.

Committee: James Lein (Advisor) Subjects: Environmental Sciences

Doctor of Philosophy, University of Akron, 2008, Civil Engineering

During the entire life cycle of a pavement, highway agencies are expected to monitor and maintain an adequate surface roughness (texture) to facilitate friction between car tires and pavement surface. Skid resistance is a measure of the resistance of pavement surface to sliding or skidding of the travelling vehicles. Maintaining adequate texture and proving resistance to polishing due to the effects of traffic are of prime importance in providing skidding resistance. Polishing of the aggregate is the reduction in microtexture, resulting in the smoothing and rounding of exposed aggregates. This process is caused by particle wear on a microscopic scale. It is a well recognized fact that the lower the skid resistance value, the higher the percentage of the traffic accidents, especially during the wet seasons. Traditionally the repair method for a surface friction-deficient or texture-deficient pavement surface, once the friction falls below certain threshold value, is the application of a new surfacing layer. The practice of monitoring and remedying the low skid resistance pavement sections is important; however, it is a passive approach toward the problem. A more proactive approach would be to test the hot mix asphalt in the laboratory during its initial mix design stage to ensure that aggregate combinations used in the asphalt pavement will provide adequate friction as expected over the life of the pavement.To achieve the screening task for polishing and friction behavior of the HMA during its mix design stage, a laboratory-scale accelerated polishing device that can mimic the actual abrasion and polishing behavior between the vehicle rubber tire and the HMA surface has been developed. The developed polishing device possesses some practical characteristics. These included the versatility of testing HMA specimens that can be prepared with the conventional compaction methods, the specimens can be prepared as part of mix design procedure, test duration is reasonably sho (open full item for complete abstract)

Committee: Robert Liang PhD (Advisor) Subjects: Civil Engineering

Doctor of Philosophy, The Ohio State University, 2024, Food Science and Technology

In response to high consumer demand, the food industry is increasingly focused on developing plant-based beverages (PBMA) that mimic the desirable sensory properties of animal-based beverages. While extensive research has characterized the differences in appearance, aroma, taste, and flavor between PBMAs and cow's milk, limited research exists that comprehensively characterizes the textural and mouthfeel differences between these two types of beverages. Moreover, the complexity of texture and mouthfeel perception poses a challenge in formulating these beverages. Formulation changes that have minimal impact on analytical measurements can still result in significant perceptual differences in texture and mouthfeel sensations. Finally, there has been little research exploring the mechanical underpinnings of textural and mouthfeel perception of food within oral cavity. To address these gaps, this dissertation aimed to comprehensively characterize textural and mouthfeel differences between PBMAs and cow's milk and relate these sensations back to oral tactile sensitivity. Utilizing a “top-down” approach, trained panelists were used to develop a comprehensive texture and mouthfeel lexicon to characterize sensory differences between animal and plant-based beverages. Sixteen unique texture and mouthfeel attributes were identified and used by trained panelists to evaluate 14 different liquid beverages, categorized by protein content: low protein (LP; 8g of protein/8fl. oz) and high protein (HP; 13g of protein/8fl. oz). Each beverage group included two types of animal-based beverages (commercial skim milk [CSM] and milk protein isolate [MPI]) and five types of plant-based beverages (pea protein isolate [PPI], soy protein concentrate [SPC] and three types of soy protein isolates [SPI 1-3]). Textural and mouthfeel similarities were evident between LP animal-based beverages, while only nuanced differences were observed within the LP-SPIs. In contrast, LP-SPC was significantly di (open full item for complete abstract)

Committee: Christopher Simons (Advisor); Rafael Jimenez-Flores (Committee Member); Osvaldo Campanella (Committee Member); Devin Peterson (Committee Member) Subjects: Food Science

Master of Computer Science, Miami University, 2023, Computer Science

In various fields, computer-generated architecture plays a pivotal role, especially in tasks like historical scene reconstruction and real estate marketing. However, reverse-engineering architectural details from images poses significant challenges due to diverse constraints. Our research focuses on one central architectural element—windows—as a stepping stone towards 3D modeling of complex scenes. We employ Physically Based Rendering (PBR) texture mapping to accurately represent material information, including transparency and reflectance, in window surfaces. Estimating PBR textures, including depth and albedo maps, is crucial for capturing the 3D appearance of windows. Given limited or unavailable ground truth data, we adopt unsupervised learning methods. Our approach utilizes a Resnet-50 backbone for window image viewpoint estimation and implements a neural network for unsupervised PBR texture estimation. This work aims to enhance the efficiency and accuracy of 3D modeling in architectural contexts, addressing the inherent challenges of reverse-engineering architectural features from images.

Committee: John Femiani (Advisor); Khodakhast Bibak (Committee Member); Daniela Inclezan (Committee Member) Subjects: Computer Science

PHD, Kent State University, 2023, College of Arts and Sciences / Department of Biological Sciences

Environmental variation is an important determinant in the geographic distribution of species. Observing phenotypic variation in geographic space also hints at the environment differentially affecting phenotypes. In gynodioecious species, among population variation can range between 0-100% females. There appears to be a geographic pattern of variation in female percentage observed across populations of Lobelia siphilitica L., as high-female populations are more common in the south-central part of the species range. The long-term persistence of these populations comes into doubt, as increasing percentage of females leads to less pollen supply from the fewer and fewer hermaphrodites. In my dissertation, I first confirm a geographic pattern, and then, I explore potential environmental causes for the structuring of these high-female populations. Chapter I introduces the mechanisms that have been confirmed for female maintenance in these populations and where geography and environmental variation potentially affect female maintenance. In Chapter II, I present the findings from ecological niche models used in predicting female percentage across the species range. Variable importance metrics indicate that high-female populations are associated with warmer climates in nutrient-limited soils. Chapter III details findings from a field survey and greenhouse experiments. Female percentage is positively correlated with soil clay content and, consequently, negatively associated with soil sand content. Female percentage is also positively correlated with concentrations of magnesium, copper, calcium, and potassium. The greenhouse experiments document soil conditions altering leaf and soil production, but with a lack of flowering, I could not draw conclusions about relative differences between females and hermaphrodites. Chapter IV explores gynodioecy from a species perspective. High-female populations are associated with what are considered more stressful conditions. I used distrib (open full item for complete abstract)

Committee: Andrea Case (Advisor); Christopher Blackwood (Committee Member); David Ward (Committee Member); Christina Caruso (Committee Member); Timothy Assal (Committee Member); He Yin (Committee Member) Subjects: Biology; Geographic Information Science

Master of Science, The Ohio State University, 2022, Food, Agricultural and Biological Engineering

The use of plant-based proteins to replace animal proteins is gaining interest from consumers and food processors, leading to forecasts that plant proteins to become a major ingredient commodity. Even though the major commercial plant proteins used today (e.g., soybean and pea) are good texturizing proteins as evidenced by their widespread use in plant-based analogues, they do not show good cohesive and viscoelastic properties, which are provided by incorporation of some seriously criticized ingredients (e.g., gluten, methylcellulose). In this study, we described some natural and food-friendly ways to promote viscoelasticity and cohesiveness of the corn protein, zein, to be used in plant-based analogues instead of wheat gluten or hydrocolloids to confer the desired viscoelastic properties. Rheological and textural measurements showed that arginine and calcium hydroxide addition to zein-pea protein blends significantly increased elasticity and cohesiveness. Secondary structure composition analysis through FT-IR emphasized the importance of -sheet content in enhancing the material viscoelastic properties, specifically arginine and calcium hydroxide-incorporated blends exhibited the highest -sheet content. Imaging on blends through scanning electron and confocal microscopy displayed that zein formed viscoelastic fibrils holding the structure together through a dispersed network. Overall, zein, along other natural and food approved ingredients, can be the primary agent providing cohesiveness and viscoelasticity to plant-based formulations and the basis to generate green-label products formulations and products. Furthermore, the zein-pea protein-based formulation allows for the adjustment of the textural properties by varying the calcium hydroxide concentration in the formulation to match the textural properties of commercial meat and cheese products. Concerning the formulations viscoelastic iii properties, weak frequency dep (open full item for complete abstract)

Committee: Osvaldo Campanella (Advisor); Rafael Jimenez-Flores (Committee Member) Subjects: Food Science

Doctor of Philosophy, The Ohio State University, 2022, Industrial and Systems Engineering

In the present study, a multiscale modeling approach is developed to investigate the metallurgical and mechanical characteristics of tubular material undergoing tube hydroforming and subsequent annealing processes. This study is performed in three steps. First, a modeling setup based on the Cellular Automata (CA) model is developed to predict the kinetic of static recrystallization (SRX) in hydroformed steel tubes undergoing the isothermal annealing process. To assess the accuracy of the CA model, experimental and predicted results are compared in terms of grain topology data including the grain size and aspect ratio distributions, as well as the rate of softening during annealing. Second, a hierarchically coupled CA model, crystal plasticity finite element method (CPFEM), and thermal finite element (FE) model is developed to predict the softening kinetics of the bulged steel tube during non-isothermal annealing. Through the developed model, the kinetics of softening mechanisms including static recovery (SRV) and SRX, as well as the recrystallization texture are predicted. The corresponding experimental data are utilized to calibrate and verify the implemented CPFEM model for simulation of tube hydroforming process, thermal FE model for prediction of the local temperature over annealing time, and CA algorithm for modeling of the softening kinetics and texture evolution throughout the annealing process. Third, a multiscale modeling approach based on CPFEM algorithm is proposed to predict the mechanical properties in the deformed and annealed specimens. To that end, CPFEM modeling of the deformation behavior in metals consisting of second phase particles is performed based on Representative Volume Element (RVE) models. The RVE model is generated based on the CA predictions for different anneal specimens. The calibrated CPFEM model is used for the simulation of deformation behavior at macro scale based on RVE models. To validate the developed multiscale approach, the t (open full item for complete abstract)

Committee: Farhang Pourboghrat (Advisor); Michael Groeber (Committee Member); Alan Luo (Committee Member) Subjects: Engineering; Materials Science; Mechanical Engineering

Master of Science (MS), Wright State University, 2021, Computer Science

The additive manufacturing (AM) field is striving to identify anomalies in laser powder bed fusion (LPBF) using multi-sensor in-process monitoring paired with machine learning (ML). In-process monitoring can reveal the presence of anomalies but creating a ML classifier requires labeled data. The present work approaches this problem by printing hundreds of Inconel-718 coupons with different processing parameters to capture a wide range of process monitoring imagery with multiple sensor types. Afterwards, the process monitoring images are encoded into feature vectors and clustered to isolate groups in each sensor modality. Four texture representations were learned by training two convolutional neural network texture classifiers on two general texture datasets for clustering comparison. The results demonstrate unsupervised texture-driven clustering can isolate roughness categories and process anomalies in each sensor modality. These groups can be labeled by a field expert and potentially be used for defect characterization in process monitoring.

Committee: Tanvi Banerjee Ph.D. (Advisor); Thomas Wischgoll Ph.D. (Committee Member); John Middendorf Ph.D. (Committee Member) Subjects: Computer Science; Materials Science

Doctor of Philosophy, The Ohio State University, 2021, Food Science and Technology

Food texture, and its perception, are of unquestionable importance to food liking and product choice. However, unlike many other critical attributes, such as taste and flavor, where compounds are detected by a series of identified receptors and pathways, the mechanisms of texture perception in the mouth are relatively unknown. While recent work has identified potential receptors involved in oral texture perception, connections between this anatomical information and perceptual ability is lacking. To address this gap, this dissertation aimed to develop a series of methods to assess tactile acuity within the oral cavity and relate this acuity to differences in oral anatomy. We began by assessing the efficacy and usability of previously-created sets of stimuli – used to assess acuity in epidermis – for use on the tongue (Chapter 3). Findings indicated that while the tongue was significantly better (p<0.02) than the finger at perceiving differences in “purely-tactile” stimuli, it was significantly worse (p=0.018) at the more cognitively-loaded, letter-recognition task. To determine if the fingertip's superiority at letter recognition was due to differences in tissue sensitivity or to differences in cognitive ability, “purely-tactile” stimuli were developed to assess a similar percept, and testing was repeated (Chapter 4). When assessing acuity using the updated tiles, the tongue was found to be significantly better than the finger (p<0.001) at discriminating between tiles suggesting that while the tongue is highly sensitive to tactile cues, it is ill-suited for shape-identification tasks. The suite stimuli were then used to assess the relative acuity of three different oral tissues, the tongue, the hard palate, and the gums (Chapter 5). The tongue was significantly more sensitive than the gums (p<0.01) or the palate (p<0.02) for all tested percepts, and the palate was more sensitive than the gums for the percept of surface roughness (p=0.013). Findings highlighted the (open full item for complete abstract)

Committee: Christopher Simons (Advisor); Farnaz Maleky (Committee Member); Monica Giusti (Committee Member); Susan Travers (Committee Member) Subjects: Food Science

Doctor of Philosophy, The Ohio State University, 2021, Materials Science and Engineering

Crystal plasticity modeling was used to understand the deformation process of FCC metals and alloys. Firstly, we investigated the issue of cube texture development during static recrystallization of FCC metals, which has been vigorously debated over the last 70 years. A Full-field elasto-viscoplastic fast-Fourier transform (EVP-FFT)based crystal plasticity solver coupled with dislocation density based constitutive model was employed to understand the deformation process in copper under plane strain compression. Simulation results revealed that the grains with initially cube orientation retained a small fraction of the cube component in the deformed state, whereas, some of the grains with initially non-cube orientations developed the cube component during the deformation. For strain up to 0.46, non-cube grains which are within 10-20 deg from the ideal cube orientation showed the highest affinity to develop the cube component during deformation. However, the cube component developed during the deformation was unstable and rotated away from the cube orientation with further deformation. With increasing strain up to 1.38, some of the grains with higher angular deviation from the ideal cube orientation also developed the cube component. No particular axis preference was observed for the non-cube grains, rather, the evolution of the cube component becomes dynamic at larger strain. Rotation of the non-cube grains towards the cube component is mainly driven by the local relaxation of the imposed boundary conditions. Significant changes in lattice rotation and slip activity were observed with different relaxed constraints. Best correlation was found for the e13 strain component and the development of cube component. Analysis of the disorientation angle and the dislocation density difference with the neighboring locations showed that the cube component developed during the deformation can play a significant role during nucleation. (open full item for complete abstract)

Committee: Stephen Niezgoda (Advisor); Michael Mills (Committee Member); Yunzhi Wang (Committee Member) Subjects: Materials Science; Mechanics; Metallurgy

Doctor of Philosophy (PhD), Wright State University, 2021, Engineering PhD

Materials with a layered structure consist of crystalline layers that are bonded to each other with secondary bonds (van der Waals interactions). The bonding between crystalline layers is relatively weak compared to the interatomic bonding within the crystalline layers. This research hypothesizes that under appropriate conditions, such as elevated temperature, the deformation of layered materials occurs like the classic “deck of cards” with no preferred direction of slip on the slip planes. The motivation for this study is from the experimental texture evolution of one such material, Bismuth Telluride ,(Bi2Te3), commonly used materials for thermoelectric applications in the range of temperatures from −20°C to 150°C. Bismuth telluride has a trigonal crystal structure consisting of five layers of hexagonally close-packed Bi and Te atoms in an alternating sequence: {푇푒(1) − 퐵푖 − 푇푒(2) − 퐵푖 − 푇푒(1)}. These quintuples are bonded with van der Waal interaction between adjacent layers of 푇푒(1) atoms. Experimental results show that after deformation at 80 to 90% of the absolute melting temperature, this material develops characteristic textures depending on the type of deformation. For example, when (Bi2Te3), is subjected to rolling, the material develops a texture in which [0001] is oriented perpendicular to the rolling plane and〈11-20〉directions are randomly oriented in the plane of rolling, and when subjected to extrusion, the [0001] is oriented perpendicular to the extrusion axis and〈11-20〉directions are aligned in the extrusion direction. In this study, the texture development during the deformation of a layered material is modeled using relationships between the rotation of a slip-plane and the orientation of the loading axis relative to the slip plane normal and the slip direction. An orientation evolution matrix, which relates the applied principal (normal) strains to a change in slip plane orientation for a layered material, has been devel (open full item for complete abstract)

Committee: Raghavan Srinivasan Ph.D. (Advisor); Ahsan Mian Ph.D. (Committee Member); Daniel Young Ph.D. (Committee Member); Joy Gockel Ph.D. (Committee Member) Subjects: Engineering; Materials Science

MFA, Kent State University, 2021, College of the Arts / School of Art

This series of weavings creates a sensory experience that heightens an awareness of tactility created by phenomenological movements of wave patterns. These works are influenced by my interest in swimming. Dynamic actions such as floating, diving, splashing, bending, and submerging provide inspiration for the development of the undulating structural transformation of the weave patterns. These rhythms vibrate with motion, color, and are meant to provide an immersive experience through their optical physicality. Similar to the action of swimming, directional forces and elasticity are at play, intersect and break up with moments of activity and rest.

Committee: Janice Lessman-Moss (Advisor); Andrew Kuebeck (Committee Member); Peter Christian Johnson (Committee Member) Subjects: Fine Arts; Textile Research

Doctor of Philosophy, The Ohio State University, 2021, Food Science and Technology

Texture of food plays a determining role in food liking. Foods with textures perceived as negative are often avoided more than positively-connotated textures are sought out signifying negativity bias. The first objective of the present research was to quantitatively determine whether consumers' food choices are motivated more by avoiding disliked food textures or seeking out liked food textures. An adaptive choice-based conjoint analysis survey was created to ascertain which food textures drive consumer choice behavior. In total, 30 attributes within 8 overall texture categories were assessed by 54 adults. Hierarchical Bayes estimation calculated utility scores (part-worths) for each of the 30 attributes and importance scores for the 8 categories. The survey also retrieved information regarding whether any of these 30 attributes were “unacceptable” or “must-have” textures. The results of a one-way analysis of variance (ANOVA) indicated that the most important texture categories were mouthfeel, particulate, and bite resistance signaling that these categories were driving consumers' choice and that attributes within the groups were most relevant in decision-making. Across all participants, 137 attributes were identified as unacceptable. The distribution of unacceptable attributes across textural categories was uneven (χ2= 321.73; p<0.001) with most unacceptables falling within the first, second, and third most important categories for each participant. No must-have attributes were selected in the survey, indicating participants place less weight on positive textures. This conjoint survey was further utilized in Objective 2 to assess the selectivity (pickiness) of children toward food textures. Three children populations aged 10-15 were sampled: neurotypical non-picky, neurotypical picky with food texture, and children with autism spectrum disorder (ASD) who are picky with food texture. Three metrics were assessed to quantify selectivity. The first was the number of un (open full item for complete abstract)

Committee: Christopher Simons (Advisor) Subjects: Food Science

Master of Arts, Miami University, 2020, Speech Pathology and Audiology

Purpose: This study parameterized the texture of heated baby foods to increase clinical knowledge of the use of these purees in patients with oropharyngeal dysphagia. The data from this study was combined with previously acquired data to construct a statistical model describing the effect of significant independent variables on the resultant IDDSI level. Methods: A two-team, regimented heating and IDDSI protocol was applied to 62 regionally-available baby food purees across three brands (Beechnut, Gerber, and Earth's Best). The resultant data was combined with data from room temperature and cooled trials to construct a mixed-effects ANCOVA model controlling for the unwanted extraneous effects. Results: The heated samples exhibited a lower average IDDSI level than other serving temperatures. The significant independent variables from the ANCOVA model included brand, manufacturer-labeled stage, serving temperature, and whether the product contained meat. The relationships between these variables and the IDDSI levels differed from brand to brand. Conclusions: Clinicians and caregivers need to understand the effect that serving temperature and constituent ingredients have on the texture of baby food purees. They also need to consider and test foods on an individual basis as there is lack of evidence for generalizable trends between brands.

Committee: Donna Scarborough (Advisor); Michael Bailey-Van Kuren (Committee Member); Susan Brehm (Committee Member) Subjects: Food Science; Speech Therapy

Doctor of Musical Arts (DMA), Bowling Green State University, 2020, Contemporary Music

This document examines the ways in which Kaija Saariaho uses texture and timbre to shape time in her 2008 opera, Emilie. Building on ideas about musical time as described by Jonathan Kramer in his book The Time of Music: New Meanings, New Temporalities, New Listening Strategies (1988), such as moment time, linear time, and multiply-directed time, I identify and explain how Saariaho creates linearity and non-linearity in Emilie and address issues about timbral tension/release that are used both structurally and ornamentally. I present a conceptual framework reflecting on my performance choices that can be applied in a general approach to non-tonal music performance. This paper intends to be an aid for performers, in particular conductors, when approaching contemporary compositions where composers use the polarity between tension and release to create the perception of goal-oriented flow in the music.

Committee: Emily Freeman Brown D.M.A. (Advisor); Brent E. Archer Ph.D. (Other); Elaine Colprit Ph.D. (Committee Member); Nora Engebretsen-Broman Ph.D. (Committee Member) Subjects: Music

PhD, University of Cincinnati, 2019, Allied Health Sciences: Communication Sciences and Disorders

The purpose of this study was to examine and explore implementation strategies for the International Dysphagia Diet Standardization Initiative (IDDSI) in terms of training effects and on the classification and preparation of texture-modified foods and liquids for people with dysphagia. The IDDSI is a culturally sensitive, international rating system for the classification and preparation of texture-modified foods and thickened liquids for people with swallowing disorders. This is important in that providing inaccurately classified foods and liquids to people with swallowing disorders may be adverse, resulting in aspiration of swallowed material into the airway which may lead to negative respiratory health outcomes or asphyxiation. Though self-study materials for the IDDSI are available online at no cost, no investigations to date have formally evaluated self-study as an educational modality and the associated effects on classification and preparation accuracy for food and liquid consistencies. Furthermore, no investigations to date have evaluated whether additional training beyond self-study may be necessary to produce reliable and accurate classification and preparation of foods and drinks. Finally, no published research to date has investigated the generalization of a training program to patients and potential caregivers without formal dysphagia experience. This investigation used a quantitative, between and within-subjects research design. Sixty-eight participants completed three tasks: (a) knowledge quiz, (b) diet classification, and (c) preparation. Participants were enrolled in self-study (SS) or self-study plus hands-on training (SS+) tracks. In summary, both SS and SS+ groups improved in their ability to accurately classify foods and drinks using IDDSI testing methods however they were not statistically significantly different from one another. Second, there were no significant predictive factors for performance found in any task for socioeconomic status (open full item for complete abstract)

Committee: Lisa Kelchner Ph.D. (Committee Chair); James Canfield Ph.D. (Committee Member); Sarah Couch Ph.D. (Committee Member); Aimee Dietz Ph.D. (Committee Member); Kathy Groves Ph.D. (Committee Member); Noah Silbert Ph.D. (Committee Member) Subjects: Speech Therapy

Master of Science, The Ohio State University, 2019, Mechanical Engineering

To measure the exact fuel consumption of a vehicle, it is essential to determine the total road load being imparted on it. One of the main methods to calculate road load is by performing a coast-down test. In order to get accurate results, there must be an understanding of the impact that each component has on the vehicle/tire. Rolling resistance is one of the primary forces acting against the motion of the vehicle. The major factors that contribute to rolling resistance losses are tire design and operation, ambient conditions and road design. Current standards tend to assume that the impact of a specific road surface in a coast-down test is a constant parameter. However, after performing multiple coast-down tests in the same track, this will cause the road surface texture to degrade. Even with a small degradation, this will possibly affect the results since the rolling resistance coefficient is increasing as well as the road load affecting the vehicle. This thesis provides a framework for road surface degradation due to coast-down testing during a span of one and a half years. First, an overview of road surface texture and its impact on fuel consumption is introduced. Surface texture is composed by 4 wavelengths, each one affecting in different ways the vehicle/tire interaction. This thesis focuses on the two smallest wavelengths -macrotexture and microtexture. Advantages and disadvantages of different methods for measuring road surface are discussed. Then, experimental data was collected with an optical profilometer in a coast-down track before and after it was repaved. This thesis aims to quantify the degradation that each wavelength experienced and to analyze the data as thorough as possible. Also, additional measurements were collected to study the impact of weather effect in the long run. By assuming a linear degradation, a mathematical model is developed to estimate the surface texture value. With more tight fuel consumption and emission regulations, it (open full item for complete abstract)

Committee: Giorgio Rizzoni (Advisor) Subjects: Mechanical Engineering