Doctor of Philosophy (PhD), Ohio University, 2024, Civil Engineering (Engineering and Technology)

Colloidal aggregation is a critical phenomenon influencing various environmental processes. However, limited research has been conducted on the aggregation of particles with heterogeneous physical and chemical properties, which are more representative of practical environmental systems than homogeneous particles. The central hypothesis of this dissertation is that primary particle size polydispersity along with chemical and material heterogeneity of primary particles exert non-trivial effects on the aggregate growth rate and the fractal dimensions of aggregates. In this dissertation, the aggregation and stability of heterogeneous nano- and sub-micrometer particles in aqueous systems were investigated using in situ microscopy and image analysis. Initially, the study examined the growth kinetics and structures of aggregates formed by polystyrene microplastics in mono- and bidisperse systems. Findings indicated that while the primary particle size distribution did not affect the scaling behavior of aggregate growth, it delayed the onset of rapid aggregation. Structural analysis revealed a power law dependence of the aggregate fractal dimension in both mono- and bidisperse systems, with mean fractal dimensions consistent with aggregates from diffusion-limited cluster aggregation. The results also suggested that aggregate fractal dimension was insensitive to shape anisotropy. The dissertation further explored the structure of DLCA aggregates in heterogeneous systems composed of particles with varying sizes, surface charges, and material compositions. The fractal dimensions of DLCA aggregates in these heterogeneous particle systems were similar, ranging from 1.6 to 1.7, and consistent with theoretical predictions and experimental evidence for homogeneous DLCA aggregates. This confirmed the universality of aggregate structures in the DLCA regime, regardless of particle composition. Additionally, a scaling relationship was demonstrated between aggregat (open full item for complete abstract)

Committee: Lei Wu (Advisor); Guy Riefler (Committee Member); Daniel Che (Committee Member); Sumit Sharma (Committee Member); Natalie Kruse Daniels (Committee Member) Subjects: Chemical Engineering; Civil Engineering; Environmental Engineering; Physical Chemistry

Master of Science (MS), Wright State University, 2022, Human Factors and Industrial/Organizational Psychology MS

This research examines the effects of fractal dimensionality on ratings of beauty, relaxation, and interest, when these patterns are incorporated in a built space. Previous findings suggest that fractal patterns can be used to mimic the beneficial psychological and physiological effects that arise from viewing nature. This research focuses on studying the impact of fractal patterns when presented within urban environments. The findings here are primarily consistent with previous research. Medium D patterns are preferred over the other pattern complexities. Low D patterns are consistently rated as more relaxing. High D patterns are rated as being more interesting over low D patterns, but the difference between high D and medium D might be smaller than previously thought. These collective findings support the further investigation of the implementation of fractal patterns to promote a form of mental enrichment for inhabitants and a reduction of the stress in an urban environment.

Committee: Assaf Harel Ph.D. (Advisor); Joori Suh Ph.D. (Committee Member); Ion Juvina Ph.D. (Committee Member) Subjects: Psychology

Master of Fine Arts, The Ohio State University, 2020, Design

This thesis analyzes the efficacy of Designer Fractal Patterns (DFPs), a proposed new category of fractal patterns which synthesizes nature's visual organization, to promote human psychological wellbeing in interior built environments. The questions set by this study explore the role of fractal dimension and the fractally fluent range (D1.3-1.5) on the wellbeing responses indicated through mood and emotional responses. The psychological and self-reported wellbeing responses are collected through both a positivistic paradigm, which is built on precedence methodology, and a naturalistic paradigm, which is focused on maintaining ecological validity. The thesis results confirm the predictive quality of fractal dimension but complicate the fractal fluency range for Designer Fractal Patterns. The naturalistic paradigm adds lived experience, memories, and prior associations as determinants of a psychological wellbeing response. The limitation of the study is the small size of the participant sample and the non-uniform distribution of the DFPs' fractal dimensions. The significance of this study lies in the endless possible applications of DFPs in interior space where the direct introduction of natural vegetation is difficult. A clear need for promoting human psychological wellbeing in interior built environments is seen in hospital rooms of their varied kinds, in libraries and classrooms, as well as the proposed pod habitation spaces designed for Mars.

Committee: Elizabeth Sanders (Advisor); Jeff Haase (Committee Member); Krystal Taylor (Committee Member) Subjects: Design; Mathematics

Master of Science in Biomedical Engineering (MSBME), Wright State University, 2016, Biomedical Engineering

The hierarchical structure of bone alone is not comprehensive enough to provide morphological explanation of how the size and arrangement of the trabeculae within cancellous bone affect load distribution, particularly concerning deterioration of bone in elderly patients. The collagen network and hydroxyapatite play a large role in defining the shape of trabeculae in cancellous bone despite that the arrangement and size is seemingly random. The growth of plates and rods in cancellous bone is mainly due to loading and stress lines within the bone, but mathematical predictive models can be developed using fractal analysis to show how bone may grow under different circumstances and what fractal density infers about the arrangement of the trabeculae as well as the strength of the bone, as fractal density is a better indicator of bone strength than bone mineral density. Using a micro-CT scan of the distal end of a human radius, the plate and rod quantity and length was measured along with the angles between the plates and rods. The volume fraction of plates and rods was calculated for each slice. The fractal density was analyzed using the box-counting method, and within this method, different combinations of scaling methods, grid placement, and rotation were utilized to see which was the most accurate. Relationships between the measured parameters were examined to see which had the greatest effect on the perceived strength of the bone.

Committee: Tarun Goswami D.Sc. (Advisor); Jaime Ramirez-Vick Ph.D. (Committee Member); Richard Laughlin M.D. (Committee Member) Subjects: Biomedical Engineering

Doctor of Philosophy, Case Western Reserve University, 2006, Biomedical Engineering

It has been reported that brain white matter (WM), which conducts information to and from the cerebral cortex, experiences degeneration in normal aging. Age-related WM changes have increasingly been explored in brain research due to its possible prognostic significance for diseases such as motor function impairment, cognitive deficits, and depression. Current clinical diagnosis of WM degeneration still depends primarily on visual rating scales due to the lack of objective measurements. Although volume analysis based on magnetic resonance (MR) images, a conventional technique in WM quantification research, is appropriate in assessing brain atrophy, it only captures one of multiple aspects of a full structural characterization of WM and tells little about WM shape adaptations. The goal of this research was to develop a shape analysis method (fractal dimension [FD] analysis) to study the structurally complex changes of WM as a result of aging in three parts. First, the FDs of human cerebellum (CB) WM interior structure (skeleton) in MR images were measured in healthy young subjects using traditional FD methods, which served as a feasibility study for this research. The results showed that the CB skeleton was a highly fractal structure and no differences of CB FDs were detected between men and women. Second, a three-dimensional (3D) FD method was developed and validated to measure the FD of human brain WM interior structure, WM surface and WM general structure simultaneously. The results indicated that the method was accurate in quantifying 3D brain WM structures and sensitive in detecting age-related degeneration of these structures. Finally, a cross-sectional study was conducted to investigate WM complexity changes in normal aging. WM FDs of old people were found to be significantly smaller than those of young people, suggesting that WM structural complexity declined with normal aging. These findings provided new information in describing brain WM structural complexit (open full item for complete abstract)

Committee: Guang Yue (Advisor) Subjects:

Doctor of Philosophy, The Ohio State University, 2024, Art Education

This dissertation shares the process of developing and implementing a fractal inspired design curriculum. The methodology takes on a combination of a participatory action research methodology and several codesign methods. The results of the dissertation are presented in a three-article format. The first article shares the experiences of implementing the fractal curriculum with the general public through three guided nature walks. The second article follows the process of codesigning a fractal curriculum with experienced educators. The third article describes the implementation of the fractal curriculum in an undergraduate studio.

Committee: Dana Kletchka (Advisor); Shari Savage (Advisor); JT Richardson (Committee Member); Rachel Skaggs (Committee Member); Elizabeth Sanders (Committee Member) Subjects: Art Education; Design

Doctor of Philosophy, The Ohio State University, 2022, Mathematics

We explore lattice point counting and the method of stationary phase through the lens of questions about the number of lattice points on and near surfaces with vanishing curvature. Our focus is on spheres arising from the Heisenberg groups. In particular, we prove an upper bound on the number of points on and near large dilates of the unit spheres generated by the anisotropic Heisenberg norms for α ≥ 2. We accomplish this through a transformative process that takes a number theory question about counting lattice points and translates it into that of an analytical estimation of measure. This process relies on truncating and scaling the n-dimensional integer lattice to produce a fractal-like set. By introducing a measure on this resulting set and using elementary Fourier analysis, the counting problem is transformed into one of bounding an energy integral. This process uses principles of fractal geometry and oscillatory integrals. Primary challenges that arise are the presence of vanishing curvature and uneven dilations. Following a discussion and formal estimate of the curvature of the Heisenberg spheres, we utilize the method of stationary phase to compute a bound on the Fourier transform of their surface measures. Our work is inspired by that of Iosevich and Taylor (2011) and Garg, Nevo, and Taylor (2015). We present an extension of the main result in the former to surfaces with vanishing curvature. Furthermore, we utilize the techniques developed here to estimate the number of lattice points in the intersection of two such surfaces. Additionally, we present a mini-course on the basics of stationary phase—a quick-start guide to stationary phase in practice. This includes a discussion of the formulation of oscillatory integrals and their solutions with a focus on the impact of geometric properties (e.g. curvature) on the estimates for the decay of the Fourier transform. It further serves as a supplement to [Shakarchi and Stein, Functional Analysis: Chapter (open full item for complete abstract)

Committee: Krystal Taylor (Advisor); Rodica Costin (Committee Member); Barbara Keyfitz (Committee Member) Subjects: Mathematics

Doctor of Philosophy, The Ohio State University, 2022, Physics

The success of modern digital electronics relies on compartmentalizing logical functions into individual gates, and controlling their order of operations via a global clock. In the absence of such a timekeeping mechanism, systems of connected logic gates can quickly become chaotic and unpredictable -- exhibiting analog, asynchronous, autonomous dynamics. Such recurrent circuitry behaves in a manner more consistent with neural networks than digital computers, exchanging and conducting electricity as quickly as its hardware allows. These physics enable new forms of information processing that are faster and more complex than clocked digital circuitry. However, modern electronic design tools often fail to measure or predict the properties of large recurrent networks, and their presence can disrupt other clocked architectures. In this thesis, I study and apply the physics of complex networks of self-interacting logic gates at sub-ns timescales. At a high level, my unique contributions are: 1. I derive a general theory of network dynamics and develop open-source simulation libraries and experimental circuit designs to re-create this work; 2. I invent a best-in-class digital measurement system to experimentally analyze signals at the trillionth-of-a-second (ps) timescale; 3. I introduce a network computing architecture based on chaotic fractal dynamics, creating the first `physically unclonable function' with near-infinite entropy. In practice, I use a digital computer to reconfigure a tabletop electronic device containing millions of logic gates (a field-programmable gate array; FPGA) into a network of Boolean functions (a hybrid Boolean network; HBN). From within the FPGA, I release the HBN from initial conditions and measure the resulting state of the network over time. These data are transferred to an external computer and used to study the system experimentally and via a mathematical model. Existing mathematical theories and FPGA simulation tools produce in (open full item for complete abstract)

Committee: Daniel Gauthier (Advisor); Emre Koksal (Committee Member); Gregory Lafyatis (Committee Member); Antonio Boveia (Committee Member) Subjects: Applied Mathematics; Computer Engineering; Computer Science; Condensed Matter Physics; Electrical Engineering; Electromagnetics; Electromagnetism; Engineering; Experiments; High Temperature Physics; Information Science; Information Systems; Information Technology; Low Temperature Physics; Materials Science; Mathematics; Medical Imaging; Nanotechnology; Particle Physics; Physics; Quantum Physics; Scientific Imaging; Solid State Physics; Systems Design; Technology; Theoretical Physics

Master of Science (MS), Wright State University, 2021, Earth and Environmental Sciences

The non-native invasive shrub Amur honeysuckle, Lonicera maackii (Rupr.) Herder (Gorchov and Trisel, 2003), is one of the most prolific invasive plant species across Midwestern and Northeastern landscapes of the United States. The locations of 2,095 individual Amur honeysuckle stems were geolocated using handheld GPS units in the understory of mixed growth forests at two study sites located approximately 5 km apart in northwestern Greene County, OH. Each site has undergone different levels of anthropogenic disturbance through time. The stem position data was used to measure the spatial clumping distribution and the density of Amur honeysuckle. The spatial clumping of Amur honeysuckle stems was measured using the fractal box counting method at each study site without regard for streams, trails, or elevation. The density of Amur honeysuckle (number of stems per square meter) was measured in zones as a function of the horizontal distance perpendicular to the edge of streams, trails, and within elevation (area between contour lines). Amur honeysuckle density is found to be uncorrelated with its proximity to streams, trails, and elevation. The density of Amur honeysuckle as a function of distance from streams and trails does not reveal an edge effect. The fractal dimension (scaling exponent) was computed to be ~1.5 at each of the two sites which means that the spatial clustering is the same for actively managed (partial Amur honeysuckle removal) and unmanaged sites. These results suggest that the invasion potential of Amur honeysuckle is robust, and its distribution may not be constrained in riparian forests by the variables included in this study.

Committee: Christopher Barton Ph.D. (Advisor); David Peterman Ph.D. (Committee Member); Ryan McEwan Ph.D. (Committee Member) Subjects: Biology; Earth; Ecology; Environmental Science

Doctor of Philosophy, The Ohio State University, 2020, Mathematics

Finite metric spaces are increasingly being used to model data sets that come equipped with a dissimilarity measure. Some examples of such data sets are DNA sequences, news articles, social networks and feature vectors in machine learning applications. Due to the ever increasing volume of data, the simplification of finite metric spaces is a fundamental problem in data analysis. In this thesis, new methods of simplification of compact metric spaces are introduced. There are three primary concepts used for simplification - fractal dimension, clustering and representative subspaces. The first concept is that of fractal dimension of a finite metric space. The effect of fractal dimension on the computational complexity of geometric problems such as the Traveling Salesman Problem, and the Independent Set of Unit Balls problem is studied. The running time lower bounds for these problems on metric spaces with non-integer dimension are computed and it is shown that these bounds nearly match previously known upper bounds. The second notion that is used for simplification of a compact metric space is clustering. Precisely, a new method for approximating a compact metric space with a k-point metric space is introduced. It is shown that approximating with k points is equivalent to certain notions of clustering of the input metric space into k blocks. Similar results are obtained for metric measure spaces. Using the equivalence results for metric spaces and metric measure spaces, polynomial time algorithms for computing the k-point approximations are developed. Another method of simplification of compact metric spaces is developed through identifying finite representative subspaces of the input metric space. The subspaces are representative because they store the entire metric information of the original metric space, up to a loss of epsilon. As a result, this method identifies a finite compression of the input metric space. A polynomial time exact algorithm is developed for com (open full item for complete abstract)

Committee: Facundo Memoli (Advisor); Anastasios Sidiropoulos (Advisor); Tamal Dey (Committee Member); David Sivakoff (Committee Member) Subjects: Applied Mathematics; Mathematics

Master of Science, The Ohio State University, 2018, Civil Engineering

Membrane technology is a growing option for treatment of source and waste waters. As implementation of membrane treatment grows, however, the classical challenge of biofouling persists. Biofilm growth separates the membrane from well-mixed flow, leading to increased hydraulic resistance and ultimately higher costs. Despite best efforts by current technology to remove fouling layers, foulant build-up followed by membrane death is a matter of course. In highly fouling environments, a mechanism to de-foul the membranes in-situ would greatly improve treatment. Ultrasonic cleaning is known to provide a high level of cleaning via acoustic cavitation. Benefits of ultrasonic cleaning include high efficacy, synergy with chemical treatment, and potential to degrade recalcitrant compounds. Prior studies have demonstrated the ability of ultrasound to increase membrane flux and deter fouling. However, ultrasonic cleaning is usually investigated using model foulants. It is a stated problem that these model compounds do not exhibited the same fouling properties as biofilms, and indeed previous researchers have found that biofilm removal by ultrasonic cleaning is not always effective. In this study, membranes were biofouled using a laboratory-scale bioreactor. Membrane-adherent biofilms were then exposed to 0, 20, or 60 s of 205.5 kHz ultrasound at a power intensity of 6.38 W cm-2. Confocal laser scanning microscopy coupled with image analysis quantified biofilm thickness, biomolecular composition, and spatial autocorrelation of membrane-adherent biofoulant. 16S rRNA gene sequencing determined changes in biofilms' microbial communities. Results demonstrated that 20 s of ultrasound – under the chemical, physical, and geometric conditions tested here – removed upper biofilm progressively, resulting in a biofilm population which exhibited a continuum of thicknesses ranging from 0 to 370 µm. Surface layers of biofilms sometimes retained some thickness after sonication, and exhibit (open full item for complete abstract)

Committee: Linda Weavers Dr. (Advisor); Paula Mouser Dr. (Advisor); Hendrik Verweij Dr. (Committee Member) Subjects: Civil Engineering; Engineering; Environmental Economics; Environmental Engineering; Ethnic Studies; Microbiology

Doctor of Philosophy, The Ohio State University, 2018, Computer Science and Engineering

We study two aspects of metric spaces that affect the computational complexity of geometric problems. First, we study directed cut problems and the associated multi-commodity flow-cut gap on different classes of directed graphs. We look at generalizations of classical metric embedding results to the case of quasimetric spaces; that is, spaces that do not necessarily satisfy symmetry. Quasimetric spaces arise naturally from the shortest-path distances on directed graphs. Random embeddings are arguably one of the most successful geometric tools in the context of algorithm design. We extend this set of tools to the quasimetric case to obtain the following results. We present a t log O(1) n-approximation algorithms for the Directed Non-Bipartite Sparsest-Cut and the Directed Multicut problems on n-vertex graphs of treewidth t, with running time polynomial in both n and t. We also give O(1)-approximation algorithms for the Uniform Directed Non-Bipartite Sparsest-Cut and the Directed Multicut problems on series-parallel digraphs and digraphs of bounded pathwidth. We also show that any n-point quasimetric space supported on a graph of treewidth t admits a random embedding into quasiultrametric spaces with distortion O(t log 2 n), where quasiultrametrics are a natural generalization of ultrametrics. For directed cycles and directed trees we show an embedding into directed ℓ 1 space with constant distortion. The above results are obtained by considering a generalization of random partitions to the quasimetric case, which we refer to as random quasipartitions. Using this definition and a construction of [Chuzhoy and Khanna 2009] we derive a polynomial lower bound on the distortion of random embeddings of general quasimetric spaces into quasiultrametric spaces. We also establish a lower bound for embedding the shortest-path quasimetric of a graph G into graphs that exclude G as a minor. Finally, we show an Ω(n) lower bound for random non-contracting embeddings of di (open full item for complete abstract)

Committee: Anastasios Sidiropoulos (Advisor); Yusu Wang (Advisor); Facundo Mémoli (Committee Member) Subjects: Computer Science

Master of Science in Mechanical Engineering, Cleveland State University, 2018, Washkewicz College of Engineering

Fractal grids, having patterns that repeat at different length scales, mimic the multi-scale characteristic of objects of complex appearance in nature, such as branching pulmonary network, river network, trees, and cumulus clouds. Understanding the role that multiple length scales have in momentum and energy transport is essential for effective utilization of fractal grids in a wide variety of engineering applications. While previous studies have solidified the dominant effect of the largest scale of fractal grids, effects of the additional scales on the generated turbulent flow remain unclear. This research is to determine how the smaller fractal scales (and the interaction between the multiple scales) influence the turbulence statistics of the induced flow and the pressure drop across a fractal square grid using well-controlled water-tunnel experiments. Instantaneous and ensemble-averaged velocity fields are obtained by a planar Particle Image Velocimetry (PIV) for a set of fractal square grids (N = 1, 2, 3 and 4) at the Reynolds number of 3400. The static pressure drop across the grids are measured by a differential pressure transducer. Flow fields indicate that the multiple jets, wakes and the shear layers produced by the multiple scales are the fundamental flow physics that promote momentum transport in the turbulence. In addition, the multiple scales cause a redistribution of the turbulent kinetic energy and a change in the vortex shedding mechanism behind the grids. The grid of 3 fractal scales (N = 3) produces higher turbulence intensity levels than the grid with 4 fractal scales (N = 4) despite having a lower blockage ratio and pressure drop, owing to the suppression of vortex shedding by the additional scale of the grid of N = 4. To better predict the location of peak turbulence intensity, the wake interaction length scale model is modified to incorporate the effects of the interactions among multiple scales by the effective mesh size and an empirical scal (open full item for complete abstract)

Committee: Wei Zhang PhD (Advisor); Asuquo Ebiana PhD (Committee Member); Thijs Heus PhD (Committee Member) Subjects: Fluid Dynamics

Master of Science (MS), Wright State University, 2018, Physics

Finite element method analysis is used to conduct electromagnetic simulations to characterize fractal antennas. This work considers wire (1D) antennas such as the triadic Koch curve, zig zag, and quadratic Koch curve of varying heights, iterations, and cross-sectional areas. Carpet (2D) antennas, including the Sierpinksi carpet, of varying heights, iterations, and deterministic and stochastic iterations are analyzed. The antenna shapes were generated in MATLAB and then modeled with finite element analysis using COMSOL Multiphysics®. The focus of this study is to determine what role various fractal patterns and iterations have on the S11 return loss and far field radiation patterns. Specifically, changes in directionality of the emitted radiation and return loss over a range of frequencies, between 0.2 to 2.2 GHz, are examined. Parameters that are manipulated to assess the influence on antenna performance include varying the fractal generator, varying the number of iterations to make more complex shapes, scaling the antennas, and varying the cross sectional area. Antennas with fractal shapes are shown to have multiple operational bandwidths and exhibit directionality changes with frequency in the far field radiation pattern.

Committee: Sarah Tebbens Ph.D. (Advisor); Jason Deibel Ph.D. (Committee Member); Christopher Barton Ph.D. (Committee Member) Subjects: Physics

Master of Science (MS), Wright State University, 2018, Earth and Environmental Sciences

Previous fractal analyses of shoreline roughness have measured the fractal dimension of long segments of shoreline, e.g. Mandelbrot (1983) quantified the shoreline of the west coast of Britain and Feder (1988) quantified the shoreline of Norway. Consequently, changes in roughness along short segments are not captured by the analysis. In this study, the fractal dimension of the mainland shoreline of the contiguous United States has been measured in 125, 250, and 375 km segments using the box-counting method. The box counting method is based on the equation N = c x b where N is number of occupied boxes, C is a constant, x is box side length, and b is the fractal dimension (scaling exponent). A MATLAB code was written to measure the fractal dimension using the box-counting method. The fractal dimension measures the scaling property of a pattern not at any one length but over a range of lengths. In this study, the box-counting method counts occupied boxes over a range of box sizes along a segment of shoreline to measure the fractal dimension as it changes at different scales along the shoreline. The result is that the fractal dimension of the shoreline will continue to change as the segment length decreases. Thus, the single value of the fractal dimension reported by Richardson (1961) and Mandelbrot (1983) for the shoreline of the west coast of Britain or by Feder (1988) for the shoreline of Norway are each an approximation of the average fractal dimensions at smaller segment lengths. The shoreline analyzed in this study is the NOAA Medium Resolution Shoreline. Source map scales range from 1:10,000 to 1:600,000 with an average of 1:70,000. In the current study, sequentially numbered X-Y coordinate points in UTM Zone 18N, spaced 50 meters apart, as measured continuously along the shoreline comprised the shoreline. Fractal scaling was found on every section of the contiguous United States shoreline for each segment length (125, 250, 375 km) sampled. The range (open full item for complete abstract)

Committee: Chris Barton Ph.D. (Committee Chair); Sarah Tebbens Ph.D. (Committee Member); Mateen Rizki Ph.D. (Committee Member) Subjects: Earth; Geology; Geophysics

MA, University of Cincinnati, 2018, Arts and Sciences: Psychology

Agent-environment systems are composed of processes acting on different time scales and thus system behavior typically exhibits multiscale dynamics. Systems that exhibit multiscale dynamics tend to be well approximated by scale-free distributions, meaning that changes in behavior or behavioral fluctuations maintain a certain level of statistical regularity across temporal scales. This pattern of statistical regularity or self- similarity is referred to as fractal behavior. Although previous research has provided cursory support for the hypotheses that (i) human behavior is characterized by fractal, multi-scaled dynamics, and (ii) that these dynamics are co-related to the structural complexity of environmental and/or task activities (e.g., Bassingthwaighte et al., 1994; Rigoli et al., 2015; Van Orden et al., 2011), nearly all of this previous work has focused on only a small subset of simple or contrived behaviors within the context of controlled and supervised laboratory tasks. The aim of the current study was to investigate the degree to which the fractal structure of human behavior vary as a function of different naturalistic environmental and task structures, as well as the degree to which pairs exhibit complexity matching as a function of these task structures. To achieve this aim, 78 undergraduate students (26 individuals and 26 pairs) participated in a 1.5-hour experiment involving six different self-paced (unsupervised), semi-structured activities around the University of Cincinnati (UC) campus. This methodology thereby grounded the investigation within the context of everyday student activity, while minimizing strict or unnatural behavioral constraints or laboratory manipulations. Wearable technology (Empatica E4 bio-sensing wristbands and iPhones) was used to record acceleration at the wrist and waist. Detrended fluctuation analysis (DFA) was employed to index the fractal dynamics of the participants' behavioral signals as a function of the different (open full item for complete abstract)

Committee: Tamara Lorenz Ph.D. (Committee Chair); Tehran Davis Ph.D. (Committee Member); Rachel Kallen Ph.D. (Committee Member); Michael Richardson Ph.D. (Committee Member); Paula Silva Ph.D. (Committee Member) Subjects: Psychology

PhD, University of Cincinnati, 2018, Engineering and Applied Science: Materials Science

Many commercially and industrially important materials aggregate to form nanoscale mass-fractal structures. Ultra-small angle X-ray scattering is coupled to a hierarchical scattering model, the Unified scattering function, to obtain topological parameters, binary interaction strength and the thermodynamics of nanoparticle aggregation. The mass-fractal structures of fumed silica aggregates were determined using literature methods and the ramified structures are then compared with simple visualizations of fractal aggregates. Surprisingly, a single parameter, the sticking probability, can empirically account for complex topological differences in the materials studied. Samples of higher specific surface area display higher branch fraction and hyper-branched structures are observed for samples of highest specific surface area. Unlike hard aggregates such as fumed silica, pigment-based inks consist of weakly bound nanoparticles stabilized by a surfactant. Bound by relatively weak van der Waals forces, these soft aggregates can easily break apart and re-form. The emergence of structure under semi-dilute conditions is related to the structure of the dilute particles, the particle spacing (mesh size), processing history, and the interaction potential. The final properties of a pigment emerge from a complex interplay between nanoparticle aggregation and dispersion of aggregates as a function of concentration. Emergent properties may enable prediction of properties such as brilliance and opacity. Samples of pigment yellow 14 were milled to four primary particle sizes to study the effect of particle size on emergence. The interactions between surfactant-stabilized PY14 aggregates in an aqueous media were quantified by the second virial coefficient, A2, in the expansion of osmotic pressure. In such systems, A2 describes long-range binary interactions. A2 was then translated into a repulsive interaction potential for use in dissipative particle dynamics simulations t (open full item for complete abstract)

Committee: Gregory Beaucage Ph.D. (Committee Chair); Jude Iroh Ph.D. (Committee Member); Dale Schaefer Ph.D. (Committee Member); Vesselin Shanov Ph.D. (Committee Member); Karsten Vogtt Ph.D. (Committee Member) Subjects: Materials Science

Master of Science in Mechanical Engineering, Cleveland State University, 2017, Washkewicz College of Engineering

An experimental study of the effect of the fractal square grid (FSG) iteration number (N) parameter on heat transfer is carried out. Using four different FSGs with four different N, the experiment is done using a closed system water tunnel. Particle Image Velocimetry (PIV) is used to characterize the turbulence of the flow downstream of the grids. Heat transfer measurements are performed around a circular cylinder centerline circumference without the grids first for Reynolds number range of 8473 = Re = 27192. After, measurements of heat transfer are done around the cylinder downstream each grid for four different stream-wise distances from the cylinder with Reynolds number of 8473 that is based on the cylinder diameter (d). Turbulence intensities (Tu) of all grids show relatively different trends with higher Tu for grids with higher N. Heat transfer results under laminar free-stream condition show a good agreement with the predicted empirical correlations in literature. Heat transfer measurements are performed downstream of two grids only, which are FSG4 and SSG. Results indicated that FSG4 introduces higher heat transfer enhancement in general than SSG. While, SSG outperforms FSG4 in heat transfer enhancement for relatively close distances from the cylinder.

Committee: Wei Zhang Ph.D. (Committee Chair); Asuquo Eliana Ph.D. (Committee Member); Thijs Heus Ph.D. (Committee Member) Subjects: Mechanical Engineering

Doctor of Philosophy, The Ohio State University, 2017, Food, Agricultural and Biological Engineering

Moisture diffusion in lipids is difficult to quantify due to the low magnitude of moisture diffusivity coefficients in lipids and limitations in measurement techniques. Owing to these challenges, the correlations between the structure of the lipid network and diffusion coefficients have not been fully developed yet. The overall objectives of this research were: to quantify the effect of (i) shearing, (ii) cooling rate, and (iii) constituent ratios on moisture diffusion in lipids using nuclear magnetic resonance microimaging (NMR microimaging, or MRI), (iv) develop a structure-based model for predicting effective moisture diffusivities (Deff) in lipid samples of defined structural characteristics, and (v) measure structural attributes of lipid samples prepared under different crystallization conditions, needed as inputs to validate the proposed model. The lipid samples used in this investigation were either of industrial importance such as cocoa butter (CB), palm kernel oil (PO), and 20% w/w cocoa powder in palm kernel oil (CPPO), or, model systems containing pure monoacid triacylglycerols trilaurin (LLL) and triolein (OOO). The lipid samples were layered on top of agar gel, a moisture source, and stored isothermally. Using MRI, the effect of shearing on moisture diffusion was studied in industrial lipid samples CB, PO, CPPO over three months of storage at 20 °C. It was found that shearing at 250/s significantly decreased equilibrium moisture uptake (M∞) by 13%-77% depending on the composition of samples. Using Fick's II law, Deff values were obtained to be ~10^-13 m^2/s; and shearing decreased diffusivities by 50% in CB and 33% in PO samples. Diffusion decreased most severely in sheared CPPO samples as they did not reach equilibrium moisture uptake in the duration of this study. Due to the complex chemical composition of CB, PO, CPPO; model fat systems made from LLL and OOO were used to study the effect of constituent ratios and cooling rate at constant shear rate (open full item for complete abstract)

Committee: Dennis Heldman (Advisor); Farnaz Maleky (Advisor); Sudhir Sastry (Committee Member); Hannah Shafaat (Committee Member) Subjects: Agricultural Engineering; Chemical Engineering; Food Science

MS, University of Cincinnati, 2016, Engineering and Applied Science: Mechanical Engineering

Elastomers, being soft and viscoelastic, when slid over a hard substrate, drape the surface roughness of the substrate and experience oscillating forces from the surface asperities of the substrate. The friction force thus generated is composed of two principal components, hysteresis and adhesion. The hysteresis component of friction depends on the surface topography, whereas the adhesion component is a surface effect and depends on the contact area. Also, when elastomers are squeezed against the substrate, they in general do not make complete contact everywhere within the nominal contact area. Thus, understanding the surface topography of the substrate and evaluating the real contact area are of huge importance to calculate the friction force generated accurately. This study uses Persson's contact theory to calculate the real contact area. The study also uses two analytical models developed by Persson to understand the adhesion and hysteresis contributions to friction. The models are used to study the effect of velocity, load, surface roughness, and cutoff magnification on the real contact area and the adhesion and hysteresis contributions to friction. Once the major parameters affecting elastomer friction are understood, a finite element model is developed to study the real contact area when a rigid surface interacts with a deformable elastomer. The elastomer is modeled as linear viscoelastic material and the surfaces are modeled as fractals. The results from the finite element model are analyzed and compared with the results from Persson's analytical models and the limitations of the computational model are studied. The results show that the finite element model is stiffer than Persson's analytical model and over-predicts the apparent area of contact. Also, in this study, a single parameter based on Hurst exponent to quantify surface isotropy is proposed. The efficiency of this parameter is tested on numerically generated isotropic and a (open full item for complete abstract)

Committee: Kumar Vemaganti Ph.D. (Committee Chair); Woo Kyun Kim Ph.D. (Committee Member); Yijun Liu Ph.D. (Committee Member) Subjects: Engineering; Mechanical Engineering