Master of Science (MS), Wright State University, 2019, Human Factors and Industrial/Organizational Psychology MS

The effect of masker spatial variability and masker fringe on the perception of a brief tone in noise was investigated in a detection task. Simpson (2011) found large effects of spatial variability (randomizing masker locations from trial to trial) in a masked localization experiment, as well as two effects of masker fringe (masking noise before the onset of the target): 1) cuing the masker location (spatial cuing effect) and 2) temporally separating the onset of the masker and the onset of the target (onset effect). In contrast, in detection studies, the effects of masker spatial variability are small (e.g., Bernstein & Trahiotis, 1997) and the possibility of a spatial cuing effect has not been directly examined. However, onset effects of similar magnitude to those observed by Simpson have been reported (e.g., McFadden, 1966). To determine whether these differences in the effect of masker variability between localization and detection could be attributed to the fact that in localization experiments there is also trial-to-trial variability in the target, we conducted a detection experiment via headphones using a 2 Masker Variability (variable & fixed) x 2 Target Variability (variable & fixed) x 2 Masker Fringe (no fringe & fringe) factorial design. We used a 60-ms, 500-Hz sinusoidal target and a 60-ms Gaussian noise masker (and a 500-ms Gaussian noise masker fringe in the fringe conditions). Masker and/or target location was varied laterally by varying the interaural time difference (ITD) of the fine structure. We found little effect of masker variability, in agreement with previous detection studies, and the presence or absence of target spatial variability did not alter the pattern of results. Because the effect of masker variability was small, there was limited opportunity to observe a spatial cuing effect, but there was an onset effect of fringe that was of similar magnitude to that observed in previous detection studies. In the binaural detection literature, (open full item for complete abstract)

Committee: Robert H. Gilkey Ph.D. (Advisor); Brian D. Simpson Ph.D. (Committee Member); Scott N.J. Watamaniuk Ph.D. (Committee Member) Subjects: Acoustics; Psychology

PhD, University of Cincinnati, 2025, Engineering and Applied Science: Civil Engineering

Flooding is widespread throughout the United States, leading to billions of dollars of damages to property and infrastructure every year. However, most of the previous studies are mainly focusing on the deterministic analysis or ignore the soil spatial variability for the stability of the flood protection infrastructures in the face of natural hazards (e.g., floods and earthquakes). This dissertation aims to re-evaluate the stability of widely used flood protection infrastructures (e.g., earthen levees, pile-founded T-walls) considering soil spatial variability under hazard conditions. The responses of the flood protection infrastructures in the face of earthquakes and floods are simulated by the physics-based numerical models with the finite element method (FEM) and the finite difference method (FDM). The soil spatial variability is characterized by random field theory and its effect on the stability of flood protection infrastructures is investigated by parametric studies. The Point Estimate Method (PEM) and efficient subdomain sampling method are adopted for the reliability analysis of the flood protection infrastructures. To design high-resilient flood protection infrastructures in spatially variable soils under the flooding hazard, a new robustness measure is proposed and a design optimization framework incorporating the proposed robustness measure is established for the robust design of flood protection infrastructures. To simplify the design procedures and improve the computational efficiency of this framework, a new method for a dynamic architecture of Convolutional Neutral Networks (CNN) is proposed to efficiently and accurately estimate the probability of failure of earthen levees under rising flood water elevations. This method maximizes the capacity of CNN in predicting the probability of failure by globally searching for the optimal configuration of CNN. This dissertation can be regarded as a complement to the previous stability analyses of flood protec (open full item for complete abstract)

Committee: Lei Wang Ph.D. (Committee Chair); Manish Kumar Ph.D. (Committee Member); Sara Khoshnevisan Ph.D. (Committee Member); Munir Nazzal Ph.D. (Committee Member) Subjects: Civil Engineering

Master of Science, The Ohio State University, 2020, Environmental Science

Understanding of within-field spatial and temporal variability of crop yield and the potential drivers for such variability is critical for site-specific crop management (a.k.a precision agriculture) from both economic and environmental perspectives. The objectives of this study are to 1) improve crop yield estimates at a field scale by integrating remote sensing data to assess spatial and temporal within-field yield variability, and 2) evaluate how the design of management zones varies using crop yield data of various spatial resolutions. To meet these objectives, yield monitor data of three fields (~32.5 hectares) that are in corn-soybean and corn-wheat rotations over the period 2016-2019 in the Molly Caren Agriculture Center at London, Ohio were used. Crop grain yield data collected from yield monitor was integrated with topographic variables derived from digital elevation model (DEM) (0.76 m) and vegetation indices derived from high- and medium-resolution remotely sensed imagery (0.3 m to 3 m) using linear regression (LR) and random forest (RF) algorithms to create high-and medium-resolution crop yield maps at a field scale. Yield monitor data were cleaned using Yield Editor software. Topographic variables, such as slope, elevation and wetness index, were calculated using DEM data. Remotely sensed imagery were preprocessed and analyzed, and various vegetation indices (e.g., normalized difference vegetation index (NDVI), Green NDVI (GNDVI), Excess Greenness (ExG)) were calculated. Using high-and medium-resolution yield maps, temporal and spatial standard deviations (SD) of crop yields were calculated. Based on SD and average crop yield, areas within a field were classified into four zones (z), with z1 and z2 having consistently higher and lower yield than average yield, respectively; z3 with variable but below average yield, and z4 with variable but above average yield. DEM derived topographic variables were used to assess their impact on yield variability within (open full item for complete abstract)

Committee: Sami Khanal Dr. (Committee Chair) Subjects: Environmental Science

Master of Science in Engineering, University of Akron, 2017, Civil Engineering

In design of supported excavation, many codes and criteria can be utilized to insure the usability and stability of excavation structure. However, due to the variation and spatial variability in soil parameters, the deterministic analysis in codes may not always be safe. This paper considers the uncertainties of standard penetration blow count ((N1)60) and execute reliability analysis on the geotechnical and structural responses in supported excavation in sand, including lateral wall deflection, bending moment in wall, shear force in wall and strut force. Random field theory is adopted to generate values of (N1)60 considering effect of variation and spatial variability on sand layers. Different levels of COV and scale of fluctuation are considered to simulate various scenarios in the real field. Random finite element method and Monte-Carlo simulation are used to execute reliability analysis and the failure probabilities of multiple failure modes are estimated. An automation procedure is purposed to enhance the efficiency and accuracy in parameters input and results output. The importance and influence of spatial variability on the design of supported excavation are shown according to the analysis results.

Committee: Zhe Luo (Advisor); Junliang Tao (Committee Member); Qindan Huang (Committee Member) Subjects: Civil Engineering

Doctor of Philosophy, University of Akron, 2013, Civil Engineering

With the implementation of load and resistance factor design (LRFD) by the U.S. Federal Highway Administration, the design of deep foundations is migrating from Level I (e.g., allowable stress design) codes to Level II codes (e.g., LRFD). Nevertheless, there are still unsolved issues regarding the implementation of load and resistance factor design. For example, there is no generally accepted guidance on the statistical characterization of soil properties. Moreover, the serviceability limit check in LRFD is still deterministic. No uncertainties arising in soil properties, loads and design criteria are taken into account in the implementation of LRFD. In current practice, the load factors and resistances are taken as unity, and deterministic models are applied to evaluate the displacements of geotechnical structures. In order to address the aforementioned issues of LRFD, there is a need for a computational method for conducting reliability analysis and computational tools for statistically characterizing the variability of soil properties. The objectives of this research are: 1) to develop a mathematically sound computational tool for conducting reliability analysis for deep foundations; and 2) to develop the associated computational method that can be used to determine the variability model of a soil property. To achieve consistency between the strength limit check and the serviceability limit check of the LRFD framework, performance-based design methodology is developed for deep foundation design. In the proposed methodology, the design criteria are defined in terms of the displacements of the structure that are induced by external loads. If the displacements are within the specified design criteria, the design is considered satisfactory. Otherwise, failure is said to occur. In order to calculate the probability of failure, Monte Carlo simulation is employed. In Monte Carlo simulation, the variability of the random variables that are involved in the reliability a (open full item for complete abstract)

Committee: Robert Liang Dr. (Advisor); Lan Zhang Dr. (Committee Member); Qindan Huang Dr. (Committee Member); Xiaosheng Gao Dr. (Committee Member); Chien-Chung Chan Dr. (Committee Member) Subjects: Civil Engineering; Statistics

PhD, University of Cincinnati, 2003, Engineering : Environmental Science

Accurate estimation of saturated hydraulic conductivity (Ks) in soils is essential for various hydrological applications. Ks is difficult to characterize because of its high variability even over short distances, and measurement methods typically require considerable time and resources. Consequently, researchers often use a limited number of measurements for characterizing Ks or use various soil properties for indirect estimation via pedotransfer functions. This dissertation investigates spatial patterns of Ks at different sampling scales along a complex forested soilscape ranging from relatively clay-rich to sand-rich soils. Although there is a tight link in the spatial patterns of soil texture, color and mineralogy as a function of topography along this tropical rainforest catena (i.e. toposequence), there is no similar dependency with Ks, and univariate statistics show no significant (a = 0.05) difference in Ks between soil types. Spatial patterns of Ks are linked to topography and texture only where soils are comprised of >80% sand. Based on surface and subsurface analyses of physical and biological processes, it appears that Ks is largely controlled by macropores from roots and animals (i.e. biopores), but textural porosity largely controls Ks for coarse textures with >80% sand. At all sampling scales (lags of 25, 10, 1 and 0.25 m), there is little to no autocorrelation in Ks and no apparent link with topography or soil properties, and structure does not emerge from noise, except for transects extending over soil boundaries separating coarse (> 80% sand) and less coarse textures. Because pedotransfer functions using readily available soil information do not properly account for macropores generated by bioturbation, these functions do not provide reasonable point estimates of Ks or accurate descriptions of its spatial patterns. This data-intensive study demonstrates a general lack of spatial structure and predictability in Ks for forest soils at all scales that (open full item for complete abstract)

Committee: Dr. Shafiqul Islam (Advisor) Subjects:

MS, University of Cincinnati, 2001, Engineering : Environmental Engineering

The widespread arsenic contamination of groundwater in Bangladesh has been recognized as posing a serious health problem to millions of people in the region. Understanding of the complex lateral and vertical variability in its countrywide distribution is an important step towards better spatial estimation and also improved understanding of the controlling physical processes. A geostatistical characterization of arsenic followed by classical and indicator variogram analysis was performed in an attempt to study the partitioning of variability across scales and thereby identify the dominant physical process controls on arsenic. The results suggest that 'large-scale' (greater than tens of km approx.) geological and physical features control a significant fraction of the spatial variability in shallow wells (55%) as well as in the deeper wells (88%). The indicator variogram analysis also suggested the possibility of deeper aquifers acting as potential safe water sources in the country. The usefulness of countrywide spatial contamination maps is brought into question. The residual, small-scale variability appears to be unrelated to the widespread application of groundwater towards irrigation in the country. Experiments on the sampling pattern of wells in the arsenic datasets revealed that the variability in the arsenic distribution is proportional to the degree of spatial clustering of sampled tube wells. The result suggests that the nugget value in the variograms is not governed by purely random variability and instead, is controlled by distinct 'small-scale' processes. It is proposed that the prevalence of higher arsenic concentrations of arsenic in shallow wells is because of these 'small-scale' processes (less than tens of km approx.) exerting a greater degree of control at shallower depths in the sediments. A possibility of the right-skewedness of arsenic distribution leading to completely random variations in the arsenic concentrations yielding an increased number o (open full item for complete abstract)

Committee: Dr. Shafiqul Islam (Advisor) Subjects: Engineering, Environmental

Doctor of Philosophy (PhD), Ohio University, 2010, Environmental and Plant Biology (Arts and Sciences)

Aquatic organisms are excellent indicators of human impacts on stream ecosystems because they provide valuable services and integrate the effects of multiple stressors over time and space, which would be difficult to assess if only considering water chemistry. Agriculture and acid mine drainage (AMD) from historic coal mining contribute to the concentrations of nutrients, conductivity, and pH of streams; all of which are important to the presence and abundance of diatom species. Based on diatom responses to environmental conditions, this research (1) developed indices and examined relationships of metrics with chemistry and land use variables throughout the Western Allegheny Plateau of Ohio and two watersheds, (2) identified how habitat heterogeneity and sampling methods affect diatom diversity patterns and biomonitoring, (3) examined how spatial factors influence diatom assemblage structure and bioassessments, (4) characterized how temporal variability in seven reference and seven non-reference streams affects bioassessments, and (5) documented how AMD impacts biofilm succession, structure, and function as measured by extracellular enzymes. In anthropogenically impacted streams, diatom assemblages showed significant decreased similarity to reference sites, increased % high nutrient diatoms, increased % motile diatoms, and decreased % low nutrient diatoms associated with increased agriculture, and increased % acidophilic diatoms was associated with reduced alkalinity caused by AMD impacts. Intermediate percentages of epilithic habitat promoted diatom diversity, and multiple habitat samples had stronger relationships with watershed impacts than epilithic habitat samples. Spatial factors contributed to diatom assemblage structure likely because of species dispersal within watersheds and the region, but metrics were influenced less by spatial factors. Diatom metrics responded to two-week lags in PO4-P concentrations, and samples collected toward the end of summer were (open full item for complete abstract)

Committee: Morgan Vis (Advisor); Jared DeForest (Committee Member); Kelly Johnson (Committee Member); Brian McCarthy (Committee Member) Subjects: Biology; Botany; Ecology; Environmental Science

MA, Kent State University, 2009, College of Arts and Sciences / Department of Geography

The Great Lakes Region of the United States is an area of great climatic diversity. Research analyzing diurnal temperature range (DTR) has noted that in late autumn and early winter an abrupt decrease in the mean temperature range for stations near the Great Lakes occurs. Reasons for this rapid change are likely related to cloud cover amounts and frequencies of specific weather-types. In this thesis, temporal trends and correlations of several weather variables were conducted to assist in the explanation of the rapid change in the region's climate. This variability was then correlated to the teleconnection phases of PNA (Pacific/North American) and NAO (North Atlantic Oscillation). Through statistical and spatial analysis of 54 first order weather stations it was found that the timing and magnitude of breakpoints in DTR, cloud cover, and MP (moist polar weather-type) were the most significantly related. The breakpoint for DTR decrease and cloud cover (CC) increase occurs in early November in the east and late October in the west, generally seen with increased MP frequency as well. DTR breakpoint occurs on the same day, typically in late October to early November, or a few days after CC while MP is typically a few weeks after DTR. Changes in the magnitude of the breakpoint, relative to teleconnection phase, were much more significant than the timing of the breakpoint. PNA phase demonstrated greater and stronger influence on the western Great Lakes Region while NAO on the eastern and strong lake-effect areas.

Committee: Scott Sheridan PhD (Advisor); Thomas Schmidlin PhD (Committee Member); Donna Witter PhD (Committee Member) Subjects: Atmosphere; Earth; Geography

Master of Science, University of Toledo, 2012, Biology (Ecology)

The estimation of abundance is complicated by factors contributing to spatial and temporal variability. Many organisms are highly variable across both of these scales, thereby violating assumptions of conventional abundance estimation methods. Larval walleye in the Maumee River are extremely variable; however estimates of abundance and mortality are important in understanding anthropogenic impacts on this spawning group and their role in Lake Erie walleye recruitment. Bayesian hierarchical models were used to quantify spatial and temporal variability, and estimate abundance and mortality within the river while accounting for spatial and temporal uncertainty. We sampled larval walleye at the river mouth and in the intake canal of a water-cooled power plant in 2010 and at an additional upstream site near the spawning grounds in 2011. Temporal variability and uncertainty was greater than spatial variability at all sites and years during the study. Daily abundance at each site and year was related to patterns in river discharge and temperature. Larval walleye abundance decreased in a downstream fashion, with an estimated annual natural mortality rate of 63.7% in 2011. Downstream (B) and power plant abundance (C) varied between years leading to a decrease in power plant entrainment mortality from 2010 to 2011, 11.1 to 2.8% respectively. Total in-river mortality was estimated at 64.8% when entrainment mortality was included. Quantifying sources of variability lead to an adjustment in sampling protocol, which increased precision in estimated values. Bayesian hierarchical models provided an optimal framework for understanding sources of variability and estimating larval fish abundance and mortality in this large river system.

Committee: Christine Mayer PhD (Committee Chair); Craig Stow PhD (Committee Member); Edward Roseman PhD (Committee Member); Jonathan Bossenbroek PhD (Committee Member) Subjects: Applied Mathematics; Aquatic Sciences; Biology; Ecology; Environmental Science; Freshwater Ecology; Statistics