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  • 1. Arya, Pulkit Creating Conversational Systems with Temporal Reasoning in Zero Resource Domains with Synthetic Data and Large Language Models

    Master of Science, The Ohio State University, 2023, Computer Science and Engineering

    Creating conversational systems for niche domains is a challenging task, further exacerbated by lack of quality datasets. In this work, we have created a data generation pipeline that can be adapted to new domains to generate a minimum viable dataset to bootstrap a semantic parser. We experimented with methods for automatic paraphrasing and tested the ability of language models to answer questions that require temporal reasoning. Based on our findings, large language models with emergent capabilities (GPT-3.5 and GPT-4) present a viable alternative to crowd sourced paraphrasing. We determined that conversational systems that rely upon language models' ability to do temporal reasoning struggle to provide accurate responses. Our proposed system outperforms such language models by performing temporal reasoning based on an intermediate representation of the user query.
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    Committee: Michael White (Committee Member); Eric Fosler-Lussier (Advisor) Subjects: Computer Science

  • 2. Shen, Zixing It's About Time: The Temporal Impacts of Information and Communication Technology (ICT) on Groups

    Doctor of Philosophy, Case Western Reserve University, 2009, Management

    The widespread use of information and communication technology (ICT) at work impinges upon time in organizations. This dissertation examines the temporal impacts of ICT on groups. I first construct a conceptualization of time, which characterizes time to have both structural and interpretive dimensions. Structural dimension describes the objective and external aspects of time, and interpretive dimension characterizes the subjective and internal aspects of time. Next, guided by such conceptualization, I assess how time has been studied in the IS literature on ICT-mediated groups. My analysis reveals that the knowledge on the temporal impacts of ICT on groups is very fragmented. They are only studied from the structural dimension. The impacts of ICT on structural time are only recognized in how ICT modifies the temporal conditions under which groups operate. The impacts of ICT on interpretive time are largely un-researched. I then conduct a case study of three groups of IT professionals to advance the empirical understanding of the temporal impacts of ICT on groups. The empirical investigation finds that ICT impacts both structural and interpretive time. ICT shifts the temporal location, frequency, duration, and sequence of tasks and events, and furthers constant prioritizing, flexible scheduling, and concurrent executing of tasks. Temporal values and temporal norms are shaped by changes, occasioned by ICT, in structural time. ICT directly impinges upon senses of time and blurs temporal boundary between work and non-work. Structural and interpretive time, as shaped by ICT, in turn contribute to procrastination of work on new development, improved efficiency, and better understanding and communication in groups.
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    Committee: Kalle Lyytinen (Committee Chair); Youngjin Yoo (Committee Co-Chair); Dick Boland (Committee Member); Brian Pentland (Committee Member) Subjects: Information Systems; Management

  • 3. Scheible, Colleen THE USE OF SPATIAL, TEMPORAL, AND METAPHORICAL TERMS BY CHILDREN WITH AUTISM SPECTRUM DISORDER

    Master of Arts, Miami University, 2019, Speech Pathology and Audiology

    Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder impacting social communication. In people with ASD, social uses of language, including non-literal uses are often universally impaired. Prepositions are used in concrete ways as spatial concepts (e.g., in the house) and in abstract ways as temporal concepts (e.g., in the morning) or metaphorical concepts (e.g., in love). This study examined the production of prepositions by children with ASD. We predicted participants with ASD would exhibit difficulties with abstract uses of prepositions. Narratives of participants with ASD (N=19) and typical development (TD) (N=20), matched for language, age, and intelligence, were analyzed for the production of prepositions. We found TD participants produced significantly more prepositions and spatial prepositions than participants with ASD. However, contrary to our hypothesis, children with ASD did not produce fewer abstract terms than TD children. Number of prepositions was significantly related to the age of participants; older participants produced more prepositions than younger participants, suggesting a developmental trajectory. Severity of ASD symptoms was negatively related to the number of prepositions produced, although both ASD and TD participants used prepositions flexibly. These findings suggest prepositions may be an area of weakness for fluent children with ASD.
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    Committee: Aaron Shield (Advisor); Amber Franklin (Committee Member); Arnold Olszewski (Committee Member); Emily Zane (Committee Member) Subjects: Speech Therapy

  • 4. Brown, Stephanie Speech-in-Speech Recognition: Understanding the Effect of Different Talker Maskers

    Master of Arts, Case Western Reserve University, 2019, Communication Sciences

    Speech-in-speech recognition tasks can vary considerably depending upon the combination of the target and masker speech. The importance of temporal similarity between the target and masker speech, and its influence on informational masking, is explored. A speech-in-speech recognition task, using different two-talker maskers, was completed by 20 normal-hearing, monolingual listeners. Four conditions were tested with a different combination of target and masker talkers in each. Three temporal analyses were conducted including: modulation spectrum area of the curve, normalization covariance, and envelope correlation index. No relationship was observed between normalization covariance and envelope correlation index with sentence recognition. Better sentence recognition was observed for conditions with greater modulation spectrum area. More research is needed to further explore the effect of temporal similarity on informational masking. Limitations included differences in signal-to-noise ratios used across conditions, a small number of target/masker combinations, and linguistic differences in stimuli across the target/masker speech.
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    Committee: Lauren Calandruccio (Committee Chair); Barbara Lewis (Committee Member); Lee Thompson (Committee Member) Subjects: Audiology

  • 5. Wehmann, Adam A Spatial-Temporal Contextual Kernel Method for Generating High-Quality Land-Cover Time Series

    Master of Arts, The Ohio State University, 2014, Geography

    In order to understand the variability, drivers, and effects of the currently unprecedented rate, extent, and intensity of land-cover change, land change science requires remote sensing products that are both highly accurate and spatial-temporally consistent. This need for accuracy is exacerbated from the shift in the discipline from the detection of change between two points in time to the analysis of trajectories of change over time. As the length of temporal record increases, the problem becomes more severe. This follows because the accuracy of change detection is bounded below by the product of the accuracies of the source maps. Without exceedingly high accuracy at individual dates, the accuracy of change detection will be low, as map errors simply and vastly outweigh the occurrence of real change. Land-cover classifiers that can better utilize spatial and temporal information offer the chance to increase the accuracy of change detection and the consistency of classification results. By increasing the spatial and temporal dependence of errors between classification maps, the overall area among maps subject to error may be minimized, producing higher quality land-cover products. Such products enable more accurate and consistent detection, monitoring, and quantification of land-cover change and therefore can have wide-reaching impacts on downstream environmental, ecological, and social research. To address these problems fundamental to the creation of land-cover products, this thesis seeks to develop a novel contextual classifier for multi-temporal land-cover mapping that fully utilizes spatial-temporal information to increase the accuracy of change detection, while remaining resistant to future advances in the spatial and spectral characteristics of remote sensor technology. By combining the complementary strengths of two leading techniques for the classification of land cover – the Support Vector Machine and the Markov Random Field – through a novel s (open full item for complete abstract)
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    Committee: Desheng Liu (Advisor); Ningchuan Xiao (Committee Member); Brian Kulis (Committee Member) Subjects: Computer Science; Geographic Information Science; Geography; Remote Sensing

  • 6. Raghavan, Preethi MEDICAL EVENT TIMELINE GENERATION FROM CLINICAL NARRATIVES

    Doctor of Philosophy, The Ohio State University, 2014, Computer Science and Engineering

    Extracting information from disparate clinical data sources in electronic health records is crucial to building intelligent systems that can reason with clinical variables and support decision making. This dissertation describes a novel framework for representing and reasoning with medical events and temporal information, in unstructured clinical narratives, by using linguistic insights from clinical text in training machine learning models for timeline extraction. Importantly, we leverage both temporal and semantic representations of medical events in learning structured relationships within and across clinical data sources and creating a longitudinal timeline of events over the patient's history. To this end, the main problems addressed in this work are medical event coreference resolution and temporal relation learning, both in intra- and cross-document settings, and information fusion across structured and unstructured data. While prior work in clinical informatics has addressed some of these problems in a limited capacity, other problems like cross-narrative temporal ordering and information fusion are being addressed for the first time in this dissertation. The generated timeline has important implications in various clinical applications with temporal constraints such as patient recruitment for clinical trials, medical document summarization, adverse drug reaction mining, question answering and clinical decision making. We explore the utility of the timeline in resolving temporal eligibility criteria for clinical trial recruitment.
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    Committee: Eric Fosler-Lussier (Advisor); Albert Lai (Advisor); Brian Kulis (Committee Member) Subjects: Computer Science

  • 7. Loyden, Jennifer Predictors of Cognitive and Seizure Outcome Post Anterior Temporal Lobectomy

    PhD, University of Cincinnati, 2007, Arts and Sciences : Psychology

    The purpose of this study was to determine significant predictors of seizure and cognitive outcome following surgery for epilepsy. Participants included 41 patients who had undergone anterior temporal lobectomy (ATL) with pre- and post-surgical neuropsychological data. Seizure-related characteristics and cognitive functioning were determined from medical records and from evaluations using multiple measures of cognitive functioning (e.g., intelligence, memory, and language). Logistic regression analyses were conducted to examine predictors of seizure control. Hierarchical multiple regression analyses were employed to evaluate predictors of overall cognitive functioning using a conglomerate score. Results of the current study suggest that patients who develop seizures at a later age and demonstrate higher pre-surgical overall intelligence and lower nonverbal memory skills were more likely to be seizure free following ATL. Further, surgery in the hemisphere contralateral to language functioning and higher pre-surgical intelligence scores were predictive of better cognitive outcome post surgery. Identifying pre-surgical factors related to surgical outcome is important for counseling patients about the potential risks and benefits of surgery.
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    Committee: Dr. Bruce Schefft (Advisor) Subjects: Psychology, Clinical

  • 8. Thakur, Mahesh Kumar Singh Advanced Color Projector Design Based on Human Visual System

    Master of Science (M.S.), University of Dayton, 2011, Electrical Engineering

    The current designs of color video projectors suffer from color errors commonly referred to as rainbow artifact. In this thesis, we propose new projector hardware and processing technique that reduce these artifacts signicantly. The hardware and preprocessing designs are tested in simulation by generating a video sequence that can be viewed on a 120 Hz monitor. Our contributions are in three parts. We first propose the notion of rendering error, or an error in the projected image perceived by the human visual system. We then propose spatial-temporal color wheel design incorporated the requirements for the zero rendering error condition and amplitude demodulation. The output was satisfactory, although it required a modied color wheel. Finally, we propose preprocessing scheme that reduce rendering error by introducing a correction term to cancel the predicted rendering error. This technique can be applied to traditional color wheels, and it was veried on RGB and RGBWW color wheels.
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    Committee: Keigo Hirakawa PhD (Committee Chair); Russell Hardie PhD (Committee Member); Eric Balster PhD (Committee Member) Subjects: Electrical Engineering; Engineering

  • 9. Gurram, Bhaskar A Multimodal Neuroimaging Method for the Prediction of Visual Stimuli

    MS, University of Cincinnati, 2024, Engineering and Applied Science: Computer Science

    This thesis explores the potential of multimodal neuroimaging techniques, particularly Electroencephalography (EEG) and Functional Magnetic Resonance Imaging (fMRI), for the classification of visual stimuli. EEG provides excellent temporal resolution, capturing the fast-changing dynamics of brain activity, while fMRI excels in spatial resolution, offering detailed insight into the brain's spatial activation patterns. This dual-fold thesis encompasses both a comprehensive survey and an experimental study. The first part presents a survey of multimodal neuroimaging techniques, focusing on fMRI, EEG, and their integration for improved neural activity analysis, evaluating current approaches and highlighting challenges, limitations, and opportunities in the fusion of these modalities for cognitive and clinical neuroscience applications. The second part involves an experimental study where EEG and fMRI data were fused to classify visual stimuli in a face recognition task. EEG's fine-grained temporal resolution was aligned with fMRI's detailed spatial resolution through temporal matching and feature concatenation. By combining temporal dynamics from EEG and spatial patterns from fMRI, classification performance was enhanced. The fused data classified visual stimuli into three categories: familiar faces, unfamiliar faces, and scrambled faces. Various neural network architectures were tested to capture both temporal and spatial information. The results demonstrate that integrating EEG and fMRI improves the accuracy of visual stimuli classification over single-modality approaches, showcasing the potential of multimodal neuroimaging for a more robust and comprehensive analysis of brain activity. Finally, this study highlights the importance of testing the proposed fusion approach on larger datasets to further validate its effectiveness and explore the generalizability of the results across broader contexts.
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    Committee: Vikram Ravindra Ph.D. (Committee Chair); Vesna Novak Ph.D. (Committee Member); Jun Bai Ph.D. (Committee Member) Subjects: Computer Science

  • 10. Babu, Diya Liz Performance of Regression Algorithms for Predictive Pilot Manual Control in Standard Rate Turn

    Master of Computer Science (M.C.S.), University of Dayton, 2024, Computer Science

    Predicting pilot performance is crucial for enhancing flight safety, efficiency and training effectiveness. This study explores the predictive capabilities of regression–based machine learning algorithms for pilot behavior during a standard rate turn maneuver. Temporal data was collected from 16 certified pilots using flight simulators under different virtual reality visual conditions. The aim was to accurately predict six target variables that represented both final standard rate turn, or task, performance and manual control inputs used by pilots to achieve such performance. These six target variables included: (1) final heading error; (2) final yaw rate; (3) final altitude error; (4) maximum positive aileron input; (5) maximum negative aileron input; (6) total aileron input. Six regression-based machine learning algorithms were employed based on top performers within the literature with respect to predicting piloting behavior and general human movement. These six algorithms included: (1) Random Forest; (2) Gaussian Process Regressor; (3) Gradient Boosting Regressor; (4) Linear Regressor; (5) Decision Tree; (6) k-Nearest Neighbor. Gaussian Process Regression performed the best followed closely by Random Forest, with single–output models performing equally well as multi-output models indicating weak correlations among the target variables. Four of the six target variables were found to be determined with high predictive accuracy temporally early on, while the final altitude error and the maximum positive aileron input required the longest temporal information to achieve the highest predictive accuracy. These findings support the usage of such algorithms in automated pilot training systems for providing early indicators in identifying pilots who may require additional guidance. Additionally, environmental factors such as visual richness, or visibility with respect to percentage of cloud cover, seem to affect pilot performance but do not significantly impact the pe (open full item for complete abstract)
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    Committee: Timothy Reissman (Committee Member) Subjects: Aerospace Engineering; Computer Science

  • 11. Yoo, Minhee Computational and Neural Mechanisms Underlying Preference-Based Decisions

    Doctor of Philosophy, The Ohio State University, 2024, Psychology

    Preference-based decisions are prevalent in our daily lives. They are influenced by several factors such as the timing of information arrival, surrounding context, and attention. In my dissertation, I explored the impact of these factors on preference-based decisions by employing sequential sampling models along with the analysis of behavioral, neural, and eye-tracking data. In the second chapter, I found that individuals assign different weights to information depending on when the information enters the decision-making process. Some individuals put higher weight on recent information, while others put higher weight on early information. This temporal weighting considerably varied across individuals but remained consistent within an individual across different decision domains. In the third chapter, I showed the positive influence of context on the choice and valuation of consumer products. Notably, this positive context effect was prominent when decisions were made quickly, suggesting the role of context in shaping prior expectations. In the fourth chapter, I found that preference ratings were more variable and made slowly when the strength of preference was weak. Also, participants explored more ratings when their preference strength was weak. These results suggest a stochastic nature to the preference rating process. Preference ratings may change moment-to-moment due to the inherent randomness in the preference rating process. Collectively, these studies showed that preference decisions follow a stochastic process, wherein noisy samples of decision evidence accumulate over time to make a decision. Furthermore, findings showed the potential for developing an integrative model in future research to deepen our understanding of preference-based decisions.
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    Committee: Ian Krajbich (Advisor); Brandon Turner (Committee Chair); Andrew Leber (Committee Member); Julie Golomb (Committee Member) Subjects: Psychology

  • 12. Ellis, Robert The role of melodic and temporal accent patterns in the perception of meter /

    Master of Arts, The Ohio State University, 2006, Graduate School

    Committee: Not Provided (Other) Subjects:

  • 13. Cimatu, Ryanne What a Waste: Nitrogen Runoff and Rates in the Maumee River (Ohio, USA)

    Master of Science (MS), Wright State University, 2024, Biological Sciences

    Excess anthropogenic nitrogen (N), primarily from agricultural field fertilization, causes nutrient runoff that stimulates harmful algal blooms (HABs) in western Lake Erie. As a critical tributary to Lake Erie, nutrient loading from the Maumee River drives the intensity of the annual summer HABs in the western basin. Knowledge gaps around rates of N transformations in the Maumee River currently hinder the calibration of in-river parameters in Soil and Water Assessment Tool (SWAT) models for the Maumee watershed. To address these gaps, this research quantified rates of ammonium uptake, ammonium remineralization, nitrification, and bacterial respiration alongside physicochemical parameters of the river. Monthly sampling was conducted along the Maumee River at International Park (river mile 4.53), Mary Jane Thurston (river mile 31.88), and Independence Dam (river mile 59.31) over the course of a year. Ammonium uptake rates ranged from 1.2 to 8.7 µmol N L-1 hr-1 for water samples incubated under light conditions and from 0.2 to 1.9 µmol N L-1 hr-1 under dark conditions, while ammonium regeneration ranged from <0.01 to 12.0 µmol O2 L-1 hr-1. Bacterial respiration rates averaged 525.0 ± 28.5 µM O2. Respiration and both NH₄⁺ uptake & regeneration rates correlated overall with seasonal temperatures and biomass. Respiration rates closely followed temperature, with warmer months having the highest rates. November 2022 samples exhibited higher rates of respiration and both NH₄⁺ uptake & regeneration at all sites as chlorophyll was >200 µg/L during the fall river bloom. Despite not being at peak temperature in the study, the highest rates of microbial activity in April and May., with the lowest observed during the coldest months, January and March. The timing of peak rates at the three sites along the river-to-lake continuum shifted with biomass, indicating the importance of parameterizing the SWAT model with models with spatially and temporally dynamic values. These findin (open full item for complete abstract)
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    Committee: Stephen J. Jacquemin Ph.D. (Committee Chair); Silvia E. Newell Ph.D. (Committee Co-Chair); Katie Hossler Ph.D. (Committee Member) Subjects: Biogeochemistry; Environmental Management; Environmental Science; Environmental Studies; Hydrologic Sciences; Hydrology; Water Resource Management

  • 14. Gurjar, Swanaya Emotion Regulation in Nonsuicidal Self-Injury: An Examination of Temporal Sequencing in Daily Life

    Master of Arts in Psychology, Cleveland State University, 2024, College of Arts and Sciences

    Research has consistently established that nonsuicidal self-injury (NSSI) is associated with difficulties in regulating one's distress and that it co-occurs with other maladaptive behaviors. However, much of the extant literature assesses emotion regulation (ER) related processes instead of specific ER responses and/or relies on retrospective self-report which may not accurately reflect response tendencies and their effectiveness. As such, relatively little is known about when NSSI is deployed, whether it is used in isolation or with other ER responses, and how it alleviates distress relative to other maladaptive ER strategies. The current study examined the temporal sequencing of NSSI relative to other ER efforts and its differential effects on distress reduction. Specifically, we tested whether NSSI and its timing influenced dispositional ER, average ER responses in daily life, and deviations from these averages to predict changes in negative affective states. 22 adults currently engaging in NSSI completed survey measures, structured interviews, and a 14-day ecological momentary assessment (EMA) protocol in which 88 NSSI behaviors were recorded. Findings indicated that NSSI was chosen as the first response to distress more often than not and that it served an emotion regulatory function, with increases in negative affect before engagement and reductions following it. Across self-report and EMA, individuals also experienced the highest distress reduction when they deployed a maladaptive ER response before NSSI. In contrast, individuals experienced lower distress alleviation when they utilized higher maladaptive ER than was usual for them. While preliminary, findings have the potential to contribute to research on the temporal order of ER responses in daily life as well as inform treatment targets for NSSI.
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    Committee: Ilya Yaroslavsky Ph.D. (Committee Chair); Kathleen Reardon Ph.D. (Committee Member); Elizabeth Goncy Ph.D. (Committee Member) Subjects: Clinical Psychology; Psychology

  • 15. Kron, Brian Effects of a Highly Modified Landscape on Diversity of Anuran Communities in Northwestern Ohio

    Doctor of Philosophy (Ph.D.), Bowling Green State University, 2024, Biological Sciences

    As human-modified landscape and climate changes proliferate, maintaining biodiversity and understanding the function and quality of available habitat is imperative. Anurans (frogs/toads) can be indicator species of habitat quality and ecosystem productivity, due to their permeable skin, small body size and ectothermy. We explored the relationship between Anurans and habitat quality by assessing the effects of spatial and temporal heterogeneity on the presence of Anurans. Across the Toledo Metropolitan Area (TMA), including the biodiversity hotspot Oak Openings Region (OOR), we surveyed across three years, 67 different wetland sites (N=1800). There was a difference in community assemblage between rural and suburban/urban habitats driven by factors related to human-modification (impervious surface), composition (landcover type) and productivity (e.g., NDVI). Areas with more impervious surface, lower amounts of swamp forest, and lower NDVI had fewer species. The differences in spatial structure but lack of differences in temporal variables among sites suggest spatial factors dominated. We also developed spatial models for predicting species richness across the region to evaluate spatial variables driving community composition and ecosystem productivity. The amount of cropland best predicted species richness, followed by amount of swamp forest. Among individual species, the most important variables differed; cropland (Acris blanchardi, Lithobates catesbeianus, Anaxyrus americanus, Anaxyrus fowleri and Hyla versicolor), floodplain forest (Lithobates clamitans), wet prairie (Lithobates pipiens), and swamp forest (Pseudacris crucifer, Pseudacris triseriata, Lithobates sylvaticus) were leading influences. Finally, we surveyed 304 local residents to assess their views on topics from support of new parks/preserves to fees to utilize parks, before a 25-minute presentation on Anurans, and resurveying them. There was strong support for many conservation-oriented questions, but (open full item for complete abstract)
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    Committee: Karen Root Ph.D. (Advisor); Paul Moore Ph.D. (Committee Member); Ashley Ajemigbitse Ph.D. (Other); Jeffrey Miner Ph.D. (Committee Member); Helen Michaels Ph.D. (Committee Member) Subjects: Biology; Conservation; Ecology; Wildlife Conservation; Wildlife Management

  • 16. Assylkhan, Karlygash Time Perspectives and Job Engagement

    Doctor of Philosophy, Case Western Reserve University, 2024, Organizational Behavior

    Job engagement remains a pervasive challenge for organizations, with just over one-third of U.S. employees fully engaged in their work according to Gallup (2024). This dissertation seeks to enhance our comprehension of job engagement by examining the influence of time-related factors. While conventional perspectives emphasize the allocation of energy based on job demands, resources, and psychological factors (Bakker & Demerouti, 2017; Kahn, 1990; Rich et al., 2010), this dissertation takes a novel approach by incorporating time perspectives (Bluedorn, 2002; Lewin, 1948 & 1951; Zimbardo & Boyd, 1999) and social roles (Abele, 2003; Eagly, 1987; Koenig & Eagly, 2014). This integration offers a comprehensive understanding of job engagement as a dynamic, flexible, temporally relative, and social construct. Comprising two studies with survey samples, Study 1 involved 491 U.S. employees through a cloud research platform. Building on these findings, Study 2 further investigated the topic using multi-wave data from 80 current business students and alumni at two East Coast Universities. The results illuminate several significant insights. The findings from both Study 1 and Study 2 provide robust support for various aspects of time perspectives and their impact on psychological antecedents and job engagement. Specifically, current temporal focus demonstrates positive links with both psychological antecedents and job engagement. Additionally, future temporal focus is found to positively influence job engagement, while past temporal focus has a negative impact on it. Positive feelings towards different timeframes are shown to contribute positively to job engagement while negative feelings have negative impact. Finally, individuals with a future temporal focus tend to exhibit communal behaviors, highlighting the importance of fostering such behaviors in the workplace. Overall, this dissertation emphasizes the importance of considering time perspectives as critical antecede (open full item for complete abstract)
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    Committee: Diana Bilimoria Dr. (Committee Chair) Subjects: Organizational Behavior

  • 17. Regmi, Aakriti Analysis of Temporal Variations in River Widths Due to Dam Construction

    MS, University of Cincinnati, 2023, Engineering and Applied Science: Environmental Engineering

    The construction of dams and reservoirs is essential for various purposes, such as regulating fresh water supply, generating hydropower, and controlling floods. However, these structures can significantly affect river's geomorphic processes, leading to various environmental disturbances. While avoiding dam construction is not always possible, it is crucial to document the effects of existing dams on upstream and downstream regions to understand their short term and long-term impact on river channel properties and their subsequent environment. In this study, we analyzed 21 dams constructed between 2000 and 2007 around the world using the Global Reservoir and Dam database (GRanD) and Global LOng-term river Width (GLOW) datasets. The study period was divided into two phases: pre-construction and post-construction, while excluding the dam construction period. This division allowed us to study the natural flow regime before dam construction and the altered flow regime after dam completion. To ensure data consistency and meaningful analysis, we focused on dams with a minimum of 12 years of available data during both pre- and post-construction phases. In our analysis, we examined the temporal variations in river widths before and after dam construction, considering both upstream and downstream reaches. By comparing these changes, we identified three distinct patterns of dam impacts on river widths: narrowing, stable and widening which were furthered termed as consistent or inconsistent based on their temporal nature. These findings enhance our understanding of how human-made physical structures, such as dams, can have dynamic and diverse effects on river morphology.
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    Committee: Dongmei Feng Ph.D. (Committee Chair); Drew McAvoy Ph.D. (Committee Member); Xi Chen Ph.D. (Committee Member) Subjects: Environmental Engineering

  • 18. Abrol, Shivam Interactive and Exploration Techniques for Trajectory Analysis and Visualization: Travis

    MS, University of Cincinnati, 2023, Engineering and Applied Science: Computer Science

    According to Moore's Law, the cost of devices drops by half every two years. This ongoing price reduction has led to a surge in data collected via GPS sensors, accelerometers, and magnetometers. The increased availability of ways to collect movement data has significantly boosted our ability to understand and work with movement-related information. Movement data takes various forms, such as people navigating cities, cars traveling on highways, or animals roaming through forests. However, on its own, movement data lacks intrinsic meaning. It must be integrated with environmental, physical, and temporal contexts to gain a comprehensive understanding. Compared to their human counterparts, who move in urban environments with abundant semantic information, animals' movements often lack such semantics and context. Researchers or domain experts who deeply understand the environment grounded in extensive research data often provide contextual information. Domain expertise is an indispensable asset in initiating practical data analysis. Visualization is pivotal in fostering collaboration between domain scientists and data analysts. This collaboration empowers both parties to collectively contribute context and significance to the data. Moreover, visualization plays a crucial role in understanding movement, as seeing the data visually prompts deeper comprehension. It aids in understanding how movement interacts with the environment. However, visualization comes with its challenges; with the exponential increase in data, clutter has emerged as a significant issue in movement visualization. Dealing with large datasets can be overwhelming; the sheer number of data points poses a considerable challenge for interpretation. Moreover, errors in the data, often originating from sensor inaccuracies, further complicate the analysis. Designing tools with the end-user in mind is crucial to address these challenges effectively. User-centric design not only helps simplify the interpreta (open full item for complete abstract)
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    Committee: Jillian Aurisano Ph.D. (Committee Chair); Raj Bhatnagar Ph.D. (Committee Member); Tanya Y. Berger-Wolf Ph.D. (Committee Member) Subjects: Computer Science

  • 19. Jin, Kang Identifying cellular and perturbational patterns from single-cell data using ToppCell and CellDrift – a case study of COVID-19

    PhD, University of Cincinnati, 2023, Medicine: Biomedical Informatics

    Single-cell sequencing technologies have significantly advanced our understanding of complex biological systems. Researchers and initiatives like the Human Cell Atlas (HCA) and the Brain Research through Advancing Innovative Neurotechnologies Initiative (BICCN) have successfully created Cell Atlases for different tissues and developmental stages. The growing volume of single-cell data has created a demand for tools that offer comprehensive capabilities, encompassing gene signature visualization, exploration, and harmonization. The widespread adoption of single-cell technologies and the decreasing cost of single-cell experiments have led researchers to utilize these techniques in studies involving perturbations, including diseases, treatments, genetic mutations, time-series analyses, and more. This application has accelerated the exploration of transcriptional profiles across different perturbation states, enabling comparisons with control conditions. However, the analysis of single-cell data presents several emerging computational challenges that need to be addressed. Firstly, there is a need for a deeper understanding of diverse biological covariates and technical effects that may impact the data. Secondly, it is crucial to develop user-friendly visualization and interaction methods capable of handling large-scale datasets, particularly at the atlas-level. Lastly, there is a pressing need to comprehend the effects of perturbations across multiple dimensions, with a particular emphasis on temporal dynamics. These computational hurdles demand innovative solutions to effectively tackle the complexities associated with single-cell data analysis. To address these challenges, we have developed two tools, namely ToppCell and CellDrift, that facilitate comprehensive exploration of large-scale single-cell data and enhance our understanding of temporal perturbation effects. To demonstrate the capabilities of our tools, we utilized the context of COVID-19 and uncove (open full item for complete abstract)
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    Committee: Bruce Aronow Ph.D. (Committee Chair); Nathan Salomonis Ph.D. (Committee Chair); Rhonda Szczesniak Ph.D. (Committee Member); Surya Prasath Ph.D. (Committee Member); ChangHui Pak PhD (Committee Member) Subjects: Bioinformatics

  • 20. Hardy, Kate Characterizing a new early-life stress model: effects on perception of sounds relevant for communication in the Mongolian gerbil

    PHD, Kent State University, 2023, College of Arts and Sciences / School of Biomedical Sciences

    Recent research shows that early-life stress (ELS) in gerbils affects neural function in the auditory pathway and is associated with poor neural and behavioral detection of a temporally-varying sound – specifically, detection of short gaps, a feature vital for understanding speech and vocalizations (Ter-Mikaelian et al., 2013; Ye et al., 2022). This dissertation evaluates the general hypothesis that ELS affects such aspects of sound perception that are important for basic auditory communication. Because auditory-related behavior can only be elucidated with awareness of top-down influences, the first step (i.e., Chapter 2) must be to characterize the ELS gerbil by assessing higher-level functions (those related to cognition, learning, memory, and anxiety). Only with this knowledge can behavioral responses to acoustic communication sounds be accurately interpreted for ELS animals. I ran the gerbils through a battery of behavioral tests that included multiple measures of locomotion, anxiety, memory, and learning. Chapter 3 explores the effects of ELS on the behavioral detection of amplitude modulations, an important auditory feature of speech and vocalizations. Perception of speech-related sounds like gap detection and amplitude modulations is vital for survival, cooperation, mediation, and reproduction in countless species. I tested gerbils with increasingly difficult signals to determine whether ELS changes temporal sensitivity. This aim also provided valuable information about learning differences in ELS animals.The findings presented in Chapter 3 evaluate the hypothesis that an ELS-induced deficit in gap detection (Ye et al., 2022) can be extrapolated to a deficit in another type of a temporally-varying sound: amplitude modulations (AM). The highly vocal Mongolian gerbil is a well-established model used to assess temporal processing via behavioral detection of amplitude modulations (AM) in sound. For this reason, I trained gerbils with operant conditioning to detec (open full item for complete abstract)
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    Committee: Merri J. Rosen (Advisor); Julia J. Huyck (Committee Member); Jeffrey J. Wenstrup (Committee Member); Lee Gilman (Committee Member) Subjects: Acoustics; Animal Sciences; Animals; Audiology; Behavioral Psychology; Behavioral Sciences; Behaviorial Sciences; Biology; Biomedical Research; Cognitive Psychology; Developmental Biology; Developmental Psychology; Experiments; Language; Linguistics; Neurobiology; Neurosciences; Psychobiology; Psychological Tests; Psychology; Welfare
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