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This research contributes to understanding the effects of local government urban regulatory policy and actions of private actors on a neighborhood’s housing market using the fast-growing city of Charlotte, North Carolina, as a case study.
The first article of this research examines private actors in the rental housing market and their impact on neighborhood outcomes. The analysis focuses on how exclusionary criteria used in online rental advertisements vary spatially and how they potentially impact neighborhood outcomes. It also focuses on how various factors such as race, income, and platform (Zillow vs. Craigslist) influence the presence of exclusionary criteria in rental advertisements.
The second article situates private actors' actions within the scope of a neighborhood’s changing characteristics and their effects on a neighborhood’s capital investment exhibited through housing renovation activity. The analysis employs 10-year longitudinal parcel-level permitting data on housing renovation activity, housing and neighborhood-specific variables, and spatial statistical techniques to assess if a change in a neighborhood’s prevailing characteristics influences housing renovation activity.
The third article analyzes the effects of local government regulatory policies on a neighborhood's housing market, specifically housing code violations that are resolved with repairs. The chapter hypothesizes that housing code violations, when solved with repairs, will significantly affect a neighborhood’s housing market by increasing home sales and rental prices or contribute to the loss of affordable housing as landlords withdraw their property from the housing market. To test this hypothesis, the research uses longitudinal data on home sales prices, gross rent, housing code violations, and other housing and neighborhood-specific variables. It employs spatial statistics techniques to model their longitudinal relationships.
These three articles collectively contribute to our understanding of neighborhood housing markets analyzed through the lens of private investments and practices and urban regulatory policy adopted by local governments in fast-growing cities like Charlotte. Furthermore, these chapters create a framework that shows how spatial statistics tools, natural language processing techniques, and novel and traditional data can be used to understand the relationship between a neighborhood’s housing market and neighborhood change.
The recently introduced class of two-dimensional materials, monolayer Transition Metal Dichalcogenides (TMDs), are emerging as highly promising candidates to enhance data transfer capacity in the field of Valleytronics. Strong “atomic spin-orbit interaction” in monolayer TMDs locks spin of electrons to degenerate valleys with different momenta. These locked valley-spin pairs respond differently to different circular polarizations of light. However, this feature vanishes at room temperature. To address this issue, the coupling between the exciton emissions and photonic modes are under extensive investigation.
This dissertation explores the control over TMD valley-polarized emission by coupling the exciton emission to the plasmonic mode. Specifically, we take advantage of the strong coupling between monolayer WS2 and metallic nanogrooves to enhance information routing, thereby achieving higher data capacity.
The first part of this study is focused on analyzing the interdependence between the nanogroove parameters and the coupling condition. In the second part, we will demonstrate the k-space separation of valley excitons in monolayer TMDs through the "optical spin-orbit interaction." This separation implies that the helicity of photons determines a preferred emission direction.
This research can serve as a guideline for designing structures and pave the way to transport and read out the spin and valley degrees of freedom in two-dimensional materials. By addressing current challenges in the field of Valleytronics, it offers guidance for future advancements in this area.
Transit Signal Priority (TSP) is a traffic signal control strategy that can provide priority to transit vehicles and thus improve transit service. However, this control strategy generally causes adverse effects on other traffic, which limits its widespread adoption. The development of Connected Vehicle (CV) technology enables the real-time acquisition of fine-grained traffic information, providing more comprehensive data for the optimization of traffic signals. Simultaneously, optimization algorithms in the field of TSP have been advancing at a rapid pace. Artificial intelligent (AI)-powered techniques, such as Deep Reinforcement Learning (DRL), have become promising approaches for addressing TSP problems recently. In this study, we developed adaptive TSP control frameworks for both isolated intersection scenarios and multiple intersection scenarios, assuming the implementation of CV technology. Leveraging the comprehensive traffic data obtained from CVs, our frameworks employ both single-agent DRL and multi-agent DRL techniques to address optimization problems. The controllers, based on our proposed frameworks, were tested in simulation environments and compared with various widely used traffic signal controllers across different scenarios, demonstrating superior performance.
Federal legislation for students with disabilities mandates that all students receive appropriate and relevant instruction across environments to improve postsecondary outcomes across domains. Teachers and parents alike have found that one way to meet individual student needs and increase instructional opportunities for students with disabilities is through the use of purposeful and meaningful community-based instruction (CBI). For students with extensive support needs (ESN), however, the practical implementation of CBI within the classroom and community setting may pose several barriers and relies heavily on teacher and family knowledge of community engagement strategies. Previous research in the area of CBI indicates that through the use of evidence-based practices CBI is effective in teaching skills across the four identified domains, which include leisure, vocational, community engagement, and daily living. In an attempt to bridge gaps in the available literature and research in the area of CBI, this study evaluated the effects of an intervention package comprised of three evidence-based practices (video modeling, visual supports, and system of least prompts), goal setting, and collaboration, through peer-implemented instruction, in order to teach leisure skills to young adults with ESN in relevant community settings. The experimental design was a multiple probe across skills replicated across two participants. Two young adults, ages 21 and 22 with ESN participated in the study, along with two of their same aged peers, and relevant team members/key stakeholders (i.e., program director at their university, parents). Three community-based leisure skills across three environments were chosen with a specific skill targeted at each location. The intervention was effective for teaching these leisure skills to the participants across all three community locations. In addition, they were able to generalize and maintain these skills at the conclusion of the study. Social validity measures indicated that all participants felt that these were relevant skills for the participants and that their role in this process was valuable. The findings from this study can be used to guide future research in the area of CBI with students of all ages to support them as they access community settings.
Random antireflective surface nanostructures (rARSS) enhance transmission by reducing the electromagnetic impedance between optical indices across a boundary, serving as alternatives for traditional coating techniques. Understanding and quantifying the role of randomness of the surface nanostructures remain elusive, without a comprehensive model that can accurately predict the wideband spectral response of randomly nanostructured surfaces based on causal physical principles. Effective-medium approximations (EMA) emulate the randomly structured surface as a sequence of homogeneous film layers, failing to predict the critical (or cut-off) wavelength above which the enhancement effect is observed and below which bidirectional optical scatter is prominent. Analyzing near-field or far-field radiance due to wavefront propagation through randomly nanostructured surfaces requires high computational budgets, which are challenging for randomly distributed features with varying-scale boundary conditions.
Deterministic periodicity is considered a sufficient surface geometrical descriptor for regular (or long-range repetitive) nanostructured surfaces, whereas characterizing random surface features is based on first-order statistical evaluations or macroscopic averages, such as autocorrelation lengths, which introduce significant ambiguity in subwavelength scales. What constitutes the "randomness" of rARSS, beyond standard surface topography measures, is subjective. Conventional optical surface structure characterization, disregards aspects of nanoscale morphological attributes, mainly spatial configuration or organization, due to resolution limitations of metrological instruments. The organizational aspect of nanostructured features can significantly impact the macroscopic Fresnel reflectivity radiance, bidirectional scattering, and axial transmission enhancement (cooperative-interference effect).
In this work, transverse granule population distributions and their corresponding granular organization at the nanoscale, is determined using a variation of the Granulometric image processing technique. Various rARSS surfaces were fabricated, resulting in unique surface modifications and spectral performance, as observed with respectively scanning electron microscope (SEM) micrographs and spectral photometry. The approach to quantify randomness or complexity of the nanostructures, presented in this work, is based on Shannon’s entropy principles. Resolution limitations from conventional characterization techniques using non-invasive confocal microscopy and spectroscopic ellipsometry is discussed. Statistical quantification of nano-structural randomness using Shannon’s entropy is proposed as a solution to characterize the unique degree of disorder on the surfaces. A figure-of-merit is derived and computed from surface organization state variables, and it is proposed as a heuristic parameter to predict the transition from spectral scattering to the transmission enhancement region. This multivariate problem is addressed by accounting for the conditional probability dependence of granule populations as functions of granule dimensions and their corresponding proximity distributions, thereby laying the foundations for a surface microcanonical ensemble model, establishing a link between surface morphological descriptors and spectral variables.
This study explored teacher and student perspectives on mandated school uniforms. Debate exists over the appropriateness of uniforms, with some stakeholders suggesting positive outcomes while others bemoan limits on student expression. This study sought to fill a gap in research specific to middle school uniform use by exploring teachers' and students' perceptions. This research also considered the intersection of gender and diversity issues with uniform policies because these topics are becoming more prominent in the discussion. Four focus groups were conducted, two at a suburban school and two at an inner-city school. Findings suggested that teachers and students at the suburban middle school experienced uniforms more positively than their counterparts in the inner city. Additionally, findings indicated that female students had more negative experiences with uniform policies and their enforcement. From a social identity perspective, this study suggests that the group experience of the same uniform could have a positive or negative impact. When people feel the need for a positive group self, they demonstrate ingroup bias, which could help or hamper the implementation of school uniforms. This research helps bridge the gap in empirical literature within the context of social groups and critical theory to offer recommendations for administrators and policymakers regarding school uniforms in public middle schools. Results can direct further research while raising awareness of issues administrators should address when considering the implementation of a school uniform policy.
Large cohort studies under simple random sampling could be prohibitive to conduct with a limited budget for epidemiological studies seeking to relate a failure time to some exposure variables that are expensive to obtain. In this case, two-phase studies are desirable. Failure-time-dependent sampling (FDS) is a commonly used cost-effective sampling strategy in such studies. To enhance study efficiency upon FDS, counting the auxiliary information of the expensive variables into both sampling design and statistical analysis is necessary.
In survival analysis, it's commonly assumed that all subjects in a study will eventually experience the event of interest. However, this assumption may not hold in various scenarios. For example, when studying the time until a patient progresses or relapses from a disease, those who are cured will never experience the event. These subjects are often labeled as ``long-term survivors'' or ``cured'', and their survival time is treated as infinite. When survival data include a fraction of long-term survivors, censored observations encompass both uncured individuals, for whom the event wasn't observed, and cured individuals who won't experience the event. Consequently, the cure status is unknown, and survival data comprise a mixture of cured and uncured individuals that can't be distinguished beforehand. Cure models are survival models designed to address this characteristic.
Chapter~2 discusses the semiparametric inference for a two-phase failure-time-auxiliary-dependent sampling (FADS) design that allows the probability of obtaining the expensive exposures to depend on both the failure time and cheaply available auxiliary variables. Chapter~3 considers the generalized case-cohort design for studies with a cure fraction. A few directions for future research are discussed in Chapter~4.
Autonomous exploration using mobile robots, commonly referred to as robotic exploration, entails simultaneously performing robot perception, localization, and motion planning to explore an unknown environment. Most prior indoor robotic exploration algorithms focus on exploring the entire environment. We consider exploration under deadlines dynamically imposed either by the robot’s battery or by the environment. Such time-sensitive robotic exploration is critical in dangerous environments as it provides vital initial information about the geometric structure and layout of the environment for subsequent operations. For instance, firefighters can utilize an initial map generated by this deadline constrained robotic exploration to rapidly navigate a building on fire. In the presence of deadlines, the robots should identify the semantically significant regions of the environment (e.g., corridors) and prioritize those that enable them to determine the environment's geometric structure and return to the starting position before the deadline.
This dissertation addresses the problem of autonomous exploration in indoor environments with dynamic deadlines. The problem is NP-hard and requires exponential time to solve optimally. Therefore, we present a short-horizon exploration algorithm, the priority-based greedy exploration algorithm, and several long-horizon exploration algorithms; these include adaptations of the orienteering problem and the profitable tour problem for single-robot and multi-robot exploration of unknown environments with dynamic deadlines. Furthermore, we present a test suite of environments and exploration metrics to benchmark the real-world efficiency of exploration algorithms in office-like environments. Our single-robot experiments reveal that the priority-based greedy exploration algorithm, which focuses on exploring semantic regions with higher connectivity, consistently outperforms the baseline cost-based greedy exploration algorithm in terms of environment layout identification and exploration efficiency. Moreover, the priority-based greedy algorithm was found to be on par with the computationally expensive long-horizon exploration algorithms in terms of percent of the area explored within the deadline. Long-horizon exploration algorithms on the other hand exhibit consistent performance with low variance over repeated experiments. Moreover, the multi-robot priority-based greedy exploration algorithm demonstrated better performance compared to the multi-robot baseline exploration algorithm and performed on par with the multi-robot long-horizon based exploration algorithm while being computationally faster.
Planning for secondary transition includes identification of postsecondary goals in the areas of continued education, employment, and independent living or community engagement (IDEA, 2004). Young adults with intellectual and developmental disabilities (IDD) lag behind their same-aged peers in outcomes related to community engagement (Lipscomb et al., 2017a); specifically, challenges related to travel and transportation are a well-documented barrier to community engagement that young adults with IDD experience (Deka et al., 2016; Kersten et al., 2020). The purpose of this dissertation was to examine the effects of constant time delay instruction on the ability of young adults with IDD to program and follow walking routes to unfamiliar community locations of their choice using the Google Maps application. Results indicated a functional relation between constant time delay instruction and the percent of steps three young adults with IDD completed for programming and following a Google Maps walking route. Additional measures included generalization to use of the Apple Maps application; social validity of the intervention, as reported by the participants and their special education teachers; and participants’ ability to problem-solve common issues that may occur when following a pedestrian route. Finally, study limitations, suggestions for future research, and implications for practice are described.
In recent years, there has been an increase in attacks, including advanced persistent threats (APTs), and the techniques used by the attacker in these attacks have reached unprecedented sophistication. Threat hunters use various monitoring tools to monitor and collect all these attack actions (which blend in with benign user activities) for cyber threat hunting—the end devices store monitored activities as generated logs/events. Moreover, Organizations like NIST and CIS provide guidelines (CSC) to enforce cyber security and defend against those attacks.
Although the end hosts and networking devices can record all benign user and adversary actions, it is infeasible to monitor everything. In existing approaches, high memory usage and communication overhead to transfer events to the central server create scalability issues on the monitored network. Single event matching on the end-host devices approach to detect attacks generates false alerts, causing the alert fatigue problem. This dissertation presents a distributed hierarchical monitoring agent architecture to overcome those limitations of existing tools and research works.
Additionally, there are no well-defined automated measures and metrics to validate the enforcement of CSC. Manually analyzing and developing measures and metrics to monitor and implementing those monitoring mechanisms are resource-intensive tasks and massively dependent on the security analyst's expertise and knowledge. To tackle those problems, we use LLM as a knowledge base and reasoner to extract measures, metrics, and monitoring mechanism implementation steps from CSC descriptions to reduce the dependency on security analysts with the help of few-shot learning with chain-of-thought prompting. This dissertation presents CSC enforcement assessment with the help of our distributed hierarchical monitoring agent architecture and prompt engineering.
