Dissertation Defense Announcements

Candidate Name: Swapneel Rao Kodupuganti
Title: Modeling Operational Performance of Urban Roads With Heterogenous Traffic Conditions
 November 15, 2021  10:00 AM
Location: https://uncc.zoom.us/j/99589832554?pwd=SmVyc2w0K2xGWEtQeU1OQmttcGtQUT09

Several urban areas are building new facilities to encourage users of alternative modes of transportation (e.g., public transportation, walking, and bicycling). The existing infrastructure is changed to accommodate/ encourage these alternative mode users. However, there is not enough evidence to justify whether such plans are instrumental in improving mobility and enhancing safety of the transportation system from a multimodal perspective Therefore, the goal of this research is to model the operational performance and safety of urban roads with heterogeneous traffic conditions to improve the safety, reliability, and mobility of people and goods. A two-step approach is used to assess the operational performance of the urban roads with heterogeneous conditions. Firstly, a travel time reliability-based modeling and analysis was conducted to analyze the transportation system performance comprehensively accounting for all the modes of transportation. Secondly, a simulation-based modeling and analysis was conducted to assess the effect of light rail transit (LRT), pedestrian activity, bicyclist activity, and traffic, individually or combined, on the operational performance of the transportation system. Further, surrogate safety assessment was conducted to check the effect of LRT, pedestrian activity, bicyclist activity, and traffic, individually or combined, on the overall safety performance of the transportation system. Notable effects on operational and safety performance were observed between the modeled and evaluated hypothetical scenarios, emphasizing the need to plan and build infrastructure by evaluating complex mobility patterns and interactions between all the mode users. The proposed methodological framework is cross-disciplinary, transferable, and can be applied to other regions.

Candidate Name: Arun Suresh
 November 11, 2021  11:00 AM
Location: EPIC 2354

In recent years the grid modernization and rapid growth in distributed energy resources due to environmental consciousness have resulted in distribution grids becoming more active which has led to significant interaction between transmission and distribution grids. In this dissertation, novel approaches in modeling and management tools are proposed considering integrated power transmission and distribution systems with Distributed Energy Resources (DERs). First, new power methods for power distribution system considering DERs is proposed in a single-phase, three-phase, and three sequence domain. Second, an integrated transmission and distribution (T\&D) grid model where transmission and distribution systems are considered as a single unit is proposed. A coalescing Ybus approach is used to obtain the bus admittance matrix of the combined T\&D system. Further, to successfully capture the effect of unbalances in the system at the same time reducing computational burden owing to the larger size, a three-sequence modeling framework is used for a unified system. A three-sequence-based multi-period power flow method is used to accurately capture the time-varying aspects of the system. Next, a three-sequence fault analysis method capable of conducting short circuit analysis on a DER integrated unbalanced distribution system is developed. All these sequence-based methods are then used for steady-state analysis of the integrated T\&D system. Finally, a sensitivity-based coordinated voltage control scheme using reactive power support from DERs is proposed which can lead to reduced voltage regulator operations and tighter voltage profiles. The proposed methods have been validated using large-scale IEEE T\&D feeders to prove the real-life implementation capabilities of the models and tools

Candidate Name: Md Mazharul Islam
Title: Active Cyber Defense Planning and Orchestration
 November 08, 2021  12:00 PM
Location: Virtual Zoom meeting (please email me at mislam7@uncc.edu for meeting link)

The overwhelming number of recent data breaches reported hundreds of terabytes of highly sensitive information, including national, financial, and personal, have been stolen from different organizations, indicating clear asymmetric disadvantage defender faces against cyber attackers. Modern attackers are well organized, highly stealthy, and stay persistent in the network for years; therefore, known as an advanced persistent threat (APT). Existing detection and prevention based cyber defense techniques usually approach the target for specific, known attack signatures, descriptions, and behaviors. However, APT attackers can easily avoid such detection techniques employing reconnaissance, fingerprinting, and social engineering. It is often very challenging and sometimes infeasible for defenders to prevent the information gathering of the adversary and patch all the vulnerabilities in the system. Therefore, a proactive defense approach is needed to break such asymmetry.

Active Cyber Defense (ACD) is a promising paradigm to achieve this goal. ACD can proactively mislead adversaries and enables a unique opportunity to engage with them to learn new attack tactics and techniques. ACD enhances real-time detection, analysis, and mitigation of APT attacks. ACD can be achieved through cyber agility and cyber deception. Cyber Agility, such as moving target defense (MTD), enables cyber systems to defend proactively against sophisticated attacks by dynamically changing the system configuration parameters (called mutable parameters) in order to deter adversaries from reaching their goals. On the other hand, Cyber Deception is an intentional misrepresentation of the system's ground truth to manipulate adversaries' actions.

Although cyber deception and MTD have been around for more than decades, static configurations and the lack of automation made many of the existing techniques easily discoverable by attackers and too expensive to manage, which diminishes the value of these technologies. Sophisticated APTs are very dynamic and thereby require a highly adaptive and embedded defense that can dynamically create honey resources and orchestrate the ACD environment appropriately according to the adversary behavior in real-time.

To overcome these challenges, this dissertation introduced an autonomous resilient ACD framework, having the following aspects: (1) developing multistrategy ACD policies that leverage an optimal dynamic composition of various MTD and deception techniques to maximize the defense utility, (2) a policy specification language and an extensible rich API integrated with a synthesis engine for developing different MTD techniques without consulting about the low-level network and system configuration management, (3) a theoretical framework and implementation for an autonomous goal-oriented cyber deception planner that optimizes deception decision-making.

Candidate Name: Sara Kamanmalek
 November 12, 2021  10:00 AM
Location: Email for Zoom Link/Virtual

Occurrences of antibiotics and antibiotic resistance have been reported in various environmental settings, posing a global concern due to associated human and ecological risks. Therefore, the main goal of this research was to develop an integrative approach to identify and assess watershed vulnerability to contamination of antibiotics and antibiotic resistance and to use the developed approach to inform field study centered in North Carolina streams. In doing so, we quantified antibiotic concentrations at WWTP discharge sites and identified streams more susceptible to antibiotic resistance under varying streamflow conditions across the U.S. Then, we assessed watershed vulnerability to antibiotic resistance occurrence by the development of the multimetric index that incorporates potential antibiotic point and nonpoint pollution, hydrologic condition, and climate change. Lastly, we conducted a targeted field study quantifying selected antibiotics and antibiotic resistance genes within three North Carolina watersheds that are modeled to be most impacted by potential antibiotic pollution. This study presented a holistic approach to assess spatial hazards of antibiotics and antibiotic resistance, and such information can be used to prioritize watershed management, control, and mitigation strategies in impacted watersheds.

Candidate Name: Sarah Abdellahi
 November 11, 2021  10:00 AM
Location: Online/ Woodward 243

Candidate Name: Tamera Moore
 November 12, 2021  10:00 AM
Location: Online

Service-learning combines academic coursework with volunteer community service experiences. Its components include the coursework, community service, course credit, and reflection on the experience. Critical service-learning emphasizes social justice (Mitchell, 2008). The broader literature explores both service-learning and critical service-learning, which result in more connections to local communities. Yet, both maintain a central focus on the students engaged in community service, overlooking the rich history of volunteer service within the communities being served. African American communities have been woven together with rich histories of service to the community. Without this historical knowledge, the future of service-learning is destined to continue to utilize an unsustainable model that relies on outside volunteers who come into underserved communities for short periods of time and return to their own lives, leaving the communities to wait on the next wave of volunteers to enter. If the outcomes of service-learning are to impact marginalized communities significantly, then service-learning programs must consider the rich histories of volunteering within these communities. The implications of this study suggest that traditional service learning programs should expand their understanding of the valuable history of volunteering within the Black community.

Candidate Name: Daniel Yonto
Title: Gentrification in Charlotte: A Tale of Urban Redevelopment
 November 15, 2021  11:30 AM
Location: email for Zoom link

Gentrification research almost exclusively focuses on traditional postindustrial cities. Despite a growing number of scholars emphasizing the importance of understanding gentrification outside of traditional urban areas, its presence and modalities in mid-sized cities remains underexplored. This holds particularly true in the U.S. South where unique historical processes of industrialization, segregation, and immigration form low-density spatial patterns of urbanization that set Southern cities apart from other U.S. regions. A group of rapidly emerging mid-size U.S. Sunbelt cities – known as the New South – share concerns over a number of converging and interrelated trends: urban core revitalization, rising housing costs, lagging economic mobility, investing in public infrastructure, and shifting demographics. In this context, the New South is an ideal region for investigating longitudinal neighborhood development trends within a gentrification framework. Using a case study approach in Charlotte, my dissertation explores the spatial, temporal, and spatial-temporal aspects of contemporary gentrification. A survival analysis also tests the relationships between gentrification and changes in housing renovation, urban amenities, proximity to light rail development, and other factors. Results reveal that administrative data at the parcel level is more precise at pinpointing where gentrification occurs and how it diffuses overtime. Findings also identify substantial differences between area estimates of gentrification hot spots calculated from parcel data, demonstrating that spatial aggregation error may lead to significant errors in measuring gentrification. Findings suggest that aggregating data to census blocks or tax parcel spatial unit provide more precise measurements of gentrification. Key findings from the survival analysis identify that neighborhood parks and greenways increase the likelihood of gentrification. Results also highlight a strong spatial effect, demonstrating that neighborhood effects do influence spatial patterns of gentrification. Unexpectedly, light rail variables do not increase the likelihood of gentrification. Additional variables that increase the likelihood of gentrification include parcels with older homes, parcels in and around historical areas, lower home values per square foot, proximity to quality education, proximity to highways, and proximity to commercial areas increase the likelihood of gentrification. Thus, at a time when urban areas are rapidly changing and considering how to accommodate future growth, a local level understanding of gentrification aids policy makers and community organizers to tailor more effective public policy.

Candidate Name: Masoumeh Sheikhi Kiasari
Title: Distance Based Linear Regression Model and Its Application to Microbiome Association Studies
 November 11, 2021  1:00 PM
Location: Conference Room, Department of Mathematics and Statistics

In the past few decades, pairwise distance based statistical methods have been developed to identify spatial and/or temporal clusters of disease, study the association between the dissimilarity of ecological communities and distance in geographical locations. With emergence of high-throughput technologies, pairwise distance base methods are widely used in the analysis of genetics and genomics data, especially when the data structure fails the fundamental assumptions of classical multivariate analysis, including independency and normality. However, much of existing knowledge has been around non-parametric or semi-parametric estimations usually employing permutation techniques to assess statistical significance, which are known to be computationally expensive and sensitive to the choice of permutation.

Majority of this thesis focuses on linear regression of pairwise distance matrices. We consider the pairwise correlation structure between the distances and investigate the large sample properties of the ordinary least square estimator of the model coefficients. Extensive simulations are conducted to evaluate the performance of our method with finite sample size.

Another major component of the thesis is the human microbiome data analysis. We analyze the integrative Human Microbiome Project (iHMP) data set of composition of microbial communities in the digestive tracts of humans by using multiple statistical methods, including our proposed method. The results are presented and interpreted. Existing challenges and future works are also discussed.

Candidate Name: Anuprabha Ravindran Nair
Title: Advanced Control Approaches For Renewable Energy Integration To Improve Overall Stability And Reliability Of Power Grid
 November 08, 2021  3:00 PM
Location: EPIC 2354

Renewable energy-based electric power generation is receiving more attention due to the increasing power demand and environmental concerns. The interconnection of these distributed resources to the grid is based on the grid code standards to ensure power quality, reliability, and security. The intermittent nature of the renewable penetrated grid, along with phasing out of conventional generation units, new HVDC lines, and long-distance transmissions from remote areas, impose several challenges to grid stability. Hence it is crucial to explore control approaches that can efficiently control these Distributed Energy Resources (DERs) to improve the overall power quality and reliability. This dissertation presents modeling, stability studies, and advanced control architectures that can support and coordinate the Wind Energy Conversion Systems (WECS) and other inverter-based Distributed Energy Resources (DERs) to improve the quality of the generation, transmission, and distribution systems. The first part of the work proposes advanced adaptive-based robust sensorless control approaches for rotor side and grid side control of DFIG based WECS.
Further, the dissertation discusses the challenges of transferring high power of renewable penetrated grid and some possible solutions and control approaches. Finally, the work efficiently coordinates the available resources to ensure power quality in a distribution network. All the proposed designs are validated using simulation results developed by dynamic models or through real-time simulators, which proves the ability of the advanced controllers to improve grid reliability. The quantification based on standard metrics used for performance improvements discussed in each design shows that the designs have exceptional advantages compared with conventional controllers.

Candidate Name: Tinghao Feng
Title: Visual Analytics Approaches to Exploring Multivariate Time Series Data
 November 15, 2021  9:00 AM
Location: https://uncc.zoom.us/j/91741396988?pwd=cWpUZ3ZjZ1ZlSEIvSzMySmMyeTJmUT09

Time-oriented data analysis has attracted the attention of researchers for decades, across many research domains, including but not limited to medical records, business, science, engineering, biographies, history, planning, and project management. However, the complexities of time-oriented data with a large number of variables and varying time scales hinder scientists from completing more than the most basic analyses. In this dissertation, I present two design studies where multivariate time series data are involved. In the first design study, I developed an interactive interface, \textit{t}-RadViz, for a manufacturing company to visually monitor and analyze real-time streaming multivariate testbench data with continuous numeric values. In the second design study, I developed a visual analytics prototype named EVis for analyzing and exploring how recurring environmentally driven events are related to high dimensional time series of continuous numeric environmental variables. In both design studies, I closely collaborated with domain users in the whole process of requirement analysis, design, and evaluation. Besides a rich set of fundamental graphic charts for supporting basic analysis functions, new visual analytics techniques were developed in the design studies for addressing challenging tasks, such as a novel trajectory-based multivariate time series visual analytics approach in EVis for exploring temporally lagging relationships between events and antecedent conditions. The effectiveness and efficiency of the prototypes are illustrated by case studies conducted with real users and feedback from domain experts.