The traditional post-earthquake damage assessment of buildings is done visually through building-by-building inspections, which for a major event might take weeks to months to be completed. The main goal of this study is to establish a framework to quantify the post-event seismic performance of a portfolio of existing structures, in this case soft-story (SS) residential buildings, in near-real-time and an ultra-high resolution. Two main ingredients are required; 1) input ground motion time-series at the sites of the buildings that excite the structures, and 2) well-established nonlinear structural computer models representing the buildings. By having these two ingredients, nonlinear dynamic analyses of the existing structures can be performed, and seismic performance of the portfolio buildings can be quantified. The number of currently available recording instruments is sparse. Therefore, using the Gaussian Process Regression (GPR), a novel approach was developed to generate ground motion time series at un-instrumented target sites (i.e., the sites of the buildings) employing the surrounding recorded ground motions. The methodology has been validated for the physics-based scenario earthquakes in Northern California as well as two events recorded in Southern California: the 2019 M7.1 Ridgecrest and 2020 M4.5 South El Monte earthquakes. In addition, an approach was introduced to generate multiple realizations of ground motions at each target site. The results illustrated that the instrumentation density closer to the target site is crucial in producing accurate and reliable ground motions, especially at long periods. The second ingredient of the framework needs an accurate nonlinear computer structural model for soft-story buildings. There is an inventory of OpenSees models constructed to represent the 13,500 SS buildings in Los Angeles. This inventory includes thirty-two models categorized through four main key features: first-story wall layout, the number of stories, floor plan dimension, and wood-frame shear wall material. I have utilized machine learning methodologies to classify the first two features via visual recognition. I extracted images for 2,681 buildings within Los Angeles via Google Street View (GSV) service through an automated algorithm to train the Convolutional Neural Network (CNN) models for that purpose. The accuracy of the trained model is 80.1% and 91.4% for first-story wall layout and the number of stories classification tasks, respectively. I, then, utilized the OpenStreetMap (OSM) to detect the floor plan area of the target buildings to classify the third required feature. Having both a structural model and input ground motion time series, one can conduct a nonlinear response history analysis to assess the seismic performance of each specific building. As an application of the framework, I generated ground motion time series at over 2,000 SS buildings’ locations within Los Angeles for a scenario M6.7 earthquake based on the 2020 M4.5 El Monte rupture mechanism. I also estimated the structural features through the GSV extracted images to identify the “closest” available OpenSees model. Lastly, a map of the estimated damage state for the 2,000 SS buildings is produced. The results demonstrated that more than 50% of the SS buildings are either severely damaged or collapsed after such an earthquake in Los Angeles, while approximately 15% of them experienced slight damage. The methodology and framework developed in this research can be implemented to assess, in a near-real-time, seismic performance of a large portfolio of structures in moderate-to-severe earthquake events.

Improvement of cognitive function is of great value to many aspects of society. However, identifying robust procedures for training cognitive processes with generalizable benefits remains elusive. Here we present a novel attention regulation training paradigm that incorporates skill application in multiple learning environments. We hypothesized that our training procedure would enhance goal-oriented cognitive function and cognitive control mechanisms. We evaluated training effects using both computerized assessments and self-report questionnaires designed to probe cognitive control in everyday life. We tested for specificity of training effects by employing multiple active control conditions and for generalizability by using assessments that were significantly different from the training tasks as well as a set of secondary self-report questionnaires to explore the extent of far transfer. Training substantially improved performance on multiple tasks that involve goal-oriented cognitive functioning and cognitive control, and these gains were often specific to the group that received attention regulation training and also applied learned skills in varied environments.

In this work, we study interactions between the curvature of a Riemannian manifold and the geometry of its submanifolds. In particular, we consider manifolds with intermediate Ricci curvature bounded below and manifolds with integral curvature bounds.

First, we develop the tools for studying manifolds with intermediate Ricci curvature bounds. In particular, we prove a comparison theorem for the Hessian of the distance function to a submanifold based on a lower bound for the k-Ricci curvature.

The main result is a generalization of the inequality of E. Heintze and H. Karcher for the volume of tubes around minimal submanifolds to an inequality based on integral bounds for k-Ricci curvature. Even in the case of a pointwise bound, this generalizes the classical inequality by replacing a sectional curvature bound with a k-Ricci bound. This theorem is motivated by the estimates of Petersen-Shteingold-Wei for the volume of tubes around a geodesic and generalizes their result.

Finally, we give several applications of these comparison theorems to the geometry and topology of submanifolds in spaces with curvature bounded below. The first is a uniform lower bound on the volume of minimal submanifolds in spaces with integral curvature bounds. We then bound the relative growth of the fundamental group of a closed minimal submanifold in terms of the growth of the fundamental group of the embedding space when the latter has nonnegative intermediate Ricci curvature. We conclude with an application of the comparison theory for intermediate Ricci curvature to certain geometric inequalities which are motivated by questions in general relativity.

Objectives:Medication-Related Osteonecrosis of the Jaw (MRONJ) is a serious and debilitating condition most often associated with dentoalveolar trauma or surgical interventions such as tooth extractions. Although MRONJ has been extensively investigated, spontaneous MRONJ—occurring without any identifiable local precipitating factors—remains poorly understood and scarcely researched. The clinical importance of spontaneous MRONJ requires better understanding to enhance prevention and management approaches. This study aimed to identify associations between known MRONJ-inducing medications, their clinical administration methods, treatment duration, therapeutic indication, and the development of spontaneous MRONJ. To date, this represents the largest single-center case-control study focused specifically on trauma-independent MRONJ. Methods: This retrospective case-control study was conducted at the University of California, Los Angeles (UCLA) School of Dentistry and included patients diagnosed with MRONJ between 2012 and 2024. Cases were defined as patients with a definitive diagnosis of MRONJ (Stage I to III per AAOMS criteria) who had not undergone any dentoalveolar procedures, including extractions, implant placement, or intraoral trauma following initiation of MRONJ-associated medications. The control group included patients who received the same medications without developing MRONJ and without a dental trauma history. The study excluded patients who received medications not related to MRONJ and patients who received head and neck radiation or had jaw metastases or chronic necrotic conditions. The research team extracted demographic information along with comorbidities and medication details, including type, indication of treatment, administration route, and duration of treatment, from clinical records. The research included 121 spontaneous MRONJ cases together with 54 control participants. The statistical analysis used Firth’s logistic regression to determine which factors were significantly linked to spontaneous MRONJ. Results: The analysis showed that the antiresorptive agent zoledronate (Reclast) had the most significant relationship with spontaneous MRONJ. The analysis showed that subcutaneous drug administration was found to significantly reduce the risk of spontaneous MRONJ (p < 0.05). Among medical indications, colon cancer showed the highest effect size (β = 2.20), followed by osteoporosis (β = 0.83); however, none of the medical conditions were statistically significant predictors (p > 0.05), and confidence intervals were wide. While longer durations of medication use (1–3 years) trended toward higher MRONJ risk, the correlation did not reach statistical significance. The research results demonstrate that spontaneous MRONJ develops from multiple factors, which include drug pharmacokinetics together with systemic disease and exposure duration.

Conclusion:This study shows that medication classification together with administration route and treatment duration significantly influences the development of spontaneous MRONJ. The results demonstrate the need for personalized risk evaluations and team-based approaches before starting antiresorptive or antiangiogenic treatments. The research findings from this study enhance the understanding of MRONJ while providing guidance for its early detection and prevention and management strategies. These results can inform predictive algorithms by identifying key patient factors—such as medication type, treatment duration, and administration route—that contribute to spontaneous MRONJ risk.

Multi-Objective Optimization Problems (MOPs) deal with optimizing several objectives simultaneously and have diverse applications in engineering, economics, logistics, etc. The methods for solving MOPs can generally be classified into stochastic and deterministic approaches. Deterministic approaches are capable of finding the global solution even though they are computationally burdensome. Stochastic methods, on the other hand, can save on computations significantly, although they do not guarantee to find the global solution.

In engineering applications, MOPs can become nonlinear, multi-modal, high dimensional, and have complex structured solutions that makes them more challenging.

This theses follows two major goals. Firstly, it presents new methods and algorithms for solving engineering MOPs by hybridizing the existing methods and comparing their effectiveness by using benchmark problems. The hybrid method combines an evolutionary algorithm with a cell mapping method in order to reduce the computational time while maintaining the quality of the solution. Implementation details on parallel CPU/GPU programming of such methods are discussed as well. The second goal of this thesis is to introduce new applications of MOPs in different areas of engineering such as control design, path planning, fractional systems and airfoil design.

Most modern communication networks suffer from both intentional and unintentional interference. In this dissertation, we investigate three separate issues in modern wireless communication networks: sensing disruption attacks on cognitive radio networks (CRNs), sharing disruption attacks on cognitive radio non-orthogonal multiple access (CR-NOMA), and error analysis in millimeter-wave (mmWave) communication under unintentional interference environments.In the first problem, we propose a different approach for sensing disruption attacks in CRNs. We examine the optimal strategy for an intelligent adversary who aims to manipulate busy bands so that they appear to be free. This approach involves contaminating noise power measurements, as demonstrated through a two-step sensing scheme that combines energy detection with noise power estimation by secondary users. We demonstrate that the optimal strategies for sensing link disruptions include equal-power and partial-band flipping, from deriving the maximum average number of missed detections under specific power constraints of the adversary. Secondly, we examine the vulnerabilities of spectrum sharing in a CR-NOMA network, proposing a new type of attack termed sharing disruption. This attack disrupts the channel estimation phase, leading to a denial-of-service (DoS) for secondary users. We derive the optimal power allocation to maximize disruption, calculating the maximum average number of DoS bands under specific adversary power constraints. Additionally, we compare optimal power allocation with uniform power allocation. Lastly, we investigate the error performance of mmWave bands in the presence of unintentional interference. This analysis is motivated by the anticipated increase in number of users in near future. We examine M-ary quadrature amplitude modulation (M-QAM) across Nakagami-m channels, taking into account the impacts of directional antennas and blockage. We derive the average probability of error with employing a stochastic geometry framework that provide different insights for mmWave networks, particularly in device-to-device (D2D) communications.

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