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Familiar Faces of Young Adulthood
Get PDFExploring the connection between myself and my subjects through portraiture, I focus on capturing the fleeting, uncelebrated moments of everyday life. My work reflects my desire to document intimate, spontaneous experiences shared with friends, often recorded through candid iPhone photos. These seemingly mundane moments, such as shared laughter or quiet reflection, are immortalized through painting and elevate the ephemeral to something worth considering. Influenced by artists like Alice Neel, Jordan Casteel, and Nan Goldin, I utilize portraiture as a tool for recording the emotional and social zeitgeist of my generation. I emphasize the power of painting to preserve the essence of life’s moments, creating lasting representations that offer a deeper, more intimate understanding of my friendships and the complexities of youth culture
Islamic Finance, Dividend Policy, and Performance of Insurance Firms
Get PDFThe dissertation investigates the impact of Covid-19 pandemic and dividend policy on the performance of insurance firms. It examines the impact of the Covid-19 pandemic on the performance of Islamic insurance versus conventional insurance firms in the Organization of Islamic Cooperation (OIC) member countries. Using Dynamic Capabilities theory and Resource Dependence Theory, the study examines the reasons behind the more significant performance reduction in Islamic insurance firms during the pandemic. Using firm- and country-level panel data from 425 insurance firms for 7 years (2016-2022) and employing different regression models, the analysis focuses on Return of Assets (ROA) and Asset Turnover Ratio as performance measures. The results indicate that Islamic insurance firms exhibited a greater reduction in performance, during the pandemic, compared to conventional firms, primarily due to weaker liquidity management and operational flexibility. Cash from operating activities (COA) was the key factor of lack of liquidity management, contributing to the underperformance of Islamic insurance firms during the pandemic. The findings highlight the need for improved liquidity management approaches in Islamic insurance firms to increase their resilience to future economic shocks. The dissertation also investigates the impact of dividend policy on the performance of 688 insurance firms globally from 2014 to 2022. Employing different econometric models, we find that dividend policy significantly increases both accounting (ROAA) and market (MKTCAP) performance. The findings align with the Dividend Signaling Theory and the Agency Theory of Free Cash Flow, which emphasize that dividend payouts signal a stable financial health and mitigate agency problems. Our findings demonstrate that consistent dividend payments (CONDIV) increase ROAA and MKTCAP by 0.007 and 0.139, respectively, which underlines the importance of consistent dividends. Channel analysis shows that corporate governance board committees, CEO duality, and board gender diversity amplify the positive effect of dividend policy, while larger board size diminishes it. This provides a global understanding of dividend policy significant impact on insurance firm performance. Jointly, the dissertation’s findings provide a complete picture to understand the financial decisions and governance structures that affect the performance of insurance firms during economic shocks such as Covid-19 pandemic and on the global stage
Liquidity Creation, Bank Funding, and Risk-Taking: The Role of ESG
Get PDFThis dissertation explores two critical types of risks faced by banks that include liquidity risk and credit risk. Furthermore, it tests whether bank regulations such as adopting the environmental, social, and governance (ESG) standards in addition to diversifying funding resources play crucial roles in mitigating them. Also, this dissertation aims to provide evidence of whether these risks vary depending on banks sizes. The final sample consists of 136 U.S. commercial banks covering the period from 2005 to 2022. Furthermore, a variety of econometric methods are applied that include OLS regression, random effects (RE), two-step system Generalized Method of Moments (GMM), regression discontinuity (RD), the bias-corrected Least-Squares with Dummy Variables (LSDVC), and the Two-Stage Least Squares regression (2SLS).
The first chapter investigates whether ESG performance plays a mediating role in the effect of funding costs on bank liquidity creation. The findings of this chapter reveal that funding costs significantly reduce liquidity creation, implying that higher funding costs decrease banks’ ability to create liquidity. Additionally, adopting ESG principles increases banks’ ability to create more liquidity. Moreover, ESG performance of the sampling banks plays a significant role in mediating the relationship between funding costs and liquidity creation, which implies that depositors accept low interest payments due to the good ESG performance of the sampling banks, which suggests increasing the ability of the sampling banks to create liquidity.
The second chapter examines the effect of bank liquidity creation and bank funding diversification on bank risk-taking, as represented by non-performing loans (NPLs). Moreover, the chapter aims to explore the mediating role of bank size in these relationships. The findings of this chapter show that NPLs increase significantly as the sampling banks create more liquidity. Furthermore, funding diversification significantly reduces NPLs and enhances the stability of the sampling banks. Finally, bank size significantly moderate the impact of bank liquidity creation and bank funding diversification on NPLs, which is more evident for the case of large banks
“Taste is right at the core of this, isn’t it?” An excerpt from Unmasked, an in-progress zine series about The Phantom of the Opera
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Real-time Fiducial Marker Based Localization for Autonomous Unmanned Aerial Vehicle Navigation
No full textBy harnessing fiducial markers as visual landmarks in the environment, Unmanned Aerial Vehicles (UAVs) can rapidly build precise maps and navigate spaces safely and efficiently, unlocking their potential for fluent collaboration and coexistence with humans. Existing fiducial marker methods rely on handcrafted feature extraction, which sacrifices accuracy. On the other hand, some deep learning pipelines for marker detection fail to meet real-time runtime constraints crucial for navigation applications. In this work, I propose YoloTag- a real-time fiducial marker-based localization system. YoloTag uses a lightweight YOLO v8 object detector to accurately detect fiducial markers in images while meeting the runtime constraints needed for navigation. The detected markers are then used by an efficient perspective-n-point algorithm to estimate UAV states. However, this localization system introduces noise, causing instability in trajectory tracking. To suppress noise, I design a higher-order Butterworth filter that effectively eliminates noise through frequency domain analysis. I evaluate our algorithm through real-robot experiments in an indoor environment, comparing the trajectory tracking performance of our method against other approaches in terms of several distance metrics
Improving Coastal Hydrodynamic Model Performance through Parameter Sensitivity Analysis and Optimization
No full textThis thesis presents a framework for parameter sensitivity analysis and optimization of coastal hydrodynamic models. The research uses the Generalized Likelihood Uncertainty Estimation (GLUE) method to quantify parameter sensitivity and identify optimal values for currents, wave height, and water level. A variance-based technique guides parameter selection when GLUE yields competing values or when observations are unavailable.
The framework is demonstrated through two case studies: Hurricane Michael (2018) and the ExCaliBur field study (2022) near Oceanside, CA. Results from Hurricane Michael show higher optimal hydrodynamic model bottom friction values near the hurricane\u27s landfall location. The Oceanside study identifies the hydrodynamic model\u27s bottom friction coefficient as a key parameter, with an optimized value of 0.043 improving model skill for this location and conditions. The wave model\u27s bottom friction coefficient is important for accurately modeling significant wave height, while horizontal eddy viscosity affects alongshore currents.
This thesis discusses implications for data-scarce settings and outlines future research, including transferring optimized parameter sets and developing predictive relationships. This research improves coastal model accuracy where observations are available and establishes a foundation for enhanced predictions in data-scarce coastal regions through informed parameter selection
Oral History Interview with Daniel Castellanos (Part 2)
No full textDaniel Castellanos is a reconstruction worker and organizer who was born in Lima, Peru, on November 21, 1970. He grew up in Lima, where his family worked in a wholesale fruit market, an experience he described as his first university. He studied industrial engineering at the Universidad Nacional Federico Villarreal but did not complete his degree due to increasing political violence and personal circumstances. He later became a successful entrepreneur in the textile industry before facing financial ruin. He immigrated to the U.S. in 2004, first to Virginia and then to New Orleans in 2006. In New Orleans, he became a prominent labor organizer, co-founding the New Orleans Workers\u27 Center for Racial Justice and the Congresso de Jornaleros, drawing on his personal experiences with exploitation as a guest worker.https://scholarworks.uno.edu/ejrloh/1024/thumbnail.jp