USFSP Digital Archive
Not a member yet
132282 research outputs found
Sort by
HIV Stalks Bodies Like Mine: An Autoethnography of Self-Disclosure, Stigmatized Identity, and (In)Visibility in Queer Lived Experience
No full textThis dissertation examines self-disclosure of Human Immunodeficiency Virus (HIV) status within the context of communication between long-standing friends. For the purposes of my study, I define this type of friendship as those who have known me for at least two years and with whom I communicate regularly. These are friends who tend to know a variety of personal details about me, ranging from superficial to private and trivial to essential. I use autoethnography to ground the study in my lived experience. By doing so, I present intimate accounts of my communication with others across my lifespan to function as background for disclosures I make in the present. My aim is to answer two questions: a) what does it mean relationally to disclose one’s HIV status to people who are long-standing friends; and, b) what do these conversations demonstrate about self-disclosure amid the tension of stigmatized identity, the perpetuation of silence, unhealthy rituals of relational communication, and diminished visibility for people living with HIV (PLHIV) and illness? These questions underscore the importance, complexity, and dimensions of disclosure as a moral duty, a social responsibility, and the consequences of interactions that entail non-disclosure in everyday life. As I write “through” disclosure, these questions guide but will not limit my inquiry. I remain aware that disclosure remains complex, and that the context of disclosure is organic and subject to change over time and relative to the given writer and story. Finally, I write this dissertation from a place of privilege owed to my body, its geographical location, socio-economic status, and legal standing because others, especially those who live with HIV, too often cannot
Deep Reinforcement Learning Based Optimization Techniques for Energy and Socioeconomic Systems
No full textOptimization, which refers to making the best or most out of a system, is critical for an organization\u27s strategic planning. Optimization theories and techniques aim to find the optimal solution that maximizes/minimizes the values of an objective function within a set of constraints. Deep Reinforcement Learning (DRL) is a popular Machine Learning technique for optimization and resource allocation tasks. Unlike the supervised ML that trains on labeled data, DRL techniques require a simulated environment to capture the stochasticity of real-world complex systems. This uncertainty in future transitions makes the planning authorities doubt real-world implementation success. Furthermore, the DRL methods have limitations for different application environments; slow convergence, unstable learning, and being stuck in local optima are a few of them.
We address these challenges in our environmental, healthcare, and energy systems projects by carefully (1) modeling the system dynamics we achieved through research and collaboration with domain experts and (2) state-of-the-art DRL techniques for experimental analysis. Our experimental results and comparative analysis with the other optimization methods demonstrate the efficacy of DRL-based techniques. The success lies in appropriately modeling the critical decision-making features, reward function, and state transitions. In the process, we have developed novel DRL (Multi-agent and Multi-objective) algorithms
Prepare for, Respond to, Recover, and Learn from Disasters: Using Data-Driven Methods to Model and Understand Disaster Resilience
No full textCommunity resilience reflects the ability of human communities to prepare for, respond to, recover, and learn from disastrous events. Community resilience carries different meanings in different phases of disaster management (i.e., preparedness, response, recovery, and mitigation). With the emergence of new geospatial data sources, human activities now can be captured through social media, mobile signals, and nighttime illuminations, which makes it possible to describe the conditions among various communities before, during, and after disasters. Therefore, this dissertation explored the use of different types of geospatial data sources (social media, nighttime light remote sensing, land-use data, and census survey data) during three types of disasters (winter storm, hurricane, flooding) in the following three aspects of community resilience during four phases of disaster: 1) understanding information diffusion patterns and user networks in social media during disasters (preparedness and response phases); 2) using multi-source, multi-scale geospatial data to monitor human dynamics in disasters and find meaningful indicators to measure community resilience (response and recovery phases); 3) analyzing spatiotemporal changes of disaster exposure to understand geographic disparities in hazard mitigation and long-term community resilience (mitigation phases). This study explores geospatial data sources to develop quantitative models to model community resilience in four case studies (Winter Storm Diego, Hurricane Sandy, Winter Storm Uri, and flooding). The goal of this study is to evaluate communities\u27 capacity to effectively prepare for disasters (preparedness), minimize damage during the event (response), recover and regain functionality (recovery), and implement long-term strategies to mitigate future risks (mitigation). The developed approach is composed of four elements (exposure, damage, recovery, and mitigation) and three aspects linking the four elements (information diffusion, recovery trajectories, and population exposure). The outcomes of this study include a summary of disaster-related information diffusion patterns, an evaluation of various potential resilience indicators based on recovery trajectories, and a comprehensive assessment of long-term disaster exposure change within the contiguous United States. The outcomes will set an example of using different types of geospatial data sources to detect the socioeconomic impact on human activities during disasters. This project also sheds lights on improving the current community resilience framework by finding better indicators
Rehabilitation Technologies to Abate Infiltration in Sanitary Sewers
No full textIn urban coastal environments, sewer infrastructure is plagued by infiltration which seeps into aging and compromised pipes. Infiltration increases pumping costs, reduces treatment efficiency, and may trigger release of partially treated wastewater at water reclamation facilities. To restore pipes and abate infiltration, local utilities rely on structural and non-structural trenchless rehabilitation technologies. These methods are commonly used throughout the United States and the world due to their numerous benefits compared to traditional open-cut construction. However, limited data is available regarding their efficacy in reducing infiltration in sewer systems. In this study, we analysed infiltration in a small urban sewershed in Pinellas County, FL to assess the magnitude of the problem. Flow meters were deployed in different sections of the sewershed over the period of 2015 - 2016 to evaluate spatial and seasonal variabilities in infiltration. To abate infiltration, three trenchless sewer rehabilitation technologies were applied in 2019. The flow meters were re-deployed in 2021 at the same locations to compare pre- and post- rehabilitation conditions. The applied technologies were: Joint grouting, Expanded-in-Place PVC lining (EX liner), and Cure-in-Place Pipe (CIPP). Initially, average infiltration for the entire sewershed was around 2,555 gpd/mi/in during the wet season and 2,110 gpd/mi/in during the dry season Pipe rehabilitation reduced infiltration by 43% during the dry season and 49% during the wet season, but effectiveness varied by technology. Groundwater levels dropped by around 2 feet between the two monitoring periods. Accordingly, calculated infiltration was normalized. Data revealed that CIPP and EX liners were not as effective as joint grouting, probably because groundwater seeps in the space between the pipe and liner. Closed-Circuit Television (CCTV) was employed to investigate the pipes’ condition at the end of the monitoring period. CCTV showed instances of water pooling within the pipes, which is an indicator of inconsistent pipe grading. Findings suggest that poor pipe grading led to faulty joints that became the main source of infiltration. This is particularly proved by the higher efficiency of joint grouting compared to the other technologies. Additionally, the data set was used to develop a relationship between groundwater levels, pipe elevations, and infiltration to assist utilities in estimating infiltration without the need for sophisticated monitoring equipment
Threat and Enhancement: Strength of Gamer Identity Moderates Affective Response to Messages about Gaming.
No full textAdvancing hypotheses derived from social identity theory, we investigated the influence of gamer identity affiliation on affective responses to identity threats and enhancements. Participants viewed a message that either devalued (i.e., threatened) or elevated (i.e., enhanced) the status of gamers when associating them with a mass shooting event. Relative to a control condition that neither threatened nor enhanced identity, our data demonstrated that gamer identity affiliation moderated affect. Specifically, greater gamer affiliation increased negative affect experienced after a threatening message. By contrast, greater gamer affiliation increased positive affect and reduced negative affect experienced after an enhancement message. Analyses of participants’ emotional reactions to the messages revealed that individuals with stronger gamer identity affiliation reported relatively more homogeneous emotions relative to individuals less affiliated with gamer identity. We discuss these response patterns with respect to how emotions may shape intergroup interaction in online communication. (PsycInfo Database Record (c) 2023 APA, all rights reserved
Method of selecting an optimal propagated base signal using artificial neural networks
No full textA system and method of propagating signal links by using artificial neural networks using a relay link selection protocol to predict an optimal link or path, providing a reliable mechanism to meet 5G-new radio requirements. The artificial neural networks used in the method classify training and testing datasets into sufficient signal strengths and insufficient signal strengths, such that paths are evaluated for predicted propagation links, and such that the strongest propagation link can be selected. Specifically, a multilayer perceptron method is used to identify and characterize new link candidates using the path loss parameter or the received signal strength, such that optimal links can be selected and updated. To determine the sufficiency of a signal, a threshold energy strength is determined (for example, a threshold of −120 dBm can be used; any energy strength below the threshold is considered a poor propagation and is classified as an insufficient signal)
Electrochemical cells for harvesting and storing energy and devices including the same
No full textDescribed herein are electrochemical cells that include composite gel positioned between the first electrode and second electrode, wherein the composite gel comprises an electrolyte, a polyaryl amine, and an oxidant. The composite gels described herein are easy to produce at a low-cost, which makes them suitable in a number of different applications electrochromic devices, supercapacitors, solar cells, and hybrid photoactive supercapacitors
Mitigating adversarial attacks on medical imaging understanding systems
No full textThe present disclosure describes a multi-initialization ensemble-based defense strategy against an adversarial attack. In one embodiment, an exemplary method includes training a plurality of conventional neural networks (CNNs) with a training set of images, wherein the images include original images and images modified by an adversarial attack; after training of the plurality of conventional neural networks, providing an input image to the plurality of conventional neural networks, wherein the input image has been modified by an adversarial attack; receiving a probability output for the input image from each of the plurality of conventional neural networks; producing an ensemble probability output for the input image by combining the probability outputs from each of the plurality of conventional neural networks; and labeling the input image as belonging to one of the one or more categories based on the ensemble probability output
Exogenous ketone supplements for reducing anxiety-related behavior
No full textMethods of treating anxiety disorders or reducing anxiety-related behaviors. The methods include administering a therapeutically effective amount of ketone supplementation, such as butanediol, ketone esters (e.g., 1,3-butanediol-acetoacetate diester) and/or ketone salts (e.g., beta-hydroxybutyrate-mineral salt), chronically, sub-chronically, or acutely, with or without admixture with a medium chain triglyceride or in combination. It was determined herein that ketone supplementation reduced anxiety in rats on elevated plus maze as measured by less entries to closed arms, more time spent in open arms, more distance travelled in open arms, and delayed latency to entrance to closed arms, when compared to control. Along with reducing anxiety-related behavior, the chronic, sub-chronic, and acute ketone supplements also caused significant elevation of blood βHB levels and changed blood glucose levels
An in-depth analysis of the impact of cyberattacks on the profitability of commercial banks in the United States
No full textThis study examined the effects of cyberattacks on the profitability of U.S. public and private commercial banks using a sample of 120 data breaches across various institutions. The results showed that cyberattacks negatively influence bank profitability, with effects more robust in the 12 quarters following a breach, especially from non-hack breaches. Large and private banks suffer more than small and public banks, with breaches resulting in decreased deposits and loans and increased liquidity. These changes are confirmed as independent channels reducing bank profitability. The results were robust after controlling for factors like multicollinearity, non-stationarity, cross-sectional dependence, and heteroskedasticity