Electronic Thesis or Dissertation

Freshwater salinization and expanding desertification threaten global agriculture. Promise lies in salt resistance genes found in Salicornia europaea, a halophyte that thrives in high-salt conditions partly due to protein action. We focused one of its genes, SeNN24. It enhanced salt resistance in yeast and shows promise in improving crop resilience. Our research introduced SeNN24 into tobacco via agrobacterial transformation, testing the plants under salt and drought conditions. The transformed tobacco showed superior tolerance of up to 400mM NaCl and drought, maintaining health and even flowering under stress. This suggests that SeNN24 could potentially confer significant salt and drought resistance to vital crops, protecting them from environmental challenges and enhancing agricultural sustainability.

Friendships convey developmental advantages. Adolescents without friends suffer from a host of difficulties. Much more is known about which friendships are likely to be stable over time, than about maternal contributions to friendship stability. To this end, the current study examines characteristics of mother-child relationship quality (i.e., child reported social support, negativity and relationship importance) and maternal parenting practices (i.e., child-reported behavioral control and psychological control) that predict the dissolution of children’s friendships in a sample of primary school (ages 10 to 11) and middle school (ages 11 to 14) students attending seven public schools in Lithuania. A total of 574 participants (290 female, 284 male) completed identical surveys at six time points across two consecutive school years. Peer nominations provided an index of peer status (i.e., acceptance or liking and rejection or disliking), which were also included as predictors in order to control the contribution of peer status. Friendships were defined as dyads in which both partners nominated each other as friends. Dissolved Friendships were defined as dyads that were reciprocated at Time 1 but one or both partners failed to nominate the other as a friend as a subsequent time point.
Discrete time survival analyses were conducted to predict friendship dissolution from maternal parenting practices variables, mother-child relationship quality variables, peer status variables, and demographic variables (sex, dyad sex, nutrition, household structure, relationship rank). Two sets of analyses were conducted. The individual model explored the degree to which individual scores on each variable predicted friendship dissolution. The dyadic model the degree to which dyadic differences (i.e., the absolute difference between friend scores) on each variable predicted friendship dissolution.

This thesis presents the development of an innovative Geographic Information System (GIS)-based Interactive Online Watershed Dashboard aimed at flood risk assessment and mitigation in Charlotte County, Florida. The research leverages advanced GIS techniques, including flood inundation simulations using CASCADE 2001, integrating LiDAR DEM data and GIS layers such as impervious surfaces, waterbodies, and soil characteristics to model flood behavior in 61 inundation probability scenarios. Key results include detailed flood inundation probability maps categorizing risk levels based on Z-scores, providing actionable insights for flood risk management and emergency planning. Spatial analysis reveals demographic vulnerabilities, with population density and ethnic compositions intersecting flood vulnerability. The study assesses flood impacts on transportation infrastructure and prioritizes critical facilities for resilience strategies. The dashboard's design integrates diverse datasets and analytical results, allowing users to interactively explore flood risk scenarios, critical infrastructure vulnerabilities, and demographic impacts. This research contributes essential tools for informed decision-making, enhancing flood resilience and disaster preparedness in Charlotte County, Florida.

Connectivity and automation have expanded with the development of autonomous vehicle technology. One of several automotive serial protocols that can be used in a wide range of vehicles is the controller area network (CAN). The growing functionality and connectivity of modern vehicles make them more vulnerable to cyberattacks aimed at vehicular networks. The CAN bus protocol is vulnerable to numerous attacks as it lacks security mechanisms by design. It is crucial to design intrusion detection systems (IDS) with high accuracy to detect attacks on the CAN bus. In this dissertation, to address all these concerns, we design an effective machine learning-based IDS scheme for binary classification that utilizes eight supervised ML algorithms, along with ensemble classifiers, to detect normal and abnormal activities in the CAN bus. Moreover, we design an effective ensemble learning-based IDS scheme for detecting and classifying DoS, fuzzing, replay, and spoofing attacks. These are common CAN bus attacks that can threaten the safety of a vehicle’s driver, passengers, and pedestrians. For this purpose, we utilize supervised machine learning in combination with ensemble methods. Ensemble learning aims to achieve better classification results through the use of different classifiers that are combined into a single classifier. Furthermore, in the pursuit of real-time attack detection and classification, we use the Kappa architecture for efficient data processing, enhancing the IDS’s accuracy and effectiveness. We build this system using the most recent CAN intrusion dataset provided by the IEEE DataPort. We carried out the performance evaluation of the proposed system in terms of accuracy, precision, recall, F1-score, and area under curve receiver operator characteristic (ROC-AUC). For the binary classification, the ensemble classifiers outperformed the individual supervised ML classifiers and improved the effectiveness of the classifier. For detecting and classifying CAN bus attacks, the ensemble learning methods resulted in a robust and accurate multiclassification IDS for common CAN bus attacks. The stacking ensemble method outperformed other recently proposed methods, achieving the highest performance. For the real-time attack detection and classification, the ensemble methods significantly enhance the accuracy the real-time CAN bus attack detection and classification. By combining the strengths of multiple models, the stacking ensemble technique outperformed individual supervised models and other ensembles.

The worldwide threat of Diabetes Mellitus (DM) has been increasing rapidly, and is now an estimated 463 million, of which 55 million people originate from Middle East and Nort African (“MENA”) region (international Diabetes Federation [“IDF’], 2020). In Saudi Arabia, the prevalence of diabetes has roughly reached a ten-fold increase in the past three decades, placing Saudi Arabia’s incidence as one of the highest globally (Almubark et al., 2022). The purpose of the study was to examine the relationship between health and diabetes self-management among Saudi adults with Type 2 Diabetes Mellitus (T2DM). The study further aimed to explore how Saudi adult with T2DM seek and utilize diabetes knowledge to self-manage their diabetes. The study was guided by Leininger’s Culture Care Diversity and Universality Theory (2002). Leininger’s Sunrise Enabler- Model provided a framework to explore the various factors that affect diabetes self-management through a cultural lens. This model provides a comprehensive understanding pf multiple factors influencing diabetes self-management.
A sample of 66 Saudi adults with T2DM aged 40-61 and older was recruited from diabetes center and Primary Healthcare Center (PHCC) at National Guard Hospital King Abdulaziz Medical City Jeddah, Saudi Arabia. A Parallel Mixed Method (PPM) design was applied, using semi-structured interviews, Diabetes Self-management Questionnaire (DSMQ), Short Test of Functional Health Literacy in Adult (S-TOFHLA), and sociodemographic surveys.

The Internet of Things (IoT) refers to a network of interconnected nodes constantly engaged in communication, data exchange, and the utilization of various network protocols. Previous research has demonstrated that IoT devices are highly susceptible to cyber-attacks, posing a significant threat to data security. This vulnerability is primarily attributed to their susceptibility to exploitation and their resource constraints. To counter these threats, Intrusion Detection Systems (IDS) are employed. This study aims to contribute to the field by enhancing IDS detection efficiency through the integration of Ensemble Learning (EL) methods with traditional Machine Learning (ML) and deep learning (DL) models. To bolster IDS performance, we initially utilize a binary ML classification approach to classify IoT network traffic as either normal or abnormal, employing EL methods such as Stacking and Voting. Once this binary ML model exhibits high detection rates, we extend our approach by incorporating a ML multi-class framework to classify attack types. This further enhances IDS performance by implementing the same Ensemble Learning methods. Additionally, for further enhancement and evaluation of the intrusion detection system, we employ DL methods, leveraging deep learning techniques, ensemble feature selections, and ensemble methods. Our DL approach is designed to classify IoT network traffic. This comprehensive approach encompasses various supervised ML, and DL algorithms with ensemble methods. The proposed models are trained on TON-IoT network traffic datasets. The ensemble approaches are evaluated using a comprehensive metrics and compared for their effectiveness in addressing this classification tasks. The ensemble classifiers achieved higher accuracy rates compared to individual models, a result attributed to the diversity of learning mechanisms and strengths harnessed through ensemble learning. By combining these strategies, we successfully improved prediction accuracy while minimizing classification errors. The outcomes of these methodologies underscore their potential to significantly enhance the effectiveness of the Intrusion Detection System.

Cerebrospinal fluid (CSF) has a role, in keeping the brain and spinal cord safe and nourished within the nervous system (CNS). This clear and colorless fluid is produced in the ventricles of the brain. Surrounds these structures acting as a protective cushion. CSF plays a role in maintaining nervous system health and ensuring optimal functioning. CSF accomplishes four objectives.
Protection: The brain and spinal cord are shielded from harm due to CSFs natural shock absorbing properties. This effectively safeguards these structures, from injuries caused by impacts or collisions.
Nutrition It ensures a favorable environment for neural cells to perform at their peak by supplying essential nutrients and removing waste products from the brain and spinal cord.

Capturing pedestrian mobility patterns with high fidelity provides a foundation for data-driven decision-making in support of city planning, emergency response, and more. Due to scalability requirements and the sensitive nature of studying pedestrian movements in public spaces, the methods involved must be passive, low-cost, and privacy-centric. Pedestrian localization based on Received Signal Strength Indicator (RSSI) measurements from Wi-Fi probe requests is a promising approach. Probe requests are spontaneously emitted by Wi-Fi-enabled devices, are readily captured by of-the-shelf components, and offer the potential for anonymous RSSI measurement. Given the ubiquity of Wi-Fi-enabled devices carried by pedestrians (e.g., smartphones), RSSI-based passive localization in outdoor environments holds promise for mobility monitoring at scale. To this end, we developed the Mobility Intelligence System (MobIntel), comprising inexpensive sensor hardware to collect RSSI data, a cloud backend for data collection and storage, and web-based visualization tools. The system is deployed along Clematis Street in the heart of downtown West Palm Beach, FL, and over the past three years, over 50 sensors have been installed.
Our research first confirms that RSSI-based passive localization is feasible in a controlled outdoor environment (i.e., no obstructions and little signal interference), achieving ≤ 4 m localization error in more than 90% of the cases. When significant time-varying signal fluctuations are introduced as a result of long-term deployment, performance can be maintained with an overhaul of the problem formulation and an updated localization model. However, when the outdoor environment is fully uncontrolled (e.g., along Clematis Street), the performance decreases to ≤ 4 m error in fewer than 70% of the cases. However, the drop in performance may be addressed through improved sensor maintenance, additional data collection, and appropriate domain knowledge.

“Death Conjunct Living” is a collection of flash essays that explores the interconnectedness between life and death—births, miscarriages, childhoods, funerals— as well as the term “empty stomach.” How a stomach can be empty of child or empty of food; how it can indicate a birth, a miscarriage, or an eating disorder. “Death Conjunct Living” is an exploration of the flash medium and how micro nonfiction can tackle macro themes.

The present study investigates therapist factors (such as conversation analysis, affectual interactions, and therapist facilitative skills) on client premature termination and the therapeutic relationship. The interactions of clients and therapists in a total sample of 76 psychotherapy sessions were analyzed using Hills Skills System (2017), Gottman, Woodin, and Coan’s (1998) Specific Affect Coding System, and scales of the Working Alliance Inventory (WAI) and Real Relationship Inventory (RRI). Coded data were analyzed using Kruskal-Wallis and Mann-Whitney U tests which found significant differences between clients who dropout and the types of questions being asked in session (HSS). There were also significant differences between clients who dropout and the therapist and client SPAFF scores, SPAFF and HSS scores on the WAI and RRI, as well as the quality of questions being asked (HSS) over time (from initial session to fourth session). Coded data for differences between clients who dropout and the therapist and client assessment of the quality of the working alliance and real relationship were assessed using Kruskal-Wallis and Mann-Whitney U tests and found no significant differences. Analysis of the results support the presence of therapist factors on the therapeutic relationship and client premature termination. These findings can also be added to the literature regarding the outcomes of the therapeutic relationship on client premature termination. The implications for psychotherapy practice, education, and research are discussed.