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Cyber resiliency framework and mechanisms for software defined tactical networks
No full textTraditional tactical networks fail to achieve cyber resiliency for many reasons, but the most prevalent causes include flat designs, the absence of cyber detection capabilities at the lowest level, and immutable resource allocations after instantiation. These design choices allow network threats direct visibility to each device on the network, and the lack of detection allows infections to proliferate. Furthermore, tactical battlefield networks are difficult to secure because of the lack of persistent oversight by an intelligent agent that can exercise control over the network\u27s topology or resources in real-time. However, advances in software-defined networking (SDN) provide an opportunity to address many of these shortcomings through the use of intelligent and automated network slicing (NS), network function virtualization (NFV), and dynamic network resource control via orchestration. Recent developments in SDN control plane capabilities allow for the deployment of network slices, which are logically segregated virtual networks that share a common infrastructure, while simultaneously guaranteeing quality service (QoS) and resource control in each slice. When NS is coupled with intent based programming, the potential exists to orchestrate the creation of elastic network slices so that a network topology can be changed on-the-fly by an orchestrator to secure the enclave. Intents are an SDN abstraction, and allow for an intelligent orchestrator (or human) to avoid programming network behavior in the traditional sense at the command line, and instead issue only intents so that the network controller can create the necessary conditions required for the intent to be realized. These intents can range from adding communication paths for new hosts, to completely reshaping the network based upon some new stimuli. Similarly, network function virtualization allows for services such as firewalls, intrusion detection systems, and many more to be virtualized in different parts of the network and called into action only when needed. Their use can lower the computational, storage, and network costs when compared to traditional hardware-based services. Software-defined networking also presents the opportunity to dynamically manage resources in network slices, so that network service delivery can be adjusted in response to increases or changes in network resource demand. With these exciting SDN concepts in mind, we seize upon the opportunity to answer the following questions in this work: 1) Can elastic and reconfigurable SDN slices together with dynamic NFV increase cyber resilience when threats penetrate the network? 2) What is the overhead cost associated with SDN slicing and NVF deployment in a small platoon-sized tactical network? and 3) How can SDN slicing and NFV be applied using network and battlefield intelligence to reconfigure the network to support a commander\u27s mission? In this dissertation we describe how all of these capabilities (SDN, NS, VNF) can be combined in an automated and intelligent framework to increase network resiliency by identifying network threats in real-time, reshaping the network to respond, mitigate, or anticipate threat effects, recover the network into a secure state, and reallocate resources in support of mission requirements on-the-fly. Furthermore, this dissertation will address the network overhead costs associated with such a technique by measuring the overhead costs at multiple levels. Validation results will illustrate how the framework can be implemented with currently available software, and data will show the effectiveness of the framework at providing cyber defense, attack mitigation, and mission-based resource reallocation
The Depths of Horror: Exploring the Manifestation of Fear in the Natural World
No full textThe horror genre is uniquely powerful in that it is one of the only hands of fiction with the ability to reach off the page and follow you. The feeling of fear originally formed in the confines of a scary story can lie dormant in the reader after the story has finished and be resurfaced later by a related trigger. On a late night after work walking through a deserted car garage, home alone in the middle of a storm, or stumbling into an abandoned hospital, the reader is imbued with an ability to detect a threat that was previously undisclosed. Horror as a written artform acts as a mirror and a microscope, investigating the roots of what the reader is afraid of and allowing them to fully process that emotion. The traditions established within the horror genre have become typified as a result of differentiating the intended impact of horror, especially the body versus the mind. The impact of my poetics is empowered by the rich lineage of horror content tempering the reader\u27s expectations, beginning with the 18th century Gothic horror movement. My contribution to the increasingly diverse and segmented horror landscape is a collection of short stories exploring the emotional depth of fear by invoking occult phenomena to engage with the reader\u27s latent sense of everyday horrors
The Feasibility And Impact Of Self-Administered Neuromuscular Electrical Stimulation On Glycemic Control In Hyperglycemia Population- A Pilot Study
No full textIntroduction: Neuromuscular electrical stimulation (NMES) has shown promise in improving glycemic outcomes in supervised, laboratory-based settings. However, its feasibility and effectiveness in real-world, community-based applications remain underexplored, particularly among sedentary, overweight/obese individuals at risk for type 2 diabetes (T2D). This study aims to assess the feasibility and effectiveness of self-administered NMES use on glycemic control and substrate utilization among sedentary, overweight/obese population with hyperglycemia. Methods: Ten participants (N=10 [Males:4; Females:6] Age: 38.9 ± 14.1 years; BMI: 36.0 ± 8.7 kg/m2) sedentary Overweight/obese participants with hyperglycemia were randomized to either an NMES or control group and were trained and instructed to self-administer NMES at home (minimum of 30 minutes/session, 3 sessions/week) for 8 weeks. Additionally, participants underwent an acute session of NMES during week 1 and week 8 when glucose levels, energy expenditure and respiratory exchange ratio (RER) were assessed in a laboratory setting. Feasibility was assessed through adherence to NMES protocol, device-recorded stimulation data, and user satisfaction surveys. Glycemic control was evaluated using continuous glucose monitoring (CGM), oral glucose tolerance test (OGTT), and HbA1c. Results: A single 30-minute NMES session significantly reduced post-stimulation glucose levels. Energy expenditure significantly increased during NMES (p \u3c 0.05), and no significant changes were observed in RER (p \u3e 0.05). Glycemic variability and time above range were significantly lower on the day of stimulation compared to control day (p\u3c0.05). Additionally, 24-hour average glucose level (p= 0.1) and high glucose excursion (p = 0.08) tended to be lower on the day of the stimulation compared to the day without the stimulation. Retention was 100%, with 90% adhering to the prescribed minimum stimulation protocol. Participants reported high levels of satisfaction, comfort, and confidence with using NMES independently. Conclusion: Acute NMES application suggests beneficial effects on glycemic regulation and metabolic demand, supporting its potential as a non-pharmacological strategy for improving glycemic control in population who are sedentary, and overweight/obese. Preliminary data indicates that self-administered NMES is a feasible and well-accepted intervention among individuals with hyperglycemia in a free-living condition
Charging Infrastructure Needs For Electric Trucks in Texas
No full textThis thesis focuses on the design of a methodology for determining the optimal location of charging stations for heavy-duty electric trucks. The work focuses primarily on long-distance trips that require charging the truck battery outside cities, where the range of charging stations is not extensive. In addition to design the algorithm, the thesis also focuses on implementing the algorithm on a specific case - the implementation of charging stations in the I-10 Freeway corridor between El Paso and San Antonio. The output of the work is not only a description of the procedure, but also an application to a real situation. Hopefully, this work will serve as a basis for designing an optimal and efficient charging infrastructure for heavy-duty electric trucks
Leveraging Predictive Analytics To Improve Prognostic Models For Uterine Cancer
No full textUterine Corpus Endometrial Carcinoma (UCEC) presents notable disparities in incidence and outcomes across racial groups, warranting deeper investigation through transcriptomic and predictive modeling approaches. This thesis presents a comprehensive transcriptomic analysis of RNA-Seq data from 177 individuals, comprising both tumor and control samples, specifically from the UCEC_CN_High molecular subtype. The cohort was stratified by race, focusing on differences between Black and White individuals to explore race-associated gene expression patterns. To uncover genes with prognostic significance, LASSO (Least Absolute Shrinkage and Selection Operator) regression was applied for feature selection, identifying a subset of genes most strongly associated with overall survival. These selected genes were then utilized in a Cox Proportional Hazards Model to estimate patient-specific risk scores. The dataset was divided into a training set (70%) for model development and a testing set (30%) for performance evaluation. The integration of transcriptomic profiling with survival analysis enables a biologically informed risk stratification, providing insight into molecular drivers of UCEC and potential race-specific biomarkers. The findings highlight the potential of using survival modeling in cancer genomics to enhance prognostic accuracy and address health disparities. This study contributes to the growing body of research focused on personalized cancer therapy and racial equity in biomedical outcomes
Simultaneous Selection of Inflations and Variables in Multiple Inflations Poisson Model (MIP)
No full textCount data frequently arise in biomedical, economic, and social science research and are often characterized by structural excesses at specific count levels. To accommodate such patterns, Su et al. (2013), among others, introduced the Multiple-Inflation Poisson (MIP) model, which allows for multiple inflated counts within the distribution. However, two critical challenges remain in modeling such data: (i) identifying the true inflation points where excess counts occur, and (ii) selecting the relevant covariates that explain variation in the inflation and count process. This dissertation addresses these issues by advancing the MIP model through a novel methodology that enables the simultaneous selection of inflated count levels and influential predictors. The proposed approach integrates a fused regularization technique with a continuous approximation to the L0-norm, and leverages subtle uprooting penalty Su (2015) to encourage sparsity and enhance model interpretability. Subtle uprooting is a tuning-free penalty, thereby eliminating the need for exhaustive grid searches over tuning parameters and significantly reducing the computational burden common to existing regularized methods. Its formulation as a single-step continuous optimization makes it particularly well-suited for a finite mixture model (FMM) such as the MIP model. Extensive simulation studies confirm the selection consistency and estimation accuracy of the proposed method, demonstrating its ability to recover both the correct inflation structure and associated covariates. The methodology is further applied to a real-world dataset involving doctor visit counts, illustrating its practical efficacy in applied health data analysis. The dissertation concludes by outlining potential extensions to the MIP framework, including its application to longitudinal and spatial count data, and opportunities for integrating post-selection inference and Bayesian techniques
Control Of Industrial Robots Based On Artificial Intelligence
No full textIndustrial robots are vital in developing smart factories, creating the need for more efficient and modern control systems. As a result, investigators and scholars are dedicating great effort to advancing this field et al. [27]. Literature showcases significant progress in various areas, including the control of articulated arms and advancements in human-robot interfaces, self-decision-making, object recognition, decision-making, and routing planning. This manuscript describes a novel technique for predicting the movement of a robotic arm based on artificial neural networks. We have implemented an artificial intelligence method based on artificial neural networks to analyze the possible routing of a robotic arm with the configuration of three degrees of freedom (3-DOF), solving the direct kinematics problem without manually calculating and solving mathematical equations or using large computational centers. By training the artificial intelligence, it can identify and provide the correct solution from a given dataset. The AI was developed in Python using the TensorFlow library but utilizing the Industrial Internet of Things (IIoT), as it uses cloud computing power through Google Colab. The system uses a dataset that collects information from 2,162 angle combinations, which feed and train the artificial neural network. The Case Study features a virtual robot with 3-DOF, with defined dimensions for the base and two arms involving the robot parts. We can also vary the theoretical movement angles of each robot element, i.e., at each joint, thus allowing us to simulate a range of angle combinations and obtain the end-effector\u27s position using the Denavit-Hartenberg methodology. The simulation helped us to train the previously described artificial intelligence, which later will find the end-effector\u27s position without the traditional control method. This research demonstrated how artificial neural networks can help develop control strategies and increase robotics efficiency
Computing Optimal Multi-Level Stress Testing Plans Using A Combined Variable Neighborhood Search Algorithm Under Progressive Type-II Censoring Scheme
No full textIn multi-level stress life tests under Type-II progressive censoring, determining optimal allocation poses significant computational challenges due to the vast solution space. Efficient methods are essential for exploring the admissible censoring schemes effectively. This thesis introduces a novel meta-heuristic algorithm, the Combined Variable Neighborhood Search (CVNS), which computes optimal schemes at different stress levels simultaneously. Unlike methods focusing on marginal stress levels or one-step progressive censoring, this approach leverages a unified framework to ensure enhanced computational efficiency and solution quality. By integrating the components of the design parameters into a cohesive optimization process, the algorithm effectively reduces computational time while providing near-optimal solutions. This CVNS algorithm demonstrates consistency with exhaustive search results for small-scale scenarios. Extensive numerical studies reveal its applicability across diverse stress levels and censoring proportions, offering robust solutions for maximizing the determinant of the Fisher Information Matrix under D-optimality and other relevant criteria. Additionally, the algorithm accommodates constraints on the degree of censoring and sample allocation, making it versatile for practical experimental designs. The proposed method addresses gaps in existing approaches by incorporating general Type-II progressive censoring for optimal multi-level stress tests and expands upon earlier works that were limited to simpler models or smaller scales. This advancement provides a valuable tool for experimenters seeking to optimize life-testing plans under complex conditions
Life Beyond 77510: One Queer\u27s Journey To Understand Self And Set The Record Straight
No full textThis thesis examines the intersections of identity, sexuality, and race relations within the creative nonfiction memoir genre, rooted in the author\u27s personal experiences. Through a first-person narrative voice, it reflects on the author\u27s coming of age as a gay white male in rural, conservative South Texas while navigating the demands of family and the systemic racism faced by Black people, with which he identifies through similarities in his own treatment, as well as with internalized gay shame and loneliness. The memoir\u27s foundation draws on Foucault\u27s theories of surveillance and power to frame the manuscript\u27s exploration of personal memory as both a form of resistance and a reclamation of self. It is structured as a series of short stories, vignettes, and poems that reflect on the key fragmented memories of his past, told nonlinearly about the process of unlearning by discovering who he wanted to be. Music threads throughout the work, sewing together the narrative as a symbol of freedom and self-definition, all on the author\u27s own terms. Influenced by a broad spectrum of writers such as Jeanette Winterson, Karla Cornejo Villavicencio, and Hanif Abdurraqib, who offer their vulnerability alongside their craft and their ability to speak truth in various ways, this project confronts silence and prejudice with direct honesty, vulnerability, and literary craftsmanship
Temporal Partitioning And Coexistence Among Four Sympatric Felid Species In The Northern Jaguar Reserve
No full textCoexistence among sympatric carnivores is shaped by behavioral strategies that mediate competition and mortality risk in heterogeneous landscapes. Understanding how sympatric carnivores, such as felids, use space and time in relation to one another requires models that account for both spatial and temporal overlap across landscapes. Models incorporating time-to-event probabilities provide a powerful framework for detecting behavioral dynamics across shared landscapes. This study evaluated the spatiotemporal responses of two apex predators â?? the jaguar (Panthera onca) and mountain lion (Puma concolor) - and two mesopredators - bobcat (Lynx rufus) and ocelot (Leopardus pardalis) - across five years of camera trap data from the Northern Jaguar Project in Sonora, Mexico. Jaguars in particular have experienced significant range contraction due to habitat loss, and the Northern Jaguar Project seeks to protect the critical breeding populations restricted to remote areas in northern Sonora, Mexico (Blust 2019). I used Piecewise Exponential Additive Mixed Models (PAMMs) to test four hypotheses of time-to-detection of mesopredators following apex predator presence, which included (1) no effect of spatial or temporal avoidance, (2) spatial avoidance, (3) spatial following, or (4) temporal displacement. Models comparing mountain lions to ocelots and bobcats supported no significant spatial avoidance or temporal displacement. In contrast, jaguar-mesopredator models revealed structured temporal dynamics. Ocelot detections peaked 10-15 days after jaguar events indicating temporal displacement while bobcats showed a sustained decline in detection throughout the 30-day interval indicating spatial avoidance. These patterns are consistent with the spatial following hypothesis (H2). Vegetation cover was not a significant predictor in any model, and no statistical evidence supported habitat-based spatial avoidance. These findings suggest that jaguars, but not mountain lions, might be altering mesopredator behavior patterns through temporal behavioral mechanisms. This study highlights the value of time-to-event models for detecting subtle interspecific interactions and advances for understanding how sympatric carnivores partition time to mitigate competition and mortality risk across shared landscapes. Furthermore, understanding how felids such as jaguar, mountain lion, bobcat, and ocelot use space and time in relation to one another can inform conservation strategies that prioritize shared habitat use