203435 research outputs found
Sort by
Service Dog Knowledge and Utilization in Occupational Therapy Practice
Get PDFThe assumed efficacy behind service dog intervention is based on the well-known potential for the development of a strong, emotional bond between humans and dogs. Occupational therapists can use knowledge of their specific scope of practice to aid in the referral process, and the assessment process, and to help assist clients with training and comfortability throughout the service dog placement process. Existing literature on service dog use and implementation within occupational therapy (OT) practice is limited and lacking in rigor, internal and external validity, and reliability. The aims of the current study include evaluating the level of education occupational therapy (OT) students and practitioners have received regarding service dog referrals, certifications, and the laws and requirements for possessing a service dog. Student researchers aimed to gain insight into current perceptions on the utilization of service dogs for therapeutic intervention in OT. A 31-question survey was developed and disseminated to current students in OT programs and licensed OT practitioners. Results from 109 participants indicated a significant lack of education and exposure to service dog utilization and implementation in OT practice within the studied geographic region of West Virginia (WV), Pennsylvania (PA), Kentucky (KY), Tennessee (TN), Ohio (OH), Virginia (VA), Maryland (MD), and North Carolina (NC). Based on the data obtained from the survey participants, the decreased exposure to service dog education in OT curriculums is attributed to the lack of competence and confidence in utilizing service dogs in current practice. As a result, the therapeutic potential of the human-dog dyad has not been fully capitalized within occupational therapy practice
Becoming a Homemaker in Appalachia: Building Bridges in Women\u27s Flat Track Roller Derby
Get PDFThis study sought to explore how six women in Central Appalachia negotiated embodied subjectivity and agency across discourses and gendered power relations. Women living in central Appalachia who play roller derby occupy a unique social position in which to position this study, as many are simultaneously challenging traditional gender norms and engaging in a highly physical and competitive sport that involves using their bodies relationally. Specifically, by conceiving women as vehicles of power and women’s flat track roller derby as a public pedagogy, I sought to trace what ‘doing’ a roller derby identity does to transform and advance their knowledge about themselves. Tracing their affective encounters across ‘growing up girl’ and participation in roller derby, this narrative inquiry highlights how dialogic learning in informal spaces of learning are productive for women’s development of embodied agency. I employed a theoretical frame based in the relational ontology of dialogic learning by bringing together the works of Mikhail Bakhtin, Michel Foucault and Judith Butler to examine narratives within a region where economic conditions lead ‘the most educated’ to leave for better opportunities. While negative stereotypes of people within the region persist, these educated, capable, and strong Appalachian women have settled on ‘loving’ instead of ‘leaving,’ carving out their own space to challenge power relations. Their cultural production of community in roller derby reimagines what ‘homemaking’ means in a region where a gendered division of labor persists, maintaining motherhood and becoming a wife as the most ‘natural’ and available option to many. Their stories surface how roller derby can be a transformative social practice for women to learn and (un)learn relationships with other women and with their bodies as tools outside normative gendered ideologies
Autonomous Object Search Planning in Large-Scale Environments
Get PDFThe advancement of autonomous search holds significant promise for applications ranging from emergency response to planetary exploration. This thesis investigates strategies to enhance autonomous search performance in large-scale environments. The main contribution of this work is its practical application in real-world scenarios, where efficient search methods are essential for managing vast amounts of data, particularly in large environments. Effective search planning requires navigating complexities such as limited prior information and managing large state spaces, necessitating advanced strategies to plan with this limited information. Additionally, balancing exploration and exploitation is crucial for optimizing the search process, as it ensures thorough coverage of the search area while efficiently utilizing resources. These challenges highlight the need for innovative approaches as traditional methods often fall short in extensive environments. State-of-the-art tools such as Monte Carlo Tree Search (MCTS) play a crucial role in advancing autonomous search technologies. MCTS is effective at handling large, complex state spaces and balancing exploration and exploitation, making it well-suited for scenarios with vast search spaces and limited prior information. However, MCTS can be computationally expensive and limited in its planning horizon, leading to potential suboptimal decisions in long-term scenarios. To address these challenges, this thesis introduces a novel approach that formulates the search problem as a belief Markov decision process with options (BMDP-O). This formulation integrates sequences of actions to navigate between regions of interest (ROIs), enabling efficient scaling to large environments. This allows for more efficient planning by breaking down the search into manageable segments and focusing resources on the most promising regions. The proposed method proves particularly efficient in environments with multiple ROIs. A robot can search within an ROI and transition to nearby ROIs without extensive searching in low-probability areas. This strategy ensures a balanced and efficient search in extensive environments. Results demonstrate that this method finds objects faster in large environments compared to traditional MCTS while still maintaining computational efficiency
Planar Semiconductor-Superconductor Topological Quantum Devices: Realistic Modeling Tools
Get PDFPlanar semiconductor-superconductor (SM-SC) heterostructures hold significant promise for realizing topological superconductivity and hosting Majorana zero modes (MZMs) --- particle-hole symmetric quasiparticle excitations that emerge as localized zero-energy modes either at the edges of one-dimensional topological SCs or at the vortex cores of two-dimensional topological SCs. Due to their non-Abelian exchange statistics and topological protection, MZMs are prime candidates for constructing topological qubits and achieving fault-tolerant topological quantum computation. Despite notable progress, significant experimental and engineering challenges remain to realizing functional MZM-based qubits. One major challenge is the small topological gap achievable in these systems, rendering MZMs fragile against disorder. Additionally, inhomogeneity can induce low-energy states that mimic MZM signatures, complicating the interpretation of experimental observations. To address these challenges, it is crucial to identify optimal material combinations, optimize structural designs, and thoroughly characterize disorder effects through comprehensive device modeling.
In this work, we develop state-of-the-art modeling tools to investigate the emergence of topological superconductivity and the stability of MZMs in planar SM-SC structures. First, to address the problem of the small topological gap, we propose a spatially modulated Josephson junction (JJ) structure and we characterize in detail its low-energy physics by numerically solving an effective model that explicitly incorporates proximity effects and geometric features. We demonstrate that the modulated JJ structure exhibits an enhanced topological gap, as compared to standard JJ structures, and we identify the optimal parameter regime for operating the device. We show that for a given set of heterostructure parameters, the modulated device can be tuned to an optimal regime via junction gate potential adjustments. Second, to examine the impact of disorder on the stability of MZMs in SM-SC planar devices, we construct an effective model based on a microscopic description of various types of disorder and develop an efficient numerical scheme based on a recursive Green’s function approach. We consider two types of disorder: randomly distributed charge impurities in the SM component and surface roughness characterizing the superconducting film. Our results demonstrate that JJ structures exhibit greater resilience to disorder as compared to nanowire structures, given similar geometrical parameters, impurity density, and disorder strength. Third, we consider a planar SM-SC system that represents a crossover between nanowires and JJs, tunable using electrostatic gate potentials, and we identify the corresponding phase diagrams and optimal topological gap regimes. The insights generated by our modeling are crucial for correctly interpreting the experimental results and provide practical guidance for advancing the development of robust topological qubits that could eventually enable the realization of a quantum computer
On Confidence and Sense of Belonging in Cybersecurity Students: Analysis & Prediction
Get PDFIn recent years, there has been a rapid expansion of cybersecurity programs across higher education institutions in response to the widening skills gap in the cybersecurity job market. This study adopts quantitative and qualitative approaches to identify factors influencing West Virginia University (WVU)’s LANE Department of Computer Science and Electrical Engineering (LCSEE) students’ confidence and sense of belonging in the cybersecurity field. The results are based on data collected from surveys administered to LCSEE students in April 2022 and April 2023. The responses were analyzed using descriptive & inferential statistics and logistic regression techniques. Additionally, the 2023 data was utilized for predictive modeling using machine learning.
The results indicate that traditional demographic factors such as minority status have a lesser impact on student confidence and sense of belonging compared to financial and familial background factors. Specifically, variables such as caregiver educational attainment significantly influenced student confidence and sense of belonging, which can subsequently affect retention rates in academic programs. Despite small sample sizes, these findings suggest that to effectively recruit and retain students in undergraduate cybersecurity programs similar to those at WVU, institutions should not only promote Diversity, Equity, and Inclusion (DEI) initiatives but also ensure that students are provided with resources and co-curricular activities that support the completion of their degrees. This comprehensive support is crucial for equipping students to succeed academically and contribute to addressing the cybersecurity workforce shortage
Translational Approaches to Address the Metastatic/Resistant (MR) Phenotype of Non-Small Cell Lung Cancer Brain Metastases
No full textLung cancer remains the most frequently diagnosed malignant neoplasm shared in men and women worldwide, second only to the sexually dimorphic prostate cancer in men and breast cancer in women. Overall, lung cancer was responsible for 12.5% of new cancer cases globally in the year 2022. Of these cases, 80-85% are non-small cell lung cancer (NSCLC), a highly genotypically diverse sub-type of lung cancer. Over the past three decades, extraordinary improvement in screening, smoking cessation, early diagnosis and targeted therapy have been made, improving NSCLC survival markedly. However, as patients are surviving their primary disease better, there is a temporal relationship with increased incidence of morbidity and mortality of secondary sequelae stemming from NSCLC. One major contributor to this burden is lung cancer brain metastases (LCBM) of NSCLC. The magnitude of this problem is made evident by the fact that most common brain tumor in adults is not attributable to a primary brain malignancy, but rather metastases of lung cancer. Historically, nearly 50% of NSCLC patients acquire LCBM at some point during the natural history of their disease and up to two thirds will have multifocal CNS disease. LCBM from NSCLC portends poorer prognoses a median overall survival of only 12 months following initial diagnosis. This poor survival is largely perpetuated by primary and acquired therapy resistance. Considering the current landscape of care, new therapies and treatment paradigms should be developed to improve the survival and function of patients suffering from LCBM. Such strategies include identification of new targets, combination therapies with synergistic agents, and development of therapies based on unique tumor dynamic and kinetic characteristics. Beyond the classical barriers to brain tumor treatment, one significant hurdle in the implementation of these strategies is the evolution of the NSCLC lesion as it is treated and disseminates. Considerations must be made for: 1) the tumor’s new environment, 2) the acquisition of therapy resistance, and 3) the internal cellular environment that allows NSCLC to thrive in its new environment. Previously regarded as discrete clinical entities, resistance and metastasis may not be as exclusive to one another as once suggested. In fact, for LCBM there are many overlaps in the co-permissive factors that constellate into metastatic/resistant (MR) phenotype which fosters growth and development within neural tissue. Examining these changes, both in the transcriptome and the proteome, of these lesions have revealed targetable nexi that relieve the cells of the MR phenotype. This dissertation aims to explore these commonalities and leverage the exploitation of these pathways to help improve survival in LCBM patients by addressing both metastasis and resistance
The identification and characterization of novel and improved antigens for next-generation pertussis vaccines
Get PDFWhooping cough, or pertussis, is a highly infectious respiratory disease caused by infection with the Gram-negative bacterial pathogen Bordetella pertussis. Pertussis affects all age groups and populations; however, it is most severe in infants who are too young to receive vaccinations and remains a global health concern with more than 200,000 cases reported globally in 2023. Vaccines against pertussis have been available in the United States since the 1940’s with the development and widespread use of whole cell pertussis vaccines. Following their introduction, the incidence of pertussis was controlled and limited to fewer than 5,000 cases per year. Despite their demonstrated efficacy, whole cell pertussis vaccines were highly immunogenic and variable in nature and often led to worrisome, severe side effects. Due to this, whole cell vaccines were replaced with lesser reactogenic acellular pertussis vaccines in the 1990’s, which are still in use to this day in most industrialized countries. After the introduction of acellular pertussis vaccines, there has been an increase in reported pertussis cases and several noteworthy pertussis outbreaks, such as the U.S. outbreak in 2012 which resulted in over 50,000 reported cases and 20 deaths, and the 2024 outbreak in the Czech Republic which experienced its highest pertussis incidence since the 1960s. This reemergence of pertussis calls for the development of next-generation pertussis vaccines which address and overcome the pitfalls of current acellular vaccines. There are many hypotheses for why acellular pertussis vaccines have triggered this resurgence, including bacterial evolution in response to selective pressure on acellular vaccine antigens, increased rates of reporting, as well as the short-lived duration of immunity that follows acellular vaccination. The overall objective of the worked covered in this thesis is to address an additional pitfall of acellular pertussis vaccines, that being their limited breadth of antigen coverage and lack thereof of functional immune responses that can promote bacterial clearance. In these studies, we employed a murine model of vaccination and B. pertussis challenge in which novel and redesigned pertussis antigens were assessed for their capability to invoke robust humoral immune responses and subsequently protect against respiratory infection of B. pertussis. At the onset of this work, we identified several immunogenic proteins contained in whole cell vaccines that were immunogenic in mice when formulated as peptide-based conjugate vaccines. Although immunogenic, these peptides were insufficient to elicit protection against B. pertussis in mice. Next, we characterized the immunogenicity and protective efficacy of several peptide-based vaccines derived from iron acquisition surface receptors expressed by B. pertussis that were previously found to be upregulated upon infection. Similarly, these vaccines were immunogenic, however they failed to confer protection against B. pertussis in mice either alone or in combination with a subprotective dose of DTaP. Finally, instead of characterizing novel antigens, we sought to redesign an antigen in acellular vaccines known as filamentous hemagglutinin. Toward this goal, we designed a truncated antigen based on the mature C-terminal domain of filamentous hemagglutinin that was fused to virus-like particles. This vaccine was demonstrated to be highly immunogenic and conferred significant protection against B. pertussis when evaluated against a B. pertussis strain lacking pertussis toxin. Overall, this work identified several immunogenic antigens, either novel or redesigned, that have promise as protective antigens against B. pertussis that could be included in the next generation of pertussis vaccines. Additionally, this work sheds light on the complexity of characterizing vaccine antigens against B. pertussis in preclinical model and lays the groundwork for future exploration of other antigens and vaccine platforms
On Uncertainty for Ill-Posed Robot Decision Problems
Get PDFAs robots adopt more real world responsibilities, they will be expected to solve more complicated problems. In some cases limited prior knowledge will result in unmodelled environmental conditions; in others, multiple users may have competing perspectives on how to frame a decision problem. Many existing frameworks, namely Markov decision processes (MDP) presuppose users have identified a specific problem with models sufficient to solve or learn a problem. If we wish to extend MDPs to novel problems or those heavily dependent on user feedback, autonomous decision makers must be able to identify limitations in how a given problem is framed and use this to produce better representations.
Central to the framing of a decision problem, and knowledge more broadly, is uncertainty. Unfortunately, prominent concepts of uncertainty preclude decision makers from considering alternative problem formulations. This is due to their conceptual emphasis on the “aleatoric/epistemic divide”—loosely organized around whether or not randomness inherent in the environment. Despite its widespread use in fields from robotics to healthcare to public policy and economics, such a distinction is a rather fluid boundary and has led to conflicting terminology. Instead, this work proposes to frame uncertainties with respect to a decision maker’s subjective understanding to better frame their knowledge of a problem. This, in turn, motivates the need for decision makers to account for equally valid alternatives (ambiguity) and identify the presence of unmodelled behaviors (ignorance). This work focuses on the prior though both of have gone understudied.
These conceptual breakthroughs are used to develop new decision making algorithms. Early work integrates ambiguous representations of uncertainty into the learning process. This lets the agent solve for a set of policies at once. Based on user preference, the agent selects how many unmodelled risks it is willing to take on. Results from a sailing environment show an agent can mitigate unmodelled risks while reaching its goal effectively. Later work introduces a formulation for multiple model MDPs (MM-MDP) for representing ill-posed decision problems. This MM-MDP allows for models to vary in state space, action space, transition models, and rewards. Thus, users with different objectives and knowledge about systems can frame problems at different scopes within a common framework. Using a foraging case study, an algorithm is introduced to balance the use of the supplied models. This case study considers scenarios isolating specific categories of MDPs. Thus, users with different objectives and knowledge about systems can frame problems at different scopes within a common framework
Comparing Resurgence Following Positive and Negative Reinforcement Using a Human-Operant Approach
Get PDFResurgence is a type of relapse that consists of the recurrence of a previously eliminated response following worsening reinforcement conditions for an alternative response. Resurgence can occur following a history of positive or negative reinforcement; however, no previous evaluations have directly compared resurgence following these processes. In the present set of experiments, college students responded on a computer program to earn points (positive reinforcement) and to avoid losing points (negative reinforcement). Experiment 1 evaluated resurgence when both target and alternative responses were maintained by the same reinforcer class (i.e., in the positive-reinforcement component the target and alternative responses were positively reinforcement, and in the negative-reinforcement component, the target and alternative responses were negatively reinforced). Experiment 2 isolated the impact of target response reinforcement history on resurgence (i.e., the alternative response was positively reinforced across both the positive- and negative-reinforcement components). Across both experiments, resurgence (i.e., defined as an increase in responding relative to control responding) occurred for two of nine participants in the positive-reinforcement component and no participants in the negative-reinforcement component. Conclusions about differences between positive and negative reinforcement on resurgence may be limited by the arrangement of the present experiment
Inherently Porous Metal Structures with Sufficient Mechanical Properties Through Direct Writing of Bio-based Inks
No full textThere is currently a burgeoning interest in developing 3D printed metal structures with inherent porosity and sufficient mechanical properties for mass customization in the biomedical field. Current approaches for 3D printing of biocompatible metals rely on powder-bed fusion methods which are energy intensive and utilize large amounts of metal powder that is challenging to recycle. In addition, such methods aim on producing dense parts and porosity generation, within the printed part, is challenging due to the complex physics of the melting pool. Here, a relatively new and promising approach is undertaken to study the 3D printing of inherently porous structures with appropriate mechanical properties towards biomedical applications. In particular, the direct ink writing (DIW) method is employed as a means to induce porosity within the printed parts by formulating, printing, and post-processing model ‘bio-inks’. During DIW a yield stress fluid (i.e., ink) is robotically extruded on digitally predefined substrate locations to build parts in a layer-by-layer fashion. Advantages of DIW include almost no material waste, ease of fabrication, and ink formulation using disparate starting precursors. Ink design and formulation studies using biopolymer binders such as xanthan gum and cellulose along with a stainless-steel hard phase led to formulation of controllably extrudable viscoelastic fluids for a range of resolutions. Ink characteristics such as yield stress, flow transition index - determining ink ductility/brittleness - are elucidated and related to printability while the limitations of current rheological models for non-Newtonian inks are highlighted. Furthermore, post-processing studies are conducted to establish relationships between different bio binder compositions (and their binary concentrations) and the hard phase, induced porosity, and resulting mechanical properties. Taking advantage of different binder conformations, the induced porosity and mechanical behavior of 3D printed and sintered parts are found to exhibit new synergies. Macro porosities and mechanical properties within a range required for potential biomedical parts are achieved and demonstrated