Digital Commons Network^™

Clinical Decision Making And Risk Appraisal Using Electronic Risk Assessment Tools For Cancer Diagnosis: A Qualitative Study Of Gp Experiences, Alex Burns, Emily Fletcher, Elizabeth Shephard, Raff Calitri, Mark Tarrant, Adrian Mercer, William Hamilton, Sarah Dean

School of Psychology

Background: Electronic risk assessment tools (eRATs) are intended to improve early primary care cancer diagnosis. eRATs, which interrupt a consultation to suggest a possibility of a cancer diagnosis, could impact clinical appraisal and the experience of the consultation. This study explores this issue using data collected within the context of the Electronic RIsk-assessment for CAncer (ERICA) trial. Aim: To explore views and experiences of GPs who used the ERICA eRATs, how the tools impacted their perception of risk and diagnostic thinking, and how this was communicated to patients. Design & setting: Qualitative interviews with GPs from English general practices undertaking …

From Attention To Action: Advancing Inclusivity In The Landscape Of Anatomy Education, Siobhan M. Moyes, Lily Evans, Katie Snow, Aiden Jayanth

Peninsula Medical School

Conversations about inclusivity in anatomy education are at a critical juncture. While the need for a more equitable discipline is widely recognised, significant challenges remain. As this project aimed to reimagine what a truly inclusive learning experience could like and how it might be best achieved, an inclusive approach to research methodology was essential to ensure robust and representative results. Due to the paucity in anatomical literature on this topic, the authors applied concepts from sociological research methodology and innovative methods of demographic data collection to their own discipline.

The study utilised questionnaires and focus groups to collect diverse data …

Systematic Review Of Paediatric Sudep And Epilepsy-Related Deaths To Inform A Safety Counselling Checklist For Clinical Practice, Rohit Shankar

Peninsula Medical School

BackgroundOne in 200 children - in the UK have epilepsy. Approximately, 1 in 1,000 children with epilepsy die yearly from Sudden unexpected death in epilepsy (SUDEP), a leading cause of death in epilepsy. Various risk factors contributing to epilepsy mortality particularly SUDEP, some modifiable, have been identified. Structured discussion of risks results in behavioural change that reduces individual risk factors.AimTo investigate and collate risk factors for SUDEP and epilepsy mortality in children to propose a structured communication tool (“Checklist”) for person centred communication.MethodsA Systematic review for children’s epilepsy mortality risk factors was conducted. The search strategy was developed by clinical …

Tracking Palaeotemperatures In Coniacian–Maastrichtian Seas, Gregory D. Price, Bryan Low Kai Sheng

School of Geography, Earth and Environmental Sciences

In this study the stable isotopes of belemnites, are presented from the Coniacian–Maastrichtian interval (∼76–66 Ma) derived from the chalks of Yorkshire and Norfolk, UK, deposited on the western North Atlantic shelf. Cathodoluminescence and elemental geochemistry of the belemnites reveals that most of the rostra were well preserved. If interpreted in terms of temperature, our oxygen isotope record reveals that during the Coniacian (at ∼43 °N) the climate was relatively warm, with maximum mean temperatures of ∼26 °C, followed by cooling to <∼21 °C during the Campanian and Maastrichtian. This overall stratigraphic trend is similar to other records, suggesting that the cooling pattern was not a regional trend and, therefore, driven predominantly by global mechanisms. Within our belemnite data, we also observe a decline in δ¹³C at the Campanian- Maastrichtian boundary, again consistent with other records. This trend has been interpreted as …

Changes In The Spatiotemporal Patterns Of Covid-19 In Japan From 2020 Through 2023, Atsuna Tokumoto, Kazuaki Jindai, Tomoki Nakaya, Mayuko Saito, Clive E Sabel, Hitoshi Oshitani

School of Geography, Earth and Environmental Sciences

BACKGROUND: The spread of coronavirus disease 2019 (COVID-19) varied among countries. The spatiotemporal trends of COVID-19 in Japan remain understudied. Therefore, this study aimed to conduct a detailed investigation of the spatiotemporal evolution of infections/deaths across prefectures in Japan, to analyze the changing patterns of COVID-19 circulation in metropolitan and nonmetropolitan areas.

METHODS: We extracted data from nationally represented open-source data from January 15, 2020, to May 9, 2023, and we calculated the incidence rate of infection and the mortality. Further the ratios were obtained by dividing those rates in prefectural level by those in national level to make them …

Consensus-Based Research Priorities For Post-Collision Care In The Western Cape Province Of South Africa, Naseef Abdullah, Tim Nutbeam, Colleen J. Saunders, Craig Wylie, Nigel Lang, Willem Stassen

Peninsula Medical School

Introduction: Road traffic injuries constitute a significant global health burden, causing 1.3 million deaths and 50million injuries annually, with 92 % of fatalities occurring in low-and middle-income countries (LMICs). Despitethis disproportionate impact, research priorities in post-collision care often reflect high-income country contexts,creating a critical misalignment between evidence generation and contextual realities in LMICs.Methods: This mixed-methods study employed a three-phase approach to identify research priorities for postcollision care in the Western Cape, South Africa. Phase 1 consisted of a comprehensive literature review toidentify preliminary thematic areas and research questions. Phase 2 involved a stakeholder engagement workshop using modified nominal group techniques …

Care‐Ful Encounters: A Case For Empathetic Youthful Encounters With Coastal Environments, Mark Holton

School of Geography, Earth and Environmental Sciences

This paper extends discourse of environmental stewardship and coastal management by examining the intersecting and overlapping relationships between notions of care and encounter in generating and maintaining forms of environmental empathy among young people at various socio-spatial scales. Exploring ‘care-ful encounters’ matters in terms of developing new ways of understanding how the choreographies of care and self-care shape, and are shaped by, encounters with human and non-human others, and prompts critical engagement and reflection with care as felt and experienced, that could otherwise be considered as distanced and ‘over there‘. Two key contributions emerge – first, that the combined role …

Anisotropic Third-Harmonic Vortex Beam Generation With Ultrathin Germanium Arsenide Fork Gratings, Jayanta Deka, Jie Gao, Xiaodong Yang

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Optical vortices have the tremendous potential to increase data capacity by leveraging the extra degree of freedom of orbital angular momentum. On the other hand, anisotropic 2D materials are promising building blocks for future integrated polarization-sensitive photonic and optoelectronic devices. Here, highly anisotropic third-harmonic optical vortex beam generation is demonstrated with fork holograms patterned on ultrathin 2D germanium arsenide flakes. It is shown that the anisotropic nonlinear vortex beam generation can be achieved independent of the fork grating orientation with respect to the crystallographic orientation. Furthermore, 2D fork hologram is designed to generate multiple optical vortices having different topological charges …

Generative Adversarial Networks For Dimensionality Reduction In Evtol Aircraft Takeoff Trajectory Optimization, Samuel Sisk, Farzaam Khorasani-Gerdehkouhi, Abdulaziz Abutunis, K. Chandrashekhara, Xiaosong Du

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Electric vertical takeoff and landing (eVTOL) aircraft play a key role in urban air mobility (UAM), which aims to alleviate traffic congestion in urban areas. Despite their value, eVTOL aircraft suffer from battery energy consumption, which affects their range and endurance in real world flight tasks. Especially, the takeoff process has been identified for excessive power demands. Multidisciplinary analysis and optimization manage to discover optimal takeoff trajectories with minimum energy consumption while balancing multidisciplinary trade-offs, such as short distance takeoff and passengers' comfort. However, conventional parametrization methods (such as B-spline curves) leverage an empirically high-dimensional design space to include real …

Hands-Free Uav Control: Real-Time Eye Movement Detection Using Eog And Lstm Networks, Niloofar Zendehdel, Khosro Ghorbani Zadeh, Haodong Chen, Yun Seong Song, Ming C. Leu

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Industry 4.0 has created a growing need for effective human-robot collaboration (HRC). As robots and humans work more closely together, efficient communication becomes essential for coordinating their actions seamlessly. While speech may seem like the obvious choice for communication, noisy factory environments can render it impractical. Additionally, workers often have their hands occupied with assembly tasks, making hand-controlled interfaces less practical for controlling robots. To address these challenges, this paper presents a novel, hands-free method for robot control using electrooculography (EOG) signals–specifically, eye movements and blinks–with unmanned aerial vehicles (UAVs) used as the demonstration platform. We developed a real-time system …

High-Order Dynamic Mode Decomposition For Multidimensional Harmonic Retrieval, Yanming Zhang, Steven Gao, Lijun Jiang

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Estimating channel parameters such as azimuth, elevation, Doppler shift, and delay is a key challenge in wireless communication, often formulated as a multidimensional harmonic retrieval (MHR) problem. To address this, we propose a high-order dynamic mode decomposition (HODMD) framework for robust frequency estimation from high-dimensional signals in noisy environments. The HODMD approach combines high-order singular value decomposition (HOSVD) to decompose tensor data into a core tensor and mode matrices, with dynamic mode decomposition (DMD) to extract frequencies from the imaginary parts of the DMD eigenvalues. Simulation examples validate the effectiveness of the proposed method, demonstrating its efficiency in solving MHR …

Anisotropic And Temperature-Tunable Second-Harmonic Vortex Generation In Ferroelectric Nbocl2 Holograms, Jayanta Deka, Xiaodong Yang, Jie Gao

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Optical vortex beams with helical phase fronts have immense potential to enhance data capacity due to the unbounded values of orbital angular momentum. Chip-scale platforms for producing vortex beams are of paramount importance for a variety of applications. On the other hand, 2D materials with unique optical properties are essential for developing multifunctional ultrathin photonic devices. Here, anisotropic and temperature-tunable second-harmonic vortex beam generation is demonstrated with ultrathin ferroelectric niobium oxide dichloride (NbOCl₂) fork holograms. The polarization-resolved Raman measurements are performed on the NbOCl₂ crystal to understand the anisotropic behavior of the Raman modes. It is demonstrated …

Design And Testing Of An Intrusive Arm For Supersonic Flow Sampling, Caleb Roberts, Davide Viganò

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Intrusive probes are critical tools in supersonic wind tunnel testing, enabling direct measurement of flow properties such as pressure and heat flux. To support these measurements, a single-axis intrusive arm was designed and implemented in the Missouri S&T Supersonic Wind Tunnel. The system is designed to maintains precise probe positioning, while withstanding the aerodynamic loads, and preserving a dynamic seal between the test section and ambient environment. Its modular design accommodates a range of probe types for various research applications. This paper presents the design methodology, including structural, mechanical, and aerodynamic considerations, and details the integration process. System performance was …

Deep Reinforcement Learning-Based Optimal Takeoff Trajectory Design Of An Evtol Drone, Nathan M. Roberts, Bingling Huang, Xiaosong Du

Mechanical and Aerospace Engineering Faculty Research & Creative Works

The continuing development of electric vertical take-off and landing (eVTOL) aircraft presents promising opportunities to alleviate transportation congestion. However, designing and executing takeoff trajectories that optimally balance energy usage, passenger comfort, and aircraft constraints remains challenging. Conventional design optimization provides a solution for pre-defined flight conditions and environments but is not ideal in real-world applications. In contrast, deep reinforcement learning (DRL) implements optimal policy making real-time decisions with no assumption of mathematical models. Seeing the lack of literature on DRL-based takeoff trajectory design of eVTOL aircraft, we implement and conduct DRL-based optimal takeoff trajectory designs for the Airbus³ Vahana …

Optimizing Uav Swarm Deployment For Efficient Communication Signal Strength Alignment In Disaster Scenarios, Mina Khalilzadeh Fathi, Chaoying Pei

Mechanical and Aerospace Engineering Faculty Research & Creative Works

In disaster scenarios, establishing reliable communication infrastructure is critical, and unmanned aerial vehicle (UAV) swarms offer a promising solution as temporary base stations. This study models communication demand in disaster-affected areas by applying Gaussian kernels to building data, forming a spatial demand distribution. Signal strength is estimated using the normalized inverse Free Space Path Loss (FSPL) to account for realistic attenuation. To guide UAV placement, we extract high-demand regions from the demand distribution using a gradient-based thresholding method. Based on this information, we develop a greedy algorithm to iteratively position UAVs for optimal coverage in areas with the greatest communication …

Few-Shot Learning-Enhanced Tiered Path Planning For Mars Rover Navigation, Ziyi Wang, Di Yu, Mina Khalilzadeh Fathi, Chaoying Pei

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Path planning for Mars rovers presents significant challenges due to the diverse terrain, ranging from easily navigable areas to hazardous zones. Traditional methods typically classify terrain simply as passable or impassable, failing to account for the nuances of more moderately challenging areas. In this paper, we introduce a tiered terrain-aware path planning strategy, employing few-shot learning to classify and segment Martian terrain into levels of difficulty. The few-shot learning model, trained on Earth, is sent to the rover, enabling real-time processing of images from satellites or helicopters. The flexibility of few-shot learning, which requires minimal data and training time, enables …

Improving The Operator-Swarm Dynamic Under Mental Fatigue Constraints In Search And Rescue Operations, Jordan Morrow

Masters Theses

"Human-robot applications that allow for work to be done remotely are largely dependent on the lassitude of the operators. The exhaustion of these operators is a result of work completion and duration. Previous research attempts to evaluate the impact on reaction by quantiying human weariness. This paper examines how human weariness affects the human-robot dynamic in UAV-assisted search and rescue missions. An explanation of the connection between mental exhaustion and operator responsiveness over prolonged durations is provided by the search and rescue operations using UAV swarms (SAROUS) model. Through the use of artificial intelligence, SAROUS is modernized. This allows the …

Evaluation Of The Impact Of Secondhand E-Cigarette Aerosols On Ros-Mediated Alterations Of Epigenetic Events: Implications For Copd Pathogenesis And Assessment Of The Efficacy Of Cold Atmospheric Plasma In Degrading Organophosphate Pesticides On Kevlar Materials, Ta-Chun Lin

Masters Theses

"We explored 1) the toxicological impacts of e-cigarette aerosols and 2) the decontamination efficacy of Cold Atmospheric Plasma (CAP) technology over organophosphates. In examining the effects of firsthand and secondhand e-cigarette aerosol exposure on A549 and BEAS-2B lung cells, we observed significant cytotoxicity, increased ROS levels, and epigenetic modifications, including heightened DNA methylation and downregulation of histone deacetylases (HDACs). These molecular alterations may contribute to chronic respiratory conditions, such as COPD, underscoring the health risks of e-cigarette exposure.

We investigated CAP’s effectiveness in degrading organophosphate pesticide (OP) residues on fabric surfaces, revealing that CAP treatment effectively decomposes OPs such as …

Transfection Of Ionizable Lipid Nanoparticles On Raw 264.7 And Mda-Mb-231, Lavanya Bhargava

Masters Theses

"This thesis investigates the preparation, characterization, and transfection efficiency of various ionizable lipid nanoparticles (LNPs) formulated for the delivery of mRNA encoding enhanced green fluorescent protein (EGFP) in RAW 264.7 macrophages and MDA-MB-231 breast cancer cell lines. The ionizable cationic lipid studied are ALC-035, C12-200, C14-4, PPZ-A10, DLin-MC3-DMA. The study addresses the need for effective gene delivery systems to provide a safe and versatile platform that protects and transports nucleic acids into target cells. LNPs were synthesized using microfluidic mixing methods, incorporating lipid components such as the cationic lipids, DSPE, DOPE, cholesterol and PEG lipids to achieve controlled particle sizes, …

Lidar From The Skies: A Uav-Based Approach For Efficient Object Detection And Tracking, Baya Cherif

Masters Theses

"Recently, there has been a growing interest in deploying the Light Detection and Ranging (LiDAR) technology to gain traction in the autonomous vehicle industry, its applications are expanding into areas like smart cities, agriculture, and renewable energy. This work proposes an advanced approach to enhance aerial traffic monitoring using Li- DAR. We aim to provide accurate, real-time object detection and tracking from an aerial perspective by integrating Unmanned Aerial Vehicle (UAV) with LiDAR, culminating in a smart UAV-integrated LiDAR (A-LiD) sensor for traffic surveillance. We introduce an adapted version of one of the newest methods of the cutting-edge 3D object …

Long Short-Term Memory (Lstm) -Based Neural Network Model For Optimizing Composite Manufacturing Process Using Autoclave, Sourav P. Bolar

Masters Theses

The combination of high pressure and controlled heat plays a critical role in ensuring the uniform curing of composite materials, leading to parts with superior mechanical properties. In this study, three composite samples of IM7/CYCOM 5320-1, each cut into 12x12-inch squares, were placed in an autoclave at three different locations, spaced 6 inches apart. Sixteen thermocouples were randomly distributed across the setup to monitor the curing process as the autoclave temperature was systematically ramped up and down while maintaining constant pressure, creating a fully controlled curing environment. The primary objective was to optimize the curing locations to reduce machine runtime …

On Optimizing Sensor Data Collection, Processing, And Storage For Industrial Additive Manufacturing, Steven Thompson

Masters Theses

The widespread adoption of digital data management methods for transformative technologies, such as additive manufacturing (AM), within the aerospace industry is impeded by poor interoperability between AM component manufacturing processes. Moreover, data quality may be compromised due to sensor failures or other corruptions. Additionally, massive amounts of data are collected during these processes, often needing to remain accessible for decades. These storage costs can place a significant financial burden on smaller suppliers. This work aims to make digital data management methods more affordable and, therefore, approachable for smaller suppliers.

First, the design and initial implementation of an affordable and adaptable …

Investigation Of Arm Stiffness Modulation During Overground Physical Human-Human Interaction, Henry Tien

Masters Theses

The vision of advancing robots capable of direct physical engagement with humans begins with an understanding of how humans interact with each other. Even rudimentary interactive tasks, such as shaking hands, provides tremendous challenges for modern robotics and current research into the biomechanics of such interactions is limited. Thus, further analysis of physical Human-Human Interaction (pHHI) will bolster efforts to enhance collaborative robot capabilities. To this end, a custom Force Perturbation Handle (FPH) was developed, including the requisite mechanical, electrical, and software systems, to investigate the unique role of arm stiffness modulation in haptic communication between humans. The novel device …

Emi Mitigation, Material Characterization, Transformer Equivalent Circuit Modeling, Reza Vahdani

Masters Theses

Modern high-frequency electronic systems demand precise characterization and modeling techniques to ensure signal integrity and electromagnetic compatibility. This thesis presents three core studies focused on real-world challenges in high-speed and power electronics: EMI mitigation using 3D printed absorbers, wideband liquid dielectric characterization, and accurate transformer modeling.

The first study demonstrates a targeted approach to mitigating electromagnetic interference (EMI) in a commercial router. By using holography imaging to identify radiation hotspots, custom absorber structures were designed with commercially available materials, fabricated via 3D printing, and applied directly to emission sources. Radiated emission tests in a reverberation chamber showed up to 9 …

Augmentation Of Quality Of Service, Security, And Trust In Edge-Enhanced Iot Networks Leveraging Blockchain, Kyle Matthew Whitlatch

Masters Theses

The meteoric rise of the Internet of Things (IoT) has led to multiple architectural schemas to handle the data these devices create. Edge-enhancement is a technique where groups of IoT report to a median layer to aggregate the data before relaying to the endpoint. These edges also open opportunities to perform more operations to ensure devices are behaving properly before committing the data to long term storage. By interconnecting these edges with a technology like blockchain, it is possible to have an interconnected and responsive system to ensure the Quality of Service (QoS) of the IoT devices within the architecture …

Ground Testing Of Cryogenic Environments In Vacuum Conditions, Lucas A. Scott

Masters Theses

The environment in the vacuum of space is unforgiving and ever-changing. Not only facing the lack of a protective atmosphere, but powerful radiation and extreme temperature ranges as well. Equipment destined for space must be hardened to withstand them. Just launching satellites that are theoretically able to survive these conditions is unfeasibly costly in the event of failure. Because of this, spacecraft must be tested in a simulated environment that can match the harsh climate in space. Therefore, it is the goal of the Gas and Plasma Dynamics Lab's (GPDL) to test and prove specific methods of recreating these conditions. …

Auto-Tuning And Applications Of Transient Voltage Suppressor Models In Full-Wave Simulations, Daniel Peter Szanto

Masters Theses

System efficient electrostatic design (SEED) combines full-wave geometry information with SPICE models of non-linear protection devices, typically transient voltage suppression (TVS) diodes, to allow optimization and validation of electrostatic design (ESD) protection early in the design process. TVS models have previously been developed which may be used in SPICE simulation tools like Keysight ADS, but these models could not be used directly in full-wave simulation tools like CST Studio. A process was developed for converting existing ADS models of TVS devices to a form that can be used within a CST full wave/SPICE hybrid simulation. Three TVS models were converted …

Frequency-Tracker And Power Supply For Piezoelectric Lunar Dust Removal Actuator, Praneeth Uddarraju

Masters Theses

"This work presents a reconfigurable phase-locked loop (PLL)-based control system for the resonant excitation of piezoelectric actuators aimed at automated removal of particulate contaminants from photovoltaic (PV) surfaces—particularly in extraterrestrial environments such as the lunar surface. Regolith or lunar dust buildup on solar panels is a serious hazard to the effectiveness of energy harvesting on extended missions. By using high-frequency structural excitation and inertial forces the suggested system removes surface impurities. Tunable Sallen-Key low-pass filters for reliable feedback conditioning are used in conjunction with a digitally implemented PLL on an FPGA (XLR8 platform) to precisely lock the drive frequency to …

Aerodynamic Design Workflow Optimization For Fsae: Advanced Cfd And Hpc Automation, Kyle Maynor

Masters Theses

This research identifies and resolves inefficiencies in the Computational Fluid Dynamics (CFD) workflow of the Missouri S&T Racing Formula SAE Team’s aerodynamic development. Key bottlenecks were found in simulation runtime, laborious simulation preparation, an overly manual simulation submission process, and unchecked mesh quality. A fully reimagined workflow was implemented with simulation parameters to adjust vehicle attitudes. From this, an automated design sweep could be executed to generate aero maps of the developing vehicle using the Design Manager Project. A Python-based submission app handles the terminal interfacing and macro editing that significantly slowed the job submission process before. Using a university …

Development Of Stalling Detection Algorithms And Operator Behavior Models For Evaluating Hydraulic Excavator Performance, Mateo Fernando Montenegro Defaz

Masters Theses

This study aims to develop a reliable algorithm for stalling detection and investigate how operator behaviors impact the truck-loading process, focusing on swing and bucket rotations. While the study of excavator digging conditions and equipment performance is well understood, describing stalling from telemetry data and the influence of operators’ behavior on overall system efficiency remain underexplored. This study seeks to: (i) develop a reliable algorithm for stall detection by analyzing key operational variables such as velocity and angles; (ii) implement statistical and machine learning techniques, specifically a Support Vector Machine (SVM) classification algorithm, to differentiate between ideal and non-ideal digging …