Engineering Commons^™

Evaluation Of A Blockchain-Based Prescription System And Data Source For National Research And Development, Sean Chan, Aedin Clay, Lance Tan, Christian E. Pulmano

Department of Information Systems & Computer Science Faculty Publications

In the Philippines, healthcare providers, government agencies, and research institutions use data from patient prescriptions to generate reports for health planning and decision-making. However, current e-prescription systems have vulnerabilities, including erroneous information, hacking attempts, a single point of failure, and medical fraud. In addition to affecting the quality of data reporting, these issues violate a patient's rights to data privacy. One promising solution is a blockchain-based prescription system. Blockchain's immutable ledger accurately traces medical fraud and erroneous information, while its decentralized nature reduces the impact of failures. Performance is an important consideration, as healthcare systems need to be scalable and …

The Impact Of Modern Buildings On Traditional Urban Environments And Urban Identity, Ann S. Ibrahim

Al-Esraa University College Journal for Engineering Sciences

Contemporary cities face significant challenges in integrating modern buildings into traditional urban environments while preserving urban identity. This study focuses on the relationship between modern architecture and urban heritage. The research problem revolves around how modern buildings affect urban identity in traditional areas, and whether they can interact positively with the urban context without distorting its traditional character.

The study aims to analyze this impact by examining a range of global examples using architectural indicators such as visual integration, spatial integration, material harmony, urban scale, accessibility, and environmental impact. A three-point scale (weak, partial, excellent) was applied to assess each …

Conflict Profiles And Team Outcomes In Cross-Disciplinary Teams: An Integrated Latent Profile Analysis And Natural Language Processing Approach, Francisco Cima, Pilar Pazos

Engineering Management & Systems Engineering Faculty Publications

Team conflict is a naturally emerging phenomenon resulting from individuals' interactions during project execution. Cross-disciplinary teams can experience higher levels of conflict than single-discipline teams because of the increased diversity of knowledge and perspectives. Research has shown that team conflict can emerge from different types of disagreements (cognitive and interpersonal), which have different implications for team functioning. Past empirical research has focused on the impact of both conflict types independent from each other while overlooking their combined effects. This work examines the conflict profiles resulting from the combined levels of interpersonal and cognitive disagreements and their association with team outcomes. …

Collaborative Robotic Finishing Platform For Metal Part Processing Towards Industry 5.0, Seyedhossein Hajargarbashi, Gabriel Côté, Jonathan Boisvert, Ramy Meziane, Chen Xu, Corentin Hubert, Sabrina Jocelyn, Clément Gosselin

Articles dans des actes de congrès

Manual finishing operations in aerospace and ground transportation industries are often associated with health-and-safety-related issues such as musculoskeletal disorders, productivity loss, and challenges in workforce renewal. This work presents an innovative automated solution to address these challenges, prioritizing the ease of implementation and affordability for small and midsize enterprises (SMEs). Our proposed solution is a collaborative robotic (cobotic) finishing platform designed to eliminate labor-intensive work while keeping human operators in the loop to manage unforeseen situations. This platform aims to eliminate health risks, enhance repeatability, improve product quality, and increase productivity. This paper describes the mechanical design of the platform, …

The Effects Of Various Nanoplastics On The Inflammatory Response In Primary Alveolar Epithelial Cells, Eunice J. Pak

Theses and Dissertations

As plastic pollution begins to multiply in the environment and the workplace, gradual degradation of these plastics creates an unseen threat to biological health: nanoplastics. These nanoplastics are common in day-to-day life, and have been found in various internal organs of the human body. The biological and health effects of these nanoplastics are still widely unknown, and only recently has research focused on the impacts of these particles on human health at the cellular level.

In this thesis, poly(methyl methacrylate) and three forms of polystyrene (carboxyl-modified, amine-modified, and neutral surface charge) have been tested on submerged primary C57BL/6 mouse alveolar …

The Effect Of 17Β Estradiol On Local Production Of Active Vitamin D3 Metabolites In Laryngeal Squamous Cell Carcinoma, Tillat Batool

Theses and Dissertations

Laryngeal squamous cell carcinoma (LSCC) has garnered attention as a sex-hormone-dependent cancer with notable responsiveness to 17β-estradiol (E₂), in which it’s tumorigenesis can be mediated through estrogen receptors (ER). The ESR1 gene encodes for the classical full-length ERα66, as well as splice variants ERα36 and ERα46. Previous studies have shown that an active vitamin D₃metabolite, 24R,25(OH)₂D_3. has pro-tumorigenic or anti-tumorigenic responses in LSCC cells with different ER profiles, and that the tumor cells have the capability of localized production of vitamin D metabolites at the cellular level when treated with 25(OH)D₃-d6 …

Leveraging Large Language Models For Enhancing Well-Being In The Digital Age, Xiaobo Guo

Dartmouth College Ph.D Dissertations

The 21st century has seen dramatic shifts in human interactions with information, peers, and the environment, primarily driven by the proliferation of online platforms and social media. These advancements offer more access to information and global connectivity, but also present challenges such as information overload, misinformation, online harms, and biased reporting that can negatively impact user well-being. This thesis examines the role of Large Language Models (LLMs) — advanced forms of artificial intelligence that understand and generate human-like text — in enhancing well-being in the digital age. The study begins by exploring the potential of LLMs to detect early signs …

Efficient Connectivity Management And Path Planning For Iot And Uav Networks, Amirahmad Chapnevis

Theses and Dissertations

This dissertation explores how to better manage resources in mobile networks, especially for enhancing the performance of Unmanned Aerial Vehicles (UAV)-supported IoT networks. We explored ways to set up a flexible communication architecture that can handle large IoT deployments by making good use of mobile core network resources like bearers and data paths. We developed strategies that meet the needs of IoT networks and enhance network performance. We also developed and tested a system that combines traffic from several mobile devices that use the same user identity and network resources within the core mobile network. We used everyday smartphones, SIM …

Laws And Green Incentives: Guiding China’S New Biomass Energy Future, Qian Li, Cihui Liu, Jennifer S. Stevenson

Faculty Articles

In this article, we focus on green incentives and laws guiding China’s new biomass energy future. We offer proposals to reinforce green incentives and legal standards in this field.

Photoluminescence Of Beryllium-Related Defects In Gallium Nitride, Mykhailo Vorobiov, Mykhailo Vorobiov

Theses and Dissertations

This study explores the potential of beryllium (Be) as an alternative dopant to magnesium (Mg) for achieving higher hole concentrations in gallium nitride (GaN). Despite Mg prominence as an acceptor in optoelectronic and high-power devices, its deep acceptor level at 0.22 eV above the valence band limits its effectiveness. By examining Be, this research aims to pave the way to overcoming these limitations and extend the findings to aluminum nitride and aluminum gallium nitride (AlGaN) alloy. Key contributions of this work include. i)Identification of three Be-related luminescence bands in GaN through photoluminescence spectroscopy, improving the understanding needed for further material …

Shape Inverse Prediction Of Magnetic Field-Actuated Soft Robots By Neural Network Machine Learning, Lineth J. Perez Monsalve

Theses and Dissertations

Soft robotics has drawn tremendous interest in recent years because the compliance and motion of soft robotics enable biocompatibility and versatility for many applications, such as human-machine interaction, wearable and assistive devices, and health monitoring. This study introduces a novel predictive modeling approach using neural networks for shape control of magnetic soft robots. The robots are made of silicone materials embedded with hard magnetic particles, which respond to the external magnetic field provided by a ring-type of permanent magnet. These robots, free from physical connections to external devices, i.e., non-tethered actuation, hold significant potential for applications in healthcare, such as …

Investigation Of Spalart-Allmaras Turbulence Model For Vortex Flows, Abigail Rayna Kerestes

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Conventional turbulence models often predict behaviors opposite as to what is observed in flows subject to rotation. In this type of flow scenario, rotation typically induces turbulence suppression. To address this limitation, a modification to the Spalart Allmaras Model with Rotation Correction (SA-R) was proposed to enhance the original Spalart Allmaras Model’s sensitivity to rotation and curvature. To test the validity and accuracy of this modification, two cases were investigated. The first case involved an axisymmetric rotating pipe. A Reynolds Number of 37,000 was implemented and the initial and boundary conditions established by Zaets et. al. were utilized. Initially non-rotating, …

Understanding Impact Of Graph Structure On Knowledge Graph Embedding, Brandon Dave

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The effectiveness of a deployed knowledge graph is commonly evaluated with defined use-cases from domain experts. This poses challenges during the development cycle in determining how to represent data. Developers of a knowledge graph can optionally include semantics into a knowledge graph by abstracting the data representation in such a way that mirrors information as it exists in the real world. Consequently, the abstraction is represented by additional layers, resulting in performant differences in knowledge graph embedding; such as, the embedded model's ability to infer facts between entities through link predictions. This thesis presents a comprehensive analysis of the performance …

Ai-Enabled Hardware Security Approach For Aging Classification And Manufacturer Identification Of Sram Pufs, Harshdeep Singh

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Semiconductor microelectronics integrated circuits (ICs) are increasingly integrated into modern life-critical applications, from intelligent infrastructure and consumer electronics to the Internet of Things (IoT) and advanced military and medical systems. Unfortunately, these applications are vulnerable to new hardware security attacks, including microelectronics counterfeits and hardware modification attacks. Physical Unclonable Functions (PUFs) are state-of-the-art hardware security solutions that utilize process variations of integrated circuits for device authentication, secret key generation, and microelectronics counterfeit detection. The negative impact of aging on Static Random Access Memory Physical Unclonable Functions (SRAM PUFs) has significant consequences for microelectronics authentication, security, and reliability. This research thoroughly …

Ml-Assisted Side Channel Security Approaches For Hardware Trojan Detection And Puf Modeling Attacks, Niraj Prasad Bhatta

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Hardware components are becoming prone to threats with increasing technological advances. Malicious modifications to such components are increasing and are known as hardware Trojans. Traditional approaches rely on functional assessments and are not sufficient to detect such malicious actions of Trojans. Machine learning (ML) assisted techniques play a vital role in the overall detection and improvement of Trojan. Our novel approach using various ML models brings an improvement in hardware Trojan identification with power signal side channel analysis. This study brings a paradigm shift in the improvement of Trojan detection in integrated circuits (ICs). In addition to this, our further …

Rf Steganography To Send High Security Messages Through Sdrs, Megan K. Patrick

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This research illustrates a high-security wireless communication method using a joint radar/communication waveform, addressing the vulnerability of traditional low probability of detection (LPD) waveforms to hostile receiver detection via cyclostationary processing (CSP). To mitigate this risk, RF steganography is used, concealing communication signals within linear frequency modulation (LFM) radar signals. The method integrates reduced phase-shift keying (RPSK) modulation and variable symbol duration, ensuring secure transmission while evading detection. Implementation is validated through software-defined radios (SDRs), demonstrating effectiveness in covert communication scenarios. Results include analysis of message reception and cyclostationary features, highlighting the method's ability to conceal messages from hostile receivers. …

Experimental Validation Of Two Highly Loaded Low Pressure Turbine Blades At High Speed Low Reynolds Number Conditions, Ryan Sauder

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In the constant search for more efficient engines, one approach to gain performance is to reduce the weight of the low pressure turbine (LPT) module. This module can account for up to 30% of the total engine weight [1], and a reduction in LPT weight results in clear gains to engine performance and a reduction in engine cost. High lift airfoils accomplish this weight reduction by each blade extracting a larger amount of work from the flow and thus requiring fewer blades to drive the compressor when compared to conventional blades. However, high lift LPT blades, quantified by a high …

Measured Phase History Data For Target Recognition Studies, Gregory A. Seltzer

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Performing automatic target recognition (ATR) on full-size aircraft targets using inverse synthetic aperture radar (ISAR) data is challenging and expensive. The use of scale models and radar systems of such large targets saves time and reduces facility requirements. This study examines the feasibility of performing ATR on 1:144 scale model airplanes at Ka-band. The scale model and Ka-band radar simulate the collection of full-scale targets at VHF-band. The phase history measurement collections were completed in the Sensors and Signals Exploitation Laboratory (SSEL) at Wright State University. To ensure sufficient data for training and testing, the phase history data was augmented …

Application Of Multiple Data Augmentation Techniques To Improve Training With Synthetic Sar Data In Common Cnn, Stephanie M.V. Saich

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To address the issues of limited target data in the Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) problem set, synthetic data is often used to aid in filling the gap. This paper covers an in depth look at the use of colorization, dynamic range adjustment, and target extraction as data augmentation techniques to improve the accuracy of deep learning networks trained on synthetic SAR data. The use of multiple different data augmentations combine to dramatically improve the accuracy of a common Convolutional Neural Network (CNN) over the use of standard synthetic data. A comparison of increasing fraction of measured …

Multi-Semantic-Stage Neural Networks For Robust And Interpretable Deep Learning, Christopher J. Menart

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Deep neural networks have great representational power. However, most deep neural nets today optimize directly for performance on a single task defined only by labeled training data. This excludes potential sources of knowledge and ways of learning which could improve their performance, and address challenges, such as explainability, which are pressing to the field. We propose a framework for neural network architecture which generalizes it to a graph of many semantically-meaningful variables. We call it the Multi-Semantic-Stage Neural Network (MSSNN). An MSSNN models its domain as a web of conditional probabilities, i.e. a collection of inter-related tasks which can learn …

Electrochemical-Thermal Model Of A Lithium-Ion Battery, Paul Kalungi

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Lithium-ion batteries are an integral component of energy storage systems for renewable energy applications owing to their high energy density. Extensive research has therefore been carried out, utilizing both experimental and computational methods, to aid in a deeper understanding of lithium-ion batteries. Challenges related to efficiency, safety and thermal management persist, particularly during high current draw, extreme temperature conditions and extreme dynamic current operation such as in electric vehicles. This thesis work presents an electrochemical-thermal model of a lithium-ion battery that simulates and analyzes the variation of electrical behavior, chemical behavior and thermal behavior. The electrochemical model is developed by …

Empirical Investigation Of Calibration Targets In Thz In The Near Field From 550 To 700 Ghz, Anais Kypris L. Rawson

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The uncertainty of the standard calibration procedure for radar cross-section (RCS) measurement is studied for different targets measured in the near-field from 550 to 700 GHz. Using common calibration spheres and squat cylinders mounted on a styrofoam pedestal at waterline (zero-degrees elevation), the calibration difference measure is determined for each target. Similarly, the difference metric is determined for square trihedral and tophat targets placed on a ground plane and measured at different elevation angles. The mean calibration measure is calculated using the dual calibration target method and repeated measurements in an anechoic chamber. The specific THz system is described and …

Pneumonia Detection With Limited And Imbalanced Data Using Energy-Based Out-Of-Distribution Technique, Jasbin Karki

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The automated detection of pneumonia through chest X-ray presents a critical challenge in medical diagnostics, particularly due to the restrictions of limited and imbalanced chest X-ray data for training AI models. Traditional methods that depend on softmax confidence scores can be overconfident even when generating erroneous outputs especially when they are processing completely new inputs, leading to unreliable diagnostic results. This research addresses challenges in AI models which aim to develop a robust pneumonia detection system using an Energy-Based Out-of-Distribution (OOD) technique that can work effectively even with limited and imbalanced data. The study focused on creating a more reliable …

Test-Time Backdoor Attack Using Universal Perturbation, Jesse Alexander Smith

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The rapid growth and widespread reliance on machine learning (ML) systems across critical applications such as healthcare, autonomous driving, and cybersecurity have un- derscored their transformative potential and heightened their susceptibility to adversarial attacks and vulnerabilities. This thesis investigates vulnerabilities in ML models, focusing on backdoor attacks, including naive backdoor attack, feature collision backdoor attack, hidden trigger backdoor attack, and test-time backdoor attack using universal perturbation technique. These methodologies demonstrate how adversaries can automate and conceal malicious behaviors to achieve specific objectives, posing significant challenges to ML model integrity and trustworthiness. The research provides a comprehensive analysis of the theoretical …

Performance Degradation Of Gan Hemts Under Rf Aging: Implications For Wireless Communications Standards, Nathan Grant

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This study examines the aging effects of GaN HEMTs, focusing on the CG2H40010 device under conditions that mimic the high-power, high-frequency environments of wireless communication systems. With the increasing adoption of GaN technology in RF applications, understanding its degradation mechanisms under CW stress and modulated signal characterization is essential for predicting device lifetime and ensuring performance standards for modern communication systems. RFALT was employed to stress the device using CW signals, while key performance metrics, such as gain compression, gate leakage, ACP, and EVM, were assessed using W-CDMA signals to replicate real-world dynamic stresses. The findings reveal that CW stress …

Novel Approaches To Treatment Of Hyperexcitability In Skeletal Muscle, Phillip Vance Walker Ii

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Myotonia Congenita (MC) is a rare, inherited ion channelopathy caused by a loss-of-function mutation in the CLCN1 gene. The resulting downregulation of the skeletal muscle chloride channel (ClC-1) results in hyperexcitable skeletal muscle fibers that fire action potentials involuntarily. Patients with MC suffer from debilitating stiffness due to myotonia, described clinically as delayed muscle relaxation following voluntary contraction. Skeletal muscle is a unique system we use in this study to advance our understanding of the pathophysiology underlying MC, and other channelopathies characterized by hyperexcitable cells (i.e., forms of epilepsy and cardiac arrhythmia). Furthermore, re-assessing what makes anti-myotonic drugs such as …

A Novel In-Vitro Approach To Investigate The Molecular Interaction Between Inhaled Particles And The Human Pulmonary Surfactant System, Bryan Mayville

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The deployment of military personnel to austere environments poses significant pulmonary health risks from inhalation of particulates including sand, dust, pollution, and heavy machinery exhaust. When particles are less than 10 µm in diameter, they can penetrate deep within the alveoli and embed within the protective lung surfactant monolayer. Here, absorption of surfactant lipids to the particle surface disrupts the essential monolayer configuration needed to reduce surface tension and prevent atelectasis, thus leading to disease. Currently, there are no in vitro surfactant-producing lung cell models capable of studying the effects of inhaled particles on the human pulmonary surfactant system. A549 …

Insights Into Hyperkalemic Periodic Paralysis: Novel Mechanisms And Treatments, Christopher Dupont

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Hyperkalemic Periodic Paralysis (hyperKPP) is an inherited channelopathy that leads to incapacitating episodes marked by severe skeletal muscle weakness or total paralysis, often accompanied by muscular stiffness (myotonia). The periods of muscle dysfunction are thought to be caused by the elevation in extracellular potassium (K). This autosomal dominant disorder is associated with mutations in the skeletal muscle voltage-gated sodium channel (Nav1.4). A multitude of inciting factors have been documented, including rest post-exertion, a potassium-rich diet, exposure to cold temperatures, psychological stressors, and fatigue. Given the variability of the triggers and the debilitating manifestation of symptoms, individuals afflicted by hyperKPP have …

Semantics-Aware Text-Guided Aerial Image Synthesis By Feature Augmented Diffusion Models, Douglas J. Townsell

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Aerial imagery provides crucial insights for various fields, including remote monitoring, environmental assessment, and autonomous navigation. However, the availability of aerial image datasets is limited due to privacy concerns and imbalanced data distribution, impeding the development of robust deep learning models. While recent text-guided generative models have shown promise in synthesizing high-quality images, they fall short in handling the unique challenges of aerial imagery, including densely packed objects, intricate spatial relationships, and the absence of paired text-aerial image datasets. To tackle these limitations, we propose STARS, a groundbreaking framework for Semantic-aware Text-guided Aerial image Refinement and Synthesis. STARS introduces a …

Comparative Analysis Of Motoneuronal C-Bouton Structural Alterations In Als And Aging, Shelby Nicole Ward

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Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease characterized by motor neuron (MN) death resulting in paralysis and eventually death. ALS has greater prevalence in older populations sharing characteristics with aging like muscle weakness and MN type specific degeneration. MNs innervate skeletal muscles and control muscle contraction through their excitability which is altered in both conditions. C-Boutons are a cholinergic, excitatory synaptic input to MNs and have been studied in ALS and aging but have produced inconsistent findings and undesired gaps. We used immunohistochemistry to label mouse lumbar spinal cord and separate MN types. 60x imaging and automated analysis was …