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Articles 31 - 60 of 3445
Full-Text Articles in Engineering
Leveraging Counterfactuals For Enhanced Natural Language Inference: A Multitask Knowledge Distillation Approach, Brian J. Davis
Leveraging Counterfactuals For Enhanced Natural Language Inference: A Multitask Knowledge Distillation Approach, Brian J. Davis
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Natural-language inference (NLI) asks whether a hypothesis is entailed by, contradicts, or is neutral with respect to a premise. Modern transformers reach high raw accuracy on benchmarks such as SNLI, MNLI, and ANLI, yet they often rely on brittle lexical shortcuts and provide little insight into their decision process. This thesis shows that counterfactual-augmented knowledge distillation can simultaneously boost robustness and supply faithful, token-level explanations—without scaling model size. Four T5-v1_1 students (60M, 220M, 770M, 3B parameters) are trained under four curricula: (1) standard fine-tuning, (2) fine-tuning with free-text rationales, (3) multi-task distillation with naive counterfactuals, and (4) multi-task distillation with …
Hardware Trojan Detection In A Segmented Mixed-Signal Circuit Via Leakage Current, Christopher James Otey
Hardware Trojan Detection In A Segmented Mixed-Signal Circuit Via Leakage Current, Christopher James Otey
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As computers and integrated circuits become more commonplace, the risk of a Hardware Trojan attack becomes more worrisome. Trojans can exploit design flaws or be inserted between essential components to leak information, change the circuit function, or destroy the circuit altogether. Several methods of trojan detection and prevention have been introduced, however few can handle combined analog and digital circuits, known as mixed-signal circuits. This thesis demonstrates a Hardware Trojan detection method implemented in an Analog-to-Digital Converter (ADC), which is a mixed-signal circuit. The detection method involves splitting the circuit into segments with approximately equal leakage currents (a large part …
Computational Analysis Of A Hafnium-Titanium Alloy Mechanical Properties From First Principles, Abdul Mughni
Computational Analysis Of A Hafnium-Titanium Alloy Mechanical Properties From First Principles, Abdul Mughni
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Hafnium and titanium, along with zirconium, are refractory metals with unique properties suitable for extreme-environment applications. Utilizing alloys based on these elements can provide suitable materials with engineered properties. Understanding their mechanical properties is necessary to determine appropriate applications. This thesis research aims at employing quantum-based atomistic simulations to estimate mechanical properties of pristine hafnium, titanium and an alloy based on these elements. The results are compared to available experimental data and the corresponding implications are explored.
Data-Driven Prediction Of Temperature Distribution In Multi-Laser Powder Bed Fusion Using Convolutional Neural Networks, Majid Dousti
Data-Driven Prediction Of Temperature Distribution In Multi-Laser Powder Bed Fusion Using Convolutional Neural Networks, Majid Dousti
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Additive Manufacturing (AM), particularly Laser Powder Bed Fusion (L-PBF), has gained significant traction in fabricating complex, high-performance metallic components. However, the inherent complexity and computational cost of high-fidelity simulations pose challenges for real-time monitoring and optimization of multi-laser powder bed fusion processes. This study proposes a data-driven surrogate modeling approach using a deep learning architecture to efficiently and accurately predict three-dimensional temperature distributions during ML-PBF. A 3D convolutional neural network (CNN) model, named Decoder-CNN, is developed and trained on a dataset of simulated thermal fields corresponding to various process configurations, including different laser power, scanning speed, and beam arrangements. The …
Optimizing Cloud Computing Resources For Operational Cost And Application Performance Using Machine Learning, Isaac K. Matthew
Optimizing Cloud Computing Resources For Operational Cost And Application Performance Using Machine Learning, Isaac K. Matthew
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As AI-driven workloads accelerate the growth of cloud initiatives and spending, resource waste also increases due to persistent inefficiencies in cloud compute and infrastructure management. Overprovisioned resources and suboptimal configurations often lead to operational inefficiencies and unnecessary financial overhead. These challenges arise from the difficulty of anticipating resource demands in dynamic workloads and selecting suitable virtual machines to ensure optimal performance. Our research proposes a holistic, data-driven framework for managing cloud compute resources that reduces costs without compromising application performance. We integrate a predictive, model-driven, threshold-based autoscaling solution for cloud-native applications with an optimized instance right-sizing approach to select cost-effective …
Evaluating Geometric Accuracy In 3d Printing (3dp) Comparative Study Of Different 3dp Processes Using Cmm And Vision Measurement Tools, Alexander Adams
Evaluating Geometric Accuracy In 3d Printing (3dp) Comparative Study Of Different 3dp Processes Using Cmm And Vision Measurement Tools, Alexander Adams
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This study investigates how various additive manufacturing (AM) technologies and parameters influence part quality by fabricating three uniquely designed artefacts. Artefact 1 is a rectangular block with stepped arches, thin and solid extruded features, and holes of varying shapes and sizes. Artefact 2 consists of a base plate with angled overhangs from 15 to 90 degrees. Artefact 3 includes a tall cylinder, a five-step cylinder, and two half arches of different scales. Each artefact was printed ten times using: metal PBF (Inconel 718), nylon PBF (Nylon 12), Vat Polymerization (GRY photopolymer), and material extrusion (nylon carbon fiber). Dimensional analysis was …
Reducing Operator Training Time Through Virtual Reality: A Case Study On The Lpkf Protomat E44 Machine, Joshua C. Patel
Reducing Operator Training Time Through Virtual Reality: A Case Study On The Lpkf Protomat E44 Machine, Joshua C. Patel
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This thesis presents the development of an immersive virtual reality (VR) simulation that replicates the operation of the LPKF ProtoMat E44 PCB milling machine. Aimed at reducing operator training time and improving procedural understanding, the simulation offers an interactive and realistic environment where users can safely engage with machine workflows and start-up sequences. The emphasis is on accurate representation, usability, and maintaining immersion to support intuitive learning. Although formal evaluation is outside the scope of this work, the system is designed to serve as a foundation for cost-effective, scalable training in technical and manufacturing contexts, offering a modern alternative to …
Study Of Fiber-Loaded Slurries For Ceramic Matrix Composite Fabrication By Additive Manufacturing, Gaspard Matondo
Study Of Fiber-Loaded Slurries For Ceramic Matrix Composite Fabrication By Additive Manufacturing, Gaspard Matondo
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We studied the additive manufacturing of an alumina-matrix composite reinforced with alumina fibers using the Admatec Admaflex 3D printer, which utilizes digital light processing technology. Oxide-oxide composites are composite materials in which both the matrix and the reinforcing element are ceramic oxides. Monolithic alumina ceramic exhibits a good combination of thermal and mechanical properties, including thermal shock resistance, high melting point, thermal oxidation resistance, good thermal conductivity, hardness, and mechanical strength. However, it is very brittle. Introducing alumina fiber as a reinforcing material into the alumina matrix is expected to enhance mechanical properties, particularly toughness, making the ceramic matrix composite …
Reinforcement Learning For Adversarial Systems Using Relational Observations, Sophia Christine Gilson
Reinforcement Learning For Adversarial Systems Using Relational Observations, Sophia Christine Gilson
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This thesis investigates the integration of relational observations with the reinforcement learning (RL) framework for improved generalization capability. A hide-and-seek simulation environment is designed in Unity for proof-of-concept demonstration. Two observation representations—relational (analogical) and standard positional—are designed to evaluate agent learning and generalization capabilities. Agents are trained using the Proximal Policy Optimization (PPO) and Soft Actor Critic (SAC) algorithms in a random-room environment and tested in both the random-room environment and a novel environment with greater spatial complexity and path obstructions. Comparative studies indicate that relational representation of objects in the adversarial environment could potentially improve the generalization capability of …
Scalable Real-Time Stream Clustering For Unbounded Text Streams, Nathaniel C. Crossman
Scalable Real-Time Stream Clustering For Unbounded Text Streams, Nathaniel C. Crossman
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Social media, AI systems, IoT sensors, and other platforms generate vast amounts of streaming data. Given this vast volume of information, techniques that can reduce and aggregate data into meaningful topics are essential. One such technique is the two-phase stream clustering approach. In the first, online micro-clustering phase, the system forms micro-clusters from the incoming data stream, incrementally merges new items into related existing micro-clusters, and prunes or fades micro-clusters as they become inactive, producing a constantly updating yet compact set of micro-clusters representing potential topics and subtopics of the stream. In the second, offline macro-clustering phase, these micro-clusters are …
Patient Subset Classification Using Encoded Embeddings And Knowledge Graph Retrieval-Augmented Generation, Benjamin A. Holmes
Patient Subset Classification Using Encoded Embeddings And Knowledge Graph Retrieval-Augmented Generation, Benjamin A. Holmes
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The widespread adoption of electronic medical records has created a vast reservoir of clinical data that can be leveraged to better understand how interventions relate to patient outcomes. Much of this information, however, exists as unstructured free-text, posing significant challenges for traditional statistical and machine-learning methods. Solving these challenges would allow the extraction of specific patient subpopulations (clinically relevant cohorts of individuals who share overlapping symptoms, risk factors, or diagnostic criteria), which could be used in precision medicine. Despite this promise, extracting these subpopulations from unstructured medical notes is an ongoing challenge due to the variability of clinical language and …
Using Unsupervised Machine Learning To Experimentally Categorize Separation On Low-Pressure Turbine Blades, Aaron B. Suter
Using Unsupervised Machine Learning To Experimentally Categorize Separation On Low-Pressure Turbine Blades, Aaron B. Suter
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Laminar boundary layer separation can significantly degrade the efficiency of Low-Pressure Turbine (LPT) blades. While active flow control (AFC) methods can mitigate these losses, energy-efficient implementation requires activating the system only when performance decreases. This study validates an unsupervised machine learning framework that utilizes sparse, discrete surface-pressure measurements to distinguish between high and low aerodynamic loss states. A fuzzy c-means (FCM) clustering model was trained on limited pressure data obtained in a low-speed linear cascade across Reynolds numbers from 30,000 to 160,000 and used to categorize the flow regime in real time. At Re = 40,000, vortex generator jet (VGJ) …
Vagus Nerve Stimulation Ameliorates Cognitive Impairment Caused By Hypoxia, Birendra Sharma
Vagus Nerve Stimulation Ameliorates Cognitive Impairment Caused By Hypoxia, Birendra Sharma
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Hypoxia disrupts brain function due to the high demand for oxygen, leading to significant cognitive impairments. While vagus nerve stimulation (VNS) has been shown to enhance cognition, its ability to counteract hypoxia-induced deficits remains unclear. In this study, male Sprague–Dawley rats were assigned to sham, hypoxia, or VNS + hypoxia groups, with VNS delivered during hypoxia (8% oxygen) using biphasic pulses (100 μs, 30 Hz, 0.8 mA). Cognitive performance was evaluated using multiple behavioral paradigms, and hippocampal tissue was analyzed for neurotrophin expression through quantitative PCR and immunohistochemistry. Among the behavioral measures, hypoxia specifically impaired performance on the passive avoidance …
Utilizing Optimization Tools For Passive Flow Control Passage Loss Reduction In Low-Pressure Turbines, Bryant Robert Duane Burton
Utilizing Optimization Tools For Passive Flow Control Passage Loss Reduction In Low-Pressure Turbines, Bryant Robert Duane Burton
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Low-pressure turbines (LPTs) play a crucial role in fuel efficiency and thrust generation of aero-engines. Traditional LPT designs, however, involve multiple stages and numerous blades, resulting in increased weight and manufacturing costs. The challenge of modern- day researchers is to reduce the weight and cost but maintain the high efficiency of LPTs. To address this, one approach is to increase the aerodynamic loading of individual blades, reducing the blade count. However, this can lead to increased secondary losses caused by flow separation, particularly in the endwall regions. This research focuses on optimizing the blade profile at the junction with the …
Quantification Of Porosity In Hfb2–20%Sic Using Convolutional Neural Networks, Cameron J. Floyd
Quantification Of Porosity In Hfb2–20%Sic Using Convolutional Neural Networks, Cameron J. Floyd
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Ultra-high-temperature ceramics, commonly used in aerospace applications, operate in high-temperature oxidizing environments where a surface scale forms and regulates oxy gen access. For hafnium diboride–silicon carbide (HfB2–SiC), that scale consists of a borosilicate glass layer over a porous HfO2 skeleton. Oxygen transport through this poros ity governs the kinetics of mechanistic models, requiring reproducible inputs for accurate pore fraction (PF), pore-size distributions, and ultimately tortuosity. This thesis replaces rule-based SEM thresholding with a convolutional neural network that segments pores and predicts the pore-radius distribution for transport models. The resulting calibrated porosity maps and size distributions transfer within the acquisition domain …
Effect Of Build Orientation And Environmental Conditions On Mechanical And Geometric Properties Of Polymeric Parts Fabricated By The Powder Bed Fusion Process, Adedamola O. Adeyemi
Effect Of Build Orientation And Environmental Conditions On Mechanical And Geometric Properties Of Polymeric Parts Fabricated By The Powder Bed Fusion Process, Adedamola O. Adeyemi
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Polyamide-12 (aka PA12) is a synthetic thermoplastic polymer that has been used in various applications, including the electronic and electrical, oil and gas industrial manufacturing and automotive industry. There are several ways to manufacture parts with PA12 and PA12 based composites. Injection molding, a traditional manufacturing process, has been the standard manufacturing route for polymers for a long time. Recently, however, additive manufacturing (AM) processes, such as powder bed fusion (PBF) and material extrusion (ME) have been introduced. PBF is a manufacturing process characterized by the selective sintering of successive layers of powdered material. It is the most effective AM …
Generative Adversarial Networks (Gans) For High-Dimensional Biological Data, Harigovind Harikumar
Generative Adversarial Networks (Gans) For High-Dimensional Biological Data, Harigovind Harikumar
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This thesis investigates the application of Generative AI models, mainly Generative Adversarial Network (GAN) models to high dimensional and low sample size biological datasets like Motion Sickness, Breast Cancer, Crohn, and Melanoma. We utilized and compared three generative AI frameworks: Vanilla GAN, Wasserstein GAN (WGAN), Locality-Sensitive Hashing GAN (LSH-GAN) and Omics GAN. To address the challenges associated with high-dimensionality and low sample size, which was leading to very poor outputs of biological synthetic samples, we came up with an approach to stop the model when it reaches its saturation level. That is, we printed the loss plots to see where …
The Role Of Trpm7 In Mouse Development And Immune Cell Function, Jananie Rockwood
The Role Of Trpm7 In Mouse Development And Immune Cell Function, Jananie Rockwood
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Transient receptor melastatin 7 (TRPM7) functions both as an ion channel and a protein kinase. TRPM7 has been implicated in Mg2+ homeostasis, embryogenesis, cardiac automaticity, and immunity. The purpose of this research was to deepen our understanding of TRPM7 channel and kinase functions. To this end, we used two transgenic mouse models: the TRPM7 gain-of-function (GOF) and TRPM7 kinase-dead (KD) mice to address the consequences of increased channel activity and kinase inactivation, respectively. Global deletion of TRPM7 or the kinase domain alone are embryonic lethal, therefore, we used the TRPM7 GOF mouse to investigate germline transmission. We examined embryo development …
Pixmix Attack: Implementation And Evaluation Of A Novel Pixel Injection On Digital Video Port (Dvp) Interface In Embedded Camera Systems With Pcb Hardware Trojan, Sayed Md Tashfi Nowroz
Pixmix Attack: Implementation And Evaluation Of A Novel Pixel Injection On Digital Video Port (Dvp) Interface In Embedded Camera Systems With Pcb Hardware Trojan, Sayed Md Tashfi Nowroz
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Image sensors are at the heart of machine vision systems in robotics, industrial automation, and surveillance systems which ideally operate with minimal human supervision and only occasional maintenance. The image sensors convert visible light into electrical signals which are locally decoded to image on the printed circuit board (PCB) by an ordinary embedded processor System on Chip (SoC). This thesis investigates a critical vulnerability in such systems, targeting the communication protocol at the signal level during runtime. Specifically, it focuses on a novel attack in the Digital Video Port (DVP) protocol, possible to exploit with PCB-based hardware Trojans, to craft …
Induced Reduction Of N-Glycosylation Leads To Heart Failure, Anthony M. Young
Induced Reduction Of N-Glycosylation Leads To Heart Failure, Anthony M. Young
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Cardiovascular disease is the leading cause of death and contributes to the increasing global prevalence of heart failure (HF). N-glycosylation is a common co-/post-translational modification where branching oligosaccharides are bound to the extracellular domain of membrane proteins. This creates a diverse range of glycan structures and requires the coordination of hundreds of regulated genes. Inherited mutations in glycan synthesis frequently present with cardiomyopathies leading to HF with reduced ejection fraction (HFrEF). Additionally, gene expression studies of HFrEF patients have shown altered expression of glycosylation-related genes, including alpha-1,3-mannosyl-glycoproten 2-beta-N acetlyglucosaminyltransferase (Mgat1). This gene encodes N-acetylglucosaminyl transferase 1 (GlcNAcT1) which is required …
A Secure Ml-Assisted Framework For Resilient And Efficient Prediction Of Physiotherapy Sequence In Bilateral Carpal Tunnel Syndrome, Pratik Pandurang Kharat
A Secure Ml-Assisted Framework For Resilient And Efficient Prediction Of Physiotherapy Sequence In Bilateral Carpal Tunnel Syndrome, Pratik Pandurang Kharat
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Bilateral idiopathic carpal tunnel syndrome (CTS) is a neuromuscular disorder characterized by compression of the median nerve at both wrists, leading to symptoms such as pain, numbness, tingling, and muscle weakness. Unlike unilateral cases, bilateral idiopathic CTS presents distinct therapeutic challenges due to the simultaneous involvement of both hands and the lack of an identifiable underlying cause. This study explores the application of machine learning techniques to predict the optimal sequence of physiotherapeutic interventions Stretching followed by Myofascial Mobilization (S/M) or the reverse (M/S) in female patients with bilateral idiopathic CTS and right hand dominance. Data were drawn from a …
The Impact Of Ahr And Hs1.2 Enhancer Genetic Variations On Igh Expression And Antibody Production In Human B Cells, Mili Santosh Bhakta-Yadav
The Impact Of Ahr And Hs1.2 Enhancer Genetic Variations On Igh Expression And Antibody Production In Human B Cells, Mili Santosh Bhakta-Yadav
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Antibody production is an essential component of the immune response against pathogens. The immunoglobulin heavy chain (IgH) gene codes for the heavy chain of antibodies. The IgH constant regions Cμ, Cδ, Cγ1-4, Cα1-2, and Cε encode the five major classes of antibodies, i.e., IgM, IgD, IgG1-4, IgA1-2, and IgE, respectively. The transcription of the IgH gene and class switch from IgM to other isotypes is regulated by two 3’ IgH regulatory regions (3’IgHRRs), each of which is a cluster of three enhancer regions (hs3, hs1.2 and hs4). The genetic variations in the hs1.2 enhancer have been identified; a ~53 bp …
Learning Under Data Scarcity: Reasoning And Negative Distillation For Texts And Graphs, Calvin T. Greenewald
Learning Under Data Scarcity: Reasoning And Negative Distillation For Texts And Graphs, Calvin T. Greenewald
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Modern machine learning (ML) models rely on large amounts of high-quality labeled data to achieve optimal performance. However, in many real-world domains, such as cyber security, acquiring sufficient labeled data is often infeasible due to cost, privacy concerns, and the rapid evolution of underlying phenomena. This challenge underscores the importance of learning under data scarcity. This thesis addresses this challenge by proposing distinct, modality-specific techniques for text and graph domains, which allow models to generalize effectively with minimal data. For text classification task, we incorporate distilled rationales from large language models and adversarial perturbations into the input space to improve …
Green Design Of Plant Based Pharmaceutical Drugs: Example Of A Wound Healing Topical Cream With Plectranthus Bojeri (Benth) Hedge Lamiaceae Extract, Helga Rim Farasoa, Marie Louise Razafindravao, Rojo Fanambinantsoa Andriamiarantsoa, Gerard Cecilien Raboanary, Jean Marie Razafindrakoto, Voahangy Ramanandraibe Vestalys
Green Design Of Plant Based Pharmaceutical Drugs: Example Of A Wound Healing Topical Cream With Plectranthus Bojeri (Benth) Hedge Lamiaceae Extract, Helga Rim Farasoa, Marie Louise Razafindravao, Rojo Fanambinantsoa Andriamiarantsoa, Gerard Cecilien Raboanary, Jean Marie Razafindrakoto, Voahangy Ramanandraibe Vestalys
Journal of Bioresource Management
To ensure the perennity of natural resources, the valorisation process of herbal pharmaceuticals must be assessed for sustainability from the very beginning of its design. No specific tools have been developed for this particular field so far. We demonstrate in this study that existing green design tools can be adapted to evaluate plant-based products manufacturing process. As the example of a topical cream using Plectranthus bojeri (Benth) Hedge LAMIACEAE extract was considered, we first confirmed the traditional use of this plant for wound healing. It acts by accelerating the re-epithelialisation phase. Using the Vermeer Cosmolife version 0.24 software tool the …
Experimental Study Of Drag Characteristics And Dust Removal Performance Of A Hybrid Wet-Filter Precipitator, Hang Yi, Zifeng Yang, Deqiang Chang, Xinjiao Tian, Jingxian Liu
Experimental Study Of Drag Characteristics And Dust Removal Performance Of A Hybrid Wet-Filter Precipitator, Hang Yi, Zifeng Yang, Deqiang Chang, Xinjiao Tian, Jingxian Liu
Mechanical and Materials Engineering Faculty Publications
With their advantages of high dust removal efficiency and low drag characteristics, hybrid wet-filter precipitators have great potential for dust control in coal mines, but the underlying mechanisms are not well understood. In this study, to help fill this knowledge gap, a hybrid wet-filter precipitator consisting of a 40-layer metal filter and a defogger device is designed and a prototype is constructed. Experiments are conducted to investigate its drag characteristics under wind velocities from 0.85 to 5.68 m/s and its dust removal performance under wind velocities of 2 and 4 m/s. On the basis of results with the initial design, …
Design And Development Of A Pineapple Peeling Machine, Dare E. Ibiyeye, Oluwatoyin O. Olunloyo, Oluwatosin A. Adesida, Taiye R. Afolabi, Abisayo O. Akala, Funmilayo B. Okanlawon
Design And Development Of A Pineapple Peeling Machine, Dare E. Ibiyeye, Oluwatoyin O. Olunloyo, Oluwatosin A. Adesida, Taiye R. Afolabi, Abisayo O. Akala, Funmilayo B. Okanlawon
Journal of Bioresource Management
The objective of this research aimed to design, modify and develop a manually operated cost effective pineapple peeling and coring machine with a reduced time of operation, easy to operate and maintain. The material of construction is stainless steel. The designed pineapple peeling and coring machine has two cylindrical cutting blades which simultaneously removes pineapple skin and core. Other components included; spring coring plate, core remover, and spring loaded handle. The peeling operation includes; cutting of crown and bottom of pineapple with a knife, placing pineapple on coring plate and applying pressure to spring loaded handle downward over the pineapple …
Using Dft On Ultrasound Measurements To Determine Patient-Specific Blood Flow Boundary Conditions For Computational Hemodynamics Of Intracranial Aneurysms, Hang Yi, Zifeng Yang, Luke Bramlage, Bryan Ludwig
Using Dft On Ultrasound Measurements To Determine Patient-Specific Blood Flow Boundary Conditions For Computational Hemodynamics Of Intracranial Aneurysms, Hang Yi, Zifeng Yang, Luke Bramlage, Bryan Ludwig
Mechanical and Materials Engineering Faculty Publications
Boundary conditions (BCs) is one pivotal factor influencing the accuracy of hemodynamic predictions on intracranial aneurysms (IAs) using computational fluid dynamics (CFD) modeling. Unfortunately, a standard procedure to secure accurate BCs for hemodynamic modeling does not exist. To bridge such a knowledge gap, two representative patient-specific IA models (Case-I and Case-II) were reconstructed and their blood flow velocity waveforms in the internal carotid artery (ICA) were measured by ultrasonic techniques and modeled by discrete Fourier transform (DFT). Then, numerical investigations were conducted to explore the appropriate number of samples (N) for DFT modeling to secure the accurate BC by comparing …
Numerical Investigation Of Supersonic Flow Over A Wedge By Solving 2d Euler Equations Utilizing The Steger–Warming Flux Vector Splitting (Fvs) Scheme, Mitch Wolff, Hashim H. Abada, Hussein Awad Kurdi Awad Kurdi Saad
Numerical Investigation Of Supersonic Flow Over A Wedge By Solving 2d Euler Equations Utilizing The Steger–Warming Flux Vector Splitting (Fvs) Scheme, Mitch Wolff, Hashim H. Abada, Hussein Awad Kurdi Awad Kurdi Saad
Mechanical and Materials Engineering Faculty Publications
Supersonic flow over a half-angle wedge (θ = 15°) with an upstream Mach number of 2.0 was investigated using 2D Euler equations where sea level conditions were considered. The investigation employed the Steger–Warming flux vector splitting (FVS) method executed in MATLAB 9.13.0 (R2022b) software. The study involved a meticulous comparison between theoretical calculations and numerical results. Particularly, the research emphasized the angle of oblique shock and downstream flow properties. A substantial iteration count of 2000 iteratively refined the outcomes, underscoring the role of advanced computational resources. Validation and comparative assessment were conducted to elucidate the superiority of the Steger–Warming flux …
Evaluation And Modulation Of The Circadian Clock In Human Keratinocytes And Epidermal Skin, William Harold Cvammen Iv
Evaluation And Modulation Of The Circadian Clock In Human Keratinocytes And Epidermal Skin, William Harold Cvammen Iv
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The circadian clock is a fundamental biological mechanism that regulates various physiological processes, including DNA repair, to synchronize with the day-night cycle. In human skin, exposure to ultraviolet (UV) light poses a significant challenge, inducing DNA damage that must be efficiently repaired to maintain genomic integrity and prevent carcinogenesis. This study delved into the complex interplay between the circadian clock, UV light exposure, DNA repair, and modulation of circadian transcriptional machinery in human skin. Initially, we examined the transcriptomic profile of the circadian clock in humans through in silico-based approaches and in vivo studies, revealing that core clock gene expression …
Understanding Patient Profiles In Sickle Cell Disease Using Unsupervised Machine Learning, Raj Kamal Somavarapu
Understanding Patient Profiles In Sickle Cell Disease Using Unsupervised Machine Learning, Raj Kamal Somavarapu
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Sickle Cell Disease (SCD) is one of the most prevalent genetic blood disorders affecting millions of people worldwide. It is often accompanied by acute and/or chronic pain leading to increased healthcare costs and adverse outcomes. Effective management of SCD requires an understanding of the diverse physiological profiles. This study employs unsupervised machine learning, specifically K-means clustering to categorize the patients suffering with SCD into different clusters based on their vital signs. The main aim is to identify the groups that reflect similarities in physiological and pain profiles, allowing an in-depth analysis to reveal distinctive features distinguishing patient clusters. The project …