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Articles 121 - 150 of 3445

Full-Text Articles in Engineering

Unfolding The Link Between The Axon Initial Segment, Endoplasmic Reticulum Stress, And Cognitive Impairment In Type 2 Diabetes, Jennae Shelby Jan 2023

Unfolding The Link Between The Axon Initial Segment, Endoplasmic Reticulum Stress, And Cognitive Impairment In Type 2 Diabetes, Jennae Shelby

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Dementia and mild cognitive impairment are well recognized comorbidities of type 2 diabetes, termed diabetic encephalopathy, that lack in course modifying treatment options. The axon initial segment (AIS) is a critical mediator of neuronal output. Shortening of AIS length has been shown to decrease neuronal excitability and is implicated in diabetic encephalopathy. AIS shortening may be reversible, hence investigating the mechanism responsible could address this significant gap in patient care by providing new therapeutic targets. Endoplasmic reticulum (ER) stress, a cellular condition that activates the unfolded protein response has been implicated in diabetic encephalopathy. Here, using immunocytochemistry in primary mouse …

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Solidity Compiler Version Identification On Smart Contract Bytecode, Lakshmi Prasanna Katyayani Devasani Jan 2023

Solidity Compiler Version Identification On Smart Contract Bytecode, Lakshmi Prasanna Katyayani Devasani

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Identifying the version of the Solidity compiler used to create an Ethereum contract is a challenging task, especially when the contract bytecode is obfuscated and lacks explicit metadata. Ethereum bytecode is highly complex, as it is generated by the Solidity compiler, which translates high-level programming constructs into low-level, stack-based code. Additionally, the Solidity compiler undergoes frequent updates and modifications, resulting in continuous evolution of bytecode patterns. To address this challenge, we propose using deep learning models to analyze Ethereum bytecodes and infer the compiler version that produced them. A large number of Ethereum contracts and the corresponding compiler versions is …

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The Open Charge Point Protocol (Ocpp) Version 1.6 Cyber Range A Training And Testing Platform, David Elmo Ii Jan 2023

The Open Charge Point Protocol (Ocpp) Version 1.6 Cyber Range A Training And Testing Platform, David Elmo Ii

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The widespread expansion of Electric Vehicles (EV) throughout the world creates a requirement for charging stations. While Cybersecurity research is rapidly expanding in the field of Electric Vehicle Infrastructure, efforts are impacted by the availability of testing platforms. This paper presents a solution called the “Open Charge Point Protocol (OCPP) Cyber Range.” Its purpose is to conduct Cybersecurity research against vulnerabilities in the OCPP v1.6 protocol. The OCPP Cyber Range can be used to enable current or future research and to train operators and system managers of Electric Charge Vehicle Supply Equipment (EVSE). This paper demonstrates this solution using three …

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Prediction Of Ka-Band Radar Cross Section With Thz Scale Models With Varying Surface Roughness, Andrew J. Huebner Jan 2023

Prediction Of Ka-Band Radar Cross Section With Thz Scale Models With Varying Surface Roughness, Andrew J. Huebner

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Radar cross section (RCS) of electrically large targets can be challenging and expensive to measure. The use of scale models to predict the RCS of such large targets saves time and reduces facility requirements. This study investigates Ka-band (27 to 29 GHz) RCS prediction from scale model measurements at 500 to 750 GHz. Firstly, the coherent quasi-monostatic turntable RCS measurement system is demonstrated. Secondly, three aluminum 18:1 scale dihedrals with surface roughness up to 218 icroinches are measured to investigate how the roughness affects the Ka-band prediction. The measurements are compared to a parametric scattering model for the specular response, …

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A Secure And Efficient Iiot Anomaly Detection Approach Using A Hybrid Deep Learning Technique, Bharath Reedy Konatham Jan 2023

A Secure And Efficient Iiot Anomaly Detection Approach Using A Hybrid Deep Learning Technique, Bharath Reedy Konatham

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The Industrial Internet of Things (IIoT) refers to a set of smart devices, i.e., actuators, detectors, smart sensors, and autonomous systems connected throughout the Internet to help achieve the purpose of various industrial applications. Unfortunately, IIoT applications are increasingly integrated into insecure physical environments leading to greater exposure to new cyber and physical system attacks. In the current IIoT security realm, effective anomaly detection is crucial for ensuring the integrity and reliability of critical infrastructure. Traditional security solutions may not apply to IIoT due to new dimensions, including extreme energy constraints in IIoT devices. Deep learning (DL) techniques like Convolutional …

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Merit Study Of Battery Or Hydrogen Energy Storage For Large Scale, Combined Wind And Solar Electricity Generation, Ashley K. Moore Jan 2023

Merit Study Of Battery Or Hydrogen Energy Storage For Large Scale, Combined Wind And Solar Electricity Generation, Ashley K. Moore

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In the past several years, the energy sector has experienced a rapid increase in renewable energy installations due to declining capital costs for wind turbines, solar panels, and batteries. Wind and solar electricity generation are intermittent in nature which must be considered in an economic analysis if a fair comparison is to be made between electricity supplied from renewables and electricity purchased from the grid. Energy storage reduces curtailment of wind and solar and minimizes electricity purchases from the grid by storing excess electricity and deploying the energy at times when demand exceeds the renewable energy supply. The objective of …

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Monitoring Blood Flow In Animal Models Using A Camera-Based Technique, Dharminder Singh Langri Jan 2023

Monitoring Blood Flow In Animal Models Using A Camera-Based Technique, Dharminder Singh Langri

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Blood flow dynamics plays a critical role in maintaining tissue health, as it delivers nutrients and oxygen while removing waste products. It is especially important when there is a disruption in cerebral autoregulation due to trauma, which can induce ischemia or hyperemia and can lead to secondary brain injury. Thus, there is a need for noninvasive techniques that can allow continuous monitoring of blood flow during intervention. Optical techniques have become increasingly practical for measuring blood flow due to their non-invasive, continuous, and relatively lower-cost nature. This research focused on developing a low-cost, scalable optical technique for measuring blood flow …

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Direct Ink Write Processing Of Signal Crossovers Using Aerosol Jet Printing Method, Lucas A. Clark Jan 2023

Direct Ink Write Processing Of Signal Crossovers Using Aerosol Jet Printing Method, Lucas A. Clark

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Electronics in different applications, such as in medical imaging devices, radar systems, communication transmitters, and optical drives, often require various power and signal lines to be integrated at board level. In such cases, different lines may cross over one another in three-dimensional space for efficient electronic integration. Crossovers are usually achieved by adding additional layers to a PCB. However, these additional layers increase the cost, weight, and complexity of the component. By creating a process and structure to offer board-level heterogenous integration, these factors may be reduced. RF-DC crossovers were designed and additively manufactured using an aerosol jet printer. Benzocyclobutene …

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Investigating The Application And Sustained Effects Of Stochastic Resonance On Haptic Feedback Sensitivity In A Laparoscopic Task, Kara Liane Wilcox Jan 2023

Investigating The Application And Sustained Effects Of Stochastic Resonance On Haptic Feedback Sensitivity In A Laparoscopic Task, Kara Liane Wilcox

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Stochastic resonance (SR) is a phenomenon that can enhance the detection or transmission of weak signals by adding random noise to a non-linear system. SR introduced into the human motor control system as a subthreshold mechanical vibration has shown promise to improve sensitivity to haptic feedback. SR can be valuable in a laparoscopic surgery application, where haptic feedback is critical. This research sought to find if applying SR to the human motor control system improves performance in a laparoscopic probing task, if the performance differs based on the location of stochastic resonance application, and if there are sustained effects from …

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Identifying A Novel Ferrocene Derivative As A K-Ras Inhibitor, Kristen Marie Rehl Jan 2023

Identifying A Novel Ferrocene Derivative As A K-Ras Inhibitor, Kristen Marie Rehl

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Ras proteins are small GTPases that regulate cell proliferation, differentiation and survival at the plasma membrane (PM). There are three Ras isoforms ubiquitously expressed in mammalian cells: H-, N- and K-Ras. Constitutively active Ras mutations are found in ~19% of all human cancers, with ~75% of those being in K-Ras. There are K-Ras inhibitors in clinic but they only target the oncogenic K-RasG12C mutant, which only makes up a small sub-set of K-Ras-driven cancers. Thus, there still exists a need for a pan anti-K-Ras drug. Ferrocene derivatives are a class of compounds that have been shown to inhibit the growth …

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Data-Driven Strategies For Pain Management In Patients With Sickle Cell Disease, Swati Padhee Jan 2023

Data-Driven Strategies For Pain Management In Patients With Sickle Cell Disease, Swati Padhee

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This research explores data-driven AI techniques to extract insights from relevant medical data for pain management in patients with Sickle Cell Disease (SCD). SCD is an inherited red blood cell disorder that can cause a multitude of complications throughout an individual’s life. Most patients with SCD experience repeated, unpredictable episodes of severe pain. Arguably, the most challenging aspect of treating pain episodes in SCD is assessing and interpreting the patient’s pain intensity level due to the subjective nature of pain. In this study, we leverage multiple data-driven AI techniques to improve pain management in patients with SCD. The proposed approaches …

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Processing And Characterization Of Inkjet Printed Batio3/Su-8 Nanocomposite Dielectrics, Mustapha A. Muhammad Jan 2023

Processing And Characterization Of Inkjet Printed Batio3/Su-8 Nanocomposite Dielectrics, Mustapha A. Muhammad

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The persistent demand for flexible and wearable electronic components in healthcare, aerospace, media, and transit applications has led to a significant shift from traditional electronics processes to printed electronics. Printed electronics are anticipated to establish itself as the industry's dominant force due to their enhanced flexibility, rapid prototyping capabilities, and seamless integration with everyday objects. They are cost-effective and have the scalable option for large-scale production because additive manufacturing techniques are used. Among the various printing methods available, inkjet printing has recently gained popularity for printing electronics, especially capacitors that require precise and complex structures on different substrates. Inkjet printing …

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A Novel Knowledge-Based Federated Deep Learning Approach For Enhancing Security And Privacy Preservation In Iot Edge Computing Applications, Tabassum Simra Jan 2023

A Novel Knowledge-Based Federated Deep Learning Approach For Enhancing Security And Privacy Preservation In Iot Edge Computing Applications, Tabassum Simra

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The Internet of Things (IoT) infrastructure encompasses smart devices and real-time sensors connected through the Internet, facilitating the exchange of large datasets among these devices. This interconnected network of IoT sensors generates a significant volume of data for processing and analysis by embedded IoT Edge Computing systems. IoT Edge Computing systems enable efficient real-time analysis and data communications. Furthermore, IoT Edge Computing emerges to enhance the overall efficiency of IoT applications, making them adept at handling the dynamic demands of AI-based and large data-driven applications. The integration of IoT Edge Computing introduces several unique research challenges. Unfortunately, IoT Edge Computing …

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Brain Morphometry From Neuroimaging As A Biomarker For Alzheimer's Disease, Nonyelum Benedicta Aniebo Jan 2023

Brain Morphometry From Neuroimaging As A Biomarker For Alzheimer's Disease, Nonyelum Benedicta Aniebo

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Alzheimer’s disease (AD) is the seventh leading cause of death globally with an estimated 6.5 million Americans aged 65 and above living with Alzheimer’s dementia in 2022 and at a projected national cost of $321 billion. AD is characterized by a progressive and irreversible neurodegenerative dysfunction with clinical symptoms such as deterioration in cognition and memory loss. The Alzheimer’s Disease Neuroimaging Initiative (ADNI) is a multi-site, public-private global research initiative that supports both investigation and development of treatments that slow or terminate AD progression. The study included 60 participants, comprising 30 AD and 30 control cohorts respectively. All participants were …

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Accelerating Precision Station Keeping For Automated Aircraft, James D. Anderson Jan 2023

Accelerating Precision Station Keeping For Automated Aircraft, James D. Anderson

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Automated vehicles pose challenges in various research domains, including robotics, machine learning, computer vision, public safety, system certification, and beyond. These vehicles autonomously handle navigation and locomotion, often requiring minimal user interaction, and can operate on land, in water, or in the air. In the context of aircraft, one specific application is Automated Aerial Refueling (AAR). Traditional aerial refueling involves a "tanker" aircraft using a mechanism, such as a rigid boom arm or a flexible hose, to transfer fuel to another aircraft designated as the "receiver". For AAR, the boom arm may be maneuvered automatically, or in certain instances the …

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Comparative Adjudication Of Noisy And Subjective Data Annotation Disagreements For Deep Learning, Scott David Williams Jan 2023

Comparative Adjudication Of Noisy And Subjective Data Annotation Disagreements For Deep Learning, Scott David Williams

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Obtaining accurate inferences from deep neural networks is difficult when models are trained on instances with conflicting labels. Algorithmic recognition of online hate speech illustrates this. No human annotator is perfectly reliable, so multiple annotators evaluate and label online posts in a corpus. Labeling scheme limitations, differences in annotators' beliefs, and limits to annotators' honesty and carefulness cause some labels to disagree. Consequently, decisive and accurate inferences become less likely. Some practical applications such as social research can tolerate some indecisiveness. However, an online platform using an indecisive classifier for automated content moderation could create more problems than it solves. …

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Enhancing Graph Convolutional Network With Label Propagation And Residual For Malware Detection, Aravinda Sai Gundubogula Jan 2023

Enhancing Graph Convolutional Network With Label Propagation And Residual For Malware Detection, Aravinda Sai Gundubogula

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Malware detection is a critical task in ensuring the security of computer systems. Due to a surge in malware and the malware program sophistication, machine learning methods have been developed to perform such a task with great success. To further learn structural semantics, Graph Neural Networks abbreviated as GNNs have emerged as a recent practice for malware detection by modeling the relationships between various components of a program as a graph, which deliver promising detection performance improvement. However, this line of research attends to individual programs while overlooking program interactions; also, these GNNs tend to perform feature aggregation from neighbors …

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Rheological Modeling And Inkjet Printability Of Electrode Ink Formulation For Miniature And Interdigital Lithium-Ion Batteries, Habib Temitope-Adebayo Ajose Jan 2023

Rheological Modeling And Inkjet Printability Of Electrode Ink Formulation For Miniature And Interdigital Lithium-Ion Batteries, Habib Temitope-Adebayo Ajose

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The rapid advancement of technology has resulted in a greater need for effective energy storage systems to meet the demands of the transportation and electronics industries. Among various energy storage systems, batteries are the most widely used, primarily because of their ability to store significant amounts of energy. In addition, lithium-ion batteries are prevalent for powering portable electronic devices due to their long cycle life, high energy density, and high operating voltage. The traditional doctor-blade approach has been used over the years for producing batteries. Currently, research is being directed to additively manufacture Li-ion batteries via Drop-on-Demand Inkjet Printing with …

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Effective Systems For Insider Threat Detection, Muhanned Qasim Jabbar Alslaiman Jan 2023

Effective Systems For Insider Threat Detection, Muhanned Qasim Jabbar Alslaiman

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Insider threats to information security have become a burden for organizations. Understanding insider activities leads to an effective improvement in identifying insider attacks and limits their threats. This dissertation presents three systems to detect insider threats effectively. The aim is to reduce the false negative rate (FNR), provide better dataset use, and reduce dimensionality and zero padding effects. The systems developed utilize deep learning techniques and are evaluated using the CERT 4.2 dataset. The dataset is analyzed and reformed so that each row represents a variable length sample of user activities. Two data representations are implemented to model extracted features …

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Fuzzing Php Interpreters By Automatically Generating Samples, Jacob S. Baumgarte Jan 2023

Fuzzing Php Interpreters By Automatically Generating Samples, Jacob S. Baumgarte

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Modern web development has grown increasingly reliant on scripting languages such as PHP. The complexities of an interpreted language means it is very difficult to account for every use case as unusual interactions can cause unintended side effects. Automatically generating test input to detect bugs or fuzzing, has proven to be an effective technique for JavaScript engines. By extending this concept to PHP, existing vulnerabilities that have since gone undetected can be brought to light. While PHP fuzzers exist, they are limited to testing a small quantity of test seeds per second. In this thesis, we propose a solution for …

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Modeling, Simulation, And Hardware Testing Of A Noise-Canceller Adc Architecture, Ethan R. Rando Jan 2023

Modeling, Simulation, And Hardware Testing Of A Noise-Canceller Adc Architecture, Ethan R. Rando

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Analog-to-Digital Converters (ADCs) are essential elements of most complex electronic devices. ADCs allow for an analog signal to be converted into the digital domain, and thus interpreted by a digital circuit or model. While ADCs are extremely common, they are not immune from common tradeoffs when being designed and implemented. The most prominent tradeoff when selecting or designing an ADC is whether to pursue a high conversion rate or a high resolution on the digital output. There are some ADC designs that allow for relatively high resolution while maintaining a respectable conversion rate, however these designs often come at the …

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Calpains In The Peripheral Nerve And During Demyelination And Degeneration In Rodents, John A. Miller Jan 2023

Calpains In The Peripheral Nerve And During Demyelination And Degeneration In Rodents, John A. Miller

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Myelin formed around axons ensures rapid and efficient action potential propagation. Disruption of myelinated axons in the peripheral nervous system (PNS) often causes nerve conduction failure, neurological symptoms and can lead to long-term disability. Calpains are calcium dependent cysteine proteases that play many roles during both normal physiology and pathogenesis. Calpain-1 (CAPN1) and calpain-2 (CAPN2) are key calpain paralogs found in the nervous system and potentially have opposing roles, with CAPN2 having a more neurodegenerative function. In the central nervous system (CNS), calpains have a role in developmental myelination as well as in the pathological demyelination of multiple sclerosis and …

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Icing Mitigation Via High-Pressure Membrane Dehumidification In An Aircraft Thermal Management, Danielle D. Hollon Jan 2023

Icing Mitigation Via High-Pressure Membrane Dehumidification In An Aircraft Thermal Management, Danielle D. Hollon

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Icing, or the formation of ice from water via freezing or water vapor via desublimation, is a phenomenon that commonly occurs within air cycle-based refrigeration systems and requires thermal control that limits system performance. In aircraft applications icing frequently occurs in the heat exchangers and turbine(s) that are part of the air cycle machine, the refrigeration unit of the environmental control system. Traditionally, water vapor is removed from an air cycle machine via condensing in a heat exchanger and subsequent high-pressure water separation. This approach is not capable of removing all of the vapor present at low altitude conditions, corresponding …

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Unsupervised-Based Distributed Machine Learning For Efficient Data Clustering And Prediction, Vishnu Vardhan Baligodugula Jan 2023

Unsupervised-Based Distributed Machine Learning For Efficient Data Clustering And Prediction, Vishnu Vardhan Baligodugula

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Machine learning techniques utilize training data samples to help understand, predict, classify, and make valuable decisions for different applications such as medicine, email filtering, speech recognition, agriculture, and computer vision, where it is challenging or unfeasible to produce traditional algorithms to accomplish the needed tasks. Unsupervised ML-based approaches have emerged for building groups of data samples known as data clusters for driving necessary decisions about these data samples and helping solve challenges in critical applications. Data clustering is used in multiple fields, including health, finance, social networks, education, and science. Sequential processing of clustering algorithms, like the K-Means, Minibatch K-Means, …

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Experimental And Numerical Studies On The Projective Dye Visualization Velocimetry In A Squared Vertical Tube, Mark Bradley Johnson Jan 2023

Experimental And Numerical Studies On The Projective Dye Visualization Velocimetry In A Squared Vertical Tube, Mark Bradley Johnson

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In fluid flow experiments, there have been numerous techniques developed over the years to measure velocity. Most popular techniques are non-intrusive such as particle image velocimetry (PIV), but these techniques are not suitable for all applications. For instance, PIV cannot be used in examining in-vivo measurements since the laser is not able to penetrate through the patient, which is why medical applications typically use X-rays. However, the images obtained from X-rays, in particular digital subtraction angiography, are projective images which compress 3D flow features onto a 2D image. Therefore, when intensity techniques, such as optical flow method (OFM), are applied …

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Effects Of Elastic Anisotropy On Residual Stress Measurements Performed Using The Hole-Drilling Technique, Joshua T. Ward Jan 2023

Effects Of Elastic Anisotropy On Residual Stress Measurements Performed Using The Hole-Drilling Technique, Joshua T. Ward

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In the present work, the variation in through-thickness residual stress profiles driven by elastic anisotropy is investigated using the incremental hole-drilling method. The standardized hole-drilling technique allows for the calculation of in-plane stresses based on measured surface strains, however, these calculations assume elastic isotropy. The assumption of elastic isotropy allows for the material constants to be reduced down to two values, however, this assumption is invalid for many materials used in aerospace design. These materials are often times elastically anisotropic, which leads to inaccuracy and uncertainty in measured stress profiles. An interference fit ring and plug sample was designed, using …

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Effect Of Size And Shape Parameters On Microstructure Of Additively Manufactured Inconel 718, Showmik Ahsan Jan 2023

Effect Of Size And Shape Parameters On Microstructure Of Additively Manufactured Inconel 718, Showmik Ahsan

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Additive Manufacturing (AM) methods are promising in applications where complex part geometries, exotic materials and small lot sizes are required. Aerospace manufacturing stands to use AM methods extensively in the future, and frequently requires temperature- and corrosion-resistant alloy materials such as Inconel 718. However, the microstructural evolution of Inconel 718 during additive manufacturing is poorly understood and depends on part size and shape. We studied the microstructure of Inconel 718 parts manufactured by Laser Powder Bed Fusion in order to further elucidate these dependencies. Microstructural analysis, SEM imaging, EBSD scans and Microhardness testing were performed.

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Rankine Cycle Investigation On Meeting Power And Thermal Requirements Of High-Speed Aircraft, Jacob J. Spark Jan 2023

Rankine Cycle Investigation On Meeting Power And Thermal Requirements Of High-Speed Aircraft, Jacob J. Spark

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This work is investigating a dual mode Rankine cycle for aircraft applications, specifically meeting vehicle thermal and power requirements. This multiconfigurational approach allows the Thermal Management System (TMS) to be controlled based on aircraft needs. In this design, waste heat is removed from critical areas of the aircraft (e.g., propulsion, structure, subsystems) using the fuel as a heat sink. Hot fuel is then forced through a heat exchanger actively boiling water. The vapor byproduct is fed to a turbine coupled to a generator, providing power. The low-pressure steam is then condensed using cold fuel drawn from its tank; however, when …

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Multi-Variable Phase And Gain Calibration For Multi-Channel Transmit Signals, Ryan C. Ball Jan 2023

Multi-Variable Phase And Gain Calibration For Multi-Channel Transmit Signals, Ryan C. Ball

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A method for software-defined radio array calibration is presented. The method implements a matched filter approach to calculate the phase shift between channels. The temporal stability of the system and calibration coefficients are shown through the standard deviation over the course of four weeks. The standard deviation of the phase correction was shown to be less than 2 deg. for most channels in the array and within 8 deg. for the most extreme case. The standard deviation in amplitude scaling was calculated to be less than 0.06 for all channels in the array. The performance of the calibration is evaluated …

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Anomaly Detection In Multi-Seasonal Time Series Data, Ashton Taylor Williams Jan 2023

Anomaly Detection In Multi-Seasonal Time Series Data, Ashton Taylor Williams

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Most of today’s time series data contain anomalies and multiple seasonalities, and accurate anomaly detection in these data is critical to almost any type of business. However, most mainstream forecasting models used for anomaly detection can only incorporate one or no seasonal component into their forecasts and cannot capture every known seasonal pattern in time series data. In this thesis, we propose a new multi-seasonal forecasting model for anomaly detection in time series data that extends the popular Seasonal Autoregressive Integrated Moving Average (SARIMA) model. Our model, named multi-SARIMA, utilizes a time series dataset’s multiple pre-determined seasonal trends to increase …

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