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Articles 1 - 30 of 1234
Full-Text Articles in Engineering
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 …
Additively Manufactured Polymeric Surface-Based Lattice Structures For Vibration Attenuation, Imabin Kelvin Ekpelu
Additively Manufactured Polymeric Surface-Based Lattice Structures For Vibration Attenuation, Imabin Kelvin Ekpelu
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The focus of this study was to select triply periodic minimal surface (TPMS) structures made of 3D-printed polymers. The primary variables in this study were: TPMS shape, lattice volume ratio, and lattice material. Vibration absorption was characterized by damping ratio via transmissibility at the system’s natural frequency. The vibration testing was performed using an electro-dynamic shaker, a known mass, an input/control accelerometer, and an output/response accelerometer. The 3D-printed absorber/lattice was mounted to the shaker baseplate and a mass will be mounted on top of the absorber. One accelerometer will be mounted to the shaker baseplate and the other will be …
Fuzzing Php Interpreters By Automatically Generating Samples, Jacob S. Baumgarte
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 …
Comparative Adjudication Of Noisy And Subjective Data Annotation Disagreements For Deep Learning, Scott David Williams
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. …
Enhancing Graph Convolutional Network With Label Propagation And Residual For Malware Detection, Aravinda Sai Gundubogula
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 …
Anomaly Detection In Multi-Seasonal Time Series Data, Ashton Taylor Williams
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 …
A Secure And Efficient Iiot Anomaly Detection Approach Using A Hybrid Deep Learning Technique, Bharath Reedy Konatham
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 …
Data-Driven Strategies For Pain Management In Patients With Sickle Cell Disease, Swati Padhee
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 …
Friend Or Foe? The Role Of Transforming Growth Factor-Β (Tgfβ) Signaling In Calcineurin Inhibitor-Induced Renal Damage, Adaku Ume
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With its incorporation into clinical practice in the early 1980s, the class of pharmacological agents known as calcineurin inhibitors (CNIs) quickly became the cornerstone of immunosuppressive therapy post-organ transplantation. However, its use is limited by irreversible kidney damage in the form of renal fibrosis. The molecular mechanism by which CNIs induce renal fibrosis remains to be better understood, and to date, there are no specific therapeutic strategies to mitigate this damage. This dilemma presents a critical need to explain mechanisms by which CNIs cause renal damage. Kidneys of patients on chronic CNI therapy show increased expression of the proinflammatory cytokine …
Unsupervised-Based Distributed Machine Learning For Efficient Data Clustering And Prediction, Vishnu Vardhan Baligodugula
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, …
Data-Driven Strategies For Disease Management In Patients Admitted For Heart Failure, Ankita Agarwal
Data-Driven Strategies For Disease Management In Patients Admitted For Heart Failure, Ankita Agarwal
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Heart failure is a syndrome which effects a patient’s quality of life adversely. It can be caused by different underlying conditions or abnormalities and involves both cardiovascular and non-cardiovascular comorbidities. Heart failure cannot be cured but a patient’s quality of life can be improved by effective treatment through medicines and surgery, and lifestyle management. As effective treatment of heart failure incurs cost for the patients and resource allocation for the hospitals, predicting length of stay of these patients during each hospitalization becomes important. Heart failure can be classified into two types: left sided heart failure and right sided heart failure. …
Identifying A Novel Ferrocene Derivative As A K-Ras Inhibitor, Kristen Marie Rehl
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 …
Brain Morphometry From Neuroimaging As A Biomarker For Alzheimer's Disease, Nonyelum Benedicta Aniebo
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 …
Contributors To Pathologic Depolarization In Myotonia Congenita, Jessica Hope Myers
Contributors To Pathologic Depolarization In Myotonia Congenita, Jessica Hope Myers
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Myotonia congenita is an inherited skeletal muscle disorder caused by loss-of-function mutation in the CLCN1 gene. This gene encodes the ClC-1 chloride channel, which is almost exclusively expressed in skeletal muscle where it acts to stabilize the resting membrane potential. Loss of this chloride channel leads to skeletal muscle hyperexcitability, resulting in involuntary muscle action potentials (myotonic discharges) seen clinically as muscle stiffness (myotonia). Stiffness affects the limb and facial muscles, though specific muscle involvement can vary between patients. Interestingly, respiratory distress is not part of this disease despite muscles of respiration such as the diaphragm muscle also carrying this …
Encryption And Compression Classification Of Internet Of Things Traffic, Mariam Najdat M Saleh
Encryption And Compression Classification Of Internet Of Things Traffic, Mariam Najdat M Saleh
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The Internet of Things (IoT) is used in many fields that generate sensitive data, such as healthcare and surveillance. Increased reliance on IoT raised serious information security concerns. This dissertation presents three systems for analyzing and classifying IoT traffic using Deep Learning (DL) models, and a large dataset is built for systems training and evaluation. The first system studies the effect of combining raw data and engineered features to optimize the classification of encrypted and compressed IoT traffic using Engineered Features Classification (EFC), Raw Data Classification (RDC), and combined Raw Data and Engineered Features Classification (RDEFC) approaches. Our results demonstrate …
Solidity Compiler Version Identification On Smart Contract Bytecode, Lakshmi Prasanna Katyayani Devasani
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 …
Digital Beamforming Array Phase Calibration Techniques For Multi-Pass Interferometric Sar, Kelly Cheung
Digital Beamforming Array Phase Calibration Techniques For Multi-Pass Interferometric Sar, Kelly Cheung
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Calibration plays a critical role in the optimal performance of algorithms in digital beamforming arrays. Phase incoherency between elements results in poor beamforming with decreased gain and higher sidelobes, leading to a decrease in accuracy and sensitivity of measurements. A similar problem exists in performing multi-pass interferometric SAR (IFSAR) processing of SAR data stacks to generate topological maps of the scene, where phase errors translate to height errors. By treating each SAR image in the data stack like an element of a uniform linear array, this thesis explores several phase calibration techniques that can be used to calibrate digital beamforming …
Characterization Of Aerosol Jet Printed Silver Thin Films Sintered By A Scanning Laser, William A. Metzger
Characterization Of Aerosol Jet Printed Silver Thin Films Sintered By A Scanning Laser, William A. Metzger
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Direct write printing, which is part of additive manufacturing (AM) technology, offers unique capabilities that can complement traditional methods of electronics fabrication. Printing of electrical interconnects via aerosolization is one of the areas in AM that is very important in electronics fabrication. Post-print sintering is a critical step in printed electrical interconnects because it strongly influences the electrical resistivity of the interconnects. Interconnects require the lowest possible resistivity to achieve better performance. Thermal sintering is the most common technique employed in printed interconnects. However, it is limited to substrates that can handle the high temperature requirement for sintering. For polymers …
Efficient Cloud-Based Ml-Approach For Safe Smart Cities, Niveshitha Niveshitha
Efficient Cloud-Based Ml-Approach For Safe Smart Cities, Niveshitha Niveshitha
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Smart cities have emerged to tackle many critical problems that can thwart the overwhelming urbanization process, such as traffic jams, environmental pollution, expensive health care, and increasing energy demand. This Master thesis proposes efficient and high-quality cloud-based machine-learning solutions for efficient and sustainable smart cities environment. Different supervised machine-learning models for air quality predication (AQP) in efficient and sustainable smart cities environment is developed. For that, ML-based techniques are implemented using cloud-based solutions. For example, regression and classification methods are implemented using distributed cloud computing to forecast air execution time and accuracy of the implemented ML solution. These models are …
Bandgap Engineering Of 2d Materials And Its Electric And Optical Properties, Kumar Vishal
Bandgap Engineering Of 2d Materials And Its Electric And Optical Properties, Kumar Vishal
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Since their invention in 1958, Integrated Circuits (ICs) have become increasingly more complex, sophisticated, and useful. As a result, they have worked their way into every aspect of our lives, for example: personal electronic devices, wearable electronics, biomedical sensors, autonomous driving cars, military and defense applications, and artificial intelligence, to name some areas of applications. These examples represent both collectively, and sometimes individually, multi-trillion-dollar markets. However, further development of ICs has been predicted to encounter a performance bottleneck as the mainstream silicon industry, approaches its physical limits. The state-of-the-art of today’s ICs technology will be soon below 3nm. At such …
Modeling, Simulation, And Hardware Testing Of A Noise-Canceller Adc Architecture, Ethan R. Rando
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 …
Path-Safe :Enabling Dynamic Mandatory Access Controls Using Security Tokens, James P. Maclennan
Path-Safe :Enabling Dynamic Mandatory Access Controls Using Security Tokens, James P. Maclennan
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Deploying Mandatory Access Controls (MAC) is a popular way to provide host protection against malware. Unfortunately, current implementations lack the flexibility to adapt to emergent malware threats and are known for being difficult to configure. A core tenet of MAC security systems is that the policies they are deployed with are immutable from the host while they are active. This work looks at deploying a MAC system that leverages using encrypted security tokens to allow for redeploying policy configurations in real-time without the need to stop a running process. This is instrumental in developing an adaptive framework for security systems …
Processing And Characterization Of Inkjet Printed Batio3/Su-8 Nanocomposite Dielectrics, Mustapha A. Muhammad
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 …
Multi-Variable Phase And Gain Calibration For Multi-Channel Transmit Signals, Ryan C. Ball
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 …
The Open Charge Point Protocol (Ocpp) Version 1.6 Cyber Range A Training And Testing Platform, David Elmo Ii
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 …
Prediction Of Ka-Band Radar Cross Section With Thz Scale Models With Varying Surface Roughness, Andrew J. Huebner
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, …
Effect Of Size And Shape Parameters On Microstructure Of Additively Manufactured Inconel 718, Showmik Ahsan
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.
Effective Systems For Insider Threat Detection, Muhanned Qasim Jabbar Alslaiman
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 …
Comparative Study Of Mof's In Phosphate Adsorption, Eniya Karunamurthy
Comparative Study Of Mof's In Phosphate Adsorption, Eniya Karunamurthy
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High concentrations of phosphate are known to adversely affect the environment. Excess phosphate can lead to eutrophication that eventually fosters uncontrollable growth of aquatic plants and algae. This can result in depletion of oxygen content which adversely impacts underwater organism’s survival rates. Metal organic frameworks (MOFs) consist of organic linkers in conjunction with metal ions or clusters arranged within a crystalline structure. They are highly porous and have larger surface area due to their ability to possess extensive void spaces while remaining bulky in nature. MOFs can absorb phosphate from aqueous solutions. We have investigated the use of commercially available …
Effects Of Elastic Anisotropy On Residual Stress Measurements Performed Using The Hole-Drilling Technique, Joshua T. Ward
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 …