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Articles 1 - 30 of 781
Full-Text Articles in Physical Sciences and Mathematics
Direct Parameter Fitting Of Action Potentials In Skeletal Muscle Cells Which Include Longitudinal Segments, Tyme Suda
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Excitation of skeletal muscle cells triggers a large voltage spike known as an action potential (AP), leading to muscle contraction. Modeling of an AP is typically done using the method developed by scientists Hodgkin and Huxley (HH). In the HH method, voltage and time gated Na+ and K+ ionic currents are simulated, along with a positive “Leak” ionic current and capacitive current. Due to the complexity and the computational time required for simulation, direct fitting of HH parameters to experimental APs has rarely been attempted. A previous thesis at Wright State performed direct fitting for the case of a single …
Green Synthesis Of Nanoparticles And Their Utilization In Electrochemical Detection Of Catechol- Or Phenol-Based Substances, Daniel Laurence Myers Iii
Green Synthesis Of Nanoparticles And Their Utilization In Electrochemical Detection Of Catechol- Or Phenol-Based Substances, Daniel Laurence Myers Iii
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PART A: GREEN SYNTHESIS OF COPPER NANOPARTICLES AND THEIR UTILIZATION IN THE DETECTION OF NEUROTRANSMITTERS Neurotransmitters, such as dopamine and epinephrine, are chemicals frequently found in the brain and are responsible for a number of human moods, needs, and emotions.1, 2 Detection of such neurotransmitters allows for a better judgement of a person’s physical and mental state, a utility that is vital in determining the presence of disease and mental illnesses.2 Electrochemical detectors used for such detections are often modified by materials such as metal nanoparticles.3 However, synthesizing nanoparticles can involve or produce chemicals, ammonium hydroxide among them, that are …
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 …
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. …
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 …
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 …
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 …
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 …
Application Of The Hvsr Technique To Map The Depth And Elevation Of The Bedrock Underlying Wright State University Campus, Dayton, Ohio, Devika L. Ghuge
Application Of The Hvsr Technique To Map The Depth And Elevation Of The Bedrock Underlying Wright State University Campus, Dayton, Ohio, Devika L. Ghuge
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Estimating sediment thickness and bedrock surface geometry is critical for many hydrogeologic studies. The horizontal-to-vertical spectral ratio (HVSR), a passive seismic method is a unique, non-invasive technique for speedily estimating bedrock depth. To record ambient seismic noise, the H/V method employs a single broadband three-component seismometer. A field assessment was conducted on the Wright State University Campus in Dayton, Ohio, to determine the depth (z) and elevation of the bedrock. Data were collected at 60 different locations. A known value for the depth of bedrock on campus was determined using the log from a local water well available from the …
Effects Of Phosphorus-Binding Agents On Nutrient Dynamics And A Planktothrix Bloom In A Shallow, Semi-Enclosed Lake Area, Joseph Lee Davidson
Effects Of Phosphorus-Binding Agents On Nutrient Dynamics And A Planktothrix Bloom In A Shallow, Semi-Enclosed Lake Area, Joseph Lee Davidson
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Grand Lake St. Marys is the largest (52 km2) inland lake in Ohio, USA, and receives high nutrient loadings (90th percentile for total nitrogen (N) and phosphorus (P) concentrations in the USA) from a watershed dominated by agricultural row-crops and livestock production. Eutrophication has led to cyanobacterial harmful algal blooms, dominated by non-N2 fixing Planktothrix, that persist year-round, including in winter months. In summer 2020 and 2021, multiple treatments using P-binding agents within a 3.5 ha swimming enclosure were conducted to remove excess dissolved P from the water column. The objective of this study was to examine pre-and-post treatment biogeochemical …
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 …
Quantifying The Effects Of Dredged Sediment Application On Soil Properties And Plant Responses In Combination With Common Agricultural Field Management Practices, Ashley N. Julian
Quantifying The Effects Of Dredged Sediment Application On Soil Properties And Plant Responses In Combination With Common Agricultural Field Management Practices, Ashley N. Julian
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Successful crop production relies on soils with balanced physical, chemical and biological properties. Demand for greater crop yields has led to the breakdown of soil properties through detrimental agricultural practices. To combat soil degradation, farmers employ field management practices including cover crop application, crop rotation strategies and organic soil amendment addition. These practices, used independently or in combination, can improve soil stability, increase soil nutrient content and functions of beneficial soil microbiota while increasing crop yield. Despite showing promise as an organic soil amendment, dredged sediments are still not well understood, due in part to the fresh or weathered conditions …
Merit Study Of Battery Or Hydrogen Energy Storage For Large Scale, Combined Wind And Solar Electricity Generation, Ashley K. Moore
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 …
Direct Parameter Fitting Of Action Potentials In Skeletal Muscle Cells Which Include Longitudinal Segments, Tyme Suda
Browse all Theses and Dissertations
Excitation of skeletal muscle cells triggers a large voltage spike known as an action potential (AP), leading to muscle contraction. Modeling of an AP is typically done using the method developed by scientists Hodgkin and Huxley (HH). In the HH method, voltage and time gated Na+ and K+ ionic currents are simulated, along with a positive “Leak” ionic current and capacitive current. Due to the complexity and the computational time required for simulation, direct fitting of HH parameters to experimental APs has rarely been attempted. A previous thesis at Wright State performed direct fitting for the case of a single …
Accelerating Precision Station Keeping For Automated Aircraft, James D. Anderson
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 …
Semantics-Driven Abstractive Document Summarization, Amanuel Alambo
Semantics-Driven Abstractive Document Summarization, Amanuel Alambo
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The evolution of the Web over the last three decades has led to a deluge of scientific and news articles on the Internet. Harnessing these publications in different fields of study is critical to effective end user information consumption. Similarly, in the domain of healthcare, one of the key challenges with the adoption of Electronic Health Records (EHRs) for clinical practice has been the tremendous amount of clinical notes generated that can be summarized without which clinical decision making and communication will be inefficient and costly. In spite of the rapid advances in information retrieval and deep learning techniques towards …
Building An Understanding Of Human Activities In First Person Video Using Fuzzy Inference, Bradley A. Schneider
Building An Understanding Of Human Activities In First Person Video Using Fuzzy Inference, Bradley A. Schneider
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Activities of Daily Living (ADL’s) are the activities that people perform every day in their home as part of their typical routine. The in-home, automated monitoring of ADL’s has broad utility for intelligent systems that enable independent living for the elderly and mentally or physically disabled individuals. With rising interest in electronic health (e-Health) and mobile health (m-Health) technology, opportunities abound for the integration of activity monitoring systems into these newer forms of healthcare. In this dissertation we propose a novel system for describing ’s based on video collected from a wearable camera. Most in-home activities are naturally defined by …
Using Network Analysis To Contrast Three Models Of Student Forum Discussions, Hannah N. Benston
Using Network Analysis To Contrast Three Models Of Student Forum Discussions, Hannah N. Benston
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There is much research about how actors and events in social networks affect each other. In this research, three network models were created for discussion forums in three semesters of undergraduate general physics courses. This study seeks to understand what social network measures are most telling of a online forum classroom dynamic. That is, I wanted to understand more about things like what students are most central to the networks and whether this is consistent across different network models. I also wanted to better understand how students may or may not group together. What relationships (student to student, student to …
Harmful Algal Blooms In Caesar Creek Lake And Their Relationship To Riparian Cover, Morgan C. Grunden
Harmful Algal Blooms In Caesar Creek Lake And Their Relationship To Riparian Cover, Morgan C. Grunden
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Caesar Creek Lake (CCL) in Warren County, OH has recently been experiencing harmful algal blooms (HABs) which are most likely attributed to an excess of phosphorus (P) from fertilizers and manures applied to surrounding fields. Sediments act as a sink for P later supplying a source of P in lakes for HABs when waters become thermally stratified and anoxic. This study seeks to determine the relationship between HABs in CCL and riparian cover at the main tributaries, Anderson Fork and Caesar Creek. In order to do this, sediment samples were collected from four sample sites along Anderson Fork and three …
Mercury Methylation In Oxic Sub-Polar Marine Regions Linked With Nitrification, Marissa Collins Despins
Mercury Methylation In Oxic Sub-Polar Marine Regions Linked With Nitrification, Marissa Collins Despins
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Methylmercury (MeHg) is a neurotoxin that bioaccumulates to potentially harmful concentrations in Arctic marine wildlife and in those that consume them. Monitoring and modeling MeHg bioaccumulation and biogeochemical cycling in the ocean requires understanding of the mechanisms behind net mercury (Hg) methylation. The key functional gene for Hg methylation, hgcAB, is widely distributed throughout ocean basins and spans multiple microbial phyla. While multiple microbially-mediated anaerobic pathways for Hg methylation are known, in the ocean, the majority of hgcA homologs have been found in oxic subsurface waters, in contrast to other ecosystems. In particular, microaerophilic Nitrospina, a genera of nitrite-oxidizing bacteria …
Has Winter Weather In Southwest Ohio Been Affected By The El Niño Southern Oscillation, The North Atlantic Oscillation, The Pacific Decadal Oscillation, And The Atlantic Multidecadal Oscillation?, John A. Blue
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Winter temperature and precipitation in Southwest Ohio over the last century were examined for anomalies attributable to teleconnections with large-scale atmospheric perturbations caused by the El Niño Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), the Pacific Decadal Oscillation (PDO), and the Atlantic Multidecadal Oscillation (AMO). The record of temperature gives evidence of a teleconnection with the NAO, ENSO, and PDO, with the strongest link being for phases of the NAO. Most winters during positive NAO phases had mean monthly temperature warmer than the century long mean, and the majority of negative NAO phase winters had colder temperatures. The difference …
Gene Vectors With Fluorescence Tracking Capabilities, Sophia Despina Angelopoulos
Gene Vectors With Fluorescence Tracking Capabilities, Sophia Despina Angelopoulos
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This project focuses on the optimization of benzothiazole-based chromophores to utilize them as fluorescent tags functionalized onto stimuli-responsive, or “smart,” polymers as non-viral gene delivery vectors for gene therapy applications. Blue-fluorescent emissive chromophores will allow tracking capabilities for the transfection pathway of the vector to be monitored as it delivers the DNA payload within intercellular space. The most appropriate chromophore must be covalently bound to a free amine within the vector, which is accomplished through NAS chemistry from fluorophenyl-benzothiazoles (F-BTZ-CBz) derivatives to amidated hyperbranched poly(ethyleneimine)(HPEI-IBAm0.61). HPEI is considered to be a synthetic polycation, which promotes tunable solubility through characteristics of …