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Undergraduate And Graduate Course Descriptions, Spring 2024, Wright State University

Course Descriptions

Wright State University undergraduate and graduate course descriptions from Spring 2024

The Guardian The Month Of April 2024, Wright State Student Body

The Guardian Student Newspaper

News articles from The Guardian for the Month of April 2024. The Guardian is the official student-run newspaper for Wright State University. It has been published regularly since March of 1965.

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Desalination As A Source Of Freshwater, Jacob Pensky

Best Integrated Writing

Jacob Pensky's article deals with technology we use to make saltwater drinkable. Drought-stricken coastal communities need desalination plants, especially as Earth's climate warms, but they are expensive and energy-intensive. This article describes ways to reduce the environmental and monetary costs.

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Toxicity Of Three Species Of The Solanaceae Family Growing In Algeria Against Culiseta Longiareolata Mosquitos 4th Stage Larvae, Saliha Benhissen, Abdelmaddjid Yagoub Asloum, Nora Belkhiri, Zakaria Hedjouli, Siham Bounadji, Wafa Habbachi, Khellaf Rebbas

Journal of Bioresource Management

Mosquitoes have always been considered a source of harm to humans and animals mainly because they can be vectors of disease. This work represents a study on the effect of aqueous extract of three plants (Hyoscyamus albus, Solanum elaeagnifolium, Solanum nigrum) of the Solanaceae family on the mortality of 4th stage larvae of Culiseta longiareolata larvae. Extract of the high impact is of S. nigrum followed by H. albus and S. elaeagnifolium, respectivley of C. longiareolata larvae.

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Faculty Senate Meeting Agenda And Minutes, March 24, 2024

Faculty Senate Minutes and Agendas

Agenda and minutes from the Wright State University Faculty Senate Meeting held on, March 24, 2024.

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The Guardian The Month Of February 2024, Wright State Student Body

The Guardian Student Newspaper

News articles from The Guardian for the Month of February 2024. The Guardian is the official student-run newspaper for Wright State University. It has been published regularly since March of 1965.

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Wright State University Magazine, 2024, Wright State Alumni Association And Wright State Foundation

Wright State University Magazine

Fifty-two page issue of the Wright State University Magazine. This magazine is published once a year and focuses on news related to Wright State alumni, faculty, staff, and friends of the university.

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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 …

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Enhancing Robustness Of Graph Neural Network Against Adversarial Attacks By Balancing Local And Global Perspectives, Bibek Raj Joshi

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Graph Neural Networks (GNNs) have increasingly gained popularity as tools for analyzing graph data in areas like biology, knowledge-graphs, social networks, biology, and recommendation systems. However, their vulnerability to adversarial attacks - small, targeted manipulations of graph structures or node features - raises serious concerns about their reliability in real-world applications. Existing defense strategies, such as adversarial training, edge filtering, low-rank approximations, and randomization-based methods, often suffer from high computational costs, scalability issues, or reduced clean-data performance. Unlike these methods, the proposed approach integrates multi-hop relationships, applies adaptive regularization, and maintains a balance between feature-based and structural embeddings, ensuring improved …

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Meta-Learning-Based Model Stacking Framework For Hardware Trojan Detection In Fpga Systems, Mani Rupak Gurram

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In today's technological landscape, hardware devices are integral to critical applications such as industrial automation, autonomous vehicles, and medical equipment, relying on advanced platforms like FPGAs for core functionalities. However, the multi-stage manufacturing process, often distributed across various foundries, introduces substantial security risks, notably the potential for hardware Trojan insertion. These malicious modifications compromise the reliability and safety of hardware systems. This research addresses the detection of hardware Trojans through side-channel analysis, utilizing power and electromagnetic signal data, combined with meta-learning techniques, specifically model stacking. By employing diverse base models and a meta-model to consolidate predictions, this non-invasive approach effectively …

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Adsorption Of Perfluoroalkyl Substances With Activated Carbon: Characterizing Non-Hazardous Simulants, Jennifer Hensley

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Per- and polyfluoroalkyl substances (PFAS) are of great interest recently because some members of this class exhibit human health risks at extremely low levels, making them the current subject of proposed regulation and policy. Treatment of PFAS contaminated wastewater often is performed through adsorptive processes, e.g., with activated carbon. Due to complexities with PFAS laboratory analysis, assessing treatment efficacy through direct measurement of PFAS is expensive and time consuming, potentially delaying the design and implementation of site-specific treatment systems. This project identified and characterized potential suitable simulants for PFAS during wastewater treatment for which analysis can be timely and economical. …

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Investigating The Impact Of Stress And Irradiation Flux On Latent Track Formation In Tio2 Under Swift Heavy Ion Irradiation: A Phase Field Study, Ebrahim Ebrahimi

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Swift Heavy Ions (SHI) irradiation, characterized by high kinetic energy ions, induces significant material/structural modification, e.g., latent track. However, the intricate interaction among various physics, i.e., mechanical stress, phase transition, and heat transfer, has been ignored in the continuum-based approaches in favor of simplicity. Here, we developed a two-dimensional coupled phase-field inelastic-thermal spike (PF-iTS) model to investigate the effect of thermal crosstalk, elastic energy, and irradiation flux on latent track formation. A particular focus is placed on investigating the influence of internal mechanical stress on latent track formation. Simulation results reveal a shift in critical stopping energy and a reduction …

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Evaluating The Deductive Competence Of Large Language Models, Spencer M. Seals, Valerie L. Shalin

Psychology Faculty Publications

The development of highly fluent large language models (LLMs) has prompted increased interest in assessing their reasoning and problem-solving capabilities. We investigate whether several LLMs can solve a classic type of deductive reasoning problem from the cognitive science literature. The tested LLMs have limited abilities to solve these problems in their conventional form. We performed follow up experiments to investigate if changes to the presentation format and content improve model performance. We do find performance differences between conditions; however, they do not improve overall performance. Moreover, we find that performance interacts with presentation format and content in unexpected ways that …

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Enhancing Cross-Modal Contextual Congruence For Crowdfunding Success Using Knowledge-Infused Learning, Trilok Padhi, Ugur Kursuncu, Yaman Kumar, Valerie L. Shalin, Lane Peterson Fronczek

Psychology Faculty Publications

The digital landscape continually evolves with multimodality, enriching the online experience for users. Creators and marketers aim to weave subtle contextual cues from various modalities into congruent content to engage users with a harmonious message. This interplay of multimodal cues is often a crucial factor in attracting users' attention. However, this richness of multimodality presents a challenge to computational modeling, as the semantic contextual cues spanning across modalities need to be unified to capture the true holistic meaning of the multimodal content. This contextual meaning is critical in attracting user engagement as it conveys the intended message of the brand …

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2024-2025 Common Data Set - Wright State University Lake Campus, Office Of Institutional Research & Effectiveness, Wright State University, Princeton Review

Common Data Set

The 2024-2025 Common Data Set of Wright State University Dayton Campus from the Princeton Review. It includes data on admissions, student enrollment, faculty, class size, financial aid and other measures, using standard definitions that aid comparisons between schools.

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2024-2025 Common Data Set - Wright State University Dayton Campus, Office Of Institutional Research & Effectiveness, Wright State University, Princeton Review

Common Data Set

The 2024-2025 Common Data Set of Wright State University Dayton Campus from the Princeton Review. It includes data on admissions, student enrollment, faculty, class size, financial aid and other measures, using standard definitions that aid comparisons between schools.

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The Easy-Ai Symbology, Alexis Ellis, Cogan Shimizu

Computer Science and Engineering Faculty Publications

As artificial intelligence (AI) surges into the forefront of research and the lives of everyday people, challenges in understanding and communicating how these systems operate are becoming more prevalent. The need for a common language for AI systems that allows for multidisciplinary understanding and communication is a prevalent topic within the field. In this work, we take the visual framework EASY-AI and create a symbolic system that overlays the framework’s ontology to facilitate such communication and understanding. Poster submission.

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The Impact Of Mental Health On Academic Performance In Ohio In 2022, Lily Chen, Samantha Gilbert

Scholarship in Medicine - All Papers

Background: Previous research has shown a significant link between mental health issues and low academic performance. Our study explores the delicate connection between mental health issues and academic performance in Ohio school children, in particular, how the dynamic may differ between rural and non-rural communities. Methods: We utilized County Health Rankings data from the years 2016 and 2022 from all 88 counties in Ohio, which we further substratified as 44 rural and 44 non-rural counties. From CHR, we chose data categories to represent measures of academic performance, internalizing mental health problems, and access to mental health services, and utilized SPSS …

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An Ontology Design Pattern For Role-Dependent Names, Rushrukh Rayan, Cogan Shimizu, Pascal Hitzler

Computer Science and Engineering Faculty Publications

We present an ontology design pattern for modeling Names as part of Roles, to capture scenarios where an Agent performs different Roles using different Names associated with the different Roles. Examples of an Agent performing a Role using different Names are rather ubiquitous, e.g., authors who write under different pseudonyms, or different legal names for citizens of more than one country. The proposed pattern is a modified merger of a standard Agent Role and a standard Name pattern stub.

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A Pade-Eno Flux Reconstruction For High-Speed Flows, Blake Martin

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The development of high order numerical schemes has been instrumental in advancing computational fluid dynamics (CFD), particularly for applications requiring high resolution of discontinuities and complex flow phenomena prevalent in high-speed flows. This thesis introduces the Pade-ENO scheme, a high-order method that integrates Essentially Non-Oscillatory (ENO) techniques with compact Pade stencils to achieve superior accuracy, up to 7th order, while maintaining stability in harsh environments. The scheme’s performance is evaluated through benchmark tests, including the advection equation, Burgers’ equation, and the Euler equations. For high Mach number flows, such as the sod shock tube the Pade-ENO method demonstrates its ability …

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Advanced Digital Wideband Receiver Design: High Dynamic Range And Enhanced Multi-Signal Detection With Fpga-Based Custom Fft And Nyquist Folding, Kiran Jayarama

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In modern wideband receiver standards, efficient frequency spectrum utilization is essential to meet demands for high data rates, reduced latency, and enhanced connectivity. The Fast Fourier Transform (FFT) stands as a pivotal technology, particularly in radar signal processing, where it supports tasks such as target detection, range estimation, and velocity estimation by analyzing the frequency content of the received radar signals. This dissertation introduces the design of an advanced digital wideband receiver featuring a high dynamic range for multiple signals, with a focus on improved performance, compact size, and reduced power consumption, implemented on an FPGA using custom hardware. Key …

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The Hip Ontology: A Formal Framework To Support Disaster Risk Reduction And Management, Shirly Stephen, Mark Schildhauer, Krzysztof Janowicz, Kitty Currier, Pascal Hitzler, Cogan Shimizu, Colby K. Fisher, Dean Rehberger

Computer Science and Engineering Faculty Publications

Open data initiatives and knowledge graphs, in synergy, have contributed to an increasing volume of disaster-related data in the Semantic Web. Synthesizing and enriching these data is critical to support all aspects of data-driven disaster risk reduction and management. A standard template that coherently defines, maps, and classifies the wide range of hazards to which communities are exposed is a key input for this task. The UNDRR-ISC Hazard Information Profiles (HIPs) provide evidence-informed standardization of hazard nomenclature and definitions and a “science-backed” classification. Unfortunately, they are not in a machine-readable format. This paper develops the HIP Ontology as its FAIR …

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An Efficient And Trusted Deep Learning Framework For Real-Time Ppe Detection In Secure Iomt Environment, Anusha Verma

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Occupationally-acquired infections impact thousands of healthcare workers (HCWs) in the U.S., with many cases preventable through proper use of personal protective equipment (PPE). This study seeks to develop a robust system to enhance PPE compliance and reduce infection risks among HCWs. The objectives of this thesis are twofold: (1) to create a hybrid machine learning model that combines object detection and keypoint detection to ensure correct donning and doffing of PPE, and (2) to design a real-time feedback system using LED indicators and a display interface to offer actionable guidance to HCWs during PPE usage. The goal is to optimize …

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An Enhanced Real-Time Object Detection Of Helmets And License Plates Using A Lightweight Yolov8 Deep Learning Model, Mounika Thatikonda

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Traffic surveillance and enforcement heavily depend on the real-time detection of helmets and license plates, particularly in high-density urban environments. This study presents a dynamic and optimized lightweight model, the proposed G-YOLOv8n, designed for resource constrained edge devices like the Raspberry Pi. By integrating the GhostNet module into the YOLOv8n architecture, this research achieves a nearly 50% reduction in model size and computational load, while maintaining comparable detection accuracy to the original YOLOv8n. These enhancements enable real-time processing capabilities crucial for traffic monitoring operations. The growing demand for real-time, low-power solutions in intelligent transportation systems necessitates lightweight, efficient detection models. …

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A Systematic Study Of Freezing Behavior In Earthworms In Response To Auditory And Vibratory Stimuli, Navjot Singh, A. Burton, Dragana Ivkovich Claflin

Celebration of Undergraduate & Graduate Research, Scholarship, and Creative Activities Materials

This study examined the parameters needed to reliably induce a freezing fear response to a predator-like auditory stimulus in the earthworm species Eisenia Fetida. Previous work from our lab found that a grunting noise was more reliable in causing freezing compared to a mole sound, i.e. artificial versus natural predators of E. Fetida (Worthen et al., 2024). In the present study, 8 amplitude levels of grunting sound were presented in either serial or random order. The speaker location was varied so that it either did or did not touch the apparatus, thus producing a mechanical vibration in addition to the …

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Peer-Assisted Learning In Miller Analogies Tasks, Preston S. Menke

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The present study aimed to investigate the role of peer-assisted learning (PAL) on individual performance using a relatively complex Miller Analogies Task (MAT). I found that low-ability learners benefitted from PAL. Furthermore, this may be explained by a significant trust mechanism indicating that learners who correctly identified trustworthy peers had greater performance. I did not find support for a significant difference between PAL and individual learning conditions. This study provides evidence for the role of scaffolding, where low-ability learners may benefit from identifying who to guide them when engaging in a social peer-learning task.

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A Domain-Agnostic Neurosymbolic Approach For Big Social Data Analysis: Evaluating Mental Health Sentiment On Social Media During Covid-19, Vedant Khandelwal, Manas Gaur, Ugur Kursuncu, Valerie L. Shalin, Amit P. Sheth

Psychology Faculty Publications

Monitoring public sentiment via social media is potentially helpful during health crises such as the COVID-19 pandemic. However, traditional frequency-based, data-driven neural network-based approaches can miss newly relevant content due to the evolving nature of language in a dynamically evolving environment. Human-curated symbolic knowledge sources, such as lexicons for standard language and slang terms, can potentially elevate social media signals in evolving language. We introduce a neurosymbolic method that integrates neural networks with symbolic knowledge sources, enhancing the detection and interpretation of mental health-related tweets relevant to COVID-19. Our method was evaluated using a corpus of large datasets (approximately 12 …

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Void Fraction And Quality Correlation Analysis Using The Separated Flow Model For Pulsed-Power Heat Loads, Zachary Joseph Carner

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Aircraft platforms are continually upgraded with increasingly high quantities of high-powered electronics. As such, efficient thermal management systems are crucially important to overcome system instabilities and support pulsed power profiles. To create effective thermal control systems, it is imperative to thoroughly examine and account for a variety of potential system behaviors caused by transient changes in the flow regime. If left unconstrained, these changes will create thermal instabilities, which can severely damage electronics and hurt the overall reliability of the aircraft. These instabilities can be described by both void fraction and quality. Electrical Capacitance Tomography (ECT) allows for the collection …

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Integrating Knowledge Graphs With Large Language Models For Natural Language Querying, Rakesh Kandula

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This research explores the integration of knowledge graphs with large language models that have already been trained on a vast pool of unstructured text data. Large language models trained on this type of data have a tendency to hallucinate and produce factually inaccurate results. This behavior is primarily due to the data being trained is unstructured and huge text corpus, and large language model uses predictive text analysis methods to obtain a response. These issues can be addressed by applying Retrieval Augmented Generation and Fine-tuning to large language models, employing an underlying domainspecific knowledge graph. Integrating knowledge graph and large …

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Prediction Interpretations Of Ensemble Models In Chronic Kidney Disease Using Explainable Ai, K M Tawsik Jawad

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Chronic Kidney Disease (CKD) poses significant health and financial threat to millions of patients all around the world. The irreversible nature of this disease not just leads to comorbid diseases like Diabetes Mellitus, Hypertension, Anemia, Bone Disease, Neurological Implants etc. It can permanently damage the kidney by progressing to Acute Kidney Injury (AKI) or End Stage Renal Diseases (ESRD). The risk factors of CKD become more dangerous as patients suffering from it have little to no idea about the presence of CKD in their body until it takes the shape of AKI or ESRD. There are severe economic burdens for …

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Full-Text Articles in Entire DC Network

Undergraduate And Graduate Course Descriptions, Spring 2024, Wright State University

Course Descriptions

The Guardian The Month Of April 2024, Wright State Student Body

The Guardian Student Newspaper

Desalination As A Source Of Freshwater, Jacob Pensky

Best Integrated Writing

Toxicity Of Three Species Of The Solanaceae Family Growing In Algeria Against Culiseta Longiareolata Mosquitos 4th Stage Larvae, Saliha Benhissen, Abdelmaddjid Yagoub Asloum, Nora Belkhiri, Zakaria Hedjouli, Siham Bounadji, Wafa Habbachi, Khellaf Rebbas

Journal of Bioresource Management

Faculty Senate Meeting Agenda And Minutes, March 24, 2024

Faculty Senate Minutes and Agendas

The Guardian The Month Of February 2024, Wright State Student Body

The Guardian Student Newspaper

Wright State University Magazine, 2024, Wright State Alumni Association And Wright State Foundation

Wright State University Magazine

Understanding Patient Profiles In Sickle Cell Disease Using Unsupervised Machine Learning, Raj Kamal Somavarapu

Browse all Theses and Dissertations

Enhancing Robustness Of Graph Neural Network Against Adversarial Attacks By Balancing Local And Global Perspectives, Bibek Raj Joshi

Browse all Theses and Dissertations

Meta-Learning-Based Model Stacking Framework For Hardware Trojan Detection In Fpga Systems, Mani Rupak Gurram

Browse all Theses and Dissertations

Adsorption Of Perfluoroalkyl Substances With Activated Carbon: Characterizing Non-Hazardous Simulants, Jennifer Hensley

Browse all Theses and Dissertations

Investigating The Impact Of Stress And Irradiation Flux On Latent Track Formation In Tio2 Under Swift Heavy Ion Irradiation: A Phase Field Study, Ebrahim Ebrahimi

Browse all Theses and Dissertations

Evaluating The Deductive Competence Of Large Language Models, Spencer M. Seals, Valerie L. Shalin

Psychology Faculty Publications

Enhancing Cross-Modal Contextual Congruence For Crowdfunding Success Using Knowledge-Infused Learning, Trilok Padhi, Ugur Kursuncu, Yaman Kumar, Valerie L. Shalin, Lane Peterson Fronczek

Psychology Faculty Publications

2024-2025 Common Data Set - Wright State University Lake Campus, Office Of Institutional Research & Effectiveness, Wright State University, Princeton Review

Common Data Set

2024-2025 Common Data Set - Wright State University Dayton Campus, Office Of Institutional Research & Effectiveness, Wright State University, Princeton Review

Common Data Set

The Easy-Ai Symbology, Alexis Ellis, Cogan Shimizu

Computer Science and Engineering Faculty Publications

The Impact Of Mental Health On Academic Performance In Ohio In 2022, Lily Chen, Samantha Gilbert

Scholarship in Medicine - All Papers

An Ontology Design Pattern For Role-Dependent Names, Rushrukh Rayan, Cogan Shimizu, Pascal Hitzler

Computer Science and Engineering Faculty Publications

A Pade-Eno Flux Reconstruction For High-Speed Flows, Blake Martin

Browse all Theses and Dissertations

Advanced Digital Wideband Receiver Design: High Dynamic Range And Enhanced Multi-Signal Detection With Fpga-Based Custom Fft And Nyquist Folding, Kiran Jayarama

Browse all Theses and Dissertations

The Hip Ontology: A Formal Framework To Support Disaster Risk Reduction And Management, Shirly Stephen, Mark Schildhauer, Krzysztof Janowicz, Kitty Currier, Pascal Hitzler, Cogan Shimizu, Colby K. Fisher, Dean Rehberger

Computer Science and Engineering Faculty Publications

An Efficient And Trusted Deep Learning Framework For Real-Time Ppe Detection In Secure Iomt Environment, Anusha Verma

Browse all Theses and Dissertations

An Enhanced Real-Time Object Detection Of Helmets And License Plates Using A Lightweight Yolov8 Deep Learning Model, Mounika Thatikonda

Browse all Theses and Dissertations

A Systematic Study Of Freezing Behavior In Earthworms In Response To Auditory And Vibratory Stimuli, Navjot Singh, A. Burton, Dragana Ivkovich Claflin

Celebration of Undergraduate & Graduate Research, Scholarship, and Creative Activities Materials

Peer-Assisted Learning In Miller Analogies Tasks, Preston S. Menke

Browse all Theses and Dissertations

A Domain-Agnostic Neurosymbolic Approach For Big Social Data Analysis: Evaluating Mental Health Sentiment On Social Media During Covid-19, Vedant Khandelwal, Manas Gaur, Ugur Kursuncu, Valerie L. Shalin, Amit P. Sheth

Psychology Faculty Publications

Void Fraction And Quality Correlation Analysis Using The Separated Flow Model For Pulsed-Power Heat Loads, Zachary Joseph Carner

Browse all Theses and Dissertations

Integrating Knowledge Graphs With Large Language Models For Natural Language Querying, Rakesh Kandula

Browse all Theses and Dissertations

Prediction Interpretations Of Ensemble Models In Chronic Kidney Disease Using Explainable Ai, K M Tawsik Jawad

Browse all Theses and Dissertations