Physical Sciences and Mathematics Commons^™

Impact Of Wastes On Some Properties Of Soil Around An Active Dumpsite In Ibadan, Southwestern Nigeria, Oluwatoyin Opeyemi Akintola, Gabriel Oladapo Adeyemi, Oluwayemisi Samuel Olokeogun, Idayat Adewunmi Bodede

Journal of Bioresource Management

Recently most farmers in developing country like Nigeria has resulted to the use of solid wastes as compost to replenish the deteriorated soils while some are farming on the abandoned waste dumpsite due to their richness in organic matter. This study assessed the soil nutrient and fertility status by investigating the influence of wastes (if any) on physical and chemical properties of soils in and around Lapite dumpsite for environmental sustainability. Ten soil samples each collected from three locations: dumpsite, downslope and upslope sites at depth of 0-20cm were analyzed for soil texture, bulk density, porosity, electrical conductivity, pH, organic …

Uncertainty-Aware Visualization In Medical Imaging - A Survey, Christina Gillmann, Dorothee Saur, Thomas Wischgoll, Gerik Scheuermann

Computer Science and Engineering Faculty Publications

Medical imaging (image acquisition, image transformation, and image visualization) is a standard tool for clinicians in order to make diagnoses, plan surgeries, or educate students. Each of these steps is affected by uncertainty, which can highly influence the decision-making process of clinicians. Visualization can help in understanding and communicating these uncertainties. In this manuscript, we aim to summarize the current state-of-the-art in uncertainty-aware visualization in medical imaging. Our report is based on the steps involved in medical imaging as well as its applications. Requirements are formulated to examine the considered approaches. In addition, this manuscript shows which approaches can be …

Nomophobia Before And After The Covid-19 Pandemic-Can Social Media Be Used To Understand Mobile Phone Dependency, Vaishnavi Visweswaraiah, Tanvi Banerjee, William Romine, Sarah Fryman

Computer Science and Engineering Faculty Publications

No abstract provided.

Hydrochemical Assessment Of Groundwater Around Lapite Dumpsite For Irrigation Water Quality In Ibadan, Southwestern Nigeria, Oluwatoyin Opeyemi Akintola, Gabriel Oladapo Adeyemi, Adewunmi Idayat Bodede, Oluwatoyin Oluwatoyin Adekoya, Kekinde O. Babatunde

Journal of Bioresource Management

Due to the increase in population and industrialization growth, most countries in the world depend on groundwater to meet agriculture demands for food production. The increase in water contamination due to indiscriminate solid wastes has necessitated the assessment of water quality and its suitability for agricultural usage. Twenty four groundwater and ten stream water samples were randomly collected from the downslope and upslope side of the dumpsite for all the major physio-chemical parameters. The pH of water samples indicates slightly acidic to alkaline in nature. High concentrations of nitrate, total dissolved solids and electrical conductivity suggest the impact of the …

The Wright State – Lake Campus 2020 – 2021 Scholarly Review, Wright State University - Lake Campus

Lake Campus Research Symposium Reports

This report provides a listing of the scholarly endeavors from Lake Campus during the 2020 calendar year, spanning across disciplines.

This document contains the Annual Research Report from 2020 and the Research Symposium Program from 2021.

Neuro-Symbolic Deductive Reasoning For Cross-Knowledge Graph Entailment, Monireh Ebrahimi, Md Kamruzzaman Sarker, Federico Bianchi, Ning Xie, Aaron Eberhart, Derek Doran, Hyeongsik Kim, Pascal Hitzler

Computer Science and Engineering Faculty Publications

A significant and recent development in neural-symbolic learning are deep neural networks that can reason over symbolic knowledge graphs (KGs). A particular task of interest is KG entailment, which is to infer the set of all facts that are a logical consequence of current and potential facts of a KG. Initial neural-symbolic systems that can deduce the entailment of a KG have been presented, but they are limited: current systems learn fact relations and entailment patterns specific to a particular KG and hence do not truly generalize, and must be retrained for each KG they are tasked with entailing. We …

Leveraging Natural Language Processing To Mine Issues On Twitter During The Covid-19 Pandemic, Ankita Agarwal, Preetham Salehundam, Swati Padhee, William Romine, Tanvi Wright State University - Main Campus

Computer Science and Engineering Faculty Publications

The recent global outbreak of the coronavirus disease (COVID-19) has spread to all corners of the globe. The international travel ban, panic buying, and the need for self-quarantine are among the many other social challenges brought about in this new era. Twitter platforms have been used in various public health studies to identify public opinion about an event at the local and global scale. To understand the public concerns and responses to the pandemic, a system that can leverage machine learning techniques to filter out irrelevant tweets and identify the important topics of discussion on social media platforms like Twitter …

Topic-Centric Unsupervised Multi-Document Summarization Of Scientific And News Articles, Amanuel Alambo, Cori Lohstroh, Erik Madaus, Swati Padhee, Brandy Foster, Tanvi Banerjee, Krishnaprasad Thirunarayan, Michael Raymer

Computer Science and Engineering Faculty Publications

Recent advances in natural language processing have enabled automation of a wide range of tasks, including machine translation, named entity recognition, and sentiment analysis. Automated summarization of documents, or groups of documents, however, has remained elusive, with many efforts limited to extraction of keywords, key phrases, or key sentences. Accurate abstractive summarization has yet to be achieved due to the inherent difficulty of the problem, and limited availability of training data. In this paper, we propose a topic-centric unsupervised multi-document summarization framework to generate extractive and abstractive summaries for groups of scientific articles across 20 Fields of Study (FoS) in …

Can Subjective Pain Be Inferred From Objective Physiological Data? Evidence From Patients With Sickle Cell Disease, Mark J. Panaggio, Daniel M. Abrams, Fan Yang, Tanvi Banerjee, Nirmish R. Shah

Computer Science and Engineering Faculty Publications

Patients with sickle cell disease (SCD) experience lifelong struggles with both chronic and acute pain, often requiring medical interventMaion. Pain can be managed with medications, but dosages must balance the goal of pain mitigation against the risks of tolerance, addiction and other adverse effects. Setting appropriate dosages requires knowledge of a patient's subjective pain, but collecting pain reports from patients can be difficult for clinicians and disruptive for patients, and is only possible when patients are awake and communicative. Here we investigate methods for estimating SCD patients' pain levels indirectly using vital signs that are routinely collected and documented in …

An Analysis Of C/C++ Datasets For Machine Learning-Assisted Software Vulnerability Detection, Daniel Grahn, Junjie Zhang

Computer Science and Engineering Faculty Publications

As machine learning-assisted vulnerability detection research matures, it is critical to understand the datasets being used by existing papers. In this paper, we explore 7 C/C++ datasets and evaluate their suitability for machine learning-assisted vulnerability detection. We also present a new dataset, named Wild C, containing over 10.3 million individual opensource C/C++ files – a sufficiently large sample to be reasonably considered representative of typical C/C++ code. To facilitate comparison, we tokenize all of the datasets and perform the analysis at this level. We make three primary contributions. First, while all the datasets differ from our Wild C dataset, some …

Augmented Reality Headset Facilitates Exposure For Surgical Stabilization Of Rib Fractures, T. Sensing, Pratik Parikh, Claire Hardman, Thomas Wischgoll, Sadan Suneesh Menon

Computer Science and Engineering Faculty Publications

Recent advances in augmented reality (AR) technology have made it more accessible, portable, and powerful. AR headsets differentiate themselves from virtual reality in that they allow the wearer an unobstructed view of the “real world” but with an image superimposed upon it. The technology has many potential applications in medicine, including surgical planning, simulation, and medical education. The aim of this project was to provide proof of concept that using an AR headset during surgical stabilization of rib fractures (SSRF) is feasible. We theorized that the use of AR could allow for more precise localization of fractures, allowing for smaller …

Pain Intensity Assessment In Sickle Cell Disease Patients Using Vital Signs During Hospital Visits, Swati Padhee, Amanuel Alambo, Tanvi Banerjee, Arvind Subramaniam, Daniel M. Abrams, Gary K. Nave, Nirmish Shah

Computer Science and Engineering Faculty Publications

Pain in sickle cell disease (SCD) is often associated with increased morbidity, mortality, and high healthcare costs. The standard method for predicting the absence, presence, and intensity of pain has long been self-report. However, medical providers struggle to manage patients based on subjective pain reports correctly and pain medications often lead to further difficulties in patient communication as they may cause sedation and sleepiness. Recent studies have shown that objective physiological measures can predict subjective self-reported pain scores for inpatient visits using machine learning (ML) techniques. In this study, we evaluate the generalizability of ML techniques to data collected from …

A Unified Approach For Constructing Confidence Intervals And Hypothesis Tests Using H-Function, Weizhen Wang

Mathematics and Statistics Faculty Publications

We introduce a general method, named the h-function method, to unify the con- structions of level- exact test and 1− exact confidence interval. Using this method, any confidence interval is improved as follows: i) an approximate interval, including a point estimator, is modified to an exact interval; ii) an exact interval is refined to be an interval that is a subset of the previous one. Two real datasets are used to illustrate the method.

Coloring Permutation-Gain Graphs, Daniel Slilaty

Mathematics and Statistics Faculty Publications

Correspondence colorings of graphs were introduced in 2018by Dvoˇr ́ak and Postle as a generalization of list colorings of graphswhich generalizes ordinary graph coloring. Kim and Ozeki observed thatcorrespondence colorings generalize various notions of signed-graph col-orings which again generalizes ordinary graph colorings. In this notewe state how correspondence colorings generalize Zaslavsky’s notionof gain-graph colorings and then formulate a new coloring theory ofpermutation-gain graphs that sits between gain-graph coloring and cor-respondence colorings. Like Zaslavsky’s gain-graph coloring, our newnotion of coloring permutation-gain graphs has well defined chromaticpolynomials and lifts to colorings of the regular covering graph of apermutation-gain graph

Analysis Of Amur Honeysuckle Stem Density As A Function Of Spatial Clustering, Horizontal Distance From Streams, Trails, And Elevation In Riparian Forests, Greene County, Ohio, Greg Michael Grierson Jr.

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The non-native invasive shrub Amur honeysuckle, Lonicera maackii (Rupr.) Herder (Gorchov and Trisel, 2003), is one of the most prolific invasive plant species across Midwestern and Northeastern landscapes of the United States. The locations of 2,095 individual Amur honeysuckle stems were geolocated using handheld GPS units in the understory of mixed growth forests at two study sites located approximately 5 km apart in northwestern Greene County, OH. Each site has undergone different levels of anthropogenic disturbance through time. The stem position data was used to measure the spatial clumping distribution and the density of Amur honeysuckle. The spatial clumping of …

Sample Mislabeling Detection And Correction In Bioinformatics Experimental Data, Soon Jye Kho

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Sample mislabeling or incorrect annotation has been a long-standing problem in biomedical research and contributes to irreproducible results and invalid conclusions. These problems are especially prevalent in multi-omics studies in which a large set of biological samples are characterized by multiple types of omics platforms at different times or different labs. While multi-omics studies have demonstrated tremendous value in understanding disease biology and improving patient outcomes, the complexity of these studies may increase opportunities for human error. Fortunately, the interrelated nature of the data collected in multi-omics studies can be exploited to facilitate the identification and, in some cases, correction …

Finite Different Time-Domain Simulation Of Terahertz Waves Propagation Through Unmagnetized Plasma, Aditha Srikantha Senarath

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In order to support ongoing terahertz time-domain spectroscopic experiments involving plasma characterization, it is beneficial to simulate the interaction of THz pulses with varying plasma configurations. In this approach, a 1-D Finite Difference Time Domain (FDTD) model was constructed to simulate the interaction of terahertz radiation with a plasma medium. In order to incorporate the plasma properties into the simulation, a Z-transformation was applied. This model is capable of simulating the following properties of plasmas including electron density, collision frequency, and the interaction length of the plasma medium. The simulated model was characterized using terahertz time-domain spectroscopy. The effects of …

Investigation Into The Source Of Contamination Of Surface Waters Flowing Through The Wright State University Woods, Nnadozie Kennedy Okeke

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This investigative research was carried out with the purpose of determining the source of contaminants present in the surface waters flowing through the Wright State University woods. Five sample sites going from upstream to downstream namely; Inflow to Nutter Center Pond (INNCP), Nutter Center Pond (NCP), Outfall 21 (OTF 21), Burley, and Outfall 15 (OTF 15), were sampled over a time period spanning from June 2020 to January 2021. Samples collected were analyzed for Escherichia coli (E. coli) using 3M™ Petrifilm™ E. coli/Coliform Count (EC) plates, select anions (Phosphate PO43-, Nitrate NO3-, Sulfate SO42-, Fluoride F- and Chloride Cl-) using …

Edge Processing Of Image For Uas Sense And Avoidance, Christopher J. Rave

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Today there is a large market for Unmanned Aerial Systems. Although most current systems are remotely piloted by operators on the ground, increasingly, many of these systems will use some sort of automatic flight controller to help mitigate new challenges, due to their deployment at growing scale. These challenges include, but are not limited to, shortage of FAA-certified UAS pilots, transmission bandwidth and delay constraints and cyber security threats associated with wireless networking, profitability of operations constrained by energy capacity and efficiency and air dynamics planning, and etc. In order to address these rising challenges, this thesis is a part …

North American Freshwater Snails As Paleoecologic Proxies In Crystal Lake, Medway, Ohio, Jaclyn R. Manker

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This study combines various paleoecological proxies found within a sediment core extracted from Crystal Lake, Medway, Ohio in order to assess the lake’s sensitivity to past climate changes and how that may have affected lake water levels. Crystal Lake is a natural kettle lake formed at the end of the Wisconsin glaciation. It is now surrounded by approximately 500 residential homes and is privately owned by the HOA of Crystal Lake. A sediment core was extracted from Crystal Lake in 2007 and has been carbon dated to 18000 years before present, indicating that it contains a complete sedimentary history from …

Cytoviva Hyperspectral Imaging For Comparing The Uptake And Transformation Of Agnps And Ag+ In Mitochondria, Kristina Steingass

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Nanomaterials have attracted significant attention in the last decade, with applications in everyday products. Amongst all known nanomaterials in use, silver is the leading metal, present in 531 different types of products, owing to their unique optical, electrical, antimicrobial, and thermal properties. Though silver nanoparticles (AgNPs) come in contact regularly with the general population, there is little known about their toxicity mechanism due to limited techniques for thorough analysis. CytoViva Hyperspectral Imaging (HSI) shows potential for filling these gaps. In this study, borohydride-capped AgNPs with an approximate diameter of 10 nm were synthesized using the modified Creighton method and characterized …

Computer Modeling Of Solar Thermal System With Underground Storage Tank For Space Heating, Mohammad Yousef Mousa Naser

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Space heating is required in almost every dwelling across the country for different periods of time. The thermal energy needed to meet a heating demand can be supplied using different conventional and/or renewable technologies. Solar energy is one example of a renewable resource that can be used for supplying heating needs. It can be utilized either by using photovoltaic panels to generate electricity, that in turn can be used to operate heaters, or by using solar thermal panels. Solar thermal panels obtain higher operating efficiencies than photovoltaic panels, but solar energy for heating purposes suffers from a mismatch between supply …

Recommending Collaborations Using Link Prediction, Nikhil Chennupati

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Link prediction in the domain of scientific collaborative networks refers to exploring and determining whether a connection between two entities in an academic network may emerge in the future. This study aims to analyze the relevance of academic collaborations and identify the factors that drive co-author relationships in a heterogeneous bibliographic network. Using topological, semantic, and graph representation learning techniques, we measure the authors' similarities w.r.t their structural and publication data to identify the reasons that promote co-authorships. Experimental results show that the proposed approach successfully infer the co-author links by identifying authors with similar research interests. Such a system …

A Rebellion Framework With Learning For Goal-Driven Autonomy, Zahiduddin Mohammad

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Modeling an autonomous agent that decides for itself what actions to take to achieve its goals is a central objective of artificial intelligence. There are various approaches used to build autonomous agents including neural networks, state machines, utility functions, learning agents, and cognitive architectures. In this thesis, we focus on cognitive architectures. Our approach uses specific knowledge of the world, the goals they pursue, and the actions being performed. Most agents do what they are told (i.e., achieve the goals given to them by a human), but a genuinely autonomous agent does more. It can formulate its own goal or …

Effect Of Cloud Cover On Optimum Orientations Of Fixed Solar Panels For Maximum Yearly Energy Collection, Prethew Prasad

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The amount of cloud cover present in the sky is a significant factor when determining the solar radiation impinging on a solar panel. The optimum tilt required to achieve maximum energy impingement on a surface is also influenced by the amount of cloud cover. This work presents a method for determining the optimum tilt angle for a fixed solar panel when a set amount of cloud cover is present in the sky. Fixed tilt angles that have the most incident solar energy over the course of a year as a function of cloud cover, latitude, and azimuthal angle orientation are …

Benzotriazole And Tolytriazole Analysis In Select Surface Waters Near Wilmington Air Park, Lee A. Raska

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Previous investigations into the presence of benzotriazole (BTZ) and corresponding analogs done in early 2019 found elevated levels near the Wilmington Air Park in Wilmington, Ohio. The analogs detected were 4-methyl-1H-benzotriazole and 5-methyl-1H-benzotriazole: known together as tolytriazole (TTZ). BTZ and TTZ are emerging environmental contaminants of concern that are often found in aircraft de-icing solutions, anti-icing solutions and detergents. The Wilmington Air Park has two facilities used to pre-treat runoff water before its subsequent release into surrounding streams. Three sites were chosen: Lytle Creek, Indian Run, and Cowan Creek. For the 2019 and 2019/2020 investigative projects, Cowan Creek was designated …

Computational Simulation And Analysis Of Neuroplasticity, Madison E. Yancey

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Homeostatic synaptic plasticity is the process by which neurons alter their activity in response to changes in network activity. Neuroscientists attempting to understand homeostatic synaptic plasticity have developed three different mathematical methods to analyze collections of event recordings from neurons acting as a proxy for neuronal activity. These collections of events are from control data and treatment data, referring to the treatment of neuron cultures with pharmacological agents that augment or inhibit network activity. If the distribution of control events can be functionally mapped to the distribution of treatment events, a better understanding of the biological processes underlying homeostatic synaptic …

Deep Learning For Compressive Sar Imaging With Train-Test Discrepancy, Morgan R. Mccamey

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We consider the problem of compressive synthetic aperture radar (SAR) imaging with the goal of reconstructing SAR imagery in the presence of under sampled phase history. While this problem is typically considered in compressive sensing (CS) literature, we consider a variety of deep learning approaches where a deep neural network (DNN) is trained to form SAR imagery from limited data. At the cost of computationally intensive offline training, on-line test-time DNN-SAR has demonstrated orders of magnitude faster reconstruction than standard CS algorithms. A limitation of the DNN approach is that any change to the operating conditions necessitates a costly retraining …

Content Adaption And Design In Mobile Learning Of Wind Instruments, Neha Priyadarshani

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People in today's world seek things that are simple to use. Learning is one of the most crucial aspects of the ongoing digital transformation. Everything is now accessible with a single click on mobile devices, making access to instructional materials faster, easier, and more comfortable. It takes time and effort to build abilities and become an expert in the fields of learning, training, and teaching; and music learning demands a great deal of both practice and mentoring. Initially, music teachers and band directors must maintain a steady attention and devote a significant amount of time to manually teaching materials. This …

A Deep Understanding Of Structural And Functional Behavior Of Tabular And Graphical Modules In Technical Documents, Michail Alexiou

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The rapid increase of published research papers in recent years has escalated the need for automated ways to process and understand them. The successful recognition of the information that is contained in technical documents, depends on the understanding of the document’s individual modalities. These modalities include tables, graphics, diagrams and etc. as defined in Bourbakis’ pioneering work. However, the depth of understanding is correlated to the efficiency of detection and recognition. In this work, a novel methodology is proposed for automatic processing of and understanding of tables and graphics images in technical document. Previous attempts on tables and graphics understanding …