Physical Sciences and Mathematics Commons^™

Plastic Fast, Maureen E. Schlangen

Roesch Library Staff Publications

In the era of Silent Spring, the first Earth Days and the “Keep America Beautiful” campaigns in the 1970s, littering was just the tip of the pollution iceberg. It still is. The bigger problems are demand and production. A June 2018 article in National Geographic found that worldwide, we produce almost 500 million tons of plastic every year — and about 40 percent is tossed after one use. According to the nonprofit Plastic Oceans, more than 8 million tons of plastic go into the oceans each year; production of plastic water bottles alone has grown from 3.8 billion in 1996 …

Human Rights, Environmental Justice, Social Justice, Faith Values And Ethics: Building Stronger Partnerships For The Common Good By Understanding The Differences, Theresa Harris, Leanne M. Jablonski, Sarah Fortner, Malcolm Daniels

Biennial Conference: The Social Practice of Human Rights

Partnerships between human rights practitioners, local communities, scientists, engineers, and health professionals have shown potential to address deeply rooted, systemic human rights concerns. These collaborations are essential for achieving the UN Sustainable Development Goals (SDGs), and for engaging the perspectives and expertise of all constituents. However, even when the individuals in these partnerships or the organizations they represent have common goals, their motivations, analyses, and solutions often come from different perspectives. Members of good will can inadvertently alienate one another when attempting to work together. The fields of human rights, social justice, environmental justice, and ethics have each developed their …

Quasilinearization And Boundary Value Problems At Resonance, Kareem Alanazi, Meshal Alshammari, Paul W. Eloe

Mathematics Faculty Publications

A quasilinearization algorithm is developed for boundary value problems at resonance. To do so, a standard monotonicity condition is assumed to obtain the uniqueness of solutions for the boundary value problem at resonance. Then the method of upper and lower solutions and the shift method are applied to obtain the existence of solutions. A quasilinearization algorithm is developed and sequences of approximate solutions are constructed, which converge monotonically and quadratically to the unique solution of the boundary value problem at resonance. Two examples are provided in which explicit upper and lower solutions are exhibited.

Reachnn: Reachability Analysis Of Neural-Network Controlled Systems, Chao Huang, Jiameng Fan, Wenchao Li, Xin Chen, Qi Zhu

Computer Science Faculty Publications

Applying neural networks as controllers in dynamical systems has shown great promises. However, it is critical yet challenging to verify the safety of such control systems with neural-network controllers in the loop. Previous methods for verifying neural network controlled systems are limited to a few specific activation functions. In this work, we propose a new reachability analysis approach based on Bernstein polynomials that can verify neural-network controlled systems with a more general form of activation functions, i.e., as long as they ensure that the neural networks are Lipschitz continuous. Specifically, we consider abstracting feedforward neural networks with Bernstein polynomials for …

Mittag–Leffler Stability Of Systems Of Fractional Nabla Difference Equations, Paul W. Eloe, Jaganmohan Jonnalagadda

Mathematics Faculty Publications

Mittag-Leffler stability of nonlinear fractional nabla difference systems is defined and the Lyapunov direct method is employed to provide sufficient conditions for Mittag-Leffler stability of, and in some cases the stability of, the zero solution of a system nonlinear fractional nabla difference equations. For this purpose, we obtain several properties of the exponential and one parameter Mittag-Leffler functions of fractional nabla calculus. Two examples are provided to illustrate the applicability of established results.

An Introduction To Declarative Programming In Clips And Prolog, Jack L. Watkin, Adam C. Volk, Saverio Perugini

Computer Science Faculty Publications

We provide a brief introduction to CLIPS—a declarative/logic programming language for implementing expert systems—and PROLOG—a declarative/logic programming language based on first-order, predicate calculus. Unlike imperative languages in which the programmer specifies how to compute a solution to a problem, in a declarative language, the programmer specifies what they what to find, and the system uses a search strategy built into the language. We also briefly discuss applications of CLIPS and PROLOG.

Nitrate Contaminant Tracing In Surface And Groundwater In The Great Miami River Watershed: Environmental Isotope Approach, Rachel Kristine Buzeta

Honors Theses

The global population has increased exponentially causing several challenges surrounding sustainability, including greater food production needs. To meet these demands and boost agricultural productivity, more efficient practices and fertilizers are used. Synthetic fertilizers and other nutrient sources have resulted in water quality degradation and pollution. Much of the Great Miami River Watershed’s streams and aquifers in southwestern Ohio are affected by nitrate contaminants originating from anthropogenic sources including synthetic and organic fertilizer used for agriculture, human wastes (domestic, industrial, and municipal wastes), and urbanization. High nitrate concentrations cause ecological disturbances across all trophic levels. Nitrate levels greater than 10 mg/L …

The Number Of Fixed Points Of And-Or Networks With Chain Topology, Lauren Geiser

Honors Theses

Boolean networks are sets of Boolean functions, which are functions that contain Boolean variables and the logical operators AND, OR, and NOT. In the simple case, the variables can be in one of two states—either 1 or 0, which can be interpreted in different ways such as ON or OFF, or TRUE or FALSE, depending on the application. Arranging model systems into Boolean functions, we can study steady states of these networks. This refers to the overall state of the dynamical system given an initial condition and another theoretical condition such as a subsequent point in time. Boolean networks have …

Topology Of Fractals, Amelia Pompilio

Honors Theses

No abstract provided.

Identifying Natural Inhibitors Of Bacterial Efflux Pumps, Marrisa Therriault

Honors Theses

Antibiotic resistance is a constantly progressing epidemic. Many strains of bacteria have developed a resistance to antibiotics, resulting in prolonged sickness and death. Resistance can be to a specific drug (single drug resistance) or to multiple drugs (multi-drug resistance). This resistance can be caused by a tripartite protein pump called an efflux pump that extends through the inner and outer membranes of the bacterium to pump antibiotics from the inside of the cell to the extracellular environment. In E. coli the efflux pump is called AcrAB-TolC. In the efforts to combat this problem, this experiment focuses on the inactivation of …

Computer-Assisted Graphic Correlation Of Ordovician Conodonts And Graptolites From The Argentine Precordillera And Western Newfoundland Using Constrained Optimization (Conop9), Andrea Marie Bryan

Honors Theses

The correlation of rock units is the foundation of geological research. Correlation is the process of proving two geologic events are time equivalent. Most importantly, it is used to establish time boundaries in the geologic time scale. This paper uses computer assisted graphic correlation (CONOP9) to correlate the ages of graptolites and conodonts from the Ordovician found in rocks from the continent Laurentia, and arranges them in a composite range chart. These two organisms lived in different environments and, therefore, are found in different biofacies. The Argentine Precordillera and the western Newfoundland region are places where these two fossils co-exist …

Tracking Disparate Colony Morphological Trends With Thermus Scotoductus, Matthew P. Leverick

Honors Theses

When plating most mesophilic bacteria, the colony shape, size, and color tends to be uniform when a single strain is present. When plating defined cell densities of T. scotoductus, however, the colonies were not of uniform size and shape while it grew on the surface. In this project, we sought to observe trends in colony morphology (shape and size) changes using the thermophile Thermus scotoductus on nutrient rich agar plates at 60°C. A general planktonic growth curve was also created to help characterize the activity of this bacterium. This project was our first attempt to characterize if this unusual phenomenon …

Analysis And Review Of The Effects Of Bacterial Competition On Efflux Pump Inhibition, Robert Leszcynski

Honors Theses

Multidrug resistant antibacterial strains are a dangerous problem for modern medical professionals. When cells of two different strains are grown together, they must compete with each other for nutrients. This competition can lead to the production of harmful compounds that are toxic to the competing strain. One such compound may be a compound that inhibits the efflux of antibiotics from the cell. When the cells compete with one another, one or both of the cells will produce compounds that are harmful to the competing strain.

Boron Dipyrromethenes: Synthesis And Computational Analysis, Eduardo Rivé Lockwood

Honors Theses

Recently, there has been a growing interest in the boron dipyrromethene (BODIPY, 4,4′- difluoro-4-bora-3a,4a-diaza-s-indacene) compounds. BODIPY compounds have fascinating properties that allow for the absorption and emission of light in the near infrared region of the electromagnetic spectrum. These molecules are highly modifiable making them ideal chemicals for the use of photoelectric energy conversion such as for commercial use in dye sensitized solar cells (DSSCs). It has been previously shown that different meso compounds have only a slight effect on the absorptive capabilities of these BODIPY compounds. We believe that the BODIPY compounds’ lack of planarity is one of the …

A State-Of-The-Art Survey On Deep Learning Theory And Architectures, Md Zahangir Alom, Tarek M. Taha, Christopher Yakopcic, Stefan Westberg, Paheding Sidike, Mst Shamima Nasrin, Mahmudul Hasan, Brian C. Van Essen, Abdul A. S. Awwal, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

In recent years, deep learning has garnered tremendous success in a variety of application domains. This new field of machine learning has been growing rapidly and has been applied to most traditional application domains, as well as some new areas that present more opportunities. Different methods have been proposed based on different categories of learning, including supervised, semi-supervised, and un-supervised learning. Experimental results show state-of-the-art performance using deep learning when compared to traditional machine learning approaches in the fields of image processing, computer vision, speech recognition, machine translation, art, medical imaging, medical information processing, robotics and control, bioinformatics, natural language …

An Interactive, Graphical Simulator For Teaching Operating Systems, Joshua W. Buck, Saverio Perugini

Computer Science Faculty Publications

We demonstrate a graphical simulation tool for visually and interactively exploring the processing of a variety of events handled by an operating system when running a program. Our graphical simulator is available for use on the web by both instructors and students for purposes of pedagogy. Instructors can use it for live demonstrations of course concepts in class, while students can use it outside of class to explore the concepts. The graphical simulation tool is implemented using the React library for the fancy ui elements of the Node.js framework and is available as a web application at https://cpudemo.azurewebsites.net. The goals …

Developing A Contemporary And Innovative Operating Systems Course, Saverio Perugini, David J. Wright

Computer Science Faculty Publications

This birds-of-a-feather provides a discussion forum to foster innovation in teaching operating systems (os) at the undergraduate level. This birds-of-a-feather seeks to generate discussion and ideas around pedagogy for os and, in particular, how we might develop a contemporary and innovative model, in both content and delivery, for an os course—that plays a central role in a cs curriculum—and addresses significant issues of misalignment between existing os courses and employee professional skills and knowledge requirements. We would like to exchange ideas regarding a re-conceptualized course model of os curriculum and related pedagogy, especially in the areas of mobile OSs and …

A New Way To Detect Cyberattacks Extracting Changes In Register Values From Radio-Frequency Side Channels, Ronald A. Riley, James T. Graham, Ryan M. Fuller, Rusty O. Baldwin, Ashwin Fisher

Computer Science Faculty Publications

The Internet of Things (IoT) and the Internet of Everything (IoE) have driven processors into nearly every powered de- vice, from thermostats to refrigerators to light bulbs. From a security perspective, the IoT and IoE create a new layer of sig- nals and systems that can provide insight into the internal opera- tions of a device via analog side channels. Our research focuses on leveraging these analog side channels in IoT/IoE processors to detect intrusions. Our goal is to defend against cyberattacks that insert malware into IoT devices by detecting deviations in the code running on their processors from known …

Quasilinearization And Boundary Value Problems At Resonance For Caputo Fractional Differential Equations, Saleh S. Almuthaybiri, Paul W. Eloe, Jeffrey T. Neugebauer

Mathematics Faculty Publications

The quasilinearization method is applied to a boundary value problem at resonance for a Caputo fractional differential equation. The method of upper and lower solutions is first employed to obtain the uniqueness of solutions of the boundary value problem at resonance. The shift argument is applied to show the existence of solutions. The quasilinearization algorithm is then developed and sequences of approximate solutions are constructed that converge monotonically and quadratically to the unique solution of the boundary value problem at resonance. Two applications are provided to illustrate the main results.

Avery Fixed Point Theorem Applied To A Hammerstein Integral Equation, Paul W. Eloe, Jeffrey T. Neugebauer

Mathematics Faculty Publications

Abstract. We apply a recent Avery et al. fixed point theorem to the Hammerstein integral equation (see paper for equation). Under certain conditions on G, we show the existence of positive and positive symmetric solutions. Examples are given where G is a convolution kernel and where G is a Green’s function associated with different boundary-value problem.

Quasilinearization And Boundary Value Problems For Riemann-Liouville Fractional Differential Equations, Paul W. Eloe, Jaganmohan Jonnalagadda

Mathematics Faculty Publications

We apply the quasilinearization method to a Dirichlet boundary value problem and to a right focal boundary value problem for a RiemannLiouville fractional differential equation. First, we sue the method of upper and lower solutions to obtain the uniqueness of solutions of the Dirichlet boundary value problem. Next, we apply a suitable fixed point theorem to establish the existence of solutions. We develop a quasilinearization algorithm and construct sequences of approximate solutions that converge monotonically and quadratically to the unique solution of the boundary value problem. Two examples are exhibited to illustrate the main result for the Dirichlet boundary value …

Comparison Of Green's Functions For A Family Of Boundary Value Problems For Fractional Difference Equations, Paul W. Eloe, Catherine Kublik, Jeffrey T. Neugebauer

Mathematics Faculty Publications

In this paper, we obtain sign conditions and comparison theorems for Green's functions of a family of boundary value problems for a Riemann-Liouville type delta fractional difference equation. Moreover, we show that as the length of the domain diverges to infinity, each Green's function converges to a uniquely defined Green's function of a singular boundary value problem.

The Large Contraction Principle And Existence Of Periodic Solutions For Infinite Delay Volterra Difference Equations, Paul W. Eloe, Jaganmohan Jonnalagadda, Youssef Raffoul

Mathematics Faculty Publications

In this article, we establish sufficient conditions for the existence of periodic solutions of a nonlinear infinite delay Volterra difference equation. (See paper for equation.)

We employ a Krasnosel’skii type fixed point theorem, originally proved by Burton. The primary sufficient condition is not verifiable in terms of the parameters of the difference equation, and so we provide three applications in which the primary sufficient condition is verified.

Active Recall Networks For Multiperspectivity Learning Through Shared Latent Space Optimization, Theus Aspiras, Ruixu Liu, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

Given that there are numerous amounts of unlabeled data available for usage in training neural networks, it is desirable to implement a neural network architecture and training paradigm to maximize the ability of the latent space representation. Through multiple perspectives of the latent space using adversarial learning and autoencoding, data requirements can be reduced, which improves learning ability across domains. The entire goal of the proposed work is not to train exhaustively, but to train with multiperspectivity. We propose a new neural network architecture called Active Recall Network (ARN) for learning with less labels by optimizing the latent space. This …

Recurrent Residual U-Net For Medical Image Segmentation, Md Zahangir Alom, Christopher Yakopcic, Mahmudul Hasan, Tarek M. Taha, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

Deep learning (DL)-based semantic segmentation methods have been providing state-of-the-art performance in the past few years. More specifically, these techniques have been successfully applied in medical image classification, segmentation, and detection tasks. One DL technique, U-Net, has become one of the most popular for these applications. We propose a recurrent U-Net model and a recurrent residual U-Net model, which are named RU-Net and R2U-Net, respectively. The proposed models utilize the power of U-Net, residual networks, and recurrent convolutional neural networks. There are several advantages to using these proposed architectures for segmentation tasks. First, a residual unit helps when training deep …

Predicting Public Opinion On Drug Legalization: Social Media Analysis And Consumption Trends, Farahnaz Golrooy Motlagh, Saeedeh Shekarpour, Amit Sheth, Krishnaprasad Thirunarayan, Michael L. Raymer

Computer Science Faculty Publications

In this paper, we focus on the collection and analysis of relevant Twitter data on a state-by-state basis for (i) measuring public opinion on marijuana legalization by mining sentiment in Twitter data and (ii) determining the usage trends for six distinct types of marijuana. We overcome the challenges posed by the informal and ungrammatical nature of tweets to analyze a corpus of 306,835 relevant tweets collected over the four-month period, preceding the November 2015 Ohio Marijuana Legalization ballot and the four months after the election for all states in the US. Our analysis revealed two key insights: (i) the people …

Deep Temporal Convolutional Networks For Short-Term Traffic Flow Forecasting, Wentian Zhao, Yanyun Gao, Tingxiang Ji, Xili Wan, Feng Ye, Guangwei Bai

Electrical and Computer Engineering Faculty Publications

To reduce the increasingly congestion in cities, it is essential for intelligent transportation system (ITS) to accurately forecast the short-term traffic flow to identify the potential congestion sites. In recent years, the emerging deep learning method has been introduced to design traffic flow predictors, such as recurrent neural network (RNN) and long short-term memory (LSTM), which has demonstrated its promising results. In this paper, different from existing work, we study the temporal convolutional network (TCN) and propose a deep learning framework based on TCN model for short-term city-wide traffic forecast to accurately capture the temporal and spatial evolution of traffic …

A Survey Of Techniques For Mobile Service Encrypted Traffic Classification Using Deep Learning, Pan Wang, Xuejiao Chen, Feng Ye, Zhixin Sun

Electrical and Computer Engineering Faculty Publications

The rapid adoption of mobile devices has dramatically changed the access to various net- working services and led to the explosion of mobile service traffic. Mobile service traffic classification has been a crucial task that attracts strong interest in mobile network management and security as well as machine learning communities for past decades. However, with more and more adoptions of encryption over mobile services, it brings a lot of challenges about mobile traffic classification. Although classical machine learning approaches can solve many issues that port and payload-based methods cannot solve, it still has some limitations, such as time-consuming, costly handcrafted …

Reachability Analysis For Neural Feedback Systems Using Regressive Polynomial Rule Inference, Souradeep Dutta, Xin Chen, Sriram Sankaranarayanan

Computer Science Faculty Publications

We present an approach to construct reachable set overapproxi- mations for continuous-time dynamical systems controlled using neural network feedback systems. Feedforward deep neural net- works are now widely used as a means for learning control laws through techniques such as reinforcement learning and data-driven predictive control. However, the learning algorithms for these net- works do not guarantee correctness properties on the resulting closed-loop systems. Our approach seeks to construct overapproxi- mate reachable sets by integrating a Taylor model-based flowpipe construction scheme for continuous differential equations with an approach that replaces the neural network feedback law for a small subset of …