Physical Sciences and Mathematics Commons^™

Autodocking Studies Of Oxygenated Fullerenes As Inhibitors Of The Hiv Protease, Michael Kaminski

All Student Theses and Dissertations

In a previous study, several oxygenated fullerene compounds were produced by ozonation of C60 (Buckminsterfullerene) and were identified by elemental analysis and by SALI (surface analysis by laser ionization). Some of these compounds, especially a batch, SK-5, a mixture of C60O1-8, were shown to inhibit activities of HIV-Protease with IC50 (concentration for 50% inhibition) of 1 mg/mol in the in-vitro studies. The oxygenated fullerenes were shown to have epoxide, ketones, and hydroxyl functionalities. As expected, C60 interacted with ozone with alkene functionality and not as an aromatic compound. It was postulated that C60O had epoxide functionality, as a product of …

Connecting Number Theory With High School Mathematics, Andre Richmond

All Student Theses and Dissertations

Number theory is the study of natural numbers and one of the oldest branches of mathematics. Elementary number theory concepts are integrated into K-12 learning experience. This paper will identify ideas and methods in elementary number theory that could be connected to K-12 education and taught in high school classrooms. In fact, Common Core Standards in Mathematics include some basic concepts and skills in elementary number theory. In this study, we will focus on the greatest common divisor, Euclid's algorithm, least common multiple, factorization and divisibility criteria (divisible by 2, 3, 4, 5, 6, 8, 9, and 11). We hope …

Cybersecurity For Cloud-Based Systems And Applications, Sathyavrath Karrela

All Student Theses and Dissertations

The rapid proliferation of cloud computing has transformed the way organizations manage and deploy their systems and applications. This paradigm shift, while offering numerous benefits such as scalability, cost efficiency, and accessibility, has also introduced a plethora of cybersecurity challenges. This thesis delves into the critical aspects of cybersecurity in the context of cloud-based systems and applications. It provides an in-depth analysis of the unique security considerations that arise from the shared responsibility model between cloud service providers and their customers.

A comprehensive review of various cloud deployment models, including public, private, hybrid, and multi-cloud configurations, offers insights into how …

Effect Of Decorating Super Paramagnetic Iron Oxide Nanoparticles With Silver Nanoparticles On Their Magneto-Photo Thermal Heating Efficiency, Anthony Joseph Afful

All Graduate Theses, Dissertations, and Other Capstone Projects

Cancer treatment is rather dangerous to the body, often involving many secondary effects, including nausea, hair loss, and weight fluctuations. The search for non-invasive, highly efficient, and targetable treatments ameliorates these issues. Super paramagnetic iron oxide nanoparticles (SPIONS) have been used for other medical purposes such as magnetic resonance imaging contrast agent and is being extensively studied as a potential candidate for many cancer therapeutic and diagnostic approaches due to its biocompatibility and superior magnetic properties. When subjected to an external alternating magnetic field SPIONS generate heat mainly due to the friction of the SPIONS against the fluid it is …

Abmr: An R Package For Agent-Based Model Analysis Of Large-Scale Movements Across Taxa, Benjamin Gochanour, Javier Fernández-López, Andrea Contina

School of Integrative Biological & Chemical Sciences Faculty Publications

1. Agent-based modelling (ABM) shows promise for animal movement studies. However, a robust, open-source and spatially explicit ABM coding platform is currently lacking.
2. We present abmR, an R package for conducting continental-scale ABM simulations across animal taxa. The package features two movement functions, each of which relies on the Ornstein–Uhlenbeck (OU) process.
3. The theoretical background for abmR is discussed and the main functionalities are illustrated using example populations.
4. Potential future additions to this open-source package may include the ability to specify multiple environmental variables or to model interactions between agents. Additionally, updates may offer opportunities for disease ecology and integration with …

Classification On Boundary-Equilibria And Singular Continuums Of Continuous Piecewise Linear Systems, Hebai Chen, Zhaosheng Feng, Hao Yang, Linfeng Zhou

School of Mathematical & Statistical Sciences Faculty Publications

In this paper, we show that any switching hypersurface of n -dimensional continuous piecewise linear systems is an (n−1) -dimensional hyperplane. For two-dimensional continuous piecewise linear systems, we present local phase portraits and indices near the boundary equilibria (i.e. equilibria at the switching line) and singular continuum (i.e. continuum of nonisolated equilibria) between two parallel switching lines. The index of singular continuum is defined. Then we show that boundary-equilibria and singular continuums can appear with many parallel switching lines.

Quantization For A Set Of Discrete Distributions On The Set Of Natural Numbers, Juan Gomez, Haily Martinez, Mrinal Kanti Roychowdhury, Alexis Salazar, Daniel J. Vallez

School of Mathematical & Statistical Sciences Faculty Publications

The quantization scheme in probability theory deals with finding a best approximation of a given probability distribution by a probability distribution that is supported on finitely many points. In this paper, first we state and prove a theorem, and then give a conjecture. We verify the conjecture by a few examples. Assuming that the conjecture is true, for a set of discrete distributions on the set of natural numbers we have calculated the optimal sets of n-means and the nth quantization errors for all positive integers n. In addition, the quantization dimension is also calculated.

Approximations Of Practice And Equity: Surveying The Spaces, Bima Sapkota, Carrie Lee, Liza Bondurant, Heather Howell

School of Mathematical & Statistical Sciences Faculty Publications

After three years of meeting as a working group at PME-NA, we will be editing a book on how several models of approximations of practice support or hinder the creation of equitable mathematics learning and thinking spaces. Participants and working group leaders will serve as critical friends as we use a fishbowl format and small groups to provide spaces for refinement and challenge. Participants will leave with a network of thought partners and a draft chapter. At the end of the working group, we will identify overarching themes about affordances and limitations of several models of digital and non-digital forms …

An Intertwined Cauchy––Schwarz–Type Inequality Based On A Lagrange–Type Identity, Iosif Pinelis

Michigan Tech Publications

Based on an apparently new Lagrange-type identity, a Cauchy–Schwarz-type inequality is proved. The mentioned identity is obtained by using certain “macro” variables; it is hoped that such a method can be used to prove or produce other identities and inequalities.

Autodesc: Facilitating Convenient Perusal Of Web Data Items For Blind Users, Yash Prakash, Mohan Sunkara, Hae-Na Lee, Sampath Jayarathna, Vikas Ashok

Computer Science Faculty Publications

Web data items such as shopping products, classifieds, and job listings are indispensable components of most e-commerce websites. The information on the data items are typically distributed over two or more webpages, e.g., a ‘Query-Results’ page showing the summaries of the items, and ‘Details’ pages containing full information about the items. While this organization of data mitigates information overload and visual cluttering for sighted users, it however increases the interaction overhead and effort for blind users, as back-and-forth navigation between webpages using screen reader assistive technology is tedious and cumbersome. Existing usability-enhancing solutions are unable to provide adequate support in …

Evaluating Human Eye Features For Objective Measure Of Working Memory Capacity, Yasasi Abeysinghe, Enkelejda Kasneci (Ed.), Frederick Shic (Ed.), Mohamed Khamis (Ed.)

Computer Science Faculty Publications

Eye tracking measures can provide means to understand the underlying development of human working memory. In this study, we propose to develop machine learning algorithms to find an objective relationship between human eye movements via oculomotor plant and their working memory capacity, which determines subjective cognitive load. Here we evaluate oculomotor plant features extracted from saccadic eye movements, traditional positional gaze metrics, and advanced eye metrics such as ambient/focal coefficient , gaze transition entropy, low/high index of pupillary activity (LHIPA), and real-time index of pupillary activity (RIPA). This paper outlines the proposed approach of evaluating eye movements for obtaining an …

Cellbrf: A Feature Selection Method For Single-Cell Clustering Using Cell Balance And Random Forest, Yunpei Xu, Hong-Dong Li, Cui-Xiang Lin, Ruiqing Zheng, Yaohang Li, Jinhui Xu, Jianxin Wang

Computer Science Faculty Publications

Motivation

Single-cell RNA sequencing (scRNA-seq) offers a powerful tool to dissect the complexity of biological tissues through cell sub-population identification in combination with clustering approaches. Feature selection is a critical step for improving the accuracy and interpretability of single-cell clustering. Existing feature selection methods underutilize the discriminatory potential of genes across distinct cell types. We hypothesize that incorporating such information could further boost the performance of single cell clustering. Results

We develop CellBRF, a feature selection method that considers genes’ relevance to cell types for single-cell clustering. The key idea is to identify genes that are most important for discriminating …

Efficient Gpu Implementation Of Automatic Differentiation For Computational Fluid Dynamics, Mohammad Zubair, Desh Ranjan, Aaron Walden, Gabriel Nastac, Eric Nielsen, Boris Diskin, Marc Paterno, Samuel Jung, Joshua Hoke Davis

Computer Science Faculty Publications

Many scientific and engineering applications require repeated calculations of derivatives of output functions with respect to input parameters. Automatic Differentiation (AD) is a method that automates derivative calculations and can significantly speed up code development. In Computational Fluid Dynamics (CFD), derivatives of flux functions with respect to state variables (Jacobian) are needed for efficient solutions of the nonlinear governing equations. AD of flux functions on graphics processing units (GPUs) is challenging as flux computations involve many intermediate variables that create high register pressure and require significant memory traffic because of the need to store the derivatives. This paper presents a …

Hashes Are Not Suitable To Verify Fixity Of The Public Archived Web, Mohamed Aturban, Martin Klein, Herbert Van De Sompel, Sawood Alam, Michael L. Nelson, Michele C. Weigle

Computer Science Faculty Publications

Web archives, such as the Internet Archive, preserve the web and allow access to prior states of web pages. We implicitly trust their versions of archived pages, but as their role moves from preserving curios of the past to facilitating present day adjudication, we are concerned with verifying the fixity of archived web pages, or mementos, to ensure they have always remained unaltered. A widely used technique in digital preservation to verify the fixity of an archived resource is to periodically compute a cryptographic hash value on a resource and then compare it with a previous hash value. If the …

Mitigating Anomalous Electricity Consumption In Smart Cities Using An Ai-Based Stacked-Generalization Technique, Arshid Ali, Laiq Khan, Nadeem Javaid, Safdar Hussain Bouk, Abdulaziz Aldegheishem, Nabil Alrahjeh

Computer Science Faculty Publications

Energy management and efficient asset utilization play an important role in the economic development of a country. The electricity produced at the power station faces two types of losses from the generation point to the end user. These losses are technical losses (TL) and non-technical losses (NTL). TLs occurs due to the use of inefficient equipment. While NTLs occur due to the anomalous consumption of electricity by the customers, which happens in many ways; energy theft being one of them. Energy theft majorly happens to cut down on the electricity bills. These losses in the smart grid (SG) are the …

Enabling Customization Of Discussion Forums For Blind Users, Mohan Sunkara, Yash Prakash, Hae-Na Lee, Sampath Jayarathna, Vikas Ashok

Computer Science Faculty Publications

Online discussion forums have become an integral component of news, entertainment, information, and video-streaming websites, where people all over the world actively engage in discussions on a wide range of topics including politics, sports, music, business, health, and world affairs. Yet, little is known about their usability for blind users, who aurally interact with the forum conversations using screen reader assistive technology. In an interview study, blind users stated that they often had an arduous and frustrating interaction experience while consuming conversation threads, mainly due to the highly redundant content and the absence of customization options to selectively view portions …

An Approach To Developing Benchmark Datasets For Protein Secondary Structure Segmentation From Cryo-Em Density Maps, Thu Nguyen, Yongcheng Mu, Jiangwen Sun, Jing He

Computer Science Faculty Publications

More and more deep learning approaches have been proposed to segment secondary structures from cryo-electron density maps at medium resolution range (5--10Å). Although the deep learning approaches show great potential, only a few small experimental data sets have been used to test the approaches. There is limited understanding about potential factors, in data, that affect the performance of segmentation. We propose an approach to generate data sets with desired specifications in three potential factors - the protein sequence identity, structural contents, and data quality. The approach was implemented and has generated a test set and various training sets to study …

Progenitor Cell Isolation From Mouse Epididymal Adipose Tissue And Sequencing Library Construction, Qianglin Liu, Chaoyang Li, Yuxia Li, Leshan Wang, Xujia Zhang, Buhao Deng, Peidong Gao, Mohammad Shiri, Fozi Alkaifi, Junxing Zhao, Jacqueline M. Stephens, Constantine A. Simintiras, Joseph Francis, Jiangwen Sun, Xing Fu

Computer Science Faculty Publications

Here, we present a protocol to isolate progenitor cells from mouse epididymal visceral adipose tissue and construct bulk RNA and assay for transposase-accessible chromatin with sequencing (ATAC-seq) libraries. We describe steps for adipose tissue collection, cell isolation, and cell staining and sorting. We then detail procedures for both ATAC-seq and RNA sequencing library construction. This protocol can also be applied to other tissues and cell types directly or with minor modifications.

For complete details on the use and execution of this protocol, please refer to Liu et al. (2023).¹

*¹ Liu, Q., Li, C., Deng, B., Gao, P., …

Optimization Of Ported Cfd Kernels On Intel Data Center Gpu Max 1550 Using Oneapi Esimd, Mohammad Zubair, Aaron Walden, Gabriel Nastac, Eric Nielsen, Christoph Bauinger, Xiao Zhu

Computer Science Faculty Publications

We describe our experience porting FUN3D’s CUDA-optimized kernels to Intel oneAPI SYCL.We faced several challenges, including foremost the suboptimal performance of the oneAPI code on Intel’s new data center GPU. Suboptimal performance of the oneAPI code was due primarily to high register spills, memory latency, and poor vectorization. We addressed these issues by implementing the kernels using Intel oneAPI’s Explicit SIMD SYCL extension (ESIMD) API. The ESIMD API enables the writing of explicitly vectorized kernel code, gives more precise control over register usage and prefetching, and better handles thread divergence compared to SYCL. The ESIMD code outperforms the optimized SYCL …

Dial "N" For Nxdomain: The Scale, Origin, And Security Implications Of Dns Queries To Non-Existent Domains, Gunnan Liu, Lin Jin, Shuai Hao, Yubao Zhang, Daiping Liu, Angelos Stavrou, Haining Wang

Computer Science Faculty Publications

Non-Existent Domain (NXDomain) is one type of the Domain Name System (DNS) error responses, indicating that the queried domain name does not exist and cannot be resolved. Unfortunately, little research has focused on understanding why and how NXDomain responses are generated, utilized, and exploited. In this paper, we conduct the first comprehensive and systematic study on NXDomain by investigating its scale, origin, and security implications. Utilizing a large-scale passive DNS database, we identify 146,363,745,785 NXDomains queried by DNS users between 2014 and 2022. Within these 146 billion NXDomains, 91 million of them hold historic WHOIS records, of which 5.3 million …

Comparison Of Physics-Based Deformable Registration Methods For Image-Guided Neurosurgery, Nikos Chrisochoides, Yixun Liu, Fotis Drakopoulos, Andriy Kot, Panos Foteinos, Christos Tsolakis, Emmanuel Billias, Olivier Clatz, Nicholas Ayache, Andrey Fedorov, Alex Golby, Peter Black, Ron Kikinis

Computer Science Faculty Publications

This paper compares three finite element-based methods used in a physics-based non-rigid registration approach and reports on the progress made over the last 15 years. Large brain shifts caused by brain tumor removal affect registration accuracy by creating point and element outliers. A combination of approximation- and geometry-based point and element outlier rejection improves the rigid registration error by 2.5 mm and meets the real-time constraints (4 min). In addition, the paper raises several questions and presents two open problems for the robust estimation and improvement of registration error in the presence of outliers due to sparse, noisy, and incomplete …

Toward A Generative Modeling Analysis Of Clas Exclusive 2𝜋 Photoproduction, T. Alghamdi, Y. Alanazi, M. Battaglieri, Ł. Bibrzycki, A. V. Golda, A. N. Hiller Blin, E. L. Isupov, Y. Li, L. Marsicano, W. Melnitchouk, V. I. Mokeev, G. Montaña, A. Pilloni, N. Sato, A. P. Szczepaniak, T. Vittorini

Computer Science Faculty Publications

AI-supported algorithms, particularly generative models, have been successfully used in a variety of different contexts. This work employs a generative modeling approach to unfold detector effects specifically tailored for exclusive reactions that involve multiparticle final states. Our study demonstrates the preservation of correlations between kinematic variables in a multidimensional phase space. We perform a full closure test on two-pion photoproduction pseudodata generated with a realistic model in the kinematics of the Jefferson Lab CLAS g11 experiment. The overlap of different reaction mechanisms leading to the same final state associated with the CLAS detector’s nontrivial effects represents an ideal test case …

Unttangling Irregular Actin Cytoskeleton Architectures In Tomograms Of The Cell With Struwwel Tracer, Salim Sazzed, Peter Scheible, Jing He, Willy Wriggers

Computer Science Faculty Publications

In this work, we established, validated, and optimized a novel computational framework for tracing arbitrarily oriented actin filaments in cryo-electron tomography maps. Our approach was designed for highly complex intracellular architectures in which a long-range cytoskeleton network extends throughout the cell bodies and protrusions. The irregular organization of the actin network, as well as cryo-electron-tomography-specific noise, missing wedge artifacts, and map dimensions call for a specialized implementation that is both robust and efficient. Our proposed solution, Struwwel Tracer, accumulates densities along paths of a specific length in various directions, starting from locally determined seed points. The highest-density paths originating …

Charged Track Reconstruction With Artificial Intelligence For Clas12, Gagik Gavalian, Polykarpos Thomadakis, Angelos Angelopoulos, Nikos Chrisochoides

Computer Science Faculty Publications

In this paper, we present the results of charged particle track reconstruction in CLAS12 using artificial intelligence. In our approach, we use neural networks working together to identify tracks based on the raw signals in the Drift Chambers. A Convolutional Auto-Encoder is used to de-noise raw data by removing the hits that do not satisfy the patterns for tracks, and second Multi-Layer Perceptron is used to identify tracks from combinations of clusters in the drift chambers. Our method increases the tracking efficiency by 50% for multi-particle final states already conducted experiments. The de-noising results indicate that future experiments can run …

Msdrp: A Deep Learning Model Based On Multisource Data For Predicting Drug Response, Haochen Zhao, Xiaoyu Zhang, Qichang Zhao, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Motivation: Cancer heterogeneity drastically affects cancer therapeutic outcomes. Predicting drug response in vitro is expected to help formulate personalized therapy regimens. In recent years, several computational models based on machine learning and deep learning have been proposed to predict drug response in vitro. However, most of these methods capture drug features based on a single drug description (e.g. drug structure), without considering the relationships between drugs and biological entities (e.g. target, diseases, and side effects). Moreover, most of these methods collect features separately for drugs and cell lines but fail to consider the pairwise interactions between drugs and cell …

Identifying The Serious Clinical Outcomes Of Adverse Reactions To Drugs By A Multi-Task Deep Learning Framework, Haochen Zhao, Peng Ni, Qichang Zhao, Xiao Liang, Di Ai, Shannon Erhardt, Jun Wang, Yaohang Li, Jiianxin Wang

Computer Science Faculty Publications

Adverse Drug Reactions (ADRs) have a direct impact on human health. As continuous pharmacovigilance and drug monitoring prove to be costly and time-consuming, computational methods have emerged as promising alternatives. However, most existing computational methods primarily focus on predicting whether or not the drug is associated with an adverse reaction and do not consider the core issue of drug benefit-risk assessment-whether the treatment outcome is serious when adverse drug reactions occur. To this end, we categorize serious clinical outcomes caused by adverse reactions to drugs into seven distinct classes and present a deep learning framework, so-called GCAP, for predicting the …

Drone Images Afford More Detections Of Marine Wildlife Than Real-Time Observers During Simultaneous Large-Scale Surveys, Amanda J. Hodgson, Nat Kelly, David Peel

Research outputs 2022 to 2026

There are many advantages to transitioning from conducting marine wildlife surveys via human observers onboard light-aircraft, to capturing aerial imagery using drones. However, it is important to maintain the validity of long-term data series whilst transitioning from observer to imagery surveys. We need to understand how the detection rates of target species in images compare to those collected from observers in piloted aircraft, and the factors influencing detection rates from each platform. We conducted trial ScanEagle drone surveys of dugongs in Shark Bay, Western Australia, covering the full extent of the drone’s range (∼100 km), concurrently with observer surveys, with …

The Wacdt, A Modern Vigilance Task For Network Defense, Oliver A. Guidetti, Craig Speelman, Peter Bouhlas

Research outputs 2022 to 2026

Vigilance decrement refers to a psychophysiological decline in the capacity to sustain attention to monotonous tasks after prolonged periods. A plethora of experimental tasks exist for researchers to study vigilance decrement in classic domains such as driving and air traffic control and baggage security; however, the only cyber vigilance tasks reported in the research literature exist in the possession of the United States Air Force (USAF). Moreover, existent cyber vigilance tasks have not kept up with advances in real-world cyber security and consequently no longer accurately reflect the cognitive load associated with modern network defense. The Western Australian Cyber Defense …

A Provable Secure And Efficient Authentication Framework For Smart Manufacturing Industry, Muhammad Hammad, Akhtar Badshah, Ghulam Abbas, Hisham Alasmary, Muhammad Waqas, Wasim A. Khan

Research outputs 2022 to 2026

Smart manufacturing is transforming the manufacturing industry by enhancing productivity and quality, driving growth in the global economy. The Internet of Things (IoT) has played a crucial role in realizing Industry 4.0, where machines can communicate and interact in real-time. Despite these advancements, security remains a major challenge in developing and deploying smart manufacturing. As cyber-attacks become more prevalent, researchers are making security a top priority. Although IoT and Industrial IoT (IIoT) are used to establish smart industries, these systems remain vulnerable to various types of attacks. To address these security issues, numerous authentication methods have been proposed. However, many …

Modification Of Cellulose Ether With Organic Carbonate For Enhanced Thermal And Rheological Properties: Characterization And Analysis, Ghulam Abbas, Abdul H. Tunio, Khalil R. Memon, Aftab A. Mahesar, Faisal H. Memon, Ghazanfer R. Abbasi

Research outputs 2022 to 2026

Reduction in viscosity at higher temperatures is the main limitation of utilizing cellulose ethers in high thermal reservoir conditions for petroleum industry applications. In this study, cellulose ether (hydroxyethyl methyl cellulose (HEMC)) is modified using organic carbonates, i.e., propylene carbonate (PC) and diethyl carbonate (DEC), to overcome the limitation of reduced viscosity at high temperatures. The polymer composites were characterized through various analytical techniques, including Fourier-transform infrared (FTIR), H-NMR, X-ray diffraction (XRD), scanning electron microscope (SEM), thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), -potential measurement, molecular weight determination, and rheology measurements. The experimental results of structural and morphological characterization confirm …