Physical Sciences and Mathematics Commons^™

The Role Of Ai In Enhancing Teamwork, Resilience And Decision-Making: Review Of Recent Developments, Satyadhar Joshi

Harrisburg University Other Works

This paper explores the transformative impact of artificial intelligence (AI) on organizational teamwork, decision-making, and resilience. This paper furthur reviews recent literature on the integration of Artificial Intelligence (AI) in various organizational functions, focusing on its impact on innovation management, leadership paradigms, and organizational resilience. We provide groundwork required to enhance frameworks that can integrate cognitive scaffolding with antifragile team dynamics, employing behavioral economics and neurocognitive principles. We introduce methodologies for enhancing team resilience through adaptive AI systems, cross-training interventions, and pre-mortem simulation techniques. The framework addresses key challenges in confirmation bias mitigation, cultural dimension alignment, and vigilance decrement prevention. …

Elucidating The Mechanisms Of Collagenase H From Hathewaya (Clostridium) Histolytica In Collagen Degradation And Developing A Cross-Linked Platform For Efficient Capture And Release Of Adeno-Associated Virus Serotype 2 (Aav2), Adjoa Otubea Bonsu

Graduate Theses and Dissertations

In biological systems, Hathewaya (Clostridium) histolytica secretes collagenases that degrade structural collagen in host tissues and cause tissue necrosis. Despite their detrimental effects, these enzymes have significant therapeutic potential by effectively digesting various collagen subtypes to treat connective tissue disorders. The bacterium produces two homologous collagenases, ColG and ColH, with a catalytic module crucial for hydrolyzing collagen and binding domains, including collagen-binding domains (CBDs) and polycystic kidney disease-like domains (PKDs), for substrate interaction.

Although the crystal structure of the catalytic module of collagenase G (ColG) has been determined, shedding light on its chew-and-digest mechanism, the structure of collagenase H (ColH) …

A New Method Using Lidar And Well Logs To Identify And Correlate The Pennsylvanian Atoka Formation Subdivisions In The Surface Outcrops Of The Boston Mountains, Arkansas, Robert Brent Boyd

Graduate Theses and Dissertations

The surface outcrops of the Boston Mountains in northwest Arkansas primarily consist of the Pennsylvanian Atoka formation. Where fully preserved in the Arkoma Basin to the south, the Atoka formation exceeds 18,000 ft of sand and shale. However, it remains undifferentiated on the surface geological map of Arkansas. This study investigates the possibility of identifying and correlating the subsurface subdivisions of the Atoka Formation to the surface outcrops of the Boston Mountains using a new method. Recognizing the Atoka subdivisions in surface outcrops using standard lithology and biostratigraphy correlation techniques has been challenging. The subdivision of the Atoka has instead …

Leveraging Machine Learning Models For Enhanced Landslide Prediction In Western North Carolina, Andrew Edmonds

Graduate Theses and Dissertations

Landslides pose significant hazards to human safety, infrastructure, and the environment, particularly in regions of high elevation that experience extended periods of heavy rainfall. This research focuses on preparing and evaluating landslide susceptibility maps (LSMs) for the Blue Ridge Mountains, a portion of the Appalachian Mountains in western North Carolina, utilizing three machine learning algorithms: Logistic Regression, Random Forest, and Gradient Boosting Regression. Sixteen landslide conditioning factors, reflecting topographic, geological, environmental, and anthropogenic influences, were identified for model input. The landslide inventory database, comprising 7,350 locations, was randomly divided into training (80%) and testing (20%) sets. The performance of each …

Culture And Context: How Frames Of Teaching And Of Learning Mathematics Form And Change For Graduate Student Instructors, Johan Benedict Arroyo Cristobal

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Why do instructors teach the way that they do? This question is key in understanding how and why asset-based or deficit-based practices are enacted in the classroom. In consequence, this question also gives insight into how the environment where students learn is shaped. Mathematics education research, in particular, has been concerned with how instructors respond to students’ contributions in the classroom and how these ways of responding can (dis)empower students.

One way to answer this question is to understand how instructors frame their own teaching and their students’ learning. Frames are mental constructs which help individuals filter details, interpret information, …

Towards Advancing Streamflow And Peak Flow Prediction With Machine Learning: Identifying Infrastructure At Risk, Sudan Pokharel

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Due to climate change and its impact, the need for adaptive strategies for natural disaster mitigation and resource management has never been more urgent. Central to this is water resource management, which is essential for sustainable human activities, ecological balance, and the mitigation of natural hazards like floods. Streamflow is a crucial element of water resource management and plays a vital role in planning and building water infrastructure, implementing emergency response plans, supporting flood mitigation initiatives, and regulating agricultural and industrial use. However, accurate prediction of streamflow still remains a challenge due to the complex non-linear and non-stationary interaction between …

Robot-Integrated 4d Building Information Modeling (4d Bim): Framework For Planning Safe Autonomous Construction Operations In Dynamic Environments, Hafiz Oyedimeji Oyediran

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

In the construction industry, the use of autonomous robots is considered a solution to overcome the heavy reliance on human workers to perform repetitive, strenuous, and hazardous tasks. While these robots offer the advantage of autonomous operation, ensuring their safe and efficient integration within construction sites requires precise planning. Such planning must account for the varying project complexities such as scope, site layout, tasks, timelines, existence of human workers, and other spatiotemporal conditions of the construction site. Currently, there are no methods to safely plan autonomous robot operations considering these factors within the overarching construction planning process. Thus, autonomous robots …

Considerations And Techniques For Producing Urban Tree Canopy Maps Using Freely Available And Accessible Methods, Hugh Reed Ellerman

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Urban forests are valued as green infrastructure for the variety of benefits they provide across ecological, social, public health, and economic domains. To understand the distribution of trees, maps are required. Many methods of mapping tree cover use expensive and data-intensive datasets such as hyperspectral and LiDAR imagery, making these methods inaccessible to municipal forest managers. Variations in the modelling approaches of existing, freely available methods are not addressed in sufficient enough detail to understand the trade-offs implicit in these variations. Further, characteristics of urban tree canopy mapping (high spatial resolution imagery, heterogeneous urban environments, the importance of the tree …

Synthesis And X-Ray Characterization Of Nickel Phosphide Nanostructures For Water Oxidation, David Thompson

Graduate Theses and Dissertations

The pursuit of earth-abundant 3d transition metal catalysts for alkaline water electrolysis remains an active area of research. While NiFe layered double hydroxides (LDH) are currently the most active catalysts for the oxygen evolution reaction (OER), doping Ni-based catalysts with phosphorus to form Ni metal phosphides has emerged as a promising alternative. However, the complex crystalline and amorphous phases of NiP_x have hindered their characterization, and the OER mechanism and origin of activity remain unclear. This dissertation aims to elucidate the synthesis, OER performance, and in situ reconstruction of amorphous NiP_x-based nanomaterials, with a focus on in …

Constraining Neutron Star Kicks Through Gravitational Wave Detection, James Phillips

Graduate Theses and Dissertations

Neutron stars have, on average, much higher proper motions than those of hot and bright O and B type main sequence stars. Thus, it is widely suspected that asymmetries in core-collapse supernovae provide a substantial kick to these compact objects at birth. Gen- eral Relativity predicts that such supernova asymmetries will generate bursts of gravitational radiation, though no such burst has been yet detected. Here we argue that the polarization amplitudes of those gravitational waves can be used to calculate the kicks imparted on that nascent neutron star. We begin by reviewing the evidence for natal kicks, then we describe …

Nonparametric Methods For Bayesian Community Detection In Complex Networks, Kedran Young

Graduate Theses and Dissertations

Network analysis is becoming an increasingly popular interdisciplinary area of study, with emerging interest in fields like sociology, biology, economics, and ecology. Within the niche of network analysis, capturing the community structure of a network is one important achievement that many statisticians have been working toward over recent decades. The most popular modeling technique for latent community detection is the Stochastic Block Model (SBM), which falls into the category of latent variable models and will serve as the baseline model throughout this thesis. SBM is widely regarded as the most effective community detection method as it detects latent community membership …

Sabrina Vs Steph: The Battle Between The Wnba And Nba, Naysha Mcgriff

Symposium of Student Scholars

The average salary of a Women’s National Basketball Association (WNBA) player is 110 times less than a National Basketball Association (NBA) player’s. Despite growing WNBA viewership, gender inequality in sports remains high, with critics claiming female athletes are less skilled. Gender bias in sports is severely understudied, making direct comparisons to men’s leagues unfair due to long-term lack of investment in women’s sports. This study investigates whether the perceived disparity in skill levels between WNBA and NBA players' is genuine or influenced more by external factors by developing an unbiased measure of player efficiency to compare athletic performance. This dataset …

Structure Disorder And Magnetic Behavior Of An Olivine-Type Cathode Material, Hamida Hassan, Madalynn Marshall

Symposium of Student Scholars

Our research investigates the structure disorder and magnetic behavior of Li(Mn,Fe)PO₄, an olivine-type material relevant for lithium-ion battery applications. Using single-crystal neutron diffraction at Oak Ridge National Laboratory, we precisely determined Mn and Fe occupancy, revealing a 56% Fe and 44% Mn distribution at the atomic 4c site within the Pnma space group. Additionally, we observed significant lithium site vacancies (78% occupied), influencing the material's electrochemical and magnetic properties. The varying Mn/Fe ratio affected spin reorientation transitions, shifting from antiferromagnetic alignment along the a-axis to the b-axis. Our findings provide crucial insights for optimizing olivine-based cathodes, enhancing energy …

Computational Study Of The Proton Transfer In The H7o3+ Cluster, Anna James, Martina Kaledin

Symposium of Student Scholars

Proton transfer (PT) from one molecule to another is among the most studied phenomena in chemistry. PT requires the bond cleavage and the formation of a new one, AH⁺ + B -> A+ BH⁺. In protonated water clusters, such a process consists of the interconversion of hydrogen bonds. Experimentally, such a process can be observed as a significant increase of a dipole moment. However, other vibrational transitions often occur with small changes in the dipole moment while large changes in polarizability. In this work, we study the PT process in a protonated water cluster, H₇O …

Overcoming Motor Imagery Bci Illiteracy: Adaptive Decoding And Knowledge Transfer In Eeg-Based Brain-Computer Interfaces, Zaid Shuqfa Shuqfa

Thesis/ Dissertation Defenses

Brain Computer Interface (BCI), Also known as brain-machine interface (BMI) is a mean of controlling machines without the need to activate peripheral nerves or muscles. It has received the attention of research for decades. Motor imagery-based BCI is a paradigm that is characterized by its user friendliness where users can generate control commands at their freewill, without waiting for a que from the BCI module. Motor imagery brain–computer interface (MI–BCI) has considerable potential in increasing the quality of the lives for people with mobility impairment and the healthy ones as well. Though, its diffusion in application still has many pitfalls …

Large Language Model Enabled Mental Health App Recommendations Using Structured Datasets, Kris Prasad, Md Abdullah Al Hafiz Khan

Symposium of Student Scholars

The increasing use of large language models (LLMs) in mental health support necessitates detailed evaluation of their recommendation capabilities. This study compares four modern LLMs—GPT-4o, Claude 3.5 Sonnet, and dataset-enhanced Gemma 2 and GPT-3.5-Turbo—in recommending mental health applications. We constructed a structured dataset of 55 mental health apps using RoBERTa-based sentiment analysis and keyword similarity scoring, focusing on depression, anxiety, ADHD, and insomnia. Standard LLMs demonstrated inconsistent accuracy and often relied on outdated or generic information. In contrast, our retrieval-augmented generation (RAG) pipeline enabled lower-cost models to achieve up to 55% higher accuracy than baseline models while recommending apps with …

Properties Of Eigenvalues Of The Fractal Laplacian, Eric Stachura, Andrew Chincea

Symposium of Student Scholars

We investigate the properties of the eigenvalues of the fractal Laplacian. We begin by defining the fractal Laplacian operator in one dimension and formulate the corresponding Dirichlet eigenvalue problem. Analytical solutions are obtained for specific fractal parameters, and computational results illustrate the structure of eigenvalues and their associated eigenfunctions. We extend our analysis to two dimensions using separation of variables. Our findings contribute to a deeper understanding of how fractal geometry affects the spectral characteristics of differential operators.

Older Groundwater Reservoirs In Nebraska, Marvin P. Carlson, Steven S. Sibray

Conservation and Survey Division: Faculty and Staff Publications

Sedimentary rocks below Nebraska's regional water table are saturated down to the crystalline igneous and metamorphic rocks of Precambrian age. The total thickness of these sedimentary rocks ranges from about 600 feet to over 10,000 feet. Nearly all current wells in Nebraska produce water from the near-surface, unconsolidated or partly consolidated rocks. Most studies to date -- interpretive reports, test drilling, inventory of wells, monitoring water levels, and water quality determinations -- have concentrated on these near-surface sources of groundwater.

Opioid Epidemic In Maine: An Analysis Of Increasing Overdose-Related Deaths Following The Coronavirus, Aysel S. Hamlin

Thinking Matters Symposium

The rate of drug overdose resulting in death doubled in Maine following the COVID-19 pandemic from the onset of the COVID-19 pandemic in late 2019 through 2022. The correlation between increased isolation during the pandemic and overdose death rates sheds a concerning light on the insufficient resources for people struggling with Opioid Use Disorder (OUD) throughout Maine. The increasing trade and access to fentanyl following the pandemic accounted for the majority of drug-related deaths in Maine in 2021 and 2022. This study examines the need for long-term access to drug treatment in rural and urban Maine, both environments with varying …

Predicting Healthcare Service Quality Based On A Kalman-Optimized Bi-Lstm-Inspired Deep Learning Model, Mohammed K. Al-Khafaji, Eman S. Al-Shamery

Karbala International Journal of Modern Science

Health is one of the most important aspects of human well-being, and access to high-quality healthcare is essential for a good quality of life. Providing top-level health services at all times is crucial. However, the research in healthcare poses significant challenges due to the diversity and variations of medical practices across different hospitals. This paper aims to tackle the challenge of data missing and scattering during data collection. Then, the quality of services (QoS) offered by healthcare facilities will be analyzed and predicted from the patient's perspective. The model begins preprocessing data by data cleaning, handling missing values, and scattering …

The Role Of Individual Values In Bryant University's Sustainability Efforts, John Boccuzzi Iii

Honors Projects in Data Science

This research examines student, faculty, and staff perspectives on sustainability at Bryant University, with the goal of understanding how individual values align with the university's environmental initiatives. The objective is to assess perceptions of Bryant's current sustainability practices, explore how effectively these efforts are communicated across campus, and identify potential gaps between institutional action and community awareness. To achieve this, the study gathers qualitative data through an open-ended survey and applies sentiment analysis to interpret student attitudes toward sustainability. By analyzing these responses alongside Bryant's sustainability marketing efforts, this research will identify gaps between student engagement and institutional messaging The …

Promoting Sustainable Drinking Water Consumption At Bryant University, Matthew Gerdenich

Honors Projects in Biological and Biomedical Sciences

Many higher education institutions have either banned or restricted the sale of disposable plastic water bottles to address environmental pollution and climate change. This project seeks to gather knowledge and data related to disposable water bottle consumption at Bryant University that can be used to provide recommendations for promoting reusable water bottle use on campus. Additionally, the project seeks to find out why community members choose single-use water bottles over reusables ones and how to make reusable water bottle use more attractive and accessible. Primary research included a survey distributed to undergraduate students on campus as well as water samples …

Exploring The Evolution Of Global Warming Discourse: A Twitter-Based Analysis Across The United States, United Kingdom, And India, Trevor Christensen

Honors Projects in Data Science

Global warming has gained increasing attention over the past decade, with public discourse intensifying on social media platforms, particularly on Twitter. This increased discussion stems from political controversies surrounding climate change and the rise in extreme weather events. This study explores the evolution of global warming discourse on Twitter, with a focus on the United States, United Kingdom, and India. This research used a dataset of historical tweets ranging from 2010 to 2023 containing the keyword "global warming." Using natural language processing (NLP) techniques such as emotion analysis, word cloud visualization, and topic modeling (LDA), approximately twenty-eight million tweets were …

Synthesis Of Surface Bound Ligands Through Click Chemistry For A Tethered Catalyst System Towards The Reduction Of Carbon Dioxide, Emma J. Goehner

Departmental Honors & Graduate Capstone Projects

The electrochemical reduction of carbon dioxide (CO₂) offers a promising strategy for reducing excess atmospheric CO₂ by converting it into valuable chemical products. This process can be catalyzed by transition metal complexes, particularly those with extended aromatic ligands such as terpyridines which coordinate with metals in a tridentate fashion. When tethered to an electrode, the terpyridine-metal catalyst complex allows for stable binding and reusability without the need for chemical separation to recover the catalyst. Self-assembled monolayers (SAMs) of (3-bromopropyl) phosphonic acid were formed on copper surfaces, enabling attachment of terpyridine ligands through a click reaction between a …

Heathy Aging In A Sustainable Manner, Karin Volkwein-Caplan

Sustainability Research & Practice Seminar Presentations

Dr. Karin Volkwein-Caplan, Kinesiology, presents "Healthy Aging in a Sustainable Manner".

Artificial Intelligence In Orthopedic Medical Education: A Comprehensive Review Of Emerging Technologies And Their Applications, Kyle Sporn, Rahul Kumar, Phani Paladugu, Tejas Sekhar, Swapna Vaja, Tamer Hage, Ethan Waisberg, Chirag Gowda, Ram Jagadeesan, Nasif Zaman, Alireza Tavakkoli

SKMC Student Presentations and Publications

Integrating artificial intelligence (AI) and mixed reality (MR) into orthopedic education has transformed learning. This review examines AI-powered platforms like Microsoft HoloLens, Apple Vision Pro, and HTC Vive Pro, which enhance anatomical visualization, surgical simulation, and clinical decision-making. These technologies improve the spatial understanding of musculoskeletal structures, refine procedural skills with haptic feedback, and personalize learning through AI-driven adaptive algorithms. Generative AI tools like ChatGPT further support knowledge retention and provide evidence-based insights on orthopedic topics. AI-enabled platforms and generative AI tools help address challenges in standardizing orthopedic education. However, we still face many barriers that relate to standardizing data, …

Mortgage Default Classification Modeling For Variable Analysis, Brendan R. Goggins

Honors College Theses

The financial crisis of the early 2000’s is a prime example of the severe consequences that mortgage default and borrower insolvency can have on economies at large. Mortgage default specifically is a prime case with the popularization of mortgage backed securities and the commonality of this loan structure. Multiple hypotheses and models have been formed to understand the reasons, causes, and consequences of mortgage default. This paper uses both machine learning and statistical classification models to inform an understanding of the variables most significant and impactful to the default outcome of mortgages. Consideration is given to both loan-level microeconomic variables …

Artificial Intelligence Applications For Grid-Connected Solar Inverters, Utkirjon Ubaydullaev, Sarvinoz Mirzaeva, Hasan Mustafoev

Chemical Technology, Control and Management

The increasing global demand for renewable energy has highlighted the importance of grid-connected solar inverters in ensuring efficient and stable power conversion. However, challenges such as fluctuations in solar energy generation, grid disturbances, and power quality issues necessitate advanced control strategies. The integration of artificial intelligence (AI) into solar inverters presents a transformative solution, enhancing performance, adaptability, and reliability in real-world applications.

This review explores the role of AI techniques, including machine learning (ML), deep learning (DL), fuzzy logic, and reinforcement learning (RL), in optimizing key inverter functionalities such as maximum power point tracking (MPPT), fault detection, power quality enhancement, …

Development Of Fuzzy Ontology For Explainable Artificial Intelligence For Decision-Making In Fuzzy Environment, Pavel Kosov

Chemical Technology, Control and Management

In modern artificial intelligence systems, there is an acute need to understand the decision-making logic of "black box" algorithms. Our research proposes an innovative method for increasing the transparency of such systems through the formalization of fuzzy explanatory mechanisms. We have developed an extension of existing ontological approaches by introducing the concept of fuzziness into the structure of explanatory properties, which allows overcoming the fundamental limitations of traditional XAI methods. The proposed formalization is based on the theory of collective mental models and principles of fuzzy logic, providing a more accurate reflection of uncertainty and subjectivity in expert knowledge. Our …

Infusing Aboriginal Perspectives In Cyber Education, John Shannahan, Mohiuddin Ahmed

Research outputs 2022 to 2026

While human factors are important in cyber security, the discipline has largely not explored incorporating indigenous perspectives—or, more specifically, in an Australian context, Aboriginal perspectives—in its curricula. In this paper, we introduce a promising approach for aligning Aboriginal perspectives with the needs of cyber security graduates and incorporating diverse perspectives into cyber degrees. The approach advocates for the centrality of good curriculum design fundamentals: backward design, constructive alignment, and student outcomes. The paper ends by reflecting on challenges and lessons from the first implementation and review of the material. It provides recommendations for other cyber practitioners exploring ways of incorporating …