Physical Sciences and Mathematics Commons^™

Impact Of Fiscal Policy On Financial Inclusion And Development In Nigeria, Okwanya Innocent, Taiwo A. Olusegun, Aimua E. Peace

CBN Journal of Applied Statistics (JAS)

This paper examines the effect of fiscal policy on financial inclusion and development in Nigeria. The study employs the Autoregressive Distributed Lag (ARDL) model and impulse response function (IRF) to determine the extent and response of financial inclusion and development to fiscal policy changes in Nigeria. The study derives a financial inclusion index from three core indicators: access, usage and quality of financial services, while financial development is measured as the ratio of money supply to GDP (M2/GDP). The results show that government expenditure has a significant positive effect on financial inclusion and development, while tax revenue exerts a negative …

Perceptions And Aspirations Of Undergraduate Computer Science Students Towards Generative Ai: A Qualitative Inquiry, James Hutson, Theresa Jeevanjee

Faculty Scholarship

This article presents a comprehensive study conducted during the spring semester of 2024, aimed at exploring undergraduate computer science students’ perceptions, awareness, and understanding of generative artificial intelligence (GAI) tools within the context of their Artificial Intelligence (AI) courses. The research methodology employed qualitative techniques, including human-subject research and focus groups, to delve into students’ insights on the evolution of AI as delineated in the seminal textbook by Russell and Norvig. The study-initiated discussions on the historical development of AI, prompting students to reflect on the aspects that intrigued them the most, and to identify which historical concepts and methodologies, …

Predictive Power Of Machine Learning Models On Degree Completion Among Adult Learners, Emily Barnes, James Hutson, Karriem Perry

Faculty Scholarship

The integration of machine learning (ML) into higher education has been recognized as a transformative force for adult learners, a growing demographic facing unique educational challenges. This study evaluates the predictive power of three ML models—Random Forest, Gradient-Boosting Machine, and Decision Trees—in forecasting degree completion among this group. Utilizing a dataset from the academic years 2013-14 to 2021-22, which includes demographic and academic performance metrics, the study employs accuracy, precision, recall, and F1 score to assess the efficacy of these models. The results indicate that the Gradient-Boosting Machine model outperforms others in predicting degree completion, suggesting that ML can significantly …

Einstein Field Equations And The Solutions For Uncharged Black Holes, Yogesh Mahat

Masters Theses

The General Theory of Relativity, formulated by the brilliant mind of Albert Einstein, stands as one of the most fundamental and revolutionary pillars of modern physics. This elegant theory of gravity not only offers a comprehensive explanation of the workings of the universe on a large scale, but it has also paved the way for groundbreaking advancements in the field of mathematics. This thesis begins by providing a concise overview of the key mathematical principles that are crucial to understanding Einstein’s theory. These principles form the basis for deriving the field equations that Einstein introduced. From there, these equations are …

Confronting Algorithms: Conscience Catching In The Criminal Trial And Beyond, Sherman J. Clark

University of Michigan Journal of Law Reform

Using the question of how to treat algorithmic evidence under the Confrontation Clause as an entry point, I argue that the use of AI in ethically salient situations presents a risk. It may cause us to avoid confronting our own responsibility. This matters because facing up to what we do, including what we delegate, can help us grow and thrive. Bearing responsibility can help us nurture vital capacities, including forms of empathy, honesty, and dignity. In the language of ethics, these are eudaimonist virtues—traits and capacities that can help us live well and fully. We should thus find ways of …

Iowa Waste Reduction Center Newsletter, June 2024, University Of Northern Iowa. Iowa Waste Reduction Center.

Iowa Waste Reduction Center Newsletter

Contents:

--- Rota, Spain: A Unique Hub for U.S. Military Vehicle Painting
--- Get involved with the Solid Waste Educators Group
--- USDA announces Composting and Food Waste Cooperative Agreements
--- Library Compost Training Series visits West Bend, IA
--- Important Reminders

Time-Dependent Behavior Of Radiation Induced Conductivity In Polymers, Tyler Heggenes, Jenny R. Whiteley, Jodie Corbridge Gillespie, Joshua Boman, Jr Dennison

Conference Proceedings

Ionizing radiation can induce conductivity in polymers via inelastic scattering which imparts energy to electrons in the valence band and trapped states, exciting them into the conduction band without depositing charge into the material. This radiation-induced conductivity (RIC) can impact space charge dissipation within highly insulating materials used in spacecraft in harsh space plasma environments. Previous USU research analyzed only the equilibrium portions of an extensive RIC database for polymeric materials, including Kapton HN^TM. This confirmed that equilibrium RIC follows a standard theoretical model that is temperature and dose-dependent. The current study provides a new analysis of RIC's …

Comparison Of Absolute Electron Emission Yields Of Extreme Insulators: Round Robin Tests Of Polyimide And Low Density Polyethylene, Jr Dennison, Matthew Robertson, Christopher Vega, Mohamed Belhaj, Juste Sarah Dadouch, Isabel Montero, María E. Dávila, Kazuhiro Toyoda

Posters

No abstract provided.

Temperature Dependent Radiation Induced Conductivity Of Polymeric Spacecraft Materials, Jodie Corbridge Gillespie, Jr Dennison

Posters

No abstract provided.

Analysis Of The Effects Of Surface Modifications And Other Extrinsic Factors On Electron Yield With A “Patch” Model, Matthew Robertson, Christopher Vega, Trace Taylor, Jr Dennison

Presentations

Electron yield (EY) is a material attribute of central importance to understanding and modeling spacecraft charging. EY is defined as the ratio of emitted electrons to incident electrons, when irradiated with an electron beam. It depends on incident energy and is unique for each material as determined by its chemical composition, crystal structure, and electronic configurations. Dynamic surface modifications and other extrinsic factors—including surface morphology, composition, contamination, oxidation, and charging— can significantly affect EY and consequently spacecraft charging. This research proposes a “patch” model to provide a simple theoretical framework to model more complex materials comprised of any number of …

Time-Dependent Behavior Of Radiation Induced Conductivity Of Polymers, Tyler Heggenes, Jenny R. Whiteley, Jodie Corbridge Gillespie, Joshua Boman, Jr Dennison

Presentations

The conductivity of insulating materials can be enhanced above the baseline dark conductivity by incident radiation via inelastic scattering that imparts energy to electrons in trapped states and excites them into the conduction band, without depositing charge. Such radiation-induced conductivity (RIC), caused by ionizing radiation present in harsh space plasma environments, can play a critical role in space charge dissipation within highly insulating materials used in spacecraft. An equilibrium value for RIC, 𝜎_RIC, is attained after prolonged exposure to an incident dose rate; this follows a standard theoretical power law model proposed by Rose/Fowler/Vissenberg, 𝜎_RIC(T …

Analysis Of The Effects Of Surface Modifications And Other Extrinsic Factors On Electron Yield With A “Patch” Model, Matthew Robertson, Christopher Vega, Trace Taylor, Jr Dennison

Conference Proceedings

Electron yield (EY) is a material property of central importance to understanding and modeling spacecraft charging. It depends on incident energy and is unique for each material. Dynamic surface modifications and other extrinsic factors—including composition, surface morphology, contamination, oxidation, and charging— can significantly affect EY and consequently spacecraft charging. This research proposes a “patch” model to provide a simple theoretical framework to model more complex materials comprised of any number of different types of constituent materials in terms of the EY contribution of each constituent material or extrinsic factor. The “patch” model merges the unique EY curve contribution of each …

Effects Of Differing Radiation Methods On Charge Transport In Polymers, Zachary J. Gibson, Jr Dennison, Virginie Griseri

Conference Proceedings

Spacecraft charging issues are understood and mitigated through an understanding of material properties. Material properties are dynamic in the harsh environment of space. Approximations must be made to simulate the space environment in the laboratory. This paper reports on the investigation of the approximation that energy deposition causes the same aging effects in the materials regardless of the radiation source. Samples of polytetrafluoroethylene (PTFE) and polyether-etherketone (PEEK) were irradiated with x-rays, γ-rays, or electrons at total ionizing dose (TID) of either 2 x 10⁴, 2 x 10⁵, or 2 x 10⁶ rad. Charge was then …

Matching The Scales Of Planning And Environmental Risk: An Evaluation Of Community Wildfire Protection Plans In The Western Us, Matthew Hamilton, Cody Evers, Max Nielsen-Pincus, Alan A. Ager

Environmental Science and Management Faculty Publications and Presentations

Theory predicts that effective environmental governance requires that the scales of management account for the scales of environmental processes. A good example is community wildfire protection planning. Plan boundaries that are too narrowly defined may miss sources of wildfire risk originating at larger geographic scales whereas boundaries that are too broadly defined dilute resources. Although the concept of scale (mis)matches is widely discussed in literature on risk mitigation as well as environmental governance more generally, rarely has the concept been rigorously quantified. We introduce methods to address this limitation, and we apply our approach to assess scale matching among Community …

A Guide To Successful Management Of Collaborative Partnerships In Quantitative Research: An Illustration Of The Science Of Team Science., Alyssa Platt, Tracy Truong, Mary Boulos, Nichole E Carlson, Manisha Desai, Monica M Elam, Emily Slade, Alexandra L Hanlon, Jillian H Hurst, Maren K Olsen, Laila M Poisson, Lacey Rende, Gina-Maria Pomann

Biostatistics Faculty Publications

Data-intensive research continues to expand with the goal of improving healthcare delivery, clinical decision-making, and patient outcomes. Quantitative scientists, such as biostatisticians, epidemiologists, and informaticists, are tasked with turning data into health knowledge. In academic health centres, quantitative scientists are critical to the missions of biomedical discovery and improvement of health. Many academic health centres have developed centralized Quantitative Science Units which foster dual goals of professional development of quantitative scientists and producing high quality, reproducible domain research. Such units then develop teams of quantitative scientists who can collaborate with researchers. However, existing literature does not provide guidance on how …

Agricultural Groundcover Update April 2024, Justin Laycock

Natural resources published reports

• In April, over 12% (1,876,000 ha) of the arable farmland in the south-west of Western Australia had less than 50% vegetative groundcover, which is inadequate to prevent wind erosion.
• Northern grainbelt had the highest risk of wind erosion and over 26% of this farmland had inadequate groundcover, predominantly found on landscapes known for sandy soils.
• About 1.5% (238,900 ha) of arable land had a high to very high risk of wind erosion because groundcover was less than 30%.

Architectural Elements Contributing To Interpretability Of Deep Neural Networks (Dnns), Emily Barnes, James Hutson

Faculty Scholarship

The interpretability of Deep Neural Networks (DNNs) has become a critical focus in artificial intelligence and machine learning, particularly as DNNs are increasingly used in high-stakes applications like healthcare, finance, and autonomous driving. Interpretability refers to the extent to which humans can understand the reasons behind a model's decisions, which is essential for trust, accountability, and transparency. However, the complexity and depth of DNN architectures often compromise interpretability as these models function as "black boxes." This article reviews key architectural elements of DNNs that affect their interpretability, aiming to guide the design of more transparent and trustworthy models. The primary …

Navigating The Complexities Of Ai: The Critical Role Of Interpretability And Explainability In Ensuring Transparency And Trust, Emily Barnes, James Hutson

Faculty Scholarship

The interpretability and explainability of deep neural networks (DNNs) are paramount in artificial intelligence (AI), especially when applied to high-stakes fields such as healthcare, finance, and autonomous driving. The need for this study arises from the growing integration of AI into critical areas where transparency, trust, and ethical decision-making are essential. This paper explores the impact of architectural design choices on DNN interpretability, focusing on how different architectural elements like layer types, network depth, connectivity patterns, and attention mechanisms affect model transparency. Methodologically, the study employs a comprehensive review of case studies and experimental results to analyze the balance between …

Herbicide Application, Department Of Primary Industries And Regional Development, Western Australia

Grains and other field crops factsheets

Herbicides can be applied by a variety of means, including boom sprayers, aerial spraying, misters, blanket wipers, rope wick applicators, weed seekers and back-pack sprayers. This fact sheet reviews the different methods, including nozzles and calibration of equipment, used to apply herbicides.

Combinatorial Creativity: Knowledge Graphs And Idea Generation In Crowdsourcing Innovation, Zhi Wei Vincent Mack

Dissertations and Theses Collection (Open Access)

This dissertation explores the dynamic interplay between combinatorial creativity and technology-driven innovation within various knowledge-intensive fields. It critically examines the role of combinatorial creativity in generating groundbreaking innovations by amalgamating existing ideas and technologies. This research incorporates a detailed examination of how knowledge, whether tacit or explicit, can be transformed into actionable data to foster innovation in crowdsourcing contexts. Chapter 2 provides an overview of the relevant literature on how Artificial Intelligence and Knowledge Management Systems can support combinatorial creativity. The study further delves into the transformative impact of knowledge management systems, particularly focusing on crowdsourcing platforms that leverage collective …

Evaluating Methods For Assessing Interpretability Of Deep Neural Networks (Dnns), Emily Barnes, James Hutson

Faculty Scholarship

The interpretability of deep neural networks (DNNs) is a critical focus in artificial intelligence (AI) and machine learning (ML), particularly as these models are increasingly deployed in high-stakes applications such as healthcare, finance, and autonomous systems. In the context of these technologies, interpretability refers to the extent to which a human can understand the cause of a decision made by a model. This article evaluates various methods for assessing the interpretability of DNNs, recognizing the significant challenges posed by their complex and opaque nature. The review encompasses both quantitative metrics and qualitative evaluations, aiming to identify effective strategies that enhance …

Photodegradation Of Microplastics Through Nanomaterials: Insights Into Photocatalysts Modification And Detailed Mechanisms, Yiting Xiao, Yang Tian, Wenbo Xu, Jun Zhu

Biological and Agricultural Engineering Faculty Publications and Presentations

Microplastics (MPs) pose a profound environmental challenge, impacting ecosystems and human health through mechanisms such as bioaccumulation and ecosystem contamination. While traditional water treatment methods can partially remove microplastics, their limitations highlight the need for innovative green approaches like photodegradation to ensure more effective and sustainable removal. This review explores the potential of nanomaterial-enhanced photocatalysts in addressing this issue. Utilizing their unique properties like large surface area and tunable bandgap, nanomaterials significantly improve degradation efficiency. Different strategies for photocatalyst modification to improve photocatalytic performance are thoroughly summarized, with a particular emphasis on element doping and heterojunction construction. Furthermore, this review …

Topological Indices And Their Applications In Designing Drugs, Fedaa Ismail Abunawa

Theses

This research delves into the use of indices, in the field of drug design with a specific focus on anticancer medications. Topological indices, values derived from representations of chemical structures provide meaningful connections to the physical and chemical characteristics of molecules. These Indices act as tools for predicting behavior playing a vital role in crafting therapeutic drugs. The study primarily delves into topological indices like the Sombor index, Randi´c index, and Atom Bond Connectivity (ABC) index, which are calculated for structures and their relationships with physical properties such as molar volume, refractive index, and flash point are explored using statistical …

Phase Error Scaling Law In Two-Wavelength Adaptive Optics, Milo W. Hyde Iv, Matthew Kalensky, Michael J. Spencer

Faculty Publications

We derive a simple, physical, closed-form expression for the optical-path difference (OPD) of a two-wavelength adaptive-optics (AO) system. Starting from Hogge and Butts’ classic OPD variance integral expression, we apply Mellin transform techniques to obtain series and asymptotic solutions to the integral. For realistic two-wavelength AO systems, the former converges slowly and has limited utility. The latter, on the other hand, is a simple formula in terms of the separation between the AO sensing (i.e., the beacon) and compensation (or observation) wavelengths. We validate this formula by comparing it to the OPD variances obtained from the aforementioned series and direct …

Qwixx Strategies Using Simulation And Mcmc Methods, Joshua W. Blank

Master's Theses

This study explores optimal strategies for maximizing scores and winning in the popular dice game Qwixx, analyzing both single and multiplayer gameplay scenarios. Through extensive simulations, various strategies were tested and compared, including a scorebased approach that uses a formula tuned by MCMC random walks, and race-to-lock approaches which use absorbing Markov chain qualities of individual score sheet rows to find ways to lock rows as quickly as possible. Results indicate that employing a scorebased strategy, considering gap, count, position, skip, and likelihood scores, significantly improves performance in single player games, while move restrictions based on specific dice roll sums …

Causal Inference Using Bayesian Network For Search And Rescue, Amanda Belden

Master's Theses

People who are considered missing have much higher probabilities of being found dead compared to those who are not considered missing in terms of Search and Rescue (SAR) missions. Dementia patients are incredibly likely to be declared missing, and in fact after removing those with dementia the probability of the mission being regarded as missing person case is only about 10%. Additionally, those who go missing are much more likely to be on private land than on protected areas such as forests and parks. These and similar associations can be represented and investigated using a Bayesian network that has been …

Dehn's Problems And Geometric Group Theory, Noelle Labrie

Master's Theses

In 1911, mathematician Max Dehn posed three decision problems for finitely

presented groups that have remained central to the study of combinatorial

group theory. His work provided the foundation for geometric group theory,

which aims to analyze groups using the topological and geometric properties

of the spaces they act on. In this thesis, we study group actions on Cayley

graphs and the Farey tree. We prove that a group has a solvable word problem

if and only if its associated Cayley graph is constructible. Moreover, we prove

that a group is finitely generated if and only if it acts geometrically …

Semantic Structuring Of Digital Documents: Knowledge Graph Generation And Evaluation, Erik E. Luu

Master's Theses

In the era of total digitization of documents, navigating vast and heterogeneous data landscapes presents significant challenges for effective information retrieval, both for humans and digital agents. Traditional methods of knowledge organization often struggle to keep pace with evolving user demands, resulting in suboptimal outcomes such as information overload and disorganized data. This thesis presents a case study on a pipeline that leverages principles from cognitive science, graph theory, and semantic computing to generate semantically organized knowledge graphs. By evaluating a combination of different models, methodologies, and algorithms, the pipeline aims to enhance the organization and retrieval of digital documents. …

Design And Implementation Of A Vision-Based Deep-Learning Protocol For Kinematic Feature Extraction With Application To Stroke Rehabilitation, Juan Diego Luna Inga

Master's Theses

Stroke is a leading cause of long-term disability, affecting thousands of individuals annually and significantly impairing their mobility, independence, and quality of life. Traditional methods for assessing motor impairments are often costly and invasive, creating substantial barriers to effective rehabilitation. This thesis explores the use of DeepLabCut (DLC), a deep-learning-based pose estimation tool, to extract clinically meaningful kinematic features from video data of stroke survivors with upper-extremity (UE) impairments.

To conduct this investigation, a specialized protocol was developed to tailor DLC for analyzing movements characteristic of UE impairments in stroke survivors. This protocol was validated through comparative analysis using peak …

Morp: Monocular Orientation Regression Pipeline, Jacob Gunderson

Master's Theses

Orientation estimation of objects plays a pivotal role in robotics, self-driving cars, and augmented reality. Beyond mere position, accurately determining the orientation of objects is essential for constructing precise models of the physical world. While 2D object detection has made significant strides, the field of orientation estimation still faces several challenges. Our research addresses these hurdles by proposing an efficient pipeline which facilitates rapid creation of labeled training data and enables direct regression of object orientation from a single image. We start by creating a digital twin of a physical object using an iPhone, followed by generating synthetic images using …