Physical Sciences and Mathematics Commons^™

A Mathematical Model For Mapping The Insecticide Resistance Trend In The Anopheles Gambiae Mosquito Population Under Climate Variability In Africa, Komi Mensah Agboka, Mark Wamalwa, James Mutuku Mutunga, Henri E.Z. Tonnang

All Peer-Reviewed Publications

The control of arthropod disease vectors using chemical insecticides is vital in combating malaria, however the increasing insecticide resistance (IR) poses a challenge. Furthermore, climate variability affects mosquito population dynamics and subsequently IR propagation. We present a mathematical model to decipher the relationship between IR in Anopheles gambiae populations and climate variability. By adapting the susceptible-infected-resistant (SIR) framework and integrating temperature and rainfall data, our model examines the connection between mosquito dynamics, IR, and climate. Model validation using field data achieved 92% accuracy, and the sensitivity of model parameters on the transmission potential of IR was elucidated (e.g. μPRCC = …

Interactions Between Bacillus Thuringiensis And Selected Plant Extracts For Sustainable Management Of Phthorimaea Absoluta, Terry A. Ochieng, Komivi S. Akutse, Inusa J. Ajene, Dora C. Kilalo, Maina Muiru, Fathiya M. Khamis

All Peer-Reviewed Publications

Phthorimaea absoluta is a global constraint to tomato production and can cause up to 100% yield loss. Farmers heavily rely on synthetic pesticides to manage this pest. However, these pesticides are detrimental to human, animal, and environmental health. Therefore, exploring eco-friendly, sustainable Integrated Pest Management approaches, including biopesticides as potential alternatives, is of paramount importance. In this context, the present study (i) evaluated the efficacy of 10 Bacillus thuringiensis isolates, neem, garlic, and fenugreek; (ii) assessed the interactions between the most potent plant extracts and B. thuringiensis isolates, and (iii) evaluated the gut microbial diversity due to the treatments for …

Dual Nature Of Magnetism Driven By Momentum Dependent F-D Kondo Hybridization, Byungkyun Kang, Yongbin Lee, Liqin Ke, Hyunsoo Kim, Myoung Hwan Kim, Chul Hong Park

Physics Faculty Research & Creative Works

The intricate nature of magnetism in uranium-based Kondo lattices is a consequence of correlations between U-5f and conduction electrons. Previously, the source of magnetism has been ascribed to either Mott physics or Ruderman-Kittel-Kasuya-Yosida interaction, both of which are not fully applicable to uranium-based Kondo lattices. Using linearized quasiparticle self-consistent GW plus dynamical mean-field theory, we demonstrate a crossover from incoherent to coherent f-d Kondo cloud in the paramagnetic phase of UTe₂, USbTe and USbSe. As the transition occurs, we observe an augmented f-d coherence and Pauli-like magnetic susceptibility, with a substantial frozen magnetic moment of U-5f persisting. We …

Traittrainr: Accelerating Large-Scale Simulation Under Models Of Continuous Trait Evolution, Jenniffer Roa Lozano, Mataya Duncan, Duane D. Mckenna, Todd A. Castoe, Michael Degiorgio, Richard Adams

Entomology and Plant Pathology Faculty Publications and Presentations

Abstract

The scale and scope of comparative trait data are expanding at unprecedented rates, and recent advances in evolutionary modeling and simulation sometimes struggle to match this pace. Well-organized and flexible applications for conducting large-scale simulations of evolution hold promise in this context for understanding models and more so our ability to confidently estimate them with real trait data sampled from nature. Results

We introduce TraitTrainR, an R package designed to facilitate efficient, large-scale simulations under complex models of continuous trait evolution. TraitTrainR employs several output formats, supports popular trait data transformations, accommodates multi-trait evolution, and exhibits flexibility …

Nestings Of Bibds With Block Size Four, Marco Buratti, Donald L. Kreher, Douglas R. Stinson

Michigan Tech Publications

In a nesting of a balanced incomplete block design (or BIBD), we wish to add a point (the nested point) to every block of a (Formula presented.) -BIBD in such a way that we end up with a partial (Formula presented.) -BIBD. In the case where the partial (Formula presented.) -BIBD is in fact a (Formula presented.) -BIBD, we have a perfect nesting. We show that a nesting is perfect if and only if (Formula presented.). Perfect nestings were previously known to exist in the case of Steiner triple systems (i.e., (Formula presented.) -BIBDs) when (Formula presented.), as well as …

Decoding Fgf/Fgfr Signaling: Insights Into Biological Functions And Disease Relevance, Oshadi Edirisinghe, Gaetane Ternier, Zeina Alraawi, Thallapuranam Krishnaswamy Suresh Kumar

Chemistry & Biochemistry Faculty Publications and Presentations

Fibroblast Growth Factors (FGFs) and their cognate receptors, FGFRs, play pivotal roles in a plethora of biological processes, including cell proliferation, differentiation, tissue repair, and metabolic homeostasis. This review provides a comprehensive overview of FGF-FGFR signaling pathways while highlighting their complex regulatory mechanisms and interconnections with other signaling networks. Further, we briefly discuss the FGFs involvement in developmental, metabolic, and housekeeping functions. By complementing current knowledge and emerging research, this review aims to enhance the understanding of FGF-FGFR-mediated signaling and its implications for health and disease, which will be crucial for therapeutic development against FGF-related pathological conditions.

Cellulose–Starch Composite Aerogels As Thermal Superinsulating Materials, Safoura Ahmadzadeh, Angelina Sagardui, David Huitink, Jingyi Chen, Ali Ubeyitogullari

Chemistry & Biochemistry Faculty Publications and Presentations

The demand for sustainable packaging materials is rapidly increasing due to growing environmental concerns over the impact of plastic waste. In this study, biodegradable, porous, lightweight, and high-surface-area microcrystalline cellulose–starch (MCC-S) hybrid aerogels were synthesized via supercritical carbon dioxide (SC–CO₂) drying. The samples were generated using five different MCC-S weight ratios and characterized for their morphology, crystallinity, and structural and thermal properties. When MCC and S were used together, aerogels with superior properties were obtained compared to those made from each component individually. Specifically, the 1:2 MCC-S aerogel exhibited the highest porosity (97%), the lowest density (0.058 g/cm …

Debtor Eligibility Prediction Using Deep Learning With Chatbot-Based Testing, Reski Noviania, Enny Itje Sela, Luther Alexander Latumakulita, Steven R. Sentinuwo

Knowledge Engineering and Data Science

Predicting debtor eligibility is essential for effective risk management and minimizing lousy credit risks. However, financial institutions face challenges such as imbalanced data, inefficient feature selection, and limited user accessibility. This study combines Recursive Feature Elimination (RFE) and Deep Learning (DL) to improve prediction accuracy. It integrates a chatbot interface for user-friendly testing. RFE effectively identifies critical features, while the DL model achieves a validation accuracy of 97.62%, surpassing previous studies with less comprehensive methodologies. The chatbot's novel design not only ensures accessibility but also enhances user engagement through flexible input options, such as approximate values, enabling non experts to …

Manifold Learning And Undersampling Approaches For Imbalanced Class Sentiment Classification, L.M. Risman Dwi Jumansyah, Agus Mohamad Soleh, Utami Dyah Syafitri

Knowledge Engineering and Data Science

Movie reviews are crucial in determining a film's success by influencing audience decisions. Automating sentiment classification is essential for efficient public opinion analysis. However, it faces challenges such as high-dimensional data and imbalanced class distributions. This study addresses these issues by applying manifold learning techniques, Principal Component Analysis (PCA) and Laplacian Eigenmaps (LE) to reduce data complexity and undersampling strategies (Random Undersampling (RUS) and EasyEnsemble) to balance data and improve predictions for both sentiment classes. On reviews of The Raid 2: Berandal, EasyEnsemble achieved the highest average G-Mean of 0.694 using Term Frequency-Inverse Document Frequency (TF IDF) features with a …

Constructing Qur’An Recitation Classification Using Alexnet Algorithm, Harits Ar Rosyid, Dzulkifli Abdullah, Mohammed S. Alqahtani

Knowledge Engineering and Data Science

The growing demands for accurate and efficient methods in the Qur'an recitation classification highlight the limitations of existing models, particularly in assisting the memorization process. This study aims to address these challenges by implementing the AlexNet Convolutional Neural Network architecture, widely recognized for its effectiveness in image classification, to classify the Qur'an recitations using the Mel Frequency Cepstral Coefficient (MFCC) as the feature extraction method. The research involves several stages, including data collection, preprocessing (audio segmentation by verse), data augmentation, feature extraction, and classification using the AlexNet architecture, followed by performance evaluation. Key results demonstrate that the combination of MFCC …

Deep Learning Approach For Dental Anomalies X-Ray Imaging Using Yolov8, Amelia Ritahani Ismail, Md Salim Sadman Taseen

Knowledge Engineering and Data Science

Dental X-ray imaging is a critical diagnostic tool for identifying various dental anomalies. However, manual interpretation is time-consuming, prone to human error, and requires specialized expertise. Deep learning models, particularly object detection frameworks like YOLO, have demonstrated promising results in automating medical image analysis. This study aims to develop and evaluate a YOLOv8-based deep learning model for automated detection and classification of 14 dental anomaly categories, including Caries, Crowns, Fillings, Implants, and Periapical lesions. The proposed approach addresses limitations in previous YOLO versions by leveraging anchor-free detection and enhanced feature extraction for improved accuracy. The model was trained on a …

A Hierarchical Density-Based Spatial Clustering Of Applications With Noise (Hdbscan) Approach For Identifying Potential Villages In Buleleng Regency, Dina Nur Amalina, Achmad Fauzan

Knowledge Engineering and Data Science

Buleleng Regency, located in Bali Province, possesses diverse village potential, including agricultural production and tourist attractions. However, this potential has not been fully optimized. Therefore, it is important to enhance village potential by clustering villages based on their specific characteristics to identify and prioritize those requiring special attention. This approach aims to promote equitable village development and reduce poverty levels. This study clusters villages in Buleleng Regency based on their potential using the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) method. The data utilized in this study comprises village potential data obtained from the Buleleng Regency Statistics Office …

Classification Of Anxiety Levels Entering The World Of Work In Final Year Students Using The Neighbor Weighted K-Nearest Neighbor Method, Awang Hendrianto Pratomo, Muhammad Fahmi Adam, Dessyanto Boedi Prasetyo

Knowledge Engineering and Data Science

This study evaluates the accuracy of the Neighbor Weighted K-Nearest Neighbor (NWKNN) method in classifying the anxiety levels of final-year students as they prepare to enter the workforce, particularly in cases of unbalanced data distribution. The system was developed using the prototype method, and NWKNN was applied to classify anxiety levels into low, medium, and high categories. Testing using the Confusion Matrix demonstrated strong performance, achieving an accuracy of 94% based on a dataset of 1009 students, with a 90:10 ratio of training to test data. The results indicate that NWKNN effectively provides classification input values, making it a reliable …

Impact Of Snow Accumulation On Structural Integrity: Present And Future Perspectives, Kenneth K. Pomeyie

All Graduate Theses and Dissertations, Fall 2023 to Present

In the United States, accommodating the weight of accumulated snow on buildings is a crucial consideration in building design. Engineers are tasked with determining the design snow load, which is defined as the weight of accumulated snow that a structure should withstand to limit the risk of building collapse to an acceptably low level. Typically, this process involves analyzing historical data of the annual maximum snow accumulations for each snow season. However, accurately assessing these design snow loads entails navigating through a series of statistical challenges. This dissertation, composed of three papers, is dedicated to addressing these statistical hurdles in …

Testing For Patterns Of Deformation From The Yellowstone Hotspot Along The Gallatin River, Sw Montana, Jack Willard

All Graduate Theses and Dissertations, Fall 2023 to Present

Yellowstone has fascinated humans for thousands of years. Geologists have addressed many of the region’s mysteries, including the underlying mantle hotspot, the chain of calderas tracking the motion of the North American Plate over this hotspot, and the risks posed by the super volcano. However, the way that the hotspot impacts regional tectonics and topography remains under-studied. Influential previous work recognized the Yellowstone Crescent of High Terrain (YCHT), an arc of high topography around the Yellowstone Plateau with limbs extending to the west and south into central Idaho and northern Utah. The YCHT is also hypothesized to be the pattern …

Feature Selection In Multivariate Time Series Data For Enhanced Solar Flare Classification, Yagnashree Velanki

All Graduate Theses and Dissertations, Fall 2023 to Present

Solar flares are powerful eruptions of energy from the Sun that can cause disruptions to technology here on Earth, like communication systems, GPS, and power grids. To help manage these risks, it’s important to accurately identify and classify these solar flares before they cause problems. In our research, we focused on improving how we classify solar flares by looking at large sets of complex data collected over time. We used several techniques to find the most important factors that help us tell different types of solar flares apart. Each method has its strengths, so instead of relying on just one, …

Understanding And Influencing Public Concerns About Prescribed Fire Use In Utah, Brooke Richards

All Graduate Theses and Dissertations, Fall 2023 to Present

Prescribed fire is beneficial for improving forest health and reducing wildfire risk, which is especially important for residents who live near forested areas. To expand the use of prescribed fire, Utah land managers must first ensure there is public support. Therefore, better understanding public concerns and what shapes their beliefs about prescribed fire can help managers improve outreach and education methods. Through interviews with south-central Utah land managers, private landowners, and Health departments employees, a focus group discussion with Park City natural resource managers, and observation of Summit County public commentary, two outreach messages were suggested to address the five …

Insights From Diverse Environments: Investigating The Fate And Transport Of Semivolatile Organic Contaminants In Alpine, Arctic, And Arid Regions, Jeffrey Perala-Dewey

All Graduate Theses and Dissertations, Fall 2023 to Present

This dissertation investigates the fate and transport of pollutants through four field-based studies in a diverse array of environments. These pollutants travel through the atmosphere and end up in places far from their original sources. In this work I explore how chemical properties of the pollutants and the environmental processes that are present change the way the pollutants travel through the environment. In the first study, pollutants from car engines were quantified in the vegetation along transects in three alpine valleys. Using these measurements, I investigated how the chemical properties of the pollutants impacted their interplay with wind systems in …

Supervised Generative Adversarial Networks For Time Series Generation In Embedding Space, Mohammadreza Eskandarinasab

All Graduate Theses and Dissertations, Fall 2023 to Present

Time series data, such as weather forecasts, stock market trends, or heart rate monitors, plays a vital role in many areas of our lives. However, creating realistic synthetic time series data for research and testing purposes has been a significant challenge due to limitations in existing methods, which often struggle with accuracy and consistency. In this study, we developed two new approaches to generate high-quality time series data more effectively. The first method introduces a dual-feedback system that helps the model learn and replicate real data patterns more accurately by providing guidance at different stages of the learning process. The …

Asthma Prevalence Among United States Population Insights From Nhanes Data Analysis, Sarya Swed, Bisher Sawaf, Feras Al-Obeidat, Wael Hafez, Amine Rakab, Hidar Alibrahim, Mohamad Nour Nasif, Baraa Alghalyini, Abdul Rehman Zia Zaidi, Lamees Alshareef, Fadel Alqatati, Fathima Zamrath Zahir, Ashraf I. Ahmed, Mulham Alom, Anas Sultan, Abdullah Almahmoud, Agyad Bakkour, Ivan Cherrez-Ojeda

All Works

Asthma is a prevalent respiratory condition that poses a substantial burden on public health in the United States. Understanding its prevalence and associated risk factors is vital for informed policymaking and public health interventions. This study aims to examine asthma prevalence and identify major risk factors in the U.S. population. Our study utilized NHANES data between 1999 and 2020 to investigate asthma prevalence and associated risk factors within the U.S. population. We analyzed a dataset of 64,222 participants, excluding those under 20 years old. We performed binary regression analysis to examine the relationship of demographic and health related covariates with …

The Palaeoclimate Potential Of Continental Scientific Drilling, Jonathan Obrist-Farner, Lesleigh Anderson, Paul Baker, Melissa A. Berke, Emily J. Beverly, Julie Brigham-Grette, Erik Brown, Isla S. Castañeda, Alan L. Deino, Sherilyn C. Fritz, Steven L. Goldstein, Natalie M. Kehrwald, Matthew Kirby, Kenneth G. Miller

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

No abstract provided.

Correlation And Causation Analysis For Cross-Sectional And Panel Data, Barry Nuqoba

Dissertations and Theses Collection (Open Access)

This dissertation investigates how data, algorithms, and expert knowledge can be harnessed to better understand human behavior and enhance well-being. It emphasizes the critical importance of interdisciplinary collaboration to bridge knowledge gaps and foster insights that support preventive care, causal theory advancement, and policy development.

The first study, part of the SHINESeniors project, shed light on the potential usefulness of passive, unobtrusive sensors for detecting nocturia and poor sleep quality, symptoms commonly observed in chronic diseases, thereby enabling live-alone older adults to age in place. Utilizing machine learning techniques on sensor-derived features, the study can identify nocturia and poor sleep …

Causality Analysis For Neural Network Security, Bing Sun

Dissertations and Theses Collection (Open Access)

While neural networks are demonstrating excellent performance in a wide range of applications, there has been a growing concern on their reliability and dependability.Similar to traditional decision-making programs, neural networks inevitably have defects that need to be identified and mitigated at times. Neural networks are usually inherently black-boxes and do not provide explanations on how and why decisions are made. As a result, these defects are more ``hidden" and more challenging to eliminate. It is thus crucial to develop systematic approaches to identify and mitigate defects in a neural network in a rigorous way.

In this dissertation, we focus on …

Robust Learning With Probabilistic Relaxation Using Hypothesis-Test-Based Sampling, Zilin Wang

Dissertations and Theses Collection (Open Access)

In recent years, deep learning has been a vital tool in various tasks. The performance of a neural network is usually evaluated by empirical risk minimization. However, robustness issues have gained great concern which can be fatal in safety-critical applications. Adversarial training can mitigate the issue by minimizing the loss of worst-case perturbations of data. It is effective in improving the robustness of the model, but it is too conservative, and the plain performance of the model can be unsatisfying. Probabilistic Robust Learning (PRL) empirically balances the average- and worst-case performance while the robustness of the model is not provable …

Towards Robust, Secure, And Privacy-Aware Large Language Models Of Code, Zhou Yang

Dissertations and Theses Collection (Open Access)

The field of software engineering has witnessed a surge in large language models specifically tailored to understand and process code, which we call large language models for code (LLM4Code). The increasing popularity of LLM4Code is inseparable from three key factors: the availability of extensive datasets compiled from diverse data sources, the advancements in deep learning algorithms and computational power that facilitate the training of these powerful models, and the active engagement and collaboration within the research community fostering innovation and the rapid exchange of ideas and methodologies. As evidenced by a series of studies, LLM4Code has been experiencing rapid development …

Observation Of Dense Collisional Soliton Complexes In A Two-Component Bose-Einstein Condensate, Sean M. Mossman, Garyfallia C. Katsimiga, Simeon I. Mistakidis, Alejandro Romero-Ros, Thomas M. Bersano, Peter Schmelcher, Panayotis G. Kevrekidis, Peter Engels

Physics Faculty Research & Creative Works

Solitons Are Nonlinear Solitary Waves Which Maintain their Shape over Time and through Collisions, Occurring in a Variety of Nonlinear Media from Plasmas to Optics. We Present an Experimental and Theoretical Study of Hydrodynamic Phenomena in a Two-Component Atomic Bose-Einstein Condensate Where a Soliton Array Emerges from the Imprinting of a Periodic Spin Pattern by a Microwave Pulse-Based Winding Technique. We Observe the Ensuing Dynamics Which Include Shape Deformations, the Emergence of Dark-Antidark Solitons, Apparent Spatial Frequency Tripling, and Decay and Revival of Contrast Related to Soliton Collisions. for the Densest Arrays, We Obtain Soliton Complexes Where Solitons Undergo Continued …

Publisher Correction: Observation Of Dense Collisional Soliton Complexes In A Two-Component Bose-Einstein Condensate (Communications Physics, (2024), 7, 1, (163), 10.1038/S42005-024-01659-W), Sean M. Mossman, Garyfallia C. Katsimiga, Simeon I. Mistakidis, Alejandro Romero-Ros, Thomas M. Bersano, Peter Schmelcher, Panayotis G. Kevrekidis, Peter Engels

Physics Faculty Research & Creative Works

In the first paragraph of the Methods section, the sentence "We begin with an elongated BEC of approximately 9 x 10587Rb atoms…," has erroneously been written without a space between the two superscripts "5" and "87". This has now been corrected in both the HTML and PDF of the article, and the same expression reads: We begin with an elongated BEC of approximately 9 x 105 87Rb atoms…"

Author Correction: Dual Nature Of Magnetism Driven By Momentum Dependent F-D Kondo Hybridization (Communications Physics, (2024), 7, 1, (186), 10.1038/S42005-024-01631-8), Byungkyun Kang, Yongbin Lee, Liqin Ke, Hyunsoo Kim, Myoung Hwan Kim, Chul Hong Park

Physics Faculty Research & Creative Works

Correction to: Communications Physicshttps://doi.org/10.1038/s42005-024-01631-8, published online 12 June 2024 In this article the grant number DE-AC02-07CH11358 relating to Ames National Laboratory was omitted. The original article has been corrected.

Publisher Correction: Observation Of Dense Collisional Soliton Complexes In A Two-Component Bose-Einstein Condensate (Communications Physics, (2024), 7, 1, (163), 10.1038/S42005-024-01659-W), Sean M. Mossman, Garyfallia C. Katsimiga, Simeon I. Mistakidis, Alejandro Romero-Ros, Thomas M. Bersano, Peter Schmelcher, Panayotis G. Kevrekidis, Peter Engels

Physics Faculty Research & Creative Works

In the first paragraph of the Methods section, the sentence "We begin with an elongated BEC of approximately 9 x 10587Rb atoms…," has erroneously been written without a space between the two superscripts "5" and "87". This has now been corrected in both the HTML and PDF of the article, and the same expression reads: We begin with an elongated BEC of approximately 9 x 105 87Rb atoms…"

Density Effects On The Interferometry Of Efimov States By Modulating Magnetic Fields, G. Bougas, S. I. Mistakidis, P. Giannakeas

Physics Faculty Research & Creative Works

Dynamical association of Efimov trimers in thermal gases by means of modulated magnetic fields has proven very fruitful in determining the binding energy of trimers. The latter was extracted from the number of remaining atoms, which featured oscillatory fringes stemming from the superposition of trimers with atom-dimers. Subsequent theoretical investigations utilizing the time-dependent three-body problem revealed additional association mechanisms, manifested as superpositions of the Efimov state with the trap states and the latter with atom-dimers. The three atoms were initialized in a way to emulate a thermal gas with uniform density. Here, this analysis is extended by taking into account …