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Public Pool Usage As Adaptation Against Urban Heat, Stefan Borsky, Eric Fesselmeyer May 2026

Public Pool Usage As Adaptation Against Urban Heat, Stefan Borsky, Eric Fesselmeyer

Research Collection College of Integrative Studies

This paper examines the relationship between urban heat and outdoor public pool usage. Using attendance data from all 53 outdoor public pools in New York City, we analyze nonlinear effects of heat on pool usage across socioeconomic contexts. Pool attendance rises sharply with heat, especially in low-income neighborhoods where alternative coping options are likely limited. We also find that public pools reduce heat-related emergency medical service calls. Our findings highlight the need for equitable investment in blue infrastructure to enhance urban climate resilience and demonstrate how this type of adaptive infrastructure can play a critical role in managing urban heat.

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Final Silver Bow Creek Conservation Area Materials Management Plan, Pioneer Technical Services, Inc. May 2026

Final Silver Bow Creek Conservation Area Materials Management Plan, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

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Primitive Pythagorean Triples In Lean And Reduction Modulo Odd Prime Powers, Luke Biddle May 2026

Primitive Pythagorean Triples In Lean And Reduction Modulo Odd Prime Powers, Luke Biddle

Mathematical Sciences Undergraduate Honors Theses

Primitive Pythagorean triples (PPTs) are (a,b,c) triples that satisfy the Pythagorean theorem and share no other common factors outside of 1. This project examines these PPTs reduction modulo odd prime powers by combining proof writing and number-theoretical analysis with the process of verification and formalization in the Lean proof coding language. Using the parameterization of PPTs generated by using the unit circle with additional conditions, we investigate how these triples behave modulo  for odd primes , with emphasis on counting the number of elements in the set of PPTs (a,b,c) modulo pn . By using cases based on initial …

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Turbulent Plenum Jet-Crossflow Validation Via Subgrid Scale Model Variation And Upstream Forcing Under Dynamic Hybrid Rans-Les, Cole W. Mccallum May 2026

Turbulent Plenum Jet-Crossflow Validation Via Subgrid Scale Model Variation And Upstream Forcing Under Dynamic Hybrid Rans-Les, Cole W. Mccallum

Mechanical Engineering Undergraduate Honors Theses

In modern gas turbine design, film cooling has become ubiquitous as a method for limiting heat transfer between high temperature gases post-combustion and the surface of downstream blades. This paper validates the use of various computational fluid dynamics techniques in recreating an experiment measuring adiabatic effectiveness over a surface downstream of a compound-angle N2 plenum jet incident on a turbulent-air boundary layer [1]. To do this, both RANS and Dynamic Hybrid RANS-LES (DHRL) methods are implemented and compared to previous research [2]. The latter method is then modified through implementation of a different subgrid scale (SGS) model and through addition …

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Enhancing Control Charting Schemes And Exploring New Assessment Metrics To Advance Quality Control And Cyber-Attack Detection In Manufacturing, Ahmad Al Majali May 2026

Enhancing Control Charting Schemes And Exploring New Assessment Metrics To Advance Quality Control And Cyber-Attack Detection In Manufacturing, Ahmad Al Majali

Dissertations

The increasing integration of digital technologies and industrial control systems in modern manufacturing has introduced new cybersecurity vulnerabilities within cyber–physical production environments. Malicious actors can exploit these vulnerabilities to induce subtle process deviations that degrade product quality while remaining undetected by conventional statistical monitoring tools. Such attacks can be deliberately engineered to manipulate process behavior through transient shifts that vary in magnitude, duration, and frequency. Despite extensive research on transient shifts caused by assignable causes in Statistical Process Control (SPC), limited attention has been given to intelligently designed cyber–physical attacks that exploit the structural characteristics and limitations of control charting …

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Selective Concolic Testing, Guofeng Zhang, Zhenbang Chen, Ziqi Shuai, Jun Sun, Weijiang Hong, Yufeng Zhang, Ji Wang, Yang Liu May 2026

Selective Concolic Testing, Guofeng Zhang, Zhenbang Chen, Ziqi Shuai, Jun Sun, Weijiang Hong, Yufeng Zhang, Ji Wang, Yang Liu

Research Collection School Of Computing and Information Systems

The principled combination of symbolic execution and random testing lacks a formal foundation, especially in deciding which inputs to symbolize. We propose selective concolic testing, a cost-aware framework that formulates this choice as an optimized policy problem of a MDP (Markov Decision Process). We model program exploration over a finite control-flow graph, where MDP states represent covered statements, actions partition path constraints into symbolic and random fragments, rewards reflect coverage gain, and costs account for SMT solving effort and sampling inefficiency. Our framework yields the first formal characterization of selective symbolization as policy synthesis in a probabilistic system. We prove …

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Detecting Doubt In Reflective Learning: A Learning Analytics Study With Large And Small Language Models, Eng Lieh Ouh, Kar Way Tan, Siaw Ling Lo, Yuhao Zhang May 2026

Detecting Doubt In Reflective Learning: A Learning Analytics Study With Large And Small Language Models, Eng Lieh Ouh, Kar Way Tan, Siaw Ling Lo, Yuhao Zhang

Research Collection School Of Computing and Information Systems

Reflective learning enhances understanding, especially when instructors promptly address difficulties raised in student reflections. Automated doubt detection can reduce time for instructors, yet existing classification approaches take substantial time for manual annotation and model training. This paper investigates whether large and small language models (LLMs, SLMs) can automate doubt detection without time-consuming training. Using a dataset of anonymized student reflections, we evaluate zeroshot, few-shot prompting, and multi-step reasoning against prior supervised classification baselines. We show that LLMs (GPT-4o, Claude-4, Gemini-2.5) surpass earlier F1 scores without prompting, while prompting further improves their performance. However, using proprietary LLMs can raise cost and …

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Gencode: A Generic Data Augmentation Framework For Boosting Deep Learning-Based Code Understanding, Zeming Dong, Qiang Hu, Xiaofei Xie, Maxime Cordy, Mike Papadakis, Yves Le Traon, Jianjun Zhao May 2026

Gencode: A Generic Data Augmentation Framework For Boosting Deep Learning-Based Code Understanding, Zeming Dong, Qiang Hu, Xiaofei Xie, Maxime Cordy, Mike Papadakis, Yves Le Traon, Jianjun Zhao

Research Collection School Of Computing and Information Systems

Pre-trained code models lead the era of code intelligence, with multiple models designed with impressive performance. However, one important problem, data augmentation for code data that automatically helps developers prepare training data lacks study in this field. In this paper, we introduce a generic data augmentation framework, GenCode, to enhance the training of code understanding models. Simply speaking, GenCode follows a generation-and-selection paradigm to prepare useful training code data. Specifically, it employs code augmentation techniques to generate new code candidates first and then identifies important ones as the training data by influence scores. To evaluate the effectiveness of GenCode, we …

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2026 May, Morehead State University. Office Of Communications & Marketing. May 2026

2026 May, Morehead State University. Office Of Communications & Marketing.

Morehead State Press Release Archive, 1961 to the Present

Press releases for May of 2026.

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Predicting Flight Fares With Machine Learning: Enhancing Aviation Industry Pricing Forecasting, Reem Almulla May 2026

Predicting Flight Fares With Machine Learning: Enhancing Aviation Industry Pricing Forecasting, Reem Almulla

Theses

This research investigates the increasing challenge of accurate flight fare prediction for travel agencies that are functioning and working in the post-pandemic aviation market. As the prices fluctuate while demand is unstable and competition pressure increases, therefore it influences decision-making and profitability. In most cases, traditional ticket pricing methods often can be ineffective when capturing complex and non-linear relationships within factors that influence the ticket fare dynamics. As a result, highlighting the urge of more adaptive pricing and data-driven predictive machine learning models. In response to this challenge, the research examines the effectiveness of machine learning techniques for enhancing flight …

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A Catalogue Of Orbital Periods Of Cataclysmic Variables And Candidates From Tess Observations, Meryem K. Dag, Simone Scaringi, Kieran O’Brien, Martina Veresvarska, Nikita Rawat, Yusuke Tampo, Santiago Hernández-Díaz, Liliana Rivera Sandoval, Wendy Mendoza, Ryan J. Oelkers, Jan Kára May 2026

A Catalogue Of Orbital Periods Of Cataclysmic Variables And Candidates From Tess Observations, Meryem K. Dag, Simone Scaringi, Kieran O’Brien, Martina Veresvarska, Nikita Rawat, Yusuke Tampo, Santiago Hernández-Díaz, Liliana Rivera Sandoval, Wendy Mendoza, Ryan J. Oelkers, Jan Kára

Physics & Astronomy Faculty Publications

We present a systematic analysis of 2544 cataclysmic variable systems and related candidates observed by the Transiting Exoplanet Survey Satellite (TESS), with the aim of compiling a comprehensive catalogue of orbital periods. Using 2-min photometric time-series data, we applied an automated algorithm to generate Lomb–Scargle periodograms and identify the most significant coherent periodic signals, which were subsequently verified through visual inspection. This process yielded a confident sample of 910 sources exhibiting at least one periodic signal, hereafter referred to as the Cataclysmic Variable Confident Catalogue (CCC). For each object, we report the most likely orbital period together with …

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Optical Counterparts To X-Ray Sources In Lsst Dp1, Yuankun David Wang, Eric Bellm, Robert I. Hynes, Yue Zhao, Poshak Gandhi, Liliana E. Rivera Sandoval, Sandro Campos, Neven Caplar, Melissa Delucchi, Konstantin Malanchev May 2026

Optical Counterparts To X-Ray Sources In Lsst Dp1, Yuankun David Wang, Eric Bellm, Robert I. Hynes, Yue Zhao, Poshak Gandhi, Liliana E. Rivera Sandoval, Sandro Campos, Neven Caplar, Melissa Delucchi, Konstantin Malanchev

Physics & Astronomy Faculty Publications

We present a crossmatch between a combined catalog of X-ray sources and the Vera C. Rubin Observatory Data Preview 1 (DP1) to identify optical counterparts. The six fields targeted as part of DP1 include the Extended Chandra Deep Field South (E-CDF-S), the Euclid Deep Field South, the Fornax Dwarf Spheroidal Galaxy (Fornax dSph), 47 Tucanae (47 Tuc), and science validation fields with low Galactic and ecliptic latitude (SV_95_-25 and SV_38_7, respectively). We find matches to 2314 of 3830 X-ray sources. We also compare our crossmatch to DP1 in the E-CDF-S field to previous efforts to identify optical counterparts. The probability …

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Long Run Effect Of Industrial Place-Based Policy On County Level Complexity, An Investigation Of The Tennessee Valley Authority, Anita Ifeyinwa Umunnah May 2026

Long Run Effect Of Industrial Place-Based Policy On County Level Complexity, An Investigation Of The Tennessee Valley Authority, Anita Ifeyinwa Umunnah

Master's Theses

This paper studies the long-term effects of large federal investments in place-based industrial policies on regional economic development, contributing new evidence on how industrial policies shape regional local productive capabilities over time. While extensive research show that industrial policies impact regional development by advancing productive technology (capabilities), the fundamental question remains how to measure this technological transformation. The study employs the use of Economic Complexity Index (ECI) as a measure of “technological advancements” resulting from large federal investments in place-based industrial policies. Previous evaluations of such investment were based on livelihood, employment and income outcomes, with productive capability of receiving …

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The Quality Assurance Machine – A Software Quality Assurance Architecture For Ml-Enabled Systems, Shane E. Downing May 2026

The Quality Assurance Machine – A Software Quality Assurance Architecture For Ml-Enabled Systems, Shane E. Downing

All-Inclusive List of Electronic Theses and Dissertations

This dissertation evaluates whether a reusable assurance architecture, the Quality Assurance Machine (QAM), can provide effective product and process quality assurance for ML-enabled software platforms. The QAM is a system-level SQA architecture that turns plans and policies into versioned configurations, executes them in controlled environments, and produces preserved run evidence that supports traceability, auditability, and controlled change. The study follows Design Science Research and evaluates the instantiated artifact using eight assurance requirements (AR1–AR8) synthesized from standards-based guidance, including IEEE 730 and ISO/IEC/IEEE 15026. A four-year longitudinal evaluation combines two methods. First, operational evidence from routine regression and release-validation runs, defect …

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Analysis Of Volumetric Reconstruction Methods In Archaeology, Cade O'Fallon May 2026

Analysis Of Volumetric Reconstruction Methods In Archaeology, Cade O'Fallon

All-Inclusive List of Electronic Theses and Dissertations

The use of Structure from Motion (SfM) photogrammetry in archaeological projects is entering a period of transition; a method of producing 3D data that is traditionally embraced as tool for documentation is being explored for its analytic potential. One such way SfM photogrammetry can be used analytically is through the calculation of volumes using photogrammetric data. The tools exist for archaeologists to be able to create and quantify volumetric models; however, the discourse on these methods is still so new there is no consensus on the best method for conducting volumetric work. Different methods of creating and isolating volumetric space …

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Microplastic Composition And Exposure Pathways In Freshwater Systems: A Multi-Matrix Comparison Between Streams And Retention Ponds In Hamilton County, In, Mya Whaley May 2026

Microplastic Composition And Exposure Pathways In Freshwater Systems: A Multi-Matrix Comparison Between Streams And Retention Ponds In Hamilton County, In, Mya Whaley

All-Inclusive List of Electronic Theses and Dissertations

Since the early 1900s, the use of plastics has increased exponentially. The breakdown of plastic materials has exposed all ecosystems to microplastics (small particles of plastic < 5 mm), which are now ubiquitous in natural settings. Microplastics have a relatively large surface area, and many chemicals adsorb to these small particles, impacting their fate and transport of pollution. While microplastics have been found in virtually every setting on Earth, research is necessary to understand their impact on freshwater ecosystems and biogeochemical cycles. The focus of this research was to investigate microplastic compositions in crayfish, sediments, and water samples collected in streams and retention ponds. We analyzed trends in microplastic counts across different land-cover classes, waterbody types, and species, and compared concentrations among matrices to improve our understanding of microplastic exposure pathways in freshwater ecosystems. As a secondary objective, lead and arsenic concentrations were analyzed from sediment samples to investigate metal burdens and compare them to microplastic concentrations. Our findings revealed that, in general, urban streams had the highest microplastic concentrations within land cover categories, and retention ponds had higher microplastic counts than streams. Crayfish, such as F. rusticus had higher microplastic concentrations than F. virilis and F. propinquus, although these results may be influenced by study design. The most common microplastic type was fibers, and most common color was transparent. There was no significant correlation between microplastic and metal concentrations in sediments. We found crayfish to be adequate bioindicators of microplastic pollution, because they were less impacted by environmental variability than sediment and water samples. This research presents the first evidence of microplastics in our study species, F. propinquus, F. rusticus, and F. virlis. This work presents new evidence of microplastic accumulation in crayfish, sediments, and water from freshwater systems in the Midwestern United States. Microplastics were found in every sample type—crayfish, water, and sediment—across urban, rural, and forested landscapes, highlighting their pervasive distribution. The findings provide foundational data for assessing emerging ecological and human-health risks and emphasize the importance of sustained monitoring and research on MP contamination in freshwater environments.

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Market Reactions To Deceptive Language In Fake News: Implications From Language Expectancy Theory And Transfer Learning, Ka Chung Ng, Ping Fan Ke, Ping Fan, Mike So, Tam, Kar Yan May 2026

Market Reactions To Deceptive Language In Fake News: Implications From Language Expectancy Theory And Transfer Learning, Ka Chung Ng, Ping Fan Ke, Ping Fan, Mike So, Tam, Kar Yan

Research Collection School Of Computing and Information Systems

The advent of generative artificial intelligence (AI) has heightened the proliferation of fake news. A key challenge is the limited real-world data to investigate the societal impact of fake news produced by generative AI. In this paper, we examine stock market reactions to financial news articles that exhibit stylometric similarity to human-crafted and AI-crafted fake financial news. Grounded in language expectancy theory, we employ a style-based transfer learning model, pre-trained to recognizing deceptive language employed in various types of fake news intricacies. We then apply this model to a comprehensive dataset of financial news, assigning a “veracity style score” to …

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Quantitative Bounds On Resource Usage Of Probabilistic Programs, Krishnendu Chatterjee, Amir Kafshdar Goharshady, Tobias Meggendorfer, Dorde Zikelic May 2026

Quantitative Bounds On Resource Usage Of Probabilistic Programs, Krishnendu Chatterjee, Amir Kafshdar Goharshady, Tobias Meggendorfer, Dorde Zikelic

Research Collection School Of Computing and Information Systems

Cost analysis, also known as resource usage analysis, is the task of finding bounds on the total cost of a program and is a well-studied problem in static analysis. In this work, we consider two classical quantitative problems in cost analysis for probabilistic programs. The first problem is to find a bound on the expected total cost of the program. This is a natural measure for the resource usage of the program and can also be directly applied to average-case runtime analysis. The second problem asks for a tail bound, i.e. ‍given a threshold t the goal is to find …

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Optimisation Of Photosensitive Recording Materials For Broadband Holographic Optical Elements, Michael Murray May 2026

Optimisation Of Photosensitive Recording Materials For Broadband Holographic Optical Elements, Michael Murray

Doctoral

The introduction of broadband (white) LED outdoor lighting has led to significant energy savings. However, this has come at the cost of increased light pollution which has negative impacts both on ecological systems and human health. This light pollution is largely the result of the lack of control measures for the directionality of the light emitted by outdoor LED lighting. The lack of directionality also results in higher energy consumption in order to compensate for the light scattered to the atmosphere and sufficiently illuminate the target area. In this thesis holographic optical elements (HOEs) are proposed as a complementary technology …

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Brief Virtual Reality And Mixed Reality Mindfulness Breathing Exercise For Emotional Well-Being And Cognitive Functions In University Students: Within-Subjects Experimental Design Study, Zoey Khai Yee Eun, Charmaine Jiali Koh, Hwajin Yang, Adalia Yin Hui Goh, Meilan Hu, K Tennakoon Appuhamillage Sandeeshwara Kasturiratna, Andree Hartanto May 2026

Brief Virtual Reality And Mixed Reality Mindfulness Breathing Exercise For Emotional Well-Being And Cognitive Functions In University Students: Within-Subjects Experimental Design Study, Zoey Khai Yee Eun, Charmaine Jiali Koh, Hwajin Yang, Adalia Yin Hui Goh, Meilan Hu, K Tennakoon Appuhamillage Sandeeshwara Kasturiratna, Andree Hartanto

Research Collection School of Social Sciences

Background: Mindfulness has been shown to enhance emotional well-being and cognitive performance, yet much of this evidence stems from interventions requiring prolonged practice, making them time-consuming and less accessible. Recent studies suggest that brief mindfulness sessions may also yield positive outcomes, but the effectiveness of such interventions in virtual reality (VR) and mixed reality (MR) remains underexplored. Objective: This study investigates the effects of brief mindfulness breathing exercises delivered through VR and MR on attentional and emotional restoration and self-control capacity. Methods: Using a within-subjects experimental design, 102 undergraduate participants (n=83, 81.4% female; mean age 20.87, SD 1.89) completed a …

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Regional Assessment Of South Texas Wells Produced Water, Hunter T. Barnett May 2026

Regional Assessment Of South Texas Wells Produced Water, Hunter T. Barnett

Water Resources Science and Technology Theses and Graduate Research Reports (Archived)

This study presents a regional assessment of produced water (PW) quality from oil and gas wells in South Texas, with a focus on variability and its implications for treatment and reuse. As oil and gas production continues to expand, PW waste streams will continue to grow. With a focus on the Eagle Ford Shale, understanding the chemical composition is critical for effective PW management. A total of 28 PW samples were collected from wells located in Pleasanton, Jourdanton, and Tilden, Texas. Samples were analyzed for key parameters including total dissolved solids (TDS), chloride, total hardness, and calcium. The results showed …

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Antidistillation Sampling For Classification Models, Khawaja Abaid Ullah May 2026

Antidistillation Sampling For Classification Models, Khawaja Abaid Ullah

Theses

Knowledge distillation enables adversaries to replicate the functionality of proprietary machine learning models by querying their APIs and training surrogate student models on the returned soft-label distributions. Antidistillation Sampling (ADS), recently proposed for large language models, perturbs the output distribution of a teacher model at inference time to degrade the quality of the resulting distilled model while preserving utility for legitimate users. We adapt ADS to the supervised classification setting and identify a structural obstacle to its direct transfer: the high-confidence, near-one-hot output distributions characteristic of well-trained classifiers leave insufficient probability mass on non-target classes for the additive penalty to …

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Atmospheric Noise Analysis In Observations From The Tomographic Ionized-Carbon Mapping Experiment, Audrey Dunn May 2026

Atmospheric Noise Analysis In Observations From The Tomographic Ionized-Carbon Mapping Experiment, Audrey Dunn

Theses

The Tomographic Ionized-carbon Mapping Experiment (TIME) instrument is a ground-based millimeter-wavelength grating spectrometer that illuminates a cryogenically cooled array of 1920 transition-edge sensor (TES) bolometers. The goal of TIME is to generate line intensity maps of singly ionized carbon ([CII]) during the Epoch of Reionization, when hydrogen became reionized by stellar radiation and the first galaxies were forming. This measurement requires a detailed understanding of the noise, most notably the 1/f noise from the time-varying atmosphere. In my thesis work, I performed an analysis of the power spectral density (PSD) and a traditional principal component analysis (PCA) on TIME data …

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Measurements And Scaling Of Ion Propulsion Impulse During Driven Magnetic Reconnection, Fatima Ebrahimi, Nicholas A. O'Gorman, Kush Maheshwari, Jongsoo Yoo, Alexandre Sainterme, Hantao Ji May 2026

Measurements And Scaling Of Ion Propulsion Impulse During Driven Magnetic Reconnection, Fatima Ebrahimi, Nicholas A. O'Gorman, Kush Maheshwari, Jongsoo Yoo, Alexandre Sainterme, Hantao Ji

Faculty Publications

Impulse scaling during magnetic reconnection, the magnetic energy conversion to kinetic energy, via direct Mach probe measurements in Magnetic Reconnection Experiment is examined. Ion exhaust velocity and impulse scalings with reconnecting magnetic field during the push phase of driven reconnection are presented. The outflows and impulse measurements are compared with global MHD simulations. Both measurements and simulations reveal a favorable scaling, greater than linear, of impulse with reconnecting field. These scaling results establish that magnetic reconnection could be utilized for plasma propulsion.

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Preservice Teachers’ Noticing Of Students’ Quantitative And Covariational Reasoning In Dynamic Mathematical Situations, Alfred M. Limbere, Joseph Dinapoli, Steven Greenstein May 2026

Preservice Teachers’ Noticing Of Students’ Quantitative And Covariational Reasoning In Dynamic Mathematical Situations, Alfred M. Limbere, Joseph Dinapoli, Steven Greenstein

Department of Mathematics Faculty Scholarship and Creative Works

Understanding rate of change requires reasoning about measurable quantities and how one quantity changes relative to another. To support this kind of reasoning, teachers should develop the ability to notice students’ quantitative and covariational reasoning. This study examines how preservice teachers (PSTs) attend to, interpret, and respond to students’ quantitative and covariational reasoning in video-based analyses of water-filling rate-of-change tasks. Drawing on relevant research, professional noticing is examined through the lenses of quantitative reasoning and covariational reasoning. Using a design-based qualitative approach, secondary PSTs participated in structured analyses of students’ problem-solving discussions related to rate of change. Data were collected …

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Enhancing Traffic Safety Through Ai-Driven, Privacy-Preserving, And Secure Impaired Driving Detection Systems, Razan Alsulieman May 2026

Enhancing Traffic Safety Through Ai-Driven, Privacy-Preserving, And Secure Impaired Driving Detection Systems, Razan Alsulieman

Dissertations

Drunk driving remains a major threat to road safety worldwide, contributing significantly to traffic injuries and fatalities each year. Traditional detection approaches are largely reactive and vehicle-centric, relying on in-vehicle sensors, breathalyzers, or post-incident enforcement. These methods often depend on driver cooperation, intrusive hardware installations, or limited monitoring environments, restricting their scalability and effectiveness in large transportation systems. At the same time, modern cities increasingly deploy roadside cameras, surveillance networks, and drone- based monitoring systems, creating new opportunities for proactive intoxication detection at the infrastructure level. However, leveraging such external monitoring introduces challenges related to secure data collection, reliable AI-based …

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Parenthood And Mental Health Among University Populations In Sub-Saharan Africa: A Cross-Sectional Study, Tuwani A. Rasengane, Ngozika Esther Ezinne, Antor Ndep, Nnamdi John Eseme, Susarah Maria Richter, Kingsley E. Agho, Ebele Gertrude Ejidike, Michael Agyemang Kwarteng, Kelechukwu Enyinnaya Ahaiwe, Nnaemeka Meribe, Obed Adonteng-Kissi, Edith Daniel-Nwosu, Mchillary Ogiemudia Osamudiamen, Ike Onyebuchi Oforbuike, Grace Obumneke Ogbonna, Isaura Ilorena D.Alva Brito Dos Santos, Okechi Ulumma Amaechi, Ugochukwu E. Uzuegbu, Khathutshelo Percy Mashige, Uchechukwu Levi Osuagwu May 2026

Parenthood And Mental Health Among University Populations In Sub-Saharan Africa: A Cross-Sectional Study, Tuwani A. Rasengane, Ngozika Esther Ezinne, Antor Ndep, Nnamdi John Eseme, Susarah Maria Richter, Kingsley E. Agho, Ebele Gertrude Ejidike, Michael Agyemang Kwarteng, Kelechukwu Enyinnaya Ahaiwe, Nnaemeka Meribe, Obed Adonteng-Kissi, Edith Daniel-Nwosu, Mchillary Ogiemudia Osamudiamen, Ike Onyebuchi Oforbuike, Grace Obumneke Ogbonna, Isaura Ilorena D.Alva Brito Dos Santos, Okechi Ulumma Amaechi, Ugochukwu E. Uzuegbu, Khathutshelo Percy Mashige, Uchechukwu Levi Osuagwu

Research outputs 2022 to 2026

Background and Aims: This study examined the association between parenthood status and psychological well-being among university staff and students in sub-Saharan Africa (SSA). Methods: A cross-sectional, web-based survey was conducted across 22 universities in four SSA countries using convenience sampling. A total of 1189 university staff and students, including parents and non-parents, participated. Mental health outcomes were assessed using the Depression, Anxiety, and Stress Scales (DASS-21). Multinomial logistic regression models were used to examine associations between parenthood status and mental health outcomes after adjusting for sociodemographic and contextual factors. Results: Participants with children had lower odds of moderate depression (adjusted …

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A Generative Ai Method For Minority Class Handling In Anomaly Detection With Drift And Explainability Analysis, Kelvin J. Mwiga, Mussa A. Dida, Ahmad Mohsin, Iqbal H. Sarker May 2026

A Generative Ai Method For Minority Class Handling In Anomaly Detection With Drift And Explainability Analysis, Kelvin J. Mwiga, Mussa A. Dida, Ahmad Mohsin, Iqbal H. Sarker

Research outputs 2022 to 2026

Artificial Intelligence, particularly machine learning (ML) algorithms, plays a crucial role in detecting cyberattacks, including anomalies and intrusions. However, machine learning models trained on imbalanced cybersecurity datasets often struggle to accurately detect minority data instances and potential threats, thereby weakening overall system security. Despite extensive research, a persistent challenge is the inadequate explanation for model predictions concerning minority data classes. This study aims to address these limitations by developing a generative AI-based approach to manage minority classes in anomaly detection, incorporating concept drift handling and explainability analysis. We introduce an over-sampling technique, CGGReaT, designed to enhance the presence of minority …

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Full Issue May 2026

Full Issue

The Bridge

No abstract provided.

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Fixed Perimeter Analogues Of Some Partition Results, Gabriel Gray, Emily Payne, Holly Swisher, Ren Watson May 2026

Fixed Perimeter Analogues Of Some Partition Results, Gabriel Gray, Emily Payne, Holly Swisher, Ren Watson

School of Mathematical & Statistical Sciences Faculty Publications

Euler's partition identity states that the number of partitions of n into odd parts is equal to the number of partitions of n into distinct parts. Strikingly, Straub proved in 2016 that this identity also holds when counting partitions of any size with largest hook length (perimeter) n. This has inspired further investigation of partition identities and inequalities in the fixed perimeter setting. Here, we explore fixed perimeter analogues of some well-known partition results inspired by Euler's partition identity.

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