Digital Commons Network^™

Methods And Applications For Bayesian Semiparametric Survival Analysis, Zile Zhao

Theses and Dissertations

Survival analysis is a cornerstone of biomedical and clinical research and plays an important role in fields as diverse as engineering, actuarial science, and sociology. In this dissertation, we develop new semiparametric Bayesian methodology for three problems from survival analysis: 1) adjustment for treatment crossover in randomized controlled trials (RCTs), 2) multilevel modeling of clustered survival outcomes when the cluster size is also informative, and 3) divide-and-conquer Bayesian inference for massive survival data. Our methods are semiparametric in the sense that we assume the covariates have a linear effect with regard to the log-hazard or the log- survival time; however, …

Determination Of Structure-Function Relationships In Hybrid Catalysts On Metal Oxide Supports, Joseph John Kuchta

Theses and Dissertations

Transition metal catalysis has revolutionized chemical synthesis, enabling efficient and selective transformations critical to the pharmaceutical, agricultural, and materials industries. However, traditional methods often rely on expensive, non-sustainable metals and harsh reaction conditions that generate significant waste. This dissertation explores a series of innovations aimed at bridging the gap between homogeneous and heterogeneous catalysis through the development of hybrid catalysts. These systems combine the molecular precision of homogeneous catalysts with the stability and practicality of heterogeneous supports, offering a pathway to more sustainable and scalable catalytic processes.

The first area of focus investigates nickel-based hybrid catalysts as a cost-effective and …

Hardware Accelerated Simulation Of Buck Converters Using Physics-Informed Neural Networks, James Clayton Crews

Theses and Dissertations

Physics-informed neural networks (PINNs) are an emerging machine learning method for learning the behavior of physical systems described by governing differential equations. Dc-dc power-electronic converters are used in a variety of industry applications such as motor drives or power supplies where real-time simulation is critical for control and safety. This thesis investigates physics-informed machine learning as an approach to develop a real-time digital twin for dc-dc power converters. Traditional numerical integration methods are used to approximate discretized behavior, and the results are compared with a trained PINN model. Modern ML frameworks (such as PyTorch and TensorFlow/Keras) are used to quickly …

Explainable Process Recommendation Through Multi-Contextual Grounding Of Dynamic Multimodal Process Knowledge Graphs, Revathy Venkataramanan

Theses and Dissertations

Can I eat this food or not? Is this food suitable for diabetes and why? Which AI pipeline is best suited for a given task and dataset? How should an end-to-end pipeline be constructed? These questions differ from factual question-answering tasks. Recipes and AI pipelines are processes consisting of several entities interacting with each other. A recipe consists of ingredients, cooking methods, and their interactions, while an AI pipeline includes datasets, preprocessing techniques, models, hyperparameters, tasks, and results. Each entity must be analyzed individually, and collective inferencing is performed to derive the final decision. This decision-making process, known as compositional …

A Spatial Scan Statistic For Group Testing Data, Vincent Onyame

Theses and Dissertations

Group testing involves pooling specimens from multiple individuals and offers an efficient means to surveil low-prevalence pathogens, but poses challenges for spatial cluster detection when only pooled results are observed. In this thesis, we develop a spatial scan statistic tailored to group-testing data with variable pool sizes. The statistic compares a null hypothesis of a homogeneous infection rate across all clusters to an alternative hypothesis that infection probabilities differ inside and outside a candidate cluster, with both models fitted by maximum likelihood estimation. We approximate the null distribution of the maximum likelihood ratio test via Monte Carlo simulation.

Through a …

Enhancing Phenotyping Accuracy For Selection Of Urochloa Spp. Tolerant Genotypes To Red Spider Mite (Oligonychus Trichardti), Paula Espitia-Buitrago, José Miguel Cotes Torres, Luis Miguel Hernández, Juan Andres Cardoso, Frank Chidawanyika, Rosa N. Jauregui

All Peer-Reviewed Publications

The red spider mites are a major biotic limitation to productivity for Brachiaria (Urochloa spp.) grasses in tropical Eastern Africa, causing severe economic losses mainly in the dry seasons. However, due to the lack of reports of integrated pest management strategies, genetic resistance can play a crucial role in Brachiaria-based livestock systems, minimising the impact of this pest. The objective of this study was to develop and validate a methodology for accurately categorising resistance responses in Urochloa genotypes to identify potential sources of resistance to the Western Kenya red spider mite Oligonychus trichardti (Meyer 1974). Accordingly, 25 genotypes of Urochloa …

Multi-Task Deep Learning Approach For Segmenting And Classifying Competitive Swimming Activities Using A Single Imu, Mark Shperkin

Theses and Dissertations

Competitive swimming performance analysis has traditionally relied on manual video review and multi-sensor systems, both of which are resource-intensive and impractical for everyday training use. This study investigates whether a single wrist-worn inertial measurement unit (IMU) can be used to automatically segment and classify swimming activities with high accuracy. We propose a multi-task deep learning pipeline based on the MTHARS (Multi-Task Human Activity Recognition and Segmentation) architecture introduced by Duan et al. to perform stroke classification, lap segmentation, stroke count estimation, and underwater kick count estimation. Data were collected from eleven collegiate-level swimmers wearing left-wrist-mounted IMUs, each performing five 100-yard …

Process-Grounded Knowledge-Infused Learning And Decision Making, Kaushik Roy

Theses and Dissertations

This dissertation introduces process-grounded knowledge-infused learning and reasoning, a novel framework for integrating domain-expertise-based process knowledge into the learning and reasoning mechanisms of artificial intelligence systems. This approach is designed to produce controlled, transparent, and reliable predictions in critical tasks such as medical diagnosis and recommendation. By focusing on the case study of mental illness diagnosis and recommendation—where decision-making must be grounded in processes such as disorder-specific diagnostic criteria—this work demonstrates methods to embed structured decision-making directly into the system architecture during both training and inference. This integration facilitates end-to-end training and reasoning while ensuring that outputs strictly adhere to …

Quantifying Biomass Burning Impacts On Soil Nox Emissions And Reactive Nitrogen Cycling, Olivia Rae Steinbeck

Theses and Dissertations

Nitrogen oxides (NOx = NO + NO₂) are trace gases that play a critical role in the atmosphere by influencing air quality, oxidation chemistry, and the deposition of fixed nitrogen. Historically, NOx emissions have been primarily linked to anthropogenic sources such as fossil fuel combustion, industrial activities, and agriculture. However, as successful legislation has significantly reduced these emissions, the relative importance of natural NOx sources has become an increasing concern. Soil NOx emissions are of particular interest due to their strong temperature dependence and their connection to global climate change. Rising global temperatures are expected to accelerate soil NOx emissions …

Elevating Next Generation Wireless Devices Towards Contactless Sensing For Healthcare Applications, Aakriti Adhikari

Theses and Dissertations

There is an increasing interest in technologies that can understand and perceive at-home human activities to provide personalized healthcare monitoring, aimed at early detection of disease markers and assisting physicians in making clinical decisions. Existing approaches, such as wearables, require users to wear sensors that can be cumbersome and cause discomfort. Vision based solutions, such as optical cameras, IRs, LiDARs, etc., can be used to design contactless at-home monitoring systems. However, these systems are limited by poor lighting and occlusion, and they are privacy-invasive. Fortunately, high-frequency millimeter-wave wireless devices provide an effective alternative to the existing systems to enable fine-grained …

Real-World Continuous Smartwatch-Based User Authentication, N. Al-Naffakh, N. Clarke, F. Li, P. Haskell-Dowland

Research outputs 2022 to 2026

User authentication is often regarded as the "gatekeeper"of cyber security. It has, however, long suffered from significant usability issues that have resulted in research focussing upon frictionless and transparent biometric approaches. Activity-based user authentication - a technique that authenticates a user by what they are physically doing at a specific point in time has attracted significant attention, particularly due to the increasing popularity of smartwatches. This research aims to overcome limitations in prior work by exploring the viability of the approach in real-world conditions. The study presents two principal experiments, one focused upon a constrained environment to provide a control …

Winds Of Change: Charting A Pathway To Ecosystem Monitoring Using Airborne Environmental Dna, Rachel L. Tulloch, Clare I.M. Adams, Matthew A. Barnes, Elizabeth L. Clare, Henrik C. Van De Ven, Andrew Cridge, Francisco Encinas-Viso, Kristen Fernandes, Dianne M. Gleeson, Erin Hill, Anna J.M. Hopkins, Anna M. Kearns, Gracie C. Kroos, Anna J. Macdonald, Francesco Martoni, Angela Mcgaughran, Todd G.B. Mclay, Linda E. Neaves, Paul Nevill, Andrew Pugh, Kye J. Robinson, Fabian Roger, Tracey V. Steinrucken, Mieke Van Der Heyde, Cecilia Villacorta-Rath, Jenny Vivian

Research outputs 2022 to 2026

Airborne environmental DNA (airborne eDNA) analysis leverages the globally ubiquitous medium of air to deliver broad species distribution data and support ecosystem monitoring across diverse environments. As this emerging technology matures, addressing critical challenges and seizing key opportunities will be essential to fully realize its potentially transformative impact. In June 2024, the Southern eDNA Society convened over 100 researchers, industry leaders, and biodiversity management stakeholders in a landmark workshop to evaluate the current state of airborne eDNA research and chart a course for future development. Participants explored opportunities for integrating airborne eDNA into existing monitoring systems, but they unanimously agreed …

Evaluating The Influence Of Carbon Quantum Dots On Starch-Based Bioplastics: Toward Potential Food Packaging Applications, Shima Jafarzadeh, Mitra Golgoli, Zeinab Qazanfarzadeh, Mehrdad Forough, Peng Wu, Wendy Timms, Colin J. Barrow, Minoo Naebe, Masoumeh Zargar

Research outputs 2022 to 2026

Developing biodegradable food packaging films is crucial for reducing dependence on petroleum-based plastics. In this study, nitrogen-doped carbon quantum dots (CDs) were synthesized from citric acid and ethylenediamine via hydrothermal treatment and incorporated into sago starch films at concentrations of 0.5 %, 1 %, 3 %, and 4 % w/w of total solids using a solution casting method. The effects of CDs on structural, thermal, antioxidant, optical, and physicochemical properties were systematically investigated. CD addition enhanced the UV-shielding ability of the films. At 4 % CD content, UV transmittance decreased by 56.4 % (UVA), 66.7 % (UVB), and 73.9 % …

The Response And Recovery Of Carbon And Water Fluxes In Australian Ecosystems Exposed To Severe Drought, C. Stephens, B. Medlyn, L. Williams, J. Knauer, A. Inbar, E. Pendall, S. K. Arndt, J. Beringer, C. M. Ewenz, N. Hinko-Najera, L. B. Hutley, P. Isaac, M. Liddell, W. Meyer, C. E. Moore, J. Cranko Page, R. Silberstein, W. Woodgate

Research outputs 2022 to 2026

Climate change-driven increases in drought risk pose a critical threat to global carbon and water cycles. However, ecosystem-scale responses remain poorly quantified, particularly for severe, multiyear drought events. We addressed this gap by examining ecosystem-scale carbon and water flux sensitivity to the extreme 2018–19 drought in Australia using data from 14 eddy covariance flux sites. The ecosystems span grasslands and semi-arid woodlands to tropical and temperate forests. The driest sites (classed as “grass” and “very dry”) experienced drastic productivity impacts, with a 65% decrease in Gross Primary Productivity (GPP) over 2 years relative to the pre-drought average. However, fluxes in …

Behavioral Responses Of The California Two-Spot Octopus Octopus Bimaculoides To Changes In Color And Sound, Sofia I. Ramirez, Nathalie Reyns

McNair Summer Research Program

Recreational and commercial boat activity, military and construction operations, and seismic surveys among other human activities produce excessive sound that is classified as anthropogenic noise. Because the intensity and frequency of anthropogenic noise is often higher than that of natural underwater acoustic stimuli, marine animals may experience physiological damage to their sound-detecting organs which in turn, affects their communication and orientation abilities. In our study, we aimed to analyze the differences in behaviors exhibited by the California two-spot octopus (Octopus bimaculoides) when exposed to various volumes of noise at 54 Hz. To further investigate behavioral changes under varying conditions, we …

Performance Comparison Of Quantum And Classical Machine Learning Models For Chronic Kidney Disease Prediction, Parama Sridevi, Paramita Basak Upama, Masud Rabbani, Sheikh Iqbal Ahamed

Computer Science Faculty Research and Publications

In this study, we develop and compare quantum and classical machine learning-based chronic kidney disease prediction models. We used the "Chronic_Kidney_Disease Data Set" of the UCI Machine Learning Repository. We performed data preprocessing and applied feature engineering techniques to select the best features. We developed two quantum machine learning-based models and two classical machine learning-based models. We used a hybrid classical-quantum environment for building quantum machine learning models. Finally, we compared the performances of all four models. We found that the Quantum Support Vector Machine performs best among the quantum models. The model’s accuracy was 95% with a k-fold cross-validation …

O-Mapl: Offline Multi-Agent Preference Learning, The Viet Bui, Tien Mai, Hong Thanh Nguyen

Research Collection School Of Computing and Information Systems

Inferring reward functions from demonstrations is a key challenge in reinforcement learning (RL), particularly in multi-agent RL (MARL). The large joint state-action spaces and intricate inter-agent interactions in MARL make inferring the joint reward function especially challenging. While prior studies in single-agent settings have explored ways to recover reward functions and expert policies from human preference feedback, such studies in MARL remain limited. Existing methods typically combine two separate stages, supervised reward learning, and standard MARL algorithms, leading to unstable training processes. In this work, we exploit the inherent connection between reward functions and Q functions in cooperative MARL to …

Empowering Weight Loss: A Pragmatic Randomized Controlled Trial Of A Theory-Driven Self-Regulation Mobile App For Young Adults With Excess Body Weight, H. S. J. Chew, J. W. Ngooi, R. C. Du, P. Z. Chan, M. Jansson, B. Zhu, Y. Cao, Chong-Wah Ngo, R. Foo, A. Shabbir, D. Ho, N. Sevdalis, K. Y. Ngiam

Research Collection School Of Computing and Information Systems

Background/Introduction: Obesity is projected to affect more than half of the global population by 2035, posing significant health and economic challenges. While lifestyle modification is considered a cornerstone of weight management, its effectiveness often relies on substantial support systems. Purpose: This study aimed to evaluate the effectiveness of a 12-week, standalone Temporal Self-Regulation Theory (TST)-based weight loss mobile application, which integrates self-regulation techniques, food logging, and dietary nudging, in promoting weight loss among young adults with excess body weight. Methods: A two-arm, parallel-group, 1:1 randomized controlled trial was conducted, adhering to the CONSORT-Outcomes 2022 Extension guidelines. Participants completed a face-to-face …

Towards Efficient Privacy-Preserving Deep Learning: He-Friendly Structures, Flexible Pruning, He-Efficient Architectures, And Secure Transformer Token Drop, Yifei Cai

Electrical & Computer Engineering Theses & Dissertations

Deep learning (DL) has become a powerful tool for solving complex problems, but developing DL models typically requires vast datasets, high computational resources, and expert knowledge—barriers that limit accessibility. Machine Learning as a Service (MLaaS) addresses this challenge by allowing resource-rich providers to deliver pre-trained DL models as services. However, privacy concerns arise: clients hesitate to share sensitive data, while providers protect their proprietary models. To address this, privacy-preserving MLaaS integrates cryptographic techniques into DL computations, as seen in frameworks like Cryptonets, SecureML, GAZELLE, CrypTFlow2, Cheetah, and BOLT. Among them, Homomorphic Encryption (HE) enables computation on encrypted data but remains …

Position: Trustworthy Ai Agents Require The Integration Of Large Language Models And Formal Methods, Yedi Zhang, Yufan Cai, Xinyue Zuo, Xiaokun Luan, Kailong Wang, Zhe Hou, Yifan Zhang, Zhiyuan Wei, Meng Sun, Jun Sun, Jing Sun, Jin Song Dong

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have emerged as a transformative AI paradigm, profoundly influencing broad aspects of daily life. Despite their remarkable performance, LLMs exhibit a fundamental limitation: hallucination—the tendency to produce misleading outputs that appear plausible. This inherent unreliability poses significant risks, particularly in high-stakes domains where trustworthiness is essential. On the other hand, Formal Methods (FMs), which share foundations with symbolic AI, provide mathematically rigorous techniques for modeling, specifying, reasoning, and verifying the correctness of systems. These methods have been widely employed in mission-critical domains such as aerospace, defense, and cybersecurity. However, the broader adoption of FMs remains constrained …

Advancing Food Nutrition Estimation Via Visual-Ingredient Feature Fusion, Huiyan Qi, Bin Zhu, Chong-Wah Ngo, Jingjing Chen, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Nutrition estimation is an important component of promoting healthy eating and mitigating diet-related health risks. Despite advances in tasks such as food classification and ingredient recognition, progress in nutrition estimation is limited due to the lack of datasets with nutritional annotations. To address this issue, we introduce FastFood, a dataset with 84,446 images across 908 fast food categories, featuring ingredient and nutritional annotations. In addition, we propose a new model-agnostic Visual-Ingredient Feature Fusion (VIF2 ) method to enhance nutrition estimation by integrating visual and ingredient features. Ingredient robustness is improved through synonym replacement and resampling strategies during training. The ingredient-aware …

An Incentive Mechanism For Privacy Preserved Data Trading With Verifiable Data Disturbance, Man Zhang, Xinghua Li, Bin Luo, Yanbing Ren, Yinbin Miao, Ximeng Liu, Robert H. Deng

Research Collection School Of Computing and Information Systems

To motivate data owners’ (DOs’) trading willingness, the existing incentive mechanisms allow DOs to independently disturb data following data consumer's (DC’s) availability requirement. However, they cannot motivate DOs’ honest disturbance, which is attributed to DOs’ independent disturbance without any supervision. Thus, we implement an incentive mechanism for privacy preserved data trading with verifiable data disturbance where an honest-but-curious disturbance generator (DG) is additionally introduced to supervise DOs’ local disturbance and assist disturbance verification between DOs and DC. Specifically, DG generates the disturbance strategies and secretly distributes to DOs following private information retrieval, guaranteeing DOs's local disturbance's privacy and verifiability with …

The Effects Of Heavy Metals Applied In Digested Sewage Sludge To Grassland, A Desira Buttigieg, D A. Klessa, D A. Hall

IGC Proceedings (1977-2023)

The application of potentially toxic elements in sewage sludge to grassland has implications to environmental pol­lution and the health of grazing rumiµants. In Scotland, mod­erately acid grassland receives most of the sewage sludge applied to agricultural land and there is therefore a risk of heavy metal mobilisation and uptake by herbage. In addition, soil ingestion (Fleming, 1986) and intake of herbage contami­nated with sludge adhering to leaf surfaces may present an important means by which potentially toxic elements are absorbed by ruminants. In this paper, two aspects of sewage sludge use on grassland are studied, namely : 1. Factors affecting …

Fedevd: A Federated Road Traffic Event Detector From Social Networks, Ahmad Traboulsi, May Itani, Layal Abu Daher, Ali Haidar

BAU Journal - Science and Technology

The increasing population and the corresponding rise in the number of vehicles, coupled with inadequate public transportation, is increasing the already existing traffic problem. One solution to this problem has been the implementation of Traffic Monitoring Systems (TMS). However, TMS deployment entails significant costs, including (1) the installation of hardware in key areas and (2) the employment of dedicated monitoring personnel. Simultaneously, Online Social Networks (OSNs) have become global platforms with rapidly growing user bases, leading to a surge in user engagement. This widespread participation has transformed social networks into invaluable sources of data for analytics, business intelligence, and decision …

Phytochemical Analysis And Antimicrobial Activity Of Micromeria Barbata Leaf And Stem Extracts Against Pseudomonas Aeruginosa: Insights From Molecular Docking And In Vitro Assays, Shiraz Rawas, Dalia El-Badan, Nawal Al Hakawati

BAU Journal - Science and Technology

The present study provides the characterization of the phytochemical content and the antibacterial activity of ethanolic extracts from the leaves (LM) and stems (SM) of Micromeria barbata (M.barbata) against Pseudomonas aeruginosa . Important functional groups were determined by analyzing the FTIR spectra of LM and SM. The phytochemical profiles were analyzed by GC-MS, and these characterized the chemicals according to retention periods and peak regions. The binding affinities of discovered compounds with the P. aeruginosa LasR and PqsD proteins were evaluated using molecular docking approach. Assays for biofilm formation, MIC, MBC, and agar well diffusion were used to assess the …

Towards Effective Academia-Industry Collaborations: The Case Of Higher Learning Institutions In Tanzania, Fatuma S. Ikuja

Tanzania Journal of Engineering and Technology (TJET)

Industries are the main consumers of products from higher learning institutions (HLIs); graduates for employment and research outputs for socio-economic development. Research outputs from HLIs are commercialized as services or products facilitated by academia-industry collaborations. The collaborations are expected to address mismatch between labour market needs and HLIs’ products, which has resulted in graduates’ employability challenges. Despite their importance, effective academia-industry collaborations remain challenging. This study explores the effectiveness of Academia-Industry collaborations established by HLIs in implementing the Higher Education Economic Transformation (HEET) project (2021-2026) in Tanzania. One of the project objectives is to build functional linkages between industry and …

Towards Future Sustainable Infrastructure: The Role Of Technical Audit In Tanzania’S Public Works, George C. Haule

Tanzania Journal of Engineering and Technology (TJET)

This study aimed to investigate the vital role and impact of technical audits in promoting sustainable infrastructure development in Tanzania. The role and effects of technical audits in long-term infrastructure development were studied using a mixed-methods approach with both quantitative and qualitative parts. Data were collected through analysis of technical audit documentation, a semi-structured questionnaire, and stakeholder interviews. The study revealed the various dimensions of infrastructure investment projects, including initiation and planning, design, procurement of contractors and consultants, contract management, environment, health, and safety. The technical audit findings reported weaknesses or non-performance issues in infrastructure planning at the national level …

Review On Gas-To-Liquids Conversion Technology: Lessons From Case Studies And Potential Strategies For Implementation In Tanzania, Joseph H. Kihedu

Tanzania Journal of Engineering and Technology (TJET)

This review paper explores the transformative potential of Gas-to- Liquids (GTL) technology for harnessing Tanzania's vast natural gas resources. With significant discoveries of natural gas reserves totalling up to 57 Tcf in fields such as Songosongo, Mnazi Bay, Block 1, 2, 3 and 4. Tanzania is positioned to leverage GTL technology to convert these resources into high-value liquid fuels like gasoline, diesel and naphtha. Review of GTL process, its products and applications has been done. By analysing successful GTL projects globally and drawing lessons applicable to Tanzania, this paper provides strategic recommendations for policymakers and stakeholders to foster GTL development. …

Teaching Materials Containing Kse To Support Character Education In Mathematics Subjects In Elementary Schools, Kunaenah Kunaenah, Rasiman Rasiman, Bagus Ardi Saputro

Jurnal Pendidikan Sains

The purpose of the study was to determine the validity, practicality, and effectiveness of teaching materials containing KSE to support character education in mathematics lesson content. This study employs a development research approach based on Borg and Gall's research design. The subjects of the study were in class V at public elementary school Kaliboyo. The data collection instruments used were validity sheets, response questionnaires, validation sheets, and observation sheets. The findings show that experts carried out the validity test, achieving a value of 93.30% in the very valid category. The practicality test results were derived from various sources: students' questionnaires, …

Ethnoscience-Based Science Learning Through Composite Board Making Utilizing Eggshell Waste And Water Hyacinth Fiber (Eichornia Crassipes), Asep Khaidir Arpan, Yulia Sukmawardani, Pina Pitriana, Ade Yeti Nuryantini

Jurnal Pendidikan Sains

The demand for boards continues to increase every year, while overexploitation of wood leads to environmental degradation and forest loss. On the other hand, eggshell and water hyacinth waste continues to grow without optimal utilization. This research aims to make composite boards based on eggshell waste and water hyacinth fibers and analyze the potential of ethnoscience in science learning. This research used a qualitative descriptive method with a literature and documentation study approach. The materials used in this experiment are eggshell waste, water hyacinth fiber, and polyester resin. The composite board was made by mixing water hyacinth fiber and eggshell …