Digital Commons Network^™

Individualized Bayesian Inference Identifies Novel Genetic Variants For Parkinson's Disease, Jin Ren, Yasaman J. Soofi, Md Asad Rahman, Qing Lu, Jinling Liu

Engineering Management and Systems Engineering Faculty Research & Creative Works

Parkinson's disease (PD) is a complex neurodegenerative disorder with a significant genetic component. While genome-wide association studies (GWAS) have been instrumental in identifying genetic variants associated with PD, the reliance on large sample sizes and population-level analyses may overlook variants with lower minor allele frequencies or individual-specific relevance. Individualized Bayesian Inference (IBI) offers a promising method to complement GWAS by identifying and prioritizing candidate genetic markers at both the individual and patients-like-me subgroup levels. This study evaluates the application of IBI to PD genetics, using GWAS as a baseline for comparison. We analyzed genetic data from the Fox Insight online …

Trustworthy And Explainable Malware Threat Intelligence Through Social Media Analytics And Nature-Inspired Optimization, Feras Al-Obeidat, Muhammad Saad Rashad, Muhammad Amin, Waqas Ali, Bilal Khan, Sajid Anwar

All Works

The convergence of media analytics, Cyber threat Intelligence (CTI) and trustworthy artificial intelligence has become essential for modern cybersecurity systems operating over large-scale, heterogenous data sources. In particular, Social Media Intelligence (SOCMINT) and Open Source Intelligence (OSINT) provide high-volume, real-time signals that complement structured CTI frameworks for early-stage malware and adversarial threat detection. However, integrating these unstructured and dynamic sources with Structured Threat Information Expression (STIX) remains challenging due to its hierarchical complexity, semantic redundancy, and computational overhead in resource-constrained environments. This paper proposes an explainable and optimized intelligence pipeline (BERT-STIX) that unifies SOCMINT, OSINT, and STIX-based CTI using deep …

Which Factors Are Most Important For Understanding Neighborhood-Scale Electrification Transitions In New York City, And How Do These Factors Interact Within Urban Energy Systems?, Aaryan Nair

Publications and Research

Building electrification has emerged as a central strategy for reducing greenhouse gas emissions from urban areas, yet its implementation remains uneven across cities due to differences in buildings, infrastructure, and local conditions. This literature review examines the factors that influence neighborhood-scale electrification transitions in New York City and explores how these factors interact within urban energy systems. Drawing on research from building science, energy systems, infrastructure planning, and sustainability transitions, the paper identifies five recurring categories of influence: building characteristics, demand characteristics, infrastructure conditions, planning and investment capacity, and socioeconomic conditions. The review finds that building age, typology, density, and …

Bayesian And Multi-Objective Decision Support For Incident Mitigation In Cyber-Physical Systems, Shaofei Huang, Christopher M. Poskitt, Lwin Khin Shar

Research Collection School of Computing and Information Systems

Cyber-physical systems increasingly rely on interconnected physical and digital systems whose security incidents can escalate rapidly into safety and operational failures. Existing decision-support approaches struggle to support incident response because they rely on static assumptions, incomplete vulnerability data, and single-objective risk models that do not adequately capture trade-offs between attack success likelihood, impact severity, and system availability. This paper proposes an adaptive decision-support framework for incident mitigation in cyber-physical systems that integrates hierarchical Bayesian Network modelling, confidence-calibrated exposure estimation, and multi-objective optimisation into a unified, adaptive pipeline. The framework constructs probabilistic models from system architecture and vulnerability data, incorporating complementary …

Stochastic Control Of Inventory And Scheduling Decisions In An Open Network Of Repairables, Erhun Özkan

Turkish Journal of Mathematics

We study maintenance operations for capital goods with repairable components. Because the downtime of a capital good is costly, the maintenance provider keeps an inventory of spare parts to quickly replace broken parts of the capital goods. After the replacement, the broken part is added to the repair facility’s repair queue. Upon a part breakdown, if there is no spare part in the inventory, then the maintenance provider has two options: (i) it can backorder the spare part demand, (ii) it can execute an emergency repair, which is done immediately and quickly, and is followed by a quick installation of …

A Full-Scale Watertight Workflow Numerical Study For A Triangular Fin And Tube Heat Exchanger, Hamdi̇ Selçuk Çeli̇k, Bahadir Doğan, Lati̇fe Berri̇n Erbay

Turkish Journal of Mathematics

In this study, the thermo-hydraulic characteristics of an air-cooled triangular finned tube heat exchanger were analyzed numerically. The full-scale heat exchanger was modeled using Ansys Fluent watertight workflow, considering water as the primer fluid to precisely investigate the effects of geometrical factors. The effects of fin height, fin width, and fin spacing of the triangular fins on the performance of the heat exchanger were examined using k-ω turbulence model. A basic finned-tube heat exchanger model, which was manufactured as a monolithic structure without tube-fin contact resistance by milling from aluminum 5083 material, was tested in an air tunnel to verify …

Aligning Tenure With Institutional Values: Supporting And Rewarding Engaged Scholarship In College Tenure Policies, Sian Bareket, Alexander R. Barron, Abigail O’Meara, Charavee Basnet Chettri

Environmental Science and Policy: Faculty Publications

Scholars have described the current era as a polycrisis, with climate change overlapping and interacting with other social justice and environmental challenges. While the expertise and capacity housed in academia has tremendous potential to help society face these threats, scholars and others have long critiqued academia as too isolated from the practical problems of society and front-line communities. While engaged scholarship (ES) connects academia and real-world problems, tenure policies are usually designed to primarily reward “traditional” scholarship focused on journal articles and academic books. This study sought to investigate the contrast between higher education institutions’ publicly shared values aligned with …

State Of The Fisheries: Status Reports Of The Fisheries And Aquatic Resources Of Western Australia 2024/25, Stephen J. Newman, Ainslie Denham, Lebo Selemela, Jenny Moore

Status Reports of the Fisheries and Aquatic Resources

The Status Reports of the Fisheries and Aquatic Resources of Western Australia (SRFAR) provides an annual update on the state of the fish stocks and other aquatic resources of Western Australia (WA). These reports outline the most recent assessments of the cumulative risk status for each of the aquatic resources (assets) within WA’s six bioregions using an Ecosystem Based Fisheries Management (EBFM) approach. The Department of Primary Industries and Regional Dveelopment’s (DPIRD) risk based EBFM framework is the WA Government’s basis for management of all of WA’s aquatic resources.

Assets examined in each bioregion include the meso-scale ecosystems and aquatic …

Towards More Inclusive Ai Systems In Cities, Siew Ying Shee, Orlando Woods

Research Collection School of Social Sciences

Artificial Intelligence (AI) is increasingly embedded in urban infrastructures and governance, shaping how people, spaces, and futures are classified, prioritised, and managed. Yet, most AI systems are developed within a narrow set of linguistic and geopolitical contexts and exported globally, embedding particular epistemic assumptions into diverse urban environments. Even where formal inclusion metrics are met, such asymmetries can render certain populations and realities less legible within algorithmic systems. Prevailing approaches in digital inclusion—centred on fairness metrics, representation, or access—presume technologies as politically inert and bounded. Yet, the adaptive and probabilistic behaviour of contemporary AI disrupts this premise, challenging the idea …

Potential Energy Landscape Formalism For Quantum Liquids, Yang Zhou

Dissertations, Theses, and Capstone Projects

Atomic delocalization due to nuclear quantum effects (NQE) remains poorly understood in low-temperature liquids near the glass state and during vitrification. Many liquids can be described accurately by treating their nuclei as classical particles, but this approximation fails for light elements such as He and H₂, small hydrogen-containing molecules such as water, and systems in which zero-point motion or isotope-substitution effects are important. Developing a general thermodynamic and statistical-mechanical description of such liquids has been challenging. This dissertation extends the potential energy landscape (PEL) formalism, originally developed for classical liquids and glasses, to liquids that obey quantum mechanics and exhibit …

Rethinking Road Racing Standards: Comparing New York Road Runners Race Results To Running Industry Standards, Jennie Coughlin

Dissertations, Theses, and Capstone Projects

Running has undergone a third boom in participation since 2020, driven largely by people seeking fitness options that were outside and socially distanced during the COVID-19 pandemic. Social media has also allowed runners from groups that did not traditionally participate to find community. As the road racing population has expanded to include more people, some road racing industry standards have not kept up. This project assesses what the road racing community looks like and measures industry standards against the community of participants to see what changes in those standards would be needed to make them inclusive for all participants.

To …

Collecting Holistic Evidence On Agroecology Performance To Accelerate Sustainable Food System Transitions, Sarah K. Jones, Andrea Cecilia Sánchez, Chris Dickens, Matthias S. Geck, Chaturangi Wickramaratne, Veronique Alary, Peter Bolo, Dennis Junior Choruma, Somphasith Douangsavanh, Modou Gueye Fall, Gatien Falconnier, Shweta Gupta, Chris Kettle, Smitha Krishnan, Sylvia Nyawira, Guillermo Orjuela-Ramirez, Boko Michel Orounladji, Piedad Pareja, Telma Sibanda, Christine Lamanna

All Peer-Reviewed Publications

A growing body of research shows the potential of agroecology for enabling a shift to planet-friendly and socially just agrifood systems through context-specific transition pathways. We conduct a scoping review to identify and evaluate the potential of 42 existing agroecology adherence and/or agricultural performance assessment tools to generate credible, legitimate, salient and transferable evidence to inform these transitions. Results show that while multiple relevant tools exist, each of them can be strengthened in at least one area. While most tools are transferable (low time and resource requirements) and have legitimacy (open access with transparent methods), tools have variable saliency (holistic …

Machine Learning-Enabled Chemical Ecology For Integrated Pest Management: From Volatiles To Field Applications, Steve B.S. Baleba, Victor O. Omondi, Pascal Aigbedion-Atalor, Emmanuel Peter, Souleymane Diallo, Komi Mensah Agboka

All Peer-Reviewed Publications

Machine learning is transforming chemical ecology by accelerating the discovery and deployment of semiochemical-based tools for precision pest management. These advances are particularly important in the face of climate change, pesticide resistance, and the growing need for sustainable agricultural intensification. This review synthesizes how machine learning can be applied across the semiochemical discovery and implementation pipeline, from chemical signal detection to field deployment and decision support for integrated pest management. We review major machine learning approaches and demonstrate how they extract biologically relevant information from high-dimensional chemical, electrophysiological, behavioral, sensor, and field datasets. These methods accelerate semiochemical discovery, prioritize candidate …

Pseudo-Traveling Waves And Bumps In Quantum And Classical Hierarchical Cellular Neural Networks, Wilson A. Zuniga-Galindo, B. A. Zambrano-Luna, Chayapuntika Indoung

School of Mathematical & Statistical Sciences Faculty Publications

We study the existence of pseudo-traveling waves and bump solutions for two classes of hierarchical cellular neural networks (CNNs) defined over the ring of p-adic integers Zp. The first type is a p-adic CNN described by a reaction-diffusion equation, while the second type is its quantum analog obtained via Wick rotation. The p-adic CNNs are hierarchical versions of the classical Chua-Yang CNNs; these networks have a tree-like hierarchical architecture with infinitely many cells and hidden layers. The states are governed by integro-differential equations on Zp. The p-adic traveling waves behave fundamentally differently from their Archimedean counterparts. A traveling wave restricted …

Investigating The Impact Of Outreach Programmes On Second Level Students In Formal And Informal Computer Science Education In Ireland, Karen Nolan

Doctoral

Computer Science (CS) education at second-level in Ireland has undergone significant development, including the introduction of the Leaving Certificate Computer Science (LCCS). However, the existing literature examined demonstrated that access to and participation in CS education is uneven, resulting in many students having limited exposure CS at second-level before making educational choices. At the same time, students who elect to study LCCS require appropriate support as they engage with programming and other aspects of a relatively new formal curriculum. This thesis investigates the function of school-based CS outreach in helping students at these various phases of their second-level CS education …

Toward Equitable Energy Futures: The Influence Of Digital Government And Financial Development On Clean Energy Justice, Martin Spraggon, Muhammad Hafeez, Sana Ullah, Chanyanan Somthawinpongsai, Zurul Aisya Osman, Sidra Sohail

All Works

Fossil fuel energy is the largest source of carbon emissions and the main driver of climate change and global warming. Therefore, the world is switching towards clean and green energy sources as an alternative to fossil fuel-based energy sources. Nevertheless, the equitable distribution of clean energy resources in urban and rural areas among every segment of society has become a concern for policymakers. Thus, urban-rural clean energy justice is an investigation topic that needs our attention. In this analysis, our main focus is on investigating the influence of financial development and digital government on clean energy justice using an advanced …

Sea-Level Rise And Regional Economic Adjustment On The U.S. Gulf Coast, Md A M Hasif, Juhee Lee, Robin A. Choudhury

School of Earth, Environmental, & Marine Sciences Faculty Publications

Sea-level rise (SLR) poses escalating risks to the U.S. Gulf Coast, where critical industries, infrastructure, and populations are concentrated in low-lying areas. This study estimates the short- and long-run economic effects of SLR using a panel autoregressive distributed lag-pooled mean group framework applied to county-level data from 26 coastal counties across five Gulf Coast states from 2005 to 2021. Over this period, mean sea level increased by approximately 11 cm on average across counties, with cumulative changes ranging from about 4 cm to over 24 cm and substantial cross-county variation, providing a meaningful empirical setting for identifying economic adjustment. We …

Remote Sensing Integrated Modeling Of Fall Armyworm Parasitoid Interactions In Africa Using Fractional Differential Equations, Marian Adan, Steeven Belvinos Affognon, Henri E.Z. Tonnang, Klaus Greve, Christian Borgemeister, Georg Goergen

All Peer-Reviewed Publications

The invasion of fall armyworm (Spodoptera frugiperda; FAW) into Africa poses a major threat to agricultural systems, particularly maize production, necessitating effective biological control strategies. This study presents an ecological modelling framework that integrates temperature data with fractional differential equations (FDEs) to investigate interactions between FAW and its parasitoid natural enemies. By incorporating temperature-driven effects on life-history traits and ecological memory dynamics, the model captures key features of host–parasitoid interactions under variable environmental conditions. Equilibrium states corresponding to FAW extinction, parasitoid extinction, and coexistence were analytically derived, and sensitivity analysis of the FAW reproduction number highlighted its central role in …

From Green Presence To Perceived Greenery: Everyday Mobility And Perceptual Availability In Singapore, Ziheng Zeng, Sonny Rosenthal, Orlando Woods

Research Collection College of Integrative Studies

Urban greenery may be abundant in transit-oriented cities, yet green presence does not necessarily become perceptually available to people moving through it. This paper distinguishes green supply, perceived greenery, and green experience, and examines the step at which greenery enters awareness during travel. The study combined 21 days of ecological momentary assessment from 68 participants in Singapore, yielding 6,794 in-trip moments, and 25 route-reconstruction interviews. A satellite normalized difference vegetation index (NDVI) provided a top-down baseline of green supply, and a street-level green view index (GVI) from Google Street View (GSV), matched to 5,824 moments, provided a co-located eye-level baseline. …

The Application Of Machine Learning And Deep Learning On Demand Forecasting Across Time-Critical Industries: A Systematic Review, Asmaa Seyam, Sujith Samuel Mathew, May El Barachi, Cheng Zhang, Jun Shen

All Works

The applications of machine learning and deep learning in demand forecasting have attracted increasing attention, as they offer remarkable predictive capabilities that help automate forecasting processes and achieve higher accuracy. While numerous review studies have examined solutions within specific industries, there is a lack of comprehensive literature review investigating these solutions across different sectors. Therefore, this study overviews machine learning and deep learning applications in demand forecasting across time-critical industries, including power, tourism, water, transportation, and food. A two-tier classification framework is proposed to categorize demand forecasting studies by both application industry and methodological architecture. In addition, the most popular …

Prune: A Patching Based Repair Framework For Certifiable And Privacy-Robust Unlearning Of Neural Networks, Xuran Li, Jingyi Wang, Xiaohan Yuan, Peixin Zhang

Research Collection School Of Computing and Information Systems

Machine unlearning has emerged as a key mechanism for enabling the “right to be forgotten” in neural network models, allowing the selective removal of specific training data upon request. Existing approaches typically rely on retraining models with the remaining data, which is computationally expensive and difficult to verify, especially when deployed models are distributed or resource-constrained. To address this challenge, our prior conference work introduced PRUNE, a patching-based framework that formulates unlearning as a neural network repair problem. PRUNE achieves targeted forgetting by learning lightweight patch networks that redirect model predictions on the data to be unlearned while preserving performance …

Neural Symphony Of Flow Experience: Evidence For High-Dimensional Metastable Dynamics, Abdelrahman B. M. Eldaly, Kris Zhangguang Kang, Fiona Fui-Hoon Nah, Leanne Lai-Hang Chan, Keng Siau, Xiao Fan Liu, Richard Huskey, Langtao Chen, Tejaswini Yelamanchili, Rene Weber

Research Collection School Of Computing and Information Systems

Flow, an optimal experience characterized by deep immersion and engagement in an activity, has been extensively studied in behavioral research. However, its neural dynamic mechanism remains poorly understood. In a within-subject video gaming experiment, we captured neural activity underlying flow, boredom, and anxiety using a 64-channel electroencephalogram (EEG) system. Compared to boredom and anxiety, flow exhibits the highest global functional connectivity, metastability, and dimensionality of dynamic functional connectivity patterns, suggesting that flow is a highly adaptable process that is supported by high-dimensional neural dynamics. Unlike previous studies that focused on identifying static or localized brain activity, we examine the neural …

Generalized Logit Adjustment: Improved Fine-Tuning By Mitigating Label Bias In Zero-Shot Vision Models, Beier Zhu, Qianru Sun, Xun Yang, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Foundation models like CLIP allow zero-shot transfer on various tasks without additional training data. Yet, the zero-shot performance is less competitive than a fully supervised one. Thus, fine-tuning and ensembling are also commonly adopted to better fit the downstream tasks. However, we argue that such prior work has overlooked the inherent biases in foundation models. Due to the highly imbalanced Web-scale training set, foundation models are inevitably skewed toward frequent semantics, and thus the subsequent fine-tuning or ensembling is still biased. In this study, we systematically examine the biases in foundation models and demonstrate the efficacy of our proposed Generalized …

Allochthonous Particulate Organic Carbon In Blue Carbon Ecosystems: Implications For Accounting, Stability, And Policy, Anirban Akhand, Inés Mazarrasa, Stacey M. Trevathan-Tackett, K. M.G. Mostofa, Abhra Chanda, Hongbin Liu, Hiroshi Sakugawa, Si Liang Li, Cong Qiang Liu, Catherine E. Lovelock, Paul Lavery, Hilary Kennedy, Peter I. Macreadie

Research outputs 2022 to 2026

Blue carbon ecosystems (BCEs) store a significant amount of organic carbon (OC) in their soils, including OC sequestered by other ecosystems and transported into these ecosystems, referred to as allochthonous OC. This study reviews the state-of-the-art knowledge on the abundance and sources (including still unexplored ones) of allochthonous particulate organic carbon (POC) and reviews the ways allochthonous POC is managed under the current blue carbon policy frameworks for carbon benefits accounting in blue carbon restoration and conservation projects. Based on a review of 102 studies, we find that 56 ± 25% of the soil organic carbon (SOC) deposits in BCEs …

Soil Organic Carbon Stocks And Sequestration Rates In Western Australia’S Mangroves, Enric Gomis, Cristian Salinas, Madison Williams-Hoffman, Nicole E. Said, Marta Sánchez Alarcón, Connor Gorham, Axel Werner, Paul S. Lavery, Karina Inostroza, Tegan Davies, Pauline F. Grierson, Sharyn Hickey, Ben Radford, Enrique Ballesteros, Jeffrey J. Kelleway, Pere Masqué, Eduard Serrano, Peter I. Macreadie, Catherine E. Lovelock, Anna Lafratta, Micheli Duarte De Paula Costa, Ruth Reef, Vladimir Levchenko, Fiona Doessel, Matthew A. Hayes, Stacey M. Trevathan-Tackett, I. Noyan Yilmaz, Pawel Waryszak, Rory Crofts, Oscar Serrano

Research outputs 2022 to 2026

Mangroves are widely recognized for their contribution to climate change mitigation. This study provides the first comprehensive assessment of top 1-m and 30-cm soil organic carbon (OC) stocks and accumulation rates across Western Australia’s mangroves, spanning multiple bioregions, geomorphological settings, and intertidal positions. The results revealed (mean ± SE) 236 ± 13 Mg and 83 ± 4 Mg OC ha−1 in top 1-m and 30-cm soils, respectively, accumulated at 443 ± 83 and 215 ± 22 kg OC ha−1 yr−1 over the past 100 and 500 years, respectively. Mangroves across Western Australia exhibited up to 3-fold differences in top 1-m …

Paper Wasp Species Polistes Fuscatus (Hymenoptera: Vespidae) Tracks Urban Lead (Pb) Levels More Than Polistes Dominula, Lindsey R. Kemmerling, Anna L. Lovat, Allison A. Hanka, Emilie C. Snell-Rood

The Great Lakes Entomologist

Lead (Pb) is a persistent, toxic pollutant with widespread but inequitably distributed impacts, especially for humans in urban areas. The impacts of lead pollution on other organisms is largely unknown, even as they commonly experience exposure to lead alongside humans. We tested hypotheses regarding the lead concentrations of paper wasps across a gradient of pollution relative to soil and human lead levels, and correlations with wasp body condition. We collected two species of paper wasp across an urban gradient of lead pollution: Polistes fuscatus (Fabricius) (Hymenoptera: Vespidae), native to our study location, and Polistes dominula (Christ), nonnative to our study …

Long-Term Monitoring And Response Of Papaipema Sciata Bird (Lepidoptera: Noctuidae) To Prescribed Fire In Michigan (Usa), Gregory J. Norwood, Logan M. Rowe, Brett A. Degregorio, David L. Cuthrell, Aaron D. Meredith, Steven M. Gray

The Great Lakes Entomologist

The Culver’s root borer moth (Papaipema sciata) (Lepidoptera: Noctuidae) is a rare Noctuid that requires Culver’s root (Veronicastrum virginicum) to complete its lifecycle within remnant native grasslands and open woodlands in the midwest and northeast United States. We report on 26 years of monitoring this species’ colonization of Culver’s root stems at Algonac State Park (ASP) in Michigan, where prairies have been maintained by prescribed fire since 1990. P. sciata is considered fire sensitive because eggs spend the dormant season in the combustible prairie litter and duff near host plants. The objectives were to determine the …

Between Digital Transformation And Regulatory Vacuum: Cybersecurity Of Public Services In Mozambique, Faztudo Languisse Eng.

Journal of Cybersecurity Education, Research and Practice

The rapid expansion of digital public services in Mozambique—including e-government platforms, digital health systems, and electronic tax administration—has outpaced the development of a coherent legal framework for cybersecurity. While Law No. 3/2017 (Electronic Transactions Law) of 9 January 2017 introduced foundational data-protection principles, Mozambique long lacked a dedicated cybersecurity regulatory authority, mandatory security standards, and formal incident-notification mechanisms. This regulatory vacuum exposed critical public services to escalating cyber risks as digital transformation was actively promoted as a development priority. This article examines the legal and institutional gaps in Mozambique's cybersecurity governance framework prior to the 2026 Cybersecurity and Cybercrime Laws, …

Exposing And Addressing Machine Learning Brittleness Through Constraint Solving, Muyeed Ahmed

Dissertations

Machine Learning (ML) implementations are fundamentally brittle: nondeterministic, inconsistent, and prone to overfitting; however, constraint solving can be used to systematically expose, quantify, and address this brittleness.

This dissertation first establishes that widely-used implementations of popular ML algorithms are nondeterministic (producing different outputs on the same input, across different runs) and inconsistent (different implementations of the same algorithm producing different outputs on the same input). This is more prevalent in Unsupervised Learning (UL) implementations where, due to the lack of a ground truth, subtle execution errors can go unnoticed and are difficult to verify. Nondeterminism and inconsistency also introduce security …

Development Of Ultrafast Protein Digestion And Standards Free Quantitation Methods For Mass Spectrometric Analysis, Praneeth Ivan Joel Fnu

Dissertations

A wide variety of biologically important molecules, such as enzymes, antibodies, hormones, transporters and receptors are proteins by composition and they play key roles in biological functions such as cellular regulation, communication, metabolism and physiological function. Protein dysfunctions and abnormalities are associated with numerous diseases, making proteins critical targets for understanding disease mechanisms and developing therapeutic interventions. Protein-based therapeutics such as monoclonal antibodies, hormones and vaccines gained popularity due to their high specificity, efficacy, and ability to treat complex diseases that are often difficult to address with small-molecule drugs.

Given the important role of proteins in a variety of biological …