Digital Commons Network^™

Trace: Intervention-Aware Temporal Representation Learning For Video Understanding, Hassan Nazeer Chaudhry, Farzana Kulsoom, Syed Muhammad Mohsin, Sheraz Aslam, Nouman Ashraf

Research Outputs: 2025-Present

Learning generalizable video representations from unlabeled data remains a fundamental challenge in computer vision, as existing self-supervised learning approaches primarily rely on modeling statistical correlations rather than capturing structured temporal dependencies in video data. While recent methods such as masked video modeling and contrastive learning achieve strong empirical performance, they often fail to explicitly account for how changes in motion influence future states, limiting robustness and generalization under distribution shifts. In this work, we propose TRACE, a self-supervised framework for learning intervention-aware spatiotemporal representations by incorporating temporal interventions and counterfactual learning objectives. TRACE operates in latent space by decomposing video …

Plant And Algal Mucilage: A Comprehensive Review On Sources, Extraction, Functional Properties, Health Benefits, And Emerging Trends In Food, Divya Singh Chauhan, Disha Mishra, Hamid, Vijay Singh Sharanagat, Amit K. Jaiswal

Research Outputs: 2025-Present

Plant- and algal-derived mucilage has gained attention as a sustainable, multifunctional biopolymer for food and allied applications, driven by the demand for clean-label ingredients, green processing technologies, and alternatives to petroleum-based additives. Mucilage is a complex hydrocolloid matrix primarily composed of heteropolysaccharides (e.g. pectins, arabinoxylans, galactomannans, glucomannans, and alginates), associated proteins, and bioactive compounds that provide hydration, rheological, emulsifying, gelling, and film-forming properties. This review aims to provide a comprehensive and critical synthesis of information on plant and algal mucilage, focusing on sources, extraction strategies, chemical composition, structural–functional relationships, health attributes, and emerging trends in food applications. This review systematically …

Regulatory Invisibility And Energy Poverty: Epc Exclusion, Traveller Mobile Homes And Ireland’S Low-Carbon Transition, Paul Lally, Ehiaze Ehimen, Mary Galvin, Ciara Ahern

Research Outputs: 2025-Present

Energy Performance Certificate (EPC) systems play a central role in residential decarbonisation by determining how dwellings are assessed, benchmarked and made eligible for retrofit support. However, these systems are largely designed around conventional, permanent, site-built housing and can exclude non-standard dwelling types. This paper examines the exclusion of permanently occupied Traveller mobile homes from Ireland’s EPC framework and considers the implications for energy poverty, retrofit policy and energy justice. Using a critical narrative synthesis, the study integrates academic literature, statutory reports, policy documents and community-based evidence to trace the relationship between Traveller accommodation policy, regulatory classification and contemporary decarbonisation frameworks. …

Acid-Mediated Extraction Of Gelatine From Chicken By-Products: Impact On Yield, Molecular Characteristics, And Structural-Thermal Properties, Omaima Aidat, Mohamad Khairi Zainol, Ilham Ben Amor, Zamzahaila Mohd Zin, Humam Shaaban Barhoum, Huda Alsaeedi, Mikhael Bechelany, Ahmed Barhoum

Research Outputs: 2025-Present

Despite the growing interest in utilizing poultry processing by-products as sustainable sources of gelatine, the influence of extraction conditions on gelatine quality, structure, and functionality remains insufficiently understood. This study investigated the effect of acetic acid concentration on the yield, molecular characteristics, and structural-thermal properties of gelatin extracted from chicken feet and heads. Gelatine was extracted using 3 v/v% (CBBG3) and 5 v/v% (CBBG5) acetic acid. CBBG5 produced a higher extraction yield (13.2%) than CBBG3 (10.49%), indicating enhanced collagen solubilization at higher acid concentration. However, CBBG5 also showed higher ash content (4.45 w/w% vs. 2.40 w/w%) and more pronounced yellow-green …

Inorganic Nanoparticle-Bacteria Interaction Systems: Mechanistic, Functional And Agricultural Applications, Gouri Nilakshika Atapattu, Michelle Giltrap, Furong Tian

Research Outputs: 2025-Present

BackgroundInorganic nanoparticles (NPs) are capable of interacting with bacteria. Due to this interaction, the cell exterior and interior of bacteria maybe altered. As a result, bacterial growth inhibition might occur due to the antimicrobial properties exerted by inorganic nanoparticles. Inorganic nanomaterials with antimicrobial activity have been discussed within the literature under the food, pharmaceutical, biomedicine and agriculture sectors. Currently, nanoparticle-mediated control of plant bacterial pathogens is advancing in agriculture.AimThis review aims to highlight the potential of bacteria-inorganic nanoparticle systems in agriculture and how they may yield agricultural benefits beyond antimicrobial activity.MethodsWe analysed literature published between 2020 and 2024 on how …

Characterization Of Nutritional And Flavor Profiles Of Aromatic Black Rice, India, Babli Waribam, Raju Sasikumar, T. Selva Kumar, Phani Kumar Garlapati, Sandeep Janghu, S. Chakkaravarthi, Paul Mansingh, Vidisha Tomer, Amit K. Jaiswal

Research Outputs: 2025-Present

Chakhao is an indigenous aromatic black rice variety from Manipur, India, valued for its distinctive flavor, high anthocyanin content, and associated health benefits. Despite its geographical indication status, limited information exists on how regional cultivation conditions influence its compositional attributes. This study characterizes and compares the nutritional composition, mineral content, volatile profile, and fatty acid methyl esters (FAMEs) composition of Chakhao sourced from five major cultivation regions in Manipur. Proximate analysis quantified moisture, protein, ash, fiber, fat, and carbohydrate content, whereas GC-MS/MS and ICP-MS were employed to profile volatile compounds, FAMEs, and mineral elements. Carbohydrates were the predominant macronutrient (73.88%–74.21%), …

Agriculture Contributions To Water Pollution And Sustainable Policy Solutions In Europe, Jemma Nolan, Azza Silotry Naik

Research Outputs: 2025-Present

Freshwater is essential for sustaining the health of humans, animals, and ecosystems; however, agricultural activities remain a major source of water pollution globally. This review examines how crop production, livestock farming, and aquaculture contribute to water contamination, the effectiveness of current European policies, and the potential of sustainable mitigation strategies. Evidence from the research identified pesticides, herbicides, veterinary antibiotics, nutrient runoff, aquaculture effluents, and microplastics as the primary agricultural pollutants affecting surface and groundwater quality. These contaminants have been linked to ecosystem degradation, biodiversity loss, endocrine disruption, antimicrobial resistance, and adverse human health outcomes. Despite extensive regulatory frameworks, including the …

Advancing Sustainable Tourism Behavioural Research: A Metacognitive Perspective, Arghavan Hadinejad, Kourosh Esfandiar, Liubov Skavronskaya

Research outputs 2022 to 2026

The current scholarly inquiry on sustainable tourism behaviour research predominantly relies on cognitive behavioural approaches, particularly exemplified by the widespread application of the theory of planned behaviour among other theories. Given recent advancements in psychology underscoring the necessity to expand beyond conventional cognitive frameworks, the current research aims to integrate metacognitive perspectives in sustainable tourism behaviour scholarship. This perspective explores the reflective awareness and control individuals exert over their cognitive processes which can uncover nuanced factors shaping sustainable tourism behaviour that may be overlooked in traditional cognitive models. Using a critical review approach, this research proposes the adoption of alternative …

Pathways To Prosperity: How Tourism Industry Training Reduces Poverty, Panpan Sun, Songshan Huang, Ghialy Yap, Zhibin Lin, Lei Zhao

Research outputs 2022 to 2026

This study investigates the effects of tourism industry training (TIT) on poverty alleviation, considering both direct and indirect pathways. Integrating labour mobility theory, human capital theory, and the tourism-led growth (TLG) hypothesis, we propose a new theoretical framework. Based on panel data from 29 Chinese provinces between 2000 and 2019, the empirical results show that TIT has a significant negative effect on poverty level, depth, and severity, with a larger direct impact than the indirect effect through tourism development. We also find significant regional differences in the impact of TIT on poverty reduction, with TIT having a more substantial impact …

An Association Test For Ordinal Outcomes In Clustered Data With Informative Cluster Size, Hasika K. Wickrama Senevirathne, Sandipan Dutta

Mathematics & Statistics Faculty Publications

In cluster-correlated data, the number of observations in a cluster can be associated with the outcome from that cluster. This phenomenon is known as informative cluster size which can occur in cluster-randomized clinical trial data. Several studies have found that ignoring the issue of informative cluster size can produce biased results in the analysis of clustered data. Most of the existing methods for addressing informative cluster size are suited to continuous outcomes. However, ordinal outcomes and covariates are often encountered in clustered data obtained from large clinical studies. The existing methods for ordinal association testing in clustered data can produce …

Changepoint Analyses Confirms Global Tropical Cyclone Frequency Decline, Michael Wehner, Thomas Fisher, Norou Diawara, Robert Lund

Mathematics & Statistics Faculty Publications

Changes in tropical cyclone frequencies as the climate warms is a topic of significant current debate [1, 2]. There is no accepted theory of how tropical cyclogenesis might respond to a warmer ocean-atmosphere system as multiple controlling factors exist [3–7]. Anthropogenic warming of surface ocean temperatures due to increased greenhouse gas concentrations [8] increases the potential for tropical cyclogenesis [9–11]; however, realized cyclogenesis also requires an initial local disturbance [12–16] to develop. Most multi-decadal tropical cyclone permitting climate models (i.e. resolutions of 15-50km) exhibit frequency decreases in warmer climates, despite the increase in tropical cyclogenesis potential [17–25]. In this paper, …

From Screens To Stress: Public Health Implications Of Cancer Worry In A Digitally Connected World, Shreya Mathur, Ethan Burns, Michael Pokojovy, Tzu-Liang Bill Tseng, Sunil Mathur

Mathematics & Statistics Faculty Publications

Aims:

This study examines how digital information environments, genetic testing experiences, health behaviors, and psychological distress influence cancer-related worry among adults in the United States. It further considers the public health implications of elevated or reduced cancer worry for prevention and risk communication.

Methods:

Data were drawn from a nationally representative survey of 6,252 U.S. adults. Measures included reliance on social media for health decision-making, smoking status, history of genetic testing, and psychological distress. Multivariable analyses assessed associations between these factors and levels of cancer worry.

Results/Findings:

Reliance on social media for health decisions was associated with greater cancer worry, …

A Composite Narxnn Approach To Photovoltaic Power Forecasting With Integrated Weather Inputs And Uncertainty Quantification, Denisse Urenda Castañeda, Sharmin Abdullah, Jackson Morgan, Honglun Xu, Michael Pokojovy, Tzu-Liang Tseng

Mathematics & Statistics Faculty Publications

Solar photovoltaics (PV) are a major source of sustainable energy. Yet, their power output is highly sensitive to environmental variability, particularly solar irradiance, cloud cover, wind, and temperature. Accurate forecasting of PV power is essential for efficient grid integration and energy planning, especially in applications requiring reliable longer-term forecasting rather than one-step-ahead predictions. This study presents a PV power forecasting approach using Nonlinear Autoregressive models with Exogenous Inputs (NARX), integrating large-scale numerical weather historical data as exogenous variables. Although NARX models effectively capture temporal dependencies, they can become overly dependent on historical power values, reducing responsiveness to real-time weather changes. …

A New Parallel-In-Time Direct Inverse Method For Nonlinear Differential Equations, Nail K. Yamaleev, Subhash Paudel

Mathematics & Statistics Faculty Publications

We propose a new method for parallelization of the first-order backward difference discretization (BDF1) of the first-order time derivative in nonlinear partial differential equations, such as conservation law equations. The time derivative term is discretized by using the method of lines based on the implicit BDF1 scheme, while the inviscid and viscous terms are approximated by conventional 2nd-order central discretizations of the 1st- and 2nd-order derivatives in each spatial direction. The global system of nonlinear discrete equations in the space-time domain is solved by the Newton method for all time levels simultaneously. For the BDF1 discretization, this all-at-once system at …

The Adaptive Solution Of High-Frequency Helmholtz Equations Via Multi-Grade Deep Learning, Peiyao Zhao, Rui Wang, Tingting Wu, Yuesheng Xu

Mathematics & Statistics Faculty Publications

The Helmholtz equation is fundamental for modeling wave propagation in acoustics, electromagnetics, and geophysics; however, high-frequency regimes remain notoriously difficult due to the severe numerical “pollution effect.” We propose FD-MGDL, an adaptive framework that synergizes finite difference discretizations with Multi-Grade Deep Learning (MGDL) to efficiently resolve high-frequency wavefields. Unlike standard physics-informed neural networks (PINNs), which frequently suffer from spectral bias and heavy automatic differentiation overhead, FD-MGDL employs a progressive, grade-wise training strategy that incrementally incorporates shallow sub-networks to refine residual errors. By leveraging ReLU activations in refinement grades, the framework reformulates the highly non-convex global optimization problem into a sequence …

Mg-Spair: Multi-Grade Sparse-Guided Implicit Representation For Training-Data-Free Image Restoration, Jianmin Liao, Lei Huang, Ronglong Fang, Ashley Prater-Bennette, Lixin Shen, Yuesheng Xu

Mathematics & Statistics Faculty Publications

MG-SpaIR is a training-data-free framework for restoring a clean image from a single observation corrupted by a mixture of blur, downsampling, noise, and missing pixels. Building on implicit neural representations (INRs), we introduce a multi-grade residual hierarchy that progressively refines the reconstruction from low to high spatial frequencies across grades, improving representational fidelity and mitigating spectral limitations. To stabilize reconstruction optimization and suppress INR-induced artifacts, we further propose an explicit sparse proximal regularization (e.g., ℓ0 type) applied directly in the high-resolution image domain, which discourages spurious high-frequency patterns while preserving sharp structures. The resulting optimization is solved efficiently via a …

Juncture Models: Trivariate Nonlinear Regression Models And Application To In-Hospital Quality Of Care, Manasi Sheth, N. Rao Chaganty

Mathematics & Statistics Faculty Publications

Mortality, readmission, and length of stay (LOS) are commonly used measures for quality of hospital care. These measures are correlated with each other, so we propose a new way to combine these three variables to evaluate hospital care quality of care. In this paper, we investigate the joint probability distribution of these three variables using what we named as the Juncture Distribution Models and characteristics of the Juncture models.

Integrating Genetic Modifier Genotype With Serum Proteomics In Duchenne Muscular Dystrophy Clinical Trials Links Ltbp4 Genetic Modifier To Il-23/Cd93 Pathways In Muscle, Utkarsh J. Dang, Yuan Fang, Daniele Sabbatini, Elena Pegoraro, Luca Bello, Paula R. Clemens, Michela Guglieri, John Van Den Anker, Jesse Damsker, Laura Hagerty, Yetrib Hathout, Michael Ziemba, Lauren Morgenroth, Surajit Bhattacharya, Kanneboyina Nagaraju, Jyoti K. Jaiswal, Eric P. Hoffman

Mathematics & Statistics Faculty Publications

Genetic modifiers of Duchenne muscular dystrophy (DMD) that alter disease severity or response to therapy have been reported using natural history or registry data sets of older corticosteroid-treated patients. We tested associations of genetic modifiers on motor function outcomes in young (4 to <  7 years) steroid naïve clinical trial participants. Participants in clinical trials (VBP15-002/003 [n = 48]; VBP15-004 [n = 121]; DNA available for n = 110) were genotyped for eight published genetic modifier loci, and associations of genotypes with baseline motor function defined via an age-adjusted linear model. Corticosteroid drug response was modeled by genotype-stratified placebo vs. steroid treatment at 12- and 24-weeks posttreatment (mixed model for repeated measures). …

Governance, Development, Technology: Histories And Futures, Nikhilesh Dholakia, Zeynep Ozdamar Ertekin, Delphine Godefroit-Winkel

Markets, Globalization & Development Review

No abstract provided.

Influencer Marketing Through The Decades: Evolution And Future Pathways, Raisha Yesmin, Chandan Goswami

Markets, Globalization & Development Review

Influencer marketing has evolved over the past few years, shifting from the conventional way of celebrity endorsement to a data-driven digital world. A systematic review has outlined the history of influencer marketing from the early days of the use of traditional mass media to development of social media platforms, the development of micro and nano Influencer marketing, and the different perceptions about authenticity and trust in consumer.

This study focuses on 37 peer-reviewed articles and books published between 1956 and 2025 from databases such as Scopus, Web of Science, Google Scholar, Emerald Insight, ScienceDirect and Taylor & Francis. Studies shows …

Digital Transformation Of Food Systems In Algeria: Challenges, Opportunities And Managerial Implications, Kamel Chikhi

Markets, Globalization & Development Review

This article provides a critical analysis of the transformations generated by digitalization in the contemporary Algerian food system. Based on contributions to the symposium "Digital and Food Practices" organized by the Social Sciences and Health Research Unit (GRAS) of the University of Oran 2 (Algeria) in January 2026, it reconstructs and analyzes the debates around four main themes: digitalization and representations of food; digital food environments and consumption practices; health, nutrition, and algorithmic mediation; and agricultural digitalization and territorial disparities. The article offers a thematic and critical read of the analogies, contradictions and complementarities among the various approaches discussed at …

Weak Enforcement And Low Compliance In Pci Dss: A Comparative Security Study, Soonwon Park, John Hastings

Research & Publications

Although credit and debit card data continue to be a prime target for attackers, organizational adherence to the Payment Card Industry Data Security Standard (PCI DSS) remains surprisingly low. Despite prior work showing that PCI DSS can reduce card fraud, only 32.4% of organizations were fully compliant in 2022, suggesting possible deficiencies in enforcement mechanisms. This study employs a comparative analysis (qualitative and indicator-based) to examine how enforcement mechanisms relate to implementation success in PCI DSS in relation to HIPAA, NIS2, and GDPR. The analysis reveals that PCI DSS significantly lags far behind these security frameworks and that its sanctions …

The Need For Standardized Evidence Sampling In Cmmc Assessments: A Survey-Based Analysis Of Assessor Practices, Logan Therrien, John Hastings

Research & Publications

The Cybersecurity Maturity Model Certification (CMMC) framework provides a common standard for protecting sensitive unclassified information in defense contracting. While CMMC defines assessment objectives and control requirements, limited formal guidance exists regarding evidence sampling, the process by which assessors select, review, and validate artifacts to substantiate compliance. Analyzing data collected through an anonymous survey of CMMC-certified assessors and lead assessors, this exploratory study investigates whether inconsistencies in evidence sampling practices exist within the CMMC assessment ecosystem and evaluates the need for a risk-informed standardized sampling methodology. Across 17 usable survey responses, results indicate that evidence sampling practices are predominantly driven …

Unsupervised Baseline Clustering And Incremental Adaptation For Iot Device Traffic Profiling, Sean Alderman, John Hastings

Research & Publications

The growth and heterogeneity of IoT devices create security challenges where static identification models can degrade as traffic evolves. This paper presents a two-stage, flow-feature-based pipeline for unsupervised IoT device traffic profiling and incremental model updating, evaluated on selected long-duration captures from the Deakin IoT dataset. For baseline profiling, density-based clustering (DBSCAN) isolates a substantial outlier portion of the data and produces the strongest alignment with ground-truth device labels among tested classical methods (NMI 0.78), outperforming centroid-based clustering on cluster purity. For incremental adaptation, we evaluate stream-oriented clustering approaches and find that BIRCH supports efficient updates (0.13 seconds per update) …

Gravity Falls: A Comparative Analysis Of Domain-Generation Algorithm (Dga) Detection Methods For Mobile Device Spearphishing, Adam Dorian Wang, John Hastings

Research & Publications

Mobile devices are frequent targets of eCrime threat actors through SMS spearphishing (smishing) links that leverage Domain Generation Algorithms (DGA) to rotate hostile infrastructure, avoid individual domain blocks, and bypass perimeter enterprise defenses. Despite this, DGA research and evaluation largely emphasize malware C2 and email phishing datasets, leaving limited evidence on how well detectors generalize to smishing-driven domain tactics outside enterprise perimeters. This work addresses that gap by evaluating traditional and machine-learning DGA detectors against Gravity Falls, a new dataset derived from smishing links delivered between 2022 and 2025. Gravity Falls captures a single threat actor's evolution across four technique …

Context-Based Adversarial Attacks On Ai Code Generators: Vulnerability Analysis And Implications, Walther Del Orbe, John Hastings, Varghese Vaidyan

Research & Publications

AI-powered code generation systems have transformed software development but introduce critical inference-time security vulnerabilities. This research presents a systematic investigation of context-based adversarial attacks, where strategically crafted contextual inputs, including comments, documentation, variable names, bias large language models toward generating exploitable code. Through 2,800 controlled experiments across CodeT5+, CodeLlama, GPT-3.5-Turbo, and GPT-4, we quantify attack effectiveness and defense mechanisms. Results demonstrate that adversarial conditions increase vulnerability generation 11.4x for individual models, with an overall pooled increase of 2.1x (from 16.1% to 34.1%), with direct instruction attacks achieving 88.8% success on GPT-3.5-Turbo. Cross-model transferability reaches 58-100%, indicating systemic architectural vulnerabilities rather …

Bridging The Smart City Cybersecurity Data Gap Through Ai-Driven Synthetic Dataset Generation, Stephanie Polczynski, John Hastings, Varghese Vaidyan, Kyle Korman

Research & Publications

Smart cities rely on interconnected cyber-physical systems that integrate sensors, IoT devices, cloud platforms, and AI-driven services and decision-making. While these systems enhance city services, they also introduce complex cybersecurity challenges due to their large attack surfaces, heterogeneous data flows, and evolving threat vectors. Developing and validating cybersecurity tools for smart cities requires high-quality datasets that accurately represent real operational conditions. However, real-world datasets are often incomplete, contain privacy-sensitive data, are difficult to access, or lack sufficient malicious activity to support tool development. This research addresses this critical gap by proposing an AI-based synthetic data generation (SDG) framework designed specifically …

Enhancing Attack Detection Capabilities In Bacnet/Ip Networks Using Machine-Learning Models, Derek Manzella, John Hastings

Research & Publications

Building Automation Systems (BAS) manage critical building functions using protocols such as BACnet/IP, yet defenders have limited tooling and few labeled datasets for detecting BACnet-specific attacks. This work addresses these gaps through three contributions. First, CISA's Zeek BACnet parser is modified to produce a unified per-packet log, simplifying feature engineering for machine-learning (ML) pipelines. Second, a simulated BACnet/IP testbed is developed using bacpypes3 to model a small commercial HVAC system with physics-based device behavior, schedule-aware controller logic, and per-packet attack labeling. Third, five unsupervised anomaly detection models are evaluated using baseline traffic and six BACnet attack types, including denial of …

Security Vulnerability Patterns In Ai-Generated Code: A Cross-Model Comparative Study, Shanna Kahn, John Hastings

Research & Publications

LLM-based coding tools enable non-expert users to generate routine automation scripts that may enter enterprise workflows without meaningful security review. This study examines that risk directly. Code was collected from ChatGPT, Microsoft Copilot, and Google Gemini using identical prompts across three automation domains. Claude Code performed a standardized vulnerability review. Each identified vulnerability was scored using CVSS v3.1 and mapped to the OWASP Top 10:2021 and the MITRE ATT\&CK frameworks. Every script contained exploitable vulnerabilities. Nine of the 17 identified vulnerability classes appeared in code from all three models, while 14 of the 17 vulnerability classes appeared in at least …

Workplace Surveillance And Insider Threat Risk Management: Legal Limits And Privacy Harms, Haywood Gelman, John Hastings, Suvineetha Herath, Quentin Covert

Research & Publications

Workplace surveillance is used by organizations to protect corporate assets and monitor employee productivity. This research presents two central arguments on workplace surveillance: although surveillance serves legitimate organizational purposes, over-surveillance can violate legal requirements and data privacy principles; and a primary security objective of workplace surveillance is the detection of insider threats (InT). InT are comprised of individuals with authorized resource access whose intentional or unintentional actions may damage or compromise corporate assets. This paper investigates InT personas to understand behavioral and psychological detection criteria. Employee surveillance tools and techniques are reviewed to characterize the employee surveillance landscape. Workplace privacy …