Digital Commons Network^™

Living Next Door To Mcdonald’S: A Qualitative Study Of Community Submissions And Resident Realities Pre- And Post Development, Roslyn Giglia, Jacinta Francis, Jana Buckle, Clare Whitton, Ros Sambell, Frith Klug, Lukar Thornton, Siobhan Hickling, Katherine Kent, Gina Trapp

Research outputs 2022 to 2026

Background: The proliferation of fast-food outlets (FFOs) in residential areas has raised growing concerns about their impact on community health, safety, and wellbeing. This qualitative study examined both pre-development community consultation submissions and post-development resident experiences surrounding the development of a McDonald’s outlet in a newly established suburb on the urban fringe of Perth, Western Australia (WA). Methods: Using a qualitative descriptive design, the study analyzed 183 community submissions lodged with the local council prior to development and conducted semi-structured interviews with 11 residents living within 300 m of the outlet after construction. Results: Content analysis revealed strong opposition, with …

Local Government Policies Restricting Unhealthy Outdoor Food Advertising In Australia: Stakeholder Perspectives Identify Political Willingness Is Key To Overcoming Barriers, Alexia Bivoltsis, Frith Klug, Jacinta Francis, Clare Whitton, Gina S.A. Trapp, Claire E. Pulker

Research outputs 2022 to 2026

Background: Unhealthy outdoor food advertising is strongly linked to poor diet, highlighting the need for policies. Western Australian (WA) local governments (LGs) can restrict advertising on their owned infrastructure through their Public Health Plans, but limited policy action has occurred. This study aimed to explore the perspectives of WA LGs and public health organizations (PHOs) on current and potential policies restricting unhealthy outdoor food advertising on LG owned infrastructure to (1) identify barriers and enablers to the policy making process, (2) assess the current level of political willingness (i.e. political want, political can and political must) and (3) inform development …

Chlorinated Drinking Water Exposure Enriches Antimicrobial Resistance Pathways In The Infant Gut Microbiome: A Randomized Trial, Kimberley Parkin, Claus T. Christophersen, Valerie Verhasselt, Debra J. Palmer, Matthew N. Cooper, Susan L. Prescott, Desiree Silva, David Martino

Research outputs 2022 to 2026

Background: Water chlorination is essential for controlling harmful microbes in drinking water; however, the antimicrobial effects of chlorine-based disinfectants present in tap water may influence early-life gut microbial ecology.

Objective: To investigate the functional and compositional impact of chlorinated drinking water on the gut microbiome of infants.

Design: The waTer qUality and Microbiome Study (TUMS) was an Australian-based double-blinded, randomised controlled trial. Six-month-old infants (n = 197) received either de-chlorinated drinking water via benchtop filtration (treatment, n = 99), or regular chlorinated water (control, n = 98) for twelve months. Tap water and stool samples were collected at baseline and …

Free-Fall Normalised Peak Braking Velocity For Countermovement Jump Monitoring, Karol Kruczek, André Rebelo, Jason Lake, Michał Nowak

Research outputs 2022 to 2026

Background: Peak braking velocity (PBV) during the countermovement jump (CMJ) is used to describe descent strategy, but its interpretation is affected by countermovement depth. This study introduced free-fall normalised peak braking velocity (PBVFF), a dimensionless index expressing PBV relative to the displacement-specific theoretical free-fall velocity limit, and evaluated its reliability, performance associations and ranking consequences. Methods: Seventy injury-free male academy footballers (18.7 ± 1.6 years) performed three maximal hands-on-hips CMJs on dual force plates. Using retrospective observational routine monitoring data, one representative testing day per athlete was analysed. Two PBVFF variants were examined: PBVFF normalised to countermovement depth (PBVFFCMD) and …

Lessons Learned From A Randomised Controlled Trial Of A Digital Intervention Targeting Eating Disorder Symptoms In Cisgender Sexual Minoritized Men, Emma Austen, Matthew Fuller-Tyszkiewicz, Siân A. Mclean, Beth O’Gorman, Katie Bishop, Hannah K. Jarman, Susan J. Paxton, Rachel F. Rodgers, An Dang, Emily A. Harris, Felicity Angelopoulos, Wesley Grey, Suku Sukunesan, Alexandra Parker, Adrian Shatte, Long Khanh-Dao Le, Scott Griffiths

Research outputs 2022 to 2026

Background: There is an unmet need for evidence-based and accessible interventions designed to treat eating disorders in sexual minoritized men, given their heightened vulnerability to developing disordered eating. Digital interventions are a promising avenue for eating disorder treatment because of their accessibility and flexibility in delivery. Here, we present a randomised controlled trial of Seed, a 10-week treatment-focussed digital intervention for eating disorders, among sexual minoritized men.

Methods: Participants (N = 126 sexual minoritized men) with a binge-spectrum eating disorder diagnosis (e.g., bulimia nervosa, binge-eating disorder) were randomly allocated to one of three conditions: (1) a waitlist control (2) a …

The Effects Of Eps Level And Presentation Format Of Analysts’ Forecast Deviation On Non-Professional Investors’ Investment Judgments, Clarence Goh, Prasart Jongjaroenkamol, Poh-Sun Seow

Research Collection School Of Accountancy

We experimentally investigate how the presentation format of the extent to which a firm's earnings per share (EPS) diverges from analysts' EPS forecasts (i.e. deviation information) and a firm's EPS level affect the investment judgments of non-professional investors (referred to hereafter as “investors”). Our results suggest that investors' investment judgments are more positive when firms with low (high) EPS levels disclose deviation information in percentage (absolute) terms. Furthermore, when the percentage of forecast deviation is held constant, investment judgments are more positive when EPS levels are high versus low if the deviation information is expressed in absolute terms. By contrast, …

Long Range Battery-Free Wireless Power Transfer Testbed For Underground Mines Iot And Lpwan Devices, Anabi Hilary Kelechi, Samuel Frimpong, Sanjay Madria

Mining Engineering Faculty Research & Creative Works

Underground mines are susceptible to occasional roof falls and cave-ins, temporarily destroying the existing wireless communications and telemetry infrastructure. During this temporary outage, intermittent provision of electrical energy wirelessly to the already deployed low-power wireless area networks (LPWAN) and Internet of Things (IoT) devices assumes a fundamental requirement. In this article, we propose and design a long-range far-field radio frequency (RF) wireless power transfer (WPT) testbed to power LPWAN and IoT devices at 35 m in an underground mines facility. Class AB external power amplifier (PA) was introduced to achieve a long-distance RF WPT, in the 880 MHz band. Thus, …

Evaluating The Impact Of Research Equipment Availability On Scientific Rigor, Reproducibility, And Innovation Across Stem Disciplines, Lyndi G. Hraban

Honors Program: Senior Projects (Public)

Research infrastructure plays a central role in shaping the quality and innovative capacity of scientific research. This study evaluates how the availability of research equipment influences scientific rigor, reproducibility, and innovation across STEM disciplines. Drawing on literature from science-to-science research, policy studies, and discipline-specific analyses, this project synthesizes existing scholarship to explore the relationship between access to infrastructure and key research outcomes. Findings across the literature indicate that access to advanced and well-maintained equipment can strengthen scientific rigor by enabling more precise measurements, improved experimental control, and the implementation of standardized research protocols. Shared and centralized facilities are also associated …

Evaluation Of A No-Code Ai Model For Detecting Periapical Radiolucencies: Impact Of Anatomical Region On Diagnostic Performance, Manal Hamdan, Kevin W. Yu, Marguerite Miller, Stephanie J. Sidow, Zaid Badr, Sergio E. Uribe

School of Dentistry Faculty Research and Publications

Background

To develop a no-code artificial intelligence (AI) model for the detection of apical radiolucent lesions and to assess how lesion location influences the model’s diagnostic performance.

Methods

312 periapical radiographs were retrospectively collected, each accompanied by a corresponding cone-beam computed tomography (CBCT) scan obtained within six months and a radiology report authored by board-certified oral and maxillofacial radiologists. These reports served as the reference standard, and all findings were cross-verified by the primary investigator through CBCT review. The dataset included 181 images with at least one apical radiolucent lesion and 131 lesion-free controls. Using the no-code AI platform LandingLens …

Methods For Tagging Whale Sharks: Insights Into Performance And Best Practices With A Focus On Clamp Attachments, Freya C. Womersley, Sofia Green, Alberto Garcia-Baciero, Ronan Conlon, Amy L. Jeffries, Matt J. Waller, Sara S. Ratão, Nuno Queiroz, Pedro Afonso, Gonzalo Araujo, Adam Barnett, Christine Barry, Michael L. Berumen, Farukhkha Bloch, Ginevra Boldrocchi, Camrin D. Braun, Ryan Caillouet, Constance Chapman, Jesse E.M. Cochran

Faculty Publications

Background: Biologging and telemetry have transformed our understanding of marine megafauna movement ecology. Yet, methodological constraints continue to limit data quality and deployment duration. Devices recording whale shark (Rhincodon typus) behaviours and movements have been used for decades, but they remain challenging to deploy and vary in success. Recently, spring-loaded clamp-based systems have emerged as one of the most widely used approaches to attach electronic tags to the fins of this globally endangered species. Currently, however, no consensus guidelines exist as to how to optimise this approach, potentially leading to continued underperforming deployments limiting analysis potential. Here, we synthesise experiences …

Critical Environmental Accounting And Accountability In Anti-Democratic Times: Agonistic Repoliticization And Critical Environmental Politics, Judy Brown, Jesse Dillard

Business Faculty Publications and Presentations

To foster and advance critical environmental accounting and accountability (CEAA) research, we argue there is a need to foreground the political dimensions of environmental issues by addressing plurality, conflict, contestability and power. Contemporary environmental accounting and accountability (EAA), especially as it is increasingly mainstreamed, often remains entrenched in allegedly apolitical technocratic-managerial and market logics. At the same time climate denialism and authoritarian neoliberalism intensify pressures against critical research. Drawing on critical environmental politics scholarship, we interpret these developments as part of broader depoliticizing and anti-democratic formations that narrow the questions EAA asks, how problems are represented, and the socio-ecological futures …

A Global High-Resolution Comprehensive Heat Indices Dataset From 1950 To 2024, Abdul Malik, Sateesh Masabathini, Mohsin Ahmed Shaikh, Qinqin Kong, Muhammad Usman, Dasari Hari Prasad, Ibrahim Hoteit

All Works

Heatwaves are becoming more intense and frequent as global temperatures rise, affecting vulnerable populations, particularly in low-income communities. Addressing the impacts of heatwaves requires high-resolution data to assess their influence on labour productivity, public health, and climate risk. We introduce the Comprehensive Heat Indices (CHI) dataset, a high-resolution (0.1° × 0.1°) hourly dataset from 1950 to 2024, derived from the ERA5 and ERA5-Land reanalyses. The CHI dataset encompasses thirteen heat stress indices, including wet-bulb temperature, universal thermal climate index, mean radiant temperature, wind chill, and lethal heat stress index (Ls). Thresholds for Ls are empirically linked to mortality, enabling the …

Quantifying Customer Sentiment For Automobile Brand Perception Analysis Using Machine Learning On Twitter, Sujith Samuel Mathew, Kadhim Hayawi, Neethu Venugopal, May El Barachi

All Works

Social networking sites provide a platform for individuals to express their opinions publicly. Brand managers actively use these platforms to gain insights into brand perceptions, as users often share their views on products and services. In this study, we use sentiment analysis to assess customer sentiment towards five leading automobile brands, analyzing text content shared on Twitter(or X). The research models the ’Brand Polarity Score’, which indicates whether customers perceive the brand positively or negatively. This score is further weighted based on the tweet’s influence, characterized by the engagement metrics of the tweet and the author’s follower count. We also …

Dblp: Noise Bridge Consistency Distillation For Efficient And Reliable Adversarial Purification, Chihan Huang, Belal Alsinglawi, Islam Al-Qudah

All Works

Recent advances in deep neural networks (DNNs) have led to remarkable success across a wide range of tasks. However, their susceptibility to adversarial perturbations remains a critical vulnerability. Existing diffusion-based adversarial purification methods often require intensive iterative denoising, severely limiting their practical deployment. In this paper, we propose Diffusion Bridge Distillation for Purification (DBLP), a novel and efficient diffusion-based framework for adversarial purification. Central to our approach is a new objective, noise bridge distillation, which constructs a principled alignment between the adversarial noise distribution and the clean data distribution within a latent consistency model (LCM). To further enhance semantic fidelity, …

X-Threat Framework For Adaptive And Explainable Deep Learning-Based Minority-Class And Zero-Day Cyber Threat Detection, Samina Naz Qaisarani, Sahar K. Badri, Asad Khattak, Daniyal M. Alghazzawi, Mohammed Yahya Alghamdi, Mona Alkhozae, Abeer Almakky, Rania M. Alhazmi, Muhammad Zubair Asghar

All Works

The dynamic nature of cyber threats—particularly minority-class and zero-day attacks—poses significant challenges to existing intrusion detection systems (IDS), which often lack adaptability, interpretability, and robustness. This paper presents X-THREAT, an adaptive and explainable deep learning framework designed to improve detection of rare and previously unseen cyber threats. Unlike prior approaches that treat detection, augmentation, and explainability separately, X-THREAT integrates these components within a unified pipeline. A hybrid VAE–GAN module enhances minority-class representation during training, while a CNN–BiLSTM–attention model performs threat detection, and a context-aware SHAP mechanism provides interpretable insights into model decisions. Experimental results on three benchmark datasets—CICIDS2017, UNSW-NB15, and …

Evolving Ai Integration In Complex Medical Decision-Making And Multidisciplinary Transplant Care: A Systematic Review Of Human-Ai Collaboration, Rachel L. Dzieran, Cihan H. Dagli, Robert J. Marley

Engineering Management and Systems Engineering Faculty Research & Creative Works

Purpose of Review: Artificial intelligence (AI) in healthcare has evolved dramatically from early expert systems, which were initially considered replacements for clinical judgment, to today's collaborative frameworks that aim to augment physician decision-making. This evolution is particularly crucial in domains such as transplant surgery, where decisions carry irreversible consequences and require the integration of complex, often ambiguous data. Drawing on peer-reviewed literature from 2019 to 2025, we conducted a systematic review that analyzed key elements distinguishing successful human-AI partnerships from those that fail. Recent Findings: The ideal balance incorporates human expertise into AI systems through weighted integration approaches, rather than …

The Airport And The City: Toward A Philosophy Of The Aerotropolis, Shane Ray Epting

Arts, Languages and Philosophy Faculty Research & Creative Works

As a city centered on its airport's vast influence, the aerotropolis's existence has an inherent tension: It is a stable city for people to stay put and dwell, with the notion of leaving at the foreground of urban life. As a conception, the aerotropolis requires investigation to reveal its significance for humankind and its place in an ontology of cities. When the aerotropolis is situated in such a manner, we see how it differs significantly from other kinds of cities throughout history that depend on travel, such as river and port cities. After unpacking these points, a view emerges illustrating …

Will Your Ai Turn Backers Away? Evidence From Kickstarter's Ai-Use Disclosure Policy, Sonika Singhal, Yeongin Kim, Seonjun Kang, Victoria Yoon

Business and Information Technology Faculty Research & Creative Works

Generative artificial intelligence (GenAI) is reshaping how creative work is produced, raising new questions about authenticity, originality, and human effort in entrepreneurial settings. We examine how platform-mandated AI-use disclosure is associated with crowdfunding outcomes, using Kickstarter's 2023 disclosure policy and a dataset of over 63,000 campaigns launched between 2022 and 2024. Drawing on signaling theory, we argue that an AI-use label functions as a platform-governed cue that backers may interpret as signaling reduced human effort, potentially shaping how they evaluate creators and campaigns. Empirically, we find that AI-use disclosure is associated with a significantly lower likelihood of campaign success, with …

Reduced Mediators Released By Cyanobacteria During Exoelectrogenesis Detected Using Differential Pulse Voltammetry, Laura T. Wey, Monica Brachi, Yagut Allahverdiyeva, Shelley D. Minteer

Chemistry Faculty Research & Creative Works

Cyanobacteria generate electrical current through exoelectrogenesis (extracellular electron transfer) downstream of photosynthesis, yet the identity of the endogenous redox mediator(s) responsible remains unresolved. Here, we apply differential pulse voltammetry (DPV) to detect and characterise redox-active species released by Synechocystis sp. PCC 6803 under illumination. To enable sensitive detection, we developed an electrolyte intermediate between BG11 (Blue-Green-11) growth medium and MOPS (3-(N-morpholino)propanesulfonic acid) buffer that minimises background electrochemical interference while maintaining short-term cellular functionality. DPV revealed multiple light-enhanced oxidation peaks (0.1–0.65 V vs. saturated calomel electrode (SCE)) that were not resolvable using cyclic voltammetry, providing evidence that cyanobacteria release reduced compounds …

Generative Ai Adoption And Solvers' Popularity On Supply-Driven Crowdsourcing Platforms: The Dual Role Of Price Signals, Zimeng Zhu, Carol Hsu, Fiona Fui-Hoon Nah, Na Liu

Research Collection School Of Computing and Information Systems

Purpose – We investigate the effect of solvers’ adoption of Generative AI (GenAI) on their popularity in a supply-driven crowdsourcing platform. We also examine the impact of price signals as well as their heterogeneous impact based on the solvers’ membership duration on the platform. Design/methodology/approach – Our analysis focuses on solvers who adopt GenAI for design-related gigs on the supply-driven crowdsourcing platform. By combining propensity score matching (PSM) with multi-period difference-in-differences (DID), we examine how GenAI adoption impacts solvers’ popularity and how price signals affect this main effect. Findings – Our findings reveal that solvers who adopt GenAI tend to …

Artificial Intelligence (Ai) For Social Innovation In Health Education: Promoting Health Literacy Through Personalized Ai-Driven Learning Tools – A Systematic Review, Dina Mansour Tbaishat, Maha Waleed Elfadel

All Works

Background: Artificial Intelligence (AI) is transforming health education by enabling personalized, adaptive, and scalable approaches that may enhance aspects of health literacy. Despite rapid adoption, comprehensive synthesis of AI tools’ impact on health literacy as social innovation is limited. Understanding these effects guides educators, developers, and policymakers in designing potentially effective, inclusive, and ethical AI interventions. This review examines generative AI models, chatbots, and adaptive learning systems in supporting health literacy globally. Methods: A systematic review was conducted following PRISMA guidelines. Literature was identified primarily through PubMed/Medline, Scopus, and ScienceDirect. Connectedpapers.com was used exclusively as a citation chasing tool, performing …

Radio Frequency Tagging–Enabled Patient Monitoring: Integrating Mobility Tracking With Early Warning Systems For Enhanced Safety, Ahed Abugabah, Prashant Kumar Shukla, Piyush Kumar Shukla, Abhishek Dwivedi

All Works

Ensuring patient safety in healthcare environments requires continuous monitoring systems capable of identifying early warning signs of clinical risk. Traditional surveillance methods often fail to capture meaningful patterns in patient movement, limiting their ability to prevent incidents such as falls, prolonged immobility, or unnoticed health deterioration. Radio Frequency Tagging technology has been increasingly adopted for real-time patient tracking; however, existing systems are generally limited to location detection and lack predictive insights into patient behaviour. To overcome these limitations, this study presents a Radio Frequency Tagging-based patient monitoring framework that integrates mobility tracking with an early warning mechanism to enable proactive …

The Association Between Cyber Behaviors And Hedonic And Eudaimonic Well-Being: The Moderating Role Of Personality Traits, Areej Elsayary, Juan Calmaestra, Mercedes Gómez-López

All Works

The increasing integration of digital technologies into everyday life has intensified engagement in various cyber behaviors, raising important questions about their relationship with individuals' well-being. This study examined the associations between online moral disengagement, problematic internet use (compulsive internet use), benign and toxic disinhibition, cyberaggression, and cybervictimization with both hedonic and eudaimonic well-being among emerging adults in the United Arab Emirates. Furthermore, it explores the moderating role of personality traits in these associations. Data were collected from 671 emerging adults (46.8% women) aged 18–29 years (M = 22.56; SD = 3.04). Results showed that cybervictimization and cyberaggression exhibited the strongest …

Understanding Chatbot-Assisted Collaborative Learning Among Female Undergraduate Students, Mohammad Amin Kuhail, Ahmed Shuhaiber, Sinan Salman, Nazik Alturki

All Works

Computer programming can be daunting for beginners due to complex concepts and syntax. Traditional teaching methods, while engaging through gamification and active learning, often lack personalized approaches. Recent advancements in artificial intelligence (AI), particularly large language models (LLMs), present new possibilities for personalized and interactive learning environments. This study introduces a chatbot-assisted collaborative learning environment (CCLE) that leverages an LLM (GPT-4) to enhance collaborative programming education. The CCLE enables real-time guidance and collaboration through natural language interactions, allowing students to work together on programming tasks, edit code collaboratively, and engage with both peers and the educational chatbot. We conducted an …

Engineering Sulfur Concrete Mixes: A Data-Driven Approach To Aggregate Packing And Binder Efficiency, Vasilia Al Khaldi, Abdel Hamid I. Mourad, Maisa El Gamal

All Works

Processing window (mass loss < 3%). A first-order, order-of-magnitude carbon-footprint estimate—presented with explicit caveats rather than as a full life-cycle assessment—indicates that replacing Portland cement with by-product sulfur is the dominant contributor to a substantial reduction in embodied CO₂ per cubic metre relative to equivalent-strength Portland cement concrete. The valorization of steelmaking by-products aligns with circular economy principles, enabling high-performance, recyclable, and low-carbon construction materials without chemical binder modification.

An Advanced Healthcare System With An Automated Vit Model For Dermoscopic Skin Cancer Identification, Ahed Abugabah, Prashant Kumar Shukla, Suchi Mishra, Abhishek Dwivedi

All Works

Early and reliable diagnosis of skin cancer from dermoscopic images remains challenging due to class imbalance, subtle inter-class variations, lesion boundary ambiguity, and illumination inconsistency, which can degrade the robustness of conventional convolutional neural networks (CNNs). To address these limitations, this study proposes an automated smart healthcare framework for dermoscopic skin cancer diagnosis using an Enhanced Vision Transformer (E-ViT) that improves global-context modeling through self-attention while strengthening fine-grained lesion representation learning. Unlike standard ViT configurations, the proposed architecture integrates multi-scale patch embedding and attention refinement to better capture border irregularities and color–texture heterogeneity that are critical for melanoma discrimination. Furthermore, …

Fair And Explainable Educational Recommendations With A Hybrid Graph-Gru Framework, Edmund Evangelista, Syed M.Salman Bukhari

All Works

Artificial Intelligence (AI) recommender systems are increasingly used in education to personalize learning and help students navigate large collections of digital learning resources. However, many existing approaches emphasize predictive accuracy over fairness, robustness, diversity, and transparency. This creates an important educational challenge. The students with limited participation histories may receive less reliable support, while highly popular resources may dominate recommendation lists and limit access to other useful learning materials. To address this challenge, this study aims to develop and evaluate a responsible educational recommender framework that supports personalized learning resource navigation while making recommendation behavior more fair, stable, diverse, and …

Large Language Models In Nlp: Evolution, Architectural Trends, And Open Challenges, Haseeb Javed, Babar Shah, Farman Ali, Daehan Kwak

All Works

The rise of Large Language Models (LLMs) has transformed how Natural Language Processing (NLP) and its subdomains are approached. Recent technological advancements have driven this transformation. This study offers researchers a detailed overview of LLMs, comparing them with traditional rule-based systems, statistical techniques, machine learning, neural networks, and the rise of transformer-based architectures. From a wider perspective, language models such as GPT, BERT, T5, PaLM, and LLaMA have facilitated the transformation of entire sectors, including healthcare and business, due to their highly scalable nature. Despite their wide range of applications, LLMs face numerous challenges, such as output biases, limited interpretability, …

Anticipatory Governance And Leadership For Ai Implementation In Higher Education: A Scoping Review, Sandra Baroudi

All Works

As the education sector attempts to address rapid changes caused by Artificial Intelligence (AI), it becomes crucial to examine approaches to leadership at the organizational and system levels. This scoping review explores how anticipatory models of governance are conceptualised and operationalised globally within higher education settings in the context of AI-related transformations. Using a scoping review design, academic and grey literature published between 2020 and 2025 was searched across Scopus, EBSCOhost, ERIC, Google Scholar, and ProQuest. Nineteen sources were selected based on clear inclusion and exclusion criteria and analysed thematically. Findings revealed that while AI offers promising opportunities to transform …

Microbial Treatment Of Refinery Wastewater Enables Fluted Pumpkin Growth And Food Safety, Eziafakaego M. Ibo, Odera R. Umeh, Oju R. Ibor, Chukwunonso P. Okoli, Azubuike V. Chukwuka, Emmanuel D. Omogbemi, Michael U. Orji, Aina O. Adeogun

Montclair State University Scholarship & Creative Works

Global water scarcity necessitates the reuse of agricultural wastewater, but safety concerns persist about contaminant transfer to plants. This study examined how treated and untreated petroleum refinery wastewater affected the growth and development of fluted pumpkins over a six-week cultivation period. The novelty lies in our comprehensive assessment of both growth performance and contaminant bioaccumulation in fluted pumpkins. The experimental design planted fluted pumpkin seeds in soils irrigated with 100% treated wastewater, untreated wastewater, or tap water (control). Weekly growth indicators (leaf count and height) and post-harvest parameters (root length, chlorophyll concentration, fresh and dry weight) were measured, and heavy …