Digital Commons Network^™

Design And Tissue Engineering Applications Of Microporous Annealed Particle (Map) Scaffolds, Nic D. Leipzig, Oluwaseyi Shofolawe-Bakare

University Research

Microporous annealed particle (MAP) scaffolds are emerging as a facile, yet sophisticated biomaterial platform with tremendous potential for tissue regenerative applications. These scaffolds are fabricated in situ through interparticle crosslinking of microgels using a variety of covalent or non-covalent techniques and possess excellent tunability and biocompatibility. The microporous structure of MAP scaffolds allows cellular infiltration and vascularization independent of degradation rate, making them more advantageous than traditional nanoporous bulk hydrogels. Moreover, the incorporation of in situ crosslinking allows MAP scaffolds to retain the key advantages of traditional microgel-based scaffolds—such as injectability and modularity—while providing the added benefit of a fixed …

Identification Of Microplastics And Additives From The Lake Erie Watershed And The Cuyahoga River Via Maldi-Ms And Dart-Ms, Chrys Wesdemiotis, Calum Bochenek, Luciana Rivera Molina, Robert Brand

University Research

Micro(nano)plastics have received increased attention as environmental contaminants due to their harmful effects on ecosystems and human health. Conventional extraction methods for microplastic analysis are often lengthy and complicated, resulting in the loss of important chemical components such as additives. In this study, we provide a complementary approach, utilizing matrix-assisted laser desorption/ionization (MALDI) and direct analysis in real time (DART) mass spectrometry (MS) techniques, with minimal extraction and sample preparation to preserve both polymer and additive information. MALDI-MS was applied to aqueous samples collected along the shores of Lake Erie and the Cuyahoga River, successfully identifying the base polymer(s) in …

No Name Ditch Filter Request, Chad Pritchard, Sonja Durr

EWU PFAS Research Collection

This is a one-page request showing the need for filtration at No Name Ditch at the SE corner of Fairchild Air Force Base (FAFB), West Plains, Spokane County, Washington.  Surface water levels in the ditch and stormwater ponds that feed the system can exceed 20,000 ppt for PFOS while groundwater can be up to 2,000 ppt for PFOS, per a remedial investigation report for FAFB in 2025.  The federal drinking water standard for PFOS is 4 ppt. While surface water and stormwater are not regulated for PFAS levels, these polluted waters enter the groundwater just off base and likely contaminate …

Watersmart Farms - Desalination: Summary Report, Wendell Ela, Abdulla Rasheed, Sharmina Begum, Vaibhav Patel, Goruck Soubans, John K. Ruprecht, V Ravisankar, S Aktar, B Husbands, L Li

Natural resources commissioned reports

In 2026, Murdoch University completed a series of investigations into farm and community scale desalination in the Wheatbelt of Western Australia for the Department of Primary Industries and Regional Development (DPIRD) and Water Corporation. The key studies investigated farm scale desalination, community scale desalination, best practice techniques for desalination in agriculture and brine management review.

The key findings of the studies are presented here, followed by an overview of the considerations concerning the potential for desalination across the Wheatbelt. Readers are referred to the Technical Reports for more detailed information.

This report is an overview of the following reports:

Ela, …

Trust Transference And Cognitive Affective Trust Dichotomy In Tiktok Short-Form Videos: The Moderating Role Of Source Homophily, Rajat Subhra Chatterjee, Mosharrof Hosen, Irfan Hameed, Hafiz Mudassir Rehman

Plymouth Business School

This study explores the under-researched area of cognitive-affective trust transference within the context of short-form videos (SFV) on the TikTok platform. It also provides new insights into the impact of source homophily that arises from viewing SFV. The study incorporates trust transfer theory into the Stimulus-Organism-Response (SOR) model perspective to examine trust transference. Data were gathered from 411 online users in Malaysia using a purposive sampling technique. Partial least squares structural equation modeling has been used to analyze the data via SmartPLS. The findings suggest that video characteristics, platform trust, and source credibility of SFVs on TikTok affect affective trust …

Opencil: Benchmarking Out-Of-Distribution Detection In Class Incremental Learning, Wenjun Miao, Guansong Pang, Trong-Tung Nguyen, Ruohuan Fang, Jin Zheng, Xiao Bai

Research Collection School Of Computing and Information Systems

Class incremental learning (CIL) aims to learn a model that can not only incrementally accommodate new classes, but also maintain the learned knowledge of old classes. Out-of-distribution (OOD) detection in CIL is to retain this incremental learning ability, while being able to reject unknown samples that are drawn from different distributions of the learned classes. This capability is crucial to the safety of deploying CIL models in open worlds. However, despite remarkable advancements in the respective CIL and OOD detection, there lacks a systematic and large-scale benchmark to assess the capability of advanced CIL models in detecting OOD samples. To …

Comprehensively Evaluating The Perception Systems Of Autonomous Vehicles Against Hazards, Xiaodong Zhang, Jie Bao, Jianlei Chi, Jun Sun, Zijiang Yang

Research Collection School Of Computing and Information Systems

Perception systems are vital for the safety of autonomous driving. In complex autonomous driving scenarios, autonomous vehicles must overcome various natural hazards, such as heavy rain or raindrops on the camera lens. Therefore, it is essential to conduct comprehensive testing of the perception systems in autonomous vehicles against these hazards, as demanded by the regulatory agencies of many countries for human drivers. Since there are many hazard scenarios, each of which has multiple configurable parameters, the challenges are (1) how do we systematically and adequately test an autonomous vehicle against these hazard scenarios, with measurable outcome; and (2) how do …

Interpretable Machine Learning For Personalized Profiling Of Mild Cognitive Impairment From Daily Activities, Budhitama Subagdja, Ah-Hwee Tan, Kenneth Kwok, Iris Rawtaer

Research Collection School Of Computing and Information Systems

Continuous monitoring of individual daily activities is essential to detect mild cognitive impairment (MCI) wherein timely intervention can still be applied to prevent more severe mental decline. Recent approaches in predicting MCI are mostly considering digital biomarkers across individuals but often neglecting specific indicators from a single person over a long period of time. Making this personalized, dynamic, and highly noisy prediction model with irregular distribution of missing information to be explainable and actionable for clinical use, remains a challenge. This paper presents a study on a personalized MCI prediction and profiling from an in-home and mobile cognitive health monitoring …

Improving Credit Card Transaction Fraud Detection Using Cvqboosting, Bethel Hui Ting Loke, Nirvik Sahoo, Bingyan Guan, Minrui Xu, Dev Verma, Paul R. Griffin

Research Collection School Of Computing and Information Systems

This paper introduces a novel hybrid quantum-classical approach to credit card fraud detection using CVQBoost, a hybrid quantum-classical boosting algorithm executed on the photonic Dirac-3 processor from Quantum Computing Inc. (QCi). By integrating a diverse set of weak classifiers, which includes K-nearest neighbours (KNN), linear discriminant analysis, logistic regression, and XGBoost, within a hybrid quantum-classical ensemble, the proposed method demonstrates significant improvements over the latest published classical benchmarks. Experiments on a Kaggle credit card fraud dataset show that the quantum-enhanced model achieves a mean AUC-PR score of over 0.8, corresponding to an approximately 9% relative improvement over the best published …

A Knowledge Transfer-Based Membrane Evolutionary Algorithm For Solving Large-Scale Sorted Waste Collection Problem With Timeliness, Wenxue Zhang, Boquan Gao, Aldy Gunawan, Yunyun Niu, Jianhua Xiao

Research Collection School Of Computing and Information Systems

The sorted collection of municipal solid waste has emerged as an effective waste management strategy due to varying timeliness requirements across different waste types, giving rise to the critical research challenge of timeliness-based waste collection. While existing algorithms primarily focus on small-scale versions of this problem, solving large-scale timeliness-based waste collection problems remains particularly challenging. To tackle this issue, this paper proposes a knowledge transfer-based membrane evolutionary algorithm. Specifically, the original problem and simplified problem are constructed in different membranes respectively, and the knowledge transfer learning mechanism is incorporated into the membrane evolutionary algorithm, enabling effective information exchange between the …

Learning To Search For Vehicle Routing With Multiple Time Windows, Kuan Xu, Zhiguang Cao, Chenlong Zheng, Lindong Liu

Research Collection School Of Computing and Information Systems

In this study, we propose a reinforcement learning-based adaptive variable neighborhood search (RL-AVNS) method designed for effectively solving the Vehicle Routing Problem with Multiple Time Windows (VRPMTW). Unlike traditional adaptive approaches that rely solely on historical operator performance, our method integrates a reinforcement learning framework to dynamically select neighborhood operators based on real-time solution states and learned experience. We introduce a fitness metric that quantifies customers’ temporal flexibility to improve the shaking phase, and employ a transformer-based neural policy network to intelligently guide operator selection during the local search. Extensive computational experiments are conducted on realistic scenarios derived from the …

Addressing Graph Heterogeneity And Heterophily From A Spectral Perspective, Kangkang Lu, Yanhua Yu, Ruopei Guo, Nan Cheng, Zhiyong Huang, Yunshan Ma, Meiyu Liang, Yuling Wang, Xiting Qin, Yimeng Ren, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Graph Neural Networks (GNNs) face two key challenges, heterogeneity and heterophily, which often degrade performance. Existing approaches either focus narrowly on specific meta-paths, limiting their expressiveness, or are expressive but cannot effectively leverage higher-order neighbors. In this paper, we propose the Heterogeneous Heterophilic Spectral Graph Neural Network (H2SGNN), which combines local independent filtering to adaptively handle meta-path subgraphs with varying homophily ratios, and global hybrid filtering to capture high-order neighbor interactions with linear computational complexity. On five heterogeneous graph benchmarks—DBLP, ACM, IMDB, AMiner, and Yelp—H2SGNN consistently outperforms strong baselines, for example, achieving +1.0% Macro-F1 and +1.3% Micro-F1 on IMDB. It …

Mini-Course On Community Resources For Patients With Disabilities, Abigail Weisse, Vinita Kiluk, Kyle Lien, Nora Newcomb, Aishwarya Vuppala, Franklin Sun, Shannon Mccarthy, Anita Narkhede, Marjorie Fitzsimmons

USF-LVHN SELECT

No abstract provided.

Trends In Alcohol Withdrawal With Relation To Race/Ethnicity, Angela Nguyen, Alexandra Amaducci Do

USF-LVHN SELECT

No abstract provided.

Preoperative Weight Loss Among Morbidly Obese Patients Undergoing Total Knee Arthroplasty: It May Not Affect Outcomes, Kiran Kittur, Jordan H. Larson, Christopher Leibold, Spencer Neaville, Richard J. Rivera, Brian Palumbo

USF-LVHN SELECT

No abstract provided.

Edgeclip: Injecting Edge-Awareness Into Visual-Language Models For Zero-Shot Semantic Segmentation, Jiaxiang Fang, Shiqiang Ma, Guihua Duan, Fei Guo, Shengfeng He

Research Collection School Of Computing and Information Systems

Effective segmentation of unseen categories in zero-shot semantic segmentation is hindered by models’ limited ability to interpret edges in unfamiliar contexts. In this paper, we propose EdgeCLIP, which addresses this by integrating CLIP with explicit edge-awareness. Based on the premise that edge variation patterns are similar across both seen and unseen class objects, EdgeCLIP introduces the Contextual Edge Sensing module. This module accurately discerns and utilizes edge information, which is crucial in complex border areas where conventional models struggle. Further, our Text-Guided Dense Feature Matching strategy precisely aligns text encodings with corresponding visual edge features, effectively distinguishing them from background …

Diy Archives: Organize And Preserve Your Own Materials, Abbie Weiser Olivas, Ada Negraru

Projects

Zine with information about how to organize and preserve your own records. Based on a 2024 presentation for the Historical Society of New Mexico's annual meeting.

Navigating Ethical Considerations And Implications Of Ai Chatbots In Higher Education: A Systematic Review, Ons Al-Shamaileh, Ramy Hammady, Mahmoud Abdelrahman, Omar Mubin

All Works

This systematic review explores the ethical challenges associated with the use of AI-based chatbots in higher education, focusing on their implications for students, educators, institutions, and administrative stakeholders. Following PRISMA guidelines, peer-reviewed literature published between 2014 and 2024 was systematically identified across eight major academic databases, yielding a total of 109 eligible studies. A thematic analysis of the included literature indicates that concerns related to academic integrity are most frequently discussed, alongside recurring issues involving data privacy and security, algorithmic bias, overreliance on automated systems, and the risk of inaccurate or misleading outputs. The findings further demonstrate considerable variation in …

Neutrophil Gelatinase-Associated Lipocalin And Neutrophil-To-Lymphocyte Ratio As Indicators Of Nephropathy In Type 2 Diabetic Mellitus Patients In Ghana: A Case-Control Study, Allwell Adofo Ayirebi, Wina Ivy Ofori Boadu, Stephen Twumasi, Samuel Kwarteng, Benedict Sackey, Lilian Antwi Boateng, Joseph Boachie, Samuel Essien-Baidoo, Daniel Nii Martey Antonio, John Agyemang Sah, Afua Marfowaa, Mayfair Adwapa Mpiani, Joachim Baba Domosie, Abiba Khalifah, Emmanuel Ekow Korsah, Ebenezer Senu, Eugene Arele Ansah, Joseph Yorke, Enoch Odame Anto

Research outputs 2022 to 2026

Introduction: Serum neutrophil gelatinase-associated lipocalin (sNGAL), a renal tubular marker, and neutrophil-to-lymphocyte ratio (NLR), a hematological inflammatory marker are two biomarkers that have recently received attention, because of their association with kidney disease. This study examined the diagnostic value sNGAL and NLR in type 2 diabetes mellitus (T2DM) patients with nephropathy. Materials and Methods: In this hospital-based case-control research, 97 T2DM participants and 70 healthy subjects were included. Participants' information was documented using a structured questionnaire and patient case records. Venous blood was drawn from each participant and early morning midstream urine samples were collected for blood and urine measurements …

Graph Convolution Neural Network And Deep Q-Network Optimization-Based Intrusion Detection With Explainability Analysis, Kelvin Mwiga, Mussa Dida, Leandros Maglaras, Ahmad Mohsin, Helge Janicke, Iqbal H. Sarker

Research outputs 2022 to 2026

As networks expand in size and complexity, coupled with an exponential increase in intrusions on network and IoT systems, this leads to traditional models failing to capture increasingly intricate correlations among network components accurately. Graph Convolution Networks (GCNs) have recently acquired prominence for their capacity to represent nodes, edges, or entire graphs by aggregating information from adjacent nodes. However, the correlations between nodes and their neighbours, as well as related edges, differ. Assigning higher weights to nodes and edges with high similarity improves model accuracy and expressiveness. In this paper, we propose the GCN-DQN model, which integrates GCN with a …

Design, Testing, And Safety Performance Of Movable Guardrail Systems: A Prisma-Based Systematic Review, Navid Hashemi Taba, Ahdieh Sadat Khatavakhotan, Majid Tolouei-Rad

Research outputs 2022 to 2026

Movable guardrail systems are increasingly used in work zones, reversible lanes, and temporary traffic operations; however, evidence on their crashworthiness, material performance, and operational reliability remains dispersed across multiple design typologies and regulatory frameworks. This PRISMA-compliant systematic review synthesizes 78 studies involving full-scale crash tests, validated finite-element simulations, field performance evaluations, and compliance evaluations under MASH, EN 1317, NCHRP 350, and AS/NZS 3845.1. The findings indicate that modular rigid barriers reliably achieve TL-3/TL-4 performance when joint alignment and foundation conditions are properly controlled; semi-rigid steel systems provide a practical balance between containment capacity and redeployability, but remain sensitive to post …

Remedying Copyright For Indigenous Australians: A Stolen Generations Perspective, Elizabeth Serventy, Koutras

Research outputs 2022 to 2026

This study presents a Judicial Intersectionality method that incorporates knowledge from arts, humanities, natural sciences, and social sciences. It aims to examine how historical legal, political, and social injustices against Indigenous Australians have persisted in various spheres. When considering the Stolen Generations period, attention is directed on addressing the existing insufficient copyright rights and enhancing future legal results for Indigenous artists. In order to achieve that objective, a unique and distinct copyright system is regarded as the most suitable resolution. Philosophically and pragmatically, this approach was aligned with the Australian Government's 2023 Referendum, which aimed to grant constitutional status to …

Magnetically Driven Engineering Of Magnetic Hydroxyapatite/Ca Ro Membranes: Enhancing Surface Physicochemical Properties For High-Performance Water Desalination, Fariba Oulad, Ali Akbar Zinatizadeh, Sirus Zinadini, Amir Razmjou, Sara Eavani

Research outputs 2022 to 2026

This research focuses on the development of innovative hydrophilic reverse osmosis (RO) hydroxyapatite/cellulose acetate (HAP/CA) and magnetic-hydroxyapatite (M-HAP)/CA membranes, utilizing a phase-inversion technique with varying nanoparticles (NPs) concentration. The positioning of magneto-responsive M-HAP NPs within the advanced M-HAP/CA membranes was adeptly controlled using magnetic guidance during immersion- precipitation process in a coagulation bath. This precise control enabled a densely organized arrangement of M-HAP NPs, shifting from the membrane's core to its upper separation layer. Consequently, the barrier properties, mechanical integrity, and surface characteristics of the advanced M-HAP/CA membranes saw significant enhancement due to the uniform distribution of hydrophilic porous NPs …

A High-Fidelity Multimodal Synthetic Dataset Generation Framework For Off-Road Unstructured Terrain Navigation Training Of Autonomous Robots, Liyana Wijayathunga, Dulitha Dabare, Alexander Rassau, Douglas Chai, Syed Mohammed Shamsul Islam

Research outputs 2022 to 2026

The success of deep learning methods in a wide range of application areas has inspired many recent developments in the urban and off-road autonomous navigation domain. In particular, techniques for semantic scene understanding, a key aspect of the navigation pipeline, have been researched extensively, resulting in many real-world and synthetic datasets. However, in comparison to urban semantic segmentation datasets, the availability of datasets for off-road environments remains sparse. In this paper, we aim to overcome this challenge by introducing a methodology capable of efficiently generating photorealistic synthetic datasets for off-road environments with support for multiple sensor modalities. The developed approach …

An Explainable Transformer-Based Model For Phishing Email Detection: A Large Language Model Approach, Mohammad Amaz Uddin, Md Mahiuddin, Iqbal H. Sarker

Research outputs 2022 to 2026

Phishing email is a serious cyber threat that tries to deceive users by sending false emails with the intention of stealing confidential information or causing financial harm. Attackers, often posing as trustworthy entities, exploit technological advancements and sophistication to make the detection and prevention of phishing more challenging. Despite extensive academic research, phishing detection remains an ongoing and formidable challenge in the cybersecurity landscape. In this research paper, we present a fine-tuned transformer-based masked language model, RoBERTa (Robustly Optimized BERT Pretraining Approach), for phishing email detection. In the detection process, we employ a phishing email dataset and apply the preprocessing …

Explainable Artificial Intelligence Models For Detecting Suspicious Bank Transactions, Narasimha Kumar Narasapuram, Syed Afaq Ali Shah, Mohd Fairuz Shiratuddin, Ferdous Sohel

Research outputs 2022 to 2026

Detecting financial crime is a complex challenge due to evolving criminal strategies and fragmented detection systems, particularly in the areas of money laundering and fraud. While it is easy to implement, traditional rule-based approaches lack adaptability to new threats, and machine learning models, though more effective, often function as opaque "black boxes," limiting their practical use in regulated domains like banking, where interpretability and accountability are essential. This research presents a novel framework that combines intrinsic and post-hoc XAI techniques to detect suspicious bank transactions. Intrinsic methods provide model-inherent transparency, while post-hoc methods offer behavior-level explanations, enabling robust cross-verification of …

Pleural Metastases From Acral Lentiginous Melanoma Diagnosed By Medical Thoracoscopy, Ad Rian Chong, Khai Lip Ng, Nai-Chien Huan, Nur Husna Mohd Aminudin, Fazilah Hassan, Kasuma Mohamed Nordin

Research outputs 2022 to 2026

Acral lentiginous melanoma (ALM) with pleural metastases is rare and under-recognised. We report an 86-year-old woman with progressive dyspnoea and pleuritic chest pain. 18 months ago, she was diagnosed with stage IIC ALM of the right heel, and was treated with wide local excision, but she declined adjuvant therapy. Imaging demonstrated a large left pleural effusion with diffuse pleural thickening, pulmonary mass, and hepatic metastases. Brownish pleural fluid, along with thoracoscopic identification of multiple pigmented nodules, led to a histologically confirmed diagnosis. Talc pleurodesis was performed for symptom control, and best supportive care was pursued. This case highlights the diagnostic …

Skeletal Muscle Health In Childhood Cancer Survivors: A Systematic Review And Meta-Analysis, Anna Maria Markarian, Dennis R. Taaffe, Daniel A. Galvão, Carolyn J. Peddle-Mcintyre, Jodie Cochrane Wilkie, Francesco Bettariga, Nicholas G. Gottardo, Mayank Dhamija, Santosh Valvi, Catriona M. Buchanan, Kerrie Graham, Robert U. Newton

Research outputs 2022 to 2026

Purpose: Childhood cancer survivors (CCS) are at risk of long-term skeletal muscle deficits following intensive therapies during critical periods of growth. This review aimed to synthesize approaches for assessing muscle quantity, quality, and function in CCS and to quantify deficits relative to healthy peers. Methods: A systematic search was conducted in CINAHL, Embase, PubMed, SPORTDiscus, and Web of Science from inception to June 2024, with an update in November 2025. Studies including CCS who had completed cancer treatment and reported measures of muscle quantity, quality, or physical function were eligible. A three-level mixed-effects model meta-analysis was conducted. Associations between muscle …

Patient And Chondral Defect-Specific Characteristics And Associated Matrix-Induced Autologous Chondrocyte Implantation Conversion Rates Following Biopsy, Derrick M Knapik, Daniel C Touhey, S Ryan Pierson, Christopher K Bozorgmehr, Matthew V Smith, Matthew J Matava, Robert H Brophy

2020-Current year OA Pubs

BACKGROUND: Focal chondral defects in the knee lead to swelling, discomfort, and mechanical symptoms, resulting in disability and loss of function. Matrix-induced autologous chondrocyte implantation (MACI) has emerged as a viable treatment option, with satisfactory clinical outcomes reported compared with microfracture. Patient and lesion-specific variables associated with MACI after biopsy remain largely underreported.

PURPOSE: To examine patient- and defect-specific characteristics associated with conversion to MACI after biopsy from a single institution.

STUDY DESIGN: Cohort study; Level of evidence, 3.

METHODS: A retrospective chart review of prospectively collected data from June 2018 to March 2025 was performed. Inclusion criteria included patients …

Active Bone Marrow Dose Reconstruction In A Large-Scale Cohort Study Of Cancer Patients Treated With Photon Radiotherapy, Keith T Griffin, Kishan J Pithadia, Yeon Soo Yeom, Lior Braunstein, Kelly L Bolton, Lindsay M Morton, Choonsik Lee

2020-Current year OA Pubs

BACKGROUND: Previous studies have investigated the dose-response relationship between external beam radiotherapy (EBRT) and leukemia by reconstructing the mean active bone marrow (ABM) dose as part of the exposure assessment. However, no prior study has leveraged electronic medical records (EMR) to reconstruct ABM dose and dose-volume in an EBRT patient population.

PURPOSE: To support future studies on the relationship between radiation exposure and adverse health effects among EBRT patients, we demonstrate methods to retrospectively calculate ABM dose and dose-volume metrics in a large patient cohort using EMR.

METHODS: We retrieved complete EMR for 639 individuals (five pediatric and 634 adult) …