Digital Commons Network^™

Prioritising Follow-Up For People With Suspected Epilepsy Using A Digital Eeg Biomarker, Rohit Shankar, John Terry

Peninsula Medical School

Lengthy waits for follow-up testing are common for people with suspected epilepsy. This delays diagnosis, prolongs uncertainty and increases seizure risk. Initial EEGs are frequently inconclusive, yet follow-ups are often dictated by referral date, and there is no established method for risk-based prioritisation. Here, we tested whether an established digital EEG biomarker could help prioritise those most likely to have epilepsy for expedited follow-up EEG testing. We analysed 196 normal non-contributory (non-diagnostic) initial EEGs collected from six National Health Service (NHS) sites in England. From these recordings, we extracted eight previously validated computational features that quantify the likelihood that the …

Sounding W. B. Yeats’S “Cuchulain Comforted”, David Sergeant

School of Law, Humanities and Social Sciences

No abstract provided.

Integrated Biotechnological Strategies For Planetary Health And Ecosystem Restoration, Duygu T. Yildirim, A. Baki Yildirim, Donald Martin, Tommaso Beccari, Oscar Vicente, Iza Radecka, Fideline Tchuenbou-Magaia, Robert Marks, Ratnesh Lal, Satya Prakash, Adam Mechler, Mario Petrov Milkov, Svetlana Fotkova Georgieva, Ilia Iliev, Radka Kaneva, Nuriye Gokce, Galina Aleksieva Yaneva, Nadya Vasileva Agova, Nikoleta Dobromirova Ivanova, Izem Olcay Sahin, Mariya Kiryakova Tsvetkova, Ivelin Rosenov Iliev, Michel Salzet, Kisung Ko, Michele Maffia, Matteo Bertelli, Isabelle Fournier, Lejla Pojskic, Qun Sun, Lembit Nei, Reynir Armgrisson

Research Outputs: 2025-Present

The escalation of ecological degradation, marked by climatic instability, precipitous biodiversity loss, and systemic chemical infiltration, necessitates a shift toward integrated biotechnological intervention. This analysis considers how the trajectory of diverse advanced methodologies, including environmental DNA (eDNA) surveillance, CRISPR-based genetic restitution, and synthetic biology-driven bioremediation, might be synthesized into a unified Planetary Health Framework. By exploring the convergence of high-precision genomic engineering, AI-augmented bioinformatic modeling, and the tenets of a circular bio-economy, we examine the capacity of technological intervention to ameliorate ecological stressors and restore critical ecosystems. Moving beyond the constraints of conventional conservation, this discourse explores frontier solutions in …

Corrigendum To “Advanced Techniques In Quartz Wafer Precision Processing: Stealth Dicing Based On Filament-Induced Laser Machining” [Opt. Laser Technol. 171 (2024) 110474] (Optics And Laser Technology (2024) 171, (S0030399223013671), (10.1016/J.Optlastec.2023.110474)), Yun Wang, Yutang Dai, Farhan Mumtaz, Kaiyan Luo

Electrical and Computer Engineering Faculty Research & Creative Works

The authors regret, that the affiliation for author Yun Wang was incomplete. To accurately reflect both the author's academic affiliation and the research platform where the work was conducted. The correct affiliation for Yun Wang is updated as above. The authors would like to apologize for any inconvenience caused.

Portrait Shadow Removal Via Self-Exemplar Illumination Equalization, Qian Huang, Cheng Xu, Guiqing Li, Ziheng Wu, Shengxin Liu, Shengfeng He

Research Collection School Of Computing and Information Systems

We introduce the Self-Exemplar Illumination Equalization Network, designed specifically for effective portrait shadow removal. The core idea of our method is that partially shadowed portraits can find ideal exemplars within their non-shadowed facial regions. Rather than directly fusing two distinct classes of facial features, our approach utilizes non-shadowed regions as an illumination indicator to equalize the shadowed regions, generating deshadowed results without boundary-merging artifacts. Our network comprises cascaded Self-Exemplar Illumination Equalization Blocks (SExmBlock), each containing two modules: a self-exemplar feature matching module and a feature-level illumination rectification module. The former identifies and applies internal illumination exemplars to shadowed areas, producing …

Patchgpt: Multi-Agent Patch Backporting Without Model Fine-Tuning, Ye Liu, Ruidong Han, Chengyan Ma, Yuqing Niu, David Lo

Research Collection School Of Computing and Information Systems

Patch backporting is crucial and prevalent in the maintenance of modern open-source software such as Linux kernels and forked repositories. However, porting patches across program versions remains a challenging problem due to the complexity of synergizing diverse patches with divergent program versions. In this paper, we propose PatchGPT, an agentic patch backporting framework for fine-grained patch generation. PatchGPT encompasses three agents: Miner for decomposing a sequence of atomic change steps as the original patch plan, Adapter for adapting the patch plan, and Executor for executing the adapted patch plan according to predefined change semantics. We conduct experiments on the PPatHF’s …

Understanding Codebase Like A Professional! Human-Ai Collaboration For Code Comprehension, Jie Gao, Yue Xue, Xiaofei Xie, Junming Cao, Soemin Thant, Erika Lee, Bowen Xu

Research Collection School Of Computing and Information Systems

Understanding an unfamiliar codebase is an essential task for developers in various scenarios, such as during the onboarding process. Especially when the codebase is large and time is limited, achieving a decent level of comprehension remains challenging for both experienced and novice developers, even with the assistance of large language models (LLMs). Existing studies have shown that LLMs often fail to support users in understanding code structures or to provide user-centered, adaptive, and dynamic assistance in real-world settings.To address this, we propose learning from the perspective of a unique role, code auditors, whose work often requires them to quickly familiarize …

Fully Decentralized Hierarchical Federated Learning At The Edge With Post-Quantum Secure Communication, Tariq Qayyum

Dissertations

Federated learning (FL) enables collaborative model training without centralizing raw data, but deploying FL at scale in real edge environments remains challenging because iterative training and aggregation must operate over heterogeneous, resource-constrained, and often mobile devices with time-varying connectivity. Conventional hierarchical federated learning (HFL) partially mitigates communication cost by introducing fog/edge aggregation, yet many designs retain cloud-based global aggregation and cloud-centric coordination. This places wide-area network latency on the critical path of every training round, creates a single point of failure, and limits responsiveness as model sizes and federation scale grow. Moreover, moving coordination and aggregation closer to the edge …

Traditional And Machine-Learning Equalization Techniques For Bandwidth-Limited Short-Reach Optical Communication Channels, Abdullah Khawatmi

Theses

This thesis investigates equalization techniques for bandwidth-limited short-reach optical communication systems, with a focus on Visible Light Communication (VLC) and Step-Index Plastic Optical Fiber (SI-POF) links. Commercial light-emitting diodes and photodiode receivers impose severe bandwidth constraints, inter-symbol interference, and noise sensitivity, which fundamentally limit achievable data rates. The work addresses these impairments through systematic evaluation of traditional digital signal processing–based equalizers and modern machine-learning-based post-equalization methods. The primary aim of this thesis is to enhance the achievable data rate and reliability of commercial short-reach optical links while maintaining practical computational complexity. Specifically, the objectives are to (i) design and experimentally …

A Computational Assessment Of Halobacterium Salinarum Glutamate Dehydrogenase Enzymes, Maaz Abdalla

Theses

Glutamate dehydrogenase (GDH) is a hexameric enzyme. GDH is involved in several pathways and cellular processes such as oxidation-reduction homeostasis, ammonia metabolism, lipid biosynthesis, insulin and lactate production, and acid-base equilibrium. The main objective of this thesis is to understand the structural and biochemical properties of this enzyme and why some organisms, for e.g., Halobacterium salinarum, have more than one GDH with different coenzyme specificities. The catabolism of glutamate is linked to NAD+ specific GDHs, meanwhile, NADP+ specific GDHs play an anabolic role in ammonia assimilation. Molecular docking and binding free energy calculations were employed followed by long-scale (500 nanoseconds) …

Intelligent Deep Learning-Based Sign Language Translation System, Nada Rasem Shahin

Dissertations

The Deaf and Hard of Hearing (DHH) community uses sign language as a primary means of communication. However, the shortage of sign language interpreters and the existence of hundreds of sign languages limit accessibility and inclusion. Sign Language Machine Translation (SLMT) systems present a promising solution for bridging the communication gap between the DHH and the hearing individuals, supporting inclusive societies. In smart cities, such systems play an essential role in improving the quality of life on a community level. In particular, as the population’s well-being is critical, developing intelligent assistive technologies, such as SLMT systems, is necessary to provide …

Utilizing And Optimizing Forecasting Models For Nursing Demand: A Narrative Review, Kalpana Singh, Liyan Ajit D. Souza, Ananth Nazarene, Amane Mounia, Mahmoud Abdelwahab Khedr, Ahmad Mousa, Rebecca Rejo George, Abdulqadir J. Nahwan

Research outputs 2022 to 2026

Background: Accurately forecasting nursing demand is essential for effective workforce planning in the context of increasing patient volumes and the growing complexity of healthcare systems. Reliable forecasting supports optimal staffing, enhances patient care quality, and reduces operational risks. However, traditional forecasting approaches are increasingly challenged by evolving clinical demands, rising chronic disease burden, and rapid technological advancements. Aim: This narrative review aimed to (1) map existing models used for predicting nursing demand, (2) identify key predictive methodologies, data inputs, and evaluation metrics, and (3) highlight existing gaps and implications for healthcare policy and workforce management. Methods: A comprehensive narrative literature …

Abca4 Versus Prph2-Associated Retinopathy: Clinical And Electrophysiological Findings, Rachael C. Heath Jeffery, A. Thompson, Johnny Lo, Andrea L. Vincent, Minal Patil, Lorenzo Bianco, Maurizio Battaglia Parodi, Lucia Ziccardi, Carmen Dell’Aquila, Lucilla Barbano, Wei Chao Tang, Choi Mun Chan, Camiel J.F. Boon, Jonathan Hensman, Ta Ching Chen, Chien Yu Lin, Pei Lung Chen, Ajoy Vincent, Anupreet Tumber, Elise Heon, John R. Grigg, Robyn V. Jamieson, Elisa E. Cornish, Benjamin N. Nash, Jeremy Chou, Tina M. Lamey

Research outputs 2022 to 2026

Purpose. The purpose of this study was to characterize the clinical features, electrophysiology, and variant spectrum in ABCA4- and PRPH2-retinopathies and to identify novel electrodiagnostic biomarkers to differentiate between these two genotypes. Methods. We conducted an international multicenter case-control study of patients with a clinical and genetic diagnosis of ABCA4- or PRPH2-retinopathy. Age at symptom onset, best-corrected visual acuity (BCVA), electroretinography (ERG) components, and fundus autofluorescence (FAF) imaging were compared. Subgroup exploratory analysis was performed on those patients with a phenotype similar to central areolar choroidal dystrophy (“CACD-like”), those with flecks distributed throughout the posterior pole (“fleck-like”) and a healthy …

Turner V. Jp Morgan Chase Bank National Association

All Decisions

In this illegal lockout proceeding, the Civil Court of the City of New York, Kings County, following an inquest due to the landlord-respondents' non-appearance, awarded a final judgment of possession to the tenant-petitioner. The court found that the tenant was unlawfully deprived of possession when the apartment doors were sealed. The landlord was ordered to provide the tenant with a key and remove structures barring entry, allowing access to belongings. Full possession is contingent upon the lifting of an HPD vacate order, which was issued due to the landlord's failure to maintain essential services like heat and hot water.

The Anchored: Issue 3, Spring 2026, Writer's Club Of The Hope-Western Prison Education Program

The Anchored

The Anchored is a quarterly publication of the Writer’s Club of the Hope-Western Prison Education Program.

Uncovering Ethical Values In Information Systems Development Methodologies, Zahidah Zulkifli, Noor Hayani Abd Rahim, Tahmid Alam, Fakhrul Hazman Yusoff, Mohammad Masudur Rahman

Malaysian Journal of Computing (MJoC)

Information system development involves a lot of tools, infrastructures, and humans. The many phases in system development need humans to interact and work with each other to obtain the goal: to complete information system development. These interactions of humans require the right ethics to ensure that the information system development can be successful. For example, ethical leadership is a vital component in determining the success of any project related to information system development in organizations. The aim of this study is to identify the ethical values which are important in information system development methodology phases. This systematic literature review (SLR) …

Addressing Class And Demographic Imbalance In E-Commerce Behavior Prediction: A Case Study Using Resampling Techniques, Nurul Ain Mustakim, Maslina Abdul Aziz, Shuzlina Abdul Rahman, Rahmiati Rahmiati

Malaysian Journal of Computing (MJoC)

In e-commerce predictive modeling, imbalanced data remains a critical challenge, particularly when both class labels and demographic attributes are unequally distributed. This study investigates a combined approach of Synthetic Minority Oversampling Technique (SMOTE) and demographic resampling to improve the performance of models predicting online purchasing behavior in Malaysia. Using a dataset of 1,126 survey responses, six classifiers (J48, Random Tree, REPTree, JRip, PART, and OneR) were evaluated under three conditions: unbalanced, after SMOTE, and after SMOTE with demographic balancing. The results displayed clear improvements in model performance. For example, J48’s accuracy increased from 62.85% (unbalanced) to 98.69% (fully balanced), while …

Textual Adversarial Example Generation Using Bigram Unigram-Semantic Preservation Optimization Algorithm, Noor Adam Noor Azmi, Haslizatul Fairuz Mohamed Hanum

Malaysian Journal of Computing (MJoC)

The vulnerability of Natural Language Processing (NLP) models to adversarial attacks remains a critical challenge in the field of cybersecurity and AI robustness. While deep learning models have achieved high performance in sentiment analysis, they are susceptible to subtle input perturbations that induce misclassification. This study presents the design and practical implementation of a web-based system (Proof of Concept) that automates the generation of textual adversarial examples using the Bigram Unigram-Semantic Preservation Optimization (BU-SPOF) algorithm. Rather than proposing a novel attack algorithm, our primary contribution is the architectural integration of a dual-source candidate generation strategy (WordNet and OpenHowNet) and a …

Commentary: If You’Re Unhappy At Work, A Hasty Job Change May Not Help, Nick Chiam

Research Collection Yong Pung How School Of Law

Finding joy at work isn’t about having the perfect job, but seeing work as part of the larger story of a life well lived, says SMU lecturer Nick Chiam.

Exploring The Effect Of Counterstereotype And Perspective Taking In Reducing Bias And Promoting Empathy In Police, Sydney C. Timmer-Murillo, Keara Kangas, Nakia Gordon

Psychology Faculty Research and Publications

Racism in policing continues to be a serious issue in the United States (U.S.). While most efforts to reduce racism, focus on changing implicit (i.e., nonconscious) racial biases, other factors may also influence behavior. Intergroup emotion theory posits that emotions, especially empathy, may play a powerful role in changing behavior. In this study, using community-based participatory research design we created and tested a virtual reality (VR) experience designed to reduce implicit racial bias through perspective taking and counter-stereotypical information. Fifty-six police officers from a large city took part in the study. They completed questionnaires and an implicit association task (IAT) …

Chimes: April 1, 2026, Calvin University

Chimes

Kelvin president to be replaced with AI, sources say by I Am Choking

Rodrick Heffley selected as Kelvin's next provost by Alex De Old

Kelvin announces transition to subscription model by June B. Under

Less-Than-Creative Dining explains the disappearance of meat week by Zachiachus Kolonel

Kelvin adds artificial intelligence department, cuts engineering and business programs by Henry B. Len

Student workers at Peter's to be replaced with campus squirrels by Inca Left-Nonbinary

Multiscale Effects Of Landscape Structure, Biodiversity, And Chronic Physiological Stress In Atlantic Forest Small Mammals, Lizette Arroyo

Theses and Dissertations from DePaul University

Habitat loss and fragmentation are major drivers of biodiversity decline and can alter the physiological condition of wildlife through changes in habitat quality, resource availability, and community composition. The Atlantic Forest of South America is one of the world’s most threatened biodiversity hotspots. This study examined relationships among biodiversity, landscape structure, and chronic stress in nonvolant small mammals within the Reserva Natural Tapytá in eastern Paraguay using hair glucocorticoid concentrations as indicators of long-term physiological stress. Small mammals were sampled from forest fragments representing multiple patch sizes between 2023 and 2025. Hair cortisol and corticosterone concentrations were quantified using enzyme …

Maintenance Insights For Power Transformers In Energy Networks, Saleh Hassan Al-Ali

Theses

This thesis examines machine learning approaches for predicting failures in electrical power distribution transformers, with the goal of helping utility operators intervene before outages occur. The dataset covers 16,000 distribution transformers operated by Compa ˜n´ıa Energ ´etica de Occidente (CEO), a Colombian utility serving 42 municipalities in the Cauca Department. Each transformer record includes geographic location, rated power capacity, self-protection features, ceramic insulation criticality levels, removable connector configurations, customer categories, user counts, estimated un-supplied energy, installation types, network topology, and secondary line lengths. Failure event histories were also included, which allowed the problem to be framed as a supervised binary …

Emote - A Modular Action Figure For Childhood Emotional Growth, Terrence Li

Theses

Emotional regulation and communication are one of the most important skills that we can learn. This skill allows us not only to recognize and effectively communicate our feelings to others but also allows us to recognize them in others. Although learning and recognizing these emotions may be a pursuit in which progress varies from person to person, this skill is especially invaluable to young children. Beginning as early as the age of 3, many children begin to show early awareness of their own emotions, such as reacting to discomfort or comfort, or starting to use words for feelings. This learning …

Twitter, Tiktok, And Terrorism Torts: Using Product Liability To Hold Social Media Companies Accountable For Their Enablement Of Terrorism, Bailey Wilson

Vanderbilt Law Review

Social media platforms have become powerful tools for terrorist organizations, allowing them to recruit and radicalize actors who incite violence. Yet victims of terrorist attacks have been unsuccessful in holding these platforms legally accountable. The Antiterrorism Act imposes a demanding proximate-cause standard, and Section 230 of the Communications Decency Act broadly shields platforms from liability as publishers of third-party content. Together, these doctrines create a nearly impenetrable barrier for plaintiffs seeking redress from companies that profit while their algorithms amplify terrorist propaganda and promote violence. This Note argues that product liability offers a path forward. Courts have increasingly recognized digital …

Giving Meaningful Effect To Victims’ Rights: The Canadian Victims Bill Of Rights As Quasi-Constitutional Legislation, Benjamin Perrin

All Faculty Publications

This research paper is the first detailed examination of the quasi-constitutional status of the Canadian Victims Bill of Rights ("CVBR") and the implications for its interpretation and application. It aims to provide a doctrinal account of this extraordinary legal recognition and to serve as a resource for scholars, judges, lawyers, and other justice system participants to ensure that the CVBR is interpreted and applied consistently with its aim of serving as a catalyst for transformative change to how the criminal justice system has historically treated victims.

The Effects Of A Financial Literacy-Based Classroom Economy Model On Student Behavior, Engagement, And Financial Literacy, Liahna Sauter

School of Education and Leadership Student Capstone Theses and Dissertations

The Effects of a Financial Literacy-Based Classroom Economy Model on Student Behavior, Engagement, and Financial Literacy by Liahna Sauter employed a mixed methods research approach and used surveys, assessments, frequency charts, finance reports, and observations to learn more about the financial literacy-based classroom economy model. Different classroom economy models, namely token economies, are used widely across the educational sphere; though, financial literacy-based approaches do not have well-documented data surrounding their effectiveness. The financial literacy model aims to instruct students on real-world financial literacy concepts while simulating a real-world economy and incentivising positive behavior. Influenced by financial literacy-based classroom economy models …

Towards Reliable And Trustworthy Deep Learning Through Explainability And Interpretability, Dipkamal Bhusal

Theses

Deep neural networks achieve state-of-the-art performance across many domains, yet their deployment in high-stakes settings is constrained by two challenges: opaque decision-making and vulnerability to adversarial manipulation. This thesis investigates explainability and interpretability as principled mechanisms for improving the reliability and trustworthiness of deep learning models. First, we develop new post-hoc explanation methods that improve feature attribution and concept-based explanations. These methods provide faithful decision cues by modeling meaningful feature interactions and extracting faithful coherent concepts, enabling more reliable understanding of why a model predicts a given label. Second, we show that explanation quality is not solely a property of …

How Policing Data Visualizations Affect Comprehension, Decision Confidence, And Perceptions Of Racial Disparities, Abeer Mustafa

Theses

Police departments use public dashboards to share use-of-force data for policymaking and public awareness, but it remains unclear how visualization formats affect how people interpret this information. This between-subjects study with 64 participants compares absolute use-of-force incident counts (Totals) and population-adjusted rates (Rates) across four United States cities. The research included a quantitative analysis of graph comprehension, policy prioritization, confidence ratings, and attitude change, as well as a qualitative examination of open-ended responses. Results showed a strong framing effect: those who viewed absolute numbers prioritized Aurora, Colorado (highest incidents) for policy intervention, often disregarding population baselines, while those viewing per-capita …

A Comparative Study Of Inference-Time Scaling Strategies For Large Language Models, Oluwamayowa Owolabi

Theses

Large language models (LLMs) have demonstrated strong performance on a range of reasoning tasks, however, their reliability often depends not only on model size or training data, but also on inference-time strategies. However, existing inference-time methods are typically evaluated in isolation and under differing experimental assumptions, making it difficult to draw systematic conclusions about their relative effectiveness. This thesis proposes a controlled empirical study of inference-time scaling strategies for large language models under fixed inference-time compute budgets. The findings reveal that no single strategy dominates uniformly. PRM guided selection with the IBM Granite verifier achieves the highest absolute accuracy across …