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2026

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Energy-Efficient Mixed-Criticality Multicore Systems, Fayyaz Ali, Saud Wasly, Amjad Ali, Shahid Iqbal, Asad Masood Khattak, Bashir Hayat, Shah Khalid Jan 2026

Energy-Efficient Mixed-Criticality Multicore Systems, Fayyaz Ali, Saud Wasly, Amjad Ali, Shahid Iqbal, Asad Masood Khattak, Bashir Hayat, Shah Khalid

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Balancing energy efficiency with stringent timing guarantees in real-time mixed-criticality systems (MCS) is a key challenge, especially in multicore architectures. This paper introduces a novel energy-aware scheduling framework that integrates dynamic voltage and frequency scaling (DVFS) with a Decreasing-Criticality-Decreasing-Utilization (DCDU) allocation approach. The optimal operating frequencies are obtained at each criticality level; high-criticality tasks are assigned to cores at full operating frequency to maintain timing guarantees, while low-criticality tasks are allocated using worst-case execution times scaled to their optimal frequency. A fixed-priority response-time analysis is used for schedulability in low mode, high mode, and during mode changes. The extensive simulations …

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Edge-Aware Ris-Assisted Dynamic Channel Allocation With Lightweight Llm Decision Agent For Interference Mitigation In Low-Altitude Remote Sensing Networks, Safiya Nasser Al-Jaradi, Mohammad Kamrul Hasan, Nabeel Al-Qirim, Shayla Islam, Muhammad Attique Khan, Rashid A. Saeed, Hashim Elshafie, Bishwajeet Kumar Pandey, Khairul Akram Zainol Ariffin Jan 2026

Edge-Aware Ris-Assisted Dynamic Channel Allocation With Lightweight Llm Decision Agent For Interference Mitigation In Low-Altitude Remote Sensing Networks, Safiya Nasser Al-Jaradi, Mohammad Kamrul Hasan, Nabeel Al-Qirim, Shayla Islam, Muhammad Attique Khan, Rashid A. Saeed, Hashim Elshafie, Bishwajeet Kumar Pandey, Khairul Akram Zainol Ariffin

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Low-altitude remote sensing networks are increasingly important for applications, such as environmental monitoring, disaster response, infrastructure inspection, and real-time sensing services. However, when many sensing nodes share limited spectrum resources, severe cochannel interference can degrade communication reliability and delay sensing-data delivery. This challenge becomes more critical in edge-enabled deployments, where control decisions must be made under strict latency, memory, and computational constraints. To address this issue, this article proposes a large language model (LLM)-enhanced edge-aware lightweight reconfigurable intelligent surface (RIS)-assisted dynamic channel allocation (EL-RIS-DCA) framework for interference mitigation in dense low-altitude remote sensing networks. The novelty of the proposed framework …

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Lightweight Tinyml-Enhanced Task Offloading In Vanets For Next-Generation Intelligent Transportation Systems, Muhammad Ali, Tariq Qayyum, Asadullah Tariq, Zouheir Trabelsi, Irfan Ud Din, Shabir Ahmed Jan 2026

Lightweight Tinyml-Enhanced Task Offloading In Vanets For Next-Generation Intelligent Transportation Systems, Muhammad Ali, Tariq Qayyum, Asadullah Tariq, Zouheir Trabelsi, Irfan Ud Din, Shabir Ahmed

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Vehicular Ad Hoc Networks (VANETs) face resource constraints, high node mobility, and stringent latency requirements, especially in safety-critical applications such as collision avoidance, path planning, and emergency braking. Task offloading to nearby vehicles or Roadside Units (RSUs) mitigates local computational limits, but dynamic conditions, unreliable nodes, and rapid topology changes complicate dependable node selection. This paper proposes a Tiny Machine Learning (TinyML)-enhanced, credibility-based task offloading framework for real-time decision-making in vehicular networks. RSUs evaluate vehicle reliability through a three-component Credibility Assessment Module: a Task Assignment Component that distributes lightweight test tasks and filters unreliable nodes via TinyML inference; a Verification …

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A Preliminary Exploratory Assessment Of Chatgpt To Generating Stride Data Flow Diagrams, Hassan Alsayegh, Mohamed El-Attar Jan 2026

A Preliminary Exploratory Assessment Of Chatgpt To Generating Stride Data Flow Diagrams, Hassan Alsayegh, Mohamed El-Attar

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Threat modeling is a core activity in security-by-design practices, enabling early identification of architectural weaknesses before system implementation. The drawings used during STRIDE analysis are typically Data Flow Diagrams (DFDs), referred to as “STRIDE diagrams” in this paper. STRIDE diagrams provide a visual approach for categorizing security threats; however, constructing accurate STRIDE diagrams require experience and is often time-consuming. Recent advances in Large Language Models (LLMs), such as ChatGPT, raise important questions about their suitability for supporting structured security modeling tasks. This study presents a preliminary exploratory assessment of ChatGPT’s ability to generate, analyse, and iteratively refine STRIDE diagrams from …

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Conceptualizing A Model For Transforming Academic Search With Retrieval-Augmented Generation, Nikesh Narayanan Jan 2026

Conceptualizing A Model For Transforming Academic Search With Retrieval-Augmented Generation, Nikesh Narayanan

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This study conceptualizes the transformative potential of RetrievalAugmented Generation (RAG) in academic research, addressing limitations of traditional search methods reliant on keyword matching, Boolean logic, and metadata analysis. RAG combines precise data retrieval with the generative capabilities of advanced AI models, synthesizing dispersed information into coherent outputs. By bridging retrieval accuracy and contextual generation, RAG offers a framework for providing researchers with comprehensive, nuanced, and up-todate insights tailored to their queries. This article explores the integration of tools like ChatGPT and LangChain to outline a RAGbased system, illustrating its potential to enhance academic discovery by harmonizing natural language processing, semantic …

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Optimized Hybrid Beamforming For Ris-Assisted Multi-User Mimo In 6g Mmwave Networks: A Low-Complexity Approach To Spectral Efficiency And Interference Mitigation, Safiya Nasser Al-Jaradi, Mohammad Kamrul Hasan, Nabeel Al-Qirim, Shayla Islam, Thippa Reddy Gadekallu, Hana Mujlid, Hashim Elshafie, Rashid A. Saeed Jan 2026

Optimized Hybrid Beamforming For Ris-Assisted Multi-User Mimo In 6g Mmwave Networks: A Low-Complexity Approach To Spectral Efficiency And Interference Mitigation, Safiya Nasser Al-Jaradi, Mohammad Kamrul Hasan, Nabeel Al-Qirim, Shayla Islam, Thippa Reddy Gadekallu, Hana Mujlid, Hashim Elshafie, Rashid A. Saeed

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The joint optimization of hybrid beamforming and reconfigurable intelligent surface (RIS) phase shifts in multi-user millimeter-wave (mmWave) MIMO systems is a challenging problem, mainly due to high computational complexity and the lack of adaptive interference management. Existing approaches typically rely on fixed Zero-Forcing (ZF) or Maximum-Ratio Transmission (MRT) designs or require iterative optimization with high overhead, limiting their practical use in dense 6G environments. To overcome these challenges, this research proposes a RIS-Aided Adaptive Zero-Forcing and Maximum-Ratio Transmission Hybrid Precoding (RA-ZMHP) framework for 6G mmWave multi-user MIMO systems. The main novelty of the method lies in an adaptive ZF–MRT mixing …

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Integrative Machine Learning Of Genetic And Lifestyle Factors For Personalized Skin Health, Yassine Benachour, Lina Maloukh, Barbara Geusens Jan 2026

Integrative Machine Learning Of Genetic And Lifestyle Factors For Personalized Skin Health, Yassine Benachour, Lina Maloukh, Barbara Geusens

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Objective: To develop an AI framework that combines genetic, phenotypic, and lifestyle data for profiling skin-health patterns and generating hypothesis-supporting summaries for potential decision support. Methods and procedures: A dataset of 5,254 individuals integrates six genes (FLG, AQP3, MMP-1, MMP-3, SOD2, GPX), six phenotype severities, and 20+ lifestyle factors. Mutation burden and interactions are tested by ANOVA. K-modes clustering identifies four interpretable dermatological profiles within the cohort and is embedded in leakage-free nested cross-validation (train-only selection; test labels from training centroids). Subtypes are predicted from genetics plus lifestyle using an XGBoost (XGB) classifier; explainability uses gain, permutation importance, and SHAP …

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A Novel Bernstein Operational Matrix Approach For Tempered Fractional Differential Equations: Convergence And Stability Analysis, Jalal Al Hallak, Mohammed Alshbool, Ishak Hashim, Eddie Shahril Ismail, Shaher Momani Jan 2026

A Novel Bernstein Operational Matrix Approach For Tempered Fractional Differential Equations: Convergence And Stability Analysis, Jalal Al Hallak, Mohammed Alshbool, Ishak Hashim, Eddie Shahril Ismail, Shaher Momani

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Tempered fractional differential equations (TFDEs) incorporate exponential decay into fractional operators to account for truncated memory and semi-long-range dependence in a variety of applications, including anomalous diffusion, viscoelasticity, transport phenomena, geophysical processes, and financial dynamics. In this work, a tempered fractional Bernstein method (TFBM) was proposed for the numerical solution of TFDEs involving Caputo-type derivatives. The proposed formulation combined a Bernstein polynomial approximation with an analytic representation of the Caputo–tempered fractional derivative through operational matrices. On this basis, two collocation-based variants were developed, namely, a Chebyshev-type method (TFBM-C) and a Legendre-type method (TFBM-L). For the linear setting, a convergence analysis …

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Empirically Evaluating The Accessibility Of A Pon-Enable Feature Diagrams Notation By The Red-Green Colorblind Community, Mohamed El-Attar, Sarah Kohail, Rima Grati Jan 2026

Empirically Evaluating The Accessibility Of A Pon-Enable Feature Diagrams Notation By The Red-Green Colorblind Community, Mohamed El-Attar, Sarah Kohail, Rima Grati

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In 2016, an enhanced version of a feature diagram notation developed using the Physics of Notations (PoN) framework was introduced. Empirical evidence demonstrated that this revised notation was more cognitively effective than the original. However, the new notation relies on color, specifically red, which poses accessibility challenges for individuals with red–green color vision deficiency, as they cannot perceive the notation as originally intended. Consequently, the cognitive effectiveness of a red–green–deficient (RGD) version of the new notation relative to the original notation remained unknown. Although the PoN framework specifies several principles that may be satisfied with or without the use of …

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Leveraging Quantum Storage Mechanism For Digital Forensic Readiness Towards Smart City Security, Bashaer Aljeneibi, Richard Ikuesan Jan 2026

Leveraging Quantum Storage Mechanism For Digital Forensic Readiness Towards Smart City Security, Bashaer Aljeneibi, Richard Ikuesan

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The increasing digitization of urban infrastructure has introduced advanced efficiency and connectivity in smart cities while exposing them to sophisticated cybersecurity threats. This study explores how Quantum Storage Mechanisms (QSM) can be integrated with digital forensic readiness systems to enhance smart city security and incident response. Through a simulated environment, the research evaluates the effectiveness of QSM against three critical cyberattack scenarios: Distributed Denial of Service (DDoS), sensor spoofing, and supply chain firmware attacks. The findings reveal that QSM-enabled systems outperform traditional cybersecurity tools by ensuring tamper-proof evidence collection, real-time threat detection, and secure long-term data retention. The study also …

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Formation-Based Ai Ethics: Ethical Formation, Responsibility, And Opportunity Cost In Ai Ecosystems, Maurice Emelu Jan 2026

Formation-Based Ai Ethics: Ethical Formation, Responsibility, And Opportunity Cost In Ai Ecosystems, Maurice Emelu

2026 Faculty Bibliography

Recent efforts in AI ethics have been dominated by the proliferation of high-level principles intended to guide responsible development and deployment. Yet persistent implementation failures suggest that principlism and tool-based approaches alone do not cultivate the moral capacity required to govern sociotechnical systems. This article advances a formation based framework for AI ethics that situates moral agency throughout the developmental conditions of design, use, and the AI lifecycle. Drawing on Aristotelian-MacIntyrean virtue ethics applied to media ecology, and sociotechnical responsibility, the argument reframes AI as a formative environment that shapes patterns of attention, judgment, delegation, and accountability over time. Against …

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Translating Writing Studies Into Institutional Practice: Embedding Rhetorical Pedagogy In A Jesuit Core Curriculum, Tom Pace Jan 2026

Translating Writing Studies Into Institutional Practice: Embedding Rhetorical Pedagogy In A Jesuit Core Curriculum, Tom Pace

2026 Faculty Bibliography

This article provides an account of how John Carroll University redesigned the role of writing in its core curriculum to embed rhetorical pedagogy across all four years of a student’s academic career. Rather than viewing general education changes as a bureaucratic hurdle, this study demonstrates how writing program administrators can align an institution’s local identity, in this case the Jesuit traditions of purposeful communication (eloquentia perfecta) and holistic care (cura personalis), with contemporary writing stud- ies research to inspire cross-disciplinary faculty ownership. Moving beyond a basic description of requirements, I present a narrative of rhetorical advo- cacy that operates across …

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Data-Driven Decision-Making Frameworks For Energy Procurement And Dynamic Pricing In Electric Vehicle Charging Networks, Pallavi Khanal Jan 2026

Data-Driven Decision-Making Frameworks For Energy Procurement And Dynamic Pricing In Electric Vehicle Charging Networks, Pallavi Khanal

Electronic Theses and Dissertations

The continued growth of electric vehicles (EVs) is increasing the demand for charging infrastructure and creating new operational challenges for charging network operators (CNOs). Uncertainty in charging demand, electricity market prices, and user charging behavior can significantly impact both the economic performance and reliability of charging network operations. To address these challenges, this thesis develops an integrated data-driven framework that combines charging demand modeling, risk-aware energy procurement, machine learning, and reinforcement learning for intelligent EV charging network operation. The research begins by developing queueing-based models to characterize EV charging demand under time-varying and stochastic arrival conditions. Time-varying Mt/M/1 and Mt/M/c …

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Modeling Heterogeneous Trajectories Of Chronic Kidney Disease Progression With Finite Mixture Models Using The All Of Us Dataset, Yahaya Mumuni Musah Jan 2026

Modeling Heterogeneous Trajectories Of Chronic Kidney Disease Progression With Finite Mixture Models Using The All Of Us Dataset, Yahaya Mumuni Musah

Electronic Theses and Dissertations

Background: Chronic kidney disease (CKD) does not progress uniformly across patients, and a single average trajectory often fails to capture this heterogeneity. Understanding variation in disease progression is important for improving risk stratification and clinical management. 
Objectives: This study aims to characterize the heterogeneity of chronic kidney disease (CKD) progression using longitudinal estimated glomerular filtration rate (eGFR) data and to identify factors associated with distinct progression trajectory patterns. 
Methods: A longitudinal cohort study was conducted using data from the All of Us} Research Program collected between April 23, 2002, and October 1, 2023. Adult participants with repeated eGFR measurements were …

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Confession Contamination And Dna Exonerations, 1989-2025, Brandon L. Garrett Jan 2026

Confession Contamination And Dna Exonerations, 1989-2025, Brandon L. Garrett

Faculty Scholarship

No abstract provided.

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Llm-Assisted Legal Propositions Identification From Party Arguments In The U.S. Supreme Court Briefs, Heng Zheng, Alex Zhang Jan 2026

Llm-Assisted Legal Propositions Identification From Party Arguments In The U.S. Supreme Court Briefs, Heng Zheng, Alex Zhang

Faculty Scholarship

Merits briefs are central to U.S. litigation, serving as the primary means for parties to present arguments and persuade judges. Legal propositions in these merits briefs are the atomic units of arguments, whose relationships evolve throughout litigation and inform court decisions and precedent. Large language models (LLMs) have been applied to legal document review, but there is limited evidence on their ability to identify legal propositions in merits briefs. Given the labor-intensive nature of the task, we evaluate a human-AI collaborative approach to identifying legal propositions in the U.S. Supreme Court merits briefs, in which legal annotators review and revise …

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Structured Interactions With Llms To Support Ltl: Supplemental Information, Karenna Kung, Maanas Punuru, Alicia M. Grubb, Paola Spoletini Jan 2026

Structured Interactions With Llms To Support Ltl: Supplemental Information, Karenna Kung, Maanas Punuru, Alicia M. Grubb, Paola Spoletini

Computer Science: Faculty Publications

This repository contains the supplemental information for the paper: "Structured Interactions with LLMs to Support LTL", which investigates how students interact with and can be supported by LLM explanations while translating linear temporal logic formulae into English natural language.

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Oompa 2025.08: A First Cut Of The Toolkit For Object-Oriented Modeling For Planning And Acting, Mark Roberts, David H. Chan, Dana S. Nau, Jamie C. Macbeth Jan 2026

Oompa 2025.08: A First Cut Of The Toolkit For Object-Oriented Modeling For Planning And Acting, Mark Roberts, David H. Chan, Dana S. Nau, Jamie C. Macbeth

Computer Science: Faculty Publications

OOMPA is a partially implemented Python 3.13+ toolkit for modeling hierarchical planning domains as annotated Python classes, without writing a separate PDDL or HDDL domain file. State properties, actions, and hierarchical methods attach to domain classes via decorator syntax; OOMPA projects the resulting model into a flat dictionary of dictionaries, like the Pyhop family of planners. We describe OOMPA’s motivation and architecture as we demonstrate its use in a restaurant planning domain. There are many unrealized features, so we end with a discussion of limitations and future work.

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