Digital Commons Network^™

General Kernel Machine Methods For Multi-Omics Integration And Genome-Wide Association Testing With Related Individuals, Amarise Little, Ni Zhao, Anna Mikhaylova, Angela Zhang, Wodan Ling, Florian Thibord, Andrew D. Johnson, Laura M. Raffield, Joanne E. Curran, John Blangero

School of Medicine Publications

Integrating multi-omics data may help researchers understand the genetic underpinnings of complex traits and diseases. However, the best ways to integrate multi-omics data and use them to address pressing scientific questions remain a challenge. One important and topical problem is how to assess the aggregate effect of multiple genomic data types (e.g. genotypes and gene expression levels) on a phenotype, particularly while accommodating routine issues, such as having related subjects' data in analyses. In this paper, we extend an existing composite kernel machine regression model to integrate two multi-omics data types, while accommodating for general correlation structures amongst outcomes. Due …

Infants’ Resting-State Functional Connectivity And Event-Related Potentials: A Multimodal Approach To Investigating The Neural Basis Of Infant Novelty Detection, Dana Kanel, Santiago Morales, Kathryn Altman, John E. Richards, Anderson M. Winkler, Daniel Samuel Pine, Nathan A. Fox, Courtney A. Filippi

School of Medicine Publications

Individual differences in how the brain responds to novelty are present from infancy. A common method of studying novelty processing is through event-related potentials (ERPs). While ERPs possess millisecond precision, spatial resolution remains poor, especially in infancy. This study aimed to balance spatial and temporal precision by combining ERP data with functional magnetic resonance imaging (fMRI) data. Twenty-nine infants (15 female) underwent resting-state fMRI (Mage = 4.73 months) and electroencephalography (EEG) during a three-stimulus auditory oddball task (Mage = 5.19 months). The mismatch response (MMR) and P3 were computed from ERP data, and resting-state functional connectivity …

Forecasting Covid-19 Cases, Hospital Admissions, And Deaths Based On Wastewater Sars-Cov-2 Surveillance Using Gaussian Copula Time Series Marginal Regression Model, Hueiwang Anna Jeng, Norou Diawara, Cynthia Jackson, Rekha Singh, Kyle Curtis, Raul Gonzalez, David Jurgens, Sasanka Adikari

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

Modeling efforts are needed to predict trends in COVID-19 cases and related health outcomes, aiding in the development of management strategies and adaptation measures. This study was conducted to assess whether the SARS-CoV-2 viral load in wastewater could serve as a predictor for forecasting COVID-19 cases, hospitalizations, and deaths using copula-based time series modeling. SARS-CoV-2 RNA load in wastewater in Chesapeake, VA, was measured using the RT-qPCR method. A Gaussian copula time series (CTS) marginal regression model, incorporating an autoregressive moving average model and Gaussian copula function, was used as a forecasting model. Wastewater SARS-CoV-2 viral loads were correlated with …

Machine Learning Models For Pancreatic Cancer Survival Prediction: A Multi-Model Analysis Across Stages And Treatments Using The Surveillance, Epidemiology, And End Results (Seer) Database, Aditya Chakraborty, Mohan D. Pant

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

Background: Pancreatic cancer is among the most lethal malignancies, with poor prognosis and limited survival despite treatment advances. Accurate survival modeling is critical for prognostication and clinical decision-making. This study had three primary aims: (1) to determine the best-fitting survival distribution among patients diagnosed and deceased from pancreatic cancer across stages and treatment types; (2) to construct and compare predictive risk classification models; and (3) to evaluate survival probabilities using parametric, semi-parametric, non-parametric, machine learning, and deep learning methods for Stage IV patients receiving both chemotherapy and radiation. Methods: Using data from the SEER database, parametric models (Generalized Extreme Value, …

Engineering Ethics Education Bingo 1.0, Shannon Chance, Mike Murphy, Celina P. Leão, M. Tobosaru, Mary Nolan

Research Outputs: 2025-Present

This paper introduces Engineering Ethics Education (EEE) Bingo 1.0, an active learning tool designed to support ethics education among engineering educators. Drawing inspiration from game-based learning, EEE Bingo provides a playful yet purposeful framework for exploring diverse pedagogical approaches in engineering ethics. The game stimulates discussion and reflection on the alignment between ethical issues and suitable teaching methods, promoting greater pedagogical awareness and collaboration. This paper outlines the motivation behind the game, its structure and rules, and illustrative examples. EEE Bingo encourages critical engagement with teaching strategies, reinforcing the importance of contextualized, interdisciplinary, and reflective ethics education.

Accreditation Considerations In Engineering Ethics Education: Bridging Global Standards And Local Practices: Bridging Global Standards And Local Practices, Louise O'Gorman, Alison Gwynne-Evan, Leah Ridgeway, Marek Rebow, Shannon Chance

Research Outputs: 2025-Present

Accreditation can drive curricular innovation and change in engineering and can have a role in encouraging students' development of ethics-related competencies. Despite engineering educators' competence in the technical aspects of design and research, there can be an element of discomfort in planning and delivering lessons relating to ethics that span social justice, diversity and inclusion, environmental sustainability, and ethical theories. Because engineering accreditation can encourage and/or require the inclusion of ethical content in accredited engineering courses, the recently published Routledge Handbook of Engineering Ethics Education dedicated an entire section to the topic. This paper critically assesses the handbook's accreditation section …

Laugh, I Nearly Cried: A Qualitative, Reflexive Thematic Analysis Of The Impact Of Humour In Psychotherapy From Client And Therapist Perspectives, Elina Broholm

School of Psychology Theses

Humour in psychotherapy is commonly occurring, yet seldom discussed as a resource in the training and education of therapists, nor considered from the perspective of clients in therapy. This qualitative study explored how clients and therapists perceive the impact of humour in psychotherapy on the therapeutic alliance, experience, and process. 12 clients and eight therapists contributed their perspectives on humour’s impact on the therapeutic experience, considering both its helpful and unhelpful contributions. Not all humour is equal, and special consideration was given to how different humour styles may impact psychotherapy. Reflexive thematic analysis from 20 semi-structured interviews suggested that humour …

Exploring The Relationship Between Attachment State Of Mind, Emotional Development And Distress In Adults With Intellectual Disabilities – A Pilot Study, Amy Pantridge

School of Psychology Theses

BackgroundAttachment theory and emotional development are increasingly considered important for understanding distress in people with intellectual disabilities, yet no prior studies have empirically explored relationships between these constructs.MethodAdults with intellectual disabilities and informants were recruited from UK community organisations. Participants completed an attachment measure (AAP), brief cognitive measure, and questionnaire on perceived emotional support. Informants completed a distress measure (HoNOS-LD), emotional development measure (SED-S), and provided feedback on the SED-S. ResultsNo significant relationship was found between attachment and emotional development, though some specific associations emerged. Unresolved attachment was common. Discrepancies were observed between informant-reported and participant-reported data. Informants found elements …

Micro(Nano)Plastics In The Fish Gastrointestinal Tract: A Mini Review And Relevance To One Health Perspective, Farhan R. Khan, Nathaniel Clark, Elvis Genbo Xu

School of Biomedical Sciences

Micro(nano)plastics (MNPs) research in the piscine gastrointestinal tract has been a significant area of study. Understanding the environmental presence of microplastics, their potential for both localised and systemic effects, and their role as a gateway for MNPs and associated chemicals to enter the tissues and the food web, including the human food chain, is crucial. The fish gut, as a critical intersection between environmental, biotic, and human health, aligns with the One Health concept. We review the likely uptake mechanisms and toxicological damage caused by ingesting MNPs and their associated chemicals in fish, contextualised within the realms of digestive physiology …

Multiplex Lateral Flow Test Sensitivity And Specificity In Detecting Influenza A, B And Sars-Cov-2 In Adult Patients In A Uk Emergency Department, Rahul Batra, Edward Blandford, Raghavendran Kulasegaran-Shylini, Matthias E Futschik, Abbie Bown, Matthew Catton, Hermione Conti-Frith, Alexandra Alexandridou, Rebecca Gill, Clara Milroy, Sean Harper, Holly Gettings, Maryann Noronha, Hooi-Ling Harrison, Sam Douthwaite, Gaia Nebbia, Paul E Klapper, Sarah Tunkel, Richard Vipond, Susan Hopkins, Tom Fowler

School of Biomedical Sciences

BACKGROUND: Rapid identification of individuals with acute respiratory infections is crucial for preventing nosocomial infections. For rapid diagnosis, especially in EDs, lateral flow devices (LFDs) are a convenient, inexpensive option with a rapid turnaround. Several 'multiplex' LFDs (M-LFDs) now exist, testing for multiple pathogens from a single swab sample. We evaluated the real-world performance of M-LFD versus PCR testing in detecting influenza A, B and SARS-CoV-2) in the ED setting.

METHODS: After preliminary evaluation of an M-LFD (SureScreen) with laboratory-grown virus and PCR-negative clinical samples, it was evaluated in a real-world setting at the ED of St Thomas' Hospital (London, …

Does The Use Of Audits Decrease The Infection Rate In A Medical Care Setting?, Kylie Borchert, Kristel K. Bakker

Annual Research Symposium

Nosocomial infections are a significant health concern in medical settings. Reports of low compliance rates with hand hygiene standards, guidelines of which are outlined and mandated by the CDC and WHO, are frequent. Factors contributing to nonadherence include lack of knowledge and an unclear understanding of correct techniques (4). Evidence shows that improved hand hygiene can reduce infection rates (1), especially when healthcare providers are included in interventions that aim to improve compliance (4).

Tintin: A Unified Hardware Performance Profiling Infrastructure To Uncover And Manage Uncertainty, Ao Li, Marion Sudvarg, Zihan Li, Sanjoy Baruah, Chris Gill, Ning Zhang

Computer Science Faculty Research & Creative Works

Hardware performance counters (HPCs) enable the measurement of microarchitectural events, which are crucial for tracking and predicting program behavior. High-fidelity measurement and precise attribution are essential for accurate profiling. However, existing profiling tools have fundamental challenges in both aspects. In measurement, numerous events compete for limited hardware monitoring resources; while for attribution, applications have diverse requirements, but systems provide limited support. Existing tools mitigate the former limitation through event multiplexing, but this approach introduces non-trivial errors. The latter limitation, however, remains largely unaddressed. This paper introduces Tintin, an HPC profiling infrastructure with a modular three-component design that addresses both challenges. …

Using Preformed Particle Gels To Control Transport In Geothermal Reservoirs: Mathematical Modeling, Philip Winterfeld, Baojun Bai, Yu Shu Wu

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

We are developing swellable Preformed Particle Gels (PPG), which can control preferential fluid and heat flow through fracture networks to increase the performance of EGS reservoirs. Part of this development is a mathematical model and numerical simulator to simulate PPG treatments by considering coupled thermal-hydraulic-mechanical effects, and gel swelling kinetics and plugging efficiency, from which an optimized gel treatment design and operation can be achieved. The starting point for our mathematical model is the TOUGH2-CSM formulation and code. The TOUGH2-CSM fluid and heat flow formulation is based on the TOUGH2 one for multiphase, multicomponent, and multi-porosity systems, with the latter …

Delayed Tooth Eruption As An Early Clinical Indicator Of Genetical Tooth Agenesis: A Family-Based Study Of Cases With Whole-Exome Sequencing, Yu-Chen Jeng, I-Ting Chen, Hui-Ching Cheng, Hsin-Yu Huang, Chao-Kai Hsu, Yu-Fen Yen

Journal of Dental Sciences

Delayed tooth eruption is frequently considered a benign developmental variation but may represent an early sign of genetically driven tooth agenesis. We evaluated a 22-month-old female presenting with markedly delayed eruption (only eight primary teeth) and a positive family history of ectodermal features. Given the inconclusive early radiographic findings, whole-exome sequencing (WES) was utilized. WES revealed the proband carried a heterozygous EDARADD variant (NM_080738:c.328G>T; p.Asp110Tyr). Subsequent familial segregation analysis identified an additional heterozygous WNT10A variant (NM_025216:c.637G>A; p.Gly213Ser) within the broader family. The EDARADD variant alone was associated with varying presentations from normal dentition to delayed eruption. Furthermore, the …

Parallel Multi Objective Shortest Path Update Algorithm In Large Dynamic Networks, S. M. Shovan, Arindam Khanda, Sajal K. Das

Computer Science Faculty Research & Creative Works

The multi objective shortest path (MOSP) problem, crucial in various practical domains, seeks paths that optimize multiple objectives. Due to its high computational complexity, numerous parallel heuristics have been developed for static networks. However, real-world networks are often dynamic where the network topology changes with time. Efficiently updating the shortest path in such networks is challenging, and existing algorithms for static graphs are inadequate for these dynamic conditions, necessitating novel approaches. Here, we first develop a parallel algorithm to efficiently update a single objective shortest path (SOSP) in fully dynamic networks, capable of accommodating both edge insertions and deletions. Building …

J-Necora: A Framework For Optimal Resource Allocation In Cloud-Edge-Things Continuum For Industrial Applications With Mobile Nodes, Marco Pettorali, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi

Computer Science Faculty Research & Creative Works

In the Industrial Internet of Things (IIoT) landscape, where the Cloud-to-Things Continuum (C2TC) paradigm is now a reality, industrial applications need to cope with highly heterogeneous network and computing resources. Moreover, many industrial applications also involve Mobile Nodes (MNs). Efficient allocation of network and computing resources to meet the stringent requirements of such applications is often a very challenging task. In this paper, we propose J-NECORA (Joint NEtwork and COmputing Resource Allocation), a comprehensive analytical framework to derive the optimal joint allocation of network and computing resources in the C2TC, that guarantees the application requirements, even in the presence of …

Correction: Yolo-Based Miner Detection Using Thermal Images In Underground Mines (Mining, Metallurgy & Exploration, (2025), 10.1007/S42461-025-01249-6), Cyrus Addy, Venkata Sriram Siddhardh Nadendla, Kwame Awuah-Offei

Computer Science Faculty Research & Creative Works

In the original published article, Figure 3 appears with the Fig. 1 caption, Figure 1 appears with the Fig. 2 caption, and Figure 2 appears with the Fig. 3 caption. The article has been updated to correct this error.

Safenav: Safe Path Navigation Using Landmark Based Localization In A Gps-Denied Environment, Ganesh Sapkota, Sanjay Madria

Computer Science Faculty Research & Creative Works

In battlefield environments, adversaries frequently disrupt GPS signals, requiring alternative localization and navigation methods. Traditional vision-based approaches like Simultaneous Localization and Mapping (SLAM) and Visual Odometry (VO) involve complex sensor fusion and high computational demand, whereas range-free methods like DV-HOP face accuracy and stability challenges in sparse, dynamic networks. This paper proposes LanBLoc-BMM, a navigation approach using landmark-based localization (LanBLoc) combined with a battlefield-specific motion model (BMM) and Extended Kalman Filter (EKF). Its performance is benchmarked against three state-of-the-art visual localization algorithms integrated with BMM and Bayesian filters, evaluated on synthetic and real-imitated trajectory datasets using metrics including Average Displacement …

V-Usdt: Vision-Based Uav Swarm Detection And Tracking By Leveraging Swarm Formation Constraints, Md Hasibur Rahman, Sanjay Madria

Computer Science Faculty Research & Creative Works

The rapid proliferation of Unmanned Aerial Vehicles (UAVs) and UAV swarm technologies has raised critical concerns about security and safety in low-altitude airspace. In response, we propose a vision-based system for detecting and tracking UAV swarms, which combines a novel UAV detection mechanism with a swarm tracking strategy. Our UAV detector incorporates parallel receptive field blocks alongside an attention mechanism to enhance detection performance. This design effectively captures multiscale features of UAVs while prioritizing salient features, ensuring robust detection under diverse conditions. For swarm tracking, we leverage the inherent formation constraints typically maintained by UAV swarms. These constraints allow us …

Dynamic Resource Allocation In Cloud-To- Things Continuum For Real-Time Iot Applications, Marco Pettorali, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi

Computer Science Faculty Research & Creative Works

The proliferation of loT devices and the growing demand for real-time applications have driven a shift in the computation paradigm, from Cloud computing to Edge computing, creating the Cloud-to-Things Continuum (C2TC). Many real-time loT applications involve Mobile Nodes (MNs), which may dynamically join or leave. In addition, in future reconfigurable loT systems, applications with different requirements will coexist, and will be dynamically introduced or removed. All this asks for dynamic management mechanisms to ensure the requirements of different real-time applications, even when the system configuration changes over time. In this paper, we propose DJ-NECORA, an online algorithm for the joint …

Feature Fusion Transferability Aware Transformer For Unsupervised Domain Adaptation, Xiaowei Yu, Zhe Huang, Zao Zhang

Computer Science Faculty Research & Creative Works

Unsupervised domain adaptation (UDA) aims to leverage the knowledge learned from labeled source domains to improve performance on the unlabeled target domains. While Convolutional Neural Networks (CNNs) have been dominant in previous UDA methods, recent research has shown promise in applying Vision Transformers (ViTs) to this task. In this study, we propose a novel Feature Fusion Transferability Aware Transformer (FFTAT) to enhance ViT performance in UDA tasks. Our method introduces two key innovations: First, we introduce a patch discriminator to evaluate the transferability of patches, generating a transferability matrix. We integrate this matrix into self-attention, directing the model to focus …

Ca-Vqvae: Cortical Folding Aware Numerical Representation Of White-Matter Structure, Yanjun Lyu, Jing Zhang, Lu Zhang, Tong Chen, Xiaowei Yu, Minheng Chen, Yan Zhuang, Chao Cao, Tianming Liu, Dajiang Zhu

Computer Science Faculty Research & Creative Works

White matter (WM) serves as a fundamental component of the brain providing essential structural support and facilitating the brain cognitive processes. Thus, an accurate and efficient description of the brain's white matter structure is essential for understanding brain function connectivity and development. In this work we used the deep model to combine the information of the WM fiber bundle shape and its related cortical folding patterns together representing the WM fiber bundle from diffusion MRI tractography into a pre-defined low-dimensional space and generate the numerical representation vector. This cortical-aware vector-quantized variational encoder (CA-VQVAE) framework leverages cortical locations and folding patterns …

Classiffication Of Mild Cognitive Impairment Based On Dynamic Functional Connectivity Using Spatio-Temporal Transformer, Jing Zhang, Yanjun Lyu, Xiaowei Yu, Lu Zhang, Chao Cao, Tong Chen, Minheng Chen, Yan Zhuang, Tianming Liu, Dajiang Zhu

Computer Science Faculty Research & Creative Works

Dynamic functional connectivity (dFC) using resting-state functional magnetic resonance imaging (rs-fMRI) is an advanced technique for capturing the dynamic changes of neural activities and can be very useful in the studies of brain diseases such as Alzheimer's disease (AD). Yet, existing studies have not fully leveraged the sequential information embedded within dFC that can potentially provide valuable information when identifying brain conditions. In this paper, we propose a novel framework that jointly learns the embedding of both spatial and temporal information within dFC based on the transformer architecture. Specifically, we first construct dFC networks from rs-fMRI data through a sliding …

Secure Data Relay In Federated Digital Twins Of Iot-Enabled Smart Interconnected Factories, Anusha Vangala, Jack Wyeth, Ashok Kumar Das, Sajal K. Das

Computer Science Faculty Research & Creative Works

Smart interconnected factories allow manufacturing units from physically distanced factory sites to communicate classified information needed for additive manufacturing. Each factory has interconnected digital twins of their equipment autonomously managed by a Point-of-Contact digital twin creating a hierarchical system with federated digital twins. The data sharing between the factories must be directed through an edge node responsible for managing multiple factories. We proposed a novel lightweight protocol to prevent the leakage of classified information at any nodes other than the origin and destination digital twins. It leverages elliptic curve cryptography to design a proxy re-encryption scheme with the edge node …

When Federated Learning Meets Quantum Computing: Survey And Research Opportunities, Aakar Mathur, Ashish Gupta, Sajal K. Das

Computer Science Faculty Research & Creative Works

Quantum Federated Learning (QFL) is an emerging field that harnesses advances in Quantum Computing (QC) to improve the scalability and efficiency of decentralized Federated Learning (FL) models. This paper provides a systematic and comprehensive survey of the emerging problems and solutions when FL meets QC, from research protocol to a novel taxonomy, particularly focusing on both quantum and federated limitations, such as their architectures, Noisy Intermediate Scale Quantum (NISQ) devices, and privacy preservation, so on. With the introduction of two novel metrics, qubit utilization efficiency and quantum model training strategy, we present a thorough analysis of the current status of …

Content Subversion Against 1 Information-Based Systems, Junjie Xiong, Ian Markwood, Dakun Shen, Yao Liu, Zhuo Lu

Computer Science Faculty Research & Creative Works

We present a novel class of content subversion attacks against information-based services, causing documents to appear to humans dissimilar to the underlying content extracted by information-based services. We demonstrate the significant impact of these attacks on real-world systems through five distinct variants. Our first attack allows academic paper writers and reviewers to collude via subverting the automatic reviewer assignment systems in current use by academic conferences including INFOCOM, which we reproduced. Our second attack renders ineffective plagiarism detection software, particularly Turnitin, targeting specific small plagiarism similarity scores to appear natural and evade detection. In our third attack, we place masked …

Grace-Fl: Green Resource-Aware Communication-Efficient Federated Learning, Dipanwita Thakur, Antonella Guzzo, Giancarlo Fortino, Sajal K. Das

Computer Science Faculty Research & Creative Works

Federated Learning (FL) enables collaborative model training across distributed clients while preserving data privacy, but its deployment on resource-constrained devices is hindered by high communication overhead, inefficient energy usage, and poor convergence under non-IID data distributions. To address these challenges, we propose GRACE-FL: a Green Resource-Aware Communication-Efficient Federated Learning framework that explicitly incorporates device energy capacity into training. Each client adapts its learning rate, number of local epochs, and gradient quantization bit-width based on its available energy, allowing high-capacity devices to sustain more intensive training while low-capacity devices operate with lighter configurations. A novel energy-weighted aggregation strategy ensures that clients …

Alertble: Alert Workzone Hazards Using Hybrid Filtering And Machine-Learning-Enabled Ble, Samuel Akinyede, Sejun Song

Computer Science Faculty Research & Creative Works

Collision hazard detection in industrial work zones faces challenges from signal instability, mobility-induced fluctuations, and nonline-of-sight (NLOS) conditions. While Bluetooth low energy (BLE) offers cost-effective proximity sensing, its received signal strength indicator (RSSI) variability - fluctuating by ±10 dBm even at fixed distances - limits reliability in safety-critical applications. This article presents AlertBLE, a hybrid BLE-based hazard detection system that combines extended Kalman filter (EKF) and adaptive moving average (AMA) algorithms to achieve up to 94% RSSI variance reduction in static NLOS conditions. The system introduces speed-aware safety thresholds based on reaction time and braking distance models, dynamically expanding hazard …

The Kincare Craze In Child Protection: Romanticism, Subterfuge, And Racial Separatism, James G. Dwyer

FIU Law Review

A deluge of federal and state legislation in recent years aims to channel maltreated children removed from parents’ custody into kin placements, responding to advocates for parents who claim this is categorically better for children than entering or remaining in foster care with biologically unrelated caregivers. The claim has romantic appeal, sufficient to beguile legislators. But the research on outcomes for children in different post-removal placements does not support it. In reality, motivations other than child welfare underlie the advocacy for this legislation. The core aim is to evade federal child-welfare mandates adopted in the 1990s and long condemned by …

Clarifying Concreteness: Statutory Grants Of Standing After Spokeo And Transunion, Naomi Eid

FIU Law Review

Article III limits the federal judicial power to deciding “cases” and “controversies,” demanding that a plaintiff suffer real, concrete, de facto harm to sue in federal court. In Spokeo and TransUnion, the Supreme Court clarified the outer limits of concreteness by making clear to plaintiffs that an injury in law is not an injury in fact. The Court largely grounded the decisions in the separation of powers, setting a tone that appears less deferential to Congress in its creation of statutory rights of action. A plaintiff does not automatically satisfy the injury-in-fact requirement whenever Congress grants a right and purports …