Engineering Commons^™

Healthcare Digital Twins: A Methodological Literature Review On Integrating Iot And Ai For Personalized Medicine And Predictive Care, Sara Shahnazinia, Mahsa Tavasoli, Abdolhossein Sarrafzadeh, Ali Karimoddini

Electrical & Computer Engineering Faculty Publications

Digital Twin (DT) technology has the potential to revolutionize healthcare delivery and enhance patient outcomes through personalized and precision medicine, simulation models for operations and interventions, and drug discovery. However, successful implementation of DTs in Internet of Things (IoT) and artificial intelligence (AI) healthcare is contingent upon addressing key challenges such as privacy, ethics, and robust data security. This paper presents a methodological literature review of DT applications in healthcare, systematically analyzing the current state of research, key enabling technologies, and implementation challenges. The review summarizes DT categorization approaches (application-based, technology-based, and real-time function-based); delineates core DT components such as …

An Explainable Cs-Mitigation Triangular (Ecsmt) Framework To Secure Graph Neural Networks, Sabah Ettahri, Sergio Pallas Enguita, Chung-Hao Chen, Wen-Chao Yang

Electrical & Computer Engineering Faculty Publications

This research addresses cyber risk by defending against backdoor attacks on Graph Neural Networks (GNNs). We propose the Explainable Complex System-Mitigation Triangular (ECSMT) Framework, which integrates Robust Training, Graph Regularization, and Data Sanitization into a lightweight, hardware-efficient defense layer. To evaluate structural generalizability, we conducted empirical evaluations across three distinct benchmark domains (AIDS, MUTAG, and PROTEINS) using a Graph Isomorphism Network (GIN) backbone. Under a baseline 5% backdoor subgraph trigger injection ratio, ECSMT achieves excellent utility retention, securing a Clean Accuracy (CA) of 97.33% (±0.62%) while reducing the Attack Success Rate (ASR) from 97.00% down to 69.45% on the primary …

Application Of A Hyaluronic Acid-Based Lotions Containing Hydroxyapatite Nano-Particles Approach For Treatment Of Initial Dental Caries, Zahra Gharavi, Zahra Namazi, Fatemehsadat Pishbin, Helia Givian, Maryam Torshabi, Farhood Najafi, Parisa Amdjadi

Electrical & Computer Engineering Faculty Publications

This study evaluated the remineralization potential of a ceramic–polyelectrolyte system, based on a novel combination of hydroxyapatite nanoparticles (HAp NPs) and a hyaluronic acid (HY) matrix, for dental enamel, a tissue that remains challenging to repair in clinical dentistry. Nano-Hydroxyapatite (nanoHAp) powder was synthesized and characterized using X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), field-emission scanning electron microscopy (FE-SEM), dynamic light scattering (DLS), and zeta potential analysis to confirm their crystalline structure, functional groups, morphology, particle size distribution, and colloidal stability. HY-based suspensions (remineralizing lotions) containing 5, 10, and 12 wt% of the synthesized nanoHAp particles were formulated via a …

Automation And Habitat Development For A Space Based Marine Life Environment, Logan Trimmer, Alexander Hang

Harrisburg University Other Works

This project was a cross-collaboration between the Environmental Sciences and Advanced Manufacturing and Robotics programs for the company Monolith Space.

The goal of this project was to design an autonomous system to be able to track qualities of water in an aquaculture system designed to be sent to space.

The Efficacy Of Hybrid Manufacturing For High Stress Automotive Components, Logan Trimmer

Harrisburg University Other Works

The goal of this research was to establish the viability of using hybrid manufacturing for automotive applications. By verifying that high-stress components can be created, it can be assumed that any other lower stress part could be made to match the strength requirements. A limiting factor of adoption for hybrid manufacturing is how new the technology is. Studies on time and cost were performed allowing for comparisons with traditional manufacturing technologies (casting, forging, milling) used in automotive applications. This research utilized a Haas Automation UMC750 5-axis CNC mill with a Meltio laser wire direct energy deposition attachment. Fusion 360 was …

Rapid Urbanization Reduces Genetic Diversity And Increases Genetic Differentiation Of A Lynx Spider Oxyopes Sertatus In Central Taiwan, Ying-Yuan Lo, Chi Wei, Wan-Jyun Chen, Chung-Ping Lin

Biological Sciences Faculty Publications

Urbanization is a dominant force driving destructive and irreversible changes of natural habitats in modern times. While the effects of urbanization on community composition and phenotypic responses are well-documented, its influence on genetic diversity and population structure remains understudied, particularly for invertebrates in subtropical regions. This study tested the hypothesis that urbanization reduces genetic diversity and increases population differentiation in the lynx spider Oxyopes sertatus, a common foliage-dwelling spider in Taiwan. We sampled 245 individuals from 17 sites distributed along an urban-rural gradient and quantified urbanization intensity using land-use composition at both landscape (4 km²) and local (0.25 km²) …

Preventing G/J Tube Dislodgement: A Device And Communication Innovation, Sarah Clark

2026

The Problem
G/J tube dislodgement is a frequent complication in pediatric patients
Leads to:

• Emergency department visits
• Hospital admissions
• Delays in nutrition/medication

Impact on Patients & Families:

• IV placement (traumatic)
• Radiation exposure
• Overnight hospital stays

Impact on Nurses & System:

• Increased workload (admissions, coordination)
• Occupied inpatient beds for stable patients
• Inefficient care processes

Aims/Objectives
Aim: Reduce unplanned G/J tube dislodgements and related hospital utilization.
Objectives: Develop a breakaway connector prototype

Implementation and Evaluation
Setting: Pediatric inpatient & outpatient system
Participants: Nurses (bedside, GI, IR), caregiver, innovation team
Process:
Roundtable discussions → identified workflow gaps
Communication/workflow audit
Developed device …

Using A Study Journal To Support Class Engagement And The Development Of Entrepreneurial Mindset Habits, Otilia Popescu, Dimitrie C. Popescu

Engineering Technology Faculty Publications

Engineering Technology programs were historically introduced to support non-traditional students and offer college pathways for students working and having already a developing career. This is even more true in the current academic environment, with a large percentage of students enrolled in engineering technology programs being either fully or part-time employed, active or retired military, and at different stages in their lives, usually with families to care for. Often, non-traditional students attend classes online, either synchronously or even more often asynchronously, due to their schedule constraints. Course instructors regularly face schedule or time management constraints from the students’ side, and they …

Dystop: Dynamic Staleness Control And Topology Construction For Asynchronous Decentralized Federated Learning, Yizhou Shi, Qianpiao Ma, Yan Xu, Junlong Zhou, Ming Hu, Yunming Liao

Research Collection School Of Computing and Information Systems

Federated Learning (FL) has emerged as a potential distributed learning paradigm that enables model training on edge devices (i.e., workers) while preserving data privacy. However, its reliance on a centralized server leads to limited scalability. Decentralized federated learning (DFL) eliminates the dependency on a centralized server by enabling peer-to-peer model exchange. Existing DFL mechanisms mainly employ synchronous communication, which may result in training inefficiencies under heterogeneous and dynamic edge environments. Although a few recent asynchronous DFL (ADFL) mechanisms have been proposed to address these issues, they typically yield stale model aggregation and frequent model transmission, leading to degraded training performance …

Improving Efficiency In Noma Schemes Having Inter-User Interference Using Mechanism Design, Zory Marantz

Publications and Research

Modern wireless systems utilize non-orthogonal multiple access to increase their rate capacities; however, the efficiency of the individual utility defined in bits per Joule has yet to be considered. Multiple variations of non-orthogonal multiple access have the interference of the signal-to-interference-plus-noise ratio as a function of the received power from multiple other users due to code implementations that are non-orthogonal or non-ideal cancellation in successive-interference-cancellation methods. Game theoretic concepts are used to improve user bits-per-Joule performance. Previous solutions increment transmit power and are not based on closed form systematic methods. The mechanism design presented here led to a non-cooperative Nash …

Convergence Of High Throughput Experimentation And Machine Learning To Rapidly Advance Application-Specific Polymer Development, Mashrafee Aryan, Daniel Struble, Felix Campbell, Saroj Upreti, S. M.Ashik Abedin, Aahil Khambawla, Jeetain Mittal, Michael S. Dimitriyev, Emily B. Pentzer, Svetlana A. Sukhishvili, Xiaodan Gu, Boran Ma

Faculty Publications

Machine learning (ML) has heavily influenced the way scientific study is done with demonstrated successes in nearly every field. However, furthering performance and explainability of ML models in increasingly complex systems and with increasingly demanding outcomes requires a significant influx of high-quality data. To that end, this Review covers some of the techniques, instrumentation, and methodologies that have shown promise for significantly accelerating the discovery of polymer materials, optimization of their properties, and elucidation of property–application relationships through high throughput (HT) experimentation, characterization, and analysis. Attention is given to not only ML, but also to hardware advancements and their synergy …

Skin Type Diversity In Image Datasets, Neda Alipour

Doctoral

Image-based AI systems that analyse human skin are increasingly used in healthcare and computer vision applications. However, many human skin-based image datasets do not provide reliable information about skin type, making it difficult to assess whether these systems perform consistently across the full spectrum of skin colour. The objective of this thesis is to examine how skin type diversity is represented and measured in image datasets, and to evaluate the reliability of image-based skin type measurement methods under different imaging conditions. Using publicly available skin lesion image datasets as a well-defined and widely used sub-class of skin image datasets, this …

Applying Transfer Learning For Street-Scale Nuisance Flood Forecasting In Coastal-Urban Environments, Binata Roy, Jonathan L. Goodall, Diana Mcspadden, Chetan Kumar, Steven Goldenberg, Yidi Wang, Malachi Schram

Data Science Faculty Publications

An important challenge with Machine Learning (ML) is its transferability; that is, whether an ML model trained on one set of data can be applied to a second set of data without requiring full retraining of the model. Transfer Learning (TL) addresses this challenge by transferring knowledge learned in the source domain (the data it was trained on) to the target domain (a second set of data that is statistically different but related, which the model was not trained on). This study investigates the use of TL for street-scale nuisance flood forecasting by exploring whether an ML model trained for …

Privacy-Preserving Federated Learning With Optimized Ensemble Weighting And Knowledge Distillation For Covid-19 Detection From Non-Iid Medical Imaging Data, Richard Annan, Hong Qin, Robert Newman, Madhuri Siddula, Letu Qingge

Computer Science Faculty Publications

Medical imaging enables rapid and accurate diagnosis of COVID-19, with CT scans proving especially effective. However, data privacy concerns limit collaborative model development across hospitals. To address this issue, we introduce a novel federated learning framework. It is referred to as Independent Knowledge Distillation with post-Ensemble Federated Learning (IKDEFL). Differential Privacy (DP) is integrated into the framework to improve privacy guarantees. Three DP mechanisms are evaluated. These include Fixed Gaussian, Gaussian Adaptive, and Tree Adaptive. The evaluation has been conducted on heterogeneous and Non-Independent and Identically Distributed (Non-IID) datasets. These datasets reflect real-world hospital scenarios. Results show that IKDEFL significantly …

A Comparative Analysis Of Explainable Ai (Xai) Techniques For Transparent And Reliable Image Classification, Sovon Chakraborty, Shakib Mahmud Dipto, Kevin R. Pilkiewicz, Michael L. Mayo, Pratip Rana

Computer Science Faculty Publications

Evaluating the trustworthiness of black-box machine learning models remains a significant methodological challenge. Their lack of transparency and interpretability limits applicability, because stakeholders often seek transparency before trusting the results of black-box machine learning models. Explainable AI (XAI) methods provide for human-understandable justifications and informed decision-making of these black-box architectures. Therefore, it is imperative to select the proper XAI model tailored to specific tasks. In this research, we focus on examining four XAI techniques: PEEK, LRP, GRAD-CAM, and LIME to understand how they perform against each other for image classification tasks. We evaluate the performance, robustness, generalizability, noise stability, and …

Biosecure-Llm Framework: Protecting Llms From Cyberbiosecurity Threats And The Case For Independent Ai Safety Governance, Xavier-Lewis Palmer, Lucas Potter, Srdjan Lesaja, Sotirios Karathanasis, Mohammad Ghasemigol

Computer Science Faculty Publications

Large Language Models (LLMs) are becoming critical infrastructure in scientific, healthcare, and governmental contexts. As frontier AI laboratories increasingly partner with government agencies, a fundamental question arises: Who should control the safety and policy-enforcement layers that constrain model behavior? Current safety mechanisms (LLM guardrails) are typically designed for generic "harmlessness" and operate by detecting semantic patterns and refusing requests. However, they are inadequate governance instruments because they cannot implement auditable, domain-specific controls tied to external regulatory policy objects (e.g., control lists or rules governing personally identifying information). Even a perfectly aligned model is not able to express institution-specific policy without …

Untrained Position-Encoded Multilayer Perceptron Network For Structured Illumination Microscopy Reconstruction, Sahil Sharma, Leonidas Zimianitis, Krishnendu Samanta, Balpreet Singh Ahluwalia, Joby Joseph, Dushan N. Wadduwage

Computer Science Faculty Publications

Structured Illumination Microscopy (SIM) enables super-resolution imaging by encoding high-frequency spatial information through patterned light. While traditional Fourier-based reconstruction methods are prone to artifacts under suboptimal conditions, recent deep learning approaches often require large training datasets and lack adaptability across different imaging setups. In this work, we present Position Encoded Multi-Layer Perceptron (PEM) network that leverages implicit neural representations (INRs) and SIM forward-model-driven modeling to reconstruct super-resolved images without any training data. PEM-SIM represents each spatial coordinate as a combination of sinusoidal functions across multiple frequencies, enabling rich encoding of fine spatial detail. A forward model grounded in SIM image …

Influence Of Surface Features On Heat Transfer During Dropwise Condensation Over Superhydrophobic Surfaces In Shear Flow, Shaur Humayun, R. Daniel Maynes, Julie Crocket, Brian D. Iverson

Faculty Publications

This study investigates heat transfer during dropwise condensation (DWC) on superhydrophobic (SH) surfaces in humid air shear flow, emphasizing the effect of increased drop mobility and the influence of surface micro/nanostructure on heat transfer rates. Experiments were conducted on smooth hydrophobic, microstructured SH, nanostructured carbon-infiltrated carbon nanotube (CICNT) surfaces, and two-tiered SH surfaces with both micro and nanostructures. Heat transfer rates were measured under humid air flow rates in the range of 2–4 CFM. Experimental results demonstrate that surfaces with nanostructure (including two-tiered structures) exhibit increased drop mobility and coalescence-induced drop jumping, enhancing drop removal rates and overall heat transfer …

The Composition Of Gases From A Diffusion Flame Above Longleaf Pine Needle Fuel Beds, David R. Weise, Thomas H. Fletcher, Timothy J. Johnson, Weimin Hao, Russell G. Tonkyn, Catherine A. Banach, Javier Palarea-Albaladejo, Mahsa Alizadeh, Stephen Baker

Faculty Publications

The gas and tar composition of a wildland fire diffusion flame from longleaf pine needles is currently relatively unmeasured and more data are needed to fill in the gap between pyrolysis data and smoke plume data, thus improving physical and chemical modeling of wildland smoke formation. A pilot experiment to measure light gas and tar composition of such a flame is described for three flame zones: persistent flame (flame base), intermittent flame, and smoke plume. Flame gases from 24 experimental fires were collected in canisters and analyzed for CO2, CO, H2, CH4, and C2 to C7 hydrocarbon gases. Other light …

Cybersecurity Center For Offshore Wind Energy (Final Project Round), Sachin Shetty

Center for Secure and Intelligent Critical Systems (CSICS) Publications

This project establishes a Cybersecurity Center for Offshore Wind Energy with the objective of designing and operating a cyber-physical testbed for wind energy farms (WEFs) that enables comprehensive cybersecurity research. The testbed incorporates a Supervisory Control and Data Acquisition (SCADA) system connected to turbine models via industrial-grade programmable logic controllers (PLCs) and remote terminal units (RTUs). It supports side-channel data acquisition, implementation and analysis of various cyberattack scenarios, and development of attack detection, mitigation, and best-practice guidance tailored to wind energy systems. During the project, the team expanded the number and fidelity of mathematical turbine models (MTMs), integrated these models …

Sustainable Paper Production From Date Palm And Reed Leaves Through The Valorization Of Agricultural Waste Products, Imane Belyamani, Alreem Alameri, Jacqueline Soghman

All Works

The environmental consequences of wood-based paper production, including greenhouse gas emissions, have accelerated the search for sustainable alternatives. This study investigates the use of reed and date palm fibers as eco-friendly raw materials for paper production, focusing on starch's influence on their thermal, structural, and mechanical properties. Reed fibers exhibited a higher pulp yield (58.2 %) and lower lignin content (7.8 %) compared to date palm fibers (55.9 % yield, 14.1 % lignin), contributing to papers with smoother textures and greater flexibility. The incorporation of starch into both fiber types resulted in notable performance improvements, though the effects were more …

Low-Energy Co2 Sequestration Via Palm Waste Biochar Activated With Carbide Lime For Mineral Carbonation Of Industrial Wastes, Maisa El Gamal, Ameera F. Mohammad, Basim Abu-Jdayil, Imen Ben Salem, Suhaib Hameedi

All Works

A sustainable method for enhancing CO₂ capture and sequestration was developed by accelerating the carbonation of alkaline industrial wastes using carbide lime-activated biochar (BC(CLW)), an alkaline by-product of acetylene production. The biochar was produced from palm tree waste through slow pyrolysis at 400 °C, followed by activation with carbide lime slurry to enhance its alkalinity, surface basic sites, and reactivity toward CO₂. Three industrial residues, namely ladle furnace slag (LF), baghouse dust (BH), and cyclone silo dust (CD), were blended with 0, 5, 7, and 10 wt. % BC(CLW) and carbonated under ambient conditions. Characterization by thermogravimetric analysis (TGA), Fourier-transform …

Chronofy: A Temporal-Logical Decay Architecture For Information Validity In Time-Aware Retrieval-Augmented Generation, Muntaser Syed, Marius Silaghi, Sheikh Abujar, Sharun Akter

Electrical Engineering and Computer Science Student Publications

Retrieval-Augmented Generation (RAG) systems retrieve and integrate external knowledge to ground large language model (LLM) outputs. However, current RAG architectures treat all retrieved facts as equally valid regardless of temporal provenance, leading to temporal hallucination, where plausible but obsolete facts corrupt the output. A clinical lab reading from yesterday is actionable; the same reading from six months ago is noise. We present Chronofy, a three-layer neuro-symbolic framework implementing the Temporal-Logical Decay Architecture (TLDA) that embeds temporal validity directly into the representation, retrieval, and reasoning layers of RAG systems. Layer 1 reserves a dedicated temporal subspace within Matryoshka embeddings to make …

Epistemic Edge: Subjective Logic Guardrails For Llm-Driven Iot Actuation, Muntaser Syed, Marius Silaghi

Electrical Engineering and Computer Science Student Publications

Deploying large language models (LLMs) as decision-making agents in safety-critical Internet of Things (IoT) systems introduces risks that purely behavioral guardrails, such as action whitelists, cannot fully address. This paper presents Epistemic Edge, a four-tier neuro-symbolic pipeline that augments LLM-driven actuation with subjective logic (SL) uncertainty quantification, temporal decay, and dual guardrails combining epistemic threshold checks with behavioral whitelists. We evaluate seven locally-deployed models: three PrismML Bonsai 1-bit models (1.7B, 4B, 8B), three 4-bit quantized models (Qwen3-8B, Llama 3.2-3B, Phi-3.5-mini), and the DeepSeek-R18B reasoning model, across eight ablation conditions and five IoT actuation scenarios (2,800 controlled trials). We further validate …

Real-Time Neck Posture Classification Using A Lightweight Wearable Imu Pendant, Muntaser Syed, Alfred Sjöqvist, Noah Sedlik, Tsing Liu

Electrical Engineering and Computer Science Student Publications

Poor neck posture during prolonged device use contributes to musculoskeletal disorders affecting millions worldwide. Existing posture monitoring solutions rely on camera-based systems or complex multi-sensor arrays, limiting their practicality for continuous daily use. We present a lightweight, chest-worn pendant using a single 6-axis IMU (accelerometer and gyroscope) for real-time classification of seven neck posture states: neutral, mild flexion, moderate flexion, severe flexion, extension, lateral tilt, and lying. Our approach employs an ensemble architecture combining bidirectional LSTM, Transformer encoder, and 1D-CNN models with learnable fusion weights. To address limited training data, we apply aggressive data augmentation (30x multiplication) including noise injection, …

Workshop Outcomes Report: 2nd International Workshop On Seismic Resilience Of Arctic Infrastructure And Social Systems, Majid Ghayoomi, Daniela Morganti

Faculty Publications

The report provides an overview of the second international workshop on Seismic Resilience of Arctic Infrastructure and Social Systems. The report discusses agenda, workshop activities, interdisciplinary working groups, and results. It ends with several strategic questions and investigation plans that were developed as part of the workshop activities.

Forcing A Molecule To Switch: Quantifying Mechanical Control At The Atomic Scale, A. M. Shashika D. Wijerathna, Markus Zirnheld, Michael L. Hildebrand, Myles Perry, Marjorie Cenese, Yuan Zhang

Physics Faculty Publications

Mechanically induced conformational switching at the single-molecule level represents a fundamental mechanism for molecular functionality, yet quantitative characterization of the underlying force and energy landscape remains limited. Here, we study individual TBrPP-Co(II) molecules on Au(111) using qPlus atomic force microscopy. By reconstructing interaction potentials from 3D Δf(x,y,z) data, we determine a threshold force of ∼96 ± 8 pN and a tip-induced switching interaction energy of ∼38 ± 4 meV associate with the conformational transition. The isolated tip-molecule force follows a power law (exponent ∼6), indicating dominance of long-range van der Waals interactions. …

Optimizing Ev Battery Charging Using Fuzzy Logic In The Presence Of Uncertainties And Unknown Parameters, Minhaz Uddin Ahmed, Md Ohirul Qays, Stefan Lachowicz, Parvez Mahmud

Research outputs 2022 to 2026

The growing use of electric vehicles (EVs) creates challenges in designing charging systems that are smart, dependable, and efficient, especially when environmental conditions change. This research proposes a fuzzy-logic-based PID control strategy integrated into a photovoltaic (PV) powered EV charging system to address uncertainties such as fluctuating solar irradiance, grid instability, and dynamic load demands. A MATLAB-R2023a/Simulink-R2023a model was developed to simulate the charging process using real-time adaptive control. The fuzzy logic controller (FLC) automatically updates the PID gains by evaluating the error and how quickly the error is changing. This adaptive approach enables efficient voltage regulation and improved system …

Numerically Evaluating The Effect Of Extrusion Angle On Material Flow And Thermal Behaviour During Additive Friction Extrusion Deposition (Afed), Numan Habib, Ferdinando Guzzomi, Ana Vafadar

Research outputs 2022 to 2026

Additive Friction Extrusion Deposition (AFED), also known as “SoftTouch”, is an emerging friction-based Additive Manufacturing (AM) technology allowing material to soften before the deposition, increasing the printing speed and reducing cost [1]. However, high power is required during the process, as excessive force is needed to extrude enough material through the printing head. This study investigates how extrusion angle and tool rotational speed affect thermal distribution and material flow in AFED to minimise power consumption. A three-dimensional computational fluid dynamics (CFD) model is proposed, and ANSYS ^® Workbench CFD code (Fluent) is used to discretise the CFD model. A User-Defined …

Efficiently Stowed Flashers With Uniform-Thickness Panels Based On Non-Developable Origami, Larry L. Howell, Robert J. Lang, Specer P. Magleby, Davis Wing

Faculty Publications

We present the mathematical construction of a deployable flasher pattern with near-uniform nonzero thickness in the deployed form and efficient face-to-face packing in the stowed form. We demonstrate its fabrication, deployment, and stowage. The results facilitate the design and manufacture of deployable systems with panels whose substantial thickness is dictated by the end application, with particular utility for space systems.