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2026

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Articles 31 - 60 of 5111

Full-Text Articles in Engineering

Lrq-Solver: A Transformer-Based Neural Operator For Fast And Accurate Solving Of Large-Scale 3d Pdes, Peijian Zeng, Guan Wang, Haohao Gu, Xiaoguang Hu, Tiezhu Gao, Zhuowei Wang, Aimin Yang, Xiaoyu Song Nov 2026

Lrq-Solver: A Transformer-Based Neural Operator For Fast And Accurate Solving Of Large-Scale 3d Pdes, Peijian Zeng, Guan Wang, Haohao Gu, Xiaoguang Hu, Tiezhu Gao, Zhuowei Wang, Aimin Yang, Xiaoyu Song

Electrical and Computer Engineering Faculty Publications and Presentations

Solving large-scale PDEs on complex three-dimensional geometries remains a central challenge in scientific and engineering computing, often due to expensive pre-processing stages and high computational overhead. We present Low-Rank Query-based PDE Solver (LRQ-Solver), a physics-integrated deep learning framework for efficient CAE simulations of complex three-dimensional geometries in CAD-driven design analysis. Built upon the Parameter-Conditioned Lagrangian Modeling (PCLM) that embeds physical consistency into the learning process and the Low-Rank Query Attention (LR-QA) module that reduces attention complexity from O(N2) to O(NC2+C3) via covariance decomposition, LRQ-Solver supports multi-configuration analysis within iterative design workflows. On two benchmark datasets, it achieves a 28.6% error …

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Mind The Hazard: Modeling And Interpreting Comfort With Personalized Sensing, Yufei Zhang, Matteo Favero, Patrick Chwalek, Sailin Zhong, Denis Lalanne, A. Joseph Paradiso, Clayton Miller, Andrew Sonta Nov 2026

Mind The Hazard: Modeling And Interpreting Comfort With Personalized Sensing, Yufei Zhang, Matteo Favero, Patrick Chwalek, Sailin Zhong, Denis Lalanne, A. Joseph Paradiso, Clayton Miller, Andrew Sonta

Research Collection College of Integrative Studies

Recent advances in personalized sensing and comfort feedback have spurred the development of data-driven comfort models tailored to individual needs. However, because current models treat sequential comfort feedback independently, they are subject to unstable predictions and limited interpretability, hindering their deployment in building management. This study introduces a dynamic modeling framework that utilizes a Neural Ordinary Differential Equations-based Continuous-time Markov Chain to model the transitions in comfort states over time. Our modeling approach, developed through a field study utilizing smart glasses and mobile app feedback, tracks occupants' comfort transitions across daily activities and contexts. The results demonstrate that this model …

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Adaptive Improved Particle Swarm Optimization-Based Maximum Power Point Tracking And Energy Smoothing For Photovoltaic Hybrid Battery–Supercapacitor Storage Systems, Mohammad Aminul Islam, Guo Shaokai, Jakaria Mahdi Imam, Mohammad Khairul Basher, Nowshad Amin, Tarek Abedin, Mohammad Nur-E-Alam Oct 2026

Adaptive Improved Particle Swarm Optimization-Based Maximum Power Point Tracking And Energy Smoothing For Photovoltaic Hybrid Battery–Supercapacitor Storage Systems, Mohammad Aminul Islam, Guo Shaokai, Jakaria Mahdi Imam, Mohammad Khairul Basher, Nowshad Amin, Tarek Abedin, Mohammad Nur-E-Alam

Research outputs 2022 to 2026

The rapid development of photovoltaic (PV) systems has made them an important component of the global clean energy strategy. However, the intermittency and non-linear characteristics of photovoltaic (PV) output remain major challenges for stable renewable energy utilization. This study proposes an adaptive improved particle swarm optimization (IPSO)-based maximum power point tracking (MPPT) strategy integrated with hybrid energy storage coordination for photovoltaic systems. The IPSO introduces adaptive inertia adjustment, velocity clamping, and stagnation reinitialization, which improve the convergence robustness under dynamic irradiance and temperature conditions. The algorithm was benchmarked against Perturb & Observe (P&O), Incremental Conductance (INC), and standard PSO using …

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Green Lithium Extraction From Alkaline Brines Using A Dibenzoylmethane Octanol System, Maryam Gonbadi, Shayan Abrishami, Ozra Gholipour, Ana Vafadar, Amir Razmjou Oct 2026

Green Lithium Extraction From Alkaline Brines Using A Dibenzoylmethane Octanol System, Maryam Gonbadi, Shayan Abrishami, Ozra Gholipour, Ana Vafadar, Amir Razmjou

Research outputs 2022 to 2026

The escalating global demand for lithium necessitates the development of efficient, selective, and environmentally sustainable extraction technologies from brine resources. This study presents a novel solvent extraction system employing dibenzoylmethane (DBM) as a lithium-selective β-diketone chelating extractant and 1-octanol as a biodegradable diluent for lithium recovery from alkaline salt lake brines. The effects of critical process parameters, including aqueous phase pH, extractant concentration, and total dissolved solids (TDS), on lithium extraction efficiency and selectivity were systematically investigated. Under optimized conditions (pH 12, 0.3 M DBM in 1-octanol), the system achieved a single-stage lithium extraction efficiency of 80.8% with exceptional separation …

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Ai-Driven Biomarker Discovery & Progression Modeling For Precision Diagnosis Of Glaucoma, Cheng Huang Oct 2026

Ai-Driven Biomarker Discovery & Progression Modeling For Precision Diagnosis Of Glaucoma, Cheng Huang

Computer Science and Engineering Theses and Dissertations

This dissertation presents a comprehensive study on the integration of artificial intelligence (AI) for glaucoma diagnosis and retinal image analysis. Leveraging multimodal imaging data including fundus photography, Optical Coherence Tomography Optical Coherence Tomography (OCT) and Optical Coherence Tomography Angiography (OCTA), the research develops a suite of deep learning frameworks designed to detect early glaucomatous changes with high precision, robustness, and interpretability. A series of novel architectures are introduced, spanning vessel segmentation networks, biomarker discovery pipelines, and multimodal fusion models, all designed to enhance diagnostic accuracy and generalizability across diverse populations. To facilitate reproducible and scalable ophthalmic AI research, this work …

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Treatment Of Municipal Solid Waste Incineration Bottom Ash Using Cement For Integrated Recovery Of Hydrogen And Production Of Supplementary Cementitious Material, Muhammad Haris Javed, Wenyu Liao, Fathma Zuhra, David Vollero, David Schmidenberg, Hongyan Ma Oct 2026

Treatment Of Municipal Solid Waste Incineration Bottom Ash Using Cement For Integrated Recovery Of Hydrogen And Production Of Supplementary Cementitious Material, Muhammad Haris Javed, Wenyu Liao, Fathma Zuhra, David Vollero, David Schmidenberg, Hongyan Ma

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Municipal solid waste incineration (MSWI) bottom ash (MBA) represents both a disposal challenge and a potential resource for sustainable construction materials, including use as a supplementary cementitious material (SCM). However, the conventional use of MBA in cement-based materials is hindered by its high metallic aluminum content, which reacts in alkaline environments to release hydrogen gas and causes volumetric instability (e.g., expansion and cracking). This study investigates a cement-based alkali pretreatment method using diluted cement suspension to stabilize metallic aluminum and improve the compatibility of MBA as an SCM. Compared to conventional alkali solutions, the proposed approach avoids the direct use …

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Rheology Modifiers For Water-Based Slurry Pipeline Transportation: Overview, Efficiency, And Intensification, Ahmed Alalou, Mohammed El Asri, Ahmed Boulahna, Muthanna H. Al-Dahhan Oct 2026

Rheology Modifiers For Water-Based Slurry Pipeline Transportation: Overview, Efficiency, And Intensification, Ahmed Alalou, Mohammed El Asri, Ahmed Boulahna, Muthanna H. Al-Dahhan

Chemical and Biochemical Engineering Faculty Research & Creative Works

Owing to their cost-efficiency and eco-friendliness, water-based slurry pipelines are the state-of-the-art technology for the transportation of several types of solids such as coal, fly ash, petcoke, iron ore, phosphate rock and other minerals. This review comprehensively examines the effect of numerous commercial dispersants and novel chemical additives that are used as rheology modifiers to enhance the slurryability and fluidity of solid-water slurries at high solids loading. Several key performance parameters such as the rheological characteristics (shear stress, apparent viscosity, yields stress, and flow behavior), the potential stability, surface tension and contact angle that highlight the efficiency of the rheology …

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Ph-Compensated Hydrogen Peroxide Quantification Using A Dual-Modal Fiber-Optic Probe, Homayoon Soleimani Dinani, Bohong Zhang, Maryam Karimi, Rex E. Gerald, Shelley D. Minteer, Jie Huang Oct 2026

Ph-Compensated Hydrogen Peroxide Quantification Using A Dual-Modal Fiber-Optic Probe, Homayoon Soleimani Dinani, Bohong Zhang, Maryam Karimi, Rex E. Gerald, Shelley D. Minteer, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

We report a dual-modal fiber-optic probe that integrates electrochemical quantification of hydrogen peroxide (H₂O₂) with co-localized fluorescent pH sensing for pH-indexed interpretation of the H₂O₂ response. H₂O₂ is a reactive oxygen species involved in oxidative stress, inflammation, and cellular signaling, and local pH modulates both its production and electrochemical response. Many electrochemical H₂O₂ sensors exhibit pH-dependent sensitivity, creating ambiguity unless pH is measured and used for compensation, which is difficult in small, heterogeneous, or rapidly changing microenvironments. A three-electrode configuration—working (WE), counter (CE), and Ag/AgCl pseudo-reference (pRE) electrodes—is fabricated directly on the cylindrical surface of a 710-µm-diameter optical fiber using …

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Graphene Oxide–Driven In Situ Bismuth Reduction Enables Highly Efficient Electroreduction Of Co2 To Formic Acid, Hao Feng, Bohong Zhang, Jie Huang, Xinhua Liang Oct 2026

Graphene Oxide–Driven In Situ Bismuth Reduction Enables Highly Efficient Electroreduction Of Co2 To Formic Acid, Hao Feng, Bohong Zhang, Jie Huang, Xinhua Liang

Electrical and Computer Engineering Faculty Research & Creative Works

To enable large scale efficient electrochemical CO2 reduction reaction (CO2RR) to formic acid (HCOOH), it is important to develop catalysts that can be operated in a wide potential window with good stability. Herein, we successfully synthesized Bi2O3 catalyst supported on graphene oxide (GO) and graphene (G) and found that Bi2O3/GO catalyst had a better overall performance than Bi2O3/G. The Bi2O3/GO catalyst demonstrated an outstanding CO2RR performance with a greater than 90% faradaic efficiency (FE) across a wide applied potential window …

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Thermo-Mechanical Behaviour Of Metal-Coated Optical Fibers For Distributed High-Temperature Sensing: From Laboratory Characterization To Industrial Case Validation, Koustav Dey, Rony Kumer Saha, Bohong Zhang, Laura Bartlett, Jeffrey D. Smith, Ronald J. O'Malley, Jie Huang Oct 2026

Thermo-Mechanical Behaviour Of Metal-Coated Optical Fibers For Distributed High-Temperature Sensing: From Laboratory Characterization To Industrial Case Validation, Koustav Dey, Rony Kumer Saha, Bohong Zhang, Laura Bartlett, Jeffrey D. Smith, Ronald J. O'Malley, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Reliable distributed temperature sensing in high-temperature environments remains a significant challenge due to the thermal and mechanical limitations of conventional optical fibers. In particular, polymer-coated fibers degrade above ∼300 °C due to coating failure, mechanical fragility and hydrogen ingress. Metal-coated optical fibers offer a robust alternative for harsh environments such as Electric Arc Furnaces (EAFs), aerospace engines, nuclear systems, and oil and gas wells, owing to their superior mechanical strength and hermetic sealing. In this work, a first comprehensive experimental investigation of the thermo-mechanical behavior of metal-coated optical fibers for distributed high temperature sensing is presented over a wide temperature …

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An Eco-Inspired Emulsion Membrane Harnessed With Waste Vegetable Oil For Effective Separation Of Ibuprofen: Insights Into Stability, Performance, And Kinetics, Huma Warsi Khan, A. Vijaya Bhaskar Reddy, Parvez Alam Khan, Samsur Akm Rahman, Muhammad Moniruzzaman Oct 2026

An Eco-Inspired Emulsion Membrane Harnessed With Waste Vegetable Oil For Effective Separation Of Ibuprofen: Insights Into Stability, Performance, And Kinetics, Huma Warsi Khan, A. Vijaya Bhaskar Reddy, Parvez Alam Khan, Samsur Akm Rahman, Muhammad Moniruzzaman

Publications and Research

Emulsion liquid membranes (ELMs) have emerged as excellent alternatives for the separation and recovery of biologically active drugs (BADs) from industrial waste at trace concentrations. Conventional ELMs generally utilize toxic petroleum-based solvents (PBS) that pose environmental and health risks. Although virgin vegetable oils have been appeared as sustainable alternatives to PBS, their widespread application is limited by economic constraints, resource sustainability, and competition with the food supply. In contrast, waste vegetable oil (WVO) represents an abundant, low-cost, and eco-friendly alternative, yet its potential as a green diluent for ELMs remains largely unexplored. To address this challenge, present study investigated the …

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Sensors And Smart Food Packaging For Supply Chains: Bridging Expiration Dates And Actual Perishability, Muhammad Umar Azam, Jolina Rodrigues, Mohamed Hamid Salim, Sarath Haridas Kaniyamparambil, Khalid Askar, Imane Belyamani, Blaise L. Tardy, Nouha Alcheikh Oct 2026

Sensors And Smart Food Packaging For Supply Chains: Bridging Expiration Dates And Actual Perishability, Muhammad Umar Azam, Jolina Rodrigues, Mohamed Hamid Salim, Sarath Haridas Kaniyamparambil, Khalid Askar, Imane Belyamani, Blaise L. Tardy, Nouha Alcheikh

All Works

The food supply chain is undergoing critical changes to minimize environmental hazards, such as those associated with packaging, and reduce food waste and loss. One of the critical components of the latter is associated with the high variance in the perishability of individual foodstuffs. Herein, we review the state of the art in smart sensor systems and emphasize the critical role they could play in addressing the mismatch between “batch” based expiry dates and individual food products’ time-dependent responses to spoilage. Following a brief overview of food shelf life and associated legislation, a subsequent section summarizes the development of sensors …

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A Review Of Natural Hydrogen Generation From Iron-Rich Rocks: Mechanisms, Influencing Factors, Techniques, And Knowledge Gaps, Kaveh Moghanirahimi, Lionel Esteban, Marina Pervukhina, Muhammad Arif, Stefan Iglauer, Alireza Keshavarz Oct 2026

A Review Of Natural Hydrogen Generation From Iron-Rich Rocks: Mechanisms, Influencing Factors, Techniques, And Knowledge Gaps, Kaveh Moghanirahimi, Lionel Esteban, Marina Pervukhina, Muhammad Arif, Stefan Iglauer, Alireza Keshavarz

Research outputs 2022 to 2026

Natural hydrogen has emerged as a promising clean energy resource with significant potential for large-scale subsurface production. Among the various geological sources, water–rock interactions involving iron-bearing rocks are among the most extensively studied pathways for natural hydrogen generation. This study provides a comprehensive review of hydrogen production from iron-rich lithologies, including mafic and ultramafic rocks, iron oxides, iron carbonates, and peralkaline granites. The fundamental mechanism involves the reduction of water coupled with the oxidation of ferrous iron to ferric iron under anoxic conditions. Key processes, including serpentinization, magnetite alteration, and siderite decomposition, are critically evaluated using experimental and modelling data. …

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Decoding Surface Chemistry Effects On Polystyrene Nanoplastic Fouling In Plasma-Grafted Pes Membranes With Distinct Functional Groups, Mohadeseh Najafi, Javad Farahbakhsh, Ebrahim Mahmoudi, Michael Johns, Masoumeh Zargar Oct 2026

Decoding Surface Chemistry Effects On Polystyrene Nanoplastic Fouling In Plasma-Grafted Pes Membranes With Distinct Functional Groups, Mohadeseh Najafi, Javad Farahbakhsh, Ebrahim Mahmoudi, Michael Johns, Masoumeh Zargar

Research outputs 2022 to 2026

Nanoplastic (NP) fouling remains a key challenge for ultrafiltration (UF) membranes, yet systematic, controlled comparisons of how membrane functional groups govern NP-membrane interactions are still limited. Here, the role of membrane surface chemistry was systematically investigated using a controlled comparative framework based on plasma-grafted polyethersulfone (PES) UF membranes tailored with structurally comparable methacrylate monomers bearing distinct terminal functionalities, including mono-2-(methacryloyloxy)ethyl succinate (MMES, -COOH), N-[3-(dimethylamino)propyl] methacrylamide (DMAPMA, -N(CH3)2), and [2-(methacryloyloxy)ethyl]dimethyl-(3-sulfopropyl)ammonium hydroxide (SBMA, zwitterionic). Comprehensive characterisations supported successful surface grafting while preserving the membrane substructure. Fouling and separation were assessed using polystyrene (PS) NPs having different surface chemistries, i.e., PS, PS-COOH and …

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Nanoparticle-Enabled Superplasticity In Extruded Az91 Magnesium Alloy With Elongation Exceeding 600%, Bai Xin Dong, Hong Yu Yang, Xin Zhang, Feng Qiu, Qi Chuan Jiang, Lai Chang Zhang Oct 2026

Nanoparticle-Enabled Superplasticity In Extruded Az91 Magnesium Alloy With Elongation Exceeding 600%, Bai Xin Dong, Hong Yu Yang, Xin Zhang, Feng Qiu, Qi Chuan Jiang, Lai Chang Zhang

Research outputs 2022 to 2026

No abstract provided.

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Numerically Evaluating The Effect Of Pin Geometries On Interlayer Material Mixing And Thermo-Mechanical Characteristics During Additive Friction Stir Deposition (Afsd), Numan Habib, Ana Vafadar, Ferdinando Guzzomi Oct 2026

Numerically Evaluating The Effect Of Pin Geometries On Interlayer Material Mixing And Thermo-Mechanical Characteristics During Additive Friction Stir Deposition (Afsd), Numan Habib, Ana Vafadar, Ferdinando Guzzomi

Research outputs 2022 to 2026

Additive Friction Stir Deposition (AFSD) is an additive manufacturing technique used to fabricate large-sized components layer-by-layer in a solid state, below the melting temperature. Poor interlayer mixing between subsequent layers results in a lack of mechanical interlocking, leading to low-strength components. The role of pin design plays a crucial role in fabricating high-strength components, requiring a comprehensive understanding of thermo-mechanical behaviour and flow pattern in the deposition zone. In this study, five different pin geometries are presented and investigated using a three-dimensional (3D) computational fluid dynamics (CFD) model. A user-defined function (UDF) was used to calculate strain- and temperature-dependent viscosity. …

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Experimental Investigation Of Internal Flow Structures And Permeate Flux Behaviour In Direct Contact Membrane Distillation, Ali Kandi, Mehdi Khiadani, Yujie Yuan, Abdellah Shafieian Sep 2026

Experimental Investigation Of Internal Flow Structures And Permeate Flux Behaviour In Direct Contact Membrane Distillation, Ali Kandi, Mehdi Khiadani, Yujie Yuan, Abdellah Shafieian

Research outputs 2022 to 2026

Understanding internal hydrodynamics of Direct Contact Membrane Distillation (DCMD) is crucial for desalination performance and mitigating temperature polarization. Despite its importance, quantitative experimental characterization of hydrodynamics governing boundary-layer development and transport enhancement within DCMD channels remains limited. This study presents the first spatially resolved Particle Image Velocimetry PIV measurement of velocity fields inside a smooth, actively operating flat-sheet DCMD channel, linking observed hydrodynamic structures to permeate flux. Two-dimensional velocity fields were captured at different streamwise positions for feed flow rates ranging from 0.7 to 4 L·min−1, enabling analysis of flow evolution and turbulence characteristics relevant to mass transfer. The results …

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Modeling Preannouncement And Launch Timing Decisions For Product Line Extension Under Innovation Uncertainty And Resource-Sharing Dual-Sourcing Supply Chain, Adewole Adegbola, Venkat Allada Sep 2026

Modeling Preannouncement And Launch Timing Decisions For Product Line Extension Under Innovation Uncertainty And Resource-Sharing Dual-Sourcing Supply Chain, Adewole Adegbola, Venkat Allada

Engineering Management and Systems Engineering Faculty Research & Creative Works

This research seeks to develop a system dynamics (SD) model to address challenges associated with the product line extension (PLE) problem. In this work, we specifically consider the "Two-Generation Product Line Extension (TGPLE) Problem", where a firm introduces a product variant in the market and plans to extend the launch to include a newer generation product in a resource-sharing dual-sourcing (RSDS) environment. We begin by identifying the key factors that influence product line extension, and we developed a TGPLE-RSDS construct based on three (3) inter-related systems: Market System, Production System and Supply Chain System. The proposed TGPLE-RSDS construct also illustrates …

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Cognitive Resilience At The Edge: Hyperdimensional Computing Versus Deep Learning For Hardware-Degraded Rf Classification, Adrian B. Cisneros, Jeong Yang Sep 2026

Cognitive Resilience At The Edge: Hyperdimensional Computing Versus Deep Learning For Hardware-Degraded Rf Classification, Adrian B. Cisneros, Jeong Yang

Military Cyber Affairs

Autonomous Collaborative Combat Aircraft (CCA) operating in contested electromagnetic environments must classify Radio Frequency (RF) signals on edge silicon that degrades over the mission lifetime due to thermal stress, radiation, and manufacturing variation. Deep neural networks dominate RF classification on pristine hardware, but their weights are precise and interdependent, causing catastrophic accuracy collapse as the underlying chip ages. We investigate whether Hyperdimensional Computing (HDC), a brain-inspired paradigm that distributes information across thousands of dimensions, can provide a reliability floor where Deep Learning fails. Using the RadioML 2016.10A dataset filtered to five digital modulations relevant to drone command-and-control links, we trained …

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Hands-On Ransomware: An Experiential Wannacry Case Study For Undergraduate Cybersecurity Education, Eli Creek Richmond, Thomas R. Devine Sep 2026

Hands-On Ransomware: An Experiential Wannacry Case Study For Undergraduate Cybersecurity Education, Eli Creek Richmond, Thomas R. Devine

Military Cyber Affairs

Ransomware represents one of the most disruptive threats in the cyber landscape, yet hands-on malware analysis remains rare in undergraduate cybersecurity curricula. This paper presents the design, implementation, and evaluation of an experiential learning module centered on the WannaCry ransomware case study, deployed in a senior-level course at West Virginia University. Students performed static and dynamic analysis using industry-standard tools. Pre- and post-module assessments demonstrated measurable gains in self-reported competency across seven technical dimensions. The module's competencies align directly with DoD Cyber Workforce Framework Work Role 212, Cyber Defense Forensics Analyst, supporting education-to-workforce pipeline development.

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Breaking The Build: Detecting Software Supply Chain Vulnerabilities In Ci/Cd Pipelines, Mercedes R. Wahl, Dr. Benjamin Yankson Sep 2026

Breaking The Build: Detecting Software Supply Chain Vulnerabilities In Ci/Cd Pipelines, Mercedes R. Wahl, Dr. Benjamin Yankson

Military Cyber Affairs

This study examines whether integrating structured DevSec- Ops security controls into CI/CD pipelines can reduce software supply chain risk by preventing vulnerable components from progressing through the software development lifecycle. Software supply chain attacks frequently originate from weaknesses or compromises within dependencies, build environments, and trusted development stages, making early detection essential. A controlled sandbox experiment compared two pipeline configurations: a baseline CI/CD pipeline with no automated security enforcement and a secure DevSecOps pipeline integrating automated vulnerability scanning, SBOM generation, and artifact integrity verification. A known vulnerable dependency, the Python requests package (version 2.19.0) associated with CVE-2018-18074, was intentionally introduced …

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Mitigating Common Vulnerabilities And Exposures In Cobol-Based Critical Systems Using The Strangler-Fig Pattern, Lauren E. Caruso, Vincent J. Compeau, Assefaw H. Gebremedhin Sep 2026

Mitigating Common Vulnerabilities And Exposures In Cobol-Based Critical Systems Using The Strangler-Fig Pattern, Lauren E. Caruso, Vincent J. Compeau, Assefaw H. Gebremedhin

Military Cyber Affairs

COBOL-based legacy systems continue to underpin critical infrastructure in banking and government sectors despite their age and associated cybersecurity risks. Originally developed through a Department of Defense–sponsored initiative to standardize business computing, COBOL remains widely used in mission-critical environments. However, these systems face increasing vulnerabilities due to outdated security architectures, workforce shortages, and rising maintenance costs. This paper examines cybersecurity and operational challenges associated with COBOL systems and evaluates the Strangler Fig pattern as a modernization strategy that enables incremental replacement while maintaining continuity. The findings highlight implications for financial institutions and public-sector organizations dependent on legacy infrastructure.

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From Framework To Toolchain: Implementing Zero Trust Architecture In Cloud-Native Environments For Dow Compliance, Shelby C. Snyder Sep 2026

From Framework To Toolchain: Implementing Zero Trust Architecture In Cloud-Native Environments For Dow Compliance, Shelby C. Snyder

Military Cyber Affairs

Federal agencies face a fiscal year 2027 target for enterprise-wide Zero Trust deployment, but NIST SP 800-207A defines logical components without identifying the Kubernetes technologies that implement them. This paper proposes a three-tier mapping of the Policy Engine, Policy Administrator, and Policy Enforcement Point to service mesh, microsegmentation, and perimeter tooling, stating the criteria by which each component is classified. It then applies a defined rubric to six Zero Trust vendors across component alignment, Kubernetes capability, federal authorization posture, and evidence quality, finding that no single vendor covers all three tiers. The mapping is a testable architectural proposition; a Stage …

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Hybrid Deep (Cnn-Bilstm) Intrusion Detection For Defense And Mission-Critical Networks, Corey A Cheng, Jermaine Anim-Addo, Asma Jakir Hussain, Zion O Smith-Fox, Sanjay Goel, Yuksel Celik Sep 2026

Hybrid Deep (Cnn-Bilstm) Intrusion Detection For Defense And Mission-Critical Networks, Corey A Cheng, Jermaine Anim-Addo, Asma Jakir Hussain, Zion O Smith-Fox, Sanjay Goel, Yuksel Celik

Military Cyber Affairs

This article examines how hybrid deep learning can strengthen intrusion detection for military and defense networks. Using the CSE-CIC-IDS2018 dataset, the study evaluates a CNN-BiLSTM model designed to detect benign traffic and multiple attack categories, including DDoS, DoS, botnet, brute-force, web attack, and infiltration activity. The model achieved strong multi-class detection performance, with 0.9893 accuracy and 0.9979 ROC-AUC. The findings suggest that AI-supported intrusion detection can improve cyber defense operations, analyst triage, and protection of mission-critical networks.

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Are Large Language Models Safe? A Vulnerability Analysis Of Generated Source Code, James Richards-Perhatch, Mitchell Milander, James M. Halvorsen, Assefaw Gebremedhin Sep 2026

Are Large Language Models Safe? A Vulnerability Analysis Of Generated Source Code, James Richards-Perhatch, Mitchell Milander, James M. Halvorsen, Assefaw Gebremedhin

Military Cyber Affairs

The increasing complexity of software and demands for rapid deployment have pushed the software industry to rely more on large language models (LLMs) in developing source code. However, as this technology is still relatively recent, questions can arise about the safety of the generated code. This paper presents an analysis of seven LLMs with respect to the presence of vulnerabilities within source code. Our findings show that LLMs are more likely to produce vulnerable web applications than vulnerable C programs, and that vulnerabilities are more likely to occur when program size and complexity increases.

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Semantic Shields: Automating Critical Infrastructure Defense Via Nlp-Driven Ransomware Profiling, Henry Trowbridge, Ian Zalcberg, Ryan Schley, Carter Yagemann, Natasha Phan, Srikar Maduposu, Vimal Buck Sep 2026

Semantic Shields: Automating Critical Infrastructure Defense Via Nlp-Driven Ransomware Profiling, Henry Trowbridge, Ian Zalcberg, Ryan Schley, Carter Yagemann, Natasha Phan, Srikar Maduposu, Vimal Buck

Military Cyber Affairs

Ransomware poses a growing threat to critical infrastructure, where successful attacks can disrupt operational technology (OT) and industrial control systems (ICS) with significant public safety consequences. However, attributing ransomware incidents to specific threat actors remains challenging due to ransomware-as-a-service ecosystems, actor rebranding, and the obfuscation of traditional indicators of compromise. This paper presents Semantic Shields, an NLP-driven attribution framework that leverages BERT-generated semantic embeddings and DBSCAN clustering to profile ransomware actors through the linguistic characteristics of ransom notes. Using a dataset of 295 ransom notes from 189 distinct threat groups, the framework achieved an 87.2% true positive clustering rate and …

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Llm-Generated Countermeasures For Iot Cyberattacks, James Alger, Michael Tu Sep 2026

Llm-Generated Countermeasures For Iot Cyberattacks, James Alger, Michael Tu

Military Cyber Affairs

The rapid expansion of the Internet of Things (IoT) has introduced significant cybersecurity challenges, particularly for resource-constrained devices that traditional intrusion detection systems often fail to protect effectively. This paper proposes a novel, two-phase autonomous security pipeline designed to bridge the gap between probabilistic threat detection and deterministic network enforcement. The framework first utilizes a custom Time Series Transformer (TST) to classify multivariate network traffic and identify specific attack vectors, such as ransomware, SQL injections, and malicious file uploads. In the second phase, an agentic AI layer, comprising a locally hosted Large Language Model (LLM) orchestrated via LangGraph, processes the …

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Foreward, Todd Arnold Sep 2026

Foreward, Todd Arnold

Military Cyber Affairs

No abstract provided.

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Letter From The Director: Mastery In Practice, Joseph Schafer Sep 2026

Letter From The Director: Mastery In Practice, Joseph Schafer

Military Cyber Affairs

No abstract provided.

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Use Of Generative Ai Tools In An Undergraduate Engineering Design Course, Wei Yin, Esther Ocharo, Lillian Best, Harrison Martin Sep 2026

Use Of Generative Ai Tools In An Undergraduate Engineering Design Course, Wei Yin, Esther Ocharo, Lillian Best, Harrison Martin

International Journal of Transformative Teaching and Learning in Higher Education

Generative AI tools are widely used by college students. This study aimed to investigate the best ways to teach students the limits of AI, especially in hands-on engineering classes. A class project in a sophomore biomedical engineering class was used to test the effectiveness of generative AI tools in supporting engineering CAD design and 3D modeling. Students were tasked with designing a finger basketball toy, creating 2D engineering drawings, and creating a physical prototype using 3D modeling and printing. Students used ChatGPT, Copilot, and Gemini to support the completion of the design project. Students’ manual work demonstrated satisfactory skills in …

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