Marquette University
New Jersey Institute of Technology
CUNY Queens College; CUNY Graduate Center
Gettysburg College
College of Civil Engineering and Architecture, Xinjiang University, Urumqi 830047, China
TÜBİTAK
Al Iraqia University
Arkansas Tech University
University of Colorado Colorado Springs
Dakota State University
Investigation Of The Production Technology Of Environmentally Safe Urethane Oligomers Based On Local Raw Materials,
2026
LLC “Tashkent Research Institute of Chemical Technology”, Tashkent, Republic of Uzbekistan;
Investigation Of The Production Technology Of Environmentally Safe Urethane Oligomers Based On Local Raw Materials, Bakhtiyor Kudratovich Shaykulov, Dilafruza Ruziboevna Akbarova Gulboeva, Fayzulla Nurmuminovich Nurqulov
Technical science and innovation
This paper investigates the step-by-step poly condensation pathway for synthesizing environmentally friendly, non-isocyanate urethane oligomers derived from local and safe raw materials: urea and ethylene glycol. The influence of thermodynamic parameters on the target product yield was systematically evaluated to determine the optimum technological matrix. Specifically, a 1:2 reactant mass ratio, a stable stationary temperature of 150-155 °C, and a reaction duration of 2 hours under an inert nitrogen atmosphere resulted in a sustainable and reproducible yield of 72%. Thermal degradation and competitive side-product condensations were activated above 160 °C, leading to a sharp drop in overall chemical efficiency. The …
Could 'Real Existing Ai' Be Sentient?,
2026
Central European University
Could 'Real Existing Ai' Be Sentient?, Tim Crane
Animal Sentience
Jonathan Birch is rightly sceptical about the idea that today’s AI machines (e.g. LLMs) are candidates for sentience. Nonetheless, he gives three reasons for taking the future possibility of AI sentience seriously. I dispute them all. Birch fails to distinguish the very idea of an artificial mind from computational AI (what I call ‘Real Existing AI’); he does not adequately distinguish a simulation from a replica or copy; and he fails to acknowledge that the appeal to computational functionalism in this context is straightforwardly question-begging.
Ai-Driven Biomarker Discovery & Progression Modeling For Precision Diagnosis Of Glaucoma,
2026
Southern Methodist University
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 …
Factors Influencing Enterprise Adoption Of Ai-Enabled Computers: An Expert Judgment Quantification Approach Across Multiple Sectors,
2026
National Taiwan Normal University, Taipei
Factors Influencing Enterprise Adoption Of Ai-Enabled Computers: An Expert Judgment Quantification Approach Across Multiple Sectors, Yu Shan Su, Tugrul Daim, Chia-Hao Hung, Leong Chan, Dana Bakry
Engineering and Technology Management Faculty Publications and Presentations
This study examines the key factors influencing the adoption of Artificial Intelligence Personal Computers (AIPCs) by enterprises, exploring both the benefits and challenges of their business applications. As enterprises increasingly require real-time computing, autonomous decision-making, and improved cybersecurity, AIPC—combining artificial intelligence and edge computing—has become a strategic technology for boosting competitiveness. Particularly in scenarios with less reliance on cloud services, businesses are more likely to adopt devices with local processing and standalone AI capabilities to meet the dual needs of operational efficiency and data privacy. Through an extensive review of the literature, this study identifies four main dimensions and sixteen …
A Unified Pipeline For Network-Based Gene Pattern Discovery,
2026
University of Missouri-St. Louis
A Unified Pipeline For Network-Based Gene Pattern Discovery, Siddhartha Reddy Pullannagari, Sharlee Climer
Undergraduate Research Symposium
Large-scale genetic datasets from diverse diseases, including cancer and neurodegenerative disorders such as Alzheimer’s disease, provide unprecedented opportunities to study disease pathogenesis and generate hypotheses for drug discovery. However, identifying meaningful genetic interaction patterns within these datasets remains challenging due to combinatorial relationships amongst genes and heterogeneous biological underpinnings across subtypes. This project systematically analyzes a diverse collection of omics datasets (e.g., GSE44076, GSE97810, GSE127711, GSE112679) obtained from the NCBI Gene Expression Omnibus (GEO), spanning multiple biological conditions and data types. We use a unified computational pipeline designed to handle variation in dataset size, structure, and context. The steps in …
The Algorithmic Narcissus: Ai Validation And The Atrophy Of The 'Athletic' Social Self,
2026
University of Queensland
The Algorithmic Narcissus: Ai Validation And The Atrophy Of The 'Athletic' Social Self, Ilham Phalosa Reswara, Anggi Mayangsari
Jurnal Psikologi Sosial
Artificial intelligence systems designed around continuous affirmation and minimal friction are becoming increasingly prominent as social and relational partners in everyday life. This article examines how sustained interaction with such frictionless AI systems may reshape the developmental conditions under which social selfhood forms and is maintained. Drawing on Cooley's (1902) looking glass self, Kohut's (1971) concept of optimal frustration, and the developmental literature on social competence, the article argues that frictionless AI may distort the social mirror through which identity forms, remove the manageable frustration that appears necessary for psychological growth, weaken empathic capacity, and erode the social stakes that …
Context Matters: Evaluating Llm-Generated Knowledge Graph Schemas,
2026
University of Tübingen, German
Context Matters: Evaluating Llm-Generated Knowledge Graph Schemas, Ritvik Garimella, Riju Marwah, Atishay Jain, Khusham Bansal, Amit Sheth
Publications
Knowledge graph (KG) schema engineering is labor-intensive and resists automation at scale. We investigate whether LLMs can generate domain-specific KG schemas of sufficient quality for downstream symbolic reasoning. We propose a tiered contextual framework that varies domain context richness across four levels: zero context, domain scope, task requirements, and data distribution. Generated schemas are evaluated intrinsically on BioRED (600 PubMed abstracts, multi-type entities and relations), where automated tiered schemas match an established KG construction baseline at 79.9% EC, with edge conformance rising from 47.5% at L1 to a stable 78–80% from L2 onward. Extrinsic evaluation on a 50-record MedHop controlled …
Cognitive Resilience At The Edge: Hyperdimensional Computing Versus Deep Learning For Hardware-Degraded Rf Classification,
2026
Texas A&M University-San Antonio
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 …
Hands-On Ransomware: An Experiential Wannacry Case Study For Undergraduate Cybersecurity Education,
2026
West Virginia University
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.
Breaking The Build: Detecting Software Supply Chain Vulnerabilities In Ci/Cd Pipelines,
2026
SUNY Albany
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 …
Mitigating Common Vulnerabilities And Exposures In Cobol-Based Critical Systems Using The Strangler-Fig Pattern,
2026
Washington State University
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.
From Framework To Toolchain: Implementing Zero Trust Architecture In Cloud-Native Environments For Dow Compliance,
2026
Northeastern University
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 …
Hybrid Deep (Cnn-Bilstm) Intrusion Detection For Defense And Mission-Critical Networks,
2026
University at Albany, SUNY
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.
Are Large Language Models Safe? A Vulnerability Analysis Of Generated Source Code,
2026
Washington State University
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.
Semantic Shields: Automating Critical Infrastructure Defense Via Nlp-Driven Ransomware Profiling,
2026
The Ohio State University
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 …
Llm-Generated Countermeasures For Iot Cyberattacks,
2026
Purdue University Northwest
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 …
Foreward,
2026
Military Cyber Affairs
Letter From The Director: Mastery In Practice,
2026
Military Cyber Institute
Letter From The Director: Mastery In Practice, Joseph Schafer
Military Cyber Affairs
No abstract provided.
Challenges, Trends, And The Role Of Lstm In Ai-Based Predictive Maintenance Of Electrolyzers For Solar Hydrogen Systems: A Review,
2026
Faculty of Electrical & Electronics Engineering Technology, Universiti Malaysia Pahang Al- Sultan Abdullah, 26600 Pekan, Pahang, Malaysia
Challenges, Trends, And The Role Of Lstm In Ai-Based Predictive Maintenance Of Electrolyzers For Solar Hydrogen Systems: A Review, Yani Koerniawan Kuatno, Muhamad Zahim Sujod
Turkish Journal of Electrical Engineering and Computer Sciences
The transition toward low-carbon energy systems has increased interest in hydrogen as a clean energy carrier, with solar-driven water electrolysis emerging as a promising technology due to its high efficiency and compatibility with renewable energy sources. However, dynamic operating conditions and intermittent renewable input accelerate electrolyzer degradation, reducing reliability and system lifespan. Predictive maintenance (PdM), supported by artificial intelligence (AI), offers a data-driven approach to anticipate failures and improve operational durability. This review systematically investigates AI-based PdM approaches for electrolyzers, with an emphasis on long short-term memory (LSTM) networks and Internet of things (IoT) integration. Following PRISMA 2020 guidelines, 35 …
Dynamic Focal Loss Adjustment For Railway Defect Detection,
2026
Eskisehir Technical University
Dynamic Focal Loss Adjustment For Railway Defect Detection, Mehmet Koç, Ridvan Özdemi̇r, Ömer Gerek
Turkish Journal of Electrical Engineering and Computer Sciences
Railway infrastructure is critical to the safe and efficient operation of transportation systems, and the early detection of defects is essential for preventing catastrophic failures. Automated defect detection methods are therefore crucial for maintaining continuous safety while reducing maintenance costs. Although Focal Loss is widely used in object detection under class-imbalanced conditions, its fixed α parameter may limit its effectiveness in detecting rare defects. In this study, we propose an adaptive α-tuned Focal Loss approach that dynamically adjusts class weights based on average precision (AP) values. By iteratively optimizing α without relying on gradient-based optimization, the proposed method improves the …
