Digital Commons Network^™

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 …

Disturbance-Learning Inertia Estimation Using Artificial Neural Networks For Power System Stability, Sospeter Igaanja Gabriel, Francis Mwasilu, Peter Makolo Dr.

Tanzania Journal of Engineering and Technology (TJET)

ABSTRACT

Power systems are progressively shifting towards low inertia as a result of incorporating significant amounts of intermittent and converter-based renewable energy sources, such as wind and solar power, into the current power grid network. This integration poses considerable problems to inertia and frequency control within the network due to a reduction in the proportion of synchronous generators. Furthermore, rapid frequency deviations occur due to the disparity between supply and demand during contingencies, complicating the maintenance of frequency stability within the power system. The disturbance-learning inertia estimation method for power system stability is presented. The simulation analysis is performed using …

Feasibility Of Acetylated Icacina Trichantha Oliv. Tuber Starch As Viscosity And Fluid Loss Control Agents In Water-Based Drilling Mud. A Comparative Assessment With Commercial Starch, Kennedy I. Ogunwa, Theresa O. Uchechukwu, Ozioma Achugasim, Leo C. Osuji, Onyewuchi Akaranta

Tanzania Journal of Science

The need for sustainable, non-food drilling fluid additives has prompted the feasibility assessment of acetylated Icacina trichantha tuber starch as a viscosity modifier and fluid loss control agent in water-based muds. Native starch was acetylated to obtain two derivatives (Degree of Substitution DS = 0.38 and 0.68) and characterized using FTIR, swelling power, and physicochemical analyses. Drilling mud performance was assessed through rheology, gel strength, filtration loss, sorptivity, and model fitting. Acetylation improved swelling and reduced gelatinization temperature. The optimal formulation, Mud-ITO-ACS2 (DS = 0.68), exhibited the most favorable performance, combining low plastic viscosity (6 cP vs 9 cP for …

A Neutrosophic Memory-Integrity Calculus For Contradiction-Preserving Persistent Ai Agents, Rana Muhammad Zulqarnain, Saalam Ali

Neutrosophic Systems with Applications

Persistent AI agents increasingly convert interaction histories into long-lived memory, making memory transformation not retrieval alone—a central reliability problem. NMIC (Neutrosophic Memory-Integrity Calculus) formalizes the integrity of write, merge, consolidation, revision, and retrieval operations over persistent memory. Each proposition is represented through an evidence ledger carrying independent truth, indeterminacy, and falsity degrees together with reliability, provenance, temporal validity, contextual applicability, and inter-evidence dependence. A dependence-normalized hazard aggregation preserves simultaneous support and opposition while making the resulting state invariant to exact evidence duplication. Pure consolidation is governed by five integrity conditions: no support invention, no opposition invention, no manufactured certainty, contradiction …

Neutrosophic Indeterminacy-Transport Kolmogorov-Arnold Networks For Structured Uncertainty, Hafiz Muhammd Bilal, Kiran Naz, Anjum Ijaz

Neutrosophic Systems with Applications

Kolmogorov-Arnold Networks (KANs) replace fixed node activations with learnable univariate edge functions, but standard KAN inference treats two equal-valued features identically even when one is accompanied by an explicit quality warning. NIT-KAN introduces a neutrosophic indeterminacy-transport mechanism for this setting. Each node carries an indeterminacy state in [0, 1]; a monotone gate g(I)=⁽1-I)^α attenuates uncertain evidence on the predictive path, while a sensitivity-weighted transport rule carries indeterminacy through the underlying KAN computation. A terminal audit maps signed evidence to truth-support, falsity-support, and conflict-augmented indeterminacy without interpreting these quantities as class probabilities. We …

Neutrokoopman: Channel-Preserving Koopman Spectral Analysis Of Nonlinear Dynamics With Truth, Indeterminacy, And Falsity Evidence, Ahmed Samy, Mohamed M. Abdelhafeez, K Venkatachalam, Mohamed Abouhawwash

Neutrosophic Systems with Applications

Modern dynamical systems increasingly operate with evidence that is not merely noisy but incomplete, contradictory, or only partially trustworthy. Conventional Koopman methods represent nonlinear dynamics through linear evolution of observables, while robust and adaptive variants address parameter and model uncertainty. They do not, however, preserve the semantic distinction between support, indeterminacy, and counter-support when those conditions are compressed into a single uncertainty variable. This paper develops NeutroKoopman, a channel-preserving Koopman framework in which the physical state is augmented by a single-valued neutrosophic evidence state ν_t = ( T_t,I_t,F_t ). Deterministic and Markovian formulations are …

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 …

Decision Support System For The Selection Of Thumbprint Recognition Algorithms In Biometric Security Systems, Toqeer Jameel, Muhammad Riaz

Neutrosophic Systems with Applications

This study investigates fingerprint recognition in immigration operations, emphasizing the role of biometric verification in enhancing security, fairness, and operational efficiency in international mobility. To address the uncertainty, vagueness, and imprecision inherent in fingerprint identification, a novel decision-making framework is proposed by integrating interval-valued picture fuzzy (IVPF) information. Fairly aggregation operators are introduced to combine decision makers' evaluations, while extracted fingerprint features are modeled using positive, neutral, and negative membership degrees within the IVPF environment. Objective criterion weights are determined using the criteria importance through intercriteria correlation (CRITIC) method, and individual ranking is performed via the alternative ranking order method …

Fast Sparse Image Reconstruction Models In Through-The-Wall Radars: A Review, Aude Kileo, Hashimu U. Iddi, Abdi Abdalla

Tanzania Journal of Science

Through-the-Wall Radar Imaging (TWRI) is a modern technology that uses electromagnetic waves to detect objects behind walls, with key applications in surveillance, rescue operations, and reconnaissance. Achieving high resolution in both down-range and cross-range requires ultra-wideband signals and long apertures, resulting in large data volumes, increased acquisition time, and high memory demands. TWRI employs Compressive Sensing (CS) to reduce computational time, which has proved its significance in many recent TWRI applications. However, in CS, image reconstruction approaches shift the computational burden from the sensing stage to the recovery stage, which prolongs the reconstruction times, making it unsuitable in time-sensitive applications. …

Valorization Of Spent Vanadium Pentoxide Catalyst From The Sulfuric Acid Industry As An Economical Adsorbent For Crystal Violet Removal, Ibrahim Hamad Mr., Saddam Alaskary Mr., Abdel El-Mohsen Turky Prof., Nasser Mostafa Prof., Mai Hassan Roushdy Dr.

Chemical Engineering

Around 150 000–200 000 tons of spent vanadium pentoxide catalyst (SVC) are generated annually by sulfuric acid plants and are landfilled for $80–150 per ton. It is demonstrated here as an unmodified, zero-cost adsorbent achieving 94.5% ± 0.3% crystal violet (CV) removal at an adsorbent dose of 6.3 g L−1 and an initial concentration of 38.5 mg L−1 , corresponding to a working capacity of 6.1 ± 0.2 mg g−1 with zero material cost and no pretreatment requirement. Physicochemical characterization by XRF, XRD, BET analysis, DLS, SEM-EDX, FTIR spectroscopy, TGA, Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), and zeta potential analysis …

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 …

Efficient Acidic H2O2 Electrosynthesis Over Co Atoms Anchored On Nitrogen-Doped Hierarchical Porous Carbon, Xiao Huang, Zi-Hao Zhan, Hao-Xu Niu, Guan-Yu Luo, Jin-Tao Huang, Bo-Xuan Jin, De-Li Wang

Journal of Electrochemistry

The two-electron oxygen reduction reaction (2e⁻ ORR) presents a promising route for the on-site production of hydrogen peroxide (H₂O₂), offering a green alternative to energy-consuming anthraquinone process. However, the high selectivity toward the competing 4e⁻ ORR over the desired 2e⁻ pathway leads to low Faradaic efficiency for H₂O₂, posing a critical challenge in catalyst design. In this work, a nitrogen-doped hollow hierarchical porous carbon with anchored Co atoms (CoN/HPC) was constructed for high-performance H₂O₂ production. The as-prepared Co-N/HPC catalyst showed excellent 2e⁻ ORR performance, achieving …

Dense Platinum Nanoparticles Confined In Mn-N-C Nanocages For Robust Heavy-Duty Pemfcs, Lei Zhao, Zhen-Min Cao, Jia-Yu Zuo, Ming-Liang Yang, Hong-Yan Qiao, Jun-Song Chen, Rui Wu

Journal of Electrochemistry

High-loading Pt cathodes are essential for heavy-duty proton exchange membrane fuel cells but suffer from a critical tradeoff between ionomer sulfonate poisoning and nanoparticle instability. Herein, we report a spatial confinement strategy to encapsulate dense Pt nanoparticles (~51.8 wt%) within Mn/N-co-doped mesoporous carbon nanocages (denoted as Pt-MnNC). This architecture excludes bulky ionomers to create an ionomer-shielded environment against sulfonate poisoning, while Mn-N_x-mediated strong metal-support interactions anchor the Pt nanoparticles to prevent agglomeration and further boost durability. In the 5 × 5 cm² membrane electrode assembly tests, the Pt-MnNC catalyst delivers an exceptional power density of 1.26 W·cm …

Survey Data For Decision Support Tool Development With A Focus On Emerging Design Spaces, Tyson Humphrey, Chris Mattson, Michael L. Anderson, Colin C. Engebretsen

ScholarsArchive Data

This dataset contains the survey responses and PASE matrix used to support the derivation of nine development principles for decision support tools in emerging design spaces. The survey captures observations from a multidisciplinary DST development team, while the PASE matrix documents relationships among desired DST outcomes, architecture strategies, effects, and supporting literature.

Geotechnical Enhancement Of Noncohesive Coal Mine Overburden Using Microbially Induced Calcite Precipitation And Fly Ash Stabilization For Sustainable Pavement Applications, Atul Gautam, Laxmikant Yadu

Journal of Sustainable Mining

The majority of the waste generated from opencast mining, termed as overburden (OB), is dumped adjacent to the mine boundary and poses long-term stability issues. Various studies have been carried out to assess the risk of dump slope failure, and treatment strategies have been accordingly recommended to strengthen this OB dump. However, scant works are reported to sustainably utilize this OB for construction works. This study aims to quantify the performance of stabilized noncohesive coal mine OB collected from Gondegaon opencast mine (OCM) in India. Stabilization was achieved using two techniques, namely microbially induced calcite precipitation (MICP) through Sporosarcina pasteurii …

Scheduling The Charging Of Battery-Electric Vehicles Under Heterogeneous Scheduling Constraints, Justin Whitaker, Greg Droge, Mario Harper

Electrical and Computer Engineering Student Research

Adopting battery electric vehicles (EVs) for vehicle fleets requires scheduling charging alongside day-to-day operations. This problem is complicated by complex utility cost structures, limited battery capacity, and competition for shared charging resources. Existing methods that simultaneously schedule routes and charging neither address the full cost structure nor employ high-fidelity charging models, and no prior work analyzes fleets containing vehicles with heterogeneous routing constraints. This work addresses these gaps by combining a state-of-the-art flexible-schedule formulation with time-of-use (TOU) demand costs and a non-linear, variable-rate charging model as drawn from additional state-of-the-art works. The proposed method is validated against two state-of-the-art methods, …

Retraction Notice To "Hydrogen Recovery, Cleaning, Compression, Storage, Dispensing, Distribution System And End-Uses On The University Campus From Combined Heat, Hydrogen And Power System" [International Journal Of Hydrogen Energy 39 (2014) 647-653], Tarek A. Hamad, Abdulhakim A. Agll, Yousif M. Hamad, Sushrut Bapat, Mathew Thomas, Kevin B. Martin, John W. Sheffield

Engineering Management and Systems Engineering Faculty Research & Creative Works

This article has been retracted: please see Elsevier policy on Article Correction, Retraction and Removal (https://www.elsevier.com/about/policies-and-standards/article-withdrawal). This article has been retracted at the request of the Editor.

Multi-Solution Ternary Grover’S Algorithm For Logic-Based Quantum Machine Learning With Pseudo-Kronecker Reed–Muller Form Minimization, Sophia Lee, Ali Al-Bayaty, Marek Perkowski

Electrical and Computer Engineering Faculty Publications and Presentations

Optimization problems in machine-learning (ML) applications can be computationally challenging for classical methods, particularly when they involve large, unstructured search spaces. Quantum computing offers a promising approach to combinatorial optimization by utilizing quantum superposition and amplitude amplification. This paper presents a new methodology using logic-based quantum machine learning (QML) as a complete framework for employing a multi-solution ternary Grover’s algorithm to minimize incomplete binary functions represented by Pseudo-Kronecker Reed–Muller (PKRO) expansions, consistent with Occam’s razor principle. Unlike previous quantum approaches based on Kronecker Reed–Muller (KRO) or fixed-polarity Reed–Muller (FPRM) representations, our work introduces the first Grover-based optimization framework for PKRO …

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, 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.

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, 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.

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, 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 …

Hybrid Transformer-Bilstm Model For Early Fault Detection And Multiclass Classification Of Wind Turbine Faults Using Scada Data, Hassan Y. Mkindu

Tanzania Journal of Engineering and Technology (TJET)

Early and accurate fault detection in wind turbines is essential for improving operational reliability, reducing maintenance costs, and minimizing unplanned downtime. This study proposes a Hybrid Transformer-BiLSTM deep learning model for early fault detection and multiclass fault classification using Supervisory Control and Data Acquisition (SCADA) data. The proposed architecture combines the Transformer's self-attention mechanism to capture global temporal dependencies with the Bidirectional Long Short-Term Memory (BiLSTM) network's ability to model sequential fault evolution, enabling effective learning of multivariate time-series data. The model was developed and evaluated using the recently introduced CARE SCADA dataset, classifying five operating states: No Fault, Transformer …

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 …

Exploring The Role Of Single Pilot Operations In Night Cargo Aviation: A Phenomenological Study, Kollin Ellis

Doctoral Dissertations and Projects

The purpose of this qualitative descriptive phenomenology was to examine the lived experiences of pilots who have operated alone at night in an aviation cargo environment. The design was guided by an epistemological philosophical assumption. Extreme advancements in the technology of artificial intelligence and their applications in the aviation industry have raised the question of the value of a single operator serving in commercial cockpits. Some experts argue that a two-person crew is unnecessarily redundant. A significant gap in the current literature of experiential data existed to prompt this study. Through the contextual lens of the Dual Process Theory and …

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 …

Cuip-X25: A Real-World Network Intrusion Dataset For Next-Generation Ai-Driven Security, Arshad Iqbal, Sohail Asghar

Turkish Journal of Electrical Engineering and Computer Sciences

The efficacy of artificial intelligence (AI) in intrusion detection systems (IDS) is critically dependent on high-fidelity training data. However, as detailed in the manuscript's literature review, existing benchmark datasets are predominantly synthetic, outdated, or imbalanced and fail to capture the complexity of the contemporary threat landscape. To bridge this gap, this study introduces CUIP-X25, a novel real-world cyber-attack dataset captured over a four-month period using a dionaea honeypot deployed on a public network. Unlike synthetic alternatives, this dataset provides an authentic representation of modern adversarial tactics, techniques, and procedures, encompassing 3.16 million real events across ten distinct attack categories, including …

Empowering Edge Intelligence Through Reparameterized Lightweight Transformers And Distributed Inference, Hosein Esmaeili, Mohammad Ali Afshar Kazemi, Reza Radfar, Nazanin Pilevari

Turkish Journal of Electrical Engineering and Computer Sciences

Deploying advanced transformer-based models on resource-constrained edge devices remains a significant challenge due to their high memory footprint and substantial compute requirements. In this paper, we propose a reparameterized transformer framework that integrates High-Rank Factorization (HRF) during training, layer merging at inference, and dynamic, load-balanced distributed inference across multiple devices. To further reduce resource usage, our framework supports mixed-precision quantization down to 4-bit, enabling flexible accuracy–latency–energy trade-offs. Experimental evaluations on the ESC-50 environmental sound dataset demonstrate that our method matches or exceeds the performance of larger baseline models while using 20–30% fewer parameters, achieving up to 48% latency reduction in …