Computational Engineering Commons^™

Feasibility Analysis Of Thermal Oxidizer To Determine Remaining Life Using Fitness- For-Service Level 3 Method, Yudhi Yudistirawan, Donanta Dhaneswara, Wahyuaji Narottama Putra, Gama Widyaputra, Dewi Kurnia Suci, Agung Putra Mahardhika

Journal of Materials Exploration and Findings

An aged thermal oxidizer (TOX) in the oil and gas industry necessitates a comprehensive evaluation to ensure its continued safe operation. This study presents a Remaining Life Assessment (RLA) and a Fitness for Service (FFS) evaluation for the four main components of the TOX, in accordance with API 510, API 579/ASME FFS-1, and ASME BPVC Section VIII Div-1 standards. The investigation includes the determination of maximum stress and maximum temperature required to assess the operational viability of the reactor. The four components are radiant, convection, transition, and stack sections—were modeled using the finite element method (FEM). Following the geometric modeling, …

Assessing Passenger Electric Vehicle Growth Strategies And Their Impacts On Electricity Demand Load And Co2 Emissions In Aceh Province To Achieve Net Zero Emission Target By 2060, Taufik Hidayat, Widodo Wahyu Purwanto

Journal of Materials Exploration and Findings

The electrification of the transport sector is a crucial pathway for achieving Indonesia’s Net Zero Emissions (NZE) target by 2060. This study assesses the potential impact of passenger electric vehicle (EV) penetration on electricity demand and CO2 emissions in Aceh Province through a scenario-based modelling approach. Two policy-aligned scenarios are assessed: a low-penetration (LP) scenario and a high-penetration (HP) scenario. Using the Gompertz model and the ASIF framework, total CO2 emissions were projected from 2020 to 2060. Results show that although higher EV penetration reduces direct emissions from internal combustion engine (ICE) vehicles, total CO2 emissions increase more significantly under …

Behavioral Biases As Drivers Of Complexity In Stock Markets: An Agent-Based Modeling Approach, David Joseph Dr, Alwin Joseph, Blesson James, Kajal Dass

Northeast Journal of Complex Systems (NEJCS)

By modeling financial systems as Complex Adaptive Systems, this study investigates how behavioral biases influence emergent complexity in stock markets. The study integrates heterogeneous agents, such as rational traders, herding agents, overconfident traders, and anchoring/disposition-driven investors, within a Limit Order Book framework calibrated to both U.S. and Indian market conditions using an Agent-Based Modeling (ABM) approach implemented through the high-fidelity ABIDES simulation environment. Price dynamics, volatility patterns, and liquidity structures were analyzed by Monte Carlo simulation experiments with different behavioral compositions. The results show that behavioral biases cause nonlinear price reactions, produce heavy-tailed return distributions that distort order-book complexity, and …

Numerical Study Of Nutrient Mixing In Trabecular Bone In Microgravity, Disuse And Normogravity, Sagar Gharti

Doctoral Dissertations and Master's Theses

Mechanical loading is known to regulate bone remodeling by driving interstitial fluid flow which stimulates cells and drives nutrient transport within the trabecular network. In microgravity, the absence of mechanical stimulation or loading suppresses convective flow processes, causing diffusion-driven nutrient mixing and renewal, and accelerated bone loss. This thesis investigates how oscillation frequency and trabecular bone density jointly control nutrient mixing and wall shear stress within trabecular cavities.

A computational fluid dynamics (CFD) framework is developed in STAR-CCM+ using a soft-cap oscillation model that mimics cyclic compression. Three idealized trabecular morphologies are simulated across different frequencies to represent microgravity or …

Reconstruction Of Information System Acceptance Model In The Era Of Integrated Artificial Intelligence: A Systematic Literature Review, Ilham, Merlin Apriliyanti

Library Philosophy and Practice (e-journal)

This study aims to explain the rapid development of Artificial Intelligence (AI) which has driven significant transformations in the development and use of information systems. However, most classical information system acceptance models, such as the Technology Acceptance Model (TAM) and  (UTAUT), have not been able to fully explain the unique characteristics of AI-based systems that are autonomous, adaptive, and complex. This study aims to reconstruct the information system acceptance model in the era of integrated AI through a Systematic Literature Review (SLR) approach. This study was conducted using the PRISMA protocol on 130 leading scientific articles indexed by Scopus and …

Ai-Scm Cmm: A Capability Maturity Model For Artificial Intelligence Integration In Supply Chain Management, Lordt Becklines, Omar F. El-Gayar, Patti Brooks, Insu Park

Annual Research Symposium

Artificial intelligence is increasingly deployed in supply chain management, yet many organizations struggle to align adoption efforts with process readiness, data quality, governance, and workforce capabilities, and they still lack validated supply chain specific roadmap for assessing readiness, sequencing investments, and reducing implementation risk. This study develops and evaluates a Capability Maturity Model for Artificial Intelligence Integration in Supply Chain Management to address that gap. Using a design science research approach, the study synthesizes prior literature and practitioner knowledge to define maturity dimensions, capability indicators, and staged progression levels for AI integration in supply chain contexts. The artifact and assessment …

A Capability Maturity Model For Artificial Intelligence Integration In Supply Chain Management, Lordt Becklines

Dissertations

Artificial Intelligence (AI) is transforming Supply Chain Management (SCM), yet many organizations struggle to assess their readiness for AI adoption and to understand how AI capabilities develop across maturity stages. This dissertation addresses this gap by developing a Capability Maturity Model (CMM) for AI integration in SCM, grounded in Organizational Information Processing Theory (OIPT), the Resource-Based View, and related capability frameworks. The model provides a structured approach for evaluating an organization's information-processing requirements, resource configurations, and alignment needed for effective AI-enabled supply chain operations.

Using a design science research approach, the AI-SCM CMM and its associated assessment instrument were derived …

Trusted Forms—The Case Of Barack Obama’S Birth Certificate, Karl-Heinrich Schmidt, Frederik Schlupkothen

Proceedings from the Document Academy

Documents can be understood as containers for information that enable remote communication between people across space and time. They are therefore fundamentally subject to issues of trust. The theoretical treatment of documents as a means of remote communication has a long history and has arrived in the age of electronic document exchange, as shown by the work of R. T. Pédauque, which is taken as a starting point here.

To take theory further, form-based documents are discussed here as an example. Form-based documents control the information provided by fillers by specifying form fields. The core of this paper is to …

Mechanical Characterization And Modeling Of Rat Myocardia Under The Influence Of Epirubicin, Abdallah Mahmoud Alkhaiyat

Theses and Dissertations

Cancer remains a predominant health challenge that is responsible for a significant portion of global morbidity and mortality. Epirubicin (EPI), a chemotherapeutic anti-cancer drug, has shown remarkable efficacy in combating various malignancies. However, it is known to have undesirable side effects on the heart, collectively referred to as cardiotoxicity. This research investigates the adverse effects of chemotherapy on cardiac contractility through an in vitro examination of the mechanics of healthy and infarcted animal heart tissues. Electrically stimulated slices of rat ventricular tissue were tested using an isometric force measurement tissue bath. The tissue slices were subjected to uniaxial stretching, allowing …

A Virtual-Reality-Based Dental Simulator For Endodontics With Automated Formative Feedback, Yousef Salah Abo El Ela

Theses and Dissertations

Advancements in virtual reality (VR) and haptic technology are transforming the landscape of medical and dental education, offering new avenues for safe, immersive, and repeatable training experiences. Within dentistry, endodontics presents unique challenges for preclinical education due to anatomical complexity, limited access to extracted teeth, ethical concerns, and the shortcomings of conventional plastic models. Despite endodontics specific plastic teeth being available, they fall short of replicating the hardness of real extracted teeth, are relatively costly compared to generic plastic teeth, and are ultimately a disposable item which makes them inadequate as a sustainable long-term solution. Extracted teeth do a much …

Mapping Leo Satellite Internet Performance Using Mobile Starlink Deployment, Annika Govil, Jacob Gray

UROP Posters

High-speed, low-latency internet connectivity on the move is a critical challenge for applications in connected vehicles, disaster response, and remote education. While terrestrial networks such as 4G or 5G are widespread, they lack coverage in remote or rural geographic areas.

Low Earth Orbit (LEO) satellite constellations, such as SpaceX's Starlink, promise global high-bandwidth, low-latency internet. However, their performance is well-documented in stationary scenarios, while data for *mobile* applications is scarce. This project explores the feasibility and real-world performance of LEO satellite internet while in motion.

Current research documents the performance of LEO satellite constellations in stationary settings. However, data concerning …

Deterministic Black-Box Context-Free Grammar Inference: Scaling From Small To Big Languages, Mohammad Rifat Arefin

Computer Science and Engineering Dissertations

Context-free grammars are essential for a number of software engineering tasks like program understanding, debugging, and grammar-based testing. However, formal language descriptions (i.e. grammars) are often unavailable, outdated, or inaccessible because parsers are closed-source or cannot be instrumented. Black-box grammar inference addresses this gap using only valid sample programs and parser acceptance decisions. The problem is challenging because finite samples rarely exercise all language features or their combinations, while the inference process cannot inspect the parser’s internal representation of syntax. This dissertation develops deterministic techniques for black-box context-free grammar inference. The first tool, TreeVada introduces the structural assumption that balanced …

Probing Proficiency-Related Neural Representations With Pca And Ica, Onila R. Narayana Mudalige Don

Graduate Student Theses, Dissertations, & Professional Papers

How proficiency-related information is represented in task fMRI depends not only on the data themselves, but on the representational lens used to summarize them. This thesis examines second-language (L2) proficiency as a problem of representational organization rather than as a simple classification exercise. Using task fMRI from adult language learners performing semantic animacy judgments in their native language (L1) and second language (L2), I derive shared low-dimensional network representations with principal component analysis (PCA) and independent component analysis (ICA), then evaluate those representations under matched leakage-controlled decoding pipelines.

Across analyses, the central comparison is between representational frameworks rather than between …

Apache Hadoop Installation And Configuration Steps, Safet Jahaj

Open Educational Resources

This tutorial covers the installation and configuration steps of Apache Hadoop on a Windows operating system.

Generative Imaging For Computational Pathology, Md Jillur Rahman Saurav

Computer Science and Engineering Dissertations

Hematoxylin and eosin (H&E) staining remains central to cancer diagnosis, providing morphological information essential for pathological assessment. Immunohistochemistry (IHC) and newer multiplexed imaging technologies complement H&E by revealing molecular information critical for accurate tumor subtyping and treatment decisions. In practice, however, H&E and IHC are obtained from different consecutive sections that are not spatially aligned, comprehensive multiplexed panels are expensive and tissue-consumptive, and not all stains are available at every clinical site, limiting comprehensive molecular profiling and the full diagnostic potential of these technologies in clinical practice. This dissertation addresses these gaps through three complementary generative deep learning studies in …

Computational And Ai Frameworks For Identifying Key Regulatory Genes And Their Target Genes In Plants And Humans, Md Khairul Islam

Dissertations, Master's Theses and Master's Reports

This dissertation presents computational and AI-driven frameworks for identifying key regulatory genes and their downstream targets across plant and human biological systems. Three studies address distinct challenges in genomic regulation using advanced machine learning and bioinformatics approaches.

The first study introduces DyGAF (Dynamic Gene Attention Focus), a dual-attention transformer framework that identifies and ranks disease-relevant biomarker genes by simultaneously modeling independent molecular responses and interdependent regulatory network behavior. Two attention models provide complementary perspectives on gene importance and are fused through a novel combination metric. Applied to COVID-19 nasopharyngeal swab profiles, the attention-weighted representations achieved 94.23% classification accuracy, high sensitivity, …

Robust State Estimation And Scene Understanding For Autonomous Ground Vehicles In Challenging Environments, Nader J. Abu-Alrub

Dissertations, Master's Theses and Master's Reports

Autonomous ground vehicles require reliable state estimation and scene understanding to operate safely in challenging environments. Poor illumination, adverse weather, degraded visibility, snow-covered roads, and sensor noise can reduce the reliability of conventional localization and perception pipelines. Addressing these challenges requires sensing and algorithmic strategies that exploit the complementary strengths of different modalities. Radar is particularly attractive because of its resilience to lighting and weather variations, while inertial, camera, and lidar measurements provide additional motion, appearance, and geometric information. This dissertation investigates compact radar-based and sensor-fusion approaches for improving autonomous ground-vehicle state estimation and scene understanding under degraded sensing conditions. …

Clear Skies, Avery C. Munn

Williams Honors College, Honors Research Projects

Air quality impacts public health, environmental sustainability, and quality of life. However, accurate and easily accessible short-term air quality forecasting is challenging to find. This project, Clear Skies, presents a machine learning–based system for forecasting next-day Air Quality Index (AQI) levels across regions in Ohio. By using historical pollutant data with variables such as temperature, humidity, wind speed, and atmospheric pressure, the system finds relationships that traditional statistical models often don’t show.

Machine learning models are evaluated alongside AI techniques to find the environmental factors that influence AQI predictions. This helps reduce the “black box” nature of many AI systems …

Quantifying Hemodynamics In Implantable Cardiac And Renal Support Devices Using Computational Modeling, Alexander D. De Oliveira

Summer Research Program Abstracts

No abstract provided.

A Rapid, Accessible Active Learning Framework For Stem At Wvu Tech, Somenath Chakraborty, Zahmeeth Sayed Sakkaff

2026 Scholarly Teaching Conference: Poster Session Papers

Many undergraduates find it difficult to move from listening to doing—especially in STEM courses that require sustained practice, timely feedback, and confidence. This poster describes a rapid, low-cost active-learning routine that can be added to existing lectures at WVU Tech with minimal redesign. Instructors insert brief engagement micro-cycles every 10–15 minutes: a concept question or mini-problem, a one-minute individual attempt, a short pair discussion, and a concise explanation that targets common misconceptions.