Digital Commons Network^™

Identifying Failure Behaviors In Stereolithography From Varied Print Orientations, Kevin Veltre

Theses and Dissertations

Stereolithography (SLA) is an additive manufacturing (AM) technique that utilizes photopolymerization to create three-dimensional shapes by strategically printing layers of material. Printing parameters such as layer orientation, print resolution and post cure can affect a specimen’s homogeneity, longevity and mechanical characteristics. Because of this variability, print parameters must be carefully considered when designing products and print processes for desired components, as manufacturers often fail to include all relevant print settings when advertising mechanical properties. For this study, 130 SLA-printed specimens were created with 100-micron print layer thickness and print orientations varying between 0 and 90, including intermediate orientations, to observe …

Provocable Forgiveness In Noisy Brand--Consumer Systems:An Agent-Based Study Of Repeated Interaction, Tahere Ahmadiyan, Hamidreza Navidi, Behbod Keshavarzi

Northeast Journal of Complex Systems (NEJCS)

Repeated brand--consumer exchange is often treated as a managerial problem of loyalty, recovery, and trust. It can also be read as a small complex system: many local decisions about cooperation, retaliation, and forgiveness accumulate into market-level selection. This study uses that perspective to examine which relational rules survive when communication is imperfect. Eight canonical Iterated Prisoner's Dilemma strategies are translated into marketing archetypes and evaluated through round-robin tournaments, a six-level noise sweep, proportional-fitness ecological dynamics, and finite-population Moran invasion tests. The tournament leaderboard is calculated without same-strategy self-play, so that reported payoffs reflect inter-archetype competition rather than homogeneous self-coordination. At …

Interstitial Fluid N-Glycans Serve As A Proxy For Serum Biomarker Discovery In A Pilot Study, Radka Saldova, Talus K. Gaymore, Caitriona Walsh, Eder Zavala, Paula M. Mendes

Student Papers, Posters & Projects

Protein glycosylation is the most prevalent post-translational modification and is known to undergo profound alterations in disease. While serum N-glycosylation has been extensively studied, interstitial fluid is gaining prominence as a minimally invasive biomarker source, underscored by the widespread clinical adoption of wearable glucose monitors in diabetic patients. However, the glycomic landscape of interstitial fluid remains largely unexplored. Here, we have performed a comparative analysis of the N-glycome from interstitial fluid and matched plasma from five healthy volunteers using liquid chromatography, mass spectrometry and exoglycosidase digestions. Strikingly, the interstitial fluid N-glycome closely shows overlapping profiles with the plasma of each …

Numerical Simulation Of Centrifugal Pump Performance Under Impeller Blade And Suction Diameter Variations, Gunawan Budi Santoso, Susanto -, Akhmad Nuriyanis, Anang Budhi Nugroho, Rizka Noor Miftakhul Ulum, Suyono -

Journal of Mechanical Engineering Science and Technology (JMEST)

The objective of this study is to model variations in pump geometry to obtain optimal performance under the effects of cavitation. The resulting geometric variation models were then simulated using ANSYS Fluent software. The first geometric variation model used a standard 5-blade impeller with suction pipe diameter variations of 2, 2 ½, 2 ¾, 3, 3 ¼, 3 ½, and 4 inches. The second geometric variation model for simulation used a standard 3-inch suction pipe with impeller blade variations of 3, 4, 5, 6, 7, 8, and 9. The simulation results indicated that the number of blades without changes in …

Privacy-Preserving Intrusion Detection For The Internet Of Medical Things Using Ensemble And Federated Learning, Theyab Alsolami

Electronic Theses and Dissertations 2020 - Present

The rapid proliferation of the Internet of Medical Things (IoMT) has transformed healthcare by enabling continuous monitoring, intelligent diagnostics, and data-driven clinical decision-making. However, this increased connectivity has significantly expanded the attack surface of healthcare systems, exposing sensitive patient data and critical medical devices to cyber threats such as intrusion and data exfiltration attacks. Ensuring both strong security and strict privacy preservation in IoMT environments remains a fundamental and unresolved challenge.

This dissertation investigates the design and evaluation of robust and privacy-preserving intrusion detection systems (IDS) for IoMT networks using advanced machine learning techniques. The research first examines the effectiveness …

Explainable Machine Learning For Biomedical Diagnostics: Optical Imaging And Eeg Signal Analysis, Fozia Rajbdad

LSU Doctoral Dissertations

The growing convenience of complex biomedical data begins new roads for better disease detection and functional identification via artificial intelligence (AI). Nevertheless, conventional analysis methods often rely on basic metrics that drop sensitive biotic differences, and various AI systems are difficult to infer, limiting their clinical reliability and practical use. There is a growing need for explainable, physiologically relevant computational models that can extract key biomarkers from diverse biomedical data sources. This dissertation addresses this problem by obtaining explainable machine learning and deep learning procedures for studying biomedical signals and optical imaging data.

This dissertation is divided into two parts; …

Improving Cancer Diagnosis And Patient Outcomes With Deep Learning Models, Mariana Arriz-Jorquiera

USF Tampa Graduate Theses and Dissertations

Cancer care depends on timely and reliable decisions, from detection and diagnosis to treatment planning and patient monitoring. These decisions are often made under uncertainty because medical images and healthcare data may be noisy, incomplete, or difficult to interpret. In breast cancer imaging, ultrasound is widely used because it is safe, accessible, and complementary to other imaging modalities. However, variations in image quality, acquisition conditions, and noise can obscure lesion boundaries and texture, affecting human interpretation and artificial intelligence reliability. This dissertation develops deep learning, image-analysis, and optimization methods to improve healthcare decisions under imperfect information. Its primary focus is …

Property Tables For Thermally Perfect Gases At Low Pressure, Travis J. Moore, Matthew R. Jones

Faculty Publications

Tables giving gas properties at low pressure enable the efficient analysis of processes in which the gas is approximated as thermally perfect but not calorically perfect. In addition to specific enthalpy and specific internal energy, thermally perfect gas tables include special functions that depend only on temperature—relative pressure and relative specific volume. These functions may be used to determine pressure, volume, and temperature of thermally perfect gases undergoing hypothetical isentropic processes. However, the definitions of these functions included in widely used thermodynamics textbooks are vague, inconsistent, or incorrect. The intent of this work is to discuss common inaccuracies in the …

From Shortage To Strategy: Optimizing Scarce Resource Allocation In Healthcare, Daniela Duran Cantarino

USF Tampa Graduate Theses and Dissertations

The allocation of scarce medical resources presents one of the most consequential challenges in health care, requiring decisions that balance equity and efficiency under pervasive uncertainty. This dissertation develops optimization-based frameworks for two critical domains, organ transplantation and emergency medical services, and demonstrates how operations research methods can improve upon current practice along both dimensions simultaneously.

In the domain of deceased-donor kidney allocation, two complementary frameworks are proposed. The first is a bi-objective stochastic optimization model that jointly maximizes post-transplant survival (efficiency) and prioritizes candidates with the highest pre-transplant mortality risk (equity), subject to regional chance constraints on graft failure …

A Reduced-Order Framework For Stochastic Criticality Estimation In Granular Energetic Materials, Philip T. Melton

LSU Master's Theses

Granular energetic materials (EMs) exhibit stochastic shock initiation because pore collapse, frictional dissipation, and localized thermal activation depend on microstructural descriptors that vary between nominally identical samples. Fully resolved mesoscale and atomistic calculations can represent these mechanisms, but their computational cost limits direct ensemble evaluation of microstructure-conditioned criticality thresholds. This thesis introduces a reduced-order framework for stochastic criticality estimation in granular EMs by coupling five explicitly defined model components: first, a one-dimensional steady compaction-shock model that maps initial solid volume fraction and shock pressure to a bulk mass-specific dissipated-work budget; second, an SEM/synthetic-image segmentation workflow that extracts pore area, perimeter, …

Critical Assessment On Challenges And Advances In Coal Pillar Strength Estimation Techniques: A Focus On Geological Discontinuities, Abhishek Kumar Singh, Sahendra Ram

Journal of Sustainable Mining

Accurate estimation of coal pillar strength is essential for ensuring safety and operational efficiency in underground mining. Current assessment methods often face limitations in addressing time-dependent failure mechanisms, geological discontinuities, and dynamic loading conditions. This paper identifies future research directions aimed at enhancing the reliability of pillar strength evaluations. One important focus is the development of advanced numerical models that account for time-dependent behaviours such as creep and fatigue. Incorporating multi-scale modelling techniques, which connect micro-scale material responses to macro-scale structural performance, may lead to more precise predictions. The integration of real-time monitoring systems measuring stress, deformation, and environmental factors …

Pulse Before Sound: Reimagining Presence And Liveness In Telematic Music Performance Through Midi-Native Collaboration, Matt C. Bray

Journal of Network Music and Arts

Telematic Music Performance heralds a new paradigm for human creativity, seeking to extend the practical limits imposed by physical co-presence and to permit the cooperative, simultaneous creation of music among geographically remote collaborators. This paper examines how MIDI-native interaction provides a structurally robust alternative to audio-centric telematic systems constrained by latency, bandwidth, and waveform fidelity. Drawing on practice-led research, including over 34 hours of documented improvisation across 91 networked sessions in the SHOALZ series—a longitudinal Telemidi performance research initiative (2022–2024)—the study examines how the Telemidi system employs MIDI as a primary substrate for remote musical collaboration by establishing a synchronized …

Efficient Edge Implementation Of Midasnet For Real-Time Depth Estimation In Indoor Robotics, Muhammed Yasi̇n Adiyaman, İsmai̇l Fai̇k Başkaya

Turkish Journal of Electrical Engineering and Computer Sciences

Real-time depth estimation is crucial in many vision-related tasks, including autonomous driving, 3D reconstruction, robotics, and simultaneous localization and mapping. In recent years, many methods have been proposed to solve depth maps from images by utilizing different modality setups like monocular vision, binocular vision, or sensor fusion. However, for real-time deployment on edge devices, complex methods are not suitable due to latency constraints and limited computation capacity. For edge implementation, models should be simple, minimal in size, and hardware-friendly. Considering these factors, we implemented MiDaSNet, which works on the simplest setup of monocular vision and utilizes hardware-friendly convolutional neural network-based …

A Holistic Approach For Workforce Scheduling And Routing, Kerem Can Manalp, Ansel Kaplan Erol, Kutluhan Erol, Cem Evrendi̇lek

Turkish Journal of Electrical Engineering and Computer Sciences

The workforce scheduling and routing problem (WSRP) involves assigning tasks across multiple locations while accounting for varying travel times, service durations, time windows, and skill requirements in a wide range of industries, from healthcare to telecommunications. This paper presents a mixed-integer programming model for the WSRP that balances the trade-off between cost and customer satisfaction using a score-generation function and subsequently evaluates the trade-off between solution quality and computation time for several algorithms on well-known datasets. We demonstrate that our model effectively balances cost, service-level agreement satisfaction, and task priorities while providing high-quality solutions in a timely manner. Observing that …

Cnkg: Harnessing Large Language Models For Cognitive Neuroscience Knowledge Graph Construction, Ali Sarabadani, Kheirollah Rahsepar Fard, Hamid Dalvand

Turkish Journal of Electrical Engineering and Computer Sciences

Textual resources are among the most valuable sources of information in cognitive neuroscience (CN) for understanding and investigating brain activity and cognitive processes. Extracting and constructing knowledge graphs (KGs) from these texts can facilitate medical research by providing deeper insights into neurological diseases and brain function. In recent years, the use of large language models (LLMs) in natural language processing (NLP) has become increasingly widespread, significantly enhancing the extraction of meaningful information from large volumes of text. This study proposes a novel approach for constructing and evaluating a specialized knowledge graph, termed the cognitive neuroscience knowledge graph (CNKG), from scientific …

Erratum To “Sgsc-Kko-Lstm: A Deeplearning Classifier Model For Smart Grid” [Turkish Journal Of Electrical Engineering & Computer Sciences 34 (2) 2026 185-213], Samaniba Imchen, Dushmanta Kumar Das

Turkish Journal of Electrical Engineering and Computer Sciences

The first and second authors were incorrectly ordered in the article PDF due to a typesetting error. To rectify this oversight and ensure the accuracy of the published work, the author order have been corrected as follows: 1. Samaniba Imchen – First Author 2. Dushmanta Kumar Das – Second Author

A link to the original article can be found at: https://doi.org/10.55730/1300-0632.4170

A Revenue-Driven Approach For Enhanced Task Utilization In Vehicular Cloud Computing, Ashish Singh Saluja, Satyabrata Das, Sanjib Kumar Nayak, Sohan Kumar Pande

Turkish Journal of Electrical Engineering and Computer Sciences

Vehicular networks support intelligent transportation through vehicle-to-roadside Units (V2R) and vehicle-to-vehicle (V2V) communication but face challenges from dynamic topologies, limited RSU coverage, and bandwidth scarcity, which impact service delivery and revenue. RDA-ITU addresses these challenges by integrating V2R and V2V paradigms to maximize RSU revenue, enhance service availability, and improve system efficiency. It dynamically allocates services based on real-time network conditions and vehicle mobility, leveraging V2V relays to optimize both RSU-direct and cooperative communication. Through extensive simulations, RDA-ITU significantly outperforms four baselines: RBSM, VVMM-U, VVMM-LW, and VVMM-MA. It achieves 81.1% higher total revenue, 154.8% more completed requests, and 103.6% higher …

Scalefusion: Hierarchical Feature Aggregation For Unified Multiscale Object Detection, Ayşenur Yaylaci, Berru Kaya, Mehmet Kiliçarslan

Turkish Journal of Electrical Engineering and Computer Sciences

Detecting objects across a wide range of scales, particularly small ones, remains a significant challenge in computer vision. Existing methods often improve small object detection at the cost of performance on larger objects or introduce significant computational overhead through external techniques like image slicing. This paper introduces ScaleFusion, a novel, unified, end-to-end object detection architecture designed to provide robust performance across all scales within a single model. The core of our approach is a hierarchical feature aggregation strategy structured like a tree. ScaleFusion processes an image by running a shared backbone network only on fine-grained patches at the lowest level …

Swindeitvit: A Soft Voting Vision Transformer Ensemble For Accurate And Explainable Solar Panel Fault Detection, Mahe Zabin

Turkish Journal of Electrical Engineering and Computer Sciences

Solar panels are becoming very essential in providing sustainable energy but they are usually affected by defects on the surface like dust, snow, bird droppings, physical damages and electrical faults which interfere with their performance. These faults must be identified accurately and in a timely manner to enhance energy efficiency, lower the maintenance cost, and supplement the traditional manual methods of inspection which are labor-intensive, time-consuming and subject to human errors in judgment. The most common methods, such as traditional CNNs and hybrid architectures tend to be less accurate, less explainable and cannot be properly evaluated to be deployed in …

Contemporary Cybersecurity Challenges In Emerging Technologies: A Systematic Literature Analysis, Faztudo Languisse Prof

Journal of Cybersecurity Education, Research and Practice

The accelerating convergence of artificial intelligence (AI), the Internet of Things (IoT), cloud computing, blockchain, and quantum computing has fundamentally transformed the global threat landscape, introducing cybersecurity challenges of unprecedented complexity and scale. This systematic literature review synthesizes findings from peer-reviewed publications, institutional reports, and regulatory documents published primarily between 2020 and 2025 to provide an integrated analysis of contemporary cybersecurity challenges across five key emerging technology domains. The review identifies critical vulnerabilities inherent to each domain, documents the evolution of threat actors and attack methodologies — including AI-powered ransomware, adversarial machine learning, and harvest-now-decrypt-later quantum attacks — and evaluates …

Assessing Computer Vision Based Conflict Detection In Uas Traffic Monitoring Under Secure Communication Constraints, Fadjimata Issoufou Anaroua

Doctoral Dissertations and Master's Theses

The rapid growth of Unmanned Aircraft Systems (UAS) and Advanced Air Mobility (AAM) is creating a new low-altitude airspace ecosystem where drones, air taxis, service suppliers, communication networks, sensors, and ground-based monitoring systems must work together safely. Within this ecosystem, UAS Traffic Management (UTM) is expected to provide a digital framework for coordinating operations beyond traditional air traffic control. However, reliable integration also requires resilient monitoring methods that can detect non-cooperative aircraft, protect communication links, and maintain timely situational awareness under real-world constraints.

This dissertation examines how computer vision can support cooperative monitoring systems such as Remote ID and ADS-B …

Direct Measurement Of Stem Position For Gas-Lift Valves While Flowing At High Pressure, Paulo E. Reitz

LSU Master's Theses

The present work challenges the idea of estimating the gas lift valves true dynamic stem position by providing an alternative capable of tracking the real stem position during flow tests. This is of interest for the improvement of performance determination tests and procedures that involve critical safety concerns, complex training and considerable investments, both in time and money. This scenario, together with the strong interest of industry for valve optimization and the scarcity of works available in literature, encouraged this study. An apparatus named Vision System was developed for tracking and measuring the stem position during flow tests. The developed …

Fuel Tank Seismic Stability Program-Informed Displacement Maps Of Portland's Critical Energy Infrastructure Hub, Ana Tijerina Esquino

Dissertations and Theses

Oregon's Critical Energy Infrastructure (CEI) Hub in northwest Portland contains a large portion of the state's liquid fuel storage and distribution capacity and is located in an area susceptible to liquefaction and earthquake-induced permanent ground deformation during a Cascadia Subduction Zone earthquake. Initial Seismic Vulnerability Assessments submitted under Oregon's Fuel Tank Seismic Stability program provide valuable facility-specific information, but the assessments vary in subsurface characterization, treatment of fine-grained soils, ground motion selection, and deformation estimation methods. This variability complicates interpretation of regional seismic risk and motivates a consistent corridor-scale analysis.

This thesis develops CPT-informed displacement estimates for representative cross sections …

Signal And Substance: Why Methodological Sophistication Fails To Guarantee Inference, Aamir Rashid, Rizwana Rasheed

Publications and Research

Despite increasing analytical sophistication, empirical research in organization studies continues to suffer from fragile inference, uneven theoretical accumulation, and contested credibility. This paper seeks to explain why these problems persist by shifting attention from authors’ methodological choices to the evaluative dynamics of peer review. Adopting a conceptual and theory-analytic approach, the paper theorizes peer review as an inferential gatekeeping system. Drawing on research design, theory evaluation, and philosophy-of-science literatures, this study develops an analytical framework to examine how evaluative routines shape what constitutes empirical rigor during the review process. The analysis identifies five recurring reviewer blind spots: inferential scope inflation, …

Gust-Induced Aerodynamic Performance In Insects' Forward Flapping Flight, Arash Farsani, Ori Stearns, Gal Ribak, Roi Gurka

Physics and Engineering Science

Flapping-wing flight is inherently unsteady, where atmospheric gusts can substantially degrade the aerodynamic performance when their characteristic time scales are comparable to the wingbeat period. This study presents a numerical investigation and time–frequency characterization of gust-induced aerodynamic response of flapping wings of varying size, inspired by the flight of a longhorn beetle, Batocera rufomaculata. Geometrically similar wings spanning the biological range were simulated in forward flight under identical prescribed kinematics and a transient frontal gust with a smoothly ramped profile. Three-dimensional unsteady Reynolds-averaged Navier–Stokes simulations using the shear stress transport k–ω turbulence model were performed to resolve the instantaneous pressure …

Enhanced Intrusion Detection Using Recurrent Neural Networks With Amino Acid Codon Features, Omar Fitian Rashid, Mohammed Ahmed Subhi, Safa Ahmed Abdulsahib, Mohammed Khaleel Hussein, Marwan Ali Albahar

Iraqi Journal for Computer Science and Mathematics

Intrusion Detection Systems (IDS) is the main defense mechanism deployed by the current networks to prevent cyber threats. Recurrent Neural Network (RNN) are also a novel IDS structure that replaces the conventional training and testing mechanism. The strategy encodes network traffic data as biological sequences using amino acid codons in such a fashion that the RNN is capable of effectively analyzing temporal and sequence data patterns. RNN architecture design adopts embedding layers to handle codon representations and Long Short-Term Memory (LSTM) layers to perform sequential data learning, which is followed by a fully connected network to perform classification functions, which …

Latent Space Exploration Of Stylegan2-Ada For Fashion Image Synthesis: A Practical Implementation Guide For Moderate-Sized Datasets, Ali H. Shareef, Hajer Ghodhbani, Tarek M. Hamdani, Habib Chabchoub, Adel M. Alimi

Iraqi Journal for Computer Science and Mathematics

\hlThis paper presents the use of StyleGAN2-ADA for fashion image synthesis and editing. We demonstrate that generative adversarial networks (GANs) can generate realistic and diverse fashion images even under moderate data constraints. The model trained on a dataset of 9,000 fashion images achieved a Fréchet Inception Distance (FID) of 10.8, a Garment Structure Accuracy (GSA) of 0.85, a Pattern Continuity Score (PCS) of 0.78, and an Attribute Transfer Accuracy (ATA) averaging 75% across key attributes. These quantitative results confirm the model’s ability to produce high-quality, structurally consistent, and semantically coherent fashion images. Unlike previous studies focusing on …

Divergent Risk Prioritization And Safety Governance In Metro Construction Projects: Evidence From Cairo Metro Line 3, Shrouk M. El Gebaly, Khaled M. Dewidar, Taher M. Elsamni

HBRC Journal

Safety risk management remains a critical challenge in large-scale infrastructure projects, particularly in complex urban metro systems where multiple stakeholders operate under different organizational mandates. While previous research has examined construction safety risks, limited studies have explored how project stakeholders prioritize these risks within the same project environment and how such differences influence safety governance. This study investigates stakeholder risk prioritization in metro construction through a qualitative case study of Cairo Metro Line 3 in Egypt. Data were collected through semi-structured interviews with representatives from three key stakeholder groups: the client organization, consulting engineers, and construction contractors. The data were …

Policy Analysis Of The Inclusion Of Mitigation Measures In The Re-Design And Construction Of The New Frances Scott Key Bridge, Patrick John O'Connor

Theses and Dissertations

This thesis discusses the tragic event of the container ship MV Dali impacting the Francis Scott Key Bridge on March 26, 2024, leading to the collapse of the structure and killing six people despite having collision mitigation protections in place. This research examines how late twentieth-century structures are inadequate for current shipping demands and emphasizes that updated mitigation design criteria must accommodate larger modern vessels as governments replace or upgrade these structures. This paper will examine four case studies of different collisions that have similar circumstances to the Francis Scott Key Bridge collapse and review National Transportation Safety Board reports …

Vibro-Seismic Approach For Remediation Of Near-Wellbore Damage, Mounira Ahmed Abdallah

Theses and Dissertations

The research highlights the importance of vibro-seismic technology in the oil and gas industry. The primary objective of this study is to demonstrate how the vibro-seismic method enhances formation properties which are porosity, and permeability. Ultimately, this will lead to improved productivity and its application to remove the wellbore damage.

The research discusses whether or not the formation properties will be enhanced by the optimized acoustic vibration formation parameters. The thesis details laboratory experiments on core plugs, and four key findings are presented. The experimental work first showed that the use of the proper and optimum parameters enhanced the porosity …