Digital Commons Network^™

A Storm-Ai Primer With An Operationally Representative Example, Shiu-Kai Chin, Matthew Clark, Garrett Katz, Jae C. Oh, William Young

Electrical Engineering and Computer Science - All Scholarship

A STORM-AI Primer with an Operationally Representative Example presents the Systems-Theoretic Technical and Operational Risk Management with Artificial Intelligence (STORM-AI) methodology, a transdisciplinary framework for establishing the trustworthiness of mission systems that may incorporate AI components. The core value proposition of STORM-AI is mission assurance—ensuring that mission systems behave with the predictability and proportionality required by senior leadership and demanded by international humanitarian law. STORM-AI achieves this by faithfully preserving commander's intent from high-level mission descriptions down to hardware-level behavioral specifications through a structured sequence of four formally grounded models: a Mission Model, a Protection (Loss Control) Model, a Security …

Toward Passwordless Document Encryption, Isaac Henry Teuscher

Theses and Dissertations

Document files with sensitive information are used across nearly every industry. In recent years, cyberattacks targeting cloud-based file transfer applications have resulted in millions of sensitive documents being exposed. Although document encryption methods exist, they are often limited in usability, security, or deployability. Password-based encryption, the most widely available option, remains vulnerable to brute-force attacks, unauthorized sharing, and human error. We address these gaps in three phases. First, we present a structured comparative framework adapted from the usability-deployability-security model to evaluate nine document encryption methods across 15 design properties, identifying the benefits and limitations of current approaches. Second, we design …

Calibration And Validation Of An Ogden Material Model For Thermoplastic Polyurethane In 3d-Printed Tensegrity Structures, Caleb C. Swain

Theses and Dissertations

This work is motivated by ongoing efforts within the Smart Materials, Adaptronics, and Shock Laboratory (SMASH Lab) at Brigham Young University (BYU) to develop variable-stiffness, external fixators using 3D-printed tensegrity structures. In the process of designing and evaluating these systems, the need arose to accurately model the nonlinear mechanical behavior of thermoplastic polyurethane (TPU), the primary material used in prototype fabrication. This thesis investigates how accurately a calibrated Ogden material model can predict the nonlinear mechanical response of TPU in 3D-printed tensegrity structures. Experimental data were used to calibrate the parameters of the hyperelastic constitutive model through nonlinear optimization. An …

An Access-Control Logic In Hol (Trindemossen 2), Shiu-Kai Chin

Electrical Engineering and Computer Science - All Scholarship

This manual documents the embedding of an access-control logic (ACL) — as presented in the textbook Access Control, Security, and Trust: A Logical Approach (Chin and Older, 2011) — within the Cambridge Higher-Order Logic theorem prover HOL-4. The logic is a multi-agent modal logic with Kripke semantics. Its purpose is to reason about authentication, authorization, integrity, security, and trust. The inference rules of the ACL are proved to be sound. The inference rules are a command-and-control (C2) calculus. The value of the ACL is its support for rigorous, verified, and trustworthy policies for complete mediation, i.e., where all actions are …

Engineering 3d Optical Interconnects And Low-Power Radio Frequency Gaze Tracking, Blake P. Crosby

Theses and Dissertations

This thesis presents two independent research works in optical in- terconnects and radio-frequency (RF)-based gaze tracking. The first work demonstrates a silicon optical waveguide fabricated from four through-silicon vias (TSVs) produced using two-photon-absorption- assisted photo-electrochemical etching. Unlike conventional TSV fabrica- tion methods, this approach enables highly localized, non-line-of-sight etching in silicon using a focused femtosecond laser. Four approximately 5 μm diameter TSVs were etched through a 200 μm silicon wafer in a 7 μm by 7 μm pattern, leaving a central silicon region that functions as a waveguide. The fabricated structure achieved an aspect ratio of approxi- mately 40:1 with …

Operational Feasibility Of Reinforcement Learning For Vehicle Routing Under Heterogeneous Fleet Capacity Constraints, Freddy Giovanny Aviles Moreno

Electronic Theses and Dissertations 2020 - Present

Reinforcement learning methods have demonstrated strong performance on vehicle routing benchmarks, yet their behavior under severe capacity constraints remains unexplored. This dissertation investigates whether PPO-based neural routing policies maintain operational viability when vehicle capacity is severely constrained, as occurs in resource-limited rural logistics settings.

Through controlled experiments on synthetic instances and validation on real-world rural healthcare networks in Florida, this research reveals a critical capacity threshold effect. Moderate capacity reductions from 40 to 20 produce negligible performance loss (4.1%), while severe reductions to capacity 10 trigger catastrophic failure with 243% degradation, manifested through degenerate single-customer routing patterns. Convergence analysis identifies …

A Portable Device To Elicit Sensory Trick And Monitor Forearm Muscle Activity In Pianists With Musician’S Dystonia, Josef Franczak

USF Tampa Graduate Theses and Dissertations

Musician’s Dystonia (MD) is a condition that can be acquired in professional musicians,causing involuntary contractions and the loss of fine motor control. The most common pharmaceutical treatment, botulinum toxin injections, has been shown to have side effects of muscle weakness and atrophy. The sensory trick, shown to temporarily relieve symptoms of dystonia, has potential to be used as an alternative or alongside current therapies, allowing patients with MD to return to playing music professionally. To systematically deploy the sensory trick and monitor forearm muscle activity, the first prototype of the Wearable Sensorimotor Integrator (wSMI) has been developed.

The wSMI is …

Development Of A Coupled Hydrological-Nitrogen Modeling Framework For Urban Stormwater Pond Analysis, Tione Grant

USF Tampa Graduate Theses and Dissertations

Urbanization impacts the hydrological and biogeochemical processes that regulate water quality in downstream ecosystems, leading to increased stormwater volume and nitrogen loads in receiving aquatic environments. Although stormwater ponds are designed as simple hydraulic storage basins, they function more like managed ecosystems within urban drainage systems, offering both hydraulic attenuation and passive nutrient treatment. However, nitrogen removal efficiency varies significantly and depends on interactions between hydrological and biogeochemical factors. This thesis details the development, calibration, validation, and sensitivity analysis of a coupled hydrological-nitrogen model framework for Aaran’s Pond, an urban stormwater pond in Hillsborough County, Florida.

A process-based water budget …

En-Feat: An Effective Feature Selection Method Using Ensemble Approach, Sasank Nath, Dhruba Kumar Bhattacharyya

Mansoura Engineering Journal

Feature selection is a crucial step in machine learning and data preprocessing, significantly influencing model performance and interpretability. This paper presents a comprehensive study and contributions in the domain of feature selection by integrating traditional learning techniques with ensemble-based, proposing an effective approach. We propose a Mutual Information-based feature aggregation approach applied to union sets of features, aiming to derive an optimal subset of features that maximizes accuracy. Then, we employ an ensemble method that utilizes forward selection over union sets to identify the optimal feature subsets through sequential feature selection. Our ensemble-based feature selection method called En-feat, is evaluated …

Adaptative And Structured Optimization Stockwell Transform For Robust Power-Quality Disturbances Detection, Moudjibatou Afoda, Séna Apeke, Filippo Gatti, Alessio Iovine, Léonce W. Tokam, Guy M. Toche Tchio, Ouro-Djobo S. Sanoussi

Mansoura Engineering Journal

The monitoring of electrical power quality requires advanced analysis techniques capable of accurately detecting and localizing non-stationary disturbances. This work proposes a structured and adaptive optimization of the Stockwell Transform (ST), referred to as Two-Power Optimization (TPO). A multiparametric Gaussian window governed by four parameters (m, p, k, r), is introduced, and a genetic algorithm is applied independently to each signal to optimize these parameters. The optimization process adapts the analysis scale to the local characteristics of the signal while preserving the physical consistency of the window function. Simulation results on synthetic power quality signals demonstrate that the proposed method …

Real-Time Iot-Enabled Dissolved Oxygen Monitoring And Automated Wastewater Recirculation In Constructed Wetlands, Sivasankar Pandiarajan, Vanitha Sankararajan, Devarinti Chandu

Mansoura Engineering Journal

Wastewater treatment is necessary for environmental conservation and combating water pollution. This study demonstrates real-time dissolved oxygen (DO) monitoring in constructed wetland treatment. The system would ensure an adequate supply of oxygen through automated water circulation whenever the level is below 4 mg/L, and shut down when it reaches the normal level. From experiments conducted, it was observed that within three automated circulation processes, it is possible to increase DO concentrations from 0.5 mg/L to 5.0 mg/L. Additionally, Total Dissolved Solids reduced from 1,380 ppm to 920 ppm. pH also stabilized from 8.38 to 7.9. In this system, data is …

Bridging Machine Learning And Climate Futures: A Framework For Explainable, Self-Directed (Agentic) Ai Models In Monitoring, Mitigation, And Governance Of Anthropogenic Climate Impacts, Vijayanandh Rajamanickam, Ketaki Kulkarni, Vishwanadham Mandala, Ozgur Kisi

Mansoura Engineering Journal

Environmental and societal damage due to anthropogenic climate change demands detection and mitigation strategies that are both effective and accountable. Machine Learning (ML) appears to offer a powerful set of tools but remains unexploited in these areas. A key barrier remains its poor ability to draw causal inferences about external systems, an essential requirement for meaningful continued monitoring, acting on, and shaping of the climate. Explainable, self-directed learned models operate by attributing environmental changes and deciding how best to model the resulting dynamics—they offer a natural solution. Recent developments in computational ecology, meteorology, and ML are combined to propose a …

Topographical Mobility Envelope Modeling And Geometric Analysis For Off-Road Unmanned Ground Vehicles, Huashuai Fan

ETDs from 2020-2029

This dissertation introduces topographical mobility as a quantitative framework for assessing off-road unmanned ground vehicle performance based on geometric interaction between the vehicle and terrain. Rather than relying on conventional discrete indices, the proposed formulation evaluates mobility directly from vehicle and terrain geometry, expressing vehicle capability in terms of radius constraints implied by its dimensions and suspension state. On the vehicle side, longitudinal and lateral mobility radii are derived analytically from vehicle geometry—wheelbase, track width, ground clearance, and lowest underbody point location—quantifying the minimum terrain radius negotiable without underbody interference. Suspension state is shown to alter longitudinal mobility radius by …

Nitrite Oxidation During Ozonation Revisited: Mechanisms Of Nitration Reactions, Tarek Manasfi, Christoph Dieziger, Simon A. Rath, Daisuke Minakata, Urs Von Gunten

Michigan Tech Publications

The formation of toxicologically relevant nitro compounds has been observed during ozonation of nitrite-containing secondary wastewater effluents, but their formation mechanism remains unknown. To identify key nitrating species, three reaction systems were investigated: ozonation of nitrite (O3/NO2^–), peroxynitrite (ONOOH/ONOO^–), and hydroxyl radical oxidation of nitrite with γ-radiolysis (γ/NO2^–). Nitrite ozonation (O3/NO2^–) yielded significant amounts of the nitrating agent nitrogen dioxide ^•NO2 (9.4% at pH 7 to 22% at pH 12) besides the main product nitrate. No peroxynitrite formation was detected during ozonation of nitrite-containing waters, suggesting that ^•NO2 is the …

Optimizing Graphene Oxide In Self-Compacting Concrete: A Sustainable Nano-Material Strategy For Enhanced Durability And Structural Performance, Mohammed Shakeebulla Khan, Vijaya G. S

Journal of Sustainable Construction Materials and Technologies

In order to reduce the environmental impact of the constructed world, advanced and sustainable construction materials are needed. This work is a summary of the comprehensive study on a novel self-compacting concrete with nano-engineered material (graphene oxide) modified self-compacting concrete (GO-SCC). The optimum dosage of GO was determined using Taguchi method, Analysis of Variance (ANOVA) and Response Surface Methodology (RSM) multi-objective statistical optimization method, and was found to be 0.10% by weight of cement (bwoc). This dosage of 0.10% GO led to the overall desirability score of 0.87, which confirmed that this was the optimum dosage for six response variables …

Adaptive Intervention Strategies In Co-Evolving Multiplex Networks: A Reinforcement Learning Approach To Real-Time Misinformation Containment, Anjali Ashokrao Bhadre Dr., Harshvardhan Prabhakar Ghongade Dr.

Northeast Journal of Complex Systems (NEJCS)

The growing transmission of misinformation via social media creates serious challenges to public health, democracy and social cohesion. To date, methods used to contain misinformation rely upon static representations of networks and set rules for interventions. In contrast, this study presents the first Multiplex Adaptive Reinforcement Intervention Network (MARIN), a framework for real-time adaptive intervention in the context of dynamic misinformation transmission using co-evolving multiplex networks and deep reinforcement learning. Unlike past studies that have assumed static network structures, MARIN has the ability to allow for dynamic changes in network topology as a result of both misinformation transmission and intervention …

Tinyvgg-Based Real-Time Degradation Classification For Adverse Driving Scenes Using A Newly Collected Iraqi Driving Dataset, Yousif N. Abbas, Matheel E. Abdulmunim, Nada H. Ali, Ismail A. Mageed

Journal of Soft Computing and Computer Applications

Environmental conditions such as low-light at night, fog scattering, glare artifacts, rain streaks, and rain smear distortions are significant issues of camera-based perception in Autonomous Vehicles (AVs). These degradations alter the statistics of the scene, mask structure, introduce non-uniform noise, and adversely affect downstream vision processes, including detection and tracking. To overcome this shortcoming, this paper presents a lightweight TinyVGG-based degradation classification system that runs in real time. The network extracts discriminative spatial features with hierarchical convolutional encoding and projects them to a lower-dimensional semantic representation with fully connected layers and a multi-class predictor based on SoftMax. In addition, a …

A Comprehensive Review Of 1d Deep Learning Approaches In Facial Analysis: Face Recognition, Landmark Detection, And Mesh Modeling, Duaa J. Al Hammami, Rehab F. Hassan

Journal of Soft Computing and Computer Applications

Facial Analysis has progressed rapidly with deep learning and its 2D image-based models, especially Convolutional Neural Networks (CNNs), which have been the most popular methods. In recent years, 1D deep learning models have gained traction in the search for efficient solutions for face recognition, facial landmark detection, and 3D face mesh modeling. 1D models encode the facial structure as sequences, curves, or temporal signals, resulting in high computational efficiency, a small memory footprint, and good interpretability, making them well-suited for real-time and edge devices. This review is a step-by-step, organized exploration of 1D deep learning analysis of the face, its …

Skin Lesion Classification Using Cnn Model And Augmented Dataset, Mohammed Nawzad Mohammed-Ramzi, Aso M. Aladdin

Journal of Soft Computing and Computer Applications

Skin cancer is a deadly disease. Skin lesion classification is a critical challenge due to its prevalent and deadly nature. Skin lesions are difficult for dermatologists to detect using eye examination, which is time-consuming and variable. A deep learning model of skin lesions classification has been proposed using a Convolutional Neural Network (CNN) trained on the HAM10000 dataset of 10,015 dermatoscopies. To improve resilience and address the dataset's extreme class imbalance, data augmentation techniques such as geometric transformations, brightness/contrast adjustments, blurring, noise addition, histogram equalization, color space alterations, and elastic deformations are used. With a carefully balanced 10% test set, …

Comparative Analysis Of Random Forest And Artificial Neural Networks For Predicting In-Situ Soil Density, Eng. Jinan Ali Abd Al-Kareem Al-Maliki, Dr Ammar Salman Dawood, Dr. Ihsan Al-Abboodi

HBRC Journal

This study suggests that RF and ANN are proven to be robust algorithms in predicting in-situ soil density, which is considered a significant geotechnical parameter. The research is based on 86 soil samples and focuses on five main input parameters: Gravel Percentage (G%), Plastic Limit (PL%), Sand Percentage (S%), Fines Percentage (F%), and Liquid Limit (LL%). The models developed here utilize five commonly recorded index properties (G%, S%, F%, LL, and PL) for all field samples taken from the Basra-Faw Road project. The influence of moisture content and compressive energy was ignored, as all field samples acquired the same moisture …

Beneficial Investigation Of Sustainable Recycled Waste Plastic In Treating The Consolidation Behavior Of Fine Soil, Aram Mohammed Raheem, Cumaraswamy Vipulanandan, Mohammed Hiwa Abdullrahman, Ali Tariq Omer

Journal of Sustainable Construction Materials and Technologies

Recycled waste plastic is one of the regular garbage utilized as a sustainable material in soil remediation. This study examines the efficacy of utilizing local recycled waste plastic as an ecological quantifiable along with soil containing fine particles to analyze the consolidation behavior. Various recycled waste plastic contents ranging from 2% to 10% were employed experimentally in two patterns where in the first pattern the recycled waste plastic were mixed as dry material with the fine soil particles while in the second pattern a sandwich layer of 10% of the recycled waste plastic was placed in the middle of the …

Development And Standardization Of Teds Actuator Templates Under Ieee 1451 Framework, Jim Kang

Theses and Dissertations

The IEEE 1451 standard is a family of standards that defines a framework for smart transducers, including both sensors and actuators, to support consistent, interoperable, and cost-effective integration across diverse applications. However, the current IEEE 1451.4 standard templates only define the method for encoding Transducer Electronic Data Sheet (TEDS) information for a broad range of sensor types and applications; they do not address actuator TEDS. Specific types of sensors and actuators are being developed to assess underground environmental conditions in cold regions with widespread permafrost. Evaluating subsurface conditions before construction can help prevent high construction expenses for structures built on …

Robust Real-Time Uav Target Tracking With Onboard Vision-Based Yaw Control, Rylan Malarchick, Jose Castelblanco, Enrique Amaya, Carmen Dimario, Graysen Brinkman, Chirag Kumar, Kiwon Yoon, Sajid Berhane

Beyond: Undergraduate Research Journal

Autonomous tracking of agile unmanned aerial vehicles (UAVs) presents significant challenges for real-time perception and control systems. This work presents AIRHOUND (Autonomous Intelligent Rotorcraft for Hostile Object Unified Navigation and Detection), a UAV platform implementing vision-based yaw tracking through a modular ROS2 software architecture. The system employs YOLOv8 object detection optimized with NVIDIA TensorRT for embedded deployment on an NVIDIA Jetson Orin companion computer. Detected targets are processed through a geometric tracking module that converts pixel coordinates to angular yaw errors using pinhole camera intrinsics, with a proportional controller generating rate-limited yaw commands. These commands are streamed to a PX4 …

Bias Before Generation: Attention-Based Preemptive Fairness Signals In Large Language Models, Aniket Das

Master’s Dissertations

Warning: This paper includes examples of language that may be perceived as inappropriate or offensive. Large language models (LLMs) are known to propagate social biases embedded in their training corpora, producing outputs that disproportionately disadvantage individuals based on sensitive attributes such as gender, religion, race, sexual orientation and nationality. Existing mitigation strategies are either computationally prohibitive, require access to model parameters, or apply corrections only after biased content has already been generated. This work addresses a different question: can the model’s own internal attention dynamics, observed at inference time, serve as a reliable early-warning signal for bias, enabling intervention before …

Literature Landscape Of Government, Academic, And Industry Efforts In Responsible, Secure, And Trustworthy Ai, Jae C. Oh, Shiu-Kai Chin, Garrett Katz, William E. Young, Matt Clark

Electrical Engineering and Computer Science - All Scholarship

This report presents a systematic analysis of 54,628 AI research papers published between 2021 and 2025, examining the extent to which current government, academic, and industry efforts address the trustworthiness requirements of mission-critical AI systems. Papers were classified along two dimensions: organizational tier (from AI model to mission/business pro- cess) using NIST SP 800-39, and systems engineering lifecycle phase using ISO/IEC/IEEE 15288. Classification combined automated LLM-assisted screening with human validation, achieving inter-rater reliability measured by Krippendorff’s α and Gwet’s AC1. Only 16.8% of papers examined addressed any aspect of AI trustworthiness, reliability, or security, and virtually all were concentrated at …

Flexible-Mode Multi-Skilled Resource-Constrained Project Scheduling Problem With Setup Times: Application In The Oil And Gas Industry, Safinaz M. El Dawody

Theses and Dissertations

Turnaround maintenance (TAM) in the petroleum refining sector requires coordinating geographically dispersed project portfolios under stringent resource, schedule, and budget constraints. Conventional scheduling models treat tasks in isolation, failing to account for shared resources, crew fatigue, or inter-site transit. To bridge this gap, this study introduces and empirically validates a Flexible-Mode Multi-Skilled Resource-Constrained Project Scheduling Problem with Setup Times (FM-MS-RCPSP-ST). Applied to a real-world portfolio of 76 TAM projects across five governorates in Egypt, the framework is operationalized through an 11-stage pipeline integrating NSGA-II multi-objective optimization, GIS-based routing, and 500-trial Monte Carlo disruption simulations.

Results demonstrate that incorporating mandatory crew-rest …

Air-Writing Recognition, Gaurang Shukla

Master’s Dissertations

Air-writing is the act of tracing characters or words in free space with a fingertip, recorded by a camera, giving a touch-free input modality for smart displays, augmented and virtual reality, and assistive interfaces. It is difficult because the finger never lifts: connecting strokes join adjacent letters with no pen-up signal to mark boundaries, and the same word varies widely in scale, position, and slant across writers. The WiTA benchmark of Kim et al. provides a large, person-disjoint dataset and a baseline that treats each clip as RGB video, recognised by a spatio-temporal 3D residual network trained with a CTC …

Case Study On Operational Performance Improvement By Turbocharger Nozzle-Ring Modification In Shipboard Generators, Jong-Hun Im, Mukyeong Kang, Sang-Deuk Lee

Journal of Marine Science and Technology–Taiwan

Owing to increasingly stringent emission regulations imposed by the International Maritime Organization and rising fuel prices, the demand for enhanced energy efficiency and emission control in marine power systems is increasing. This study presents a case study in which the performance of an aging auxiliary generator is restored by modifying the turbocharger nozzle ring. The vessel, a 12-year-old crude-oil tanker, experienced repeated operational issues with its No. 3 generator. This included elevated exhaust gas temperatures, reduced intake air pressure, and frequent turbocharger surges. To address these challenges, the nozzle-ring blade angle was reduced by approximately 2° using spare components, while …

Towards Sustainable Marine Education: Assessing High School Students’ Understanding Of Conservation Concepts, Cheng-Chieh Chang, Tsuyoshi Sasaki, Ya-Yin Liao, Ling-Ya Hung, Liang-Ting Tsai

Journal of Marine Science and Technology–Taiwan

This study assesses the concepts of marine resource conservation and sustainability among Taiwanese high school students by developing a Chinese version of the Marine Resources Conservation and Sustainability Scale. Grounded in the cognitive, socio-emotional, and behavioral learning objectives outlined in UNESCO’s Education for Sustainable Development Goals (ESDGs), the scale targets students aged 16 to 18 to examine gender differences in marine knowledge and conservation concepts. The scale comprises 31 items across four dimensions: the first section evaluates marine knowledge, while the remaining dimensions utilize a four-point Likert scale to assess cognitive, socio-emotional, and behavioral learning objectives, respectively. The results indicate …

Dynamic Governance Of Transboundary Fisheries Through Deep Learning And High-Frequency Vessel Tracking Data For Climate-Resilient Management: An Application To The East China Sea, Qin Lin, Yaqing Shu, Manel Grifoll, Hongxiang Feng

Journal of Marine Science and Technology–Taiwan

Transboundary fisheries governance is increasingly challenged by jurisdictional conflicts, regulatory asymmetries, and climate-induced stock shifts in fish stocks, especially in contested regions like the East China Sea (ECS). This study capitalizes on China’s high-frequency BeiDou Navigation Satellite System (BDS) data (June 2021–May 2022) to pioneer a CNN-BiLSTM deep learning model to dynamically identify fishing hotspots and evaluate policy effectiveness. By integrating vessel trajectories, environmental variables, and regulatory events, we examine spatiotemporal patterns of fishing effort relative to seasonal bans, extreme weather, and quota systems. Key findings reveal: (1) post-moratorium (September–December) concentration of fishing intensity, peaking in November; (2) significant weather-driven …