Digital Commons Network^™

Slender And Nonslender Delta Wing Simulation And Analysis, Aashish Gyawali, Brinda Bhattarai, Nishesh Bista, Sundeep Rao Dr

Journal of Aviation Technology and Engineering

Stability, controllability, and maneuverability are critical factors for aircraft with short takeoff and landing distances, such as modern fighter aircraft and unmanned aerial vehicles. Delta wings are commonly employed in these aircraft due to their efficient aerodynamics, enabling high maneuverability, and performance at both low and high speeds. Nonslender wings are used for low-speed performance and agility, while slender wings offer reduced drag and are suited for high-speed operations. In flight, an aircraft encounters different airflow patterns including vortices that circulate from the higher-pressure lower side of the wing to the lower-pressure upper side, contributing to lift generation. However, as …

The Waldo Dataset, Mary E. Koone, Rosie Kallie, Vassilis Athisos, Laurel S. Stvan

Computer Science and Engineering Datasets - Archive

Distinct from the task of predicting the author of a document (authorship attribution), we focus on addressing the issue of how to estimate the similarity between the written language styles of authors. To do so, we present a dataset of metadata derived by asking human annotators, who were presented with three documents, to identify which two were written by the same author and which was written by a different author. The dataset has over 400 such annotations, creating a companion to the Amazon Web Services (AWS) customer review dataset, laying the groundwork for crowdsourcing applications to other natural language processing …

Exploiting Chaotic Antenna Arrays For Rf Fingerprint Authentication And Physical Layer Security In Wireless Communications, Joshua K. Thomas Ranstrom

USF Tampa Graduate Theses and Dissertations

Modern wireless networks support a wide range of applications including consumer devices, healthcare monitoring, industrial automation, smart grids, and military platforms, in which transmitted information is often highly sensitive and subject to stringent confidentiality and integrity requirements. As wireless communication operates over an open broadcast medium it exposes systems to physical layer (PHY) threats such as eavesdropping, in which an adversary passively intercepts transmitted information, and spoofing, in which a forged transmission misleads the receiver about device identity. Cryptographic defenses address both threats but require computational assumptions and key management infrastructure that may not scale to dense or resource-constrained deployments. …

Meteorological Monitoring With Real-Time Data Processing Using Uas, Htoo Poe Sar, Cal Coopmans, Sierra Young

Spring Runoff Conference

Background and Motivation 

• Agricultural water management requires information about crop water use and water consumption 
• Remote sensing methods are used to model and estimate environmental processes – but they require weather data 

Influence Of Gender-Specific Data Imbalance On Scgpt Fine-Tuning For Single-Cell Genomics, Mohammad Aman Ullah Al Amin, Daniil Filienko, Hong Qin

Knowledge and Creativity Expo

The transformer-based foundation model scGPT has demonstrated strong capabilities in analyzing high-dimensional single-cell RNA sequencing data. However, the impact of demographic factors, particularly gender, on model performance remains insufficiently understood. Gender is known to influence cell-type compositions in the immune system. Here, using the gender-sensitive cell-type composition in immune system, we comprehensively evaluated how the gender-sensitive imbalance of training data influences the performance of scGPT in cell-type predictions. We fine-tuned scGPT on male-only, female-only, and mixed-gender subsets from two large-scale datasets containing immune cells. We used a logit difference to measure the confidence gap between the true label and the …

A Surface Wave Inversion Method Incorporating Interface Constraints And Its Application In The Assessment Of Ground Vibrations Caused By Mining-Induced Seismicity, Li Cheng, Qian Changbao, Liu Ying, Cheng Yi, Ni Shengjun, Yao Huajian

Coal Geology & Exploration

Objective In mining areas of western China, underlying interfaces with significant fluctuations affect seismic exploration accuracy while also posing challenges to the assessment of ground vibrations triggered by dynamic hazards such as mining-induced seismicity. To enhance the characterization of shallow velocity structures and to provide support for dynamic hazard prevention and control in mining areas, this study developed an inversion method incorporating prior information about geological interfaces.Methods A dense array consisting of 489 single-component and 108 three-component seismometers was deployed within a mining area in Longkou Town, Junggar Banner, Inner Mongolia. Accordingly, continuous ambient noise data for about one …

Multi-Modal Tensor Fusion For Alzheimer’S Disease Recognition, Mason Li, Tiffany Le, Jiajing Huang, Yuxin Wen

Engineering Faculty Articles and Research

Accurate and early diagnosis of Alzheimer’s disease (AD) is critical for effective intervention, disease monitoring, and patient care. Traditional diagnostic approaches rely on a single modality, such as clinical assessments, neuroimaging, or genetic markers, which may fail to capture the complex, multifaceted nature of AD. Multimodal learning has therefore been explored to integrate complementary information across data sources. However, conventional fusion strategies, including early feature concatenation and late decision-level fusion, often model modalities independently and fail to capture high-order cross-modal interactions. To address these limitations, we propose a multimodal tensor fusion network (MTFN) that integrates heterogeneous data sources, including visual …

Secure Machine Learning In Networking Systems, Wenwei Zhao

USF Tampa Graduate Theses and Dissertations

With the rapid integration of Machine Learning (ML) into networking systems, ensuring the security and trustworthiness of these intelligent frameworks has become a paramount concern. While ML offers unprecedented capabilities in spectrum management and collaborative learning, it also introduces novel vulnerabilities that can be exploited by sophisticated adversaries. This dissertation investigates and addresses critical security challenges across three key dimensions of ML-driven networking: adversarial spectrum sensing, the security of privacy-preserving unlearning processes, and efficient post-attack model recovery.

First, we address the threat of adversarial spectrum attacks in cognitive radio networks, where malicious nodes manipulate sensing reports to disrupt spectrum access. …

Rigorous Electromagnetic Beamforming Simulations For Realistic Vehicular-To-Infrastructure (V2i) Scenarios In 5g Systems, Mohanad Hussam Aldean Ahmed Almuzian

Theses and Dissertations

Millimeter-wave (mmWave) Vehicle-to-Infrastructure (V2I) communication is a key enabling technology for next-generation intelligent transportation systems, offering high data rates and low latency to support safety-critical and autonomous driving applications. However, mmWave communication at 28 GHz suffers from severe path loss, sensitivity to blockage, and strong dependence on antenna directivity and beam alignment, particularly in complex urban environments. As a result, advanced beamforming and multi-antenna techniques are essential to ensure reliable and high-capacity V2I links. This thesis investigates mmWave V2I communication performance using realistic electromagnetic modeling and beamforming techniques. Full-wave antenna simulations and deterministic ray-tracing are employed using Ansys Electronics Desktop, …

The Writing On The Wall: The Rise Of Applied Ai And The Life-Or-Death Choice Every Ceo Must Make Now, Gary Sheng, Ron Roberts

Digital Laboratory: Publisher of Internet Journal

Applied AI is putting AI to its highest and best use: running your organization as autonomously as possible so you can deliver value for humanity while maximizing the scale of the value. The economy is splitting. Organizations that adopt applied AI are expanding their capacity, accelerating their impact, and pulling ahead. Those that don't are quietly becoming irrelevant, not because they're doing bad work, but because the gap between what they can do and what the moment requires is widening every day. This is not a technology question. It is a leadership question. This paper is written for organizational leaders, …

Marine Vehicle Dynamics Using Koopman Operator Theory With Hybrid Observables, Mikhalib A L Green

LSU Master's Theses

Accurate modeling of marine vehicle dynamics remains challenging due to strong nonlinear hydrodynamic effects, environmental disturbances, and sensitivity to configuration changes, particularly for small-scale platforms. Classical physics-based models require extensive parameter identification and often exhibit degraded performance outside narrow operating regimes, while purely data-driven approaches may lack structure or impose high computational cost. This thesis presents a data-driven Koopman operator framework with hybrid observables for modeling the dynamics of unmanned marine vehicles. The proposed approach combines structured monomial observables with a learned neural network embedding to construct a lifted state representation in which the nonlinear vehicle dynamics are approximated by …

Large-Scale File Fragment Classification Via Multi-View Learning, Samuel Hildebrand

LSU Master's Theses

File reassembly is one of the most fundamental tasks in digital forensics, enabling recovery of data from potentially damaged storage media even when file system metadata is unavailable. This thesis reviews more than two decades of work in the realm of file carving, with a particular focus on fragmented file carving, which remains a focus of research, and file fragment classification, a principal component of fragmented file carving. This thesis serves a literature review of both file carving and fragmented file carving, surveys the massive amounts of data needed for the task of fragment classification and the datasets that serve …

Arid: Agglomerative Regionalization Via Information Divergence, A Novel Clustering Algorithm For Geo-Spatial Data, Joshua David Sills

Dissertations and Theses

Regionalization is a clustering problem that seeks to partition geospatial data into geographically contiguous regions while remaining internally homogeneous in their attributes. It has been successfully applied towards the development of urban planning, natural resource discovery, and ecological analysis. Existing popular approaches optimize homogeneity using Euclidean or variance-based criteria, which ignore distributional differences such as variance shifts, multimodality, and higher-order dependence. This thesis introduces ARID (Agglomerative Regionalization via Information Divergence), a spatially constrained agglomerative clustering framework that replaces distance-based merging with an information-theoretic, Ward-like criterion that utilizes the Kullback-Leibler (KL) divergence. ARID constructs a neighborhood graph from coordinate space and …

Sustainable Cement-Blast Furnace Slag Mortar, Richa Jain, Harshit Jain, Ashish Nim

Journal of Sustainable Construction Materials and Technologies

New building materials that required and produced less energy are need of today's infrastructure development. The ability of such building materials to promote sustainable development always puts its demand in active mode. With huge consumption of naturally occurring materials in building construction, the problem related to environment degradation is now common for world. Along with this, world is also facing the problem of reuse, recover and scientifically approved disposal of non-biodegradable wastes. Blast furnace slag (BFS) is one of those steel industry wastes that are abundantly available and their disposal arises as prime challenge for industries. This research work makes …

Enhanced Cuckoo Search-Based Optimization For Single Distributed Generation Placement And Sizing In Radial Distribution Systems, Samson Oladayo Ayanlade, Abdulrasaq Jimoh, Richard Oladayo Olarewaju, Ignatius Kema Okakwu, Israel O. Adejumobi, Joseph B. Samson, Oluwadare A. Adebisi, Oluwadare O. Akinrogunde

Al-Bahir

This paper presents an Enhanced Cuckoo Search Algorithm (ECSA) to optimally place and size Distributed Generation (DG) in radial distribution systems to minimize real power loss within operating constraints. The proposed ECSA has exponentially decaying adaptive Lévy flights, constraint-aware solution repair with dynamic penalty coefficients, and diversity-directed stochastic replacement to enhance search robustness and convergence speed. It was tested with 30 independent runs on the IEEE 33-bus, IEEE 69-bus, and a practical Nigerian 32-bus distribution network. The simulations show that the ECSA lowers the active power loss of the IEEE 33-bus system from 201.58 kW to 102.75 kW (49.03%), and …

Voltage-Mode Driver With Sar-Based Termination Calibration, Moustafa M. Elsayed, Abeer T. Khalil, Sameh A. Ibrahim, Mohy Eldin A. Abo-Elsoud

Mansoura Engineering Journal

This paper introduces a DAC-based four-level pulse-amplitude modulation (PAM-4) driver capable of operating at data rates up to 80 Gb/s in 65-nm CMOS technology. A replica-based termination calibration loop is proposed to preserve driver linearity across process, voltage, and temperature (PVT) variations. The proposed driver achieves a relative level mismatch (RLM) of 99.3%. In addition, the transmitter demonstrates a vertical eye opening of 134.8 mV and a horizontal eye opening of 0.44 UI under worst-case conditions.

Information Manager (Bim) Role Profiles, Davitt Lamon, Robert Moore, Stephen Lynam

Reports

This document, Information Manager (BIM) Role Profiles, has been prepared in support of the Irish Government’s Building Information Modelling (BIM) Mandate. It is the result of a collaborative effort between Build Digital, the Department of Public Expenditure, Infrastructure, Public Service Reform and Digitalisation (DPER), the Infrastructure Division, and the National Standards Authority of Ireland (NSAI). Its primary purpose is to define the competencies, responsibilities, and experience required for Information Management (BIM) roles to facilitate the successful digital delivery of public works projects. It is important to recognise that under the ISO 19650 series of standards, Information Management is defined as …

Graph-Based And Uncertainty-Aware Machine Learning For Academic Performance Prediction, Anca Olivia Muresan

Electronic Theses and Dissertations 2020 - Present

Early identification of students at risk of academic failure is essential for timely pedagogical interventions and reducing dropout rates. While Artificial Intelligence (AI) has significantly advanced predictive modeling in education, two primary challenges persist: effectively modeling the complex, evolving relationships within heterogeneous educational data, and ensuring the reliability of model outputs for high-stakes decision-making. This dissertation addresses these challenges by proposing a comprehensive framework for early and continuous student performance prediction applied to the Open University Learning Analytics (OULA) dataset. First, we introduce a Heterogeneous Graph Neural Network (HGNN) approach that utilizes metapath structures to capture latent interactions between diverse …

A Novel Optuna-Vmd-Ml Framework For Enhanced Settlement Prediction Of Buildings Around Foundation Pits, Jing Zhang, Zhenlin Wang, Shiyu Sheng, Sifan Shen, Chuang He, Huafeng Shan, Haijie He, Li Ai

Civil Engineering Faculty Publications

The prediction of building settlement around foundation pit is of vital importance to ensure the safety and stability of urban construction projects. However, current predictions of buildings surrounding foundation pit face numerous challenges. Therefore, this study proposes a framework that integrates Optuna-based hyperparameter optimization, variational mode decomposition (VMD), and machine learning (ML) for accurate and timely settlement prediction in practical engineering scenarios, referred to as the Optuna–VMD–ML framework. Optuna is employed to tune the hyperparameters of both VMD and the ML models; the Optuna-tuned VMD decomposes the settlement time series into mode components, which are then used to train the …

Tusqh: Topological Control Of Volume-Fraction Meshes Near Small Features And Dirty Geometry, Brian Reese Shawcroft

Theses and Dissertations

This work develops a framework to create meshes with user-specified homology from potentially dirty geometry by coupling background grids, persistent homology, and a generalization of volume fractions. For a mesh with fixed grid size, the topology of the output mesh changes predictably and monotonically as its volume-fraction threshold decreases. Topological anti-aliasing methods are introduced to resolve pinch points and disconnected regions that are artifacts of user choice of grid size and orientation, making the output meshes suitable for downstream processes including analysis. The methodology is demonstrated on geographical, mechanical, and graphics models in two-dimensions (2D) and three-dimensions (3D) using a …

Safe-Calibrated Tcn–Transformer Transfer Learning For Reliable Battery Soh Estimation Under Lab-To-Field Domain Shift, Ehab Bayoumi

Mechanical Engineering

Battery state-of-health (SoH) estimation is central to transportation electrification because it conditions safety limits, warranty accounting, power capability management, and long-horizon fleet optimization. Although deep temporal architectures can achieve high laboratory accuracy, field deployment is frequently limited by laboratory (Lab)-to-field (L2F) domain shift that alters input statistics, feature definitions, and noise regimes. Under such a shift, predictors may remain strongly monotonic, preserving degradation ordering and become operationally unreliable due to systematic output distortion (e.g., compression/warping of the SoH scale). A deployment-complete L2F transfer learning pipeline is presented, built around a gated Temporal Convolutional Network (TCN)–Transformer fusion backbone, domain-specific adapters and …

Transport And Aging Pathways Of Microplastics In Urban Aquatic Systems, Jessica J. Boyer

USF Tampa Graduate Theses and Dissertations

Microplastics are pervasive in urban aquatic systems, which serve as both conduits of particle flow and incubators of microplastics. These aquatic systems transport microplastic particles while simultaneously altering their physical, chemical, and biological properties. Despite their growing environmental relevance, constructed wetlands and riverine systems remain underrepresented in studies of microplastic fate and transport. This dissertation aims to assess the patterns influencing microplastic transport and aging across urban aquatic environments through a combination of field experiments and mechanistic modeling.

Four interconnected studies were conducted to address this objective. The first investigated the horizontal and vertical transport of microplastics within the Se7en …

The Developing Role Of Ai In Modern Engineering Research, Rianna Pais

The Cardinal Edge

No abstract provided.

Targeting Glioblastoma: Mechanistic And Clinical Perspectives From Uofl Research, Swetha Senthil Nathan

The Cardinal Edge

Glioblastoma (GBM) remains one of the most aggressive and treatment-resistant brain tumors, with median survival lingering at 12–15 months despite surgery, radiation, and chemotherapy. This article explores cutting-edge research and clinical efforts at the University of Louisville aimed at improving GBM treatment outcomes. Dr. Joseph Chen investigates the tumor microenvironment, revealing how physical properties such as stiffness and porosity drive GBM cell proliferation, migration, and resistance to apoptosis. His lab’s findings suggest that porosity, rather than stiffness alone, may be a more accurate predictor of tumor invasion and progression-free survival. Additionally, his research highlights the role of hyaluronic acid and …

Latex: Overleaf, Anette Moreno-Lozano Phd, John Connolly Phd

Day Family Research Lab Workshop Series

This workshop introduces participants to LaTeX for scientific writing. Attendees will learn to create and compile documents in Overleaf, insert and format equations, figures, tables, and references, use collaboration and version control tools, and find discipline-specific resources and templates.

Task-Based Analysis Of Augmented Reality In Collaborative Robotic Programming For Manufacturing Assembly, Medhavi Kamran, Snehesh Shrestha, Vinh Nguyen

Michigan Tech Publications

Augmented Reality (AR) is often promoted as a solution to the cognitive and physical demands of traditional Teach Pendant (TP) programming for collaborative robots. Although prior work has suggested advantages of the AR interface, many evaluations have been limited in scope and may not fully represent the complexities of real-world manufacturing tasks. This study compares the performance of an AR interface to that of a standard TP interface for manufacturing assembly tasks of varying difficulty. In a between-groups study, one group of operators completed standardized assembly tasks using the TP interface, while a separate group used the AR interface instead. …

Understanding And Defending Large-Scale Networked Systems: From Wi-Fi Localization To Provenance- Based Intrusion Detection, Xiao Han

USF Tampa Graduate Theses and Dissertations

Modern networked systems increasingly depend on wireless sensing and large-scale telemetry to provide critical services and maintain security. However, many of these systems function as black boxes or encounter significant scalability limitations, which leave them vulnerable to sophisticated adversaries. In this dissertation, I present my research on enhancing the security and efficiency of networked systems through measurement-driven vulnerability analysis and the design of scalable defense mechanisms.

First, I present my research on the security and privacy risks of Wi-Fi–based localization services. Although location spoofing attacks against Wi-Fi positioning systems have been studied for over a decade, constructing practical attacks in …

A Data-Driven Framework For Modeling Car-Following Behavior Using Conditional Transfer Entropy And Dynamic Mode Decomposition, Poorendra Ramlall

Student Research Symposium (SRS)

Accurate modeling of car-following behavior is essential for understanding traffic dynamics and enabling predictive control in intelligent transportation systems. This study presents a novel data-driven framework that combines information-theoretic input selection via conditional transfer entropy (CTE) with dynamic mode decomposition with control (DMDc) for identifying and forecasting car-following dynamics. In the first step, CTE is employed to identify the specific vehicles that exert directional influence on a given subject vehicle, thereby systematically determining the relevant control inputs for modeling its behavior. In the second step, DMDc is applied to estimate and predict the dynamics by reconstructing the closed-form expression of …

Best Practices For Electrical Safety At Home, Joshua Henderson, Derek Centrone

Student Research Symposium (SRS)

The household, although it’s a comfortable and relaxing environment, can in fact be riddled with everyday hazards that homeowners and residents overlook or are too complacent in acknowledging or even recognizing their presence. One such common oversight is electrical hazards. This may be due to the high usage of electricity in people’s day-to-day lives, especially since the perceived risk of electrical hazards by the public can be very low. In a household, there are many hazards present each and every day. One such hidden deadly hazard is electricity. While on the surface it doesn’t seem that dangerous, in reality, it …

Enhancing Aviation Safety: A Framework For Predictive Maintenance Using Generative Data Augmentation And Uncertainty Quantification, Muhammad Najjar

Student Research Symposium (SRS)

Predictive maintenance is a critical component of aviation safety, yet the development of accurate data-driven models is often hindered by the severe class imbalance inherent in real-world flight data; healthy flights are abundant, while flights preceding a failure are rare. This imbalance biases machine learning models, leading to poor detection of critical maintenance needs. This project addresses this challenge by leveraging the NGAFID aviation maintenance dataset to develop and validate an integrated predictive framework. We propose training a Time-series Generative Adversarial Network (TimeGAN) to synthesize high-fidelity, realistic "pre-maintenance" flight data. This synthetic data is used to create a balanced training …