Digital Commons Network^™

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 …

Engineering 50 Years From Today: A Purdue Engineering Community Perspective, Arvind Raman

Purdue University Press Books

Since opening its doors in 1874, Purdue University’s College of Engineering and its graduates have been at the forefront of technological breakthroughs and are now poised to lead the next generation of engineers in the United States and around the globe. Featuring more than twenty-five essays from former Boilermakers, Engineering 50 Years From Today both celebrates the program’s 150th anniversary and looks ahead, exploring what the field of engineering might look like half a century from now. Leading luminaries from Purdue’s engineering community, including prominent university professors as well as innovators working in the private sector, reveal how technologies related …

Scenarioxp: A Complete Scenario–Based Testing Framework For The Exploration And Exploitation Of Autonomous Vehicle Validation Scenarios, Quentin Goss

Student Research Symposium (SRS)

Today is an age of exiting emerging technology where cutting-edge research in autonomous vehicles (AVs) reduces the active human participation in driving and extends awareness beyond human limitations of perception and reaction, improving driving safety and quality of the user experience as a result. The ever-increasing complexity of these autonomous systems poses many challenges towards the validation and verification (V\&V) of these complex systems under time and resource constraints, as the use of artificial intelligence and also the intricacy of the operating environment means that these systems are also black-box and non-deterministic. Scenario-based V\&V testing of such systems, which involves …

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 …

Building Services Engineering January/February 2026

Building Services Engineering

No abstract provided.

Enhanced Antenna Selection Techniques For Energy-Efficient Code Index Modulation Aided Spatial Modulated Wireless Communication Systems, Fati̇h Çögen, Burak Ahmet Özden, Erdoğan Aydin

Turkish Journal of Electrical Engineering and Computer Sciences

This study proposes an integrated multiple-input multiple-output (MIMO) transceiver framework, termed CIM-HQAM-SM, which combines code index modulation (CIM) and spatial modulation (SM) with energy-efficient hexagonal quadrature amplitude modulation (HQAM). In the proposed bit mapping, the information bits jointly select (i) the active transmit-antenna index, (ii) the Walsh–Hadamard spreading-code indices for the in-phase and quadrature branches, and (iii) an HQAM symbol. Hence, the payload is conveyed through the constellation symbol as well as through antenna and code indices. For the considered Rayleigh-fading scenarios and matched spectral-efficiency settings, the proposed framework offers BER improvements over conventional SM and quadrature SM (QSM), while …

An Ensembled Two-Phase Deep Learning Approach For A Psychiatric Disorder Detection, Prajna Paramita Debata, Midhun Chakkaravarthy, Brojo Kishore Mishra

Turkish Journal of Electrical Engineering and Computer Sciences

Computational Psychiatry represents a burgeoning realm within scientific inquiry, delving into the intricate interplay of neurobiology within the brain. The escalating prevalence of mental illness underscores the urgency to confront this challenge. Among the prevalent disorders, Schizophrenia and Bipolar Disorder loom large, affecting a significant portion of the population at some point in their lives. However, pinpointing psychiatric disorders poses a formidable challenge. Genetic predispositions significantly influence the development of mental illnesses, with intriguing overlaps observed among certain disorders. This convergence complicates accurate diagnosis. Here, a deep learning approach is considered for significant gene biomarker identification and classification of Schizophrenia …

Sentisec: Combining Keyword Heuristics And Sentiment Modeling For Ai-Powered Threat Detection, Ridho Surya Kusuma, Erum Ashraf, Selvakumar Manickam, Shankar Karuppayah

Turkish Journal of Electrical Engineering and Computer Sciences

This work presents SENTISEC, a hybrid LLM-based threat detection framework designed to classify security logs by integrating keyword heuristics, domain-adapted sentiment scoring, and Retrieval-Augmented Generation (RAG). The system achieves an overall accuracy of 93.67%, with 91.46% macro recall, 89.07% macro F1, and 95.15% threat recall, while maintaining a low false-positive rate of 1.68%. Its methodology incorporates strict keyword and IOC matching, a domain-tuned DistilBERT sentiment module, hybrid BM25–MiniLM retrieval enhanced with BGE reranking, adaptive quantile-based threshold calibration, and SHAP-based explainability. Comparative evaluations against keyword-only, sentiment-only, classical machine-learning models, and DistilBERT-only baselines show that SENTISEC consistently improves both true-positive and true-negative …

Optimal Network Reconfiguration Based On Discrete Metaheuristic Techniques For Reduction Of Power Loss And Carbon Emission In Distribution Networks, Asad Ali, Hazlie Mokhlis, Nurulafiqah Nadzirah Mansor, Hussain Shareef, Hasmaini Mohamad, Munir Azam Muhammad

Turkish Journal of Electrical Engineering and Computer Sciences

Power distribution systems play a crucial role in transmitting electrical power from generation sources to end users. During transmission, significant power losses occur in the form of heat as the current flowing along the lines/cables has resistance. To minimize power losses, distribution network reconfiguration (DNR) has been widely adopted. This paper proposes optimal DNR based on metaheuristic techniques with discrete mutation feature targeting active power loss reduction, which subsequently lowers carbon emissions and operational costs. Through the discrete mutation feature, computational time to find optimal solution has been reduced significantly with fewer iterations compared to conventional mutation techniques. The proposed …

Sgsc-Kko-Lstm: A Deeplearning Classifier Model For Smart Grid, Dushmanta Kumar Das, Samaniba Imchen

Turkish Journal of Electrical Engineering and Computer Sciences

Maintaining smart grid stability is crucial for the reliable operation of decentralized electricity networks, especially as the energy sector becomes more complex. The process of ensuring grid stability begins with collecting consumer data and comparing it to power supply requirements. Ultimately, consumers receive a report showing their energy use and pricing details. However, this process is time-consuming and can be improved by leveraging artificial intelligence to predict smart grid stability more efficiently. Specifically, an optimized Long Short-Term Memory (LSTM) network is proposed to predict smart grid stability, addressing the challenges associated with traditional data collection and evaluation methods. Simulations from …

Reducing Complexity In Versatile Video Coding Intra-Coding Through Machine Learning-Based Optimization Of Partitioning And Prediction, Amina Kessentini, Amna Maraoui, Imen Werda, Fatma Ezahra Sayadi

Turkish Journal of Electrical Engineering and Computer Sciences

The escalating demand for high-resolution multimedia content has necessitated more efficient video compression solutions. The Versatile Video Coding (VVC) standard, despite achieving remarkable compression gains, introduces significant computational complexity, primarily due to its exhaustive Rate-Distortion Optimization (RDO) process. To address this, we propose an intelligent approach leveraging supervised machine learning techniques to streamline the VVC encoding process. Specifically, we introduce a Lightweight Neural Network (LNN) for efficient coding unit partitioning decisions and a Decision Tree (DT) classifier for optimizing the intra prediction process. This dual-method framework, tailored for All Intra coding configuration, significantly reduces encoder complexity while maintaining compression performance …

Designing Risk-Aware Mixed-Mode Evacuation Strategies For Tsunamis: Insights From İstanbul, Vedat Bayram, Doruk Ergez, Ada Arikanoğlu

Turkish Journal of Electrical Engineering and Computer Sciences

​Tsunamis pose severe and time-critical risks to densely populated coastal cities, where limited warning times and infrastructure constraints demand carefully coordinated evacuation strategies. This study develops an integrated, risk-aware optimization framework that jointly considers vertical and horizontal sheltering options together with mixed pedestrian-vehicular evacuation dynamics. The proposed mixed-integer second-order cone programming (MISOCP) model simultaneously determines vertical shelter location, evacuee assignment, road-use designation for pedestrians and vehicles, and route selection under congestion, capacity, and budget constraints. Vehicle travel times incorporate congestion effects through a convex flow-dependent function, while pedestrian routing ensures convergent and conflict free evacuation paths. A risk-minimization objective accounts …

A Joint Optimization-Based Novel Attack For Genomic Beacon Reconstruction, Kousar Saleem, Si̇nem Sav

Turkish Journal of Electrical Engineering and Computer Sciences

Genomic data sharing has become an essential component of biomedical research, enabling large-scale collaborations and accelerating discoveries in human genetics. To balance the need for accessibility with privacy concerns, several controlled-access mechanisms have been proposed, including genomic beacons. Genomic beacons answer simple presence/absence queries about specific genetic variants. However, prior work has demonstrated that beacons remain vulnerable to genome reconstruction attacks, where an adversary can recover large portions of participants’ genomes using summary statistics. Building on insights from prior reconstruction attacks, we introduce an approach that unifies SNP correlation and allele frequency alignment objectives within a single-stage joint optimization framework. …

Automated Software Size Measurement Using Multilingual Domain-Adapted Language Models, Samet Tenekeci̇, Hüseyi̇n Ünlü, Burak Keçeci̇, Muhammed Efe İnci̇r, Onur Demi̇rörs

Turkish Journal of Electrical Engineering and Computer Sciences

Software Size Measurement (SSM) is crucial for estimating required project effort as well as budget and schedule. However, many small and medium-sized companies struggle to apply objective SSM due to limited resources and lack of expertise. This often leads to inaccurate estimates and project overruns. There is a need for practical, low-resource solutions that support these tasks without requiring expert involvement. Motivated by this challenge, this study proposes an automated software size measurement approach that formulates the measurement task as supervised regression over natural language requirements, using domain-adapted transformer models. We construct large-scale Turkish and English software engineering corpora to …

Ionic Liquid Pilocarpine Serves As Therapeutic Cosolvent And Permeation Enhancer For Glaucoma Medication, Ashish Trital, Lei Xu, Burhan Ates, Tzu Chen Wang, Vimalin Mani, Hu Yang

Chemical and Biochemical Engineering Faculty Research & Creative Works

The efficacy of hydrophobic ocular drugs is significantly hindered by their low bioavailability, for which poor water solubility is a major contributing factor. In this work, we synthesized pilocarpine-derived ionic liquid [Pilo-OEG] Cl (PO) and evaluated its cosolvent properties for the codelivery of the hydrophobic antiglaucoma drug brimonidine (BM) for glaucoma therapy. Pilocarpine was quaternized with 2-[2-(2-chloroethoxy) ethoxy] ethanol in a one-pot reaction to yield PO, and its structure was confirmed using ¹H NMR and FT-IR spectroscopy methods and further characterized for its rheological property and thermal stability. The HET-CAM assay and cell viability study showed that PO was …

Potential And Chloride Concentration As Thresholds For Crevice Corrosion Initiation In Ss 316l, Y Shorrab, R S. Lillard

University Research

This work investigated the relationship between crevice chloride concentration ([Cl−]) and the potential necessary for initiation in stainless steel 316L (SS 316L). Real-time fluorescence measurements were used to measure [Cl−] inside SS 316L crevice assemblies as a function of bulk [Cl−] and potential. Crevice initiation at potentials slightly less than or equal to the bulk repassivation potential was associated with chloride transport, owing to passive dissolution, and an increase in the crevice [Cl−]. Once the [Cl−] within the crevice increased to the concentration necessary to initiate SS 316L pitting at the applied potential, crevice corrosion was initiated. For example, in …

Impact Of Glass Powder As A Sustainable Material On The Workability, Strength, And Durability Properties In High-Performance Basalt Fiber Reinforced Concrete: A Statistical Analysis, Ahmed Fathi Mohamed Salih

Journal of Sustainable Construction Materials and Technologies

The global construction sector is increasingly challenged to balance environmental sustainability with the growing demand for durable, high-performance materials. As cement production continues to be a major source of anthropogenic CO2 emissions, the incorporation of alternative, low-impact binders has become a key strategy for reducing the environmental footprint of concrete. In this context, glass powder (GP)—a finely ground industrial by-product rich in amorphous silica—has emerged as a promising partial replacement for Portland cement. Its pozzolanic reactivity contributes to improved hydration and matrix densification, while its use also supports circular economy principles by diverting waste glass from landfills. At the same …

Developing And Delphi-Validating A Phase-Based Sustainable Bsc Framework For Contractors' Sustainable Productivity Management In Developing Countries, Truong Van Luu, Le Minh Long Nguyen

HBRC Journal

This study develops and validates, using the Delphi method, a conceptual decision-support framework for construction contractors' sustainable productivity management (CSPM) in developing-country settings. Guided by KAMET rules, semi-structured interviews were conducted with 15 experts to elicit, refine, and reach consensus on a set of productivity management attributes that construction contractors can realistically apply. The outcome is 29 feasible productivity management attributes, organized under the four Sustainable Balanced Scorecard (SBSC) perspectives and mapped onto five management phases (planning, organizing, staffing, leading/coordination, and controlling). Rather than predicting or demonstrating productivity gains, the framework provides a practical roadmap for diagnosing capability gaps, prioritizing …

Symmetry And Scale: The Precise Engineering Of Chitosan-Based Polyhedral For The Delivery Of Therapeutic Materials At The Nanoscale, Ahmed. J. Jasim, Noor Malik Saadoon

AUIQ Technical Engineering Science

Preparation of chitosan-based drug delivery carriers requires converting linear polysaccharide chains into individual 3D objects. By building on the natural structural properties of chitin-based polymers, in addition to control the geometry of organically derived raw material from feedstock to submicron polyhedral or spherical dimensions. This geometric change is governed by a surface area-to-volume ratio that maximizes the exposure of the therapeutic payload to the external environment. The approach centres on the development of such carriers using ion-complexation and emulsification methodologies, which involve electrostatic cross-linking of ion-binding pairs to define an impenetrable boundary separating a liquid from a solid phase. Critical …

Etv2 Mediated Differentiation Of Human Pluripotent Stem Cells Results In Functional Endothelial Cells For Engineering Advanced Vascularized Microphysiological Models, Shun Zhang, Zhengpeng Wan, Lei Wang, Caihong Wu, Junkai Zhang, Sarah Spitz, Xun Wang, Marie A. Floryan, Mark F. Coughlin, Francesca M. Pramotton, Liling Xu, Ron Weiss, Roger D. Kamm

Michigan Tech Publications

Patient-specific microphysiological models have become a valuable tool for broad applications, revolutionizing biomedical research. However, limitations persist, with functional vasculature being a significant challenge. With the discovery of ETV2's determinant role in specifying EC lineages during differentiation, researchers have adopted techniques involving ETV2 overexpression to produce h-iECs more efficiently and consistently. Here, we generated multiple h-iPSC lines with inducible ETV2 expression, and subsequently differentiated them into h-iECs, which were validated functionally and by key endothelial markers and RNA-seq analysis. These cells are capable of reproducibly self-organizing into stable microvascular networks (MVNs) in a microfluidic chip, forming lumenized and functional vessels …

Quantification Of Interfacial Defects In Frp-Strengthened Rc Member Using Non-Destructive Testing Methods, Yuliya Tyrak

USF Tampa Graduate Theses and Dissertations

Externally bonded fiber-reinforced polymer (FRP) composites are widely used to strengthen reinforced concrete (RC) structures. However, the long-term performance of these systems depends on the integrity of the FRP–concrete bond. Interfacial defects such as debonding, delamination, cracking, and voids may reduce structural capacity and are often difficult to detect using conventional inspection techniques. This study investigates the effectiveness of nondestructive testing (NDT) methods for detecting interfacial defects in FRP-strengthened RC members. It explores the potential for automated defect identification using thermal imaging and machine learning.

Laboratory specimens with predefined defects were fabricated and tested using visual inspection (VI), tap testing …

Roadmap: Integrating Artificial Intelligence In Structural Health Monitoring Systems, Simon Laflamme, Erik Blasch, Flippo Ubertini, Zheng Liu, John Wertz, Christine Knott, Matthew Cherry, Eric Lindgren, Fu-Kuo Chang, Amrita Kumar, Jack Poole, Keith Worden, Austin Downey, Jie Wei, Patrick F. Musgrave, Adrian S. Wong, Guiseppe Quaranta, Marco Martino Rosso, Giuseppe Carlo Marano, Yu Chen, Et. Al.

Faculty Publications

Advances in computing and machine learning (ML) methods have led to a rapid rise in artificial intelligence (AI) research and applications in many fields. AI research benefitted from advances in computation hardware, collection and distribution of large data sets, and proliferation of software techniques. AI techniques include ML for provable results, deep learning for data exploration, reinforcement learning for control, and active learning for adaptive systems. Likewise, AI algorithms can handle large amounts of data, construct unknown representations, and provide a direct link between data and classification for decision making. These unmatched capabilities have been seen as a path to …

Comparison Of The Impact Of Probabilistic Versus Deterministic Ground Motions On Structural Responses Of A Steel Moment Building In Salt Lake City, Utah, O. Murphy, M. Zaker Esteghamati, B. R. Cox

Civil and Environmental Engineering Student Research

Earthquakes on the Wasatch Fault pose a significant hazard to Utah’s people and built environment. Code-based seismic design of civil infrastructure in Utah is governed by ground motions determined from regional probabilistic seismic hazard analysis (PSHA). However, with a large earthquake overdue along several segments of the Wasatch Fault, there is growing concern among Utah engineers that PSHA-based ground motions are significantly lower than those from a deterministic scenario. This study compares the structural responses of a four-story steel moment frame in Salt Lake City, Utah, under two ground-motion sets derived from risk-targeted probabilistic and deterministic seismic-hazard analyses. The preliminary …

An Ambient Acoustic Ice-Fracturing Dataset Taken In Shallow Freshwater, John Case, Andrew Barnard, Daniel Brown

Michigan Tech Publications

This paper describes an acoustic dataset collected on a frozen shallow freshwater lake between February and March of 2024. This collection took place over one full week on Portage Lake in the Upper Peninsula of Michigan, USA. The first sub-dataset consists of ambient ice and environmental noises collected by an array of hydrophones, microphones and geophones placed below, above and on the ice respectively. The second sub-dataset consists of instrumented force hammer impacts at a series of locations on the the ice with the corresponding response at each acoustic sensor. All acoustic data were recorded at a sample rate f …

Quantifying And Integrating Community Hardship Into Two-Stage Stochastic Grid Reliability Optimization With Battery Storage, Fredrica Arthur

LSU Master's Theses

Traditional reliability planning for conventional distribution systems is largely utility-oriented, with a focus on collective system performance metrics like Expected Energy Not Supplied (EENS), where implicitly all unserved energy is considered of equal weight in terms of post-outage economic hardship. Yet, it is well understood that extended outage durations cause an uneven level of hardship to socioeconomically disadvantaged communities. This thesis proposes a community-informed reliability planning framework where the hardship caused by outages is explicitly considered in the battery energy storage system (BESS) location and sizing problem. First, a hardship-weighted Energy Not Supplied (WENS) measure is proposed, where income, education, …

Dual-Modal Tumor Imaging Using Functionalized Iron-Oxide Nanoparticles For Mri And Fluorescence, Kevin Secades

USF Tampa Graduate Theses and Dissertations

Accurate tumor imaging is essential for early cancer detection and treatment monitoring [1]. Technetium-99m (Tc-99m) is a commonly used radioisotope for medical imaging, but its reliance on gamma-ray emission poses challenges related to safety, accessibility, and regulatory constraints[2-5]. This research proposes the development of a dual-modal imaging approach by functionalizing iron-oxide nanoparticles (IONPs) with the fluorescent chromophore Cy5, enabling both MRI contrast enhancement and optical imaging for improved tumor specificity. IONPs provide strong T2-weighted MRI contrast [6], while conjugated near-infrared (NIR) chromophores allow for high-resolution fluorescence imaging [7], making this proposed system adaptable for both in vitro and in vivo …

Availability Model To Evaluate Ai Data Centers’ Role In Grid Stability, Troy Mcsimov, Trevor S. Kunz, Jeffrey Billo

SMU Data Science Review

The United States has made it clear; it is imperative that the US wins the global AI race. This paper focuses on one of the most challenging puzzle pieces surfaced at the POWER Data Center conference (San Antonio, Sept. 30.); for Electric Reliability Council of Texas (ERCOT) the limiting factor is not generation alone but the need to balance generation and load to preserve grid reliability.

The regulatory landscape fundamentally changed with the passage of Texas Senate Bill 6 in June 2025, which mandates new large loads must "contribute to the recovery of the interconnecting electric utility’s costs" (Texas Legislature, …

Reducing Range Anxiety Through Predictive Modeling Of Ev Battery Degradation, Caleb Thornsbury, Christian Castro, Bivin Sadler

SMU Data Science Review

Electric Vehicles (EV) range anxiety remains one of the top barriers for broader adoption. Range anxiety can be attributed to battery pack age and degradation over time. This paper plans to explore how to address this issue by creating a machine learning model that can predict degradation based on usage, temperature, battery chemistry, charging habits and exploring whether other factors tie into range degradation. This research will be using real world charging data along with lab tested chemistry data to build a model that can be chemistry specific for degradation. This paper will help perspective used-EV buyers learn about battery …

Time-Robust Evaluation For Multi-Dataset Intrusion Detection Reveals Temporal Shortcuts And Strong Baselines, Kyle A. Mccleary

LSU Master's Theses

Pooled multi-dataset benchmarks are an attractive way to evaluate intrusion detection systems (IDS) across heterogeneous public corpora, but they can quietly reward shortcut features tied to capture schedules and dataset identity. This work introduces TRACER, an auditable benchmark specification that standardizes seven public IDS corpora into a shared transaction-window prediction unit and a shared label ontology, enabling controlled comparisons between compact sequence backbones and strong tabular baselines under matched splits, training budgets, and scoring rules.

Under this protocol, absolute clock time is a strong shortcut under pooled random splits. Enforcing time-robust controls (timestamp rebasing, circular shifts, and schedule-token masking) reduces …

Comparative Assessment Of Uav-Based Tseb And Field-Calibrated Aquacrop For Evapotranspiration On The Arid Coast Of Peru, Roxana Peña-Amaro, José Huanuqueño-Murillo, Lia Ramos-Fernández, Abel Ramos-Ayala, David Quispe-Tito, Lena Cruz-Villacorta, Elizabeth Heros-Aguilar, Edwin Pino-Vargas, Alfonso Torres-Rua

Civil and Environmental Engineering Faculty Publications

Precise estimation of evapotranspiration (ET) is essential for sustainable water management in arid agroecosystems, particularly for high-water-demand crops such as rice. This study integrated very-high-resolution UAV thermal–multispectral imagery with a Two-Source Energy Balance model (UAV–TSEB) and a field-calibrated AquaCrop model to quantify daily ET and its components under continuous flooding on the arid Peruvian coast during the 2024–2025 season. A network of 24 drainage lysimeters provided an independent observational benchmark (ET_lys); to represent the treatment-level response, lysimeter observations were aggregated as the mean across the 24 units for each UAV campaign. Thirteen UAV surveys supplied radiometric surface temperature …