Digital Commons Network^™

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 …

What Holds High-Speed Rail Back: Lessons From Global Systems And California’S Experience, Deepak K. Sharma, Rajnish Lekhi, Scott Parr

Mineta Transportation Institute

This report examines benefits and challenges of integrating California High-Speed Rail (CHSR) with other rail systems and reviews California legislation impacting HSR’s contracting and financing alternatives. The research compares CHSR with high-speed rail systems around the world to identify similarities and differences in costs, speeds, ridership, and operating conditions. It also reviews Japan’s Land Readjustment Act as a model for value capture and land assembly and explores international experiences on rail integration, along with U.S. cases from the East Coast and Los Angeles. A review of California’s transportation legislation highlights how laws for highways and bridges differ from those for …

Polymer Microstructures For Advanced Biomanufacturing, Tongyao Wu

LSU Doctoral Dissertations

With the continued growth of the biopharmaceutical industry, the demand for scalable, robust, and resource-efficient platforms for large-scale mammalian cell culture is amplified. Recent developments in microfluidic technology, such as precise control of the microenvironment, showed the potential to improve the performance of cell culture systems. However, constrained by scalability and operational efficiency, applying such approaches to large-scale cell culture and biopharmaceutical production presents challenges. This dissertation addresses these challenges through three independent but conceptually related technological developments. First, a roll-to-roll (R2R) fabrication process was developed for the scalable production of hollow microcarriers (HMCs). HMCs provide three-dimensional microenvironments suitable for …

Sustainable Guiding Framework Of The Egyptian Rural Development Initiative (Hayah Karima), Eman Badawy Ahmed

Mansoura Engineering Journal

The Hayah Karima initiative launches to improve the standard of living for the most needy community groups across the Egyptian countryside, and it also contributes to improving the quality of services provided to the neediest villages. The research aims to propose a guiding framework for sustainability in villages of the Hayah Karima Initiative to achieve Egypt’s Sustainable Vision 2030. To achieve the goal, the research will study the goals, strategies, and principles of the Hayah Karima Initiative, the Egypt Vision 2030, COP 27, the sustainability evaluation criteria, and the Green Villages Initiative. Consequentially, in addition to A questionnaire to measure …

Generalizing Networks To Address Data Imbalance For Facial Action Unit Recognition, Andrew W. Sumsion

Theses and Dissertations

Facial action unit (AU) recognition is used in a variety of downstream applications, including sign language translation, dementia detection, pain detection, and other high-impact tasks that require understanding facial motion. Although AU recognition is a well-established field, a number of unresolved issues remain. An initial problem is the need to improve overall performance on the benchmark datasets, as measured by the average F1 score. Also, the performance across all individual AUs, not just the average F1 score, needs to be increased. Recently, the field has shifted its attention towards ensuring that AU recognition models generalize beyond the distributions represented in …

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 …

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

The Cardinal Edge

No abstract provided.

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 …

Artificial Intelligence In Aviation: Benefits, Concerns, And Regulations, Aarohi Srivastava

Student Research Symposium (SRS)

As artificial intelligence (AI) becomes increasingly integrated into aviation systems, understanding its implications for safety and regulation is critical. This research examines the current state of AI implementation in aviation, exploring both transformative benefits and significant concerns in this high-stakes environment. AI's strengths, including rapid large-scale data processing, predictive capabilities, and pattern recognition, offer substantial operational advantages. However, these benefits must be balanced against key concerns including liability issues, potential loss of pilot competency through over-reliance, the "black box" problem of AI explainability, emergent system behaviors, and training requirements.  This project analyzes current regulatory frameworks from major aviation authorities including …

Building Services Engineering January/February 2026

Building Services Engineering

No abstract provided.

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 …

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 …

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 …

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 …

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 …

Analysis Of The Yeast Surface Display Platform As A Tool For Tyrosine Kinase Phosphorylation Assays, Alexandra Maritza Tang

USF Tampa Graduate Theses and Dissertations

Developing and optimizing platforms to study complex protein-protein interactions is important to maintain fidelity of the modeled activity. Yeast surface display has been used as a high-throughput platform to model substrate phosphorylation by tyrosine kinases, a key enzymatic process that is essential for most cell signaling pathways. However, existing characteristics of the yeast surface display platform limit the ease-of-use, reproducibility, and feasibility of screening enzymatic modification processes like tyrosine phosphorylation. The cytoplasmic domain of T cell costimulatory receptor CD28 and lymphocyte kinase (LCK) were used as a model enzyme-substrate pair to showcase molecular cloning issues that can hinder reproducibility and …

A Transcendental Phenomenological Study Of Cognitive Overload In Manufacturing Engineers During Online Training In An Industrial Environment, Sarah J. May

Doctoral Dissertations and Projects

The purpose of this transcendental phenomenological study was to explore experiences of cognitive overload among engineers in manufacturing during digital training in industrial environments. Engineers in manufacturing manage substantial physical and cognitive demands in order to maintain safe and efficient industrial environments. In addition to these responsibilities, engineers are often expected to engage in digital training within active work settings, frequently without physical or cognitive separation from job-related tasks. A qualitative research design was employed, with data collected from 10 participants and analyzed through triangulation and thematic coding. Data collection methods included a qualitative questionnaire, individual interviews, and a letter-writing …

Mitigating Uas Airspace Risks Through Policy Innovation, Christopher Daniel Sidor

Student Research Symposium (SRS)

Uncrewed Aircraft Systems (UAS), commonly known as drones, have become an everyday part of our lives. Once a technology reserved for the defense industry, UAS are now widely available and affordable in the commercial market. These systems have been used for intelligence, surveillance, and reconnaissance (ISR) missions, route mapping, and kinetic deployment of munitions. In modern warfare, drones have been at the forefront, leveraging new tactics, techniques, and procedures to enhance lethality and destruction. The integration of fiber-optic (FO) connected drones, first-person view (FPV) technology, and 3D printed munition-dropping devices in particular demonstrates a dire need for legislative intervention. These …

Beyond The Sayable: Wittgenstein, Theory Of Mind And Affective Simulation Of Llms, Haomiaomiao Wang, Lili Zhang, Tomás E. Ward

Women+ in Early Career Research Symposium

Wittgenstein’s distinction between what can be said and what can only be shown frames a limit of propositional language. Affect and aesthetic are not primarily matters of correct description but of how expressions function within shared forms of life. Large language models (LLMs), however, increasingly produce fluent language that resembles such understanding by reproducing the patterns through which people ordinarily talk about emotion and perspective.

This paper argues that apparent Theory of Mind (ToM) in LLMs is understood as competence in the publicly shared patterns of mental-state language, rather than as grounded understanding. Using abstract artworks, we show that LLMs …

When Irl Is Not An Option: Social Media Bans And The Exclusion Of Autistic Youth, Melanie Gruben, Ashley Sheil, Anne Ryan Duffy, Jacob Camillleri, Michelle O'Keeffe, Hazel Murray

Women+ in Early Career Research Symposium

After the roll-out of the social media ban in Australia in 2025, policy makers are considering psychosocial benefits and logistical implications. However, the needs of autistic people, who may rely more on the Internet for social connection earlier in life, should be taken into account. This white paper proposes a responsibility to support alternative means of social connection for autistic young people where bans are considered, as IRL (in real life) socialising is not always accessible.

Enhancing Engineering Education For Students With Intellectual Disabilities Through Universal Design For Learning, Bree Jimenez, Ginevra Courtade

Journal of Science Education for Students with Disabilities

This study explores the impact of a Universal Design for Learning (UDL) aligned engineering curriculum on teacher confidence and student engagement in special education classrooms, specifically focusing on students with intellectual and developmental disabilities (IDD). The research investigates how the curriculum, combined with targeted professional development (PD), supports teachers in delivering high-quality engineering instruction. Through a mixed methods approach, including surveys, interviews, focus groups, and classroom observations, data revealed significant improvements in teacher self-efficacy, particularly in areas such as scaffolding, differentiation, and accessibility. Teachers reported increased confidence in their ability to implement engineering lessons and foster engineering mindsets in their …

Multimodal Thermal And Mechanical Characterization Of Cryopreservation Effects In Biological Cells, Subhrajyoti Sourav Kumar Kundu

LSU Master's Theses

Cryopreservation is critical for long-term storage of cells, tissues, organs, and reproductive cells in medicine, biotechnology, and conservation. However, its success is limited by ice formation and cryoinjury, prompting extensive research into analytical tools for understanding and improving cryopreservation outcomes. We outline the principles of each technique and how they are used to detect key thermal and physical events such as ice nucleation, vitrification, devitrification, and cryoinjury. DSC enables quantitative thermal characterization including critical cooling/warming rates (ranging from 1-200°C/min for various systems) and glass transition temperatures. Cryomicroscopy provides direct real time visualization of ice crystal dynamics, distinguishing intracellular versus extracellular …