Engineering Commons^™

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.

Experimental Study Of Dimpled Serpentine Reactors For Hydrogen Production Via Methanol Steam Reforming, Mohamed I. Zeid, Mahmoud A. Shouman, Osama Abdelrehim, Ahmed M. Hamed

Mansoura Engineering Journal

Hydrogen is widely regarded as a clean energy carrier, with methanol steam reforming (MSR) emerging as a promising route for portable and on-board applications due to its favorable operating conditions and high hydrogen yield. Reactor geometry plays a decisive role in hydrogen productivity, pressure drop, and energy efficiency, yet the balance among these factors remains insufficiently addressed. In this study, three serpentine aluminum microreactors: a plain serpentine reactor (PSR), a rhombusdimple reactor (RDR), and a hemispherical-dimple reactor (HDR) were fabricated and coated with a copper (II) oxide, zinc oxide, aluminum oxide (CuO/ZnO/Al₂O₃) catalyst to experimentally investigate the effect of dimple …

Preliminary Proportion Limits And Ultimate Strengths Of Steel Box Girder Using Different Codes, Amira Akram Ahmed Tolba, Nabil Said Mahmoud, Saad Eldeen Mostafa Abdrabou

Mansoura Engineering Journal

— Steel Box Girder (SBG) bridges represent a widely used structural solution for both straight and curved bridges of moderate spans, owing to their high torsional rigidity, significant bending resistance, and rapid construction characteristics. This study provides a comprehensive theoretical comparison of the preliminary proportion limits, shear resistance design strength, and flexural resistance design strength in both positive (sagging) and negative (hogging) moment regions of SBG bridges. The comparison encompasses major bridge design specifications (BDS), including those of the American Association of State Highway and Transportation Officials (AASHTO), the Egyptian Code of Practice (ECP 207 – Part 6), and the …

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. …

Optimizing High-Density Uas Integration In Tbo Airspace: A Federated Contingency Management Framework For Singapore, Jianxiang Wang

Student Works

In Singapore's high-density urban airspace, integrating Unmanned Aircraft Systems (UAS) and Advanced Air Mobility (AAM) into NextGen Trajectory-Based Operations (TBO) faces a significant scalability challenge due to reliance on centralized, legacy Command and Control (C2C) architectures. The problem lies in the latency and inefficiency inherent to centralized Air Traffic Management (ATM) systems when managing heterogeneous, low-altitude traffic. This is particularly true during non-nominal events, such as unpowered ballistic descents. Currently, these events necessitate disruptive airspace segregation, such as Temporary Flight Restrictions (TFRs). This paper presents a conceptual and architectural framework for federated UAS Traffic Management (UTM), utilizing intent sharing and …

Impact Of Urban Roadway Work Zones On Road Users And Other Stakeholders, Lyndsey Renee Harris

Lyles School of Civil Engineering Graduate Student Reports

As urban populations continue to rise at an unprecedented rate, the need for highly maintained infrastructure in cities -- buildings, utilities, and transportation networks -- has become increasingly critical. An urban work zone refers to a designated area within a city where roadway construction, maintenance, or rehabilitation activities are taking place. These zones are typically characterized by high traffic volumes, complex roadway networks, and proximity to residential, institutional, and commercial land uses. Urban work zones often involve lane closures, detours, reduced speed limits, and temporary traffic control measures to ensure safety for road workers and road users. Due to the …

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 …

Wasting The Risk, Or Risking The Waste? Understanding The Trends Of Critical Raw Material Loss Into Waste Streams During Copper And Aluminium Processing, Ella Lausberg, Joël Brugger, Rahul Ram, John R. Owen, Deanna Kemp, Micheal S. Moats, Jonathan Hamisi, Vanessa N.L. Wong

Materials Science and Engineering Faculty Research & Creative Works

Many critical raw materials (CRM) necessary in the transition to carbon-neutral energy reside in the waste streams of mining projects, as they are by-products and often not recovered alongside the primary metal. This work aims to document (i) the loss and potential recovery of by-product metals during the host commodity processing and (ii) the consequences of non-recovery, via a multi-scale risk–reward analysis. The information on the deportment of by-product metals through processing circuits is crucial for treating them as a resource, without which they risk near-permanent loss when treated as 'waste'. We review the deportment of tellurium and selenium as …

Improving Hydrogen Storage Kinetics Of Polymeric Composites Via Additions Of La0.6Ce0.4Ni5 And Carbon Particles, Muhammad M. Rahman, Fenil J. Desai, Ramazan Asmatulu, Md. Nizam Uddin, Karina Suarez-Alcantara

Faculty Publications

Nanostructured metal hydrides have garnered interest in their potential to store hydrogen safely and effectively as an energy carrier. By partially substituting lanthanum (La) with cerium (Ce), the structure of lanthanum pentanickel (LaNi₅) was maintained to create a novel encapsulated La–Ce–Ni-based metal hydride (La_0.6Ce_0.4Ni₅). The incorporation of metal-polymer composites can protect metal hydrides from oxidation and enhance cyclic stability. Carbon-based materials such as graphene and multi-walled carbon nanotubes (MWCNTs) not only store hydrogen but also improve the reaction kinetics and address thermal management issues. This study presents La_0.6Ce_0.4Ni …

Project Comet Verification Testing And Mbse, David Chiaravalle

Student Research Symposium (SRS)

Embry-Riddle’s Project COMET is developing a CubeSat mission to test mmWave communications technology in partnership with the University Nanosatellite Program (UNP). As the design continues to mature, the mission’s growing complexity has introduced challenges in defining and managing technical specifications across multiple subsystems. This Graduate Research Project (GRP) focuses on creating verification test plans at the component, subsystem, and system levels to ensure the spacecraft meets all mission requirements. To better understand and manage interactions between systems, a Model-Based Systems Engineering (MBSE) approach using MBSE software has been applied to provide a digital model representation of the spacecraft. This includes …

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 …

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 …

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 …

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 …

Ergonomics Of A Pilot Workstation And Aircraft Interactions On A Cessna 172 Nav Iii, Nicole Beaulieu

Student Research Symposium (SRS)

This study analyzes the ergonomic aspects of the Cessna 172 Navigator III, a common flight training aircraft. The research investigated the interactions between pilots and the aircraft, focusing on pilot fatigue, workstation design, anthropometric data, and pilot interactions with controls. The study included surveys, interviews, and anthropometric measurements involving three pilots with varying experience levels, alongside physical measurements of the cockpit and a workstation ergonomic assessment.. Participants reported issues with limited physical space, particularly impactful to the pilot’s posture and ability to manipulate controls. Display visibility and interaction were areas of concern, often requiring pilots to lean in awkward postures. …

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 …

Cover And Contents

Turkish Journal of Electrical Engineering and Computer Sciences

No abstract provided.

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 …

Layer-Wise Printing Parameter Optimization For Laser Powder Bed Fusion, Chaoran Dou, Rongxuan Wang, Raghav Gnanasambandam, Jianzhi Li, Zhenyu James Kong

Manufacturing & Industrial Engineering Faculty Publications

Additive manufacturing (AM) is a transformative technology that enables the fabrication of complex geometries layer by layer. However, metal parts produced via AM processes such as laser powder bed fusion (LPBF) are prone to various defects, including porosity and deformation. These defects often result from suboptimal printing parameter settings. Traditional approaches typically aim to reduce defects by optimizing a fixed set of parameters for the entire part. However, such methods do not account for layer-wise variations in printing conditions caused by changes in geometry, heat transfer, and re-heating effects. While optimizing parameters for each layer could improve part quality, it …