Digital Commons Network^™

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 …

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 …

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 …

Cover And Contents

Turkish Journal of Electrical Engineering and Computer Sciences

No abstract provided.

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 …

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 …

Electrochemical Deactivation Of High-Strength, Catechol-Based Adhesives Incorporated With Anhydrous Proton And Electron Conducting Elements, Han Peng, Zhongtian Zhang, Vedika Khare, Abhilash Arjan Das, Fatemeh Razaviamri, Kan Wang, Bruce P Lee

Michigan Tech Publications

Catechol offers switchable adhesion in response to electrochemical redox reaction. However, electrochemistry requires water for effective proton transport, but water weakens adhesive performance. Here, we incorporate proton and electron conducting elements (sulfonic acid-containing monomer and multiwalled carbon nanotube, respectively) into a water-free catechol-based adhesive to create a high-strength adhesive that is also susceptible to electrochemical control. These additions increase the proton and electrical conductivity by over 100-fold. The adhesive also exhibits elevated lap shear adhesion strength (4.6 MPa) to metal substrates and outperforms a commercial epoxy glue. Under mild electrical stimulation (9 V), the adhesive strength decreases by over 90%. …

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 …

The Development And Validation Of A Portable Monitoring System For Real-World Prosthetic And Orthotic Performance Assessment, Emily Wyatt

USF Tampa Graduate Theses and Dissertations

Laboratory-based motion capture systems are widely used for gait analysis but are limited by high cost, infrastructure requirements, and restricted external validity. Inertial measurement units (IMUs) offer a portable and low-cost alternative for assessing gait in both controlled and real-world environments; however, their accuracy and clinical utility require validation. The purpose of this thesis was to develop and evaluate a portable monitoring system using IMUs to quantify spatiotemporal gait parameters and gait asymmetry in both laboratory and community settings, with a focus on prosthetic and orthotic users.

Phase 1 of this study evaluated the agreement between IMU-derived gait metrics and …

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 …

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 …

Lightweight And Resilient Cryptographic Protocols For Next-Generation Networking Systems, Saif Eddine Nouma

USF Tampa Graduate Theses and Dissertations

The security of our increasingly interconnected world is strongly dependent on the strength and practicality of the cryptographic tools we deploy. As billions of embedded devices join the Internet of Things (IoT) and interact with remote cloud infrastructures for data processing, analysis, and actuation, they evolve into so-called digital twins. This rapid expansion introduces numerous attack vectors, particularly targeting constrained IoT devices, which greatly increase the attack surface and often become the root cause of large-scale infrastructure compromises. At the same time, the rise of quantum computing threatens the hardness of today’s intractability assumptions that underpin cryptographic primitives currently deployed. …

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 …

Redesigning Online Graduate Orientation To Foster Academic Resilience And Prevent Underperformance, Stella Michael-Makri, David E. Rodriguez

Journal of Academic Underperformance

Graduate students in fully online programs often begin their academic journey without adequate preparation for the emotional, structural, and cultural challenges of graduate-level work. For students who are first-generation, racially marginalized, international, or returning to education after time away, this lack of scaffolding can lead to early disengagement, underperformance, or attrition. Orientation, often treated as a checklist of logistical tasks, represents a missed opportunity for meaningful academic intervention. This manuscript proposes a five-module conceptual model for online graduate orientation designed to proactively support online graduate students in the domains of emotional regulation and academic identity, time management and executive functioning, …

Developing Low-Cost Gnss Remote Sensing Hardware To Measure Precipitable Water Vapor For Flash Flood Nowcasting In Southern Appalachia, Austin Gleydura

Student Research Symposium (SRS)

Flash flood prediction in Southern Appalachia is particularly challenging due to steep terrain, narrow valleys, highly localized rainfall patterns, and limited measurement coverage. Traditional remote sensing methods, such as Doppler radar and microwave radiometry, suffer from reduced resolution at extended range and signal blockage by mountains, while satellite instruments like MODIS lack sufficient spatio-temporal resolution for sub-kilometer measurements critical to flash flood nowcasting. GNSS-Meteorology offers an established alternative for measuring precipitable water vapor (PWV) and is currently integrated into several numerical weather models. Recent research demonstrates that GNSS-derived PWV products can be used to accurately predict rainfall intensity and timing …

Scheduling As An Organizational Capability, Felix Delmonte

Mechanical and Civil Engineering Faculty Publications

This study synthesizes evidence on schedule governance as an organizational capability integrating Lean practices, BIM, Digital Twin, and AI to convert scheduling into continuous, data driven decision making that links front line observations to executive risk appetite and funding choices, showing how 4D/5D BIM, clash detection, automated material takeoff, and IoT enabled twins shorten planning horizons, reduce design errors by up to 80%, improve timeline adherence by 20%–30%, and, when AI is applied, further cut delays by about 18% and procurement costs by approximately $2.5 million; a four layer Integrated Lean–Digital Framework is proposed to align strategic intent, collaborative digital …

The Ripple Effect: How The Hse Data Breach Shaped Public Opinion, Mugdha Srivastava, Abhishek Kaushik, Roisin Loughran, Fergal Mccaffery

Women+ in Early Career Research Symposium

The digital world is rapidly evolving, and healthcare systems now depend heavily on data-driven and connected technologies. While these tools bring many benefits, they also increase exposure to data breaches and cyberattacks, raising concerns about privacy, security, and trust. Although earlier research has focused on technical and policy solutions for data protection, less attention has been paid to how major cyber incidents influence public attitudes. This study addresses that gap by examining how a major cyberattack relates to people’s views on data security and trust in digital health. We surveyed individuals about their personal cybersecurity practices and their opinions on …

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 …

Bridging Research And Practice: Your Guide To Implementing Evidence-Based Gender Equality Actions In Computing Education, Alina Berry

Women+ in Early Career Research Symposium

What if you could access proven strategies for promoting gender equality without spending months reviewing academic and grey literature? TechMate makes this possible. This free, open-access toolkit transforms scattered research into actionable guidance (actions) that anyone can use, whether you are a lecturer wanting to make your classroom more inclusive, a department head developing new policies, or a student advocate pushing for change. Actions are structured into four context-based categories and relevant subcategories. Each action comes with implementation instructions, where relevant, with ready-to-use templates, and links to the supporting evidence. While developed for computing higher education, TechMate’s research-driven approach is …

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.

A Human-Centred View Of Maritime Autonomous Surface Ships Operations Using The Shell Model, Natasya Habibah, Paul Hegarty, Breda Kenny

Women+ in Early Career Research Symposium

The concept of Maritime Autonomous Surface Ships (MASS) has existed for decades and is advancing rapidly as a result of developments in artificial intelligence. Despite these technological advancements, human involvement remains essential to ensure safety and reliability. This study applies the Software, Hardware, Environment, and Liveware (SHELL) model to develop a human-centric framework for examining the integration of human elements with intelligent and automated technologies in MASS. The framework considers software, with particular attention to regulatory aspects; hardware, including general system and sub-system architecture; environmental factors, including both natural and artificial, as well as dynamic and static elements; and liveware, …

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 …

Decentralized Q-Learning Supervisory Control For Coordinated Multi-Loop Tuning In Pump Stations, David Brattley, Wayne Weaver

Michigan Tech Publications

This paper introduces a reinforced learning-based supervisory control architecture that oversees multiple Recursive Least Squares (RLS) based self-tuning pump controllers and determines when each loop is permitted to adapt its gains. The supervisor learns adaptation policies that minimize interaction between loops while preserving responsiveness to changing hydraulic conditions. A two-loop pump station simulation is used to evaluate performance under product changes and transient flow disturbances. The results show that the supervisory layer reduces the number of simultaneous adaptation events by over 70%, leading to a 32% lower pressure-tracking error and 45% fewer gain-induced oscillations compared to conventional independent adaptive control. …

Demonstration Of Coherent Optical Phase-Shift Keying With A Watt-Class 1550 Nm Tapered Diode Laser Amplifier For Free-Space Optical Communication Applications, Charlotte K. Duda, Aaron Cross, Chadwick Lindstrom, Julie Smith

Space Dynamics Laboratory Publications

High-power semiconductor optical amplifiers (SOAs) are a promising alternative to erbium-doped fiber amplifiers (EDFAs), which typically amplify the weak signals used in free-space optical (FSO) communications. In this work, we demonstrate that an Aura TM SOA operating with 1 W output power can be used to transmit phase-shift key (PSK) protocols required of next-generation FSO communication networks. Firstly, we demonstrate successful transmission of binary phase-shift keying (BPSK) for bit rates up to 50 Gbps. Secondly, we demonstrate successful transmission of quadrature phase-shift keying (QPSK) for bit rates up to 100 Gbps. For both PSK protocols, we demonstrate comparable constellation error-vector …

Using Facial Landmarks For Lip Motion Interpretation Applications, Kimi S. Wright

Theses and Dissertations

Facial motion, particularly lip movement, carries rich information about human speech that can be leveraged for a variety of computer vision and speech-processing tasks. Unlike other pixel-based methods, this work investigates the use of facial landmarks as a compact, privacy- preserving, and computationally efficient representation for lip-motion analysis. We evaluate landmark-based models across multiple tasks, including visual voice activity detection (VVAD), lip–audio synchroniza- tion, and visual speech recognition (VSR) for liveness detection. Using LRS3-VVAD1 and LRS22 datasets, we demonstrate that landmark-only models can achieve performance comparable to pixel-based systems for VVAD and synchronization, while significantly reducing parameter counts and inference …

Evaluating The Response Of Ageing Rc Bridge Piers To The 2023 Kahramanmaraş Earthquake Using Bouc-Wen Hysteretic Models, Negin Fayaz, S. Kocakaplan Sezgin, E. Ahmadi, M. M. Kashani, M. Zaker Esteghamati

Civil and Environmental Engineering Student Research

This study examines the seismic response of reinforced concrete (RC) bridge piers subjected to ground motion (GM) records from the 2023 Kahramanmaraş earthquake by comparing the impact of pulse-like versus no-pulse records. A suite of 300 nonlinear Bouc-Wen hysteretic models was developed to represent piers with varying nonlinear behavior. Each Bouc-Wen model was assessed under 24 pulse-like and 28 no-pulse GMs, resulting in 15,600 nonlinear time-history simulations. Subsequently, the relationship between Bouc-Wen parameters (i.e., period and yield displacement) and seismic responses (i.e., maximum displacement, residual displacement, and maximum acceleration) was analyzed. The results show that pulse-like records produce 5 times …