Digital Commons Network^™

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

Day Family Research Lab Workshop Series

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

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

Michigan Tech Publications

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

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

USF Tampa Graduate Theses and Dissertations

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

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

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

Student Research Symposium (SRS)

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

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

Student Research Symposium (SRS)

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

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

Student Research Symposium (SRS)

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

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 …

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. …

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 …

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 …

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 …

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 …

Interfacial Chemistry Involved In Selective Separation Of Nmc/Lmo And Lco/Lmo Binary Cathode Materials By Froth Flotation Using Oleic Acid, Richard Kofi Oboh, Kaiwu Huang, Seoung Bum Son, Lei Pan

Michigan Tech Publications

The variability in cathode compositions within recycled lithium-ion battery (LIB) feedstocks poses a significant challenge to efficient downstream refining processes. This study demonstrates the feasibility of using froth flotation with oleic acid as a collector to selectively separate lithium nickel-manganese-cobalt oxide (NMC) and lithium cobalt oxide (LCO) from lithium manganese oxide (LMO) materials. Laboratory-scale flotation tests achieved an 80% separation efficiency in a single stage, producing a froth product with >90% purity of NMC/LCO at approximately 90% yield. Concurrently, the LMO materials were enriched in the sink product with ∼90% purity and ∼90% yield. This approach was further validated using …

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 …

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 …

Predicting Tart Cherry Stem Water Potential Using Uav Multispectral Imagery And Environmental Data Via Symbolic Regression, Anderson L. S. Safre, Alfonso Torres-Rua, Kurt Wedegaertner, Brent Black, Brennan Bean, Burdette Barker, Matt Yost

Civil and Environmental Engineering Faculty Publications

Tart cherry is an important fruit crop in Utah, where irrigation is essential due to arid conditions. Precision irrigation requires reliable indicators of plant water status, and stem water potential (Ψ_stem), is among the most sensitive though labor-intensive and spatially limited. This study develops Ψ_stem estimation models using high-resolution multispectral Unmanned Aerial Vehicle (UAV) imagery combined with meteorological and soil moisture data, applying Symbolic Regression (SR). Results show a stronger correlation between optical bands and Ψ_stem during the pre-harvest period. Among 85 vegetation indices, the Red Chromatic Coordinate (RCC) index performed best (R² = 0.67). …

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 …

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 …

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 …

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 …

Corrigendum Notice: Motion Fusion: A Robust Ensemble Learning Framework For Accurate Sensor-Based Human Activity Recognition, Iraqi Journal For Computer Science And Mathematics

Iraqi Journal for Computer Science and Mathematics

NOTICE OF CORRIGENDUM FOR: Almulla, Hussein K.; Mohammed, Hussam J.; Al-Waisy, Alaa S.; Al-Fahdawi, Shumoos; Had, Ahmed Adnan; and AL-Attar, Bourair (2025) ``MotionFusion: A Robust Ensemble Learning Framework for Accurate Sensor-Based Human Activity Recognition,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 3, Article 14.
DOI: https://doi.org/10.52866/2788-7421.1289.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss3/14.
Reason for Corrigendum: In the published version of the above article, a number of typographical and formatting errors were identified. These corrections do not affect the study design, results, or conclusions, and are provided below to ensure accuracy and consistency. 1) Feature subset sizes …

Corrigendum Notice: Machine Learning Algorithms To Detect Cyber-Attack In The Internet Of Things Platform, Iraqi Journal For Computer Science And Mathematics

Iraqi Journal for Computer Science and Mathematics

NOTICE OF CORRIGENDUM FOR: Al-anni, Maad Kamal; Almuttairi, Rafah M.; Al-Hamadani, Ammar A.; Zidan, Khamis A.; Alsaadi, Husam Ibrahiem Husain; and Al-Sultany, Ghaidaa A (2025) ``Machine Learning Algorithms to Detect Cyber-Attack in the Internet of Things Platform,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 2, Article 26.
DOI: https://doi.org/10.52866/2788-7421.1268.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss2/26.
Reason for Corrigendum: In the published version of this article, the authors and publisher wish to correct the following items:

1. DOI correction: The DOI printed in the article PDF as ``10.52866/ijcsm.0000'' is incorrect and should be ``10.52866/2788-7421.1268.''
2. Dataset split correction: …

Corrigendum Notice: Finding General Mathematical Formulas For Extraction The Minimal Path Sets Of Complex Parallel-Series Networks, Iraqi Journal For Computer Science And Mathematics

Iraqi Journal for Computer Science and Mathematics

NOTICE OF CORRIGENDUM FOR: Lafta, Mariem Hassan and Hassan, Zahir Abdul Haddi (2025) ``Finding General Mathematical Formulas for Extraction the Minimal Path Sets of Complex Parallel-Series Networks,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 1, Article 11.
DOI: https://doi.org/10.52866/2788-7421.1237.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss1/11.
Reason for Corrigendum: In the published article, the definition of minimal path set is stated incorrectly. The text defines a minimal path set as ``a set of components whose failure leads to failure of the whole CPSN,'' which corresponds to a minimal cut set, not a minimal path set. Correction: A …

Corrigendum Notice: Federated Learning-Driven Iot And Edge Cloud Networks For Smart Wheelchair Applications, Iraqi Journal For Computer Science And Mathematics

Iraqi Journal for Computer Science and Mathematics

NOTICE OF CORRIGENDUM FOR: Mohammed, Mazin Abed; Abd Ghani, Mohd Khanapi; Lakhan, Abdullah; AL-Attar, Bourair; and Khaled, Waleed (2025) ``Federated Learning-Driven IoT and Edge Cloud Networks for Smart Wheelchair Applications,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 1, Article 9.
DOI: https://doi.org/10.52866/2788-7421.1241.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss1/9.
Reason for Corrigendum: In the published version of this article, the authors and publisher wish to correct the following items:

1. Table 5 (Task Status entry) — data cell error
In Table 5, the entry for Device ID 3, Iteration 5 contains an incorrect/misaligned value in the Task Status …

Corrigendum Notice: A New Approach For Multiprocessor System-On-Chip Application Scheduling In Hybrid Flow Shop, Iraqi Journal For Computer Science And Mathematics

Iraqi Journal for Computer Science and Mathematics

NOTICE OF CORRIGENDUM FOR: Khraibet, Tahani Jabbar; Kalaf, Bayda Atiya; and Jasim, Ahmed Abbas (2025) ``A New Approach for Multiprocessor System-On-Chip Application Scheduling in Hybrid Flow Shop,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 3, Article 45.
DOI: https://doi.org/10.52866/2788-7421.1318.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss3/45.
Reason for Corrigendum: This note corrects (i) author-affiliation metadata, (ii) minor textual duplications/typographical errors, (iii) a naming inconsistency of the proposed algorithm, and (iv) duplicated sentences in the dataset description. These corrections do not change the experimental results or conclusions; they improve clarity and metadata accuracy. 1) Correction to author affiliation metadata …