Digital Commons Network^™

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

Purdue University Press Books

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

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

Student Research Symposium (SRS)

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

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 …

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

AUIQ Technical Engineering Science

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

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

Michigan Tech Publications

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

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

LSU Master's Theses

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

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 …

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 …

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). …

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, …

Characterizing Atmospheric Turbulence With The Lunar Step Response Method, Patrick D. Carattini, Caleb J. Stilp, Katelyn M. Atkinson, Stephen C. Cain

Faculty Publications

Most methods that astronomers use to characterize the strength of atmospheric turbulence in and around their observatories use differential image motion monitors observing a star to provide the necessary data for the measurement. With the Moon becoming a greater national priority, the need to characterize atmospheric paths between observatories on Earth and the Moon is potentially going to grow in the future. To this end, the differential image motion monitor is not an ideal instrument for characterizing turbulence along paths between observatories and the Moon as the bright Moon makes it difficult to detect and locate stars in its vicinity. …

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 …

Retraction Notice: Reliability-Based Design Optimization Using Differential-Algebraic Equations, Iraqi Journal For Computer Science And Mathematics

Iraqi Journal for Computer Science and Mathematics

NOTICE OF RETRACTION FOR: Abed, Saad Abbas; Ghassan, Mona; Latef, Shaimaa Qais; and Hassan, Hind S. (2025) ``Reliability-Based Design Optimization Using Differential-Algebraic Equations,'' Iraqi Journal for Computer Science and Mathematics: vol. 6, Iss. 3, Article 8. DOI: https://doi.org/10.52866/2788-7421.1280.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss3/8.

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 …