Digital Commons Network^™

Effect Of Concentration And Temperature Of Tio2-Al2o3 Hybrid Nanofluid On The Corrosion Rate Of Aluminum Radiator, Ratna Monasari, Rizky Maulana Ramadhan, Nike Nur Farida, Ahmad Hanif Firdaus, Supa Kusuma Aji, Yuniarto Agus Winoko, Rizkyansyah Alif Hidayatullah

Journal of Mechanical Engineering Science and Technology (JMEST)

Corrosion of aluminum radiators can reduce cooling system reliability and shorten service life in automotive applications. This study examines the effect of TiO₂-Al₂O₃ hybrid nanofluid concentration and temperature on the corrosion rate of commercially available aluminum radiator material. Hybrid nanoparticles with a TiO₂-Al₂O₃ mass ratio of 50:50 were dispersed in distilled water at concentrations of 0.6 wt.%, 0.8 wt.%, and 1.0 wt.%. Corrosion behavior was evaluated using a 14-day accelerated immersion test at 60°C and 80°C, and the corrosion rate was determined using the gravimetric mass loss method. The results …

Developing A Natural Language Interface For Knowledge Graphs, Ruth Assefa, Sarah Mendoza, Luke Voinov, Oyku Serap Ogut, Nurcan Yuruk

SMU Journal of Undergraduate Research

This paper proposes to solve the challenge of making databases more user-friendly by interfacing them with OpenAI's ChatGPT-3.5 model. We implemented this solution to assist researchers in easily finding others with similar research interests. Our study involves 184 researchers from 14 departments at Southern Methodist University (SMU). We collected researchers' areas of expertise and biographies and stored them in a Neo4j graph database. We used OpenAI's embedding models to create vector representations of the collected data, allowing for accurate similarity assessments via Neo4j's built-in algorithms. By integrating this system with LangChain, we enabled natural language queries. The results demonstrated high …

Equivocation Analysis Across Continuous And Discrete Memoryless Channels, Md Munibun Billah

Theses and Dissertations

Physical-layer security uses the noise already present in the channel to keep a message secret without making any assumption about the eavesdropper's computing power. In Wyner's wiretap channel model, coset coding uses randomness to map the message to many codewords and protects against information leakage from an active eavesdropper. The secrecy provided by such codes is measured by their equivocation, which is the eavesdropper's remaining uncertainty about the message after she observes her channel. So computing equivocation is an important aspect of analyzing the wiretap channel model. Many channels of practical interest have no closed-form expression for equivocation, and exact …

Co-Designing New Website Ideas In The Age Of Ai, Rami Huu Nguyen

Paul English Applied Artificial Intelligence (AI) Institute Publications

This workshop introduced students to the principles of website co-design in the age of artificial intelligence through a collaborative, project-based learning experience. Participants first explored the mission of the Paul English Applied Artificial Intelligence Institute and the instructor's career journey before learning the fundamentals of e-commerce website design. Students were organized into five teams to develop original website concepts in areas including travel, fashion, sports, and gaming. Each team conducted research using search engines and generative AI tools, including ChatGPT, Gemini, Claude, and Perplexity, created website wireframes, and presented their ideas using Canva. The workshop emphasized teamwork, creativity, user-centered design, …

Development And Characterization Of Mycelium-Based Biocomposites From Ganoderma Lucidum Using Spent Yerba Mate And Residual Sunflower Oil For Sustainable Insulation In Construction, Paula V. Alfieri, Guadalupe Canosa

Journal of Sustainable Construction Materials and Technologies

The construction industry urgently requires sustainable alternatives to petroleum-based insulation materials such as expanded polystyrene (EPS) and mineral wool. Mycelium-based biocomposites offer a promising solution by valorizing agricultural waste while delivering competitive thermal and acoustic performance. In this study, developed and characterized biocomposites using Ganoderma lucidum mycelium combined with spent yerba mate and residual sunflower oil (70 g/30 mL), both commonly generated as waste in Argentine institutions. Two formulations were evaluated: high density (HD: 280 ± 5 kg/m³) and low density (LD: 185 ± 5 kg/m³). Samples were cultivated for 90 days at 28 °C and 65% relative humidity and …

Transmogrifying Coding Into A Social Studies Technology Tool: Using Participatory Design With Engineers And Social Studies Educators To Design Data Visualization Tools For Social Studies Data Literacy Education, Bahare Naimipour, Jessica Zhang, Tamara Shreiner, Mark Guzdial

Journal of Pre-College Engineering Education Research (J-PEER)

Our work aims to inform, design, and create data visualization tools that are meant to be integrated specifically into social studies classes. In this essay we discuss the use of participatory design methods to learn what social studies teachers (both pre-service and in-service) want in their classrooms and test the usability of real tools with these participants. Through this approach we iteratively engage our users, and they inform the design and development of learning tools for the learning and instruction of social studies data literacy. Using a social construction of technology lens, we describe the scaffolding embedded in our tools …

Provocable Forgiveness In Noisy Brand--Consumer Systems:An Agent-Based Study Of Repeated Interaction, Tahere Ahmadiyan, Hamidreza Navidi, Behbod Keshavarzi

Northeast Journal of Complex Systems (NEJCS)

Repeated brand--consumer exchange is often treated as a managerial problem of loyalty, recovery, and trust. It can also be read as a small complex system: many local decisions about cooperation, retaliation, and forgiveness accumulate into market-level selection. This study uses that perspective to examine which relational rules survive when communication is imperfect. Eight canonical Iterated Prisoner's Dilemma strategies are translated into marketing archetypes and evaluated through round-robin tournaments, a six-level noise sweep, proportional-fitness ecological dynamics, and finite-population Moran invasion tests. The tournament leaderboard is calculated without same-strategy self-play, so that reported payoffs reflect inter-archetype competition rather than homogeneous self-coordination. At …

Singulars: Performing The Reverse Turing Test, Halim Madi

ELO (un)supervised 2026

Singulars is an ongoing series of performance systems in which I co-create poetry with a language model trained on an anthology of English poetry alongside my own writing. Across three works—carnation.exe, versus.exe, and reinforcement.exe—I stage live reinforcement loops in which my poems and the model’s responses compete for audience votes. The audience functions as an embodied feedback mechanism, shaping the evolution of both the machine and the human poet in real time.

This paper examines what happens when a poet becomes both author and training data. Drawing from creativity research, metacognition, and social cognition, I reflect …

Case Study: Feasibility Of Creating A Simulated Mobile Data Center For Cross-Disciplinary Academic Programs, Stanley Mierzwa, Christoper J. Schultz, Iassen Christov, Michael Fagioli, Thomas Ikeda, Reinaldo Jaramillo, Giolian Sanagustin

Center for Cybersecurity

This case study examines the potential to envision, create, and deploy a simulated mobile micro data center solution that can be easily replicated and transported between locations and educational settings. The coined term for this solution is the Mobile AI-Centered Data Center (Mobile ACDC), which provides students with a platform to construct, in a hands-on fashion, such a solution and navigate the product to gain greater competencies and understanding of the components found in a data center. Instructor and student feedback assessments from the pilot classroom modules and laboratory experiential learning activities indicate that such a solution helps to improve …

A Reduced-Order Framework For Stochastic Criticality Estimation In Granular Energetic Materials, Philip T. Melton

LSU Master's Theses

Granular energetic materials (EMs) exhibit stochastic shock initiation because pore collapse, frictional dissipation, and localized thermal activation depend on microstructural descriptors that vary between nominally identical samples. Fully resolved mesoscale and atomistic calculations can represent these mechanisms, but their computational cost limits direct ensemble evaluation of microstructure-conditioned criticality thresholds. This thesis introduces a reduced-order framework for stochastic criticality estimation in granular EMs by coupling five explicitly defined model components: first, a one-dimensional steady compaction-shock model that maps initial solid volume fraction and shock pressure to a bulk mass-specific dissipated-work budget; second, an SEM/synthetic-image segmentation workflow that extracts pore area, perimeter, …

Proceedings: Nownet Arts Conference 2018, Sarah Rose Weaver, Chris Chafe, Margaret Schedel, Min Xiao-Fen

Journal of Network Music and Arts

Proceedings of the NowNet Arts Conference 2018, “Network Music: Artistic and Technological Strategies for Public and Private Networks.” This was the first of the NowNet Arts Conferences, which have been held annually from 2018 to the present. The conference took place from April 19–22, 2018, at the Institute for Advanced Computational Science (IACS), Stony Brook University, and at multiple remote locations connected via the internet.

Listening Ahead Ever So Slightly, Chris Chafe, Mike Dickey

Journal of Network Music and Arts

This paper presents a novel packet loss concealment system called “Regulator,” designed for interactive network audio applications, particularly server-mediated “jam rooms” in the cloud, where large ensembles of musicians perform together synchronously. The system addresses the critical challenge of maintaining continuous high-quality audio output while minimizing latency penalties in unreliable network environments including Wi-Fi. The Regulator architecture combines four core components: a central Regulator class orchestrating packet loss concealment operations; a BurgAlgorithm class implementing maximum entropy autoregressive prediction to reconstruct missing audio samples; a Channel class providing per-channel independent modeling for multi-channel streams; and a RegulatorWorker class offering asynchronous processing …

Pulse Before Sound: Reimagining Presence And Liveness In Telematic Music Performance Through Midi-Native Collaboration, Matt C. Bray

Journal of Network Music and Arts

Telematic Music Performance heralds a new paradigm for human creativity, seeking to extend the practical limits imposed by physical co-presence and to permit the cooperative, simultaneous creation of music among geographically remote collaborators. This paper examines how MIDI-native interaction provides a structurally robust alternative to audio-centric telematic systems constrained by latency, bandwidth, and waveform fidelity. Drawing on practice-led research, including over 34 hours of documented improvisation across 91 networked sessions in the SHOALZ series—a longitudinal Telemidi performance research initiative (2022–2024)—the study examines how the Telemidi system employs MIDI as a primary substrate for remote musical collaboration by establishing a synchronized …

Efficient Edge Implementation Of Midasnet For Real-Time Depth Estimation In Indoor Robotics, Muhammed Yasi̇n Adiyaman, İsmai̇l Fai̇k Başkaya

Turkish Journal of Electrical Engineering and Computer Sciences

Real-time depth estimation is crucial in many vision-related tasks, including autonomous driving, 3D reconstruction, robotics, and simultaneous localization and mapping. In recent years, many methods have been proposed to solve depth maps from images by utilizing different modality setups like monocular vision, binocular vision, or sensor fusion. However, for real-time deployment on edge devices, complex methods are not suitable due to latency constraints and limited computation capacity. For edge implementation, models should be simple, minimal in size, and hardware-friendly. Considering these factors, we implemented MiDaSNet, which works on the simplest setup of monocular vision and utilizes hardware-friendly convolutional neural network-based …

Evolutionary Neural Architecture Search: A Survey, Ferda Nur Özçeli̇k, Mehmet Önder Efe

Turkish Journal of Electrical Engineering and Computer Sciences

Deep Neural Networks (DNNs) have achieved remarkable success across diverse machine learning applications, yet designing effective architectures remains a laborious, expert-driven process. Neural Architecture Search (NAS) was introduced to automate this process, with Evolutionary NAS (ENAS) emerging as one of the most effective and widely adopted NAS paradigms. This survey provides a comprehensive and systematic review of 164 ENAS studies published between 2020 and 2024, categorized according to the specific evolutionary algorithm employed as the search strategy. Unlike prior surveys—which either treat evolutionary methods at a high level or focus on general NAS pipelines—this study is, to the best of …

A Holistic Approach For Workforce Scheduling And Routing, Kerem Can Manalp, Ansel Kaplan Erol, Kutluhan Erol, Cem Evrendi̇lek

Turkish Journal of Electrical Engineering and Computer Sciences

The workforce scheduling and routing problem (WSRP) involves assigning tasks across multiple locations while accounting for varying travel times, service durations, time windows, and skill requirements in a wide range of industries, from healthcare to telecommunications. This paper presents a mixed-integer programming model for the WSRP that balances the trade-off between cost and customer satisfaction using a score-generation function and subsequently evaluates the trade-off between solution quality and computation time for several algorithms on well-known datasets. We demonstrate that our model effectively balances cost, service-level agreement satisfaction, and task priorities while providing high-quality solutions in a timely manner. Observing that …

Cnkg: Harnessing Large Language Models For Cognitive Neuroscience Knowledge Graph Construction, Ali Sarabadani, Kheirollah Rahsepar Fard, Hamid Dalvand

Turkish Journal of Electrical Engineering and Computer Sciences

Textual resources are among the most valuable sources of information in cognitive neuroscience (CN) for understanding and investigating brain activity and cognitive processes. Extracting and constructing knowledge graphs (KGs) from these texts can facilitate medical research by providing deeper insights into neurological diseases and brain function. In recent years, the use of large language models (LLMs) in natural language processing (NLP) has become increasingly widespread, significantly enhancing the extraction of meaningful information from large volumes of text. This study proposes a novel approach for constructing and evaluating a specialized knowledge graph, termed the cognitive neuroscience knowledge graph (CNKG), from scientific …

Dual-Stream Bilstm Framework With Histogram-Based Shape Features For Household Load Forecasting, Chang Xu, Wong Jee Keen Raymond, Hazlee Azil Illias, Hazlie Mokhlis

Turkish Journal of Electrical Engineering and Computer Sciences

This study proposes a dual-stream BiLSTM framework for household load forecasting that integrates time-series dynamics with histogram-based daily shape features. Unlike existing models relying on weather or external data, the proposed method extracts intrinsic load-shape information directly from normalized daily curves. A multihead attention module fuses temporal and shape representations, enabling adaptive weighting of informative dimensions. Experiments on three real-world datasets show consistent improvements over the baseline BiLSTM, with up to 30.12%, 24.27%, and 19.03% reductions in MAE, RMSE, and SMAPE, respectively. The results highlight the framework’s robustness and efficiency for fine-grained load forecasting without external inputs.

Robust Variable-Gain Backstepping Control For Nonlinear Systems With Real-Time Application To Induction Motor, Fadi Alyoussef, İbrahi̇m Kaya, Ahmad Akrad, Rabia Sehab, Cristina Morel

Turkish Journal of Electrical Engineering and Computer Sciences

This study proposes a novel variable-gain mechanism with a minimal number of tuning parameters to enhance the performance of conventional backstepping controllers for nonlinear systems while avoiding singularity and peaking phenomena. The proposed approach is simple, computationally efficient, and well suited for real-time implementation without imposing a significant computational burden. Its effectiveness is validated through real-time experiments conducted using a dSPACE DS1104 controller board and a 7.5-kW induction motor (IM). Simulation results demonstrate that the proposed controller outperforms the conventional backstepping controller. Robustness analyses under variations in stator resistance, load inertia, and viscous friction coefficient reveal substantial reductions in the …

Enhancement Of Nested Hexagonal Fractal Antenna Performance For Multiband Wireless Applications, Abdelbasset Azzouz, Rachid Bouhmidi, Mohammed Chetioui, Redouane Berber, Ahmed Jamal Abdullah Al-Gburi

Turkish Journal of Electrical Engineering and Computer Sciences

This work focuses on developing a compact multiband antenna to meet the growing demand for versatile and efficient radiating structures in modern wireless communication systems. A hexagonal fractal antenna is proposed and analyzed for applications such as mobile communications, WLAN, industrial, scientific and medical (ISM) bands, Wi-Fi, satellite links, radar systems, and military communications. By iteratively modifying the antenna geometry with larger hexagonal elements, the design enhances multiband behavior and improves key performance parameters including gain, S11, voltage standing wave ratio (VSWR), and radiation characteristics. The antenna is modeled using high-frequency structure simulator (HFSS)® and fabricated on a low-cost 0.8 …

Parameter Optimization Of Dual-Qsg Based Pll For Real-Time Control Of Grid-Connected Ev Chargers, Gaurav Yadav, Sudhanshu Mittal, Vineet Kumar, Sombir Kundu, Praveen Bansal

Turkish Journal of Electrical Engineering and Computer Sciences

Dual-Quadrature Signal Generator (D-QSG) based Phase lock loop (PLL) has been recently proposed to handle the nonideal grid voltage conditions. However, selecting the parameter for D-QSG based controller has been a great challenge, especially for higher-order systems. Inappropriate parameter selection tends to increase settling time both in terms of amplitude as well as harmonics attenuation. Hence, in the proposed work, the main focus is on parameter selection to achieve a faster response. Here, a fourth-order Quasi-Synchronous Generator has been realized by cascading the two nonidentical second order generalized integrators (NISOGIs). Furthermore, the parameters of both the NISOGIs are selected in …

Erratum To “Sgsc-Kko-Lstm: A Deeplearning Classifier Model For Smart Grid” [Turkish Journal Of Electrical Engineering & Computer Sciences 34 (2) 2026 185-213], Samaniba Imchen, Dushmanta Kumar Das

Turkish Journal of Electrical Engineering and Computer Sciences

The first and second authors were incorrectly ordered in the article PDF due to a typesetting error. To rectify this oversight and ensure the accuracy of the published work, the author order have been corrected as follows: 1. Samaniba Imchen – First Author 2. Dushmanta Kumar Das – Second Author

A link to the original article can be found at: https://doi.org/10.55730/1300-0632.4170

A Revenue-Driven Approach For Enhanced Task Utilization In Vehicular Cloud Computing, Ashish Singh Saluja, Satyabrata Das, Sanjib Kumar Nayak, Sohan Kumar Pande

Turkish Journal of Electrical Engineering and Computer Sciences

Vehicular networks support intelligent transportation through vehicle-to-roadside Units (V2R) and vehicle-to-vehicle (V2V) communication but face challenges from dynamic topologies, limited RSU coverage, and bandwidth scarcity, which impact service delivery and revenue. RDA-ITU addresses these challenges by integrating V2R and V2V paradigms to maximize RSU revenue, enhance service availability, and improve system efficiency. It dynamically allocates services based on real-time network conditions and vehicle mobility, leveraging V2V relays to optimize both RSU-direct and cooperative communication. Through extensive simulations, RDA-ITU significantly outperforms four baselines: RBSM, VVMM-U, VVMM-LW, and VVMM-MA. It achieves 81.1% higher total revenue, 154.8% more completed requests, and 103.6% higher …

Class-Aligned Frequency Augmentation Using Variational Mode Decomposition Forfew-Shot Image Classification, Leila Boussaad

Turkish Journal of Electrical Engineering and Computer Sciences

Few-shot image classification benefits from data augmentation, yet most existing methods operate in pixel space with limited control over spectral semantics. We introduce a lightweight, frequency-guided augmentation strategy based on Variational Mode Decomposition (VMD). Our method constructs an offline, per-class ModeBank by decomposing downsampled luminance patches and retaining midband modes that encode class-specific texture patterns. During episodic training, VMD is never executed online: instead, for each support image, a same-class midband mode is selected and blended using PSNR-targeted scaling with a luminance energy cap, ensuring perceptual consistency. The augmentation is fast, reproducible, class-consistent, and integrates seamlessly into standard metric-based pipelines …

Scalefusion: Hierarchical Feature Aggregation For Unified Multiscale Object Detection, Ayşenur Yaylaci, Berru Kaya, Mehmet Kiliçarslan

Turkish Journal of Electrical Engineering and Computer Sciences

Detecting objects across a wide range of scales, particularly small ones, remains a significant challenge in computer vision. Existing methods often improve small object detection at the cost of performance on larger objects or introduce significant computational overhead through external techniques like image slicing. This paper introduces ScaleFusion, a novel, unified, end-to-end object detection architecture designed to provide robust performance across all scales within a single model. The core of our approach is a hierarchical feature aggregation strategy structured like a tree. ScaleFusion processes an image by running a shared backbone network only on fine-grained patches at the lowest level …

Swindeitvit: A Soft Voting Vision Transformer Ensemble For Accurate And Explainable Solar Panel Fault Detection, Mahe Zabin

Turkish Journal of Electrical Engineering and Computer Sciences

Solar panels are becoming very essential in providing sustainable energy but they are usually affected by defects on the surface like dust, snow, bird droppings, physical damages and electrical faults which interfere with their performance. These faults must be identified accurately and in a timely manner to enhance energy efficiency, lower the maintenance cost, and supplement the traditional manual methods of inspection which are labor-intensive, time-consuming and subject to human errors in judgment. The most common methods, such as traditional CNNs and hybrid architectures tend to be less accurate, less explainable and cannot be properly evaluated to be deployed in …

Analysis Of The Fracture Toughness Of Ercual A2 Cladding On Api X70 Using The Instrumented Charpy Impact Test, Martín Aguirre-Pulido, Francisco Fernando Curiel-López, Jose Jaime Taha-Tijerina, Jorge Alejandro Verduzco-Martínez, Víctor Hugo López-Morelos, Heriberto Granados-Becerra, Ariosto Medina-Flores

Mechanical Engineering Faculty Publications

The degradation of steel used in the oil industry has become a serious problem due to the high costs associated with material loss from corrosion. Applying thin layers of corrosion-resistant material promises to be a viable alternative for extending the service life of pipelines. ERCuAl-A2 electrode claddings were applied to API X70 carbon steel using the MIG brazing process with direct current (DC) and pulsed current (P). The cladding was applied under three conditions: base material (BM) at room temperature, BM preheated to 120 °C, and Ni-buttered BM. Microhardness profiles and Charpy impact tests were performed on the MB and …

Chemistry Of Co2 Capture And Storage: From Molecular Interactions To Advanced Functional Materials, Nany Hairunisa, Hazim F. Abbas, Amamer M. Redwan

Al-Mustaqbal Journal of Sustainability in Engineering Sciences

In light of the industrial development taking place, the burning of fossil fuels for transportation and electricity generation, and the accompanying carbon dioxide emissions, there is an urgent and continuing need to innovate methods of capture and storage, as well as competitive materials to address increasing carbon emissions. This review describes the importance of this procedure and the technologies that help solve this problem, some of which have been adopted by many countries and industrial institutions and differ in their efficiency, cost, and applicability.

Contemporary Cybersecurity Challenges In Emerging Technologies: A Systematic Literature Analysis, Faztudo Languisse Prof

Journal of Cybersecurity Education, Research and Practice

The accelerating convergence of artificial intelligence (AI), the Internet of Things (IoT), cloud computing, blockchain, and quantum computing has fundamentally transformed the global threat landscape, introducing cybersecurity challenges of unprecedented complexity and scale. This systematic literature review synthesizes findings from peer-reviewed publications, institutional reports, and regulatory documents published primarily between 2020 and 2025 to provide an integrated analysis of contemporary cybersecurity challenges across five key emerging technology domains. The review identifies critical vulnerabilities inherent to each domain, documents the evolution of threat actors and attack methodologies — including AI-powered ransomware, adversarial machine learning, and harvest-now-decrypt-later quantum attacks — and evaluates …

Upar-Guided Dendrimer Gel Nanoparticles Reprogram Inflammation To Stabilize Atherosclerotic Plaques, Hsin Yin Chuang, Huari Kou, Yue-Wern Huang, Hu Yang

Biological Sciences Faculty Research & Creative Works

Atherosclerosis is characterized by lipid deposition, chronic inflammation, and apoptosis within the arterial wall, leading to plaque progression and instability. Current lipid-lowering therapies fail to fully address residual cardiovascular risk driven by local inflammation and cell death. Here, we report the development of uPA-functionalized, rapamycin-encapsulated dendrimer nanoparticles (G5PM-uPA/RA) that preferentially accumulate in urokinase plasminogen activator receptor (uPAR)-enriched atherosclerotic plaque, including macrophage- and apoptosis-rich lesion microenvironments. G5PM-uPA/RA was constructed by cross-linking reaction-enabled flash nanoprecipitation in a custom-made multi-inlet vortex mixer, followed by thiol-maleimide conjugation of uPA for uPAR-guided targeting. The nanoparticles demonstrated uniform morphology, favorable stability, and efficient rapamycin loading. In …