Digital Commons Network^™

From Resource Mapping To Mechanism Identification: A Lunar Tethered-Satellite Exploration System And China’S Strategic Opportunities, Wei Yong, Zhiping He, Yanchu Yang, Ke Wang, Junqiang Zhu

Bulletin of Chinese Academy of Sciences (Chinese Version)

Lunar exploration is undergoing a paradigm shift from resource mapping to mechanism identification. Key processes, such as the modulation of volatiles by magnetic anomalies and the contribution of Earth wind to surface hydration, require strictly synchronized observations of magnetic-field gradients, particle fluxes, spectrally detected volatiles, and subsurface hydrogen within the same spatiotemporal window. However, a still-unfilled “near-lunar-surface intermediate layer” gap exists between conventional single-spacecraft orbiters and landed rovers: the capability for multi-altitude, multi-parameter, strictly synchronized process observations. A tethered dual-satellite system, consisting of a main satellite and a subsatellite connected by a 5–10 km short tether, naturally provides a dual-altitude …

Community Energy Storage: A Framework For Enhanced Energy Trading And Cost Efficiency, Bassam Al-Hanahi, Aziz

Research outputs 2022 to 2026

The integration of renewable energy sources (RES) into modern electricity grids introduces substantial challenges, primarily due to their inherent variability and the critical need for real-time supply-demand balancing. Community Battery Storage system (CBSS) has emerged as an effective strategy to address these challenges, enhancing grid stability and providing localized economic benefits through optimized energy storage and management. This paper presents a novel energy trading framework that prioritizes community welfare by balancing the reduction of user energy costs with CBSS revenue generation. The proposed model employs a multi-objective optimization approach, utilizing the epsilon-constraint method to strike an optimal balance between minimizing …

Structural Design And Finite Element Analysis Of A 54 Ton Freight Wagon Bolster Test Rig Under Vertical Center Plate Load, Shandy Dwi Prayoga, I Dewa Gede Ary Subagia, I Ketut Adi Atmika, Abdul Rohman Farid, Yunior Lanang Satrio

Journal of Mechanical Engineering Science and Technology (JMEST)

Bolster is one of the bogie's components that receives direct loading from the car body and freight movement. One of the problems that occurs in the bolster is permanent deflection during vertical and transversal static loading. Therefore, a static loading test is required to evaluate the structural strength of the bolster. During the static loading test, a rig is required to hold the bolster so that the bolster is in a fixed position. The rig owned by PT. INKA (Persero) showed a plastic deformation at the vertical center plate load (VCP 5.22P). Therefore, a modified design of the rig is …

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 …

Numerical Simulation Of Centrifugal Pump Performance Under Impeller Blade And Suction Diameter Variations, Gunawan Budi Santoso, Susanto -, Akhmad Nuriyanis, Anang Budhi Nugroho, Rizka Noor Miftakhul Ulum, Suyono -

Journal of Mechanical Engineering Science and Technology (JMEST)

The objective of this study is to model variations in pump geometry to obtain optimal performance under the effects of cavitation. The resulting geometric variation models were then simulated using ANSYS Fluent software. The first geometric variation model used a standard 5-blade impeller with suction pipe diameter variations of 2, 2 ½, 2 ¾, 3, 3 ¼, 3 ½, and 4 inches. The second geometric variation model for simulation used a standard 3-inch suction pipe with impeller blade variations of 3, 4, 5, 6, 7, 8, and 9. The simulation results indicated that the number of blades without changes in …

Privacy-Preserving Intrusion Detection For The Internet Of Medical Things Using Ensemble And Federated Learning, Theyab Alsolami

Electronic Theses and Dissertations 2020 - Present

The rapid proliferation of the Internet of Medical Things (IoMT) has transformed healthcare by enabling continuous monitoring, intelligent diagnostics, and data-driven clinical decision-making. However, this increased connectivity has significantly expanded the attack surface of healthcare systems, exposing sensitive patient data and critical medical devices to cyber threats such as intrusion and data exfiltration attacks. Ensuring both strong security and strict privacy preservation in IoMT environments remains a fundamental and unresolved challenge.

This dissertation investigates the design and evaluation of robust and privacy-preserving intrusion detection systems (IDS) for IoMT networks using advanced machine learning techniques. The research first examines the effectiveness …

Improving Cancer Diagnosis And Patient Outcomes With Deep Learning Models, Mariana Arriz-Jorquiera

USF Tampa Graduate Theses and Dissertations

Cancer care depends on timely and reliable decisions, from detection and diagnosis to treatment planning and patient monitoring. These decisions are often made under uncertainty because medical images and healthcare data may be noisy, incomplete, or difficult to interpret. In breast cancer imaging, ultrasound is widely used because it is safe, accessible, and complementary to other imaging modalities. However, variations in image quality, acquisition conditions, and noise can obscure lesion boundaries and texture, affecting human interpretation and artificial intelligence reliability. This dissertation develops deep learning, image-analysis, and optimization methods to improve healthcare decisions under imperfect information. Its primary focus is …

Technological Advancements Of Hybrid Rocket Engines For Sustainable And Competitive In-Space Propulsion Applications, Ryan J. Thibaudeau, Stephen A. Whitmore, Jared Coen, Joshua Sorenson, Logan Mecham, Ava Wilkey

Mechanical and Aerospace Engineering Student Publications and Presentations

Hybrid rocket engines (HREs), which pair a fluid oxidizer with a solid fuel, offer safety and handling advantages, can reduce environmental impact relative to selected legacy systems, and are capable of deep throttling and restart, making them strong candidates for “green” in-space propulsion applications. However, until very recently, there has not been any flight heritage of an HRE used in a spaceflight mission. Over the past decade, the Propulsion Research Laboratory at Utah State University (PRL-USU) has matured a portfolio of HRE technologies—low-energy arc ignition, digital throttling, additively manufactured sustainable fuels, the Nytrox green oxidizer, and electroplated thruster assemblies—that together …

Property Tables For Thermally Perfect Gases At Low Pressure, Travis J. Moore, Matthew R. Jones

Faculty Publications

Tables giving gas properties at low pressure enable the efficient analysis of processes in which the gas is approximated as thermally perfect but not calorically perfect. In addition to specific enthalpy and specific internal energy, thermally perfect gas tables include special functions that depend only on temperature—relative pressure and relative specific volume. These functions may be used to determine pressure, volume, and temperature of thermally perfect gases undergoing hypothetical isentropic processes. However, the definitions of these functions included in widely used thermodynamics textbooks are vague, inconsistent, or incorrect. The intent of this work is to discuss common inaccuracies in the …

From Shortage To Strategy: Optimizing Scarce Resource Allocation In Healthcare, Daniela Duran Cantarino

USF Tampa Graduate Theses and Dissertations

The allocation of scarce medical resources presents one of the most consequential challenges in health care, requiring decisions that balance equity and efficiency under pervasive uncertainty. This dissertation develops optimization-based frameworks for two critical domains, organ transplantation and emergency medical services, and demonstrates how operations research methods can improve upon current practice along both dimensions simultaneously.

In the domain of deceased-donor kidney allocation, two complementary frameworks are proposed. The first is a bi-objective stochastic optimization model that jointly maximizes post-transplant survival (efficiency) and prioritizes candidates with the highest pre-transplant mortality risk (equity), subject to regional chance constraints on graft failure …

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 …

Applied Research In Field Crop Pathology For Indiana, 2019, Darcy E. P. Telenko

Purdue University Press Books

Applied Research in Field Crop Pathology for Indiana, 2019 serves as a summary of the applied field crop pathology research trials conducted in 2019 under the direction of the Purdue Field Crop Pathology program in the Department of Botany and Plant Pathology at Purdue University. In Indiana, there are more than 57,600 farms, 16,000 crop producers, and 900 certified crop advisers, Extension educators, and crop-protection industry representatives who manage more than 11.4 million crop-production acres with a total value of $6.3 billion annually. There are more than twenty diseases affecting corn, soybeans, and wheat that may decrease crop production and …

Techno-Economic And Environmental Analysis Of A Hybrid Renewable Energy System With V2h Support For Remote Australian Communities, Tushar Kanti Roy, K. Das, Md Apel Mahmud

Research outputs 2022 to 2026

Remote Australian communities continue to experience persistent energy insecurity and increased greenhouse gas emissions due to their reliance on diesel-based microgrids. In response, this study presents a comprehensive techno-economic and environmental assessment of a hybrid renewable energy system integrating solar photovoltaics (PVs), wind turbines (WTs), battery energy storage systems (BESSs), and electric vehicles (Evs) with vehicle-to-home (V2H) functionality. Three system configurations are evaluated over a full-year simulation horizon using realistic solar irradiance, wind speed, household electricity demand, and driving profiles of electric vehicles: (i) off-grid (PV–WT–diesel generator (DG)), (ii) on-grid (PV–WT-BESS–Grid), and (iii) off-grid with V2H. System operation is coordinated …

Proceedings: Nownet Arts Conference 2019, Sarah Rose Weaver, Chris Chafe, Scott Oshiro, Margaret Schedel

Journal of Network Music and Arts

Proceedings of the 2nd Annual NowNet Arts Conference 2019 “Social Purpose in Contemporary Network Arts.” The conference was held November 7–10, 2019. The primary in-person site was the Institute for Advanced Computational Science (IACS), Stony Brook University. Satellite in-person sites included Center for Computer Research in Music and Acoustics at Stanford University, Edinburgh Napier University, Orpheus Institute in Belgium, and Electronic Studios at the Technical University, Berlin. More locations participated remotely via the internet.

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.

Comparing Text Score Strategies For Online Music Making, Craig Pedersen, Lindsay R. Vickery, Stuart James

Journal of Network Music and Arts

This paper investigates a range of approaches to using text scores in online and networked music performance, focusing on their alignment with strategies proposed by Wilson (2020) for aesthetic and technical approaches to networked music performance. Text scores, emerging from the experimental music movement of the 1960s, communicate musical ideas through words rather than traditional notation, taking instructional, allusive, and hybrid forms. Although there are many aesthetic and pragmatic approaches to text score composition, the temporal openness of many such works makes the medium well-suited to the latency-challenged practice of telematic performance. The study evaluates four text scores—Craig Pedersen’s July …

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 …

Editorial, Sarah Rose Weaver

Journal of Network Music and Arts

No abstract provided.

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 …