Digital Commons Network^™

Optimized Hybrid Watermarking: Dual-Scheme Strategies For Enhanced Robustness, Ooi Jessie, Liew Siau Chuin, Syifak Izhar Bt Hisham, Khor Hui Liang, Khoo Bee Ee, Jasni Mohamad Zain

Iraqi Journal for Computer Science and Mathematics

Digital watermarking is crucial in content identification and copyright protection, particularly multimedia and medical imaging. This paper introduces two novel hybrid watermarking methods, Entropy-Guided Singular Embedding (EGSE) and Entropy-Guided Hybrid Embedding (EGHE), that improve upon existing techniques by integrating entropy-based adaptive block selection with Particle Swarm Optimization (PSO) for dynamic embedding strength determination. Unlike traditional methods, which rely on fixed embedding regions or manual parameter tuning, the proposed approaches automatically identify high-entropy regions to embed watermark signals, ensuring stronger resistance to distortion while maintaining image quality. EGSE employs Integer Wavelet Transform (IWT) and Singular Value Decomposition (SVD), whereas EGHE enhances …

Retracted: Efficient Multi-User Computation Offloading And Reducing Latency In Mobile-Edge Computing For Iot Applications, Sarmad T. Abdul-Samad, Osamah Al-Hwaidi, Ali Abd Al-Rasool Muslem

Iraqi Journal for Computer Science and Mathematics

Mobile Edge Computing (MEC) is an inventive paradigm for computing that has the potential to notably diminish latency and energy consumption by transferring computationally demanding jobs to edge clouds near intelligent mobile users. This investigation aims to reduce offloading and latency between multiple users and edge computing in the context of Internet of Things (IoT) applications in the fifth generation (5G) by utilizing an optimization algorithm called the Bald Eagle Search Optimization Algorithm. Although employing deep learning methods might increase time consumption and computational complexity, an edge computing system enables devices to transfer their demanding jobs to edge servers, decreasing …

Retracted: Iot Flow Parameters Classification Based On Machine Learning Techniques, El-Sayed M. El-Kenawy, Marwa M. Eid, Ban Salman Shukur, Amel Ali Alhussan, Doaa Sami Khafaga

Iraqi Journal for Computer Science and Mathematics

In recent years, there has been a highly remarkable convergence of artificial intelligence (AI) and the Internet of Things (IoT), which has made rapid progress in smart city initiatives by developing smart devices for such cities. Since these devices are increasingly diversified, they require a resilient communication network to demonstrate high performance in managing consistent traffic flows. A machine learning model intended for identifying network parameters from diverse devices, in addition to proposing modifications meant for network performance enhancement, is developed in this study. In relation to packet data as a network traffic parameter, employing gateway devices can facilitate its …

Retracted: Metaguard: A Federated Learning Approach To Hybrid Xgboost And Meta-Learning Models For Proactive Cyber Threat Hunting, Shatha H. Jafer Al-Khalisy, Ghada Al-Kateb

Iraqi Journal for Computer Science and Mathematics

In an increasingly interconnected world, cybersecurity threats have become more sophisticated, necessitating advanced, scalable, and privacy-preserving solutions. MetaGuard emerges as a novel framework that integrates federated learning with hybrid machine learning models, specifically XGBoost and meta-learning, to enhance proactive cyber threat detection. This framework offers a robust, distributed approach to cybersecurity, ensuring high detection accuracy while preserving user privacy through the implementation of differential privacy and homomorphic encryption. MetaGuard leverages distributed nodes to collaboratively train a global model, enabling rapid adaptation to new threats without the need for centralized data aggregation. Experimental evaluations using the CYBER-2024 dataset demonstrate that MetaGuard …

Hourly Simulated Power Production Data With Snow Loss Model At Queued Utility-Scale Pv Sites Simulated As Single-Axis Tracking Systems In The U.S. Eastern Interconnection For Weather Year 2015, Shelbie Wickett, Ana Dyreson

Simulated Solar Capacity Including Snow Cover in the Eastern U.S.

Using 2015 weather data, we ran PySAM power production simulations for utility-scale PV sites in the U.S. Eastern Interconnection queue. Site IDs, capacities, and locations (counties) were extracted from Lawrence Berkeley National Laboratory’s Queued Up: 2024 Edition dataset. No panel mount information was provided, so all sites were assumed to be single-axis tracking systems. Sites’ latitudes and longitudes were assumed to be the centers of the installation counties. See queued_site_metadata.csv file for individual site metadata.

Hourly Simulated Power Production Data With Snow Loss Model At Queued Utility-Scale Pv Sites Simulated As Single-Axis Tracking Systems In The U.S. Eastern Interconnection For Weather Year 2019, Shelbie Wickett, Ana Dyreson

Simulated Solar Capacity Including Snow Cover in the Eastern U.S.

Using 2019 weather data, we ran PySAM power production simulations for utility-scale PV sites in the U.S. Eastern Interconnection queue. Site IDs, capacities, and locations (counties) were extracted from Lawrence Berkeley National Laboratory’s Queued Up: 2024 Edition dataset. No panel mount information was provided, so all sites were assumed to be single-axis tracking systems. Sites’ latitudes and longitudes were assumed to be the centers of the installation counties. See queued_site_metadata.csv file for individual site metadata.

Hourly Simulated Power Production Data With Snow Loss Model At Queued Utility-Scale Pv Sites Simulated As Single-Axis Tracking Systems In The U.S. Eastern Interconnection For Weather Year 2014, Shelbie Wickett, Ana Dyreson

Simulated Solar Capacity Including Snow Cover in the Eastern U.S.

Using 2014 weather data, we ran PySAM power production simulations for utility-scale PV sites in the U.S. Eastern Interconnection queue. Site IDs, capacities, and locations (counties) were extracted from Lawrence Berkeley National Laboratory’s Queued Up: 2024 Edition dataset. No panel mount information was provided, so all sites were assumed to be single-axis tracking systems. Sites’ latitudes and longitudes were assumed to be the centers of the installation counties. See queued_site_metadata.csv file for individual site metadata.

Hourly Simulated Power Production Data With Snow Loss Model At Queued Utility-Scale Pv Sites Simulated As Single-Axis Tracking Systems In The U.S. Eastern Interconnection For Weather Year 2017, Shelbie Wickett, Ana Dyreson

Simulated Solar Capacity Including Snow Cover in the Eastern U.S.

Using 2017 weather data, we ran PySAM power production simulations for utility-scale PV sites in the U.S. Eastern Interconnection queue. Site IDs, capacities, and locations (counties) were extracted from Lawrence Berkeley National Laboratory’s Queued Up: 2024 Edition dataset. No panel mount information was provided, so all sites were assumed to be single-axis tracking systems. Sites’ latitudes and longitudes were assumed to be the centers of the installation counties. See queued_site_metadata.csv file for individual site metadata.

Hourly Simulated Power Production Data With Snow Loss Model At Queued Utility-Scale Pv Sites Simulated As Single-Axis Tracking Systems In The U.S. Eastern Interconnection For Weather Year 2018, Shelbie Wickett, Ana Dyreson

Simulated Solar Capacity Including Snow Cover in the Eastern U.S.

Using 2018 weather data, we ran PySAM power production simulations for utility-scale PV sites in the U.S. Eastern Interconnection queue. Site IDs, capacities, and locations (counties) were extracted from Lawrence Berkeley National Laboratory’s Queued Up: 2024 Edition dataset. No panel mount information was provided, so all sites were assumed to be single-axis tracking systems. Sites’ latitudes and longitudes were assumed to be the centers of the installation counties. See queued_site_metadata.csv file for individual site metadata.

Hourly Simulated Power Production Data With Snow Loss Model At Queued Utility-Scale Pv Sites Simulated As Single-Axis Tracking Systems In The U.S. Eastern Interconnection For Weather Year 2020, Shelbie Wickett, Ana Dyreson

Simulated Solar Capacity Including Snow Cover in the Eastern U.S.

Using 2020 weather data, we ran PySAM power production simulations for utility-scale PV sites in the U.S. Eastern Interconnection queue. Site IDs, capacities, and locations (counties) were extracted from Lawrence Berkeley National Laboratory’s Queued Up: 2024 Edition dataset. No panel mount information was provided, so all sites were assumed to be single-axis tracking systems. Sites’ latitudes and longitudes were assumed to be the centers of the installation counties. See queued_site_metadata.csv file for individual site metadata.

Hourly Simulated Power Production Data With Snow Loss Model At Queued Utility-Scale Pv Sites Simulated As Single-Axis Tracking Systems In The U.S. Eastern Interconnection For Weather Year 2021, Shelbie Wickett, Ana Dyreson

Simulated Solar Capacity Including Snow Cover in the Eastern U.S.

Using 2021 weather data, we ran PySAM power production simulations for utility-scale PV sites in the U.S. Eastern Interconnection queue. Site IDs, capacities, and locations (counties) were extracted from Lawrence Berkeley National Laboratory’s Queued Up: 2024 Edition dataset. No panel mount information was provided, so all sites were assumed to be single-axis tracking systems. Sites’ latitudes and longitudes were assumed to be the centers of the installation counties. See queued_site_metadata.csv file for individual site metadata.

Hourly Simulated Power Production Data With Snow Loss Model At Queued Utility-Scale Pv Sites Simulated As Single-Axis Tracking Systems In The U.S. Eastern Interconnection For Weather Year 2022, Shelbie Wickett, Ana Dyreson

Simulated Solar Capacity Including Snow Cover in the Eastern U.S.

Using 2022 weather data, we ran PySAM power production simulations for utility-scale PV sites in the U.S. Eastern Interconnection queue. Site IDs, capacities, and locations (counties) were extracted from Lawrence Berkeley National Laboratory’s Queued Up: 2024 Edition dataset. No panel mount information was provided, so all sites were assumed to be single-axis tracking systems. Sites’ latitudes and longitudes were assumed to be the centers of the installation counties. See queued_site_metadata.csv file for individual site metadata.

Performance Properties Of Treated Jute Fabric Laminated By Electrospun Recycled Pet Nanofibers, Md Abdus Shahid, Md Golam Mortuza Limon, Imam Hossain, Md Tanvir Hossain, Tarikul Islam, Md Moslem Uddin

Michigan Tech Publications

Transforming from plastic to environmentally friendly materials is essential for both human health and the protection of the environment. In this work, a modified jute fabric (MJF) laminated with electrospun recycled polyethylene terephthalate (rPET) nanofibers with silver nitrate (AgNO3) is presented. The purpose to apply the silver nitrate and rPET nanofiber mat is to enhance the performance properties of packaging materials like mechanical strength, thermal insulation, moisture resistance, and antibacterial properties. The jute fabric was pretreated with alkali to make it compatible with rPET electrospun nanofibers, which improved breathability with a diameter of 24.70 ± 7.79 nm and an average …

Deep Phenotyping Of Obesity: Electronic Health Record-Based Temporal Modeling Study, Xiaoyang Ruan, Shuyu Lu, Liwei Wang, Andrew Wen, Sameer Murali, Hongfang Liu

Faculty, Staff and Student Publications

Background: Obesity affects approximately 40% of adults and 15%-20% of children and adolescents in the United States, and poses significant economic and psychosocial burdens. Currently, patient responses to any single antiobesity medication (AOM) vary significantly, making obesity deep phenotyping and associated precision medicine important targets of investigation.

Objective: This study aimed to evaluate the potential of electronic health records (EHR) as a primary data source for obesity deep phenotyping. We conducted an in-depth analysis of the data elements and quality available from obesity patients prior to pharmacotherapy and applied a multimodal longitudinal deep autoencoder to investigate the feasibility, data requirements, …

Us Education Secretary Visits University, Staff Report

University of Mississippi News

OXFORD, Miss. – U.S. Secretary of Education Linda McMahon toured the University of Mississippi and met with campus leaders to discuss literacy, early learning, workforce development, and health and wellness during a visit to Mississippi.

Scene Generation Method For Maritime Target Recognition Based On Detection Parameters, Yuxuan Run, Dezhen Yang, Yeyang Liu, Wei Deng, Xiangyu Xing, Yi Ren

Journal of System Simulation

Abstract: Traditional scene generation methods for maritime target recognition consider only the effects of different environments on the generated scene data, while overlooking the changes in scene information caused by sensor detection parameters, resulting in a lack of accuracy and authenticity in generated scenes. To address this issue, a detection parameter-based scene generation method for maritime target recognition was proposed. For the task of maritime target recognition, key detection parameters affecting scene generation quality and essential scene features were analyzed. An association relationship modeling method based on Bayesian networks was proposed to construct a mapping relationship model between scene features …

Ship Fire Prediction Method Based On Evidence Theory With Fuzzy Reward, Chunyu Yang, Chuang Zhang, Xiaofan Zhang

Journal of System Simulation

Abstract: A multi-source information fusion approach based on the dempster-shafer (D-S) evidence theory with a fuzzy reward-penalty mechanism was proposed to address the issues of underreporting and false reporting in the early prediction of ship fires. PyroSim was utilized to construct a ship's laboratory model for fire simulation. Variations in carbon monoxide, temperature, and smoke concentration were recorded for data acquisition, followed by the application of a sigmf function for membership assignment. By leveraging the classical D-S theory, a reward-penalty mechanism was applied in weighted evidence fusion. Reward-penalty factors were utilized to differentiate various basic probability assignments, with unified belief …

Current Mechanobiological Pathways And Therapies Driving Spinal Health, Rahul Kumar, Kyle Sporn, Harlene Kaur, Akshay Khanna, Phani Paladugu, Nasif Zaman, Alireza Tavakkoli

SKMC Student Presentations and Publications

Spinal health depends on the dynamic interplay between mechanical forces, biochemical signaling, and cellular behavior. This review explores how key molecular pathways, including integrin, yeas-associated protein (YAP) and transcriptional coactivator with PDZ-binding motif (TAZ), Piezo, and Wingless/Integrated (Wnt) with β-catenin, actively shape the structural and functional integrity of spinal tissues. These signaling mechanisms respond to physical cues and interact with inflammatory mediators such as interleukin-1 beta (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor alpha (TNF-α), driving changes that lead to disc degeneration, vertebral fractures, spinal cord injury, and ligament failure. New research is emerging that shows scaffold designs that can …

Offline Contextual Bandits In The Presence Of New Actions, Ren Kishimoto, Tatsuhiro Shimizu, Kazuki Kawamura, Takanori Muroi, Yusuke Narita, Yuki Sasamoto, Kei Tateno, Takuma Udagawa, Yuta Saito

Cowles Foundation Discussion Papers

Automated decision-making algorithms drive applications in domains such as recommendation systems and search engines. These algorithms often rely on off-policy contextual bandits or off-policy learning (OPL). Conventionally, OPL selects actions that maximize the expected reward within an existing action set. However, in many real-world scenarios, actions—such as news articles or video content—change continuously, and the action space evolves over time compared to when the logged data was collected. We define actions introduced after deploying the logging policy as new actions and focus on the problem of OPL with new actions. Existing OPL methods cannot learn and select new actions because …

Commonwealth Times 2025-08-20

Commonwealth Times, 1969-

No abstract provided.

Project-Based Learning With Odes: Modeling Straw Rocket Motion With Air Resistance, Viktoria Savatorova, Ethan Dyer, Aleksei Talonov

CODEE Journal

This paper presents a hands-on project that guides students through building and validating a mathematical model of projectile motion. The project starts with the idealized case of motion under gravity without air resistance and then introduces air drag : first as a linear force, and then as a nonlinear quadratic force, with the Reynolds number providing the justification for the quadratic model. Students perform experiments with vertical and angled launches, capturing and analyzing motion data using video analysis software. Vertical launch data allows parameter estimation via least squares fitting of the nonlinear drag model, yielding values for initial velocity and …

August 20, 2025, Faculty, Illinois Wesleyan University

2025-2026

No abstract provided.

Office Of Research, Economic Engagement And Outreach Update, August 20, 2025, Office Of Research, Economic Engagement And Outreach

Office of Research, Economic Engagement and Outreach

No abstract provided.

Acoustic Characterization And Modeling Of Heated Supersonic Tactical Jet Aircraft And Launched Rockets, Logan Troy Mathews

Theses and Dissertations

Understanding and accurately modeling the acoustic sources and propagation of high-intensity tactical jet aircraft and rocket noise is essential for vehicle design, environmental impact assessment, and mitigation strategies. Despite progress in the understanding of these sources, significant challenges persist in both predictive modeling and noise mitigation. This dissertation advances both the characterization of full-scale supersonic tactical jet aircraft noise sources and the development of modern empirical prediction models for rocket launch noise. Across multiple measurement campaigns and modeling efforts, the work integrates advanced array signal processing, high-fidelity field measurements, and improved empirical methods to expand the physical understanding of source …

Carbon-Infiltrated Carbon Nanotube Coated Ti6al4v Substrate Tensile And Torsion Raw Data, Jacquelyn Monroe, Brian Jensen

ScholarsArchive Data

This data set contains the raw tensile and torsional Instron measurements reported in Carbon-Infiltrated Carbon Nanotube Coating Effect on Bacterial Resistance and Ti6Al4V Substrate Material Properties. The data in these files was used to generate the stress–strain curves presented in the study.

Dynamic Testing Architecture Of Intelligent Unmanned Systems Based On Parallel Battlefields, Dayong Liu, Zhiming Dong, Qisheng Guo, Wenjun Zhang, Jiancheng Gao

Journal of System Simulation

Abstract: To improve the inadequacy of traditional test and identification systems, this paper proposed an overall architecture for dynamic testing across the entire lifecycle based on the concept of parallel battlefield (integration of physical, virtual, and cognitive battlefields), meeting the new requirements for the testing of intelligent unmanned systems. This architecture included high-fidelity mapping between virtual and physical battlefields, red-blue adversarial deductions and model optimization, simulation to reality (Sim2Real), human-machine collaboration, and cloud-end integrated control, as well as multidimensional assessment and confidence analysis. Centered on the principles of "mutual driving between virtual and physical battlefields, dynamic closed-loop, human-machine collaboration, and …

Research On Digital Simulation Method For Cognitive Load Evaluation Of Pilots, Zeng Fan, Mingjun He, Xiangyu Xing

Journal of System Simulation

Abstract: The operator's cognitive load constitutes a critical determinant of task performance. Pilots, as the primary operators of aircraft, must face an overwhelming volume of information during complex missions, which significantly heightens the risk of cognitive overload and operational errors. Evaluating cognitive load during tasks helps reduce human errors and improve system safety by optimizing design schemes. A simulation model was established to dynamically predict the pilots' cognitive load during tasks for multi-task scenarios. Based on the multiple resource theory, a method for quantifying cognitive load in multi-task conditions was established. By considering cognitive capacity, task time constraints, task priority, …

Study On Semi-Physical Simulation Method Of Air Turbo Rocket Engine In Startup Process, Xuesen Yang, Wei Zhao, Binglong Zhang, Sanqun Ren, Xiaorong Xiang, Qingjun Zhao

Journal of System Simulation

Abstract: To satisfy the requirements for validating the control law of air turbo rocket (ATR) engines, a semi-physical simulation approach was proposed based on serial communication. This platform integrated a rapid prototype system, a supply system, a measurement and control system, a signal simulator, a fault injection system, and a real-time computer. A digital model of the engine was developed based on cross-compilation technology, enabling the coupling and semi-physical simulation of the engine control system and the supply system. A semi-physical simulation of the ATR engine in the startup process was carried out, and the fault handling strategy of the …

Research On 3d Visualization Of Safety Monitoring And Early Warning For Steel Continuous Casting Scenarios, Wei Zhang, Wei Sheng, Yidan Cao, Tingsheng Zhao

Journal of System Simulation

Abstract: In order to improve the visualization and integration of production safety monitoring and fault warning, a three-dimensional (3D) visualization model architecture for whole-process industrial production safety monitoring and early warning for steel continuous casting scenarios was designed. By using 3ds Max and Unity3D, a multi-dimensional and multi-scale model was built, and functional modules such as visualization display and multi-level early warning for safety monitoring data were developed. By combining WebGL technology and Node. js runtime environment, the visualization of whole-process industrial production safety monitoring based on Web terminal was realized. The alarm threshold determination method for whole-process industrial production …

Digital Testing And Evaluation: Current Status, Challenges, And Prospects, Bo Sun, Kai Zheng

Journal of System Simulation

Abstract: Digital testing and evaluation (DTE) represents a novel paradigm in the evolution of testing and evaluation methodologies within the digital era. It is achieved through the integration of multiple digital theories and technologies to conduct testing and evaluations in the digital domain. This paper analyzed the characteristics of test objects across different historical periods, reviewed the core features of testing and evaluation techniques in each stage, and unveiled the paradigm shifts within the testing and evaluation technology system. Building upon this foundation, it explored the new demands placed on testing by test objects in the information age, clarifying the …