Digital Commons Network^™

Retracted: Hotspot Issue Handling And Reliable Data Forwarding Technique For Ocean Underwater Sensor Networks, Omar Adil Mahdi, Yusor Rafid Bahar Al-Mayouf, Sameer Sami Hassan Al-Obaidi, Bourair Al-Attar, Hamed Balogun, Suleman Khan

Iraqi Journal for Computer Science and Mathematics

Underwater Wireless Sensor Networks (UWSNs) have emerged as a promising technology for a wide range of ocean monitoring applications. The UWSNs suffer from unique challenges of the underwater environment, such as dynamic and sparse network topology, which can easily lead to a partitioned network. This results in hotspot formation and the absence of the routing path from the source to the destination. Therefore, to optimize the network lifetime and limit the possibility of hotspot formation along the data transmission path, the need to plan a traffic-aware protocol is raised. In this research, we propose a traffic-aware routing protocol called PG-RES, …

Building Multimodal Knowledge Graphs: Automation For Enterprise Integration, Ritvik Garimella, Hong Yung Yip, Revathy Venkataramanan, Amit P. Sheth

Publications

As enterprises increasingly aim to incorporate artificial intelligence into their workflows to tackle complex, multimodal tasks, the demand for intelligent, robust, and trustworthy systems is paramount. While multimodal large language models offer initial capabilities for processing diverse data streams, their dependence on embedding-based representations limit their effectiveness in delivering semantically grounded explanations and reasoning, as well as qualities essential for enterprise-grade applications. Neurosymbolic approaches provide a promising alternative by enabling traceable, context-aware decision making. However, constructing enterprise-level multimodal knowledge graphs (MMKGs) that enable neurosymbolic approaches remains largely impractical. Although prior efforts have explored MMKG construction, they fall short in addressing …

Event-Based Sensor Noise Modeling For Space-Based Space Domain Awareness, Rachel Oliver, Brian Mcreynolds, Dmitry Savransky

Faculty Publications

Building off the foundation of a physics-based end-to-end model for event-based vision sensors (EVS) observing resident space objects (RSOs), we apply new techniques to model realistic low-light sensor noise. While previous approaches simulate memorized current leakage and apply temporal noise models, our methods improve on these approaches and additionally account for current-following noise as an event source. These improvements are key components for accurate event-generating simulators which can advise requirements and concepts of operations for dedicated event-based Space Domain Awareness (SDA) architectures. The EVS pixel’s independent and asynchronous recording of changes in photocurrent produces data with high temporal resolution and …

The Green Approach Of Arabic Gum-Based Adsorbent In Wastewater Treatment, Atheel H. Alwash

AUIQ Technical Engineering Science

The natural polysaccharide Arabic gum is a multifunctional, sustainable material used widely in wastewater treatment technology. Its active functional groups such as hydroxyl, carboxyl, and amino groups facilitate the efficient removal of heavy metals, dyes, pesticides, pharmaceuticals, and persistent organic pollutants. The high surface area and active functional groups facilitate its modification to form various structures such as hydrogels, nanocomposites, or biochar integrated into hybrid adsorption–photocatalysis systems. This review highlights the most familiar forms of Arabic gum in wastewater treatment technology, such as hydrogels, hydrogels nanocomposite, stabilizing agents, coating agents, and bio-activated carbon derived from Arabic gum using different preparation …

Memf-Net: A Mega-Ensemble Of Multi-Feature Cnns For Classification Of Breast Histopathological Images, Alaa Hussein Abdulaal, Ali H. Abdulwahhab, Aqeel Majeed Breesam, Zahra Hasan Oleiwi, Riyam Ali Yassin, Morteza Valizadeh, Saja Nafea Mohsin

Iraqi Journal for Computer Science and Mathematics

Pathological anatomical images play a pivotal role in diagnosing diseases, notably breast cancer, which affects women globally. These images, obtained through biopsies or post-mortem examinations, are preserved to maintain their structural integrity. Software tools, like computer-aided diagnosis, aid doctors in early detection and treatment planning, contributing to reduced mortality rates. In this context, convolutional neural networks (CNNs) have emerged as valuable tools for diagnosing benign and malignant breast cancers. This paper introduces a Mega Ensemble Net method, leveraging multi-scale combination features on the breast histopathology dataset. Three fine-tuned deep learning models, namely ResNet-18, ResNet-34, and ResNet-50, are integrated into this …

Retracted: Capsule Network Model For Detecting Spoofing Attack In The Internet Of Medical Things (Iomt), Mohammad A. Alsharaiah, Mohammed Amin Almaiah, Mansour Obeidat, Rami Shehab

Iraqi Journal for Computer Science and Mathematics

The Internet of Medical Things (IoMT) has transformed healthcare delivery through real-time monitoring and data exchange. However, this integration of smart medical devices has also introduced critical cybersecurity threats, particularly spoofing attacks, which can compromise patient safety and system reliability. Conventional Intrusion Detection Systems (IDS) often fail to address IoMT-specific challenges such as class imbalance, computational constraints, and the need for real-time adaptability. This study proposes a Capsule Network (CapsNet)-based IDS that leverages spatial dependency modeling and hierarchical feature relationships to detect spoofing attacks in IoMT environments. Using the CICIoMT2024 dataset, we implemented a binary classification framework where spoofing instances …

Modeling Of Urea Drying And Granulation Process In Fluidized Bed, Jalolitdin Pakhritdinovich Mukhitdinov, Aleksey Viktorovich Schulz

Chemical Technology, Control and Management

This article is devoted to the mathematical modeling of urea drying and granulation processes in a fluidized bed. A brief description is provided for the functional blocks included in the mathematical model, along with the required parameters that form an integrated representation of the technological process. The SR-POLAR model is used to describe a phase equilibrium between the components involved. The interconnections between functional blocks are shown in the process flow diagram. Block diagrams for modeling a multi-chamber granulation unit and the cooling system for the resulting granules are presented. Granule growth in the fluidized bed is described using a …

Increasing The Robustness Of A Control System For A Complex Dynamic Plant By Correcting Nonlinearity In The Warping Process, Tukhtamurod Khayitmurodovich Avezov, Zokhid Ergashboyevich Iskandarov

Chemical Technology, Control and Management

The paper discusses the challenges of enhancing the robustness of a control system for a complex dynamic plant by addressing nonlinearity in the warping process. Devices that ensure the stability of the control system against parameter non-stationarity on the warping machine are referred to as state controllers. The operating principle of these devices relies on providing artificial nonlinearity to the rear connection circuit of the control system's actuator. However, this nonlinearity is implemented using components that consider the parameters of low control quality. Therefore, it is necessary to continuously adjust the nonlinearity parameters to, on one hand, reduce the load …

Optimizing Beer Fermentation Through Intelligent Control, Azizbek Nodirbekovich Yusupbekov, Mirjalol Yusupov

Chemical Technology, Control and Management

This paper presents an intelligent control approach for optimizing the beer fermentation process using fuzzy logic and adaptive neuro-fuzzy inference systems. By incorporating multivariable inputs—temperature error and pH deviation—the proposed system effectively handles the nonlinear dynamics and biological variability inherent in fermentation. Simulation results demonstrate improved control accuracy, responsiveness, and robustness compared to conventional methods, making the approach suitable for integration in modern brewery automation systems.

A C++ Library For Turbulent Mixing Simulation Using Hierarchical Parcel Swapping (Hips), Masoomeh Behrang, Tommy Starick, Heiko Schmidt, David O. Lignell

Faculty Publications

Turbulence models are crucial for simulating flows at all scales, capturing both large-scale structures and small-scale mixing. Software libraries that implement such models should support modular integration, customization, and scalability across different simulation frameworks. This paper presents Hierarchical Parcel Swapping (HiPS), a C++ library documented with Doxygen and available on GitHub. HiPS supports both mixing and reactions and can be used as a standalone model or as a subgrid model in CFD simulations. The code includes examples for users to run it as a standalone model. Additionally, considerations for using it as a subgrid model are provided.

Performance Analysis Of Flapping-Foil Energy Harvesters Under Shear-Flow Conditions, Maqusud Alam, Bubryur Kim, Shehnaz Akhtar, Sujeen Song, Zengshun Chen, Jinwoo An

Civil Engineering Faculty Publications

Flapping-foil energy harvesters represent a promising technology for extracting energy from fluid flows, although their performance under shear-flow conditions is poorly understood. To address this research gap, we investigated the impact of power-law shear flow on the performance of flapping-foil energy harvesters. We conducted numerical simulations at different Reynolds numbers (Re = 1000, 50 000, and 500 000) under uniform-flow and shear-flow conditions. Based on the results, shear flow minimally affected the power outputs at Re = 1000 and 50 000, where viscous forces dominate the flow dynamics. However, at Re = 500 000, shear flow significantly reduced the …

Algorithms For The Synthesis Of Adaptive Control Systems Based On The Speed-Gradient Method, Oxunjon Boborayimov

Chemical Technology, Control and Management

This paper discusses the synthesis algorithms for adaptive control systems based on the speed-gradient method. Adaptive control systems with implicit reference and adjustable models are synthesized using speed-gradient techniques, which reduce the requirements for the main control loop structure and the completeness of measurement data. Stable adaptive decentralized control algorithms are developed for a class of interconnected systems with nonlinear local dynamics and uncertainties, ensuring the stability of individual subsystems and the overall system while accounting for their interactions. To incorporate inter-subsystem interactions into the overall control law, an adaptation algorithm based on the speed-gradient method is introduced. A synthesis …

Modern Methods Of Self-Monitoring, Diagnostics, And Fault Tolerance In Flow Measurement Systems, Elbek Ortiqov

Chemical Technology, Control and Management

This article investigates various methods and tools for self-monitoring and fault tolerance in flow measurement transducers used in industrial processes. The study focuses on key techniques such as the use of redundancy, generation of reference values, analysis of measurement signals, and control of disturbance variables. These methods allow transducers to detect potential faults, ensure reliable operation, and maintain measurement accuracy even under adverse conditions. The article highlights how self-monitoring contributes to improving system safety, increasing reliability, and reducing downtime. It also discusses the integration of intelligent monitoring systems that support predictive maintenance and real-time diagnostics. Fault-tolerant sensors with self-monitoring capabilities …

Finding The Shortest And Optimal Path With Metaheuristic And Madm Methods, Narmin Ibrahim Hasanli

Chemical Technology, Control and Management

The article had described a method for finding the shortest and most optimal path among cities. The data had been taken from the TSPLIB library, which had provided standard examples for the Traveling Salesman Problem. This approach had integrated the advantages of the meta-heuristic technique and the Multi Attribute Decision Making method to solve the problem effectively. In the first stage, the population based meta-heuristic method ACO (Ant Colony Optimization) had found optimal solutions in large search spaces. The use of pheromone trails, heuristic information and an iterative search process had given the opportunity to find the best or near-best …

Research And Development Of Intelligent Measurement Systems, Odil Abdujalilovich Jumaev, Mahmudov Giyosjon Baqoyevich

Chemical Technology, Control and Management

The article discusses modern methods for developing intelligent measuring systems. Intelligent measuring systems are systems based on intelligent technologies that not only accurately measure physical or chemical quantities, but also have the ability to self-analyze, diagnose and make management decisions. The article comprehensively examines the architecture, components of such systems, the organization of their software and hardware, the relationship of sensors and artificial intelligence algorithms. It also analyzes the practical application and prospects of intelligent measuring systems in such areas as industry, medicine, energy, ecology, transport.

Evaluation Of Deep Learning Techniques In Road Sign Recognition, Latafat Abbas Gardashova, Haji Fakhraddin Hajiyev

Chemical Technology, Control and Management

Deep learning has transformed the computer vision field and greatly improved the performance and efficiency of road sign recognition systems. This research compares different deep learning methods, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and hybrid models, in terms of their ability to effectively detect and classify road signs under various conditions. The study compares performance measures such as accuracy, processing speed, and robustness to environmental conditions like low lighting, occlusion, and adverse weather. The results show that CNN-based methods, especially those with transfer learning and ensemble techniques, have better performance in real-time scenarios. Problems like computational …

Application Of Neural Networks For Intelligent Processing Of Sensor Signals In The Control Of Technological Process Parameters, N.R. Yusupbekov, Yu.Sh. Avazov, G.Kh. Rashidov

Chemical Technology, Control and Management

This scientific article investigates the problem of analyzing technological process parameters in the fields of chemistry, energy, and metallurgy based on sensor data and applying intelligent signal processing methods. The main objective is to evaluate the effectiveness of artificial intelligence and deep learning models for intelligent analysis, forecasting, and anomaly detection of data obtained from sensors. Time-series data collected from industrial sensors were analyzed using LSTM (Long Short-Term Memory) and Autoencoder neural networks, as well as the Kalman filter. At the first stage of the study, sensor signals were denoised and their true state was estimated using the Kalman filter. …

Algorithms For Assessing Soil Salinity Levels Based On Remote Sensing Imagery, Bobomurod Mamitjonovich Tojiboev

Chemical Technology, Control and Management

This article investigates methods for assessing soil salinity levels based on satellite (remote sensing) imagery and their calculation algorithms. Determining the degree of salinity plays a crucial role in the rational use of land resources and increasing agricultural efficiency. The study analyzes indices for determining soil salt content using remote sensing technologies, particularly multispectral images obtained from satellite systems such as Landsat and Sentinel (for example, SI - Salinity Index, NDVI - Normalized Difference Vegetation Index, and others). Furthermore, algorithms are developed based on these indices that enable automatic determination of salinity assessments. Artificial intelligence, machine learning, and geographic information …

Synthesis Of A Control System For Thermal Energy Facilities Using The Quantum Photon Spin Method, Isomiddin Siddikov, N.S Yakubova

Chemical Technology, Control and Management

Increasing energy efficiency and reducing fuel consumption in the process of generating electricity and heat at thermal power plants is one of the urgent tasks. Such systems operate under conditions of random changes in external and internal influences, as well as measurement uncertainties, which reduce the quality of control. In order to overcome this problem, it was proposed to develop an intelligent control system using the quantum photon-spin method to control technological units of thermal power plants. In the proposed approach, a multi-dimensional heating boiler device was taken as a control object, and the simulation modeling of the control system …

Novel Percutaneous Repair Of Femoral Pseudoaneurysms Using Perclose Proglide™: A Case Series, Ryan Mancoll, Emily Burnett, Nicholas Bandy, Benjamin Samberg, Thomas Cook, Jacob Hoffman, Christopher Murter, Matthew Rossi, David Dexter, Hosam El Sayed, Animesh Rathore Md

Cardiovascular Research Symposium

BACKGROUND - This case series details the novel use of the Perclose ProGlide closure device to successfully repair three separate cases of iatrogenic femoral pseudoaneurysms (PSAs) without attempting other modalities first. Conventional treatment methods of ultrasound-guided compression, duplex-directed thrombin injection (DDTI), or open surgical repair were contraindicated in these patients due to unique anatomy or advanced comorbidities.

METHODS – Details were gathered via a retrospective chart review.

RESULTS - In the first case, a 73-year-old female had an access site PSA off the superficial femoral artery (SFA) with concomitant arteriovenous fistula (AVF) and advanced cardiac disease. The ProGlide device was …

Retracted: Automated Diagnosis Of Orthopedic Patients With Vertebral Column Disorders Using Advanced Mathematical Modeling, Chen Feng, Zhenhua Sun, Xinheng Dai, Hongli Wen

Iraqi Journal for Computer Science and Mathematics

Orthopedic disorders are multifactorial, making accurate diagnosis a significant challenge. This study introduces a novel method for classifying patients into three categories—normal, disc herniation, and spondylolisthesis—using biomechanical parameters derived from diagnostic datasets. To enhance classification accuracy, two meta-heuristic optimization algorithms—the Zebra Optimization Algorithm (ZOA) and Chaos Game Optimization (CGO)—are integrated with Adaptive Boosting (ADAC) and Light Gradient Boosting Machine (LGBM) classifiers. The experimental results reveal that ZOA significantly improves model performance, particularly in the ADAC classifier. The baseline ADAC model achieved a mean accuracy of 0.916, which increased to 0.952 after optimization with ZOA (referred to as the ADZO model). …

Deep Learning And Texture Analysis For Lung And Colon Cancer Predicting, Mohamed M. Neamah, Laith A. Al-Ani, Loay E. George

Iraqi Journal for Computer Science and Mathematics

Cancer remains a major cause of death worldwide, with lung and colon (LC) cancers presenting significant challenges to healthcare systems due to their high rates of occurrence and mortality. Early and precise diagnosis is essential for better patient outcomes. This research utilizes recent advances in deep learning (DL) and texture analysis (TA) to create a reliable predictive model for detecting LC cancer through histopathological images (HPI). A hybrid method is proposed that combines a gray-level co-occurrence matrix (GLCM) for extracting texture features with an adaptive modified EfficientNet B2 model (AM-EfficientNet B2) for deep feature extraction. These features are used to …

Retracted: Software Engineering-Oriented Text Generation And Analysis Using Gpt-2, Nadia Mahmood Hussien, Aumama Mohammed Farhan, Yasmin Makki Mohialden, Qabas Abdal Zahraa Jabbar, Shahbaa Mohammed Abdulmaged, Asmaa Hatem Arif

Iraqi Journal for Computer Science and Mathematics

The research focuses on developing an improved system for generating and analyzing text by combining GPT-2, LSTM, and CNN models to address challenges in automated content creation for software engineering tasks. The system targets specific applications such as requirements engineering, software documentation, and code comment generation. It generates 150-token text samples based on over 100 user-provided prompts. These generated texts are first processed through an LSTM layer to capture semantic meaning, then passed through a CNN module to extract syntactic and semantic features. All outputs are stored in structured CSV files to support future analysis. Evaluation results demonstrate positive impacts …

09.02.2025 Ored Connect, Liz Williamson

ORED Newsletter

• NIH Specific Aims Statement RED Talk
• NSF Research Security Training 

Irrigation Scheduling Methods: Checkbook Vs Fao-56, Adarsha Neupane, Vidya Samadi

Forestry and Natural Resources

Irrigation scheduling addresses two critical questions: when to irrigate and how much water to apply. Providing the optimal amount of water at the right time enhances water use efficiency, leading to improved crop production, while minimizing water losses and ensuring sustainable water management. This article compares two widely used soil water balance-based irrigation scheduling methods—Checkbook and FAO-56—by evaluating their principles, advantages, and limitations, with a focus on how each method tracks and manages soil moisture. To illustrate their differences, we present example calculations demonstrating how each method estimates irrigation timing and amount. The Checkbook method, widely adopted for its simplicity, …

A Pilot Study On Tissue Deformation Using An Integrated Sensor–Actuator Blood Collection Setup In Aquaculture (Salmo Salar), Ishrak Siddiquee, Md Ebne Al Ashad, Ahmed Hasnain Jalal

Electrical and Computer Engineering Faculty Publications

This pilot study presents a sensor–actuator setup designed to evaluate tissue deformation in Atlantic Salmon (Salmo salar) during needle insertion. The system integrates three types of low-cost, commercially available force sensors to capture force profiles and identify biomechanical events associated with tissue layer transitions. Controlled insertions were performed on a deceased specimen, and the resulting force data were analyzed to quantify insertion dynamics and estimate tissue deformation. A simulation model based on the recorded force values was developed to calculate stress distribution and deformation, which ranged from 0.001 µm to 8.4 µm and from 0.3 N/m2 to 4.9 N/m2, respectively. …

The Atmospheric Waves Experiment (Awe), L. Scherliess, M. J. Taylor, P.-Dominique Pautet, Y. Zhao, B. Lamborn, H. Latvakoski, G. Cantwell, P. Sevilla, E. Syrstad, J. Forbes, S. Eckermann, D. Fritts, D. Janches, H. Liu, J. Snively

Space Dynamics Laboratory Publications

Modern theory and modeling indicate that atmospheric gravity waves (AGWs) play an important role in vertical coupling of the atmosphere-ionosphere system. Although small-scale AGWs (horizontal wavelengths ~30–300 km) are thought to account for the dominant energy and momentum inputs at Ionosphere-Thermosphere-Mesosphere (ITM) altitudes, the sources, variability, and influences of small-scale AGWs are still major unknowns. NASA’s Atmospheric Waves Experiment (AWE) mission is a Heliophysics Small Explorers Mission of Opportunity designed to investigate how terrestrial weather affects space weather, via small-scale AGWs produced in Earth’s atmosphere. Installed on the International Space Station (ISS) in November 2023, AWE began its 2-year mission …

Data Compression In Additive Manufacturing: Recent Progress And Opportunities, Dongmin Ethan Kang, Wenmeng Tian

Publications

A crucial aspect of quality control for Additive Manufacturing (AM) processes is the acquisition of diverse data from the entire lifecycle of the product. AM data has grown significantly in terms of diversity and volumes, resulting in diverse data formats of increasing volumes, including time series, images, and point clouds. Large quantities of these data are essential for effective in-situ process monitoring and ex-situ non-destructive evaluation. However, this will result in large manufacturing and inspection datasets that are difficult to manage for users, which will delay the broader adoption of AM for mission critical applications. This motivates the urgent need …

Magnetic Nanoparticles Tethered With Zn–Dpa For The Removal Of Bacteria From Red Blood Cell Suspension, Tochukwu P. Okonkwo, Rajendra P. Gautam, Jacob B. Limburg, Breckin L. Forstrom, Bowen J. Houser, Aaron Rappleyea, Tyler P. Green, Joseph P. Talley, Alexander D. Daum, Stacey J. Smith, Karine Chesnel, William G. Pitt, Roger G. Harrison

Faculty Publications

Bacterial infections continue to drive the need for more effective and rapid methods for bacterial analysis. To address this, magnetic nanoparticles (MNPs) have emerged as promising tools, especially when their surfaces are modified with bacteria binders. The bis-zinc–dipicolylamine (Zn–DPA) complex is known for its broad affinity to bacteria. We have synthesized MNPs via a thermal decomposition method, encapsulated them in silica, modified their surface with Zn–DPA, and tested their ability to remove bacteria. The MNPs retain their superparamagnetic properties and crystallite structure after being encapsulated. The MNPs coated with silica and Zn–DPA effectively bind and remove both Gram-positive and Gram-negative …

Analyzing Lf/Vlf Lightning Waveforms To Estimate D-Region Electron Density Profiles, William R. Smith, Erin H. Lay, Kyle E. Fitch, Daniel J. Emmons

Faculty Publications

Lightning waveforms in the low frequency (LF; 30–300 kHz) and the very low frequency (VLF; 3–30 kHz) bands can be exploited to produce data-driven ionospheric D-region electron density profile (EDP) estimates with significantly higher spatial and temporal coverage than previously available. The lightning waveforms used in this paper are signals detected in the LF/VLF of negative cloud-to-ground lightning by the Earth Networks Total Lightning Detection Network. Each waveform contains a ground wave and a time-delayed ionospheric reflection. The time delay between the ground wave and ionospheric reflection has previously been used to estimate a single specular reflection altitude, where LF/VLF …