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A Model Combining Self-Attention And Weight Sharing For Human Activity Recognition, Lun Ma, Yue Yang, Daihe Wang, Guisheng Liao, Xing Li

Journal of System Simulation

Abstract: With the prevalence of wearable devices, human activity recognition based on wearable sensor data has garnered significant attention. The central issue in this field is how to extract effective behavioral information from raw sensor data to form corresponding feature vectors. Currently, convolutional neural networks and recurrent neural networks have been widely utilized for feature extraction from multisensory data. However, these networks struggle to globally capture the crucial temporal features inherent of human activity over time. To address this, a multi-CNN-BiLSTM-self attention (Multi-CBSA) model based on self-attention and weight sharing has been proposed, taking into consideration the logical correlations among …

Bioresources And Bioproducts: A New Open Access Journal, Ronald C. Sims

Biological Engineering Faculty Publications

Greetings to the bioresource and bioproducts community! I will serve as the Editor-in-Chief to collaborate with you as we promote the aims and scope of this new MDPI journal [1], with the help of its distinguished Editorial Board, by publishing Special Issues, Topics, Topical Collections, and Editorials, and by providing high-impact scholarship through the outstanding published work of each of our contributors.

Comparative Analysis Of Life Cycle Assessment Of Traditional And Contemporary Timber Buildings, Sadık Akşar, Gökçe Tuna

Journal of Sustainable Construction Materials and Technologies

Wood has long been used as a primary building material, but with the Industrial Revolution and the spread of multi-story structures, its structural limitations became evident. In the 21st century, engineered timber products have reintroduced wood as a sustainable alternative, offering advantages such as lightness, rapid assembly, and recyclability. At the same time, buildings are responsible for significant environmental impacts, making assessment methods essential. Life Cycle Assessment (LCA) is one of the most widely used tools for evaluating these impacts across all stages of a building’s life cycle. This study applies LCA to timber construction systems, focusing on both traditional …

Sustainable Paving Block Development: Utilizing Rejected Coal As Filler And Assessing Its Impact On Paving Block’S Properties, Siti Nikmatin, Allen Kurniawan, Heriansyah Putra, Rima Fitria Adiati, Riawan Ma’Ruf, Dwi Arso Yedi Irwanto

Journal of Sustainable Construction Materials and Technologies

This study investigated the potential use of rejected coal as a fine aggregate filler in paving block production. Comprehensive characterization of the rejected coal was conducted, including toxicity, physical, chemical, and mineralogical analyses of the coal. The results showed that the rejected coal met environmental safety standards (TCLP) and had suitable properties as an aggregate. Paving blocks were produced by incorporating different proportions of rejected coal fillers and were tested for compressive strength, water absorption, and density. The optimal mix with 35% rejected coal filler as a replacement of stone ash achieved a compressive strength of 14.61 MPa, water absorption …

Smart Dressings Accelerating Wound Healing With Tranexamic Acid-Infused Aligned Electrospun Nanofibers: In Vitro And In Vivo Assessments, Samar A. Salim, Abdullah Elbadry, Eman Gomaa, Mennatullah Faisal, Elbadawy A. Kamoun

Nanotechnology Research Centre

The development of advanced wound dressings capable of accelerating healing and achieving effective hemostasis remains a critical challenge in managing traumatic and surgical wounds. This study reports the fabrication and comprehensive evaluation of a novel multilayered, sandwich-structured nanofiber scaffold composed of Tranexamic acid (TXA), chitosan (CS), polyvinyl alcohol (PVA), L-arginine, and polylactic acid (PLA) for biodegradable wound dressing applications. Using sequential electrospinning, a four-layered scaffold was developed as a smart wound dressing, comprising an immediate-release TXA-CS-PVA layer for rapid clotting, a hydrophobic PLA barrier layer for protection, a sustained-release L-arginine-PVA layer to promote tissue regeneration, and a final PLA protective …

Recent Trends In Network Technologies: A Comprehensive Review, Atheer Y. Oudah, Raaid Alubady, Lina M. Shaker

AUIQ Technical Engineering Science

The networking domain is undergoing profound transformation, driven by the rapid maturation of artificial intelligence, edge computing, and advanced virtualization technologies. This review analyzes the major networking trends that have emerged between 2010 and 2025, emphasizing their technological foundations, practical implementation challenges, and long-term implications for the evolution of digital infrastructure. Specifically, it analyzes advances in network function virtualization, AI-enabled network operations, edge computing integration, and the evolution of next-generation connectivity standards. This review highlights the interplay and convergence of these technologies, arguing that their combined adoption is not merely incremental but represents a paradigm shift in network design, deployment, …

Internet Of Batteries: An Integrated System Of Battery Health Surveillance System For Electric Vehicles (Evs), Ayodeji Akinsoji Okubanjo, Ignatius Kema Okakwu, Alexander Akpofure Okandeji, Oluwasogo Emmanuel Seun, Olusesi Ayobami Taiwo

AUIQ Technical Engineering Science

Progress in storage technologies is vital to the efficiency of electric vehicles in combating climate change. The automobile industry’s transition to electric mobility has significantly increased the demand for battery storage systems (BSS) in terms of performance, reliability, monitoring, and evaluation of key parameters. However, poor safety and monitoring strategies for battery storage systems in electric vehicle applications pose an increasing threat to the vehicles’ sustainability and mobility. This calls for intelligent battery storage monitoring systems to mitigate carbon footprint and preserve battery lifespan of electric vehicles. This paper focuses on battery storage sustainability in electric vehicles with special emphasis …

Tissue-Like Fracture Toughness And Stress–Relaxation Ability In Pva-Agar-Based Hydrogels For Biomedical Applications, Ismael Lamas Jr., Bhuvana L. Chandrashekar, Cláudia C. Biguetti, Mohammad R. Islam

Mechanical Engineering Faculty Publications

Soft tissues exhibit remarkable stretchability, fracture toughness, and stress–relaxation ability. They possess a large water content to support cellular processes. Mimicking such a combination of mechanical and physical properties in hydrogels is important for tissue engineering applications but remains challenging. This work aims to develop a hydrogel that can combine excellent mechanical properties with cellular viability. The research focused on polyvinyl alcohol (PVA)/agar double-network (DN) hydrogels, fabricated by thermal gelation and freeze–thawing methods. Their mechanical properties were characterized through tension, compression, fracture, and stress–relaxation tests, and their cellular viability was measured through cytotoxicity tests. The results show that the PVA/agar …

Non-Invasive Wearables In Pediatric Healthcare: A Comprehensive Review Of Uses And Implications, Kyra-Angela Magsayo, Seyedeh F. Khatami Firoozabadi

Pacific Faculty Work

Wearable technology is rapidly evolving, with increasing efforts to integrate a wide range of sensors capable of capturing real-time physiological and behavioral health data from users. These devices have shown significant promise in supporting health monitoring and promoting well-being by providing continuous, objective feedback based on data analytics. Importantly, they enable early detection of potential health issues, allowing for timely intervention and more personalized healthcare. While a wide variety of commercially available wearable devices are designed for adults—tracking metrics such as physical activity, heart rate, body temperature, electrocardiograms (ECG), and oxygen saturation—there remains a notable gap in the availability and …

Novel Biomolecule-Infused Gelatin Injectable For Treatment Of Recurrent Laryngeal Nerve Injury, Ananya Tadikonda, Sunjay Anekal, Lena W. Chen, Gabriel Sobczak, Troy Wesson, Carmilya Jackson, Marisa A. Egan, Julie C. Liu, Patrick R. Finnegan, Stacey Halum

School of Chemical Engineering Faculty Publications

Objective

Unilateral vocal fold paralysis (UVFP) due to recurrent laryngeal nerve injury (RLN) is a major cause of voice disorders. We have recently identified three biomolecules (agrin, acetylcholine, and neuregulin) with the potential to promote reinnervation after RLN injury. This study aimed to determine if a gelatin injectable with the reinnervating biomolecules will induce site-specific amplification of neurotrophic factor release and reinnervation after RLN injury in unilateral vocal fold paralysis.

Methods

C57BL/6 mice underwent RLN transection with the following treatment allocations: saline control (N = 16), biomolecule cocktail only (N = 16), and biomolecule-infused gelatin (N = …

09.15.2025 Ored Connect, Liz Williamson

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Coumarin/Nitrogen-Bearing Heterocyclic Hybridloaded Electrospun Pmma/Pvp Nanofibrous Scaffolds For Accelerating Topical Wound Healing Rates: Synthesis And In Vitro Bio-Evaluation, Samar A. Salim, Mohamed Ali, Tasneem Abed, El Badawy Kamoun

Nanotechnology Research Centre

Coumarin-nitrogen heterocyclic compounds (e.g. quinoline, acridine, and phthalazine) were synthesized by facile reactions and elucidated by FTIR, 1H, and 13C-NMR analyses, which showed consistency with the expected structures. Coumarin-quinoline (drug A)- and coumarin-acridine (drug B)-loaded electrospun PMMA/PVP nanofibrous scaffolds were fabricated using electrospinning techniques. Results showed a successful PMMA/PVP blend, which formed the matrix that was used as the main scaffold for drug loading/release. The drugs (A and B) were encapsulated within the matrix as verified by IR and SEM results. Bio-evaluation through cytotoxicity and anticancer screening was conducted using the MTT assay against lung fibroblast (Wi-38), colon carcinoma (Caco-2), …

Two-Factor Authentication Software For Bluetooth Pairing Between Mobile And Pc Operating Systems, Sundos A. Hameed Alazawi, Abbas A. Abdulhameed, Mostafa Abdulghafoor Mohammed, Thekra Abbas

Iraqi Journal for Computer Science and Mathematics

Bluetooth devices actively broadcast software when pairing to connect. Even during the connection process, the connection can be monitored to view information about the transmission. Using this information, anyone can hijack your existing connection and steal data. Bluetooth connections can be single or multiple. Thus, while connecting to a device, the same device could be simultaneously connected to another device. To avoid this problem, a new software is proposed to support the ID-based authentication process for paired devices by integrating an authentication method based on the biometric features of the device owner. The proposed two-factor security authentication system for pairing …

Ontology Features-Based Arabic Text Augmentation Using Word2vec, Enas Tariq Khudair, Onsa Lazzez, Mourad Zaied, Tarek M. Hamdani, Ahmed T. Sadiq, Habib Chabchoub, Adel M. Alimi

Iraqi Journal for Computer Science and Mathematics

Text augmentation plays a major role when data is scarce. In this context, there are few Arabic news texts for specific purposes, and hence, there is a dire need to generate Arabic text, especially news. This paper presents an enhanced approach to Arabic text augmentation based on Arabic ontology features. The Arabic part of speech, particularly adjectives, verbs, and prepositions, and the ontology properties regarding such parts to create new texts, make up the first stage of the system, which has multiple stages. Word2Vector (Word2Vec) plays a pivotal role in giving Arabic ontology features to the specific Arabic Part of …

Transparent Eeg Analysis: Leveraging Autoencoders, Bi-Lstms, And Shap For Improved Neurodegenerative Diseases Detection, Badr Mouazen, Ahmed Bendaouia, Omaima Bellakhdar, Khaoula Laghdaf, Aya Ennair, El Hassan Abdelwahed, Giovanni De Marco

Manufacturing & Industrial Engineering Faculty Publications

Highlights

• Novel hybrid architecture: Combined autoencoders with bidirectional LSTM networks for enhanced EEG signal classification, achieving 98% accuracy in distinguishing AD, FTD, and healthy controls.

• Explainable AI integration: Implemented SHAP (SHapley Additive exPlanations) framework to enhance model transparency and identify entropy as the most influential feature for neurodegenerative disease detection.

• Optimal temporal segmentation: Demonstrated that 5-s EEG windows with 50% overlap provide the best balance between classification accuracy and computational efficiency.

• Comprehensive feature extraction: Utilized Power Spectral Density (PSD) analysis across standard frequency bands (Delta, Theta, Alpha, Beta, Gamma) following autoencoder-based dimensionality reduction.

• Superior performance validation: Outperformed traditional machine learning …

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Leveraging Computational Modelling/Simulation, Machine Learning, And Additive Manufacturing For Customized Design Of Periodontal And Bone Tissue Scaffolds, Rakesh Pemmada

All ETDs from UAB

The current dissertation presents an overarching pipeline for the development and production of custom-made scaffolds for periodontal tissue regeneration using finite element analysis (FEA), machine learning (ML), and additive manufacturing (AM).CAD anatomically realistic scaffolds were generated from CT-based maxilla and mandible models. The FEA simulation results under masticatory (100 N), parafunctional (500–550 N), and traumatic (800–850 N) loads showed region-specific distribution of stresses and strains, which guided structural reinforcement strategies. Over 1000 3D-printed PCL scaffolds were printed using various pore diameters (200–300 µm) and filament diameters. Five ML models were constructed for predicting print quality based on process factors. Classification …

09.08.2025 Ored Connect, Liz Williamson

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Detecting Electrical Submersible Pump (Esp) Failures And Estimating Run Life Using Artificial Neural Networks, Mostafa Ahmed Sobhy

Theses and Dissertations

Electric Submersible Pumps (ESPs) are one of the important artificial lift methods for sustaining production in mature and high-water-cut wells; but may suffer frequent failures due to mechanical, electrical, hydraulic, chemical, and operational failures. These failures can yield substantial deferred production and intervention costs. Plenty of ESP installations are fitted with downhole sensors. Yet, it is observed that the current industry practice underutilizes the wealth of available sensor and operational data and lacks standardized, explainable failure-type identification and classification.

In this thesis, a comprehensive Machine Learning (ML) and Deep Learning (DL) framework was introduced for ESPs that simultaneously estimates remaining …

Impact Of Composite Materials Research On Underrepresented Communities, Neil Mahesh Bedagkar, Ramanan Sritharan, Wyatt Barnes

Mechanical Engineering

This research assesses how advancements in composites material research affect communities from lower socioeconomic backgrounds, using a dual track strategy to quantitatively and qualitatively examine their impact. A keyword-based classification algorithm was applied to a sample of research papers from ScienceDirect spanning the last 24 years to quantify which engineering industries benefit most from this research. The findings indicate that the construction industry benefits the most, followed by the automotive, defense, renewable energy, and biomedical sectors. A broader qualitative analysis of the social implications of these industries was also conducted to provide context. Each sector were found to have either …

Activity Design Principles To Support Engineering Engagement For Families With Preschool-Age Children From Low-Income English-And Spanish-Speaking Communities, Scott A. Pattison, Smirla Ramos Montañez, Viviana López Burgos, Gina Svarovsky, María Quijano, Amy Corbett, Catherine Wagner, Diana Contreras

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

Existing evidence highlights the significance of family STEM learning experiences during early childhood. However, there is a lack of research specific to early childhood family-based learning in the field of engineering, especially with preschool-age children (three to five years old). To address this gap and inform engineering education programs for young children, we conducted a design-based research study in collaboration with 15 Spanish-and English-speaking families with preschool-age children from low-income communities. Our study aimed to develop and test a series of family-based engineering design activities while also identifying underlying design principles. Guided by an asset-based family learning framework, which acknowledges …

Ada-Application Of Decision Analysis For Developing A Healthcare System To Predict Fetal Health, Melfi Alrasheedi, Theyazn.H.H Aldhyani

Iraqi Journal for Computer Science and Mathematics

A fatal health condition involves an unborn baby that persists throughout the embryonic stage until delivery. The fetus grows and develops during each trimester of pregnancy. Obstetricians may detect fetal anomalies and select medical interventions based on cardiotocogram (CTG) data. However, the obstetrician's visual assessment of CTG data can sometimes be subjective or inaccurate. Therefore, automated analysis using machine learning approaches for CTG data is essential. This research employs decision analysis techniques, including decision trees (DT), gradient boosting (GB), and type-2 fuzzy neural networks (FNN), for prenatal analysis and prediction. The system was tested using a standard dataset consisting of …

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 …

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 …

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 …

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.

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 …

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 …

09.02.2025 Ored Connect, Liz Williamson

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