Digital Commons Network^™

Skeleton-Based Human Action Recognition Using Spatio-Temporal Latent Features With A Gcn Model, Avazjon Marakhimov, Kabul Khudaybergenov, Mominov Zakhriddin

Chemical Technology, Control and Management

Owing to its resilience to visual noise and viewpoint variations, skeleton-based analysis has become a cornerstone of human action recognition research. Despite its practical significance, existing methodologies often suffer from a reliance on single-stream skeletal representations, which fail to encompass the full complexity of action features. This study introduces Latent Features for Human Action Recognition (LFHAR), a novel architecture designed to overcome these limitations by utilizing diverse spatio-temporal latent representations for improved feature extraction. The approach applies graph-based transformations to individual skeletal frames in temporal sequences, then arranges the derived graph features into spatio-temporal matrices. Evaluation of standard datasets demonstrates …

Models And Algorithms Of Control Mechanisms In Information Exchange Processes, Madina M. Fozilova, Dilshoda N. Uchqunova

Chemical Technology, Control and Management

In modern digital systems, efficient and reliable information exchange is essential for the stability of corporate systems. Traditional data management models struggle to detect and eliminate invalid, incomplete data at early stages, resulting in reduced accuracy and system inefficiency. This article proposes an advanced framework for controlling information exchange processes through the development of a Verification and Filtering algorithm. The algorithm operates within a multi-layered conceptual model that includes data input, control, validation, optimization, and decision layers. Acting as the core component, the Verification and Filtering algorithm distinguishes valid from invalid records in real time, ensuring data integrity before storage. …

Performance Assessment Of Operators Under Digital Simulator-Based Training In Automated Industrial Systems, Kamola Abdullaeva

Chemical Technology, Control and Management

This article proposes a comprehensive approach to training process operators, based on computer simulators integrated with digital twins, SCADA/DCS systems, real production data, and artificial intelligence algorithms. This approach is particularly relevant given the increased requirements for safety, productivity, and reliability of industrial facilities, as human factors are often the main cause of accidents (up to 60-80% of cases). The architecture of the simulator complex is designed to accurately emulate steady-state, transient, and pre-emergency operating modes of equipment, which are not achievable under actual production conditions. The experiment compared the training effectiveness of two groups of operators: one that used …

Development And Evaluation Of A Smart Vacuum-Assisted Suspension System For Transtibial Prostheses, Zahraa Kathem Ali, Wajdi Sadik Aboud

Al-Bahir

A prosthetic socket is essential for achieving comfort, gait symmetry, and overall satisfaction for amputees. Effective suspension systems are crucial for securely attaching prostheses, thereby optimizing comfort, functional performance, and long-term limb health. This study aimed to develop and evaluate an intelligent vacuum-assisted suspension system for transtibial prostheses to improve socket fit, gait stability, and reduce residual limb movement. The system integrates force-sensitive resistors (FSRs) and an inertial measurement unit (MPU6050) with accelerometer and gyroscope sensors, continuously monitoring interface pressure and limb motion. Real-time data from these sensors dynamically adjusts vacuum pressure based on the user's gait pattern .Quantitative gait …

Seemingly Unrelated Exponetiated Exponential Geometric Regression Model, Oluwaseun Michael Famoni, Bamidele Mustapha Oseni

Al-Bahir

In the class of seemingly unrelated regression models, the dispersion nature of the dependent variable can greatly impact the efficiency and reliability of the parameter estimates for the model. Despite this, the seemingly unrelated Poisson regression model and seemingly unrelated negative binomial model are two most commonly used count data models for these class of regression models. This study introduces the seemingly unrelated exponentiated exponential geometric regression (SUEEGR) for modelling count data which might be equi-, under, or over-dispersed. Parameters estimation for the model was carried out using the method of maximum likelihood. A simulation study was carried out to …

Virtual Process Modeling Of Metal Additive Manufacturing Basedon Direct Energy Deposition In-Situ Failure, Jachin J. Ramirez

2025 Fall Honors Capstones Projects - Archive

This study builds a mesoscale finite element simulation to examine how internal stresses form during additive metal manufacturing using directed energy deposition. The goal is to track how heat and stress develop layer by layer and determine where a failure criterion could appear during the print. The model uses temperature-dependent material properties for Inconel 718 and a moving heat source defined with custom G-code.

Thermal results are mapped into a mechanical simulation to watch stress accumulate as new layers are added. Different scan paths were tested to determine whether varying heat exposure could reduce the extent of a region exceeding …

Precision Education In Health Sciences Education: A Call-To-Action For Developing An Interprofessional Strategy, Arvie Vitente, Jennifer Bosworth, Robert Sweet, Rebecca Cozzi, Christopher Galloway, Piotr Szczurek, Melanie Obispo-Young, Eron Bozec, Erica Brkovic, Tina Bobo, Jennifer Lubinski

Philippine Journal of Physical Therapy

Introduction. Growing learner diversity, expanding biomedical knowledge, and rapid clinical digitization have exposed the limits of on-size-fits-all health sciences training. Competency-Based Education (CBE) has sharpened the focus on outcomes, yet many programs still move learners through fixed sequences, infrequent assessments, and retrospective remediation. Precision Education (PE) offers a complementary, data-enabled approach that uses learning analytics, artificial intelligence (AI), and continuous feedback loops. Embedded within CBE, PE converts competencies from static milestones into dynamic trajectories that can be measured, visualized, and adjusted in real time across the curriculum. This abstract highlights the need for an interprofessional strategy to design, fund, and …

Developing A Vietnamese Text Summarization Large Language Model On Limited Hardware, Tin Pho

Master's Theses

Text summarization models have achieved significant growth during the last few years because of major Large Language Model (LLM) technological advancements. The application of LLMs are widely used in news distribution (TL;DR news), translation tools (DeepL Translate), or virtual assistants (ChatGPT, DeepSeek, Claude, etc.). However, the progress has not yet reached all languages equally. The Vietnamese language is used by more than 90 million people, but the language is not as highly developed for LLM as it has many homophones, five different tones that affect meaning of words, and irregular grammar compared to other languages (e.g. English, Spanish, etc.). Also, …

Enhanced Pothole Detection In Urban Environments Using Yolo-Nas With Adaptive Image Augmentation Techniques, Saluky, Aisya Fathimah, Onwardono Rit Riyanto

Baghdad Science Journal

Pothole detection in urban environments is a critical task for maintaining road infrastructure and ensuring vehicular safety. Recent advancements in deep learning, particularly the YOLO (You Only Look Once) framework, have shown promise in object detection tasks. However, achieving high accuracy and robustness in real-world conditions remains a challenge. This study introduces an enhanced pothole detection system utilizing the YOLO-NAS (Neural Architecture Search) model, optimized through adaptive image augmentation techniques. The YOLO-NAS model is fine-tuned on a custom dataset of urban road images, with various augmentation strategies applied to simulate different environmental conditions such as shadows, low light, and occlusions. …

Collagen Type Ii-Targeting Lentiviral Gene Therapy For Mucopolysaccharidosis Iva, Betul Celik, Sampurna Saikia, Shaukat Khan, Krishna Sai Musini, Shunji Tomatsu

Department of Pediatrics Faculty Papers

Mucopolysaccharidosis (MPS IVA) is caused by pathogenic variations in the GALNS gene, leading to the accumulation of glycosaminoglycans in tissues and causing progressive skeletal lesions. While conventional lentiviral vectors (LVs) provide long-term stable expression, they do not deliver therapeutic levels to bone and cartilage. We hypothesized that engineering the LV envelope with a collagen type II-targeting peptide (WYRGRL) increases the binding affinity of the LVs for bone and cartilage. These modified vectors carrying the CBh and COL2A1 promoters delivered the GALNS gene to MPS IVA newborn mice via intravenous (IV) or intraarticular (IA) administration. The peptide-modified LVs exhibited markedly increased …

Optimizing Runtime Memory Size Of Smith-Waterman Algorithm For Long Sequences Alignment, Imad Qasim Habeeb, Zeyad Qasim Habeeb, Hanan Najm Abdulkhudhur

Baghdad Science Journal

Sequence alignment is used to help researchers see areas of similarity between two sequences. Hence, it is a key component of many applications, such as DNA matching, plagiarism detection, and spelling correction. The Smith-Waterman algorithm (SWA) is widely used to calculate the sequence alignment because it is guaranteed to find an optimal solution. This algorithm creates a matrix of the size n * m where the symbols n, m refers to the lengths of two sequences needed to be aligned. Therefore, it requires impersonal hardware with a large amount of main memory at runtime to align long sequences. Furthermore, it …

Enhanced Network Anomaly Detection Using Hybrid Deep Learning Network Based On Interactive Threshold, Maythem S. Derweesh, Sundos A. Hameed Alazawi, Anwar H. Al-Saleh

Baghdad Science Journal

In recent years, the growing use of the internet by both governments and private companies has led to a major increase in individual online activity. This expand lead to make the systems more effected to the threats and cyber attacks, and need more strong solutions to cyber security. Recently, deep learning (DL) and machine learning (ML) have become powerful tools in the cybersecurity field, especially for tasks such as detecting malware and filtering spam. This study present new multi layer method to detect the abnormal activities by busing advanced deep learning techniques. The proposed system work in tow main steps. …

A Spatiotemporal Model Of Cxcl10 As A Master Regulator Of Immune Evasion And Metastasis In Osteosarcoma, Benjamin B Gyau, Tsz-Kwong Man

Faculty, Staff and Students Publications

The C-X-C motif chemokine ligand 10 (CXCL10) is implicated in the progression of osteosarcoma (OS), the most aggressive pediatric bone malignancy. However, its role often presents a profound clinical paradox: although high circulating levels are strongly linked to poor prognosis, its canonical function is to recruit anti-tumor immune cells. This review unravels these contrasting roles by proposing a novel spatiotemporal model. We argue that in the early stages, immune-evading OS cells initiate the formation of a pre-metastatic niche (PMN) in the lungs, creating a localized inflammatory environment that becomes the primary source of elevated circulating CXCL10. As the disease progresses, …

Information System To Elucidate Dna Sequencing Based On Machine And Deep Learning Techniques, Suhiar Mohammed Zeki Abd Alsammed

Baghdad Science Journal

The elucidation of DNA sequencing provides great importance in increasing the comprehension of organisms' genomic functions. However, the investigation of concealed structural information preserved within Deoxyribonucleic Acid (DNA) sequencing represents an outstanding challenge. Recently, machine and deep learning have become the techniques of preference for various tasks of genomics modeling, involving the prediction of genetic variation influence on the mechanisms of gene regulation like DNA receptivity and splicing. Therefore, this paper presented an information system for elucidating DNA sequencing data using diverse machine and deep learning techniques. In this system, two encoding methods are utilized for modifying the DNA sequencing …

Intrusion Detection System For Iot/Cloud Networks Using Federated Learning And Lightweight Cryptography, Ayad Al-Adhami, Rajaa K. Hasoun, Sanaa Ali Jabber, Soukaena H. Hashem

Baghdad Science Journal

This study presents a secure solution that utilizes lightweight cryptography (LWC) and intrusion detection systems (IDS) to safeguard cloud networks and Internet of Things (IoT) from cyberattacks. Federated Learning (FL) is suggested for identifying zero-attacks to guarantee the security of different local IoT networks connected to global server of cloud. The proposed federated learning utilizing data from all local IoT network devices to create a generalized Intrusion Detection System (IDS). Local IoT networks consist of clients that communicate updates to their parameters with a central server located in the global cloud. This server integrates these changes and deploys an improved …

Folding Architecture “Origami Art-Inspired” Applicability To Sustainable Architecture- Biomuseo As A Case Study, Vitta A. Ibrahim

Mansoura Engineering Journal

The growing need for sustainable architectural solutions in the contemporary era underscores the necessity for interactive architectural applications that can adapt to changing requirements. In architecture, folding systems refer to three-dimensional, foldable structural forms that create unique spatial configurations and possess a wide range of capabilities. Drawing inspiration from origami techniques, this study emphasizes the role of folding systems in generating innovative spatial designs. The research problem arises from the fact that the built environment is a significant contributor to greenhouse gas emissions and energy consumption, necessitating the implementation of smart solutions. The goal of this study is to identify …

Supply Chain Network Based On Blockchain And Intelligent Agent, Hiba Hamdi Hassan, Rana Fareed Ghani

Journal of Soft Computing and Computer Applications

In agricultural supply chains, the complexity and indeterminacy pose serious challenges to traceability, reliability and confidence today. This challenge is especially acute in the olive oil industry where adulteration, wrong labeling, and uneven chemical quality threaten the actual well-being of producers and consumers. The project aims to design a blockchain-based hybrid architecture with intelligent agents (FNNs) to enhance transparency, reliability and responsiveness in the olive oil supply chain. The Blockchain component enables a completely open, tamper-proof ledger to be built in a very decentralized way and preserved as an archive of every account of its transactions. The intelligent agents contribute …

Intelligent Extensible Markup Language Encryption Using Type-2 Fuzzy Logic, Faiez Musa Lahmood Alrufaye, Seham Ahmed Hashem

Journal of Soft Computing and Computer Applications

Financial and commercial institutions increasingly rely on Extensible Markup Language (XML) files as a standard means of exchanging data. However, this extensive use has created serious security challenges due to the fact that these files contain sensitive information such as bank card numbers and expiration dates. Relying on traditional full file encryption methods achieves a high degree of security, but it causes problems related to the large file sizes that consume memory and the long encryption and decryption times, which reduces the efficiency of systems when dealing with a large number of daily transactions. Methods based on Type-1 Fuzzy Logic …

Enhanced Generative Convolutional Networks: A Hybrid Algorithm For Refinement Video Classification, Dalal Thair Mahjoub, Hala Bahjat Abdulwahab, Kesra Nermend

Journal of Soft Computing and Computer Applications

Video classification is a vital area of research due to the growing volume of video content in various applications. Accurate category across various resolutions poses challenges, which include adapting to scaling, resizing, and compression. Therefore, this paper introduces an innovative Generative Convolutional Network (GCN) set of rules tailored for multi-resolution video classes. The proposed GCN model utilizes Convolutional Neural Networks (CNNs) combined with generative modeling to enhance the extraction of functions across varying video resolutions, which is crucial for maintaining class robustness in the face of common video adjustments, such as scaling, resizing, and compression. In contrast, traditional fashions frequently …

Review Of Video Steganography By Using Deep Learning Methods: Datasets, Techniques, And Evaluations, Noor Fahem Sahib, Soukaena Hassan Hashem, Ekhlas Falih Naser

Journal of Soft Computing and Computer Applications

The growing prevalence of cyber threats, including fraud and attacks, has intensified the demand for secure methods of safeguarding confidential information exchanged between users. As telecommunications increasingly rely on multimedia data, video steganography has become a prominent technique to address these concerns. By embedding sensitive data within video files, this approach enhances protection against unauthorized access and common internet-based attacks, offering a robust layer of security in an era of escalating digital risks. With the introduction of Deep Learning (DL) steganography methods recently, video steganography can be defined as a rapidly developing subject within information security. This study provides a …

Real-Time Hand Gesture Recognition System For Abductees Rescue Using Deep Learning Techniques, Aws Saood Mohamed, Nidaa Flaih Hassan, Abeer Salim Jamil

Journal of Soft Computing and Computer Applications

Hand gesture recognition is a challenging problem in computer vision, particularly in terms of security surveillance applications. This study presents the first efficient system for abduction-related hand gesture real-time detection based on deep learning. The most critical problem is to detect and recognize hand gestures in real surveillance conditions and to be computationally effective for real-time multi-hand tracking in various lighting situations while allowing reliable surveillance beyond the 1–4 meters limitation. The proposed system consists of three main parts: The adaptive hand tracking algorithm, which has been used to create the Abductees-Rescue dataset. Introduced pose estimation You Only Look Once …

Hate Speech Detection Using Optimized Feature Representation Via Spiral-Grey Wolf Optimizer-Based Machine Learning Approaches, Noor S. Farhan, Matheel E. Abdulmunim, Hasanen S. Abdullah

Journal of Soft Computing and Computer Applications

Hate speech detection is crucial as social media diversifies. This research present a lightweight, scalable system using traditional machine learning methods along with a new approach called Spiral-Grey Wolf Optimizer (S-GWO).

S-GWO effectively selects key features that consider both meaning and content from the Term Frequency Inverse Document Frequency (TF-IDF) space, leading to high-quality representation without excessive computing power.

The propoused system was tested on Arabic and another English datasets using six machine learning methods: SVM, RF, LR, KNN, NB, and SGD. It achieved 92% accuracy and F1 score on the Arabic dataset, while reaching 100% accuracy on the English …

Voluntary Aspects Of Involuntary Conversions, Eli Gerver

Tax Adviser

No abstract provided.

(R2141) Analysis Of Map^I_1 , Ph^(Oa)_2 / Ph^I_1 , Ph^O_2 / 1 Retrial Inventory Queue With Two Way Communication, (S, S) Replenishment Policy, Feedback, Bernoulli Vacation And Impatient Customers, G. Ayyappan, V. Ganesan

Applications and Applied Mathematics: An International Journal (AAM)

This work discusses about the topic as the two-way communication retrial inventory queue model, the (s, S) replenishment policy, immediate feedback, Bernoulli vacations, and impatient customers. The assumption we make is that arrivals follow a Markovian arrival process, and the server provides phase type services. When the server is idle and there is a positive inventory, an arriving customer immediately receives service. If not, arriving customers goes to orbit with infinite capacity. Only in the account of positive inventory the server renders rapid feedback for incoming call arrivals, otherwise customer departs. Outgoing calls will only be made by the server …

Dynamic Characteristic Simulation And Optimization Of Ground Test System For Airborne Launch Rack, Yuguang Bai, Sheng Zhang, Yushun Cao, Xiaoshi Zhang, Hu Huang

Journal of System Simulation

Abstract: To solve the ground equivalent test problem of the airborne launch system, an optimization method for the dynamic characteristics of the ground launch rack test system based on a multi-variable optimization approach was proposed. Through the discussion on the boundary conditions of the foundation, an effective dynamic simulation model of the ground launch test system was established. By comparing the dynamic characteristics of the launch rack structure in the airborne state and the ground test state, the objectives and constraints of the optimization design were determined. The dynamic characteristics of the ground test system were optimized and designed. …

Satellite-Mediated Quantum Clock Synchronization: Towards Precise Timing At A Global Scale, Sage B. Ducoing

LSU Doctoral Dissertations

Accurate timekeeping is essential for scientific and technological advancements, particularly in areas of communication, networking, navigation, and high precision measurements. While many methods of time resolution are already established using classical resources, they fail to combine high precision outcomes with large-scale implementations. Additionally, numerous quantum networking architectures using satellite-assisted methods have demonstrated quantum communication on scales exceeding ground-based methods. For these reasons, we propose the use of a time synchronization method by which pairs of highly time-correlated photons are exchanged between clocks on satellites and clocks on Earth, al- lowing users to reconstruct the time offsets between their clocks. This …

Digital Resistance Against The Lasting Manipulation Of Capitalism: Ai, Luddite Rebellion, And The Haunted Future Of Cyberpunk, James Sweeting

School of Art, Design and Architecture

This paper examines the complex relationship between artificial intelligence, labour, and resistance amidst contemporary capitalism. Big Tech presents AI as revolutionary innovation, yet this narrative obscures its function in maintaining growth and preventing alternative economic forms from emerging. Through the lens of "enshittification," we observe a strategic shift toward AI deployment despite its beta state of development, threatening workers' livelihoods. This tension echoes historical patterns of technological implementation and resistance, exemplified by the Luddite movement whose meaning elites have successfully corrupted over time. The Luddites, organised under the mythical figurehead Ned Ludd (a Robin Hood-esque figure), recognised technological change as …

Scheduling Method For Virtual Power Plants Based On Analysis And Forecasting Of Heterogeneous Load Characteristics, Runzhao Zhang, Yanbo Chen, Tao Huang, Haoxin Tian, Tuben Qiang, Zhi Zhang

Journal of System Simulation

Abstract: To improve the electricity supply-demand situation by rationally utilizing demand response resources, a two-layer optimal scheduling model for virtual power plants (VPPs) based on the analysis and forecasting of heterogeneous load characteristics was proposed. With the differences in response characteristics of multi-type loads considered, a demand response model for multi-type loads was constructed by using a customer baseline load (CBL) curve forecasting method that integrated dynamic scenario generation and K-means++ clustering. A two-layer optimal scheduling model for VPPs that incorporated load aggregators and demand response was established. In this model, the upper layer conducted optimal scheduling targeting maximizing the …

Spatiotemporal Graph Convolution-Based Demand Forecasting And Simulation Analysis For Automotive Parts Supply Chain, Xiaobin Li, Bing Hu, Chao Yin, Bo Li, Jun Ma

Journal of System Simulation

Abstract: To address complex automotive after-sales parts supply network operations with insufficient demand forecasting accuracy, slow response, and low service efficiency, this study proposed a spatiotemporal graph convolution-based method for automotive parts supply chain demand forecasting. Sales network data of the automotive parts sales network was constructed as a heterogeneous graph, integrating node features like parts sales volume and value to build multi-dimensional node dependencies. A node update mechanism of the graph convolutional neural network was designed, combined with long short-term memory neural networks to capture temporal features, using spatiotemporal attention to integrate temporal and spatial features into updated nodes …

Improved Pid Search Algorithm For Uav Path Planning In Mountainous Environments, Yi Peng, Yunkui Lei, Qingqing Yang, Hui Li, Jianming Wang

Journal of System Simulation

Abstract: To address the challenges of UAV path planning in mountainous environments, including high computational complexity and suboptimal optimization performance, and the disadvantages of the PIDbased search algorithm, such as low optimization accuracy and slow convergence rate, this paper proposed an improved PID search algorithm (IPSA). The method introduced a good point set to ensure a more uniform population distribution, thereby enhancing population diversity and global search capability. The Q-learning algorithm was employed to adapt PID parameter adjustments, incorporating an exploration rate factor to further improve the algorithm's exploration and computational capabilities. A lens imaging opposition-based learning mechanism was also …