Digital Commons Network^™

Interoperability Model And Application Of Military Training System For Combination Of Virtuality And Reality, Jianxing Gong, Hai Hu, Haihui Ren, Ruixiang Wu

Journal of System Simulation

Abstract: With the development of AI technology, VR technology, and combat simulation technology, in order to achieve the practical training effect of "how to fight and how to train soldiers", virtual and real training has become a widely popular military training mode. It has become a trend to integrate digital systems, virtual equipment, semi-physical models, physical models, and other heterogeneous systems to carry out training in the same training environment. Therefore, it is necessary to study the interoperability model and application of training systems for the combination of virtuality and reality. This paper proposed the definition of interoperability between virtuality …

Analysis Of Optimal Spectral Bands For Thermal Infrared Hyperspectral Image Reconstruction Driven By Physical Simulation Model, Yonghao Yang, Xiaoyu He

Journal of System Simulation

Abstract: To achieve accurate reconstruction of thermal infrared hyperspectral images under limited spectral bands, this paper proposed a reconstruction method based on physical modeling and simulation. Semi-global decomposition algorithm was adopted to invert the thermophysical properties of the scenario based on the physical model of thermal radiation, simulating and generating full-band hyperspectral data. An optimal spectral band selection strategy driven by a physical model was proposed, which integrated the sensitivity of temperature inversion and the separability of material spectra. Experiments were conducted on both simulated and measured datasets to evaluate the performance of material identification, temperature inversion, and spectral …

Research On Pac-Bayes-Based A2c Algorithm For Multi-Objective Reinforcement Learning, Xiang Liu, Qiankun Jin

Journal of System Simulation

Abstract: To address the theoretical challenges of exploration and exploitation trade-offs and uncertainty modeling in multi-objective reinforcement learning (MORL), this study developed a learning framework, MO-PAC, based on PAC-Bayes theory. By introducing a multi-objective stochastic Critic network and a dynamic preference mechanism, the framework extended the conventional A2C architecture, enabling adaptive and efficient approximation of complex Pareto fronts. Experimental results demonstrate that in multi-objective MuJoCo environments, MO-PAC outperforms baseline algorithms, achieving approximately 20% improvement in hypervolume and 60% increase in expected utility, while exhibiting superior convergence efficiency and robustness. It verifies both theoretical value and practical performance advantages in …

Comprehensive Study For Characterization Of Blending And Heat Treatment Of Organic-Based Polymer Thin Films, Ruaa M. Ali, Atyaf H. Kazem, Khalidah G. Hasan

Baghdad Science Journal

Thin films of Polypyrrole (PPy)/Cupper Phthalocyanine (CuPc) have been prepared by the spin coating technique on glass substrates, Samples were prepared in a blending ratio (50:50) in addition to pure PPy and CuPc at room temperature, these samples were annealed at (473K) and then characterized by X ray diffraction (XRD), Energy Dispersive Spectroscopy (EDX) and Scanning electron microscopy (FESEM) at different magnifications power. XRD patterns reveal that pure polypyr role is amorphous. A wide peak was seen at roughly 2θ= 24° with an average crystallite size of roughly 56 nm and CuPc had a polycrystalline structure. Annealing results …

A Social Media-Based Framework For Marketing Architectural Design Firms In Egypt During Global Economic Downturn, Ayman Ahmed Ezzat Othman, Yara Ahmed Maarek

Architectural Engineering

Purpose – The global economic downturn has greatly impacted the construction industry and architectural design firms (ADFs). Social media, however, provides a strategic avenue to enhance the visibility and resilience of ADFs. This paper aims to develop a framework integrating social media as an innovative marketing approach for mitigating the impacts of the global economic downturn on ADFs in Egypt.

Design/methodology/approach – A qualitative and quantitative research approach was designed to accomplish four objectives. The literature review examined the global economic downturn and marketing ADFs. Three case studies investigated the role of social media in marketing ADFs during economic uncertainties. …

Nuclear Binding Energy Prediction For Some Odd-Mass Number Nuclei By Artificial Neural Network (Ann), Ruya H. Ibrahim, Akram Mohammed Ali

Baghdad Science Journal

Machine learning models called artificial neural networks (ANNs) are widely used in many fields and real-world applications. The parameter vector that forms the basis of these models needs to be evaluated computationally. We calculated the ground-level binding energy of 146 nuclei with an odd mass number using three different models:the integrated nuclear model,the liquid drop model and the experimental model. The results of these models were compared with our theoretical results calculated by the artificial intelligence network. The mean squared error of the target and output values and how close they are to zero were calculated, and the degree of …

Dual-Channel Supply Chain Network Equilibrium Model Under Retailers’ Risk Aversion, Hongchun Wang, Caifeng Lin, Xinyi He, Haiyue Yin

Journal of System Simulation

Abstract: To study the network equilibrium problem of dual-channel supply chains under the background of retailers' risk aversion, a dual-channel supply chain network equilibrium model including multiple competitive suppliers, manufacturers, retailers, and demand markets was established. The Mean-CVaR method was employed to quantify retailers' risk aversion characteristics, and variational inequalities were utilized to characterize the equilibrium conditions of decision-makers at each tier of the supply chain. The projection contraction algorithm was applied to solve the model and conduct numerical analysis, thereby revealing the impact of retailers' risk aversion behavior on equilibrium outcomes. The simulation results indicate that a higher …

Using Physics-Informed Neural Networks For Solving 2Nd-Order Volterra Integro-Differential Equation By Deepxde Library, Oday Ahmed Jasim, Abdulghafor M. Al-Rozbayani

Baghdad Science Journal

Many disciplines widely use deep learning (DL) as a key tool for investigating the behavior of various systems. Deep learning (DL) has recently been utilized to solve differential equations using physics-based input. Physics-informed neural networks (PINNs) are a novel DL model that excels at solving both inverse and forward non-linear PDE problems. PINNs may be trained as surrogate models for approximation solutions to the VIDE without label data by embedding the physical information outlined by PDEs in feedforward NNs. The objective of this study is to solve the second-order Volterra integral-differential equations (2nd-order VIDEs) for the first time using PINN …

Robust Emergency Dispatch Method Considering Dynamic Frequency Security And N-K Contingency, Tao Huang, Zhi Zhang, Yujie Ding, Yanbo Chen, Jing Wang, Wenqian Zhang

Journal of System Simulation

Abstract: To address the risk of system inertia loss and frequency instability caused by grid integration of high-proportioned new energy and unit failures, an N-k robust emergency dispatch method considering dynamic frequency security constraints was proposed. With the consideration of the frequency response characteristics of variable-speed pumped storage, a dynamic frequency response model incorporating variable-speed pumped storage was constructed, and the nadir frequency constraint was established through second-order cone transformation. Information entropy theory was employed to quantify the uncertainty of unit failures, and an uncertainty set considering N-k unit failures was developed. A twostage robust emergency dispatch model considering N-k …

Vibration Control Of Offshore Wind Turbine Towers Based On Eddy Current Nonlinear Energy Sink, Xiangxing Yu, Yandong Zhao, Baolin Zhang

Journal of System Simulation

Abstract: To address the issue of tower vibrations induced by wind loads, which can damage the structure of wind turbines, a vibration control method for monopile offshore wind turbine towers based on an eddy current-nonlinear energy sink (EC-NES) was proposed. The dynamic model of monopile offshore wind turbines based on EC-NES was constructed according to the Euler-Lagrange equation, and based on the output response of FAST software, the unknown parameters of the model and the wind loads were identified in terms of parameters. The optimal parameters of EC-NES stiffness and damping were obtained using PSO. The eddy current damper …

Fault Diagnosis Method For Photovoltaic Systems Based On Multi-Strategy Fusion, Bin Li, Yuchuo Wang

Journal of System Simulation

Abstract: To address the problem of frequent PV system faults, a multimodal fusion fault diagnosis model based on the optimization of the improved lemming algorithm was proposed. The one-dimensional time series signals of PV currents and voltages were converted into two-dimensional images by Markov transformation field, and the spatial features of the original waveforms were mined by using multiscale CNN (MCCNN); BiGRU was used to extract the temporal dynamic features of the original waveforms, and complementary enhancement of the temporal and spatial features was realized by the feature fusion layer. The improved lemming algorithm was innovatively introduced to adaptively optimize …

Numerical Simulations Of Ship Liquid Tank Sloshing Based On Graph Neural Networks, Wenkang Zhang, Xiaofeng Sun, Yiping Zhong, Yong Yin

Journal of System Simulation

Abstract: To address the high consumption of computational resources in simulating ship liquid tank sloshing using computational fluid dynamics simulation methods, a data-driven numerical simulation model was proposed based on graph neural networks. An encoder-processor-decoder framework was employed in the proposed model. The encoder extracted features of fluid particles from the first five time steps. The processor learnt latent motion patterns of fluid and updated features, and the decoder predicted features of particles at subsequent time steps. The processor incorporated a self-attention mechanism to enable dynamic adjacency weight allocation and emphasize the influence of irregular tank wall regions. Training …

Lightweight Assembly Workpiece Detection Algorithm Based On Improved Yolov8, Shuheng Wu, Yongkui Liu, Lin Zhang, Yingying Xiao, Lihui Wang

Journal of System Simulation

Abstract: To address the issues of low recognition accuracy and slow detection speed with existing deep learning-based object detection algorithms for robotic automatic assembly tasks, a lightweight assembly workpiece object detection algorithm based on YOLOv8 was proposed. The PConv was introduced to improve the C2f module, and a new Faster_C2f module was designed to enhance the detection speed of the model. The SIoU loss function was employed to optimize the location prediction accuracy of the CIoU loss function and improve the localization accuracy of small targets. The high-level screening-feature fusion pyramid networks (HS-FPN) structure was used to improve the Neck …

Emotive Immersion: Eeg-Driven Ai Art In Immersive Visual Spaces (Full Dome), Yuming Chen, Gianni Corino

School of Art, Design and Architecture

This paper proposes a rethinking of the “Future Screen” through the integration of affective computing, generative AI, and immersive spatial display. As an advanced screen paradigm, the full dome environment transcends flat, passive interfaces and enables dynamic, multisensory, and emotionally adaptive media experiences (Von Chamier-Waite, 2013). Building on this potential, the research presents Coids, an interactive dome installation. In this work, real-time emotional states are captured using EEG-based emotion recognition. These inputs dynamically shape AI-generated visuals within a shared immersive setting.Drawing on theories of immersion, presence, AI generated visuals and affect, the study explores how physiological data, even from low-resolution …

Advanced Ni-Co And Sn-Based Alloys Electrodeposition From Aqueous And Deep Eutectic Solvents, Narmin M. Hamadamin, Hassan H. Abdallah

Polytechnic Journal

Electrodeposition is a pivotal technique in materials science, enabling the fabrication of metal and alloy coatings with tailored properties for diverse industrial applications. Traditional aqueous electrolytes, while widely used, present limitations such as narrow electrochemical windows, hydrogen evolution, and environmental concerns. Deep eutectic solvents (DESs) have emerged as promising alternatives, offering broader electrochemical windows, reduced toxicity, and enhanced solubility for metal salts. This review comprehensively examines the principles and advancements in electrodeposition from both aqueous and DES media, focusing on the deposition of Ni-Co and Sn-based alloys, the role of additives, the electrodeposition of metal powders, and surface analysis techniques. …

Detection Of The Linac Jaw Face Angle Misalignment Using High Energy Electron Beam Symmetry, Song Gao, Andres Cibrian, Jared Ohrt, Peter Balter

Faculty, Staff and Student Publications

Purpose: To demonstrate the usage of a high energy electron beam symmetry measured at different collimator angles to detect jaws face angle misalignments on Varian TrueBeam linear accelerators (linac) during acceptance and commissioning processes.

Methods: During the linac acceptance, all beams were steered to a symmetric shape with the gantry and collimator at 0°. We noted that the symmetries of higher energies electrons (e.g., 16 MeV, 20 MeV) changed over 2% when measured with the collimator at 90° and/or 270° for two linacs commissioned within 5-months of each other. Misaligned applicators were ruled out as the cause of this issue. …

Sex Differences In The Association Of Vascular Risk And Apoe Genotype With Cognitive Decline And Dementia: Evidence From A U.S. Longitudinal Study, Longjian Liu, Jintong Hou, Saishi Cui, Xiaopeng Zhao, Zuolu Liu, J. Craig Longenecker, Nathalie S May, Jin Jun Luo, Rose Ann Dimaria-Ghalili, Howard J. Eisen

Division of Cardiology Faculty Papers

BACKGROUND: Sex differences in the association between vascular factors and cognitive outcomes remain unclear. We aimed to investigate the associations of blood pressure metrics (hypertension, systolic blood pressure [SBP), pulse pressure, ankle and brachial pressures, and ankle to brachial pressure index [ABI]) with the risk of cognitive decline and dementia.

METHODS: We conducted a population-based longitudinal analysis using data from the Atherosclerosis Risk in Communities (ARIC) study (begun in 1987-1989) in the United States. We analyzed a total of 12,268 participants aged 45-64 years who had validated exposure measurements, cognitive function tests (first administrated 1990-1992), and followed up for incidence …

Classifying Glaucoma Disease Images Via Deep Neural Network Technique, Maysoon Jaaffar Rahim, Ali Abid Dawood Al-Zuky, Fatin Ezzat Muhy Al-Dean Al-Obaidi

Baghdad Science Journal

A class of eye diseases known as glaucoma refers to a category of eye disease which is often associated with increased intraocular pressure, and can damage the eye by destroying nerve cells in the retina in addition to the eye's optic nerve. The accuracy of diagnosing glaucoma eye disease depends on the knowledge and experience of the ophthalmologist. Therefore, the need for automatic feature extraction from the retinal image is crucial for the diagnosis of glaucoma eye disease. The current article introduces a multi-layer deep neural network model with convolution and classification layers. The originality of the current search focuses …

Hybrid Pre-Trained Models With Machine Learning Classifier For Skin Cancer Classification, Toqa A. Sadoon, Asaad F. Qasim, Mohammed Khalaf, Ali M. Hasan

Baghdad Science Journal

Classification of skin lesions can be challenging because of the subtle variation on the skin surface due to the appearance of these lesions. In the other hand, the digital dermoscopy has been widely used by dermatologist to diagnose cancer. For accurate detection, the clinicians should have a lot of experiences, but human nature is prone to error, forgetfulness, tension, and speed in diagnosis, all of which can affect the accuracy of detection. For these reasons, automated skin lesion classification has been used by many researchers to help dermatologists in making the right and accurate decision during diagnosis. A preprocessing image …

Volumetric Video With Skateboarders: Creative Opportunities Of Working With An Emerging Screen Technology Beyond The Studio, Dani Landau, Diego Zamora

School of Art, Design and Architecture

Volumetric video is an emerging moving image technology in which each pixel is recorded witha location in three dimensions. Volumetric videos are normally created in studios using rigs made upof multiple cameras with LiDAR sensors and computers that combine the moving images. We tookthis technology out of the studio to explore variations of this technique in public skateparks. We foundthat as the technology is still in developing stages there are creative constraints: the lidar cameras sense at a limited distance to the subject, the image is of a relatively low resolution, and there are often glitches in theimage. In our …

The Gamification Of Online Dating: Exploring The Intersection Of Play, Choice, And Romance In Digital Spaces, Emma Taylor

School of Law, Humanities and Social Sciences

The rise of online dating platforms has transformed the way individuals approach romantic relationships, with many users increasingly engaging in these platforms as a form of entertainment in addition to finding meaningful connections. Central to this shift is the growing phenomenon of gamification, the incorporation of game-like elements, into non-game contexts. This article proposes to explore the gamification of online dating, examining how these game mechanics shape user behaviours, influence romantic choices, and impact relationship outcomes. By analysing the ways in which dating platforms integrate game-inspired features, this study will also assess the implications of gamified experiences for the nature …

Allfromair™ Ires (Gen 3) – System Architecture, Process Physics & Compliance Verification, Vesa Olavi Lius

Defensive Publications Series

This disclosure describes the ALLFROMAIR™ IRES (Gen 3), a unified thermodynamic platform integrating heating, cooling, ventilation, and atmospheric water generation (AWG) into a single hardware unit. The system utilizes an ”Anticubic” U-channel geometry with airflow velocities <2.5 m/s to minimize fan power according to the Cube Law. Key innovations include a ”Three-Stage Rocket” thermodynamic process and a ”Turbo Effect” where regenerative pre-cooling reduces compressor temperature lift (\Delta T \approx 5K), enabling practical COPs >30.

The disclosure specifically details a pressure-modulated spray nozzle matrix capable of switching between ”Flash Evaporation” (<300 µm droplets) and ”Film Wetting” modes to maximize latent heat transfer on Heresite P-413/Hydrophilic coated exchangers. The system solves historical displacement ventilation stratification (”The Bahco Problem”) via a scavenger heat exchanger (HX4) coupled to a hydronic floor loop. An integrated ”Water Ring” harvests and purifies condensate to EU Directive 2020/2184 standards, enabling decentralized potable water production for residential, commercial, and tropical applications.

Keywords:

Integrated Regenerative Energy System (IRES), ALLFROMAIR, Anticubic U-Channel, Low Lift Heat Pump, Atmospheric Water Generator (AWG), Pressure Modulated Spray Matrix, Flash Evaporation, Thermodynamic Up-Gearing, Heresite P-413, Hydrophilic Topcoat, Radiant-Assisted Displacement Ventilation, Twin-Leg Air Separation, EU Directive 2020/2184, Three-Stage Rocket Process.

Rack Thermal Topography Gans, N/A

Defensive Publications Series

The technology relates to a framework for designing the thermal layout of compute resource racks to proactively mitigate heat concentration zones and optimize cooling efficiency. The framework utilizes an artificial intelligence (AI) model, specifically a generative adversarial network (GAN), to automate the layout design process. The generative network is configured to ingest physical and operational constraints and produce a simulated layout. The initial layout estimation is then verified by a discriminator network, which acts as a neural network approximation of fluid dynamics, providing a closed-loop feedback mechanism to refine the generated result. The framework allows for the exploration of a …

Carbon Nanotubes Enhanced Tio2/G-C3N4 As A Potential Nanocomposite Material For Impedance-Type Humidity Sensors, Mohamed Morsy

HBRC Journal

The development of high-performance humidity sensors based on nanotechnology has become critical for different applications. This work focuses on the fabrication and characterization of impedance-type humidity sensors based on graphitic carbon nitride, titanium dioxide, and carbon nanotubes. The prepared materials comprise g-C3N4, g-C3N4/TiO₂, and g-C3N4/TiO₂/CNTs, which have been synthesized through the co-precipitation method. The properties of the nanocomposites have been verified through different characterization techniques that confirm the phase composition and morphology. The humidity-sensing response of the prepared materials was tested within a humidity range of 11%–97% RH at different testing frequencies. The unique structure of …

A Generalized Convolutional Autoencoder Framework For Seismic Waveform Compression, Emad B. Helal, Mostafa M. Ibrahim, Ali G. Hafez

Almaaqal Journal of Sustainability and Emerging Technology

Effective data compression methods have become crucial as the amounts of seismic data are growing exponentially. Due to extensive surveys and ongoing real-time monitoring, compression not only reduces storage requirements but also optimizes communication bandwidth between remote seismic stations and central processing hubs. In this paper, a generalized autoencoder-based compression framework is proposed.. Its primary innovation is a single, fixed network architecture that delivers robust performance across low, medium, and high compression ratios (CRs). Performance was compared with a dynamic pooling-based model and three wavelet-based methods (db4, sym8, coif3) across CRs from 2 to 100. Experimental results demonstrate that the …

A Review Of Machine Learning Methods In Energy Management System, Mays Sattar Al-Fatllah, Zaipatimah Ali, Aidil Azwin Zainul Abidin

Almaaqal Journal of Sustainability and Emerging Technology

Traditional energy sources are vital for energizing residences and industries. Nonetheless, they meet considerable obstacles, such as deteriorating infrastructure, ecological decline, resource exhaustion, price fluctuations, and a disparity between real-time demand and supply. Conversely, renewable energy sources offer cleaner and more sustainable energy sources. Renewable energy mitigates the pollution caused by carbon and greenhouse gas emissions in traditional power systems. Incorporating renewable energy into a power system needs an efficient management system to keep stability and improve power use. Modern energy management systems (EMS) face a significant challenge in controlling renewable and non-renewable energy sources. The challenges encompass intermittent and …

Multivariate Optimization Of Nonlinear Response Functions Using Central Composite Design, Hussein M. Hussein, Bassma H. Elwakil, Esraa Abdelhamid Moneer, Sara H. Akl, Yahya H. Shahin

Almaaqal Journal of Sustainability and Emerging Technology

This study addresses critical challenges in multivariate optimization by: (1) developing efficient strategies for optimizing nonlinear response functions in high-dimensional parameter spaces, (2) quantifying comparative performance of traditional versus statistical design methodologies, (3) establishing mathematical frameworks for parameter interactions, and (4) validating results through comprehensive statistical analysis. A dual-phase optimization framework combined One-Variable-At-a-Time (OVAT) screening with Central Composite Design-based Response Surface Methodology (CCD-RSM). Five parameters were evaluated using rotatable CCD (α= 2.378) with 50 design points. Statistical analysis included analysis of variance (ANOVA), regression modeling, interaction quantification, and cross-validation. CCD-RSM demonstrated superior performance with model predictability improving from …

Machine Learning Framework For Automatic Classification Of Nano-Seismic Signals Preceding Earthquakes, Ghada Ali, Ali G. Hafez, Sayed Hasaneen, Hamed Nofel, Nabeel Al-Atwan, Ahmed Mohamed

Almaaqal Journal of Sustainability and Emerging Technology

Nano-seismicity preceding earthquakes has many applications in fault prediction, in structure health monitoring, and in enhancing fault and source parameter determination. Not all earthquakes are preceded by these precursors; therefore, there is a need to identify P-wave arrivals that are preceded by these nano signals, which will eliminate errors in P-wave arrival timing. The current work introduces topologies capable of automatically categorizing the arrivals with nano-seismic precursors. The proposed methods are trained on datasets extracted from stations belonging to Egyptian National Seismic Network (ENSN). These records contain both P-wave arrivals preceded by such nano precursors and other records without these …

Optimizing Bioethanol Production Using Design Of Experiments For Sustainable Biofuel Development, Hussein M. Hussein, Yahya H. Shahin, Ihab A. Moneam

Almaaqal Journal of Sustainability and Emerging Technology

This study presents a statistically optimal approach for bioethanol production from sugarcane bagasse (SCB) using alkaline pretreatment, enzymatic hydrolysis, and fermentation processes. To maximize cellulose yield, four extraction parameters—solvent type, concentration, temperature, and time—were optimized using a Taguchi statistical model. The Taguchi-optimized extraction yields 11.3% increased cellulose recovery relative to cellulose recovered via non-optimized extraction methods (i.e., alkaline treatment with 6% NaOH at 90 °C for 3 hours). This yielded 45.20% cellulose (w/w), with 37.80% weight loss (predicted weight loss was 36.94%). Statistical analysis of extraction results verified solvent type (p = 0.0022) and temperature (p = 0.0007) to be …

A Smart City Transformation Survey: Using Intelligent Buildings To Promote Smart Cities, Salma A. Alhashimi, Majid A. Alwan, Ali N. Aliedani

Almaaqal Journal of Sustainability and Emerging Technology

The challenges of urbanization have been greatly aided by the integration of cutting-edge technology into smart buildings and cities. In smart cities and buildings, machine learning and the Internet of Things are essential for boosting sustainability, increasing safety, and optimizing resource use. In order to enable more effective, connected, and comfortable living in urban settings, this work has introduced the relationship between creative technology and buildings. It also discusses the regulatory frameworks for smart cities and emphasizes the importance of standardization in this regard.