Bilad Alrafidain Journal for Engineering Science and Technology
Bilad Alrafidain Journal for Engineering Science and Technology (BAJEST) is a peer-reviewed, open access journal founded in 2022. BAJEST welcomes full research articles in the area of Engineering Sciences including the following subject areas: Civil Engineering, Mechanical Engineering, Electrical and Electronics Engineering, Communication Engineering, Information Technology. Authors are invited to submit articles with the English Language that have not been published previously and are not under consideration elsewhere. Review articles and special issues are also welcome. BAJEST is a quarterly journal intended to be interesting and useful for researchers and practitioners in academic and industrial communities. Journal Scope: - Civil Engineering - Mechanical Engineering - Electrical, Electronic and Communication Engineering - Information Technology Journal Frequency: semi-annual International standard serial number (ISSN): 2957-9651 Publishing Timeline 8 days Time to first decision 60 days Review time 90 days Submission to acceptance
Enhanced Multi-Chaotic Fredkin-Logic-Based Image Encryption for Satellite Imagery with Adaptive Hash-Driven Key Generation
The secure transmission of satellite imagery is essential for contemporary remote sensing, surveillance, and defense applications. Exchanging large, high-resolution datasets over insecure channels presents significant challenges. Conventional cryptographic algorithms are frequently inefficient for image data because of inherent redundancy and pixel correlations. This study introduces an enhanced encryption framework that combines adaptive hash-driven key generation, multi-chaotic synchronization, and reversible Fredkin logic to achieve both high security and computational efficiency. Dynamic encryption keys are generated using the SHA-256 hash of the input image. Unlike some existing encryption methods that prioritize pixels based on spatial frequency and local contrast features, the proposed framework generates dynamic image-dependent keys using a tri-chaotic system combined with SHA-256 hashing. This approach enhances randomness and key sensitivity while improving resistance to statistical and differential attacks in satellite image encryption. . Fredkin reversible logic gates facilitate bit-level swapping, ensuring complete reversibility of the encryption process. Reed–Solomon error correction, a coding technique for detecting and correcting data errors, is incorporated into the pipeline to enhance robustness in satellite communication and enable recovery from transmission errors. Experimental results on grayscale images demonstrate that the proposed scheme approaches ideal entropy (7.999), resists differential attacks (with NPCR ≈ 99.6% indicating the percentage of pixels changing between encrypted images after a single pixel change in the original, and UACI ≈ 33.2% measuring the average intensity change), achieves low pixel correlation, and passes all NIST SP 800-22 randomness tests. The framework offers a large key space exceeding 2²⁵⁶ and achieves real-time encryption at 0.19 seconds per frame using GPU-based parallel processing. These results confirm the scheme's security, efficiency, and robustness for real-time satellite image transmission.
Copyright (c) 2026 Bilad Alrafidain Journal for Engineering Science and Technology https://creativecommons.org/licenses/by/4.0
Static and Dynamic Bahavior of Dry Saline Soil
In this paper, two samples of dry natural saline soil were studied and physical and chemical tests were conducted. The first sample was from Diyala Governorate, Al-Khalis District, and was taken at a depth of 1 meter. This sample had a sodium chloride salt concentration of 54%. The second sample was prepared in the soil laboratory at the College of Engineering, University of Diyala, by mixing the first sample in known weight ratios with clean, salt-free sand to obtain a second soil sample with a sodium chloride salt concentration of 32%. After that, the container in which the laboratory tests were carried out was prepared and made locally with dimensions of 50 cm × 50 cm × 55 cm and was painted yellow. The behavior of the two samples was studied under static and dynamic loading. Through the latter, we knew the dynamic response parameters, represented by speed, acceleration, displacement amplitude, and settlement, all with respect to time. It was found that the behavior of the soil with a sodium chloride salt concentration of 54% was much weaker compared to the behavior of the soil with a sodium chloride salt concentration of 32%. The weakness is represented by an approximate percentage of about 50%. In the static state, the bearing capacity decreased from (48 to 32) kilopascals for the two percentages of 32% and 54%, respectively. However, it was also found that increasing the salt content in the soil leads to increased vibration amplitude, decreased damping, and faster sedimentation, especially at frequencies of 15 Hz and 20 Hz. These effects are attributed to the weak internal structure of the salt bonds between soil particles, which weaken rapidly under cyclic loading. At the end of the study, it was found that reducing the salt content of the soil in the laboratory by mixing it with clean, dry sand in known weight ratios significantly improved the soil properties and dynamic behavior. The results of this study have contributed to a better understanding of the behavior of dry sabkha soils for the design of better and safer foundations in areas where saline soils predominate.
Mechanical Properties and Porosity Evaluation of the Casted Composite (Al1050/B4C/FA) after Multi-Cycles of SPD
Aluminum composite products have become a significant component in advanced engineering applications, due to their lightweight nature and excellent mechanical properties. In this article, pure aluminum grade 1050 with a content 93%, boron carbide of 2%, and fly ash of 5% were mixed using the stir casting process to fabricate Al1050/B4C/FA composite rods. Severe plastic deformation (SPD) using equal channel angular pressing (ECAP) was adopted to enhance the mechanical properties and to assess the porosity state. The two-channel angles 120° and 135° with multi-cycles of ECAP (1,2,3,4,5, and 6) under room temperature were used to determine the mechanical properties and porosity state of the Al1050/B4C/FA composite. The results revealed that the number of ECAP cycles (passes) can improve both the yield tensile stress, ultimate tensile stress, and hardness due to strain hardening and microstructural changes. The sixth cycle of ECAP and a channel die angle of 120° have the greatest impact on enhancing the mechanical properties compared to the others. Additionally, the porosity magnitudes recorded discrepancies with multiple ECAP cycles and channel die angles. The increase in ECAP passes showed a slight rise in porosity; moreover, the die angle of 120° gave a greater effect than 135°.
Sunflower Seed Hulls as a Sustainable Material in the Composites Industry: An Overview
Due to the abundance of sunflower by-products and the difficulty of digesting them, they constitute a large percentage of agricultural waste. Our goal here is to transform this waste into sustainable materials that can be used in many industrial applications. The multiple uses of sunflower have been discussed in our lives as a concrete stabilizer and corrosion inhibitor, in addition to its environmental benefits and mechanical, thermal and acoustic properties. Advanced studies have proven that sunflower husks are more than just waste, due to their promising future benefits when combined with other materials to become useful and environmentally friendly compounds It has chemical components that make it a material with a future in the composite materials industry, as the peel consists of hemicellulose, lignin, phenolic compounds and fatty acids, which enhances its mechanical properties when combined with polymers. Studies have also shown that combining peels with polymers such as polypropylene or epoxy will improve tensile and compressive resistance, in addition to having good thermal stability. From the above, it is clear that sunflower peels represent a vital resource for use in various industries. In this study, the uses of sunflower seed shells in various industrial applications will be highlighted, through which these applications will be developed.
Superplastic Deformation Processes of the Mg Alloys: A Detailed Review
The superplasticity of magnesium alloys (Mg) exhibits excellent behavior at high elongations while maintaining mechanical properties and a deformation mechanism that depends on intergranular sliding. These alloys can be achieved at lower stress levels with relatively high temperatures. Furthermore, the microstructure is fine-grained and homogeneous, which gives the alloy a very high potential for forming then, obtaining complex components of various weights. The microstructure and mechanical properties make magnesium alloys a successful choice for many industrial applications, including the automotive, aviation, and medical device industries, which require lightweight materials with high strength. The superplastic behavior of magnesium alloys treated by various manufacturing operations, such as differential speed rolling (DSR), equal channel angular pressing (ECAP), and friction stir processing (FSP) was studied. These operations contribute to reducing grain size and enhancing intergranular sliding, which is the basic mechanism responsible for superplasticity.
Behavior of Retaining Wall Nearby Machine Foundation in Sandy Soil
This study experimentally investigates the frequency-dependent dynamic soil-structure interaction response for a retaining wall resting on sandy soil and a focus on identifying critical behavioral regimes under varying excitation frequencies. Controlled vibration tests were conducted at 10 Hz, 15 Hz, and 20 Hz in order to study the characteristics of vibration velocity, acceleration, displacement amplitude and settlement. The results show that when frequency increases, the response of elastic stability for progressive failure tends to decrease. At 10 Hz, the base exhibited stable elastic behavior and minor deformation. This meant it was operating safely. The places where the observations were made suggest that at the frequency of 15 Hz, the lateral dislocation ratio is more than 1.0. So, we conclude that plastic deformation happened. We also see the maximal dynamic response and vibration speed (1.23-4.61 mm/s) and acceleration (1.1-3.8 m/s²). The system experienced extreme compounded damage and settled nearly -20mm. Cyclic stress concentration and densification of soil also cause a large degree of lateral spreading. The results indicate a critical resonance (15 Hz) beyond which the dynamic instability builds up and design measures should be taken into consideration in terms of resonant amplification and long-term cyclic degradation to secure foundation safety and serviceability on vibrations caused by machines.
Ammonia Removal from Wastewater Using ZnO-Modified Ceratophyllum as a Low-Cost Eco-Friendly Biosorbent
The results of its detrimental effects on water quality and toxicity to aquatic life, as well as ammonia contamination in wastewater, indicated that noticeable environmental risk. Moreover, there was another important factor in this field are conventional treatment techniques lead to pollution and operative budget. It is important to produce a balance between the low-cost treatment and keeping the sustainability and efficiency in the environmentally friendly adsorbents. Here, in this current work, an efforts were conducted to investigate an eco-friendly bio sorbent for ammonia extraction, that is depends on Ceratophyllum and is modified with zinc oxide (ZnO). To do the creation of adsorbent, ZnO nanoparticles have been impregnated into dried Ceratophyllum powder. The evaluation of the results was conducted by utilising scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), Brunauer-Emmett-Teller (BET) surface area analysis, and X-ray diffraction (XRD). To measure the influence of temperature, stirring speed, pH, and contact time on removal efficiency, batch adsorption tests were carried out. SEM, BET, FTIR, and XRD investigations verified the ZnO-modified Ceratophyllum's enlarged surface roughness, better porosity, and the emergence of Zn–O functional groups. The bio sorbent demonstrated a notable enhancement over the raw material, attaining an 82% removal efficiency for ammonia under optimal conditions (pH = 8, contact duration = 60 min, stirring speed = 200 rpm, and ambient temperature = 25 °C). The results showed that zinc oxide-modified Ceratophyllum is a cheap, environmentally friendly, and highly effective absorbent for large-scale wastewater treatment, providing a sustainable alternate to traditional technologies.
Cyclic Behavior of Sheet Pile Wall Under Seepage Anchored Conditions
Sheet pile walls have been considered the hoariest anchoring techniques used in civil engineering projects. In this study, experimental work was achieved on a laterally anchored sheet pile wall installed below a cycling load in sandy soil with a ratio (CLR) of 40% for 100 cycles. Several parameters were considered, including anchor rod length of (40 cm), pile length (30 cm), and angles between batter piles 100, 150, and 250). In addition, a linear load of 5 kPa applied on ground surface near sheet pile wall and a spacing between anchors of 10 cm was considered. The batter piles were installed at a distance of 40 cm from the sheet pile wall. The test results showed that the increase of angles between batter piles from 10° to 15° and 25° leads to a reduction of the lateral displacement of the sheet pile wall by 7.1% and 64.3%, respectively. Strip footing tilting and total settlement near the sheet pile wall reduced as the angles between anchored batter piles increased from 10° to 15° and 25°. The wall system, which is made from anchored sheet pile, has been exposed to cyclic load was effective and safer when the inclination angle between the batter piles is 25°.
Influence of Metakaolin Replacement on the Early Compressive Strength of Heat-Cured Fly Ash–Based Geopolymer Mortar
Geopolymer materials have gained significant attention as sustainable alternatives to traditional concrete, as they reduce carbon dioxide emissions associated with the cement production industry. This environmentally friendly material consists of industry waste materials that have been chemically activated by an activation solution. This study attempts to evaluate the effects of replacing metakaolin (MK) in fly ash (FA)-based geopolymer mortars with sand at 10%, 20%, and 30% with an emphasis on early strength within the first few hours. Na2SiO3 (2:1) and 12 M NaOH were combined to create an alkaline activator. After being formed into 50 x 50 x 50 mm cubes, the samples were first heat-cured at 90°C for initial durations. At two, five, and twenty-four hours, the bulk density and compressive strength were measured. The results showed that density gradually decreased over time, and that replacing some of the sand with MK significantly increased both early and later compressive strengths while initially reducing density. The increased active aluminosilicate content and quicker N-A-S-H gel formation are responsible for this improvement. MK20 showed the best workability, microcompactness, and reactivity. On the other hand, because of its higher viscosity and potential for microporosity, MK30 was marginally weaker than MK20 but showed greater strength in comparison to the reference. The results support the use of medium replacement proportions of MK in applications that require both structural stability and quick strength growth
Performance Evaluation of Biomedical ECG Suction Electrodes with Different Materials
A suction electrode is often used as a precordial electrode in clinical electrocardiograms. It can be used frequently and has no need for adhesive or a strap. Higher source impedance since the contact area is small. The present study aims to 1) design and implement biomedical suction electrodes with different types of materials (Copper and Aluminum) and determine the dimensions of each electrode, 2) apply these constructed electrodes on subjects in hospitals and laboratories to extract their electrocardiogram waveform, and 3) finally, make a comparison between these constructed electrodes (Copper and Aluminum) with the standard electrode made from Ag/AgCl. The results obtained indicate that the best material for a construction electrode after the Ag/AgCl material is the Copper material, which gives the same electrocardiogram signal with high efficiency and accuracy. In addition, Copper material is less expensive than silver, so it is desired to use it in the reading of electrocardiogram signals. While Aluminum material was never used in the electrode construction, this was obtained from the reading of its electrocardiogram signal
Development of A Multi-Stage MATLAB Algorithmic Quantification Using Computational Processing of Stress-Strain Data in Carbon/Glass Epoxy Composites
Composite materials have long been in great demand for industrial purposes, particularly in the field of lightweight automobile and aircraft applications. The theory of numerical analysis provides a useful account of how Fibre-reinforced composites can be developed to prevent failure analyses and produce new composite materials. Therefore, to get an advantage from this theory, an effort has been made in this research. A systematic, Multi-stage algorithm based on MATLAB software was established to generate a code that can manipulate data of tensile and flexural tests for composite materials made from glass fibre epoxy composites (GF/Ep) and carbon fibre epoxy composites (CF/Ep). The current numerical framework was implemented using suggested code to handle data from stress-strain curves and predict ultimate strength, strain, and fracture toughness values, undergoing a validation process. The key findings were the GF/Ep-based composite showed better strain failure and an energy absorption value of 12.3 MJ/m³/and 7.0 MJ/m³ in tension and flexure, respectively. This is suggested to have better performance in fracture toughness. Furthermore, the outcome results also showed that on GF/Ep, it is less stiff, with a value of 21.3 GPa and a bending failure value of 400 MPa.
Hybrid Deep Learning Architectures for Structural Health Monitoring via PZT Signals: A Multi-Scale Attention AI with Uncertainty Quantification
To keep pace with rapid developments in infrastructure, an AI-based damage detection tool is crucial for ensuring safety. However, the rapid deterioration of civil infrastructure necessitates intelligent, autonomous structural health monitoring (SHM) systems capable of real-time damage detection, localization, and quantification. In this article, a novel method for smart structural damage identification is proposed. a novel hybrid architecture integrating electromechanical impedance ( ) sensing with multi-scale attention-based deep learning. Unlike conventional approaches reliant on hand-crafted features, our method employs a Squeeze-and-Excitation Residual Convolutional Network ( ) coupled with multi-head self-attention mechanisms for automatic feature extraction from raw PZT-EMI signals. The model was designed and trained using an effective approach for optimal feature learning from the raw PZT-EMI response signals. Further, Bayesian optimization has increased the network’s reliability. It substantially increases the network performance and knowledge acquisition process. To validate the proposed approach experimentally, PZT-EMI signals have been obtained from a beam with various structural conditions. The model demonstrates 100% classification accuracy across four damage severity levels (healthy, 25%, 50%, 75% stiffness reduction), with an RMSE of 0.137 cm for damage localization. Comparative study against state-of-the-art methodologies, such as GNN reviles the superior efficiency of the introduced approach. This research establishes a new paradigm for intelligent SHM, bridging the gap between data-driven pattern recognition and physics-aware structural assessment
Deepfake Detection via Active Perception: A Deep Q-Learning Framework for Interpretable Visual Forensics
The rapid development of generative models has resulted in the widespread synthesis of realistic deepfake media, and thus raises fundamental threats to digital trust and media verification. The majority of current deepfake detection methods are based on supervised convolutional neural networks (CNNs) and work with global image presentations, which may easily have a weak generalization ability and lack interpretive power. This paper presents a different paradigm for the detection of deepfakes: to cast the problem as a sequential decision and adopt reinforcement learning to solve it. For that purpose, the paper proposes a patch-based Deep Q-Learning (DQN) approach that enables the agent to selectively explore local face regions and detect manipulation artifacts. Unlike the typical feedforward classifiers, this approach is capable of fine-grained spatial exploration as well as making the model interpretable: it highlights regions that are informative for the decision. This research is offered as a pilot study to examine feasibility and interpretability rather than sample size and benchmarking. Experiments were performed on balanced partial deepfake image datasets released for public use (2400 images), and each image was considered as a single RL episode. Experimental results on a practical deepfake dataset show that the proposed approach has good performance with an AUC of 0.92. Remarkably, despite processing only 18.2% pixels on average per image, the proposed approach achieves this level of performance, confirming the efficiency and forensic potential of the proposed active perception framework
Federated Learning Based Intelligent Spectrum Management for Secure Cognitive IoT Communications
The increasing density of IoT devices has intensified spectrum scarcity and security challenges in modern wireless communication systems. Conventional spectrum allocation mechanisms are centralized, static, and vulnerable to privacy leakage and malicious attacks. This paper proposes a federated learning–based intelligent spectrum management framework for cognitive IoT networks. Unlike traditional centralized learning, the proposed approach enables distributed IoT nodes to collaboratively train a global spectrum decision model without sharing raw data, thereby preserving privacy and enhancing security. Each node locally learns spectrum availability patterns and interference characteristics, while a lightweight aggregation mechanism updates the global model. Simulation results show that the proposed framework improves spectrum utilization efficiency, reduces interference, and enhances communication security compared to centralized and non-intelligent spectrum management schemes.
Improving The Planning and Scheduling of Road Maintenance Projects Using Crack and Climate Data Analysis
Road infrastructure in dry and semi-dry climates has become more susceptible to rapid deterioration because of climate pressures and changing pavement performance. This research proposes a data-based predictive model to help assess road conditions and inform maintenance strategies by combining long-term climatic variables (ex, effective rainfall, evapotranspiration, climatic water balance) with pavement performance data collected from vibration sensors and static structural assessments; both datasets are analyzed together using a Random Forest classification model. The results show that incorporating climate data into the analysis produces improvement in the estimated timeliness of construction activity and could promote proactive maintenance through adaptation to climate conditions. Methodologically, this finding is significant in that it creates a single machine-learning framework by combining several independent data sources (i.e., dynamic measure of sensing) into one value using a quantifiable climate index. Practically, this research provides decision-making support for maintenance timing, decreases uncertainty in deterioration estimates, and improves the sustainability of road networks in dry climates.
Multi-Task EEG Signal Classification for Emotion Recognition and Epilepsy Detection Using Intelligent Learning Models
Electroencephalography (EEG) provides rich information and a representation of brain activity for intelligent healthcare applications. This study proposes a framework for automated emotion recognition and epileptic seizure classification using machine learning (ML) and deep learning (DL) techniques. The framework was tested on three EEG datasets to cover areas of anomaly detection, multi-class emotion recognition, and binary seizure prediction. EEG signals were first normalized and then segmented into fixed-length windows depending on each participant's recording traits, thus maintaining subject-specific temporal patterns. Models’ evaluation was done with properly separated training and testing data to guarantee a trustworthy performance assessment. For emotion recognition, the model based on Gated Recurrent Units (GRU) obtained 96% accuracy on the test data, whereas ensemble learning with Random Forest got 98%, thereby proving its excellent discriminative power on structured EEG features. Anomaly detection without supervision through Histogram-Based Outlier Score (HBOS) was able to detect the abnormal single-channel EEG segments accurately. In seizure classification, a convolutional neural network (CNN) trained on log, scaled time, frequency spectrograms yielded 95. 75% accuracy with an AUC of 0. 996 on the test dataset, thus successfully differentiating interictal and preictal states. The findings confirm that ML models provide robust and computationally efficient performance on engineered EEG features, whereas DL models effectively capture complex temporal and spectral representations across multiple EEG analysis tasks
Enhancement of the Project Cost by Mapping the Influence of the Supply Chain Attributes
Controlling construction project costs is a critical factor in achieving project success, particularly in complex construction environments. Previous studies have shown that weaknesses in supply chain management are among the primary causes of cost overruns. Accordingly, this study investigates the impact of supply chain attributes (SCA) on construction project costs by identifying key characteristics through a comprehensive review of prior literature and developing a framework to evaluate their influence on cost outcomes. Data were collected from construction projects and analyzed using statistical techniques, including correlation analysis and Structural Equation Modeling (SEM). The results reveal a statistically significant negative correlation between project costs and SCA, indicating that improved supply chain performance contributes to cost reduction. The SEM results validate the proposed framework, with a chi-square value of χ² = 9.865 and a probability value of p = 0.275, which exceeds the acceptable threshold of 0.05 and confirms an adequate model fit. Further findings demonstrate that supply chain functions, quality management, and human resource management (HRM) significantly enhance project cost efficiency. Among these factors, HRM plays a pivotal role in strengthening supply chain functions and improving overall performance, highlighting its strategic importance within construction organizations. By statistically modeling the relationships and pathways between SCA and project costs, this study provides a systematic approach to understanding how effective supply chain management can improve cost efficiency in construction projects
An Experimental Work into the Effect of Soil Saturation on the Bearing Capacity of Under-reamed Piles
This study examines the behavior and load-bearing capacity of under-reamed piles embedded in cohesive soil at different degrees of saturation (100%, 80%, 60%, and 40%). The major objective is to examine the impact of matric suction (MS), cohesion (Cu), and saturation ratio (Sr) on the axial response of both conventional and under-reamed foundations under unsaturated conditions. Three different pile configurations were tested in laboratory models: a single under-reamed pile (SURP), a double under-reamed pile (DURP), and a straight pile without under-reaming (NP). The ultimate bearing capacity and settlement behavior were evaluated using load-displacement curves and axial compression tests conducted inside a steel soil chamber. The results show that decreasing the degree of saturation significantly increases matric suction, thereby improving the bearing capacity of all pile configurations. For the straight pile (L/D = 22.5), the load capacity increased by 259%, 348%, and 48% at saturation rates of 80%, 60%, and 40%, respectively. The increases observed for the single under-reamed pile were 260%, 346%, and 64%, respectively. Under the same conditions, the double under-reamed pile showed increases of 210.5%, 238%, and 24%. When comparing the different types of piles, it was found that the single under-reamed pile achieved capacity improvements of 42-57% compared to the straight pile. On the other hand, the double under-reamed pile provided even more significant improvements, which ranged from 58% to 109% depending on the degree of saturation. The study highlights the strong interaction between saturation, suction, and pile geometry, offering useful insights for foundation design in partially saturated cohesive soils.
Mapping of Allowable Bearing Capacity for Square and Strip Footing of Al-Khalis City
This research uses Geographic Information Systems (GIS) to assess the allowable bearing capacity of shallow foundations in AI-Khalis District, located along the Diyala River about 20 kilometres northeast of Baghdad within the Diyala Governorate. From 12 exploration sites, each containing several boreholes, around 35 test records were gathered at different depths. The database contained unit weights, soil classification, and results from the Standard Penetration Test (SPT). The correlation and modification of the SPT results were used to calculate the undrained shear strength (Cu). Subsequently. Terzaghi's equations were used to determine the permissible soil bearing capacity for two types of foundations: square (B= 1.5 m) and strip (B 1 m) at excavation depths of 1 and 2 metres. The data were then collected in an Excel file and entered into the Geographic Information System (GIS) to produce maps. The results revealed a significant variation in the permissible soil bearing capacity across different depths, i.e., it gradually increases with depth. This study aims to provide geotechnical engineers with an initial view of the type of foundation and the number of layers.
The Experimental Seismic Performance of Positive and Negative Batter in a 2 x 2 Piles Group in Loose Sand
In the event of an earthquake, pile foundations help to keep a structure stable. Batter piles, which are a sort of angled foundation, can resist both vertical and horizontal loads better than a typical vertical pile. The primary objective of this research is to address the seismic response of negatively battered piles in 2 × 2 pile groups embedded in loose sand through an experimental approach. The experimental study used 2 × 2 pile groups embedded in a loose sandy soil with 31.2% relative density, and seismic loading was replicated using a shaking table. Ground motion in the El Centro and Kobe earthquakes was applied to piles with batter angles of -5°, 0°, and +5°. The purpose was to assess and contrast the case of a negatively battered pile with vertical and positively battered piles, about the lateral and vertical displacements, and the acceleration. Experimental findings demonstrated that negative battering notably amplifies pile group displacements under seismic excitation. Specifically, for the El Centro earthquake, changing the batter angle from 0° to 5° increased the maximum lateral displacement by 24.287%, while adjusting it from 0° to +5° reduced it by 3.877%. Similarly, vertical displacement rose by 19.923% for the negatively battered piles, whereas positive battering resulted in a 7.511% reduction in lateral displacement for the same event.
Integration of High-Efficiency DC Generators with AI-Driven Maximum Power Point Tracking in Hybrid Solar-Wind Microgrids
The combination of renewable power with microgrids also requires intelligent control systems that get the most power being utilized and regulate the power flow in an even manner. We have developed a new paradigm that comprises high-performance DC generators coupled with artificial intelligence-based maximum power point tracking (AI-MPPT) for hybrid solar-wind microgrids. Our system possesses an embedded 36-kW solar photovoltaic panel array, a 10-kW wind turbine system, and 50 kWh battery storage with dynamic grid interaction ability. We developed a deep Q-network (DQN) energy management system to harvest maximum real-time power from renewable sources. It controls charge and discharge cycles of batteries and grid interaction according to time-of-use electricity rates. The AI-MPPT algorithm performs better than traditional algorithms in tracking optimal operating points under changing environmental parameters. It learns and dynamically adapts. Simulation efficiency for 24-hour operation cycles proves the AI-based system has a daily operating cost reduction of 67% compared to traditional rule-based operating practices. The system proves energy autonomy with successful peak load shaving at 57% and grid autonomy at 57.3%. Battery life was enhanced through intelligent scheduling of dynamic electricity prices. The model provides an expandable platform for residential and commercial microgrids. It shows real economic benefit while promoting the integration of renewable power and grid stability. Our approach provides meaningful enhancement in economic efficiency and system reliability for distributed energy consumption
Influence of Composite Coating (Tin/BN) On Microstructure and Properties of Laser-Cladded Martensitic Stainless-Steel Coating
The current work used the laser cladding method to prepare a composite coating of titanium nitride (TiN) combined with boron nitride (BN) on 420 stainless steels. The microstructure, micro-hardness, wear resistance, and bio-corrosion resistance of the coatings were analyzed using scanning electron microscopy (SEM), energy-dispersive spectroscopy (EDS), a micro-hardness tester, a wear tester, a bio-corrosion resistance test, and electrochemical techniques. The results indicated that a strong metallurgical bond was successfully formed between the composite coating and the substrate. SEM analysis revealed that the sample produced at low power demonstrated superior performance compared to the high-power sample with respect to the microstructure. The path coefficients related to adhesion factors, such as microhardness, were higher in the low-power sample, suggesting stronger and more consistent interactions between the coating and the substrate. EDS results showed that the high-power sample had a lower iron (Fe) signal, indicating superior performance in that regard; however, the low-power sample displayed strong titanium (Ti) and BN components. Micro-hardness decreased with high power and increased with low power. The high-power sample exhibited a higher wear rate compared to the low-power counterpart. In the bio-corrosion test, the uncoated martensitic steel displayed a more negative and unstable open circuit potential (OCP) profile. In contrast, the TiN + BN-coated sample showed a less negative and more stable OCP response, indicating enhanced corrosion resistance. Electrochemical techniques demonstrated that the "Mix" sample had superior electrochemical performance compared to the "Base" sample. It exhibited a broader current density range, a higher current response, and clearer separation of anodic and cathodic reactions, making it more effective for applications in medical instruments such as orthopedic implants, dental implants (abutments and dental screws), surgical tools (scalpels, forceps, bone drills), and spinal implants and plates. Electrochemical techniques demonstrated that the "Mix" sample had superior electrochemical performance compared to the "Base" sample. It exhibited a broader current density range, a higher current response, and clearer separation of anodic and cathodic reactions, making it more effective for applications in medical instruments.
Copyright (c) 2025 Bilad Alrafidain Journal for Engineering Science and Technology https://creativecommons.org/licenses/by/4.0
The Impact of Blockchain Technology on Database Management Systems Healthcare Networks Use Case
In recent years, blockchain technology has gained significant attention due to its ability to revolutionize various industries. One area that has seen interest is database management systems (DBMS). Blockchain technology's ability to be integrated with database management systems could transform healthcare networks. With regard to enhanced privacy, security, scalability, and regulatory compliance, this study examines the impact of blockchain on healthcare data management. Blockchain ensures data integrity and authenticity by utilizing cryptographic techniques such as hashing, encryption, and digital signatures, which mitigate vulnerabilities in traditional systems. In addition to patient-centric care, interoperability, and research advancements, blockchain has an experienced impact on healthcare. Health care systems undergo a revolution as patient data ownership, secure sharing, and compliance management are revolutionized, fostering trust among stakeholders and spurring collaboration. It is undeniable that blockchain will revolutionize healthcare data management, ushering in a new era of patient-centered, transparent healthcare. It will also discuss potential healthcare use cases, implementation strategies, and best practices for hybrid systems, as well as a comparative analysis of blockchain and traditional database systems.
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
An Effective Prediction of Power Consumption Using Deep Learning Models
Effective prediction of electric power consumption is vital for accurate energy management systems, particularly in growing urban regions. Conventional statistical techniques usually fail to capture the complicated patterns and non-linear tendencies in time-series energy data. The development of deep learning models has demonstrated encouraging findings in a variety of prediction tasks. Therefore, this paper concentrates on the application of diverse deep learning models, comprising One-dimensional Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Bidirectional LSTM, and a hybrid model for power consumption prediction based on a dataset gathered from January 1, 2017, until January 1, 2018, in Tetouan, Morocco. These applied models were assessed utilizing diverse robust assessment metrics, and the findings demonstrated that the hybrid model (One-dimensional CNN with LSTM) reached superior performance with a Mean Squared Error (MSE) of 0.42548, Root-MSE of 0.65228, Mean Absolute Error (MAE) of 0.54395, and Median-AE of 0.51401, surpassing the other standalone and relevant models. The attained findings emphasize the merits of incorporating recurrent and convolutional structures to obtain more effective and accurate predictions of energy consumption time series
Copy-Move Image Forgery Detection Using Deep Learning Approaches: An Abbreviated Survey
Images play a fundamental role in digital media, and altering digital images can present a significant risk since it contributes to disseminating false information. The rapid advancement of technology in digital image forensics has significantly improved the quality of forged images to the extent that many forgeries are now indistinguishable. Digital image authenticity and reliability are becoming more significant as evidence. Some people invalidate photos by adding or removing sections. Therefore, image forgery detection and localization are crucial. Image manipulation techniques have made this a major computer vision issue. Images can be obtained from many origins and may appear in multiple formats. Consequently, passive techniques for detecting image forgeries are generally favored, which do not necessitate prior knowledge about an image. The prevalent forms of passive image forgery detection encompass the identification of copy-move and image-splicing forgeries. Recently, deep learning techniques have become prevalent in image manipulation detection. These techniques demonstrated superior accuracy to traditional approaches due to their ability to extract features from images effectively. This study comprehensively examines deep learning methodologies utilized in detecting copy-move forgery, and it has mostly focused on studies conducted in recent years, and data sets commonly used in detecting copy-move forgery have been mentioned. This abbreviated survey concluded that combining conventional image processing methods with pre-trained CNN approaches could leverage the strength of both, exhibit significant efficiency, and decrease the requirement for labeled image datasets. Furthermore, utilizing ensemble techniques to integrate multiple approaches improves overall forgery detection performance.
Advancements and Challenges in Low-Quality Fingerprint Identification: A Comprehensive Survey
Low-quality fingerprint identification has played a significant role in the field of biometric identification. Especially for those who are experts in the use of sophisticated biometric technology in jail security and criminal identification in recent years. Many factors affect the accuracy of low-quality fingerprint identification, such as the quality of the fingerprint image, the accusation device, the extraction tools, etc. Many approaches have been proposed in this field to improve low-quality fingerprint identification. In this paper, we discuss the main factors of the low-quality fingerprint, the main approach of the human biometric data, as well as the main factors of the low-quality of the fingerprint data. Also, we list the state of the art, most recent machine learning and deep learning approaches that have been used recently in this field. As the end, this field of research is still in progress to improve the quality of the identification progress
Hybrid Deep Learning Model Based on Transformer Encoder for Sleep Stages Classification
Sleep is the cornerstone of overall health, and the process of sleep staging involves classifying sleep data into specific stages. Key signals such as EEG, EOG, and EMG are useful in analysing and categorizing sleep data, but it is a complex and time-consuming task. This paper focuses on designing a hybrid deep learning model to accurately classify sleep data using the Sleep Heart Study (SHHS) dataset. Considering that sleep signals show similar temporal patterns to the time series data, we also use transform encoders to extract essential features and facilitate the discrimination of sleep stages. By leveraging the power of transform encoders to capture crucial temporal patterns, we have successfully enhanced the classification of sleep data into five stages. Through comprehensive evaluation using various criteria, we measure the performance of our model and compare it with cutting-edge methods. The results show a significant accuracy of 0.883 and 0.836 for accuracy and Cohen's kappa, respectively, confirming the effectiveness of our approach. The results also highlight the robustness and efficiency of our approach in accurately diagnosing sleep states, ultimately contributing to the advancement of sleep analysis and overall health monitoring.
A Review of Data Aggregation Algorithms for Enhancing IoT Performance
With the exponential growth of the Internet of Things, efficient data aggregation is necessary to tackle the issues of network congestion, energy consumption, and latency. This paper presents an adaptive data aggregation algorithm that applies clustering, in-network processing, and optimized routing. The proposed algorithm outperforms traditional methods such as LEACH and PEGASIS with 50 ms latency, 0.5 J/node energy efficiency, and 350 kbps throughput. Key features include dynamic clustering, energy optimization, and real-time adaptation, which offer high scalability and are fit for use in smart cities, healthcare, and industrial IoT. However, future challenges like interoperability and security will still be taken into account on the ground. This paper advances scalable, efficient, and secure IoT systems towards technological advancement.
Experimental Model for the Machine Foundation Response with Skirt Under Earthquake Loading in Sandy Soil
The machine foundation sometimes carries the vibration load developed from the machine run and the seismic load simultaneously. This issue is not covered enough in previous research, especially in the case of laboratory models. Therefore, this paper presents a laboratory model for the response of singly and doubly skirted foundations subjected to seismic loads in sandy soil. The soil was prepared to attain a relative density of 70% in a steel container with a dimension of (50*50*55) cm using the rain technique. Tests were conducted under the dynamic response with a frequency of machine of 30 Hz. A shake table is used; it is exposed to the Halabja earthquake with an intensity of (0.1 g). In addition, three directions of the machine concerning the axis of an applied load (0ᵒ,45ᵒ,90ᵒ) are selected. The ratio of the skirt depth to the machine foundation width (Ds/B) (single and double skirt) was (0,0.25,0.5). The results showed that high displacement can be obtained when the direction of rotation of the machine’s operation is parallel to the direction of the earthquake, also reduced when the angle is 90ᵒ compared with 00. The best case for reducing lateral and vertical displacement is a double skirt at (Ds/B) =0.5 reached (15% and 13%) comparing to single and without skirt respectively
Finite Element Analysis of Pre-stress Concrete Box-Girder under Thermal Loading
Bridge design and structural evaluation should consider thermal load, particularly for statically indeterminate and cable-supported bridges. However, there is limited study and test data on the thermal impact of pre-stress concrete girder, focusing on models developed through numerical analysis and field measurements. Temperature distributions on concrete bridges are nonlinear, causing stress distributions to self-equilibrate, to discuss the stresses, deflections, and moments caused by temperature variations. Three-dimensional finite element ANSYA R3 V19 finite element software is used to analyze pre-stress concrete box girder bridge specimens with trapezoidal cross sections. A parametric analysis was conducted to examine the effect of thermal loading on the behavior of prestressed concrete box girders. Six cases were considered to study the thermal behavior of the box girder. In each of these six cases, thermal loading was applied to three different regions of the box girder. The results showed that girder 3 and girder 6 with bottom thermal had the lowest deflected shape value of 0.84 mm when the thermal load was applied to the bottom flange of the prestressed concrete box girder, with reduced stresses observed in girder 2 and girder3 with top thermal, where the longitudinal stresses in the Z direction were 6.3 Mpa