KUFA JOURNAL OF ENGINEERING

The infiltration of hazardous chemicals into the soil causes soil pollution which poses significant risks to ecosystems and human health. For this reason, accurate predicting the diffusion of pollution in soils is important and critical for monitoring and protection the environmental state. In this study we have compared advanced machine learning ML models to predict vertical and horizontal pollution diffusion using complex and multimodal soil experimental datasets. Support vector regression, linear regression, gradient boosting regression, xgboost regression, k-nearest neighbours, and artificial neural networks were employed to build predicted models and compared with each other. The comparison criteria are measuring mean squared error, root mean squared error, mean absolute error, and R-squared as the metrics used to evaluate the predictive models performance. The observed results demonstrate that ensemble methods XGBoost and random forest, outperform other models in predicting pollution diffusion while XGBoost achieving the highest accuracy. On the other hand, linear regression was the least effective while k-nearest neighbours and artificial neural networks showed moderate performance

Satellite images provide a wealth of information that is used in various applications such as urban planning, environmental monitoring and terrain analysis. However, converting raw satellite data into a grid image suitable for these applications remains a challenge. this study proposes a three-stage methodology that integrates advanced image processing techniques to transform satellite image surfaces into a mesh image to enhance the utility in e geometric topology. The first phase involves applying intelligent detectors technology to identifying the edges of objects within the satellite image. By detecting high-density change points which are characteristic points at edge intersections especially at corners or angular features formed by objects. In the second stage, these features are processed using SIFT which ensures that scale-invariant features are extracted across different images. The final stage utilizes Delaunay triangulation to create the mesh, effectively converting the satellite image surfaces into a mesh representation. This mesh representation is a type of graph consisting of nodes (representing extracted features) and edges (connecting these nodes). Such a representation opens up new possibilities for analysis and study and providing an organized and detailed depiction of the Earth's surface. The mesh image produced through this methodology can be applied to numerous scientific and practical applications, bridging the gap between raw satellite data and its practical utilization in various fields. The satellite images used in this study are high-resolution raster optical images, commonly employed in applications such as urban mapping, environmental monitoring, and geographic analysis. These types of images are typically captured by satellites like Landsat. Such raster optical imagery provides detailed visual information that is essential for analyzing surface features and patterns. The results demonstrate the effectiveness of the proposed system, achieving a Ki metric value of (104.46) compared to (78.51) in previous approaches, indicating superior mesh quality

The most recent wireless technology is ultra-wideband (UWB), which makes use of a broad frequency range for efficient communication, fast data transmission, and precise position recognition. In this paper, a small antenna architecture based on CST software is developed with a width of 9 mm, length of 14 mm, and a height of 1 mm. The antenna is proposed in the frequency range 3-11 GHz, which is the ultra-wideband frequency spectrum. The simulation result of the above software includes a first frequency band of 0.542 GHz and a second frequency band of 3.4309 GHz, which covers several frequency bands. The new design is unique in being a Rubik's Cube antenna. The antenna we want has a reflection coefficient of less than -10 dB at all frequencies. The gain value lies between 0.5 dB and 3.8 dB, and the radiation efficiency and antenna efficiency vary from 60% to 88%. These outstanding characteristics position the proposed antenna in an essential position in contemporary wireless networks that utilize ultra-wideband technology

The rapid developments of additive technology bestirred the interested to application of this technology for different filed. In the biomedical and prosthesis engineering, this technology especially contributes to solve the sophisticated problems from generating the surgical models, and tissue engineering to detect the defected zone of anatomy. In the introduced paper a suggested strategies have been adopted to create a printable model for the human’s head depending on the row data from the CT or MRI with DICOM format and the software of medical data processing. The additive manufacturing based on 3D printing technology has been used to produce the 3D physical models of the face parts. Surface roughness for the created models have been measured, which ranged (0.65-1.846) ℳm for the printed modes, also an image processing technique have been adopted to compare the dimensional accuracy between the prototypes and the DICOM image, where the maximum deviation value was (0.8mm). The adopted strategies were simplified to use by any interested in biomedical domain, and proved a good effectualness to create any suggested human body parts

The current study was an attempt to examine the new application of the Adaptive Exploration Artificial Bee Colony algorithm in evolutionary clustering. An optimized version of the traditional Artificial Bee Colony (ABC) algorithm, AEABC relies heavily on a probability parameter that is based on distance. In this regard, a balanced relationship of exploration and exploitation leads to the dynamic parameter adaption, resulting in the spatial distribution of solutions. As a result, AEABC provides an efficient performance for solving dynamic clustering tasks, involving consistent data changes and constant reformation of cluster structures. To conduct the present study, the performance of AEABC on evolutionary clustering was compared with that of other metaheuristic algorithms, namely Genetic Algorithm (GA), Differential Evolution (DE), and Particle Swarm Optimization (PSO). The results confirmed that AEABC produced lower performance error rates than competing algorithms in all three scenarios representing best case, worst case and average case performance. This established both its reliability as well as robust efficiency. AEABC indicated convergence performance because the algorithm initiates with quick development in the first stages then reached stable points effectively, which shows its ability to position itself well in changing data environments. Moreover, the findings could confirm AEABC as an effective means for evolutionary clustering, which makes it a suitable candidate for applications involving dynamic systems, such as social network analysis, customer segmentation, and anomaly detection. By applying AEABC to evolutionary clustering, this paper contributes to expanding the range of applications for swarm intelligence algorithms in handling real-world, dynamic data challenges

As mobile technology develops rapidly, social media becomes a rich source of platform for people to voice for his or her opinions and point of views. In order to support business decision making and policy making, we need to quantify public moods, sentiments by conducting sentiment analysis which emerges as a key research area in recent year. Many research efforts have been put into performing sentiment analysis, many tools and algorithms are devised to determine the sentiment as positive, negative or neutral, in social media. We improve the sentiment classfier by training a classical machine learning model on three different data sets. Although BERT has shown good performance in sentiment analysis, we need to find a way to raise the accuracy. We propose four Deep Learning models by combining BERT with Bidirectional Long ShortTerm Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU) algorithms. Our paper uses pre, trained word embedding vectors as inputs and aims to increase the accuracy of sentiment analysis and test the effect of BERT‘s combination with BiGRU and BiLSTM layers, DistilBERT and RoBERTa. We test the classification of text sentiments with and without use of emoji symbols. A comparative analysis of 2 pre, trained BERT models and 7 classical machine learning models suggests that the models with BiGRU layers achieve best performance in our sentiment analysis pipelines

This study evaluates the efficacy of shrimp shell powder as a coagulant in wastewater treatment, specifically targeting batik effluent. The primary aim is to compare the performance of shrimp shell powder to traditional alum in reducing key parameters such as Turbidity, Total Suspended Solids (TSS) and color (pt-co) levels. The shrimp shells were rinsed, air dried, grounded. Jar tests were performed, and the effectiveness of coagulation tests was compared to the use of alum. In comparison to using shrimp shells powder at the prescribed dose of 25g of shrimp shell powder in milliliters of the effluent provided outstanding results (the mixture gives a slightly clear water) and alum at 20g. The effectiveness of shrimp powder and alum reduced turbidity by 37.22% and 44.49%, TSS by 25.35% and 37.98%, color 14.14% and 36.13% respectively. In contrast to alum, the shrimp shell powder increases the pH due to the presence of alkaline elements (Calcium 45.0 wt%, Magnesium 15.3 wt%, and sodium 2.0wt%) and also increased the electrical conductivity which reduced the removal efficiency by -1.30% because of the presence of high Calcium content (45.0 wt%) Calcium ions have a high charge density, making them effective at carrying electrical charge. The experimental results indicate that shrimp shell powder substantially diminishes these parameters, underscoring its potential as an environmentally friendly and sustainable substitute for conventional coagulants. The results show that shrimp shell powder works like alum and could be a good way to make water treatment technologies less harmful to the environment. Using shrimp shell powder could also help turn waste into something useful by turning a byproduct of the seafood business into something valuable. This study paves the way for more research aimed at optimizing the utilization of shrimp shell powder in extensive water treatment applications, highlighting its potential for sustainable and efficient wastewater management

In order to lower environmental effect in cloud computing, sustainability calls for effective resource management. Often ignoring server heterogeneity, traditional load balancing techniques include the Power-of- d paradigm result in poor performance and significant energy consumption. This work presents an adaptive, energy-aware load balancing method dynamically distributing workloads depending on several server parameters: bandwidth, memory, latency, CPU speed, connection count, and cost. Task allocation is guided by a probabilistic model derived from Ant Colony Optimization (ACO). Furthermore, advanced metaheuristics like both Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) as well as conventional algorithms like Round Robin and Least Connections are greatly outperformed by the suggested method according to experimental data. It reduces response times by 18%, uses resources up to 22%, and runs 15% less energy. This method aligns performance goals with environmental responsibility and presents a sustainable and effective solution for cloud infrastructure.

This paper proposes DOLDIR, a Dynamic Online Learning, based Defect Identification and Reliability framework, that helps to predict software quality and risk much more effectively. Positioned as an alternative to existing static online reliability models, DOLDIR is able to take into account that the changes in software version through detection of feature drift, optimal selection of test cases, latent topic modelling and combination of reliability scores is possible. The algorithm devised used cosine similarity to trace feature drift in consecutive software versions, Latent Dirichlet Allocation (LDA) to model the failure density and a combined reliability score which is a function of previous reliability, structural attributes of the software modules and training failure indices. Extensive experiments have been carried out using standard benchmark datasets like NASA PROMISE and SoftRel Bench. The effectiveness of the proposed framework has been tested using DOLDER and Reduckum, and experimental results in terms of RMSE, MAE and prediction accuracy proved that DOLDIR outperform the models. The classification accuracy for failure risk, reached 92.8%. DOLDIR has proved to be better than six recently published models in a cross examination

Mechanical brake systems usually suffer from major defects such as friction, noise, high wear rate, lack of accuracy, and lack of smoothness, so it has become necessary to replace them with alternative braking technologies that address these problems. One such technology is the hysteresis brake. This brake is one of the unique and effective braking technologies that uses the principle of magnetic hysteresis to generate braking force. It has features such as powerful torque, silent operation, no wear parts or mechanical friction, precise control, and no need for regular maintenance. The most influential aspect of the performance of the hysteresis braking system is the material that is used to make the brake disk. In this study, the COMSOL Multiphysics platform was applied to analyze the impact of four types of magnetic materials: cobalt, nickel, neodymium, and iron on the performance of the braking system. This was achieved by studying the equations of the governing braking system. This system proves to be great in determining the braking parameters to ensure that a good choice in terms of coil, core, and magnet specifications is made to enable good performance. These parameters were determined after numerous experiments and watching the outcome. Effects of material properties, including permeability, electrical conductivity, and thermal conductivity, on the braking force and system efficiency were studied by simulation through the use of the finite element analysis (FEM) method. It was demonstrated that neodymium is the best core material used in this brake, due to its high magnetic permeability and excellent electrical conductivity. Neodymium enhances the brake with maximum braking force superior to cobalt, nickel, and iron materials by 20, 100, 150%, respectively. The results also showed that using cobalt cores leads to improved design, as it generates the highest braking efficiency under load, with a value ranging between 90-98%.

The rapid deployment of 5G networks promises ultra-low latency and high-speed communication, yet meeting the stringent requirements of latency-sensitive applications remains a challenge. Traditional cloud computing architectures struggle to deliver real-time processing due to the physical distance between data centers and end-users. This paper addresses the problem of reducing latency in 5G networks by enhancing edge computing capabilities. Our contribution lies in developing an optimized edge computing framework that dynamically allocates resources and offloads tasks to the network edge, thereby reducing response times. While previous works focus primarily on cloud-based solutions or static edge configurations, this paper introduces a hybrid approach that combines adaptive multi-tier edge orchestration with blockchain coordination (AMTEO-BC) for enhanced security and reliability by integrating edge computing with machine learning models to predict and adapt to real-time network condition cases to achieves faster data processing, reduced network congestion, and improved overall system performance. By leveraging advanced techniques such as distributed caching, task offloading, and intelligent resource allocation, we aim to bring computation and storage closer to the network edge, thereby minimizing the distance data travel. Simulation results demonstrate a significant reduction in latency compared to existing methods, offering a practical solution for time-critical 5G applications such as autonomous vehicles and real-time analytics. The Hierarchical Distributed Edge Computing Model (HDEC) could be a game-changer in reducing latency in 5G edge computing, especially for applications that require near-instantaneous response times

This paper focuses on target detection, emphasizing its significance and exploring the implementation of Constant False Alarm Rate (CFAR) detection algorithms. Explores the performance of CA (Cell Average), GO (Greatest of), and SO (Smallest of) detectors in the existence of many jamming techniques including Barrage Jamming, Spot Jamming, Sweep Jamming, Noise AM Jamming, and Noise FM Jamming signals. Evaluate the CFAR algorithm's realization with various scenarios like Single target, Closed multi-target, Multi target, and the presence of a Clutter edge environment. The study aims to assess the behavior of detection algorithms for accurate detection and how they perform in challenging environments

Existing models for predicting students’ academic performance show limitations which result in their inability to make accurate predictions. The system achieved operational efficiency together with precise predictions, which created problems for its application in extensive online educational platforms. The system fails to predict accurately because it depends solely on basic dataset attributes, which prevents it from understanding complex user behavior patterns. The research introduces a refined Deep Neural Network (DNN) classifier to predict student performance. The research connects these study gaps with results obtained from UK Open University Learning Analytics Dataset (OULAD) which includes Social Science and STEM courses. The study introduced new features that track student behaviors through different activities which include Engagement, Total Pre-Course Activities, Average, Studying, Discussing, Examining, Working, and Total Post-Course Activities. The research team selected their most predictive features through Particle Swarm Optimization (PSO), which resulted in a feature reduction from 40 to 12. The research team optimized the hyperparameters through Grid Search, which tested learning rates between 0.0001 and 0.1 and assessed three hidden unit configurations: 64, 128, and 256. The proposed model achieved accuracies of 90% (Social Science) and 88% (STEM), which showed better performance than previous studies that used the same datasets with lower computational requirements. The results enable educators to perform personalized educational interventions which will improve student results during online courses

In this work, wire mesh metallic fibers and the nanomaterial titanium oxide TiO₂ were used to reinforce the epoxy matrix to create the hybrid composite material specimens by the hand lay-up method. The effects of adding specific fractions of TiO₂ nano material on the dynamic characteristics of wire mesh hybrid composites were investigated. The vibrational characteristics (natural frequency and damping ratio) and mechanical characteristics (tensile and bending stresses) were examined. The numerical analysis for Bending and vibration cases was performed using ANSYS ABDL 2017 R2. Experimental results showed that the addition of TiO₂ nanoparticles significantly improved tensile strength and flexural strength in epoxy-steel wire-TiO₂ composites. However, increasing the nanoparticle ratio to the maximum values led to reduced performance. The composites showed a balance between stiffness and strength when optimized at 5 wt% TiO₂ and moderate layer counts. Natural frequencies and damping characteristics were affected by fiber volume, layering, and nanoparticle content

Although epoxy compounds are often used in engineering and industrial environments, their poor resistance to wear in high-friction environments requires changes to their mechanical and surface properties. To improve wear properties, tungsten carbide nanoparticles WC at concentrations (1%, 1.5%, and 2%) were used to reinforce hybrid epoxy composites with chopped glass fibers (8%) and chopped sisal fibers (4%). A pin-on-disc devise was used to perform the dry wear test according to ASTM G99 guidelines. The best result was obtained with the addition of 1.5% WC compared to pure epoxy, with a 46.4% improvement in wear rate for the glass fiber-reinforced specimen with a friction coefficient of 0.20 and a 33.9% increase for the sisal fiber-reinforced specimen with a friction coefficient of 0.29

This study employed the friction stir approach in conjunction with a stationary shoe tool system to fabricate high-density polyethylene matrix surface composite with adding various filler material particles, such as copper, graphene, silicon carbide, and silicon dioxide. Here, L9 Taguchi method was used to design of experiment and to show the effect of FSP parameters such as tool rotational speed, traverse speeds, and amount of particles on the wear rate behavior. According to the findings, fabricated HDPE composite\'s wear resistance have improved due to the presence of hard particles in the HDPE sheet. At the optimum set of parameters (rotational speed of 679 rpm, traverse speed of 22 mm/min, and volume fraction of 20%), the wear rate of the HDPE/SiC composite decreased by 84.81 % when compared with the base HDPE. But, the (SiO2, Cu, and C) reinforced composites wear rate decreased with (76.94, 72.61, and 69.74) % respectively

With significant technological advancements, deep learning models have become powerful and essential tools for image classification and object recognition, particularly in classifying land cover images captured by satellite sensors. However, these models require a substantial amount of high-quality training data. This study introduces a novel strategy for enhancing and increasing the size of the time series of training samples. Zonal statistics are used to eliminate class contamination within the buffer area. This research aims to utilize time series data and select suitable bands to improve classification accuracy through multiple Long Short-Term Memory (LSTM) cells. By integrating data from Sentinel-1 (S1) and Sentinel-2 (S2) sensors, the study provides comprehensive insights for the research area of Karbala City, Iraq. The results demonstrated that the proposed system achieved high accuracy compared to prior studies, the overall accuracy reaching 98.66% and 96.75% for the Kappa coefficient by using zonal statistics

The conventional dental amalgam has some multifarious drawbacks which lead to increasing demand for improving them to overcome the present limitations. The present study aimed to increase the dimensional stability and improve the mechanical properties by introduction of hybrid composite. The improvement is carried out by the addition of zirconia powder in both nano and micron particle size. Nano zirconia increases the structural cohesion of the amalgam due to the high surface area, which improves the bond between the particles and the matrix. While the micron sized zirconia acts as internal reinforcements that prevent the growth and propagation of microcracks, improving long-term compressive strength. The addition resulted in an improvement in compression strength in one hour by 48% and by 10 % for 24 hours. Creep resistance improved by 44% and dimensional stability improved by 96%. In overall, the hybrid modification of nano and micro ZrO₂ provides a synergistic effect, enhancing the durability and performance of dental amalgam. This modification can be a promising approach to overcoming the conventional limitations of dental amalgam, leading to more reliable and long-lasting restorative fillings

The construction industry plays a crucial role in driving economic growth, particularly in developing nations. At the same time, there are many obstacles that are encountered within the construction industry including project budget over-runs, project delays and quality-related concerns. Although Value Engineering (VE) can provide numerous benefits to the construction industry, the use of VE within the construction industry of Jordan has been minimal thus far. This research aims to identify the barriers and drivers that influence VE adoption in Jordan. To achieve this goal, a survey was sent to construction professionals in different areas of construction. The findings indicate that there is a moderate level of awareness regarding VE among construction professionals and that the barriers to VE adoption can be subdivided into four categories: Stakeholder Issues; Training Issues; Client Issues; and Government Issues. The main barriers have been identified as: Lack of awareness of VE; a lack of supportive legislation; and a lack of incentive from government to the construction industry. However, Education regarding VE in higher education institutions, Economic conditions of the country, Demand from clients and financial incentives for participants have been identified as the most significant drivers of VE adoption. The research provides practical guidance for improving VE implementation by providing educational programs; developing VE policy; and providing stakeholder involvement in the process of VE implementation

The research work is intended to develop sustainable cement mortar by means of partial substitution of cement with rice husk ash (RHA) at 4%, 8%, 12%, 16%, and 20% by weight and replacement of mixing water with solvent, treated expanded polystyrene (EPS) at 3%, 6%, 9%, and 12% by volume. The mortar was prepared by using 1:3 cement, to, sand and 0.5 water, to, cement ratios. The mix with 8% RHA and 6% EPS produced the highest mechanical performance at the age of 28 days with compressive strength and flexural strength of 34.72 MPa and 2.34 MPa, respective 22.16% and 5.88% increments over the control mix. At the highest substitution, drying shrinkage was reduced from 0.008% to 0.006%, and thermal conductivity was lowered by 24%, from 0.927 W/mK to 0.704 W/mK. Water absorption rose slightly by 8.2%. Although higher replacement levels reduced strength, the combination of 20% RHA and 12% EPS produced the best insulation and shrinkage performance. The results demonstrate that combining RHA and treated EPS improves durability and thermal efficiency, making the mortar suitable for non-load-bearing applications

This study aims to develop a formula to estimate rainfall intensity depending on Intensity Duration Frequency (IDF) curves. The rainfall IDF relationship is one of the tools used in water resources engineering to classify rainfall intensity for a given return period. Daily rainfall data for 20 years from (2003 to 2022) from the Iraqi Metrological Organization and Seismology was used to generate IDF curves for Hilla City. Three methods, namely, Gumbel, Log Normal, and Log Pearson III, were used to derive IDF curves using MATLAB software for six durations (1, 2, 3, 4, 5, and 6 hr) and six return periods (2, 5, 10, 25, 50, and 100 years). Then, empirical equations were developed to estimate rainfall intensity for the various rainfall durations and frequency periods. The results showed an excellent correlation coefficient (R2) for these empirical equations with an average (94.4 %) the maximum rainfall intensity was 48.6 mm/hr during a 1 hr return period and a 100-year return period. However, the 2.7 mm/hr was the minimum rainfall intensity that occurred during the 6-hour duration period and 2-year return period

All age groups are affected by oral diseases that are common worldwide. The dentist relies on Dental radiographs were used to explore the characteristics of oral diseases. Dental X-ray image segmentation and analysis pose significant challenges compared with other medical images. This secondary challenge makes dental radiography challenging. Because dental images are captured at a lower resolution, the segmentation of a tooth and its related complications can be unreliable because they are not resolvable. Dental X-ray Image Segmentation (DXIS) is one of the most fundamental and important steps in obtaining relevant information concerning oral diseases. In dentistry, DXIS is an important step in obtaining many different pathologies of gingival tissues. The next proposed methodology uses Vision Transformers (ViTs) to identify dental caries from dental X-ray images. In contrast to traditional CNN-based approaches, this approach uses attention mechanisms to dissect each patch of the image in more detail and yields more accurate results with earlier detection of caries. The results showed that ViTs are better than CNN, the proposed performance accuracy reached 95% compared to the accuracy of the convolutional neural network, which reached 86%

Early detection of breast cancer significantly improves patient outcomes through timely and accurate diagnosis. This study proposes a hybrid image reconstruction method combining the Maximum Likelihood Expectation Maximization (MLEM) algorithm with the Distance Driven Method (DDM) for stationary digital breast tomosynthesis, aimed at producing high-resolution three-dimensional (3D) breast images. The method was initially validated using simulated projection data of a digital breast phantom modeled with two spheres of varying radii and attenuation coefficients. Fifteen projection images were generated over a 15° angular range (from +7° to –7° with 1° increments) to replicate realistic tomosynthesis acquisition. The focus plane was set 45 mm above the detector with a pixel size of 0.14 mm. Reconstruction results demonstrated enhanced image quality, with spatial resolution quantitatively evaluated using the Line Spread Function (LSF) across the smaller sphere. Compared to the Ray Driven Method (RDM), the MLEM-DDM approach provided better contrast, sharper edges, and fewer artifacts. Subsequently, the method was applied to experimental data from a real breast phantom. Volumetric reconstructions revealed detailed tissue structures across multiple planes, with spatial resolution assessed by line profiling through two aligned calcifications in the focus plane. The MLEM-DDM method preserved the shape and sharpness of these calcifications more effectively than MLEM-RDM. Overall, the proposed MLEM-DDM framework enhances spatial resolution and visualization quality in stationary digital breast tomosynthesis, demonstrating strong potential for improved early breast cancer detection and diagnostic accuracy

This is because the corrosion of embedded steel reinforcements in concrete because of the ingress of chloride ions is one of the most important durability factors in the construction sector. This experiment was used to test the efficacy of Coconut Shell (CSh) as a partial substitute to coarse aggregate and Rice Husk Ash (RHA) as a partial substitute to cement to minimize chloride ion induced corrosion in reinforced concrete. The research was a response to the necessity to find long-term building material and discuss its usefulness in the sustainable construction, by substituting the 1:2:4 mix ratio CSh with RHA by weight at 0%, 5%, 10%, 15% and 20%. Prism samples 100 x 100 x 500 mm of reinforced concrete were placed in a solution of 5.844g/L NaCl to replicate a chloride rich environment and 150 mm cube samples were left to cure under normal laboratory conditions. The ingress of chloride ions was measured using the gravimetric analysis of the weight loss of the steel reinforcement, compressive strengths were measured at 7, 14, and 28 days. The findings indicated that compressive strength reduced progressively with the replacement rates with the 10 per cent mix providing a good compromise of 16.09 MPa at 28 days. The corrosion rates reduced considerably to 18.52 mm/year in the control mix and 6.17 mm/year in 10 percent replacement, which demonstrates the beneficial effect of RHA and CSh on the corrosion resistance of concrete. Nonetheless, corrosion rates were higher in 15-percent and 20-percent levels, meaning that the matrix integrity was reduced with the increase in the replacements

Vehicle identification processes through traffic management systems depend heavily on automatic license plate recognition capabilities. The presented system uses advanced methods from deep learning and machine learning to recognize Iraqi vehicle license plates. The system uses the Tkinter-developed graphical user interface alongside YOLO for detection and EasyOCR for text extraction together with the SVM and KNN classifiers for plate classification. The system successfully divides license plates into important parts which enables the identification of governmental entities alongside vehicle class types. The evaluation metrics reveal outstanding classification performance because both SVM and KNN models delivered 100% accuracy together with 100% precision, recall and F1-score. The proposed approach performs well when compared to existing studies because its analysis demonstrates both strength and effectiveness. The obtained findings demonstrate that the system can efficiently serve real-world needs in vehicle identification and law enforcement and intelligent transportation systems

Given the increasing reliance on cloud services, a crucial area for future research is to investigate the security and privacy implications associated with the growing concentration of data and critical infrastructure within a few dominant cloud providers. This research would provide valuable insights into the evolving landscape of cloud computing and help inform the development of more secure, resilient, and equitable cloud ecosystems

Internet of Things (IoT) is rapidly expanding in smart city usage in applications such as smart traffic systems, power management, ecological monitoring, security in cities, and some more vital services. Quality of connectivity and response time have significant effects on how well these systems function. It is thus the crucial factor with respect to their functioning performance. The emerging requirement is then for IoT technologies with enhanced connectivity efficiency and data-loss minimization capabilities. To reduce Packet Error Rate (PER) higher Quality of Service (QoS) levels are used but at the expanse of imposing more load on the broker which should serve multiple publishers and subscribers. In this paper, it is proposed to improve the PER at a target subscriber by the assistance of a peer neighbor subscriber. The conducted tests show the capability of the proposed Peer-Assistance (PA) scheme to enhance PER up to 65% with respect to conventional QoS schemes tested under the same conditions. Although, this enhancement is at the expense of an increase in system latency, it provides a useful trade-off enabling design customization to suit the requirements of the specific application. Moreover, the proposed PA scheme introduces a mechanism that redistributes the retransmission load from the broker to neighboring subscribers, thereby enhancing the broker's availability to serve its clients

Methane (CH₄) is a strong greenhouse gas and dangerous in industrial settings, so highly sensitive and reliable sensors are needed to detect it at low concentrations. In this study, ZnO-based thick-film gas sensors were fabricated using a screen-printing technique on Kapton substrates and evaluated for methane sensing at room temperature. The device consists of an interdigitated electrode (IDE) and a ZnO sensing layer, both deposited onto the Kapton substrate via screen printing. The prepared sensors were then tested with methane at 6700 ppm and 7500 ppm concentrations. Results show that both ZnO sensors have good linearity in I-V measurements meaning that they exhibit ohmic behavior. ZnO2 sensitivity was higher than ZnO1 probably due to some microstructural differences. Resistance changes during gas exposure were used as the sensor response

Two major issues associated with the production of lightweight concrete are high-water absorption and low unit-weight. To minimize the absorption of lightweight aggregate LWA (pumice) with maintaining low unit-weight, pumice particles were treated using different coating materials. The research evaluates the effect of coating materials and technique on enhancing the properties of pumice based on absorbency and unit-weight. The research included 36 samples were prepared and coated in different materials, quantity, layers, and initial moisture content. The samples were coated with either cement paste (CP), CP containing silica fume (SF), or CP containing polymer, and applied in either one or two layers; the samples were either oven-dried or surface-saturated state. The results showed that the treatment of LWA significantly reduces the absorption of pumice. The pumice coated with CP containing SF exhibited the highest reduction in absorption by up to 65% when applied in two layers and by 57% when applied in a single layer under OD conditions. The results also presented that the smaller quantity of coating materials, the greatest effect on the pumice properties. Consequently, pumice coated with 500 gm showed the greatest reduction (9%) in absorption compared to pumice coated with 750 and 1000 gm, especially at OD conditions

The modeling and simulation of single phase five level t-type multilevel is presented in the paper. Solar photovoltaic inverter tailored inverter. The suggested topology is intended to improve. conversion efficiency and power quality and minimized total harmonic distortion. The inverters modeled in grid-connected and islanded operating conditions with PSIM, showing that it is flexible and can perform in different load and environmental conditions. The switching strategy and output waveform shaping is analyzed in detail. emphasizing the benefits of a lower number of switches and a better pulse width. modulation techniques. Sinusoidal output voltage generation is provided by the control system with. strong voltage regulation of both operating modes. The simulation findings support the use of the five-level. T-MLI has a stable operation, good power transfer and low harmonics hence making it. a promising technology to next-generation PV grid integration and standalone energy systems

Given the importance of speed control in switched-reluctance motor (SRM) drives for industrial applications, this study proposes a super-twisting sliding-mode controller (STSMC) implemented in MATLAB/Simulink to control the speed of a 6/4 SRM. Another control strategy based on speed control is implemented: conventional sliding-mode control, an optimized cascaded PID, and a traditional PID. Simulation results indicate that STSMC outperforms all other controllers, reducing speed error by 82.1% (at full load) compared to SMc and 98.2% relative to cascaded PID and PID. Regarding settling time, STSMC responds 23.1% faster than SMC (at 50 nm load) and 95% faster than Cascaded PID (at no load). Torque ripple is minimized by 47.7% and 86.7% relative to SMC and PID-based controllers, respectively. Moreover, STSMC significantly mitigates chattering in SMC, resulting in smoother control signals. These results confirm STSMC as an accurate, robust, and efficient solution for high-performance applications in SRM drives.

This study demonstrated the effectiveness of laboratory-prepared nano-silica derived from Iraqi natural sand using sol-gel technology in providing effective corrosion protection. The inhibitor was evaluated on low-carbon steel surfaces using dynamic polarization, Tafel plots, polarization curves, and weight loss. Concentrations of 400-1000 ppm and temperatures of 25-55°C were used for the study. The results indicated that the synthesized inhibitor was highly effective in reducing corrosion by forming a thin film on the surface of A106 Grade B steel. while the weight loss ranged from 95.8-76.4% in 1M-HCl medium. Characterization methods such as FTIR, SEM, XRD, and AFM were used to study the preparation process and evolution of the protective layer. The results indicated the stability of the protective activity of the inhibitor. The performance was described as economical, sustainable, scalable, highly efficient, and low toxicity

This paper proposes a control method for battery energy storage systems (BESS) connected to a 220 kV transmission network to enhance grid stability when renewable energy sources (RES), such as wind and solar, are integrated. This problem leads to significant issues developing for grid stability, including frequency instability, transmission line overloads, and grid failures. The research mainly focuses on the 220 kV transmission line between the 500 kV Pleiku and Dak Nong substations in Vietnam's Tay Nguyen area. Using PSS®E and DigSILENT Power Factory modeling tools, the findings reveal that combining three BESS units, each with a rated power capacity of 200 MW and an energy storage capacity of 673 MWh, effectively reduces grid congestion and stabilizes frequency fluctuations. Simulation findings estimate a decrease of 2,057 MWh in surplus power production. in case with the BESS connection, the minimum system frequency during rapid RES production increases to 49.17 Hz, up from 48.91 Hz without BESS, ensuring that power flows remain within acceptable operating limits. These simulation results illustrate the proposed method BESS, can enhance grid performance, decrease faults, preserve frequency stability during RES fluctuation, and facilitate Vietnam's clean energy transition, notably in the Tay Nguyen area. This proposed method also meets the frequency stability standards outlined in Circular No. 25/2016/TT-BCT issued by the Vietnamese Ministry of Industry and Trade

This paper presents the implementation of a distance protection relay using a programmable logic controller (PLC). Power systems widely use distance relays to detect and isolate faults based on the impedance between the relay and the fault location. By leveraging a PLC, the design benefits from flexibility, real-time processing, and integration with a monitoring system. The paper is mostly about using voltage and current inputs to figure out impedance, finding fault zones, and putting protection logic into place using the Function Block Diagram (FBD) in PLC programming environments. The proposed system is reliable, cost-effective, and easy to modify for various protection schemes

The research reviews the behavior of pile-raft foundations and the impact of pile driving in clayey soil, focusing on the resulting soil movement, pore water pressures, and their effects on slope stability, especially in the city of Nasiriyah, southern Iraq, where clayey soil prevails. Previous studies reviewed include load-bearing capacity using experimental and numerical models to determine the maximum load and the relationship between load and settlement, with observations on changes in pore pressures after pile driving , pile-raft foundations through numerical and experimental research studying the distribution of loads between the piles and the raft and the impact of pile configuration, raft thickness, and soil type on performance . Local studies have shown that the soil in nasiriyah is predominantly composed of high or low plasticity clay (CL–CH), with a small percentage of sand, and there is a lack of realistic studies on the local soil. The main findings included advancement in modeling methods, but a deficiency in real field studies of clayey soil in nasiriyah, absence of standardized criteria for assessing the risks of pile driving in clay and need for comprehensive models that consider the interaction between the soil and the structure during execution

Modernly speaking, reviewing large numbers of Magnetic Resonance Imaging (MRI) images and manually discovering a brain tumor by a person is a slow and inaccurate process. It may have effects on the correct medical treatment of the patient. Additionally, it could be a slow and laborious task due to the numerous amounts of image datasets involved. Because brain tumors appear similarly to healthy tissue, tumor region segmentation can be difficult. Therefore, there is a need for a high-quality automatic tumor detection system. CNNs are one type of deep learning technique which are often used for image recognition and image classification tasks currently. CNNs are also commonly used to identify Brain Tumors. In our research, we proposed a CNN model for the purpose of classifying images from MRI scans of brains into two classes (Normal or Tumor). Our proposed model was able to achieve a recall of 97.51%, accuracy of 97.889%, F1-score of 97.84%, precision of 98.18%, specificity of 97.62% and an AUC of 97.57%. Our CNN model will help doctors to find brain tumors in MRI images with great efficiency , therefore, greatly increasing the amount of time saved when treating patients

Vehicular ad-hoc networks (VANETs) have the potential to revolutionize the transportation industry by enabling intelligent transportation systems and improving road safety. However, VANETs are vulnerable to various security threats, such as attacks on communication links and malicious nodes. In this paper, we propose a fully homomorphic encryption-based trust-aware clustering-based routing (FHE-TACBR) protocol for secure and reliable VANET communications. FHE-TACBR uses clustering-based routing to group vehicles based on their geographic proximity and assigns a cluster head to act as a communication hub. Trust metrics are used to evaluate the reliability of vehicles in the network based on their past behavior, current behavior, and willingness to cooperate. The choice of FHE over conventional encryption schemes (e.g., AES, ECC) is motivated by its ability to enable computations on encrypted data without decryption, thereby eliminating potential attack windows. By lowering the possibility of compromised data this feature improves confidentiality while preserving the integrity of intermediate computations. It also slightly increases end-to-end latency because of encryption overhead but it still achieves higher throughput through fewer retransmissions and secure routing. To further improve communication security and dependability FHE-TACBR also uses message authentication intrusion detection and quick response techniques. According to simulation results FHE-TACBR provides a much higher security level while outperforming baseline protocols in PDR end-to-end delay and throughput. Furthermore, it is feasible for real-time vehicular communication because its time-complexity is competitive with cutting-edge VANET routing protocols.

Breast cancer continues to be one of the world’s leading causes of death for women and early detection is essential to increasing survival rates and simplifying treatment. Even though they work well traditional diagnostic methods frequently require a lot of time and resources and are prone to human interpretation errors. Although training deep neural networks from scratch requires large-scale annotated datasets which are frequently unavailable in medical domains recent developments in deep learning have demonstrated great promise in medical image analysis. This work suggests a Transfer Learning-Driven Deep Neural Network Framework for early and precise breast cancer detection in order to overcome this difficulty. The framework makes use of pre-trained convolutional neural network (CNN) architectures that have been refined on publicly accessible mammography and histopathological datasets including VGG19 ResNet and DenseNet. To improve model generalization and lessen overfitting extensive preprocessing procedures like image normalization noise reduction and data augmentation are used. The suggested method outperforms traditional CNNs trained from scratch with accuracy exceeding 97% precision and recall above 96% and an F1-score of 0. 97 according to experimental evaluations on benchmark datasets like BreakHis and CBIS-DDSM. The findings demonstrate how transfer learning can enable reliable data-efficient breast cancer detection systems opening the door for clinical applications with limited resources and real-time capabilities

Given its direct effects on human health productivity at work and general well-being stress detection has emerged as a crucial area of affective computing research. Self-reports and physiological sensors which are either subjective invasive or expensive to implement at scale are frequently used in traditional stress assessment methods. This study suggests an Enhanced VGG-Based Approach for Facial Expression-Driven Stress Detection (EVSD-Net) which uses subtle facial cues to accurately identify stress levels in order to overcome these limitations. By adding lightweight convolutional blocks to eliminate unnecessary parameters a hybrid channel–spatial attention mechanism to highlight stress-relevant areas (like wrinkles on the forehead tight lips and strained eyes) and a fine-tuned transfer learning strategy initialized with pretrained ImageNet weights the suggested framework enhances the standard VGG16 architecture. Both binary (stressed and non-stressed) and Softmax classifiers are supported by a hybrid classification layer that integrates fully connected (FC) layers. non-stressed) and multi-class stress classification (high medium and low). Preprocessing and augmentation of facial datasets improved feature extraction using the modified VGG network dimensionality reduction and regularization for better generalization and Softmax classification comprise the four main stages of the methodology. Accuracy Precision Recall F1-score and AUC metrics were used to evaluate the experimental results using four benchmark facial expression and stress-related datasets. The results show that EVSD-Net outperforms baseline models like standard VGG16 ResNet18 and MobileNetV3 with an overall accuracy of 97.84%. The superiority of the suggested model is further confirmed by a comparison with previous research which shows significant improvements in detection accuracy and robustness under various illumination pose and demographic circumstances

The autonomous navigation of robots could be carried out by path planning that enables these robots to move efficiently and avoid obstacles even in complex areas. This work deployed metrics such as task completion time and path length to enhance the performance of path planning. Surpassing the limitations in the traditional approaches, this study improved the decision making in hard environments and ensured safe interaction between human and robots. In addition, this paper used cloud computing to increase the speed of the tasks and work easily in real time with a chance of scalability in the future. The main challenge in the path planning framework is to optimize both time and distance in single agent systems. Therefore, this challenge could be solved by presenting a hybrid path planning method that combined tow algorithms that are Particle Swarm Optimization (PSO) and Elman Neural Network (ENN). Also, this work adopted Message Queuing Telemetry Transport (MQTT) as a lightweight communication protocol to efficient exchange data between robot and cloud. The results showed that the proposed method achieved shortest completion times 5.42 seconds for case study 1, 6.82 seconds for case study 2 and a shortest path of 592.9 centimeter for case study 3. This research contributed to more efficient and reliable robotic path planning that offering a robust solution for navigating complex and dynamic environments

The rapid urbanization of the modern cites, alongside growing concerns about urban heat islands and climate change, has presented increasing challenges to cities, particularly regarding their resilience to these changes and ensuring their environmental sustainability. Urban configuration plays a pivotal in influencing the climate conditions, and this influence differs according to environmental conditions of the region. Hence the importance of studying and analyzing each urban configuration separately based on its unique environmental characteristics. Based on the foregoing, this research paper aims to address the following question: How do urban configuration contribute to affecting the level of thermal comfort within urban spaces? The research adopts an analytical approach and employing the simulation Ecotect software to investigates the degree to which urban form affects thermal comfort in outdoor spaces, by an analytical study conducted on two different urban patterns in Baghdad, Iraq: the gridiron pattern and the organic pattern. The study results showed that The organic planning prevalent in ancient cities, achieved the best thermal performance in hot, dry climates. Where, the results showed that the Mean Radiant Temperature (MRT) was decreased by more than 15 degrees in comparison to the gridiron pattern. In conclusion, the results confirm that adopting an appropriate urban pattern significantly contributes to enhancing external thermal comfort and mitigating the effects of climate change