KUFA JOURNAL OF ENGINEERING

Wire Electrical Discharge Machining (WEDM) process is one of the most important and effective technologies in modern manufacturing due to its versatility in producing complex-shaped components with high precision. It can be used to machine various hard materials and non-ferrous metals regardless of their hardness. In general, surface integrity includes several tests such as surface roughness tests, recast layer measurement, microhardness testing, residual stress measurement, microstructural analysis, chemical and metallurgical tests and fatigue and wear testing. The present study focused on evaluating surface roughness and recast layer of stainless steel 1610 which has many applications like tubes industry and tools, also it has been noticed that a little studies focused on it in the survey. The study was conducted by selecting three variable parameters: servo feed (400, 500,600 m/min), pulse-on time (24,28,32 µs) and pulse-off time (36,40,44 µs) which have the greatest role on the process, while keeping the other machine parameters constant. The obtained data were analyzed using (ANOVA) which was satisfied for predicting and explain the relation between the input parameters and the process characteristics, and it was found that a smoother product surface can be achieved by reducing the values of servo feed (SF) and pulse on time (TON) while increasing the pulse off time (TOFF). As for the recast layer, it increased proportionally with SF and TON and began to stabilize at higher levels of TOFF

Global demand for sustainable construction materials and concerns about environmental pollution from by-products of manufacturing industries have intensified research for viable alternatives to aggregates in concrete production. This research investigated steel slag aggregate (ssa) as a partial replacement for coarse aggregates. Samples of 150 mm concrete cubes and 100 x 100 x 500 mm prisms were prepared at a 1:2:4 mix ratio and a water-cement ratio of 0.5, with ssa replacing coarse aggregates at 0%, 15%, 30%, 45%, and 60% by weight for 7, 14, and 28 days compressive and flexural strength tests. In addition, a validated random forest (rf) and multiple linear regression (mlr) algorithm were developed to predict the compressive strength of samples. The analysis of ssa for x-ray fluorescence (xrf) revealed a high 34.125% silicon dioxide (sio₂) composition and a minimum of 0.196% for strontium oxide (sro), a specific gravity of 3.07, 1680 kg/m³ bulk density, 21.28% and 8.37% aggregate crushing value and impact value were evaluated, respectively. The 15%, 30%, 45%, and 60% ssa replacement samples exhibited improved strengths of 15.63 n/mm², 17.07 n/mm², 18.30 n/mm², and 19.10 n/mm², while the flexural strengths increased up to 45% ssa (4.45 n/mm²) before declining at 60% (3.85 n/mm²). The mean absolute error (mae), mean square error (mse) and a coefficient of determination (r²) for rf were 1.20, 2.07 and 0.85, while mlr recorded 1.46, 3.77 and 0.72, respectively. The xrf suggests an improved aggregate bonding potential, and the physical characterisation revealed that ssa was within the acceptable limits for structural applications. The compressive and flexural strengths increased with ssa content up to 45%, after which strength properties declined, offering optimal mechanical performance. The mlr achieved a robust prediction accuracy with an r² value of 0.85. In conclusion, the research supports the potential of industrial by-products in promoting greener and more cost-effective construction practices

Shia Muslims believe the holy city of Karbala (Iraq) to be the location of the shrines of Imam Hussein and Imam Hussein's brother Abbas ibn Ali at-Turayhi, making it the 4th holiest responsory in the Muslim world. And this also, we don't just become related here to the places ideologically, but rather through the cultural squares one or the other mark this as a landmark and even social bookmarking . This paper aims to examine the correlation between psychological attachment to Ahl al-Bayt (a.s.) and concerns in maintaining sustainability of religious architecture, i.e., religious buildings in Karbala due to environmental stressors as well as urban sprawl. The study is based on a multi-level method, we associated analyses of survey data from the usual community study and field architectural case studies to understand the influence that religious sentiment and visitor density have over preservation of integrity in these structures. The findings demonstrate that community engagement (altiyaw, kelenya and altee) is essential to the maintenance of the shrines but are frustrated by problems with material obsolescence, environment decay and nonexistence of technical monitoring. As such, the study suggest an integrated model of spiritual motivation and engineering measures to enhance earthquake resistance. through a cultural-based repair method using green materials. Such a model of integration could offer a way of safeguarding religious buildings and thus ensuring a role for the buildings both in physical and symbolic terms

This article explores photocatalytic degrading that occurs in Amoxicillin (AMX) inside the aqueous medium using a BiVO4 MnZnO heterojunction catalyst in UV and visible. The catalyst, which was prepared by hydrothermal synthesis has shown an impressively higher photocatalytic efficiency in comparison to traditional catalysts. The improvement can be attributed to the synergistic effects that occurs between BiVO4 and MnZnO that allows for efficient charge separation as well as the reactive species, such as radicals hydroxyl. The system has demonstrated significant removal of AMX under both conditions of irradiation and has proven its effectiveness in practical applications in water treatment. Furthermore, (HPLC-MS) revealed the progressive breakdown of AMX into lower-molecular-weight by-products through oxidative degradation pathways. These findings support the use of BiVO4-MnZnO as a promising and sustainable photocatalyst for the remediation of antibiotic-contaminated waters

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 simulation and experimental findings are presented to validate the proposed distance protection relay system's effectiveness. The proposed system is reliable, cost-effective, and easy to modify for various protection schemes

Landslides are one of the biggest natural dangers around the world. They can cause serious damage to properties and infrastructure and even threaten people's lives. Therefore, researchers and engineers throughout the world have come up with many methodologies to evaluate slopes stability and to find ways to reduce these dangerous risks. Using both analytical and numerical methods, this study provides an analysis of landslide stability in Kherrata, located in the Wilaya of Bejaia, northeastern Algeria. Specifically, in this two-dimensional analysis, the Limit Equilibrium (LEM) methods were compared with the Finite Element Method (FEM) calculations performed in the PLAXIS 2D V8.2 software to evaluate the safety Factor (FoS). The results clearly show that the Factor of Safety values are significantly less than unity (FoS < 1.0), both the LEM and FEM analyses yield similar findings, confirming that the slope is in an unstable state

Concentrated solar power (csp) plants offer a viable solution to the electricity issue and the environmental repercussions of traditional energy generating. This paper studies the technical and economic assessment of two concentrated solar power configurations parabolic trough collector systems and a solar power tower utilizing the system advisor model (sam) created by national renewable energy laboratory (nrel). The selected location is zakho city, which experiences an annual average direct normal irradiation (dni) surpassing 6.61 kwh/m²/day, rendering it very suitable for csp implementation. The analysis evaluates a 99.9 mwₑ parabolic trough plant with a 101.66 mwₑ solar power tower, focusing on monthly and annual energy outputs, system efficiencies, and levelized cost of energy (lcoe). The findings demonstrate that the parabolic trough attains a levelized cost of energy (lcoe) of 12.75 cents per kilowatt-hour and a capacity factor (cf) of 38.1%, but the solar power tower exhibits superior economic performance at 9.33 cents per kilowatt-hour and capacity factor 58.7%. Both technologies exhibit robust energy outputs and the capacity to substantially enhance the local energy composition. The findings indicate that csp projects in high-dni areas such as zakho can improve energy security, diminish greenhouse gas emissions, and promote sustainable development. Recommendations are offered for enhancing plant design, increasing cost efficiency, and incorporating csp into iraq's long-term renewable energy strategy.

This study investigates the influence of liquid metal ga additions (1-2.5 wt%) on the structural, physical, and biological properties of ti metal, which are fabricated by the powder metallurgy method. The alloys were manufactured by sintering at 1250 °c to improve the metallic implants for biotribological applications. Xrd, sem and edx analyses were used to identify the predominant compounds and phases before and after gallium addition. The structural examination by xrd and sem showed an improvement in the crystal structure distribution and a phase transformation, with a significantly smaller grain size due to the increased gallium content. A change in the proportions of the predominant phases was observed, with the appearance of intermetallic compounds tiga₃ and ti₃ga. The density, porosity, wettability, and young's modulus were also tested. The results indicate a decrease in the density, wettability, and young's modulus of the samples at different concentrations and an increase in the porosity of the samples with the lowest density occurring at 1% ga, attributed to the formation of a disordered solid solution with decreased atomic packing efficiency, which reduces the density overall. This resulting in increased porosity due to incomplete densification during sintering. In addition, ga reacts with ti at higher ga concentrations to form an intermetallic compound tiga₃ and ti₃ga, through metal bonding. This intermetallic compound forms a chemically compatible interface, thereby reducing the contact angle and enhancing wettability. The low young's modulus of the samples is due to the very low young's modulus of ga, about (9.8 gpa), and to distortion of the crystal lattice, increased atomic void volume, and the formation of weakly bonded intermetallic compounds. Finally, cytotoxicity and biocompatibility tests were performed on the samples. The results showed low cytotoxicity to mcf-10 at concentrations of up to 50 µg/ml for all samples, and this gradually increased with higher concentrations up to 100 µg/ml, due to the potential release of gallium ions. The increase in gallium led to increased cytotoxicity, but it remained within acceptable limits. This makes all samples promising candidates for biomaterials applications

This study evaluated the quality of surface drinking water using ten hydro-chemical variables at three stations on the shatt al arab river. A water quality index (wqi) was established, with values ranging from poor to unsuitable for the three stations. Statistical data revealed that all parameters at st1 and st3 were outside the standard limits. Furthermore, na+ > ca+2 > mg+2 > k+ were found for all three stations. Pumping specific amounts of water from the al-badaa canal for specific periods of time improved water quality, but it remained unfit for human consumption. The variables primarily responsible for spatial variation were identified using a discriminant analysis (da) approach. Sulfate, magnesium, calcium, and alkalinity were found to be the most significant characteristics that characterized different locations and contributed to spatial variations in surface water quality. The results of this study provide decision makers with useful information to address the important issue of water quality management and protection

Human Faces as a biometric feature are applied in multiple applications where both public and private organizations have preserved facial images for membership cards, passports, or personal identifications. They have been used as referential databases in investigations and match facial images of victims, witnesses, or offenders. Moreover, the widespread usages of smartphones and digital cameras have made it simple to share created facial images using social networks. Thus, Human Facial Recognitions (HFRs) have been an exciting and quickly expanding field of study. Real time applications include HFRs for identifications, access controls, forensics, and human-computer interactions. Though studies have been proposed for HFRs, there are areas that are wanting in implementations. Hence, this work attempts to fill up these gaps in HFRs with its suggested AI Based Recognitions of Faces (AIBRF). The schema uses Deep Learning (DL) techniques for identifying faces. The schema is trained using Yale Face dataset and achieves 99% accuracy in HRFs

This article provides a full numerical analysis of blood flow dynamics in a major vascular segment from the lateral surface of the cerebral peduncle to the posterior edge of the midbrain (P2P), which possessed an aneurysm that was identified at a point of bifurcation. In order to realistically model the non-Newtonian behavior of blood, with particular emphasis on its shear-thinning nature, the modified Krieger model 5 (MKM5) was utilized with physiologically realistic parameters. As it is not possible to have inflow data directly for the P2P segment, inlet flow waveform was reconstituted by employing the closest available pulsatile profile of the basilar artery.The results indicate that standard Carreau and basic Newtonian models are unable to adequately model the hemodynamic response in this complex geometry, but an approximation by a shear-rate-dependent Newtonian model, scaled to the local environment of shear rates, can achieve the accuracy of the more advanced MKM5 model. This result provides a possible simplification to computational fluid dynamics (CFD) simulations in similar research, which might reduce computer expenses without compromising predictive accuracy. These results enable patient-specific modeling strategies in cerebrovascular hemodynamics with specific emphasis on regions of high anatomical complexity and pathologic relevance

Prosthetic feet are frequently manufactured using composite laminates to balance strength, weight, and comfort. Since these structures undergo varying stresses during gait, optimizing their mechanical properties is critical. This study investigated the influence of nanomaterial reinforcement on stress distribution in composite foot prostheses composed of carbon fiber, Kevlar, and Perlon. A mathematical model was employed to determine the effective modulus of elasticity, while finite element analysis was used to evaluate Von Mises stress, strain, and deformation. The suggested foot was analyzed in the case of heel strike. Results showed that incorporating SiO₂ nanoparticles at a 2.5% weight fraction improved the proposed layer sequence. (1Perlon+2Carbon+1Kevlar+4Perlon+1Kevlar+2Carbon+1Perlon) achieved the most significant improvements, reducing stress by 50.36%, strainby61.64%, and deformation by 35.50%. Al₂O₃ nanoparticles provided slightly lower but comparable performance. Overall, nano-reinforced composites demonstrate potential for developing lighter, stronger prosthetic feet that improve durability and patient comfort

The experimental investigation of the effect of adding alumina oxides Al2O3 nanoparticles on mechanical properties of unsaturated polyester resin has been studied. The Mechanical properties were evaluated by tensile, hardness and impact test. The study involved adding alumina oxide nanoparticles with size 45nm in different weight ratio which are 0,3,5,7, and 9%. The specimens were manufactured using hand-layup technique. The results showed that the increasing of AL₂O₃ into the polyester matrix can improve the tensile strength, elastic modulus, hardness, and impact strength of polyester to 18.91, 10.44, 1.21, and 221.53%, respectively, when the AL₂O₃ weight fraction reached 5%. However, the mechanical properties decreased when the Al2O3 nanoparticles weight fraction reached 7% and 9% . The Optical microscopes observations revealed that the nanoparticles were more homogeneously dispersed at 5%.

The use of artificial intelligence has entered into many fields, especially difficult and complex ones. It has also entered into the field of construction projects in countries around the world in several matters, but this has not been done in construction projects in Iraq. These projects are in dire need of this use because of the problems and difficulties that they have been suffering from during the various stages of the construction project life, for several reasons. The benefits of using artificial intelligence in construction projects have been demonstrated through previous research. The problems that construction projects in Iraq suffer from in the various stages of the project have been explained. Although artificial intelligence will be useful in solving these problems, there are reasons that hinder its use. To find out these reasons, it has been organizing a questionnaire for a number of experts in this field to explain these reasons. Seven reasons have been identified with experts that hinder the use of artificial intelligence. Through the analysis of the questionnaire it was found that the most important of which was the lack of awareness and lack of knowledge of the benefits and advantages of this use. It was concluded that there are five basic points for these seven

The current research aims to develop the mechanical properties by treating the fiberglass with nano materials. The methodology that was adopted can be summarized as prepare the (tensile, impact, and vibration) molds with proper ASTM dimensions by CNC machine, after that preparation of nanoparticle suspension that consists of H2O and (1wt%, 3wt%, and 5wt% SiO2) respectively. Firstly, magnetic stirring was used for mixing H2O and Silica powder for 10 minutes. Secondly, the ultrasonic was used to disperse nanoparticles in water for 15 minutes. The mechanism that was adopted in treating fiberglass was immersion the pieces of fiberglass in nanoparticle suspension for one minute at room temperature and after that heated the fiber at 50C in thermal furnace for 10 minutes to accelerate the drying process, after that drying the fibers for two hours to remove the moisture of fibers. The hand lay up process was used for preparation the samples that fabricated of epoxy and 2layers of E-glassfiber at RT and normal humidity. The control case was the results of samples fabricated of epoxy with fiberglass without treatment with nano silica. The comparison between the results that occurred from the samples fabricated of epoxy and two layers fiberglass that immersed in nanoparticle suspension and the control case to know the development in mechanical properties.The key findings showed that highest value of impact was 36kJ/m^2 in samples fabricated of epoxy and fiberglass that immersed in nanoparticles suspension with 1wt% SiO2 and 99wt% H2O with increasing percentage 43% as compared to control case. The highest value of tensile was 45MPa in control case. The highest value of natural frequency was in samples manufactured from epoxy and 2layers fiberglass treated by 1wt% SiO2 with increasing percentage13% as compared to control case

Composite materials are widely used in engineering applications due to their high strength-to-weight ratio and tunable mechanical properties. This research aims to investigate the effect of equi-biaxial pre-tensioning load on the mechanical performance of epoxy composites reinforced with glass fibers at weight fraction of 0.35%. Four pretension load levels (0, 400, 800, and 1200 N) were applied to the fibers prior to fabrication. Tensile, impact, and flexural tests were performed to evaluate the composite behavior. The results showed that preloading significantly improved the mechanical properties, with optimal performance achieved at 800 N. At this level, tensile strength, elastic modulus, and impact strength increased by 50.14, 27, and 49.75%, respectively. and flexural properties also improved, while the flexural stress and modulus increasing by 263.3% and 76%, respectively. In contrast, increasing the pretension load to 1200 N resulted in a reduction in properties. These findings indicate that applying an optimal equi-biaxial pre-tension load during fabrication effectively enhances the overall mechanical performance of glass fiber-reinforced epoxy composites

This research aims to investigate the active behavior of expansive soils stabilized with ground granular kiln slag (GGBS) and cement-activated slag (GGBS/C) subjected to cyclic loading. Key active parameters shear modulus, Young's modulus, damping ratio, and deviatoric stress are basically assessed, complemented by Fourier Transform Infrared Spectroscopy (FTIR) analysis to characterize the underlying chemical mechanisms. Untreated expansive soils exhibit very low stiffness and strength, making them highly susceptible to cyclic degradation. The results show that stabilization with GGBS and GGBS/C substantially improves the mechanical stability: Young 's modulus increases by 5200 % to 7200 %. And this is key: and shear modulus increases by 2000 % to 5700 %, while the damping ratio decreases by more than 90 %. Resistance to deviatoric stress also increases, increasing by 440% to 545% depending on the limiting stress. Also increasing the confining pressure from 100 to 300 kPa further strengthens the stiffness and cyclic resistance, resulting in more stable performance under repeated loading conditions

Hospital services play a crucial role in people's lives, and continuous improvement in these services is essential. The prediction is paramount to determine the quality and suitability of the hospital's services. Yet the complexity of predicting increases, especially when dealing with intangible services such as hospital services. Many traditional prediction methods suffer from a gradual decline in accuracy and a simultaneous increase in the error rate especially in time series data. This paper introduces a new model, called Deep Fuzzy Learning Prediction (DFLP), for accurately predicting hospital service quality. The model combines fuzzy logic and deep learning neural network techniques and utilizes a set of quality parameters. DFLP's nodes represent different memberships among fuzzy sets, and the model adapts its internal fuzzy membership functions based on input data and time. Each node within the DFLP model box represents a different membership among the fuzzy sets. The membership scenarios vary when adding or removing a group of hospitals from the prediction process. This is due to changes in the fuzzy membership centers, resulting in membership changes. The DFLP model offers an effective way to predict and adapt to the dynamic nature of hospital services across time series. The model is tested on data from more than 4500 hospitals in the United States collected by the Centers for Medicare and Medicaid Services (CMS). The results show a significant convergence between the model's prediction and the actual services provided by hospitals. The results were compared with Long Short-Term Memory (LSTM) networks and Bidirectional LSTM (Bi-LSTM) networks and showed the superiority of the proposed model over traditional prediction networks. The proposed model achieved an accuracy of 96.68%, while LSTM and Bi-LSTM obtained 92.30% and 94.45%, respectively

Ternary neural network with three values of the weight (-1, 0, +1) is an effective solution for artificial intelligence applications in edge computing due to their energy efficiency, which is achieved through their deployment on memristor crossbars. However, the implementation on a memristor crossbar often has problems related to programming energy and defects. Therefore, this paper proposes a training method that uses both weight pruning and defect-aware training to optimize the number of programming pulses while maintaining a high recognition rate for the network. By using this method, at a 20% defect rate and 80% sparsity, the network achieves a 95% recognition rate on MNIST and 90% on CIFAR-10, compared to only 73.7% and 60.4% without using defect-aware training. At the same sparsity, the number of programming pulses, which is indicative of programming energy, is reduced by 49.1% compared to 20% sparsity

The Jordanian construction sector faces persistent fragmentation, delays, and inefficiencies despite its vital role in national development. Building Information Modeling (BIM), widely recognized as a solution to such challenges, remains underutilized in Jordan. This study aims to investigate the current state of BIM adoption, identifies key barriers and drivers, and assesses potential benefits to propose a localized framework for digital transformation. The methodology involved a quantitative survey of 443 professionals was analyzed using Relative Importance Index (RII) and descriptive statistics, with high reliability (Cronbach’s α = 0.937). Results revealed major barriers: lack of BIM awareness (RII = 0.745), limited training centers (RII = 0.729), and insufficient government support (RII = 0.706). Key benefits included improved 3D visualization (RII = 0.854), successful project execution (RII = 0.845) and improved decision making (RII = 0.831). The study recommends government BIM mandates, training centers, and curriculum integration to advance sustainable adoption and enhance project performance

The Wireless Sensor Networks (WSNs) are widely used in environmental monitoring, healthcare, and smart farming. However, energy consumption remains critical since battery-powered sensor nodes directly affect network lifetime. The conventional clustering and multi-hop routing algorithms are prone to collapse when used in dynamic environments, resulting in poor energy consumption and frequent node failures. This paper proposes a novel reinforcement learning (RL) routing algorithm based on Q-learning to enhance energy savings in WSNs. The algorithm is dynamic in assigning routes based on node energy, communication distance, and packet size, and adapts in real-time to network changes. It ensures that data transfer is efficient and the load distribution throughout the network is even by updating routing decisions with Q-learning. These simulation outcomes indicate that the suggested algorithm can save up to 25% of energy per round relative to traditional protocols and increase network life to 50 times that of the LEACH protocol

In this study, waste glass was utilized as a sintering agent and silica source in red ceramic products and investigated its effect on the properties of these products. The sample composition was prepared from waste glass, kaolin clay, and natural sand. Formulations of samples were contained (0- 40) % of glass waste. After preparation of the samples by the powder technology technique, some properties such as apparent porosity, bulk density, linear shrinkage, fracture strength, hardness, SEM, and XRD were examined. From the results of this investigation, the highest bulk density of the tested specimens was 2.19 g/cm3, the lowest apparent porosity was 3.2%, the highest strength was 59 MPa and the highest hardness was 219 MPa. From results of XRD analysis for the specimens, it can be indicated that the similarity in crystalline patterns confirms that the improvement mechanism is physical (liquid phase sintering) and not chemical (formation of new compounds), which is a positive aspect that enhances the simplicity of industrial application. The SEM image for the specimen with 40% of glass waste shows that the glassy phase content considerably increases, while the pores significantly reduce. Therefore, the utilization of glass waste improves mechanical properties and reduces the large quantity of the inherent imperfections in the microstructures of ceramic products. Thus, industrial economic and environmental benefits can be obtained by this replacement from the side of avoiding landfilling with these wastes, saving energy, and reducing material consumption

Reverse osmosis (RO) is widely used for desalination and water treatment, but its efficiency depends on several operational factors. This study evaluates the performance of a commercial RO unit in treating concentrate water and applies an artificial neural network (ANN) model to predict permeate total dissolved solids (TDS) and RO removal efficiency. Data was collected over 60 days monitoring period, water quality parameters including pH, total dissolved solids (TDS), electrical conductivity (EC), and temperature were measured for feed, permeate, and concentrate water of the RO unit. The results indicated that the RO unit has shown good performance in removing salt from concentrate water and achieved an average removal efficiency of 95.7% with permeate water meeting Iraqi and WHO drinking water standards. A slight decrease in efficiency was observed with rising feedwater temperature, with the highest performance near (96.9%) recorded near 21.8 °C. The ANN model showed moderate predictive accuracy for permeate TDS (R² = 0.870) with TDS variability explaining the difference in model performance. In addition, the ANN model presented a strong predictive performance for removal efficiency (R² = 0.902). Compared with previous studies, this work demonstrates the feasibility of using ANN to model and predict RO performance with small experimental datasets. The results provide practical insights for employing RO units for the reuse of RO concentrate through a secondary RO unit, which could minimize water wastage and mitigate environmental impacts

The problems of partial observability and sensor shortage pose a significant challenge for autonomous Unmanned Aerial Vehicles (UAVs) as they prove to be challenging for conventional Deep Reinforcement Learning (DRL) methods to undertake well under such conditions. In this paper, a memory-augmented Proximal Policy Optimization (PPO) model extended using a Long Short-Term Memory (LSTM) network is proposed as a solution to such challenges. The observation space is constructed from 2D LiDAR and Inertial Measurement Unit (IMU) data to sense simultaneously external observation and internal state of motion, whereas the action space consists of continuous velocity commands. A shaped reward function is optimized for encouraging safe target approaching, obstacle avoidance, and convergence speed. Experimental outcomes show that the PPO-LSTM described herein achieves smoother paths, more robust reward convergence, and a much lower rate of collision than regular PPO. It also generalizes to new environments with movable obstacles. Qualitatively, the success rate increased from 64.5% to 83.9%, collision frequency reduced by over 70%, and path efficiency increased from 0.60 to 0.85, without suffering from unstable training behavior

Anemia appears as a common medical condition which affects pregnant women internationally since it produces serious health issues for mothers and their developing babies. The main cause of anemia comes from red blood cell and hemoglobin deficiencies specifically related to iron deficiency while vitamin B12 deficiency and chronic diseases contribute to its progression. Tangible methods for early diagnosis matter to deliver proper management of pregnancy-related issues yet they prove hard to achieve in areas with limited resources. The study tackles the requirement for advanced anemia in pregnancy predictive methods by implementing machine learning algorithms to strengthen diagnostic performance. A team compiled the dataset at sulaimaniya maternity hospital which contained socio-demographic information and medical histories and blood test results from pregnant females. The collected dataset presented data on age as well as occupation and gestational age together with dietary habits and chronic diseases and blood parameters measured as hemoglobin and iron levels. The study optimizes model performance through the use of multiple feature selection methods including PCA, SelectKBest, Chi-Square and RFE. The study trained and evaluated eight machine learning models which included Random Forest (RF), Gradient Boosting (GB), AdaBoost, Extra Trees, Support Vector Machines (SVM), Logistic Regression (LR), K-Nearest Neighbors (KNN) and Decision Trees (DT) and Naive Bayes (NB). Each model received evaluation through assessments of accuracy, precision and recall and F1-score measures. The integration of feature selection led to substantially better model performance because RF, Extra Trees, SVM and LR demonstrated near-perfect results. The performance of KNN remained significantly lower than other models because it demonstrated weak abilities in selecting features. Among the evaluated algorithms SelectKBest and Chi-Square along with RFE demonstrated superior performance than PCA for obtaining 100% accuracy levels. This research shows that machine learning systems have potential in predicting anemia in pregnant women thus promoting improved diagnostic strategies in underprivileged healthcare settings. This research utilizes maternity hospital information to demonstrate the advantages of data-based approaches in maternal healthcare and presents machine learning as a tool for better healthcare results

Alzheimer's disease AD, also known as dementia, is a disease resulting from an irreversible brain disorder that can develop gradually as the patient ages. Its distinguishing features include cognitive decline, memory loss, and psychological problems. Early diagnosis of this disease becomes difficult due to the complexity of the initial pathological changes. Other problems overlap with other types of diseases. Consequently, the computational complexity of analyzing neuroimaging data has become a new method, especially magnetic imaging MRI and PET positron tomography are possible thanks to recent advances in ML machine learning and DL deep learning. In this study, four basic learning methods —convolutional neural networks (CNNs), recurrent neural networks (RNNs), discrete polynomial networks (DPNs), and k-nearest neighbors (KNN)— were evaluated to detect and classify Alzheimer's disease. CNN networks have proven their high ability in binary and multi-category classification tasks, successfully distinguishing between people with the disease. Simultaneously, RNN architectures such as Long Short-Term Memory networks (LSTM) and Gated Recurrent Units (GRU) are particularly skilled at examining temporal trends in longitudinal datasets that monitor the development of diseases over a number of patient visits. Discontinued polynomial networks are beneficial for learning and improving the interpretability of linear representations. An investigation into methods for raising the polynomial order of extracted features will be carried out in order to learn more about this field. Even simple models like KNN might be a good place to start when used on tiny datasets that are obtained via volumetric measurements or clinical evaluations. These models are put through a thorough evaluation process using a number of benchmark databases, including ADNI, OASIS, MIRIAD, and AIBL. Their diagnostic performance metrics are evaluated using accuracy rates, sensitivity levels, specificity standards, and Area Under Curve-Receiver Operating Characteristic (AUC-ROC) scores. Our findings indicate that frameworks based on CNNs and RNNs demonstrate comparable performance to Naive Bayes classifiers; furthermore, ensembles of these neural network architectures show impressive generalization capabilities. In this article we emphasize the growing significance of integrating deep learning techniques across multiple modalities aimed at facilitating early diagnosis ，ongoing monitoring ，and personalized treatment strategies for Alzheimer’s Disease

Lubricating oils play a crucial role in reducing friction in engines and other moving machine parts. In this study, nano-additives (ZnO and SiO2) were successfully prepared using a sol-gel method, yielding nanomaterials with specific measured values such as the purity, diameter, and surface area, and blended with commercial 5w-20 lubricating oil. Additionally, a mixture of these two nanomaterials was used together in the commercial oil. The nanomaterials were blended at different ratios (0.1%, 0.3%, 0.5%, 0.7%, and 1.0 % wt ) with the lubricating oil using two steps. First, a magnetic stirrer with the presence of oleic acid as a surfactant, and second, with a high-speed homogenizer electric mixer. The physical properties of the nano-oil, such as viscosity index, were enhanced from 117 to 121.5 with ZnO and from 117 to 119.5 with SiO2. The enhancement was from 117 to 120.2 when the two types of nanomaterials were used together

Person Re-Identification_(ReID) is a crucial task in computer vision with growing importance in security and engineering applications, particularly in surveillance and smart city systems. The hand-crafted feature-based existing approaches that consider texture and color struggle with complex real challenges involving lighting; person pose; and variable backgrounds. This survey offers an updated and focused review of deep learning-based ReID methods, encompassing research from 2020 to 2025. It investigates in-depth the engineering aspects, including system integration, real-time performance, and sensor constraints, which are often overlooked in reviews of earlier work. Techniques discussed in this study involve CNNs and transformers, triplet loss and contrastive learning, GANs, and methods that enhance matching accuracy and generalization. The paper compares recent methods; presenting their strengths and weaknesses, and setting directions for future research. The survey aims to provide a practical reference for engineers and researchers interested in developing robust and scalable ReID systems in real-world environments

Many approaches have been proposed to locate autonomous mobile platforms such as robotic trucks, aircrafts, and underwater vehicles. Relying on satellite navigation only is associated with permanent errors due to the passage of radio signals through the atmosphere. Furthermore, the use of satellite navigation is simply not enough in certain environments such as crowded or enclosed spaces. Therefore, it is essential to propose and implement another effective, simple, and flexible device to ascertain the actual situation. This device generally consists of a spatial sensor and an efficient processor that receives and processes signals from cellular network towers in the surrounding area. For this solution, it is crucial to determine the relative movement of the mobile platform based on new algorithms and models. This research paper outlines the problems and challenges of this solution, clarifying them for practical applications. Finally, it would be advantageous to easily equip this mechanism on a UAV platform based on its efficiency, features, and technologies

Glass-ceramics of the lithium aluminosilicate (LAS) system are widely employed in dentistry; yet optimizing their clinical performance requires specific control of heat treatment parameters. This study aims to investigate the impact of varied processing parameters on the resulting microstructure, mechanical properties, and surface roughness of these materials. The methodology involves systematic control of nucleation temperature (520°C) and crystallizations temperatures (750°C) and (900°C). Materials were micro-structurally characterized and investigated using X-ray diffraction (XRD) and Scanning Electron Microscopy (SEM); mechanically using Knoop hardness tests and ultrasound wave velocity (to obtain an elastic modulus; and physically using a surface roughness tester. XRD confirmed the presence of LiAlSi2O6 as the main phase and Li2Si2O5 as a minor phase. Furthermore, the optimal heat treatment of glass-ceramics significantly increased the knoop hardness amounted to ~ 7.66±0.44GPa and the elastic modulus to ~ 96.35±0.50GPa. Surface roughness analysis also revealed a minimum (Ra) of 0.34±0.04μm for the specific heat treatment

This paper proposes a hybrid deep learning framework for Arabic handwritten digit recognition by optimizing Convolutional Neural Network (CNN) hyperparameters using the Crow Search Algorithm (CSA). Due to the high variability and structural complexity of Arabic handwritten digits, achieving optimal CNN performance requires efficient and automatic hyperparameter tuning. In the proposed approach, CSA is employed to optimize key CNN hyperparameters, including filter size, number of filters, mini-batch size, and learning rate, with the objective of minimizing classification error. The MADBase dataset is used for evaluation, and preprocessing steps such as normalization, image reshaping, one-hot encoding, noise reduction, and data shuffling are applied to enhance training efficiency and model robustness. The CNN architecture is trained using the optimized hyperparameters obtained through CSA iterations. Experimental results show that the proposed CSA-optimized CNN achieves 99% accuracy on the testing set, demonstrating strong generalization capability and stability. The findings confirm that CSA effectively improves CNN performance while eliminating the need for manual hyperparameter tuning, making the framework suitable for other image classification tasks.

Monitoring water quality is essential for environmental protection and sustainable water resource management. This study employed a Multilayer Perceptron (MLP) neural network to model the Groundwater Quality Index (GWQI) based on key physicochemical parameters. The model was developed using MATLAB’s Neural Network Toolbox (nftool), with data divided into training, validation, and testing sets. Performance was evaluated using MSE, RMSE, MAE, and the correlation coefficient (R). The MLP model achieved good predictive accuracy:(MSE = 2.3, R ≈ 1.0) in training;( MSE = 7.6983, R = 0.99992) in validation; and (MSE = 15.8, R = 0.997) in testing. These results confirm the model’s ability to capture nonlinear relationships and generalize well without overfitting. The stability of the model is supported by its error distribution and training convergence. A comparison with previous studies shows improved performance, reinforcing the potential of neural networks in predicting water quality and supporting data-driven environmental decision-making

Hepatitis C Virus (HCV) is a disease that infects the liver with multiple stages that spread through blood, requiring blood tests and body symptoms for diagnosis. The diagnosis should decide which stage the patient reaches. This work suggests a hybrid classification method based on optimization and machine learning techniques. A Long Short-Term Memory Neural Network (LSTM) is used as a classifier to identify the stages of the disease, based on the four stages, using 28 features comprising body symptoms and blood tests. To find the optimal number of hidden cells in the hidden layers of LSTM, the Transit Search Algorithm (TSA) is used, TSA identifies the best integer value for the number of hidden cells and selects the best features combined with this number of cells that will give the highest accuracy of classification. The preprocessing step is required to enhance the quality of the dataset and resizing it. In multiclass classification, a large dataset is essential for the network to learn effectively through all classes. To achieve this, Synthetic Minority Oversampling Techniques (SMOTE) is used to generate similar data that will increase the size of the dataset. The proposed TSA-LSTM method achieves high performance, with classification accuracy exceeding 99% outperforming previous works

We have experimentally investigated and simulated the diversity of unbalanced Mach-Zehnder interferometer (UMZI) designs. The group index and free spectral range (FSR) are measured for both the transverse electric (TE) and transverse magnetic (TM) designs for a silicon (Si) strip waveguide that has a 500 nm width and a 220 nm height. The simulated TE UMZI with arm's length difference of 50 µm shows 11 nm and 4.33 of the FSR and group index, respectively. The group index is 3.103, and FSR is 13.5 for the TM UMZI. These designs show a slight variation compared with the simulation results due to fabrication variation. Overall, the TM UMZI design is more sensitive to the manufacturing variation than the TE UMZI designs. Photonic interferometers have been widely used in photonic devices, and such photonic devices have potential applications in photonic modulators, sensors, filters, and future computing via photonic integrated circuits (PICs)

This study investigates the potential of using ground walnut shell powder as a natural reinforcement for unsaturated polyester resin for sustainability and recycling, as an alternative to synthetic reinforcements. The main objective is to assess the effect of various filler contents 5%, 15%, and 25% wt. on the thermal and mechanical performance of the prepared composites. Hand lay-up technique was used to fabricate the casts, and the designated test specimens were cut according to the dimensions in the standard specification of each test (Impact strength, flexural strength, water uptake, and thermal conductivity). The results showed a significant improvement in the impact and flexural strengths at 15% wt. reinforcement, while thermal conductivity dropped with increasing filler weight fraction, suggesting enhanced thermal insulation. Water uptake increased at the first few weeks until a steady state is reached, after which the 25%wt. specimens showed the highest rate of water absorption, followed by the 15% and 5%wt. specimens, respectively. These findings show the suitability of using locally-available and inexpensive agricultural waste as an eco-friendly reinforcement for polymeric composites

The reliability of deep aquifers makes groundwater a vital resource in semi-arid and arid regions. However, less precipitation and more droughts are predicted due to climate change. Iraq has been significantly affected by a prolonged drought, leading to a notable increase in groundwater use over the past decade. Consequently, utilizing groundwater has emerged as the most viable option for meeting water supply requirements. This study aims to construct a groundwater potential map (GPM) for the Dammam confined aquifer in Iraq's southern desert zone using integrated RS, GIS, and multi-criteria decision-making (MCDM) approaches, including the analytical hierarchy process (AHP) to facilitate groundwater management. A multiclass GPM is created by extracting relevant thematic layers based on factors that influence groundwater potential and integrating them into a normalized Pairwise comparison matrix using the AHP technique. The findings of this study demonstrate that the created GPM classifies the study area into five zones, including very good, good, moderate, poor, and very poor groundwater potential zones (GWPZs). The distribution of these zones as a percentage of the total area of 76732.71 Km2 is as follows: 15.91%, 39.02%, 33.01%, 6.49%, and 5.57%, respectively. The AHP method accurately delineates a groundwater potential map with 85.4% accuracy. Specifically, out of 349 well locations, 298 were correctly identified using this method. Such a study will significantly contribute to early planning of groundwater exploration by providing preliminary, reliable information on the optimal zone before drilling, especially in arid areas with limited groundwater information, to target appropriate drilling sites for groundwater wells

Three kinds of asphalt were used in this study: conventional 40/50 asphalt, PG 76V-10, and PG 76E-10 modified asphalt to investigate the effect of modified asphalt on the performance of asphalt mixtures in binder and surface layers. The performance of the mixtures was assessed through tests measuring mixture stability, flow, moisture resistance, and crack resistance. The modified mixtures showed a decrease in optimal asphalt content (OAC), indicating improved aggregate coating and compaction. Mixtures containing polymer-modified asphalt—specifically PG 76E-10—achieved higher stability and density, lower flow and void ratio, and significantly improved resistance to moisture damage compared to conventional mixtures. Mixtures modified with PG 76E-10 also recorded the highest CT index (Crack Tolerance Index) values, indicating excellent resistance to cracking from repeated loading or thermal changes. The PG 76V-10 mixtures showed improved performance compared to conventional asphalt, but PG 76E-10 showed more pronounced improvements in most properties, especially in enhancing internal cohesion and reducing permeability. These results emphasize the importance of using polymer-modified asphalt in both layers to achieve better paving performance and emphasize the need for careful selection of the type of modified asphalt according to the function of the layer and the expected service conditions

Because next-generation electronic devices are compact and have high power densities, efficient cooling techniques are required to improve a heat sink's thermal performance and keep the temperature at a safe level. A graphite layer was applied to the aluminium substrate. Two aluminum specimens were prepared for testing; one has a graphite covering, while the other specimen is left uncoated for comparison. A wind tunnel was developed to study the heat transfer coefficient to comprehend the performance of the Al heat sink coated with graphite. To replicate the energy produced by a heat sink, a plate electrical heater is affixed to the specimen's bottom. The distribution of body temperature, thermal transmittance, and Nusselt number with four Reynolds number values under turbulent flow conditions for each sample was examined experimentally and numerically using a three-dimensional CFD-solid-mechanics model. The findings demonstrated that graphite enhances thermal performance. An improvement of approximately almost 15.2% in Nusselt number and 38% in thermal transmittance was observed compared to the uncoated case, with the graphite-coated sample exhibiting the most effective thermal dissipation and the uncoated sample exhibiting the worst

This work described the on-grid strategy to provide energy for lighting loads. This technique included connecting a lighting lamp directly to the public utility grid shared with solar PV array in order to reduce the consumption of energy during peak hours in day. A solar car parking prototype model has been designed practically consisted of 66 WP PV array included PV modules and smart inverter (700 W). Case study illustrated successful implementation of on-grid solar PV system across various lamp load. The results showed the on-line monitoring data on power generation in kWh/kWp for each lamp under different irradiance and constant temperature (25oC). Finally, it has been concluded that the maximum power generated obtained by smart inverter was (18.7W) at irradiance (760W/m2)

To achieve optimal wireless fidelity (Wi-Fi) coverage, start by assessing the location and evaluating the needs of users and devices. This study compares various UniFi devices (Mesh, Lite, Pro, and HD) to identify which one can provide free Wi-Fi coverage throughout Najaf International Airport. To cover the passenger terminals, 19 UniFi Mesh or UniFi Lite devices, 12 UniFi Pro devices, and 8 UniFi HD devices are needed. It was observed that the UniFi HD device provides extensive coverage and supports over 500 users. It delivers high-speed data transmission, reaching 800 Mbps on the 2.4 GHz band and 1733 Mbps on the 5 GHz band. Additionally, it used UniFi HD devices to enhance coverage, reduce costs, and minimize signal interference, aligning with recent research objectives. Furthermore, it was observed that the coverage rate is 60% with UniFi Mesh or UniFi Lite devices, 72% with UniFi Pro devices, and increases to 92%-100% with UniFi HD devices. It was concluded that increasing the number of devices in airport terminals can hinder signal transmission due to varied operating ranges and obstacles, particularly in Non-Line-of-Sight (NLoS) regions. Materials like concrete can obstruct signals, while wood and iron scatter them, and bricks may absorb them

Utilizing the same parameters for operation, 50 Hz frequency, and 380 V line voltage, every single inverter is represented mathematically on the MATLAB/Simulink environment. The shape of the generated voltage's harmonics is examined using Fast Fourier Transform (FFT) approaches. The results demonstrate the benefits and drawbacks of each design by taking into account the THD values for each examined inverter with the exact same applied frequency and supply voltage. This paper uses modelling to analyse three-phase, three-level (CHB) and (NPC) inverters. Total harmonic distortion (THD), pattern efficacy, and structural complexity are all examined. Simulation findings show that the NPC architecture needs fewer DC sources and is simpler to control, whereas the CHB inverter generates less THD (~4.7%) compared with the NPC architecture (~9.2%). Because they provide better waveforms, less acoustic noise, and less switching strain, inverters that have less harmonic distortion are favoured in medium- and high-power applications

This study developed experimental and statistical predictive models to determine the higher heating value (HHV) of lignocellulosic biomass using proximate, ultimate, and structural composition analysis data. Multiple linear regression (MLR) and response surface methodology (RSM) were utilized to establish a mathematical relationship between important compositional parameters and HHV. Results indicate that carbon, hydrogen, fixed carbon, and lignin contents positively influenced HHV, whereas increased oxygen content decreases energy density. The RSM model gives a better prediction accuracy as compared to the MLR model because it takes into consideration the interaction and nonlinear effects of biomass constituents. The lignin inclusion as a structural parameter contributes greatly to the strength of the model. The suggested method is fast and economical compared to bomb calorimetry and can be applied to screen biomass, select feedstock, as well as optimize combustion, gasification, and pyrolysis systems