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
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
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
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
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
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
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
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
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
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
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
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
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
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
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.
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 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
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
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
