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
