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Objective: To assess lesions of the posterior segment in pre-operative cataract patients using B-Scan ultrasonography and to determine various risk factors contributing to posterior segment lesions. Study Design: A Descriptive Cross-Sectional Study Place and Duration of the Study: The study was conducted in the Department of Radiology, Sahiwal Teaching Hospital from 10 June 2022 to 10 December 2022. Materials and Methods: We enrolled 290 cases of cataracts including both genders and all ages, both with and without history of trauma. All cases underwent a B-Scan ultrasound to assess posterior segment pathologies. Patients having congenital lesions and with any history of surgery were excluded from the study. The data was analyzed using SPSS version 25.0. The frequencies and percentages of posterior segment pathologies observed on the B-Scan were calculated. The percentage of various co-morbidities contributing to posterior segmental lesions was also analyzed. Results: The mean age of the subjects was 36.0 + 23.2 years. 137 (47.2%) cases were females and 153 (52.7%) were males. Cataract was observed in 253 (87.2%) subjects without any trauma to eye. While studying various risk factors that increase the risk of lesions of the posterior segment, the most common were hypertension (17.5%) followed by diabetes mellitus (16.5%). Conclusion: We concluded that B-Scan is a valuable and easily available modality to detect posterior segment pathologies in cataract patients.

This article is based in one of the scientific legacies by Mahmud Zamakhshari on the work on spiritual education and morality "Atvaq uz-zahab fil-mawo'iz val-khutab" ("Golden Necklaces of teachings and exhortations") and the transmission of knowledge, the exhortation to know its documents and evidence. Az-Zamahshari's request for knowledge refers to his travels to Bukhara, Khorasan, Damascus, Iraq, and the Hijaz, where he received permission from his teachers for various books and sciences, as well as the permission given by the scholar to many of his students. At the same time, a sample of Zamakhshari's authorized letters (letters ijaza) is given. At the end of our article, we will focus on some important and necessary golden advices in the spiritual education of students from the work of the great scholar Mahmud Zamakhshari "Atvaq uz-zahab fil-mawaiz val-khutab".

In the distributed computing era, cloud computing has completely changed organizational operations by facilitating simple access to resources. However, the rapid development of the IoT has led to collaborative computing, which raises scalability and security challenges. To fully realize the potential of the Internet of Things (IoT) in smart home technologies, there is still a need for strong data security solutions, which are essential in dynamic offloading in conjunction with edge, fog, and cloud computing. This research on smart home challenges covers in-depth examinations of data security, privacy, processing speed, storage capacity restrictions, and analytics inside networked IoT devices. We introduce the Trusted IoT Big Data Analytics (TIBDA) framework as a comprehensive solution to reshape smart living. Our primary focus is mitigating pervasive data security and privacy issues. TIBDA incorporates robust trust mechanisms, prioritizing data privacy and reliability for secure processing and user information confidentiality within the smart home environment. We achieve this by employing a hybrid cryptosystem that combines Elliptic Curve Cryptography (ECC), Post Quantum Cryptography (PQC), and Blockchain technology (BCT) to protect user privacy and confidentiality. Additionally, we comprehensively compared four prominent Artificial Intelligence anomaly detection algorithms (Isolation Forest, Local Outlier Factor, One-Class SVM, and Elliptic Envelope). We utilized machine learning classification algorithms (random forest, k-nearest neighbors, support vector machines, linear discriminant analysis, and quadratic discriminant analysis) for detecting malicious and non-malicious activities in smart home systems. Furthermore, the main part of the research is with the help of an artificial neural network (ANN) dynamic algorithm; the TIBDA framework designs a hybrid computing system that integrates edge, fog, and cloud architecture and efficiently supports numerous users while processing data from IoT devices in real-time. The analysis shows that TIBDA outperforms these systems significantly across various metrics. In terms of response time, TIBDA demonstrated a reduction of 10–20% compared to the other systems under varying user loads, device counts, and transaction volumes. Regarding security, TIBDA's AUC values were consistently higher by 5–15%, indicating superior protection against threats. Additionally, TIBDA exhibited the highest trustworthiness with an uptime percentage 10–12% greater than its competitors. TIBDA's Isolation Forest algorithm achieved an accuracy of 99.30%, and the random forest algorithm achieved an accuracy of 94.70%, outperforming other methods by 8–11%. Furthermore, our ANN-based offloading decision-making model achieved a validation accuracy of 99% and reduced loss to 0.11, demonstrating significant improvements in resource utilization and system performance. [ABSTRACT FROM AUTHOR]