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Chemical warfare agents (CWAs) are extremely lethal substances used in warfare and terrorism, capable of causing permanent damage even in small doses, despite medical intervention. Therefore, detection, protection, and detoxification of CWAs are vital for the safety of first responders, military personnel, and civilians, driving significant research in this area. Herein, we designed and synthesized a poly(diallyldimethylammonium chloride) (PDDA) mediated cupric oxide (CuO) functionalized activated carbon fabric (ACF), termed ACF@PDDA-CuO, as an adsorbent filter material for self-detoxifying chemical protective clothing. PDDA, a positively charged polyelectrolyte, effectively binds in-situ synthesized CuO to the negatively charged ACF surface, serving as a suitable binder. This study demonstrates the synergistic effects of PDDA-CuO functionalization on ACF, where PDDA treatment enhanced mechanical and comfort properties, and CuO crystal growth significantly improved detoxification efficacy against the CWA Nerve Agent Sarin. Comprehensive analyses, including FTIR, BET surface area analysis, SEM, EDS, TEM, STEM, TGA, XPS, and XRD, confirmed the uniform deposition of CuO and PDDA on the ACF surface. The Cu content on ACF@PDDA-CuO samples was measured via iodometric titration. The materials were evaluated for tensile strength, air permeability, water vapor permeability, nerve agent (Sarin) detoxification, and blister agent (Sulfur Mustard) breakthrough time to assess their applicability for protective clothing. The optimized PDDA-CuO on ACF detoxified 82.04% of Sarin within 18 h, compared to 25.22% by ACF alone, and enhanced tensile strength by 23.67%, air permeability by 24.63%, and water vapor permeability by 3.94%, while maintaining protection against Sulfur Mustard for 24 h. These findings indicate that ACF@PDDA-CuO is a promising candidate for CWA protective clothing, offering robust protection with enhanced comfort. [Display omitted]

Deep Learning is the newest and the current trend of the machine learning field that paid a lot of the researchers' attention in the recent few years. As a proven powerful machine learning tool, deep learning was widely used in several applications for solving various complex problems that require extremely high accuracy and sensitivity, particularly in the medical field. In general, the brain tumor is one of the most common and aggressive malignant tumor diseases which is leading to a noticeably short expected life if it is diagnosed at a higher grade. Based on that, brain tumor classification is an overly critical step after detecting the tumor in order to achieve an effective treating plan. In this paper, we used Convolutional Neural Network (CNN) which is one of the most widely used deep learning architectures for classifying a dataset of 3064 T1 weighted contrast-enhanced brain MR images for grading (classifying) the brain tumors into three classes (Glioma, Meningioma, and Pituitary Tumor). The proposed CNN classifier is a powerful tool and its overall performance with an accuracy of 98.93% and sensitivity of 98.18% for the cropped lesions, while the results for the uncropped lesions are 99% accuracy and 98.52% sensitivity and the results for segmented lesion images are 97.62% for accuracy and 97.40% sensitivity.