Copper oxide (CuO) nanoparticles were synthesized through the thermal decomposition of a copper(II) Schiff-base complex. The complex was formed by reacting cupric acetate with a Schiff base in a 2:1 metal-to-ligand ratio. The Schiff base itself was synthesized via the condensation of benzidine and 2-hydroxybenzaldehyde in the presence of glacial acetic acid. This newly synthesized symmetric Schiff base served as the ligand for the Cu(II) metal ion complex. The ligand and its complex were characterized using several spectroscopic methods, including FTIR, UV-vis, 1H-NMR, 13C-NMR, CHNS, and AAS, along with TGA, molar conductivity and magnetic susceptibility measurements. The CuO nanoparticles were produced by thermally decomposing the

Precise and interpretable classification of autism-related behaviors is important for initial diagnosis, personalized intervention, and support arrangements. This study proposes an interpretable machine learning (ML) model using Light Gradient Boosting Machine (LightGBM) and Categorical Boosting (CatBoost) to classify behavioral patterns into four categories (normal, mild, moderate, and severe) associated with Autism Spectrum Disorder (ASD) based on a custom 377-instance survey dataset from Iraqi parents and teachers of children aged 6-12. The model observes 16 key features across communication and social interaction, repetitive behaviors, language, and adaptive skills, preprocessed via interquartile range (IQR) outlier removal, me

The use of sugarcane molasses (SCM) as a sustainable stabilizing agent for geomaterials, particularly granular subbase soils, has recently attracted growing attention as an alternative to traditional stabilization methods employing cement, lime, or bitumen, which are often associated with high costs and environmental concerns. This study presents a mechanistic–experimental evaluation of SCM for stabilizing granular subbase materials at dosages of 2.5%, 5%, 7.5%, and 10% by weight of dry granular material. A comprehensive testing program was conducted, including compaction characteristics, California Bearing Ratio (CBR), resilient modulus (Mr), permanent deformation under repeated loading, optical microscopy, and FTIR spectroscopy.

Discrete Racah polynomials (DRPs) are considered essential for several applications such as approximation theory, quantum physics, and digital signal processing. However, the computation of high-order DRPs presents significant numerical and computational challenges. Even though the recent advancements in algorithms have improved numerical stability, the inherently sequential nature of these algorithms continues to be a bottleneck for large-scale implementations. This paper proposes a new multithreading-based algorithm for computing the DRP coefficients. The proposed algorithm is designed to accelerate the computation of high-order DRPs by considering the advantages of the independence of different tasks and the ability to process mu

The study aimed to examine the impact of audit committee characteristics on the practices of intellectual capital disclosure in the annual reports of Bank and Insurance companies listed on Palestine Exchange, through performing content analysis of the annual reports for the study sample which totaled thirteen companies, including six banks and seven insurance companies. To achieve the study objectives, the study employed a content analysis approach in order to analyze the content of the intellectual capital disclosure practice, in addition, the study used cross-sectional with longitudinal data for time series for a period of time between 2014-2019. The empirical results indicated that financial expertise and the number of meeting

Aim of the study: Using surface roughness and tensile bond strength tests, the objective of this investigation was to ascertain the impact of laser surface modification on the binding strength of injectable thermoplastic acrylic denture base material to acrylic-based soft-liner material. Materials and methods: Acrylic base soft liner material was bonded to injectable thermoplastic acrylic resin (Deflex). Forty specimens were created (20 disc, 20 dumbbells) 10 of each specimen type as control specimens, and 10 were treated with nano pulse Nd: YAG laser. The data were analyzed using the Kruskal-Wallis test and unpaired t-test (a=.05) and the roughness test was performed utilizing a double column universal test machine. Results: Compar

In this study, a packed bed was used to remove pathogenic bacteria from synthetic contaminated water. Two types of packing material substrates, sand and zeolite, were used. These substrates were coated with silver nanoparticles (AgNPs), which were prepared by decomposition of Ag ions from AgNO3 solution. The prepared coated packings were characterized using scanning electron microscopy, energy-dispersive X-ray spectroscopy and transmission electron microscopy. The packed column consisted of a PVC cylinder of 2 cm diameter and 20 cm in length. The column was packed with silver nanoparticlecoated substrates (sand or zeolite) at a depth of 10 cm. Four types of bacteria were studied: Escherichia coli, Shigella dysenteriae, Pseudomonas aerugi

In this work, multilayer nanostructures were prepared from two metal oxide thin films by dc reactive magnetron sputtering technique. These metal oxide were nickel oxide (NiO) and titanium dioxide (TiO2). The prepared nanostructures showed high structural purity as confirmed by the spectroscopic and structural characterization tests, mainly FTIR, XRD and EDX. This feature may be attributed to the fine control of operation parameters of dc reactive magnetron sputtering system as well as the preparation conditions using the same system. The nanostructures prepared in this work can be successfully used for the fabrication of nanodevices for photonics and optoelectronics requiring highly-pure nanomaterials.

Deep learning has recently received a lot of attention as a feasible solution to a variety of artificial intelligence difficulties. Convolutional neural networks (CNNs) outperform other deep learning architectures in the application of object identification and recognition when compared to other machine learning methods. Speech recognition, pattern analysis, and image identification, all benefit from deep neural networks. When performing image operations on noisy images, such as fog removal or low light enhancement, image processing methods such as filtering or image enhancement are required. The study shows the effect of using Multi-scale deep learning Context Aggregation Network CAN on Bilateral Filtering Approximation (BFA) for d