Transmission lines are generally subjected to faults, so it is advantageous to determine these faults as quickly as possible. This study uses an Artificial Neural Network technique to locate a fault as soon as it happens on the Doukan-Erbil of 132kv double Transmission lines network. CYME 7.1-Programming/Simulink utilized simulation to model the suggested network. A multilayer perceptron feed-forward artificial neural network with a back propagation learning algorithm is used for the intelligence locator's training, testing, assessment, and validation. Voltages and currents were applied as inputs during the neural network's training. The pre-fault and post-fault values determined the scaled values. The neural network's p

Early detection of eye diseases can forestall visual deficiency and vision loss. There are several types of human eye diseases, for example, diabetic retinopathy, glaucoma, arteriosclerosis, and hypertension. Diabetic retinopathy (DR) which is brought about by diabetes causes the retinal vessels harmed and blood leakage in the retina. Retinal blood vessels have a huge job in the detection and treatment of different retinal diseases. Thus, retinal vasculature extraction is significant to help experts for the finding and treatment of systematic diseases. Accordingly, early detection and consequent treatment are fundamental for influenced patients to protect their vision. The aim of this paper is to detect blood vessels from

This study relates to synthesis of bentonite-supported iron/copper nanoparticles through the biosynthesis method using eucalyptus plant leaf extract, which were then named E-Fe/Cu@B-NPs. The synthesised E-Fe/Cu@B-NPs were examined by a set of experiments involving a heterogeneous Fenton-like process that removed direct blue 15 (DB15) dye from wastewater. The resultant E-Fe/Cu@B-NPs were characterised by scanning electron microscopy, Brunauer–Emmet–Teller analysis, zeta potential analysis, Fourier transform infrared spectroscopy and atomic force microscopy. The operating parameters in batch experiments were optimised using Box–Behnken design. These parameters were pH, hydrogen peroxide (H₂O₂

In this study, a new class of polymeric nanocomposites was synthesized and characterized.  One mole of dimethyl adipate and two moles of thiosemicarbazide in ethanol first reacted to form the compound [C1]. Compound [C1] then reacted with sodium hydroxide to produce compounds [C2]. Hydrazine hydrate reacted with compound [C2] to generate compound [C3]. Compound [C4] was synthesized from compound [C3] and maleic anhydride. A polymer [C5] is formed by the reaction of the compound [C4] with ammonium persulfate as an initiator. This polymer was then combined with nano: ZnNPs, AgNPs, SiNPs, or IONPs using a hotplate stirrer for 3 hours to produce nanocomposites [C6-C9]. FTIR, 1H-NMR, and Field Emission Scanning Electron Microscope (FESEM) were

The deposition method of perovskite solar cell layers significantly impacts device functionality and the achievement of industrial goals. Aluminum (Al) nanoparticles with rutile titanium oxide (TiO2) nanoparticle thin films are fabricated on Fluorine Tin Oxide (FTO) glass substrates by nanosecond pulsed fiber laser deposition (PLD) to be used as a plasmonic electron transport layer (ETL) in perovskite solar cell (PSC). The effect of various pulsed fiber laser parameters on the structural, optical, and surface morphology on Al/TiO2 films is extensively examined utilizing a variety of measurement techniques; X-ray diffraction (XRD), Ultraviolet–visible (UV–Vis) spectroscopy, Field emission scanning electron microscopy (FE-SEM) and Atomic

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