This paper aims to improve the voltage profile using the Static Synchronous Compensator (STATCOM) in the power system in the Kurdistan Region for all weak buses. Power System Simulation studied it for Engineers (PSS\E) software version 33.0 to apply the Newton-Raphson (NR) method. All bus voltages were recorded and compared with the Kurdistan region grid index (0.95≤V ≤1.05), simulating the power system and finding the optimal size and suitable location of Static Synchronous Compensator (STATCOM)for bus voltage improvement at the weakest buses. It shows that Soran and New Koya substations are the best placement for adding STATCOM with the sizes 20 MVAR and 40 MVAR. After adding STATCOM with the sizes [20MVAR and 40MV

The ligand 4-amino-N-(5-methylisoxazole-3-yl)-benzene-sulfonamide(L1) (as a chelating ligand) was treated with Pd(II),Pt (IV) and Au(III) ions in alcoholic medium in order to prepare a series of new metal complexes. Mixed ligand complexes of this primary ligand were prepared in alcoholic medium in presence of the co-ligand 4,4'-dimethyl-2,2'-bipyridyl(L2) with Cu(II) ,Pd(II) and Au(III) ions. The complexes were characterized in solid state using flame atomic absorption, elemental analysis C.H.N.S, FT-IR, UV-Vis Spectroscopy, conductivity and magnetic susceptibility measurements. The nature of some complexes formed in ethanolic solution has been studied following the molar ratio method, also stability constant was studied and the complexes f

A robust and sensitive analytical method is presented for the extraction and determination of six pharmaceuticals in freshwater sediments.

This study proposes a hybrid predictive maintenance framework that integrates the Kolmogorov-Arnold Network (KAN) with Short-Time Fourier Transform (STFT) for intelligent fault diagnosis in industrial rotating machinery. The method is designed to address challenges posed by non-linear and non-stationary vibration signals under varying operational conditions. Experimental validation using the FALEX multispecimen test bench demonstrated a high classification accuracy of 97.5%, outperforming traditional models such as SVM, Random Forest, and XGBoost. The approach maintained robust performance across dynamic load scenarios and noisy environments, with precision and recall exceeding 95%. Key contributions include a hardware-accelerated K