In study of effective bioactive compounds, we have synthesized the Co((ІІ), Mn(ІІ), Fe(ІІ), Cu(ІІ), Ni(ІІ), and Zn(ІІ) complexes of the Schiff base derived from trimethoprim and2'-amino-4-chlorobenzophenone and characterized by spectroscopic (NMR, IR, Mass, UV–vis,), analytical, TGA studies and magnetic data .The solution electronic spectral study suggests the stoichiometry of the synthesized complexes and Elemental analysis detected the square planer and octahedral geometry of the compounds. The prepared metal complexes presented promoted efficiency versus the screened bacterial (Escherichia Coli and Staphylococcus aureus) antibacterial efficacy against (Staphylococcus aureus, Salmonella spp., E. coli, Vibrio spp., Pseudomona

A new series of transition metal complexes of Cu(II), Ni(II), Co(II) and Fe(III) have been synthesized from the Schiff base (L1) and (L2) derived from Semicarbazide hydro chloride and 4-chlorobenzaldehyde or 4-bromobenzaldehyde. The structural features have been arrived from their elemental analyses, magnetic susceptibility, molar conductivity, IR, UV-Vis. and 1H NMR spectral studies. The data show that the complexes have composition of [M(L)2](NO3)2 and [Fe(L)2 (NO3)2](NO3) where the M=Co(II),Ni(II) and Cu(II) ;L=L1and L2 type. The magnetic susceptibility and UV-Vis spectral data of the complexes suggest a square planer geometry for Co(II) and Cu(II) but Fe(III) octahedral geometry and Ni(II) tetrahedral geometry around the central metal i

The method of predicting the electricity load of a home using deep learning techniques is called intelligent home load prediction based on deep convolutional neural networks. This method uses convolutional neural networks to analyze data from various sources such as weather, time of day, and other factors to accurately predict the electricity load of a home. The purpose of this method is to help optimize energy usage and reduce energy costs. The article proposes a deep learning-based approach for nonpermanent residential electrical ener-gy load forecasting that employs temporal convolutional networks (TCN) to model historic load collection with timeseries traits and to study notably dynamic patterns of variants amongst attribute par

This work evaluates the influence of combining twisted fins in a triple-tube heat exchanger utilised for latent heat thermal energy storage (LHTES) in three-dimensional numerical simulation and comparing the outcome with the cases of the straight fins and no fins. The phase change material (PCM) is in the annulus between the inner and the outer tube, these tubes include a cold fluid that flows in the counter current path, to solidify the PCM and release the heat storage energy. The performance of the unit was assessed based on the liquid fraction and temperature profiles as well as solidification and the energy storage rate. This study aims to find suitable and efficient fins number and the optimum values of the Re and the inlet tem

In this study, the Halder-Wagner method was used for an analysisX-ray lines of Tio2 nanoparticles. Where the software was used to calculate the FWHM and integral breath (β) to calculate the area under the curve for each of the lines of diffraction. After that, the general equation of the halder- Wagner method is applied to calculate the volume (D), strain (ε), stress (σ), and energy per unit(u). Volume (β). Where the value of the crystal volume was equal to (0.16149870 nm) and the strain was equal to (1.044126), stress (181.678 N / m2), and energy per unit volume (94.8474 J m-3).The results obtained from these methods were then compared with those obtained from each of the new paradigm of the HalderWagner method, the Shearer developm