The orogenic gold deposit of Tamilouw – Haya is hosted by slate and metapelitic rocks within Tehoru metamorphic complex. Gold and polymetallic sulfides mineralization at study area is predominantly formed in the form of veins, stockwork and breccia although minor dissemination is slightly appeared in the rock float samples. They are trapped and controlled by NE-SW and NNE-SSW trending geologic structure occurred during orogeny process from Late Miocene to Pliocene. The common ore minerals assemblage at Tamilouw – Haya deposit are dominated by native gold, chalcopyrite, pyrite, sphalerite, galena, pyrrhotite, tetrahedrite-tennantite (sulphosalt), marcasite,realgar, kalininite and arsenopyrite as hypogene minerals and accompanied by co

In the present work, Response Surface Methodology (RSM) was utilized to optimize process variables and find the best circumstances for indirect electrochemical oxidation of mimicked wastewater to remove phenol contaminants using prepared ternary composite electrode. The electrodeposition process is used for the synthesis of a ternary composite electrode of Mn, Co, and Ni oxides. The selected concentrations of metal salts of these elements were 0.05, 0.1, and 1.5 M, with constant molar ratio, current density, and electrolysis time of 1:1:1, 25 mA/cm2, and 2 h. Interestedly, the gathered Mn-Co-Ni oxides were deposited at both the anode and cathode. X-ray diffraction (XRD) and scanning electron microscopy (SEM) facilitated the qualitative char

In this work, an experimental research on a low voltage DC magnetron plasma sputtering (0-650) volt is used for coating gold on a glass substrate at a constant pressure of argon gas 0.2 mbar and deposition time of 30 seconds. We focused on the effects of operating conditions for the system such as, electrode separation and sputtering current on coated samples under the influence of magnetic flux. Electron temperature and electrons and ions densities are determined by a cylindrical single Langmuir probe. The results show the sensitivity of electrode separation lead to change the plasma parameters. Furthermore, the surface morphology of gold coated samples at different electrode separation and sputtering current were studied by atomic forc

This assay rapidly detects chlorpromazine hydrochloride using its ability to reduce gold ions to form nanoparticles. Its low cost, resilience to interferences and short analysis time could facilitate environmental monitoring and biomedical analysis.

This assay rapidly detects chlorpromazine hydrochloride using its ability to reduce gold ions to form nanoparticles. Its low cost, resilience to interferences and short analysis time could facilitate environmental monitoring and biomedical analysis.

Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a

New series of metal ions complexes have been prepared from the new ligand [4-Amino-N-(5- methyl-isaxazol-3-yl)-benzenesulfonamide] derived from Sulfamethoxazole and 3-aminophenol. Accordingly, mono-nuclear Mn(II), Fe(III), Co (II), and Rh(III) complexes were prepared by the reaction of previous ligand with MnCl2.4H2O, CoCl2.6H2O, FeCl3.6H2O and RhCl3H2O, respectively. The compounds have been characterized by Fourier-transform infrared (FTIR), ultraviolet–visible (UV–vis), mass, 1H-, and 13C-nuclear magnetic resonance (NMR) spectra and thermo gravimetric analysis (TGA& DSC) curve, Bohr magnetic (B.M.), elemental microanalyses, metal ions, chloride containing, and molar conductance.These reviews uncovered octahedral geometries for complex

This paper includes an experimental study of hydrogen mass flow rate and inlet hydrogen pressure effect on the fuel cell performance. Depending on the experimental results, a model of fuel cell based on artificial neural networks is proposed. A back propagation learning rule with the log-sigmoid activation function is adopted to construct neural networks model. Experimental data resulting from 36 fuel cell tests are used as a learning data. The hydrogen mass flow rate, applied load and inlet hydrogen pressure are inputs to fuel cell model, while the current and voltage are outputs. Proposed model could successfully predict the fuel cell performance in good agreement with actual data. This work is extended to developed fuel cell feedback