Porous silicon (PS) layers were formed on n-type silicon (Si) wafers using Photo- electrochemical Etching technique (PEC) was used to produce porous silicon for n-type with orientation of (111). The effects of current density were investigated at: (10, 20, 30, 40, and50) mA/cm2 with etching time: 10min. X-ray diffraction studies showed distinct variations between the fresh silicon surface and the synthesized porous silicon. The maximum crystal size of Porous Silicon is (33.9nm) and minimum is (2.6nm) The Atomic force microscopy (AFM) analysis and Field Emission Scanning Electron Microscope (FESEM) were used to study the morphology of porous silicon layer. AFM results showed that root mean square (RMS) of roughness and the grain size of p

Densities
 and viscosity
 of serine in 20, 40, and 60% (w/w) dimethyl sulfoxide (DMSO)-water mixtures were measured at 298.15, 303.15 and 308.15k. From these experimental data, apparent molal volume v  , limiting apparent molal volume v  o , the slop v S , transfer volume  v  o(tr), Jones-Dole coefficients A and B were calculated. The results are
v  odiscussed the solute-solvent and solute-solute interaction, and showed that serine behaves as structure-breaker in aqueous DMSO solvent

   Density data of  alum chrom in water and in aqueous solution of poly (ethylene glycol) (1500) at different temperatures (288.15, 293.15, 298.15) k have been used to estimate the apparent molar volume (Vθ), limiting apparent molar volume (Vθ˚) experimental slope (Sv) and the second derivative of limiting partial molar volume [δ2 θ v° /δ T2] p .The viscosity data have been analyzed by means of Jones –Dole equation to obtain coefficient A, and  Jones – Dole coefficient B, Free activation  energy of activation per mole of solvent, Δμ10* solute, Δμ20* the activation enthalpy ΔH*,and entropy, ΔS*of activation of viscous flow. These results have been discussed

The purpose of this research is to investigate the impact of corrosive environment (corrosive ferric chloride of 1, 2, 5, 6% wt. at room temperature), immersion period of (48, 72, 96, 120, 144 hours), and surface roughness on pitting corrosion characteristics and use the data to build an artificial neural network and test its ability to predict the depth and intensity of pitting corrosion in a variety of conditions. Pit density and depth were calculated using a pitting corrosion test on carbon steel (C-4130). Pitting corrosion experimental tests were used to develop artificial neural network (ANN) models for predicting pitting corrosion characteristics. It was found that artificial neural network models were shown to be

Structure of unstable 21,23,25,26F nuclei have been investigated
using Hartree – Fock (HF) and shell model calculations. The ground
state proton, neutron and matter density distributions, root mean
square (rms) radii and neutron skin thickness of these isotopes are
studied. Shell model calculations are performed using SDBA
interaction. In HF method the selected effective nuclear interactions,
namely the Skyrme parameterizations SLy4, Skeσ, SkBsk9 and
Skxs25 are used. Also, the elastic electron scattering form factors of
these isotopes are studied. The calculated form factors in HF
calculations show many diffraction minima in contrary to shell
model, which predicts less diffraction minima. The long tail

Viscosities (η) and densities (ρ) of atenolol and propranolol hydrochloride in water and in concentrations (0.05 M) and (0.1 M) aqueous solution of threonine have been used to reform different important thermodynamic parameters like apparent molal volumes fv partial molal volumes at infinite dilution fvo , transfer volume fvo (tr), the slop Sv , Gibbs free energy of activation for viscous flow of solution ΔG*1,2 and the B-coefficient have been calculated using Jones-Dole equation. These thermodynamic parameters have been predicted in terms of solute-solute and solute-solvent interaction.

Botnet detection develops a challenging problem in numerous fields such as order, cybersecurity, law, finance, healthcare, and so on. The botnet signifies the group of co-operated Internet connected devices controlled by cyber criminals for starting co-ordinated attacks and applying various malicious events. While the botnet is seamlessly dynamic with developing counter-measures projected by both network and host-based detection techniques, the convention techniques are failed to attain sufficient safety to botnet threats. Thus, machine learning approaches are established for detecting and classifying botnets for cybersecurity. This article presents a novel dragonfly algorithm with multi-class support vector machines enabled botnet