The main objective of this research is to design and select a composite plate to be used in fabricating wing skins of light unman air vehicle (UAV). The mechanical properties, weight and cost are the basis criteria of this selection. The fiber volume fraction, fillers and type of fiber with three levels for each were considered to optimize the composite plate selection. Finite element method was used to investigate the stress distribution on the wing at cruise flight condition in addition to estimate the maximum stress. An experiments plan has been designed to get the data on the basis of Taguchi technique. The most effective parameters at the process to be find out by employing L₉

Submerged arc welding (SAW) process is an essential metal joining processes in industry. The quality of weld is a very important working aspect for the manufacturing and construction industries, the challenges are made optimal process environment. Design of experimental using Taguchi method (L9 orthogonal array (OA)) considering three SAW parameter are (welding current, arc voltage and welding speed) and three levels (300-350-400 Amp. , 32-36-40 V and 26-28-30 cm/min). The study was done on SAW process parameters on the mechanical properties of steel type comply with (ASTM A516 grade 70). Signal to Noise ratio (S/N) was computed to calculate the optimal process parameters. Percentage contributions of each parameter are validated by using an

Unlike fault diagnosis approaches based on the direct analysis of current and voltage signals, this paper proposes a diagnosis of induction motor faults through monitoring the variations in motor's parameters when it is subjected to an open circuit or short circuit faults. These parameters include stator and rotor resistances, self-inductances, and mutual inductance. The genetic algorithm and the trust-region method are used for the estimation process. Simulation results confirm the efficiency of both the genetic algorithm and the trust-region method in estimating the motor parameters; however, better performance in terms of estimation time is obtained when the trust-region method is adopted. The results also show the po

Mixed ligand metal complexes are synthesized from oxalic acid with Schiff base, and the Schiff base was obtained from trimethoprim and acetylacetone. The synthesized complexes were of the type [M(L1)(L2)], where the metal, M, is Ni(II), Cu(II), Cr(III), and Zn(II), L1 corresponds to the trimethoprim ((Z)-4-((4-amino-5-(3,4,5-trimethoxybenzyl)pyrimidine-2-yl)imino)pentane-2-one) as the first ligand and L2 represent the oxalate anion ( ) as a second ligand. Characterization of the prepared compounds was performed by elemental analysis, molar conductivity, magnetic measurements, 1H-NMR, 13C-NMR, FT-IR, and Ultraviolet-visible (UV-Vis) spectral studies. The recorded infrared data is reinforced with density functional theory (DFT) calcul

Mixed ligand metal complexes are synthesized from oxalic acid with Schiff base, and the Schiff base was obtained from trimethoprim and acetylacetone. The synthesized complexes were of the type [M(L1)(L2)], where the metal, M, is Ni(II), Cu(II), Cr(III), and Zn(II), L1 corresponds to the trimethoprim ((Z)-4-((4-amino-5-(3,4,5- trimethoxybenzyl)pyrimidine-2-yl)imino)pentane-2-one) as the first ligand and L2 represent the oxalate anion (𝐶􀬶𝑂􀬶 􀬶􀬿) as a second ligand. Characterization of the prepared compounds was performed by elemental analysis, molar conductivity, magnetic measurements, 1H-NMR, 13C-NMR, FT-IR, and Ultraviolet-visible (UV-Vis) spectral studies. The recorded infrared data is reinforced with density functional th

Recurrent strokes can be devastating, often resulting in severe disability or death. However, nearly 90% of the causes of recurrent stroke are modifiable, which means recurrent strokes can be averted by controlling risk factors, which are mainly behavioral and metabolic in nature. Thus, it shows that from the previous works that recurrent stroke prediction model could help in minimizing the possibility of getting recurrent stroke. Previous works have shown promising results in predicting first-time stroke cases with machine learning approaches. However, there are limited works on recurrent stroke prediction using machine learning methods. Hence, this work is proposed to perform an empirical analysis and to investigate machine learning al