Thispaperpresentsthesynthesisandstudyofsomenewmixed-liagnd complexescontainingtowaminoacids[Alanine(Ala)andphenylalanine(phe)]withsome metals .Theresultsproductswerefoundtobesolidcrystallinecomplexeswhichhave been characterized by using (FT-IR,UV-Vis) spectra , melting point, elemental analysis (C.H.N) , molar conductivity and solubiltyThe proposed structure of the complexes using program , chem office 3D(2000) .The general formula have been given for the prepared complexes :[M(A-H)(phe-H)]M(II): Hg , Mn ,Co , Ni , Cu ) , Zn , Cd(II) .Ala = Alanine acid = C3H7NO2Phe = phenylalanine = C9H11NO2

This paper presents the synthesis and study of some new mixed-liagnd complexes containing tow amino acids[Alanine(Ala) and phenylalanine (phe)] with some metals . The results products were found to be solid crystalline complexes which have been characterized by using (FT-IR,UV-Vis) spectra , melting point, elemental analysis (C.H.N) , molar conductivity and solubilty The proposed structure of the complexes using program , chem office 3D(2000) . The general formula have been given for the prepared complexes : [M(A-H)(phe-H)] M(II): Hg , Mn ,Co , Ni , Cu ) , Zn , Cd(II) . Ala = Alanine acid = C3H7NO2 Phe = phenylalanine = C9H11NO2

The use of real-time machine learning to optimize passport control procedures at airports can greatly improve both the efficiency and security of the processes. To automate and optimize these procedures, AI algorithms such as character recognition, facial recognition, predictive algorithms and automatic data processing can be implemented. The proposed method is to use the R-CNN object detection model to detect passport objects in real-time images collected by passport control cameras. This paper describes the step-by-step process of the proposed approach, which includes pre-processing, training and testing the R-CNN model, integrating it into the passport control system, and evaluating its accuracy and speed for efficient passenger flow

Background: The purpose of this study was to evaluate and compare centering ability and canal transportation of simulated S-shaped canals instrumented with four different types of rotary nickel-titanium systems. Materials and Methods: Forty simulated S-shaped canals in resin blocks were divided into four groups of ten each and were instrumented to an apical size 25 by different instrumentation technique using ProTaper Universal files (group A), ProTaperNext (group B), Reciproc (group C) and WaveOne (group D).Centering ability and canal transportation was measured at (11) measuring points from D0 to D10 bysuperimposion of the pre- and post-operative images obtained by using digital camera in standardized manner. An assessment of the canals

In this paper new methods were presented based on technique of differences which is the difference- based modified jackknifed generalized ridge regression estimator(DMJGR) and difference-based generalized  jackknifed ridge regression estimator(DGJR), in estimating the parameters of linear part of the partially linear model. As for the nonlinear part represented by the nonparametric function, it was estimated using Nadaraya Watson smoother. The partially linear model was compared using these proposed methods with other estimators based on differencing technique through the MSE comparison criterion in simulation study.

A new derivative of PAM, acrylamide was copolymerized with succinic anhydride, and the reaction product reacted with three dyes, anthocyanin, bromophenol, and thymol. The prepared polymers were characterized by X-ray diffraction, FT-IR and UV-visible spectroscopy, proton nuclear magnetic resonance spectrometry, and thermal analysis. FT-IR spectroscopy showed the disappearance of two bands near 3450 and 3380 cm-1 for the stretching vibrations of the primary amine which indicates for the formation of amides. The UV-photolysis of aqueous solutions of different concentrations of the polymers was studied. Polyacrylamide-g-succinic anhydride showed an increase in polymerization under light. An increase of ~ 50% was observed for a 200 mg/L

The proposal of nonlinear models is one of the most important methods in time series analysis, which has a wide potential for predicting various phenomena, including physical, engineering and economic, by studying the characteristics of random disturbances in order to arrive at accurate predictions.

In this, the autoregressive model with exogenous variable was built using a threshold as the first method, using two proposed approaches that were used to determine the best cutting point of [the predictability forward (forecasting) and the predictability in the time series (prediction), through the threshold point indicator]. B-J seasonal models are used as a second method based on the principle of the two proposed approaches in dete

Lithium-rich layered oxide cathodes have attracted considerable attention due to their high energy density, but have suffered from voltage drop, structural instability, and limited electrical conductivity. In this study, the electrochemical performance of the lithium-rich cathode material Li[Li0.20Mn0.54Ni0.13Co0.13]O2 was evaluated after modification by zinc doping and composition with graphene oxide or graphene. The zinc-doped powders were synthesized by the sol-gel method, while the graphene-based composites were prepared by the hydrothermal route. The structural, morphological and electrochemical characteristics of the modified materials were examined using X-ray diffraction, Fourier transform infrared spectroscopy, field emission scann