A new ligand complexes have been synthesis from reaction of metal ions of MnII , CoII , NiII , CuII , ZnII , CdII and PdII with schiff base [(E)-1-((2-amino-5-(3, 4, 5-trimethoxybenzyl) pyrimidin-4-ylimino) methyl) naphthalen-2-ol [HL)]. The prepared [HL] was characterized by FT-IR, UV-Vis spectroscopy, 1H13CNMR spectra Mass spectra and melting point. The compounds were characterized by techniques UV-Vis and FT-IR spectral studies, micro analysis (C.H.N), determination of atomic absorption, chloride content, molar conductivity measurements, magnetic susceptibility and melting point. The ligand acts as a monobasic tridentate, coordinating through deprotonated phenolic O and azomethine N atoms. The compounds are neutral electrolytic in dimeth

    This article aims to explore the importance of estimating the a semiparametric regression function ,where we suggest a new estimator beside the other combined estimators and then we make a comparison among them by using simulation technique . Through the simulation results we find  that the suggest estimator is the best with the first and second models ,wherealse for the third model we find Burman and Chaudhuri (B&C) is best.

The Cu(II) was found using a quick and uncomplicated procedure that involved reacting it with a freshly synthesized ligand to create an orange complex that had an absorbance peak of 481.5 nm in an acidic solution. The best conditions for the formation of the complex were studied from the concentration of the ligand, medium, the eff ect of the addition sequence, the eff ect of temperature, and the time of complex formation. The results obtained are scatter plot extending from 0.1–9 ppm and a linear range from 0.1–7 ppm. Relative standard deviation (RSD%) for n = 8 is less than 0.5, recovery % (R%) within acceptable values, correlation coeffi cient (r) equal 0.9986, coeffi cient of determination (r2) equal to 0.9973, and percentage capita

In this paper, a new equivalent lumped parameter model is proposed for describing the vibration of beams under the moving load effect. Also, an analytical formula for calculating such vibration for low-speed loads is presented. Furthermore, a MATLAB/Simulink model is introduced to give a simple and accurate solution that can be used to design beams subjected to any moving loads, i.e., loads of any magnitude and speed. In general, the proposed Simulink model can be used much easier than the alternative FEM software, which is usually used in designing such beams. The obtained results from the analytical formula and the proposed Simulink model were compared with those obtained from Ansys R19.0, and very good agreement has been shown. I

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Objective(s): To assess the effect of removable partial dentures on the abutment teeth in comparison with that of the
remaining dentition, and to investigate the effect of regular check up on periodontal health for patients using this
type of prosthesis.
Methodology: One hundred patients wearing removable partial dentures for at least three years included in the
study. Teeth used as direct or in direct retainers for the removable partial denture were considered as the study
group, while the remaining dentition in the same jaw was considered as the control group. The following periodontal
parameters were registered for each tooth, plaque and gingival indices, clinical attachment level and tooth mobility.
Patients were