The aim of this paper is to approximate multidimensional functions by using the type of Feedforward neural networks (FFNNs) which is called Greedy radial basis function neural networks (GRBFNNs). Also, we introduce a modification to the greedy algorithm which is used to train the greedy radial basis function neural networks. An error bound are introduced in Sobolev space. Finally, a comparison was made between the three algorithms (modified greedy algorithm, Backpropagation algorithm and the result is published in [16]).

Background: This in vitro study was carried out to evaluate the effects of various veneering dentin ceramic thicknesses and repeated firings on the color of lithium disilicate glass-ceramic (IPS e.max Press) and zirconium-oxide (IPS ZirCAD) all-ceramic systems, measured by clinical spectrophotometers (Easyshade Advance 4.0) . Materials and methods: The 72specimens cube-shaped have the dimension of about 11 mm in width, 14 mm in length, 1mm in thickness, these cores divided into 3 groups according to the type of material each group have (24)core specimens. Each group had been divided into three sub-groups (each having 8 specimens) according to veneering with dentin ceramic thicknesses: as 0.5, 1, or 2 mm (n=8). IPS e.max press and ZirCAD c

The reaction oisolated and characterized by elemental analysis (C,H,N) , 1H-NMR, mass spectra and Fourier transform (Ft-IR). The reaction of the (L-AZD) with: [VO(II), Cr(III), Mn(II), Co(II), Ni(II), Cu(II), Zn(II), Cd(II) and Hg(II)], has been investigated and was isolated as tri nuclear cluster and characterized by: Ft-IR, U. v- Visible, electrical conductivity, magnetic susceptibilities at 25 Co, atomic absorption and molar ratio. Spectroscopic evidence showed that the binding of metal ions were through azide and carbonyl moieties resulting in a six- coordinating metal ions in [Cr (III), Mn (II), Co (II) and Ni (II)]. The Vo (II), Cu (II), Zn (II), Cd (II) and Hg (II) were coordinated through azide group only forming square pyramidal

In this study, active knife and fixed knife of single-row disc silage machine has three different clearance C1, C2 and C3 (1, 3 and 5 mm) and it is tried in three different working speed V1, V2 and V3 (1.8, 2.5 and 3.7 km / h) and PTO speed (540 min-1) and machine's fuel consumption (l/h), average power consumption (kW), field energy consumption (kW/da), product energy consumption (kW/t), field working capacity (da/h), product working capacity (t/h) and Chopping size distribution characteristics of the fragmented material were determined. It has been found that knife-counter knife clearances smaller than 3 mm (1 mm) and larger (5 mm) have a negative effect on machine performance in general. In terms of fuel and power consumptions, the m

were prepared by condensation of 6-R-2amino bcnzothiazol with Salicyldehyde.These Schiff bases were found to reach with maleic anhydride and citraconic to give

Background: We aimed to investigate the accuracy of salivary matrix metalloproteinases (MMP)-8 and -9, and tissue inhibitor of metalloproteinase (TIMP)-1 in diagnosing periodontitis and in distinguishing periodontitis stages (S)1 to S3. Methods: This study was a case–control study that included patients with periodontitis S1 to S3 and subjects with healthy periodontia (controls). Saliva was collected, and then, clinical parameters were recorded, including plaque index, bleeding on probing, probing pocket depth, and clinical attachment level. Diagnosis was confirmed by assessing the alveolar bone level using radiography. Salivary biomarkers were assayed using an enzyme-linked immunosorbent assay. Results: A total of 45 patients (15

Objective: Breast cancer is regarded as a deadly disease in women causing lots of mortalities. Early diagnosis of breast cancer with appropriate tumor biomarkers may facilitate early treatment of the disease, thus reducing the mortality rate. The purpose of the current study is to improve early diagnosis of breast by proposing a two-stage classification of breast tumor biomarkers fora sample of Iraqi women.

Methods: In this study, a two-stage classification system is proposed and tested with four machine learning classifiers. In the first stage, breast features (demographic, blood and salivary-based attributes) are classified into normal or abnormal cases, while in the second stage the abnormal breast cases are

Complexes of (Co2+, Ni2+, Cu2+, Zn2+, Cd2+ and Hg2+) with the ligand Ethyl cyano (2-methyl carboxylate phenyl azo acetate) (ECA) have been prepared and characterized by FTIR, (UV-Visible), Atomic absorption spectroscopy, Molar conductivity measurements and magnetic moments measurements. The following general formula has been suggested for the prepared complexes [M(ECA)2]Cl2 where M = (Co2+, Ni2+, Cu2+ ,Zn2+, Cd2+, Hg2+) and the geometry is octahedral.