In this paper we study and design two feed forward neural networks. The first approach uses radial basis function network and second approach uses wavelet basis function network to approximate the mapping from the input to the output space. The trained networks are then used in an conjugate gradient algorithm to estimate the output. These neural networks are then applied to solve differential equation. Results of applying these algorithms to several examples are presented

A new four series of 2,2′-([1,1′- phenyl or biphenyl]-4,4′-diylbis(azanediyl)) bis(N′-((E)-1-(4-alkoxyphenyl) ethylidene) acetohydrazide) [V-XI]a,b and 1,1′-(2,2′-([1,1′- phenyl or biphenyl]-4,4′-diyl bis(azanediyl)) bis- (acetyl)) bis(3-(4-ethoxyphenyl)-1H-pyrazole-4-carbalde hyde) [XII-XVIII]a,b have been synthesized by varying terminal lateral alkoxy chain length (n = 1–3, 5–8), central linkage group (phenyl or biphenyl) and induced pyrazole heterocyclic ring in the main chain. The last two series were synthesized by the cyclization of substituted acetophenone hydrazones with Vilsmeier–Haack reagent (DMF/POCl3) to produce 4-formylpyrazole derivatives. The chemical structures of the synthesized compounds were examine

Cladophora and Spirulina algae biomass have been used for the removal of Tetracycline (TC) antibiotic from aqueous solution. Different operation conditions were varied in batch process, such as initial antibiotic concentration, different biomass dosage and type, contact time, agitation speed, and initial pH. The result showed that the maximum removal efficiencies by using 1.25 g/100 ml Cladophora and 0.5 g/100 ml Spirulina algae biomass were 95% and 94% respectively. At the optimum experimental condition of temperature 25°C, initial TC concentration 50 mg/l, contact time 2.5hr, agitation speed 200 rpm and pH 6.5. The characterization of Cladophora and Spirulina biomass by Fourier transform infrared (FTIR) indicates that the presenc

The increased use of hybrid PET /CT scanners combining detailed anatomical information along withfunctional data has benefits for both diagnostic and therapeutic purposes. This presented study is to makecomparison of cross sections to produce 18F , 82Sr and68Ge via different reactions with particle incident energy up to 60 MeV as a part of systematic studies on particle-induced activations on enriched natNe, natRb, natGa 18O,85Rb, and 69Ga targets, theoretical calculation of production yield, calculation of requiredtarget and suggestion of optimum reaction to produce: Fluorine-18 , Strontium-82 andGermanium-68 touse in Hybrid Machines PET/CT Scanners.

In this paper, a handwritten digit classification system is proposed based on the Discrete Wavelet Transform and Spike Neural Network. The system consists of three stages. The first stage is for preprocessing the data and the second stage is for feature extraction, which is based on Discrete Wavelet Transform (DWT). The third stage is for classification and is based on a Spiking Neural Network (SNN). To evaluate the system, two standard databases are used: the MADBase database and the MNIST database. The proposed system achieved a high classification accuracy rate with 99.1% for the MADBase database and 99.9% for the MNIST database