This piece of research work aims to study one of the most difficult reaction and determination due to continuous and rapid variation of reaction products and the reactants. As molybdenum (VI) aid in the decomposition of hydrogen peroxide in alkaline medium of ammomia, thus means a continuous liberation of oxygen which cuases and in a continuous manner a distraction in the measurement process. On this basis pyrogallol was used to absorbe all liberated oxygen and the result is an a clean undisturbed signals. Molybdenum (VI) was determined in the range of 4-100 ?g.ml-1 with percentage linearity of 99.8% or (4-300 ?g.ml-1 with 94.4%) while L.O.D. was 3.5 ?g.ml-1. Interferring ions (cations and anions) were studied and their main effect was reduced using mini column cataining resin, or the separation of molybdenum via its precipitation prior to injection. The reaction system and manifold were used to determine pyrogallol (4-40 ?g.ml-1, L.O.D. 80 ?M) and hydrogen peroxide (0.1-5M, L.O.D. 50 mM). Relative standard diviation was better the 1%.
By use the notions pre-g-closedness and pre-g-openness we have generalized a class of separation axioms in topological spaces. In particular, we presented in this paper new types of regulαrities, which we named ρgregulαrity and Sρgregulαrity. Many results and properties of both types have been investigated and have illustrated by examples.
In this study, the mobile phone traces concern an ephemeral event which represents important densities of people. This research aims to study city pulse and human mobility evolution that would be arise during specific event (Armada festival), by modelling and simulating human mobility of the observed region, depending on CDRs (Call Detail Records) data. The most pivot questions of this research are: Why human mobility studied? What are the human life patterns in the observed region inside Rouen city during Armada festival? How life patterns and individuals' mobility could be extracted for this region from mobile DB (CDRs)? The radius of gyration parameter has been applied to elaborate human life patterns with regards to (work, off) days for
... Show MoreIn this work the concept of semi-generalized regular topological space was introduced and studied via semi generalized open sets. Many properties and results was investigated and studied, also it was shown that the quotient space of semi-generalized regular topological space is not, in general semi-generalizedspace.
This research, involved synthesis of some new 1,2,3-triazoline and 1,2,3,4- tetrazole derivatives from antharanilic acid as starting material .The first step includes formation of 2-Mercapto-3-phenyl-4(3H)Quinazolinone (0) through reacted of anthranilic acid with phenylisothiocyanate in ethanol, then compound (0) reaction with chloro acetyl chloride in dimethyl foramamide (DMF) to prepare intermediate S-(α-chloroaceto-2-yl)-3-phenylquinazolin-4(3H)-one (1); compound (1) reacted with sodium azide to yield S-(α-azidoaceto-2-yl)-3-phenylquinazolin-4(3H)-one (2), while Schiff bases (3-10) were prepared from condensation of substituted primary aromatic amines with different aromatic aldehydes in absolute ethanol as a solvent. Compound (2)
... Show MoreImage 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
... Show MoreImage 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
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