A common field development task is the object of the present research by specifying the best location of new horizontal re-entry wells within AB unit of South Rumaila Oil Field. One of the key parameters in the success of a new well is the well location in the reservoir, especially when there are several wells are planned to be drilled from the existing wells. This paper demonstrates an application of neural network with reservoir simulation technique as decision tool. A fully trained predictive artificial feed forward neural network (FFNNW) with efficient selection of horizontal re-entry wells location in AB unit has been carried out with maintaining a reasonable accuracy. Sets of available input data were collected from the exploited grids and used in the training and testing of the used network. A comparison between the calculated and observed cumulative oil production has been carried out through the testing steps of the constructed ANN, an absolute average percentage error of the used network was reached to 4.044%, and this is consider to be an acceptable limit within engineering applications, in addition to that, a good behavior was reached with (FFNNW) and suitable re-entry wells location were identified according to the reservoir configuration (pressure and saturation distribution) output from SRF simulation model at the end of 2005.
The aim of this study was the discrimination of Salmonella isolated from chicken and their feed and drinking water for the epidemiological control of salmonellosis. Totally, 289 samples, including 217 chicken cloaca swabs, 46 water, and 26 feed samples were collected from five different farms in Karbala governorate, Iraq. Conventional bacteriology tests, API 20E, Vitek 2, and serology were used for bacterial identification. Random amplified polymorphic DNA (RAPD)-polymerase chain reaction (PCR) was applied to analyze the genetic relationships among Salmonella isolates. The isolation rate of Salmonella spp. was 21.1% (61/289). While the water samples constituted the highest rate (30.4%), a rate of
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A new series of chalcone derivatives featuring an oxadiazole-quinoline moiety were successfully synthesized through a multi-step reaction sequence, commencing with quinoline-2-carboxylic acid as the starting material. First, the carboxylic group was chlorinated to form an acid chloride, following reacted with hydrazine hydrate. The resulting product underwent cyclization with carbon disulfide in an alkaline solution to produce 5-(quinolin-2-yl)-1,3,4-oxadiazole-2-thiol, followed by alkylation using chloroacetone. In the final step, an aldol condensation reaction was carried out by grinding the acetone derivative with various aromatic aldehydes, yielding the desired chalcones. The synthesized compounds were characterized by Rf, FTIR,
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The aim of this stud to isolate and identified of A. fumigatus from different sources and study the genetic diversity among these isolates by using RAPD and ISSR markers.Collected 20 samples from 7samples were isolated A. fumigatusisolates were characterized depending on its morphological, then extracted DNA from its.RAPD markersrandomly bandingwith sitesof genome more than ISSR markers where the primer OPN-07 achieved discriminative power (19.1) and 43 bands, while ISSR6 achieved discriminative power (17.1) with 32 bands.ISSR were more efficiency in specific binding then RAPD, ISSR primers has great a binding to production unique band, when 9 primers from 01 primers, ISSR9 was produce (5) unique bands, while RAPD markers was low ability
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One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p
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The study included the investigation of fungi which associated with heavy animal's leather (Cows and Buffalos) and light (Sheep’s and Goats )through different processing stages (raw hides ,dehairing ,pickling,chrome tanned and stainning or finished stages)there were 10 genera and 25 species in addition to sterile fungi associated with animal leathers which included Alternaria ,Aspergillus,Cladosporium,Fusarium, Mucor , Penicillium , Rhizopus , and Trichoderma .Aspergillus and Penicillium have observed in all leather samples and different processing stages, and that the first time isolate two genera Helminthosporium , Stemphylium form leather for staining stage.
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