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An Application of Response Surface Methodology for Optimizing the Production of Chitin Deacetylase Enzyme by Aspergillus Flavus
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In this study, the optimum conditions for chitin deacetylase (CDA) production by Aspergillus flavus F1 in solid-state fermentation were investigated via two optimization strategies: classical optimization based on the method of one factor at a time and statistical optimization using response surface methodology. The result of classical optimization showed that corn supplemented with 2% chitin moisturized with mineral salts solution at pH=7 and five days of incubation time were the optimum conditions for increasing CDA production with approximately yield of 219.5 U/g solid substrate. Furthermore, pH, moisture level and inoculum size were systemically evaluated to improve CDA production based on a central composite design using the Design expert 7 software. Based on the enhanced regression model, a maximum predicted CDA enzyme production of 283.8 U/g could be obtained with pH 8, moisture level 1:1 (w/v) and inoculum size 3 ml/10g solid substrate, which contain 1×106 spore/ml. The verification of optimization results and determine accuracy of model showed that the actual response of CDA was 312 U/g, which approximately closed to predicted value 283.89 U/g. The crude extract of CDA was concentrated by sucrose. The results showed that 61% of CDA enzyme was yielded with a purification fold of 1.1 In addition. Then CDA was purified partially by gel filtration chromatography after concentration by sucrose with total enzyme activity and specific activity of 1476 U and 12300 (U/mg protein) respectively. Furthermore, the produced CDA enzyme showed maximum activity in pH ranged from (6-8); in which enzyme activity was 69 U/ ml. however; the enzyme stability has a wide range in acidic and alkaline pH. In addition, the enzyme was maintained its activity at temperatures from 30 to 55C˚. Whereas, the activity was declined in temperature up to 55Cº with a minimum activity (8 U/ml) observed at 80Cº.

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Publication Date
Thu Nov 29 2018
Journal Name
Iraqi Journal Of Science
Improving Extractive Multi-Document Text Summarization Through Multi-Objective Optimization
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Multi-document summarization is an optimization problem demanding optimization of more than one objective function simultaneously. The proposed work regards balancing of the two significant objectives: content coverage and diversity when generating summaries from a collection of text documents.

     Any automatic text summarization system has the challenge of producing high quality summary. Despite the existing efforts on designing and evaluating the performance of many text summarization techniques, their formulations lack the introduction of any model that can give an explicit representation of – coverage and diversity – the two contradictory semantics of any summary. In this work, the design of

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Publication Date
Fri Jul 01 2022
Journal Name
Iraqi Journal Of Science
Extractive Multi-Document Text Summarization Using Multi-Objective Evolutionary Algorithm Based Model
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Automatic document summarization technology is evolving and may offer a solution to the problem of information overload. Multi-document summarization is an optimization problem demanding optimizing more than one objective function concurrently. The proposed work considers a balance of two significant objectives: content coverage and diversity while generating a summary from a collection of text documents. Despite the large efforts introduced from several researchers for designing and evaluating performance of many text summarization techniques, their formulations lack the introduction of any model that can give an explicit representation of – coverage and diversity – the two contradictory semantics of any summary. The design of gener

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