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Determination of Optimal Conditions for the Production of Laccase Enzyme by Local Isolate of Bacillus sp.
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Fifty isolates of Bacillus sp. were subjected to the first and second screening to detect the ability to produce laccase enzyme and select the highest ones production of laccase on Petri plates containing nutrient agar supplemented with Cu2+.
Syringaldazine was used as an indicator and substrate for the determination of laccase activity. Three isolates, which consumed less time to developed pink color were tested for the production of laccase quantitatively. The effective isolate B16 with significant amounts of laccase 1.84 unit /ml was selected for laccase study.
The optimization studies revealed that the maximum laccase production was achieved when the production medium was at the following conditions: 5 days of incubation, temperature 35 C°, pH 7.0, copper sulphate 0.2 mM, galactose 3% as carbon source, 0.2% Tryptone as nitrogen source and K+ 1mM.

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Publication Date
Sat Oct 28 2023
Journal Name
Baghdad Science Journal
Diversity Operators-based Artificial Fish Swarm Algorithm to Solve Flexible Job Shop Scheduling Problem
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Artificial fish swarm algorithm (AFSA) is one of the critical swarm intelligent algorithms. In this
paper, the authors decide to enhance AFSA via diversity operators (AFSA-DO). The diversity operators will
be producing more diverse solutions for AFSA to obtain reasonable resolutions. AFSA-DO has been used to
solve flexible job shop scheduling problems (FJSSP). However, the FJSSP is a significant problem in the
domain of optimization and operation research. Several research papers dealt with methods of solving this
issue, including forms of intelligence of the swarms. In this paper, a set of FJSSP target samples are tested
employing the improved algorithm to confirm its effectiveness and evaluate its ex

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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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