An extracellular β-galactosidase from the thermophilic fungus Rhizomucor
Pusillus IB8 has been purified via several steps included precipitation by ammonium
sulphate at 80 % saturation, DEAE- Cellulose Ion exchange chromatography and gel filteration on sepharose CL-6B column. The Final purification folds and the yield of the enzyme were 42.5 and 24.8 % respectively. The purified β-galactosidase has an optimum pH for its activity between 4.5 to 5, while the optimum pH for enzyme stability was between 5 to 5.5. Futhermore, it was found that the optimum temperature for its activity was 60 C°. The purified enzyme retained approximatly 98% of its original activity when incubated at 60 C° for 60 min. However, 25 % of its activity was lost when incubated for 120 min at the same tmperaure. Activation energy for conversion of the substrate ONPG to products was 6.15 Kcal / mol, whereas, for enzyme denaturation it was 99.3 Kcal / mol. The molecular weight of the purified enzyme was 232000 dalton as determined by gel filtration on sepharose CL-6B. Kinetic studies showed that the Michaelis constant (Km) and maximum velocity (Vmax) values for the purified enzyme using ONPG as a substrate were 0.46 mM and 223 μM /min respectively.
Purification and Properties of β-Galactosidase From A Thermophilic Fungus Rhizmmucor Pusillus IB8.
Purification and Properties
β-Galactosidase
Rhizmmucor Pusillus IB8.
Publication Date
Wed Apr 25 2018
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Different Estimation Methods for System Reliability Multi-Components model: Exponentiated Weibull Distribution
Exponentiated Weibull Distribution (EWD)
Reliability of multi-component Stress – Strength models R(s
k)
Maximum likelihood estimator (MLE)
Moment estimator (MOM)
shrinkage method (Sh)
Least Squares Estimator (LS)
Rank Set Sampling (RSS). and mean squared error (MSE).
In this paper, estimation of system reliability of the multi-components in stress-strength model R(s,k) is considered, when the stress and strength are independent random variables and follows the Exponentiated Weibull Distribution (EWD) with known first shape parameter θ and, the second shape parameter α is unknown using different estimation methods. Comparisons among the proposed estimators through Monte Carlo simulation technique were made depend on mean squared error (MSE) criteria
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Publication Date
Sat Oct 01 2016
Journal Name
Journal Of Theoretical And Applied Information Technology
Factors affecting global virtual teams’ performance in software projects
Today the trend is to perform software development work via distributed geographical area among team
individual or even as an organization. However
due to the global market and international presence of many companies
there is a need to implement a global virtual teams. The global virtual team members are gradually engaged in globalized business environments across space
time and organizational boundaries via information and communication technologies. A global virtual team relies on communication
collaboration
and information exchange are the most important criteria in global virtual teams’ operations. The purpose of this paper is to answer two research questions. The first research question is to identify the factors affecting global virtual teams’ performance. A systematic literature review was conducted to answer the first research question. The second research question is on what the rank of the factor affecting the global virtual teams’ performance according to their level of effect on global virtual teams’ performance. Online survey was conducted within 103 developers and IT managers from eight IT companies to answer the second research question. The Statistical Package for Social Science (SPSS 22) was used to analyze the collected data. In this study
we investigated factors that affect global virtual teams’ performance
factors considered include cultural differences
language problems
time-zone differences
team size
technical problems
lack of trust
lack of sufficient training
and ICT problems. Also the findings indicated that lack of sufficient training is the highest level of effect on global virtual teams’ performance. On the other hand
team size is the lowest level of effect on global virtual teams’ performance. © 2005 - 2016 JATIT & LLS. All rights reserved.
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