The presence of heavy metal in environment associated with several health problems. The clean up environment from lead (Pb) and Nickel (Ni) represent major challenges. In his study, planktonic and immobilized bacteria were used to purify the water from Pb and Ni in Lab. In the present study, three bacterial isolates of Staphylococcus aureus (isolated from wound swaps), Pseudomonas aeruginosa (isolated from wound swaps) and Pantoea (isolated from urine samples) and identified using biochemical methods to check their ability to biosorb Pb and Ni. Ten PPM of Pb and Ni were added to the deionized distilled water and 107 c.f.u. of planktonic bacteria were used to biosorpe Pb and Ni. Similar experiment was repeated but in this case, the immobilized bacteria (S. aureus, Pantoea, and P. aeruginosa) on silica gel and eggshells were used. It was found that S. aureus decreased the level of Pb and Ni significantly (P<0.05) in planktonic and immobilized form. Pantoea decreases the level of Ni only in planktonic form. This bacteria decreased the level of Pb and Ni significantly when it immobilized on silica gel and eggshells (P<0.05). P. aeruginosa could not decrease the level of Pb and Ni when it was in planktonic form but it can decrease the level of heavy metals in the immobilized form on silica and eggshells (P<0.05). It can be concluded that the studied bacteria can purify water from heavy metals in immobilized status more efficiently than planktonic form.
Biosorption of Pb and Ni From Aqueous Solution by Staphylococcus Aureus, Pantoea and Pseudomonas Aeruginosa
Biosorption
Pb
Ni
Staphylococcus aureus
Pantoea and Pseudomonas aeruginosa
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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