Choosing antimicrobials is a common dilemma when the expected rate of bacterial resistance is high. The observed resistance values in unequal groups of isolates tested for different antimicrobials can be misleading. This can affect the decision to recommend one antibiotic over the other. We analyzed recalled data with the statistical consideration of unequal sample groups. Data was collected concerning children suspected to have typhoid fever at Al Alwyia Pediatric Teaching Hospital in Baghdad, Iraq. The study period extended from September 2021 to September 2022. A novel algorithm was developed to compare the drug sensitivity among unequal numbers of Salmonella typhi (S. Typhi) isolates tested with different antibacterials. According to the proposed algorithm, the predicted resistance values were more valid than the observed values. This proposed algorithm is expected to help the hospital antibiotic policy committee recommend the proper antibacterial agents for S. Typhi and further bacterial isolates.
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
RA Ali, LK Abood, Int J Sci Res, 2017 - Cited by 2
viruses are responsible for a large proportion of lower respiratory tract infections (LRTIs). Other causes of LRTIs are bacteria: Streptococcus pneumoniae, Haemophilus influenzae, Klebsiella pneumoniae, and Staphylococcus aureus being the most common. Sputum samples are commonly used in the microbiological laboratory for diagnosing lower respiratory infections. Objective: The aim of this study to evaluate the causative bacteria and antibiotics sensitivity in culture of sputum samples. Patients Methods: A retrospective study performed in the microbiology department of Al Immamin Al Kahdimin Medical laboratory in Baghdad. The results of sputum cultures collected from the files between 2016 and 2019. A tota
... Show MoreExponential distribution is one of most common distributions in studies and scientific researches with wide application in the fields of reliability, engineering and in analyzing survival function therefore the researcher has carried on extended studies in the characteristics of this distribution.
In this research, estimation of survival function for truncated exponential distribution in the maximum likelihood methods and Bayes first and second method, least square method and Jackknife dependent in the first place on the maximum likelihood method, then on Bayes first method then comparing then using simulation, thus to accomplish this task, different size samples have been adopted by the searcher us
... Show MorePetioles of nine species and four subspecies for Scutellaria L. which are growing wildly in Iraq were studied anatomically. Systmatic importance for the cross sections of those petioles were found. The winged petioles were seen in all taxa but they were different in their shapes , some of them U – shaped but they were semi ellipsoid in others , also they were different in their numbers of vascular bundles in the wings , and the shapes of central vascular bundles , so those taxa were divided into groups .
The purpose of this paper is to model and forecast the white oil during the period (2012-2019) using volatility GARCH-class. After showing that squared returns of white oil have a significant long memory in the volatility, the return series based on fractional GARCH models are estimated and forecasted for the mean and volatility by quasi maximum likelihood QML as a traditional method. While the competition includes machine learning approaches using Support Vector Regression (SVR). Results showed that the best appropriate model among many other models to forecast the volatility, depending on the lowest value of Akaike information criterion and Schwartz information criterion, also the parameters must be significant. In addition, the residuals
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