Microbial lipases today occupy a place of prominence among biocatalysts owing to their ability to catalyze awide variety of reactions in aqueous and non- aqueous media, A.baumannii were isolated from different clinical specimens from hospitalized patients from Baghdad hospitals and were detected by biochemical tests and API20E system. The percentage of isolation was (16.6%), A. baumannii is an increasingly multidrug – resistant (MDR), it showed high level of resistant to Ceftriaxon, Colistin, Piperacillin, Co-trimoxazol, Tertracycline, Carbenicillin, Amoxicillin, Penicillin G, Gentamicin and Ceftazidim , wherease the isolates were highly sensitive to Imipenem, Ciprofloxacin, Meropenem, Amikacin, and Cefotaxime.

              The current paper proposes a new estimator for the linear regression model parameters under Big Data circumstances.  From the diversity of Big Data variables comes many challenges that  can be interesting to the  researchers who try their best to find new and novel methods to estimate the parameters of linear regression model. Data has been collected by Central Statistical Organization IRAQ, and the child labor in Iraq has been chosen as data. Child labor is the most vital phenomena that both society and education are suffering from and it affects the future of our next generation. Two methods have been selected to estimate the parameter

This paper deals with constructing a model of fuzzy linear programming with application on fuels product of Dura- refinery , which consist of seven products that have direct effect ondaily consumption . After Building the model which consist of objective function represents the selling prices ofthe products and fuzzy productions constraints and fuzzy demand constraints addition to production requirements constraints , we used program of ( WIN QSB )  to find the optimal solution

From 211 urine samples, Gram negative bacteria were isolated from only 61 urine samples with isolation percentage 28.9%. Escherichia coli were isolated percentage 70.49% while Klebsiella pneumoniae and Psendomonas aeruginosa were 8.19% and 6.55%, respectively.Proteus spp. Were isolated from 9 (14.75%), P. mirablis and P. vulgaris were isolates percentage 11.47% and 3.27%, respectively. Uroepithelial Cell Adhesin (UCA) fimbriae expression by P.mirabilis isolates was detected by the high capacity to adhesion to human uroepithetial cells, the isolate p.mirabilis U7 was adhesion to human uroepithelial cells mean no.30.2 bacteria/cell when grown on luria broth at 37C for 24h, but then grown it’s on luria agar at 37C for 24h the adhesion

Pathological blood clot in blood vessels, which often leads to cardiovascular diseases, are one of the most common causes of death in humans. Therefore, enzymatic therapy to degrade blood clots is vital. To achieve this goal, bromelain was immobilized and used for the biodegradation of blood clots. Bromelain was extracted from the pineapple fruit pulp (Ananas comosus) and purified by ion exchange chromatography after precipitation with ammonium sulphate (0-80 %), resulting in a yield of 70%, purification fold of 1.42, and a specific activity of 1175 U/mg. Bromelain was covalently immobilized on functionalized multi-walled carbon nanotubes (MWCNT), with an enzyme loading of 71.35%. The results of the characterization of free and immobilized

Generally, statistical methods are used in various fields of science, especially in the research field, in which Statistical analysis is carried out by adopting several techniques, according to the nature of the study and its objectives. One of these techniques is building statistical models, which is done through regression models. This technique is considered one of the most important statistical methods for studying the relationship between a dependent variable, also called (the response variable) and the other variables, called covariate variables. This research describes the estimation of the partial linear regression model, as well as the estimation of the “missing at random” values (MAR). Regarding the

The prevalence of using the applications for the internet of things (IoT) in many human life fields such as economy, social life, and healthcare made IoT devices targets for many cyber-attacks. Besides, the resource limitation of IoT devices such as tiny battery power, small storage capacity, and low calculation speed made its security a big challenge for the researchers. Therefore, in this study, a new technique is proposed called intrusion detection system based on spike neural network and decision tree (IDS-SNNDT). In this method, the DT is used to select the optimal samples that will be hired as input to the SNN, while SNN utilized the non-leaky integrate neurons fire (NLIF) model in order to reduce latency and minimize devices

The prevalence of using the applications for the internet of things (IoT) in many human life fields such as economy, social life, and healthcare made IoT devices targets for many cyber-attacks. Besides, the resource limitation of IoT devices such as tiny battery power, small storage capacity, and low calculation speed made its security a big challenge for the researchers. Therefore, in this study, a new technique is proposed called intrusion detection system based on spike neural network and decision tree (IDS-SNNDT). In this method, the DT is used to select the optimal samples that will be hired as input to the SNN, while SNN utilized the non-leaky integrate neurons fire (NLIF) model in order to reduce latency and minimize devices