Bacterial meningitis is a leading cause of illness and death worldwide. It is crucial for clinical and public health care, as well as disease control, to identify the meningitis-causing agent promptly. Between June 2021-February 2022, a total of 100 cerebrospinal fluid (CSF) and blood samples were collected from suspected cases of meningitis admitted to Raparin Paediatric Teaching Hospital, Erbil city-Iraq. Cytochemical, cultural, and biochemical tests were conducted, and confirmed by molecular techniques. Bacterial culture findings were positive in 7% of CSF samples and just one positive among blood samples. The most common pathogens found by cultural characteristics and VITEK 2 Compact System were Staphylococcus sciuri in two cases 2%, Staphylococcus xylosus in one case 1%, Escherichia coli in two instances 2%, Enterococcus casseliflavus and Micrococcus luteus each in one case 1%. Staphylococcus sciuri, Staphylococcus xylosus, Enterococcus casseliflavus and Micrococcus luteus were first recorded as bacterial meningitis in Erbil/Iraq. All isolates were confirmed by PCR assay. All clinical isolates were screened for some antimicrobial sensitivity, meropenem and tobramycin have been shown to be totally resistant 100% to all isolated bacteria, furthermore, isolated E coli showed highly resistant 100 to cefotaxime, gentamycin, pencillin, ceftriaxone, rifampin, amoxicillin/clavulanic acid, ceftazidime, erythromycin, ampicillin, and clindamycin, while they were sensitive (100%) to amikacin and imipenem as well as all the gram positive bacteria were resistant 100% to optochin and sensitive (100%) to gentamycin, and trimethoprim. In bacterial meningitis patients, high C-reactive protein (CRP) >6 mg/dl, high CSF protein >50 mg/dl, low CSF glucose level <40 mg/dl and high leukocyte count >100 cells/mm3 were all substantially diagnostic.
The convolutional neural networks (CNN) are among the most utilized neural networks in various applications, including deep learning. In recent years, the continuing extension of CNN into increasingly complicated domains has made its training process more difficult. Thus, researchers adopted optimized hybrid algorithms to address this problem. In this work, a novel chaotic black hole algorithm-based approach was created for the training of CNN to optimize its performance via avoidance of entrapment in the local minima. The logistic chaotic map was used to initialize the population instead of using the uniform distribution. The proposed training algorithm was developed based on a specific benchmark problem for optical character recog
... Show MoreWater saturation is the most significant characteristic for reservoir characterization in order to assess oil reserves; this paper reviewed the concepts and applications of both classic and new approaches to determine water saturation. so, this work guides the reader to realize and distinguish between various strategies to obtain an appropriate water saturation value from electrical logging in both resistivity and dielectric has been studied, and the most well-known models in clean and shaly formation have been demonstrated. The Nuclear Magnetic Resonance in conventional and nonconventional reservoirs has been reviewed and understood as the major feature of this approach to estimate Water Saturation based on T2 distribution. Artific
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The heterogeneity nature of carbonate reservoirs shows sever scattering of the data, therefore, one has to be cautious in using the permeability- porosity correlation for calculating permeability unless a good correlation coefficient is available. In addition, a permeability- porosity correlation technique is not enough by itself since simulation studies also require more accurate tools for reservoir description and diagnosis of flow and non-flow units.
Evaluation of reservoir characterization was conducted by this paper for Mishrif Formation in south Iraqi oil field (heterogeneous carbonate reservoir), namely the permeability-porosity correlation, the hydraulic units (HU’s) an
... Show MoreThe heterogeneity nature of carbonate reservoirs shows sever scattering of the data, therefore, one has to be cautious in using the permeability- porosity correlation for calculating permeability unless a good correlation coefficient is available. In addition, a permeability- porosity correlation technique is not enough by itself since simulation studies also require more accurate tools for reservoir description and diagnosis of flow and non-flow units. Evaluation of reservoir characterization was conducted by this paper for Mishrif Formation in south Iraqi oil field (heterogeneous carbonate reservoir), namely the permeability-porosity correlation, the hydraulic units (HU’s) and global hydraulic elements (GHE
... Show MoreThe study dealt with The impact of dangerous goods Transportation for Maritime carrier responsibility, the study was exposed to the definition of dangerous goods. the study also dealt with the impact of IMDG code on the responsibility of the carrier, and the duties of the parties, as the study spotlights The duty of the carrier to prepare a compatible ship for dangerous goods transport, as well as the duty of the carrier to preserve the goods while highlighting to the difference in this obligation while transporting dangerous goods, and the study also reviewed the duties of the shipper, for example, his duty to inform the carrier with the dangerous nature of the goods and the role of IMDG code in dealing with the danger of these goods. t
... Show MoreThe Internet of Things (IoT) is an information network that connects gadgets and sensors to allow new autonomous tasks. The Industrial Internet of Things (IIoT) refers to the integration of IoT with industrial applications. Some vital infrastructures, such as water delivery networks, use IIoT. The scattered topology of IIoT and resource limits of edge computing provide new difficulties to traditional data storage, transport, and security protection with the rapid expansion of the IIoT. In this paper, a recovery mechanism to recover the edge network failure is proposed by considering repair cost and computational demands. The NP-hard problem was divided into interdependent major and minor problems that could be solved in polynomial t
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