This study was conducted to evaluate the hydrocarbon biodegradation abilities of Enterobacter cloacae, Staphylococcus aureus, Sphingomonas paucimobilis, and Pentoae species which were isolated from different diesel-contaminated soil samples. The isolates were identified by the Vitek 2 system. Fourier-transform spectroscopy (FT-IR) tested the potential of these isolates to biodegrade the diesel according to the peak areas, a significant decrease in the area of the peaks at 2856-2928 cm−1 corresponds to aliphatic hydrocarbons. The appearance of small peaks at 900-1032 cm−1 refers to substituted benzene derivative compounds. An appearance of some new peaks at 3010- 3030 cm−1 which indicate the presence of alcohol (-OH) and ketones (RC=O)

Finding orthogonal matrices in different sizes is very complex and important because it can be used in different applications like image processing and communications (eg CDMA and OFDM). In this paper we introduce a new method to find orthogonal matrices by using tensor products between two or more orthogonal matrices of real and imaginary numbers with applying it in images and communication signals processing. The output matrices will be orthogonal matrices too and the processing by our new method is very easy compared to other classical methods those use basic proofs. The results are normal and acceptable in communication signals and images but it needs more research works.

Image compression plays an important role in reducing the size and storage of data while increasing the speed of its transmission through the Internet significantly. Image compression is an important research topic for several decades and recently, with the great successes achieved by deep learning in many areas of image processing, especially image compression, and its use is increasing Gradually in the field of image compression. The deep learning neural network has also achieved great success in the field of processing and compressing various images of different sizes. In this paper, we present a structure for image compression based on the use of a Convolutional AutoEncoder (CAE) for deep learning, inspired by the diversity of human eye

Background: Complete seal of the root canal system following its chemo-mechanical debridement plays a pivotal role for achieving successful endodontic treatment. This can be established by reducing the gaps between the core filling material and root canal wall. Aim: To assess and compare the dislocation resistance of root canals obturated with GuttaFusion® and TotalFill BC sealer versus single cone obturation technique and TotalFill BC sealer after instrumentation of the canals with WaveOne, ProTaper Next and ProTaper Universal system. Material and Method: Sixty extracted human permanent mandibular premolars were conducted in the current study. The teeth were decorated and left the root with 15mm length; the roots were divided randoml

Zernike Moments has been popularly used in many shape-based image retrieval studies due to its powerful shape representation. However its strength and weaknesses have not been clearly highlighted in the previous studies. Thus, its powerful shape representation could not be fully utilized. In this paper, a method to fully capture the shape representation properties of Zernike Moments is implemented and tested on a single object for binary and grey level images. The proposed method works by determining the boundary of the shape object and then resizing the object shape to the boundary of the image. Three case studies were made. Case 1 is the Zernike Moments implementation on the original shape object image. In Case 2, the centroid of the s

Background: Cluster of differentiation 14 (CD14) is a serum/cell surface glycoprotein; and it is a pattern recognition receptor. CD14 expressed on the surface of various cells, or it found soluble in saliva and other body fluids. It has been proposed that soluble CD14 (sCD14) may play a protective role by controlling Gram negative bacterial infections through its capacity to bind lipopolysaccharide. This study was conducted to assess the level of soluble CD14 in saliva of patients with different periodontal diseases and healthy subjects and determine its correlation with clinical periodontal parameters. Materials & Methods: A total of 80 subjects, age ranged (25-50) years old, divided into three main groups, group ? consisted of 45 chronic

We have studied Bayesian method in this paper by using the modified exponential growth model, where this model is more using to represent the growth phenomena. We focus on three of prior functions (Informative, Natural Conjugate, and the function that depends on previous experiments) to use it in the Bayesian method. Where almost of observations for the growth phenomena are depended on one another, which in turn leads to a correlation between those observations, which calls to treat such this problem, called Autocorrelation, and to verified this has been used Bayesian method.

The goal of this study is to knowledge the effect of Autocorrelation on the estimation by using Bayesian method. F

Owing to their cost-effectiveness and the natural abundance of magnesium, magnesium-ion batteries (MIBs) were introduced as encouraging alternatives to Lithium-ion batteries. Following the successful synthesis of carbon nano-tube, its B and N doped derivatives which were doped with B and N enjoyed the attention of researchers as novel anode materials (AM) for MIBs. Here, we investigated a BC2N nano-tube (BC2NNT) as an encouraging AM for MIBs. To have a deeper understanding of the electrochemical properties, cycling stability, specific capacity (SC) and the adsorption behavior of this nano-tube, first-principles density functional theory computations were performed. By performing NMR calculations, we identified two types of non-aromatic hexa