The genic variation analysis of Pseudomonas aeruginosa after filtering the spurious variation appeared that 222 variable loci out of 5572 loci were detected. The type of variation analysis revealed that single nucleotide polymorphism was highly significant compared with other types of variation due the fact that the genome variation was achieved on the level of microevolution. Moreover, the proportional effect of functional scheme showed that genes responsible for environmental information were the highest comparable to another scheme. The genes of environmental information processing locate on outer membrane and face the defense strategy of the host therefore change in proteins coded by these genes lead to escape the immune system defense

An experimental investigation has been carried out for zinc-nickel (Zn-Ni) electro-deposition using the constant applied current technique. Weight difference approach method was used to determine the cathode current efficiency and deposit thickness. Also, the influence effect of current density on the deposition process, solderability, and porosity of the plating layer in microelectronic applications were examined. The bath temperature effect on nickel composition and the form of the contract was studied using Scanning Electron Microscope (SEM). Moreover, elemental nature of the deposition was analyzed by Energy Dispersive X-Ray (EDX).     

It has been found that the best bath temperature

Blends of Polymethyl methacrylate (PMMA)/polyvinyl alcohol (PVA) doped with 2% weight percentage of Sn were prepared with different blend ratios using casting technique. The measurements of A.C conductivity σa.c within the frequency range (25kHz – 5MHz) of undoped and Sn doped PMMA/PVA blends obeyed the relationship σ= Aw^s were the value of s within the range 0 > s > 1. The results showed that σa.c increases with the increase of frequency. The exponent s showed preceding increase with the increase of PVA content for PMMA/PVA blends doped with Sn. The dielectric constant, dielectric loss, A.C electrical conductivity are varied with the concentration of PVA in the blend and frequency of applied electrical field.

Nowadays, people's expression on the Internet is no longer limited to text, especially with the rise of the short video boom, leading to the emergence of a large number of modal data such as text, pictures, audio, and video. Compared to single mode data ,the multi-modal data always contains massive information. The mining process of multi-modal information can help computers to better understand human emotional characteristics. However, because the multi-modal data show obvious dynamic time series features, it is necessary to solve the dynamic correlation problem within a single mode and between different modes in the same application scene during the fusion process. To solve this problem, in this paper, a feature extraction framework of

Dust is a frequent contributor to health risks and changes in the climate, one of the most dangerous issues facing people today. Desertification, drought, agricultural practices, and sand and dust storms from neighboring regions bring on this issue. Deep learning (DL) long short-term memory (LSTM) based regression was a proposed solution to increase the forecasting accuracy of dust and monitoring. The proposed system has two parts to detect and monitor the dust; at the first step, the LSTM and dense layers are used to build a system using to detect the dust, while at the second step, the proposed Wireless Sensor Networks (WSN) and Internet of Things (IoT) model is used as a forecasting and monitoring model. The experiment DL system

Background: Various fluids in the oral environment can affect the surface roughness of resin composites. This in vitro study was conducted to determine the influence of the mouth rinses on surface roughness of two methacrylate-based resin (nanofilled and packable composite) and siloraine-based resin composites.

Materials and methods: Disc-shaped specimens (12 mm in diameter and 2mm in height) were prepared from three types of composi

During pregnancy, high blood pressure disorder is the most common medical complication in pregnancy. It is the foremost cause of maternal mortality and perinatal diseases. Vascular endothelial growth factor (VEGF) affects the growth of vascular endothelial cells, existence, and multiplying, which are known to be expressed in the human placenta. This study aimed to identify the expression VEGF in the placenta of hypertension and normotensive women. In this study, a cross-sectional study from november 2019 to February 2020. A total of 100 placentae involved 50 hypertensive cases and 50 normotensive groups were assessed. VEGF-A expression in two placentas groups was evaluated by immunohistochemistry techniques. Strong and moderate VEGF

 Researchers have increased interest in recent years in determining the optimum sample size to obtain sufficient accuracy and estimation and to obtain high-precision parameters in order to evaluate a large number of tests in the field of diagnosis at the same time. In this research, two methods were used to determine the optimum sample size to estimate the parameters of high-dimensional data. These methods are the Bennett inequality method and the regression method. The nonlinear logistic regression model is estimated by the size of each sampling method in high-dimensional data using artificial intelligence, which is the method of artificial neural network (ANN) as it gives a high-precision estimate commensurate with the dat

The aim of the current study is to create special norms of the second edition of Minnesota multi faces personality inventory, and the fifth edition of the sixteen personality factor questionnaire of catel. To this end, the researcher applied the Minnesota multi faces personality inventory over a sample of (1646) secondary and university students as well as plenty of disorders. She also applied the sixteen personality factor questionnaire of catel on (4700) secondary and university students. SPSS tools were used to process data.