Silymarin, a flavolignans from seeds of ‘milk thistle’ “Silybum marianum” has been widely used from ancient times because of its excellent hepatoprotective action. It has been used clinically to treat liver disorders including acute and chronic viral hepatitis, toxin/drug-induced hepatitis and cirrhosis and alcoholic liver disease. The efficacy and dose-response effect of silymarin (125, 250 and 500 mg/kg) were assessed using egg albumin-induced paw edema in rats as a model of acute inflammation. In this model, 56 rats were used and allocated into 7 subgroups each containing 8 rats. All treatments were given intraperitonealy 30 minutes before induction of inflammation by egg albumin and then the increase

This paper presents experimentally a new configuration of shear connector for Steel-Concrete-Steel (SCS) sandwich beams that is derived from truss configuration. It consists of vertical and inclined shear connectors welded together and to cover steel plates infilled with concrete. Nine simply supported SCS beams were tested until the failure under a concentrated central load (three- point bending). The beams were similar in length (1100mm), width (100mm), and the top plate thickness (4mm). The test parameters were; beam thickness (150, 200, 250, and 300mm), the bottom plate thickness (4, and 6mm), the diameter of the shear connectors (10, 12, and 16mm), and the connector spacing (100, 200, and 250mm). The test results sh

     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod

This research is devoted to investigate relationship between both Ultrasonic Pulse Velocity and Rebound Number (Hammer Test) with cube compressive strength and also to study the effect of steel reinforcement on these relationships.
A study was carried out on 32 scale model reinforced concrete elements. Non destructive testing campaign (mainly ultrasonic and rebound hammer tests) made on the same elements. About 72 concrete cubes (15 X 15 X15) were taken from the concrete mixes to check the compressive strength.. Data analyzed.Include the possible correlations between non destructive testing (NDT) and compressive strength (DT) Statistical approach is used for this purpose. A new relationships obtained from correlations results is give

Crabs belong to the crustacean family (Decapods crustacean), and their shells contain natural ingredients from which the bioactive compounds are derived. It has been used as folklore medicine in cancer treatment. We investigate the possible anti-inflammatory and anti-oxidant effects for crab shells and whole crabs. Thirty-six rats (150–200 gm) from both sexes were used, divided into six groups, the anti-inflammatory and anti-oxidant activity measured using cotton pellet induce granuloma model. Detection of tumor necrosis factor alpha (TNF α), Interleukin 1 beta (IL-1β), superoxide (SOD), and malondialdehyde (MDA) levels using ELISA Kits. The data analysis by one-way ANOVA followed by the Tukey test. Values are significant at (p < 0.05).

The uptake of Cd(II) ions from simulated wastewater onto olive pips was modeled using artificial neural network (ANN) which consisted of three layers. Based on 112 batch experiments, the effect of contact time (10-240 min), initial pH (2-6), initial concentration (25-250 mg/l), biosorbent dosage (0.05-2 g/100 ml), agitation speed (0-250 rpm) and temperature (20-60ºC) were studied. The maximum uptake (=92 %) of Cd(II) was achieved at optimum parameters of 60 min, 6, 50 mg/l, 1 g/100 ml, 250 rpm and 25ºC respectively.

Tangent sigmoid and linear transfer functions of ANN for hidden and output layers respectively with 7 neurons were sufficient to present good predictions for cadmium removal efficiency with coefficient of correlatio

The study aims to predict Total Dissolved Solids (TDS) as a water quality indicator parameter at spatial and temporal distribution of the Tigris River, Iraq by using Artificial Neural Network (ANN) model. This study was conducted on this river between Mosul and Amarah in Iraq on five positions stretching along the river for the period from 2001to 2011. In the ANNs model calibration, a computer program of multiple linear regressions is used to obtain a set of coefficient for a linear model. The input parameters of the ANNs model were the discharge of the Tigris River, the year, the month and the distance of the sampling stations from upstream of the river. The sensitivity analysis indicated that the distance and discharge

Survival analysis is widely applied to data that described by the length of time until the occurrence of an event under interest such as death or other important events. The purpose of this paper is to use the dynamic methodology which provides a flexible method, especially in the analysis of discrete survival time, to estimate the effect of covariate variables through time in the survival analysis on dialysis patients with kidney failure until death occurs. Where the estimations process is completely based on the Bayes approach by using two estimation methods: the maximum A Posterior (MAP) involved with Iteratively Weighted Kalman Filter Smoothing (IWKFS) and in combination with the Expectation Maximization (EM) algorithm. While the other