The present study was conducted to examine toxicological effects of copper sulfate (Cu) in common carp fish (Cyprinus carpio L.). The LC50 (median lethal concentrations) of copper on Cyprinus carpio were 3.64, 3.36, 3.04, 2.65 mg/L respectively. In general, behavioral responses of the fishes exposed to copper included uncontrolled swimming, erratic movements, loss of balance, swam near the water surface with sudden jerky movements. Haematological parameters such, red blood cells (RBC), white blood cells (WBC), haemoglobin (Hb), Packed cell volume (PCV), mean cell volume (MCV) mean cell haemoglobin (MCH), and mean corpuscular hemoglobin concentration (MCHC) were studied. The obtained results indicated that the (RBC) and (WBC) have increased with increasing metal concentrations and exposure period. While, haemoglobin (Hb) had slightly increased compared with control fish. Other parameters like Packed cell volume (PCV), mean cell volume (MCV) mean cell haemoglobin (MCH) and mean corpuscular haemoglobin concentration (MCHC) appeared likely being enhanced with increased exposure to studying heavy metals, and did not show any significant increase with different concentrations used in chronic exposure experiments.
Compression for color image is now necessary for transmission and storage in the data bases since the color gives a pleasing nature and natural for any object, so three composite techniques based color image compression is implemented to achieve image with high compression, no loss in original image, better performance and good image quality. These techniques are composite stationary wavelet technique (S), composite wavelet technique (W) and composite multi-wavelet technique (M). For the high energy sub-band of the 3rd level of each composite transform in each composite technique, the compression parameters are calculated. The best composite transform among the 27 types is the three levels of multi-wavelet
... Show MoreThe issue of image captioning, which comprises automatic text generation to understand an image’s visual information, has become feasible with the developments in object recognition and image classification. Deep learning has received much interest from the scientific community and can be very useful in real-world applications. The proposed image captioning approach involves the use of Convolution Neural Network (CNN) pre-trained models combined with Long Short Term Memory (LSTM) to generate image captions. The process includes two stages. The first stage entails training the CNN-LSTM models using baseline hyper-parameters and the second stage encompasses training CNN-LSTM models by optimizing and adjusting the hyper-parameters of
... Show MoreThe inhibitive action of Phenyl Thiourea (PTU) on the corrosion of mild steel in strong Hydrochloric acid, HCl, has been investigated by weight loss and potentiostatic polarization. The effect of PTU concentration, HCl concentration, and temperature on corrosion rate of mild steel were verified using 2 levels factorial design and surface response analysis through weight loss approach, while the electrochemical measurements were used to study the behavior of mild steel in 5-7N HCl at temperatures 30, 40 and 50 °C, in absence and presence of PTU. It was verified that all variables and their interaction were statistically significant. The adsorption of (PTU) is found to obey the Langmuir adsorption isotherm. The effect of temperature on th
... Show MoreA Genetic Algorithm optimization model is used in this study to find the optimum flow values of the Tigris river branches near Ammara city, which their water is to be used for central marshes restoration after mixing in Maissan River. These tributaries are Al-Areed, AlBittera and Al-Majar Al-Kabeer Rivers. The aim of this model is to enhance the water quality in Maissan River, hence provide acceptable water quality for marsh restoration. The model is applied for different water quality change scenarios ,i.e. , 10%,20% increase in EC,TDS and BOD. The model output are the optimum flow values for the three rivers while, the input data are monthly flows(1994-2011),monthly water requirements and water quality parameters (EC, TDS, BOD, DO and
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