The main aim of this study was to molecular identification and determine the antagonistic impact of rhizosphere Trichoderma spp. against some phytopathogenic fungi, including (Magnaporthe grisea) pyricularia oryzae, Rhizoctonia solani and Macrophomina phasolina. Four Trichoderma isolates were isolated from rhizosphere soils of the different host plants in different locations of Egyptian governorates. The morphological characterization of isolated Trichoderma as well as using of (ITS1-5.8S-ITS2) ribosomal gene sequence acquisition and data analyses. By comparing the results of DNA sequences of ITS region, the fungi represented one isolate were positively identified as T. asperellum (1 isolate T1) and one as T. longibrachiatum (1 isolate T2)

    The present study was performed to detect the molecular and the phylogenetic identification of species that belonging to the genus of Moniezia Blanchard, 1891 which affected intestines of sheep in Al-Diwaniyah city, Iraq; fifty intestine samples were sought for the infestation of Moniezia spp. from the city slaughterhouse from 1 October to 30 November 2017, this tapeworm was found to infest the intestines of 13 sheep.

    For morphological identify the genus of this tapeworm, eggs from one gravid proglottid of the thirteen worms were examined, polymerase chain reaction (PCR) and the PCR-product-based sequencing were applied on 4 Moniezia tapeworms targeti

The present study aimed to use the magnetic field and nanotechnology in the field of water purification, which slots offering high efficiency to the possibility of removing biological contaminants such as viruses and bacteria rather than the use of chemical and physical transactions such as chlorine and bromine, and ultraviolet light and boiling and sedimentation and distillation, ozone and others that have a direct negative impact on human safety and the environment. Where they were investigating the presence in water samples under study Coli phages using Single agar layer method and then treated samples positive for phages to three types of magnetic field fixed as follows (North Pole - South Pole - Bipolar) and compare the re

Prediction of penetration rate (ROP) is important process in optimization of drilling due to its crucial role in lowering drilling operation costs. This process has complex nature due to too many interrelated factors that affected the rate of penetration, which make difficult predicting process. This paper shows a new technique of rate of penetration prediction by using artificial neural network technique. A three layers model composed of two hidden layers and output layer has built by using drilling parameters data extracted from mud logging and wire line log for Alhalfaya oil field. These drilling parameters includes mechanical (WOB, RPM), hydraulic (HIS), and travel transit time (DT). Five data set represented five formations gathered

Lung cancer is one of the most serious and prevalent diseases, causing many deaths each year. Though CT scan images are mostly used in the diagnosis of cancer, the assessment of scans is an error-prone and time-consuming task. Machine learning and AI-based models can identify and classify types of lung cancer quite accurately, which helps in the early-stage detection of lung cancer that can increase the survival rate. In this paper, Convolutional Neural Network is used to classify Adenocarcinoma, squamous cell carcinoma and normal case CT scan images from the Chest CT Scan Images Dataset using different combinations of hidden layers and parameters in CNN models. The proposed model was trained on 1000 CT Scan Images of cancerous and non-c

Surface electromyography (sEMG) and accelerometer (Acc) signals play crucial roles in controlling prosthetic and upper limb orthotic devices, as well as in assessing electrical muscle activity for various biomedical engineering and rehabilitation applications. In this study, an advanced discrimination system is proposed for the identification of seven distinct shoulder girdle motions, aimed at improving prosthesis control. Feature extraction from Time-Dependent Power Spectrum Descriptors (TDPSD) is employed to enhance motion recognition. Subsequently, the Spectral Regression (SR) method is utilized to reduce the dimensionality of the extracted features. A comparative analysis is conducted between the Linear Discriminant Analysis (LDA) class

Bacterial strains were isolated from oil-contaminated soil, in 2018, these isolates were identified, and with the aim of finding out the ability of these isolates to degrede the oil compounds, the color change of medium which added to it isolates was read by the method of Pacto Bushnell Hans. Then the change in the petroleum compounds was read by gas chromatography, for the most effective isolates.

The nine isolated bacterial showed different degrees of color change, and the isolates (Pseudomonas, Bacillus, Micrococcus) outperformed the color change amount (78, 78, 77) %, respectively, compared to the control, and the three isolates together showed the best color change of 90.7. % Compared to the control, and the

Lung cancer is the most common dangerous disease that, if treated late, can lead to death. It is more likely to be treated if successfully discovered at an early stage before it worsens. Distinguishing the size, shape, and location of lymphatic nodes can identify the spread of the disease around these nodes. Thus, identifying lung cancer at the early stage is remarkably helpful for doctors. Lung cancer can be diagnosed successfully by expert doctors; however, their limited experience may lead to misdiagnosis and cause medical issues in patients. In the line of computer-assisted systems, many methods and strategies can be used to predict the cancer malignancy level that plays a significant role to provide precise abnormality detectio