The free piston engine linear generator (FPELG) is a simple engine structure with few components, making it a promising power generation system. However, because the engine works without a crankshaft, the handling of the piston motion control (PMC) is the main challenge influencing the stability and performance of FPELGs. In this article, the optimal operating parameters of FPELG for maximising engine performance and reducing exhaust gas emissions were studied. Moreover, the influence of adding hydrogen (H2) to compressed natural gas (CNG) fuel on FPELG performance was investigated. The influence of operating parameters on in-cylinder pressure was also analysed. The single-piston FPELG fuelled by CNG blended with H2 was used to run the expe

The family found itself confused any of the methods of dealing (Control - democracy) follow to reach the best level in education, what was favorite yesterday about a father dealing with his children control which showed generations characterized by high levels of self-esteem and decision-making, today rejected and opposed it. As some believe that a sense of control generates a negative represented by fear and frustration.
Find the goal of preparing the current scale of the father control when junior high students.
To achieve this goal, has been preparing a tool to measure the father control after that, the researcher extract the validity and reliability of the scale, and then applied to a sample of 500 students from junior high sch

It is well known that sonography is not the first choice in detecting early breast tumors. Improving the resolution of breast sonographic image is the goal of many workers to make sonography a first choice examination as it is safe and easy procedure as well as cost effective. In this study, infrared light exposure of breast prior to ultrasound examination was implemented to see its effect on resolution of sonographic image. Results showed that significant improvement was obtained in 60% of cases.

In this paper, a fast lossless image compression method is introduced for compressing medical images, it is based on splitting the image blocks according to its nature along with using the polynomial approximation to decompose image signal followed by applying run length coding on the residue part of the image, which represents the error caused by applying polynomial approximation. Then, Huffman coding is applied as a last stage to encode the polynomial coefficients and run length coding. The test results indicate that the suggested method can lead to promising performance.

In the present work, pattern recognition is carried out by the contrast and relative variance of clouds. The K-mean clustering process is then applied to classify the cloud type; also, texture analysis being adopted to extract the textural features and using them in cloud classification process. The test image used in the classification process is the Meteosat-7 image for the D3 region.The K-mean method is adopted as an unsupervised classification. This method depends on the initial chosen seeds of cluster. Since, the initial seeds are chosen randomly, the user supply a set of means, or cluster centers in the n-dimensional space.The K-mean cluster has been applied on two bands (IR2 band) and (water vapour band).The textural analysis is used

Diabetic retinopathy is an eye disease in diabetic patients due to damage to the small blood vessels in the retina due to high and low blood sugar levels. Accurate detection and classification of Diabetic Retinopathy is an important task in computer-aided diagnosis, especially when planning for diabetic retinopathy surgery. Therefore, this study aims to design an automated model based on deep learning, which helps ophthalmologists detect and classify diabetic retinopathy severity through fundus images. In this work, a deep convolutional neural network (CNN) with transfer learning and fine tunes has been proposed by using pre-trained networks known as Residual Network-50 (ResNet-50). The overall framework of the proposed