This aims tackles the importance of the organizational energy of the hotel organizations that search the success in the business field to penetrate in the whole tourist markets, and to draw the policies and firm rules which must be framed with the administrative strategies that contributed in creativity and achievement the targets besides provide a future vision due to its position among the competitive henceforth achieving the activity. This is what the chapters tackle in the theoretical side. Also many general questions have been arisen to determinate the importance of the research and many other special questions that express the problem of the study. To limit the levels of study alter

The research (The Aesthetics of Lumia Art in Contemporary Textile Arts: An Analytical Study) included four chapters, the first to explain the research problem summarized by the question: Does the art of Lumia achieve aesthetic values in contemporary textile arts?, and aims to: Identify the aesthetics of Lumia art and its applied uses in contemporary textile arts, within the time period (2013-2022). The third chapter included: research procedures and sample analysis (4) models according to the descriptive analytical approach, and the fourth chapter dealt with: results, conclusions and recommendations as well as sources.

In this study, the sonochemical degradation of phenol in water was investigated using two types of ultrasonic wave generators; 20 kHz ultrasonic processor and 40 kHz ultrasonic cleaner bath. Mineralization rates were determined as a function of phenol concentration, contact time, pH, power density, and type of ultrasonic generator. Results revealed that sonochemical degradation of the phenol conversion was enhanced at increased applied power densities and acidic conditions. At 10 mg/L initial concentration of phenol, pH 7, and applied power density of 3000 W/L, the maximum removal efficiency of phenol was 93% using ultrasonic processor at 2h contact time. Whereby, it was 87% using and ultrasonic cleaner bath at 16h contact time and 150 W

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class