Back ground: Glass ionomer materials lack resistance to wear and pressure and are susceptible to moisture during the initial stages of setting and dehydration. So this study was done to assess diametral tensile strength and microhardness of glass ionomer reinforced by different amounts of hydroxyapatite. Materials and methods: In this study a hydroxyapatite material was added to glass monomer cement at different ratios: 10%, 15%, 20%, 25% and 30% (by weight). The diametral tensile strength test described by the British standard specification for zinc polycarboxylate cement was used in this study and the microhardness test was performed using Vickers microhardness testing machine and the microhardness values were calculated and statistical c

This investigation was carried out to examine the effect of replacing partial of flour by dried Lentils (Lens culinaris) to white flour in different percentages on the chemical, sensory and storage properties of the Laboratory bread. The results revealed that replacing 0% than wheat flour by lentil powder (1) control was high significan than the replacing 25 and 35% than wheat flour by lentil powder ( 4 and 5) in flavor and chewiness . The results of sensory evaluation showed that replacing 4 were high significan different than that of replacing 1 in external layer colour. Other replacing percentages, however, did not show significant differences of in comparison with control . In regards with chemical analysis of Iron and copper, i

The research aims to identify how to enhance the quality of the human resources, focusing on four dimensions (efficiency, effectiveness, flexibility, and reliability), by adopting an adventure learning method that combines theoretical and applied aspects at the same time, when developing human resources and is applied using information technology, and that Through its dimensions, which are (cooperation, interaction, communication, and understanding), as the research problem indicated a clear deficiency in the cognitive perception of the mechanism of employing adventure learning dimensions in enhancing human resources quality, so the importance of research was to present treatments and proposals to reduce this problem. To achieve

In this paper, we investigate the automatic recognition of emotion in text. We perform experiments with a new method of classification based on the PPM character-based text compression scheme. These experiments involve both coarse-grained classification (whether a text is emotional or not) and also fine-grained classification such as recognising Ekman’s six basic emotions (Anger, Disgust, Fear, Happiness, Sadness, Surprise). Experimental results with three datasets show that the new method significantly outperforms the traditional word-based text classification methods. The results show that the PPM compression based classification method is able to distinguish between emotional and nonemotional text with high accuracy, between texts invo

In all applications and specially in real time applications, image processing and compression plays in modern life a very important part in both storage and transmission over internet for example, but finding orthogonal matrices as a filter or transform in different sizes is very complex and importance to using in different applications like image processing and communications systems, at present, new method to find orthogonal matrices as transform filter then used for Mixed Transforms Generated by using a technique so-called Tensor Product based for Data Processing, these techniques are developed and utilized. Our aims at this paper are to evaluate and analyze this new mixed technique in Image Compression using the Discrete Wavelet Transfo

This research describes a new model inspired by Mobilenetv2 that was trained on a very diverse dataset. The goal is to enable fire detection in open areas to replace physical sensor-based fire detectors and reduce false alarms of fires, to achieve the lowest losses in open areas via deep learning. A diverse fire dataset was created that combines images and videos from several sources. In addition, another self-made data set was taken from the farms of the holy shrine of Al-Hussainiya in the city of Karbala. After that, the model was trained with the collected dataset. The test accuracy of the fire dataset that was trained with the new model reached 98.87%.