Construction projects have become a changing dramatically in recent decades and that the goal of the beneficiaries of the implementation of structural project is to complete the work with less time and within the cost of the specific and the best possible quality may sometimes happen that highlights the importance of time on the rest of the items at the implementation of projects for various reasons, including the need to use the project as soon as possible possible change rapidly to customer's requests, but the high cost of the project represents the biggest obstacle for entrepreneurs with its effects on the quality and the time workers, and is a measure of those elements in monetary terms is the key to integration between them, so the

This research aims to improve the radiation shielding properties of polymer-based materials by mixing PVC with locally available building materials. Specifically, two key parameters of fast neutron attenuation (removal cross-section and half-value layer) were studied for composite materials comprising PVC reinforced with common building materials (cement, sand, gypsum and marble) in different proportions (10%, 30% and 50% by weight). To assess their effectiveness as protection against fast neutrons, the macroscopic neutron cross-section was calculated for each composite. Results show that neutron cross-section values are significantly affected by the reinforcement ratios, and that the composite material PVC + 50% gypsum is an effect

Abstract:

The achievement of economic and social welfare for individual is the main target to all policies that adopted by all countries worldwide either were economic, social, political or others. The obtaining of education by individuals and especially the higher education is one of the most important determinates in achieving the wellbeing and lasted economic development. This is because via the higher education new fields can be opened in front of individuals in order to get adequate jobs associated with their scientific specialization. This is allowing educated individuals gain higher income that can reduce the gap of income inequality.

Thus, this paper aims to analysis the n

Journalistic discourse is a fertile through which most of the segments of the society interact in all their platforms: intellectual, cultural, social, and various settings between the vital structures of the state; which makes it the link between the groups and segments of the society.

The role of discourse, moreover, engages in a vital way by establishing a culture of debate on controversial issues that provided a space in the different visions and differing perceptions on how to formulate the discourse and the magnitude of vocabulary for the diagnosis of these issues. Since there is no system of any community empty of the emergence of issues reflecting the public interest which is necessary is reflected in the context discourse

In many video and image processing applications, the frames are partitioned into blocks, which are extracted and processed sequentially. In this paper, we propose a fast algorithm for calculation of features of overlapping image blocks. We assume the features are projections of the block on separable 2D basis functions (usually orthogonal polynomials) where we benefit from the symmetry with respect to spatial variables. The main idea is based on a construction of auxiliary matrices that virtually extends the original image and makes it possible to avoid a time-consuming computation in loops. These matrices can be pre-calculated, stored and used repeatedly since they are independent of the image itself. We validated experimentally th

Two unsupervised classifiers for optimum multithreshold are presented; fast Otsu and k-means. The unparametric methods produce an efficient procedure to separate the regions (classes) by select optimum levels, either on the gray levels of image histogram (as Otsu classifier), or on the gray levels of image intensities(as k-mean classifier), which are represent threshold values of the classes. In order to compare between the experimental results of these classifiers, the computation time is recorded and the needed iterations for k-means classifier to converge with optimum classes centers. The variation in the recorded computation time for k-means classifier is discussed.

Traffic classification is referred to as the task of categorizing traffic flows into application-aware classes such as chats, streaming, VoIP, etc. Most systems of network traffic identification are based on features. These features may be static signatures, port numbers, statistical characteristics, and so on. Current methods of data flow classification are effective, they still lack new inventive approaches to meet the needs of vital points such as real-time traffic classification, low power consumption, ), Central Processing Unit (CPU) utilization, etc. Our novel Fast Deep Packet Header Inspection (FDPHI) traffic classification proposal employs 1 Dimension Convolution Neural Network (1D-CNN) to automatically learn more representational c