This research consists of two parts, the first part concern with analyzing the collected data of BOD and COD values in discharge waste water from Al-Dora refinery during 2010 to find the relationship between these two variables The results indicates that there
is a high correlation between BOD and COD when using a natural logarithm model (0.86 ln(COD)) with correlation coefficient of 0.98. This relationship is useful in predicting the BOD value using the COD value. The second part includes analyzing collected data from the same site in order to find a relationsip between BOD and other parameters COD, Phenol(phe), Temperature(T), Oil, Sulphat(SO4),pH and Total dissolved solids( TDS) discharged from the refinery. The results indicated

Optimizing the Access Point (AP) deployment has a great role in wireless applications due to the need for providing an efficient communication with low deployment costs. Quality of Service (QoS), is a major significant parameter and objective to be considered along with AP placement as well the overall deployment cost. This study proposes and investigates a multi-level optimization algorithm called Wireless Optimization Algorithm for Indoor Placement (WOAIP) based on Binary Particle Swarm Optimization (BPSO). WOAIP aims to obtain the optimum AP multi-floor placement with effective coverage that makes it more capable of supporting QoS and cost-effectiveness. Five pairs (coverage, AP deployment) of weights, signal thresholds and received s

This work aims to study the exploding copper wire plasma parameters by optical emission spectroscopy. The emission spectra of the copper plasma have been recorded and analyzed The plasma electron temperature (Te), was calculated by Boltzmann plot, and the electron density (ne) calculated by using Stark broadening method for different copper wire diameter (0.18, 0.24 and 0.3 mm) and current
of 75A in distilled water. The hydrogen (Hα line) 656.279 nm was used to calculate the electron density for different wire diameters by Stark broadening. It was found that the electron density ne decrease from 22.4×1016 cm-3 to 17×1016 cm-3 with increasing wire diameter from 0.18 mm to 0.3 mm while the electron temperatures increase from 0.741 to

Abstract

The human mind knew the philosophy and logic in the ancient times, and the history afterwards, while the semiotics concept appeared in the modern time, and became a new knowledge field like the other knowledge fields. It deals, in its different concepts and references, with the processes that lead to and reveals the meaning through what is hidden in addition to what is disclosed. It is the result of human activity in its pragmatic and cognitive dimensions together. The semiotic token concept became a knowledge key to access all the study, research, and  investigation fields, due to its ability of description, explanation, and dismantling. The paper is divided into two sections preceded by a the

Non uniform channelization is a crucial task in cognitive radio receivers for obtaining separate channels from the digitized wideband input signal at different intervals of time. The two main requirements in the channelizer are reconfigurability and low complexity. In this paper, a reconfigurable architecture based on a combination of Improved Coefficient Decimation Method (ICDM) and Coefficient Interpolation Method (CIM) is proposed. The proposed Hybrid Coefficient Decimation-Interpolation Method (HCDIM) based filter bank (FB) is able to realize the same number of channels realized using (ICDM) but with a maximum decimation factor divided by the interpolation factor (L), which leads to less deterioration in stop band at

In this paper, an algorithm is suggested to train a single layer feedforward neural network to function as a heteroassociative memory. This algorithm enhances the ability of the memory to recall the stored patterns when partially described noisy inputs patterns are presented. The algorithm relies on adapting the standard delta rule by introducing new terms, first order term and second order term to it. Results show that the heteroassociative neural network trained with this algorithm perfectly recalls the desired stored pattern when 1.6% and 3.2% special partially described noisy inputs patterns are presented.