Mobile Wireless sensor networks have acquired a great interest recently due to their capability to provide good solutions and low-priced in multiple fields. Internet of Things (IoT) connects different technologies such as sensing, communication, networking, and cloud computing. It can be used in monitoring, health care and smart cities. The most suitable infrastructure for IoT application is wireless sensor networks. One of the main defiance of WSNs is the power limitation of the sensor node. Clustering model is an actual way to eliminate the inspired power during the transmission of the sensed data to a central point called a Base Station (BS). In this paper, efficient clustering protocols are offered to prolong network lifetime. A kern

In the literature, several correlations have been proposed for hold-up prediction in rotating disk contactor. However,
these correlations fail to predict hold-up over wide range of conditions. Based on a databank of around 611
measurements collected from the open literature, a correlation for hold up was derived using Artificial Neiral Network
(ANN) modeling. The dispersed phase hold up was found to be a function of six parameters: N, vc , vd , Dr , c d m / m ,
s . Statistical analysis showed that the proposed correlation has an Average Absolute Relative Error (AARE) of 6.52%
and Standard Deviation (SD) 9.21%. A comparison with selected correlations in the literature showed that the
developed ANN correlation noticeably

A Novel artificial neural network (ANN) model was constructed for calibration of a multivariate model for simultaneously quantitative analysis of the quaternary mixture composed of carbamazepine, carvedilol, diazepam, and furosemide. An eighty-four mixing formula where prepared and analyzed spectrophotometrically. Each analyte was formulated in six samples at different concentrations thus twentyfour samples for the four analytes were tested. A neural network of 10 hidden neurons was capable to fit data 100%. The suggested model can be applied for the quantitative chemical analysis for the proposed quaternary mixture.

A Novel artificial neural network (ANN) model was constructed for calibration of a multivariate model for simultaneously quantitative analysis of the quaternary mixture composed of carbamazepine, carvedilol, diazepam, and furosemide. An eighty-four mixing formula where prepared and analyzed spectrophotometrically. Each analyte was formulated in six samples at different concentrations thus twenty four samples for the four analytes were tested. A neural network of 10 hidden neurons was capable to fit data 100%. The suggested model can be applied for the quantitative chemical analysis for the proposed quaternary mixture.

In the literature, several correlations have been proposed for bubble size prediction in bubble columns. However these correlations fail to predict bubble diameter over a wide range of conditions. Based on a data bank of around 230 measurements collected from the open literature, a correlation for bubble sizes in the homogenous region in bubble columns was derived using Artificial Neural Network (ANN) modeling. The bubble diameter was found to be a function of six parameters: gas velocity, column diameter, diameter of orifice, liquid density, liquid viscosity and liquid surface tension. Statistical analysis showed that the proposed correlation has an Average Absolute Relative Error (AARE) of 7.3 % and correlation coefficient of 92.2%. A

Novel artificial neural network (ANN) model was constructed for calibration of a multivariate model for simultaneously quantitative analysis of the quaternary mixture composed of carbamazepine, carvedilol, diazepam, and furosemide. An eighty-four mixing formula where prepared and analyzed spectrophotometrically. Each analyte was formulated in six samples at different concentrations thus twentyfour samples for the four analytes were tested. A neural network of 10 hidden neurons was capable to fit data 100%. The suggested model can be applied for the quantitative chemical analysis for the proposed quaternary mixture.

The aim of the research is to demonstrate the extent of the impact of resource consumption accounting technicality as an administrative technique that is compatible with the rapid developments and changes in the external environment, with the information it provides and scientific foundations in the allocation of indirect costs, and the identification and measurement of idle energy and its costs in a way that contributes to the rationalization of pricing decisions in economic units. In light of the intense competition and the multiplicity of alternatives, and to achieve this goal, a random sample was chosen.

In this paper, precision agriculture system is introduced based on Wireless Sensor Network (WSN). Soil moisture considered one of environment factors that effect on crop. The period of irrigation must be monitored. Neural network capable of learning the behavior of the agricultural soil in absence of mathematical model. This paper introduced modified type of neural network that is known as Spiking Neural Network (SNN). In this work, the precision agriculture system  is modeled, contains two SNNs which have been identified off-line based on logged data, one of these SNNs represents the monitor that located at sink where the period of irrigation is calculated and the other represents the soil. In addition, to reduce p

In this research, (MOORA) approach based– Taguchi design was used to convert the multi-performance problem into a single-performance problem for nine experiments which built (Taguchi (L₉) orthogonal array) for carburization operation. The main variables that had a great effect on carburizing operation are carburization temperature (^oC), carburization time (hrs.) and tempering temperature (^oC). This study was also focused on calculating the amount of carbon penetration, the value of hardness and optimal values obtained during the optimization by Taguchi approach and MOORA method for multiple parameters. In this study, the carburization process was done in temperature between (850 to 950 ᵒC) for 2 to 6

This work describes the enhancement of phenol red decolorization through immobilizing of laccase in chitosan and enzyme recycling. Commercial laccase from white rot fungus, Trametesversicolor (Tvlac), was immobilizedin to freshly prepared chitosan beads by using glutaraldehyde as a cross linker. Characterization of prepared chitosan was confirmed by FTIR and scanning electron microscope (SEM). Tvlac (46.2 U/mL) immobilized into chitosan beads at 0.8 % glutaraldehyde (v/v) within 24 hrs. Synthetic (HBT) and natural (vanillin) mediators were used to enhance dye decolorizoation. It was found that 89 % of phenol red was decolorized by chitosan beads within 180 min. in the absence of enzyme and mediator, while decolorization percenta