A microbial desalination cell (MDC) is a new approach to bioelectrochemical systems. It provides a more sustainable way to electrical power production, saltwater desalination, and wastewater treatment at the same time. This study examined three operation modes of the MDC: chemical cathode, air cathode, and biocathode MDC, to give clear sight of this system's performance. The experimental work results for these three modes were recorded as power densities generation, saltwater desalination rates, and COD removal percentages. For the chemical cathode MDC, the power density was 96.8 mW/m², the desalination rate was 84.08 ppm/hr, and the COD removal percentage was 95.94%. The air cathode MDC results were different

Abstract

The aim of the current research is to prepare an integrated learning program based on mathematics standards for the next generation of the NYS and to investigate its impact on the development of the teaching performance of middle school mathematics teachers and the future thinking skills of their students. To achieve the objectives of the research, the researcher prepared a list of mathematics standards for the next generation, which were derived from a list of standards. He also prepared a list of the teaching competencies required for middle school mathematics teachers in light of the list of standards, as well as clarified the foundations of the training program and its objectives and the mathematical

This paper describes a new finishing process using magnetic abrasives were newly made to finish effectively brass plate that is very difficult to be polished by the conventional machining processes. Taguchi experimental design method was adopted for evaluating the effect of the process parameters on the improvement of the surface roughness and hardness by the magnetic abrasive polishing. The process parameters are: the applied current to the inductor, the working gap between the workpiece and the inductor, the rotational speed and the volume of powder. The analysis of variance(ANOVA) was analyzed using statistical software to identify the optimal conditions for better surface roughness and hardness. Regressions models based on statistical m

In this paper, point estimation for parameter ? of Maxwell-Boltzmann distribution has been investigated by using simulation technique, to estimate the parameter by two sections methods; the first section includes Non-Bayesian estimation methods, such as (Maximum Likelihood estimator method, and Moment estimator method), while the second section includes standard Bayesian estimation method, using two different priors (Inverse Chi-Square and Jeffrey) such as (standard Bayes estimator, and Bayes estimator based on Jeffrey's prior). Comparisons among these methods were made by employing mean square error measure. Simulation technique for different sample sizes has been used to compare between these methods.

   A comparison of double informative and non- informative priors assumed for the parameter of Rayleigh distribution is considered. Three different sets of double priors are included, for a single unknown parameter of Rayleigh distribution. We have assumed three double priors: the square root inverted gamma (SRIG) - the natural conjugate family of priors distribution, the square root inverted gamma – the non-informative distribution, and the natural conjugate family of priors - the non-informative distribution as double priors .The data is generating form three cases from Rayleigh distribution for different samples sizes (small, medium, and large). And Bayes estimators for the parameter is derived under a squared erro

The estimation of the parameters of linear regression is based on the usual Least Square method, as this method is based on the estimation of several basic assumptions. Therefore, the accuracy of estimating the parameters of the model depends on the validity of these hypotheses. The most successful technique was the robust estimation method which is minimizing maximum likelihood estimator (MM-estimator) that proved its efficiency in this purpose. However, the use of the model becomes unrealistic and one of these assumptions is the uniformity of the variance and the normal distribution of the error. These assumptions are not achievable in the case of studying a specific problem that may include complex data of more than one model. To