Oily wastewater is one of the most challenging streams to deal with especially if the oil exists in emulsified form. In this study, electrospinning method was used to prepare nanofiberous polyvinylidene fluoride (PVDF) membranes and study their performance in oil removal. Graphene particles were embedded in the electrospun PVDF membrane to enhance the efficiency of the membranes. The prepared membranes were characterized using a scanning electron microscopy (SEM) to verify the graphene stabilization on the surface of the membrane homogeneously; while FTIR was used to detect the functional groups on the membrane surface. The membrane wettability was assessed by measuring the contact angle. The PVDF and PVDF / Graphene membranes efficiency

Coblatcomplex has been prepared by reaction between C16H19N3O3S (L) as ligand and metal salt (II). The prepared complex were characterized by infrared spectra, electromic spectra, magnetic susceptibility, molar conductivity measurement and metal analysis by atomic absorption and (C.H.N) analysis. From these studies tetrahedral geometry structure for the complex was suggested. The photodegredation of complex were study using photoreaction cell and preparednanoTiO2 catalyst in different conditions (concentration, temperatures, pH).The results show that the recation is of a first order with activation energy equal to (6.6512 kJ /mol).

Extraction of copper (Cu) from aqueous solution utilizing Liquid Membrane technology (LM) is more effective than precipitation method that forms sludge and must be disposed of in landfills. In this work, we have formulated a liquid surfactant membrane (LSM) that uses kerosene oil as the main diluent of LSM to remove copper ions from the aqueous waste solution through di- (2-ethylhexyl) phosphoric acid - D2EHPA- as a carrier. This technique displays several advantages including one-stage extraction and stripping process, simple operation, low energy requirement, and. In this study, the LSM process was used to transport Cu (II) ions from the feed phase to the stripping phase, which was prepared, using H₂SO₄. For LSM p

The Dirichlet process is an important fundamental object in nonparametric Bayesian modelling, applied to a wide range of problems in machine learning, statistics, and bioinformatics, among other fields. This flexible stochastic process models rich data structures with unknown or evolving number of clusters. It is a valuable tool for encoding the true complexity of real-world data in computer models. Our results show that the Dirichlet process improves, both in distribution density and in signal-to-noise ratio, with larger sample size; achieves slow decay rate to its base distribution; has improved convergence and stability; and thrives with a Gaussian base distribution, which is much better than the Gamma distribution. The performance depen

In this paper we use non-polynomial spline functions to develop numerical methods to approximate the solution of 2nd kind Volterra integral equations. Numerical examples are presented to illustrate the applications of these method, and to compare the computed results with other known methods.

This paper presents a meta-heuristic swarm based optimization technique for solving robot path planning. The natural activities of actual ants inspire which named Ant Colony Optimization. (ACO) has been proposed in this work to find the shortest and safest path for a mobile robot in different static environments with different complexities. A nonzero size for the mobile robot has been considered in the project by taking a tolerance around the obstacle to account for the actual size of the mobile robot. A new concept was added to standard Ant Colony Optimization (ACO) for further modifications. Simulations results, which carried out using MATLAB 2015(a) environment, prove that the suggested algorithm outperforms the standard version of AC