The evolution in the field of Artificial Intelligent (AI) with its training algorithms make AI very important in different aspect of the life. The prediction problem of behavior of dynamical control system is one of the most important issue that the AI can be employed to solve it. In this paper, a Convolutional Multi-Spike Neural Network (CMSNN) is proposed as smart system to predict the response of nonlinear dynamical systems. The proposed structure mixed the advantages of Convolutional Neural Network (CNN) with Multi -Spike Neural Network (MSNN) to generate the smart structure. The CMSNN has the capability of training weights based on a proposed training algorithm. The simulation results demonstrated that the proposed

Azo dyes like methyl orange (MO) are very toxic components due to their recalcitrant properties which makes their removal from wastewater of textile industries a significant issue. The present study aimed to study their removal by utilizing aluminum and Ni foam (NiF) as anodes besides Fe foam electrodes as cathodes in an electrocoagulation (EC) system. Primary experiments were conducted using two Al anodes, two NiF anodes, or Al-NiF anodes to predict their advantages and drawbacks. It was concluded that the Al-NiF anodes were very effective in removing MO dye without long time of treatment or Ni leaching at in the case of adopting the Al-Al or NiF-NiF anodes, respectively. The structure and surface morphology of the NiF electrode were inves

Three series of monomers, polymers and thioester cyclic compounds containing 4H-1,2,4-triazol-3-thiol moiety were synthesized and examined for their liquid crystalline properties. All monomers, polymers and thioester compounds were characterized by elemental analysis and FTIR, 1 H-NMR and mass spectroscopy. The phase transition and mesomorphic properties were investigated by polarized optical microscope (POM) and differential scanning calorimetry (DSC). The monomer with terminal phenyl substituent display dimorphism nematic and smectic A (SmA) mesophases. The corresponding polymers derived from acrylic and phenyl acrylic acid monomers show nematic mesophase. The only thioester cyclic compound derived from terephtaloyl chloride show nemati

This research discusses application Artificial Neural Network (ANN) and Geographical InformationSystem (GIS) models on water quality of Diyala River using Water Quality Index (WQI). Fourteen water parameterswere used for estimating WQI: pH, Temperature, Dissolved Oxygen, Orthophosphate, Nitrate, Calcium, Magnesium,Total Hardness, Sodium, Sulphate, Chloride, Total Dissolved Solids, Electrical Conductivity and Total Alkalinity.These parameters were provided from the Water Resources Ministryfrom seven stations along the river for the period2011 to 2016. The results of WQI analysis revealed that Diyala River is good to poor at the north of Diyala provincewhile it is poor to very polluted at the south of Baghdad City. The selected parameters wer

Water-based drilling fluids (WBDFs) are the industry-preferred medium for drilling reactive shale formations, yet they remain susceptible to shale swelling, fluid invasion, and cuttings dispersion—all of which compromise wellbore stability. Although oil-based fluids (OBFs) provide effective shale inhibition, their high cost and adverse environmental profile limit their use. Nanoparticles (NPs) derived from waste feedstocks offer a low-cost, environmentally responsible alternative for enhancing WBDF performance, but no prior study has simultaneously evaluated both waste-derived SiO2 and Al2O2 NPs within a single WBDF system. This study synthesised SiO2 NPs from Halfaya bentonite clay waste and Al2O2 NPs from recycled aluminium scrap, char