In this golden age of rapid development surgeons realized that AI could contribute to healthcare in all aspects, especially in surgery. The aim of the study will incorporate the use of Convolutional Neural Network and Constrained Local Models (CNN-CLM) which can make improvement for the assessment of Laparoscopic Cholecystectomy (LC) surgery not only bring opportunities for surgery but also bring challenges on the way forward by using the edge cutting technology. The problem with the current method of surgery is the lack of safety and specific complications and problems associated with safety in each laparoscopic cholecystectomy procedure. When CLM is utilize into CNN models, it is effective at predicting time series tasks like iden

A novel mixed natural coagulant has been developed to remove sewage pollutants and heavy metals from Qanat- al- Jayesh by using low cost adsorbent natural materials. In these materials, significant interaction contains Arabic gum mixed with extracted silica from rice husk ash (natural coagulants) by the Batch device approach, using two variables, pH values ranging from 5-8 and contact times between 0.25-5 hrs. All wastewater samples were collected after treatment by adsorbents and examined for determination of residual heavy metal concentrations: Pb, Ni, Zn and Cu by atomic absorption spectroscopy (AAS), turbidity, pH, total dissolved salts (TDS), electrical conductivity (EC) and total salinity (TS). The results obtained indicate Th

A novel mixed natural coagulant has been developed to remove sewage pollutants and heavy metals from Qanat- al- Jayesh by using low cost adsorbent natural materials. In these materials, significant interaction contains Arabic gum mixed with extracted silica from rice husk ash (natural coagulants) by the Batch device approach, using two variables, pH values ranging from 5-8 and contact times between 0.25-5 hrs. All wastewater samples were collected after treatment by adsorbents and examined for determination of residual heavy metal concentrations: Pb, Ni, Zn and Cu by atomic absorption spectroscopy (AAS), turbidity, pH, total dissolved salts (TDS), electrical conductivity (EC) and total salinity (TS). The results obtained indicate Th

The purpose of this research is to investigate the impact of corrosive environment (corrosive ferric chloride of 1, 2, 5, 6% wt. at room temperature), immersion period of (48, 72, 96, 120, 144 hours), and surface roughness on pitting corrosion characteristics and use the data to build an artificial neural network and test its ability to predict the depth and intensity of pitting corrosion in a variety of conditions. Pit density and depth were calculated using a pitting corrosion test on carbon steel (C-4130). Pitting corrosion experimental tests were used to develop artificial neural network (ANN) models for predicting pitting corrosion characteristics. It was found that artificial neural network models were shown to be

Commercial graphite (CGT) powder was used as an adsorbent surface for cationic dye, Janus green (JG), from aqueous solutions. This study aims to highlight the practical significance of using inexpensive CGT as an efficient adsorbent for the removal of JG dye from industrial wastewater. CGT was characterized by Fourier transform infrared spectroscopy, scanning electron microscopy, and X-ray diffraction. The adsorption process was investigated by examining parameters like the weight of the adsorbent, contact time, and temperature. Pseudo-second-order kinetic (PSO), pseudo-first-order, and intraparticle diffusion were used for analyzing the kinetic data. JG dye's adsorption kinetics fit the PSO kinetic model well (R2= 0.999). Furthermo

A common field development task is the object of the present research by specifying the best location of new horizontal re-entry wells within AB unit of South Rumaila Oil Field. One of the key parameters in the success of a new well is the well location in the reservoir, especially when there are several wells are planned to be drilled from the existing wells. This paper demonstrates an application of neural network with reservoir simulation technique as decision tool. A fully trained predictive artificial feed forward neural network (FFNNW) with efficient selection of horizontal re-entry wells location in AB unit has been carried out with maintaining a reasonable accuracy. Sets of available input data were collected from the exploited g

Removing Congo red (CR) is critical in wastewater treatment. We introduce a combination of electrocoagulation (EC) and electro-oxidation (EO) to address the elimination of CR. We also discuss the deposition of triple oxides (Cu–Mn–Ni) simultaneously on both anodic and cathodic graphite electrodes at constant current density. These electrodes efficiently worked as anodes in the EC-EO system. The EC-CO combination eliminated around 98 % of the CR dye and about 95 % of the Chemical Oxygen demand (COD), and similar results were obtained with the absence of NaCl. Thus, EC-EO is a promising technique to remove CR in an environmentally friendly pathway.

Density data of alum chrom in water and in aqueous solution of poly (ethylene glycol) (1500) at different temperatures (288.15, 293.15, 298.15) k have been used to estimate the apparent molar volume (Vθ), limiting apparent molar volume (Vθ˚) experimental slope (Sv) and the second derivative of limiting partial molar volume [δ2 θ v° /δ T2] p .The viscosity data have been analyzed by means of Jones –Dole equation to obtain coefficient A, and Jones –Dole coefficient B, Free activation energy of activation per mole of solvent, Δμ10* solute, Δμ20* the activation enthalpy ΔH*,and entropy, ΔS*of activation of viscous flow. These results have been discussed in terms of solute –solvent interaction and making/breaking ability of so

The objective of this study was tointroduce a recursive least squares (RLS) parameter estimatorenhanced by using a neural network (NN) to facilitate the computing of a bit error rate (BER) (error reduction) during channels estimation of a multiple input-multiple output orthogonal frequency division multiplexing (MIMO-OFDM) system over a Rayleigh multipath fading channel.Recursive least square is an efficient approach to neural network training:first, the neural network estimator learns to adapt to the channel variations then it estimates the channel frequency response. Simulation results show that the proposed method has better performance compared to the conventional methods least square (LS) and the original RLS and it is more robust a