The modified Hummers method was applied to prepare graphene oxide (GO) from the graphite powder. Tin oxide nanoparticles with different loading (10-20 wt.%) supported on reduced graphene oxide were synthesized to evaluate the oxidative desulfurization efficiency. The catalyst was synthesized by the incipient wetness impregnation (IWI) technique. Different analysis methods like FT-IR, XRD, FESEM, AFM, and Brunauer-Emmett-Teller (BET) were utilized to characterize graphene oxide and catalysts. The XRD analysis showed that the average crystal size of graphene oxide was 6.05 nm. In addition, the FESEM results showed high metal oxide dispersions on the rGO. The EDX analysis shows the weight ratio of Sn is close to its theoretical weight.

Al-Aziz's book was chosen as a subject for research as it is one of the important books in the Islamic world in general and the Shafi'i school in particular and its author Imam Abdul Karim bin Mohammed bin Abdul Karim Al-Rafi's, who in the doctrine of the jurisprudence has made prominent lines and left behind invaluable scientific treasures in the service of religion. Its importance is summarized as follows:
1. Being an explanation of the book (brief) of the argument of Islam Imam Ghazali (God's mercy), one of the five books adopted in Shafi'i jurisprudence.
2. His work is Imam Abu al-Qasim al-Rafii known for the brilliance of the investigation and the power of weighting in the doctrine.
3. It is considered an encyclopedia in Sh

In this study, a 3 mm thickness 7075-T6 aluminium alloy sheet was used in the friction stir welding process. Using the design of experiment to reduce the number of experiments and to obtain the optimum friction stir welding parameters by utilizing Taguchi technique based on the ultimate tensile test results. Orthogonal array of L9 (3³) was used based on three numbers of the parameters and three levels for each parameter, where shoulder-workpiece interference depth (0.20, 0.25, and 0.3) mm, pin geometry (cylindrical thread flat end, cylindrical thread with 3 flat round end, cylindrical thread round end), and thread pitch (0.8, 1, and 1.2) mm) this technique executed by Minitab 17 software. The results showed th

 A particle swarm optimization algorithm and neural network like self-tuning PID controller for CSTR system is presented. The scheme of the discrete-time PID control structure is based on neural network and tuned the parameters of the PID controller by using a particle swarm optimization PSO technique as a simple and fast training algorithm. The proposed method has advantage that it is not necessary to use a combined structure of identification and decision because it used PSO. Simulation results show the effectiveness of the proposed adaptive PID neural control algorithm in terms of minimum tracking error and smoothness control signal obtained for non-linear dynamical CSTR system.

Expanded use of antibiotics may increase the ability of pathogenic bacteria to develop antimicrobial resistance. Greater attention must be paid to applying more sustainable techniques for treating wastewater contaminated with antibiotics. Semiconductor photocatalytic processes have proven to be the most effective methods for the degradation of antibiotics. Thus, constructing durable and highly active photocatalytic hybrid materials for the photodegradation of antibiotic pollutants is challenging. Herein, FeTiO3/Fe-doped g-C3N4 (FTO/FCN) heterojunctions were designed with different FTO to FCN ratios by matching the energy level of semiconductors, thereby developing effective direct Z-type heterojunctions. The photodegradation behaviors of th

Phosphorus‐based Schiff base were synthesized by treating bis{3‐[2‐(4‐amino‐1.5‐dimethyl‐2‐phenyl‐pyrazol‐3‐ylideneamino)ethyl]‐indol‐1‐ylmethyl}‐phosphinic acid with paraformaldehyde and characterized as a novel antioxidant. Its corresponding complexes [(VO)₂L(SO₄)₂], [Ni₂LCl₄], [Co₂LCl₄], [Cu₂LCl₄], [Zn₂LCl₄], [Cd₂LCl₄], [Hg₂LCl₄], [Pd₂LCl₄], and [PtL

This research studies the comparison of deep neural network models and performance evaluation to predict the gold prices of time series, where the gold prices contain high fluctuations and non-linear patterns that are difficult to capture using traditional models, which makes predicting them a significant challenge. Therefore, the focus was on using deep learning models represented by (LSTM), (Bi-LSTM), (GRU) and (Bi-GRU). The results showed the superiority of the (Bi-GRU) model according to comparison criteria (MSE), (RMSE), (MAE), and (R∧2) compared to other models because it was able to understand the time patterns better by processing the data in both directions and provided superior performance, which indicates its effectiveness, eff