The removal of boron from aqueous solution was carried out by electrocoagulation (EC) using magnesium electrodes as anode and stainless steel electrodes as cathode. Several operating parameters on the removal efficiency of boron were investigated, such as initial pH, current density, initial boron ion concentration, NaCl concentration, spacing between electrodes, electrode material, and presence of carbonate concentration. The optimum removal efficiency of 91. 5 % was achieved at a current density of 3 mA/cm² and  pH = 7 using (Mg/St. St. ) electrodes, within 45 min of operating time. The concentration of NaCl was o. 1 g/l with a 0.5cm spacing between the electrodes. First and second order rate equation were applied to study adsorp

Broyden update is one of the one-rank updates which solves the unconstrained optimization problem but this update does not guarantee the positive definite and the symmetric property of Hessian matrix.

In this paper the guarantee of positive definite and symmetric property for the Hessian matrix will be established by updating the vector  which represents the difference between the next gradient and the current gradient of the objective function assumed to be twice continuous and differentiable .Numerical results are reported to compare the proposed method with the Broyden method under standard problems.

This paper proposes a new strategy to enhance the performance and accuracy of the Spiral dynamic algorithm (SDA) for use in solving real-world problems by hybridizing the SDA with the Bacterial Foraging optimization algorithm (BFA). The dynamic step size of SDA makes it a useful exploitation approach. However, it has limited exploration throughout the diversification phase, which results in getting trapped at local optima. The optimal initialization position for the SDA algorithm has been determined with the help of the chemotactic strategy of the BFA optimization algorithm, which has been utilized to improve the exploration approach of the SDA. The proposed Hybrid Adaptive Spiral Dynamic Bacterial Foraging (HASDBF)

Systemic lupus erythematosus (SLE) is an autoimmune disease with polymorphic expression. B cells have an essential contribution in immune system activation via the production of different cytokines and functioning as potent antigen-presenting cells. Therefore, a drug that particularly targets B cells may represent an ideal therapeutic approach for SLE. Rituximab (RTX), an anti-CD20 monoclonal antibody causing transient B-cell depletion, has been used to manage SLE. This study aims to assess Rituximab effects on lupus nephritis (LN) patients when added to conventional immunosuppressants. Twenty four patients, 15-32 years old, with class III/IV/V LN were involved in this study. All were on steroids before lupus nephritis occurrence. They were

Systemic lupus erythematosus (SLE) is an autoimmune disease with polymorphic expression. B cells have an essential contribution in immune system activation via the production of different cytokines and functioning as potent antigen-presenting cells. Therefore, a drug that particularly targets B cells may represent an ideal therapeutic approach for SLE. Rituximab (RTX), an anti-CD20 monoclonal antibody causing transient B-cell depletion, has been used to manage SLE. This study aims to assess Rituximab effects on lupus nephritis (LN) patients when added to conventional immunosuppressants. Twenty four patients, 15-32 years old, with class III/IV/V LN were involved in this study. All were on steroids before lupus nephritis occurrence. They were

The convergence speed is the most important feature of Back-Propagation (BP) algorithm. A lot of improvements were proposed to this algorithm since its presentation, in order to speed up the convergence phase. In this paper, a new modified BP algorithm called Speeding up Back-Propagation Learning (SUBPL) algorithm is proposed and compared to the standard BP. Different data sets were implemented and experimented to verify the improvement in SUBPL.

     Many authors investigated the problem of the early visibility of the new crescent moon after the conjunction and proposed many criteria addressing this issue in the literature. This article presented a proposed criterion for early crescent moon sighting based on a deep-learned pattern recognizer artificial neural network (ANN) performance. Moon sight datasets were collected from various sources and used to learn the ANN. The new criterion relied on the crescent width and the arc of vision from the edge of the crescent bright limb. The result of that criterion was a control value indicating the moon's visibility condition, which separated the datasets into four regions: invisible, telescope only, probably visible, and certai