In this paper, a new class of nonconvex sets and functions called strongly -convex sets and strongly -convex functions are introduced. This class is considered as a natural extension of strongly -convex sets and functions introduced in the literature. Some basic and differentiability properties related to strongly -convex functions are discussed. As an application to optimization problems, some optimality properties of constrained optimization problems are proved. In these optimization problems, either the objective function or the inequality constraints functions are strongly -convex. 

.

Introduction: The current study investigated the use of acid-treated rice husks to remove heavy metals and organic pollutants from water containing heavy metals (R2C and Cd2) and organic pollutants (phenol and atrazine). Methods: The adsorption effect of acid-treated rice husks was compared with other adsorbents such as activated carbon, chitosan, and bentonite clay. Result: both acid-treated rice husks and activated carbon were highly efficient materials, and thus, rice husks were established as a cost-effective alternative. It was revealed that acid treatment of rice husks enhanced adsorption capacity by half, and lead removal was nearly doubled. The most effective pH value for optimizing organic pollutants and heavy metals while

 Abstract 

The vegetative filter strips (VFS) are a useful tool used for reducing the movement of sediment and pesticide in therivers. The filter strip’s soil can help in reducing the runoff volume by infiltration. However, the characteristics of VFS (i.e., length) are not recently identified depending on the estimation of VFS modeling performance. The aim of this research is to study these characteristics and determine acorrelation between filter strip length and percent reduction (trapping efficiency) for sediment, water, and pesticide. Two proposed pesticides(one has organic carbon sorption coefficient, K_oc, of 147 L/kg which is more moveable than XXXX, and another one

Some of the characters of the Staphylolysin A and D enzymes purified from Pseudomonas aeruginosa P16 and P5 respectively were studied, the molecular weights of Staphylolysin A and D were 20.417 kilo dalton and 23.988 kilo Dalton respectively by SDS- polyacryl amide gel electrophoresis. The optimum pH for staphylolysin A activity was found to be 8 which gives higher activity reaches 150 unit/ml, and for enzyme stability was 7.5-8.5 in which the enzyme nearly retained its full activity, while it was 9.5 for staphylolysin D that gives higher activity of 16 unit/ml,and 8.5-9.5 for enzyme stability in which the enzyme nearly retained its full activity, Maximum activity of two enzymes was obtained at 40C in which the specific activity for st

Stereolithography (SLA) has become an essential photocuring 3D printing process for producing parts of complex shapes from photosensitive resin exposed to UV light. The selection of the best printing parameters for good accuracy and surface quality can be further complicated by the geometric complexity of the models. This work introduces multiobjective optimization of SLA printing of 3D dental bridges based on simple CAD objects. The effect of the best combination of a low-cost resin 3D printer’s machine parameter settings, namely normal exposure time, bottom exposure time and bottom layers for less dimensional deviation and surface roughness, was studied. A multiobjective optimization method was utilized, combining the Taguchi me

Drilling deviated wells is a frequently used approach in the oil and gas industry to increase the productivity of wells in reservoirs with a small thickness. Drilling these wells has been a challenge due to the low rate of penetration (ROP) and severe wellbore instability issues. The objective of this research is to reach a better drilling performance by reducing drilling time and increasing wellbore stability.

In this work, the first step was to develop a model that predicts the ROP for deviated wells by applying Artificial Neural Networks (ANNs). In the modeling, azimuth (AZI) and inclination (INC) of the wellbore trajectory, controllable drilling parameters, unconfined compressive strength (UCS), formation

Drilling deviated wells is a frequently used approach in the oil and gas industry to increase the productivity of wells in reservoirs with a small thickness. Drilling these wells has been a challenge due to the low rate of penetration (ROP) and severe wellbore instability issues. The objective of this research is to reach a better drilling performance by reducing drilling time and increasing wellbore stability.

In this work, the first step was to develop a model that predicts the ROP for deviated wells by applying Artificial Neural Networks (ANNs). In the modeling, azimuth (AZI) and inclination (INC) of the wellbore trajectory, controllable drilling parameters, unconfined compressive strength (UCS), formation