Artificial Intelligence Algorithms have been used in recent years in many scientific fields. We suggest employing flower pollination algorithm in the environmental field to find the best estimate of the semi-parametric regression function with measurement errors in the explanatory variables and the dependent variable, where measurement errors appear frequently in fields such as chemistry, biological sciences, medicine, and epidemiological studies, rather than an exact measurement. We estimate the regression function of the semi-parametric model by estimating the parametric model and estimating the non-parametric model, the parametric model is estimated by using an instrumental variables method (Wald method, Bartlett’s method, and Durbin

Abstract

The labeled research deal with (Entrepreneurship Organizations In the framework of strategic leadership practices: Field research in the Ministry of Oil), Search over the possibility of the influence of the practices of strategic leadership Which include

(Determine the strategic direction, The discovery of the fundamental estimators and maintain it, The development of the human capital, and Maintaining of  an organizational culture influential, and Find a balanced regulatory Control) In a Entrepreneurship  in its dimensions and its (innovation, risk, pre-emptive and independence) On group of heads of departments and authorities

Experimental tests were carried to control lost circulation in the Khabaz oil field using different types of LCMs including Nano-materials. A closed-loop circulation system was built to simulate the process of lost circulation into formations. Two dolomite plugs were used from different depths of the formation of Azkand in Khabaz oil field. The experimentations were carried out to study the effect of different types of LCMs, cross-linked copolymer (FLOSORB CE 300 S), SiO₂ NP, and Fe₂O₃ NP, on mud volume losses as a function of time.

The rheological measurements of the nanoparticles-reference mud system showed that both of the SiO₂ NP and Fe₂O₃ NP w

Detecting the optimum layer for well placement, which requires a diverse assortment of tools and techniques, represents a significant challenge in petroleum studies due to its critical impact on minimizing drilling costs and time. This study aims to evaluate integrated geological, petrophysical, seismic, and geomechanical data to identify the optimum zones for well placement. Three different reservoirs were analyzed to account for lateral and vertical variations in reservoir properties. The integrated data from these reservoirs provides many tools for reservoir development, especially to detect appropriate well placement zones based on evaluations of reservoir and geomechanical quality. The Mechanical Earth Model (MEM) was construct

Nimodipine (NMD) is a dihydropyridine calcium channel blocker useful for the prevention and treatment of delayed ischemic effects. It belongs to class ? drugs, which is characterized by low solubility and high permeability. This research aimed to prepare Nimodipine nanoparticles (NMD NPs) for the enhancement of solubility and dissolution rate. The formulation of nanoparticles was done by the solvent anti-solvent technique using either magnetic stirrer or bath sonicator for maintaining the motion of the antisolvent phase. Five different stabilizers were used to prepare NMD NPs( TPGS, Soluplus®, HPMC E5, PVP K90, and poloxamer 407). The selected formula F2, in which  Soluplus 

has been utilized as a stabilizer, has a par

The majority of real-world problems involve not only finding the optimal solution, but also this solution must satisfy one or more constraints. Differential evolution (DE) algorithm with constraints handling has been proposed to solve one of the most fundamental problems in cellular network design. This proposed method has been applied to solve the radio network planning (RNP) in the forthcoming 5G Long Term Evolution (5G LTE) wireless cellular network, that satisfies both deployment cost and energy savings by reducing the number of deployed micro base stations (BSs) in an area of interest. Practically, this has been implemented using constrained strategy that must guarantee good coverage for the users as well. Three differential evolution

     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod

Experimental and numerical studies have been conducted on the effects of bed roughness elements such as cubic and T-section elements that are regularly half-channel arrayed on one side of the river on turbulent flow characteristics and bed erosion downstream of the roughness elements. The experimental study has been done for two types of bed roughness elements (cubic and T-section shape) to study the effect of these elements on the velocity profile downstream the elements with respect to different water flow discharges and water depths. A comparison between the cubic and T-section artificial bed roughness showed that the velocity profile downstream the T-section increased in smooth side from the river and decrease in the rough side