It is well known that drilling fluid is a key parameter for optimizing drilling operations, cleaning the hole, and managing the rig hydraulics and margins of surge and swab pressures. Although the experimental works represent valid and reliable results, they are expensive and time consuming. In contrast, continuous and regular determination of the rheological fluid properties can perform its essential functions during good construction. The aim of this study is to develop empirical models to estimate the drilling mud rheological properties of water-based fluids with less need for lab measurements. This study provides two predictive techniques, multiple regression analysis and artificial neural networks, to determine the rheological

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Pathogenic microorganisms are becoming more and more resistant to antimicrobial agents. So the synthesis of new antimicrobial agents is very important. In this work, new 5-fluoroisatin-chalcone conjugates 5(a–g) were synthesized based on previous research that showed the modifications of the isatin moiety led to the synthesis of many derivatives that have antimicrobial activity. 4-aminoacetophenone reacts with 5-fluoroisatin to form Schiff base (3), which in turn reacts with two different groups of aromatic (carbocyclic and heterocyclic) aldehydes 4(a–g) separately to form the final compounds 5(a–g). Proton-nuclear magnetic resonance (¹H-NMR) and Fourier-transform infrared (FT-IR) spectroscopy were used to confirm the chemic

Recently, numerous the generalizations of Hurwitz-Lerch zeta functions are investigated and introduced. In this paper, by using the extended generalized Hurwitz-Lerch zeta function, a new Salagean’s differential operator is studied. Based on this new operator, a new geometric class and yielded coefficient bounds, growth and distortion result, radii of convexity, star-likeness, close-to-convexity, as well as extreme points are discussed.

Some new complexes of 4-(5-(1,5-dimethyl-3-oxo-2-phenyl pyrazolidin-4- ylimino)-3,3-dimethyl cyclohexylideneamino) -1,5- dimethyl-2- phenyl -1H- pyrazol -3(2H) –one (L) with Mn(II), Fe(III), Co(II), Ni(II), Cu(II), Pd(II), Re(V) and Pt(IV) were prepared. The ligand and its metal complexes were characterized by phisco- chemical spectroscopic techniques. The spectral data were suggested that the (L) as a neutral tetradentate ligand is coordinated with the metal ions through two nitrogen and two oxygen atoms. These studies revealed Octahedral geometries for all metal complexes, except square planar for Pd(II) complex. Moreover, the thermodynamic activation parameters, such as ?E*, ?H, ?S, ?G and K are calculated from the TGA curves using Coa

A small number of researches were done in the design and synthesis of enkephalin analogues that are able to resist degradation effect of proteolytic enzymes with good bioavailability and half-lives.Through studying structure activity relationships we tried to incorporate phthalyl group, tryptophan and lysine amino acids in different positions in the basic backbone structure of the naturally occurring opioid Leu⁵- and Met⁵- enkephalin, in the hope that such insertion of these amino acids could induce interesting addition in the biological activity of these analogues with enhancement of their bioavailability, in addition to decrease side effects as addiction liability.

These synthesized peptides are:

        In this paper, an estimate has been made for parameters and the reliability function for Transmuted power function (TPF) distribution through using some estimation methods as proposed new technique for white, percentile, least square, weighted least square and modification moment methods. A simulation was used to generate random data that follow the (TPF) distribution on three experiments (E₁ , E₂ , E₃)  of the real values of the parameters, and with sample size (n=10,25,50 and 100) and iteration samples (N=1000), and taking reliability times (0< t < 0) . Comparisons have been made between the obtained results from the estimators using mean square error (MSE). The results showed the