The current study deals with host-guest complex formation between cucurbit [7] urils as host and lansoprazole as guesti using PM3 (semi empirical molecules orbital calculations) also DFT calculations. In this complex, the formation of hydrogen bonding may be occurred through portal oxygen atoms(O2) of cucurbit [7] urils and amine groups (NH 2 )of the drug. The energies of HOMO and LUMO orbital’s have been computed for the host guest complex and its components. The result of the stabilization energy explained a complex formation.

The study was conducted in the Tigris River in Baghdad during May 2021 until March 2022 to follow the impact of climate change, rising temperatures, and the presence of pollutants on the dynamics of phytoplankton and some physicochemical variables from four sites. The results showed that the climatic conditions during different seasons, in addition to the nature of the sampling sites, have a clear and significant impact on the studied traits and, in turn, affect the phytoplankton community. The highest average temperature (30.67 ˚C) was recorded; the pH values ranged between 8.70 & 6.75; the electrical conductivity (1208.18-770.11 µS/cm ) and the total dissolved solids (TDS) (778.95- 439.49 mg/L) were evaluated. Upon measuring

We wanted to find out how selenium (Se) affects broiler chicken performance, meat physicochemical properties, and selenium deposition in the tissues of broilers. Each of the 96 experimental pens had 30 chickens and included a total of 2,880 one-day-old broilers (Cobb 500 strain). A factorial design of four-by-three (SY + SS) and eight replicates (SY + SS) was used for the 12 experimental treatments, with selenium levels ranging from 0.15 to 0.60 ppm and organic (SY) or inorganic (SS) sources of selenium and their relationship (SY + SS). There were no differences in performance (P > 0.05) across Se levels or sources. 106 g/day of ADFI, 63 g/day of ADG, and 1.6844 kg/kg of FCR were found to be the averaging values for these three parameters:

Effect of copper doping and thermal annealing on the structural and optical properties of Zn0.5Cd0.5S thin films prepared by chemical spray pyrolysis have been studied. Depositions were done at 250°C on glass substrate. The structural properties and surface morphology of deposited films were studied using X-ray diffraction (XRD) and photomicroscope (PHM) techniques. XRD studies reveal that all films are crystalline tetragonal structure. The film crystallinity are increased with 1% Cu-doping concentration and also increased for the films annealed at 300°C than the other studied cases. The lattice constant 'a' and 'c' varies with doping concentrations from 5.487Å to 5.427Å and 10.871Å to 10.757Å respectively. The grain size attained

The specifications of lubricating oil are fundamentally the final product of materials that have been added for producing the desired properties. In this research, spherical nanoparticles copper oxide (CuO) and titanium oxides (TiO₂) are added to SAE 15W40 engine oil to study the thermal conductivity, stability, viscosity of nano-lubricants, which are prepared at different concentrations of 0.1%, 0.2%, 0.5%, and 1% by weight, and also their pour point, and flash point as five quality parameters. The obtained results show that CuO nanoparticles in all cases, give the best functionality and effect on engine oil with respect to TiO₂. With 0.1 wt. % concentration, the thermal conductivity of CuO/oil and TiO₂/

Nano-structural of vanadium pentoxide (V2O5) thin films were
deposited by chemical spray pyrolysis technique (CSPT). Nd and Ce
doped vanadium oxide films were prepared, adding Neodymium
chloride (NdCl3) and ceric sulfate (Ce(SO4)2) of 3% in separate
solution. These precursor solutions were used to deposit un-doped
V2O5 and doped with Nd and Ce films on the p-type Si (111) and
glass substrate at 250°C. The structural, optical and electrical
properties were investigated. The X-ray diffraction study revealed a
polycrystalline nature of the orthorhombic structure with the
preferred orientation of (010) with nano-grains. Atomic force
microscopy (AFM) was used to characterize the morphology of the
films. Un-do

In this research Bi2S3 thin films have been prepared on glass substrates using chemical spray pyrolysis method at substrate temperature (300oC) and molarity (0.015) mol. Structural and optical properties of the thin films above have been studied; XRD analysis demonstrated that the Bi2S3 films are polycrystalline with (031) orientation and with Orthorhombic structure. The optical properties were studied using the spectral of the absorbance and transmission of films in wavelength ranging (300-1100) nm. The study showed that the films have high transmission within the range of the visible spectrum. Also absorption coefficient, extinction coefficient and the optical energy gap (Eg) was calculated, found that the film have direct ener

<span lang="EN-GB">Transmitting the highest capacity throughput over the longest possible distance without any regeneration stage is an important goal of any long-haul optical network system. Accordingly, Polarization-Multiplexed Quadrature Phase-Shift-Keying (PM-QPSK) was introduced lately to achieve high bit-rate with relatively high spectral efficiency. Unfortunately, the required broad bandwidth of PM-QPSK increases the linear and nonlinear impairments in the physical layer of the optical fiber network. Increased attention has been spent to compensate for these impairments in the last years. In this paper, Single Mode Fiber (SMF), single channel, PM-QPSK transceiver was simulated, with a mix of optical and electrical (Digi

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