Escherichia coli  infections are becoming difficult treated because of extensive resistance to antibiotic among these organisms and manufacturing extended-spectrum beta lactamases enzymes (ESBLs) make them resistant to beta-lactam antibiotics. This study aims to offer a summary of the main horizontal transmission  apparatuses  between E. coli as well as  Staphylococcus aureus and emergence resistance to antibiotics. Fifty of the E. coli and  50 of S. aureus isolates were examined to obtain minimum inhibitory concentration (MIC) results. These isolates were then tested by  conventional polymerase chain-reaction for the existence or absenc

A range of macrocyclic dinuclear metal (II) dithiocarbamate-based complexes are reported. The preparation of complexes was accomplished from either mixing of the prepared ligand with a metal ion or through a template one-pot reaction. The preparation of the bisamine precursor was achieved through several synthetic steps. The free ligand; potassium 2,2'-(biphenyl-4,4'-diylbis(azanediyl))bis(1-chloro-2-oxoethane-2,1diyl)bis(cyclohexylcarbamodithioate) (L) was yielded from the addition of CS2 to a bis-amine precursor in  KOH medium.A variety of analytical and physical methods were implemented to characterise ligand and its complexes. The analyses were based on spectroscopic techniques (FTIR, UV-Vis, mass spectroscopy and 1H, 13C-NMR sp

     In this work, the mass attenuation coefficient, effective atomic number and half value layer parameters were calculated for silicate (SiO2) mixed with various levels of lead oxide and iron oxide as reinforced materials. SiO2 was used with different concentrations of PbO and Fe2O3 (25, 50 and 75 weight %). The glass system was prepared by the melt-quenching method. The attenuation parameters were calculated at photon energies varying from 1keV to 100MeV using the XCOM program (version 3.1). In addition, the mass attenuation coefficient and half value layer parameters for selected glass samples were experimentally determined at photon energies 0.662 and 1.28 MeV emitted from radioactive sources 137Cs and 22Na respe

This paper presents a hybrid energy resources (HER) system consisting of solar PV, storage, and utility grid. It is a challenge in real time to extract maximum power point (MPP) from the PV solar under variations of the irradiance strength.  This work addresses challenges in identifying global MPP, dynamic algorithm behavior, tracking speed, adaptability to changing conditions, and accuracy. Shallow Neural Networks using the deep learning NARMA-L2 controller have been proposed. It is modeled to predict the reference voltage under different irradiance. The dynamic PV solar and nonlinearity have been trained to track the maximum power drawn from the PV solar systems in real time.

Moreover, the proposed controller i

Diamond-like carbon (DLC) homogeneous thin films were deposited from cyclohexane (Ccyclohexane (Ccyclohexane (Ccyclohexane (C cyclohexane (Ccyclohexane (Ccyclohexane (C cyclohexane (Ccyclohexane (C 6H12 ) liquid by using a plasma jet system which operates with alternating high voltage 7.5 which operates with alternating high voltage 7.5which operates with alternating high voltage 7.5which operates with alternating high voltage 7.5 which operates with alternating high voltage 7.5which operates with alternating high voltage 7.5which operates with alternating high voltage 7.5 which operates with alternating high voltage 7.5which operates with alternating high voltage 7.5 which operates with alternating high voltage 7.5which operates with al

Due to the lack of vehicle-to-infrastructure (V2I) communication in the existing transportation systems, traffic light detection and recognition is essential for advanced driver assistant systems (ADAS) and road infrastructure surveys. Additionally, autonomous vehicles have the potential to change urban transportation by making it safe, economical, sustainable, congestion-free, and transportable in other ways. Because of their limitations, traditional traffic light detection and recognition algorithms are not able to recognize traffic lights as effectively as deep learning-based techniques, which take a lot of time and effort to develop. The main aim of this research is to propose a traffic light detection and recognition model based on