Antibiotics resistant bacteria have become a global problem as a result of the unprogrammed use of antibiotics, resulting in bacterial strains resistant to many antibiotics, or to all available antibiotics. Plants are a good source of primary and secondary metabolites that have a major role in reducing silver nitrate to silver nanoparticles (AgNPs). The production of these nanoparticles were carried out by using aqueous extract of Carthamus oxycantha M.Bieb. This can be verified by color change of the reaction solution from yellow to dark brown because of the excitation of the surface plasmon resonance. AgNPs were characterized by UV-Vis spectroscopy, where they recorded the peak at 420 nm. Fourier Transformation-infrared (FTIR)

This study investigated the impact of lime stabilization on the fate and transformation of AgNPs. It also evaluated the changes in the population and diversity of the five most relevant bacterial phyla in soil after applying lime-stabilized sludge containing AgNPs. The study was performed by spiking an environmentally relevant concentration of AgNPs (2 mg AgNPs/g TS) in sludge, applying lime stabilization to increase pH to above 12 for two hours, and applying lime-treated sludge to soil samples. Transmission electron microscopy (TEM) and energy-dispersive X-ray spectroscopy (EDS) were used to investigate the morphological and compositional changes of AgNPs during lime stabilization. After the application of lime stabilized sludge to

Abstract: Background: Optical biosensors offer excellent properties and methods for detecting bacteria when compared to traditional analytical techniques.  It allows direct detection of many biological and chemical materials.  Bacteria are found in the human body naturally non-pathogenic and pathologically, as they are found in other living organisms.  One of these bacteria is Escherichia coli (E. coli) which are found in the human body in its natural and pathogenic form. E.coli bacteria cause many diseases, including Stomach, intestines, urinary system infections, and others. The aim of this study: is sensing and differentiation between normal flora and pathogenic E.coli. Material and method:

The bacterial contamination of lipsticks and face cream may become a great important matter in the medical laboratories. The present study was designed to determine the types of bacterial contamination in the face cream and lipsticks of undergraduate students. Also, the study aimed to determine the sensitivity of the isolated bacteria against many antibacterial agents. The study included 190 swabs samples from 190 face cream and lipsticks samples of the females’ students from five departments in the Medical Technology Institute, Almansour, Middle Technical University were collected in February 2018. The swab samples were collected with sterile condition and cultured on enriched Blood agar and MacConkey agar. Serial dilutions were made up

The antagonism of the rhizospheric bacteria toward pathogenic fungi Macrophomina phaseolina was investigated. Ten soil samples were collected from the rhizospheric zone around Cowpea root (Vignaunguiculata L.). These samples were used as the source of Arbuscular Mycorrhizal fungi (AMF) and rhizobacterial isolates. Twenty-five bacteria were isolated and evaluated as an antagonistic agent against pathogenic fungi. M. phaseolina was isolated from infected roots of Cowpea and used as a pathogen. Twenty-five bacteria were isolated and evaluated as an antagonistic agent against pathogenic fungi. M. phaseolina was isolated from infected roots of Cowpea and used as a pathogen. The synergistic effect between A. siccitolerans and (AMF) Glomusmosseae,

Nowadays, internet security is a critical concern; the One of the most difficult study issues in network security is "intrusion detection". Fight against external threats. Intrusion detection is a novel method of securing computers and data networks that are already in use. To boost the efficacy of intrusion detection systems, machine learning and deep learning are widely deployed. While work on intrusion detection systems is already underway, based on data mining and machine learning is effective, it requires to detect intrusions by training static batch classifiers regardless considering the time-varying features of a regular data stream. Real-world problems, on the other hand, rarely fit into models that have such constraints. Furthermor

Image pattern classification is considered a significant step for image and video processing. Although various image pattern algorithms have been proposed so far that achieved adequate classification, achieving higher accuracy while reducing the computation time remains challenging to date. A robust image pattern classification method is essential to obtain the desired accuracy. This method can be accurately classify image blocks into plain, edge, and texture (PET) using an efficient feature extraction mechanism. Moreover, to date, most of the existing studies are focused on evaluating their methods based on specific orthogonal moments, which limits the understanding of their potential application to various Discrete Orthogonal Moments (DOM