Sol-gel method was use to prepare Ag-SiO2 nanoparticles. Crystal structure of the nanocomposite was investigated by means of X-ray diffraction patterns while the color intensity was evaluated by spectrophotometry. The morphology analysis using atomic force microscopy showed that the average grain sizes were in range (68.96-75.81 nm) for all samples. The characterization of Ag-SiO2 nanoparticles were investigated by using Scanning Electron Microscopy (SEM). Ag-SiO2 NPs are highly stable and have significant effect on both Gram positive and negative bacteria. Antibacterial properties of the nanocomposite were tested with the use of Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli) bacteria. The results have shown antibacteri

Abstract: The utility of DNA sequencing in diagnosing and prognosis of diseases is vital for assessing the risk of genetic disorders, particularly for asymptomatic individuals with a genetic predisposition. Such diagnostic approaches are integral in guiding health and lifestyle decisions and preparing families with the necessary foreknowledge to anticipate potential genetic abnormalities. The present study explores implementing a define-by-run deep learning (DL) model optimized using the Tree-structured Parzen estimator algorithm to enhance the precision of genetic diagnostic tools. Unlike conventional models, the define-by-run model bolsters accuracy through dynamic adaptation to data during the learning process and iterative optimization

With the continuous downscaling of semiconductor processes, the growing power density and thermal issues in multicore processors become more and more challenging, thus reliable dynamic thermal management (DTM) is required to prevent severe challenges in system performance. The accuracy of the thermal profile, delivered to the DTM manager, plays a critical role in the efficiency and reliability of DTM, different sources of noise and variations in deep submicron (DSM) technologies severely affecting the thermal data that can lead to significant degradation of DTM performance. In this article, we propose a novel fault-tolerance scheme exploiting approximate computing to mitigate the DSM effects on DTM efficiency. Approximate computing in hardw

Facial recognition has been an active field of imaging science. With the recent progresses in computer vision development, it is extensively applied in various areas, especially in law enforcement and security. Human face is a viable biometric that could be effectively used in both identification and verification. Thus far, regardless of a facial model and relevant metrics employed, its main shortcoming is that it requires a facial image, against which comparison is made. Therefore, closed circuit televisions and a facial database are always needed in an operational system. For the last few decades, unfortunately, we have experienced an emergence of asymmetric warfare, where acts of terrorism are often committed in secluded area with no

Abstract This research scrutinizes the impact of external magnetic field strength variations on plasma jet parameters to enhance its performance and flexibility. Plasma jets are widely used for their high thermal and kinetic energy in both medical and industrial fields. The study employs optical emission spectroscopy to measure electron temperature, electron density, and plasma frequency in a plasma jet subjected to varying magnetic field strengths (25, 50, 100, 150, and 250 mT). The results indicate that a stronger magnetic field results in higher electron temperature (1.485 to 1.991 eV), electron density (5.405 × 1017 to 7.095 × 1017), and plasma frequency 7.382 × 1012 to 8.253 × 1012 Hz. As well as the research investigates the influ

The density functional B3LYP is used to investigate the effect of decorating the silver (Ag) atom on the sensing capability of an AlN nanotube (AlN-NT) in detecting thiophosgene (TP). There is a weak interaction between the pristine AlN-NT and TP with the sensing response (SR) of approximately 9.4. Decoration of the Ag atom into the structure of AlN-NT causes the adsorption energy of TP to decrease from − 6.2 to − 22.5 kcal/mol. Also, the corresponding SR increases significantly to 100.5. Moreover, the recovery time when TP is desorbed from the surface of the Ag-decorated AlN-NT (Ag@AlN-NT) is short, i.e., 24.9 s. The results show that Ag@AlN-NT can selectively detect TP among other gases, such as N2, O2, CO2, CO, and H2O.

Porosity plays an essential role in petroleum engineering. It controls fluid storage in aquifers, connectivity of the pore structure control fluid flow through reservoir formations. To quantify the relationships between porosity, storage, transport and rock properties, however, the pore structure must be measured and quantitatively described. Porosity estimation of digital image utilizing image processing essential for the reservoir rock analysis since the sample 2D porosity briefly described. The regular procedure utilizes the binarization process, which uses the pixel value threshold to convert the color and grayscale images to binary images. The idea is to accommodate the blue regions entirely with pores and transform it to white in r