The study aimed to determine the impact of energy for the north and south magnetic poles on the the growth of bacteria isolated from cases of tooth decay, 68 swabs were collected from surfaces of faulty tooth, the detected of Staphylococcus aureus

Seeds, beans, leaves, fruit peel and seeds of five plants (Ferula assa-foetida, Coffea robusta, Olea europaea, Punica granatum and Vitis vinifera, respectively) were extracted with four solvents (distilled water, 80% methanol, 80% acetone and a mixed solvent that included methanol, ethanol, acetone and n-butanol at proportions 7:1:1:1). Such manipulation yielded 20 extracts, which were phytochemically analyzed for total polyphenols (TP) and flavonoids (TF). The DPPH (2,2-diphenyl-1-picrylhydrazyl) radical scavenging activity (RSA) and DPP-4 (dipeptidyl peptidase-4) relative inhibition activity (RIA) were also assessed for each extract. The results revealed that mixed solvent extract of V.

The detection of fungi contaminating maize grain and the effect of four plant extracts Azadirachta indica, Eucalyptus globulus Glycyrrhiza glabra and Zingiber officinale on the growth of A. flavus and its ability to produce AflatoxinB1. The results showed that the incidence of Aspergillus spp., was 52.75% of the isolated fungi, of which 29.50% was due to Aspergillus flavus, followed by Penicillium spp., with an incidence of 21.06%, and then Fusarium spp., with a rate of 18.13%. The percentage of toxin-producing A. flavus isolates reached 70.8% out of 24 isolates. The results showed the effect of alcoholic plant extracts at a concentration of 10 mg/ml on the fungal growth activity of A. flavus, the alcoholic extract of neem leaves was superi

This study was aimed to evaluate atotal phenolic content, antibacterial activity, and antioxidant activity of M. communis callus extracts were evaluated. Callus induction in general Murashige and Skoog (MS) media is completed by the Benzil adenine's unique knowledge of callus formation. A well diffusion experiment was used to examine antibacterial interest in Staphylococcus aureus, Escherichia coli, Klebsiella pneumonia, and Pseudomonas aeruginosa. The DPPH radical scavenging activity test was used to measure antioxidant activity. FTIR and HPLC have been used to pinpoint the presence of polyphenol compounds in calluses. The total phenol content of plant leaves extract (0.1, 0.5, and 1) mg/ml was 42.12, 94.08, and 189 mg of Gallic ac

In this research, a novel thin film Si-GO10 and nanopowders Si-GO30 of silica-graphene oxide (GO) composite were prepared via the sol–gel method and deposited on glass substrates using spray pyrolysis. X-ray diffraction (XRD) results showed a relatively strong peak in the graphite layer that corresponds to the (002) plane. Transmission electron microscope (TEM) images showed that SiO2 nanoparticles were randomly distributed on the surface of GO plates, and the particle size in these nanopowders was below 50 nm. Field emission scanning electron microscopy (FESEM) analysis demonstrated that silica nanoparticles on the surface of GO plates exhibited almost spherical and rod-like nanoparticle shape, which in turn confirmed the formation of Si

In this research, a novel thin film Si-GO10 and nanopowders Si-GO30 of silica-graphene oxide (GO) composite were prepared via the sol–gel method and deposited on glass substrates using spray pyrolysis. X-ray diffraction (XRD) results showed a relatively strong peak in the graphite layer that corresponds to the (002) plane. Transmission electron microscope (TEM) images showed that SiO₂ nanoparticles were randomly distributed on the surface of GO plates, and the particle size in these nanopowders was below 50 nm. Field emission scanning electron microscopy (FESEM) analysis demonstrated that silica nanoparticles on the surface of GO plates exhibited almost spherical and rod-like nanoparticle shape, which in tur

The successful implementation of deep learning nets opens up possibilities for various applications in viticulture, including disease detection, plant health monitoring, and grapevine variety identification. With the progressive advancements in the domain of deep learning, further advancements and refinements in the models and datasets can be expected, potentially leading to even more accurate and efficient classification systems for grapevine leaves and beyond. Overall, this research provides valuable insights into the potential of deep learning for agricultural applications and paves the way for future studies in this domain. This work employs a convolutional neural network (CNN)-based architecture to perform grapevine leaf image classifi