In this article, the high accuracy and effectiveness of forecasting global gold prices are verified using a hybrid machine learning algorithm incorporating an Adaptive Neuro-Fuzzy Inference System (ANFIS) model with Particle Swarm Optimization (PSO) and Gray Wolf Optimizer (GWO). The hybrid approach had successes that enabled it to be a good strategy for practical use. The ARIMA-ANFIS hybrid methodology was used to forecast global gold prices. The ARIMA model is implemented on real data, and then its nonlinear residuals are predicted by ANFIS, ANFIS-PSO, and ANFIS-GWO. The results indicate that hybrid models improve the accuracy of single ARIMA and ANFIS models in forecasting. Finally, a comparison was made between the hybrid forecasting models ARIMA-ANFIS, ARIMA-ANFIS-PSO, and ARIMA-ANFIS-GWO and the results showed the superiority of the ARIMA-ANFIS-PSO model.
The new novel polymers nanocomposites based modified chitosan (CS) blending with polyvinyl alcohol (PVA) and coated gold or silver nanoparticles (AuNPs), AgNPs) were synthesized from many sequence reactions as presented in (Scheme1, 2 and 3). By utilizing 1H-NMR spectroscopy, FTIR, and Field Emission Scanning electron microscope , the synthesized compounds have been identified. Molecular docking is studied, where operations are used to predict the binding status of compounds with the enzyme and to calculate the free energy (ΔG) of the compounds prepared. Also, the antibacterial activity regarding the synthesized compounds against two resistant pathogenic bacteria (G+) S. aureus and E. coli (G-) was examined in vitro compare with standard a
... Show MoreThis study was aimed to produce AuNPs biologically using Klebsiella pneumoniae and study their synergistic effect with some antibiotics.Technologies of nanoparticles are quick and are employed in many applications in biomedicine. The potential of metallic nanoparticle as an anti-microbial agent is greatly investigated which considered as an alternative method to reduce the challenges of multi-drug resistance microbes. The present study discusses the novel approach to synthesize nanoparticles involving eco-friendly synthesis of gold nanoparticles using Klebsiella pneumoniae and study their effect as antimicrobial spectrum .Also study synergism effect of gold nanoparticles with antibiotic against Acinetobacter baumannii. These approac
... Show MoreThe present work focuses on the experimental implementation of one of the fiber optical sensors, the optical glass fiber built on surface Plasmon resonance. A type of optical glass fiber was used in this work, single-mode no-core fiber with pre-tapering diameter: (125.1 μm) and (125.3 μm), respectively. The taper method can be tested by measuring the output power of the optical fiber before and after chemical etching to show the difference in cladding diameter due to the effect of hydrofluoric acid with increasing time for the taper process. The optical glass fiber sensor can be fabricated using the taper method to reduce the cladding diameter of the fibers to (83.12 µm, 64.37 µm, and 52.45 µm) for single-mode fibers using Hydrofluoric
... Show MoreThe orogenic gold deposit of Tamilouw – Haya is hosted by slate and metapelitic rocks within Tehoru metamorphic complex. Gold and polymetallic sulfides mineralization at study area is predominantly formed in the form of veins, stockwork and breccia although minor dissemination is slightly appeared in the rock float samples. They are trapped and controlled by NE-SW and NNE-SSW trending geologic structure occurred during orogeny process from Late Miocene to Pliocene. The common ore minerals assemblage at Tamilouw – Haya deposit are dominated by native gold, chalcopyrite, pyrite, sphalerite, galena, pyrrhotite, tetrahedrite-tennantite (sulphosalt), marcasite,realgar, kalininite and arsenopyrite as hypogene minerals and accompanied by co
... Show MoreThe flavonoglycone hesperidin is recognized as a potent anti-inflammatory, anticancer, and antioxidant agent. However, its poor bioavailability is a crucial bottleneck regarding its therapeutic activity. Gold nanoparticles are widely used in drug delivery because of its unique properties that differ from bulk metal. Hesperidin loaded gold nanoparticles were successfully prepared to enhance its stability and bioactive potential, as well as to minimize the problems associated with its absorption. The free radical scavenging activities of hesperidin, gold nanoparticles, and hesperidin loaded gold nanoparticles were compared with that of Vitamin C and subsequently evaluated in vitro using 2,2-diphenyl-1-picrylhydrazyl assay. The antioxi
... Show MoreIn this study, gold nanoparticle samples were prepared by the chemical reduction method (seed-growth) with 4 ratios (10, 12, 15 and 18) ml of seed, and the growth was stationary at 40 ml. The optical and structural properties of these samples were studied. The 18 ml seed sample showed the highest absorbance. The X- ray diffraction (XRD) patterns of these samples showed clear peaks at (38.25o, 44.5o, 64.4o, and 77.95o). The UV-visible showed that the absorbance of all the samples was in the same range as the standard AuNPs. The field emission-scanning electron microscope (FE-SEM) showed the shape of AuNPs as nanorods and the particle size between 30-50 nm. Rhodamine-610 (RhB) was prepared at 10<
... Show MoreMetallic nanoparticles are increasingly studied for their biomedical applications due to their unique physicochemical and catalytic properties. Here, a broccoli-mediated gold/platinum nanohybrid (Au@Pt NH) was synthesized using an ultrasound-assisted green method with an aqueous extract of Brassica oleracea var. italica for multifunctional biomedical evaluation. XRD and TEM confirmed a crystalline nanohybrid with an average crystallite size of 7.56 nm and a mean particle diameter of 13.08 ± 7.58 nm. The broccoli extract produced no inhibition zones, whereas Au@Pt NH inhibited Staphylococcus aureus (18 mm), Staphylococcus epidermidis (21 mm), Escherichia coli (18 mm), Klebsiella pneumoniae (20 mm), and Candida albicans (21 mm). In vivo,
... Show MoreAdvanced strategies for production forecasting, operational optimization, and decision-making enhancement have been employed through reservoir management and machine learning (ML) techniques. A hybrid model is established to predict future gas output in a gas reservoir through historical production data, including reservoir pressure, cumulative gas production, and cumulative water production for 67 months. The procedure starts with data preprocessing and applies seasonal exponential smoothing (SES) to capture seasonality and trends in production data, while an Artificial Neural Network (ANN) captures complicated spatiotemporal connections. The history replication in the models is quantified for accuracy through metric keys such as m
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