A low-cost reverse flow plasma system powered by argon gas pumping was built using homemade materials in this paper. The length of the resulting arc change was directly proportional to the flow rate, while using the thermal camera to examine the thermal intensity distribution and demonstrating that it is concentrated in the centre, away from the walls at various flow rates, the resulting arc's spectra were also measured. The results show that as the gas flow rate increased, so did the ambient temperature. The results show that the medium containing the arc has a maximum temperature of 34.1 ˚C at a flow rate of 14 L/min and a minimum temperature of 22.6 ˚C at a flow rate of 6 L/min.

In the image processing’s field and computer vision it’s important to represent the image by its information. Image information comes from the image’s features that extracted from it using feature detection/extraction techniques and features description. Features in computer vision define informative data. For human eye its perfect to extract information from raw image, but computer cannot recognize image information. This is why various feature extraction techniques have been presented and progressed rapidly. This paper presents a general overview of the feature extraction categories for image.

Oil from Brassca campestris (local variety) was extracted with hexane using Soxhlet. The extracted oil was characterized and its antimicrobial activity was determined as well. The content of extracted oil was 40% with 0.5% of volatile oil .Oil was immiscible with polar solvent such as ethanol, acetone and water, while it was easily miscible with chloroform due to its hydrophobicity. The result of organoleptic tests revealed that the oil is clear yellow in color and odorless with acceptable taste. The oil was stable at 4 -25 C? for a month. Refractive index (RI) of oil was 1.4723 with density of 0.914, [both at 4-25 C?]. Boiling point 386 C?. Infra red spectroscopy (IR) indicated the presence of different chemical groups (C=C

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

ABSTRACT This study developed two adsorbents for extracting salbutamol sulphate (SAS) from water and urine samples after derivatisation with 2-aminobenzothiazole as a colour reagent. These adsorbents include cetylpyridinium chloride surfactant (CPC) modified silica and alumina-coated magnetite nanoparticles (Fe3O4/SiO2/CPC and Fe3O4/Al2O3/CPC). The derivatisation of SAS with the colour reagent resulted in an orange azo dye with maximum adsorption wavelengths of 443.0 nm. UV–Vis spectroscopy was used to identify the target analyte following the magnetic solid-phase extraction (MSPE) method. Under optimal conditions, the concentration ranges of 0.03–5.00 µg/mL and 0.05–6.00 µg/mL with good linearity (˃ 0.99), the detection limi

Flexure members such as reinforced concrete (RC) simply supported beams subjected to two-point loading were analyzed numerically. The Extended Finite Element Method (XFEM) was employed for the treatment the non-smooth h behaviour such as discontinuities and singularities. This method is a powerful technique used for the analysis of the fracture process and crack propagation in concrete. Concrete is a heterogeneous material that consists of coarse aggregate, cement mortar and air voids distributed in the cement paste. Numerical modeling of concrete comprises a two-scale model, using mesoscale and macroscale numerical models. The effectiveness and validity of the Meso-Scale Approach (MSA) in modeling of the reinforced concrete beams w

In this study, an easy, low-cost, green, and environmentally
friendlier reagents have been used to prepare CdS QDs, in chemical
reaction method by mixed different ratio of CdO and sulfur in
paraffin liquid as solvent and oleic acid as the reacting media in
different concentration to get the optimum condition of the reaction
to formation CdS QDs. The results give an indication that the
behavior is at small concentration of 4ml of the oleic acid is best
concentration which give CdS QDs of small about to 9.23 nm with
nano fiber configuration.