A simple indirect spectrophotometric method for determination of mebendazol in pure and pharmaceutical formulation was presented in this study. UV-Visible spectrophotometry using the optimal conditions was developed for determination of mebendazole in pure drug and different preparation samples. The method is based on the oxidation of drug by nbromosuccinimide with hydrochloric acid and the left amount of oxidizing agent was determined by the reaction with tartarazine and the absorbance was measured at 428 nm. Calibration curves were linear in the range of 5 to 30 µg.mL-1 with molar absorptivity 8437.2 L.mol-1 .cm-1 . The limits of detection and quantification were determined and found to be 0.7770 µg.mL-1 and 2.3400 µg.mL-1 respec

Several stress-strain models were used to predict the strengths of steel fiber reinforced concrete, which are distinctive of the material. However, insufficient research has been done on the influence of hybrid fiber combinations (comprising two or more distinct fibers) on the characteristics of concrete. For this reason, the researchers conducted an experimental program to determine the stress-strain relationship of 30 concrete samples reinforced with two distinct fibers (a hybrid of polyvinyl alcohol and steel fibers), with compressive strengths ranging from 40 to 120 MPa. A total of 80% of the experimental results were used to develop a new empirical stress-strain model, which was accomplished through the application of the parti

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an ob

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an object under de

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Anaerobic digestion is a technology widely used for treatment of organic waste for biogas production as a source for clean energy. In this study, poultry house wastes (PHW) material was examined as a source for biogas production. The effects of inoculum addition, pretreatment of the substrate, and temperature on the biogas production were taken into full consideration. Results revealed that the effect of inoculum addition was more significant than the alkaline pretreatment of raw waste materials. The biogas recovery from inoculated waste materials exceeds its production from wastes without inoculation by approximately 70% at mesophilic conditions. Whereby, the increase of biogas recovery from pretreated wastes was by 20% higher than its

Zinc sulfide (ZnS) thin films were deposited on glass substrates using pulsed laser deposition technique. The laser used is the Q-switched Nd: YAG laser with 1064nm wavelength and 1Hz pulse repetition rate and varying laser energy 700mJ-1000mJ with 25 pulse. The substrate temperature was kept constant at 100°C. The structural, morphological and optical properties of ZnS thin films were characterized with X-ray diffraction (XRD), scanning electron microscopy (SEM), atomic force microscope (AFM) and UV-VIS spectrophotometer.