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The study aims to evaluate the efficiency of using tea residues in removing two chalcone namely 1-(benzofuran-2-yl)-3-(4-chloro phenyl) prop-2-en-1-one (Ch2) and 1-(benzofuran-2-yl)-3-(4-(dimethyl amino) phenyl) prop-2-en-1-one (Ch1), which are derived from 2-acetylbenzofuran from aqueous solutions. The research included studying the influence of some factors on the efficiency and adsorption capacity, including (adsorbent dose, contact time, initial chalcone concentration, pH, and initial temperature). The highest removal percentage was (90.36%) for (Ch1) at initial concentration (20 ppm), and (80.59%) for (Ch2) at initial concentration (50 ppm). The contact time was (30 min.) for each of two chalcones in normal pH and temperature (30°C). The study also included studying the adsorption isotherm by applying four models, which are (Freundlich, Langmuir, Temkin, and Dubinin-Radushkevich), and it was found that the adsorption process follows the Langmuir model. In addition, the adsorption kinetics were studied by applying four kinetic models, and it was found that it follow the pseudo-second-order model. The study also included a thermodynamic study of the adsorption process, which revealed that the adsorption process occurs spontaneously and the reaction is exothermic, in addition to a decrease in randomness where the chalcone molecules are more ordered on the surface of the tea residues than they are in the solution. [ABSTRACT FROM AUTHOR]

The most challenging task of the Covid-19 pandemic is the accurate detection of Covid-19 affected people. To fight against this challenge, automatic detection of Covid-19 can help to diagnose patients with more reliability. In this work, we have collected chest X-rays of Covid-19 affected patients and processed the dataset for training purpose. As we have imbalanced datasets as well as data scarcity for Covid-19 class, these might create problems during training of deep neural networks. To overcome that, we have tried to develop a simple CNN model by hyperparameter optimization so that this shallow network can give better results in case of data limitation. Later we compared the performance of our developed model and the existing VGG16 model. Our model has an overall accuracy of 88.12% whereas the VGG16 model has an accuracy of 84.34%. [ABSTRACT FROM AUTHOR]

This paper discusses the use of calcium carbide residue to absorb iron (II) ions in acidic water. In this research, the acidic water was made synthetically using FeSO4. 7H2O and processed by batch adsorption methodusing calcium carbide residue as an adsorbent. This study aims to determine the characteristics of adsorbent, absorption capacity and removal efficiency. The concentrations of Iron (II) ions was varied between 40 and 100 mg/L, at the pH of solution of 2.5, adsorbent mass of 5 g, and stirring speed of 200 RPM. Contact time was ranged from 5 to 60 min. The experimental results show that at the 60 min contact time, calcium carbide residue was able to increase the pH from 2.5 to 12.8 and decreased the iron concentration by 99.73%. The process of adsorption of iron (II) metal ions with calcium carbide residue was fitted well with Langmuir’s isotherms adsorption model. [ABSTRACT FROM AUTHOR]

This paper presents a highly efficient 2.4 GHz wireless energy harvesting system comprising of a metamaterial based circularly polarized (CP) antenna and a power management circuit. The antenna is designed at 2.4 GHz using a circular slotted truncated corner square patch radiator placed on reactive impedance surface (RIS) for antenna size miniaturization, better impedance matching and to improve the front-to-back ratio. The power management system integrates a matching circuit with a single stage Dickson charge pump and an ultra low power with high efficiency DC/DC boost converter/charger. The Dickson charge pump uses three schottky diodes to minimize the losses at high frequency. The DC-DC converter (BQ25504) is capable of acquiring and managing low power levels. The measured axial ratio (boresight) is below 3-dB for the entire 2.40–2.48 GHz band and the 10-dB return loss band is from 2.35–2.49 GHz. The gain (boresight) of the antenna is around 4.6 dBic at 2.44 GHz. The proposed antenna shows an improvement in front-to-back ratio of around 3 dB with size reduction of approximately 22%. The power management system generates an output voltage level of 1.5 volts at −10 dBm and 4 volts at 0 dBm input RF power respectively. The overall efficiency of the proposed energy harvesting system is above 28%. [ABSTRACT FROM PUBLISHER]