A Comparison of Two Novel Non-Ferrous Metal Recovery Equations: Development, Similarity and Industrial Significance

The recovery of non-ferrous metals in oxidative sulfide smelting and converting processes and within reductive oxide smelting processes has been previously analyzed using two similar equations which express recovery in terms of iron transfer and a distribution coefficient. A detailed comparison will show that the equations are mathematically identical but with one equation validated only for constant distribution coefficients. The wider applicability of the equation and implications for optimization of metal recovery are discussed.

Impact of the COVID-19 pandemic on water consumption behaviour

Access to drinking water is essential for life, and an adequate and constant distribution of water is necessary during the occurrence of contagious diseases and pandemic situations. Currently, COVID-19, caused by the SARS-COV-2 coronavirus, has spread throughout the world, and in Brazil, more than 5,300,000 cases and 157,000 deaths had been reported by 26th October 2020. Water is regarded as one of the most important resources in a pandemic, in order to provide the necessary sanitary conditions. Thus, the present study aims to analyse changes in hygiene behaviour in relation to the recommendations of the World Health Organisation and the impact on water consumption before and during the pandemic. The survey was conducted using a questionnaire, which was delivered online to 149 participants between June and July 2020. The results pointed to changes in hygiene behaviour, with a consequent effect on water consumption.

Imaging the internal structure of trunks via multiscale phase inversion of ground-penetrating radar data

Ground-penetrating radar (GPR) is a geophysical technique widely used in near-surface non-invasive detecting. It has the ability to obtaining a high-resolution internal structure of living trunks. Full wave inversion (FWI) has been widely used to reconstruct the dielectric constant and conductivity distribution for cross-well application. However, in some cases, the amplitude information is not reliable due to the antenna coupling, radiation pattern and other effects. We present a multiscale phase inversion (MPI) method, which largely matches the phase information by normalizing the magnitude spectrum; in addition, a natural multiscale approach by integrating the input data with different times is implemented to partly mitigate the local minimal problem. Two synthetic GPR datasets generated from a healthy oak tree trunk and from a decayed trunk are tested by MPI and FWI. Field GPR dataset consisting of 30 common shot GPR data are acquired on a standing white oak tree (Quercus alba); the MPI and FWI methods are used to reconstruct the dielectric constant distribution of the tree cross-section. Results indicate that MPI has more tolerance to the starting model, noise level and source wavelet. It can provide a more accurate image of the dielectric constant distribution compared to the conventional FWI.

Experimental Study on the Flow Around a Rotating Cylinder

The flow around a rotating cylinder has been investigated at the experimental water basins. The tangential velocity generated by the rotation of the cylinder decreases linearly with the logarithmic value of the distance from the cylinder surface, and has a constant distribution in the radial direction regardless of the rotation speed. The distance at which the tangential velocity becomes zero is about 4.35 times of the cylinder radius from the cylinder center. The energy transfer is greater near the cylinder than far from the cylinder and proportional to the proportionality coefficient A of the linear equation of velocity distribution. In a narrow water basin, the influence of the rotation is not absorbed completely to the water and a flow was formed in the water basin.

Ü-Net: Deep-Learning Schemes for Ground Penetrating Radar Data Inversion

Electromagnetic (EM) inversion is a quantitative imaging technique that can describe the dielectric constant distribution of a target based on the EM signals scattered from it. In this paper, a novel deep neural network (DNN) based methodology for ground penetrating radar (GPR) data inversion, known as the Ü-net is introduced. The proposed Ü-net consists of three parts: a data compression unit, U-net, and an output unit. The novel inversion approach, based on supervised learning, uses a neural network to generate the dielectric constant distribution from GPR data. The GPR data can be compressed and reshaped the size using data compression unit. The U-net maps the object features to the dielectric constant distribution. The output unit meshes the dielectric constant distribution more finely. A novel feature of the proposed methodology is the application of instance normalization (IN) to the DNN EM inversion method and a comparison of its performance to batch normalization (BN). The validity of this technique is confirmed by numerical simulations. The Mean-Square Error of the test data sets is 0.087. These simulations prove that the instance normalization is suitable for GPR data inversion. The proposed approach is promising for achieving quality dielectric constant images in real-time.

Spectroscopic and thermodynamic interaction studies of anti-diabetic drug with globular proteins

Drug-protein interaction is a fundamental problem in estimating the serious side effects of the drug. Hence, the main objective of this study was to study the interaction of acarbose with three different globular proteins i.e.; bovine serum albumin (BSA), human serum albumin (HSA), and hemoglobin (Hb) via UV-Visible absorption spectroscopic analysis. We were determined physicochemical parameters, binding constant, distribution constant and thermodynamic parameters activation energy, enthalpy, entropy, and Gibbs free energy by using UV-visible data. These both properties of acarbose-protein complexes indicated that the hydrogen bonding and weak van der Waals force played a major role in the interaction for complexation. The binding of acarbose with different proteins leads to change in the structure of protein folding which confirms by physicochemical and thermodynamic analysis.

Characterization of Volume Gratings Based on Distributed Dielectric Constant Model Using Mueller Matrix Ellipsometry

Volume grating is a key optical component due to its comprehensive applications. Other than the common grating structures, volume grating is essentially a predesigned refractive index distribution recorded in materials, which raises the challenges of metrology. Although we have demonstrated the potential application of ellipsometry for volume grating characterization, it has been limited due to the absence of general forward model reflecting the refractive index distribution. Herein, we introduced a distributed dielectric constant based rigorous coupled-wave analysis (RCWA) model to interpret the interaction between the incident light and volume grating, with which the Mueller matrix can be calculated. Combining with a regression analysis with the objective to match the measured Mueller matrices with minimum mean square error (MSE), the parameters of the dielectric constant distribution function can be determined. The proposed method has been demonstrated using a series of simulations of measuring the volume gratings with different dielectric constant distribution functions. Further demonstration has been carried out by experimental measurements on volume holographic gratings recorded in the composite of polymer and zinc sulfide (ZnS) nanoparticles. By directly fitting the spatiotemporal concentration of the nanoparticles, the diffusion coefficient has been further evaluated, which is consistent to the result reported in our previous investigations.