Structure-Activity Studies Reveal Scope for Optimisation of Ebselen-Type Inhibition of SARS-CoV-2 Main Protease

The reactive organoselenium compound ebselen is being investigated for treatment of coronavirus disease 2019 (COVID-19) and other diseases. We report structure-activity studies on sulfur analogues of ebselen with the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) main protease (M^pro), employing turnover and protein-observed mass spectrometry-based assays. The results reveal scope for optimisation of ebselen/ebselen derivative- mediated inhibition of M^pro, particularly with respect to improved selectivity.     

Effect of Nickel Nitrate Concentration on the Size of Nickel Oxide Nanoparticles Bio-synthesized by Artemisia herba-alba Aqueous Leaves Extract and Improving Their Antioxidant Activities

Journal of Inorganic and Organometallic Polymers and Materials - This work aims to investigate the effect of nickel nitrate concentration on the size and antioxidant activity of nickel oxide...     

Solidification of electrically conducting liquid flow driven by shear and pressure gradients subject to viscous and Joule heating

Solidification of a liquid in motion driven by shear and pressure gradients occurs in many natural settings and technological applications. When the liquid is electrically conducting, its solidification rates can potentially be modulated by an imposed magnetic field. The shearing motion results in viscous dissipation and the Lorentz force induced by the magnetic field causes Joule heating of the fluid, which can influence the structure of the flow, thermal fields, and thereby the solidification process. In this study, a mathematical model is developed to study the combined effects of shear and pressure gradients in the presence of a magnetic field on the solidification of a liquid between two parallel plates, with one of them being insulated and under constant motion, and the other being cooled convectively and at rest. Under the quasi-steady assumption, closed-form semianalytical solutions are obtained for the instantaneous location of the solid–liquid interface, Nusselt number, and dimensionless power density as a function of various characteristic parameters such as the Hartmann number, pressure gradient parameter, Brinkman number, and Biot number. Furthermore, an interesting remelt or steady-state condition for the interfacial location is derived as arising from the competing effects of the solid side heat flux and viscous dissipation and Joule heating on the liquid side. The newly derived analytical results are shown to reduce to the various classical results in the limiting cases. A detailed systematic study is performed by the numerical solution of the semianalytical formulation, and the effects of different characteristic parameters on the solidification process are discussed.     

Synthesis and Characterization of α- Fe2O3 NPs/P-Si Heterojunction for High Sensitive Photodetector

Silicon - In this work, Fe2O3 nanoparticles (NPs) were synthesized successfully through simple chemical precipitation method. The absorption spectra of the synthesized Fe2O3 NPs was studied by...     

Factorial design–machine learning approach for predicting incident durations

This research proposes a hybrid approach for predicting incident duration that integrates the salient features of both factorial design of experiments (DOE) and machine learning (ML). This study compares DOE with another widely used technique, forward sequential feature selection (FSFS). Moreover, to confirm the effectiveness and robustness of the proposed approach, multiple ML techniques are employed, including linear regression, decision trees, support vector machines, ensemble trees, Gaussian process regression, and artificial neural networks. The study results are validated using data from the Houston TranStar incidents archive with over 90,000 records. The accuracy of the developed predictive models is compared based on multiple techniques (i.e., no feature selection–ML, FSFS–ML, and DOE–ML). The results revealed that the significant factors affecting incident duration identified by both DOE and FSFS include the type of vehicles involved, type of lanes affected, number of vehicles involved, number of emergency responses dispatched, incident severity level, and day of the week. The comparative results of the different feature selection and modeling approaches revealed that the hybrid DOE–ML approach outperformed the other tested analysis approaches. The best-performing model under the DOE–ML approach was the SVM with cubic kernel model. It reduced the modeling time by 83.8% while increasing the prediction error by merely 0.02%, which is not significant. Therefore, the prediction accuracy could be slightly downgraded in return for a substantial reduction in the number of variables utilized, resulting in substantial savings in the modeling time and required dataset.     

Optimizing the Antibacterial Activity of Iron Oxide Nanoparticles Using Central Composite Design

This work aims to optimize the antibacterial activity of iron oxide nanoparticles (IONPs) against both Gram-positive and Gram-negative bacteria. IONPs were greenly biosynthesized using Moringa oleifera leaves extract, and surface methodology (RSM) based on central composite design (CCD) was employed to investigate the combined effect of various experimental factors on the antibacterial activity of IONPs. The reaction and annealing temperatures besides precursor concentration were set as independent variables, while the antibacterial activity was set as a response to obtain the optimal conditions that maximizes IONPs antibacterial activity. Different characterization techniques such as UV–Vis, FTIR, XRD, SEM, and EDX were employed to study the properties of the biosynthesized nanoparticles. Meanwhile, the antibacterial activity was tested using the disk diffusion method. The characterizations results have confirmed the biosynthesis of Hematite (α-Fe₂O₃) nanoparticles of rhombohedral structure. The generated model has exhibited predicted values very close to the actual proving its validity to analyze and optimize the studied process. The model indicated that all the investigated parameters and their interactions have significantly affected IONPs antibacterial activity. An optimal antibacterial activity was achieved when biosynthesis factors at their lower levels (? 1). Furthermore, the effect of IONPs size on the antibacterial activity was studied and the results shown that the latter is significantly related to the nanoparticles size.

Graphical Abstract

     

An efficient FPGA-based implementation of UWB radar system for through-wall imaging

The work presented in this paper aims to develop a low-cost ultra-wideband (UWB) radar system that has the capability to image through the wall of a human target. The proposed radar system relies heavily on a field-programmable gate array (FPGA) platform. The Xilinx FPGA Kintex-7 (KC705) board is employed to integrate the main functionalities of the radar system, such as the generation and acquisition of a UWB signal. The generated signal is a monocycle signal, which has an ultra-wide bandwidth. Due to the UWB nature of this signal, the radar system achieves better penetration ability, as well as high imaging resolution. However, the major challenge of a UWB radar system lies in the stage of digitization, as it requires a high sampling rate analog-to-digital converter (ADC). In fact, such a chip is very expensive. Hence, to reach a high sampling resolution using a low-cost and low-speed ADC, an efficient sampling strategy is implemented. In contrast to other UWB sampling methods, which require a hardware delay line chip, the new sampling scheme depends only on the FPGA firmware to realize a combination of real-time and equivalent-time sampling, which provides better jitter performance. Finally, to demonstrate the imaging capability, experimental tests are conducted in an indoor environment while human targets are located in different places. The measurement results revealed that the proposed radar system has the ability to provide 2D images that accurately determine the location of the target.     

Optimal conditions for the production of exopolysaccharide by Pediococcus acidilactici and impact of the bacterium,its EPS and dextran on the quality of Kariesh cheese

The effect of some growth conditions on the production of exopolysaccharide (EPS) by Pediococcus acidilactici was studied. Furthermore, the impact of utilisation of the isolated EPS, dextran and P. acidilactici on some properties of Kariesh cheese was investigated. The maximum EPS production by P. acidilactici was obtained after 10 h of incubation at 37°C in MRS medium with an initial pH of 7. Kariesh cheese manufactured with dextran or P. acidilactici exhibited the highest (P ≤ 0.05) moisture content and the lowest hardness values. Protein, fat, ash, total bacterial and yeast and mould counts were not significantly affected by the applied treatments. However, the body and texture of Kariesh cheese were significantly improved.     

Photovoltaic Models Parameters Estimation Based on Weighted Mean of Vectors

Renewable energy sources are gaining popularity, particularly photovoltaic energy as a clean energy source. This is evident in the advancement of scientific research aimed at improving solar cell performance. Due to the non-linear nature of the photovoltaic cell, modeling solar cells and extracting their parameters is one of the most important challenges in this discipline. As a result, the use of optimization algorithms to solve this problem is expanding and evolving at a rapid rate. In this paper, a weIghted meaN oF vectOrs algorithm (INFO) that calculates the weighted mean for a set of vectors in the search space has been applied to estimate the parameters of solar cells in an efficient and precise way. In each generation, the INFO utilizes three operations to update the vectors’ locations: updating rules, vector merging, and local search. The INFO is applied to estimate the parameters of static models such as single and double diodes, as well as dynamic models such as integral and fractional models. The outcomes of all applications are examined and compared to several recent algorithms. As well as the results are evaluated through statistical analysis. The results analyzed supported the proposed algorithm’s efficiency, accuracy, and durability when compared to recent optimization algorithms.