Error estimation,adaptivity and data transfer in enriched plasticity continua to analysis of shear band localization

In this paper, an adaptive FE analysis is presented based on error estimation, adaptive mesh refinement and data transfer for enriched plasticity continua in the modelling of strain localization. As the classical continuum models suffer from pathological mesh-dependence in the strain softening models, the governing equations are regularized by adding rotational degrees-of-freedom to the conventional degrees-of-freedom. Adaptive strategy using element elongation is applied to compute the distribution of required element size using the estimated error distribution. Once a new mesh is generated, state variables and history-dependent variables are mapped from the old finite element mesh to the new one. In order to transfer the history-dependent variables from the old to new mesh, the values of internal variables available at Gauss point are first projected at nodes of old mesh, then the values of the old nodes are transferred to the nodes of new mesh and finally, the values at Gauss points of new elements are determined with respect to nodal values of the new mesh. Finally, the efficiency of the proposed model and computational algorithms is demonstrated by several numerical examples.     

Convergence analysis of perturbed hemivariational inequalities

We consider the rate of convergence for a class of perturbed hemivariational inequalities in reflexive Banach Spaces. Our results can be viewed as an extension and refinement of some previous known results in this area.     

Bioaccumulation and biosorption of stable strontium and <Superscript>90</Superscript>Sr by <Emphasis Type="Italic">Oscillatoria homogenea</Emphasis> cyanobacterium

The ability of living filamentous cells of the cyanobacterium Oscillatoria homogenea to separate stable strontium and ⁹⁰Sr from aqueous solution is demonstrated in this study. On a basis of filamentous cell biovolume, the removal were 43.78 nM·ml·(mm³)⁻¹ and 3129.48 mBq·ml·(mm³)⁻¹ after 240 hour incubation. The optimum pH for strontium uptake is 9±0.3. The increasing biovolume of the blue-green alga elevates sorption. In the liquid culture containing 21.2 mm³·ml⁻¹ filamentous cells and 1000 nM·ml⁻¹ initial strontium concentration, the maximum strontium removal was 455.34 nM·ml·(mm³)⁻¹. At 1200 Lux illumination, the maximum removal value was 58.62 nM·ml·(mm³)⁻¹, and at the initial strontium concentration of 6590 nM·ml⁻¹, 235.40 nM·ml·(mm³)⁻¹ removal was observed. The experimental data fitted to Langmuir isotherm and the model parameters and correlation coefficient (R ²) were q _max = 7.143 μg·(mm³)⁻¹, b = 0.003 and 0.99, respectively.     

Molecular Genomic Study of Inhibin Molecule Production through Granulosa Cell Gene Expression in Inhibin-Deficient Mice

Inhibin is a molecule that belongs to peptide hormones and is excreted through pituitary gonadotropins stimulation action on the granulosa cells of the ovaries. However, the differential regulation of inhibin and follicle-stimulating hormone (FSH) on granulosa cell tumor growth in mice inhibin-deficient females is not yet well understood. The objective of this study was to evaluate the role of inhibin and FSH on the granulosa cells of ovarian follicles at the premature antral stage. This study stimulated immature wild-type (WT) and Inhibin-α knockout (Inha−/−) female mice with human chorionic gonadotropin (hCG) and examined hCG-induced gene expression changes in granulosa cells. Also, screening of differentially expressed genes (DEGs) was performed in the two groups under study. In addition, related modules to external traits and key gene drivers were determined through Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm. The results identified a number of 1074 and 931 DEGs and 343 overlapping DEGs (ODEGs) were shared in the two groups. Some 341 ODEGs had high relevance and consistent expression direction, with a significant correlation coefficient (r² = 0.9145). Additionally, the gene co-expression network of selected 153 genes showed 122 nodes enriched to 21 GO biological processes (BP) and reproduction and 3 genes related to genomic pathways. By using principal component analysis (PCA), the 14 genes in the regulatory network were fixed and the cumulative proportion of fitted top three principal components was 94.64%. In conclusion, this study revealed the novelty of using ODEGs for investigating the inhibin and FSH hormone pathways that might open the way toward gene therapy for granulosa cell tumors. Also, these genes could be used as biomarkers for tracking the changes in inhibin and FSH hormone from the changes in the nutrition pattern.     

Virtual Screening of Artemisia annua Phytochemicals as Potential Inhibitors of SARS-CoV-2 Main Protease Enzyme

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a human coronaviruses that emerged in China at Wuhan city, Hubei province during December 2019. Subsequently, SARS-CoV-2 has spread worldwide and caused millions of deaths around the globe. Several compounds and vaccines have been proposed to tackle this crisis. Novel recommended in silico approaches have been commonly used to screen for specific SARS-CoV-2 inhibitors of different types. Herein, the phytochemicals of Pakistani medicinal plants (especially Artemisia annua) were virtually screened to identify potential inhibitors of the SARS-CoV-2 main protease enzyme. The X-ray crystal structure of the main protease of SARS-CoV-2 with an N3 inhibitor was obtained from the protein data bank while A. annua phytochemicals were retrieved from different drug databases. The docking technique was carried out to assess the binding efficacy of the retrieved phytochemicals; the docking results revealed that several phytochemicals have potential to inhibit the SARS-CoV-2 main protease enzyme. Among the total docked compounds, the top-10 docked complexes were considered for further study and evaluated for their physiochemical and pharmacokinetic properties. The top-3 docked complexes with the best binding energies were as follows: the top-1 docked complex with a −7 kcal/mol binding energy score, the top-2 docked complex with a −6.9 kcal/mol binding energy score, and the top-3 docked complex with a −6.8 kcal/mol binding energy score. These complexes were subjected to a molecular dynamic simulation analysis for further validation to check the dynamic behavior of the selected top-complexes. During the whole simulation time, no major changes were observed in the docked complexes, which indicated complex stability. Additionally, the free binding energies for the selected docked complexes were also estimated via the MM-GB/PBSA approach, and the results revealed that the total delta energies of MMGBSA were −24.23 kcal/mol, −26.38 kcal/mol, and −25 kcal/mol for top-1, top-2, and top-3, respectively. MMPBSA calculated the delta total energy as −17.23 kcal/mol (top-1 complex), −24.75 kcal/mol (top-2 complex), and −24.86 kcal/mol (top-3 complex). This study explored in silico screened phytochemicals against the main protease of the SARS-CoV-2 virus; however, the findings require an experimentally based study to further validate the obtained results.     

A deep learning method for estimating the boiling heat transfer coefficient of porous surfaces

Journal of Thermal Analysis and Calorimetry - Owing to the high nucleation site density and relatively robust behavior, sintered coated surfaces are of great interest for thermal management via...     

Integrated Transmission Electron and Single‐Molecule Fluorescence Microscopy Correlates Reactivity with Ultrastructure in a Single Catalyst Particle

Establishing structure–activity relationships in complex, hierarchically structured nanomaterials, such as fluid catalytic cracking (FCC) catalysts, requires characterization with complementary, correlated analysis techniques. An integrated setup has been developed to perform transmission electron microscopy (TEM) and single‐molecule fluorescence (SMF) microscopy on such nanostructured samples. Correlated structure–reactivity information was obtained for 100 nm thin, microtomed sections of a single FCC catalyst particle using this novel SMF‐TEM high‐resolution combination. High reactivity in a thiophene oligomerization probe reaction correlated well with TEM‐derived zeolite locations, while matrix components, such as clay and amorphous binder material, were found not to display activity. Differences in fluorescence intensity were also observed within and between distinct zeolite aggregate domains, indicating that not all zeolite domains are equally active.     

Determination of the Heat Transfer Coefficient of Metal Oxide Based Water Nanofluids in a Laminar Flow Regime Using an Adaptive Neuro-Fuzzy Inference System

In order to enhance the thermal properties of turbine oil (TO), three different nanoparticles (CuO, Al₂O₃, and TiO₂) are loaded into the TO. To measure the thermal performance of nanoparticle-based TO nanofluids at laminar flow and under constant heat flux boundary conditions, an experimental setup was applied. The obtained data clearly demonstrate the positive effect of all nanoparticles on the heat transfer rate of TO. As the most important factor, the heat transfer coefficient of the abovementioned two-phase systems is increased upon increasing both the volume concentration and the flow rate. An adaptive neuro-fuzzy inference system (ANFIS) is applied for modeling the effect of critical parameters on the heat transfer coefficient of nanoparticle-TO based nanofluids numerically. The results are compared with experimental ones for training and test data. The results suggest that the developed model is valid enough and promising for predicting the extant of the heat transfer coefficient. R² and MSE values for all data were 0.990208751 and 108.1150734, respectively. Based on the results, it is obvious that our proposed modeling by ANFIS is efficient and valid, which can be expanded for more general states.