This study explores the linear and nonlinear solutions of sigmoid functionally graded material (S-FGM) nanoplate with porous effects. A size-dependent numerical solution is established using the strain gradient theory and isogeometric finite element formulation. The nonlinear nonlocal strain gradient is developed based on the Reissner-Mindlin plate theory and the Von-Karman strain assumption. The sigmoid function is utilized to modify the classical functionally graded material to ensure the constituent volume distribution. Two different patterns of porosity distribution are investigated, viz. pattern A and pattern B, in which the porosities are symmetric and asymmetric varied across the plate's thickness, respectively. The nonlinear finite element governing equations are established for bending analysis of S-FGM nanoplates, and the Newton-Raphson iteration technique is derived from the nonlinear responses. The isogeometric finite element method is the most suitable numerical method because it can satisfy a higher-order derivative requirement of the nonlocal strain gradient theory. Several numerical results are presented to investigate the influences of porosity distributions, power indexes, aspect ratios, nonlocal and strain gradient parameters on the porous S-FGM nanoplate's linear and nonlinear bending responses.

The potentials of Ni_xZn_1-xFe₂O₄ (x = 0.0, 0.2, 0.4, 0.6, 0.8 and 1.0) nanoadsorbents were investigated for removal of Cd and Cr from contaminated water from an electroplating industry in Himachal Pradesh, India. Optimal values were recorded under batch adsorption experiments performed to remove dissolved heavy metal ions from industrial wastewater. The specific surface area (SSA) of nanoadsorbents perceived to vary in a range 35.75-45.29 cm²/g and was calculated from the XRD data. The influence of two operating parameters, contact time and dopant (Ni) concentration was also investigated at pH ~7 with optimum dosage. Kinetic studies were conducted within a time range of 2-10 min with rapid adsorption of cadmium and chromium ions onto Ni_0.2Zn_0.8Fe₂O₄ nanoadsorbents. Pseudo-second-order kinetic model was observed to be well fitted with the adsorption data that confirmed the only existence of chemisorption throughout the adsorption process. The maximum adsorption efficiency values observed for Cd and Cr were 51.4 mg/g and 40.12 mg/g, respectively for different compositions of prepared series of nanoadsorbents. The removal percentage of Cd and Cr was found to vary in a range of 47.7%-95.25% and 21%-50% respectively. The prepared series of nanoferrite found to be suitable enough for adsorption of both heavy metal ions.

In the current work, static and free torsional vibration of functionally graded (FG) nanorods are investigated using Fourier sine series. The boundary conditions are described by the two elastic torsional springs at the ends. The distribution of functionally graded material is considered using a power-law rule. The systems of equations of the mechanical response of nanorods subjected to deformable boundary conditions are achieved by using the modified couple stress theory (MCST) and taking the effects of torsional springs into account. The idea of the study is to construct an eigen value problem involving the torsional spring parameters with small scale parameter and functionally graded index. This article investigates the size dependent free torsional vibration based on the MCST of functionally graded nano/micro rods with deformable boundary conditions using a Fourier sine series solution for the first time. The eigen value problem is constructed using the Stokes' transform to deformable boundary conditions and also the convergence and accuracy of the present methodology are discussed in various numerical examples. The small size coefficient influence on the free torsional vibration characteristics is studied from the point of different parameters for both deformable and rigid boundary conditions. It shows that the torsional vibrational response of functionally graded nanorods are effected by geometry, small size effects, boundary conditions and material composition. Furthermore, for all deformable boundary conditions in the event of nano-sized FG nanorods, the incrementing of the small size parameters leads to increas the torsional frequencies.

The present work is an attempt to study the vibration analysis of the single-walled carbon nanotubes (SWCNTs) under the effect of the surface irregularity using Donnell's model. The surface irregularity represented by the parabolic form. According to Donnell's model and three-dimensional elasticity theory, a novel governing equations and its solution are derived and matched with the case of no irregularity effects. To understand the reaction of the nanotube to the irregularity effects in terms of natural frequency, the numerical calculations are done. The results obtained could provide a better representation of the vibration behavior of an irregular single-walled carbon nanotube, where the aspect ratio (L/d) and surface irregularity all have a significant impact on the natural frequency of vibrating SWCNTs. Furthermore, the findings of surface irregularity effects on vibration SWCNT can be utilized to forecast and prevent the phenomena of resonance of single-walled carbon nanotubes.

Nanofluids have recently triggered a substantial scientific interest as cooling media. However, their stability is challenging for successful engagement in industrial applications. Different factors, including temperature, nanoparticles and base fluids characteristics, pH, ultrasonic power and frequency, agitation time, and surfactant type and concentration, determine the nanofluid stability regime. Indeed, it is often too complicated and even impossible to accurately find the conditions resulting in a stabilized nanofluid. Furthermore, there are no empirical, semi-empirical, and even intelligent scenarios for anticipating the stability of nanofluids. Therefore, this study introduces a straightforward and reliable intelligent classifier for discriminating among the stability regimes of alumina-water nanofluids based on the Zeta potential margins. In this regard, various intelligent classifiers (i.e., deep learning and multilayer perceptron neural network, decision tree, GoogleNet, and multi-output least squares support vector regression) have been designed, and their classification accuracy was compared. This comparison approved that the multilayer perceptron neural network (MLPNN) with the SoftMax activation function trained by the Bayesian regularization algorithm is the best classifier for the considered task. This intelligent classifier accurately detects the stability regimes of more than 90% of 345 different nanofluid samples. The overall classification accuracy and misclassification percent of 90.1% and 9.9% have been achieved by this model. This research is the first try toward anticipting the stability of water-alumin nanofluids from some easily measured independent variables.

This study aimed to investigate the effects and potential mechanisms of Chikusetsusaponin V (CsV) on endothelial nitric oxide synthase (eNOS) and vascular endothelial cell functions. Different concentrations of CsV were added to animal models, bovine aorta endothelial cells (BAECs) and human umbilical vein endothelial cells (HUVECs) cultured in vitro. qPCR, Western blotting (WB), and B ultrasound were performed to explore the effects of CsV on mouse endothelial cell functions, vascular stiffness and cellular eNOS mRNA, protein expression and NO release. Bioinformatics analysis, network pharmacology, molecular docking and protein mass spectrometry analysis were conducted to jointly predict the upstream transcription factors of eNOS. Furthermore, pulldown and ChIP and dual luciferase assays were employed for subsequent verification. At the presence or absence of CsV stimulation, either overexpression or knockdown of purine rich element binding protein A (PURA) was conducted, and PCR assay was employed to detect PURA and eNOS mRNA expressions, Western blot was used to detect PURA and eNOS protein expressions, cell NO release and serum NO levels. Tube formation experiment was conducted to detect the tube forming capability of HUVECs cells. The animal vasodilation function test detected the vasodilation functions. Ultrasonic detection was performed to determine the mouse aortic arch pulse wave velocity to identify aortic stiffness. CsV stimulus on bovine aortic cells revealed that CsV could upregulate eNOS protein levels in vascular endothelial cells in a concentration and time dependent manner. The expression levels of eNOS mRNA and phosphorylation sites Ser1177, Ser633 and Thr495 increased significantly after CsV stimulation. Meanwhile, CsV could also enhance the tube forming capability of HUVECs cells. Following the mice were gavaged using CsV, the eNOS protein level of mouse aortic endothelial cells was upregulated in a concentration- and time-dependent manner, and serum NO release and vasodilation ability were simultaneously elevated whereas arterial stiffness was alleviated. The pulldown, ChIP and dual luciferase assays demonstrated that PURA could bind to the eNOS promoter and facilitate the transcription of eNOS. Under the conditions of presence or absence of CsV stimulation, overexpression or knockdown of PURA indicated that the effect of CsV on vascular endothelial function and eNOS was weakened following PURA gene silence, whereas overexpression of PURA gene could enhance the effect of CsV upregulating eNOS expression. CsV could promote NO release from endothelial cells by upregulating the expression of PURA/eNOS pathway, improve endothelial cell functions, enhance vasodilation capability, and alleviate vessel stiffness. The present study plays a role in offering a theoretical basis for the development and application of CsV in vascular function improvement, and it also provides a more comprehensive understanding of the pharmacodynamics of CsV.

The application of fibers in concrete obviously enhances the properties of concrete, also the application of natural fibers in concrete is raising due to the availability, low cost and environmentally friendly. Besides, predicting the mechanical properties of concrete in general and shear strength in particular is highly significant in concrete mixture with fiber nanocomposite reinforced concrete (FRC) in construction projects. Despite numerous studies in shear strength, determining this strength still needs more investigations. In this research, Adaptive Neuro-Fuzzy Inference System (ANFIS) have been employed to determine the strength of reinforced concrete with fiber. 180 empirical data were gathered from reliable literature to develop the methods. Models were developed, validated and their statistical results were compared through the root mean squared error (RMSE), determination coefficient (R²), mean absolute error (MAE) and Pearson correlation coefficient (r). Comparing the RMSE of PSO (0.8859) and ANFIS (0.6047) have emphasized the significant role of structural parameters on the shear strength of concrete, also effective depth, web width, and a clear depth rate are essential parameters in modeling the shear capacity of FRC. Considering the accuracy of our models in determining the shear strength of FRC, the outcomes have shown that the R² values of PSO (0.7487) was better than ANFIS (2.4048). Thus, in this research, PSO has demonstrated better performance than ANFIS in predicting the shear strength of FRC in case of accuracy and the least error ratio. Thus, PSO could be applied as a proper tool to maximum accuracy predict the shear strength of FRC.

Graphene is one of the strongest, stiffest, and lightest nanoscale materials known to date, making it a potentially viable and attractive candidate for developing lightweight structural composites to prevent high-velocity ballistic impact, as commonly encountered in defense and space sectors. In-plane twist in bilayer graphene has recently revealed unprecedented electronic properties like superconductivity, which has now started attracting the attention for other multi-physical properties of such twisted structures. For example, the latest studies show that twisting can enhance the strength and stiffness of graphene by many folds, which in turn creates a strong rationale for their prospective exploitation in high-velocity impact. The present article investigates the ballistic performance of twisted bilayer graphene (tBLG) nanostructures. We have employed molecular dynamics (MD) simulations, augmented further by coupling gaussian process-based machine learning, for the nanoscale characterization of various tBLG structures with varying relative rotation angle (RRA). Spherical diamond impactors (with a diameter of 25Å) are enforced with high initial velocity (V_i) in the range of 1 km/s to 6.5 km/s to observe the ballistic performance of tBLG nanostructures. The specific penetration energy (E_p^*) of the impacted nanostructures and residual velocity (V_r) of the impactor are considered as the quantities of interest, wherein the effect of stochastic system parameters is computationally captured based on an efficient Gaussian process regression (GPR) based Monte Carlo simulation approach. A data-driven sensitivity analysis is carried out to quantify the relative importance of different critical system parameters. As an integral part of this study, we have deterministically investigated the resonant behaviour of graphene nanostructures, wherein the high-velocity impact is used as the initial actuation mechanism. The comprehensive dynamic investigation of bilayer graphene under the ballistic impact, as presented in this paper including the effect of twisting and random disorder for their prospective exploitation, would lead to the development of improved impact-resistant lightweight materials.