Microwave-assisted synthesis of new pyrimido[4′,5′：4,5]thiazolo[3,2-α] benzimidazol-4（3H）-one derivatives in solvent-free condition

Microwave-assisted synthesis of new 2-arylpyrimido[4′,5′：4,5]thiazolo[3,2-α]benzimidazol-4（3H）-ones from 3-aminothia- zolo[3,2-α]benzimidazol-2-earboxamide and aroyl halides in solvent-free condition is described. In comparison with classical conditions the reactions are faster and the yields are higher under microwave irradiation.     

Topological efficiency under graph operations

In this paper we present explicit formulas for computing the topological efficiency index of the most important graph operations such as the Cartesian product, composition, corona, join and hierarchical product of two graphs. We apply our results to compute this distance-related invariant for some important classes of molecular graphs and nano-structures by specializing components of these graph operations.     

Using a Natural Surfactant for Synthesize of NiO and CuO Nanostructures via Simple and Fast Microwave Approach

NiO and CuO nanostructures were synthesized successfully via simple and fast microwave approach. Olive oil was chose as surfactant for stabilizing nanostructures. Different parameters such as microwave time and power and olive oil concentration were investigated on product size and morphology. The products were characterized with X-ray diffraction pattern, scanning electron microscopy, transmission electron microscopy and energy dispersive spectrometry analysis.     

Using deep learning to learn physics of conduction heat transfer

Journal of Thermal Analysis and Calorimetry - In the present study, an advanced type of artificial intelligence, a deep neural network, is employed to learn the physic of conduction heat transfer...     

Acid-induced gelation behavior of sonicated casein solutions

Casein gels were made from solutions sonicated by 24 and 130 kHz ultrasounds for 0, 60 and 120 min, followed by acidification with glucono-δ-lactone at 30 °C. The dynamics of gel formation were studied using rheological methods and microstructure of gels was monitored using scanning electron microscopy. Sonication postponed the gelation point to a lower pH value and increased the elasticity of freshly formed gels. It also resulted in gels with a more interconnected structure and smaller non-distinguishable particulates. This structure was especially dominant for the gel made from the solution already sonicated for 120 min.     

An accurate pressure–velocity decoupling technique for semi‐implicit rotational projection methods

In this paper, an accurate semi‐implicit rotational projection method is introduced to solve the Navier–Stokes equations for incompressible flow simulations. The accuracy of the fractional step procedure is investigated for the standard finite‐difference method, and the discrete forms are presented with arbitrary orders or accuracy. In contrast to the previous semi‐implicit projection methods, herein, an alternative way is proposed to decouple pressure from the momentum equation by employing the principle form of the pressure Poisson equation. This equation is based on the divergence of the convective terms and incorporates the actual pressure in the simulations. As a result, the accuracy of the method is not affected by the common choice of the pseudo‐pressure in the previous methods. Also, the velocity correction step is redefined, and boundary conditions are introduced accordingly. Several numerical tests are conducted to assess the robustness of the method for second and fourth orders of accuracy. The results are compared with the solutions obtained from a typical high‐resolution fully explicit method and available benchmark reports. Herein, the numerical tests are consisting of simulations for the Taylor–Green vortex, lid‐driven square cavity, and vortex–wall interaction. It is shown that the present method can preserve the order of accuracy for both velocity and pressure fields in second‐order and high‐order simulations. Furthermore, a very good agreement is observed between the results of the present method and benchmark simulations. Copyright © 2016 John Wiley & Sons, Ltd.     

An improved ensemble learning machine for biological activity prediction of tyrosine kinase inhibitors

Boosting is one of the most important strategies in ensemble learning because of its ability to improve the stability and performance of weak learners. It is nonparametric, multivariate, fast and interpretable but is not robust against outliers. To enhance its prediction accuracy as well as immunize it against outliers, a modified version of a boosting algorithm (AdaBoost R2) was developed and called AdaBoost R3. In the sampling step, extremum samples were added to the boosting set. In the robustness step, a modified Huber loss function was applied to overcome the outlier problem. In the output step, a deterministic threshold was used to guarantee that bad predictions do not participate in the final output. The performance of the modified algorithm was investigated with two anticancer data sets of tyrosine kinase inhibitors, and the mechanism of inhibition was studied using the relative weighted variable importance procedure. Investigating the effect of base learner's strength reveals that boosting is only successful using the classification and regression tree method (a weak to moderate learner) and does not have a significant effect using the radial basis functions partial least square method (a strong base learners). Copyright © 2015 John Wiley & Sons, Ltd.     

Prediction of High Performance Fibers Strength Using Back Propagation Neural Network

Nowadays, quantification of the effects of basic parameters such as precursor, temperature oxidation, residence time, low temperature carbonization (LTC) and high temperature carbonization (HTC) on production process polyacrylonitrile based carbon fibers is not completely understood. In this way, there is not a completely theoretical model that accomplishes to quantitatively describe production process carbon fibers very accurately which needs to be used by engineers in design, simulation and operation of that process. This paper presents the development of a back propagation neural network model for the prediction of carbon fibers produced from PAN fibers. The model is based on experimental data. The precursors, temperature oxidation, residence time, LTC and HTC have been considered as the input parameters and the strength as output parameter to develop the model. The developed model is then compared with experimental results and it is found that the results obtained from the neural network model are accurate in predicting the strength of carbon fibers.