Scaling laws in network models: porous medium property prediction during morphological evolution

A 3-D network model was used to represent a porous medium and the macroscopic properties of the network (like permeability) were simulated by Monte Carlo method. It was shown that these macroscopic properties can be related to network parameters (throat-size distribution parameters, network size and connectivity) through power law correlations, as can be inferred from percolation theory. In this way, macroscopic properties evaluation during the morphological evolution of a 3-D network requires less computational effort, which facilitates the incorporation of this model into oil flow simulators.     

The design and evaluation of the Simple Self-Similar Sequences Generator

This paper describes a new algorithm for the generation of pseudo random numbers with approximate self-similar structure. The Simple Self-Similar Sequences Generator (4SG) elaborates on an intuitive approach to obtain a fast and accurate procedure, capable of reproducing series of points exhibiting the property of persistence and anti-persistence. 4SG has a computational complexity of O(n) and memory requirements of the order of log₂(N), where N is the number of points to be generated. The accuracy of the algorithm is evaluated by means of computer-based simulations, recurring to several Hurst parameter estimators, namely Variance Time (VT) and the Wavelets-based estimator. The Hosking and the Wavelets-based methods for the generation of self-similar series were submitted to the same tests the 4SG was analysed with, providing for a basis for comparison of several performance aspects of the algorithm. Results show that the proposal embodies a good candidate not only for on-demand emulation of arbitrarily long self-similar sequences, but also for fast and efficient online simulations.     

Effect of ion-exchange temperature on mechanical properties of a dental porcelain

The objective of this study was to determine the influence of different ion-exchange temperatures on the biaxial flexural strength (σ_f), hardness (HV) and indentation fracture resistance (K_IF) of a dental porcelain. Disk-shaped specimens were divided into five groups (n = 10) and submitted to an ion-exchange procedure using KNO₃ paste for 15 min in the following temperatures (°C); (I) 430; (II) 450; (III) 470; (IV) 490; (V) 510; and control (no ion exchange). The value of σ_f was determined in artificial saliva at 37 °C. The values of HV and K_IF were obtained using 3 Vickers indentations in each specimen (19.6 N). Results showed that ion exchange increases significantly the properties of the material as compared to the control and no significant differences were found among the temperatures tested for any of the properties studied.     

Three-dimensional analysis of plastic flow during high-pressure torsion

The use of imposed plastic deformation as a single parameter to compare results of samples processed by severe plastic deformation is not always accurate. Therefore, this report describes the theoretical plastic flow occurring during high-pressure torsion and presents finite element modeling of this technique to complement the theory. The results demonstrate the influence on plastic flow of the material behavior, the sample aspect ratio, the processing pressure, and the contact friction between the sample and the anvil. It is shown that heterogeneous flow is primarily observed near the edges of the samples. The present results are in general agreement with published experimental observations.     

Towards a model for the measurement of data quality in websites

Websites are, nowadays, the face of institutions, but they are often neglected, especially when it comes to contents. In the present paper, we put forth an investigation work whose final goal is the development of a model for the measurement of data quality in institutional websites for health units. To that end, we have carried out a bibliographic review of the available approaches for the evaluation of website content quality, in order to identify the most recurrent dimensions and the attributes, and we are currently carrying out a Delphi Method process, presently in its second stage, with the purpose of reaching an adequate set of attributes for the measurement of content quality.     

Boron nitride nanotubes coated with organic hydrophilic agents: Stability and cytocompatibility studies

In the present study, Boron Nitride Nanotubes (BNNTs) were synthesized and functionalized with organic hydrophilic agents constituted by glucosamine (GA), polyethylene glycol (PEG)₁₀₀₀, and chitosan (CH) forming new singular systems. Their size, distribution, and homogeneity were determined by photon correlation spectroscopy, while their surface charge was determined by laser Doppler anemometry. The morphology and structural organization were evaluated by Transmission Electron Microscopy. The functionalization was evaluated by Thermogravimetry analysis and Fourier Transformer Infrared Spectroscopy. The results showed that BNNTs were successfully obtained and functionalized, reaching a mean size and dispersity deemed adequate for in vitro studies. The in vitro stability tests also revealed a good adhesion of functionalized agents on BNNT surfaces. Finally, the in vitro cytocompatibility of functionalized BNNTs against MCR-5 cells was evaluated, and the results revealed that none of the different functionalization agents disturbed the propagation of normal cells up to the concentration of 50 μg/mL. Furthermore, in this concentration, no significantly chromosomal or morphologic alterations or increase in ROS (Reactive Oxygen Species) could be observed. Thus, findings from the present study reveal an important stability and cytocompatibility of functionalized BNNTs as new potential drugs or radioisotope nanocarriers to be applied in therapeutic procedures.     

Effect of reverse and cyclic shear on the work-hardening of AISI 430 stainless steel

Sheet metal forming commonly involves various processing steps leading to complex strain paths. The work hardening of the metal under these circumstances is different from that observed for monotonic straining. The effect of the strain path on the hardening of materials is usually studied through sequences of standard mechanical tests, and the shear test is especially well adapted to such studies in sheet forming. Shear straining covering Bauschinger and cyclic strain paths were used in the analysis of the hardening of AISI 430 stainless steel sheets. The tests were conducted at 0°RD, 45°RD, and 90°RD (Rolling Direction) and for three effective strain amplitudes. The results indicate that the material presents Bauschinger effects and strain hardening transients that are sensitive to the testing direction. In addition, the cyclic straining leads to an oscillating stress pattern for the forward and reverse shearing cycles, which depends on the deformation amplitude.     

Artificial neural network based on genetic learning for machining of polyetheretherketone composite materials

In this paper an artificial neural network (ANN) aiming for the efficient modelling of a set of machining conditions for orthogonal cutting of polyetheretherketone (PEEK) composite materials is presented. The supervised learning of the ANN is based on a genetic algorithm (GA) supported by an elitist strategy. Input, hidden and output layers model the topology of the ANN. The weights of the synapses and the biases for hidden and output nodes are used as design variables in the ANN learning process. Considering a set of experimental data, the mean relative error between experimental and numerical results is used to monitor the learning process obtaining the completeness of the machining process modelling. Also a regularization term associated to biases in hidden and output neurons are included in the GA fitness function for learning. Using a different set of experimental results, the optimal ANN obtained after learning is tested. The optimal number of nodes on the hidden layer is searched and the positive influence of the regularization term is demonstrated. This approach of ANN learning based on GA presents low mean relative errors in learning and testing phases.