Enhancement of glass-ceramic performance by TiO2 doping: In vitro cell viability,proliferation, and differentiation

A new TiO₂-containing bioactive glass and glass-ceramics based on 50SiO₂-(45-X)CaO-(XTiO₂)-5P₂O₅ system was designed using a sol–gel technique (where X = 5, 7.5 and 10 wt %). The roles of the crystallization behavior and physicochemical characteristics of the designed glass and glass-ceramics which were played in the introduction of TiO₂ substitutions were investigated. Moreover, cell proliferation and differentiation were evaluated against human osteosarcoma cells (Saos-2). The TiO₂/CaO replacements led to the formation of a stronger glass structure and thus increased thermal parameters and the chemical stabilization of the designed materials. The FTIR data confirmed the existence of Ti within the glass and glass-ceramics samples, and no remarkable effect on their chemical integrity was observed. The XRD patterns indicated that calcium-containing minerals, including Ca₂SiO₄,Ca₃(PO₄)₂, Ca(Ti,Si)O₅, CaTiSiO₅, and Ca₁₅(PO₄)₂·(SiO₄)₆ phases were developed as a role of structure/texture under the applied heat-treatment. The results of the cytotoxicity test proved that a safe sample dose is 12–50 μg/ml, at which cell viability is ≥ 85%. The cell differentiation determined by ALP test proved the superiority of glass-ceramics compared with their native glasses. Therefore, the obtained materials could be safely used as novel biocompatible materials for the regeneration of bone tissue.     

An adaptive region segmentation combining surrogate model applied to correlate design variables and performance parameters in a transonic axial compressor

Surrogate models have been widely applied to correlate design variables and performance parameters in turbomachinery optimization applications. With more design variables and uncertain factors taken into account in an optimization design problem, the mathematical relations between the design variables and the performance parameters might present linear, low-order nonlinear or even high-order nonlinear characteristics, and are usually analytically unknown. Therefore, it is required that surrogate models have high adaptability and prediction accuracy for both the linear and nonlinear characteristics. The paper mainly investigates the effectiveness of an adaptive region segmentation combining surrogate model based on support vector regression and kriging model applied to a transonic axial compressor to approximate the complicated relationships between geometrical variables and objective performance outputs with different sampling methods and sizes. The purpose is to explore the prediction accuracy and computational efficiency of this adaptive surrogate model in real turbomachinery applications. Three different sampling techniques are studied: (1) uniform design; (2) Latin hypercube sampling method; (3) Sobol quasi-random design. For the low dimensional case with five variables, the adaptive region segmentation combining surrogate model performs better (not worse) than the single component surrogate in terms of prediction accuracy and computational efficiency. In the meanwhile, it is also noted that the uniform design applied to the adaptive surrogate model has more advantages over the Latin hypercube sampling method especially for the small sample size cases, both performing better than the Sobol quasi-random design. Moreover, a high dimensional case with 12 variables is also utilized to further validate the prediction advantage of the adaptive region segmentation combining surrogate model over the single component surrogate, and the computational results favor it. Overall, the adaptive region segmentation combining surrogate model has produced acceptable to high prediction accuracy in presenting complex relationships between the geometrical variables and the objective performance outputs and performed robustly for a transonic axial compressor problem.

     

A model for the analysis of neighbor finding in pointer-based quadtrees

A natural byproduct of the tree-like nature of the quadtree is that many basic image processing operations can be implemented as tree traversals which differ in the nature of the computation that is performed at each node. Some of these computations involve the inspection of a node's adjacent neighbors (termed neighbor finding). A new model is developed for images represented by quadtrees, and it is used to analyze various neighbor-finding techniques. The model's predicted costs for neighbor finding correlate very closely with empirical results and it is superior to the model that was used previously.     

Impact of graphene oxide nanoparticles and carbon black on the gamma radiation sensitization of acrylonitrile–butadiene rubber seal materials

Seals prepared from acrylonitrile–butadiene rubber (NBR) are primarily used in nuclear services. Nevertheless, at relatively high ionizing radiation doses, NBR seal materials may undergo radiation-induced degradation processes, leading to adverse effects on the sealing ability life. Herein, to strengthen the functional characteristics of NBR seals against radiation, graphene oxide (GO) nanoparticles were prepared and characterized by transmission electron microscopy, X-ray diffraction (XRD), Fourier transform infrared (FTIR), and ultraviolet/visible spectroscopies. Various NBR/GO composites fabricated with different ratios of GO nanoparticles and in the presence or absence of carbon black (CB) were investigated via cross-linking density, scanning electron microscopy, XRD, FTIR, and mechanical and thermal stability analyses. The synergistic effect of the simultaneous presence of GO and CB on the NBR seal sensitization to gamma radiation up to a dose of 1 MGy was studied. The physicomechanical properties were enhanced by adding GO nanosheets up to 3 phr and by incorporating 35 phr of a CB with GO until 5 phr. Further, the application of γ-irradiation resulted in an overall enhancement in the mechanical, physical, and thermal stability of the prepared composites up to 0.5 and 1 MGy with GO nanosheets in the absence or presence of CB particles, respectively. The mechanical measurements indicated significant increments by loading with GO nanosheets in the absence and presence of CB as well as by irradiation. The tensile strength elevated up to about 121%, 336%, and 366% by adding 3 phr GO, 3 GO:35 CB phr, and 5 GO:35 CB phr, respectively.     

Hash key-based image encryption using crossover operator and chaos

This paper proposes a color image encryption scheme using one-time keys based on crossover operator, chaos and the Secure Hash Algorithm(SHA-2). The (SHA-2) is employed to generate a 256-bit hash value from both the plain-image and the secret hash keys to make the key stream change in each encryption process. The SHA-2 value is employed to generate three initial values of the chaotic system. The permutation-diffusion process is based on the crossover operator and XOR operator, respectively. Experimental results and security analysis show that the scheme can achieve good encryption result through only one round encryption process, the key space is large enough to resist against common attacks,so the scheme is reliable to be applied in image encryption and secure communication.     

Different Estimation Methods for Constant Stress Accelerated Life Test under the Family of the Exponentiated Distributions

The Accelerated Life Testing (ALT) has been used for a long time in several fields to obtain information on the reliability of product components and materials under operating conditions in a much shorter time. One of the main purposes of applying ALT is to estimate the failure time functions and reliability performance under normal conditions. This paper concentrates on the estimation procedures under ALT and how to select the best estimation method that gives accurate estimates for the reliability function. For this purpose, different estimation methods are used, such as maximum likelihood, least squares (LS), weighted LS, and probability weighted moment. Moreover, the reliability function under usual conditions is predicted. The estimation procedures are applied under the family of the exponentiated distributions in general, and for the exponentiated inverted Weibull (EIW) as a special case. Numerical analysis including simulated data and a real life data set is conducted to compare the performances between these four methods. It is found that the ML method gives the best results among other estimation methods. Finally, a comparison between the EIW and the Inverted Weibull (IW) distributions based on a real life data set is made using a likelihood ratio test. It is observed that the EIW distribution can provide better fitting than the IW in case of ALT. Copyright © 2015 John Wiley & Sons, Ltd.     

Evidential data mining: precise support and confidence

Associative classification has been shown to provide interesting results whenever of use to classify data. With the increasing complexity of new databases, retrieving valuable information and classifying incoming data is becoming a thriving and compelling issue. The evidential database is a new type of database that represents imprecision and uncertainty. In this respect, extracting pertinent information such as frequent patterns and association rules is of paramount importance task. In this work, we tackle the problem of pertinent information extraction from an evidential database. A new data mining approach, denoted EDMA, is introduced that extracts frequent patterns overcoming the limits of pioneering works of the literature. A new classifier based on evidential association rules is thus introduced. The obtained association rules, as well as their respective confidence values, are studied and weighted with respect to their relevance. The proposed methods are thoroughly experimented on several synthetic evidential databases and showed performance improvement.