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.     

Dopamine receptors influence vasoactive intestinal peptide release from turkey hypothalamic explants

Amongst the elements which contributed to the success of the early lung transplants at the beginning of the 1980's we feel that the careful selection of candidates probably played a predominant role. If some of the selection criteria initially described remain somewhat intangible, others have either been eased or have been invalidated. The experience acquired over the last 15 years has enabled to precise the optimal moment to include patients on the waiting list and to refine the choice for the type of surgical procedure according to the underlying disease. This article aims to review the different selection criteria for candidates for transplantation and stresses those which have recently undergone change.     

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.

     

Consistency of randomized and finite sized decision tree ensembles

Regression via classification (RvC) is a method in which a regression problem is converted into a classification problem. A discretization process is used to covert continuous target value to classes. The discretized data can be used with classifiers as a classification problem. In this paper, we use a discretization method, Extreme Randomized Discretization, in which bin boundaries are created randomly to create ensembles. We present an ensemble method for RvC problems. We show theoretically for a set of problems that if the number of bins is three, the proposed ensembles for RvC perform better than RvC with the equal-width discretization method. We use these results to show that infinite-sized ensembles, consisting of finite-sized decision trees, created by a pure randomized method (split points are created randomly), are not consistent. We also theoretically show, using a set of regression problems, that the performance of these ensembles is dependent on the size of member decision trees.     

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.     

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.     

Reinforcement of hydroxyapatite by adding P2O5-CaO glasses with Na2O, K2O and MgO

Commercial hydroxyapatite was reinforced by adding small amounts (2 and 4 wt%) of P₂O₅-based glasses during its sintering process. The composites prepared had a chemical composition closely related to the mineral part of bone tissues in terms of trace elements usually detected, such as Na, K and Mg. X-ray diffraction analysis (XRD) showed that the glass reinforced-HA composites were composed of a HA matrix and variable amounts of tricalcium phosphate phase, depending on sintering temperature and glass composition. These composites were shown to have much higher biaxial bending strength than sintered HA, 107 MPa for Ha/2% of 35P₂O₅-35CaO-10Na₂O-10K₂O-10MgO glass composite and 28 MPa for sintered HA. The presence of -tricalcium phosphate in the microstructure of the composites is an important factor in the reinforcement process.This paper was accepted for publication after the 1995 Conference of the European Society of Biomaterials, Oporto. Portugal, 10–13 September.     

A two-stage stochastic programming model for the parallel machine scheduling problem with machine capacity

This paper proposes a two-stage stochastic programming model for the parallel machine scheduling problem where the objective is to determine the machines' capacities that maximize the expected net profit of on-time jobs when the due dates are uncertain. The stochastic model decomposes the problem into two stages: The first (FS) determines the optimal capacities of the machines whereas the second (SS) computes an estimate of the expected profit of the on-time jobs for given machines' capacities. For a given sample of due dates, SS reduces to the deterministic parallel weighted number of on-time jobs problem which can be solved using the efficient branch and bound of M’Hallah and Bulfin [16]. FS is tackled using a sample average approximation (SAA) sampling approach which iteratively solves the problem for a number of random samples of due dates. SAA converges to the optimum in the expected sense as the sample size increases. In this implementation, SAA applies a ranking and selection procedure to obtain a good estimate of the expected profit with a reduced number of random samples. Extensive computational experiments show the efficacy of the stochastic model.     

Online scheduling with rejection and withdrawal

We study an online scheduling problem with rejection, in which some rearrangement of the solution is allowed. This problem is called scheduling with rejection and withdrawal. Each arriving job has a processing time and a rejection cost associated with it, and it needs to be either assigned to a machine or rejected upon arrival. At termination, it is possible to choose at most a fixed number of scheduled jobs and withdraw them (i.e., decide to reject them). We study the minimization version, where the goal is to minimize the sum of the makespan and the total rejection cost (which corresponds to the penalty), and the maximization problem, where the goal is to maximize the sum of the minimum load and the total rejection cost (which corresponds to profit). We study environments of machines, which are the case of m identical machines and the case of two uniformly related machines, and show a strong relation between these problems and the related classic online scheduling problems which they generalize, in contrast to standard scheduling with rejection, which typically makes the scheduling problems harder.