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.

     

FAIRIO: A Throughput-oriented Algorithm for Differentiated I/O Performance

Providing differentiated service in a consolidated storage environment is a challenging task. To address this problem, we introduce FAIRIO, a cycle-based I/O scheduling algorithm that provides differentiated service to workloads concurrently accessing a consolidated RAID storage system. FAIRIO enforces proportional sharing of I/O service through fair scheduling of disk time. During each cycle of the algorithm, I/O requests are scheduled according to workload weights and disk-time utilization history. Experiments, which were driven by the I/O request streams of real and synthetic I/O benchmarks and run on a modified version of DiskSim, provide evidence of FAIRIO’s effectiveness and demonstrate that fair scheduling of disk time is key to achieving differentiated service in a RAID storage system. In particular, the experimental results show that, for a broad range of workload request types, sizes, and access characteristics, the algorithm provides differentiated storage throughput that is within 10% of being perfectly proportional to workload weights; and, it achieves this with little or no degradation of aggregate throughput. The core design concepts of FAIRIO, including service-time allocation and history-driven compensation, potentially can be used to design I/O scheduling algorithms that provide workloads with differentiated service in storage systems comprised of RAIDs, multiple RAIDs, SANs, and hypervisors for Clouds.     

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.     

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.     

POEMS: end-to-end performance design of large parallel adaptive computational systems

The POEMS project is creating an environment for end-to-end performance modeling of complex parallel and distributed systems, spanning the domains of application software, runtime and operating system software, and hardware architecture. Toward this end, the POEMS framework supports composition of component models from these different domains into an end-to-end system model. This composition can be specified using a generalized graph model of a parallel system, together with interface specifications that carry information about component behaviors and evaluation methods. The POEMS Specification Language compiler will generate an end-to-end system model automatically from such a specification. The components of the target system may be modeled using different modeling paradigms and at various levels of detail. Therefore, evaluation of a POEMS end-to-end system model may require a variety of evaluation tools including specialized equation solvers, queuing network solvers, and discrete event simulators. A single application representation based on static and dynamic task graphs serves as a common workload representation for all these modeling approaches. Sophisticated parallelizing compiler techniques allow this representation to be generated automatically for a given parallel program. POEMS includes a library of predefined analytical and simulation component models of the different domains and a knowledge base that describes performance properties of widely used algorithms. The paper provides an overview of the POEMS methodology and illustrates several of its key components. The modeling capabilities are demonstrated by predicting the performance of alternative configurations of Sweep3D, a benchmark for evaluating wavefront application technologies and high-performance, parallel architectures.     

Configuration Maintenance for Distributed Applications Management

The MANDAS project has defined a layered architecture for the management of distributed applications. In this paper we examine a vertical slice of this architecture, namely the management applications and services related to configuration management. We introduce an information model which captures the configuration information for distributed applications and discuss a repository service based on the model. We define a set of services and management applications to support maintenance of configuration information, and describe how the different types of configuration information are collected. Finally, we present two management applications that use configuration information.     

Calibrated,Registered Images of an Extended Urban Area

We describe a dataset of several thousand calibrated, time-stamped, geo-referenced, high dynamic range color images, acquired under uncontrolled, variable illumination conditions in an outdoor region spanning several hundred meters. The image data is grouped into several regions which have little mutual inter-visibility. For each group, the calibration data is globally consistent on average to roughly five centimeters and 0 1°, or about four pixels of epipolar registration. All image, feature and calibration data is available for interactive inspection and downloading at http://city.lcs.mit.edu/data.Calibrated imagery is of fundamental interest in a variety of applications. We have made this data available in the belief that researchers in computer graphics, computer vision, photogrammetry and digital cartography will find it of value as a test set for their own image registration algorithms, as a calibrated image set for applications such as image-based rendering, metric 3D reconstruction, and appearance recovery, and as input for existing GIS applications.