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.

     

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.     

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.     

Cytotoxic T Cell Expression of Leukocyte-Associated Immunoglobulin-Like Receptor-1 (LAIR-1) in Viral Hepatitis C-Mediated Hepatocellular Carcinoma

Virus-related hepatocellular carcinoma (HCC) pathogenesis involves liver inflammation, therefore, despite successful treatment, hepatitis C virus (HCV) may progress to HCC from initiated liver cirrhosis. Cytotoxic T cells (Tcs) are known to be involved in HCV-related cirrhotic complications and HCC pathogenesis. The inhibitory checkpoint leukocyte-associated immunoglobulin-like receptor-1 (LAIR-1) is expressed on Tcs. Therefore, we aimed to determine whether the Tc expression level of LAIR-1 is associated with HCC progression and to evaluate LAIR-1 expression as a noninvasive biomarker for HCC progression in the context of liver cirrhosis related to HCV genotype 4 (G4) in Egyptian patients’ peripheral venous blood liquid biopsy. A total of 64 patients with HCC and 37 patients with liver cirrhosis were enrolled in this case-controlled study, and their LAIR-1 expression on Tc related to the progression of liver cirrhosis was examined and compared to that of the apparently healthy control group (n = 20). LAIR-1 expression was analyzed using flow cytometry. Results: The HCC group had significantly higher LAIR-1 expression on Tc and percentage of Tc positive for LAIR-1 (LAIR-1+Tc%) than the HCV G4-related liver cirrhosis group. LAIR-1+Tc% was correlated with the HCC surrogate tumor marker AFP (r = 0.367, p = 0.001) and insulin resistance and inflammation prognostic ratios/indices. A receiver operating characteristic (ROC) curve revealed that adding LAIR-1+Tc% to AFP can distinguish HCC transformation in the Egyptian patients’ cohort. Upregulated LAIR-1 expression on Tc could be a potential screening noninvasive molecular marker for chronic inflammatory HCV G4 related liver cirrhosis. Moreover, LAIR-1 expression on Tc may be one of the players involved in the progression of liver cirrhosis to HCC.     

基于交叉耦合效应的车用内置式永磁电机转子位置估计

该文提出一种基于滑模观测器和基于凸极效应信号注入相结合的、考虑电机中电感交叉耦合效应的内置式永磁同步电机无速度传感器控制方法。系统在硬件在环平台中实验。直轴和交轴电感间的交叉耦合效应由有限元法算得。所提出的方法能使内置式永磁同步电机获得更好的运行性能。硬件在环平台验证了Matlab/Simulink的结果。样机实验得到的结果与Matlab仿真以及硬件在环实验的结果相吻合。实验结果表明,所提出的算法是有效的,硬件在环平台在一定程度上能模拟样机实验。     

A Hybrid Virtual Cloud Learning Model during the COVID-19 Pandemic

The COVID-19 pandemic has affected the educational systems worldwide, leading to the near-total closures of schools, universities, and colleges. Universities need to adapt to changes to face this crisis without negatively affecting students’ performance. Accordingly, the purpose of this study is to identify and help solve to critical challenges and factors that influence the e-learning system for Computer Maintenance courses during the COVID-19 pandemic. The paper examines the effect of a hybrid modeling approach that uses Cloud Computing Services (CCS) and Virtual Reality (VR) in a Virtual Cloud Learning Environment (VCLE) system. The VCLE system provides students with various utilities and educational services such as presentation slides/text, data sharing, assignments, quizzes/tests, and chatrooms. In addition, learning through VR enables the students to simulate physical presence, and they respond well to VR environments that are closer to reality as they feel that they are an integral part of the environment. Also, the research presents a rubric assessment that the students can use to reflect on the skills they used during the course. The research findings offer useful suggestions for enabling students to become acquainted with the proposed system’s usage, especially during the COVID-19 pandemic, and for improving student achievement more than the traditional methods of learning.     

Microstructure and elastic tensile behavior of polyethylene terephthalate-exfoliated graphene nanocomposites

Polyethylene terephthalate-exfoliated graphene nanocomposites were prepared by injection molding. Nanocomposites with graphene platelets of 2, 5, 10, and 15% weight fractions were molded and tested for mechanical characterization. Transmission electron microscopy imaging along with X-ray diffraction show that the graphene platelets remained intact and were dispersed into the matrix. An exponential increase in the Young’s modulus of the nanocomposites was observed, but with current limits on exfoliation they do not yet reach the potential suggested by idealized predictions.     

Synthesis and Biopharmaceutical Evaluation of Imatinib Analogues Featuring Unusual Structural Motifs

A convenient synthesis of imatinib, a potent inhibitor of ABL1 kinase and widely prescribed drug for the treatment of a variety of leukemias, was devised and applied to the construction of a series of novel imatinib analogues featuring a number of non‐aromatic structural motifs in place of the parent molecule's phenyl moiety. These analogues were subsequently evaluated for their biopharmaceutical properties (e.g., ABL1 kinase inhibitory activity, cytotoxicity). The bicyclo[1.1.1]pentane‐ and cubane‐containing analogues were found to possess higher themodynamic solubility, whereas cubane‐ and cyclohexyl‐containing analogues exhibited the highest inhibitory activity against ABL1 kinase and the most potent cytotoxicity values against cancer cell lines K562 and SUP‐B15. Molecular modeling was employed to rationalize the weak activity of the compounds against ABL1 kinase, and it is likely that the observed cytotoxicity of these agents arises through off‐target effects.