An efficient pareto set identification approach for multiobjective optimization on black-box functions

ABSTRACT Both multiple objectives and computation-intensive black-box functions often exist simultaneously in engineering design problems. Few of existing multi-objective optimization approaches addresses problems with expensive black-box functions. In this paper, a new method called the Pareto set pursuing (PSP) method is developed. By developing sampling guidance functions, this approach progressively provides a designer with a rich and evenly distributed Pareto optimal points. This work describes PSP in detail with analysis of its properties. From testing and design application, PSP demonstrates considerable efficiency, accuracy, and robustness. Theoretical proof of convergence of PSP is also given. It is believed that PSP has a great potential to be a practical tool for multi-objective optimization problems

Estimating intracluster correlation for ordinal data

Purpose: In this paper we consider the estimation of intracluster correlation for ordinal data. We focus on pure-tone audiometry hearing threshold data, where thresholds are measured in 5 decibel increments. We estimate the intracluster correlation for tests from iPhone-based hearing assessment application as a measure of test/retest reliability. Methods: We present a method to estimate the intracluster correlation using mixed effects cumulative logistic and probit models, which assume the outcome data are ordinal. This contrasts with using a mixed effects linear model which assumes that the outcome data are continuous. Results: In simulation studies we show that using a mixed effects linear model to estimate the intracluster correlation for ordinal data results in a negative finite sample bias, while using mixed effects cumulative logistic or probit models reduces this bias. The estimated intracluster correlation for the iPhone-based hearing assessment application is higher when using the mixed effects cumulative logistic and probit models compared to using a mixed effects linear model. Conclusion: When data are ordinal, using mixed effects cumulative logistic or probit models reduces the bias of intracluster correlation estimates relative to using a mixed effects linear model.Comment: 11 pages, 3 table

Boundary search and simplex decomposition method for MDO problems with a convex or star-like state parameter region

Abstract One major challenge in multidisciplinary design optimization (MDO) is the presence of couplings among state parameters, which demands an iterative and often expensive system analysis (SA) process for each function evaluation in optimization. This paper offers a new perspective and proposes a corresponding method for solving MDO problems. The proposed method, named the boundary search and simplex decomposition method (BSSDM), geometrically captures the relation among coupled state parameters with a feasible state parameter region. Given the feasible state parameter region, the SA can be avoided during the optimization of the system objective function. To identify the feasible state parameter region, a search strategy is developed to find boundary points of the region. In the boundary search process, a collaboration model (CM) is applied to maintain the feasibility of samples with respect to the SA. In search of the system optimum in the feasible region, a robust simplex decomposition algorithm is developed for convex and starlike feasible state parameter regions. The BSSDM is tested with two numerical cases, one of which is an MDO problem constrained by a convex state parameter region, and the other is a SA problem with a star-like state parameter region. All results are then validated, and the results show the promising capability of the proposed BSSDM

Signatures of Drug Sensitivity in Nonsmall Cell Lung Cancer

We profiled receptor tyrosine kinase pathway activation and key gene mutations in eight human lung tumor cell lines and 50 human lung tumor tissue samples to define molecular pathways. A panel of eight kinase inhibitors was used to determine whether blocking pathway activation affected the tumor cell growth. The HER1 pathway in HER1 mutant cell lines HCC827 and H1975 were found to be highly activated and sensitive to HER1 inhibition. H1993 is a c-MET amplified cell line showing c-MET and HER1 pathway activation and responsiveness to c-MET inhibitor treatment. IGF-1R pathway activated H358 and A549 cells are sensitive to IGF-1R inhibition. The downstream PI3K inhibitor, BEZ-235, effectively inhibited tumor cell growth in most of the cell lines tested, except the H1993 and H1650 cells, while the MEK inhibitor PD-325901 was effective in blocking the growth of KRAS mutated cell line H1734 but not H358, A549 and H460. Hierarchical clustering of primary tumor samples with the corresponding tumor cell lines based on their pathway signatures revealed similar profiles for HER1, c-MET and IGF-1R pathway activation and predict potential treatment options for the primary tumors based on the tumor cell lines response to the panel of kinase inhibitors

System identification and closed-loop control of laser hot-wire directed energy deposition using the parameter-signature-property modeling scheme

Hot-wire directed energy deposition using a laser beam (DED-LB/w) is a method of metal additive manufacturing (AM) that has benefits of high material utilization and deposition rate, but parts manufactured by DED-LB/w suffer from a substantial heat input and undesired surface finish. Hence, monitoring and controlling the process parameters and signatures during the deposition is crucial to ensure the quality of final part properties and geometries. This paper explores the dynamic modeling of the DED-LB/w process and introduces a parameter-signature-property modeling and control approach to enhance the quality of modeling and control of part properties that cannot be measured in situ. The study investigates different process parameters that influence the melt pool width (signature) and bead width (property) in single and multi-layer beads. The proposed modeling approach utilizes a parameter-signature model as F_1 and a signature-property model as F_2. Linear and nonlinear modeling approaches are compared to describe a dynamic relationship between process parameters and a process signature, the melt pool width (F_1). A fully connected artificial neural network is employed to model and predict the final part property, i.e., bead width, based on melt pool signatures (F_2). Finally, the effectiveness and usefulness of the proposed parameter-signature-property modeling is tested and verified by integrating the parameter-signature (F_1) and signature-property (F_2) models in the closed-loop control of the width of the part. Compared with the control loop with only F_1, the proposed method shows clear advantages and bears potential to be applied to control other part properties that cannot be directly measured or monitored in situ.Comment: 28 pages, 14 figures, 4 tables

Discovering and verifying DNA polymorphisms in a mung bean [V. radiata (L.) R. Wilczek] collection by EcoTILLING and sequencing

<p>Abstract</p> <p>Background</p> <p><it>Vigna radiata</it>, which is classified in the family Fabaceae, is an important economic crop and a dietary staple in many developing countries. The species <it>radiata </it>can be further subdivided into varieties of which the variety <it>sublobata </it>is currently acknowledged as the putative progenitor of <it>radiata</it>. EcoTILLING was employed to identify single nucleotide polymorphisms (SNPs) and small insertions/deletions (INDELS) in a collection of <it>Vigna radiata </it>accessions.</p> <p>Findings</p> <p>A total of 157 DNA polymorphisms in the collection were produced from ten primer sets when using <it>V. radiata </it>var. <it>sublobata </it>as the reference. The majority of polymorphisms detected were found in putative introns. The banding patterns varied from simple to complex as the number of DNA polymorphisms between two pooled samples increased. Numerous SNPs and INDELS ranging from 4–24 and 1–6, respectively, were detected in all fragments when pooling <it>V. radiata </it>var. <it>sublobata </it>with <it>V. radiata </it>var. <it>radiata</it>. On the other hand, when accessions of <it>V. radiata </it>var. <it>radiata </it>were mixed together and digested with CEL I relatively few SNPs and no INDELS were detected.</p> <p>Conclusion</p> <p>EcoTILLING was utilized to identify polymorphisms in a collection of mung bean, which previously showed limited molecular genetic diversity and limited morphological diversity in the flowers and pod descriptors. Overall, EcoTILLING proved to be a powerful genetic analysis tool providing the rapid identification of naturally occurring variation.</p