Understanding coupled factors that affect the modelling accuracy of typical planar compliant mechanisms

In order to accurately model compliant mechanism utilizing plate flexures, qualitative planar stress (Young’s modulus) and planar strain (plate modulus) assumptions are not feasible. This paper investigates a quantitative equivalent modulus using nonlinear finite element analysis (FEA) to reflect coupled factors in affecting the modelling accuracy of two typical distributed- compliance mechanisms. It has been shown that all parameters have influences on the equivalent modulus with different degrees; that the presence of large load-stiffening effect makes the equivalent modulus significantly deviate from the planar assumptions in two ideal scenarios; and that a plate modulus assumption is more reasonable for a very large out-of-plane thickness if the beam length is large

Using Non-Additive Measure for Optimization-Based Nonlinear Classification

Over the past few decades, numerous optimization-based methods have been proposed for solving the classification problem in data mining. Classic optimization-based methods do not consider attribute interactions toward classification. Thus, a novel learning machine is needed to provide a better understanding on the nature of classification when the interaction among contributions from various attributes cannot be ignored. The interactions can be described by a non-additive measure while the Choquet integral can serve as the mathematical tool to aggregate the values of attributes and the corresponding values of a non-additive measure. As a main part of this research, a new nonlinear classification method with non-additive measures is proposed. Experimental results show that applying non-additive measures on the classic optimization-based models improves the classification robustness and accuracy compared with some popular classification methods. In addition, motivated by well-known Support Vector Machine approach, we transform the primal optimization-based nonlinear classification model with the signed non-additive measure into its dual form by applying Lagrangian optimization theory and Wolfes dual programming theory. As a result, 2 – 1 parameters of the signed non-additive measure can now be approximated with m (number of records) Lagrangian multipliers by applying necessary conditions of the primal classification problem to be optimal. This method of parameter approximation is a breakthrough for solving a non-additive measure practically when there are a relatively small number of training cases available (). Furthermore, the kernel-based learning method engages the nonlinear classifiers to achieve better classification accuracy. The research produces practically deliverable nonlinear models with the non-additive measure for classification problem in data mining when interactions among attributes are considered

Extending Timoshenko Beam Theory for Large Deflections in Compliant Mechanisms

International audienceCompliant Mechanisms (CMs) have presented its inherently advantageous properties due to the fact that CMs utilize elastic deformation of the elementary flexible members to transfer motion, force and energy. Previously, the classic Euler-Bernoulli beam theory is the most used theory in terms of modeling large beam deflections in CMs. However, it has some assumptions that may decrease the modeling accuracy, such as ignoring the shear strain and the axial strain of cross sections. In this paper, to take into account the shear and axial strains, we adopt Timoshenko beam theory along with some modifications to consider the axial elongation. To simplify the complexity of the proposed governing boundary value problem (BVP), we transform the BVP into an explicit formulation and use weighted residual methods to numerically approximate the solution. We first focus on the singlebeam deflection of a straight beam and an initially curved beam (ICB) using Euler Bernoulli beam theory, Timoshenko beam theory and solid mechanics to analyze the contributions of the influences of shear and axial strains in beam deflections. Then, we prove the feasibility of the proposed modeling strategy via mechanism synthesis for a bi-stable mechanism and an ICB-based parallelogram mechanism. Finally, the deduction of the mathematical model and the numerical results are provided along with brief analysis on the mechanical performances of the studied CMs

Determining the range of allowable axial force for the third-order Beam Constraint Mode

The Beam Constraint Model (BCM) was developed for the purpose of accurately and analytically modeling nonlinear behaviors of a planar beam flexure over an intermediate range of transverse deflections (10% of the beam length). The BCM is expressed in the form of Taylor's expansion associated with the axial force. It has been found that the BCM may yield large predicting errors (> 5 %) when the applied axial force goes beyond a certain boundary, even the deflection is still in the intermediate range. However, this boundary has not been clearly identified so far. In this work, we mathematically determine the non-dimensional boundary of the axial force by the condition that the strain energy expression of the BCM is a positive definite quadratic form, and by the buckling condition relate to compressing axial force. Several examples are analyzed to demonstrate the effects of the axial force on the modeling errors of the BCM. When using the BCM for modeling, it is always suggested to check if the axial force is within this boundary to avoid large modeling errors. If the axial force is beyond the boundary, the Chained Beam Constraint Model (CBCM) can be used instead

Trend and Cycle Analysis of Annual and Seasonal Precipitation in Liaoning, China

Annual and seasonal precipitation data for 49 meteorological stations over the period of 1960–2006 in Liaoning province were analyzed. Liaoning experienced province-wide decreases in precipitation over the 47-year period, with annual precipitation decreasing by 96% of the stations, followed by 92, 84, 63, and 27%, respectively, for summer, autumn, spring, and winter precipitation. Regional trend analysis confirmed the province-wide decrease, which was detected by the site-specific analysis, but a greater number of significant declines were found for annual, summer, and autumn precipitation for Liaoning province and for three of its four subregions. Four significant cycles with alternation patterns were detected mainly at the time scales of 3–5, 10-11, 20–23, and 31.2 years for each of the four subregions (Liaodong Peninsula, Northeastern Mountain, Western Highland, and Central Plain) and the entire Liaoning province, with the dominant periodicities being 10-11 years. The 10-11-year periodic variation of Liaoning annual precipitation was negatively associated with sunspot activity and positively associated with the East Asian Summer Monsoon (EASM) at the same time scale, while the 31.2-year periodic variation of Liaoning annual precipitation was positively correlated with both the EASM and ENSO activities at the 30–33-year time scale

MethylPCA: a toolkit to control for confounders in methylome-wide association studies

Background In methylome-wide association studies (MWAS) there are many possible differences between cases and controls (e.g. related to life style, diet, and medication use) that may affect the methylome and produce false positive findings. An effective approach to control for these confounders is to first capture the major sources of variation in the methylation data and then regress out these components in the association analyses. This approach is, however, computationally very challenging due to the extremely large number of methylation sites in the human genome. Result We introduce MethylPCA that is specifically designed to control for potential confounders in studies where the number of methylation sites is extremely large. MethylPCA offers a complete and flexible data analysis including 1) an adaptive method that performs data reduction prior to PCA by empirically combining methylation data of neighboring sites, 2) an efficient algorithm that performs a principal component analysis (PCA) on the ultra high-dimensional data matrix, and 3) association tests. To accomplish this MethylPCA allows for parallel execution of tasks, uses C++ for CPU and I/O intensive calculations, and stores intermediate results to avoid computing the same statistics multiple times or keeping results in memory. Through simulations and an analysis of a real whole methylome MBD-seq study of 1,500 subjects we show that MethylPCA effectively controls for potential confounders. Conclusions MethylPCA provides users a convenient tool to perform MWAS. The software effectively handles the challenge in memory and speed to perform tasks that would be impossible to accomplish using existing software when millions of sites are interrogated with the sample sizes required for MWAS

Chromosome-scale genome assembly for the duckweed Spirodela intermedia, integrating cytogenetic maps, PacBio and Oxford Nanopore libraries

Duckweeds are small, free-floating, morphologically highly reduced organisms belonging to the monocot order Alismatales. They display the most rapid growth among flowering plants, vary ~ 14-fold in genome size and comprise five genera. Spirodela is the phylogenetically oldest genus with only two mainly asexually propagating species: S. polyrhiza (2n = 40; 160 Mbp/1C) and S. intermedia (2n = 36; 160 Mbp/1C). This study combined comparative cytogenetics and de novo genome assembly based on PacBio, Illumina and Oxford Nanopore (ON) reads to obtain the first genome reference for S. intermedia and to compare its genomic features with those of the sister species S. polyrhiza. Both species’ genomes revealed little more than 20,000 putative protein-coding genes, very low rDNA copy numbers and a low amount of repetitive sequences, mainly Ty3/gypsy retroelements. The detection of a few new small chromosome rearrangements between both Spirodela species refined the karyotype and the chromosomal sequence assignment for S. intermedia

Green Light-Emitting Devices Based on Perovskite CsPbBr3 Quantum Dots

In this paper, high quality green-emitting CsPbBr3 quantum dots (QDs) are successfully synthesized by hot-injection method. Different injection temperatures are tested to optimize the synthesis conditions. High brightness with the photoluminescence (PL) quantum yields (QYs) up to 90% and narrow size-distribution with the full width at half-maximum (FWHM) of 18.5 nm are obtained under the optimized conditions. Green light emitting diodes (LEDs) based on the CsPbBr3 QDs are successfully demonstrated by combining solution method with vapor deposition method. Composite films of poly[9,9-dioctylfluorene-co- N-[4-(3-methylpropyl)]-diphenylamine] (TFB) and bathocuproine (BCP) layers are chosen as the hole-transporting and the electron-transporting layers, respectively. The highly bright green QD-based light-emitting devices (QLEDs) showing maximum luminance up to 46,000 cd/m2 with a low turn on voltage of 2.3 V, and peak external quantum efficiency (EQE) of 5.7%, corresponding to 19.9 cd/A in luminance efficiency. These devices also show high color purity for electroluminescence (EL) with FWHM <20 nm, and no redshift and broadening with increasing voltage as well as a spectral match between PL and EL

Evaluation of association tests for rare variants using simulated data sets in the Genetic Analysis Workshop 17 data

We evaluate four association tests for rare variants—the combined multivariate and collapsing (CMC) method, two weighted-sum methods, and a variable threshold method—by applying them to the simulated data sets of unrelated individuals in the Genetic Analysis Workshop 17 (GAW17) data. The family-wise error rate (FWER) and average power are used as criteria for evaluation. Our results show that when all nonsynonymous SNPs (rare variants and common variants) in a gene are jointly analyzed, the CMC method fails to control the FWER; when only rare variants (single-nucleotide polymorphisms with minor allele frequency less than 0.05) are analyzed, all four methods can control FWER well. All four methods have comparable power, which is low for the analysis of the GAW17 data sets. Three of the methods (not including the CMC method) involve estimation of p-values using permutation procedures that either can be computationally intensive or generate inflated FWERs. We adapt a fast permutation procedure into these three methods. The results show that using the fast permutation procedure can produce FWERs and average powers close to the values obtained from the standard permutation procedure on the GAW17 data sets. The standard permutation procedure is computationally intensive