Design and finite element mode analysis of noncircular gear

The noncircular gear transmission is an important branch of the gear transmission, it is characterized by its compact structure, good dynamic equilibration and other advantages, and can be used in the automobile, engineering machine, ship, machine tool, aviation and spaceflight field etc. Studying on the dynamics feature of noncircular gear transmission can improve the ability to carry loads of, reduce the vibration and noise of, increase the life of the noncircular gear transmission machine, provides guidance for the design of the noncircular gear, and has significant theories and practical meanings. In this paper, the gear transmission technique is used to studied the design method of the noncircular gear, which contains distribution of teeth on the pitch curve, designs of the tooth tip curve and the tooth root curve, design of the tooth profile curve, the gear system dynamics principle is introduced to establish dynamics model for the noncircular gear; basic theory of finite element and mode analysis method are applied, finite element model for the noncircular gear is established, natural vibration characteristic of the noncircular gear is studied. And the oval gear is taken as an example, the mathematics software MathCAD, the 3D modeling software UG and the finite element software ABAQUS are used to realize precise 3D model of the oval gear. The finite element method is used, the natural vibration characteristic of the oval gear is studied, the main vibration types and natural frequencies of the oval gear and that of the equivalent cylindrical gears are analyzed and compared, the conclusions received reflect the dynamics performance of the oval gear, and solid foundation is laid for dynamics research and engineering application of the oval gear transmission

Guiding Non-Autoregressive Neural Machine Translation Decoding with Reordering Information

Non-autoregressive neural machine translation (NAT) generates each target word in parallel and has achieved promising inference acceleration. However, existing NAT models still have a big gap in translation quality compared to autoregressive neural machine translation models due to the enormous decoding space. To address this problem, we propose a novel NAT framework named ReorderNAT which explicitly models the reordering information in the decoding procedure. We further introduce deterministic and non-deterministic decoding strategies that utilize reordering information to narrow the decoding search space in our proposed ReorderNAT. Experimental results on various widely-used datasets show that our proposed model achieves better performance compared to existing NAT models, and even achieves comparable translation quality as autoregressive translation models with a significant speedup.Comment: Accepted by AAAI 202

Accumulation Pattern of Flavonoids in Cabernet Sauvignon Grapes Grown in a Low-Latitude and High-Altitude Region

Particular climate conditions in a low-latitude and high-altitude region endow grape berries with distinctivequality characteristics. So far, few reports have been concerned with the formation of berry flavour in such aregion. This study aimed to investigate the accumulation pattern of flavonoids in Vitis vinifera L. cv. CabernetSauvignon grape berries growing at different altitudes of the highland in southwest China in two consecutivevintages. In addition to the 3-O-monoglucosides and 3-O-acyl monoglucosides of the five main anthocyanidins(delphinidin, cyanidin, peonidin, petunidin and malvidin), some uncommon anthocyanins, such as threediglucosides of anthocyanidins and pelargonidin-3-O-glucoside, were detected in the grape berries. Higheraltitude cultivation greatly promoted the production of anthocyanins and flavonols, particularly cyanidintypeanthocyanins and quercetin-type flavonols from the F3’H branch of the flavonoid biosynthetic pathway.Flavan-3-ols from both branches were comparatively less influenced by vineyard altitude. Vintage in thishigh-altitude region also had a dramatic influence on the accumulation of flavonoids. Most of the anthocyaninand flavonol components were affected more by vineyard altitude than by vintage, whereas the accumulationof flavan-3-ols differed mainly between vintages. The present data will not only improve the understandingof flavonoid accumulation in grapes from a high-altitude region with different climates, but also providepractical guidance for the production of high-quality grapes and wine

A competitive mechanism based multi-objective particle swarm optimizer with fast convergence

In the past two decades, multi-objective optimization has attracted increasing interests in the evolutionary computation community, and a variety of multi-objective optimization algorithms have been proposed on the basis of different population based meta-heuristics, where the family of multi-objective particle swarm optimization is among the most representative ones. While the performance of most existing multi-objective particle swarm optimization algorithms largely depends on the global or personal best particles stored in an external archive, in this paper, we propose a competitive mechanism based multi-objective particle swarm optimizer, where the particles are updated on the basis of the pairwise competitions performed in the current swarm at each generation. The performance of the proposed competitive multi-objective particle swarm optimizer is verified by benchmark comparisons with several state-of-the-art multiobjective optimizers, including three multi-objective particle swarm optimization algorithms and three multi-objective evolutionary algorithms. Experimental results demonstrate the promising performance of the proposed algorithm in terms of both optimization quality and convergence speed

NumNet: Machine Reading Comprehension with Numerical Reasoning

Numerical reasoning, such as addition, subtraction, sorting and counting is a critical skill in human's reading comprehension, which has not been well considered in existing machine reading comprehension (MRC) systems. To address this issue, we propose a numerical MRC model named as NumNet, which utilizes a numerically-aware graph neural network to consider the comparing information and performs numerical reasoning over numbers in the question and passage. Our system achieves an EM-score of 64.56% on the DROP dataset, outperforming all existing machine reading comprehension models by considering the numerical relations among numbers.Comment: Accepted to EMNLP2019; 11 pages, 2 figures, 6 table

Sample Size Determination for Interval Estimation of the Prevalence of a Sensitive Attribute Under Randomized Response Models

© 2022 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.Studies with sensitive questions should include a sufficient number of respondents to adequately address the research interest. While studies with an inadequate number of respondents may not yield significant conclusions, studies with an excess of respondents become wasteful of investigators’ budget. Therefore, it is an important step in survey sampling to determine the required number of participants. In this article, we derive sample size formulas based on confidence interval estimation of prevalence for four randomized response models, namely, the Warner’s randomized response model, unrelated question model, item count technique model and cheater detection model. Specifically, our sample size formulas control, with a given assurance probability, the width of a confidence interval within the planned range. Simulation results demonstrate that all formulas are accurate in terms of empirical coverage probabilities and empirical assurance probabilities. All formulas are illustrated using a real-life application about the use of unethical tactics in negotiation.Peer reviewe

EYA4 Promotes Cell Proliferation Through Downregulation of p27Kip1 in Glioma

Background/Aims: Accumulating evidence suggests that Eyes Absent Homologue 4 (EYA4) plays an important role in tumorigenesis and progression of various cancers. However, the role of EYA4 in glioma development is still unclear. Methods: The expression of EYA4 was examined in glioma tissues by immunohistochemistry. Cell viability and apoptosis were analyzed by CCK-8, BrdU assay, and flow cytometry. Results: We found that EYA4 was upregulated in glioma, and its expression was positively correlated with advanced tumor stage. Moreover, higher expression of EYA4 predicted a worse overall survival in patients with glioma. Forced overexpression of EYA4 enhanced glioma cell proliferation, and EYA4 suppressed the expression of p27Kip1 directly in these cells. Furthermore, Six1 was required for EYA4 to suppress the expression of p27Kip1 in glioma. Conclusion: Together, we demonstrate that EYA4 promotes cell proliferation by directly suppressing the expression of p27Kip1 in glioma