物流管理案例教学“五步深化法”的探讨与实践

物流管理案例教学方法对提升物流管理专业学生的综合应用能力至关重要。在精心选取案例与科学分组的基础上,采用"五步深化法"进行案例教学,能有效增强学生的学习兴趣与参与热情,拓展学生深度思维能力,从而培养学生综合运用理论知识灵活解决复杂实际问题的应用能力。

中国典籍《菜根谭》语素英译探析——基于许渊冲的深化翻译法

典籍《菜根谭》中的语素具有表意丰富、构词精炼之特点,其英译需采用许渊冲先生提出的深化翻译法,才能达到传神的效果,被目的语读者领会和接受。语篇或语段是以动态语义链为结构组成的语义连贯统一体(continuum)。以语篇或语段为深化翻译单位,就是将语素有机地纳入语段或语篇,以原语深层内容和中心信息能动有效的传递为核心,努力取得语素翻译的神似。本文将研究这种语素的深化英译,从理论和实践两个方面,探讨其适切性和理据性。

造船报价中设备及工时的深化估算法

造船报价中设备及工时的深化估算法孔祥鼎（武汉交通科技大学）１设备深化估算法船舶设备部分包括下列３个部分：１．轮机设备：包括主机、发电机组、辅机、轴系、螺旋桨、通风、空调冷藏、管系等。２．电气设备：包括导航、通讯及其它电气设备和各种配电箱等。３．碱装设...

深化翻译法研究——以许渊冲英译《毛泽东诗词》为例

英译《毛泽东诗词》时,既传达一字一词又表达出作者的心境是一项艰巨的任务。许渊冲先生提出的深化翻译法使该诗作的译文达到传神的效果,既译诗句,也译心境。本文通过举例赏析,探究许先生如何用深化翻译法传递原文的“意美”,旨在启发译者灵活再现原诗词的深层含义,也让译者明白只有结合原诗作者的心境仔细琢磨与推敲才能得出满意的译诗,让读者感同身受。

3D density inversion of gravity gradient data using the extrapolated Tikhonov regularization

We use the extrapolated Tikhonov regularization to deal with the ill-posed problem of 3D density inversion of gravity gradient data. The use of regularization parameters in the proposed method reduces the deviations between calculated and observed data. We also use the depth weighting function based on the eigenvector of gravity gradient tensor to eliminate undesired effects owing to the fast attenuation of the position function. Model data suggest that the extrapolated Tikhonov regularization in conjunction with the depth weighting function can effectively recover the 3D distribution of density anomalies. We conduct density inversion of gravity gradient data from the Australia Kauring test site and compare the inversion results with the published research results. The proposed inversion method can be used to obtain the 3D density distribution of underground anomalies.

《雪夜林畔》译文评析——弗罗斯特诗歌译作论

通过分析弗罗斯特的诗歌《雪夜林畔》的一篇中译文,评价了它在传达原文的思想内容、语言风格及翻译技巧方面的特点。诗是内容和形式高度融合的有机统一体,诗歌的翻译要求我们传达的不仅是内容和风格,还有它的形式。

Comparison on construction of strut-and-tie models for reinforced concrete deep beams

With consideration of the differences between concrete and steel,three solutions using genetic evolutionary structural optimization algorithm were presented to automatically develop optimal strut-and-tie model for deep beams.In the finite element analysis of the first method,the concrete and steel rebar are modeled by a plane element and a bar element,respectively.In the second method,the concrete and steel are assigned to two different plane elements,whereas in the third method only one kind of plane element is used with no consideration of the differences of the two materials.A simply supported beam under two point loads was presented as an example to verify the validity of the three proposed methods.The results indicates that all the three methods can generate optimal strut-and-tie models and the third algorithm has powerful capability in searching more optimal results with less computational effort.The effectiveness of the proposed algorithm III has also been demonstrated by other two examples.

Full waveform inversion based on deep learning and optimal nearly analytic discrete method

In this study,we implement forward modeling and inversion based on deep-learning strategies using an optimal nearly analytic discrete(ONAD)method.The forward-modeling method combines the ONAD method with recurrent neural network(RNN)for the fi rst time.RNN is a type of neural network that is suitable for sequential data,which uses information from both previous and current times to obtain output information.We express the ONAD method using an RNN framework to advance the time iteration of an acoustic equation.This process can simplify programming using RNN and convolution kernels.Next,we use deep learning based on the proposed forward-modeling method to study full waveform-inversion problems.Because the main purpose of inversion is to minimize the error between real and synthetic data,inversion is essentially an optimization problem.Many new optimizers are available in the framework of deep learning,such as the Adam and Nadam optimizers,which are used for optimizing velocity model in the inversion process.We perform six numerical experiments.The first two experiments demonstrate the forward-modeling results,which indicate that the forward-modeling method can effectively suppress numerical dispersion and improve computational effi ciency.The other four experiments demonstrate the inversion results,which show that the method proposed in this paper can eff ectively realize inversion imaging.We compare several optimizers used in deep learning and find that the Nadam optimizer has faster convergence and better effectiveness based on the ONAD method combined with RNN.

Application of Modified Genetic Algorithm to Optimal Design of Supporting Structure

The modified genetic algorithm was used for the optimal design of supporting structure in deep pits.Based on the common genetic algorithm, using niche technique and reserving the optimum individual the modified genetic algorithm was presented. By means of the practical engineering, the modified genetic algorithm not only has more expedient convergence, but also can enhance security and operation efficiency.