Intelligent Design of High Strength and High Conductivity Copper Alloys Using Machine Learning Assisted by Genetic Algor

Metallic alloys for a given application are usually designed to achieve the desired properties by devising experimentsbased on experience, thermodynamic and kinetic principles, and various modeling and simulation exercises.However, the influence of process parameters and material properties is often non-linear and non-colligative. Inrecent years, machine learning (ML) has emerged as a promising tool to dealwith the complex interrelation betweencomposition, properties, and process parameters to facilitate accelerated discovery and development of new alloysand functionalities. In this study, we adopt an ML-based approach, coupled with genetic algorithm (GA) principles,to design novel copper alloys for achieving seemingly contradictory targets of high strength and high electricalconductivity. Initially, we establish a correlation between the alloy composition (binary to multi-component) andthe target properties, namely, electrical conductivity and mechanical strength. Catboost, an ML model coupledwith GA, was used for this task. The accuracy of the model was above 93.5%. Next, for obtaining the optimizedcompositions the outputs fromthe initial model were refined by combining the concepts of data augmentation andPareto front. Finally, the ultimate objective of predicting the target composition that would deliver the desired rangeof properties was achieved by developing an advancedMLmodel through data segregation and data augmentation.To examine the reliability of this model, results were rigorously compared and verified using several independentdata reported in the literature. This comparison substantiates that the results predicted by our model regarding thevariation of conductivity and evolution ofmicrostructure and mechanical properties with composition are in goodagreement with the reports published in the literature.

Quantifying Solid Solution Strengthening in Nickel-Based Superalloys via High-Throughput Experiment and Machine Learning

Solid solution strengthening(SSS)is one of the main contributions to the desired tensile properties of nickel-based superalloys for turbine blades and disks.The value of SSS can be calculated by using Fleischer’s and Labusch’s theories,while the model parameters are incorporated without fitting to experimental data of complex alloys.In thiswork,four diffusionmultiples consisting of multicomponent alloys and pure Niare prepared and characterized.The composition and microhardness of singleγphase regions in samples are used to quantify the SSS.Then,Fleischer’s and Labusch’s theories are examined based on high-throughput experiments,respectively.The fitted solid solution coefficients are obtained based on Labusch’s theory and experimental data,indicating higher accuracy.Furthermore,six machine learning algorithms are established,providing a more accurate prediction compared with traditional physical models and fitted physical models.The results show that the coupling of highthroughput experiments and machine learning has great potential in the field of performance prediction and alloy design.

Integration of“Learning to Strengthen the Country”and“Offline Teaching”Concepts in the“Curriculum Ideology and Politics”Education among Graduate Students

Since the socialism with Chinese characteristics has entered this new era,the“curriculum ideology and politics”concept has become one of the innovative achievements in the reformation of ideological and political education courses in colleges as well as universities.Based on the emphasis of“curriculum ideology and politics”among graduate students and the influence of the“learning to strengthen the country”concept,this article analyzes universities in regard to the curriculum settings,faculties,and their graduate students.It also explores the“curriculum ideology and politics”concept in consideration of the ontology of teaching,school education,social influence,etc.,and propose practical and extendable countermeasures.

Strengthen Exchanges and Mutual Learning among Civilizations,Join Hands to Create a Better Future:Keynote speech of H.E.Ji Bingxuan on the Dialogue on Exchanges and Mutual Learning among Civilizations

On November 28,the First Dialogue on Exchanges and Mutual Learning among Civilizations organized by the Chinese Association for International Understanding was staged in the Forbidden City.Some 100 participants from all over the world were present at the Dialogue.Participants made discussions themed on"building a world featuring mutual learning and harmonious coexistence among different civilizations".Ji Bingxuan,Vice Chairman of Standing Committee of the National People’s Congress and President of the Chinese Association for International Understanding attended the opening ceremony and delivered a keynote speech.

计算机等级考试E-Learning学习模式实践

本文阐述了全国计算机等级考试e-learning学习模式的设计与实践,以及系统各功能模块的技术特点.

Strategies for developing Online Learning

This paper examines the strategies of developing online learning in Chinese universities.Top-down strategies include policy,funding,Senior initiative and task-based management,etc,in which funding generally plays the most important role followed by Senior initiative and task-based management.Bottom-up strategies,especially staff training and contest are often seen as essential to successfully improve online learning.

E-learning方式下教师培训质量影响因子分析——以“国培计划”安徽省农村初中骨干教师远程培训项目为例

将E-learning方式引入到教师培训,为培训提供了先进、人本、灵活的模式。安徽广播电视大学依托省中小学教师教育网,借助E-learning的技术和手段开展教师培训已践行了近6年时间,如何提高大规模教师培训的实效性,确保培训质量一直以来都是倍受关注的焦点问题,以"国培计划"安徽省农村初中骨干教师远程培训项目为例,从培训资源、团队、教学设计和平台四个方面分析了E-learning方式下教师培训质量影响因子。

Implementation of E-Learning System: Findings and Lessons Learned

This paper describes the implementation of the e-learning system at the School of Mathematics and Computer Science, National University of Mongolia. The paper includes in-house development of Edunet 1.0 e-learning system, comparative analysis on LMS, evaluation methodology, selection of e-learning systems, and comparative analysis on implementation of Edunet, Moodle and Canvas systems.

Stock Prediction Based on Technical Indicators Using Deep Learning Model

Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable nature.The stock data is usually non-stationary,and attributes are non-correlative to each other.Several traditional Stock Technical Indicators(STIs)may incorrectly predict the stockmarket trends.To study the stock market characteristics using STIs and make efficient trading decisions,a robust model is built.This paper aims to build up an Evolutionary Deep Learning Model(EDLM)to identify stock trends’prices by using STIs.The proposed model has implemented the Deep Learning(DL)model to establish the concept of Correlation-Tensor.The analysis of the dataset of three most popular banking organizations obtained from the live stock market based on the National Stock exchange(NSE)-India,a Long Short Term Memory(LSTM)is used.The datasets encompassed the trading days from the 17^(th) of Nov 2008 to the 15^(th) of Nov 2018.This work also conducted exhaustive experiments to study the correlation of various STIs with stock price trends.The model built with an EDLM has shown significant improvements over two benchmark ML models and a deep learning one.The proposed model aids investors in making profitable investment decisions as it presents trend-based forecasting and has achieved a prediction accuracy of 63.59%,56.25%,and 57.95%on the datasets of HDFC,Yes Bank,and SBI,respectively.Results indicate that the proposed EDLA with a combination of STIs can often provide improved results than the other state-of-the-art algorithms.

基于区块链技术的国家学分银行系统研究与设计

区块链作为一种新兴技术,具有去中心化和不可篡改等特征,它能为学分银行建设提供技术支持。突破以往从理论上论证区块链技术服务于学分银行建设的可行性,以国家开放大学推进学分银行建设实践为依托,系统介绍了区块链+学分银行的应用案例。同时,对基于区块链技术的国家学分银行系统可能存在的技术风险进行分析,并提出应对方案。

“职业教育的强国”与“职业教育来强国”:耦合逻辑与实现路径

职业教育强国是教育强国的重要有机组成部分。在新时代背景下,“职业教育强国”包含“职业教育的强国”与“职业教育来强国”双重意蕴。“职业教育的强国”意为强职业教育自身,“职业教育来强国”意为强国家、强民族,二者相互依存、相互促进,共同坚持以促进民族复兴为旨趣、以培养技术技能人才为载体、以实现高质量发展为支撑。现实中,建设职业教育强国仍面临职普融通失序、产教融合失能、科教融汇失位等困境。为此,应着力绘制新图景、孕育新希望、打造新动能,促进教育强国、人才强国、科技强国建设,实现教育、人才、科技三位一体协同发展,为全面建成社会主义现代化强国提供有力支撑。

课程思政与大学英语“金课”构建研究

在打造高校英语教学“金课”工程的进程中,利用多媒体和信息技术,把课堂思政教学内容和语言课程进行有机结合,在以学生为中心,培养学生的英语自主学习能力的同时,通过通过融入习近平新时代中国特色社会主义思想,引导语言学习者牢固理想信念;融入中华优秀传统文化的英文表达,帮助学习者更加深入理解中华优秀传统文化的理论精髓与社会意义;融入对爱国、诚信等内容的现代理解,把民族精神、时代精神内化为学生的精神追求。有助于将大学英语课程打造成知识、技能和文化相融合的“金课”。

封建王朝时期西藏地区与内地各民族互动的历史考察

我国是一个历史悠久的统一的多民族国家,各民族多彩而独特的历史都是中国史不可分割的一部分,西藏地区自古以来就是中国疆域不可分割的一个部分。在古代中国广袤的土地上,西藏地区与内地各民族始终保持着密切的交往交流交融,密切程度随着时代的变迁而不断加深。本文通过系统探究封建王朝时期西藏地区与内地各民族互动的历史,研究发现,西藏地区各民族与内地各民族间之所以团结融合,源自于各民族文化上的兼收并蓄、经济上的相互依存、情感上的相互亲近,也源自于中华民族追求团结统一的内生动力。然而,在封建王朝时期,没有哪一个王朝能从根本上解决民族之间的平等交往与团结问题,直到新中国成立之后,在中国共产党的坚强领导下,西藏地区各民族与内地各民族的交往才真正形成了相互依存、交流融合、休戚与共的关系,各民族共同凝聚在中华民族旗帜下,中华民族共同体意识空前增强。

改进行为克隆与DDPG的无人驾驶决策模型

无人驾驶技术的关键是决策层根据感知环节输入信息做出准确指令。强化学习和模仿学习比传统规则更适用于复杂场景。但以行为克隆为代表的模仿学习存在复合误差问题,使用优先经验回放算法对行为克隆进行改进,提升模型对演示数据集的拟合能力;原DDPG(deep deterministic policy gradient)算法存在探索效率低下问题,使用经验池分离以及随机网络蒸馏技术(random network distillation,RND)对DDPG算法进行改进,提升DDPG算法训练效率。使用改进后的算法进行联合训练,减少DDPG训练前期的无用探索。通过TORCS(the open racing car simulator)仿真平台验证,实验结果表明该方法在相同的训练次数内,能够探索出更稳定的道路保持、速度保持和避障能力。

基于深度强化学习的农村物流运输路径自动选择研究

常规方法求解运输路径自动选择模型时,选择的运输路径的运输总成本较高,因此,提出基于深度强化学习的农村物流运输路径自动选择方法。通过分析农村流通供应链的特殊性确定目标函数,并基于目标函数设计运输路径自动选择模型,采用深度强化学习算法求解,设置状态-动作空间、奖励函数等要素完善动作选择过程,输出模型最优解,从而实现运输路径自动选择的目的。在实验论证中,所提方法所选运输路径的平均运输成本为8.35万元,相比对照方法更低。结果表明,设计的方法能够有效规划物流运输路径,降低运输成本。

讲好各民族地区红色故事助力铸牢中华民族共同体意识

小故事承载大历史。回顾党在各民族地区的奋斗历程,其中有不少美丽故事记载并默默讲述着党与各族人民间的红色情谊。红色故事是红色文化的生动表现形式,是铸牢中华民族共同体意识的一个有力抓手,其中包含的红色基因已深深融入党与各族人民的内心深处。深入挖掘并讲好各族人民中流传的红色故事,对全面铸牢中华民族共同体意识具有重要意义。