Combining reinforcement learning with mathematical programming:An approach for optimal design of heat exchanger networks

Heat integration is important for energy-saving in the process industry.It is linked to the persistently challenging task of optimal design of heat exchanger networks(HEN).Due to the inherent highly nonconvex nonlinear and combinatorial nature of the HEN problem,it is not easy to find solutions of high quality for large-scale problems.The reinforcement learning(RL)method,which learns strategies through ongoing exploration and exploitation,reveals advantages in such area.However,due to the complexity of the HEN design problem,the RL method for HEN should be dedicated and designed.A hybrid strategy combining RL with mathematical programming is proposed to take better advantage of both methods.An insightful state representation of the HEN structure as well as a customized reward function is introduced.A Q-learning algorithm is applied to update the HEN structure using theε-greedy strategy.Better results are obtained from three literature cases of different scales.

A Machine Learning-Based Distributed Denial of Service Detection Approach for Early Warning in Internet Exchange Points

The Internet service provider(ISP)is the heart of any country’s Internet infrastructure and plays an important role in connecting to theWorld WideWeb.Internet exchange point(IXP)allows the interconnection of two or more separate network infrastructures.All Internet traffic entering a country should pass through its IXP.Thus,it is an ideal location for performing malicious traffic analysis.Distributed denial of service(DDoS)attacks are becoming a more serious daily threat.Malicious actors in DDoS attacks control numerous infected machines known as botnets.Botnets are used to send numerous fake requests to overwhelm the resources of victims and make them unavailable for some periods.To date,such attacks present a major devastating security threat on the Internet.This paper proposes an effective and efficient machine learning(ML)-based DDoS detection approach for the early warning and protection of the Saudi Arabia Internet exchange point(SAIXP)platform.The effectiveness and efficiency of the proposed approach are verified by selecting an accurate ML method with a small number of input features.A chi-square method is used for feature selection because it is easier to compute than other methods,and it does not require any assumption about feature distribution values.Several ML methods are assessed using holdout and 10-fold tests on a public large-size dataset.The experiments showed that the performance of the decision tree(DT)classifier achieved a high accuracy result(99.98%)with a small number of features(10 features).The experimental results confirmthe applicability of using DT and chi-square for DDoS detection and early warning in SAIXP.

A Community with a Shared Future for Mankind:Built through Inter-Civilization Exchanges

Civilization-related issues have attracted attention because they affect international relations,global governance and international order.World politics and the Clash of Civilizations theory proposed by Western strategic circles raise risks of inter-civilization conflicts,whereas China's framework of building a community with a shared future for mankind advocates peace.Although civilizations are bound to diversify,and conflicts are endemic between vastly distinct civilizations,there is aproper basis for peaceful coexistence in exchanges and mutual learning.China holds that civilizations are diverse,equal and tolerant,and that different civilizations should learn from each other's civilization concept and actively practice it.For future relations between Chinese and Westerncivilizations,China promotes the benign interaction of inter-civilization exchanges and mutual learning,and the building of a community with a shared future.

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.

Promoting Mutual Learning and Understanding Through Exchanges——Notes on the Visit of the European-American Young Leaders Delegation to China

At the invitation of CAFIU,an 11-member European-American young leaders delegation composed of politicians,think tank researchers and media workers from France,Germany,Italy,UK and US headed by Mr.Manlio di Stefano,Member of House of Representatives of Italy and Chairman of the Five-Star

Machine Learning-based USD/PKR Exchange Rate Forecasting Using Sentiment Analysis of Twitter Data

This study proposes an approach based on machine learning to forecast currency exchange rates by applying sentiment analysis to messages on Twitter(called tweets).A dataset of the exchange rates between the United States Dollar(USD)and the Pakistani Rupee(PKR)was formed by collecting information from a forex website as well as a collection of tweets from the business community in Pakistan containing finance-related words.The dataset was collected in raw form,and was subjected to natural language processing by way of data preprocessing.Response variable labeling was then applied to the standardized dataset,where the response variables were divided into two classes:“1”indicated an increase in the exchange rate and“−1”indicated a decrease in it.To better represent the dataset,we used linear discriminant analysis and principal component analysis to visualize the data in three-dimensional vector space.Clusters that were obtained using a sampling approach were then used for data optimization.Five machine learning classifiers—the simple logistic classifier,the random forest,bagging,naïve Bayes,and the support vector machine—were applied to the optimized dataset.The results show that the simple logistic classifier yielded the highest accuracy of 82.14%for the USD and the PKR exchange rates forecasting.

Effects of Study-Abroad Experiences on Chinese Students’ L2 Learning Activities and Study-Abroad Motivations

This study explores how overseas exchange opportunities might influence Chinese students ’engagement in L2 learning activities and how far such opportunities may satisfy their motivation to study abroad. The analysis of the data, collected and filtered from carefully designed questionnaires and interviews, showed that students ’ L2 learning activities and study-abroad motivations underwent changes after their overseas experiences. Regarding the former, the overseas environment was the cause of the change because it provided students with more chances to talk with native speakers and increased the frequency of their using L2 in their daily life. Regarding the latter, the decline of the students ’ major study-abroad motivations was partly because they tended to treat L2 learning as a tool for realizing other goals and partly because the students had got other important motivations. In view of these findings, suggestions were raised to help future students get better prepared for their overseas study or short-term exchange life.

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e-learning的医学科研管理模式在学术交流领域的实践与经验

在线学术交流方式如e-learning等,克服传统学术交流模式中学科领域、经费、空间和时间的限制,能够有效提升学术交流效率。当下,越来越多的科技工作者利用e-learning等在线学术交流平台,分享科技信息、交流学术观点、追踪最新科学研究趋势,形成高效学术交流模式。本文对医学科研管理人员运用e-learning等方式协助科技工作者进行高效、共享的学术交流,总结目前的实践应用现状及经验,并为未来e-learning在学术交流领域的扩大使用提供建设性启示。

明清之际中国社会对欧洲文明的拒斥与接受

16-18世纪耶稣会传教士来中国传教,同时也带来了部分西方的科学知识。中国的儒家士人和士大夫群体中,有一部分人激烈反对、拒斥接受这些知识,也有一部分人乐于学习传教士带来的科学知识,接受并试图融合而后超越西方文化。拒斥的一方,有出于意识形态立场、政治安全角度考虑的,如《破邪集》的编辑者徐昌治,以及该书中收录的各色人等,如清初布衣杨光先;也有王夫之这样的大儒,从儒家经学中天文、地理知识的固有立场出发,批评利玛窦的地圆说和地球与太阳及诸行星的距离说。接受的一方表现出比较多元的立场,有徐光启的“超胜会通”说、方以智的“坐集千古之智”折衷说,也有以康熙皇帝为代表的“西学中源”说。“西学中源”说虽然最终不利于中国人虚心学习西方的科学知识,但也包含着一定的合理的文明交流互鉴思想,即在学习外来文明中的先进内容时,必须保持民族文化的主体性。

习近平文化思想形成历程论纲

党的十八大以来,习近平同志站在实现中华民族伟大复兴的战略高度和历史高度,面对世情、国情、党情不断变化的新形势,提出了新时代中国特色社会主义文化建设方面的一系列新思想新观点新论断,形成了习近平文化思想。作为一个不断展开的、开放式的思想体系,习近平文化思想之所以能够具备“明体达用、体用贯通”的理论品格与思想特质,必当有其渊源有自的形成历程。揆诸习近平文化思想形成历程,文化自信塑造了其理论底色,文化发展道路明确了其发展指向,文明交流互鉴彰显了其天下立场,文化强国与中华民族现代文明展现了其宏远目标。习近平文化思想体现了鲜明的实践性与总体性、民族性与时代性、现实性与理想性、包容性与原则性的高度统一,是引领新时代中国特色社会主义文化建设的行动指南。

一种自适应强制进化随机游走算法应用于换热网络综合

RWCE(强制进化随机游走)算法应用于系统热集成时,最大步长既影响当前可行搜索域的范围,又影响整型变量的进化,固定参数设置降低了更优解产生的几率。因此提出一种融合自适应步长和自适应反向学习策略的RWCE算法。建立随机动态步长,在导向参数牵引下自动激励有利步长值持续进化;在此基础上,建立自适应反向学习策略改变个体进化路径,使算法在优化的不同阶段能够自动搜索最佳步长,并挖掘尽可能多的结构,充分发挥算法全局搜索和局部开发能力。最后研究并计算H6C10、H10C10、H13C73个典型中大规模算例,结果表明该方法能够进一步提升算法的寻优能力。

基于改进型树型奇偶机的密钥交换研究

提出了一种基于改进型树型奇偶机的密钥交换方法。利用伪随机数产生器为通信双方产生共同的输入向量,降低网络同步所需的数据量;使用基于滑动窗口的学习规则来动态改变学习率,降低同步次数;并且进一步提出基于背包算法和深度优先搜索遍历算法的高效输入序列生成方法,增大正向学习概率加快同步速度。仿真实验结果表明,改进模型的通信数据量下降了60%以上,同步时间显著下降。

社会性昆虫社会身份识别机制研究进展

昆虫社会身份识别是指昆虫通过感知各种信息来辨别其他个体的身份,进而建立一定社会关系的生物学现象。社会身份识别对社会性昆虫的信息交流、天敌防御、社会分工、个体及群体生存和繁衍尤为重要。本文以蚂蚁、白蚁和蜜蜂等社会性昆虫为例,详细阐述了社会性昆虫的社会身份识别机制:社会性昆虫以信息素为关键线索,利用嗅觉实现社会身份的识别;社会性昆虫也通过其视觉观察同伴或天敌等个体的行为,实现社会身份的识别;社会性昆虫还能够识别特殊的声音信号和感知空气中的震波,并作为其社会身份识别的重要依据。此外,研究表明昆虫共生菌也参与调控社会性昆虫的身份识别。针对社会性昆虫身份识别是先天还是后天形成的这一争论,我们从基因、适应学习以及基因和环境互作等方面,阐述了昆虫可能通过先天遗传和后天学习相结合的方式实现社会身份识别的观点。社会性昆虫是研究人类和动物社会行为学的绝佳模式生物。社会身份识别的研究成果不仅可深入解析社会性昆虫的生物学特性,也对人类及其他动物的社会认知进化以及各类社会问题的研究提供重要参考。今后应加强以下两方面的研究:(1)探索社会性昆虫多样的识别方式,解码其更深层次的机制研究;(2)应用现代新型计算机技术,将社会性昆虫身份识别行为可视化分析。

因“果”相连:葡萄沿丝路中国化的亚欧文明互鉴启示

从美索不达米亚平原起源的葡萄沿丝绸之路向西传到欧洲、向东传至中国的历史表明,葡萄成为亚欧大陆通用食品、饮品、药品及艺术创作原型是亚欧文明相互传播、学习、互鉴与创新的结果。通过葡萄东传中国可以发现,葡萄中国化的具体表现为:产出适应中国不同区域的葡萄品种;以中国酿酒之法酿制葡萄酒;葡萄果实、籽、叶、根入药治疗不同疾病;葡萄成为中国各种艺术创作的原型。葡萄沿丝路东传给现代以“一带一路”为核心的亚欧命运共同体建设的启示是:尊重和崇尚自然是因“果”相连的前提;因地制宜是因“果”相连的基础;与中国不同区域文化交融、创新体现出因“果”相连的特色;在相互欣赏与互鉴中促进亚欧命运共同体建设是因“果”相连的目标。

从唯物史观看人类文明交流互鉴

唯物史观是观察研究不同人类文明形态及其关系的唯一正确方法。把综合完整的文明形态分成物质文明、精神文明、社会文明、政治文明、生态文明,以唯物史观进行观察研究,从而确定文明交流互鉴是符合历史发展规律的主流。主张文明的冲突对抗,是夸大了不同文明形态中上层建筑部分的差异,并把这种差异看作是对抗冲突的根源。正是因为物质文明是决定其他文明形式的根本力量,所以文明交流互鉴是人类解放的必然要求。

奥西里斯的远征:跨文化神话与托勒密埃及的认同政治

公元前1世纪的希腊历史学家西西里的狄奥多罗斯在《历史丛书》1.17-20中讲述了一个关于埃及国王奥西里斯远征全世界传播文明的神话。这则“文明交流互鉴”神话中的人物有埃及的,也有希腊的,故事发生地点涉及欧亚非三大洲多地。对该神话多角度的分析表明,它应该是托勒密一世时期希腊化埃及官方创造的,它构成了托勒密王朝认同政治的一项内容。

中华孝文化为全球文明交流互鉴增光添彩

中华孝文化是以“事亲行孝,明德至善”为特征的一种传统文化,其中蕴含着丰富的人文思想和文明价值。人类文明的发展需要先进文化作支撑,如今,构建全球共同文明应充分挖掘传统文化中的先进成分,并积极开展文明交流互鉴。中华孝文化中的仁爱思想、和谐精神以及大同愿景等优秀成分,可为构建全球文明价值理念增光添彩。

论习近平文化思想的原创性贡献

习近平文化思想是新时代中国共产党领导中国人民进行文化建设实践经验的理论总结。习近平文化思想以文化建设规律性的认识,科学阐述了精神文明对物质文明的能动作用,对以中国式现代化推进中华民族伟大复兴具有深刻意义。习近平文化思想鲜明体现了中国共产党对“文化领导权”和“文化主体性”的坚守,突出强调了中华优秀传统文化创造性转化、创新性发展的战略价值,是建设中华民族现代文明的思想保证和精神力量。习近平文化思想深化了对国际话语权和国家“软实力”的认识,为推动人类文明进步、应对全球共同挑战提供了强大的思想武器和科学行动指南,生动展现了构建人类命运共同体的世界胸怀和中国智慧。

“一带一路”倡议下职业教育国际合作平台的实践类型与发展路径

打造职业教育国际合作平台是扩大职业教育高水平对外开放、服务“一带一路”倡议的重要举措。自2013年以来,我国职业教育国际合作平台建设历经初步探索阶段、多元拓展阶段和高质量发展阶段,形成了助力国际人力资源开发的技术技能人才培养国际合作平台、服务企业“走出去”的应用科研与技术服务国际合作平台、促进民心相通的跨文化交流互鉴国际合作平台。步入新的发展阶段,高质量打造职业教育国际合作平台需要以专业群为基础,整合升级国际化技术技能人才培养合作平台;对接“走出去”企业技术创新需求,提升应用科研与技术服务平台效能;回应国际文化交流和“民心相通”需求,全面优化跨文化交流互鉴国际合作平台功能。