Scalable and Transferable Reinforcement Learning for Multi-Agent Mixed Cooperative–Competitive Environments Based on Hierarchical Graph Attention

Most previous studies on multi-agent systems aim to coordinate agents to achieve a common goal, but the lack of scalability and transferability prevents them from being applied to large-scale multi-agent tasks. To deal with these limitations, we propose a deep reinforcement learning (DRL) based multi-agent coordination control method for mixed cooperative–competitive environments. To improve scalability and transferability when applying in large-scale multi-agent systems, we construct inter-agent communication and use hierarchical graph attention networks (HGAT) to process the local observations of agents and received messages from neighbors. We also adopt the gated recurrent units (GRU) to address the partial observability issue by recording historical information. The simulation results based on a cooperative task and a competitive task not only show the superiority of our method, but also indicate the scalability and transferability of our method in various scale tasks.     

煤液化残渣与褐煤混煤燃烧特性的实验研究

煤液化是提高煤炭资源利用率、减轻燃煤污染的有效途径[1].在煤液化工艺过程中,煤液化残渣的主体是由液化原料煤中未转化的煤有机体、无机矿物质以及外加的液化催化剂组成,是一种高炭、高灰和高硫的物质,在某些工艺中会占到液化原煤总量的30%左右,如此多的残渣量对液化过程的热效率和经济性所产生的影响是不可低估的[2].     

促进深度学习的专题复习教学*——以“碳及其化合物”为例

以促进学生的深度学习为基本理念,围绕“自然界的碳循环”“大气中CO₂的控减排”“化学家们合成的新型碳家族成员”等板块,设计并成功实施系列情境问题,在系列问题的讨论、分析、解决过程中帮助学生建构“碳”家族成员间相互转化的知识网络,提高学生思维的纵深度,引发高阶思维,促进深度学习。     

“学习提示”模块在新版有机化学教材中的应用

一本好的有机化学教材应该对教师教学和学生自学有很好的辅助和引导作用。如何使学生从浩如烟海的内容中发现重点、难点,并做到有效掌握和融会贯通,是教材编写者需要重点考虑的问题。笔者将教学经验融入到有机化学教材改革实践,在新版教材中加入了原创的“学习提示”板块,用简练、更通俗易懂的语言对一些重要、较难理解的或是容易混淆的知识点进行辅助讲解、串联总结。笔者认为该板块可以起到协助学生理解知识难点、厘清易混淆概念、将关联知识融会贯通以及激发学生科研兴趣等作用。本文用一些实例对这一模块的编写构思、内容和预期成效进行了介绍。     

线上线下混合式教学结合翻转课堂进行酯缩合反应的教学*

采用“线上线下混合式教学”开展有机化学课程的教学工作,利用APP “学习通”将在线课堂教学和线下讲授相结合。以知识点酯缩合反应的教学为例,介绍了该教学模式的实施过程和教学效果。实践结果表明,线上线下混合式教学与翻转课堂相结合的教学模式,能够拓展化学专业学生知识面,提升学生的自主学习能力和学习的积极性,取得了较好的教学效果。     

An ensemble learning framework for potential miRNA-disease association prediction with positive-unlabeled data

To explore the pathogenic mechanisms of MicroRNA (miRNA) on diverse diseases, many researchers have concentrated on discovering the potential associations between miRNA and disease using machine learning methods. However, the prediction accuracy of supervised machine learning methods is limited by lacking of experimentally-validated uncorrelated miRNA-disease pairs. Without these negative samples, training a highly accurate model is much more difficult. Different from traditional miRNA-disease prediction models using randomly selected unknown samples as negative training samples, we propose an ensemble learning framework to solve this positive-unlabeled (PU) learning problem. The framework incorporates two steps, i.e., a novel semi-supervised Kmeans (SS-Kmeans) to extract reliable negative samples from unknown miRNA-disease pairs and subagging method to generate diverse training sample sets to make full use of those reliable negative samples for ensemble learning. Combined with effective random vector functional link (RVFL) network as prediction model, the proposed framework showed superior prediction accuracy comparing with other popular approaches. A case study on lung and gastric neoplasms further confirms the framework’s efficacy at identifying miRNA disease associations.     

高师生实验探究洋葱中挥发性化学物质

利用实验室一般仪器,对洋葱中挥发性化学成分如含硫化合物、醇类、醛类、羧酸进行了定性检测,旨在让高师生体验探究学习,掌握探究方法,发展实验设计和探究能力。     

指向学习体验的物质及其转化复习的教学实践——以“钠及其化合物”为例

学习体验是学生对学习内容、过程、方法、意义的自我感受和评估,对学生学习志趣、学习素养提升有积极正向作用。以钠及其化合物复习为例,将各类别物质间转化关系的构建、物质制备原理和条件的优选作为学习体验对象,依据学习体验的内在发展历程设计学习任务及活动,引导学生亲身体验学科认知方式、学科思维方法、学科应用价值,评价体验结果,激励学生自主学习发生。     

基于社会性科学议题的化学微项目学习*——以“物质的性质与转化”单元复习为例

在化学课堂中开展社会性科学议题的教学能够发展学生的社会参与意识,结合微项目学习模式,从课堂教学角度出发,构建选定项目、启动项目、实施项目、交流成果等4个教学流程,确立实施教学的核心问题,设计了鲁科版高一化学“物质的性质与转化单元复习课”,进行了基于社会性科学议题的化学微项目学习的实践教学,结合实践的基本情况提出4点反思。