自我评价在大学英语教学中的应用研究

小组合作学习有利于增强学生的学习兴趣和责任感,是一种有效的课堂教学组织形式。该研究探索小组合作学习中五要素之一的“小组自我学习评价和反馈”在大学英语教学中所起到的作用。实验采用了调查问卷、水平测试等试验工具进行调查和收集数据。结果表明小组合作学习有助于增强学生在语言学习中的元认知意识;其中小组自我评价在增强学生的元认知意识和促进小组活动方面起导航作用;能促使学生更多地参与学习过程,从而提高学生学习语言的效果。     

高校图书馆工作人员素质提高策略初探

现代社会的发展，给高校图书馆带来巨大的压力与挑战。图书馆员的整体素质如何，将关系到图书馆事业的未来发展。高校图书馆应从提高其馆员整体学历水平、建立和完善岗位培训制度、使全体馆员牢固树立终身学习的观念三方面来提高工作人员的整体素质，推动图书事业的持续发展。     

关于双语课程《数字电子技术》学习状况的调查与思考

以问卷调查的形式,通过描述性统计和定量分析,对我校双语课程《数字电子技术》学习状况进行了调查和分析,在此基础上,对双语教学提出了一些建议.     

研究性学习中的心理学思想探析

研究性学习视学生为一个完整的人，注重学生个性的培养，重视学生潜能的开发以及整体素质的协调，这其中渗透着激发学生学习动机、使学生达到自我实现等心理学思想．该文对研究性学习的几个特点的心理学思想进行了分析。     

大学生学习焦虑心理研究

通过分析大学生学习焦虑心理的成因以及学习焦虑控制问题。论述了大学生学习焦虑心理与学习成绩的关系。     

Using genetically-defined rodent strains for the identification of hippocampal traits relevant for two-way avoidance behavior: a non-invasive approach

Summary Genetically-defined rodent strains permit the identification of hippocampal traits which are of functional relevance for the performance of two-way avoidance behavior. This is exemplified here by analyzing the relationship between infrapyramidal mossy fibers (a tiny projection terminating upon the basal dendrites of hippocampal pyramidal neurons) and two-way avoidance learning in about 800 animals. The necessary steps include 1) identification of structural traits sensitive to selective breeding for extremes in two-way avoidance, 2) testing the robustness of the associations found by studying individual and genetical correlations between hippocampal traits and behavior, 3) establishing causal relationships by Mendelian crossing of strains with extreme structural traits and studying the behavioral consequences of such structural randomization, 4) confirming causal relationships by manipulating the structural variable in inbred (isogenic) strains, thereby eliminating the possibility of genetic linkage, and 5) ruling out the possibility of spurious associations by studying the correlations between the hippocampal trait and other behaviors known to depend on hippocampal functioning.In comparison with the classical lesion approach for identifying relationships between brain and behavior, the present procedure appears to be superior in two aspects: it is non-invasive, and it focuses automatically on those brain traits which are used by natural selection to shape behaviorally-defined animal populations, i.e., it reveals the natural regulators of behavior.     

Learning attention-based strategies to cooperate for multi-agent path finding

Multi-agent path finding (MAPF) is a challenging multi-agent systems problem where all agents are required to effectively reach their goals concurrently with not colliding with each other and avoiding obstacles. In MAPF, it is a challenge to effectively express the observation of agents, utilize historical information, and effectively communicate with neighbor agents. To tackle these issues, in this work, we proposed a well-designed model that utilizes the local states of nearby agents and outputs an optimal action for each agent to execute. We build the local observation encoder by using residual attention CNN to extract local observations and use the Transformer architecture to build an interaction layer to combine local observations of agents. With the purpose of overcoming the deficiency of success rate, we also designed a new evaluation index, namely extra time rate (ETR). The experimental results show that our model is superior to most previous models in terms of success rate and ETR. In addition, we also completed the ablation study on the model, and the effectiveness of each component of the model was proved.     

Earthshaker: A mobile rescue robot for emergencies and disasters through teleoperation and autonomous navigation

To deal with emergencies and disasters without rescue workers being exposed to dangerous environments, this paper presents a mobile rescue robot, Earthshaker. As a combination of a tracked chassis and a six-degree-of-freedom robotic arm, as well as miscellaneous sensors and controllers, Earthshaker is capable of traversing diverse terrains and fulfilling dexterous manipulation. Specifically, Earthshaker has a unique swing arm—dozer blade structure that can help clear up cumbersome obstacles and stabilize the robot on stairs, a multimodal teleoperation system that can adapt to different transmission conditions, a depth camera-aided robotic arm and gripper that can realize semiautonomous manipulation and a LiDAR aided base that can achieve autonomous navigation in unknown areas. It was these special systems that supported Earthshaker to win the first Advanced Technology & Engineering Challenge (A-TEC) championships, standing out of 40 robots from the world and showing the efficacy of system integration and the advanced control philosophy behind it.     

基于强化学习的电磁悬浮型磁浮列车悬浮控制

为了保证磁浮列车的安全、可靠运行,研究了悬浮系统在参数摄动条件下的悬浮控制问题。首先,对电磁悬浮（EMS）型磁浮列车的基本悬浮单元建模,给出了电流控制模型;然后,建立了悬浮系统的强化学习环境以及软演员-评论家（SAC）智能体,并设计了加速训练的奖励函数与“吸死”处理方案;最后,提出了基于强化学习的悬浮控制方法。与传统比例-积分-微分（PID）控制方法的对比结果表明,本方法具有更快的动态响应,在损失50%线圈匝数或磁极面积变化时具有更好的跟踪精度。     

基于深度学习的红外与可见光图像融合综述

图像融合技术是指从不同的源图像中提取并融合互补的信息,生成一幅信息量更丰富、对后续高级视觉任务提供足够支持的图像.红外与可见光图像融合（Infrared and Visible Image Fusion,IVIF）是图像融合领域的一个重要分支.近年来,深度学习技术在视觉计算领域表现出了良好的性能,尤其是基于自编码器、卷积神经网络、生成对抗网络等几种基于深度学习的IVIF技术得到了蓬勃发展.为此,对基于深度学习的IVIF算法的方法、数据集和评估指标等进行了总结和阐述;通过大量的实验,进行定性和定量的结果分析,对比了各类基于深度学习IVIF算法的性能;最后,讨论了该领域未来发展的一些前景和研究方向.