Dynamical analysis and performance evaluation of a biped robot under multi-source random disturbances

During bipedal walking,it is critical to detect and adjust the robot postures by feedback control to maintain its normal state amidst multi-source random disturbances arising from some unavoidable uncertain factors.The radical basis function（RBF）neural network model of a five-link biped robot is established,and two certain disturbances and a randomly uncertain disturbance are then mixed with the optimal torques in the network model to study the performance of the biped robot by several evaluation indices and a specific Poincar′e map.In contrast with the simulations,the response varies as desired under optimal inputting while the output is fluctuating in the situation of disturbance driving.Simulation results from noise inputting also show that the dynamics of the robot is less sensitive to the disturbance of knee joint input of the swing leg than those of the other three joints,the response errors of the biped will be increasing with higher disturbance levels,and especially there are larger output fluctuations in the knee and hip joints of the swing leg.

Blockchain-Empowered Token-Based Access Control System with User Reputation Evaluation

Currently,data security and privacy protection are becoming more and more important.Access control is a method of authorization for users through predefined policies.Token-based access control(TBAC)enhances the manageability of authorization through the token.However,traditional access control policies lack the ability to dynamically adjust based on user access behavior.Incorporating user reputation evaluation into access control can provide valuable feedback to enhance system security and flexibility.As a result,this paper proposes a blockchain-empowered TBAC system and introduces a user reputation evaluation module to provide feedback on access control.The TBAC system divides the access control process into three stages:policy upload,token request,and resource request.The user reputation evaluation module evaluates the user’s token reputation and resource reputation for the token request and resource request stages of the TBAC system.The proposed system is implemented using the Hyperledger Fabric blockchain.The TBAC system is evaluated to prove that it has high processing performance.The user reputation evaluation model is proved to be more conservative and sensitive by comparative study with other methods.In addition,the security analysis shows that the TBAC system has a certain anti-attack ability and can maintain stable operation under the Distributed Denial of Service(DDoS)attack environment.

A Critical Analysis on Evaluating the Students'Feedback of Learning

Teaching involves finding out about students'misunderstandings,intervening to change them and creating a context of learning that encourages students to engage with the subject matter.This theory of making student learning possible is very much concerned with the content of what students have to learn in relation to how it should be taught.Evaluation implies collecting information about our work,interpreting the information and making judgments about which actions we should take to improve practice.Evaluation is an analytical process that is intrinsic to good teaching.

Multi-Source Adaptive Selection and Fusion for Pedestrian Dead Reckoning

Accurate multi-source fusion is based on the reliability, quantity, and fusion mode of the sources. The problem of selecting the optimal set for participating in the fusion process is nondeterministic-polynomial-time-hard and is neither sub-modular nor super-modular. Furthermore, in the case of the Kalman filter(KF) fusion algorithm, accurate statistical characteristics of noise are difficult to obtain, and this leads to an unsatisfactory fusion result. To settle the referred cases, a distributed and adaptive weighted fusion algorithm based on KF has been proposed in this paper. In this method, on the basis of the pseudo prior probability of the estimated state of each source, the reliability of the sources is evaluated and the optimal set is selected on a certain threshold. Experiments were performed on multi-source pedestrian dead reckoning for verifying the proposed algorithm. The results obtained from these experiments indicate that the optimal set can be selected accurately with minimal computation, and the fusion error is reduced by 16.6% as compared to the corresponding value resulting from the algorithm without improvements.The proposed adaptive source reliability and fusion weight evaluation is effective against the varied-noise multi-source fusion system, and the fusion error caused by inaccurate statistical characteristics of the noise is reduced by the adaptive weight evaluation.The proposed algorithm exhibits good robustness, adaptability,and value on applications.

Course Evaluation Based on Deep Learning and SSA Hyperparameters Optimization

Sentiment analysis attracts the attention of Egyptian Decisionmakers in the education sector.It offers a viable method to assess education quality services based on the students’feedback as well as that provides an understanding of their needs.As machine learning techniques offer automated strategies to process big data derived from social media and other digital channels,this research uses a dataset for tweets’sentiments to assess a few machine learning techniques.After dataset preprocessing to remove symbols,necessary stemming and lemmatization is performed for features extraction.This is followed by several machine learning techniques and a proposed Long Short-Term Memory(LSTM)classifier optimized by the Salp Swarm Algorithm(SSA)and measured the corresponding performance.Then,the validity and accuracy of commonly used classifiers,such as Support Vector Machine,Logistic Regression Classifier,and Naive Bayes classifier,were reviewed.Moreover,LSTM based on the SSA classification model was compared with Support Vector Machine(SVM),Logistic Regression(LR),and Naive Bayes(NB).Finally,as LSTM based SSA achieved the highest accuracy,it was applied to predict the sentiments of students’feedback and evaluate their association with the course outcome evaluations for education quality purposes.

The Representation of Soil Moisture−Atmosphere Feedbacks across the Tibetan Plateau in CMIP6

Thermal processes on the Tibetan Plateau(TP)influence atmospheric conditions on regional and global scales.Given this,previous work has shown that soil moisture−driven surface flux variations feed back onto the atmosphere.Whilst soil moisture is a source of atmospheric predictability,no study has evaluated soil moisture−atmosphere coupling on the TP in general circulation models(GCMs).In this study,we use several analysis techniques to assess soil moisture−atmosphere coupling in CMIP6 simulations including:instantaneous coupling indices;analysis of flux and atmospheric behaviour during dry spells;and a quantification of the preference for convection over drier soils.Through these metrics we partition feedbacks into their atmospheric and terrestrial components.Consistent with previous global studies,we conclude substantial inter-model differences in the representation of soil moisture−atmosphere coupling,and that most models underestimate such feedbacks.Focusing on dry spell analysis,most models underestimate increased sensible heat during periods of rainfall deficiency.For example,the model-mean bias in anomalous sensible heat flux is 10 W m−2(≈25%)smaller compared to observations.Deficient dry-spell sensible heat fluxes lead to a weaker atmospheric response.We also find that most GCMs fail to capture the negative feedback between soil moisture and deep convection.The poor simulation of feedbacks in CMIP6 experiments suggests that forecast models also struggle to exploit soil moisture−driven predictability.To improve the representation of land−atmosphere feedbacks requires developments in not only atmospheric modelling,but also surface processes,as we find weak relationships between rainfall biases and coupling indexes.

基于OBE的课程目标达成度评价方法及实践

课程目标达成评价是专业人才培养目标与毕业要求达成的基础。以“工程经济学”课程为例,构建了基于OBE理念的课程目标达成度评价框架。首先,分解毕业要求指标点,确定教学目标、教学内容、考核标准和毕业要求指标点的对应支撑关系。其次,分别采用直接定量评价、间接定量评价和定性评价三种方法对课程目标达成情况进行评价。最后,将评价结果反馈给授课教师,为教学持续改进提供借鉴。结果表明,该评价体系科学合理,可以为课程教学质量的提高和促进毕业要求及培养目标的达成提供理论依据和实践参考。

地学类课程的贯穿式学习与评价——形成性评价在“古生物学”课程中的探索与应用

形成性评价是为正在进行的教学活动提供反馈信息,即贯穿全学习过程的师生互助式双向反馈,相对于总结性评价而言,其目的在于促进学习和指导教学,而非仅获得评价结果。本文以吉林大学本科创新示范课—“古生物学”课程为例,分析形成性评价体系的优势,明确该体系的设计理念及目标,详细介绍以评价内容、评价时机、评价方式和教学反馈为内容的评价体系建设及实施过程,提出了具有地学类课程特色的普适性形成性评价体系模型。

可视化反馈促进自我调节学习的实验研究

建设终身学习型社会要求学习者具备更高的自我调节学习能力。这就要求教育不仅要教会学生知识,更要教会学生学习。然而,当前关于促进基础教育中学习者自我调节学习的研究较少,并且缺乏可以应用于线下课堂的实用工具,导致测评数据无法被深入解读。立足于基础教育课堂教学,设计开发促进学习者自我调节学习的可视化反馈工具,深入分析测评数据,挖掘其中蕴藏的有效信息。该工具提供包括自我认识、学习动机、学习策略三个维度的可视化反馈报告,将知识掌握情况、错题归因、试题定位等信息呈现给学习者,助力其自我调节学习的发生。为检验该工具的效果,在两个班级开展对比实验,对两组学生的自我调节学习能力和学习成绩进行差异性检验。独立样本T检验结果显示,实验班和对照班在自我调节学习能力和学习成绩之间均存在显著性差异,并且均呈现出0.4左右的效应量。证明该工具提供的可视化反馈报告能够有效促进学习者的自我调节学习,同时能够促进学业表现。

面向多目标优化主动运维的电能表远程在线评价系统设计

针对传统电能表远程在线评价系统中存在的系统建造成本耗费较大、评价结构过于繁琐等问题,从评价系统的构建和评价算法两方面开展研究。在评价系统的设计方面,加入反馈,使得该评价系统不仅可以整合各项评价指标进行规律化的采集,还可以对评价指标进行实时反馈,提高了评价的效率和评价信息传输的效率。在评价算法方面,基于解耦评价算法对电能表的运行状态和采集数据进行评价,对数据信息进行归纳处理,并根据大量的数据样本进行条理化分析评价。通过仿真实验可知,硬件反馈线路的数据传输效率高达98.6%,具有很好的传输能力。

公共健康视角下的恢复性环境实证研究综述

社会飞速发展,人类对健康生活的追求给予了环境新的价值和意义,环境的健康效益被深度挖掘。本文利用Citespace可视化分析工具,基于Web of Science数据库的346篇文献和中国知网166篇文献,从环境和需求两个角度梳理恢复性环境基础理论及概念,分类总结研究内容和研究热点,比较分析实证研究中的材料呈现、被试筛选及实验方法。最后,针对研究内容和实验流程进行深入探讨,挖掘恢复性环境研究的新内容,思考有关样本量有效性、前期施压程度及首位效应等实验中的关键问题,以期为后续恢复性环境的实证研究提供有力参考,作为循证设计与实践结合的科学指导。

专业虚拟社区创意感知价值的影响因素——基于反馈类型的调节作用

专业虚拟社区已经成为个人以及企业进行知识共享的重要平台,社区需要辅助贡献者了解用户对创意价值评价的影响因素,但至今相关研究缺乏。因此,针对“哪些因素会影响用户对创意感知价值的评判”,基于信息采纳模型,将在线反馈行为区分为知识性反馈行为与社会性反馈行为两类反馈行为后共同纳入模型之中,并引入文本逻辑作为新的结构变量,爬取思否问答社区的用户数据,通过Tobit回归模型进行实证分析。结果显示,两类在线反馈行为、文本逻辑和贡献者社区参与均对创意感知价值有显著正向影响。其中,社会性反馈的影响程度高于知识性反馈,但知识性反馈的调节作用更稳定;清晰且有条理的文本逻辑显著正向影响创意感知价值;同一问题下的创意贡献数对创意贡献感知价值持续呈现显著负向影响,“粉丝”数和内容丰富度对创意感知价值具有显著正向影响。据此,为社区管理者在提供参考创意回答框架、可选择的创意贡献者身份特征展示以及引导营造评论区知识交流氛围等方面提出建议。

智能技术赋能虚拟科学探究学习过程评价与适应性反馈研究

虚拟科学探究学习过程的自动评价和适应性反馈是提升探究学习效果的重要支撑手段。研究首先从虚拟科学探究学习过程要素表征和分析模型、自动分析与实时评价、适应性反馈三个方面概述了智能技术在虚拟科学探究学习中的应用现状,总结了当前技术应用面临的深层次探究要素表征难、不确定探究过程刻画难、适应性反馈生成难等现实挑战。其次,在此基础上,提出了基于活动流的底层计算模型构建、复杂探究过程动态监测与自动评价、可解释性归因的自适应反馈内容生成三个关键技术。再次,基于关键技术设计了虚拟科学实验自主探究学习平台的技术架构。最后,研究总结了当前研究的创新之处并提出未来建议,为智能技术赋能虚拟科学探究学习领域开展更深入的技术探索提供有益参考。

西安市鄠邑区红色文化资源融入高校思政课教学现状研究——以西安某高校为例

为了解西安市鄠邑区红色文化资源融入高校思政课教学的现状,随机抽取西安某高校46名思政课教师进行问卷调查。结果显示,绝大部分教师对红色文化的内涵有一定了解,但一半的教师对此了解的还不充分。思政课教师主要通过实践活动和理论学习的途径了解鄠邑区红色文化资源。认为鄠邑区红色文化资源内涵丰富,应该继承和发扬的教师占97.83%。大部分思政课教师能将鄠邑区红色文化资源融入到思政课教学中,且教学效果良好,但其教学组织与设计维度的均值尚未达到理想水平。基于此,西安市高校思政教师应不断创新教学方法,灵活运用鄠邑区红色文化资源,丰富网络教学形式,有效利用鄠邑区红色文化资源,通过思政教育实现高校立德树人的目标。

形成性评估视角下的英语写作自动评阅反馈投入研究

写作自动评阅越来越受到教师和研究者的关注,但目前对自动评阅的学习者反馈投入的研究却非常欠缺。本研究从形成性评估理论出发,从修改行为、认知策略和情感反应三个角度,分析了两位大学生使用iWrite英语写作教学与评阅系统时的写作反馈和刺激回忆数据。研究发现,iWrite能够给出分数、语言和结构等多方面的反馈。学生依据反馈对作文相关部分进行了修改和替换,并在此过程中自行纠正了自己发现的问题;在修改过程中,学生运用了各类认知和元认知策略;学生认可反馈的及时性和准确性。从形成性评估角度看,在反馈投入中,学生对学习目标和成功标准的理解发挥重大作用,反馈的准确度和精细度等因素影响学生的情感投入。

上级发展性反馈对新员工绩效的影响:内部人身份感知和核心自我评价的作用机制

基于资源保存理论,本研究探讨了上级发展性反馈对新员工任务绩效和工作主动性的影响机制以及核心自我评价的调节作用。通过对3个时间点领导者—员工配对数据进行分析,结果表明:上级发展性反馈能够促进新员工的内部人身份感知,进一步提高任务绩效和工作主动性;核心自我评价在上述路径中起调节作用,对于高核心自我评价的新员工而言,上级发展性反馈通过内部人身份感知对任务绩效和工作主动性产生的积极影响更强。

现状-背景-评估-建议-反馈沟通模式在急性心肌梗死合并呼吸衰竭患者中的应用效果

目的 探讨现状-背景-评估-建议-反馈沟通模式在急性心肌梗死合并呼吸衰竭患者中的应用效果。方法 选取2020年1月—2022年3月南京市雨花医院收治的急性心肌梗死合并呼吸衰竭患者60例为研究对象,按照随机数字表法分为常规干预组和反馈沟通组,每组30例。常规干预组采用常规护理,反馈沟通组在常规干预组的基础上采用现状-背景-评估-建议-反馈沟通模式进行干预,两组均干预至患者出院。比较两组恢复情况、生命体征、护理人员对患者病情、护理质量和护理满意度。结果 反馈沟通组重症监护室干预时间、机械通气时间、住院时间均短于常规干预组,差异有统计学意义(P<0.05)。干预后,两组心率、呼吸频率均慢于干预前,氧分压高于干预前,二氧化碳分压低于干预前,且反馈沟通组心率、呼吸频率均慢于常规干预组,氧分压高于常规干预组,二氧化碳分压低于常规干预组,差异有统计学意义(P<0.05)。干预后,两组诊断和病情、护理重点、存在风险、治疗、异常化验结果评分均高于干预前,且反馈沟通组上述各评分均高于常规干预组,差异有统计学意义(P<0.05)。反馈沟通组不良事件发生率、护患纠纷发生率、护理差错率均低于常规干预组,差异有统计学意义(P<0.05)。两组护理投诉率比较,差异无统计学意义(P>0.05)。反馈沟通组满足需求的及时性、护患沟通、健康教育、业务水平、服务态度评分均高于常规干预组,差异有统计学意义(P<0.05)。结论 采用现状-背景-评估-建议-反馈沟通模式对急性心肌梗死合并呼吸衰竭患者进行干预,可有效稳定患者生命体征,提高护理人员对患者病情掌握程度,有助于患者康复,提高护理质量和护理满意度,具有较好的干预效果。

全过程、多元化的应用型本科院校课程考核改革研究与实践

课程考核是高校课程教学的重要环节,高校课程考核结果不仅可以评价学生学习成效,也可以检验教师的课程教学成效。以应用型本科人才培养为背景,通过课程评价组成、学习评价维度等方面进行探索与实践,形成全过程、多元化课程考核模式。通过科学合理的课程评价结果,不仅对学生阶段性学习进行评价,真实反映学生学习状况,通过对课程的考核结果进行分析,还可以及时发现教学过程中存在的问题。通过课程考核结果的诊断与反馈、引导与激励,促进学生的发展、指导教师改善后续教学,有效提升教学质量和学生学习成效,发挥考核评价以评促学、以评促教的作用。