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关键词： Computer science   Machine learning   Society  

摘要： In a test of the chatbot's language abilities it fails at certain languages.

关键词： 人机交互试题   科学教育测评   教育评价改革  

摘要： 面对科学教育测评中素养和能力考查的新要求,传统测评工具的短板愈发明显。在信息技术快速发展的背景下,人机交互试题在科学教育测评领域得到广泛应用,成为备受关注的新型测评工具。相较于传统的书面试题,人机交互试题已具有明显的动态特征,表现在设问呈现方式、试题模拟环境和考查过程三个方面。将人机交互试题引入我国科学教育大型考试,不仅能够弥补传统测评工具的短板,更有助于科学教育智慧考试的开发与应用。

关键词： 立德树人   五育并举   计算机学科   研究生教育   思政教育  

摘要： 新时代背景下,五育并举理念在推进教育现代化、建设教育强国中起到愈发重要的作用。研究生作为高层次储备人才,是国家发展、民族复兴的重要力量和基础保障,思政教育作为其培养中的重要一环,更应坚持以立德树人为根本任务,落实五育并举,促进人才全面发展。为此,构建五育并举视域下思政教育新模式,使五育并举赋能高校研究生思想政治教育模式创新,对焕发时代意义具有尤为重要的意义。天津大学智能与计算学部在工作中坚持强化思想引领,问需学生诉求,把握学科特色,通过分析学生在成长成才中遇到的问题,以学生喜闻乐见的活动形式,拓宽思政教育与智育、体育、美育、劳育间的协同方式,探索符合计算机学科研究生思政教育的新路径,使思政教育更具针对性。

关键词： 交互式健康教育   运动疗法   腹膜透析   运动功能   自护能力  

摘要： 目的探讨交互式健康教育联合运动疗法对腹膜透析患者运动功能、自护能力及生活质量的影响。方法选取2021年6月至2023年6月潜江市中心医院收治的80例行腹膜透析治疗患者为研究对象,采用随机数表法将其分为对照组、观察组,每组各40例,对照组予以常规护理措施,观察组在上述护理基础上加用交互式健康教育联合运动疗法,比较两组患者干预前后运动功能[6 min步行试验(6MWT)、10次坐立秒数]、自护能力(ESCA)及生活质量(SF-36)评分。结果干预后,两组患者6MWT、10次坐立秒数显著改善,且观察组6MWT高于对照组,10次坐立秒数少于对照组,差异有统计学意义(P<0.05)。干预前后,两组ESCA、SF-36量表各维度评分及总分评分显著改善,且观察组上述指标高于对照组,差异有统计学意义(P<0.05)。结论交互式健康教育联合运动疗法有助于改善腹膜透析患者运动功能,提高自我护理能力,提升生活质量,值得临床推广应用。

关键词： OBE教育理念   计算机学科   高质量发展  

摘要： 学习贯彻党的二十大精神,探究科学推进计算机学科建设的多元路径,是实现计算机学科高质量发展的题中之义。基于OBE教育理念与高校学科建设创新发展的内在契合性,文章简要分析了推动计算机学科高质量发展过程中,在人才培养、创新能力培育、学科融合、学科评估等方面所面临的核心问题;并以北京科技大学计算机与通信工程学院为例,在系统观念和成果导向的基本原则下,探讨了学科建设价值培育、赋能国家发展总体需求、建构多元人才培育体系和打造科学贯通教学体系等路径的优化作用,为新时代如何有力推动计算机学科高质量发展从政策话语走向生动实践提供参考。

关键词： 课程思政   医学专业   大学计算机   新医科   信息社会  

摘要： 开展医学类专业大学计算机课程思政建设是对“信息社会+‘新医科’”背景下医学复合型人才培养作出的积极回应。本研究提出了医学专业大学计算机课程思政的价值体现和建设原则,并基于此阐述开展该课程思政建设实施策略,即多层面挖掘思政元素、多渠道选取思政材料、多方式融入教学环节、多措施提升教师素养以及多维度开展思政评价等,以期为医学院校信息技术课程思政建设提供借鉴与参考。

关键词： 新工科   区块链   计算机类课程   教学质量   评价机制  

摘要： 为解决当前计算机类课程教学质量评价中存在的信息不透明、评价指标单一、结果认可度低等问题。面向新工科教育教学改革需求,分析了区块链与计算机类课程教学质量评价的耦合关系,发现区块链能有效解决课程教学质量评价的痛点和难点,并基于此设计了一种采用区块链技术的计算机类课程教学质量评价机制,最后结合具体教学实验介绍了该机制的设计流程和有效性验证方法。

关键词： Aircraft final assembly   Worker allocation   Human-machine collaboration   Production optimization  

摘要： The allocation of workers with diverse skills on assembly stations significantly impacts the efficiency of aircraft final assembly (AFA). In a real-life assembly process, workers are allowed to move between multiple stations, to avoid bottleneck stations. The arrangement of worker movement is a practical issue coupled with the worker allocation, but is still ignored. This study formulates a dynamic worker allocation problem in AFA (dWA-AFA), focusing on the multiobjective joint optimization of worker allocation and worker movement. The trade-off between objectives depends on the human preferences that reflect assembly requirements. To address this, a human - machine collaborative optimization method (HMC-O) is proposed, involving a bidirectional collaboration: machine-to-human preference adaption, and human-to-machine experience support. Specifically, we design an adaptive dominance operator for integrating human preferences, and combine it with a two-stage nondominated sorting approach to generate initial optimization solutions. An experience-driven neighborhood search is further developed, which uses human experience to improve solutions. The proposed method is evaluated through two scales of real cases. It is observed the dWA-AFA significantly reduces the takt time, particularly by 14.21 % and 9.25 % in two worker-shortage scenarios respectively. Meanwhile, the HMC-O is competitive in terms of convergence and preference satisfaction for solving dWA-AFA.

关键词： Probability density function  

摘要： Accurate remaining useful life (RUL) prediction plays a vital role in increasing the system operation safety and reducing maintenance costs. In industrial applications, there is usually a large amount of multi-sensor data generated. Therefore, how to construct an appropriate health index (HI) based on multi-sensor signals is very important for the RUL prediction. However, existing works treat sensor selection, HI construction, and degradation modeling independently as unrelated parts, which may result in the combination of sensors selected not constituting an optimal HI or the constructed HI not matching the degradation model. In addition, most existing works treat prior units as a whole to obtain a unique set of sensor combinations and fusion coefficients, which cannot reflect unit-to-unit heterogeneity, thus affecting the accuracy of RUL prediction. Therefore, a novel interactive feedback framework is established to construct HI, where the sensor selection, fusion coefficient calculation, and nonlinear Wiener process degradation modeling are incorporated into the feedback. Furthermore, an adaptive weight selection method based on particle swarm optimization and leave-one-out cross-validation (PSO-LV) is proposed to adjust the fusion coefficients in real-time. Then, the RUL is estimated by updating model parameters online, detecting degradation trends, and deriving the probability density function (PDF) of the RUL. Finally, two examples of engine datasets are provided to verify the effectiveness of the proposed method. © 2024 Elsevier Ltd