Panic and panacea: brain drain and science and technology human capital policy   总被引：2，自引：0，他引：2  

Sally Davenport 《Research Policy》2004,33(4):617-630

Brain drain, the diffusion of skilled human capital, particularly scientific and technical human capital (STHC), from home to host country, is of concern to many nations. Traditional brain drain ‘control’ policies target the human capital embodied in a skilled individual. Based on a case study of brain drain panic in New Zealand in 2000, this paper explores new ‘stimulation’ brain drain policy approaches, including building research excellence and exploiting the diaspora, that take into account the situated and networked nature of STHC. Diaspora policies imply a reframing of ‘national’ STHC no longer circumscribed by geographic boundaries but by national affiliation.  相似文献   

e-Learning情感计算模型设计研究     

贺斌 《远程教育杂志》2011,29(4):103-110

目前,绝大多数e-Learning系统是在传统智力观指导下设计开发而成,严重忽视了学习者情感智能的发展。脑科学研究表明,情感在注意、记忆、推理和决策等方面发挥重要作用。以情感计算学说、人工心理学、脑科学等相关理论为指导,赋予计算机和网络以情感智能,以设计出支持适应性学习的e-Learning情感计算模型。  相似文献   

运动与脑健康     

刘瑾彦  王勇 《哈尔滨体育学院学报》2008,26(4):4-6

有关人类脑健康的问题已经引起全世界的广泛关注。动物研究表明,运动可以促进动物脑的神经再生,改善人脑的认知功能,提高学习和记忆能力;并进而介导脑老化的进程,防止脑衰老;此外,运动还可促进帕金森病的行为恢复。以上这些相关研究主要以成年动物或人为研究对象,但生命早期的运动对于脑发育乃至以后整个生命过程中脑健康的影响问题是值得我们去思考和探讨的。  相似文献   

河南省社科优秀成果奖学术影响力研究     

周春雷  曹玲静 《中国科技期刊研究》2017,28(8):748-756

【目的】 研究河南省社会科学优秀成果奖的学术影响力,分析社会科学领域发展现状,提出改进建议。【方法】 以2002—2014年河南省社会科学优秀成果奖的获奖信息为研究对象,从获奖作品、作者、机构、施引者、施引期刊以及学科分布等角度深入分析,并与教育部高校社科优秀成果奖进行对比研究,力图全方位、多层次探讨获奖成果的学术影响力。【结果】 河南省社会科学优秀成果奖获奖成果的整体学术影响力较小,在国内具有优势的研究领域为文学、历史学,但是某些学科存在较为明显的学术领军人才流失现象。【结论】 建议进一步完善河南省社会科学优秀成果奖的评价体系,加强对各领域优秀学术人才的识别与管理,促进哲学社会科学的繁荣发展。  相似文献   

俄罗斯科学人才流失及其警示意义     

宋兆杰  王续琨 《科学学与科学技术管理》2006,27(6):154-158

苏联解体后,俄罗斯科学人才出现严重的流失现象,受到国际上的广泛关注.科学人才流失的原因,主要是劳动报酬过低,生活不稳定.科学人才流失造成严重的经济损失,导致科学家队伍年龄老化,技术创新能力大为降低.最近十年来,俄罗斯通过制定相关法律和法规,实行资助金制度等措施,使科学人才流失问题得到初步缓解.俄罗斯科学人才流失现象的出现,对中国具有重要的警示意义.  相似文献   

The many faces of mobility: Using bibliometric data to measure the movement of scientists     

Nicolás Robinson-Garcia  Cassidy R. Sugimoto  Dakota Murray  Alfredo Yegros-Yegros  Vincent Larivière  Rodrigo Costas 《Journal of Informetrics》2019,13(1):50-63

This paper presents a methodological framework for developing scientific mobility indicators based on bibliometric data. We identify nearly 16 million individual authors from publications covered in the Web of Science for the 2008–2015 period. Based on the information provided across individuals’ publication records, we propose a general classification for analyzing scientific mobility using institutional affiliation changes. We distinguish between migrants--authors who have ruptures with their country of origin--and travelers--authors who gain additional affiliations while maintaining affiliation with their country of origin. We find that 3.7% of researchers who have published at least one paper over the period are mobile. Travelers represent 72.7% of all mobile scholars, but migrants have higher scientific impact. We apply this classification at the country level, expanding the classification to incorporate the directionality of scientists’ mobility (i.e., incoming and outgoing). We provide a brief analysis to highlight the utility of the proposed taxonomy to study scholarly mobility and discuss the implications for science policy.  相似文献   

中小民营通信企业人才流失问题分析与对策     

陶蓉 《贺州学院学报》2011,27(4):131-133,144

首先对中小民营通信企业的行业人才结构、流动性进行了调查与分析,并着重分析了我国中小民营通信企业的人才流失问题;根据目前多数企业员工绩效管理的现状,对绩效管理存在问题的原因进行剖析,提出从企业角度出发应对人才流失需要作出的改进措施与绩效评价制度的建立。  相似文献   

TCACNet: Temporal and channel attention convolutional network for motor imagery classification of EEG-based BCI     

《Information processing & management》2022,59(5):103001

Brain–computer interface (BCI) is a promising intelligent healthcare technology to improve human living quality across the lifespan, which enables assistance of movement and communication, rehabilitation of exercise and nerves, monitoring sleep quality, fatigue and emotion. Most BCI systems are based on motor imagery electroencephalogram (MI-EEG) due to its advantages of sensory organs affection, operation at free will and etc. However, MI-EEG classification, a core problem in BCI systems, suffers from two critical challenges: the EEG signal’s temporal non-stationarity and the nonuniform information distribution over different electrode channels. To address these two challenges, this paper proposes TCACNet, a temporal and channel attention convolutional network for MI-EEG classification. TCACNet leverages a novel attention mechanism module and a well-designed network architecture to process the EEG signals. The former enables the TCACNet to pay more attention to signals of task-related time slices and electrode channels, supporting the latter to make accurate classification decisions. We compare the proposed TCACNet with other state-of-the-art deep learning baselines on two open source EEG datasets. Experimental results show that TCACNet achieves 11.4% and 7.9% classification accuracy improvement on two datasets respectively. Additionally, TCACNet achieves the same accuracy as other baselines with about 50% less training data. In terms of classification accuracy and data efficiency, the superiority of the TCACNet over advanced baselines demonstrates its practical value for BCI systems.  相似文献   

Dementia analysis from functional connectivity network with graph neural networks     

《Information processing & management》2022,59(3):102901

Deep learning methods have been widely applied for disease diagnosis on resting-state fMRI (rs-fMRI) data, but they are incapable of investigating global relationships between different brain regions as well as ignoring the interpretability. To address these issues, this paper presents a new graph neural network framework for brain disease diagnosis via jointly learning global relationships and selecting the most discriminative brain regions. Specifically, we first design a self-attention structure learning to capture the global interactions between brain regions for achieving diagnosis effectiveness, and theoretically integrate a feature selection method to reduce the noise influence as well as achieve interpretability. Experiment results on three neurological diseases datasets show the effectiveness of our method, compared to the comparison methods, in terms of diagnostic performance and interpretability.  相似文献   

学习技术典型案例：从社交机器人到大脑刺激——学习科学国际大会“学习技术”专题综述     

徐光涛  张怀浩  任友群 《现代远程教育研究》2014,(3):45-51,65

学习科学作为一门研究教与学的新兴交叉学科,自诞生以来就将学习技术设计作为重要的研究方向。在最近召开的学习科学国际大会“学习技术”专题研讨中,来自不同国家和地区的研究人员重点对四个方面进行了交流研讨。在社会性学习技术方面,社交机器人RUBI通过与儿童的对话和交互能有效提高儿童语言学习的效率;社会性教学代理（TA）能帮助学生学习并提高他们的学习和推理能力。在学习设计与分析技术方面,学习设计平台PPC能使教师在大数据的支撑下选择或设计学生学习方案;教育数据仓库DataShop能为研究者们提供研究学生学习行为、预测学生学习绩效、验证学习理论的环境。在认知描绘技术方面,CogSketch采用独特的方式促进学生对于描绘的认知理解,有利于学生高阶推理能力与空间能力的培养。在大脑刺激技术方面,经颅电刺激技术（tES）能够对大脑的功能进行塑造,有助于提高学生的数学学习效果。这些研究表明,学习技术正在从理论走向实践,逐渐影响教育决策和教育实践;学习技术的健康发展需要教育学、计算机科学、认知科学、脑科学等各领域研究者的紧密合作,只有这样才能使学习科学研究走向真实的学习境脉,促进学习者的深度学习。  相似文献