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流畅状态的认知干预:目标设置

上海体育学院学报 2013年第2期37卷 72-80页

作者：刘微娜季浏 Watson Ⅱ jack c 华东师范大学体育与健康学院上海200241 西弗吉尼亚大学体育学院美国西弗吉尼亚州摩根城26505

目标设置与流畅状态间存在着中高级的相关,这一关系很可能是源于目标设置的认知功能。以2min5点定点投篮(篮球)作为活动任务,以修订的中文版《特质流畅量表-2》和《状态流畅量表-2》作为测量工具,采用2(目标定向:自我/任务定向)×3(目... 详细信息

目标设置与流畅状态间存在着中高级的相关,这一关系很可能是源于目标设置的认知功能。以2min5点定点投篮(篮球)作为活动任务,以修订的中文版《特质流畅量表-2》和《状态流畅量表-2》作为测量工具,采用2(目标定向:自我/任务定向)×3(目标难度:容易/中等难度/困难)实验设计,探讨目标定向和目标难度对流畅状态的影响。结果表明:实验后,实验组受试的特质流畅和状态流畅得分显著高于控制组受试:目标定向对特质流畅和状态流畅的主效应均达显著水平,任务定向高于自我定向;目标难度对特质流畅和状态流畅的主效应均达到显著水平,中等难度目标优于容易目标和困难目标。

关键词：目标定向目标难度特质流畅状态流畅篮球

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剑桥国际英语教程第3版教师资源手册 3

Energy-efficient virtual sensor-based deep reinforcement learning control of indoor cO_(2)in a kindergarten

2007年

作者： jack c. richards等

本书包含课堂常用的听力、语法、词汇和口语活动，可以作为教师用书的补充。

ISBN: (数字)9787560063492

ISBN: (纸本)7560063497

本书包含课堂常用的听力、语法、词汇和口语活动，可以作为教师用书的补充。

关键词：英语

来源：畅想之星图书光盘版（南... 评论

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Frontiers of Architectural research 2023年第2期12卷 394-409页

作者： Patrick Nzivugira Duhirwe jack Ngarambe Geun Young Yun Department of Architectural Engineering Kyung Hee UniversityYongin 17104Republic of Korea

High concentrations of indoor cO_(2)pose severe health risks to building ***,mechanical equipment is used to provide sufficient ventilation as a remedy to high indoor cO_(2)***,such equipment consumes large amounts of energy,substantially increasing building energy *** the end,the issue becomes an optimization problem that revolves around maintaining cO_(2)levels below a certain threshold while utilizing the minimum amount of energy *** that end,we propose an intelligent approach that consists of a supervised learning-based virtual sensor that interacts with a deep reinforcement learning(DrL)-based control to efficiently control indoor cO_(2)while utilizing the minimum amount of energy *** data used to train and test the DrL agent is based on a 3-month field experiment conducted at a kindergarten equipped with a heat recovery *** results show that,unlike the manual control initially employed at the kindergarten,the DrL agent could always maintain the cO_(2)concentrations below sufficient ***,a 58%reduction in the energy consumption of the ventilator under the DrL control compared to the manual control was *** demonstrated approach illustrates the potential leveraging of Internet of Things and machine learning algorithms to create comfortable and healthy indoor environments with minimal energy requirements.

关键词： Indoor air quality Indoor cO_(2)control Machine learning Virtual sensor Deep reinforcement learning

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体育运动领域流畅状态的研究进展

体育科学 2009年第11期29卷 72-78页

作者：刘微娜季浏 Watson Ⅱjack c 天津体育学院运动人体科学系天津300381 华东师范大学体育与健康学院上海200241 西弗吉尼亚大学体育学院西弗吉尼亚州摩根城26505

流畅状态是一种理想的内部体验状态,在体育运动领域也被称之为个体从事活动的最佳体验。由于其可以为个体提供良好的心境状态,有助于促进个体对活动的内部动机,提高个体的活动效率,所以,流畅状态作为一个重要的研究领域在运动心理学的... 详细信息

流畅状态是一种理想的内部体验状态,在体育运动领域也被称之为个体从事活动的最佳体验。由于其可以为个体提供良好的心境状态,有助于促进个体对活动的内部动机,提高个体的活动效率,所以,流畅状态作为一个重要的研究领域在运动心理学的发展过程中发挥着重要的作用。拟对体育运动情境中流畅状态的心理特征、模型构建及研究方法进行详细的阐述;并对相关的研究成果加以评述,如流畅状态的跨文化普遍性,体育运动中流畅体验的倾向性差异,流畅-运动技能表现间的关系,影响流畅状态的因素及其相应的调控措施。在此基础上提出,本领域的未来发展应拓展对流畅状态心理特征的研究,即以那些处于非西方文化和西方文化中少数民族文化背景下的群体作为研究对象,进行不同背景的跨文化对比研究;同时,对流畅模型的进一步改进,以充实流畅状态的理论架构,也应成为未来研究的一个焦点;此外,通过目标设置等心理干预手段以促进运动员流畅状态的发生,以及为流畅状态与运动技能建立明确的中介模型,从而应用于运动技能提升的实践情境中也将是未来研究的新主题。

关键词：体育运动流畅状态心理特征流畅模型研究方法

Mixture models detect large effect QTL better than GBLUP and result in more accurate and persistent predictions

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Journal of Animal Science and Biotechnology 2016年第4期7卷 468-473页

作者： Anna Wolc Jesus Arango Petek Settar Janet E.Fulton Neil P.O'Sullivan jack c.M.Dekkers rohan Fernando Dorian J.Garrick Department of Animal Science Iowa State University Hy-Line International Dallas Center

Background： Accurate evaluation of SNP effects is important for genome wide association studies and for genomic prediction. The genetic architecture of quantitative traits differs widely, with some traits exhibiting few if any quantitative trait loci （QTL） with large effects, while other traits have one or several easily detectable QTL with large effects. Methods： Body weight in broilers and egg weight in layers are two examples of traits that have QTL of large effect A commonly used method for genome wide association studies is to fit a mixture model such as BayesB that assumes some known proportion of SNP effects are zero. In contrast, the most commonly used method for genomic prediction is known as GBLUP, which involves fitting an animal model to phenotypic data with the variance-covariance or genomic relationship matrix among the animals being determined by genome wide SNP genotypes. Genotypes at each SNP are typically weighted equally in determining the genomic relationship matrix for GBLUP. We used the equivalent marker effects model formulation of GBLUP for this study. We compare these two classes of models using egg weight data collected over 8 generations from 2,324 animals genotyped with a 42 K SNP ***： Using data from the first 7 generations, both BayesB and GBLUP found the largest QTL in a similar well-recognized QTL region, but this QTL was estimated to account for 24 % of genetic variation with BayesB and less than 1% with GBLUP. When predicting phenotypes in generation 8 BayesB accounted for 36 % of the phenotypic variation and GBLUP for 25 %. When using only data from any one generation, the same QTL was identified with BayesB in all but one generation but never with GBLUP. Predictions of phenotypes in generations 2 to 7 based on only 295 animals from generation 1 accounted for 10 % phenotypic variation with BayesB but only 6 % with GBLUP. Predicting phenotype using only the marker effects in the 1 Mb region that accounted for the largest e

关键词： Bayesian methods Bias QTL