生命教育在高校心理危机干预的实践探讨

近几年,否定生命、伤害生灵的高校心理危机事件频发,学生的生命价值观教育日益受到关注,生命教育的实践将成为高校心理危机干预教育改革的重要取向。心理危机干预的教育必须重视生命教育,重视学生的内在主体性发展,引导学生认识生命、欣赏生命、尊重生命、创造生命价值。     

不锈钢耐蚀层冷焊与热焊的研究

带极电渣堆焊广泛应用于核反应堆压力容器内壁大面积的不锈钢耐蚀层堆焊。文章针对不锈钢耐蚀层带极电渣堆焊时采用冷焊与热焊两种控温方式制备实验试件,分别进行弯曲、金相等项目的性能检验。结果表明:不锈钢耐蚀层采用冷焊与热焊方式均能满足技术条件的要求,但热焊的实验结果更优于冷焊。     

基于改进型序号总和理论的企业绩效评价研究及实证分析

序号总和理论是多方案(指标)筛选或优化的一种很好的方法,分析斯皮尔曼等级相关系数,作为该理论的关键步骤,具有先天不足。通过理论演绎,提出了改进型序号总和理论,同时,通过选取的四种绩效评价方法进行实证,对比分析发现,改进型序号总和理论,不仅能扩大原理论的运用范围,还具有原理论同样的优势,都能实现优秀方案筛选或为方案优化提供指导。     

新生代农民工的心理契约问题研究

基于文献分析和对江西省130名新生代农民工的调查,运用SPSS15.0统计分析工具分析他们的心理契约,发现新生代农民工心理契约与上一代农民工相比的特殊之处,并进一步分析产生的原因以及提出解决的对策。     

河南理工大学学生体质检测结果比较与分析

目的：研究高校在校生年级学生身体素质发展趋势。方法：对河南理工大学2010年度四个年级学生《国家学生体质健康标准》测试成绩和等级进行统计分析。结果：学生较低体重比例较大,正常体重比例几乎不到半数,同时肥胖者稍多,男生尤其突出;大四男生肺活量不及格率升高,优秀率下降。立定跳远成绩不及格率最高,优秀率最低;大四学生耐力跑成绩不及格率升高,优秀率下降;学生总分不及格率大一到大三呈下降趋势,大四又上升,大四男生良好率和优秀率下降。结论：学生身体形态和身体素质大一到大三呈上升趋势,大四呈下降。     

浅析社会体育专业学生篮球裁判员临场执裁表现不足的原因

本文运用文献资料、分析比较等方法,对关于社会体育学生裁判员的临场执裁进行分析研究,结果显示：社会体育专业篮球裁判员对规则和裁判员手册理解不透彻、基本功掌握欠缺、选位与移动不到位、临场经验不足,缺乏实践机会、临场时心理紧张,缺乏自信等因素是影响学生裁判临场执裁表现不足的主要原因。通过分析原因,为今后社会体育专业学生篮球裁判员在比赛执裁中减少不足表现提供理论参考依据。     

棒球守场员比赛时心理调控方法的初步探讨

本文目的为了探讨我国优秀棒球守场员赛时心理调控的一些方法,了解心理素质对棒球守场员的影响。棒球比赛中守场员面对对方进攻时,对不同局面做出相应的防守处理时,不仅技术上过硬,而且更重的是攻破心理这一关,尤其是棒球这项运动场上的局面瞬息万变,如不及时把自己的心理调节好很容易使场上的局面失控,造成本队失分或处于不利局面。本文主采用文献资料法对全国棒球联赛中一些队伍的守场员进行心理问题上的探讨。结果发现很多球员在比赛中的心理上有很多情况都无法控制自己,达不到比赛的最佳状态,导致比赛成绩不佳。本研究认为心理能力的强弱对棒球守场员在赛时的发挥有着极为重的作用。     

体育游戏对3-6岁学前儿童心理发展的影响

幼儿时期是心理快速发展阶段,而心理发展又离不开动作。虽然动作本身不是心理,但动作和心理发展有着密切的关系。游戏是幼儿时期的主导活动,而体育游戏在幼儿各种活动中占主导地位,它对幼儿心理发展具有全面的促进作用。通过实地观察、问卷调查、逻辑分析、归纳总结等研究方法,对体育游戏对学前儿童心理发展的影响进行研究。目的是通过研究阐明体育游戏在儿童成长中的作用。结果表明,体育游戏对提高学前儿童认知能力,丰富情感,形成良好意志品质等具有不可替代性。     

Relationship between weight status and cognition in children: A mediation analysis of physical fitness components

ABSTRACT

Cross-sectional study aimed to analyse differences in cognitive performance across fitness components categories (cardiorespiratory fitness [CRF], speed-agility and muscular fitness [MF]) and weight status in children, and to determine whether physical fitness mediates the association between body mass index (BMI) and cognitive performance. Fitness components and BMI were measured using standard procedures in 630 children aged 5-to-7 years from the provinces of Cuenca and Ciudad Real, Spain. BADyG was used to assess cognitive performance. We used ANCOVA models to test mean differences in cognition scores by BMI and fitness categories. Hayes’s PROCESS macro was used for mediation analyses. Children with normal weight scored better in spatial factor and general intelligence than their overweight/obese peers (p < 0.05), but differences were attenuated when controlling for CRF (p > 0.05). Children with better results in CRF and speed-agility scored better in all cognitive dimensions even after controlling for BMI (p < 0.05). Similarly, children with high MF obtained better scores in verbal factor (p < 0.05). All fitness components acted as mediators of the relationship between BMI and general intelligence (p < 0.05). These findings highlight the crucial role of fitness in minimising the negative effect of excess weight on children’s cognition.

Abbreviations: BMI: Body mass index; CRF: Cardiorespiratory fitness; MF: Muscular fitness; BADyG E1: Battery of general and differential aptitudes; SES: Socioeconomic status; SD: Standard deviation; IE: Indirect effect     

A machine learning approach for automatic detection and classification of changes of direction from player tracking data in professional tennis

ABSTRACT

The purpose of this study was to develop an automated method for identifying and classifying change of direction (COD) movements in professional tennis using tracking data. Three sport science and strength and conditioning experts coded match-play footage of nineteen professional tennis players (9 male and 10 female) from the Australian Open Grand Slam for COD of medium and high intensity. A total of 1,494 changes were identified and aligned with 2D player position sampled at 25 Hz based on camera tracking data. Several machine learning classifiers were trained and tested on a set of 1,128 time-motion features. A random forest algorithm was found to have the best out-of-sample performance, classifying medium and high intensity changes with an F1-score of 0.729. This research offers a novel and applicable way for utilising player tracking data and machine learning techniques to automatically identify and classify COD movements in professional tennis.