中学体育隐蔽课程精神文化要素分析

通过对江西省体育隐蔽课程在中学情况的调查分析,笔者就体育隐蔽课程的精神文化要素在中学的实施现状进行研究,提出体育精神文化要素在实施过程中存在的问题及建议。     

How reliable are students’ evaluations of teaching quality? A variance components approach

The inter-rater reliability of university students’ evaluations of teaching quality was examined with cross-classified multilevel models. Students (N = 480) evaluated lectures and seminars over three years with a standardised evaluation questionnaire, yielding 4224 data points. The total variance of these student evaluations was separated into the variance components of courses, teachers, students and the student/teacher interaction. The substantial variance components of teachers and courses suggest reliability. However, a similar proportion of variance was due to students, and the interaction of students and teachers was the strongest source of variance. Students’ individual perceptions of teaching and the fit of these perceptions with the particular teacher greatly influence their evaluations. This casts some doubt on the validity of student evaluations as indicators of teaching quality and suggests that aggregated evaluation scores should be used with caution.     

HS-SP ME-GC/MS对干、鲜杏花挥发性成分的分析

采用顶空固相微萃取( HS-SPME)技术提取干、鲜杏花的挥发性成分,经气相色谱-质谱测定,比较分析干、鲜杏花的香气成分变化.分析结果表明：鲜杏花中鉴定出2种挥发组分,占总挥发组分相对百分含量的95.09%,苯甲醛占总挥发组分的93.55%;干杏花中鉴定出20种挥发组分,占总挥发组分相对百分含量的92.17%,其中苯甲醛占总挥发组分的25.07%.可见干、鲜杏花挥发油化学成分种类和含量均有明显的差别.     

红肉河蓝蛤软体部营养成分分析与评价

本研究主要对红肉河蓝蛤软体部分营养成分进行了评价。结果显示,红肉河蓝蛤软体部分(鲜样)中的水分含量为82.02%,粗蛋白、粗脂肪和灰分的质量分数分别为9.47%、1.72%、3.25%。共检出18种常见的氨基酸,其中必需氨基酸占氨基酸总量的21.00%;油脂中,不饱和脂肪酸占油脂的46.33%,其中多不饱和脂肪酸(PUFA)占油脂的19.04%;矿物元素中钙(Ca)的质量分数最高,其次为镁(Mg),微量元素中铁(Fe)质量分数最高。结果表明红肉河蓝蛤是营养价值较高的经济贝类,具有较高的食用价值、广阔的养殖前景和较大的市场开发潜力。     

Analysis of football game-related statistics using multivariate techniques

Abstract

The purpose of this study was to explore football game-related statistics during a competition, using principal component and cluster analyses to determine if it is possible to distinguish the winning teams from the drawing and losing ones. We collected the game-related statistics of the group phase matches of the 2006 World Cup and organised them into a matrix. The principal components of the covariance matrix were calculated. The scores of the first and second components were used to represent the new data, and cluster analysis was applied to separate the elements in two groups (G1 and G2). To analyse the degree of separation between the groups, we calculated the Silhouette Coefficient for each group. Finally, we checked if the winning teams were classified into the same group. The Silhouette Coefficients found for G1 and G2 were 0.54 and 0.55, respectively. Results showed that 70.3% of the winning teams were classified into the same group (G1). Similarly, 67.8% of the drawing and losing teams were classified in G2. This study presented a different way to analyse game-related statistics that allowed the multivariate differences to be shown between successful and unsuccessful teams.