都是氣泡惹的禍
當輕敲啤酒杯時，會發覺酒杯發出之聲調隨氣泡漸漸消失而有所改變。為了解氣泡是如何影響頻率，我測試了幾種含氣泡之飲料，以探討當氣泡漸漸消失時，杯子發聲頻率之變化。實驗發現杯子內飲料之氣泡漸漸消失時，裝啤酒與可樂杯子的發聲頻率逐漸增高，然而裝沙士杯子的頻率卻逐漸降低。為解開此相互矛盾的現象，我設計了將液面上之泡沫及液面下之氣泡分開檢驗的實驗。實驗結果發現，液體中氣泡的存在會使杯子發聲頻率變高。而當液體表面受到擾動時，會降低杯子的發聲頻率。若液面上存在泡沫時，杯子的發聲頻率也會變低。這表示裝盛含氣泡飲料杯子音律之變化，須同時考量液內含泡量與液體表面之效應。此結果可以成功的解釋為何啤酒、可樂與沙士於氣泡漸漸消失時，杯子頻率會變高或變低的現象。Tapping the side of a glass of beer as the bubbles escape, one can find that the pitch will change. In order to know how the bubbles would influence the frequency, I survey the pitches of a wine glass with various drinks that would generate bubbles. As bubbles getting away from the glass, my experimental data shows that the frequencies of the tone did get higher when the glass contains beer and coke. However, I surprisingly find a different result when the same glass contains sarsaparilla. Hence I design a series of experiments to understand the possible mechanism. The data suggests that when the drinks contain bubbles in it, the frequencies of the tones will be higher. When the liquid surfaces were disturbed, the frequencies of the tones will become lower. If there were foams above the liquid surfaces, the frequencies of the tones will also be lower. This finding proposes that people needs to consider both the bubble bulk status and surface effects. The consequences of the competitions between these two effects can successfully explain how the tones are changed in the cases of beer or sarsaparilla or coke.
聚乳酸/天然纖維複合材料之研究-探討加入玉米葉纖維對機械性質之影響
本研究以玉米葉纖維做為聚乳酸纖維的補強材料，並以加入的玉米葉纖維長度為操縱變因，探討其對聚乳酸/玉米葉纖維複合材料機械性質的影響。實驗設計以純聚乳酸為對照組，以加入1mm, 2mm, 5mm, 13mm玉米葉纖維的聚乳酸複合材料為實驗組。本研究以拉伸強度和耐衝擊值來判斷機械性質的強度。 實驗數據顯示，實驗組的拉伸強度與對照組差距不大，但在耐衝擊值卻比對照組高出許多。除此之外，拉伸強度和耐衝擊值都顯示加入2mm玉米葉纖維在實驗組擁有最佳的數值。另外，加入越長的玉米葉纖維反而不會擁有較佳的機械性質。未來期待聚乳酸複合材料能夠應用在更廣的層面。
Development of Models for Performance Index (PI) and Score Index(SI) of Individual players based on 5 European Soccer Leagues
Most football managers are not aware of the need for analysis of soccer data, which is one of the dynamic sports. In this study, we developed a statistical model with performance indicators and score indicators of individual soccer player based on various event data including dynamic features such as goal, assist, pass, etc. In this study, the correlation between the dependent variables and the explanatory variables, and each explanatory variable was confirmed through the correlation analysis to solve the problem of multiple communicability from the regression model and to analyze the statistically significant preliminary model. In addition, we analyzed the correlation between individual rating of the players and the data recorded in the soccer games, and found that there has been a problem with the rating of the soccer players evaluated by the reporters and the soccer statistics site. To solve this problem, we developed a model that best fit the performance indicators of individual soccer player using the linear regression model and the beta regression model. The performance index model of the athletes was developed by comparing the R-squared value and the mean absolute percentage error of two models, the linear regression model and the beta regression model, and found out the beta regression model is better model to use. By using the estimated regression coefficients of the regression model we made new PI model. Score Index, which is the attractive point of soccer, was developed by comparing Poisson regression model and negative binomial regression model based on AIC value, and the one using negative binomial regression model was found to be better. Through the model developed by this study, it is possible to collect the event data recorded by individual athletes for each soccer game, and obtain the PI & SI index which are the athlete performance index models. This allows us to evaluate each team's players objectively, analyze the team's deficiencies, and provide tools to find players, who can fill in the missing positions of the teams. This study can also be utilized to grasp the performance of athlete in real time by simulating the resultative model.