The microvalve is one of the most important components in microfluidics. With decades of development, the microvalve has been widely used in many industries such as life science, chemical engineering, chip, and so forth. This paper presents a comprehensive review of the progress made over the past years about microvalves based on different actuation mechanisms. According to driving sources, plenty of actuation mechanisms are developed and adopted in microvalves, including electricity, magnetism, gas, material and creature, surface acoustic wave, and so on. Although there are currently a variety of microvalves, problems such as leakage, low precision, poor reliability, high energy consumption, and high cost still exist. Problems deserving to be further addressed are suggested, aimed at materials, fabrication methods, controlling performances, flow characteristics, and applications.

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The fractal analysis of data on silicon content in hot metal obtained in No. 1 blast furnace at Laiwu and No.6 blast furnace at Linfen Iron and Steel Group Co. respectively is performed using power spectrum method to examine the possible scale-invariance laws. The results confirm the existence of fractal characteristics in the investigated time series, which provides a powerful tool to explore complex blast furnace system and makes the application of fractal theory to blast furnace full of potential and attraction.KEY WORDS: blast furnace; power spectrum; fractal; silicon content in hot metal; time-dependent. points respectively. When the sampling interval of the time series is Dt, the reflected maximum frequency can be written as Therefore, frequency increases with the increment (MDt). Ϫ1each time up to f M . If a system profile is self-affine, the variation of power spectrum P( f) with the frequency f will follow a power law: The Hurst exponent measures the smoothness of fractal time series based on asymptotic behavior of the rescaled range of the process. It ranges from 0 to 1 and reflects antipersistent behavior with 0ϽHϽ0.5, persistent behavior with 0.5ϽHϽ1 and ordinary uncorrelated Brownian motion with H=0.5.The fractal dimension and Hurst exponent are estimated easily through power spectrum analysis for a given possible fractal time series. The segmentation is illustrated in Fig. 2. Results and DiscussionFor the sake of convenience, we choose R=84 and L=168 corresponding to the size of time series during a week and two weeks respectively. For each segment of length L, the power spectrum of silicon content in hot metal can be calculated as a function of frequency ( f in h Ϫ1 ) using Eq. (1). As an example, logarithmic power spectrum plots are appreciated in Fig. 3 for m=8. From Fig. 3, we will see the falloff of power spectrum with the increasing frequency during the studied period. The fundamental trend of each of these spectra appears to follow a straight line despite statistical fluctuation. The straight line is plotted to expect the slope values of 1/f power spectrum for each plots. Similar plots are obtained for other m. The power law behavior of silicon content in hot metal reveals self-similarity and indicates the existence of fractal properties roughly.Least square method is used to simulate these slope values and fractal dimension as well as Hurst exponent are calculated using Eqs. (5) and (6) respectively. The results are presented in Fig. 4. Vol. 45 (2005) Obviously, time-dependent fractal behavior is found in the investigated time series as m changes. The fractal dimension D s is fractal and low-dimensional at all the m sites for both BFs, thus pointing out a chaotic regime of the silicon content in hot metal. That is to say, there is a deterministic mechanism that governs the blast furnace process under study. Furthermore, the larger D s occurs in No. 6 BF for most of values, indicating more complex dynamics in it, in accordance with the conclusion obtained from Kolomogorov entropy 19) which...

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