教甄◆英文科題庫下載題庫

原本題目:

(43-46) Now, suppose you want to predict the weather. Then, you need two basic types of information: (1) the current weather and (2) how weather changes from one moment to the next. You could attempt to predict the weather by creating a “model world.” For example, you could overlay a globe of the Earth with graph paper and then specify the current temperature, pressure, cloud cover, and wind within each square. These are your starting points, or initial conditions. Next, you could input all the initial conditions into a computer, along with a set of equations (physical...

原本題目:

(43-46) Now, suppose you want to predict the weather. Then, you need two basic types of information: (1) the current weather and (2) how weather changes from one moment to the next. You could attempt to predict the weather by creating a “model world.” For example, you could overlay a globe of the Earth with graph paper and then specify the current temperature, pressure, cloud cover, and wind within each square. These are your starting points, or initial conditions. Next, you could input all the initial conditions into a computer, along with a set of equations (physical laws) that describe the processes that can change weather from one moment to the next. Suppose the initial conditions represent the weather around the Earth at this very moment and you run your computer model to predict the weather for the next month in New York City. The model might tell you that tomorrow will be warm and sunny, with cooling during the next week and a major storm passing through a month from now. But suppose you run the model again, making one minor change in the initial conditions, say, a small change in the wind speed somewhere over Brazil. This slightly different initial condition will not change the weather prediction for tomorrow in New York City. But for next month’s weather, the two predictions may not agree at all! The disagreement between the two predictions arises because the laws governing weather can cause very tiny changes in initial conditions to be greatly magnified over time. This extreme sensitivity to initial conditions is sometimes called the butterfly effect: If initial conditions change by as much as the flap of a butterfly’s wings, the resulting prediction may be very different. The butterfly effect is a hallmark of chaotic systems. Simple systems are described by linear equations in which, for example, increasing a cause produces a proportional increase in an effect. In contrast, chaotic systems are described by nonlinear equations, which allow for subtler and more intricate interactions. For example, the economy is nonlinear because a rise in interest rates does not automatically produce a corresponding change in consumer spending. Weather is nonlinear because a change in the wind speed in one location does not automatically produce a corresponding change in another location. Despite the name, chaotic systems are not necessarily random. In fact, many chaotic systems have a kind of underlying order that explains the general features of their behavior even while details at any particular moment remain unpredictable. In a sense, many chaotic systems— like the weather— are “predictably unpredictable.” Our understanding of chaotic systems is increasing at a tremendous rate, but much remains to be learned about them.
【題組】46. The author suggests that our knowledge of chaotic systems (A) will never allow us to make accurate predictions. (B) requires more research by the scientific community. (C) reveals detail that can be predicted quite accurately. (D) has not improved very much over the years.

修改成為

46. The author suggests that our knowledge of chaotic systems _____________(A) will never allow us to make accurate predictions. (B) requires more research by the scientific community. (C) reveals detail that can be predicted quite accurately. (D) has not improved very much over the years.