1 . As we all know, insects can be remarkably agile (灵活的) in flight. This is really hard to build into flying robots, but MIT Assistant Professor Kevin Yufeng Chen has developed an insect-sized drone (无人机) that approaches insects’ agility.

Typically, drones require wide open spaces. “If we look at most drones today, they’re usually quite big,” says Chen. “Most of their applications involve flying outdoors. The question is: Can you create an insect-sized drone that can move around in very crowded and complex spaces?”

According to Chen, he overcame many problems when building the drone. The insect-sized drone requires a fundamentally different construction from a larger one. The large drone is usually powered by a motor, but the motor loses efficiency as you shrink it. So, Chen says, “For an insect-sized drone, you need to look for alternatives.” The principal alternative until now has been employing a small, rigid actuator (执行器) built from new materials. Chen designed a more agile tiny drone using soft actuators instead of hard ones.

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1. What can we know about the actuator designed by Chen?

A．It weighs about six grams.

B．It drives the insect-sized drone.

C．It loses efficiency too much.

D．It employs conventional materials.

2 . Scientists say they have developed a system that uses machine learning to predict when and where lightning will strike. The research was led by engineers from the Swiss Federal Institute of Technology in Lausanne, Switzerland.

Lightning is a strong burst of electricity in the atmosphere. It can strike between clouds or between a cloud and the ground. Since lightning carries an extremely powerful electrical charge, it can be destructive and deadly. It is difficult to know exactly how many people die of lightning-related causes. European researchers have estimated that between 6,000 and 24,000 people are killed by lightning worldwide each year. The strikes can also cause power failure, destroy property, damage electrical equipment and start forest fires.

For this reason, climate scientists have long sought to develop methods to predict and control lightning. The system tested in the experiments uses a combination of data from weather stations and machine learning methods. The researchers developed a prediction model that was trained to recognize weather conditions that were likely to cause lightning. The model was created with data collected over a 12-year period from 12 Swiss weather stations in cities and mountain areas. The data related to four main surface conditions: air pressure, air temperature, relative humidity (湿度) and wind speed. The atmospheric data was placed into a machine learning algorithm (计算程序), which compared it to records of lightning strikes. Researchers say the algorithm was then able to learn the conditions under which lightning happens.

“Once trained, the system made predictions that proved correct almost 80 percent of the time,” the Swiss Federal Institute of Technology said in a statement. “It can now be used anywhere.”

Amirhossein Mostajabi, a PhD student at the institute, said current systems for gathering such data are slow and complex and require costly collection equipment like radar or satellites. “Our method uses data that can be obtained from any weather station,” he said. “This will improve data collection in very remote areas not covered by radar and satellite or in places where communication systems have been cut,” he added.

The researchers plan to keep developing the technology in partnership with a European effort that aims to create a lightning protection system. Scientists working on the Paris-based project are experimenting with a laser technology that could someday control lightning activity. The idea is that powerful, ground-based lasers can be positioned in the sky to direct energy from lightning.

Which is the correct order of how the system works?
① develop a prediction model.
② learn to recognize weather conditions.
③ collect related data.
④ input the data onto the computer.
⑤ make predictions.

A．①→②→③→④→⑤	B．③→④→①→②→⑤
C．①→②→④→③→⑤	D．③→①→②→⑤→④

4 . Goffin’s cockatoos, a kind of small parrot native to Australasia, have been shown to have similar shape-recognition abilities to a human two-year-old. Though not known to use tools in the wild, the birds have proved skilful at tool use while kept in the cage. In a recent experiment, cockatoos were presented with a box with a nut inside it. The clear front of the box had a “keyhole” in a geometric shape, and the birds were given five differently shaped “keys” to choose from. Inserting the correct “key” would let out the nut.

In humans, babies can put a round shape in a round hole from around one year of age, but it will be another year before they are able to do the same with less symmetrical (对称的) shapes. This ability to recognize that a shape will need to be turned in a specific direction before it will fit is called an “allocentric frame of reference”. In the experiment, Goffin’s cockatoos were able to select the right tool for the job, in most cases, by visual recognition alone. Where trial-and-error was used, the cockatoos did better than monkeys in similar tests. This indicates that Goffin’s cockatoos do indeed possess an allocentric frame of reference when moving objects in space, similar to two-year-old babies.

1. How did the cockatoos get the nut from the box in the experiment?

A．By following instructions.	B．By using a tool.
C．By turning the box around.	D．By removing the lid.

2. Which task can human one-year-olds most likely complete according to the text?

A．Using a key to unlock a door.	B．Telling parrots from other birds.
C．Putting a ball into a round hole.	D．Grouping toys of different shapes.