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Satellites are already in use for so many purposes today — from weather forecasting to communications, GPS, and more. Since the late 1970s, satellites have also been used to monitor changes in the Arctic sea ice. Data suggests that sea ice is thinning over time due to climate change. Over the last forty years, the extent of Arctic ice cover has been declining at a rate of 13%per decade. However, existing ways of measurement do not provide a clear and continuous picture of the state of sea ice.

This September, scientists developed a new method that combines computer modeling and satellite data to predict ice thickness all year round. Over the years, scientists have proposed several methods to measure ice thickness. These include flying planes over the Arctic Ocean or collecting field measurements.

Neither of these methods provides a full picture of Arctic melt, making year-long data collection difficult. Continuous data across the entire region from satellites is a great way for scientists to understand how climate change is playing out in the Arctic.

These satellites use special radar or laser-based altimeter instruments. Sea ice thickness is then calculated as the difference between the height of the ice and the top of the water(ice level — sea level). This method works well during the winter months — September to May.

Unfortunately, satellite-based sea ice thickness measurements are inaccurate during the summer months: the time of the year with the greatest melting. This melting creates a pool of liquid water on the surface. As a result, the simple calculation (ice level — sea level) does not work as radar systems are unable to differentiate between ocean water and ice, thus rendering measurements inaccurate.

New research led by Jack Landy, a scientist, seeks to tackle this age-old issue. The team used machine learning and deep learning methods to distinguish between seawater and ice.

The researchers built a model of the radar system’s predicted data and cross-referenced these values to those collected by a satellite. This model allows the researchers to obtain accurate, year-round Arctic sea ice thickness data.

【小题1】Why are satellite-based sea ice thickness measurements not accurate?
A.The ice melts in summer monthsB.The radar system doesn’t work in winter.
C.The liquid water turns ice in cold daysD.The ocean water comes onto the ice surface.
【小题2】What does the age-old issue refer to?
A.The changeable climate changeB.The incorrect ice measurements.
C.The constantly flowing ocean water.D.The great amount of melting ice.
【小题3】How do the researchers study Arctic sea ice changes in the new research?
A.By using satellites to collect data.B.By collecting field measurements
C.By flying planes over the Arctic Ocean.D.By using machine learning and deep learning methods.
【小题4】What is the best title for the passage?
A.A new device to measure ocean waterB.A new stage for the application of satellites
C.A new way to monitor Arctic sea ice changesD.A new finding about the rising sea level
22-23高一下·山东济宁·阶段练习
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Italian astronaut Luca Parmitano had a terrifying experience during a spacewalk. While working on the International Space Station(ISS), his helmet began filling with water.

At first, Parmitano wasn’t sure what it was. “My head is really wet,” he told NASA flight controllers back on the earth. As the water began to accumulate, Parmitano realized there was a problem. “It’s too much...Now it’s in my eyes,” he recalled. Concerned that he might choke on the water, ground control stopped the spacewalk. Back inside the ISS, Parmitano took off his helmet and discovered that it contained almost half a liter(two cups)of water. Where had this water come from?

NASA found out that a malfunction in the suit’s liquid cooling system had caused water to leak. Some of the water got into Parmitano’s helmet. Though NASA has taken steps to correct the problem, the experience underscores the dangers that astronauts face each time they venture outside a spacecraft.

Spacewalks are safer now than they were fifty years ago, when — in 1965 — Russian Alexei Leonov carried out the first one. However, as Parmitano’s experience illustrates, there are still risks involved. To ensure that missions are successful, astronauts train for hundreds of hours on the earth. They learn to deal with the lack of gravity in space, for example, by floating in a large tank of water, where they experience a feeling very close to the weightlessness of space.

For every hour they will walk in space, astronauts practice for ten hours in the water. They also familiarize themselves with the exact route they will take once they leave a spacecraft. They repeatedly go over this path so they know exactly what to do on a spacewalk.

Astronauts also train for emergencies that may come about during a walk. One of the most common is losing consciousness. Although spacesuits have an internal heating and cooling system, they can still get very hot, especially when astronauts are doing physically demanding work outside the spacecraft for hours. Astronauts are trained to monitor their breathing and to make sure their bodies aren’t getting overheated, which could cause them to pass out. Another potential challenge that astronauts are trained to deal with is being separated from a spacecraft. During a walk, astronauts work in pairs and are attached to the ISS for safety reasons. Every NASA spacesuit has a mini jet pack, and astronauts are trained to use it to float back to the station if they somehow become detached from the craft.

【小题1】What caused Luca Parmitano’s helmet to fill with water?
A.No one knows why it filled with water.
B.There was a crack in his helmet, which caused a leak.
C.A bag with drinking water inside his suit began to leak.
D.There was a problem with his suit’s liquid cooling system.
【小题2】How long does an astronaut need to practice in the water to prepare for a two-hour spacewalk?
A.Ten hours.B.Twenty hours.
C.Thirty hours.D.Hundreds of hours.
【小题3】What does the underlined word “it” in the last sentence refer to?
A.The walk.B.The spacecraft.C.The jet pack.D.The spacesuit.

Pessimism VS. Progress

FASTER, CHEAPER, BETTER—technology is one field many people rely on to offer a vision of a brighter future. As the 2020s dawns, however, optimism is in short supply. The new technologies that have dominated the past decade seem to be making things worse. Social media was supposed to bring people together, but today, it is better known for invading privacy. Parents worry that smartphones have turned their children into screen-addicted zombies.

This depressed mood is centered on smartphones and social media, which took off a decade ago. However, concerns have arisen that particular technologies might be doing more harm than good. The 1920s witness a resistance to cars, which had earlier been seen as a miraculous answer to the problem of horse-drawn vehicles. In the 1970s, the depression was prompted by concerns about environmental damage and the prospect of nuclear accidents.

In each of these historical cases, disappointment arose from a mix of unrealized hopes and unforeseen consequences. Technology produces the forces of creative destruction, which replaces the outdated production units, so it is natural that it leads to anxiety. For any given technology, its drawbacks sometimes seem to outweigh its benefits. When this happens with several technologies at the same time, as it does today, the result is a wider sense of techno-pessimism.

However, this pessimism can be overdone. Too often people focus on the disadvantages of a new technology while taking its benefits for granted. Worries about screen time should be weighed against the instant access to information and entertainment that smartphones make possible. Efforts to avoid the short-term cost associated with a new technology will end up denying access to its long-term benefits. Fears that robots will steal people’s jobs may promote governments to tax them, for example, to discourage their use, but in the long run, countries that wish to maintain their standard of living as their workforce ages and shrinks will need more robots, not fewer.

That points to another lesson: the remedy for technology-related problems very often involves more technology. Airbags and other improvements in safety features, for example, mean that in America, deaths in car accidents per billion miles travelled have fallen from around 240 in the 1920s to around 12 today.

The most important lesson is about technology itself. Any powerful technology can be used for good or ill. Biotechnology, for example, can raise crop yields and cure diseases, but it could equally lead to deadly weapons.

Technology itself is neutral. It is the choices people make about it that shape the world. Will technology lead to pessimism or progress? The question should be settled by a broad debate, not by a small group of technologists.

【小题1】The word “prompted” in paragraph 2 probably means _______.
A.causedB.preventedC.relievedD.removed
【小题2】According to the author, pessimism over new technologies is often resulted from the fact that _______.
A.technological innovations hardly cause unexpected problems
B.people assume the faults of new technologies to be natural
C.new technologies tend to emerge with uncertainty about future
D.new technologies cause more disadvantages than advantages
【小题3】By writing this article, the author mainly wanted to argue that _______.
A.optimism over new technologies is in short supply as the 2020 comes
B.pessimism over innovations, if not overdone, is helpful and even essential
C.people tend to care more about innovations’ problems than about their benefits
D.people’s wise decision on the use of new technologies really matters

In the world of digital health, Silicon Valley-based Mindstrong stands out. It has an amazing idea: that its app, based on mental functioning research, can help detect (探测) troubling mental health patterns by collecting data on a person’s smartphone usage—how quickly they type or scroll, for instance.

The promise of that technology has helped motivate Mindstrong a lot since it launched last year; already more than a dozen counties in California have agreed to use the company’s app for patients.

Mindstrong works by collecting information about how people are typing and running it through a machine to determine which data can predict their emotional state. The idea is to use that data to build a “normal” pattern—so it can be compared against someone’s typing habits on any given day. If the habits look abnormal, the app can send messages to a health care provider. And one of Mindstrong’s most encouraging results is that its app can even predict how a person will feel next week—kind of like a weather app for your mood.

The app can detect a seven-point change on the Hamilton Rating Scale for depression (抑郁). That kind of difference could indicate a patient who is not normally depressed now shows signs of mild or moderate (适度的) depression, or that a person with moderate depression is now showing signs of a very severe condition, knowing which could be very powerful for a clinician and for someone taking care of a patient.

Does the app live up to its promise? There’s no way to tell. Almost no one outside the company has any idea whether it works. Most of the company’s key promises aren’t yet supported by published, peer-reviewed data — leading some experts to wonder if the technology is ready for the real world.

Based on her own research, one expert in digital health and mood said she’s doubtful that Mindstrong can, in a general population, work as well as the company promises. MIT’s Picard said that while there are ways to predict or detect mood changes, you usually need more than just a single type of data to do so.

The company’s website describes five completed clinical trials (临床试验), but it has not yet published the results of any. Only a handful of other published works — all from the last year — have proved how well it works.

Besides, there are plenty of issues that could affect typing speed,which Mindstrong hasn’t figured out how to deal with yet. Sticky fingers after lunch, full hands at an airport, wearing gloves during winter, or a broken hand might also affect a person’s typing speed — and therefore the app’s performance.

However, according to the company’s founder, Dr. Paul Dagum, they’ve done several successful clinical tests on its memory and detective function for depression, for anxiety and for mental decline. “We’re confident, we’re already seeing some really exciting results.” said Dr. Dagum.

Last year, Mindstrong doubled the company’s workforce to 42 employees and it launched a partnership with Harvard T.H School of Public Health to deal with depression treatments.

And about 15 counties—including the county with the largest population in the United States, Los Angeles County—will be spending about $60 million over the next four years to bring companies like Mindstrong into their health care system to help them get better services to people with mental illnesses like depression.

【小题1】How does Mindstrong predict or detect mood changes?
A.By sending some messages to a health care provider.
B.By comparing against other people’s data in the app.
C.By building a normal pattern of people’s typing habits.
D.By analyzing the collected information of phone usage.
【小题2】People doubt Mindstrong probably because _________.
A.the single type of data is not that enoughB.its clinical trials haven’t been completed
C.nobody outside the company supported itD.it can’t be used when they are at the airport
【小题3】What is the purpose of the passage?
A.To argue for a media.B.To introduce a new app.
C.To explain a phenomenon.D.To report an event.

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