1 . Last September, Sarah, 44, slipped on a platform and
“It’s hard to explain but when I think about a
In February, Sarah returned to work and a few months later she attended a conference in Rome to give a speech about the accident.
“I do miss myself before the accident. But at the end of the day, I’m just
| A．fell | B．jumped | C．stood | D．filled |
| A．changes | B．worries | C．injuries | D．failures |
| A．moved | B．turned | C．walked | D．rushed |
| A．uncertain | B．unable | C．patient | D．eager |
| A．Naturally | B．Normally | C．Obviously | D．Luckily |
| A．powered | B．developed | C．graded | D．treated |
| A．pattern | B．signal | C．movement | D．moment |
| A．protect | B．detect | C．, connect | D．reflect |
| A．cares about | B．talks about | C．figures out | D．points out |
| A．hopeful | B．grateful | C．concerned | D．amused |
2 . Think of the words in your head: that tasteless joke you wisely kept to yourself at dinner; your unvoiced impression of your best friend’s new partner. Now imagine that someone could listen in.
Recently, scientists from the University of Texas, have made another step in that direction. In a study published in Neuroscience, the team showed it was possible to read people’s thoughts with a non-invasive brain scanner called fMRI and large language models (LLMs) built with GPT.
The study centered on three subjects, who lay in an fMRI scanner recording their brain activity by detecting changes in blood flow in parts of their brains while they listened to online stories. By integrating this information with the LLMs’ ability to understand how words relate to one another, the researchers developed an encoded (编码的) map of how each individual’s brain responds to different words. Then, the team worked backward. They recorded the fMRI activity while the participants listened to a new story. Using a combination of the patterns previously encoded for each individual and LLMs, the researchers attempted to translate this new brain activity.
While many of the sentences it produced were inaccurate, the decoder generated sentences that got the main idea of what the person was thinking. For instance, when a person heard, “I don’t have my driver’s license yet,” the decoder spat out, “She has not even started to learn to drive yet.” Alex Huth from the university said, “We were shocked and impressed that this worked as well as it does.”
The researchers also found that the technology isn’t one-size-fits-all. Each decoder was quite personalized and worked only for the person whose brain data had helped build it. Additionally, a person had to voluntarily cooperate for the decoder to identify ideas. If a person wasn’t paying attention to an audio story, the decoder couldn’t pick that story up from brain signals.
While the technology was still far from perfect, the result could ultimately lead to seamless devices that help people who can’t talk or otherwise communicate easily. However, the research also raises privacy concerns about unwelcome neural overhearing. The team said the potential of the technology was such that policymakers should proactively address how it can be legally used. Jerry Tang from the team said, “Nobody’s brain should be decoded without their permission. If one day it does become possible to get accurate decoding without a person’s will, we’ll have a regulatory foundation in place.”
1. What is the study mainly about?| A．The working principle of a smart scanner. |
| B．The potential impact of mind-reading GPT. |
| C．The advance in brain-decoding technology. |
| D．The breakthrough in large language models. |
| A．They fed the decoder data on people’s brain activities. |
| B．They employed the scanner to encode people’s thoughts. |
| C．They recorded the fMRI activity to assess thinking ability. |
| D．They used brain activity patterns to read the subjects’ mind. |
| A．The decoder worked as expected. |
| B．The decoder can get the wording right. |
| C．The decoder required willing participation. |
| D．The decoder can be applied to different people. |
| A．Personalize the technology. | B．Establish proper regulations. |
| C．Apply the technology across fields. | D．Break limitations of the technology. |
| comfort science entire anxiety motivate |
2. He seemed
3. He is intelligent enough but he lacks
4. I was so
5. We should adopt a more
4 . Microgreen Farming
Microgreens have been used in cooking since the 1980s.
As with most vegetables, it all starts with seeds. But instead of allowing the plant to grow to its full potential, microgreens are created when the plant is harvested shortly after it starts to grow. The result is a strong flavor and an ingredient that packs a variety of nutrients and health benefits in a tiny, delicious package. The Journal of Agricultural and Food Chemistry states that microgreens have increased levels of vitamins E, C and K.
One of the most significant benefits of using microgreens in dishes is that they provide an intense experience that will improve the overall flavor of any dish. Many chefs use them to add depth of flavor and to create complex flavor. Another benefit of microgreens is the convenience factor — they are incredibly easy to cook with!
Traditional plants require an extended amount of time and large amounts of land.
| A．Microgreens, on the other hand, are easy! |
| B．They were initially used as a form of decoration. |
| C．There is no prep and cutting work as with most vegetables. |
| D．They have as much as 40 times more nutrients than a mature plant. |
| E．One popular form of microgreens is a mix of purple and green radish. |
| F．Some beginners like to start with a familiar plant, such as radish, cabbage, or peas. |
| G．Mature traditional vegetables are generally harvested 2 to 4 months after they are planted. |
| accompany demonstrate evaluate break down in charge of in turn |
2. Ken agreed to
3. I can’t
4. Listen up! Please come up
5. Let me
6. A smile can
| bend abandon have an impact on boil down to leave out significant boost for instance be known as on the contrary |
2. Susan’s success in business
3. They’re building new hotels in order to
4. I still remember one time I had to
5. It’s no trouble for me at all!
6. John explained the case to his boss, being careful not to
7. The discovery of the new drug is of great
8. Near the top of the hill, the path
9. We made many improvements to our house.
10. In the third mass extinction, which
7 . The need for clarity extends beyond how we communicate science to how we evaluate it. Who can really define stock phrases such as ‘a significant contribution to research’? Or understand what ‘high impact’ or ‘world-class’ mean? Scientists demand that institutions should be clear about their criteria and consider all scholarly outputs—preprints, code, data, peer review, teaching, mentoring and so on.
My view about the practices in research assessment is that most assessment guidelines permit sliding standards: instead of clearly defined terms, they give us feel-good slogans that lack any fixed meaning. Facing the problem will get us much of the way towards a solution.
Broad language increases room for misunderstanding. ‘High impact’ can be code for where research is published. Or it can mean the effect that research has had on its field, or on society locally or globally—often very different things. Yet confusion is the least of the problems. Words such as ‘world-class’ and ‘excellent’ allow assessors to vary comparisons depending on whose work they are assessing. Academia(学术界) cannot be a fair and reasonable system if standards change depending on whom we are evaluating. Unconscious bias(偏见) associated with factors such as a researcher’s gender, ethnic origin and social background helps the academic injustice continue. It was only with double-blind review of research proposals that women finally got fair access to the Hubble Space Telescope.
Many strategies exist to improve fairness in academia, but conceptual clarity is paramount. Being clear about how specific qualities are valued leads assessors to think critically about whether those qualities are truly being considered. Achieving that conceptual clarity requires discussion with faculties, staff and students: hours and hours of it. The University Medical Center Utrecht in the Netherlands, for example, held a series of conversations, each involving 20-60 researchers, and then spent another year revising its research assessment policies to recognize social impacts.
Frank conversations about what is valued in a particular context, or at a specific institution, are an essential first step in developing concrete recommendations. Although ambiguous(模棱两可的) terms, for instance ‘world-class’ and ‘significant’, are a barrier when performing assessments, university administrators have said that they rely on flexible language to make room to reward a variety of contributions. So it makes sense that more specific language in review and promotion must be able to accommodate varied outputs, outcomes and impacts of scholarly work.
Setting specific standards will be tough. It will be inviting to fall back on the misleading standards such as impact factors, or on ambiguous terms that can be agreed to by everyone but applied wisely by no one. It is too early to know what those standards will be or how much they will vary, but the right discussions are starting to happen. They must continue.
1. Regarding the current practices in research assessment, the author is ________.| A．supportive | B．puzzled |
| C．unconcerned | D．disapproving |
| A．Bias on assessors can cause inequality. | B．Frank conversations harm scholarly work. |
| C．Specific qualities need to be clearly stated. | D．Broad language ensures academic fairness. |
| A．primary. | B．recognized. |
| C．optional. | D．accomplished. |
| A．Fix research assessment. Change slogans for clear standards. |
| B．Fix research assessment. Change evaluations for conversations. |
| C．Define research assessment. Change simplicity for specification. |
| D．Define research assessment. Change broad language for flexible one. |
8 . Bingo has been a beloved game for generations, and for good reason.
Once you have your supplies, it’s time to invite your guests. You can invite your friends and family over for a fun night of bingo, or you can even host a virtual bingo party over video chat.
Once everyone has arrived, it’s time to start the game!
The caller is responsible for drawing the numbers and calling them out to the players. Players keep marking the numbers on their cards until someone achieves a winning pattern. There are various winning patterns in bingo, such as completing a row, column, or diagonal line, or achieving a specific shape or design on the bingo card.
The goal here is to have fun. While some organization is required, try not to tun it into a military operation. After all, bingo is the type of game that can bring people together and create a fun and exciting atmosphere.
| A．Hand out the bingo cards to each player. |
| B．You can ask your guests to bring their favourite bingo balls, cards and markers. |
| C．It should be decided before the game starts, and known to all players. |
| D．To host your own bingo party, you’ll need a few supplies. |
| E．Instead of using traditional bingo numbers and letters, mix it up with different themes. |
| F．If you’re hosting an in-person party, make sure to let your guests know what time to arrive. |
| G．It’s a fun, exciting game and can be enjoyed by people of all ages. |
9 . A 12-year-old girl from Miller Middle School in San Jose has won the top prize with $25, 000 in a science fair. Her invention is a new fire detection system that can faster detect(探测) the heat sources. It’s also cheaper and more reliable than smoke detectors.
In the summer of 2022, a restaurant behind Shanya’s house was burned to the ground. Since then, Shanya’s mother became increasingly cautious about the stove in the kitchen. It wasn’t that the restaurant didn’t have smoke detectors, but they require there is a significant amount of smoke first, which can sometimes mean a fire has already started and gotten out of control. This incident inspired her to create a fire detection system to help people suffer less loss from fires.
One day, Shanya discovered that thermal cameras can detect heat loss in homes during winter months. She wondered if these cameras could also spot house fires more quickly than traditional smoke detectors.
Shanya connected an affordable thermal camera to a tiny computer. She programmed her system to differentiate between people—which were identified as warm moving objects—and heat sources, such as a turned-on gas burner, which were identified as hot objects that remained still.
The system can send a text message when it detects a fire but no human presence for a continuous 10-minute period. Shanya conducted multiple trials at various times of day and with people entering the camera’s view from different directions. In the end, Shanya’s system accurately detected human presence 98% of the time and heat sources 97% of the time.
Shanya determined that the best place for the detector would be on the wall above the stove but under the stove range—this allowed its sensors to have clear access to the most likely locations where a fire might start in a kitchen.
After her victory, the 12-year-old has said she wants to refine the device by combining it with a smartphone app. The app will allow users to quickly switch over to a camera after receiving a text message so they can see if the alert is correct.
1. Why did Shanya invent the new fire detection system?| A．To compete in the science fair. | B．To relieve her anxiety about fire. |
| C．To help reduce people’s fire loss. | D．To market it for mass production. |
| A．find out heat sources | B．give a fire alarm |
| C．record the burning process | D．identify different people |
| A．tested all kinds of sensors | B．placed it on the ceiling |
| C．conducted some experiments | D．combined it with an app |
| A．Outgoing and smart. | B．Helpful and generous. |
| C．Modest and determined. | D．Creative and hardworking. |
Go to kindergarten, elementary school, high school, and finally, college. Most American kids
