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Our economy runs on consumption, especially during the holidays—we as consumers are overwhelmed by emails and ads about the newest holiday gifts. Buying a brand new gift for a loved one, we may feel generous and not feel the environmental impact. But with millions of people doing the same, the resources spent really add up.

Journalist Annalise Griffin recommends people rethink the value of gift-giving and shop in an effective eco-friendly way.

A gift doesn’t have to be expensive, says Griffin. In the holiday shopping rash, it’s easy to forget why we give in the first place. She thinks it’s wise to adopt a more open-minded approach. For instance, pack a gift box with homemade cookies or get the whole family in on the act of paring down by tie-dyeing（扎染）white clothes from a local secondhand store. They may not be pricey but the love behind it counts.

As for green shopping, Griffin practices her way of buying used items instead of new ones. She mentions several websites and online communities where people can buy, give away or trade secondhand items. Her go-to for shopping for clothes and housing items include the more familiar eBay and Poshmark. For vintage（复古）goods, she recommends shopgoodwill. com.

Whether we’re making a commitment to cutting out extra spending or showing concern for the environment, it’s vital to share our ideas with the loved ones. It may be hard to convince a teenager not to buy the latest iPhone, so having these conversations early on can go a long way.

Leung Long-kong, 89, might not be a household name, but people around the world have admired his work, thanks to the award-winning film In the Mood for Love, which features 23 colorful qipao made by Leung.

Starting to make qipao at the age of 13, Leung witnessed the gradual shift of qipao from a dress of ethnic Manchu origins to a Chinese fashion icon (标志). The unique dress, which used to have a straight skirt covering a woman’s body, except for her head, hands and feet, became more form-fitting in the 1950s after it was brought to Hong Kong by a group of Shanghai tailors.

The glory days of qipao in the 1950s and 60s Hong Kong faded in a time of mass production of ready-to-wear clothing. “Qipao used to be so popular,” Leung says, adding that the dress was an everyday outfit (装束) among Hong Kong women, from the less well-off to women at the highest levels of society. “Now, nobody is wearing them except on grand and happy occasions.”

With less than 10 senior qipao tailors left in Hong Kong, young designers are trying to figure out a way to pass on the legacy. Fashion designer Mary Yu, 41, set up her own qipao brand in 2016. Yu says, “I fed I should tap into Chinese culture. After a period of wearing Chanel all the time, there will be a day when one looks back to traditional Chinese culture, whose elegance has been inherited (继承) for thousands of years. It is about finding the stuff that exists in your genes and suits you best.”

In the constantly developing fashion industry, qipao is catching up with the times. Zippers, digital print patterns and new materials have been introduced. Bolder ideas like 3D printed qipao have also become a reality.

Animals perform tasks that their bodies are well designed for. That’s because their intelligence evolved（进化）with their physical form as they interacted with the environment. Artificial intelligence is smart as well but it doesn’t have a body.

Agrim Gupta of Stanford University and his colleagues wondered: Does having bodies matter for the evolution of intelligence? To answer it, they created a virtual robot named “unimal” and studied how having virtual bodies affected the evolution of the unimals’ intelligence.

Unimals are pretty simple with balls for heads and cylinders for anns and legs. The test environments came in three varieties: flat areas, areas filled with hills, steps and broken stones, and areas that had the complexities of the second sort, but with added objects that unimals need to move to a target location.

At first, the unimals were in random shapes, but with identical software running them. In groups of four, Dr Gupta put them through tests of agility, stability, and ability to control objects. Then each group’s winner was allowed to give birth to a daughter with one mutation（变异）, for example, an extra leg for stability. This daughter was sent to a new group to be tested. The process repeated and the test ended after ten generations of evolution.

The diversity of the unimals’ shapes that evolved surprised the team. Some had arms as well as legs while others had only legs. Crucially, though, the researchers found the most successful unimals, which evolved in the toughest environment, learned tasks faster and better than their ancestors. This consists with the Baldwin effect, which suggests that minds evolve to make best use of the morphologies（形态）of the bodies they find themselves in.

It’s believed that the result will provide insights for developing robots that can learn to perform multiple tasks in the real world.

Science is a mental toolbox for solving problems.     16    . And they are powerful and useful in everyday life. Today, we open the toolbox: how scientists think, and how you can borrow their tricks.

•Start with an open mind. Being open-minded means that when we are asked a question, scientific thinking requires us to admit that we often simply don’t know the answer.    17    . If the evidence changes and you don’t change your mind, that’s not a good spot to be in.

•Accept uncertainty. Scientists always accept that there are things they don’t know, and things they simply cannot know for sure.    18    . It allows you to make better decisions because you’re taking the uncertainty into account. Science says it’s OK to wait for evidence before coming to a conclusion.

•    19    . Go and do your research. Pull together all the data—stuff that agrees with your argument and stuff that refutes（驳斥）it. Consider everything, rather than focusing on a single point. Then, for each piece of data, consider the quality of the evidence.

• Develop a hypothesis（假设）. A hypothesis is a theory about the world—a guess. Ask yourself: Which of my theories best explains the data I have gathered? Start with the most likely explanation and test it.    20    .