Recently researchers at the University of Zurich are adding a new member to the drone （无人机） family． Unlike most drones whose application is to make selfies （自拍） or transport something， this drone was built with first aid workers and rescue efforts in mind． Disaster sites are rarely of logical shapes and sizes． So having a drone that could change its shape and size to fit through tight spaces on the fly could prove extremely valuable．

As is often the case in experimental robotic projects， researchers met some technical shortcomings at the beginning． The airplane mode of the existing aircraft， such as passenger planes helicopters and the common drones， couldn't meet the demand of different environmental features． Eventually， they turned to animals for inspiration﹣specifically how some birds can fold their wings to fly through narrow passages．

What makes it distinguishing is that when faced with a narrow passage， it can change into an H shape． Or it can shrink itself into an O shape， arms folded into its body． And if that isn't enough it can also transform into a T shape， allowing an on﹣board camera on the central frame to， get as close as possible to its focal object． While the working concept model is impressive， the researchers aren't done with the folding drone yet They want to improve the structure of the drone so that it can fully fold in three dimensions （维度）， offering even mare shapes， And they want to create full auto operation﹣a drone that looks at the changing land features in， for example， an apartment building partly destroyed by an earthquake and changes its shape to meet is current needs． Researchers say their final goal is to give the drowse a high﹣level instruction such as "enter that building， inspect every mom and come back" and let it figure out by itself how to do it．Therefore， it's reasonable to hope the new drone will play an important supporting role in disaster rescue．

Technology is here to stay. 【小题1】. However, they are often unsure on how to help children use technology responsibly and keep a balance between online and offline time. Here are some tips.

Have a device contract

Having a device usage contract allows parents and children to work through what responsible technology usage looks like in your family and ensure that everyone is aware. 【小题2】. Kids are more likely to make sense of the reasons you have for restriction.

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Although this tip seems like common sense, it is often difficult due to the demand of work and the expectation of being constantly connected. By ensuring you build offline time into your own day and communicate that with your children, they will see the value and benefits. Whether you are reading a book or just taking a walk, letting your children be aware of your offline actions will encourage them to do the same!

Encourage non-tech activities

One great way to keep kids off their screens is to ensure that they have other activities to fill their time. 【小题4】. These activities are also great ways to help children to develop their social skills and confidence.

Tech-free family time

Once a week, pencil-in a “tech-free family time” in your schedule. It can be a meal or trip. To help increase the success of outings, try designing special activities, i. e., going to the beach to build a sand castle that looks the same as a photo you have found online. 【小题5】

No one can entirely predict where the artificial intelligence(AI)industry is taking everyone, but at least the AI is ready to reliably tell you what the weather will be like when you get there. According to a paper published in Science, a new, AI-powered 10-day climate forecasting program called GraphCast is already outperforming existing prediction tools nearly every time.

Developed by Lam, one GraphCast team member, and colleagues at Google DPMD, the technology company’s AI research division, GraphCast is trained on decades of historic weather information alongside roughly 40 years of satellite, weather station, and radar reanalysis. This stands in sharp contrast to what are known as numerical weather prediction(NWP)models, which traditionally use massive amounts of data concerning thermodynamics, fluid dynamics, and other atmospheric sciences. All those data require intense computing power. On top of all that, NWP models are slow—taking hours for hundreds of machines within a supercomputer to produce their 10-day forecasts.

GraphCast, meanwhile, offers highly accurate, medium range climatic predictions in less than a minute, all through just one of Google’s AI-powered machine learning tensor processing unit (TPU) machines. During a comprehensive performance evaluation against the industry-standard NWP system—the High-Resolution Forecast (HRES)—GraphCast proved more accurate in over 90 percent of tests. When limiting the scope to only the Earth’s troposphere, the lowest part of the atmosphere and home to most noticeable weather events, GraphCast beat HRES in an astounding 99.7 percent of test variables.

“Pioneering the use of AI in weather forecasting will benefit billions of people in their everyday lives,” Lam said. He noted GraphCast’s potential vital importance amid increasingly destroying events resulting from climate breakdown. “Predicting extreme temperatures is of growing importance in our warming world, ” Lam continued. “GraphCast can characterize when the heat is set to rise above the historical top temperatures for any given location on Earth. This is particularly useful in anticipating heat waves，disruptive and dangerous events that are becoming increasingly common. ”