If you ever get the impression that your dog can "tell" whether you look content or annoyed, you may be onto something. Dogs may indeed be able to distinguish between happy and angry human faces, according to a new study.

Researchers trained a group of 11 dogs to distinguish between images（图像）of the same person making either a happy or an angry face. During the training stage, each dog was shown only the upper half or the lower half of the person's face. The researchers then tested the dogs' ability to distinguish between human facial expressions by showing them the other half of the person's face or images totally different from the ones used in training. The researchers found that the dogs were able to pick the angry or happy face by touching a picture of it with their noses more often than one would expect by random chance.

The study showed the animals had figured out how to apply what they learned about human faces during training to new faces in the testing stage. "We can rule out that the dogs simply distinguish between the pictures based on a simple cue, such as the sight of teeth," said study author Corsin Muller. "Instead, our results suggest that the successful dogs realized that a smiling mouth means the same thing as smiling eyes, and the same rule applies to an angry mouth having the same meaning as angry eyes.”

"With our study, we think we can now confidently conclude that at least some dogs can distinguish human facial expressions," Muller told Live Science.

At this point, it is not clear why dogs seem to be equipped with the ability to recognize different facial expressions in humans. "To us, the most likely explanation appears to be that the basis lies in their living with humans, which gives them a lot of exposure to human facial expressions," and this exposure has provided them with many chances to learn to distinguish between them, Muller said.

"What would the world be if there were no hunger?" It's a question that Professor Crystal would ask her students. They found it hard to answer, she wrote later, because imagining something that isn't part of real life—and learning how to make it real—is a rare skill. It is taught to artists and engineers, but much less often to scientists. Crystal set out to change that, and helped to create a global movement. The result一an approach known as systems thinking—is now seen as essential in meeting global challenges.

Systems thinking is crucial to achieving targets such as zero hunger and better nutrition because it requires considering the way in which food is produced, processed, delivered and consumed, and looking at how those things intersect (交叉) with human health, the environment, economics and society. According to systems thinking, changing the food system—or any other network- requires three things to happen. First, researchers need to identify all the players in that system, second, they must work out how they relate to each other, and third, they need to understand and quantify the impact of those relationships on each other and on those outside the system.

Take nutrition. In the latest UN report on global food security, the number of undernourished (营养不良) people in the world has been rising, despite great advances in nutrition science. Tracking of 150 biochemicals in food has been important in revealing the relationships between calories, sugar, fat and the occurrence of common diseases. But using machine learning and artificial intelligence, some scientists propose that human diets consist of at least 26,000 biochemicals—and that the vast majority are not known. This shows that we have some way to travel before achieving the first objective of systems t hinking - which,in this example, is to identify more constituent parts of the nutrition system.

A systems approach to creating change is also built on the assumption that everyone in the system has equal power. But as some researchers find, the food system is not an equal one. A good way to redress (修正) such power imbalance is for more universities to do what Crystal did and teach students how to think using a systems approach.

More researchers, policymakers and representatives from the food industry must learn to look beyond their direct lines of responsibility and adopt a systems approach. Crystal knew that visions alone don't produce results, but concluded that "we'll never produce results that we can't envision".

Can a small group of drones（无人机）guarantee the safety and reliability of railways and, at the same time, help railway operators save billions of euros each year? That is the very likely future of applying today's "eyes in the sky" technology to making sure that the millions of kilometres of rail tracks and infrastructure（基础设施）worldwide are safe for trains on a 24/7 basis.

Drones are already being used to examine high-tension electrical lines. They could do precisely the same thing to inspect railway lines and other vital aspects of rail infrastructure such as the correct position of railway tracks and switching points. The more regularly they can be inspected, the more railway safety, reliability and on-time performance will be improved. Costs would be cut and operations would be more efficient（高效）across the board.

That includes huge savings in maintenance costs and better protection of railway personnel safety. It is calculated that European railways alone spend approximately 20 billion euros a year on maintenance, including sending maintenance staff, often at night, to inspect and repair the rail infrastructure. That can be dangerous work that could be avoided with drones assisting the crews' efforts.

By using the latest technologies, drones could also start providing higher-value services for railways, detecting faults in the rail or switches, before they can cause any safety problems. To perform these tasks, drones for rail don't need to be flying overhead. Engineers are now working on a new concept: the rail drones of the future. They will be moving on the track ahead of the train, and programmed to run autonomously. Very small drones with advanced sensors and AI and travelling ahead of the train could guide it like a co-pilot. With their ability to see ahead, they could signal any problem, so that fast-moving trains would be able to react in time.