According to the Solar Energy Industry Association, the number of solar panels installed(安装)has grown rapidly in the past decade, and it has to grow even faster to meet climate goals. But all of that growth will take up a lot of space, and though more and more people accept the concept of solar energy, few like large solar panels to be installed near them.

Solar developers want to put up panels as quickly and cheaply as possible, so they haven't given much thought to what they put under them. Often, they'll end up filling the area with small stones and using chemicals to control weeds. The result is that many communities, especially in farming regions, see solar farms as destroyers of the soil.

"Solar projects need to be good neighbors," says Jordan Macknick, the head of the Innovative Site Preparation and Impact Reductions on the Environment(InSPIRE)project. "They need to be protectors of the land and contribute to the agricultural economy." InSPIRE is investigating practical approaches to "low-impact" solar development, which focuses on establishing and operating solar farms in a way that is kinder to the land. One of the easiest low-impact solar strategies is providing habitat for pollinators(传粉昆虫).

Habitat loss, pesticide use, and climate change have caused dramatic declines in pollinator populations over the past couple of decades, which has damaged the U.S. agricultural economy. Over 28 states have passed laws related to pollinator habitat protection and pesticide use. Conservation organizations put out pollinator-friendliness guidelines for home gardens, businesses, schools, cities—and now there are guidelines for solar farms.

Over the past few years, many solar farm developers have transformed the space under their solar panels into a shelter for various kinds of pollinators, resulting in soil improvement and carbon reduction. "These pollinator-friendly solar farms can have a valuable impact on everything that's going on in the landscape," says Macknick.

"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".

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.