Growing pains aren't a disease. You probably won't have to go to the doctor for them. But they can hurt. Usually they happen when kids are between the ages of 3 and 5 or 8 and 12. Doctors don't believe that growing actually causes pains, but growing pains stop growing actually causes pains, but growing pains stop when kids stop growing. By the teen years, most kids don’t get growing pains any more.

Kids get growing pains in their legs. Most of the time, they hurt in the front of the thighs（the upper part of your legs）, in the calves（the back part of your legs below your knees）, or behind the knees. Usually, both legs hurt.

Growing pains often start to ache right before bedtime. Sometimes you go to bed without any pains, but you might wake up in the middle of the night with your legs hurting. The best news about growing pains is that they go away by morning.

Growing pains don't hurt around the bones or joints（the flexible parts that connect bones and let them move）-only in the muscles. For this reason, some doctors believe that kids might get growing pains because they've tired out their muscles. When you run, climb or jump a lot during the day, you might have aches and pains in your legs at night.

Your parents can help you feel better by giving you an over-the-counter（非处方的）painkilling drugs. Kids should not take aspirin because it can cause a rare but serious illness called Reye's syndrome（雷耶氏综合征）.

Here are three other things that might help you feel better：

（1）placing a heating pad on the spot where your legs hurt；

（2）stretching your legs like you do in gym class；

（3）having your parents massage your legs.

You might never feel any growing pains, but if you do, remember that before you know them, you will outgrow them！

Four and a half billion people worldwide live without a household toilet. Hundreds of millions of people are using facilities that are unhealthy and more than a billion people defecate (排泄) in the open.

Michael decided to take action in response to what is one of the world's greatest public health crises. He invited 32 health workers from 16 communities in the southern Highlands of Madagascar to attend a training to organize communities to completely wipe out open defecation.

After completing the initial training, the 32 health workers returned to their towns and got to work. They met with their community and began to map out their town. Together they toured the area and pointed out every spot where people often defecate. Many of them became embarrassed after realizing the high amount of human waste in their community. They then continued to make a bill for the total costs of trips to the local health center when being treated for gastrointestinal (肠胃的) diseases. They quickly began to realize the financial involvement, besides the health risks of open defecation.

The communities began to solve the problem on their own. They considered ways to reduce the cost, by using local materials. Bamboo fences were suggested as an alternative to wooden fences. Mud could be used to build walls instead of bricks. Before Michael starting his project, 267 toilets existed for the 6, 700 people living in the communities Michael aimed to reach. It was not enough at all. More than 300 additional toilets were built. He was pleasantly surprised by the enthusiasm he saw from the community towards making open defecation less.

You might think a computer would be a fair judge, but a new study from MIT finds it might be better for you to leave yourself in the hands of humans. AI won’t let people off easily when they break the rule!

This study, conducted by a team at MIT, examined how AI would interpret perceived violations (违反) of a given code. They discovered the most effective data for AI training is normative data, where humans determine whether a rule has been violated. However, data used to train machine-learning models are typically labeled descriptively — meaning people label the factual features of a situation, and AI determines whether a code has been broken.

The team gathered images of dogs that could potentially violate an apartment rule banning aggressive animals. Labelers were then asked to provide normative and descriptive responses. The descriptive team wasn’t informed about the policy on dogs, and was asked to identify whether elements, such as the dog’s aggression, were present. Their responses helped to form judgments. If a user said the photo described an aggressive dog, the policy was considered to be violated. On the other hand, the normative group was informed about the rule and was asked to determine whether each image violated the rule.

Participants were 20 percent more likely to identify a violation using the descriptive method. If the descriptive data on dog behavior had been used to program an AI model, it would be more likely to issue severe punishment. These inaccuracies could be a real-world concern. For instance, if a descriptive model is used to predict whether a person may commit the same crime more than once, it may result in longer criminal sentences.

The researchers advocate for increased data transparency (透明性) to understand how data is collected. And their future work is to investigate the impact of having experts, such as lawyers and doctors, participate in data entry to see if it will lead to the same result. “Generally, normative decisions tend to be more lenient (宽容的),” says lead author Aparna Balagopalan. “Perhaps the way people think about violations differs from how they think about descriptive data.”