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Guo Ailun, a high-profile player, unexpectedly failed to make the final list in the defence battle of China men’s basketball team. This defender has made great contributions to the national team in the past games with his excellent skills and breakthrough ability. However, in the selection of Djordevich, Guo Ailun was not recognized. The selection criteria of Djordevich caused controversy, which made the China men’s basketball team look a little weak in the guard lineup, and made people question his selection decision. Guo Ailun is a highly anticipated defender. He has excellent technology and agile breakthrough ability, and often shows extraordinary personal ability in competitions. In the past games, he scored key points for China national team many times and made great contributions to the team’s important victory.

Therefore, fans have high expectations for Guo Ailun’s inclusion in the final list. However, Guo Ailun was not selected as expected in the selection process of Djordevich. Obviously, the selection criteria of players in Djordevich are related to other factors. He may pay more attention to the players’ physical condition, tactical adaptability and teamwork spirit. This preference may lead to Guo Ailun’s failure to gain recognition in the eyes of Ciorjevic. Of course, Guo Ailun’s failure to make the final list may also be influenced by other factors. Injury is a possible factor. In sports competition, injuries are inevitable, which may affect the state and performance of players.

If Guo Ailun is recovering from injury at the time of selection, then his condition may not be ideal, which also affects his chances of being included in the list. In addition, the fierce competition in the position of defender is also a factor worthy of consideration. China men’s basketball team has many excellent defenders, and the competition is fierce. In the selection process of Djordevich, other players may perform better in some aspects, thus occupying the position in the defender lineup, which led to Guo Ailun’s failure to enter the final list. Whatever the reason, Guo Ailun failed to make the final list of China men’s basketball team, which made the team look a little weak in the defender lineup. In competitive sports, every position needs excellent players to ensure the team’s tactical diversity and ability to deal with emergencies.

Guo Ailun’s absence may have a certain impact on the overall strength of the China men’s basketball team in the guard position. The outside world began to question the selection decision of Jorjevich. Fans, media and experts expressed their dissatisfaction with Guo Ailun’s failure to make the list. To some extent, these doubts reflect the public’s concern and expectation for the China men’s basketball team. As a coach, Jorjevich must face the pressure and challenges from all sides. He needs to make decisions to balance the advantages and disadvantages among players and ensure the best performance of the team on the field. Although Guo Ailun didn’t make the final list, it may not be without reason that Djordevich made the decision.

As fans and spectators, we hope that the China Men’s Basketball Team can perform well, but at the same time, we also believe that the decision of the coaching staff is well thought out. We expect China men’s basketball team to achieve better results in future competitions. No matter who stands on the court, they will go all out to win honor for the national team.

A large group of researchers found that machine learning can be used to help metallurgists find the best metal mixture to make the required alloys. In this way, they found an alloy with a thermal coefficient lower than that currently recorded.

The research was carried out by scientists from Max Planck Institute for Iron Research, who cooperated with colleagues from darmstadt University of Technology, Delft University of Technology and Royal Institute of Technology.

For thousands of years, people have been mixing metals according to their own needs and learned a lot about alloy manufacturing. But finding the right combination always requires some inspiration, patience and luck.

Therefore, most alloys are made from a single base metal (such as iron), and the obtained characteristics are observed by adding a small amount of other metals.

However, in the past few decades, the situation has begun to change-some researchers have begun to make alloys containing several metals in equal parts.

Of course, it is much more difficult to make this alloy with specified characteristics. In this new work, researchers apply machine learning to help this process. They first reduced the test space to only one application-making alloys that will not expand and contract too much when exposed to temperature changes.

In order to create a machine learning application, researchers searched and found metal features that can be used for comparison purposes, and then trained their systems using the information in the currently available databases. In the process, they developed a process to find an alloy suitable for the required goal.

The team’s process was reduced to three main steps: First, they created a new mixture using a model based on information stored in a database describing metal characteristics. Then, they use the second model to help predict the properties of some alloys they created in the first step. The last step is to study the candidate alloys produced by the system and select some of them for practical testing.

Using this system, the researchers obtained 1000 candidates, which were narrowed down to three alloys. Then, they made three kinds of alloys using the mixture described in the system and tested their physical properties.

The team used data from real alloys to train the system and repeated the whole process. They chased it for seven times and found an alloy with a thermal coefficient lower than the current record.

In their paper published in the journal Science, the team described their three-step process and its performance in the test. Scientists from the Institute of Metals, Chinese Academy of Sciences published an article on the prospect in the same issue, describing the work done by the team on this new project.

Reference: https://techxplore.com/news/2022-10-machine-optimal-mixture-metals-desired.html.