中国梦之声

佩德里踢球时为何脸红？运动医学专家为你揭秘_我的网站

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     Lingjun Zhenwu M890 supernode instance  Photo: Courtesy of Alibaba CloudAlibaba Cloud on Tuesday officially launched its Lingjun Zhenwu M890 supernode instance in Ulanqab, North China's Inner Mongolia Autonomous Region, with the first batch of instances now available for sale in the region. 
The instance is designed to handle inference for mixture-of-experts models with up to 10 trillion parameters, the company said in a statement sent to the Global Times on Wednesday. 
This marks the first supernode-form computing architecture in China to successfully run large language models exceeding 2 trillion parameters, according to the company.
Industry expert Tian Feng told the Global Times that the commercial rollout of supernode infrastructure could significantly reduce training cycles, lower costs, and speed up iteration for AI developers requiring massive computational resources.
The company said the new instance has already been used to power commercial services for large language models such as KimiK3 and Qwen3.8Max.
The Lingjun Zhenwu M890 ­supernode instance supports FP8/FP4 low-precision computing. Through the ICNSwitch 1.0 chip, its scale-up interconnect scale has been expanded from 16 cards to 64 cards, with inter-card interconnect bandwidth boosted to 800 GB/s. Enterprises can provision 64-card, high-speed-interconnect computing units through the cloud without building their own data centers, according to the company.
In training scenarios such as autonomous driving and embodied intelligence, the instance delivers three times the training performance compared with the previous-generation Zhenwu 810E, the company said.
Ulanqab, where the supernode instance debuted, is one of Alibaba Cloud's five super data centers. The facility sources approximately 90 percent of its electricity from green energy, providing a low-carbon operating environment for high-density computing power.
Leveraging its climate, energy and network advantages, Ulanqab has transformed from "China's potato hometown" into the "token factory"  - a term increasingly used in the AI industry to describe infrastructure dedicated to producing the digital building blocks generated by large language models. 
By the end of 2025, the city had attracted 84 data center projects, including 81 intelligent computing centers, with total investment exceeding 500 ­billion yuan ($74.1 billion) and operational computing power reaching approximately 172,000 petaflops, ranking it firmly in the nation's top tier, according to domestic media reports.
On August 6, China's largest AI computing industrial park was completed and put into operation in Ulanqab. The project highlights a broader race in ­China to build massive AI data centers capable of supporting the next generation of AI models while addressing soaring electricity demand, according to Chinese experts.
In recent years, Inner Mongolia has been rapidly positioning itself as a global-scale AI computing center cluster. Major technology companies, including Huawei, Tencent, ByteDance and Alibaba; telecom operators China Mobile, China Telecom and China Unicom; as well as cyberspace infrastructure service provider VNET, have established computing facilities in the region.
As the AI industry gradually transitions from the training era to the inference era and large model parameters continue to expand, supernodes have become a central battleground for AI infrastructure.
Chinese vendors are accelerating deployments in this space. Huawei has commercially deployed more than 750 sets of its Ascend 384 supernodes across industries including internet, telecom operators, finance, education, healthcare, transportation and manufacturing. It is also the only domestic supernode to have trained state-of-the-art (SOTA) models.
Baidu AI Cloud has also launched its Tianchi 256-card supernode based on Kunlun chips, with support for major models including Wenxin, DeepSeek, GLM, and MiniMax.
Meanwhile, supercomputer manufacturer Sugon has unveiled China's first fully domestic 100,000-card AI ­supercluster Sugon 8000 (Dengfeng), integrating supercomputing and AI computing on a unified architecture. It has now been connected to the national supercomputing internet to provide computing services to government, research, and enterprise clients nationwide. 
Tian, former dean of SenseTime's Intelligence Industry Research Institute, told the Global Times that the flurry of domestic supernode launches reflects a broader inflection point as China's AI sector pivots from training capacity toward efficient, large-scale inference. 
The expert further said that the commercial viability of these systems - evidenced by Huawei's extensive deployed base, Baidu's rapid model adaptation and Sugon's integration into the national computing network - suggests domestic vendors are moving beyond proof-of-concept to genuine production-grade infrastructure, a prerequisite for sustaining the next wave of ­trillion-parameter model proliferation, Tian noted.
The move also underscores China's push for self-reliance in AI infrastructure as US chip export restrictions continue to tighten, Tian said, noting that, in the supernode domain, Chinese companies are shifting from imported graphics processing units toward homegrown interconnect chips and domestic compute clusters, a transition that could reshape value allocation across the AI industry chain.
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本文编译自西班牙《马卡报》旗下的健康频道

巴塞罗那足球俱乐部的中场球员佩德里，在比赛结束时常常满脸通红，这一现象不仅和运动强度脱不了干系，还与球员调节自身身体机能、提升竞技表现的方式息息相关。马卡报的健康频道专门采访了运动医学专家，为大家揭开这背后的科学奥秘。

佩德里・冈萨雷斯赛后的面部潮红总是格外引人注意。运动医学专家、欧洲大学 i-Shape 研究中心讲师弗朗西斯科・何塞・马丁・戈麦斯解释道：“就这位球员而言，这种反应首先是‘良性运动负荷下的正常表现，不过可能存在一定的遗传易感性’。” 但他也提醒，除了热不耐受或者运动过量之外，“面部潮红的背后，可能还潜藏着其他生理或临床因素”。脸红背后的生理机制：身体怎样应对运动产生的热量？

专家指出，人体依靠 “效率不算高的代谢过程” 为运动提供能量：“细胞代谢（比如糖酵解和线粒体三羧酸循环）释放的能量中，只有 20% - 25% 会转化为机械功（也就是肌肉收缩），剩下 75% - 80% 的能量都以热量的形式散失了。” 这意味着，在跑步、举铁或者进行任何运动时，“身体每产生 100 单位的能量，大概就有 75 单位会变成热量”。

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他打了个比方：“想象一辆汽车，它只能把 25% 的汽油转化为动能，其余的都通过排气管以热量的形式排出去了。运动时的人体就类似这种情况。”

为了防止核心温度危险升高，进而引发中暑（导致器官功能紊乱甚至衰竭），身体必须要把热量散发出去。马丁・戈麦斯着重强调：“当体温达到 38.5℃时，运动表现就会下滑，人会感觉疲劳，甚至可能抽筋；超过 40℃，就可能出现意识模糊、头晕目眩，甚至有休克的风险；要是达到 42℃，蛋白质就会变性，大脑功能衰竭，可能引发抽搐、中风、昏迷，甚至死亡。” 所以，在高强度运动中及时散热极为关键，“不然就相当于从身体内部把自己‘煮熟’了”。面部潮红：身体高效散热的反应

在运动初期，血管会先收缩，之后 “皮肤的血流量能够通过血管扩张增加 4 - 7 倍，尤其是面部和颈部的血管，以此加快热量的散发”。专家指出，这并不是什么异常情况，“而是身体为了提高散热效率，在长期进化过程中形成的一种适应性机制”。

那些能够通过蒸发（出汗）和辐射散掉更多热量的人，“在高强度运动中会更有优势，能避免体温过度升高，从而延缓运动性疲劳的出现”。为何佩德里更容易脸红？遗传因素与生理特征共同作用

专家分析，“皮肤白皙、表层毛细血管密度高的人，更容易出现面部潮红，佩德里很可能就是这种情况”。

三 | 另外，经过系统训练的运动员 “产热能力更强，同时，他们还能通过更高的皮肤血流量来提升散热效率”，这是身体适应运动的一种表现。

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研究显示，面部潮红存在一定的遗传倾向：“皮肤光型较浅（I 型和 II 型）的人，真皮透明度更高，毛细血管扩张以及运动时的交感神经反应会更明显；此外，还有部分基因与主观热感以及皮肤血管反应有关。”导致面部潮红加剧的常见因素

除了运动强度之外，还有以下这些因素可能会加重脸红的现象：

• 高温环境：环境温度越高，人体就越难通过对流或传导来散热（这两种散热方式依赖于温度差、湿度以及是否有气流）；

• 高湿度：当空气里水汽饱和时，汗液没办法蒸发，就会停留在皮肤表面，导致散热无法正常进行；

• 脱水：身体缺水时，汗液分泌会减少，散热能力也就跟着下降了；

• 着装不当：不透气的衣物会阻碍汗液蒸发，使得热量滞留在体内；

• 高强度运动：能量需求增大，产热也就更多，进而激活更强的散热机制；

• 过度的交感神经反应：比赛时肾上腺素分泌增加、现场观众营造的氛围、紧张情绪等，都会加剧血管反应；

• 散热障碍：包括尚未适应高温环境（部分与遗传有关）、糖尿病神经病变、脊髓损伤、自主神经功能紊乱等神经系统疾病，还有汗腺疾病（无汗症 / 多汗症、烧伤或者皮肤病损伤）。

需要警惕的潜在健康问题

虽说多数情况下，面部潮红属于良性反应，但还是要排查以下这些可能：

• 玫瑰痤疮：这是一种慢性皮肤病，运动会使其症状加重；

• 类癌综合征或特发性潮红：这类情况比较罕见，但要是同时伴有心悸、腹泻等其他症状，就需要及时就医；

• 材质过敏：与汗液接触的衣物材质可能会引发过敏，不过要是双侧对称发红，这种情况不太常见；

• 过度反应性充血：血管收缩之后，血液回流过度的一种循环反应；

• 热不耐受或体温调节障碍：身体在运动时，没办法有效处理体温升高的情况，只能通过极端的血管扩张来进行代偿散热。

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Published on:08:35:06