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Since December 2021, Huawei & Honor mobile phones have launched the Vientiane desktop widget function. Many convenient functions, visually optimized desktop controls, etc. have been added to many users’ mobile desktops; by August this year, the two major merchant platforms also opened up sports and health data, weather data, music data, system data, etc., allowing users to use their mobile desktops The interactive operation is more convenient, faster and more interesting, allowing users to DIY and create their own personalized desktop. Mobile desktop after adding widgets Recently, many Huawei mobile phone users have reported that they are not clear about how to add desktop widgets on Huawei and Honor mobile phones, complaining that the process is too complicated and cumbersome. In order to help everyone solve this problem, Qian Shuxian has prepared a detailed operation process, hoping to

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Title: Efficient Practice Guide for Go Language Encoding Conversion In daily software development, we often encounter the need to convert text in different encodings. As an efficient and modern programming language, Go language provides a rich standard library and built-in functions, making it very simple and efficient to implement text encoding conversion. This article will introduce practical guidelines on how to perform encoding conversion in the Go language and provide specific code examples. 1.UTF-8 encoding and string conversion In Go language, strings use UTF-8 encoding by default

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Python and C++ are two popular programming languages, each with its own advantages and disadvantages. For people who want to learn programming, choosing to learn Python or C++ is often an important decision. This article will explore the learning costs of Python and C++ and discuss which language is more worthy of the time and effort. First, let's start with Python. Python is a high-level, interpreted programming language known for its ease of learning, clear code, and concise syntax. Compared to C++, Python

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Editor | In the era of ScienceAI large models, the effects of purely data-driven meteorological and climate models are gradually catching up to or even surpassing numerical models. However, existing large-scale meteorological and climate models still have some problems. For example, the physical consistency in the model is not high enough, which limits the ability to predict complex weather and climate phenomena such as precipitation. In addition, the forecast effect of divergent wind is not satisfactory. These issues require further research and improvement to improve the prediction accuracy and reliability of the model. At present, combining physics, atmospheric dynamics and deep learning models is an important way to solve the bottleneck problem. Recently, the team of researcher Huang Gang from the Institute of Atmospheric Physics, Chinese Academy of Sciences, based on the data and computing power support of the Earth System Numerical Simulation Facility (Huan), analyzed the coupling relationship between physical variables from the perspective of
