The selection of basketball players should highlight their specific characteristics and proceed according to the essential laws of basketball. When the acquired training level becomes closer and closer, and is more and more conducive to the control of the entire training and competition, the selection of the standard paradigm of basketball players plays a key role. At present, the existing evaluation methods of basketball players are limited to the human experience of coaches, and there is a lack of further information evaluation methods. This article discusses a new type of basketball player evaluation scheme that combines wireless network and machine learning methods. First, the wireless sensor network is used to perceive basketball players' performance on the court and record various evaluation indicators. Secondly, establish a player value evaluation model through improved Bayesian algorithm and fuzzy comprehensive evaluation methods. Finally, after relevant tests and comparisons with the coaches' results, the model showed better evaluation results and a fairer value distribution.

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Deep convolution neural networks (CNNs) play a critical role in single image super-resolution (SISR) since the amazing improvement of high performance computing. However, most of the super-resolution (SR) methods only focus on recovering bicubic degradation. Reconstructing high-resolution (HR) images from randomly blurred and noisy lowresolution (LR) images is still a challenging problem. In this paper, we propose a novel Spatial Context Hallucination Network (SCHN) for blind super-resolution without knowing the degradation kernel. We find that when the blur kernel is unknown, separate deblurring and superresolution could limit the performance because of the accumulation of error. Thus, we integrate denoising, deblurring and super-resolution within one framework to avoid such a problem. We train our model on two high quality datasets, DIV2K and Flickr2K. Our method performs better than state-of-the-art methods when input images are corrupted with random blur and noise.

Aim. Assessing the national interests of the People's Republic of China and the Russian Federation in the context of their participation in the development of the Shanghai Cooperation Organization.Methodology. Comparative analysis and descriptive method were chosen as the main research methods, which were used to express the specifics as well as similarities and differences in the national interests of China and Russia within the framework of participation in the Shanghai Cooperation Organization. The role of the Shanghai Cooperation Organization as a multifaceted organization in the international space is assessed and the possibilities of Russia and China are forecasted.Results. Based on the analysis of periodicals and Internet sources, the conclusion was made that a huge role in the success and effectiveness of the Shanghai Cooperation Organization belongs to two countries: China and Russia.  The directions of the partnership between these states, as well as the current situation at the present stage of SCO development, point to problems based on the different objectives of the Russian Federation and the China and the potential for competition between the two countries for world supremacy.Research implications. The results of the study consist in the systematization of knowledge on the issues of SCO development.

The Editor-in-Chief and the publisher have retracted this article. The article was submitted to be part of a guest-edited issue. An investigation by the publisher found a number of articles, including this one, with a number of concerns, including but not limited to compromised editorial handling and peer review process, inappropriate or irrelevant references or not being in scope of the journal or guest-edited issue. Based on the investigation's findings the Editor-in-Chief therefore no longer has confidence in the results and conclusions of this article. Dong Huo has not responded to correspondence regarding this retraction. Publisher's NoteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.