In the modern aviation industry, accurate prediction of complex flow fields is of great significance for optimizing blade design and improving engine performance. Although traditional computational fluid dynamics (CFD) methods can provide high-precision flow field information, they have long calculation time and high resource consumption, making it difficult to meet the rapid response requirements of engineering practice. As an emerging machine learning model, neural networks have gradually become an effective tool for flow field prediction with their powerful nonlinear mapping capabilities and high computational efficiency. This paper aims to combine machine learning technology to construct an efficient and accurate flow field prediction model method, and introduce new theoretical support for the design and optimization of aircraft engines. This paper first explains the basic concepts of machine learning and the shortcomings of current flow field prediction methods. Then, through three cases, the basic principles and application process of neural networks are introduced, including BP neural network, RBF neural network and UNet neural network methods, and the current status and superiority of neural networks in complex flow field prediction are analyzed in detail, providing an important reference for promoting the informatization and intelligent development of the aviation manufacturing industry.

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Transverse confinement is important in enhancing the mechanical performance of ultra-high-performance concrete (UHPC) columns under axial compressive loads. This research offers a detailed review of the axial compression behaviour (ACB) of UHPC columns confined by transverse steel bars, steel tubes, and fiber-reinforced polymer (FRP) sheets, respectively. The findings indicate that using a combination of transverse steel bars, steel tubes, and FRP sheets significantly boosts the bearing capacity and ductility of UHPC columns, leading to increased peak stress and enhanced peak strain in the confined concrete. The ACB of UHPC columns restrained by transverse steel bars is influenced by the strength and volumetric ratio of the stirrups; higher strength and greater stirrup volume result a lot in enhanced performance. Similarly, the strength and thickness of the steel tube are key factors in the ACB of UHPC confined by steel tubes, with performance improving proportionally to these attributes. For FRP sheet-confined UHPC, the mechanical properties, thickness, and winding configuration of the FRP sheets critically affect the ACB. Thicker sheets and increased winding layers correlate with better mechanical properties. The insights provided in this study offer valuable guidance for engineers considering the implementation of UHPC in construction projects, providing a reliable foundation for optimizing ACB through appropriate transverse confinement strategies.

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Machine learning always requires a large amount of labeled data, and the test data may have a different distribution than the training data. Transfer learning has proven to be an essential method for solving this problem in many fields. However, achieving successful transfer in graph datasets remains challenging, as the pre-training datasets must be large enough and carefully selected. This research looks at the inherent challenges of data scarcity and the need for robust models to increase the versatility and efficiency of Graph neural networks (GNNs)in various implementation domains. By examining the performance between trained GNNs and non-pre-trained GNNs, which can further demonstrate the generalization of the pre-trained GNN strategy and the significance of transfer learning to graph data.

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In this study, we delve into the intersection of high-dimensional statistics and machine learning within the realm of sports analytics, with a particular focus on real-time prediction of NBA game outcomes. We harness cutting-edge data techniques and innovative AI models to boost our predictive capabilities and real-time performance. By combining advanced data processing with the latest in machine and deep learning, we're able to deliver more accurate and timely insights across a range of complex scenarios. Our approach integrates Bayesian statistical methods to quantify prediction uncertainty, ensuring robust and interpretable models. We utilize a combination of traditional machine learning models, such as Random Forest and Logistic Regression, alongside advanced deep learning architectures, including CNNs, RNNs, LSTMs, and Transformer networks. Our comprehensive preprocessing pipeline includes advanced statistical techniques for handling missing values and outliers, ensuring data consistency, and feature selection and dimensionality reduction methods like PCA and RFE. Implementing real-time data streaming technologies such as Apache Kafka and distributed databases like Apache Cassandra ensures high availability, scalability, and efficient handling of large volumes of data. This study highlights the significant potential of integrating high-dimensional statistics and deep learning in sports analytics, offering deeper insights, more accurate predictions, and real-time analysis capabilities, paving the way for future innovations and applications in the field.

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