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In order to solve the challenges brought by multi-source and cross-domain scenarios to online music education, this paper designs an online music education system based on advanced artificial intelligence technology, which can provide personalized learning course resource recommendations for music online learners. The system includes four layers, consisting of user interface layer, application module layer, function module layer and data storage layer. At the application module level, this paper proposes a music recommendation algorithm based on a personalized multimodal network model. The recommendation algorithm performs music information retrieval (MIR) based on the similarity judgment of the contour of music pitch and the overall change, and constructs a multimodal network model based on the user's preference for resources to achieve personalized music recommendation. This paper crawls more than one million music score data from a well-known music platform database in China to establish a dataset to evaluate the performance of this method. The comparison results with three existing works show that the method proposed in this paper has good performance and can provide users with suitable music recommendations. The artificial intelligence technology-driven online music education mechanism proposed in this paper has good prospects. [ABSTRACT FROM AUTHOR]

The low-medium maturity shale oil in China has large resource potential. The occurrence state of such resource is mainly retained hydrocarbon and unconverted organic substance, making the in-situ conversion process a requisite technology. Meanwhile, the naturally fractured shale reservoir has multiple types of pore space, making the hydrothermal coupling process more complex. In this paper, a coupled thermal-reactive compositional flow model and numerical solution method is proposed. Specifically, the apparent permeability of fractured shale oil reservoir is derived based on fractal theory. Besides, the multistage parallel chemical kinetic reactions during in-situ conversion process and evolution of reservoir fluid composition and shale porosity are incorporated. Then the accuracy of the proposed model is tested through numerical cases. Finally, the impact of key operation parameters and fractal parameters on shale oil exploitation is analyzed, such as heating temperature, heating spacing, pore size fractal dimension, fracture length fractal dimension and tortuosity fractal dimension. The proposed model is capable of accurate and efficient prediction of the fractured low-medium maturity shale oil reservoir production. [ABSTRACT FROM AUTHOR]

The purposes of this paper are to analyze the financial management and influencing factors of small and medium-sized enterprises (SMEs) and explore the nonlinear relationship in enterprises' financial market structure. The small and medium-sized enterprises in Eastern and Western China are taken as the research objects. First, the relationships between asset–liability constraints and financial management are discussed, analyzed, and explained. The development of enterprises' financial management under the asset–liability constraints system is emphasized. Second, a self-adaptive nonlinear dynamic system is proposed based on dynamic surface control and the multi-directional and uncertain control of the financial market structure. Finally, a dynamic nonlinear panel estimation model for enterprises is constructed based on the nonlinear system. The simulation and empirical analysis results confirm that the proposed nonlinear system is useful in dynamic, uncertain control problems. The statistical results of the three primary indicators, economic variables, financial indicators, and control variables, reveal the significant regional differences of different financial market structure indicators. Model estimates based on the three key sub-indicators, the leverage ratio, return on assets, and debt interest rate, reveal the differences in financing and leverage ratio of small and medium-sized enterprises located in the eastern and western regions. SMEs in Eastern China are taken as examples; the direct financing rate and insurance proportion are negatively correlated with the leverage ratio and debt interest rate. In contrast, they are positively correlated with asset returns. In conclusion, there are noticeable differences in the financial market structure between different regions. [ABSTRACT FROM AUTHOR]

The aim of this paper is to improve the accurate analysis of the sports training project, research its video data profoundly, and discover deficiencies and summarize experience from the existing videos, thereby promoting China's sports industry. According to previous works, the existing sports training target detection system has the problem of nonlinear video and low target detection accuracy, which can easily cause target loss or tracking failure. Therefore, the Kalman Filter (KF) is studied in-depth and applied to detect the targets in sports videos. Combined with the Multi-Innovation (MI) theory, an MI-Extended KF (EKF) algorithm model based on nonlinear dynamic technology is proposed. This model can effectively solve the filtering accuracy problem under the strong nonlinear system. Finally, the performance analysis through different datasets has verified the effectiveness of the model proposed further. Results demonstrate that the model proposed can effectively improve the filtering accuracy of nonlinear systems. As the angle module changes, the accuracy of sports recognition also changes. The final accuracy of the model proposed can reach above 96%. The simulation results and convergence are better than other algorithms, which also proves the effectiveness of the model proposed. The results can provide a theoretical basis for the research related to the detection of training sports targets in sports videos. [ABSTRACT FROM AUTHOR]