With the continuous progress of artificial intelligence algorithms, music creation has gradually developed from purely manual operation to intelligent assistance, a process that not only optimizes the efficiency of music creation, but also opens up new paths of creative thinking. This study explores the impact of artificial intelligence on creative thinking in music composition and teaching. By introducing AI-based music composition models and teaching methods, its performance in practical applications was analyzed. The study used scoring data from 30 professional listeners and 25 general listeners and compared them with the AI scoring system. The results show that the difference between the AI ratings and the ratings of the professional listeners is about 1.5 points, indicating that AI can more accurately simulate the perception of professional listeners in music composition evaluation. In addition, the model training generated the highest music scores at an iteration number of 60k, which were 79.249 points for the artificial rating and 80 points for the model rating, respectively. The study shows that AI has a significant auxiliary role in music creation and teaching, which can promote the diversity of music creation and personalization of teaching, and enhance students’ understanding and interest in music.
This paper collects physiological index data such as heart rate through real-time monitoring of wearable devices. Mining and extracting the relevant features of the physiological index data, we constructed a dynamic correlation model between the physiological indexes and the exercise load, and predicted the future physiological state of the athletes. The non-dominated sorting genetic (NSGA-II) algorithm is introduced to realize the multiobjective optimization and regulation of heart rate in the prediction of training load to enhance the training effect. The practical value of this paper’s method of combining real-time monitoring and genetic algorithm modeling is verified through multiple sets of experiments. The results show that the physiological data of athletes can be monitored and collected in real time at a frequency of 1 time per second by using a wearable device, and the data have research value. During the 4 stages of incremental load exercise, the muscle oxygen saturation of different muscle parts showed a decreasing trend. Combined with the method of this paper, real-time regulation was performed to maintain the decreasing muscle oxygen saturation at the 4th stage. In the physical fitness training experiment, the real-time heart rate prediction error of the athletes was optimized by the algorithm and adjusted to be consistent with the actual monitoring value, so as to realize the real-time accurate regulation of the exercise load in the training process.
Financial time series data exhibit characteristics such as complexity and high noise levels, rendering traditional clustering methods prone to limitations when processing such data. Consequently, this paper introduces rough set clustering, leveraging its advantages in financial time series analysis and forecasting to construct the macroeconomic prediction model required for research. Principal component analysis is employed to transform high-dimensional data into low-dimensional representations, thereby reducing the complexity of the prediction model. A comprehensive digital economy forecasting model is constructed, revealing its data processing workflow. This workflow emphasizes calculating the fitness of each solution and designing targeted fitness functions. Quantitative analysis across multiple datasets is conducted on the digital economy forecasting model, comparing traditional K-means clustering with the proposed model using economic indicators from multiple cities. Empirical applications demonstrate that the clustering results from the proposed macroeconomic forecasting model better reflect the developmental tiers of 36 cities, highlighting the significant value of rough clustering in financial time series analysis and forecasting.
In the era of digital justice, the integration of big data analytics into sentencing decisions has emerged as a key direction for enhancing judicial transparency and fairness. This paper proposes a novel sentencing standardization framework based on judicial big data and interpretable machine learning. Focusing on online fraud adjudication documents from the Chinese judiciary, we construct a domain-specific database using a hybrid method of keyword-based pattern matching and association rule analysis to extract structured features such as criminal intent, means, economic loss, and mitigating factors. These features are encoded into machine-readable vectors and fed into a LightGBM-based gradient boosting decision tree (GBDT) model to predict sentencing outcomes. Extensive experiments using real-world fraud cases demonstrate the model’s high predictive performance, with R² scores reaching 0.98 and minimal average deviation. A series of visual and statistical evaluations—including boxplots, Taylor diagrams, and regression fits—validate the model’s robustness and its ability to replicate human sentencing logic.
Deep generative models have a lot of promise for music production and style modeling given the quick advancement of artificial intelligence technologies. There are still several obstacles in the way of properly capturing and analyzing the principles of music style evolution using deep generative models. This study, which is based on deep generative modeling, attempts to investigate the dynamic process of music style change and develop a generative framework that can capture the traits of music style evolution. In the meantime, an optimization scheme based on the attention mechanism and multiscale modeling is proposed to improve the quality of generation and the interpretability of style evolution in to address the limitations of deep generative models when handling complex time series and multimodal music data.In terms of stylistic consistency, sound diversity, and evolution rationality, the generated music greatly surpasses the current approaches, according to experimental results, which demonstrate that the model put out in this study can successfully represent the time-series evolution characteristics of musical styles.
Education serves as a critical driver for class mobility and human capital accumulation, particularly in countries with pronounced socio-economic stratification. This study investigates the heterogeneity and temporal evolution of educational opportunity inequality in China, focusing on the urban-rural dual structure, gender disparities, and uneven regional development. The study measures how environmental factors, including household registration, income, and regional resource distribution, contribute to educational disparity using micro-level data and a parametric methodology in conjunction with the Shapley value decomposition method. Multiple intelligence theory and discrete inequality indicators, including the Theil index and Gini coefficient, are employed to measure disparities across different education stages. Empirical analysis reveals that although overall educational inequality has declined due to policy interventions, the relative share of inequality stemming from environmental factors has risen since the 1960s. This indicates that exogenous conditions, rather than individual effort, increasingly determine educational outcomes. Additionally, algorithmic optimization and robustness tests enhance the reliability of the measurement framework.
Chinese civilization, with its extensive and profound heritage, has nurtured a wealth of traditional cultural expressions that continue to exert influence in contemporary society. These cultural assets not only retain strong aesthetic appeal but also offer enduring practical significance. Across different regions, the unique geographical, historical, and social environments have led to varied manifestations of traditional culture, enriching its forms and elements. This diversity provides an essential foundation for enhancing the depth and nuance of modern artistic and design practices. Given this background, this paper explores the theme of “Integrating Chinese Traditional Elements into Visual Communication Design”, positioning it as a critical lens through which to examine the intersection of cultural inheritance and creative innovation. The study emphasizes how visual communication—through graphic design, digital media, branding, and spatial expression—can serve as a bridge between timehonored traditions and contemporary aesthetics. By embedding traditional motifs, symbols, and philosophies into modern design languages, visual communication can cultivate a more culturally resonant and nationally distinctive style. This process not only enhances the visual identity of design works but also plays a pivotal role in preserving and revitalizing intangible cultural heritage. Ultimately, the research aims to promote the integration of tradition and modernity, ensuring that Chinese cultural elements are not only passed down, but also reimagined in ways that speak to today’s audiences and future generations.
Large-scale access of distributed photovoltaic (PV) systems to low-voltage (LV) distribution networks causes voltage overruns and back-feeding problems, which seriously affect the safe and stable operation of power grids. Aiming at the voltage overrun and back-feeding overload caused by the large-scale access of low-voltage distributed photovoltaic (PV) systems, this paper proposes a user regulation and station autonomy strategy based on particle swarm optimization algorithm. A mathematical model is constructed with total power loss, voltage deviation and PV consumption ratio as the optimization objectives, and the particle swarm algorithm is used to solve the optimal access location and capacity configuration of PV system. This includes the establishment of a two-stage topology model for distributed PV systems, the design of PV MPPT control and inverter double-loop control strategies, and the use of PSO algorithm for iterative optimization of decision variables. Pilot application is carried out in a village #2 station area, and the results show that: the total voltage deviation after single-point PV optimization configuration is reduced from 1.311kV to 0.0885kV, with a voltage deviation reduction of 94.36%; the total voltage deviation of multi-point configuration is further reduced to 0.0349kV, with a reduction of 98.47%. After the implementation of the autonomous control strategy, the midday peak voltage is stabilized from over 250V to below 240V, effectively suppressing the backward flow. The optimized PV rated capacity is 970kW, which maximizes the PV consumption under satisfying the voltage constraints. It is proved that the proposed method can effectively improve the voltage quality of distribution network and guarantee the safe and efficient grid connection of distributed PV.
Grid supply-demand balance faces severe challenges, and air conditioning loads, as typical controllable loads, have significant demand response potential. Although the individual capacity is small, the large user base makes it a sizable demand-side resource after aggregation. In this paper, a grid supply and demand optimization scheduling method based on air-conditioning load is proposed for the grid supply and demand imbalance problem. Firstly, an indoor temperature prediction model is constructed based on the extreme learning mechanism to realize the indoor temperature prediction after 5 minutes using the historical temperature data as input, and the adjustable capacity of air conditioning load is determined accordingly. Second, an air conditioning load regulation strategy considering human comfort is designed, and the comfort temperature interval is set to 22-28°C, with the goal of minimizing the comfort cost for optimal scheduling. Finally, a supply-demand cooperative optimization model including time-of-use tariff and incentive-based demand response is constructed to optimize scheduling with the objective of minimizing the operating cost of the user’s optical storage microgrid. The simulation results of the algorithm show that when the TSV index is used to evaluate the central air-conditioning load clusters, the comfort users can participate in the scheduling for 7.56 minutes, and the dispatchable capacity reaches 10.3 MW, while the economy users can participate in the scheduling for up to 21.8 minutes, and the dispatchable capacity reaches 13.8 MW. In the real-time scheduling strategy, the time granularity of 5 minutes is used during the time period 17:00- 19:00 In the real-time scheduling strategy, when scheduling with 5-minute time granularity from 17:00 to 19:00, the power difference of the contact line is 0.40kW, and the number of iterations is 170, which is a significant improvement compared to the scheduling effect of 15-minute time granularity. The method in this paper provides a feasible technical path for grid supply and demand regulation.
Financial evaluation, as a key part of enterprise management, has long provided the core basis for measuring the operating results and financial health of enterprises. This paper proposes a comprehensive evaluation index system of multidimensional financial indicators with profitability, operating ability, solvency and development ability as the first-level indicators. Through the hierarchical analysis method to calculate the comprehensive weights, combined with the fuzzy comprehensive evaluation method to construct the affiliation matrix, constituting a comprehensive evaluation model of multi-dimensional financial indicators. Taking A real estate company as the research object, the proposed multidimensional financial indicators comprehensive evaluation model is used to conduct multidimensional analysis of the financial status of A company. According to the evaluation results based on the fuzzy comprehensive evaluation method, the financial performance of Company A in 2022- 2024 shows a significant downward trend, and the financial performance of Company A in 2024 belongs to the category of “poor”, and the degree of affiliation reaches 0.5332, which is high, and there is an urgent need to improve the financial performance of the company.