Given the increasing complexity and vulnerability of modern power systems, traditional electric power network (EPN) design and management methods have proven insufficient in the face of extreme weather events such as hurricanes. This paper explores the potential of digital twin (DT) technology as a dynamic tool for real-time monitoring, predictive analysis, and system optimization. By constructing a three-dimensional digital replica of the entire substation infrastructure, the study demonstrates how DT integration can significantly enhance the resilience of power grids and improve the efficiency and accuracy of engineering design processes. A thorough digital twin model of the electric power network is created in this study, which includes important system components such power plants, substations, transmission and distribution lines, and end-user connections. According to simulation results derived from this modeling technique, Hurricane Ike is predicted to result in major power disruptions that will impact the majority of users. In particular, it is estimated that 96.4% of residential structures, 96.0% of commercial buildings, and 94.2% of industrial buildings will experience power outages, illustrating the serious effects of such natural disasters on the supply of electricity in many industries.
This study investigates the biological processes that support children’s social skill development in the framework of familial interactions. The study looks at multidimensional datasets, such as physiological biomarkers, brain activity, and observed behaviors, using a attentive adversarial deep subspace clustering (SAADSC) algorithm to find patterns that connect competences to family dynamics. The results show that brain configurations that support social functioning are consistently linked to high levels of family cohesion. Furthermore, the accuracy of behavioral outcome predictions is significreased by include physiological factors in the analytical model, such as heart rate variability and cortisol concentration. Through a datadriven, interdisciplinary lens, this integrated methodology provides important insights for developmental psychology and social neuroscience by revealing how biological systems moderate environmental impacts.
In the era of big data and digital information, blended learning has emerged as a prominent instructional paradigm. With the growing demand for business Japanese education in China, both the scale and scope of instruction are rapidly evolving. This trend has prompted increasing attention to the integration of online learning environments with traditional teaching models, particularly in terms of learning assessment. This study focuses on developing a scientifically grounded evaluation index system tailored to blended learning in business Japanese, employing algorithmic optimization and experimental validation as its core methodologies. Centered around high school learners, the research draws from educational psychology and learning theory to propose strategic guidance from institutional, instructional, and individual perspectives. Empirical analysis and model testing support the proposed system, with the optimized Nivre-based evaluation framework demonstrating strong adaptability to contemporary educational demands. The model achieves an accuracy exceeding 92% across various test datasets, verified through correlation analysis and reliability-validity metrics, indicating its robustness and applicability. The study provides a valuable reference for advancing blended teaching practices and enhancing the effectiveness of business Japanese education at the secondary level.
Currently, music education in higher education institutions relies solely on traditional music composition textbooks to fulfill teaching objectives, which no longer meets the demands for music talent. Digital music technology, as a revolutionary innovation in music technology, has transformed people’s understanding of music production. This study first leverages AIGC and multimodal large-scale model technology to assist music classroom instruction, and proposes a personalized music learning path recommendation strategy based on ant colony algorithms. By aligning with students’ learning needs, it precisely recommends scientifically sound learning paths to enhance learning efficiency. Experimental results show that the recommendation algorithm achieves high prediction accuracy, aligning with students’ needs. Additionally, the recommendation system’s surprise factor and real-time performance meet the requirements of resource recommendation systems, making it suitable for implementing teaching resource recommendations. Finally, based on practical analysis of the application of learning path recommendations in music education, results indicate that after practical teaching, the experimental group’s aesthetic perception, artistic expression, cultural understanding, and creative practice levels were significantly higher than those of the control group.
This paper addresses the issue of preserving and digitizing ethnic dance movements by analyzing the characteristics of ethnic dance movements, Kinect-based motion capture technology, and skeleton-based motion recognition methods. It proposes a deep learning-based method for recognizing typical ethnic dance movements. Using Kinect sensors to collect data on typical ethnic dance movements, a dataset of typical ethnic dance movements was constructed, and 3D CNNs were used for recognition. Finally, strategies for the protection, inheritance, and digital development of ethnic dances are proposed, and effective pathways for the digital preservation of minority ethnic dances are explored. The results indicate that both the detection of two-dimensional joints and the extraction of three-dimensional joint information can to a certain extent meet the requirements for three-dimensional human motion reconstruction. Additionally, the results of the motion capture system setup and three-dimensional human motion reconstruction are also satisfactory, with experimental errors around 3%. Compared to traditional methods, the motion capture joints and angles under the proposed method are closer to the Kinect standard values, and the motion capture trajectories have the smallest error compared to the Kinect method. Furthermore, the recognition accuracy rate of the proposed method remains above 95%, with a maximum accuracy rate of 99.76%, demonstrating that the proposed method has certain feasibility and application prospects in the preservation and inheritance of ethnic dances.
Educational cooperation and exchange between Guangdong, Hong Kong, and Macao have a long history. However, the Guangdong-Hong Kong-Macao Greater Bay Area, currently under construction, faces challenges such as insufficient awareness of the importance of educational cooperation and exchange, as well as a lack of innovative momentum. In light of these issues, this study proposes a reform pathway for educational cooperation within the Guangdong-Hong Kong-Macao educational cooperation framework. The research questions and hypotheses are identified, and based on relevant materials and literature, a survey questionnaire is developed. Subsequently, the questionnaire undergoes reliability and validity testing. Under the theoretical framework of the research design, an empirical exploratory analysis of educational reform pathways within the Guangdong-Hong Kong-Macao education cooperation framework is conducted. In terms of knowledge understanding and problem-solving abilities, the experimental group scored higher than the control group, and the difference was statistically significant at the 0.05 level. This indicates that compared to traditional educational methods, Guangdong-Hong Kong-Macao educational cooperation is more effective in enhancing students’ knowledge understanding and problem-solving abilities, thereby demonstrating the practical application value of the Guangdong-Hong Kong-Macao educational reform pathways.
High-performance fiber-reinforced composite materials have emerged as a promising option in the field of construction concrete due to their unique mechanical properties and durability. This study focuses on UHMWPE high-performance fiber bundles, incorporating their structural characteristics, material properties, and mechanical properties. Two fiber content levels and single/mixed fiber addition methods were tested for concrete’s dry shrinkage rate and early crack resistance. The fracture performance and fracture mechanism of UHMWPE fiber concrete were systematically analyzed. The research conclusions are as follows: Compared to steel fiber concrete with the same fiber content, UHMWPE fiber concrete exhibits greater slump. Compared to the control group without fibers, the drying shrinkage rate of concrete with a UHMWPE fiber content of 0.20% was reduced by 51.28%. Therefore, UHMWPE fibers can increase the tensile strength within concrete and enhance its crack resistance.
As a time-honored and technically complex art form, oil painting is undergoing unprecedented transformation in its creative process and aesthetic value under the influence of AI technology. This study explores the modernization and innovation of oil painting creation from an AI technology perspective, as well as its role in driving artistic development. It primarily achieves oil painting style transfer through the optimization of the CycleGAN (Cycle-Consistent Generative Adversarial Network), proposes the introduction of spectral normalization processing, and improves the residual structure. Finally, the algorithmic model is validated through quantitative and qualitative experiments. The experiments demonstrate that the model generates oil painting images with outstanding performance in terms of clarity, diversity, and style similarity. Its IS values are 4.17% to 172.02% higher than those of the comparison methods, while its ID values are reduced by 2.99% to 63.50%. Additionally, the subjective quality evaluation and style similarity evaluation of the generated images are optimal. The model constructed in this paper improves the visual effects of image style transfer and can be used to assist artists in oil painting creation, inspiring their creative inspiration.
The rapid development of internet technology in modern society has brought about changes in the ways and channels of information dissemination, presenting new opportunities for the dissemination of classical choral works. Based on this, this paper takes the textual and video-related data of classical choral works as its research object, adopts a dissemination perspective, and employs TF-IDF algorithms and sentiment analysis to analyze the dissemination cognition and emotional effects of classical choral works. Overall, the attitudes held by the dissemination audience toward classical choral works are primarily characterized by anticipation, joy, and surprise, while negative emotions account for a relatively small proportion. Additionally, a dissemination effect model for choral works is constructed, and based on this model, linear regression and configurational analysis methods are employed to further explore the current state and existing issues of classical choral works in modern society, and targeted innovative strategies for the dissemination and development of classical choral works are proposed. According to the regression analysis results, the dissemination effects of choral works are significantly positively correlated (P<0.001) with multiple variables such as “publisher identity,” “fan base,” “editing style,” “innovation,” and “language type.”
This paper introduces deep reinforcement learning into automated penetration testing to plan and optimize penetration testing supply and defense paths. After modeling the automated penetration problem, the paper simplifies and evaluates the benefits of the DQN algorithm in deep reinforcement learning, finds the optimal penetration path through sample augmentation, and proposes the MASK-SALT-DQN algorithm. Through simulation experiments, the paper verifies the operation and effectiveness of the algorithm. In both simple and complex scenarios, the MASK-SALT-DQN algorithm achieves the fastest runtime speed, significantly enhancing the agent’s learning efficiency. The algorithm provides accurate evaluation criteria for penetration testing path planning results. Compared to penetration testing learning algorithms based on Nature DQN, the MASK-SALT-DQN algorithm demonstrates a higher convergence value in its learning curve, indicating superior convergence performance.