As economic globalization progresses, air transport has become increasingly vital to economic development due to its speed and convenience. This study examines the driving forces of airside economic construction across four levels: primary, secondary, derivative, and permanent influences. It explores the dynamic interplay between the aviation industry and airside economic construction. Using the entropy weight method to optimize the grey situation decision-making theory, the paper investigates the development strategies for Henan Province’s airside economy. Results indicate that the H2 area should be prioritized as the key construction zone, achieving the highest effect measurement score of 0.9789. Furthermore, focusing on the development of the tertiary industry or the joint advancement of secondary and tertiary industries in the H2 area yields the most significant economic impact, with effect measurement scores of 0.755 and 0.749, respectively.

This paper explores the integration of blockchain technology into the teaching quality evaluation system of universities. A practical teaching quality evaluation index system for applied technology universities is developed, ensuring data authenticity through blockchain’s de-trusting mechanism. To enhance data storage efficiency, the PBFT consensus algorithm is improved and incorporated into a technical architecture adopting an “off-chain storage + on-chain sharing” model. The algorithm scoring formula and improved PBFT consensus algorithm are analyzed to demonstrate their effectiveness. Practical applications in applied technology universities highlight the benefits of blockchain in higher education evaluation. The CBFT-based consensus algorithm achieves average CPU utilization of 13.4% compared to 18.5% in traditional algorithms, while ensuring data transparency and tamper-proofing. Additionally, the algorithm improves transaction throughput and reduces resource consumption, enabling efficient operation of the teaching evaluation system in applied sciences universities.

Translation as a cross-cultural information exchange and exchange activity has the nature of dissemination. Combining communication and translation helps make translation an open, dynamic, and comprehensive discipline. Translators play the role of gatekeepers in communication studies. The choice of a translator is affected by any change in the translator himself, such as his personal preference, motivation, life experience, aesthetic orientation, psychological factors and values, which can call for different translations to be produced. The translation of classics is not like the translation of ordinary works. It puts forward higher requirements for the translator. The beauty and subtlety of its words and characters require the translator to have a profound knowledge of the target language; its connotation and thought are broad and profound, and the translator needs to understand the source language. Transparency of this understanding. And such a master is really rare, and it is difficult to cultivate, so excellent translation works of classics are not common. In addition, translations are becoming more and more diverse, and there is inevitably a mix of people and irregularities in the intermediate translations. This paper explores the translation of classics that combines machine learning technology with the perspective of communication, and proposes an efficient translation model. The experimental results show that the model can effectively improve translation efficiency and accuracy.

We consider the generating function for increasingly labelled trees. By generalizing the proof through symbolic method, we are able to study various statistics regarding binary increasing trees with respect to height restrictions. We then apply our approach to special colorings of increasing trees in order to obtain their generating functions and, from there, derive the counting sequence for $(ak+a)$-colored recursive trees. We also present some interesting bijections between colored and non-colored increasing trees.

This paper aims to enhance the moral and vocational qualities of college students by integrating moral education elements into career planning education. The BOPPPS teaching model is constructed, comprising six modules: introduction, objectives, pre-test, participatory learning, post-test, and summary, to effectively stimulate students’ interest and initiative. Moral education elements are integrated into career planning education through an intelligent teaching platform, incorporation into teaching processes, and the use of the second classroom to promote in-class and out-of-class linkages. Additionally, a fuzzy classroom teaching evaluation system is developed to assess the effectiveness of career planning education. The results indicate high reliability and validity of the evaluation system, with an alpha coefficient exceeding 0.8, a KMO value of 0.938, and a Bartlett’s test P-value of 0.000. Students’ positive classroom mood improved significantly from 35.79% to 68.42%, alongside an enhanced evaluation of classroom learning. The findings demonstrate the practical value of this approach in advancing education reform.

The combination of thermal power units’ stability and energy storage systems’ rapid response time enhances power system frequency control. However, high costs and battery life impacts from charging/discharging strategies limit energy storage adoption. This study proposes an adaptive weight-based particle swarm optimization algorithm (APSO) to optimize energy storage control for joint thermal-storage frequency modulation (FM). By analyzing the coupling between state of charge (SOC) and charging/discharging power, the study implements “shallow charging and discharging” with dynamic SOC constraints. The improved PSO algorithm integrates adaptive weighting to overcome local optimal convergence, enhancing global search capabilities and particle migration. Simulation results, based on real-world power plant data, show improved FM accuracy, faster regulation, and reduced energy storage system loss, significantly boosting economic efficiency.

With the increasing penetration of distributed intermittent energy into distribution networks, the self-healing problem of distribution networks faces significant challenges. The load level and demand response must be considered as critical factors affecting fault recovery. This paper proposes a fault recovery strategy that combines islanding division and network reconstruction. First, a distribution network model with a distributed energy storage system is established. To optimize the use of distributed energy resources, controllable loads that can respond to demand are prioritized, and high-priority loads are included in the islanded network after a fault. Based on the islanding division results, the remaining non-faulty power loss areas are restored through main network reconstruction. The improved whale optimization algorithm is employed to solve the problem. Simulation results demonstrate that load demand response is closely linked to the islanding process, and an optimal fault recovery strategy can be achieved by utilizing the distributed energy storage system and the main network.

With the rise of digital technology, global cross-border information flows are driving significant growth in international digital commerce. This paper employs Meta-analysis to examine the impact of cross-border information flows on global trade competitiveness. It outlines the Meta-analysis paradigm, explores the relationship between data element valorization and trade competitiveness, and highlights the varying effects across different stages of the trade process. Using correlation coefficients as effect values, the study transforms and calculates data with the help of formulas and software to comprehensively analyze and test the relationship. The findings reveal rapid growth in China’s digital economy, expanding from 22.6 trillion yuan in 2016 to 51.9 trillion yuan in 2022, deeply influencing industrial structures. In global cross-border data flows, China and Russia exhibit tighter regulations, with China’s DSTRI value rising from 0.325 to 0.347 million USD, demonstrating that cross-border data flows significantly impact global trade competitiveness.

In the era of intelligent education, technology is reshaping traditional music education by enhancing teaching quality, optimizing curriculum design, and improving teacher resources. However, its redistributive effects remain underexplored. This study examines how intelligent education technology impacts resource distribution in music education, focusing on the context of music teacher certification. The research highlights the reform needs of music teacher education, including student-centered goals, improved teaching methods, and optimized curricula. It introduces a music intelligence system based on a radial basis function (RBF) neural network and evaluates its potential in promoting equitable resource distribution through interactive teaching. Findings reveal that intelligent education technology enhances student learning outcomes and music skills by enabling personalized learning paths and strengthening practical teaching. Experimental results confirm the system’s effectiveness in significantly improving students’ music grades, demonstrating its value in transforming music education.