The development of blockchain technology has driven improvements in the traceability of corporate financial data. To effectively track the review and modification of relevant business data by responsible parties in financial reports. This paper integrates blockchain technology with the Delegated Byzantine Fault Tolerance (DBFT) algorithm and Proof of Work (PoW) algorithm to construct a multi-tiered enterprise management secure accounting information system. The DBFT algorithm is used to perform user transaction queries in the blockchain-based relational database, while the PoW algorithm is employed to achieve audit consensus on financial report information, thereby enhancing enterprise financial security. Research shows that the error rate of accounting information in the blockchain-based secure accounting information system is as low as 11.62% across seven nodes, with error handling efficiency reaching up to 93.74%. Performance tests demonstrate a 35%- 466% optimization in business processing, and enterprise application systems achieve a 60%-109% improvement in business efficiency.
As teaching models increasingly integrate with the big data era, data-driven and technology-enabled approaches have become important tools for evaluating the effectiveness of ideological and political education. This paper explores reliable methods for assessing the effectiveness of ideological and political education primarily through learning analytics and behavior tracking. The research data was preprocessed by merging data, handling missing and abnormal cases, and converting features. The Gaussian mixture model clustering algorithm is selected, and a probabilistic model is introduced to handle complex distributions of various student behavior data, achieving clustering of student behavior data. Based on student learning activity sequences and cognitive styles during the ideological and political education process, a learning behavior model is constructed to analyze the quality of the ideological and political education process based on information from the learning process. Taking second-year students from a certain major at University E as the research subjects, after applying Gaussian mixture clustering to their ideological and political-related learning data, the learning behavior model proposed in this paper achieved a prediction accuracy of 0.8839 for student learning behavior and performance.
With the strengthening of cultural confidence in the context of globalization, the new Chinese-style design, as a fusion of traditional Chinese aesthetics and modern design concepts, has gradually become an important trend in high-end residential design. This study explores the integration strategies of the new Chinese-style design in modern villa space design and systematically analyzes its design methods. Based on spatial syntax theory and combined with point cloud technology, Project A was selected for quantitative analysis and case verification of the new Chinese-style villa space. Project A demonstrates superior comprehensibility, with higher global proportions of transition and circulation spaces, higher permeability of living spaces, and greater accessibility of living spaces compared to Western-style projects. Within Project A’s internal structure, core areas A4 (integration degree 0.62) and A6 (integration degree 0.69) form a dual-center structure, with core spaces A4, A6, A7, and A5 exhibiting strong spatial aggregation.
Python’s advantages in text mining provide a fast, efficient, and low-cost research path for poetry sentiment analysis. This paper uses Python to design and develop corresponding poetry text sentiment mining software to conduct in-depth sentiment word frequency analysis and visualization of Huang Tingjian’s poetry. Additionally, it combines Latent Dirichlet Allocation (LDA) and Non-Negative Matrix Factorization (NMF) to calculate the relationship between high-frequency words and emotional themes, establishing an emotional theme model. The effectiveness of the theme modeling is measured using word vector theme consistency. The results show that the precision, recall, and F1 scores of the LDA+NMF emotional theme model all exceed 90%, outperforming the six classification models compared in the same period. The model achieved an accuracy rate greater than 0.900 for the classification of three types of poems with different emotional tendencies. The proportion of the three types of emotional tendencies in Huang Tingjian’s poems showed an upward or downward trend in the early, middle, and late periods.
Against the backdrop of rapid global sustainable development and green finance, the quality of Environment, Society, and Governance (ESG) information disclosure has increasingly garnered attention from regulators and investors. However, the phenomenon of companies engaging in ESG greenwashing through symbolic disclosure is becoming increasingly common, which impairs capital market resource allocation efficiency and hampers the clear transmission of policy signals. This study takes the implementation of the 2018 Environmental Protection Tax Law as an exogenous shock and utilizes data from Chinese A-share listed companies to empirically test how the introduction of environmental protection tax affects corporate ESG greenwashing behavior using the Difference in Differences (DID) method. The findings indicate that the enforcement of environmental protection tax notably curtails such greenwashing behavior, and this outcome has been consistently confirmed through various robustness checks. Moreover, further mechanism analysis reveals that environmental protection tax indirectly curbs greenwashing behavior by increasing analyst attention, easing corporate financing constraints, and encouraging companies to reduce symbolic disclosure. Further, results from the heterogeneity analysis demonstrate a significantly stronger effect of environmental protection tax governance in the eastern region, high tax burden areas, and enterprises with high ESG scores. This paper explores the economic impacts of environmental protection tax from the perspectives of signal transmission and compliance, enriching the research path of external governance mechanisms for ESG greenwashing behavior. At the same time, using a text similarity index based on natural language processing to quantify the degree of greenwashing provides a new measurement tool for ESG research.
As there is growing emphasis on quality-oriented teacher training, the optimization of practice teaching resources in early childhood education (ECE) programs has become a strategic imperative for Chinese universities. The present article examines the allocation issues through the Analytic Hierarchy Process (AHP) method, building a decision framework incorporating pedagogical relevance, infrastructure adequacy, collaboration strength, and students’ preparedness. Using mixed-methods fieldwork and expert consultations, the study ascertains the institutional investment-actual teaching effectiveness misalignments. The study offers a systematic yet flexible prioritization framework to guide resource allocation that will allow institutions to consider local constraints while aligning institutional resource allocations with national Early Childhood Education talent development priorities. Reasonable, evidence-based design, based on participatory evaluation, can greatly contribute to the professional preparation of future preschool teachers, as stated in the research.
As Artificial Intelligence (AI) technology is developing rapidly, the application field of AI corpora in college business English teaching has been enlarging step by step, especially in language expression practice. This paper concisely introduces the basic concept and technical background of AI corpora, and their advantages in enhancing the business English expression ability of students, optimization of learning resources, and personalized learning experiences. It elaborates on the application of AI corpora in classroom instruction, including corpus analysis, expression practice, and instant feedback, based on some teaching instances. The results show that AI corpus-based training can significantly improve the business writing, oral communication, and cultural adaptability performance of students. This provides both practical proof and theoretical basis for wider integration of AI technology in future business English teaching syllabuses.
Vocational education is crucial in the context of economic growth on an international level as it equips the workforce with the expertise needed to be efficient and competitive in the business sector. Sudden industrial changes with progressing technology have rendered the need for specialists more specialized and dynamic in nature. Vocational education systems are mediums of meeting this need by transferring industry-based knowledge to students. However, with all its growing importance, vocational education systems across the world are faced with a fundamental dilemma: a mismatch between the needs of industry and the type of skills that the education systems offer. The gap in demand and supply is critical for both graduates and business organizations because it enables the graduates to fail to meet industry requirements, and business organizations cannot hire people who possess required skills. Mitigating the supply-demand deficit is essential to vocational training success and industry responsiveness. Vocational training systems must succeed at meeting changing industry demands and training employable skills in demand by the employer. This calls for a paradigm shift for vocational education institutions to operate, from static and traditional curricula to more fluid, industry-driven models of education. Only through having the education materials as closely coordinated with real industry demands as possible can vocational education systems ever have any hope whatever of preparing students with the skills and knowledge they need to thrive in an increasingly competitive job market. The aim of the following work is to examine the problems of successful industry-education integration, i.e., the demand-supply gap. It will determine the causes and ways in which this gap develops, and its implications for vocational education schools and industry, as well as ways to overcome these. The research will examine the reason for the gap, how it is a problem to students and industry, and how industry needs and vocational education courses can better be aligned. This problem, besides being highlighted in regards to the improvement of educational quality, is also vital for graduate employability. Efficient integration of industry needs into vocational training can maximize students’ performance in the labor market as well as provide quality adaptable employees to industries with specified capacity to ensure productivity and innovation. Therefore, this study aims to present useful lessons and policy recommendations to facilitate greater cooperation between industry and education to allow vocational education systems to respond to evolving labor market demands.
Based on the dataset of 2,207 listed companies in the A-share market of Shanghai and Shenzhen from 2012 to 2022, on the basis of constructing a national big data comprehensive pilot zone, this study combines multiple period Difference-in-Differences (DID) model and fixed-effects model to deeply explore the specific impact of this pilot zone on the green performance of enterprises and its mechanism. Through rigorous parallel trend tests, placebo tests, and robustness tests, the big data comprehensive pilot zone has shown significant effectiveness in improving the level of enterprise green performance. Mechanism analysis indicates that reducing the average tenure of senior executives, shortening the credit term structure, and enhancing the level of digital development can positively incentivize the development of enterprise green performance. Furthermore, the establishment of the big data comprehensive pilot zone to promote enterprise innovation shows heterogeneity. This study finds that the policy effect of the big data comprehensive pilot area in empowering enterprise green performance is more significant in high-tech industries and non-innovative provinces and non-state-owned enterprises. This study helps to clarify the impact of the construction of the big data comprehensive pilot zone on enterprise green performance, providing theoretical support and policy recommendations for driving enterprise green innovation and improving enterprise green performance through digitization in China.
This paper constructs a load regulation model of coal-fired power station based on PID control algorithm. The linear combination of proportional, integral and differential simulates the inertial delay characteristics of the unit, and the grid control instruction is input as a step signal to realize the dynamic regulation of load. The joint operation model of photovoltaic + coalfired power generation is proposed to integrate the multiple systems, and the coal-fired unit is used as a regulating power source to enhance the use of new energy. Measuring the carbon emission reduction effect through environmental benefit assessment indicators. Construct a decision model for the replacement of installed power generation units to provide decision support for the replacement of old units. Demonstrate the environmental benefit optimization ability of this paper’s method through empirical analysis. The results show that greater corporate value can be realized by replacing more coal power installations with photovoltaic installations. The difference between the ideal value and negative ideal value of the city in the three environmental benefit indicators does not exceed 0.05, and the mixed load regulation method has sensitivity. Through the method of this paper regulating the proportion of PV installation increased by 13.92% to achieve the optimization of environmental benefits.