ISSN 2512-6660 (Online) ISSN 1478-9906 (Print)
CN 10-1192/N
CN 10-1192/N
To address the issue of revenue distribution between government departments and enterprises in the operation of public data authorization, an evolutionary game model was constructed for both government and enterprise parties. The impacts of different incentive levels and revenue distribution ratios on the strategic choices and evolutionary trends of both government and enterprise were analyzed. It was found that when the government chose a strategy of weak authorization and strong regulation, enterprises showed a higher tendency to actively participate in public data sharing. In addition, when the revenue distribution ratio between government and enterprise was 3:7, the game evolution of both parties tended to be stable, reaching a balanced state that is beneficial and sustainable for both parties.
As the era of large-scale highway maintenance arrives, the maintenance strategies have transitioned to a holistic approach that prioritizes safety, economic feasibility, and environmental sustainability. This research introduces a multi-objective optimization model for highway maintenance that incorporates the interplay of decision-maker preferences across three key objectives: Highway safety performance, maintenance engineering cost, and carbon emissions. This study employs a large-sample data analysis on a subset of the Lianhuo Highway network, which includes 2,842 pavement sections. This approach mitigates the impact of outliers, ensuring a substantial data buffer that fortifies the model's capacity for generalization and bolsters its robustness. The findings reveal a Pareto-optimal relationship among the three scrutinized variables. A particularly noteworthy observation is the M-shaped trajectory of carbon emissions, which initially rise, then decline, and ultimately rebound, contingent upon the selected maintenance strategy. Furthermore, an examination of the relationship between maintenance costs and safety performance discloses a trend of diminishing marginal returns, illustrating that the incremental gains in safety performance attenuate as maintenance investment escalates.
In the domain of data governance, crimes involving virtual currencies have emerged as an integral concern that cannot be overlooked. To address challenges such as the difficulty of evidence collection and the low probability of recovering stolen funds in virtual currency crimes, this paper proposes a new mechanism for the electronic storage and retrieval of evidence using blockchain technology, elaborating on its core steps and underlying technology. Moreover, from the perspective of virtual currency transaction intermediaries, this study employs game theory to analyze the issue, constructing replicator dynamics equations, solving for the Jacobian matrix, and exploring the direction of game evolution and the factors influencing the decision-making of the participants. This analysis demonstrates that the decision-making of virtual currency criminals is impacted by this electronic evidence mechanism, which can deter illicit intermediaries from assisting in money laundering activities, thereby reducing the feasibility of committing crimes with virtual currencies. Lastly, the paper offers policy recommendations to enhance the implement ability of the evidence storage and retrieval mechanism in regulating virtual currency crimes.
Since the COVID-19 pandemic began in late 2019, economic uncertainty and supply chain disruptions have significantly affected China's real economy. This has reduced consumer reliance on physical stores, increasing market and operational risks for brick-and-mortar retailers. This study analyzes the impact of the pandemic on shopping center performance using operational data from 7, 010 stores in China alongside COVID-19-related data. The analysis focuses on two indicators: Rent levels and lease termination rates, considering both rental and tenant perspectives. Findings reveal that COVID-19 negatively impacts retail rents and lease termination rates, with regions experiencing severe outbreaks facing greater declines. During lockdowns, the negative effects on rents and termination rates were more pronounced than during the broader pandemic period. Additionally, stores with sales-based rent experience a greater adverse impact compared to those without. The study concludes that the pandemic significantly reduces store rent while the likelihood of early lease terminations remains relatively low. These empirical results offer valuable insights for shopping center operational management and crisis response strategies.
In July 2024, a shooting incident involving President Trump drew widespread public attention, highlighting the need for a deeper understanding of political rhetoric among U.S. Congressional members. This study analyzes discourse patterns on Twitter using large language models (LLMs), specifically ChatGPT and bidirectional encoder representations from transformers (BERT), to explore underlying factors that may contribute to polarizing political language. By collecting and preprocessing Twitter data, we initially labeled 20, 000 tweets using ChatGPT and then utilized the BERT-large model to classify the remaining 980, 000 tweets. The analysis identified party affiliation and geographic region as significant factors influencing political rhetoric. Republican lawmakers exhibited a higher prevalence of polarizing language, while New Jersey recorded the highest rate among the states. Newly elected Congressional members also tended to adopt more provocative language, potentially as a strategy to engage with their voter base or distinguish themselves in a competitive political environment. Temporal analysis revealed spikes in polarizing rhetoric corresponding to events such as discussions on the new fiscal year budget. This study offers insights into the dynamics of political discourse, providing a foundation for promoting constructive dialogue and fostering institutional resilience.
Data pricing is a key link to promote the efficient circulation of data in the market. However, the existing methods are still insufficient in terms of pertinence, dynamism and comprehensiveness. Therefore, we proposed a data pricing prediction model based on sparrow search optimization XGBoost, aiming to provide a reference for pricing decisions in data market. First, we crawled the data transaction information of Youedata.com and performed preprocessing operations such as outlier processing, one hot encoding and logarithmic transformation on the dataset; Secondly, we conducted exploratory data analysis to understand the distribution of data and their correlation. Then, we used the LASSO algorithm to select features for the dataset and constructed a data pricing prediction model based on SSA-XGBoost. Finally, we compared and analyzed it with six machine learning models including LightGBM, GBDT, MLP, KNN, LR and XGBoost. The experimental results show that in terms of the R-squared, the prediction results of the proposed SSA-XGBoost model exceed the above six models by 4.9%, 7.4%, 7.1%, 23.8%, 12.8%, and 2.3% respectively, and are superior to the state-of-the-art work. Furthermore, the evaluation results of the five indicators of MSE, RMSE, MAE, MAPE, and RMSPE are better than other models, showing higher stability.
With the rapid advancement of information technology, the sharing of educational resources has become an integral component of the modern educational system. However, traditional data elements in educational resources face challenges such as data security, copyright protection, and trust mechanisms. Blockchain technology, as a distributed ledger technology, offers innovative solutions for the sharing of educational resources data elements with its characteristics of immutability, decentralization, and transparency. This paper designs an educational resource sharing platform based on blockchain technology, and experiments have demonstrated that by optimizing the blockchain threshold elimination model, the overall performance of the platform can be enhanced, improving the security of data processing and achieving satisfactory results.
