Making accurate demand forecasting of iron ore of China is essential for resource planning, strategic investment, and risk management. However, the complex and nonlinear nature of iron ore demand, driven by the interplay of domestic economic factors and international market forces, poses significant challenges to conventional forecasting methods. This study proposes a hybrid forecasting framework that integrates the Archimedes Optimization Algorithm (AOA) with the Least Squares Support Vector Machine (LSSVM) to predict China's annual iron ore demand. A comprehensive dataset spanning 2014 to 2025 is constructed, comprising ten explanatory variables that capture both macroeconomic conditions, including GDP, real estate investment, urbanization rate, industrial output, and steel apparent consumption, and international market factors, including steel net exports, iron ore prices, the Baltic Dry Index, and the USD exchange rate. The AOA is employed to automatically search for the optimal LSSVM hyperparameters, overcoming the limitations of manual grid-search approaches. Using the first eight years as the training set and the remaining four years for out-of-sample evaluation, the proposed model is benchmarked against the standard LSSVM without optimization. Empirical results demonstrate that the AOA-LSSVM model consistently outperforms the baseline across all evaluation metrics, reducing the mean absolute error by 7.05%, the root mean square error by 7.47%, and the mean absolute percentage error by 0.42 percentage points. These findings confirm the efficacy of AOA in enhancing LSSVM's generalization capability and underscore the value of integrating meta-heuristic optimization with machine learning for mineral resource demand forecasting. The proposed framework offers policymakers and industry practitioners a reliable tool for generating accurate demand projections to inform import strategies and resource security planning.

In recent years, the knowledge payment industry has experienced rapid growth. To investigate the factors influencing college students' behavioral intentions in the context of the Bilibili monthly membership support service ("Monthly Charging"), this study collected 186 valid questionnaires and conducted empirical analyses using reliability and validity tests, correlation analysis, and hierarchical regression analysis. The results indicate that perceived usefulness, perceived entertainment value, and perceived cost all have significant positive effects on users' revisit intention and word-of-mouth recommendation intention, whereas technicality does not exert a significant effect. Free mentality negatively moderates the relationships between perceived usefulness and revisit intention, as well as between technicality and word-of-mouth recommendation intention, while positively moderating the relationship between perceived entertainment value and word-of-mouth recommendation intention. Emotional attachment positively moderates only the relationship between perceived usefulness and the two types of behavioral intention, although these moderating effects are only marginally significant. This study extends the application of the Value-based Adoption Model (VAM) in the field of knowledge payment and provides both theoretical insights and practical implications for platform operation and management.

China's low-altitude infrastructure construction highly relies on government finance, a commercial closed loop has not yet been formed, and investment and financing difficulties have become increasingly prominent. It is urgent to explore a localized path to transform from "government transfusion" to "market hematopoiesis". By analyzing current situation, difficulties and underlying causes of investment and financing in low-altitude infrastructure, and taking Federal Aviation Administration (FAA) eVTOL Integration Pilot Program (eIPP) as a reference, it analyzes its core logic of relying on operational certainty to activate market-oriented investment, and combines the unique institutional environment to complete localization transformation. The research found that the key to breaking the impasse lies in building an endogenous value cycle system of "scenario-data-capital-infrastructure" and establishing a dual-drive collaborative mechanism with central state-owned enterprises' "patient capital" as the backbone and social capital as the vitality engine.

Against the backdrop of the digital economy, the inclusion of data assets in financial statements has become an important institutional arrangement to unlock the value of data factors and empower enterprise innovation. Taking 1,197 A-share listed companies in 2024 as research samples, this paper empirically tests the effect and internal mechanism of data asset inclusion on enterprise innovation performance, and explores the heterogeneous roles of different types of data assets. The results show that the inclusion of data assets in financial statements significantly improves enterprise innovation performance. By raising corporate information transparency, data asset inclusion optimizes the allocation of innovation resources, thereby promoting the growth of enterprise innovation performance. Investor attention plays a positive moderating role: the higher the level of investor attention, the stronger the promoting effect of data asset inclusion on enterprise innovation performance. The heterogeneity test finds that data assets classified as development expenditures, which are embedded in the front end of R&D, have a significantly stronger promoting effect on innovation performance than intangible-asset data assets solidified in mature technologies. The conclusions provide practical reference for enterprises to optimize data asset management and improve innovation performance, and also offer policy basis for improving the data asset inclusion system and advancing the in-depth integration of the digital economy and the real economy.

Balanced rural development depends not only on income growth, but also on whether the asset base supporting livelihoods improves in a coordinated way across income classes. Drawing on the sustainable livelihoods framework, this study uses the China Labor-force Dynamics Survey (CLDS) to construct a five-capital indicator system for low-, middle-, and high-income rural households. The analytic hierarchy process is used to determine indicator weights, and a complex-system coordination model is employed to evaluate the coordinated evolution of the three income-class subsystems. The results show that the coordination degree increased from 0.086 to 0.161, but the overall level remained low; the western region displayed the weakest coordination; and fluctuations in social, financial, and natural capital were the main constraints on system-wide synergy. Substantively, the findings indicate that rural inequality cannot be understood only in terms of absolute income gaps, because the convertibility and coordination of livelihood capital across income classes also matter. From a methodological perspective, this study combines the "sustainable livelihoods" approach with coordination analysis and provides a historically comparable baseline for assessing the coordinated development of livelihood capital during the observation period of China's targeted poverty alleviation efforts.

This study uses survey data collected from southern China, a region characterized by relatively high levels of entrepreneurial activity, as its research sample. Drawing on the indicator framework developed in the Consumer Financial Literacy Survey and Analysis Report issued by the People's Bank of China, it constructs an evaluation system for financial literacy from two dimensions: financial knowledge and financial behavior. Based on this framework, the study examines the relationship between financial literacy and farmers' entrepreneurship from three perspectives: entrepreneurial choice, entrepreneurial revenue, and entrepreneurial scale. The findings indicate a strong positive correlation between financial literacy and entrepreneurial behavior among farmers. Dimension-specific analyses further reveal that both financial knowledge and financial behavior are significantly and positively associated with farmers' entrepreneurial activities. Statistical analysis of the relationship between financial literacy and entrepreneurial revenue shows that farmers with higher levels of financial literacy generally achieve greater entrepreneurial profits. However, once financial literacy exceeds a certain threshold, further improvements in financial literacy are associated with a decline in entrepreneurial profitability. This phenomenon may be attributable to the greater risk preference of farmers with higher levels of financial literacy, which may reduce the stability of their business operations. In addition, financial literacy is found to be strongly and positively correlated with entrepreneurial scale, indicating that farmers with higher levels of financial literacy tend to operate larger entrepreneurial ventures.

Against the backdrop of the in-depth advancement of regional coordinated development and the strategy of accelerating the rise of Central China, high-quality economic development has become a core issue for Henan Province to overcome traditional growth constraints and achieve transformation and upgrading. From the perspective of multidimensional factors, this study constructs a comprehensive evaluation indicator system from four dimensions: overall economic scale, economic structure, economic efficiency, and economic growth momentum. The comprehensive index method is employed to evaluate the overall development level of Henan Province from 2019 to 2023. The results indicate that the comprehensive economic development level of Henan Province has shown a steady upward trend overall; however, prominent issues remain, including lagging structural optimization, unstable growth drivers, and insufficient efficiency transformation. Among the four dimensions, overall economic scale makes the greatest contribution, while the economic growth momentum dimension exhibits the largest fluctuations. Accordingly, targeted optimization pathways are proposed from four aspects: consolidating economic scale, upgrading economic structure, improving economic efficiency, and strengthening growth momentum. This study provides theoretical references and practical insights for promoting high-quality economic development in Henan Province and other comparable provinces in Central China.

The digital-real integration is emerging as a strategic approach to invigorate industry and enhance industrial core competitiveness. This paper categorizes industrial innovation into high-end and low-end innovations, and empirically examines the influence of digital-real integration on industrial innovation in China's industrial sector, specifically on the low-end-locked dilemma. The results reveal that digital-real integration substantially fosters industrial innovation and recreates a crucial function in overcoming the challenges of low-end-locked innovation. The empowering effect of digital-real integration on industrial innovation varies under different quantile conditions and its marginal contribution increases as the level of industrial innovation improves. The industrial absorptive capacity positively moderates the driving effect of digital-real integration on industrial innovation. The analysis of heterogeneity in digital-real integration sources indicates that incorporating digital services into the real economy significantly influences high-end innovation more than digital products. This study provides valuable insights for governments to facilitate high-end industrial innovation through digital-real integration.

From 2023 to 2025, the Federal Reserve implemented its most aggressive rate hikes since the 1980s to confront high inflation, pushing the federal funds rate above 5%. This drastic tightening significantly disrupted financing conditions for U.S. Small and Medium-sized Enterprises (SMEs). This essay aims to apply the monetary policy transmission theory to analyze SME financing issues, and add new practical evidence for studies about how macro policies change individual firms' operating choices. The research relies on three methods: literature sorting, official data collation and case analysis. It sorts out how Fed rate hikes affect U.S. SMEs, lists specific driving factors and explains the transmission paths behind these influences. According to the collected data, the continuous rate hikes greatly raised borrowing costs for SMEs, hitting a nearly 20-year high, and meanwhile made it harder for small firms to get sufficient loans. The negative impacts are not evenly distributed: different industries, firm sizes and regional businesses suffer to different degrees. Besides, policy effects showed an obvious lag starting from 2024 to 2025, and many small firms face huge risks when refinancing expired loans. At the end of this paper, we put forward matching suggestions for government supportive policies and internal financing adjustments of SMEs to ease their current capital shortage problems.

To address the challenges of high-dimensional feature redundancy and nonlinear feature extraction in multisource heterogeneous financial data, in this study, key indicators are extracted from multidimensional structured data, including macroeconomic and stock market data, and unstructured news sentiment data are incorporated. Moreover, text features and time-series data are fused through a tensor fusion network to construct an automated feature engineering framework for risk indicators capable of integrating multisource heterogeneous data. Automated feature engineering can be used to automatically generate high-quality features without requiring domain knowledge or human intervention, thereby improving model performance and enhancing efficiency.