With the deepening of global agricultural trade and the acceleration of RMB internationalization, the synergistic development of cross-border fruit trade and RMB settlement has become a significant issue in international trade and finance. This paper, from a multi-agent collaborative perspective, systematically analyzes the interaction mechanisms and practical challenges of RMB settlement in cross-border fruit trade. The research indicates that the current cross-border fruit trade is characterized by scale expansion and product diversification. RMB settlement promotes trade facilitation by reducing exchange rate risks and simplifying processes. However, small and medium-sized businesses face high settlement costs and credit barriers, banks lack innovation in cross-border financial products, and policy support and regulatory systems need improvement. In response, this paper proposes a three-dimensional promotion path of "financial market opening—policy and regulation optimization—technological innovation empowerment," including developing offshore RMB markets, establishing specialized settlement mechanisms, and promoting blockchain technology applications. Benefit analysis shows that RMB settlement can enhance industrial chain stability, reduce financing costs for small and medium-sized enterprises, and provide substantial support for RMB internationalization. Finally, the paper suggests optimizing top-level design, strengthening risk prevention, and deepening international cooperation to promote the high-quality integration of cross-border fruit trade and RMB settlement, serving the new "dual circulation" development pattern.

Human resource allocation plays a pivotal role in enabling banks to adapt swiftly to financial market fluctuations and enhance operational efficacy. Addressing this challenge necessitates a comprehensive approach that accounts for various factors. To ensure optimal use of human resources, our research delves into essential considerations like position headcount, staff availability, employee competencies, and proficiency levels. Our objective is to formulate an integer optimization model, which strategically allocates personnel to distinct roles. Leveraging advanced optimization solvers, we solve this model to identify the most efficient staffing configurations. To validate our approach, we conducted a case study focusing on the front office of the Agricultural Bank of China. The results demonstrate the model's effectiveness in enhancing human resource allocation, ultimately contributing to the bank's operational efficiency and adaptability to market dynamics.

Financial accounting has been changed by using vast datas to increase the accuracy of financial statements with big data. In this paper, big data in financial accounting adoption process, insights from data driven and real world cases are studied and its implication for accuracy of financial statements is also investigated. It investigates how these set of characteristics or paradigms of big data (volume, veracity, velocity and variety) facilitate real time transaction analysis and help to minimize the errors there are in periodic reporting which are prone to errors. The study also demonstrates how machine learning plays in big data detection fraud, through PayPal’s real time fraud monitoring, such as in the case of fraud detection, the use of machine learning to spot anomalies and to prevent financial misstatements. Furthermore, it studies Google’s heavy use of search trends as a case study in enhancing financial forecasting through big data. Finally, the paper also covers auditing advances to auditing entire transaction populations and predictive analytics, as these are KPMG’s auditing practices. However, visualization tools like Power BI and the yet to be understood technology of blockchain make accuracy and transparency even better, but challenges remain at first such as infrastructure limitations and skill gap. The findings imply that big data aims to transform financial accounting, yet it is constrained by these hurdles that hinder the full potential of the data revolution in terms of more reliable and transparent reporting of the financials.

The success of multinational consumer brands in emerging Asia depends on the precise balance between standardization and localization in their marketing strategies. By analyzing the strategies of multinational companies in key emerging markets such as China, India, and Vietnam, this study explores the interplay between the two strategies. Based on an in-depth analysis of case studies, company annual reports, and market data, the research reveals the key factors influencing the choice of product and marketing strategies. The study found that while standardization strategies can improve profitability, localization is key to responding to regional cultural, economic, and political environments. The study highlights the importance of a hybrid strategy—combining the strengths of both strategies to maximize brand effectiveness and user engagement in emerging markets.

Based on macroeconomic data and commercial bank data in China from 2007 to 2022, this paper analyzes whether the cost efficiency and profit efficiency of commercial banks influence their risk management behavior. The findings indicate the following: banks with better cost efficiency tend to restrain the impulse for risk expansion induced by loose monetary policies, whereas high profit efficiency incentivizes commercial banks to pursue high returns from high-risk activities, leading to an increase in risk-taking levels. Moreover, when the capital adequacy ratio is high, the restrictive effect of high cost efficiency on risk management decreases, while the stimulating effect of high profit efficiency becomes more pronounced.

By combining behavioral finance theory and big data analysis technology, this study explores the mechanism of the impact of investor sentiment on cryptocurrency market price anomalies. Based on the fusion database of traditional exchange historical market data and social media sentiment data, the research team constructed multidimensional sentiment indicators to quantify the emotional fluctuations of market participants. The research design adopts the strict data cleaning process, feature engineering processing, and the hybrid modeling method combining the traditional statistical model and the machine learning algorithm. The empirical results show that extreme optimism or pessimism is significantly associated with abnormal price events, and the predictive ability of the composite sentiment index is better than that of the single volatility index. This research reveals the transmission pathways of cognitive biases such as overconfidence and the anchoring effect in the cryptocurrency market, confirming the significant influence of irrational psychological factors on the price formation of digital assets. The findings not only deepen our understanding of the interaction mechanism between investor psychology and market behavior, but also provide an innovative analytical framework for risk management and quantitative investment strategies in the cryptocurrency field.

The rapid growth of e-commerce has led to an increased demand for campus express delivery. Traditional self-service parcel collection methods encounter challenges such as time conflicts, long retrieval distances, and extended wait times. Autonomous vehicle delivery presents an efficient and convenient solution for campus logistics. This study utilizes a questionnaire to assess the parcel collection behaviors of faculty and students at North China University of Technology (NCUT), their satisfaction with current services, and their acceptance of autonomous delivery. Results reveal significant advantages for autonomous delivery in campus logistics, with over 60% of respondents citing difficulties due to long distances to collection points and around 50% expressing dissatisfaction with lengthy queues. Many believe autonomous delivery can effectively mitigate these issues. Furthermore, nearly 70% are willing to pay extra for autonomous services, especially in adverse weather conditions. The study also evaluates the economic and social feasibility of the delivery system within the campus context, indicating substantial market potential for autonomous vehicles at NCUT.

Against the backdrop of rapid advancements in modern information technology, digital transformation has emerged as a global trend. This process has experienced unprecedented acceleration, particularly under the impact of the COVID-19 pandemic. Such transformation extends beyond technological innovation, encompassing critical aspects of corporate future competitiveness as well as organizational survival and growth within intensely competitive markets. As the global economy gradually recovers from the pandemic, sustaining digital transformation poses a significant challenge for enterprises. Governments worldwide have clearly recognized this imperative and have formulated corresponding strategic plans. Since 2015, China has implemented the "National Big Data Strategy," aiming to leverage big data to drive comprehensive economic growth and empower various industries. The "14th Five-Year Plan and Long-Range Objectives Through 2035," proposed in 2020, further emphasized the importance of accelerating the integration of the digital economy with traditional industries and building internationally competitive digital industrial clusters. Notably, the 2022 Government Work Report mentioned the "digital economy" for the sixth consecutive year, and for the first time, dedicated an entire paragraph to this topic, underscoring the government's heightened focus on its significance. Evidently, with continuous policy support and active participation from all sectors of society, digital transformation is poised to become a crucial driver for enterprises to navigate the future. This study selects Changan Automobile as a case study to analyze the motivations, pathways, and financial performance impacts of digital transformation in the automotive industry. The findings aim to provide insights and references for other enterprises within the sector.

Social media's rise has intensified investor sentiment in financial markets, driving heightened stock price volatility and crash risk. In this context, exploring how internal control quality mediates sentiment and crash risk is vital for developing governance tools to stabilize markets in the digital age. This study investigates the relationship between investor sentiment, internal control quality, and stock price crash risk using a sample of Chinese A-share listed companies from 2007 to 2022. Leveraging financial data and a robust empirical framework, the study finds that higher investor sentiment significantly exacerbates stock price crash risk, particularly in firms with internal control deficiencies and state-owned enterprises (SOEs). Mediation analysis reveals that investor sentiment deteriorates internal control quality, amplifying crash risk and underscoring the critical role of governance mechanisms in mitigating market instability.

Urban agglomerations play a pivotal role in China's carbon peaking and carbon neutrality goals, yet few studies have provided a unified, long-term assessment of their carbon emission performance. This paper addresses this gap by analyzing panel data (2006–2022) from 16 national-level urban agglomerations. Utilizing a Non-Radial Directional Distance Function (NDDF) to calculate the Carbon Reduction Efficiency Index (CREI) and a Global Malmquist-Luenberger (GML) index to measure Total Factor Carbon Emission Productivity (TFCEP), we reveal considerable disparities across regions. Eastern "optimization-enhancing" agglomerations (e.g., Pearl River Delta, Yangtze River Delta) demonstrate consistently high efficiency, sustained by stable technological advances. In contrast, central and western "growth-enhancing" and "development-nurturing" agglomerations (e.g., the Ningxia region along the Yellow River, Central Shanxi) exhibit lower performance but significant potential for improvement. Dynamic analysis indicates an overall upward trend, largely driven by technology gains in advanced regions and efficiency catch-up in less developed ones, despite challenges such as technological lock-in. Dagum's Gini coefficient shows narrowing gaps under coordinated the carbon peaking and the carbon neutrality goals policies, although institutional barriers still restrict cross-regional technology diffusion. These findings underscore the need for region-specific low-carbon strategies that integrate industrial upgrading and innovation support, thereby promoting balanced and sustainable urban development trajectories.