Driven by the dual forces of digital transformation and intelligent education, university libraries are transitioning from traditional document storage centers to smart service hubs. This paper focuses on data-driven theory and AI technology applications, integrating the characteristics of university library service scenarios to construct a closed-loop smart service model of "data collection-intelligent analysis-precise service-feedback optimization." By analyzing current pain points in university libraries regarding resource integration, service responsiveness, and user demand matching, it explores three dimensions: intelligent resource restructuring, precise service delivery, and smart management upgrades. Specific implementation pathways are proposed by integrating big data analytics, machine learning, and natural language processing technologies. The research provides theoretical references and practical insights for university libraries to overcome service bottlenecks, enhance user experience, and strengthen educational support functions.
Keywords: Data driven, AI empowered, university libraries, intelligent services, service models

The transportation model is a special form of the Linear Programming model. This model deals with transporting goods from sources (supply centers) to destinations (demand centers). A balance between the demand requirements of destinations and the supply quantities of sources must be ensured. The aim of the transportation model is to determine the amount of goods to be transported from each source to each destination such that the total transportation cost is minimized; in other words, to optimize transportation costs. Transportation models are widely used in real life. Given that these problems may contain numerous variables and constraints, it is important to solve them accurately and rapidly. With the rapid development of computer software, various package programs have been introduced. In this study, information regarding transportation models and solution methods is provided. Subsequently, a transportation model was constructed using a dataset compiled from information available on the Turkish Hard Coal Enterprise (TTK) website. This model was solved using Python, R, and Excel, and the results were compared to provide recommendations.
Keywords: Linear programming, Transportation model, Optimization, Python, R

In the field of mathematics, Newton's iteration method stands as a significant numerical computation technique, possessing profound theoretical significance and extensive practical value. As the Gaokao increasingly demands comprehensive literacy and innovative capabilities from students, the format and content of its mathematics questions continue to evolve. Consequently, examination papers increasingly feature questions centred upon Newton's iteration method. This paper focuses on innovative questions centred around Newton's method, delving into the essence of this iterative technique and providing detailed analysis and critique through representative examples. It aims to assist students in gaining a profound understanding of the problem-solving approach and methodology for such question types, thereby enhancing their problem-solving abilities. Concurrently, it seeks to support teachers in refining their teaching strategies and elevating instructional quality.
Keywords: Newton's iteration method, Derivative，Sequence

Scientific research and development (R&D) activities constitute the core driving force for enhancing regional innovation capability, and accurate prediction of their development trends provides crucial support for formulating scientific and technological innovation policies. Taking Tai'an City as the research object, this paper selects three core indicators—total social R&D investment, R&D investment intensity, and the proportion of high-tech industry output value—in the period of 2019-2024 as the analysis dimensions, constructs a GM (1,1) prediction model based on the grey system theory, and conducts a quantitative forecast of the R&D development trends of Tai'an City from 2025 to 2030. The results show that: the average relative error of the constructed GM (1,1) model is less than 5%, reaching the first-level accuracy grade, and the prediction effect is reliable; the total social R&D investment of Tai'an City will maintain an average annual growth rate of 8%-10% from 2025 to 2030, expected to exceed 15 billion yuan in 2030, the R&D investment intensity will steadily rise to more than 3.2%, and the proportion of high-tech industry output value will surpass 75%.
Keywords: Grey System Theory; GM (1,1) Model; R&D Trend Forecast; Tai'an City ;Regional Innovation.