With developments in the express delivery industry, statistics indicate that by 2023, the number of parcels exceeded 132.07 billion. The efficiency of couriers in delivering packages has become a service issue for many companies. This thesis, grounded in the courier path problem, draws on the Traveling Salesman Problem (TSP) to construct a delivery route planning model. This model uses an annealing algorithm for solving. It views each destination as a point and sets a variable x to 0 (if the edge is not retained) or 1 (if retained) for any point-to-point edge. Experimental results from the model's solution reveal that the method significantly reduces delivery path length, enhances efficiency, and cuts down time and cost. The approach not only enhances competitiveness and customer satisfaction for food delivery enterprises but also offers valuable insights for optimizing urban logistics distribution.

The application of artificial intelligence (AI) is of significant importance in driving the innovation and development of enterprises. This paper explores how AI applications affect enterprise innovation performance from a micro-level perspective. Based on the Resource-Based View (RBV) and Dynamic Resource-Based View (DRBV), the study empirically tests the impact of AI technology application on the innovation performance of manufacturing enterprises using data from A-share listed manufacturing companies between 2015 and 2023. The research results show that: (1) The application of AI significantly enhances the innovation performance of manufacturing enterprises, and this effect remains significant across various robustness tests. This suggests that the application of AI is a key driver for the efficient utilization of production factors, improving corporate competitiveness and economic growth. Manufacturing enterprises should actively adopt AI technologies to enhance their innovation capabilities and facilitate the conversion of innovation outcomes into economic benefits. (2) Innovation and R&D resources play a significant mediating role in the process by which AI applications enhance innovation performance, with the mediating effect of R&D personnel allocation being the strongest, while the mediating effect of R&D funding allocation is relatively weaker. This finding provides new insights into optimizing the allocation of innovation resources, particularly emphasizing the irreplaceable role of human capital in technological innovation. By optimizing human resource allocation, enterprises can further enhance the marginal benefits of AI applications and promote the continuous development of innovation capabilities. This study provides a theoretical foundation and practical insights for empowering manufacturing industry innovation through AI technology.

Modern marketing strategies have transformed through the combined power of Artificial Intelligence (AI) and Business Intelligence (BI) which improve customer segmentation and personalize marketing activities. This research examines how AI recommendation systems alongside BI tools influence marketing performance through customer interaction and conversion metrics. The research shows how AI and BI technologies produce effective marketing initiatives by analyzing consumer behavior data from transaction histories, browsing patterns, and social media activities. The study shows major enhancements in essential performance metrics including click-through rates and conversion rates with increased customer satisfaction when businesses implement AI-based systems over traditional marketing techniques. The research indicates that businesses using BI tools to implement AI-based customer segmentation achieve better conversion rates across different consumer demographics. Organizations that utilize both AI and BI systems can develop market advantages by improving customer targeting methods and enhancing their advertising approaches. The study offers important information that helps businesses boost their marketing performance while keeping pace with changing consumer behaviors in a competitive environment.

The Olympic Games, organized by the International Olympic Committee, is the largest summer comprehensive games in the world, and its medal list has attracted much attention. The Olympic Games is a dynamic and complex system, and it is of extensive and far-reaching practical significance to establish a scientific and accurate prediction model for the competition results and to reveal the rules of medals. In this regard, this paper will address the following issues. For Problem 1, we first used Machine learning algorithms and Random Forest models. The goodness-of-fit index was used to judge the advantages and disadvantages of Random Forest, Logistic regression and XGBoost, and secondly, we predicted the number of medals won by each country and the number of medals won by each country in 2028, and with the help of the correlation analysis and the systematic clustering algorithm, we came up with the intrinsic connection between the host country, the amount of project changes and the amount of medal changes. For problem 2, we firstly adopt Bayesian Changepoint Detection monitoring model. We use Bayesian Changepoint Detection monitoring to determine the location of the effect point of "great coaches", then we use the factor of coach's contribution rate to determine the influence of coaches in national programs, and at the end of the question, we have conducted case studies on China, England and Brazil, and verified the reasonableness of the model by combining with the real situation in history. For question 3, we first summarized the model above, provided insights related to the Olympic medal count, and explained how each type of insight informs the Olympics. The host country's home field effect and international economic power were analyzed, and we thus made recommendations to the Olympics on infrastructure development, logistical experience, and so on, in order to provide for the next Olympic Games in Los Angeles, USA.

In recent years, the field of cold chain logistics for fruits and vegetables has emerged as a significant topic in academic research. This study adopts a bibliometric approach and utilizes the visual analysis tool CiteSpace to systematically investigate the progress of domestic research in this area. Based on 209 core articles retrieved from the CNKI (China National Knowledge Infrastructure) database from January 2007 to May 2024, the study constructs multi-dimensional knowledge graphs—including discipline co-occurrence networks, author collaboration networks, and keyword timezone maps. The analysis reveals several key findings: there is a marked upward trend in the annual number of publications, research hotspots have evolved in phases, and core research areas concentrate on the optimization of cold chain logistics systems, innovations in preservation technologies for fruits and vegetables, and the construction of agricultural product logistics networks. It is worth noting that quality control of fruits and vegetables, along with related technological challenges, may become prominent directions for future research.

With the deepening of economic globalization, China’s economy and the global economy are becoming increasingly interdependent and closely linked, resulting in a more complex environment for domestic enterprises and heightened financial risks. To enhance the risk resilience of enterprises, the research methods for assessing financial risks are becoming more diverse. Traditional financial risk analysis methods, such as the single-argument model, have certain limitations in the practical application of enterprise financial risk evaluation. These methods cannot overcome the restrictions of time, region, and industry, and their application value is not fully realized. To better assist enterprises in addressing the complexities of financial risks, fuzzy hierarchical analysis is applied to the traditional hierarchical analysis method under fuzzy optimization conditions. This method focuses on indices of measurable comparability, facilitating a more reasonable and objective financial risk evaluation of enterprises, especially when comparing different companies in the new energy vehicle industry and conducting a longitudinal comparison of Company A. Fuzzy hierarchical analysis integrates qualitative judgment with quantitative analysis, using triangular fuzzy numbers to generate a judgment matrix. The results are transformed into an objective fuzzy set, enabling the quantification and structuring of complex system indicators and improving the rationality and accuracy of the enterprise’s financial risk evaluation.

Using survey data from 450 employees in 15 engineering companies, this study reveals the transmission mechanism of green human resource management to low-carbon transformation. The analysis found that including environmental protection track record assessment in recruitment criteria, implementing special training on energy-saving skills, and implementing performance bonus systems for carbon emission reduction can increase employees' likelihood of actively participating in green actions such as saving electricity and water by 42%. Particularly in the field of civil engineering, the correlation between green HRM measures and the emission reduction effectiveness of resource-intensive companies is 0.73. The case study shows that after a road and bridge construction company implemented an environmental assessment system, the rate of material loss on the construction site decreased by 19% year-on-year. The results provide an operational implementation plan for engineering companies to promote carbon emission reduction for all employees through human resource management.

Stock price fluctuation and prediction is a problem that has attracted much attention. There exist many mathematical and statistical problems behind it. In essence, the key to solving this problem lies in capturing the linear and nonlinear characteristics in the time series to predict future price movements. This study investigates the predictive capabilities of two distinct methodologies—Long Short-Term Memory (LSTM) networks and Autoregressive Integrated Moving Average (ARIMA) models—using Apple Inc. (AAPL) stock price data spanning 2016 to 2024. By synthesizing theoretical frameworks with empirical analysis, the research evaluates how each model captures linear trends and nonlinear fluctuations, ultimately proposing a hybrid ARIMA-LSTM architecture to enhance forecasting accuracy. Finally, according to the principal characteristics of the two models, the ARIMA-LSTM hybrid model is constructed. The results show that the hybrid model significantly outperforms single models in terms of RMSE and directional accuracy. Combined with error distribution visualization and volatility analysis, the hybrid model demonstrates efficient performance in achieving prediction optimization through the decomposition of linear and nonlinear components. It provides a new methodological perspective for financial time series modeling.

With the continuous development of science and technology and other fields in today's world, statistical analysis and research have become indispensable research methods in people's daily lives. Among these, hypothesis testing plays an important role in fields such as biology, medicine, and economics, and has significant effects in most scenarios. However, the suitability and effectiveness of different hypothesis testing methods vary depending on the context, often leading to different outcomes and levels of accuracy. This paper focuses on discussing various hypothesis testing methods in statistics, such as t-test, chi-square test, z-test, F-test, etc. In addition, this study analyzes various cases in real life and optimize and improve some hypothesis testing methods. Based on a review of existing literature, the study explores how traditional hypothesis testing determines statistical significance—where the null hypothesis is rejected if the test statistic falls within the rejection region. To improve and enhance the determination of the significance level, handle uncertainty, and increase the sample size, this paper proposes alternative methods such as NHST test, Bayesian test, big data sequential test, and failure rate hypothesis test from the perspectives of medicine and kinesiology.

The paper investigates quantum stabilizer codes and our innovative construction methodology. At first, we began with representing fundamental concepts and theories of quantum computing. In order to identify quantum error correcting codes, we extend the method of using polynomials to represent qudits on square lattices to accommodate more complicated situations in general Cayley graphs. The paper briefly reviews essential definitions and examples related to graphs, groups, and stabilizer codes, and later we propose the novel method and demonstrate its application by using the dihedral groupD4.