Volume 4 Issue 2

This paper proposes an intelligent decision support framework that integrates geographic information system (GIS) and building information modeling (BIM) to optimize supply chain operation in smart building design. This framework uses intelligent algorithms to synchronously balance procurement paths, supplier selection, and construction procedures, thereby minimizing embodied carbon emissions and full life cycle costs. Taking a mid-level office building as an example, under the principle of balancing environmental and economic objectives, this method reduces embodied carbon by 18% and lowers life cycle costs by 12% compared with the traditional plan. Scenario simulation further reveals a controllable balance of carbon costs: up to 25% carbon emission reduction (with a 9% cost increase), or 18% cost savings (with only a 4% carbon emission increase). Stability tests show that when price and emission parameters are perturbed by ±10%, the fluctuation range of the optimization scheme remains at ±2%. The achievements demonstrate the potential of AI-driven spatial analysis technology to guide sustainable procurement, logistics optimization, and material selection. Subsequent research will integrate real-time data streams with renewable energy factors to support dynamic reoptimization.

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Understanding how countries allocate resources and formulate strategies between team and individual sports in the Olympic Games is crucial for uncovering broader institutional and cultural dynamics. This study, based on a visual analytics framework, explores the structural patterns of Olympic medal distribution over a century. Leveraging a large-scale athlete-event dataset, we construct three interrelated visualizations: 1) a symmetrical bar chart comparing national performance in team and individual sports; 2) a structural clustering model based on medal distribution, combining principal component analysis and K-means clustering to identify pattern types; and 3) a dynamic timeline visualization of the evolution of Australia's performance in team sports. The results reveal systematic differences in national strategic preferences, ranging from "team dominance" to "balanced" performance, and identify four structural archetypes of team sports success. Time series analysis further demonstrates how individual countries adjust their strategic priorities across Olympic cycles. The research suggests that medal structure is not simply a result of competitive performance but is also influenced by long-term strategic planning and institutional configurations. This study offers a new data-driven perspective for cross-national sports comparative analysis and demonstrates the unique value of visual analytics in revealing the underlying structure of global competitive systems.

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Gross Domestic Product (GDP) is the total market value of final goods and services produced by a country in a year. This study attempted to find the best-fit Autoregressive Integrated Moving Average (ARIMA) model for forecasting China’s GDP over the next five years (2025 to 2029). In this study, we collected historical GDP data for China from 1960 to 2024 from the World Bank. Using the Box-Jenkins approach, we examined the ACF and PACF plots, performed stationarity tests, and tested several models using the AIC criterion. We determined ARIMA(1,2,1) would be the best model to fit the data. We then used the fitted model to forecast the following five years for GDP in China, demonstrating the capabilities of ARIMA as an effective forecasting model.This study provides valuable insights for policymakers and economists in planning sustainable economic strategies for China's future development.

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