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Predicting the water-conducting fractured zone (WCFZ) height is the key to roof water disaster prevention and environmental protection. Field WCFZ data were collected from 24 working faces of Shendong, Shenfu, Yushen, and Yuheng mining areas in the northern Ordos Basin. A fitting formula was obtained by a multiple regression model and the weight of each factor was calculated by information entropy theory, allowing a prediction of the WCFZ height in the area. The formula was improved by applying a safety factor of 1.33, which met the 95.8% reliability requirements of the samples. Finally, the 31,114 working face of the Jinjie coal mine was selected as a case study. The spatial characteristics of the WCFZ in the study area were analyzed using a geographic information system (GIS) and the WCFZ height was measured using borehole TV. The predicted value was 49.6 m and the field measured value exceeded 42.4 m, making the model-predicted value much closer that predictions calculated with other empirical formulas. The results indicate that this method quantitatively predicted the WCFZ height in the study area and enabled the WCFZ height in the study area to be visualized with GIS technology. It is an effective tool for analyzing and predicting the height of the WCFZ and for preventing mine water inrush. [ABSTRACT FROM AUTHOR]

Social collaborative design rooted in online platforms has emerged as a new paradigm for product innovation, with open and dynamic design elements bringing about external design resources and internal fluctuations. To guarantee sustainable operation of the design system, assessing the impact of the uncertainty of the external environment and randomness of the internal organization on the efficiency and stability of the system's operation is essential. To that end, this paper proposes a quantitative evolutionary analysis model based on information entropy theory to assess the benefits and drawbacks of system evolution by measuring the degree of development and harmony of the system, as well as to anticipate future evolutionary trajectories using a classical time-series prediction model. The accuracy and validity of the model were verified using 794,509 design data points accumulated over 10 years on the Zhubajie platform, the largest social collaborative design platform in China. In summary, our study provides a method for stakeholders to evaluate the evolutionary trend of the design ecosystem. Platform managers can utilize this model for controlling the operating state of the system to ensure safe and effective operation. [ABSTRACT FROM AUTHOR]