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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]

The main objective of the present study was to produce a landslide susceptibility map by implementing a novel methodology that combines Information Theory and GIS-based methods for the Nancheng County, China, an area with numerous reported landslide events. Specifically, the information coefficient that is estimated from Shannon's entropy index was used to determine the number of classes of each landslide-related variable that maximizes the information coefficient, while three methods, logistic regression, weight of evidence, and random forest algorithm, were implemented to produce the landslide susceptibility map. The comparison of the various models was based on the assessment of a database of 112 past landslide events, which were divided randomly into a training dataset (70 %) and a validation dataset (30 %). The identification of the areas affected was established by analyzing airborne imagery, extensive field investigation, and the examination of previous research studies, while the morphometric variables were derived using remote sensing technology. The geo-environmental conditions in those locations were analyzed regarding their susceptibility to slide. In particular, 11 variables were analyzed: lithology, altitude, slope, aspect, topographic wetness index, sediment transport index, profile curvature, plan curvature, distance to rivers, distance to faults, and distance to roads. The comparison and validation of the outcomes of each model were achieved using statistical evaluation measures, the receiving operating characteristic, and the area under the success and predictive rate curves. Each model gave similar outcomes; however, the random forest model had a slightly higher predictive performance in terms of area under the curve (0.9220) against the ones estimated for the weight of evidence (0.9090) and the logistic regression model (0.8940). The same pattern of performance was reported when the success power of the models was calculated. Random forest was slightly better than the other two models in terms of area under the curve (0.9350) in comparison with the weight of evidence (0.9255) and logistic regression (0.9097). The predictive performance was estimated by using the validation dataset, while the success power of the models was estimated by using the training dataset. From the visual inspection of the produced landslide susceptibility maps, the most susceptible areas are located at the west and east mountainous areas, while moderate to low susceptibility values characterize the central area. [ABSTRACT FROM AUTHOR]

What is the impact of a bank failure on the whole banking industry? To resolve this issue, the paper develops a transfer entropy-based method to determine the interbank exposure matrix between banks. This method constructs the interbank market structure by calculating the transfer entropy matrix using bank stock price sequences. This paper also evaluates the stability of Chinese banking system by simulating the risk contagion process. This paper contributes to the literature on interbank contagion mainly in two ways: it establishes a convincing connection between interbank market and transfer entropy, and exploits the market information (stock price) rather than presumptions to determine the interbank exposure matrix. Second, the empirical analysis provides an in depth understanding of the stability of the current Chinese banking system. [ABSTRACT FROM AUTHOR]

Diversity of urban energy system structure (UESS) was analyzed based on information entropy. To find out the evolution law of UESS and thus provide principles to coordinate and optimize the UESS, the correlative relationship model between UESS and its influencing factors can be developed using the partial least square (PLS) regression. Here we took Xiamen City as an example to analyze the evolution tendency and the influencing factors of diversity of energy supply structure (ESS) and energy demand structure (EDS). The results showed that, on the whole, the diversity of ESS in Xiamen was rising over time as a logistic curve from 1996 to 2003, with reducing speed from 1999 though. While in the period, EDS in Xiamen City didn't improve efficiently. Urbanization level, population, gross domestic product (GDP), and the third industrial structure had significantly affected ESS. Meanwhile, number of vehicles, GDP, population, per capita disposable income, and energy eco-efficiency had obvious influences on EDS. As for Xiamen City, the improvement of diversity of EDS should be especially emphasized. [ABSTRACT FROM AUTHOR]