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In view of the frequent occurrence of rock burst in the middle layer of the steeply inclined thick coal seam in Dongxia Coal Mine, based on the three-dimensional microseismic monitoring network and the seismic source space precise positioning method, the law that the seismic source of mine earthquakes is concentrated in the floor rock roadway and its distribution trend line is perpendicular to the direction of maximum horizontal stress is determined; according to this, it is determined that the coal burst in the middle layer is caused by the decrease of the minimum principal stress (vertical direction) caused by the mining of the upper layer, which makes the floor rock reach the Mohr shear failure limit state under the strong tectonic stress, and then the blasting anti-rock burst measures for the floor rock is proposed. The result of engineering practice shows that using reasonable blasting amount of floor regularly can effectively reduce vibration frequency and energy and avoid the occurrence of rock burst.

Landscape ecological risk assessment (LERA) serves as a crucial tool for guiding effective environmental management. However, the conventional approach of LERA suffers from two notable drawbacks: the utilization of low‐resolution land‐use data (e.g., 30 × 30 m) and the application of arbitrary evaluation units (e.g., uniformly‐sized grids), both of which introduce uncertainty and inaccuracies into the assessment outcomes. Moreover, the extent to which the traditional LERA accurately reflects the true ecological risk level remains unexplored. To address these limitations, this study presents a modified LERA conducted in Xiapu, a coastal county in China, spanning the years 2013–2015. Fine‐grained land‐use data were employed to overcome the shortcomings of low‐resolution data. Additionally, spatial correlations between land‐use changes and ecological risk alterations were analyzed to unravel the mechanisms behind land‐use changes' impact on ecological risk, while also testing the accuracy of LERA results. Major findings can be summarized as follows: (1) Ecological risk changes in Xiapu during 2013–2015 were relatively minor, with high‐risk areas predominantly concentrated along the coast. (2) A total of 1137 ha of land in Xiapu County experienced changes, with construction land witnessing the most substantial increase. (3) Counterintuitive and unreasonable LERA outcomes were identified, particularly pertaining to illogical ecological risk changes arising from transformations between construction and non‐construction land. (4) Based on the counterintuitive findings, potential factors affecting the limitations and applicability of LERA were discussed. This study represents the first critical examination of the limitations of LERA, offering valuable insights to stimulate future researchers to rethink LERA and emphasize the importance of validating assessment outcomes during its application. [ABSTRACT FROM AUTHOR]

Motivation The question of how to construct gene regulatory networks has long been a focus of biological research. Mutual information can be used to measure nonlinear relationships, and it has been widely used in the construction of gene regulatory networks. However, this method cannot measure indirect regulatory relationships under the influence of multiple genes, which reduces the accuracy of inferring gene regulatory networks. Approach This work proposes a method for constructing gene regulatory networks based on mixed entropy optimizing context-related likelihood mutual information (MEOMI). First, two entropy estimators were combined to calculate the mutual information between genes. Then, distribution optimization was performed using a context-related likelihood algorithm to eliminate some indirect regulatory relationships and obtain the initial gene regulatory network. To obtain the complex interaction between genes and eliminate redundant edges in the network, the initial gene regulatory network was further optimized by calculating the conditional mutual inclusive information (CMI2) between gene pairs under the influence of multiple genes. The network was iteratively updated to reduce the impact of mutual information on the overestimation of the direct regulatory intensity. Results The experimental results show that the MEOMI method performed better than several other kinds of gene network construction methods on DREAM challenge simulated datasets (DREAM3 and DREAM5), three real Escherichia coli datasets (E.coli SOS pathway network, E.coli SOS DNA repair network and E.coli community network) and two human datasets. Availability and implementation Source code and dataset are available at https://github.com/Dalei-Dalei/MEOMI/ and http://122.205.95.139/MEOMI/. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]