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The primary purpose of this study is to investigate the relationship between leader bottom-line mentality (BLM) and employee innovative behavior, which may be interpreted by employees’ perceived creativity expectations and moderated by employee time orientation.

A multi-wave and multi-source questionnaire survey with 259 paired Chinese employee–leader dyads provided data to test the theoretical model. Hypotheses were tested with Statistical Package for the Social Sciences (SPSS).

Consistent with hypotheses, leader BLM reduces employees’ perceived creativity expectations and thus inhibits employees’ innovative behavior, and this effect is stronger for employees with short-term orientation.

Our findings highlight the negative influences of leader BLM on innovative behavior and the buffering role of employees’ long-term orientation. Organizations may incorporate BLM in leadership promotion and evaluation and provide corresponding training for leaders to overcome BLM. In addition, long-term orientation can be a valuable indicator in employee recruitment and selection.

This study contributes to a new theoretical perspective of the Pygmalion effects for understanding leader BLM’s influence on employee innovative behavior.

Based on the strong feature representation ability of the convolutional neural network (CNN), generous object detection methods in remote sensing (RS) have been proposed one after another. However, due to the large variation in scale and the omission of relevant relationships between objects, there are still great challenges for object detection in RS. Most object detection methods fail to take the difficulties of detecting small and medium-sized objects and global context into account. Moreover, inference time and lightness are also major pain points in the field of RS.

To alleviate the aforementioned problems, this study proposes a novel method for object detection in RS, which is called lightweight object detection with a multi-receptive field and long-range dependency in RS images (MFLD). The multi-receptive field extraction (MRFE) and long-range dependency information extraction (LDIE) modules are put forward.

To concentrate on the variability of objects in RS, MRFE effectively expands the receptive field by a combination of atrous separable convolutions with different dilated rates. Considering the shortcomings of CNN in extracting global information, LDIE is designed to capture the relationships between objects. Extensive experiments over public datasets in RS images demonstrate that our MFLD method surpasses the state-of-the-art methods. Most of all, on the NWPU VHR-10 dataset, our MFLD method achieves 94.6% mean average precision with 4.08 M model volume.

This paper proposed a method called lightweight object detection with multi-receptive field and long-range dependency in RS images.

This study aims to provide a comprehensive review of the existing literature on uncertainty in underground mining operations, using a bibliometric and systematic analysis covering the period from 1975 to 2024.

To achieve this, the following questions were addressed using a mixed-method approach involving bibliometrics, text mining and content analysis: How has the field of uncertainty research in underground mining operations evolved? What are the most prominent research topics and trends in uncertainty in underground mining operations? and What are the possible directions for future research on uncertainty in underground mining operations?

As a result, bibliometric networks of 327 journal articles from the Scopus database were created and examined, the main research topics were underground mining management; rock mechanics; operational optimization; and stochastic systems. Finally, the inclusive investigation of uncertainty in underground mining operations and its prominent patterns can serve as a basis for real-time direction for new research and as a tool to improve underground mining activities by implementing advanced technology for innovative practices and optimizing operational efficiency. This is fundamental to identify unknown variables that impair the planning, operation, safety and economic viability of underground mines.

This research is 100% original because there is no review research on the uncertainty present in underground mining operations.