AFM characterization of physical properties in coal adsorbed with different cations induced by electric pulse fracturing

Acknowledgements This research was funded by the National Natural Science Foundation of China (grant nos. 41830427, 42130806 and 41922016), 2021 Graduate Innovation Fund Project of China University of Geosciences, Beijing (grant no. ZD2021YC035) and the Fundamental Research Funds for Central Universities (grant no. 2-9-2021-067). We are very grateful to the reviewers and editors for their valuable comments and suggestions.Peer reviewedPostprin

Variation of adsorption effects in coals with different particle sizes induced by differences in microscopic adhesion

Acknowledgements This research was funded by the National Natural Science Foundation of China (grant nos. 41830427, 42130806 and 41922016), the Fundamental Research Funds for Central Universities (grant no. 2-9-2021-067) and the 2021 Graduate Innovation Fund Project of China University of Geosciences, Beijing (grant no. ZD2021YC035). We are very grateful to the reviewers and editors for their valuable comments and suggestionsPeer reviewedPostprin

Atomic force microscopy investigation of nano-scale roughness and wettability in middle to high rank coals, with samples from Qinshui Basin, China

Acknowledgements This research was funded by the National Natural Science Fund (grant nos. 41830427, 42130806 and 41922016), the Fundamental Research Funds for Central Universities (grant no. 2652018002), and financial support from China Scholarship Council ((No.202006400048).Peer reviewedPostprin

Interference mechanism in coalbed methane wells and impacts on infill adjustment for existing well patterns

This research was funded by the National Natural Science Foundation of China (grant nos. 41830427, 42130806 and 41922016), 2021 Graduate Innovation Fund Project of China University of Geosciences, Beijing (grant no. ZD2021YC035) and the Fundamental Research Funds for Central Universities, China (grant no. 2-9-2021-067). We are very grateful to the reviewers and editors for their valuable comments and suggestions.Peer reviewedPublisher PD

Nano-CT measurement of pore-fracture evolution and diffusion transport induced by fracturing in medium-high rank coal

Acknowledgements This research was funded by the National Natural Science Foundation of China (grant nos. 42130806, 41830427 and 41922016), 2021 Graduate Innovation Fund Project of China University of Geosciences, Beijing (grant no. ZD2021YC035) and the Fundamental Research Funds for Central Universities (grant no. 2-9-2021-067). We are very grateful to the reviewers and editors for their valuable comments and suggestions.Peer reviewedPostprin

Continual Segment: Towards a Single, Unified and Accessible Continual Segmentation Model of 143 Whole-body Organs in CT Scans

Deep learning empowers the mainstream medical image segmentation methods. Nevertheless current deep segmentation approaches are not capable of efficiently and effectively adapting and updating the trained models when new incremental segmentation classes (along with new training datasets or not) are required to be added. In real clinical environment, it can be preferred that segmentation models could be dynamically extended to segment new organs/tumors without the (re-)access to previous training datasets due to obstacles of patient privacy and data storage. This process can be viewed as a continual semantic segmentation (CSS) problem, being understudied for multi-organ segmentation. In this work, we propose a new architectural CSS learning framework to learn a single deep segmentation model for segmenting a total of 143 whole-body organs. Using the encoder/decoder network structure, we demonstrate that a continually-trained then frozen encoder coupled with incrementally-added decoders can extract and preserve sufficiently representative image features for new classes to be subsequently and validly segmented. To maintain a single network model complexity, we trim each decoder progressively using neural architecture search and teacher-student based knowledge distillation. To incorporate with both healthy and pathological organs appearing in different datasets, a novel anomaly-aware and confidence learning module is proposed to merge the overlapped organ predictions, originated from different decoders. Trained and validated on 3D CT scans of 2500+ patients from four datasets, our single network can segment total 143 whole-body organs with very high accuracy, closely reaching the upper bound performance level by training four separate segmentation models (i.e., one model per dataset/task)