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Background Recent studies have shown that deep learning can help tumor staging automatically. However, automatic nasopharyngeal carcinoma (NPC) staging is difficult due to the lack of large and slice‐level annotated datasets. Purpose To develop a weakly‐supervised deep‐learning method to predict NPC patients' T stage without additional annotations. Study Type Retrospective. Population/Subjects In all, 1138 cases with NPC from 2010 to 2012 were enrolled, including a training set (n = 712) and a validation set (n = 426). Field Strength/Sequence 1.5T, T1‐weighted images (T1WI), T2‐weighted images (T2WI), contrast‐enhanced T1‐weighted images (CE‐T1WI). Assessment We used a weakly‐supervised deep‐learning network to achieve automated T staging of NPC. T usually refers to the size and extent of the main tumor. The training set was employed to construct the deep‐learning model. The performance of the automated T staging model was evaluated in the validation set. The accuracy of the model was assessed by the receiver operating characteristic (ROC) curve. To further assess the performance of the deep‐learning‐based T score, the progression‐free survival (PFS) and overall survival (OS) were performed. Statistical Tests The Sklearn package in Python was applied to calculate the area under the curve (AUC) of the ROC. The survcomp package was used for calculations and comparisons between C‐indexes. The software SPSS was employed to conduct survival analysis and chi‐square tests. Results The accuracy of the deep‐learning model was 75.59% in the validation set. The average AUC of the ROC curve of different stages was 0.943. There were no significant differences in the C‐indexes of PFS and OS from the deep‐learning model and those from TNM staging, with P values of 0.301 and 0.425, respectively. Data Conclusion This weakly‐supervised deep‐learning approach can perform fully automated T staging of NPC and achieve good prognostic performance. Level of Evidence: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;52:1074–1082.

Geological and hydrological characteristics, joint geometric features, rock physical and mechanical properties and rock mass quality are studied in the Beishan area, preselected for China's high‐level radioactive waste (HLW) disposal engineering. A comprehensive survey method is developed to study joint geometric features in the outcrop and samples from borehole BS06 into the Xinchang rock mass were tested. The optimal joint sets are determined by rose diagrams and equal‐area lower hemisphere plots of joint poles. Results show that: 1) the distribution of joint occurrence obeys a normal distribution, while the distribution of joint spacing obeys a negative exponential distribution; 2) concentric circular and tangent circular sampling windows are applied to study the trace length and the trace midpoint density. Results indicate that tangent circular sampling window is more stable and reasonable; 3) Beishan granite shows high density, low porosity and high strength based on many laboratory tests and the physical properties and mechanical properties are closely related; and 4) a synthesis index, Joint Structure Rating (JSR), is applied to evaluate the quality of rock mass. Through the research results of rock mass characteristics, the Xinchang rock mass in the Beishan preselected area has the favorable conditions for China's HLW disposal repository site.

The accurate and reproducible delineation of tumors from uninvolved tissue is essential for radiation oncology. However, the tumor margin may be challenging to identify from magnetic resonance (MR) images of nasopharyngeal carcinomas (NPCs). Additionally, clinical diagnoses such as T-staging may already provide some information on tumor invasion. To use this information and improve the performance of tumor segmentation, we propose a novel deep learning neural network architecture that can incorporate both T-staging and image information. Based on U-Net, our model adds a T-channel composed of T-staging information and uses the attention mechanism. Since the T-staging information is defined by the extent of tumor invasion, the T-channel using T-staging information can improve the segmentation accuracy at different stages. Additionally, the addition of an attention mechanism allows our model to retain the most valuable pixels of the image, thus further improving the delineation accuracy. In our experiments, the proposed network was trained and validated based on records from 251 clinical patients using 10-fold crossvalidation. The dice similarity coefficient (DSC) and average symmetric surface distance (ASSD) were used to evaluate our network's results. The average DSC and ASSD and their standard deviation (SD) values are 0.841 ± 0.011 and 0.747 ± 0.199 mm. The unique T-channel effectively utilizes T-staging information to improve the results. With the combination of the T-channel module and the attention module, we significantly improved NPC tumor delineation performance.

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Pedestrian dead reckoning (PDR) is an essential technology for positioning and navigation in complex indoor environments. In the process of PDR positioning and navigation using mobile phones, gait information acquired by inertial sensors under various carrying positions differs from noise contained in the heading information, resulting in excessive gait detection deviation and greatly reducing the positioning accuracy of PDR. Using data from mobile phone accelerometer and gyroscope signals, this paper examined various phone carrying positions and switching positions as the research objective and analysed the time domain characteristics of the three-axis accelerometer and gyroscope signals. A principal component analysis algorithm was used to reduce the dimension of the extracted multidimensional gait feature, and the extracted features were random forest modelled to distinguish the phone carrying positions. The results show that the step detection and distance estimation accuracy in the gait detection process greatly improved after recognition of the phone carrying position, which enhanced the robustness of the PDR algorithm.