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The inference of the network traffic data from partial measurements data becomes increasingly critical for various network engineering tasks. By exploiting the multi-dimensional data structure, tensor completion is a promising technique for more accurate missing data inference. However, existing tensor completion algorithms generally have the strong assumption that the tensor data have a global low-rank structure, and try to find a single and global model to fit the data of the whole tensor. In a practical network system, a subset of data may have stronger correlation. In this work, we propose a novel localized tensor completion model (LTC) to increase the data recovery accuracy by taking advantage of the stronger local correlation of data to form and recover sub-tensors each with a lower rank. Despite that it is promising to use local tensors, the finding of correlated entries faces two challenges, the data with adjacent indexes are not ones with higher correlation and it is difficult to find the similarity of data with missing tensor entries. To conquer the challenges, we propose several novel techniques: efficiently calculating the candidate anchor points based on locality-sensitive hash (LSH), building sub-tensors around properly selected anchor points, encoding factor matrices to facilitate the finding of similarity with missing entries, and similarityaware local tensor completion and data fusion. We have done extensive experiments using real traffic traces. Our results demonstrate that LTC is very effective in increasing the tensor recovery accuracy without depending on specific tensor completion algorithms.

A double-row pile support system combined with existing and additional support piles offers an effective solution for further excavation beneath existing underground space. A large-scale test chamber was therefore built to simulate the whole construction process of underground space extension. Several parallel tests are conducted through observation, data monitoring, and analysis to study the influence of several parameters on an h-type support system containing double-row piles. The relevant parameters include pile row spacing, pile length ratio, pile-head constraint, and in-service foundation pile. The tests reveal that a significant load-transfer effect is generated between the pile rows, and increasing the spacing between pile rows within a certain range can lead to a more reasonable distribution of bending moments and pile force. The displacement of the pile top and its rate of increase are directly proportional to excavation depth, and additional excavation to the bottom of the back-row piles tends to be a critical point, after which the deformation will be significant. The stability of the system varies inversely with the reduction in pile length ratio, but is positively related to the existing pile-head constraint. Furthermore, in-service foundation piles can result in increased bending moments and reduced displacement of the pile top. Finally, the rationality of the model test results was verified according to the numerical simulation and the stability of the double-row piles support system was calculated.

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The mentoring relationship affects the growth and development of new employees. For nurses, the uncertainty of the influence of the mentoring relationship may be magnified by the unique nature of hospitals as public departments, however it is unclear whether and how nurses’ mentoring relationship influence the outcome. Protean career orientation defined as a tendency of individuals to achieve subjective career success through self-management of their career is crucial to the influence mechanism of the mentoring relationship. The aim of this study was to explore the path and boundary conditions of the influence of the nurses’ mentoring relationship on organizational commitment. As a cross-sectional sample, 371 nurses were investigated. The results showed that protégé career optimism plays an intermediary role in the influence of the mentoring relationship on organizational commitment, and protean career orientation plays a moderating role in the influence of the mentoring relationship on career optimism. The mentor relationship between mentors and protégés facilitates protégés’ career optimism, enhancing the protégés’ organizational commitment, especially for protégés with low protean career orientation. These findings contribute to the improving nurses’ organizational commitment through mentoring relationship. Hospitals should provide space for nurses to exert their abilities, enhance opportunities to improve their team cooperation ability, clearly define the scope of nurses’ work and rights, and give nurses the right to make decisions.