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The staggered distribution of joints and fissures in space constitutes the weak part of any rock mass. The identification of rock mass structural planes and the extraction of characteristic parameters are the basis of rock-mass integrity evaluation, which is very important for analysis of slope stability. The laser scanning technique can be used to acquire the coordinate information pertaining to each point of the structural plane, but large amount of point cloud data, uneven density distribution, and noise point interference make the identification efficiency and accuracy of different types of structural planes limited by point cloud data analysis technology. A new point cloud identification and segmentation algorithm for rock mass structural surfaces is proposed. Based on the distribution states of the original point cloud in different neighborhoods in space, the point clouds are characterized by multi-dimensional eigenvalues and calculated by the robust randomized Hough transform (RRHT). The normal vector difference and the final eigenvalue are proposed for characteristic distinction, and the identification of rock mass structural surfaces is completed through regional growth, which strengthens the difference expression of point clouds. In addition, nearest Voxel downsampling is also introduced in the RRHT calculation, which further reduces the number of sources of neighborhood noises, thereby improving the accuracy and stability of the calculation. The advantages of the method have been verified by laboratory models. The results showed that the proposed method can better achieve the segmentation and statistics of structural planes with interfaces and sharp boundaries. The method works well in the identification of joints, fissures, and other structural planes on Mangshezhai slope in the Three Gorges Reservoir area, China. It can provide a stable and effective technique for the identification and segmentation of rock mass structural planes, which is beneficial in engineering practice. [ABSTRACT FROM AUTHOR]

This paper investigates whether state-owned enterprise (SOE) wage premium exists in China. Our contribution is empirical and twofold. First, previous studies commonly use conventional methods, including OLS and fixed effects model, to identify SOE wage premium. It is unclear if the assumptions of these methods are met and if the evidence from these methods is subject to selection bias. In this paper, we use alternative methods to account for selection bias. The first method explores restrictions on a factor model of unobservables to identify the premium. The second method is the minimum-bias (MB) approach of Millimet and Tchernis (J Appl Econ 28:982–1017, 2013). These methods do not rely on the conventional identification assumptions and thus provide a robust check on evidence obtained from conventional methods. Our results show that significant SOE premium exists, that OLS underestimates the premium in early periods and overestimates it in recent periods, and that fixed effects method underestimates the premium. Second, we utilize data that cover a longer period than studied by previous research and thus provide estimates of the SOE wage premium for an extended period. Our results show that the premium averages at 13% in 1990–1995, 9% in 2002–2007, and 10% in 2009–2017. Our results also show considerable variation in SOE wage premium over time, between industrial sectors and between rural–urban migrant and urban local workers. [ABSTRACT FROM AUTHOR]

Trust in automation plays a leading role in human-automation interaction. As there lack of scales measuring trust in automation in China, the purpose of this study was to adapt the trust between People and Automation Scale (TPAS) into Chinese and to demonstrate its psychometric properties among Chinese adults. A total of 310 Chinese adults were randomly selected as sample 1, and 508 Chinese adults as sample 2. Results of the item analysis revealed that each item had a good quality, and the exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) suggested that the two-factor model with 12 items was the best fitting model. In addition, the TPAS was positively correlated with Interpersonal Trust Scale (ITS), proving good evidence based on relations to other variables to support the TPAS. In sum, the study suggested that the Chinese version of the TPAS could be used as an effective tool to assess trust in automation in the Chinese context. [ABSTRACT FROM AUTHOR]

Due to the rapid economic development and acceleration of industrialization, most cities in China are experiencing severe air pollution. Exposure to fine particulate matter (PM2.5) has been associated with acute lower respiratory tract infection (ALRI). To estimate associations between short-term exposure to PM2.5 and ALRI hospitalization in Yancheng City, China. This was a 6-year time-series study from 2014 to 2019. Data on hospitalization were collected from four high-ranked general hospitals, including for community-acquired pneumonia (CAP), acute exacerbation of chronic bronchitis (AECB), acute exacerbation of chronic obstructive pulmonary disease (AECOPD), and acute exacerbation of bronchiectasis (AEB), and the sum was termed total ALRIs. We obtained pollutant exposure data from five fixed monitoring stations. The association between PM2.5 and ALRI hospitalization was estimated using the generalized linear model with quasi-Poisson regression. Two-pollutant models were applied to test the robustness of the observed correlations. Subgroup analyses included sex, age, and season. During the study period, a total of 43,283 cases of total ALRIs were recorded. The average annual mean PM2.5 concentration was 45.4 ± 32.3 μg/m3. A 10-μg/m3 increase in PM2.5 concentration (lag 0) was significantly associated with increases in hospitalizations for total ALRIs (at 0.73%; 95% CI: 0.40%, 1.06%), in CAP (at 0.80%; 95% CI: 0.02%, 1.57%), in for AECOPD (1.08%; 95% CI: 0.38%, 1.78%), and AECB (0.67%; 95% CI: 0.23%, 1.11%). The estimated effects for total ALRIs and AECB were relatively robust with adjustment for other air pollutants. Associations between PM2.5 and total ALRIs were stronger in females, in the elderly, and in the cold season. PM2.5 exposure was significantly associated with ALRI morbidity, and females and older people were more susceptible to PM2.5 air pollution, especially in the cold season. [ABSTRACT FROM AUTHOR]