Construction industry is a pillar industry of China's national economy but its problems of high energy consumption, high pollution and low energy efficiency is increasingly prominent. The study on the energy efficiency of construction industry is of great significance for improving development quality and achieving the goal of energy saving and emission reduction. In this paper, a three-stage undesirable SBM-DEA model was employed to measure the energy efficiency in construction industry during 2005 -2016. The CO 2 directly emitted by the construction industry and indirectly emitted in the production of building materials were used as the undesirable output and the three-stage framework was employed to analyze and eliminate the influence of external environment. The empirical results showed that low efficiency of management in the construction industry is an important factor leading to the low level of energy efficiency in China's construction industry. For the energy efficiency value before and after adjustment, the "high-high" provinces has made full use of the superior external environment by their high management level, while the "high-low" provinces needs to fully realize the potential in promoting energy efficiency of its external environment by improving its own management of construction industry. On the contrary, the "low-high" provinces need to improve the external environment to ease its restrictions on the level of management in the construction industry. Environmental factors and management level should be considered simultaneously for different provinces to improve energy efficiency of construction industry.
The features traditionally extracted from hysteresis loops are highly sensitive to both the variation of case depth and uncontrollable factors in repeated testing cycles, thus increasing the difficulty in predicting the tensile force applied on surface-hardened steel rods with different case depths. In this study, in order to eliminate the influence of such high sensitivity, a case depth-insensitive feature (CDIF) was proposed to characterize the tensile force, and a particle swarm optimization (PSO)-optimized neural network was used to establish the correlation between the CDIF and tensile force in order to predict the tensile force applied on steel rods with different case depths. Five classical features (including remanent magnetic induction intensities, coercive force, hysteresis loss, maximum magnetic induction, and distortion factor) and the CDIF were successfully used to characterize the tensile force. Then, the linear regression model and PSO-optimized neural network model were used in turn to establish the relationship between each feature and tensile force to predict the tensile force applied on steel rods with different case depths. The CDIF was insensitive to the variation of case depth and linearly correlated with the tensile force. Even though the CDIF is affected by the unknown and uncontrollable factors in repeated testing cycles, the PSO-optimized neural network model based on it can be used to accurately predict the tensile force applied on steel rods with different case depths with a prediction error of 0.67%.
The present study implemented an objective head pose tracking technique—OpenFace 2.0 to quantify the three dimensional head movement. Children with autism spectrum disorder (ASD) and typical development (TD) were engaged in a structured conversation with an interlocutress while wearing an eye tracker. We computed the head movement stereotypy with multiscale entropy analysis. In addition, the head rotation range (RR) and the amount of rotation per minute (ARPM) were calculated to quantify the extent of head movement. Results demonstrated that the ASD group had significantly higher level of movement stereotypy, RR and ARPM in all the three directions of head movement. Further analyses revealed that the extent of head movement could be significantly explained by movement stereotypy, but not by the amount of visual fixation to the interlocutress. These results demonstrated the atypical head movement dynamics in children with ASD during live interaction. It is proposed that head movement might potentially provide novel objective biomarkers of ASD.
Lay Summary
Our study used an objective tool to quantify head movement in children with autism. Results showed that children with autism had more stereotyped and greater head movement. We suggest that head movement tracking technique be widely used in autism research.
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