Public stereotypes of recycled water end uses with different human contact: Evidence from event-related potential (ERP)

Hou, Can; Yan, Wen; He, Yuqi; Liu, Xiaojun; Wang, Mengmeng; Zhi-yin, Zhang; Fu, Hanliang

doi:10.1016/j.resconrec.2021.105464

Cited by 69 publications

(38 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, informatization has been received substantial emphasis in the construction industry [1][2][3][4][5][6]. Jung et al [7] stated that information is a critical resource in a construction project, facilitating not only effective project management, but also automation in engineering and construction.…”

Section: Introductionmentioning

confidence: 99%

Investigating the Critical Factors of Professionals’ BIM Adoption Behavior Based on the Theory of Planned Behavior

Jiang

et al. 2021

IJERPH

View full text Add to dashboard Cite

In recent years, building information modeling (BIM) has been receiving growing interest from the construction industry of China. Nevertheless, although BIM has many foreseeable advantages, many studies claimed that these advantages have not been sufficiently achieved in practice at the current stage. In this circumstance, it is interesting to investigate what really drives the adoption of BIM. Based on Ajzen’s theory of planned behavior (TPB), a hypothetical model which involves nine latent variables is initially established. Then, a questionnaire is designed and distributed to the construction professionals in the Chinese context. After reliability and validity analysis, the goodness-of-fit of the initial model and the related theoretical assumptions are tested through structural equation modeling (SEM). Based on the modification indicators, a modified model is finally derived. Results show that economic viability and governmental supervision are the most critical factors that influence construction professionals’ BIM adoption behavior in China, sharing weights of 0.37 and 0.34, respectively, whereas other factors play limited roles in this regard. The research findings revealed from this study can provide insightful references for countries that intend to promote BIM adoption in a similar circumstance.

show abstract

Section: Introductionmentioning

confidence: 99%

Investigating the Critical Factors of Professionals’ BIM Adoption Behavior Based on the Theory of Planned Behavior

Jiang

et al. 2021

IJERPH

View full text Add to dashboard Cite

show abstract

“…Relevant scholars proposed wide residual networks [22][23][24][25]. Based on residual networks, they further explored the influence of width on neural networks and proved through experiments that width is more efficient than depth [26,27]. For example, a 16-layer widened residual network has better performance than a thousand-layer residual network and can save nearly half of the training time.…”

Section: Related Workmentioning

confidence: 99%

Feature Extraction of Broken Glass Cracks in Road Traffic Accident Site Based on Deep Learning

Shu

2021

Complexity

View full text Add to dashboard Cite

This paper studies the feature extraction and middle-level expression of Convolutional Neural Network (CNN) convolutional layer glass broken and cracked at the scene of road traffic accident. The image pyramid is constructed and used as the input of the CNN model, and the convolutional layer road traffic accident scene glass breakage and crack characteristics at each scale in the pyramid are extracted separately, and then the depth descriptors at different image scales are extracted. In order to improve the discriminative power of the depth descriptor, Hellinger kernel and Principal Component Analysis (PCA) are used to perform nonlinear and linear transformations. Two aggregation strategies based on depth descriptors are proposed to form a global image representation. The classification experiment of the data set shows that Hellinger kernel, PCA transformation, and two aggregation strategies are all conducive to improving the classification accuracy. In addition, the convolutional layer road traffic accident scene glass breaking and crack feature coding can obtain better classification performance than the fully connected layer feature. We conducted dynamic impact tests on plate glass and Polyvinyl Butyral- (PVB-) laminated glass under different boundary conditions and studied the crack propagation and failure process of the glass under different impact speeds. The results show that there are radial cracks and circular cracks on the glass specimens under the impact load; the glass specimens show partial damage under high-speed impact and the characteristics of glass breaking and cracks at the scene of road traffic accidents; the four-frame plate glass is supported by sharp dagger-like fragments. This paper compares the energy absorption of glass specimens under different boundary conditions. The results show that the energy absorption effect of the four-point supporting glass specimen is generally stronger than that of the four-frame supporting glass.

show abstract

“…The errors of PSO, MFO, and IMFO algorithms are compared below, and the errors are compared and analyzed by equation (18). The results are shown in Figure 6.…”

Section: Location Prediction Algorithmmentioning

confidence: 99%

“…Literature [16] proposes an improved IMFO based on exploration/development rate control in the field of machine learning for feature selection. Literature [17,18] proposed a moth flame algorithm and solved the engineering optimization problem successfully. The improved MFO algorithm has achieved good results in specific applications.…”

Section: Introductionmentioning

confidence: 99%

Research on Underground Chemical Gas Monitoring and Target Location Based on an Improved Moth Flame Algorithm

Chen

2021

Journal of Sensors

View full text Add to dashboard Cite

The danger of downhole work is mainly due to the chemical toxic gases and flammable gases NO2, CO, SO2, H2S, CH4, CO2, etc. When the concentration reaches a certain value, it will produce very great harm. With the continuous development of sensor technology and communication technology, it is necessary to monitor the relevant geographic features below the ground. Because of the complex environmental parameters of the coal mine roadway and the interference caused by various electrical equipment, the transmission of mine electromagnetic signals will be affected, resulting in low positioning accuracy. However, the underground chemical gas leakage leads to the life of underground workers which cannot be guaranteed, so it is necessary to effectively monitor the concentration of chemical gas components in underground mines. In this paper, a moth flame algorithm based on optimized inertia weights is proposed. By continuously improving the local inertia weights, the global optimum is determined by using the change of inertia weights in the iterative process of the algorithm. By testing the convergence and optimal value of several algorithms under common test functions, IMFO can obtain the global optimal solution. Finally, the concentrations of chemical gases NO2, CO, SO2, H2S, CH4, and CO2 are monitored by setting specific areas to see if they reach the early warning values. Then, 16 coordinates in the region are used to predict the above method, and the IMFO algorithm can achieve the best prediction effect.

show abstract

Public stereotypes of recycled water end uses with different human contact: Evidence from event-related potential (ERP)

Cited by 69 publications

References 45 publications

Investigating the Critical Factors of Professionals’ BIM Adoption Behavior Based on the Theory of Planned Behavior

Investigating the Critical Factors of Professionals’ BIM Adoption Behavior Based on the Theory of Planned Behavior

Feature Extraction of Broken Glass Cracks in Road Traffic Accident Site Based on Deep Learning

Research on Underground Chemical Gas Monitoring and Target Location Based on an Improved Moth Flame Algorithm

Contact Info

Product

Resources

About