Safety climate and safety performance among construction workers in Hong Kong : the role of psychological strains as mediators

This paper examines relations among safety climate (safety attitudes and communication), psychological strains (psychological distress and job satisfaction), and safety performance (self-reported accident rates and occupational injuries). A questionnaire was administered to construction workers from 27 construction sites in Hong Kong (N=374, M=366, F=8, mean age =36.68 years). Data were collected by in-depth interviews and a survey from February to May 2000. A path analysis using the EQS-5 was employed to test the hypothesized model relating safety climate, safety performance, and psychological strains. The results provide partial support for the model, in that safety attitudes predict occupational injuries, and psychological distress predicts accident rates. Furthermore, psychological distress was found to be a mediator of the relationship between safety attitudes and accident rates. The implications of these results for psychological interventions in the construction industry are discussed

Federated Neural Radiance Fields

The ability of neural radiance fields or NeRFs to conduct accurate 3D modelling has motivated application of the technique to scene representation. Previous approaches have mainly followed a centralised learning paradigm, which assumes that all training images are available on one compute node for training. In this paper, we consider training NeRFs in a federated manner, whereby multiple compute nodes, each having acquired a distinct set of observations of the overall scene, learn a common NeRF in parallel. This supports the scenario of cooperatively modelling a scene using multiple agents. Our contribution is the first federated learning algorithm for NeRF, which splits the training effort across multiple compute nodes and obviates the need to pool the images at a central node. A technique based on low-rank decomposition of NeRF layers is introduced to reduce bandwidth consumption to transmit the model parameters for aggregation. Transferring compressed models instead of the raw data also contributes to the privacy of the data collecting agents.Comment: 10 pages, 7 figure

The impacts of airport activities on regional economy - An empirical analysis of New Zealand

This study investigates the impacts of airport activities on regional economies using annual data on 22 regions and airports in New Zealand from 1996 to 2016. Studying all regions of an island country avoids the sample selection bias, and reduces the likelihood of incorrectly capturing the effects of improvements in other transport modes. The use of panel data over an extensive period of time also contributes to a robust identification procedure. In addition to the fixed effects estimation that has been frequently used in the literature, the system generalized methods of moments (GMM) approach and the dynamic common correlated effects (CCE) estimator are applied to account for cross-sectional dependence, cross-regional heterogeneity, and feedback effects. We find that airport activities have a statistically and economically significant impact on a region’s economy. This finding is robust across fixed effects, GMM, and CCE estimations, although more significant effects are identified by the less restrictive CCE approach. Our study suggests a positive effect of aviation on regional economies, and supports local/regional policies promoting aviation activities

Statistical forecast of pollution episodes in Macao during national holiday and COVID-19

UID/AMB/04085/2019Statistical methods such as multiple linear regression (MLR) and classification and regression tree (CART) analysis were used to build prediction models for the levels of pollutant concentrations in Macao using meteorological and air quality historical data to three periods: (i) from 2013 to 2016, (ii) from 2015 to 2018, and (iii) from 2013 to 2018. The variables retained by the models were identical for nitrogen dioxide (NO2), particulate matter (PM10), PM2.5, but not for ozone (O3) Air pollution data from 2019 was used for validation purposes. The model for the 2013 to 2018 period was the one that performed best in prediction of the next-day concentrations levels in 2019, with high coefficient of determination (R2), between predicted and observed daily average concentrations (between 0.78 and 0.89 for all pollutants), and low root mean square error (RMSE), mean absolute error (MAE), and biases (BIAS). To understand if the prediction model was robust to extreme variations in pollutants concentration, a test was performed under the circumstances of a high pollution episode for PM2.5 and O3 during 2019, and the low pollution episode during the period of implementation of the preventive measures for COVID-19 pandemic. Regarding the high pollution episode, the period of the Chinese National Holiday of 2019 was selected, in which high concentration levels were identified for PM2.5 and O3, with peaks of daily concentration exceeding 55 µg/m3 and 400 µg/m3, respectively. The 2013 to 2018 model successfully predicted this high pollution episode with high coefficients of determination (of 0.92 for PM2.5 and 0.82 for O3). The low pollution episode for PM2.5 and O3 was identified during the 2020 COVID-19 pandemic period, with a low record of daily concentration for PM2.5 levels at 2 µg/m3 and O3 levels at 50 µg/m3, respectively. The 2013 to 2018 model successfully predicted the low pollution episode for PM2.5 and O3 with a high coefficient of determination (0.86 and 0.84, respectively). Overall, the results demonstrate that the statistical forecast model is robust and able to correctly reproduce extreme air pollution events of both high and low concentration levels.publishersversionpublishe