Limited information is available on the perceptions of stakeholders concerning the health co-benefits of greenhouse gas (GHG) emission reductions. The purpose of this study was to investigate the perceptions of urban residents on the health co-benefits involving GHG abatement and related influencing factors in three cities in China. Beijing, Ningbo and Guangzhou were selected for this survey. Participants were recruited from randomly chosen committees, following quotas for gender and age in proportion to the respective population shares. Chi-square or Fisher’s exact tests were employed to examine the associations between socio-demographic variables and individuals’ perceptions of the health co-benefits related to GHG mitigation. Unconditional logistic regression analysis was performed to investigate the influencing factors of respondents’ awareness about the health co-benefits. A total of 1159 participants were included in the final analysis, of which 15.9% reported that they were familiar with the health co-benefits of GHG emission reductions. Those who were younger, more educated, with higher family income, and with registered urban residence, were more likely to be aware of health co-benefits. Age, attitudes toward air pollution and governmental efforts to improve air quality, suffering from respiratory diseases, and following low carbon lifestyles are significant predictors of respondents’ perceptions on the health co-benefits. These findings may not only provide information to policy-makers to develop and implement public welcome policies of GHG mitigation, but also help to bridge the gap between GHG mitigation measures and public engagement as well as willingness to change health-related behaviors. Full article

A micro-fiber-optic Fabry-Perot interferometer (FPI) is proposed and demonstrated experimentally for ultrasonic imaging of seismic physical models. The device consists of a micro-bubble followed by the end of a single-mode fiber (SMF). The micro-structure is formed by the discharging operation on a short segment of hollow-core fiber (HCF) that is spliced to the SMF. This micro FPI is sensitive to ultrasonic waves (UWs), especially to the high-frequency (up to 10 MHz) UW, thanks to its ultra-thin cavity wall and micro-diameter. A side-band filter technology is employed for the UW interrogation, and then the high signal-to-noise ratio (SNR) UW signal is achieved. Eventually the sensor is used for lateral imaging of the physical model by scanning UW detection and two-dimensional signal reconstruction. Full article

Since 2009, great public attention has been paid in Lhasa City (Tibet, China) to mosquito bites and accompanying inflammatory complications. However, the potential contribution of knowledge levels, experiences, disease control and preventive practices (KEP) towards mosquitoes has not received much attention. To investigate community KEP concerning mosquitoes in Lhasa, a cross-sectional survey was undertaken in four sub-districts of urban Lhasa in 2012. Questionnaires were designed to collect information regarding socio-demographics and KEP concerning the harmful effects of mosquitoes on participants. The scoring for KEP was developed after consultation of literature. A total of 591 eligible questionnaires were examined. The majority of respondents were female (61.8%) with a mean age of 46 years. Nearly all of the respondents were of Tibetan nationality (97.4%) and living in registered native households (92.7%), who have less than primary school education. The averages of overall score, knowledge score, experience score, and practice score were 9.23, 4.53, 1.80, 2.90, respectively. The registered household with the highest overall score, knowledge score and practice score was non-native. Female subjects with monthly incomes between 1000 and 3000 RMB had higher experience scores. The correlation analysis revealed that significant positive linear correlations existed between knowledge and experience, knowledge and practices, and experience and practices towards mosquitoes. Past experiences with mosquitoes can result in a better knowledge of effective mosquito control practices in the present and the future. Though the average of overall scores related to mosquitoes is high among the participants in Lhasa, however, the knowledge about the ecological habits of mosquitoes should be strengthened. The findings in this study may help to develop strategies and measures of mosquito and mosquito-borne diseases in the future, not only in Lhasa, but also in similar altitude, latitude and longitude regions worldwide. Full article

This study proposes a novel method for multichannel image gray level co-occurrence matrix (GLCM) texture representation. It is well known that the standard procedure for the automatic extraction of GLCM textures is based on a mono-spectral image. In real applications, however, the GLCM texture feature extraction always refers to multi/hyperspectral images. The widely used strategy to deal with this issue is to calculate the GLCM from the first principal component or the panchromatic band, which do not include all the useful information. Accordingly, in this study, we propose to represent the multichannel textures for multi/hyperspectral imagery by the use of: (1) clustering algorithms; and (2) sparse representation, respectively. In this way, the multi/hyperspectral images can be described using a series of quantized codes or dictionaries, which are more suitable for multichannel texture representation than the traditional methods. Specifically, K-means and fuzzy c-means methods are adopted to generate the codes of an image from the clustering point of view, while a sparse dictionary learning method based on two coding rules is proposed to produce the texture primitives. The proposed multichannel GLCM textural extraction methods were evaluated with four multi/hyperspectral datasets: GeoEye-1 and QuickBird multispectral images of the city of Wuhan, the well-known AVIRIS hyperspectral dataset from the Indian Pines test site, and the HYDICE airborne hyperspectral dataset from the Washington DC Mall. The results show that both the clustering-based and sparsity-based GLCM textures outperform the traditional method (extraction based on the first principal component) in terms of classification accuracies in all the experiments. Full article