Spatial analysis of MODIS aerosol optical depth, PM2.5, and chronic coronary heart disease

<p>Abstract</p> <p>Background</p> <p>Numerous studies have found adverse health effects of acute and chronic exposure to fine particulate matter (PM_2.5). Air pollution epidemiological studies relying on ground measurements provided by monitoring networks are often limited by sparse and unbalanced spatial distribution of the monitors. Studies have found correlations between satellite aerosol optical depth (AOD) and PM_2.5in some land regions. Satellite aerosol data may be used to extend the spatial coverage of PM_2.5exposure assessment. This study was to investigate correlation between PM_2.5and AOD in the conterminous USA, to derive a spatially complete PM_2.5surface by merging satellite AOD data and ground measurements based on the potential correlation, and to examine if there is an association of coronary heart disease with PM_2.5.</p> <p>Results</p> <p>Years 2003 and 2004 daily MODIS (Moderate Resolution Imaging Spectrometer) Level 2 AOD images were collated with US EPA PM_2.5data covering the conterminous USA. Pearson's correlation analysis and geographically weighted regression (GWR) found that the relationship between PM_2.5and AOD is not spatially consistent across the conterminous states. The average correlation is 0.67 in the east and 0.22 in the west. GWR predicts well in the east and poorly in the west. The GWR model was used to derive a PM_2.5grid surface using the mean AOD raster calculated using the daily AOD data (RMSE = 1.67 <it>μg/m</it>³). Fitting of a Bayesian hierarchical model linking PM_2.5with age-race standardized mortality rates (SMRs) of chronic coronary heart disease found that areas with higher values of PM_2.5also show high rates of CCHD mortality: <inline-formula><graphic file="1476-072X-8-27-i1.gif"/></inline-formula> = 0.802, posterior 95% Bayesian credible interval (CI) = (0.386, 1.225).</p> <p>Conclusion</p> <p>There is a spatial variation of the relationship between PM_2.5and AOD in the conterminous USA. In the eastern USA where AOD correlates well with PM_2.5, AOD can be merged with ground PM_2.5data to derive a PM_2.5surface for epidemiological study. The study found that chronic coronary heart disease mortality rate increases with exposure to PM_2.5.</p

Understanding, Interpreting and Answering the Question of What Is Being Properly

We have to master six propositions and judges, which are connected each other tightly, in the process of understanding, interpreting and answering the question of what is being properly. They are :1) Being is the representative of all converted forms of the verb of be, and further the representative of all English words; 2) words are the representative of languages; 3) languages are one of the main ways in materializing the human thinking and its results, that is, languages are one of the main ways of expressing the processes of human thinking and its results; 4) the processes of human thinking is the process of subjective disposition on objects, the results of human thinking are the results of human subjective dispositions on objects; 5) subjective dispositions are the processes of humans dispositions on objects，which include the results of human subjective dispositions previously, from multi-aspects, multi-strata, multi-disciplines, in human minds, and the subjective dispositions are the contents of the activities of human minds; 6) the true function and aim of raising the question of what is being continually from ancient Greece to now by Occidental scholars is that they want to know what is subjective dispositions and the relationship between subjective dispositions and their linguistic expressions

Generalized Variable Selection Algorithms for Gaussian Process Models by LASSO-like Penalty

With the rapid development of modern technology, massive amounts of data with complex pattern are generated. Gaussian process models that can easily fit the non-linearity in data become more and more popular nowadays. It is often the case that in some data only a few features are important or active. However, unlike classical linear models, it is challenging to identify active variables in Gaussian process models. One of the most commonly used methods for variable selection in Gaussian process models is automatic relevance determination, which is known to be open-ended. There is no rule of thumb to determine the threshold for dropping features, which makes the variable selection in Gaussian process models ambiguous. In this work, we propose two variable selection algorithms for Gaussian process models, which use the artificial nuisance columns as baseline for identifying the active features. Moreover, the proposed methods work for both regression and classification problems. The algorithms are demonstrated using comprehensive simulation experiments and an application to multi-subject electroencephalography data that studies alcoholic levels of experimental subjects

Innovations in Cadres Selection and Promotion in China: the Case of Mudanjiang City

China’s new cadre selection and promotion system has been arguably considered as China’s most significant move in securing its implementation of reform strategies. Almost at the same time when he was advocating for China’s economic reform in 1978, the late Deng Xiaoping, China’s reform champion also issued a set of important speeches on reforming China’s cadre selection and promotion protocols in order that there would be enough leadership to help meeting the challenges of the reform needs. His view on what talents are is a de facto mandate leading the way for China’s cadre selection and promotion reform. This paper uses the case of Mudanjiang, a well-known city in China’s northeast, to illustrate how Deng’s Xiaoping’s cadre selection and promotion ideas are implemented. Through literature and documents review, onside observation, person to person interviews, and surveys, the authors examined how cadres are publicly nominated, competitively elected, and scientifically selected in Mudanjian city. They study shows, the cadre open selection mechanisms have worked well, it has opened the door for many talented people who otherwise would not have had the chance to be even noticed by the upper management. The study also reveals some existing problems in the current system and made suggestions for further reform

Terrain Diffusion Network: Climatic-Aware Terrain Generation with Geological Sketch Guidance

Sketch-based terrain generation seeks to create realistic landscapes for virtual environments in various applications such as computer games, animation and virtual reality. Recently, deep learning based terrain generation has emerged, notably the ones based on generative adversarial networks (GAN). However, these methods often struggle to fulfill the requirements of flexible user control and maintain generative diversity for realistic terrain. Therefore, we propose a novel diffusion-based method, namely terrain diffusion network (TDN), which actively incorporates user guidance for enhanced controllability, taking into account terrain features like rivers, ridges, basins, and peaks. Instead of adhering to a conventional monolithic denoising process, which often compromises the fidelity of terrain details or the alignment with user control, a multi-level denoising scheme is proposed to generate more realistic terrains by taking into account fine-grained details, particularly those related to climatic patterns influenced by erosion and tectonic activities. Specifically, three terrain synthesisers are designed for structural, intermediate, and fine-grained level denoising purposes, which allow each synthesiser concentrate on a distinct terrain aspect. Moreover, to maximise the efficiency of our TDN, we further introduce terrain and sketch latent spaces for the synthesizers with pre-trained terrain autoencoders. Comprehensive experiments on a new dataset constructed from NASA Topology Images clearly demonstrate the effectiveness of our proposed method, achieving the state-of-the-art performance. Our code and dataset will be publicly available

Linking stroke mortality with air pollution, income, and greenness in northwest Florida: an ecological geographical study

<p>Abstract</p> <p>Background</p> <p>Relatively few studies have examined the association between air pollution and stroke mortality. Inconsistent and inclusive results from existing studies on air pollution and stroke justify the need to continue to investigate the linkage between stroke and air pollution. No studies have been done to investigate the association between stroke and greenness. The objective of this study was to examine if there is association of stroke with air pollution, income and greenness in northwest Florida.</p> <p>Results</p> <p>Our study used an ecological geographical approach and dasymetric mapping technique. We adopted a Bayesian hierarchical model with a convolution prior considering five census tract specific covariates. A 95% credible set which defines an interval having a 0.95 posterior probability of containing the parameter for each covariate was calculated from Markov Chain Monte Carlo simulations. The 95% credible sets are (-0.286, -0.097) for household income, (0.034, 0.144) for traffic air pollution effect, (0.419, 1.495) for emission density of monitored point source polluters, (0.413, 1.522) for simple point density of point source polluters without emission data, and (-0.289,-0.031) for greenness. Household income and greenness show negative effects (the posterior densities primarily cover negative values). Air pollution covariates have positive effects (the 95% credible sets cover positive values).</p> <p>Conclusion</p> <p>High risk of stroke mortality was found in areas with low income level, high air pollution level, and low level of exposure to green space.</p

Research on trajectory tracking control for wet clutch engagement based on SMC

AbstractTo improve tracking control quality of the clutch actuator during the wet clutch engagement, models of the clutch actuator were established firstly, including the control cylinder model, flow equilibrium equation and pressure control model. Secondly, taking the clutch output speed as tracking target, the state space equation of the tracking control system was set up and the sliding mode controller (SMC) was designed. Finally, a simulation test was performed. The results show that a higher tracking accuracy as well as a better performance to resist disturbance can be achieved with the proposed sliding control method, compared to PI control. It was also shown that the exponent approaching sliding mode control can produce smaller chattering compared with the constant rate approaching sliding mode control