Improving with Metaheuristics the Item Selection in Parallel Coordinates Plot

The 2019 novel coronavirus disease (COVID-19) has become a severe public health and social problem worldwide. A limitation of the existing literature is that multiple environmental variables have not been frequently elaborated, which is why the overall effect of the environment on COVID-19 has not been conclusive. In this study, we used generalized additive model (GAM) to detect the relationship between meteorological and air pollution variables and COVID-19 in four urban agglomerations in China and made comparisons among the urban agglomerations. The four urban agglomerations are Beijing-Tianjin-Hebei (BTH), middle reaches of the Yangtze River (MYR), Yangtze River Delta (YRD), and the Pearl River Delta (PRD). The daily rates of average precipitation, temperature, relative humidity, sunshine duration, and atmospheric pressure were selected as meteorological variables. The PM2.5, PM10, sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO) contents were selected as air pollution variables. The results indicated that meteorological and air pollution variables tended to be significantly correlated. Moreover, the nature of the relationship between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and meteorological and air pollution variables (i.e., linear or nonlinear) varied with urban agglomerations. Among the variance explained by GAMs, BTH had the highest value (75.4%), while MYR had the lowest value (35.2%). The values of the YRD and PRD were between the above two, namely 45.6% and 62.2%, respectively. The findings showed that the association between SARS-CoV-2 and meteorological and air pollution variables varied in regions, making it difficult to obtain a relationship that is applicable to every region. Moreover, this study enriches our understanding of SARS-CoV-2. It is required to create awareness within the government that anti-COVID-19 measures should be adapted to the local meteorological and air pollution conditions.

Associations of particulate matter (PM) and ozone with morbidity and mortality have been reported in many recent observational epidemiology studies. These studies often considered other gaseous co-pollutants also as potential confounders, including nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO). However, because each of these air pollutants can have different seasonal patterns and chemical interactions, the estimation and interpretation of each pollutant's individual risk estimates may not be straightforward. Multi-collinearity among the air pollution and weather variables also leaves the possibility of confounding and over- or under-fitting of meteorological variables, thereby potentially influencing the health effect estimates for the various pollutants in differing ways. To investigate these issues, we examined the temporal relationships among air pollution and weather variables in the context of air pollution health effects models. We compiled daily data for PM less than 2.5 mum (PM2.5), ozone, NO2, SO2, CO, temperature, dew point, relative humidity, wind speed, and barometric pressure for New York City for the years 1999-2002. We conducted several sets of analyses to characterize air pollution and weather data interactions, to assess different aspects of these data issues: (1) spatial/temporal variation of PM2.5 and gaseous pollutants measured at multiple monitors; (2) temporal relationships among air pollution and weather variables; and (3) extent and nature of multi-collinearity of air pollution and weather variables in the context of health effects models. The air pollution variables showed a varying extent of intercorrelations with each other and with weather variables, and these correlations also varied across seasons. For example, NO2 exhibited the strongest negative correlation with wind speed among the pollutants considered, while ozone's correlation with PM2.5 changed signs across the seasons (positive in summer and negative in winter). The extent of multi-collinearity problems also varied across pollutants and choice of health effects models commonly used in the literature. These results indicate that the health effects regression need to be run by season for some pollutants to provide the most meaningful results. We also find that model choice and interpretation needs to take into consideration the varying pollutant concurvities with the model co-variables in each pollutant's health effects model specification. Finally, we provide an example for analysis of associations between these air pollutants and asthma emergency department visits in New York City, which evaluate the relationship between the various pollutants' risk estimates and their respective concurvities, and discuss the limitations that these results imply about the interpretability of multi-pollutant health effects models.

A test pattern compression scheme for test data volume and application time reduction is proposed. While compression reduces test data volume, the increased number of internal scan chains due to an on-chip, fixed-rate decompressor reduces test application time proportionately. Through on-chip decompression, both the number of virtual scan chains visible to the ATE and the functionality of the ATE are retained intact. Complete fault coverage is guaranteed by constructing the decompression hardware deterministically through analysis of the test pattern set.

In this work, we present an effective and scalable system for multivariate volume data visualization and analysis with a novel Transfer Function (TF) interface design that tightly couples parallel coordinates plots (PCP) and MDS-based dimension projection plots. In our system, the PCP visualizes the data distribution of each variate and the MDS plots project features. Together, they are integrated seamlessly to provide flexible feature classification without context switching between different data presentations during the user interaction. The proposed interface enables users to identify relevant correlation clusters and assign optical properties on them. To further support large scale multivariate volume data visualization and analysis, we develop three integrated parallel systems to accelerate the rendering of PCP, the layout of MDS, as well as parallel rendering of multivarite volume data. Our experiments show that the system is effective in multivariate volume data visualization and its performance is scalable for data sets with different sizes and number of variates.

Decomposing the association of completed suicide with air pollution, weather, and unemployment data at different time scales

Cardiovascular Effects of the Outdoor Air Pollution in Tuscany: Preliminary Results of the Riscat Study

Hand, foot, and mouth disease (HFMD) is a serious threat among children in China. Some studies have found that air pollution is associated with HFMD incidence, but the results showed heterogeneity. In this study, we aimed to explore the heterogeneity of associations between air pollutants and the number of HFMD cases and to identify significant socioeconomic effect modifiers. We collected daily surveillance data on HFMD cases in those aged less than 15 years, air pollution variables and meteorological variables from 2015 to 2017 in the basin area of Sichuan Province. We also collected socioeconomic indicator data. We conducted a two-stage multicity time-series analysis. In the first stage, we constructed a distributed lag nonlinear model (DLNM) to obtain cumulative exposure-response curves between each air pollutant and the numbers of HFMD cases for every city. In the second stage, we carried out a multivariable meta-regression to merge the estimations in the first stage and to identify significant socioeconomic effect modifiers. We found that PM10, NO2 and O3 concentrations were associated with the number of HFMD cases. An inverted V-shaped association between PM10 and the number of HFMD cases was observed. The overall NO2-HFMD association was a hockey-stick shape. For the relationships of PM10, SO2, NO2, O3 and CO with HFMD counts, approximately 58.5%, 48.4%, 51.0%, 55.6% and 52.5% of the heterogeneity could be explained, respectively. The proportion of primary school students, population density, urbanization rate, number of licensed physicians and number of hospital beds explained part of the heterogeneity and modified the relationships. Our study explored the heterogeneity of associations between air pollutants and HFMD counts. The proportion of primary school students, population density, urbanization rate, number of licensed physicians and number of hospital beds could modify the relationships. The results can serve as a reference for relevant public health decision making.

A modification of the simulated annealing (SA) algorithm for solving discrete stochastic optimization problems where the objective function is stochastic and can be evaluated only through Monte Carlo simulation is proposed. In this modification, the Metropolis criterion depends on whether the objective function values indicate a statistically significant difference at each iteration. The differences between objective function values are considered to be statistically significant based on confidence intervals associated with these values. Unlike the original SA, the proposed method uses a constant temperature. It is shown that the configuration that has been visited most often in the first k iterations converges almost surely to a global optimizer. Computational results and comparisons with other SA algorithms are presented to demonstrate the performance of the proposed SA algorithm.

This study examines the relationship of asthma emergency department (ED) visits to daily concentrations of ozone and other air pollutants in Saint John, New Brunswick, Canada. Data on ED visits with a presenting complaint of asthma (n = 1987) were abstracted for the period 1984-1992 (May-September). Air pollution variables included ozone, sulfur dioxide, nitrogen dioxide, sulfate, and total suspended particulate (TSP); weather variables included temperature, humidex, dewpoint, and relative humidity. Daily ED visit frequencies were filtered to remove day of the week and long wave trends, and filtered values were regressed on air pollution and weather variables for the same day and the 3 previous days. The mean daily 1-hr maximum ozone concentration during the study period was 41.6 ppb. A positive, statistically significant (p < 0.05) association was observed between ozone and asthma ED visits 2 days later, and the strength of the association was greater in nonlinear models. The frequency of asthma ED visits was 33% higher (95% CI, 10-56%) when the daily 1-hr maximum ozone concentration exceeded 75 ppb (the 95th percentile). The ozone effect was not significantly influenced by the addition of weather or other pollutant variables into the model or by the exclusion of repeat ED visits. However, given the limited number of sampling days for sulfate and TSP, a particulate effect could not be ruled out. We detected a significant association between ozone and asthma ED visits, despite the vast majority of sampling days being below current U.S. and Canadian standards.ImagesFigure 1. AFigure 1. BFigure 2.Figure 3.

A combined strategy of an improved simulated annealing (SA) algorithm and genetic algorithm is presented, with the goal of reducing the computational expenses. The improvements made on the SA algorithm include two parts, i.e., the adaptive regulating for the step vector, and the dynamic testing for the equilibrium of the Metropolis process. The proposed strategy has both the advantage of SA algorithm, the ability to avoid being trapped in a local optimum, and that of genetic algorithm, the ability to use the information about the searched states for the next iteration. A practical application on geometry optimization of pole shoes in large salient pole synchronous generators is effectively implemented using the strategy. The numerical results show that the number of iterations used by executing the combined strategy are only about 75% of those by executing basic SA algorithm.

SPECIALTY GRAND CHALLENGE article Front. Environ. Sci., 26 November 2013Sec. Air Pollution Volume 1 - 2013 | https://doi.org/10.3389/fenvs.2013.00003

Today’s scientific simulations generate exceptionally large volumes of data, challenging the capacities of available I/O bandwidth and storage space. This necessitates a substantial reduction in data volume, for which error-bounded lossy compression has emerged as a highly effective strategy. A crucial metric for assessing the efficacy of lossy compression is visualization. Despite extensive research on the impact of compression on visualization, there is a notable gap in the literature concerning the effects of compression on the visualization of Adaptive Mesh Refinement (AMR) data. AMR has proven to be a potent solution for addressing the rising computational intensity and the explosive growth in data volume that requires storage and transmission. However, the hierarchical and multi-resolution characteristics of AMR data introduce unique challenges to its visualization, and these challenges are further compounded when data compression comes into play. This article delves into the intricacies of how data compression influences and introduces novel challenges to the visualization of AMR data.

Summary The Simulated Annealing (SA) algorithm has been widely used in geophysical data processing. However, the seri al SA algorithm is limited by low computing efficiency. In this paper, the annealing schedule has been improved in the SA algorithm, and a constrained inversion of magnetotelluric (MT) data using the parallel SA algorithm has been applied. The test of synthetic data shows that the improved Very Fast Simulated Annealing (VFSA) method is significantly better than the traditional method. Additionally, the computing efficiency has been improved using the parallel SA inversion algorithm. This method has also been applied in MT data processing for geothermal exploration and good geological results have been achieved.

Over the past decades, parallel coordinates plots (PCPs) have been extensively applied to various tasks of multivariate data visualization. However, a main disadvantage of PCPs is the almost inevitable overlap of polylines, which causes chaotic visual effects when the number of polylines is large and the sequence of parallel coordinates is not conducive to visualization. To tackle this issue, this paper proposed an axis permutation of PCPs based on Laplacian scores (LS), which can be used to enhance the visualization performance of the original PCPs. The enhanced PCP is a combination of an unsupervised feature selection technique (LS) and the visualization of multivariate data. With the new sequence of axes based on the Laplacian scores of the corresponding features, polylines of PCPs are distributed with less clutter and overlap. The experimental results demonstrated that the proposed method made great progress in enhancing the visualization performance of PCPs.

Spatial and temporal heterogeneity of air pollution in East Africa