Influence of Mercury and Chlorine Content of Coal on Mercury Emissions from Coal-Fired Power Plants in China

China is the largest mercury emitter in the world and coal combustion is the most important mercury source in China. This paper updates the coal quality database of China and evaluates the mercury removal efficiency of air pollution control devices (APCDs) based on 112 on-site measurements. A submodel was developed to address the relationship of mercury emission factor to the chlorine content of coal. The mercury emissions from coal-fired power plants (CFPPs) in China were estimated using deterministic mercury emission factor model, nonchlorine-based and chlorine-based probabilistic emission factor models, respectively. The national mercury emission from CFPPs in 2008 was calculated to be 113.3 t using the deterministic model. The nonchlorine-based probabilistic emission factor model, which addresses the log-normal distribution of the mercury content of coal, estimates that the mercury emission from CFPPs is 96.5 t (P50), with a confidence interval of 57.3 t (P10) to 183.0 t (P90). The best estimate by the chlorine-based probabilistic emission factor model is 102.5 t, with a confidence interval of 71.7 to 162.1 t. The chlorine-based model addresses the influence of chlorine and reduces the uncertainties of mercury emission estimates

Image_4_Sex hormone-binding globulin exerts sex-related causal effects on lower extremity varicose veins: evidence from gender-stratified Mendelian randomization.tif

BackgroundThe association between serum sex hormones and lower extremity varicose veins has been reported in observational studies. However, it is unclear whether the association reflects a causal relationship. Besides, serum sex hormone-binding globulin (SHBG) has been rarely studied in lower extremity varicose veins. Here, we aim to investigate the association between serum levels of SHBG, testosterone, and estradiol and the risk of lower extremity varicose veins using Mendelian randomization (MR).MethodsWe obtained genome-wide association study summary statistics for serum SHBG levels with 369,002 European participants, serum testosterone levels with 424,907 European participants, serum estradiol levels with 361,194 European participants, and lower extremity varicose veins with 207,055 European participants. First, a univariable MR was performed to identify the causality from SHBG and sex hormone levels to lower extremity varicose veins with several sensitivity analyses being performed. Then, a multivariable MR (MVMR) was performed to further assess whether the causal effects were independent. Finally, we performed a gender-stratified MR to understand the role of genders on lower extremity varicose veins.ResultsGenetically predicted higher serum SHBG levels significantly increased the risk of lower extremity varicose veins in the univariable MR analysis (OR=1.39; 95% CI: 1.13–1.70; P=1.58×10-3). Sensitivity analyses and MVMR (OR=1.50; 95% CI:1.13-1.99; P=5.61×10-3) verified the robustness of the causal relationships. Gender-stratified MR revealed that higher serum SHBG levels were associated with lower extremity varicose veins in both sexes. However, the OR of serum SHBG levels on lower extremity varicose veins risk in females (OR=1.51; 95% CI: 1.23–1.87; P=1.00×10-4) was greater than in males (OR=1.26; 95% CI: 1.04–1.54; P=1.86×10-2).ConclusionsSerum SHBG levels are positively related to lower extremity varicose veins risk in both sexes, especially in females. This may partly explain the higher prevalence of varicose vines among females.</p

Log-Rank-Type Tests for Equality of Distributions in High-Dimensional Spaces

Motivated by applications in high-dimensional settings, we propose a novel approach for testing the equality of two or more populations by constructing a class of intensity centered score processes. The resulting tests are analogous in spirit to the well-known class of weighted log-rank statistics that are widely used in survival analysis. The test statistics are nonparametric, computationally simple, and applicable to high-dimensional data. We establish the usual large sample properties by showing that the underlying log-rank score process converges weakly to a Gaussian random field with zero mean under the null hypothesis and with a drift under the contiguous alternatives. For the Kolmogorov-Smirnov-type and the Cramér-von Mises-type statistics, we also establish the consistency result for any fixed alternative. Cutoff points for the test statistics are obtained by permutations or a simulation-based resampling method. The new approach is applied to a study of brain activation measured by functional magnetic resonance imaging when performing two linguistic tasks and also to a prostate cancer DNA microarray data set.</p

Image_1_Sex hormone-binding globulin exerts sex-related causal effects on lower extremity varicose veins: evidence from gender-stratified Mendelian randomization.tif

BackgroundThe association between serum sex hormones and lower extremity varicose veins has been reported in observational studies. However, it is unclear whether the association reflects a causal relationship. Besides, serum sex hormone-binding globulin (SHBG) has been rarely studied in lower extremity varicose veins. Here, we aim to investigate the association between serum levels of SHBG, testosterone, and estradiol and the risk of lower extremity varicose veins using Mendelian randomization (MR).MethodsWe obtained genome-wide association study summary statistics for serum SHBG levels with 369,002 European participants, serum testosterone levels with 424,907 European participants, serum estradiol levels with 361,194 European participants, and lower extremity varicose veins with 207,055 European participants. First, a univariable MR was performed to identify the causality from SHBG and sex hormone levels to lower extremity varicose veins with several sensitivity analyses being performed. Then, a multivariable MR (MVMR) was performed to further assess whether the causal effects were independent. Finally, we performed a gender-stratified MR to understand the role of genders on lower extremity varicose veins.ResultsGenetically predicted higher serum SHBG levels significantly increased the risk of lower extremity varicose veins in the univariable MR analysis (OR=1.39; 95% CI: 1.13–1.70; P=1.58×10-3). Sensitivity analyses and MVMR (OR=1.50; 95% CI:1.13-1.99; P=5.61×10-3) verified the robustness of the causal relationships. Gender-stratified MR revealed that higher serum SHBG levels were associated with lower extremity varicose veins in both sexes. However, the OR of serum SHBG levels on lower extremity varicose veins risk in females (OR=1.51; 95% CI: 1.23–1.87; P=1.00×10-4) was greater than in males (OR=1.26; 95% CI: 1.04–1.54; P=1.86×10-2).ConclusionsSerum SHBG levels are positively related to lower extremity varicose veins risk in both sexes, especially in females. This may partly explain the higher prevalence of varicose vines among females.</p

Table_1_Sex hormone-binding globulin exerts sex-related causal effects on lower extremity varicose veins: evidence from gender-stratified Mendelian randomization.xlsx

BackgroundThe association between serum sex hormones and lower extremity varicose veins has been reported in observational studies. However, it is unclear whether the association reflects a causal relationship. Besides, serum sex hormone-binding globulin (SHBG) has been rarely studied in lower extremity varicose veins. Here, we aim to investigate the association between serum levels of SHBG, testosterone, and estradiol and the risk of lower extremity varicose veins using Mendelian randomization (MR).MethodsWe obtained genome-wide association study summary statistics for serum SHBG levels with 369,002 European participants, serum testosterone levels with 424,907 European participants, serum estradiol levels with 361,194 European participants, and lower extremity varicose veins with 207,055 European participants. First, a univariable MR was performed to identify the causality from SHBG and sex hormone levels to lower extremity varicose veins with several sensitivity analyses being performed. Then, a multivariable MR (MVMR) was performed to further assess whether the causal effects were independent. Finally, we performed a gender-stratified MR to understand the role of genders on lower extremity varicose veins.ResultsGenetically predicted higher serum SHBG levels significantly increased the risk of lower extremity varicose veins in the univariable MR analysis (OR=1.39; 95% CI: 1.13–1.70; P=1.58×10-3). Sensitivity analyses and MVMR (OR=1.50; 95% CI:1.13-1.99; P=5.61×10-3) verified the robustness of the causal relationships. Gender-stratified MR revealed that higher serum SHBG levels were associated with lower extremity varicose veins in both sexes. However, the OR of serum SHBG levels on lower extremity varicose veins risk in females (OR=1.51; 95% CI: 1.23–1.87; P=1.00×10-4) was greater than in males (OR=1.26; 95% CI: 1.04–1.54; P=1.86×10-2).ConclusionsSerum SHBG levels are positively related to lower extremity varicose veins risk in both sexes, especially in females. This may partly explain the higher prevalence of varicose vines among females.</p

Image_2_Sex hormone-binding globulin exerts sex-related causal effects on lower extremity varicose veins: evidence from gender-stratified Mendelian randomization.tif

BackgroundThe association between serum sex hormones and lower extremity varicose veins has been reported in observational studies. However, it is unclear whether the association reflects a causal relationship. Besides, serum sex hormone-binding globulin (SHBG) has been rarely studied in lower extremity varicose veins. Here, we aim to investigate the association between serum levels of SHBG, testosterone, and estradiol and the risk of lower extremity varicose veins using Mendelian randomization (MR).MethodsWe obtained genome-wide association study summary statistics for serum SHBG levels with 369,002 European participants, serum testosterone levels with 424,907 European participants, serum estradiol levels with 361,194 European participants, and lower extremity varicose veins with 207,055 European participants. First, a univariable MR was performed to identify the causality from SHBG and sex hormone levels to lower extremity varicose veins with several sensitivity analyses being performed. Then, a multivariable MR (MVMR) was performed to further assess whether the causal effects were independent. Finally, we performed a gender-stratified MR to understand the role of genders on lower extremity varicose veins.ResultsGenetically predicted higher serum SHBG levels significantly increased the risk of lower extremity varicose veins in the univariable MR analysis (OR=1.39; 95% CI: 1.13–1.70; P=1.58×10-3). Sensitivity analyses and MVMR (OR=1.50; 95% CI:1.13-1.99; P=5.61×10-3) verified the robustness of the causal relationships. Gender-stratified MR revealed that higher serum SHBG levels were associated with lower extremity varicose veins in both sexes. However, the OR of serum SHBG levels on lower extremity varicose veins risk in females (OR=1.51; 95% CI: 1.23–1.87; P=1.00×10-4) was greater than in males (OR=1.26; 95% CI: 1.04–1.54; P=1.86×10-2).ConclusionsSerum SHBG levels are positively related to lower extremity varicose veins risk in both sexes, especially in females. This may partly explain the higher prevalence of varicose vines among females.</p

Covariance matching based adaptive unscented Kalman filter for direct filtering in INS/GNSS integration

All rights reserved. The use of the direct filtering approach for INS/GNSS integrated navigation introduces nonlinearity into the system state equation. As the unscented Kalman filter (UKF) is a promising method for nonlinear problems, an obvious solution is to incorporate the UKF concept in the direct filtering approach to address the nonlinearity involved in INS/GNSS integrated navigation. However, the performance of the standard UKF is dependent on the accurate statistical characterizations of system noise. If the noise distributions of inertial instruments and GNSS receivers are not appropriately described, the standard UKF will produce deteriorated or even divergent navigation solutions. This paper presents an adaptive UKF with noise statistic estimator to overcome the limitation of the standard UKF. According to the covariance matching technique, the innovation and residual sequences are used to determine the covariance matrices of the process and measurement noises. The proposed algorithm can estimate and adjust the system noise statistics online, and thus enhance the adaptive capability of the standard UKF. Simulation and experimental results demonstrate that the performance of the proposed algorithm is significantly superior to that of the standard UKF and adaptive-robust UKF under the condition without accurate knowledge on system noise, leading to improved navigation precision

Image_3_Sex hormone-binding globulin exerts sex-related causal effects on lower extremity varicose veins: evidence from gender-stratified Mendelian randomization.tif

BackgroundThe association between serum sex hormones and lower extremity varicose veins has been reported in observational studies. However, it is unclear whether the association reflects a causal relationship. Besides, serum sex hormone-binding globulin (SHBG) has been rarely studied in lower extremity varicose veins. Here, we aim to investigate the association between serum levels of SHBG, testosterone, and estradiol and the risk of lower extremity varicose veins using Mendelian randomization (MR).MethodsWe obtained genome-wide association study summary statistics for serum SHBG levels with 369,002 European participants, serum testosterone levels with 424,907 European participants, serum estradiol levels with 361,194 European participants, and lower extremity varicose veins with 207,055 European participants. First, a univariable MR was performed to identify the causality from SHBG and sex hormone levels to lower extremity varicose veins with several sensitivity analyses being performed. Then, a multivariable MR (MVMR) was performed to further assess whether the causal effects were independent. Finally, we performed a gender-stratified MR to understand the role of genders on lower extremity varicose veins.ResultsGenetically predicted higher serum SHBG levels significantly increased the risk of lower extremity varicose veins in the univariable MR analysis (OR=1.39; 95% CI: 1.13–1.70; P=1.58×10-3). Sensitivity analyses and MVMR (OR=1.50; 95% CI:1.13-1.99; P=5.61×10-3) verified the robustness of the causal relationships. Gender-stratified MR revealed that higher serum SHBG levels were associated with lower extremity varicose veins in both sexes. However, the OR of serum SHBG levels on lower extremity varicose veins risk in females (OR=1.51; 95% CI: 1.23–1.87; P=1.00×10-4) was greater than in males (OR=1.26; 95% CI: 1.04–1.54; P=1.86×10-2).ConclusionsSerum SHBG levels are positively related to lower extremity varicose veins risk in both sexes, especially in females. This may partly explain the higher prevalence of varicose vines among females.</p

A self-paced learning algorithm for change detection in synthetic aperture radar images

Detecting changed regions between two given synthetic aperture radar images is very important to monitor the change of landscapes, change of ecosystem and so on. This can be formulated as a classification problem and addressed by learning a classifier, traditional machine learning classification methods very easily stick to local optima which can be caused by noises of data. Hence, we propose an unsupervised algorithm aiming at constructing a classifier based on self-paced learning. Self-paced learning is a recently developed supervised learning approach andhas been proven to be capable to overcome effectively this shortcoming. After applying a pre-classification to the difference image, we uniformly select samples using the initial result. Then, self-paced learning is utilized to train a classifier. Finally, a filter is used based on spatial contextual information to further smooth the classification result. In order to demonstrate the efficiency of the proposed algorithm, we apply our proposed algorithm on five real synthetic aperture radar images datasets. The results obtained by our algorithm are compared with five other state-of-the-art algorithms, which demonstrates that our algorithm outperforms those state-of-the-art algorithms in terms of accuracy and robustness.</p

A self-paced learning algorithm for change detection in synthetic aperture radar images

Detecting changed regions between two given synthetic aperture radar images is very important to monitor the change of landscapes, change of ecosystem and so on. This can be formulated as a classification problem and addressed by learning a classifier, traditional machine learning classification methods very easily stick to local optima which can be caused by noises of data. Hence, we propose an unsupervised algorithm aiming at constructing a classifier based on self-paced learning. Self-paced learning is a recently developed supervised learning approach andhas been proven to be capable to overcome effectively this shortcoming. After applying a pre-classification to the difference image, we uniformly select samples using the initial result. Then, self-paced learning is utilized to train a classifier. Finally, a filter is used based on spatial contextual information to further smooth the classification result. In order to demonstrate the efficiency of the proposed algorithm, we apply our proposed algorithm on five real synthetic aperture radar images datasets. The results obtained by our algorithm are compared with five other state-of-the-art algorithms, which demonstrates that our algorithm outperforms those state-of-the-art algorithms in terms of accuracy and robustness.</p