Minimization of heavy metal adsorption in struvite through effective separation and manipulation of flow field.

The admixture of heavy metals on struvite during the P recovery process from wastewater will affect its value for safe agricultural application, but it is not clear how to effectively separate heavy metals from struvite. Herein, a two-stage separation reactor (static and dynamic) has been developed to achieve efficient separation of heavy metals and struvite. The generation of struvite from real swine wastewater would naturally precipitate to the lowest layer under static conditions, leading to an enrichment of heavy metals (75 % Cu and 84 % Zn) in suspension. Meanwhile, phosphorus recovery from real swine wastewater results in the generation of a large amount of fines flowing out of the reactor due to the effects of suspended solids (SS), etc., making it necessary to recover phosphorus by static separation. For the dynamic separation step, we also analyzed the characteristics of struvite formation at different rotational speeds in a continuous reaction system. The results demonstrated that the shear rate of the fluid affects the particle size of struvite, which in turn determines the rate and the distribution of struvite in either primary or secondary recovery tanks. The implementation of zonal regulation in the flow field can produce a higher phosphorus efficiency (from 85.8 to 95.5 % at pH=8.1-8.2, from 93.8 to 98.5 % at pH=9.0-9.1) and a lower alkali consumption (55.56 % of alkali cost), which is favorable for the separation of struvite crystals and heavy metals (the amount of Cu and Zn metals separated increased by more than 50 %), and ultimately yield high quality of struvite. The findings in this study will provide insights for the separation and reduction of heavy metals through a combined method with dynamic and static in a continuous system, providing a reference for the safe application of struvite in agriculture.

Iterative image reconstruction for sparse-view CT via total variation regularization and dictionary learning.

Recently, low-dose computed tomography (CT) has become highly desirable due to the increasing attention paid to the potential risks of excessive radiation of the regular dose CT. However, ensuring image quality while reducing the radiation dose in the low-dose CT imaging is a major challenge. Compared to classical filtered back-projection (FBP) algorithms, statistical iterative reconstruction (SIR) methods for modeling measurement statistics and imaging geometry can significantly reduce the radiation dose, while maintaining the image quality in a variety of CT applications. To facilitate low-dose CT imaging, we in this study proposed an improved statistical iterative reconstruction scheme based on the penalized weighted least squares (PWLS) standard combined with total variation (TV) minimization and sparse dictionary learning (DL), which is named as a method of PWLS-TV-DL. To evaluate this PWLS-TV-DL method, we performed experiments on digital phantoms and physical phantoms, and analyzed the results in terms of image quality and calculation. The results show that the proposed method is better than the comparison methods, which indicates the potential of applying this PWLS-TV-DL method to reconstruct low-dose CT images.

Absolute distance measurement by multi-heterodyne interferometry using an electro-optic triple comb.

We experimentally demonstrate a method for absolute distance measurement using a triple-comb-based multi-heterodyne interferometer which has the capacity to simultaneously balance the non-ambiguous range, resolution, update rate, and cost. Three flat-top electro-optic combs generated via cascaded intensity and phase modulators are adopted to form a measurement scheme including rough and fine measurements, and the unknown distance is determined by detecting the phase changes of the consecutive synthetic wavelengths. Experimental results demonstrate an agreement within 750 nm over 80 m distance at an update rate of 167 µs.

[Nosocomial infection caused by Pseudomonas aeruginosa in intensive care unit].

OBJECTIVE: To investigate the risk factors of nosocomial infection caused by Pseudomonas aeruginosa in intensive care unit (ICU), in order to provide reference for an effective measure of infection control. METHODS: A retrospective study of cases of Pseudomonas aeruginosa infection occurring in ICU was made with multivariable Logistic regression analysis. The clinical data of 1 950 cases admitted from January 2002 to December 2006 were found to have nosocomial infection caused by Pseudomonas aeruginosa were analyzed in order to identify its independent risk factors. RESULTS: Sixty-four out of 1 950 patients were found to suffer from nosocomial infection caused by Pseudomonas aeruginosa, the morbidity rate was 3.3%. At the same time, and in the same department, 37 patients suffering from infection caused by Escherichia coli, served as control group. Univariate analysis showed that the risk factors for nosocomial infection caused by Pseudomonas aeruginosa were the use of corticosteroid, unconsciousness or craniocerebral trauma, abdominal surgery, thorax/abdomen drainage tube, mechanical ventilation, and tracheostomy [the use of corticosteroid: odds ratio (OR)=3.364, 95% confidence interval (95%CI) 1.445-7.830; unconsciousness or craniocerebral trauma: OR=4.026, 95%CI 1.545-10.490; abdominal surgery: OR=0.166, 95%CI 0.068-0.403; thorax/abdomen drainage tube: OR=0.350, 95%CI 0.150-0.818; tracheostomy: OR=4.095, 95%CI 1.638-10.740]. Multivariate analysis showed that the independent risk factors of nosocomial infection caused by Pseudomonas aeruginosa in ICU were: the use of corticosteroid and mechanical ventilation [the use of corticosteroid: OR=3.143, 95%CI 1.115-8.856; mechanical ventilation: OR=3.195, 95%CI 1.607-6.353, P<0.05 and P<0.01]. CONCLUSION: The independent risk factors of nosocomial infection caused by Pseudomonas aeruginosa in ICU are the use of corticosteroid and mechanical ventilation. Measures should be taken to take care of the risk factors in order to prevent nosocomial infection caused by Pseudomonas aeruginosa in ICU.