Risk assessment of Artemia egg shell-Mg-P composites as a slow-release phosphorus fertilizer during its formation and application in typical heavy metals contaminated environment.

Artemia egg shell loaded with nano-magnesium (shell-Mg) can be used to recover phosphorus from wastewater. The exhausted Artemia egg shell-Mg (denoted as shell-Mg-P) can be used as a slow-release fertilizer for phosphorus reuse. However, due to the coexistence of heavy metal ions in the environment, the application of slow-release fertilizer for phosphorus removal and reuse may have potential risks. In this paper, the potential risks of Pb2+, Cd2+, Zn2+ and Cu2+ in phosphorus wastewater and soil were studied from the formation and application process of shell-Mg-P. The result showed that shell-Mg adsorbed Pb2+, Cd2+, Zn2+ and Cu2+ in phosphate wastewater during the formation of shell-Mg-P and became shell-Mg-P-metal hybrid biomaterial. Although the experiment proved that the existence of heavy metal ions did not affect the phosphorus slow-release behavior of slow-release fertilizer, but the heavy metal ions in the shell-Mg-P-metal were also slow released. The pot experiment results confirmed that the slow-release phosphorus fertilizers (shell-Mg-P and shell-Mg-P-metal) in the soil polluted in low concentration of heavy metals can reduce the amount of heavy metals in whole wheat seedlings and promote wheat seedling growth. However, the application of slow-release fertilizers increased the translocation efficiency (TFR to SL) of metal from root (R) to aboveground part (stem and leaves, SL), promoted the transportation of heavy metals from roots to the stems and leaves, and increased the safety risk of the wheat seedling edible. Therefore, besides the positive role of slow-release fertilizers in retaining heavy metals and reducing the amount of heavy metals in whole seedlings, the risk that it may aggravate the translocation of heavy metals to stems and leaves should be paid more attention, so as to ensure the safe and reliable application of slow-release fertilizers.

TCM Constitution Analysis Method Based on Parallel FP-Growth Algorithm in Hadoop Framework.

This work is devoted to establishing a comparatively accurate classification model between symptoms, constitutions, and regimens for traditional Chinese medicine (TCM) constitution analysis to provide preliminary screening and decision support for clinical diagnosis. However, for the analysis of massive distributed medical data in a cloud platform, the traditional data mining methods have the problems of low mining efficiency and large memory consumption, and long tuning time, an association rules method for TCM constitution analysis (ARA-TCM) is proposed that based on FP-growth algorithm and the open-source distributed file system in Hadoop framework (HDFS) to make full use of its powerful parallel processing capability. Firstly, the proposed method was used to explore the association rules between the 9 kinds of TCM constitutions and symptoms, as well as the regimen treatment plans, so as to discover the rules of typical clinical symptoms and treatment rules of different constitutions and to conduct an evidence-based medical evaluation of TCM effects in constitution-related chronic disease health management. Secondly, experiments were applied on a self-built TCM clinical records database with a total of 30,071 entries and it is found that the top three constitutions are mid constitution (42.3%), hot and humid constitution (31.3%), and inherited special constitution (26.2%), respectively. What is more, there are obvious promotions in the precision and recall rate compared with the Apriori algorithm, which indicates that the proposed method is suitable for the classification of TCM constitutions. This work is mainly focused on uncovering the rules of "disease symptoms constitution regimen" in TCM medical records, but tongue image and pulse signal are also very important to TCM constitution analysis. Therefore, this additional information should be considered into further studies to be more in line with the actual clinical needs.