The photocatalytic degradation of 17beta-estradiol (E2), by Fenton like reaction was investigated as a function of E2 concentrations, organic co-solvents and co-existing estrogens, humic acid (HA) and other background anions. E2 degradation was effectively achieved by hydroxyl radicals that were generated in the heterogeneous photo-Fenton process. The degradation kinetics were fitted to Langmuir-Hinshelwood model with kr = 0.3140 microM/h and Kads = 2.2146L/micromol. The removal kinetics of E2 were initiated by a rapid decay and then followed by a much slower one in acetonitrile-water solutions while in methanol-water solutions they followed the first-kinetic model for the diffusion-control of hydroxyl radicals and competition between E2 and co-solvents. In addition, the lower level of co-existing substances did not significantly influence the oxidation efficiency of E2. The degradation rates of E2 were found to depend not only on the concentrations of hydrogen peroxide and iron content as reported before but also on pH, E2 concentrations and composition of co-solvents. Thus it is very important to look for the optimum conditions for the purpose of most efficiently eliminating E2 from drinking water.
Data in a data center are stored dispersively. The data-oriented task computing disperses big data analysis tasks to different computing nodes. The extensive use of graphics processing unit (GPU) makes it urgent and important to study how to reasonably assign heterogeneous resources to different computing frameworks. We investigate the existing big data computing framework and the GPU computing. Based on the existing cluster resource management model and the GPU management model, we propose a hybrid heterogeneous resource management model that combines CPU resources with GPU resources. The computing nodes manage local resources and implement tasks; the resource management center concertedly manage various computing frameworks. We design and implement a hybrid domain resource sharing and allocation algorithm, which allocates the hybrid domain resources to computing frameworks according to the coordinated use of them so as to fairly share the hybrid domain resources among various computing frameworks and prevent the CPU from too many tasks but the GPU or CPU from resource "hunger". The experimental results show that the allocation algorithm can increase the use of heterogeneous resources and the number of completed tasks by around 15%.
Aim. MRO2 system is a data management platform. It has the ability to manage and store all kinds of data in the product's lifecycle, that is both the mass storage capacity and the scalability are required. For the existing big-data stores for MRO2 systemeither only focus on the storage problem, or only do the scalability issue. In this paper, a two-layer data management model is proposed, in which the top layer uses the memory storage for scalability and the bottom layer uses the distributed key-value storage for mass storage. By adding a middle layer of the key group between the application and the KV storage system, the keys for real-time processing are combined to cache in a node. It satisfies the characteristics of the real-time application and improves the dynamic scalability. The present protocol of the dynamic key groups for real-time distributed computation for MRO2 system is explained in detail. And then the protocol for creating and deleting key groups is introduced. The third topic is an implement of a big-data store for supporting MRO2 system. In this topic, the delay times for creating and deleting the dynamic transaction groups to estimation are used. Finally, the experiments to appraise the present method are done. The response time of the present method is quite efficient in comparison with the other methods to be used inbig data storage systems.
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