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In recent years, the treatment of wastewater treatment plant (WWTP) effluent has gained increasing attention. However, researches on the relationships between nitrogen forms and nitrogen removal efficiency are very limited. Based on the fact that the nitrogen forms in the WWTP effluent may vary as the season changes, the nitrogen removal efficiencies of an integrated ecological floating bed (IEFB) was studied under different influent nitrogen forms. In addition, the effects of sediments in the system were also quantified during the experiment. Results showed that the total nitrogen (TN) removal rates of the IEFB were 25.61 ± 5.72% and 60.03 ± 7.00%, respectively, when the main influent nitrogen forms are nitrate and ammonia. The sediments in the system also played vital roles in the removal processes: when the sediments were covered with a polyethylene membrane, the total nitrogen (TN) removal rate of the system dropped from 27.86 ± 5.53% to 14.78 ± 4.97%, and the total phosphorus (TP), from 58.77 ± 6.20% to 33.51 ± 25.52%.

Students spend most of their time living and studying on campus, especially in Asia, and they form various types of associations in addition to those with classmates and roommates. It is necessary for university authorities to master these types of associations, so as to provide appropriate services, such as psychological guidance and academic advice. With the rapid development of the &ldquo, smart campus,&rdquo, many kinds of student behavior data are recorded, which provides an unprecedented opportunity to deeply analyze students&rsquo, associations. In this paper, we propose a visual analytic method to construct students&rsquo, association networks by computing the similarity of their behavior data. We discover student communities using the popular Louvain (or BGLL) algorithm, which can extract community structures based on modularity optimization. Using various visualization charts, we visualized associations among students so as to intuitively express them. We evaluated our method using the real behavior data of undergraduates in a university in Beijing. The experimental results indicate that this method is effective and intuitive for student association analysis.