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Zhang H.,Dalian University of Technology | Cheng S.,DalianUniversity of Technology | Yang F.,DalianUniversity of Technology
Desalination | Year: 2014

A major barrier for forward osmosis (FO) process is concentration polarization (CP) which dramatically reduces the performance of FO. This study focused on providing a simple method of mitigating CP, especially dilutive internal CP (DICP), in the absence of additional energy consumption and investigate the impact of spacer location on CP quantitatively in both FO (AL-FS) and PRO (AL-DS) mode; finally we tried to provide a spacer location that could best decrease the adverse effect of CP on FO performance. The findings of the research led to the conclusion that in FO mode, placing the spacer (1. mm. ×. 1. mm) in the draw channel with one end connecting the membrane could well mitigate DICP. Besides, placing the same spacer in both feed and draw channels could be an acceptable method of decreasing concentrative external CP (CECP) and DICP simultaneously. If locating the spacer thus, the average water flux could increase by 7.57%, 14.1% and 18.7% when the concentration of the draw solution was 1. M, 2. M, and 4. M separately. Similarly, in PRO mode we found that spacer can also mitigate concentrative ICP and dilutive ECP. Besides, the pore size in the spacer should also be considered when it was used to mitigate CP. © 2014 Elsevier B.V. Source

Zhang X.,Dalian University of Technology | Wang Y.,Tencert Inc. | Mou N.,DalianUniversity of Technology | Liang W.,DalianUniversity of Technology
ACM Transactions on the Web | Year: 2014

Semi-automatic anti-spam algorithms propagate either trust through links from a good seed set (e.g., TrustRank) or distrust through inverse links from a bad seed set (e.g., Anti-TrustRank) to the entire Web. These kinds of algorithms have shown their powers in combating link-based Web spam since they integrate both human judgement and machine intelligence. Nevertheless, there is still much space for improvement. One issue of most existing trust/distust propagation algorithms is that only trust or distrust is propagated and only a good seed set or a bad seed set is used. According to Wu et al. [2006a], a combined usage of both trust and distrust propagation can lead to better results, and an effective framework is needed to realize this insight. Another more serious issue of existing algorithms is that trust or distrust is propagated in nondifferential ways, that is, a page propagates its trust or distrust score uniformly to its neighbors, without considering whether each neighbor should be trusted or distrusted. Such kinds of blind propagating schemes are inconsistent with the original intention of trust/distrust propagation. However, it seems impossible to implement differential propagation if only trust or distrust is propagated. In this article, we take the view that each Web page has both a trustworthy side and an untrustworthy side, and we thusly assign two scores to eachWeb page: T-Rank, scoring the trustworthiness of the page, and D-Rank, scoring the untrustworthiness of the page.We then propose an integrated framework that propagates both trust and distrust. In the framework, the propagation of T-Rank/D-Rank is penalized by the target's current D-Rank/T-Rank. In other words, the propagation of T-Rank/D-Rank is decided by the target's current (generalized) probability of being trustworthy/ untrustworthy; thus a page propagates more trust/distrust to a trustworthy/untrustworthy neighbor than to an untrustworthy/trustworthy neighbor. In this way, propagating both trust and distrust with target differentiation is implemented. We use T-Rank scores to realize spam demotion and D-Rank scores to accomplish spam detection. The proposed Trust-DistrustRank (TDR) algorithm regresses to TrustRank and Anti-TrustRank when the penalty factor is set to 1 and 0, respectively. Thus TDR could be seen as a combinatorial generalization of both TrustRank and Anti-TrustRank. TDR not only makes full use of both trust and distrust propagation, but also overcomes the disadvantages of both TrustRank and Anti-TrustRank. Experimental results on benchmark datasets show that TDR outperforms other semi-automatic anti-spam algorithms for both spam demotion and spam detection tasks under various criteria. © 2014 ACM. Source

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