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The paper by Lee et al. [10] presents a knowledge-based freight management decision support system incorporating economies of scale with a multimodal minimum cost flow optimization approach.This study explicitly considers several multimodal freight transport options in terms of quantity, vehicle size, batch strategy, multi-modes, and combinations.They propose multimodal minimum cost flow problem formulation with concave equations due to economies of scale for quantity, nonlinear equations due to economies of scale for both quantity and distance, and non-continuous equations due to economies
of scale for vehicle size.The paper by Han [11] presents efficient decision support for detecting content polluters on social networks with an approach based on automatic knowledge acquisition from behavioral patterns.They propose a set of features that can be easily extracted from the messages and behaviors of Twitter users, and constructs a new breed of classifiers based on these features.
The paper by Lee et al. [10] presents a knowledge-based freight management decision support system incorporating economies of scale with a multimodal minimum cost flow optimization approach. They propose multimodal minimum cost flow problem formulation with concave equations due to economies of scale for quantity, nonlinear equations due to economies of scale for both quantity and distance, and non-continuous equations due to economies
of scale for vehicle size. This study explicitly considers several multimodal freight transport options in terms of quantity, vehicle size, batch strategy, multi-modes, and combinations. The paper by Han [11] presents efficient decision support for detecting content polluters on social networks with an approach based on automatic knowledge acquisition from behavioral patterns. This study is an efficient method for detecting content polluters on Twitter. They propose a set of features that can be easily extracted from the messages and behaviors of Twitter users, and constructs a new breed of classifiers based on these features. The selection of papers has been achieved through careful evaluation and discussion of the papers. We gratefully acknowledge the authors for their worthy contributions to this special issue. We would also like to thank all members of the ICCT Technical Program Committee and the anonymous reviewers for their help in identifying original and innovative papers from the conference program and for their careful evaluation of earlier versions of the submitted papers. Accordingly, 8 high-quality papers were selected out of the 25 submitted resulting in a 32 % acceptance rate. Last but not the least, we would like to thank Professor R.A. Patterson, Professor E. Rolland, coeditor in chiefs of the international journal on Information Technology and Management, for his valuable remarks and his undeterred help throughout the publication process of this special issue.
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