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Recently, some theoretical studies proposed introducing distance information in the handover policy in railway deployments[19, 20].
By introducing distance variables, we may also optimize the distance probability, as opposed to the contact probability used by our current ILP method.
Wang et al [ 24] propose a new topological model by extending the definition of k-clique community of algorithm CPM and introducing distance restriction, and develop a novel algorithm called CP-DR based on the new topological model to identify protein complexes.
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This paper contributes to the literature by introducing distances from origin states in Mexico to destination states in the USA as a proxy for costs of migration and uses a US expected wage measure instead of the average US real wages.
We first introduce Distance function.
We find that, if there is no distance dependence, edge effects do not artificially introduce distance dependence.
To enforce this kind of distance constraints, we shall introduce distance variables and also define their relationship with contact variables.
In our approach, in order to achieve these advantages, we propose a new topological model by extending the definition of k-clique community of algorithm CPM and introduced distance restriction.
In the view of the experienced interviewer this strategy did not introduce distance between the respondent and interviewer, indeed respondents appeared to be encouraged at the 'scientific' nature of the study.
In this paper, we propose a new topological model by extending the definition of k-clique community of algorithm CPM and introduced distance restriction, and develop a novel algorithm called CP-DR based on the new topological model for identifying protein complexes.
In this paper, we extended the definition of k-clique community of algorithm CPM, introduced distance restriction, proposed a new topological model for protein complexes and developed an algorithm CP-DR to identify protein complexes in large protein interaction networks based on the proposed topological model.
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