Abstract

Abstract
This paper addresses the problem of finding several different solutions with the same optimum performance in single objective real-world engineering problems. In this paper a parallel robot design is proposed. Thereby, this paper presents a genetic algorithm to optimize uni-objective problems with an infinite number of optimal solutions. The algorithm uses the maximin concept and e-dominance to promote diversity over the admissible space. The performance of the proposed algorithm is analyzed with three well-known test functions and a function obtained from practical real-world engineering optimization problems. A spreading analysis is performed showing that the solutions drawn by the algorithm are well dispersed. © 2013, Springer Science+Business Media Dordrecht.

Abstract
Understanding how VMI benefits serve lean purposes in healthcare and why its outcomes can be difficult to achieve in healthcare settings is the main purpose of this study. An in?depth case study of VMI is presented in the perspective of the downstream member, a public general multi?site hospital, operating as a small scale consolidated service centre in terms of material management, exploring such dimensions as: VMI benefits, risks, barriers and enablers. Despite some unawareness of VMI benefits in healthcare, it can present a waste reduction solution not only in costs but in the quality of care for freeing clinical professionals to clinical tasks, among other savings. The multiple benefits are better explored, as in any relationship building, by investing in partnership creation and overcoming the idiosyncratic barriers of the healthcare sector. Although findings of a single case study are difficult to generalize, the protocol and methodology presented allow replication in other units of analysis with the same inclusion criteria. This paper brings the lean deployment discussion out of the organization's boundaries, showing the interconnections and pointing to the need for future work that would allow healthcare managers to build a lean supply chain. By considering VMI an outsourcing alternative, this paper identifies the lean thinking intent behind such options and enhances the idiosyncratic difficulties in full deployment in the healthcare sector, a less studied setting. © 2013, Emerald Group Publishing Limited

Abstract
This paper presents a new method for automatic common carotid artery detection in B-mode ultrasonography. The proposed method is based on the location of phase symmetry patterns at apropriate scale of analysis. The local phase information is derived from the monogenic signal and isotropic log-normal band-pass filters, and the resulting common carotid artery is located using a dynamic programming optimization algorithm. The experiments show that the proposed method is more robust to noise than previous approaches, although additional research is required for robust common carotid artery detection on the more complicated cases.

Abstract

Abstract
Cluster detection methods are widely studied in Propositional Data Mining. In this context, data is individually represented as a feature vector. This data has a natural nonrelational structure, but can be represented in a relational form through similarity-based network models. In these models, examples are represented by vertices and an edge connects two examples with high similarity. This relational representation allows employing network-based algorithms in Relational Data Mining. Specifically in clustering tasks, these models allow to use community detection algorithms in networks in order to detect data clusters. In this work, we compared traditional non-relational data-based clustering algorithms with clustering detection algorithms based on relational data using measures for community detection in networks. We carried out an exploratory analysis over 23 numerical datasets and 10 textual datasets. Results show that network models can efficiently represent the data topology, allowing their application in cluster detection with higher precision when compared to non-relational methods. Copyright 2013 ACM.