Wireless sensor networks have been widely deployed in the last decades to provide various services, like environmental monitoring or object tracking. Such a network is composed of a set of sensor nodes which are used to sense and transmit collected information to a base station. To achieve this goal, two properties have to be guaranteed: (i) the sensor nodes must be placed such that the whole environment of interest (represented by a set of targets) is covered, and (ii) every sensor node can transmit its data to the base station (through other sensor nodes). In this paper, we consider the Minimum Connected k-Coverage (MCkC) problem, where a positive integer k ≥ 1 defines the coverage multiplicity of the targets. We propose two mathematical programming formulations for the MCkC problem on square grid graphs and random graphs. We compare them to a recent model proposed by (Rebai et al 2015). We use a standard mixed integer linear programming solver to solve several instances with different formulations. In our results, we point out the quality of the LP-bound of each formulation as well as the total CPU time or the proportion of solved instances to optimality within a given CPU time.
Background: Storage of platelet concentrates (PCs) has an impact on platelet quality and possibly affects their functions after transfusion. The influence of processing and storage conditions of PCs on their in vivo function upon transfusion is unknown. One option for investigating this question is to implement an ex vivo labeling of human platelets, to analyze them after transfusion into heathy volunteers and/or patients. In this study, we developed two labeling methods employing biotin. Methods: Two methods of biotinylation were compared to a control (standard PC). The "Bio-Wash" process used washing steps to label all platelets within the PC; for the other method, "Bio-Direct," one fifth of the PC were directly labeled without washing steps. The control and the two biotinylated PCs were analyzed over 7 days of storage. Labeling efficiency, platelet counts, phenotypes, and functions, along with time and costs, were evaluated to select the best process.Results: Both methods achieved a stable labeling through the storage, with similar platelet counts and metabolism in comparison to control PCs. Bio-Wash showed higher activation phenotype and lower aggregation response in comparison to the Bio-Direct method. The Bio-Direct was performed within 1.5 h versus 3 h for the Bio-Wash. However, the Bio-Direct required 12 mg of biotin instead of 8 mg for the other process. Conclusion: We set up two methods of biotinylation that can be easily implemented in a blood bank environment. The Bio-Direct process was preferred to the Bio-Wash because of its similarity, from a functional and phenotypic point of view, with standard PCs.
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