Drivers of population structure of the bottlenose dolphin (Tursiops truncatus) in the Eastern Mediterranean Sea

The drivers of population differentiation in oceanic high dispersal organisms, have been crucial for research in evolutionary biology. Adaptation to different environments is commonly invoked as a driver of differentiation in the oceans, in alternative to geographic isolation. In this study, we investigate the population structure and phylogeography of the bottlenose dolphin (Tursiops truncatus) in the Mediterranean Sea, using microsatellite loci and the entire mtDNA control region. By further comparing the Mediterranean populations with the well described Atlantic populations, we addressed the following hypotheses: (1) bottlenose dolphins show population structure within the environmentally complex Eastern Mediterranean Sea; (2) population structure was gained locally or otherwise results from chance distribution of preexisting genetic structure; (3) strong demographic variations within the Mediterranean basin have affected genetic variation sufficiently to bias detected patterns of population structure. Our results suggest that bottlenose dolphin exhibits population structures that correspond well to the main Mediterranean oceanographic basins. Furthermore, we found evidence for fine scale population division within the Adriatic and the Levantine seas. We further describe for the first time, a distinction between populations inhabiting pelagic and coastal regions within the Mediterranean. Phylogeographic analysis suggests that current genetic structure, results mostly from stochastic distribution of Atlantic genetic variation, during a recent postglacial expansion. Comparison with Atlantic mtDNA haplotypes, further suggest the existence of a metapopulation across North Atlantic/Mediterranean, with pelagic regions acting as source for coastal environments

An exact column generation-based algorithm for Bi-Objective Vehicle Routing Problems

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3DdSiPM - Digital Photon Counting Microsystem Based on 3D Integration: Architechture and Measurements on 1st Prototype

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3DdSiPM - Digital Photon Counting Microsystem Based on 3D Integration: Architechture and Measurements on 1st Prototype

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Sherbrooke’s First 3D Digital SiPM: Measurements, Recommendations and Future Work

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Column generation algorithms for bi-objective combinatorial optimization problems with a min–max objective

International audienceMany practical combinatorial optimization problems can be described by integer linear programs having an exponential number of variables, and they are efficiently solved by column generation algorithms. For these problems, column generation is used to compute good dual bounds that can be incorporated in branch-and-price algorithms. Recent research has concentrated on describing lower and upper bounds of bi-objective and general multi-objective problems with sets of points (bound sets). An important issue to address when computing a bound set by column generation is how to efficiently search for columns corresponding to each point of the bound set. In this work, we propose a generalized column generation scheme to compute bound sets for bi-objective combinatorial optimization problems. We present specific implementations of the generalized scheme for the case where one objective is a min–max function by using a variant of the ε-constraint method to efficiently model these problems. The proposed strategies are applied to a bi-objective extension of the multi-vehicle covering tour problem, and their relative performances based on different criteria are compared. The results show that good bound sets can be obtained in reasonable times if columns are efficiently managed. The variant of the ε-constraint presented is also better than a standard ε-constraint method in terms of the quality of the bound sets