1,702 research outputs found
Multiprocessor task scheduling in multistage hyrid flowshops: a genetic algorithm approach
This paper considers multiprocessor task scheduling in a multistage hybrid flow-shop environment. The objective is to minimize the make-span, that is, the completion time of all the tasks in the last stage. This problem is of practical interest in the textile and process industries. A genetic algorithm (GA) is developed to solve the problem. The GA is tested against a lower bound from the literature as well as against heuristic rules on a test bed comprising 400 problems with up to 100 jobs, 10 stages, and with up to five processors on each stage. For small problems, solutions found by the GA are compared to optimal solutions, which are obtained by total enumeration. For larger problems, optimum solutions are estimated by a statistical prediction technique. Computational results show that the GA is both effective and efficient for the current problem. Test problems are provided in a web site at www.benchmark.ibu.edu.tr/mpt-h; fsp
Modeling the Arctic Freshwater System and its integration in the global system: Lessons learned and future challenges
This is the final version of the article. Available from the publisher via the DOI in this record.Numerous components of the Arctic freshwater system (atmosphere, ocean, cryosphere, and terrestrial hydrology) have experienced large changes over the past few decades, and these changes are projected to amplify further in the future. Observations are particularly sparse, in both time and space, in the polar regions. Hence, modeling systems have been widely used and are a powerful tool to gain understanding on the functioning of the Arctic freshwater system and its integration within the global Earth system and climate. Here we present a review of modeling studies addressing some aspect of the Arctic freshwater system. Through illustrative examples, we point out the value of using a hierarchy of models with increasing complexity and component interactions, in order to dismantle the important processes at play for the variability and changes of the different components of the Arctic freshwater system and the interplay between them. We discuss past and projected changes for the Arctic freshwater system and explore the sources of uncertainty associated with these model results. We further elaborate on some missing processes that should be included in future generations of Earth system models and highlight the importance of better quantification and understanding of natural variability, among other factors, for improved predictions of Arctic freshwater system change.The first two authors have contributed
equally to the publication. The Arctic
Freshwater Synthesis has been
sponsored by the World Climate
Research Programme’s Climate and the
Cryosphere project (WCRP-CliC), the
International Arctic Science Committee
(IASC), and the Arctic Monitoring and
Assessment Programme (AMAP). C.L.
acknowledges support from the UK
Natural Environment Research Council.
M.M.H. acknowledges support from NSF
PLR-1417642. D.M.L. is supported by
funding from the U.S. Department of
Energy BER, as part of its Climate Change
Prediction Program, Cooperative
Agreement DE-FC03-97ER62402/A010,
and NSF grants AGS-1048996,
PLS-1048987, and PLS-1304220. J.A.S. is
supported by Natural Environment
Research Council grant NE/J019585/1.
Y.D. is supported by Environment
Canada’s Northern Hydrology program.
We acknowledge the World Climate
Research Programme’s Working Group
on Coupled Modelling, which is responsible
for CMIP, and we thank the climate
modeling groups for producing and
making available their model output. For
CMIP, the U.S. Department of Energy’s
Program for Climate Model Diagnosis
and Intercomparison provides
coordinating support and led
development of software infrastructure
in partnership with the Global
Organization for Earth System Science
Portals. The CMIP data and CESM-LE data
are available through the relevant Web
data portal
Bats Use Magnetite to Detect the Earth's Magnetic Field
While the role of magnetic cues for compass orientation has been confirmed in numerous animals, the mechanism of detection is still debated. Two hypotheses have been proposed, one based on a light dependent mechanism, apparently used by birds and another based on a “compass organelle” containing the iron oxide particles magnetite (Fe3O4). Bats have recently been shown to use magnetic cues for compass orientation but the method by which they detect the Earth's magnetic field remains unknown. Here we use the classic “Kalmijn-Blakemore” pulse re-magnetization experiment, whereby the polarity of cellular magnetite is reversed. The results demonstrate that the big brown bat Eptesicus fuscus uses single domain magnetite to detect the Earths magnetic field and the response indicates a polarity based receptor. Polarity detection is a prerequisite for the use of magnetite as a compass and suggests that big brown bats use magnetite to detect the magnetic field as a compass. Our results indicate the possibility that sensory cells in bats contain freely rotating magnetite particles, which appears not to be the case in birds. It is crucial that the ultrastructure of the magnetite containing magnetoreceptors is described for our understanding of magnetoreception in animals
Analysis of a mechanistic Markov model for gene duplicates evolving under subfunctionalization
Background Gene duplication has been identified as a key process driving functional change in many genomes. Several biological models exist for the evolution of a pair of duplicates after a duplication event, and it is believed that gene duplicates can evolve in different ways, according to one process, or a mix of processes. Subfunctionalization is one such process, under which the two duplicates can be preserved by dividing up the function of the original gene between them. Analysis of genomic data using subfunctionalization and related processes has thus far been relatively coarse-grained, with mathematical treatments usually focusing on the phenomenological features of gene duplicate evolution. Results Here, we develop and analyze a mathematical model using the mechanics of subfunctionalization and the assumption of Poisson rates of mutation. By making use of the results from the literature on the Phase-Type distribution, we are able to derive exact analytical results for the model. The main advantage of the mechanistic model is that it leads to testable predictions of the phenomenological behavior (instead of building this behavior into the model a priori), and allows for the estimation of biologically meaningful parameters. We fit the survival function implied by this model to real genome data (Homo sapiens, Mus musculus, Rattus norvegicus and Canis familiaris), and compare the fit against commonly used phenomenological survival functions. We estimate the number of regulatory regions, and rates of mutation (relative to silent site mutation) in the coding and regulatory regions. We find that for the four genomes tested the subfunctionalization model predicts that duplicates most-likely have just a few regulatory regions, and the rate of mutation in the coding region is around 5-10 times greater than the rate in the regulatory regions. This is the first model-based estimate of the number of regulatory regions in duplicates. Conclusions Strong agreement between empirical results and the predictions of our model suggest that subfunctionalization provides a consistent explanation for the evolution of many gene duplicates
Creatures Great and SMAL: Recovering the Shape and Motion of Animals from Video
We present a system to recover the 3D shape and motion of a wide variety of
quadrupeds from video. The system comprises a machine learning front-end which
predicts candidate 2D joint positions, a discrete optimization which finds
kinematically plausible joint correspondences, and an energy minimization stage
which fits a detailed 3D model to the image. In order to overcome the limited
availability of motion capture training data from animals, and the difficulty
of generating realistic synthetic training images, the system is designed to
work on silhouette data. The joint candidate predictor is trained on
synthetically generated silhouette images, and at test time, deep learning
methods or standard video segmentation tools are used to extract silhouettes
from real data. The system is tested on animal videos from several species, and
shows accurate reconstructions of 3D shape and pose.GlaxoSmithKlin
- …