Bat algorithm is a new swarm intelligent optimization algorithm inspired by bat seeking behaviour. Due to the fast convergent speed, it has been applied to many areas successfully. In this paper, we incorporate bat al...
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Bacterial Foraging Optimization (BFO) is a novel intelligent optimization algorithm by simulating the bacterial foraging behaviours. In this paper, BFO is applied to solve coverage optimization problem which is one cr...
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Current modeling of infectious diseases allows for the study of complex and realistic scenarios that go from the population to the individual level of description. However, most epidemic models assume that the spreadi...
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Current modeling of infectious diseases allows for the study of complex and realistic scenarios that go from the population to the individual level of description. However, most epidemic models assume that the spreading process takes place on a single level (be it a single population, a metapopulation system, or a network of contacts). In particular, interdependent contagion phenomena can be addressed only if we go beyond the scheme-one pathogen-one network. In this paper, we propose a framework that allows us to describe the spreading dynamics of two concurrent diseases. Specifically, we characterize analytically the epidemic thresholds of the two diseases for different scenarios and compute the temporal evolution characterizing the unfolding dynamics. Results show that there are regions of the parameter space in which the onset of a disease’s outbreak is conditioned to the prevalence levels of the other disease. Moreover, we show, for the susceptible-infected-susceptible scheme, that under certain circumstances, finite and not vanishing epidemic thresholds are found even at the limit for scale-free networks. For the susceptible-infected-removed scenario, the phenomenology is richer and additional interdependencies show up. We also find that the secondary thresholds for the susceptible-infected-susceptible and susceptible-infected-removed models are different, which results directly from the interaction between both diseases. Our work thus solves an important problem and paves the way toward a more comprehensive description of the dynamics of interacting diseases.
Bioinformatics has been defined many different ways, since practitioners do not always agree upon the scope of its use within the biological and computer sciences, but it is always considered a combination of both sci...
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Bioinformatics has been defined many different ways, since practitioners do not always agree upon the scope of its use within the biological and computer sciences, but it is always considered a combination of both sciences, along with other contributing disciplines. In Wikipedia, bioinformatics is defined as "as branch of biological science which deals with the study of methods for storing, retrieving and analyzing biological data, such as nucleic acid (DNA/RNA) and protein sequence, structure, function, pathways and genetic interactions. It generates new knowledge that is useful in such fields as drug design and development of new software tools to create that knowledge."
Through analyzing the type of infected population, a contact model with covert infection is introduced. The stability of this model in small world network is studied according to the property of contact network. To th...
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At present, malicious code becomes more and more harmful in common people's daily life. In order to solve this problem, the relative methods and theories of Chinese word segmentation and malicious code detection a...
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In order to train Artificial Neural Networks (ANNs), we used a new stochastic optimisation algorithm that simulates the plant growing process. It designs an artificial photosynthesis operator and an artificial phototr...
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Swarm intelligence is an umbrella for amount optimization algorithms. This discipline deals with natural and artificial systems composed of many individuals that coordinate their activities using decentralized control...
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