7,599 research outputs found

    A hybrid boundary condition for robust particle swarm optimization

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    The particle swarm optimization (PSO) technique is a powerful stochastic evolutionary algorithm that can be used to find the global optimum solution in a complex search space. However, it has been observed that there is a great variation in its performance due to the dimensionality of the problem and the location of the global optimum with respect to the boundaries of the search space. The present paper attempts to resolve this problem by proposing a simple hybrid "damping" boundary condition that combines the characteristics offered by the existing "absorbing" and "reflecting" boundaries. Simulation results on microwave image reconstruction have shown that with the proposed "damping" boundary condition, a much robust and consistent optimization performance can be obtained for PSO regardless of the dimensionality and location of the global optimum solution. © 2005 IEEE

    A microparticle swarm optimizer for the reconstruction of microwave images

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    A novel optimization technique known as the microparticle swarm optimizer (μPSO) is proposed for high-dimensional microwave image reconstruction. With the proposed μPSO, good optimization performance can be obtained especially for solving high-dimensional optimization problems. In addition, the proposed μPSO requires only a small population size to outperform the standard PSO that uses a larger population size. Our simulation results on the reconstruction of the dielectric properties of normal and malignant breast tissues have shown that the μPSO can perform quite well for this high-dimensional microwave image reconstruction problem. © 2007 IEEE

    A Microparticle Swarm Optimizer for the Reconstruction of Microwave Images

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    Assembly of normal actomyosin rings in the absence of Mid1p and cortical nodes in fission yeast

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    Cytokinesis in many eukaryotes depends on the function of an actomyosin contractile ring. The mechanisms regulating assembly and positioning of this ring are not fully understood. The fission yeast Schizosaccharomyces pombe divides using an actomyosin ring and is an attractive organism for the study of cytokinesis. Recent studies in S. pombe (Wu, J.Q., V. Sirotkin, D.R. Kovar, M. Lord, C.C. Beltzner, J.R. Kuhn, and T.D. Pollard. 2006. J. Cell Biol. 174:391–402; Vavylonis, D., J.Q. Wu, S. Hao, B. O'Shaughnessy, and T.D. Pollard. 2008. Science. 319:97–100) have suggested that the assembly of the actomyosin ring is initiated from a series of cortical nodes containing several components of this ring. These studies have proposed that actomyosin interactions bring together the cortical nodes to form a compacted ring structure. In this study, we test this model in cells that are unable to assemble cortical nodes. Although the cortical nodes play a role in the timing of ring assembly, we find that they are dispensable for the assembly of orthogonal actomyosin rings. Thus, a mechanism that is independent of cortical nodes is sufficient for the assembly of normal actomyosin rings

    Smart Contract-based Consensus Building for Collaborative Medical Decision-Making

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    Medical decision-making is moving away from the traditional one-off dyadic encounter between the patient and physician, and transitioning towards a more inclusive, shared decision-making process that also considers the inputs from other stakeholders. This ensures that a patient's decision is not only based on a medical opinion, but also includes other considerations such as impact on family members, legal and financial implications, and experiences of patients in similar situations. However, given the sensitive nature of health data and decisions, there are several challenges associated with safeguarding the privacy, security and consent of all contributors and assuring the integrity of the process. We propose a collaborative medical decision-making platform that uses a consensus building mechanism implemented using Blockchain-based Smart Contracts to address some of the above challenges, thereby giving the participants confidence that both the decision-making process and the outcome(s) can be trusted. We also present a proof-of-concept implementation using the private Ethereum Blockchain to demonstrate practicability
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