The complexity of transitions

No abstract

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

The structure and function of complex networks

Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.Comment: Review article, 58 pages, 16 figures, 3 tables, 429 references, published in SIAM Review (2003

Leveraging Microtechnology to Study Multicellular Microvascular Systems and Macromolecular Interaction

Biological systems are large-scale, complex systems comprised of many hierarchical subsystems interacting physico-chemically in a dynamic and coordinated fashion. The complex interactions of subsystems (in micro-scale) lead to the formation of emergent properties (in macro-scale); these are properties that are not visible if individual subsystems are studied. The inherent high-throughput characteristics of microfabrication technology (microtechnology) along with its ability to manipulate biological species at the micro-scale makes it an ideal tool to elucidate the mechanisms leading to the formation of emergent properties at the macro-scale. In this dissertation, by combining microtechnologies with advanced computational algorithms, we demonstrate system-level analysis of biological systems in development and disease. The abundance of high quality molecular and genetic data along with the drastic increase in computational power resulted in considerable progress in genomics, epigenomics and proteomics, but not for the so-called cellomics as we define it here: high-throughput study of single-cell phenotype and heterotypic cell-cell interaction via micromanipulation and bioinformatics analysis. Lack of high-throughput robust experimental tools is the major roadblock to cellomics. Using microtechnologies, in the context of developmental biology we studied vascular tissue morphogenesis (vasculogenesis). Formation of microvessels is of critical significance in development and for vascularizing newly engineered tissues in regenerative medicine. First, we sought to map the heterogeneous morphodynamic behavior of individual clonal cells in the process of capillary-like structure (CLS) formation (Chapter 2 and 3). Then we looked into deciphering the role of extracellular matrix (ECM) mediated mechanical signals in deriving the process of CLS formation (Chapter 4). In the second half of this thesis, we demonstrated the capabilities of microtechnologies and advanced computational algorithms in tackling the challenging problems in disease: global health diagnostics and cancer drug screening. First, we studied the performance of microfluidic-based diagnostic as a large-scale complex system under real-world constraints (Chapter 5). Then, we present the development of two microfluidic-based platforms to study the heterotypic interaction of cells in both a biomimetic in vitro and a realistic in vivo setting. We developed an implantable construct carrying a densely-packed heterogeneous panel of tumor cells. This platform could ultimately be used to test anti-cancer drug efficacy against a large number of genotypes in an in vivo setting (Appendices A and B). Together, these methods provide a powerful suite of tools for high-throughput analysis of biological species at the micro-scale and could potentially unlock the mysteries behind the emergent properties observed at the macro-scale

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 378)

This bibliography lists 185 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during Aug. 1993. Subject coverage includes: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance

Fruit-Fly Based Searching Algorithm For Cooperative Swarming Robotic System

Swarm intelligence can be described as a complex behaviour generated from a large number of individual agents, where each agent follows very simple rules. It is actually inspired by understanding the decentralized mechanisms in the organization of natural swarms such as the birds, the ants, the bees, the glowworms, and the fireflies. Observation of these biological behaviour has given birth to swarm robotics whereby robots have the capability to work with one another in a group to achieve the same kind of parallelism, robustness and collective capabilities. A collective behaviour movement strategy such as a “source search” and “aggregation” are commonly exhibited by the animals while finding their source of food. However, the situation for the robots is to find the source of odour, light, and sound. Meanwhile, there has been mounting interest, particularly for finding the deepest location in lakes and dams for bathymetric survey systems. Using the existing lawnmower methods incur substantial costs in terms of time, accuracy and reliability. Therefore, the usage of a swarming robotic system is proposed. In this thesis, a simple framework and methodology in developing a bio-inspired algorithm for cooperative swarming robotic application has been developed. The fruit flies or Drosophila Melanogaster movement strategy offers some advantages such as strategic 'search-aggregation' cycle, distribution of moving patterns with Levy Random, information sharing in real-time, and reduction of controller parameters during movements. A number of benchmark function processes were conducted to assess the performance of proposed FOA (Fly Optimisation Algorithm)

Power transmission planning using heuristic optimisation techniques: Deterministic crowding genetic algorithms and Ant colony search methods

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The goal of transmission planning in electric power systems is a robust network which is economical, reliable, and in harmony with its environment taking into account the inherent uncertainties. For reasons of practicality, transmission planners have normally taken an incremental approach and tended to evaluate a relatively small number of expansion alternatives over a relatively short time horizon. In this thesis, two new planning methodologies namely the Deterministic Crowding Genetic Algorithm and the Ant Colony System are applied to solve the long term transmission planning problem. Both optimisation techniques consider a 'green field' approach, and are not constrained by the existing network design. They both identify the optimal transmission network over an extended time horizon based only on the expected pattern of electricity demand and generation sources. Two computer codes have been developed. An initial comparative investigation of the application of Ant Colony Optimisation and a Genetic Algorithm to an artificial test problem has been undertaken. It was found that both approaches were comparable for the artificial test problem.EPRSC and National Grid Company pl