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Bacterial Colony Optimization  [PDF]
Ben Niu,Hong Wang
Discrete Dynamics in Nature and Society , 2012, DOI: 10.1155/2012/698057
Abstract: This paper investigates the behaviors at different developmental stages in Escherichia coli (E. coli) lifecycle and developing a new biologically inspired optimization algorithm named bacterial colony optimization (BCO). BCO is based on a lifecycle model that simulates some typical behaviors of E. coli bacteria during their whole lifecycle, including chemotaxis, communication, elimination, reproduction, and migration. A newly created chemotaxis strategy combined with communication mechanism is developed to simplify the bacterial optimization, which is spread over the whole optimization process. However, the other behaviors such as elimination, reproduction, and migration are implemented only when the given conditions are satisfied. Two types of interactive communication schemas: individuals exchange schema and group exchange schema are designed to improve the optimization efficiency. In the simulation studies, a set of 12 benchmark functions belonging to three classes (unimodal, multimodal, and rotated problems) are performed, and the performances of the proposed algorithms are compared with five recent evolutionary algorithms to demonstrate the superiority of BCO. 1. Introduction Swarm intelligence is the emergent collective intelligent behaviors from a large number of autonomous individuals. It provides an alternative way to design novel intelligent algorithms to solve complex real-world problems. Different from conventional computing paradigms [1–3], such algorithms have no constraints of central control, and the searching result of the group will not be affected by individual failures. What is more, swarm intelligent algorithms maintain a population of potential solutions to a problem instead of only one solution. Nowadays, most of swarm intelligent optimization algorithms are inspired by the behavior of animals with higher complexity. Particle swarm optimization (PSO) [4, 5] was gleaned ideas from swarm behavior of bird flocking or fish schooling. Ant colony optimization (ACO) was motivated from the foraging behavior of ants [6, 7]. Artificial fish swarm algorithm (AFSA) was originated in the swarming behavior of fish [8], and artificial bee colony algorithm (ABCA) [9, 10] was stimulated by social specialization behavior of bees. However, the states of the abovementioned animals are more complex, and their behaviors are difficult to describe qualitatively. As prokaryote, bacteria behave in a simple pattern which can be easily described. Inspired by the foraging behavior of Escherichia coli (E. coli) in human intestines, Passion proposed an
Computational studies of the glass-forming ability of model bulk metallic glasses  [PDF]
Kai Zhang,Minglei Wang,Stefanos Papanikolaou,Yanhui Liu,Jan Schroers,Mark D. Shattuck,Corey S. O'Hern
Physics , 2013, DOI: 10.1063/1.4821637
Abstract: Bulk metallic glasses (BMGs) are produced by rapidly thermally quenching supercooled liquid metal alloys below the glass transition temperature at rates much faster than the critical cooling rate R_c below which crystallization occurs. The glass-forming ability of BMGs increases with decreasing R_c, and thus good glass-formers possess small values of R_c. We perform molecular dynamics simulations of binary Lennard-Jones (LJ) mixtures to quantify how key parameters, such as the stoichiometry, particle size difference, attraction strength, and heat of mixing, influence the glass-formability of model BMGs. For binary LJ mixtures, we find that the best glass-forming mixtures possess atomic size ratios (small to large) less than 0.92 and stoichiometries near 50:50 by number. In addition, weaker attractive interactions between the smaller atoms facilitate glass formation, whereas negative heats of mixing (in the experimentally relevant regime) do not change R_c significantly. These studies represent a first step in the development of computational methods for quantitatively predicting glass-formability.
Computational studies of anisotropic diffuse interface model of microstructure formation in solidification
M. Benes
Abstract: The growth of microstructure non-convex patterns is studied by means of the modified anisotropic phase-field model. The numerical algorithm is designed using the finite-difference spatial discretisation in the method of lines. Results of numerical analysis of the model are based on the a-priori estimates, the compactness and monotonicity arguments. As a quantitative result, we present the convergence studies of the dendritic growth when the mesh size and the diffuse parameter tend to zero.
Comparison of low amplitude oscillatory shear in experimental and computational studies of model foams  [PDF]
Micah Lundberg,Kapilanjan Krishan,Ning Xu,Corey O'Hern,Michael Dennin
Physics , 2008, DOI: 10.1103/PhysRevE.79.041405
Abstract: A fundamental difference between fluids and solids is their response to applied shear. Solids possess static shear moduli, while fluids do not. Complex fluids such as foams display an intermediate response to shear with nontrivial frequency-dependent shear moduli. In this manuscript, we conduct coordinated experiments and numerical simulations of model foams subjected to boundary-driven oscillatory, planar shear. Our studies are performed on bubble rafts (experiments) and the bubble model (simulations) in 2D. We focus on the low-amplitude flow regime in which T1 bubble rearrangement events do not occur, yet the system transitions from solid- to liquid-like behavior as the driving frequency is increased. In both simulations and experiments, we observe two distinct flow regimes. At low frequencies $\omega$, the velocity profile of the bubbles increases linearly with distance from the stationary wall, and there is a nonzero total phase shift between the moving boundary and interior bubbles. In this frequency regime, the total phase shift scales as a power-law $\Delta \sim \omega^n$ with $n \approx 3$. In contrast, for frequencies above a crossover frequency $\omega > \omega_{p}$, the total phase shift $\Delta$ scales linearly with the driving frequency. At even higher frequencies above a characteristic frequency $\omega_{nl} > \omega_{p}$, the velocity profile changes from linear to nonlinear. We fully characterize this transition from solid- to liquid-like flow behavior in both the simulations and experiments, and find qualitative and quantitative agreement for the characteristic frequencies.
Bacterial Colony Counts During Vaginal Surgery  [PDF]
Patrick Culligan,Michael Heit,Linda Blackwell,Miles Murphy,Carol A. Graham,James Snyder
Infectious Diseases in Obstetrics and Gynecology , 2003, DOI: 10.1080/10647440300025515
Abstract: Objective: To describe the bacterial types and colony counts present before and during vaginal surgery.
Experimental and computational studies of water drops falling through model oil with surfactant and subjected to an electric field  [PDF]
?smund Ervik,Svein Magne Helles?,Svend Tollak Munkejord,Bernhard Müller
Physics , 2014, DOI: 10.1109/ICDL.2014.6893172
Abstract: The behaviour of a single sub-millimetre-size water drop falling through a viscous oil while subjected to an electric field is of fundamental importance to industrial applications such as crude oil electrocoalescers. Detailed studies, both experimental and computational, have been performed previously, but an often challenging issue has been the characterization of the fluids. As numerous authors have noted, it is very difficult to have a perfectly clean water-oil system even for very pure model oils, and the presence of trace chemicals may significantly alter the interface behaviour. In this work, we consider a well- characterized water-oil system where controlled amounts of a surface active agent (Span 80) have been added to the oil. This addition dominates any trace contaminants in the oil, such that the interface behaviour can also be well-characterized. We present the results of experiments and corresponding two-phase- flow simulations of a falling water drop covered in surfactant and subjected to a monopolar square voltage pulse. The results are compared and good agreement is found for surfactant concentrations below the critical micelle concentration.
An Authomated approach for Bacterial Colony Counter  [PDF]
Shruti Nagpal
International Journal of Computer Technology and Applications , 2012,
Abstract: Counting of bacterial colonies is complex task for microbiologist. To a large extent, accurate colony counting depends on the ability to see colonies distinctly, whether viewed by the naked eye or by an automated instrument. An increased area of focus in Microbiology is the automation of counting methods.. Further in an Industry thousands of such samples are formed per day and colonies on each sample are counted manually, then this becomes a time consuming hectic and error prone job.We proposed a method to count these colonies to save time with accurate results and fast delivery to customers. This proposed research work will count the colonies after 6 to 8 hours priori, saving a lot more time and this work will more efficient because market range for this is about 10,000 only as compare to prior systems.
Application of PSO Artificial Bee Colony and Bacterial Foraging Optimization algorithms to economic load dispatch-An analysis  [PDF]
Anant Baijal,Vikram Singh Chauhan,T Jayabarathi
International Journal of Computer Science Issues , 2011,
Abstract: This paper illustrates successful implementation of three evolutionary algorithms, namely- Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) and Bacterial Foraging Optimization (BFO) algorithms to economic load dispatch problem (ELD). Power output of each generating unit and optimum fuel cost obtained using all three algorithms have been compared. The results obtained show that ABC and BFO algorithms converge to optimal fuel cost with reduced computational time when compared to PSO for the two example problems considered.
Application of PSO, Artificial Bee Colony and Bacterial Foraging Optimization algorithms to economic load dispatch: An analysis  [PDF]
Anant Baijal,Vikram Singh Chauhan,T. Jayabarathi
Computer Science , 2011,
Abstract: This paper illustrates successful implementation of three evolutionary algorithms, namely- Particle Swarm Optimization(PSO), Artificial Bee Colony (ABC) and Bacterial Foraging Optimization (BFO) algorithms to economic load dispatch problem (ELD). Power output of each generating unit and optimum fuel cost obtained using all three algorithms have been compared. The results obtained show that ABC and BFO algorithms converge to optimal fuel cost with reduced computational time when compared to PSO for the two example problems considered.
ISDD: A computational model of particle sedimentation, diffusion and target cell dosimetry for in vitro toxicity studies
Paul M Hinderliter, Kevin R Minard, Galya Orr, William B Chrisler, Brian D Thrall, Joel G Pounds, Justin G Teeguarden
Particle and Fibre Toxicology , 2010, DOI: 10.1186/1743-8977-7-36
Abstract: The In vitro Sedimentation, Diffusion and Dosimetry model (ISDD) was tested against measured transport rates or cellular doses for multiple sizes of polystyrene spheres (20-1100 nm), 35 nm amorphous silica, and large agglomerates of 30 nm iron oxide particles. Overall, without adjusting any parameters, model predicted cellular doses were in close agreement with the experimental data, differing from as little as 5% to as much as three-fold, but in most cases approximately two-fold, within the limits of the accuracy of the measurement systems. Applying the model, we generalize the effects of particle size, particle density, agglomeration state and agglomerate characteristics on target cell dosimetry in vitro.Our results confirm our hypothesis that for liquid-based in vitro systems, the dose-rates and target cell doses for all particles are not equal; they can vary significantly, in direct contrast to the assumption of dose-equivalency implicit in the use of mass-based media concentrations as metrics of exposure for dose-response assessment. The difference between equivalent nominal media concentration exposures on a μg/mL basis and target cell doses on a particle surface area or number basis can be as high as three to six orders of magnitude. As a consequence, in vitro hazard assessments utilizing mass-based exposure metrics have inherently high errors where particle number or surface areas target cells doses are believed to drive response. The gold standard for particle dosimetry for in vitro nanotoxicology studies should be direct experimental measurement of the cellular content of the studied particle. However, where such measurements are impractical, unfeasible, and before such measurements become common, particle dosimetry models such as ISDD provide a valuable, immediately useful alternative, and eventually, an adjunct to such measurements.The rapid pace of introduction of new nanomaterials into commerce, rising human exposure through consumer products, and the
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