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This chapter covers software and hardware requirements that must be met before a cluster can be implemented. The cluster software you select must satisfy certain requirements. If an application cannot run on a server (because of software or hardware problems), the cluster software must be able to switch the applications to another node. The process of migrating a service or application from one server to another within the cluster is called failover and is a basic cluster requirement. A failover should be automatic, quick, and transparent to clients. Several other activities that accompany a failover are described in this chapter. You must make various hardware and operating system related changes before deploying cluster applications. The SCSI IDs of host-based adapters (HBAs) in a SCSI chain must be different. If the cluster must share or export certain directories (using NFS or CIFS) for clients to mount and use the directories, the file system and device drivers that manage the shared directories must have identical driver-related parameters on all cluster members. Most of the commands in this chapter pertain to the Solaris operating system and are provided as an illustration. Cluster concepts and requirements explained in this chapter are generic and apply to all platforms. If you manage several clusters (or even one cluster), implement a standard procedure for all cluster configuration issues. For example, when you change an adapter SCSI ID on one of two hosts in a cluster, do so on the host that has
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Lueking, A , Cahill, D.J. and M llner, S. (2005) Protein biochips: u a new and versatile platform technology for molecular medicine. Drug Discovery Today 10, 789 794. MacBeath, G, and Schreiber, S.L. (2000) Printing proteins as microarrays for high-throughput function determination. Science 289, 1760 1763. Predki, P.F. (2004) Functional protein microarrays: Ripe for discovery. Current Opinion in Chemical Biology 8, 8 13. A review of functional protein arrays. Taussig, M.J. and Landegren, U. (2003) Progress in antibody arrays. Targets 2, 169 176. A review of antibody arrays. Zhu, H., Bilgin, M., Bangham, R., Hall, D., Casamayor, A., Bertone, P., Lan, N., Jansen, R., Bidlingmaier, S., Houfek T. et al., (2001) Global analysis of protein activities using proteome chips. Science 293, 2101 2105. Description of protein chips in activitybased detection of proteins. Zhu, H. and Snyder, M. (2003) Protein chip technology. Current Opinion in Chemical Biology 7, 55 63. Review of protein chip technology.
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Substituting these expressions in (5.61) yields a system of three equations in a, b, and c. Closer examination reveals that both first equations of this system are linear in a and b and nonlinear in c. The third equation is nonlinear in a, b, and c. Example 5.7 shows that maximum likelihood estimation of a, and y using the likelip, hood equations implies finding all solutions of three nonlinear equations in three unknowns and investigating which solution represents the absolute maximum of the likelihood function. Since the equations to be solved are nonlinear, generally no closed-form solution is available. This is the reason why this kind of estimation problem has to be solved by iterative numerical methods. Usually, the numerical method chosen consists of directly maximizing the log-likelihood function instead of solving the likelihood equations for all possible stationary points and selecting the absolute maximum. Suitable numerical optimization methods are described in 6. On the other hand, the alternative problem of estimating the parameters a and p for a known parameter 7 requires the solution of both first equations only. Since these are two linear equations in two unknowns, the solution is closed-form and, generally, unique. Then, the maximum likelihood estimate of the parameters is computed in one step and no iterations are needed. This attractive closed-form and, typically, unique maximum likelihood estimator occurs whenever the observations are normally distributed and, in addition, the expectation model is linear in the parameters. This important special case will be the subject of Subsection 5.4.3. Finally, we mention that parameters like cr and /3 are usually called linear parameters and parameters like 7 nonlinear parameters.
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One can loosely classify the launch trajectories of pellet injection into two categories: HFS (or high- eld side) launch in which the pellet is injected from the inside of the tokamak and LFS (or low- eld side) launch in which the pellet is injected from the outside of the tokamak. It was experimentally observed that the HFS launches of the pellet lead to a signi cantly better fueling ef ciency of the core of the tokamak than the LFS launches. Identifying and quantifying the causal MHD mechanisms in HFS versus LFS pellet launches are the goals of this research. As a frozen pellet of hydrogen or deuterium is injected into the hot plasma tokamak fusion reactor, it is rapidly heated by long mean-free-path electrons streaming along magnetic eld lines, leading to ablation at the frozen pellet surface, with a shield of neutral gas and an ionized high-density plasma cloud around it. This forms a local high- 6 plasmoid, which implies a localized region of high pressure. This can trigger MHD instabilities [26]. Furthermore, the high- plasmoid expands along the magnetic eld lines, and the plasma cloud drifts in the direction of the major radius. This drift was observed in experiments on ASDEX7 Upgrade tokamak, as well as DIII-D8 and JET [16]. Thus, pellets injected on the high- eld side showed a much better penetration and fueling ef ciency than pellets injected on the low- eld side.
35. J. M. Auerbach and R. L. Schmitt, Diode-pumped monolithic Nd:YLF 1.053 m mini-laser and its application to injection seeding, Solid State Lasers, Proc. SPIE 1223, 133 141 (1990). 36. D. C. Hanna, B. Luther-Davies, and R. C. Smith, Single longitudinal mode selection of high power actively Q-switched lasers, Opto-electron. 4, 249 256 (1972). 37. Y. K. Park and R. L. Byer, Electronic linewidth narrowing method for single axial mode operation of Q-switched Nd:YAG lasers, Opt. Commun. 37, 411 416 (1981). 38. W. L. Smith, Nonlinear refractive index, in Handbook of Laser Science and Technology, Vol. 3, M. J. Weber (ed.) CRC Press, Boca Raton, FL (1986), pp. 259 264. 39. L. M. Frantz and J. S. Nodvik, Theory of pulse propagation in a laser ampli er, J. Appl. Phys. 34, 2346 2349 (1963). 40. J. M. Eggleston, L. M. Frantz, and H. Injeyan, Derivation of the Frantz Nodvik equation for zig-zag optical path, slab geometry laser ampli ers, IEEE J. Quantum Electron. 25, 1855 1862 (1989). 41. D. Eimerl, High average power harmonic generation, IEEE J. Quantum Electron. 23, 575 592 (1987). 42. R. V. Lovberg, Eric R. Wooding, and Michael L. Yeoman, Pulse stretching and shape control by compound feedback in a Q-switched ruby laser, IEEE J. Quantum Electron. 11, 17 21 (1975). 43. W. E. Schmid, Pulse stretching in a Q-switched Nd:YAG laser, IEEE J. Quantum Electron. 16, 790 794 (1980). 44. D. C. Jones and D. A. Rockwell, Single-frequency, 500-ns laser pulses generated by a passively Q-switched Nd laser, Appl. Opt. 32, 1547 1550 (1993). 45. J. Harrison, G. A. Rines, and P. F. Moulton, Stable-relaxation-oscillation Nd lasers for long-pulse generation, IEEE J. Quantum Electron. 24, 1181 1187 (1988). 46. J. P. Roberts, K. W. Hosack, A. J. Taylor, J. Weston, and R. N. Ettelbrick, Ef cient frequency-doubled long-pulse generation with a Nd:glass/Nd:YAG oscillator ampli er system, Opt. Lett. 18, 429 431 (1993). 47. A. M. Scott, W. T. Whitney, and M. T. Duignan, Stimulated Brillouin scattering and loop threshold reduction with a 2.1-mm Cr, Tm, Ho-YAG laser, J. Opt. Soc. Am. B 11, 2079 2088 (1994). 48. D. G. Voelz, K. A. Bush, and P. S. Idell, Illumination coherence effects in laser-speckle imaging: Modeling and experimental demonstration, Appl. Opt. 36, 1781 1788 (1997). 49. M. S. Mangir, J. J. Ottusch, D. C. Jones, and D. A. Rockwell, Time-resolved measurements of stimulated-Brillouin-scattering phase jumps, Phys. Rev. Lett. 68, 1702 1705 (1992). 50. K. D. Ridley and A. M. Scott, Phase-locked phase conjugation using a Brillouin loop scheme to eliminate phase uctuations, J. Opt. Soc. Am. B 13, 900 907 (1996). 51. N. G. Basov, V. F. E mkov, I. G. Zubarev, A. V. Kotov, S. I. Mikhailov, and M. G. Smirnov, Inversion of wavefront in SMBS of a depolarized pump, JETP Lett. 28, 197 201 (1978).
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