Perinatal Hypoxic-Ischemic Encephalopathy

Perinatal hypoxic-ischemic encephalopathy (HIE) is an important cause of brain injury in the newborn and can result in long-term devastating consequences. Perinatal hypoxia is a vital cause of long-term neurologic complications varying from mild behavioural deficits to severe seizure, mental retardation, and/or cerebral palsy in the newborn. In the mammalian developing brain, ongoing research into pathophysiological mechanism of neuronal injury and therapeutic strategy after perinatal hypoxia is still limited. With the advent of promising therapy of hypothermia in HIE, this paper reviews the pathophysiology of HIE and the future potential neuroprotective strategies for clinical potential for hypoxia sufferers

Zero-norm states and stringy symmetries

We identify spacetime symmetry charges of 26D open bosonic string theory from an infinite number of zero-norm states (ZNS) with arbitrary high spin in the old covariant first quantized string spectrum. We give various evidences to support this identification. These include massive sigma-model calculation, Witten string field theory calculation, 2D string theory calculation and, most importantly, three methods of high-energy stringy scattering amplitude calculations. The last calculations explicitly prove Gross's conjectures in 1988 on high energy symmetry of string theory.Comment: 6 pages. Talks presented by Jen-Chi Lee at XXVIII Spanish Relativity Meeting (ERE2005),"A Century of Relativity Physics",Oviedo,Spain,6-10 Sep 2005 and "4th Meeting on constrained Dynamics and Quantum Gravity",Cala Gonone,Sardinia,Italy,12-16 Sep 2005. To appear in the Journal of Physics: Conference Serie

Statistical system identification and applications to seismic response of structures

A pragmatic and versatile statistical system identification framework is presented and applied to seismic response records of structures. The framework is based on the interpretation of probability as a measure of plausibility and on Bayesian statistical inference. Various classical system identification techniques can be derived and viewed as the special cases of the framework. However, the framework can provide a more informative interpretation of the identified optimal model. When the number of sampled input and output data from structures is large, useful asymptotic approximations of the analytical results are available. These asymptotic approximations are incorporated into the framework by introducing the definitions of system identifiability and model identifiability. New asymptotic approximation results are derived for the system un-identifiable case. From the viewpoint of asymptotic approximations, the system identification problem is a non-trivial global optimization problem. Two generalized trajectory methods, the homotopy scheme and the relaxation scheme, are presented which can be combined to provide a very robust numerical procedure for global optimization. Both methods can also be applied to find the roots of a set of nonlinear algebraic equations. Structural model updating is useful because it can be applied to structural health monitoring and is also desirable since the theoretically based stiffness matrix of a structure can be improved by using the measured structural response data. However, no well-accepted solution to this difficult problem has emerged primarily because it is an ill-conditioned and non-unique inverse problem. A single-stage structural model updating approach using the least-squares prediction-error system identification method and a substructuring technique is proposed and applied to simulated and real structural response data