The POINT-AGAPE survey: 4 high signal-to-noise microlensing candidates detected towards M 31

We have carried out a survey of the Andromeda galaxy for unresolved microlensing (pixel lensing). We present a subset of four short timescale, high signal-to-noise microlensing candidates found by imposing severe selection criteria: the source flux variation exceeds the flux of an R = 21 magnitude star and the full width at half maximum timescale is less than 25 days. Remarkably, in three out of four cases, we have been able to measure or strongly constrain the Einstein crossing time of the event. One event, which lies projected on the M 31 bulge, is almost certainly due to a stellar lens in the bulge of M 31. The other three candidates can be explained either by stars in M 31 and M 32 or by MACHOs.

A short-timescale candidate microlensing event in the point-agape pixel lensing survey of M31

We report the discovery of a short-duration microlensing candidate in the northern field of the POINT-AGAPE pixel lensing survey toward M31. Almost certainly, the source star has been identified on Hubble Space Telescope archival images, allowing us to infer an Einstein crossing time of t(E) = 10.4 days, a maximum magnification of A(max) similar to 18, and a lens-source proper motion mu (rel) > 0.3 mu as day(-1). The event has a projected separation of 8' from the center of M31, beyond the bulk of the stellar lens population. There are three plausible identifications/locations for the lensing object: a massive compact halo object (MACHO) in either M31 or the Milky Way, or a star in the M31 disk. The most probable mass is 0.06 M-. for an M31 MACHO, 0.02 M-. for a Milky Way MACHO, and 0.2 M-. for an M31 stellar lens. While the stellar interpretation is possible, the MACHO interpretation is the most probable for halo fractions above 20%.

CHARACTERIZATION OF A LASER-PRODUCED PLASMA USING THE TECHNIQUE OF POINT-PROJECTION ABSORPTION-SPECTROSCOPY

The technique of point-projection spectroscopy has been shown to be applicable to the study of expanding aluminum plasmas generated by approximately 80 ps laser pulses incident on massive, aluminum stripe targets of approximately 125-mu-m width. Targets were irradiated at an intensity of 2.5 +/- 0.5 x 10(13) W/cm2 in a line focus geometry and under conditions similar to those of interest in x-ray laser schemes. Hydrogenic and heliumlike aluminum resonance lines were observed in absorption using a quasicontinuous uranium back-lighter plasma. Using a pentaerythrital Bragg crystal as the dispersive element, a resolving power of approximately 3500 was achieved with spatial resolution at the 5-mu-m level in frame times of the order of 100 ps. Reduction of the data for times up to 150 ps after the peak of the incident laser pulse produced estimates of the temperature and ion densities present, as a function of space and time. The one-dimensional Lagrangian hydrodynamic code MEDUSA coupled to the atomic physics non-local-thermodynamic-equilibrium ionized material package was used to simulate the experiment in planar geometry and has been shown to be consistent with the measurements.

Discrete Conditional Phase-type model (DC_Ph) for patient waiting time with a logistic regression component to predict patient admission to hospital

Discrete Conditional Phase-type (DC-Ph) models are a family of models which represent skewed survival data conditioned on specific inter-related discrete variables. The survival data is modeled using a Coxian phase-type distribution which is associated with the inter-related variables using a range of possible data mining approaches such as Bayesian networks (BNs), the Naïve Bayes Classification method and classification regression trees. This paper utilizes the Discrete Conditional Phase-type model (DC-Ph) to explore the modeling of patient waiting times in an Accident and Emergency Department of a UK hospital. The resulting DC-Ph model takes on the form of the Coxian phase-type distribution conditioned on the outcome of a logistic regression model.

A Bayesian network hybrid model for representing accident and emergency waiting times

The paper introduces a new modeling approach that represents the waiting times in an accident and emergency (A&E) department in a UK based national health service (NHS) hospital. The technique uses Bayesian networks to capture the heterogeneity of arriving patients by representing how patient covariates interact to influence their waiting times in the department. Such waiting times have been reviewed by the NHS as a means of investigating the efficiency of A&E departments (emergency rooms) and how they operate. As a result activity targets are now established based on the patient total waiting times with much emphasis on trolley waits.

64-point Fourier transform chip for digital television applications

A 64-point Fourier transform chip is described that performs a forward or inverse, 64-point Fourier transform on complex two's complement data supplied at a rate of 13.5MHz and can operate at clock rates of up to 40MHz, under worst-case conditions. It uses a 0.6µm double-level metal CMOS technology, contains 535k transistors and uses an internal 3.3V power supply. It has an area of 7.8×8mm, dissipates 0.9W, has 48 pins and is housed in a 84 pin PLCC plastic package. The chip is based on a FFT architecture developed from first principles through a detailed investigation of the structure of the relevant DFT matrix and through mapping repetitive blocks within this matrix onto a regular silicon structure.