Holographic and ultrasonic detection of bond flaws in aluminum panels reinforced with boron-epoxy

An experimental investigation was made of the application of holographic interferometry to the nondestructive detection of unbonded areas (flaws) in bonded panels. Flaw detection results were compared with results obtained with an ultrasonic flaw detector. Holography, with panel deformation accomplished by a reduction in ambient pressure, is less sensitive for flaws beneath 5 and 10 plies of boron-epoxy than the ultrasonic method, though it does have its operational advantages. A process for the manufacture of bonded panels which incorporate known unbonded areas was also developed. The unbonded areas were formed without the use of foreign materials, which makes the method suitable for the construction of reference standards for bonded panels whenever needed for the proper setup of ultrasonic flaw-detection instruments

Determination of activation volumes of reversal in perpendicular media

We discuss a method for the determination of activation volumes of reversal in perpendicular media. This method does not require correction for the self-demagnetizing field normally associated with these media. This is achieved by performing time dependence measurements at a constant level of magnetization. From the difference in time taken for the magnetization to decay to a fixed value at two fields-separated by a small increment DeltaH, the activation volume can be determined. We report data for both CoCrPt alloy films and a multilayer film, typical of those materials under consideration for use as perpendicular media. We find activation volumes that are consistent with the hysteresis curves of the materials. The activation volume scales qualitatively with the exchange coupling. The alloy films have significantly lower activation volumes, implying that they would be capable of supporting a higher data density

Time dependence in perpendicular media with a soft underlayer

In this paper we describe measurements of magnetic viscosity or time dependence in magnetic thin films suitable for use as perpendicular recording media. Generally, such effects cannot be measured using conventional magnetometry techniques due to the presence of a thin (0.1 mum) soft underlayer (SUL) in the media necessary to focus the head field. To achieve our results we have developed an ultrastable MOKE magnetometer, the construction of which is described. This has enabled us to measure nominally identical films with and without the presence of the SUL. We find that the presence of the SUL narrows the energy barrier distribution in the perpendicular film increasing the nucleation field (H-n), reducing the coercivity (H-c) and results in an increase in the squareness of the loop. This in turn results in an increase in the magnitude of the viscosity in the region of the H-c but that the range of fields over which the viscosity occurs is reduced

Functional renormalization group study of an eight-band model for the iron arsenides

We investigate the superconducting pairing instabilities of eight-band models for the iron arsenides. Using a functional renormalization group treatment, we determine how the critical energy scale for superconductivity depends on the electronic band structure. Most importantly, if we vary the parameters from values corresponding to LaFeAsO to SmFeAsO, the pairing scale is strongly enhanced, in accordance with the experimental observation. We analyze the reasons for this trend and compare the results of the eight-band approach to those found using five-band models.Comment: 11 pages, 10 figure

Optical properties of Southern Hemisphere aerosols: Report of the joint CSIRO/NASA study

This study was made in support of the LAWS and GLOBE programs, which aim to design a suitable Doppler lidar system for measuring global winds from a satellite. Observations were taken from 5 deg S to 45 deg S along and off the E and SE Australian coast, thus obtaining representative samples over a large latitude range. Observations were made between 0 and 6 km altitude of aerosol physical and chemical properties in situ from the CSIRO F-27 aircraft; of lidar backscatter coefficients at 10.6 micron wavelength from the F-27 aircraft; of lidar backscatter profiles at 0.694 microns at Sale, SE Australia; and of lidar backscatter profiles at 0.532 microns at Cowley Beach, NE Australia. Both calculations and observations in the free troposphere gave a backscatter coefficient of 1-2 x 10 to the -11/m/sr at 10.6 microns, although the accuracies of the instruments were marginal at this level. Equivalent figures were 2-8 x 10 to the -9/m/sr (aerosol) and 9 x 10 to the -9 to 2 x 10 to the -8/m/sr (lidar) at 0.694 microns wavelength at Sale; and 3.7 x 10 to the -9/m/sr (aerosol) and 10 to the -8 to 10 to the -7/m/sr (lidar) at 0.532 microns wavelength at Cowley Beach. The measured backscatter coefficients at 0.694 and 0.532 microns were consistently higher than the values calculated from aerosol size distributions by factors of typically 2 to 10

Forecasting time series by means of evolutionary algorithms

Proceeding of: 8th International Conference in Parallel Problem Solving from Nature - PPSN VIII , Birmingham, UK, September 18-22, 2004.The time series forecast is a very complex problem, consisting in predicting the behaviour of a data series with only the information of the previous sequence. There is many physical and artificial phenomenon that can be described by time series. The prediction of such phenomenon could be very complex. For instance, in the case of tide forecast, unusually high tides, or sea surges, result from a combination of chaotic climatic elements in conjunction with the more normal, periodic, tidal systems associated with a particular area. Too much variables influence the behaviour of the water level. Our problem is not only to find prediction rules, we also need to discard the noise and select the representative data. Our objective is to generate a set of prediction rules. There are many methods tying to achieve good predictions. In most of the cases this methods look for general rules that are able to predict the whole series. The problem is that usually the time series has local behaviours that dont allow a good level of prediction when using general rules. In this work we present a method for finding local rules able to predict only some zones of the series but achieving better level prediction. This method is based on the evolution of set of rules genetically codified, and following the Michigan approach. For evaluating the proposal, two different domains have been used: an artificial domain widely use in the bibliography (Mackey-Glass series) and a time series corresponding to a natural phenomenon, the water level in Venice Lagoon.Investigation supported by the Spanish Ministry of Science and Technology through the TRACER project under contract TIC2002-04498-C05-

Lessons from cardiac transplantation in infancy

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73539/1/j.1399-3046.2009.01143.x.pd

Inhibition in multiclass classification

The role of inhibition is investigated in a multiclass support vector machine formalism inspired by the brain structure of insects. The so-called mushroom bodies have a set of output neurons, or classification functions, that compete with each other to encode a particular input. Strongly active output neurons depress or inhibit the remaining outputs without knowing which is correct or incorrect. Accordingly, we propose to use a classification function that embodies unselective inhibition and train it in the large margin classifier framework. Inhibition leads to more robust classifiers in the sense that they perform better on larger areas of appropriate hyperparameters when assessed with leave-one-out strategies. We also show that the classifier with inhibition is a tight bound to probabilistic exponential models and is Bayes consistent for 3-class problems. These properties make this approach useful for data sets with a limited number of labeled examples. For larger data sets, there is no significant comparative advantage to other multiclass SVM approaches

Inhibition in multiclass classification

The role of inhibition is investigated in a multiclass support vector machine formalism inspired by the brain structure of insects. The so-called mushroom bodies have a set of output neurons, or classification functions, that compete with each other to encode a particular input. Strongly active output neurons depress or inhibit the remaining outputs without knowing which is correct or incorrect. Accordingly, we propose to use a classification function that embodies unselective inhibition and train it in the large margin classifier framework. Inhibition leads to more robust classifiers in the sense that they perform better on larger areas of appropriate hyperparameters when assessed with leave-one-out strategies. We also show that the classifier with inhibition is a tight bound to probabilistic exponential models and is Bayes consistent for 3-class problems. These properties make this approach useful for data sets with a limited number of labeled examples. For larger data sets, there is no significant comparative advantage to other multiclass SVM approaches

Seasonality of fire weather strongly influences fire regimes in south Florida savanna-grassland landscapes

Fire seasonality, an important characteristic of fire regimes, commonly is delineated using seasons based on single weather variables (rainfall or temperature). We used nonparametric cluster analyses of a 17-year (1993-2009) data set of weather variables that influence likelihoods and spread of fires (relative humidity, air temperature, solar radiation, wind speed, soil moisture) to explore seasonality of fire in pine savanna-grassland landscapes at the Avon Park Air Force Range in southern Florida. A four-variable, three-season model explained more variation within fire weather variables than models with more seasons. The three-season model also delineated intra-annual timing of fire more accurately than a conventional rainfall-based two-season model. Two seasons coincided roughly with dry and wet seasons based on rainfall. The third season, which we labeled the fire season, occurred between dry and wet seasons and was characterized by fire-promoting conditions present annually: drought, intense solar radiation, low humidity, and warm air temperatures. Fine fuels consisting of variable combinations of pyrogenic pine needles, abundant C4 grasses, and flammable shrubs, coupled with low soil moisture, and lightning ignitions early in the fire season facilitate natural landscape-scale wildfires that burn uplands and across wetlands. We related our three season model to fires with different ignition sources (lightning, military missions, and prescribed fires) over a 13-year period with fire records (1997-2009). Largest wildfires originate from lightning and military ignitions that occur within the early fire season substantially prior to the peak of lightning strikes in the wet season. Prescribed ignitions, in contrast, largely occur outside the fire season. Our delineation of a pronounced fire season provides insight into the extent to which different human-derived fire regimes mimic lightning fire regimes. Delineation of a fire season associated with timing of natural lightning ignitions should be useful as a basis for ecological fire management of humid savanna-grassland landscapes worldwide. Funding: The Avon Park Air Force Range (Department of Defense, United States Air Force) provided funding for most of this study. Additional support for this study was provided through NSF Award 0950302 (WJP, PI). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript