SIMDET - Version 4 A Parametric Monte Carlo for a TESLA Detector

A new release of the parametric detector Monte Carlo program \verb+SIMDET+ (version 4.01) is now available. We describe the principles of operation and the usage of this program to simulate the response of a detector for the TESLA linear collider. The detector components are implemented according to the TESLA Technical Design Report. All detector component responses are treated in a realistic way using a parametrisation of results from the {\em ab initio} Monte Carlo program \verb+BRAHMS+. Pattern recognition is emulated using a complete cross reference between generated particles and detector response. Also, for charged particles, the covariance matrix and $dE/dx$ information are made available. An idealised energy flow algorithm defines the output of the program, consisting of particles generically classified as electrons, photons, muons, charged and neutral hadrons as well as unresolved clusters. The program parameters adjustable by the user are described in detail. User hooks inside the program and the output data structure are documented.Comment: 30 pages, 7 figure

A new factorization property of the selfdecomposable probability measures

We prove that the convolution of a selfdecomposable distribution with its background driving law is again selfdecomposable if and only if the background driving law is s-selfdecomposable. We will refer to this as the factorization property of a selfdecomposable distribution; let L^f denote the set of all these distributions. The algebraic structure and various characterizations of L^f are studied. Some examples are discussed, the most interesting one being given by the Levy stochastic area integral. A nested family of subclasses L^f_n, n\ge 0, (or a filtration) of the class L^f is given.Comment: Published at http://dx.doi.org/10.1214/009117904000000225 in the Annals of Probability (http://www.imstat.org/aop/) by the Institute of Mathematical Statistics (http://www.imstat.org

Residual Action of Slow Release Systemic Insecticides on \u3ci\u3eRhopalosiphum Padi\u3c/i\u3e (Homoptera: Aphididae) on Wheat

Slow release formulations of acephate and carbofuran encapsulated in pearl corn­ starch or corn flour granules were applied to the soil at seeding time of potted \u27Caldwell\u27 wheat in the laboratory. Dosages of these insecticides were adjusted to a standard of IO kg/ha of a 10 10 granular formulation of carbofuran. The residual action of these insecticide treatments against Rhopalosiphum padi were compared with those obtained with that of carbofuran 150 at corresponding dosages and foliar sprays of solutions of acephate (25 10 EC) at 0.2 10 and carbofuran (4F) at 1.25 10, applied 12 d after seedling emergence. The residual action of carbofuran 150, which controlled R. padi since seedling emergence, lasted 28.5 d. The slow release granular formulations of carbofuran began to provide control (\u3e 50 10 aphid mortality) on days 13.3 and 17.9 after seeding. They controlled the insect until days 31.6 and 35.5 after seeding. The two corresponding granular formulations of acephate began to provide control on days 15.0 and 17.0 after seeding and con­ trolled the aphids until days 31.5 and 32.8 after seeding. The foliar sprays of acephate and carbofuran provided control for 18.3 and 36.2 d from application, respectively. The slow release granular formulations provided control of R. padi, an important vector of barley yellow dwarf virus, during early. stages of wheat development

Nonlinear projective filtering in a data stream

We introduce a modified algorithm to perform nonlinear filtering of a time series by locally linear phase space projections. Unlike previous implementations, the algorithm can be used not only for a posteriori processing but includes the possibility to perform real time filtering in a data stream. The data base that represents the phase space structure generated by the data is updated dynamically. This also allows filtering of non-stationary signals and dynamic parameter adjustment. We discuss exemplary applications, including the real time extraction of the fetal electrocardiogram from abdominal recordings.Comment: 8 page

DDF and Pohlmeyer invariants of (super)string

We show how the Pohlmeyer invariants of the bosonic string are expressible in terms of DDF invariants. Quantization of the DDF observables in the usual way yields a consistent quantization of the algebra of Pohlmeyer invariants. Furthermore it becomes straightforward to generalize the Pohlmeyer invariants to the superstring as well as to all backgrounds which allow a free field realization of the worldsheet theory.Comment: 17 pp, minor typos corrected, references to papers by Isaev and Borodulin added, which contain essentially the same results as reported her

Localization of non-interacting electrons in thin layered disordered systems

Localization of electronic states in disordered thin layered systems with b layers is studied within the Anderson model of localization using the transfer-matrix method and finite-size scaling of the inverse of the smallest Lyapunov exponent. The results support the one-parameter scaling hypothesis for disorder strengths W studied and b=1,...,6. The obtained results for the localization length are in good agreement with both the analytical results of the self-consistent theory of localization and the numerical scaling studies of the two-dimensional Anderson model. The localization length near the band center grows exponentially with b for fixed W but no localization-delocalization transition takes place.Comment: 6 pages, 5 figure

The influence of self-citation corrections on Egghe's g index

The g index was introduced by Leo Egghe as an improvement of Hirsch's index h for measuring the overall citation record of a set of articles. It better takes into account the highly skewed frequency distribution of citations than the h index. I propose to sharpen this g index by excluding the self-citations. I have worked out nine practical cases in physics and compare the h and g values with and without self-citations. As expected, the g index characterizes the data set better than the h index. The influence of the self-citations appears to be more significant for the g index than for the h index.Comment: 9 pages, 2 figures, submitted to Scientometric