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We present a general method, the &test, which establishes functional dependencies given a sequence of measurements. The approach is based on calculating conditional probabilities from vector component distances. Imposing the requirement of continuity of the underlying function, the obtained values of the conditional probabilities carry information on the embedding dimension and variable dependencies. The power of the method is illustrated on synthetic time-series with different time-lag dependencies and noise levels and on the sunspot data. The virtue of the method for preprocessing data in the context of feedforward neural networks is demonstrated. Also, its applicability for tracking residual errors in output units is stressed.

We present the results of an analysis of high-energy pp and pp scattering based on a model in which the energy dependence of very-high-energy processes is driven by semihard scatterings of quarks and gluons in the nucleons. We show in particular that parton-parton scattering processes at small x drive not only the observed increases in total, elastic-, and inelastic-scattering cross sections and the forward slope parameter, but also, through analyticity, a rapid increase in the ratio of the real to the imaginary part of the forward elastic-scattering amplitude. We also give a careful definition of the inclusive jet cross section and present results on a,,,, average jet multiplicities, and jet-multiplicity fluctuations at high energies.