Search for scalar top quark production in p&pmacr; collisions at sqrt

We have searched for direct production of scalar top quarks at the Collider Detector at Fermilab in 88 pb(-1) of p&pmacr; collisions at sqrt[s] = 1.8 TeV. We assume the scalar top quark decays into either a bottom quark and a chargino or a bottom quark, a lepton, and a scalar neutrino. The event signature for both decay scenarios is a lepton, missing transverse energy, and at least two b-quark jets. For a chargino mass of 90 GeV/c(2) and scalar neutrino masses of at least 40 GeV/c(2), we find no evidence for scalar top production and present upper limits on the production cross section in both decay scenarios.     

Dijet production by double pomeron exchange at the fermilab tevatron

We report the first observation of dijet events with a double Pomeron exchange topology produced in &pmacr;p collisions at sqrt[s] = 1800 GeV. The events are characterized by a leading antiproton, two jets in the central pseudorapidity region, and a large rapidity gap on the outgoing proton side. We present results on jet kinematics and production rates, compare them with corresponding results from single diffractive and inclusive dijet production, and test factorization.     

Semiempirical Modeling with Application of Artificial Neural Networks for the Photocatalytic Reaction of Ethylenediaminetetraacetic Acid (EDTA) over Titanium Oxide (TiO2)

The use of chemometric techniques, full factorial and Doehlert experimental designs, multivariate analysis by MANOVA (=multiple‐way analysis of the variance), and artificial neural networks (ANNs) for the photocatalytic reaction of ethylenediaminetetraacetic acid (EDTA) over TiO₂ in aqueous solution is described. Based on the previous knowledge of the photocatalytic system, variables such as EDTA concentration, photocatalyst concentration, pH, and irradiation time were chosen to build a set of experiments for the analysis. By means of MANOVA, the statistical significance of the individual variables and the inspection of interactions between them were analyzed. By the use of ANNs, correlation plots among variables may help to build a semiempirical modeling for understanding and prediction the behavior of the system, optimizing parameters valuable for further technological applications.     

2D mapping by Kohonen networks of the air quality data from a large city

The 15-variable environmental data (7 concentrations: CO, SO2, O3, NOx, NO, NO2, particulate matter smaller than 10 micron (PM10), and 8 weather data: cloudiness, rainfall, insolation factor (Isfi), temperature, pressure at two locations, and wind intensity with direction) in a period of 45 days with 1-h intervals were extracted from a larger database of concentrations recorded in minute intervals for the same time period. The monitoring site was located in the City of Buenos Aires in a relatively heavy traffic crossroad of two avenues. The data required special pretreatment where the hourly content of rain, wind intensity, wind velocity, and cloudiness were concerned. The new variable named insolation factor (relative UV radiation) calculated on the basis of the general meteorological data, the geographic position of the monitoring site, cloudiness, date, and the time of the recording was composed. The relative intensity of UV radiation was modeled by a Gaussian function, multiplied by a cloudiness factor. Based on the 14-variable input and the 1-variable output (ozone) data, first, the clustering of all 980 data records was made. The top map clustering showing the ozone concentration was related to the maps of all 14 variables. The link between O3 clusters, NO2, and Isfi weight levels is shown and discussed. As a preliminary result of this study some of the most interesting correlations between the maps and remaining variables are given.