The ATLAS experiment is preparing for data taking at 14 TeV collision energy. A rich discovery physics program is being prepared in addition to the detailed study of Standard Model processes which will be produced in abundance. The ATLAS multi-level trigger system is designed to accept one event in 2 • 10B to enable the selection of rare and unusual physics events. The ATLAS calorimeter system is a precise instrument, which includes liquid Argon electromagnetic and hadronic components as well as a scintillator-tile hadronic calorimeter. All these components are used in the various levels of the trigger system. A wide physics coverage is ensured by inclusively selecting events with candidate electrons, photons, taus, jets or those with large missing transverse energy. The commissioning of the trigger system is being performed with cosmic ray events and by replaying simulated Monte Carlo events through the trigger and data acquisition system.

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Pulmonary tuberculosis (PTB) remains a worldwide public health problem. Diagnostic algorithms to identify the best combination of diagnostic tests for PTB in each setting are needed for resource optimization. We developed one artificial neural network model for classification (multilayer perceptron-MLP) and another risk group assignment (self-organizing map-SOM) for PTB in hospitalized patients in a high complexity hospital in Rio de Janeiro City, using clinical and radiologic data collected from 315 presumed PTB cases admitted to isolation rooms from March 2003 to December 2004 (TB prevalence = 21.5 %). The MLP model included 7 variables-radiologic classification, age, gender, cough, night sweats, weight loss and anorexia. The sensitivity of the MLP model was 96.0 % (95 % CI ±2.0), the specificity was 89.0 % (95 % CI ±2.0), the positive predictive value was 72.5 % (95 % CI ±3.5) and the negative predictive value was 98.5 % (95 % CI ±0.5). The variable with the highest discriminative power was the radiologic classification. The high negative predictive value found in the MLP model suggests that the use of this model at the moment of hospital admission is safe. SOM model was able to correctly assign high-, medium- and low-risk groups to patients. If prospective validation in other series is confirmed, these models can become a tool for decision-making in tertiary health facilities in countries with limited resources.

This article presents the base-line design and implementation of the ATLAS Trigger and Data Acquisition system, in particular the Data Flow and High Level Trigger components. The status of the installation and commissioning of the system is also presented.

The ATLAS DAQ system online configurations database service challenge Igor.Soloviev@cern.chAbstract. This paper describes challenging requirements on the configuration service for the ATLAS experiment at CERN. It presents the status of the implementation and testing one year before the start of data taking, providing details of: the capabilities of underlying OKS object manager to store and to archive configuration descriptions, its user and programming interfaces; the organization of configuration descriptions for different types of data taking runs and combinations or participating sub-detectors; the scalable architecture to support simultaneous access to the service by thousands of processes during the online configuration stage of ATLAS; the experience with the usage of the configuration service during large scale tests, test beam, commissioning and technical runs. The paper also presents pro and contra of the chosen object-oriented implementation comparing with solutions based on pure relational database technologies, and explains why after several years of usage we continue with our approach. IntroductionThe configurations database service described in the paper is a part of the ATLAS High-Level Trigger, Data Acquisition (DAQ) and Controls system [1]. It is used to provide the overall description of the DAQ system and partial description of the trigger and detectors software and hardware. Such descriptions cover the control-oriented configuration of all ATLAS processes running during data taking (including information such as: which parts of the ATLAS systems and detectors are participating in a given run, when and where processes shall be started, what run-time environment to be created for each of them, how to check status of running processes and to recover run-time errors, when and in what order to shut down running processes, etc.) and provide configuration parameters for many of them (overall DAQ data-flow configuration, online monitoring configuration, connectivity and parameters for various DAQ, trigger and detector modules, chips and channels).

A detecção e a classificação de um navio podem ser feitas através do ruído irradiado pelo mesmo. Este ruído é conseqüência das vibrações das máquinas do seu interior e se propaga por longas distâncias na água. Neste trabalho, um sistema de classificação de navios foi implementado utilizando uma rede neural atuando sobre informação espectral. Como a dimensão do espaço de entrada de dados é bastante elevada (557 amostras), realizou-se a compactação deste espaço através de componentes principais não-lineares, a fim de reduzir a complexidade da rede neural de classificação. Utilizando apenas 20 componentes não-lineares, uma eficiência de 90.0% foi obtida, enquanto que, se utilizássemos componentes principais lineares, seriam necessárias 48 componentes para atingir um nível similar de eficiência de classificação. Projetando-se a informação espectral em 33 componentes não-lineares, a eficiência de classificação se eleva para 94.0%.

Abstract-Aiming at coping with LHC high event rate, the ATLAS collaboration has been designing a sophisticated threelevel online triggering system. A significant number of interesting events decays into electrons, which have to be identified from a huge background noise. This work proposes a highly-efficient L2 electron / jet discrimination algorithm based on artificial neural processing fed from preprocessed calorimeter information. The feature extraction part of the proposed system provides a ring structure for data description. Energy normalization is later applied to the rings, making the proposed system usable for a broad energy spectrum. Envisaging data compaction, Principal Component Analysis and Principal Component of Discrimination are compared in terms of both compaction rates and classification efficiency. For the pattern recognition section, a fullyconnected feedforward artificial neural network was employed. The proposed algorithm was able to achieve an electron detection efficiency of 96% for a false alarm of 7%.