The ALICE detector data link

The ALICE detector data link has been designed to cover all the needs for data transfer between the detector and the data-acquisition system. It is a 1 Gbit/s, full-duplex, multi-purpose fibre optic link that can be used as a medium for the bi-directional transmission of data blocks between the front-end electronics and the data- acquisition system and also for the remote control and test of the front-end electronics, In this paper the concept, the protocol, the specific test tools, the prototypes of the detector data link and the read-out receiver card, their application in the ALICE-TPC test system and the integration with the DATE software are presented. The test results on the performance are also shown. (14 refs)

Reverse-Flow Oxidation Catalyst with Supplemental Fuel Injection for Lean-Burn Natural Gas Engines

The purpose of this research is to demonstrate that the use of a reverse-flow oxidation catalyst reactor (RFOCR), both with and without supplemental fuel injection (SFI), will result in significant reductions of methane (CH4) in a simulated lean-burn natural gas exhaust mixture. Methane reduction is investigated as a function of the directional duration of the exhaust gases through the oxidation catalyst, gas hourly space velocity (GHSV), and exhaust gas temperature. The CH4 catalytic chemical reaction, at an elevated exhaust gas temperature, is an exothermic reaction and elevating the temperature across the catalyst reactor corresponds to an increase in CH4 conversion. Periodically reversing the inlet and outlet exhaust direction through the catalyst traps the heat released from the chemical reaction, raising the overall temperature of the exhaust gas through the RFOCR. This study demonstrates the ability of the RFOCR to trap heat, thereby increasing CH4 oxidation. This ability to trap heat provides a significant advantage over standard unidirectional flow catalytic converters. Additionally, to increase CH4 conversion at relatively low feed temperatures, the injection of a supplemental fuel mixture consisting of carbon monoxide (CO) and hydrogen (H2) was evaluated. The experimental results confirm that, when compared with unidirectional flow, periodically reversing the flow of exhaust mixture through a catalyst reactor can significantly improve CH4 conversion. Results also indicate that the effect of switching time (ST) on CH4 conversion vary significantly with gas hourly space velocity (GHSV) and temperature. Furthermore, results indicate that by introducing supplemental fuel into the feed mixture at low engine operating conditions CH4 conversion is notably improved by elevating the temperature across the catalyst reactor through the combustion of carbon monoxide and hydrogen. However, extended durations of increased CH4 conversion during reverse-flow operations is not possible after supplemental fuel injection is terminated

Mass Spectrometry as a Workhorse for Preclinical Drug Discovery: Special Emphasis on Drug Metabolism and Pharmacokinetics

Mass spectrometry as an instrument is popular, given its sensitivity, selectivity, speed and robustness. In this chapter, we have briefly deliberated on various mass spec platforms, their hardware components and specific applications in preclinical drug discovery with a special emphasis on drug metabolism and pharmacokinetic assays. Basic principle of operation of mass spectrometer and various ionization techniques/mass analyzers was explicitly discussed. Compatibility of mass spectrometers with ultrafast LC and various throughput techniques, enabled evaluation of thousands of compounds with quick turnaround times. Faster generation of results corresponding to in vitro ADME and in vivo pharmacokinetic assays, aid medicinal chemists to refine their combinatorial synthetic chemistry efforts and expedite the lead optimization and identification phases of drug discovery. Mass spectrometer is a powerful tool for both qualitative and quantitative applications. While quantitative applications include measurement of absolute/relative concentrations, qualitative features assist in identification of molecular structures of metabolites and putative biotransformation pathways. Qualitative inputs are more precise and accurate, with the advent of high-resolution mass spectrometry technology. Although, mass spectrometry has many built-in advantages, it also suffers from matrix effects, as the samples analyzed are mostly of biological origin and are complex in nature. In this chapter, we have defined the nature of matrix effects and various approaches by which these matrix effects can be mitigated

Probabilistic Distance for Mixtures of Independent Component Analyzers

© 2018 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] Independent component analysis (ICA) is a blind source separation technique where data are modeled as linear combinations of several independent non-Gaussian sources. The independence and linear restrictions are relaxed using several ICA mixture models (ICAMM) obtaining a two-layer artificial neural network structure. This allows for dependence between sources of different classes, and thus a myriad of multidimensional probability density functions (PDFs) can be accurate modeled. This paper proposes a new probabilistic distance (PDI) between the parameters learned for two ICA mixture models. The PDI is computed explicitly, unlike the popular Kullback-Leibler divergence (KLD) and other similar metrics, removing the need for numerical integration. Furthermore, the PDI is symmetric and bounded within 0 and 1, which enables its use as a posterior probability in fusion approaches. In this work, the PDI is employed for change detection by measuring the distance between two ICA mixture models learned in consecutive time windows. The changes might be associated with relevant states from a process under analysis that are explicitly reflected in the learned ICAMM parameters. The proposed distance was tested in two challenging applications using simulated and real data: (i) detecting flaws in materials using ultrasounds and (ii) detecting changes in electroencephalography signals from humans performing neuropsychological tests. The results demonstrate that the PDI outperforms the KLD in change-detection capabilitiesThis work was supported by the Spanish Administration and European Union under grant TEC2014-58438-R, and Generalitat Valenciana under Grant PROMETEO II/2014/032 and Grant GV/2014/034.Safont Armero, G.; Salazar Afanador, A.; Vergara Domínguez, L.; Gomez, E.; Villanueva, V. (2018). Probabilistic Distance for Mixtures of Independent Component Analyzers. IEEE Transactions on Neural Networks and Learning Systems. 29(4):1161-1173. https://doi.org/10.1109/TNNLS.2017.2663843S1161117329