Neuronal precision and the limits for acoustic signal recognition in a small neuronal network

Recognition of acoustic signals may be impeded by two factors: extrinsic noise, which degrades sounds before they arrive at the receiver’s ears, and intrinsic neuronal noise, which reveals itself in the trial-to-trial variability of the responses to identical sounds. Here we analyzed how these two noise sources affect the recognition of acoustic signals from potential mates in grasshoppers. By progressively corrupting the envelope of a female song, we determined the critical degradation level at which males failed to recognize a courtship call in behavioral experiments. Using the same stimuli, we recorded intracellularly from auditory neurons at three different processing levels, and quantified the corresponding changes in spike train patterns by a spike train metric, which assigns a distance between spike trains. Unexpectedly, for most neurons, intrinsic variability accounted for the main part of the metric distance between spike trains, even at the strongest degradation levels. At consecutive levels of processing, intrinsic variability increased, while the sensitivity to external noise decreased. We followed two approaches to determine critical degradation levels from spike train dissimilarities, and compared the results with the limits of signal recognition measured in behaving animals

Measurement of the inclusive jet cross-sections in proton-proton collisions at root s=8 TeV with the ATLAS detector

Inclusive jet production cross-sections are measured in proton-proton collisions at a centre-of-mass energy of s√=8 TeV recorded by the ATLAS experiment at the Large Hadron Collider at CERN. The total integrated luminosity of the analysed data set amounts to 20.2 fb−1. Double-differential cross-sections are measured for jets defined by the anti-kt jet clustering algorithm with radius parameters of R = 0.4 and R = 0.6 and are presented as a function of the jet transverse momentum, in the range between 70 GeV and 2.5 TeV and in six bins of the absolute jet rapidity, between 0 and 3.0. The measured cross-sections are compared to predictions of quantum chromodynamics, calculated at next-to-leading order in perturbation theory, and corrected for non-perturbative and electroweak effects. The level of agreement with predictions, using a selection of different parton distribution functions for the proton, is quantified. Tensions between the data and the theory predictions are observed

Catalysis engineering: From the catalytic material to the catalytic reactor

This chapter deals with the application of chemical reaction engineering and computational fluid dynamics (CFD) for the analysis and assessment of the interactions between mass and heat transport and chemical reactions. In the first part of the Chapter, we review fundamental concepts of chemical reaction engineering, by showing the potential impact of transport phenomena at the macroscale on the observed functionality of the catalytic material. This includes both the effect of the distribution of the residence times in the reactor and the impact of internal and external transport phenomena. In the second part, we illustrate modern approaches to catalytic reaction engineering based on CFD simulations. In particular, we present the algorithms to couple microkinetic models and kinetic Monte Carlo (kMC) simulations with CFD. The potentialities of the method are assessed by means of a showcase of the CFD-based analysis of a spectroscopic cell for operando experiments. This example clearly shows that transport artifacts in standard equipment may lead to an erroneous interpretation of the experiments if not properly accounted for