SUPER

Aims. The SINFONI survey for Unveiling the Physics and Effect of Radiative feedback (SUPER) aims to trace and characterise ionised gas outflows and their impact on star formation in a statistical sample of X-ray selected active galactic nuclei (AGN) at z ∼ 2. We present the first SINFONI results for a sample of 21 Type 1 AGN spanning a wide range in bolometric luminosity (log Lbol = 45.4–47.9 erg s−1). The main aims of this paper are to determine the extension of the ionised gas, characterise the occurrence of AGN-driven outflows, and link the properties of such outflows with those of the AGN. Methods. We used adaptive optics-assisted SINFONI observations to trace ionised gas in the extended narrow line region using the [O III] λ5007 line. We classified a target as hosting an outflow if its non-parametric velocity of the [O III] line, w80, was larger than 600 km s−1. We studied the presence of extended emission using dedicated point-spread function (PSF) observations, after modelling the PSF from the Balmer lines originating from the broad line region. Results. We detect outflows in all the Type 1 AGN sample based on the w80 value from the integrated spectrum, which is in the range ∼650–2700 km s−1. There is a clear positive correlation between w80 and the AGN bolometric luminosity (> 99% correlation probability), and the black hole mass (98% correlation probability). A comparison of the PSF and the [O III] radial profile shows that the [O III] emission is spatially resolved for ∼35% of the Type 1 sample and the outflows show an extension up to ∼6 kpc. The relation between maximum velocity and the bolometric luminosity is consistent with model predictions for shocks from an AGN-driven outflow. The escape fraction of the outflowing gas increases with the AGN luminosity, although for most galaxies, this fraction is less than 10%.

An anomaly detection method to improve the intelligent level of smart articles based on multiple group correlation probability models

Purpose The purpose of this paper is to detect abnormal data of complex and sophisticated industrial equipment with sensors quickly and accurately. Due to the rapid development of the Internet of Things, more and more equipment is equipped with sensors, especially more complex and sophisticated industrial equipment is installed with a large number of sensors. A large amount of monitoring data is quickly collected to monitor the operation of the equipment. How to detect abnormal data quickly and accurately has become a challenge. Design/methodology/approach In this paper, the authors propose an approach called Multiple Group Correlation-based Anomaly Detection (MGCAD), which can detect equipment anomaly quickly and accurately. The single-point anomaly degree of equipment and the correlation of each kind of data sequence are modeled by using multi-group correlation probability model (a probability distribution model which is helpful to the anomaly detection of equipment), and the anomaly detection of equipment is realized. Findings The simulation data set experiments based on real data show that MGCAD has better performance than existing methods in processing multiple monitoring data sequences. Originality/value The MGCAD method can detect abnormal data quickly and accurately, promote the intelligent level of smart articles and ultimately help to project the real world into cyber space in CrowdIntell Network.

Characterizations of Hemirings Based on Probability Spaces

The notion of falling fuzzyh-ideals of a hemiring is introduced on the basis of the theory of falling shadows and fuzzy sets. Then the relations between fuzzyh-ideals and falling fuzzyh-ideals are described. In particular, by means of falling fuzzyh-ideals, the charac-terizations ofh-hemiregular hemirings are investigated based on independent (prefect positive correlation) probability spaces.