Giant Radio Quasars: Composite Optical Spectra

We present the composite optical spectrum for the largest sample of giant radio quasars (GRQs). They represent a rare subclass of radio quasars due to their large projected linear sizes of radio structures, which exceed 0.7 Mpc. To construct the composite spectrum, we combined the optical spectra of 216 GRQs from the Sloan Digital Sky Survey (SDSS). As a result, we obtained the composite spectrum covering the wavelength range from 1400 Å to 7000 Å. We calculated the power-law spectral slope for the GRQ’s composite, obtaining α λ = −1.25, and compared it with that of the smaller-sized radio quasars, as well as with the quasar composite spectrum obtained for a large sample of SDSS quasars. We obtained that the GRQ’s continuum is flatter (redder) than the continuum of comparison quasar samples. We also show that the continuum slope depends on core and total radio luminosity at 1.4 GHz, being steeper for higher radio luminosity bins. Moreover, we found that there is a flattening of the continuum with the increase in the projected linear size of the radio quasar. We show that α λ is orientation-dependent, being steeper for a higher radio core-to-lobe flux density ratio, which is consistent with AGN unified model predictions. For two GRQs, we fit the spectral energy distribution using the X-CIGALE code to compare the consistency of results obtained in the optical part of the electromagnetic spectrum with broadband emission. The parameters obtained from the SED fitting confirmed the larger dust luminosity for the redder optical continuum.

The Blow-Off Impulse Equivalence of Typical Missile Homogeneous Al-Alloy under Multienergy Composite Spectrum Electron Beam and Powerful Pulsed X-ray

The electron beam, one of the most effective approaches to simulate the irradiation effects of powerful pulsed X-ray in the laboratory, plays an important role in simulating the thermodynamic effects of powerful pulsed X-ray. This paper studies the thermodynamics equivalence between multienergy composite spectrum electron beam and blackbody spectrum X-ray, which is helpful to quickly determine the experimental parameters in the simulation experiment. The experimental data of electron beam are extrapolated by numerical calculation, to increase the range of energy flux. Through calculating the blow-off impulse of blackbody spectrum X-ray irradiation, we obtained the curve of X-ray blow-off impulse varying with energy flux, and then found two categories of equivalent relations—equal-energy flux and equal-impulse—by analyzing the calculation results of electron beam and X-ray blow-off impulse. Based on such relations, we could directly or indirectly obtain the results of blackbody spectrum X-ray irradiation blow-off impulse via electron beam experiment.

An Automated Data Fusion-Based Gear Faults Classification Framework in Rotating Machines

The feasibility and usefulness of frequency domain fusion of data from multiple vibration sensors installed on typical industrial rotating machines, based on coherent composite spectrum (CCS) as well as poly-coherent composite spectrum (pCCS) techniques, have been well-iterated by earlier studies. However, all previous endeavours have been limited to rotor faults, thereby raising questions about the proficiency of the approach for classifying faults related to other critical rotating machine components such as gearboxes. Besides the restriction in scope of the founding CCS and pCCS studies on rotor-related faults, their diagnosis approach was manually implemented, which could be unrealistic when faced with routine condition monitoring of multi-component industrial rotating machines, which often entails high-frequency sampling at multiple locations. In order to alleviate these challenges, this paper introduced an automated framework that encompassed feature generation through CCS, data dimensionality reduction through principal component analysis (PCA), and faults classification using artificial neural network (ANN). The outcomes of the automated approach are a set of visualised decision maps representing individually simulated scenarios, which simplifies and illustrates the decision rules of the faults characterisation framework. Additionally, the proposed approach minimises diagnosis-related downtime by allowing asset operators to easily identify anomalies at their incipient stages without necessarily possessing vibration monitoring expertise. Building upon the encouraging results obtained from the preceding part of this approach that was limited to well-known rotor-related faults, the proposed framework was significantly extended to include experimental and open-source gear fault data. The results show that in addition to early established rotor-related faults classification, the approach described here can also effectively and automatically classify gearbox faults, thereby improving the robustness.

Application of Composite Spectrum in Agricultural Machines

Composite spectrum (CS) is a data-fusion technique that reduces the number of spectra to be analyzed, simplifying the analysis process for machine monitoring and fault detection. In this work, vibration signals from five components of a combine harvester (thresher, chopper, straw walkers, sieve box, and engine) are obtained by placing four accelerometers along the combine-harvester chassis in non-optimal locations. Four individual spectra (one from each accelerometer) and three CS (non-coherent, coherent and poly-coherent spectra) from 18 cases are analyzed. The different cases result from the combination of three working conditions of the components—deactivated (off), balanced (healthy), and unbalanced (faulty)—and two speeds—idle and maximum revolutions per minute (RPM). The results showed that (i) the peaks can be identified in the four individual spectra that correspond to the rotational speeds of the five components in the analysis; (ii) the three formulations of the CS retain the relevant information from the individual spectra, thereby reducing the number of spectra required for monitoring and detecting rotating unbalances within a combine harvester; and, (iii) data noise reduction is observed in coherent and poly-coherent CS with respect to the non-coherent CS and the individual spectra. This study demonstrates that the rotating unbalances of various components within agricultural machines, can be detected with a reduced number of accelerometers located in non-optimal positions, and that it is feasible to simplify the monitoring with CS. Overall, the coherent CS may be the best composite spectra formulation in order to monitor and detect rotating unbalances in agricultural machines.

Observations and Study of Byurakan-IRAS Galaxies: Summary

The paper is a summary and general analysis of optical spectroscopic data on 257 Byurakan-IRAS Galaxies (BIG objects) obtained with the BAO 2.6-m, SAO 6-m, OHP 1.93-m telescopes and taken from SDSS spectroscopic database. 149 star-formation regions galaxies, 42 galaxies with active nuclei, and 28 galaxies with a composite spectrum were identified. The spectra of 21 galaxies show signs of emission, but without the possibility of more precise determination of their activity type, 13 galaxies appear to have star formation rates that do not exceed normal, and 3 are absorption galaxies.

Determining Bidirectional Ground Motions for Nonlinear Response History Analysis of Buildings at Far-Field Sites

The current practice for selecting bidirectional ground motions (GM pairs) to conduct nonlinear response history analysis (RHA) of multistory buildings is restricted to those with a symmetric plan. To overcome such limitations, we propose selecting GM pairs to be consistent with a pair of target spectra defined along the structural axes, enabling a unique azimuth to be determined for each GM pair. We develop two new target spectra: (1) the s-GCMS for two horizontal components of GM and (2) the CMS-UHS Composite Spectrum. Based on nonlinear RHAs of buildings with both symmetric and unsymmetric plans, the CMS-UHS Composite Spectrum is shown to be the best alternative to the current practice of utilizing multiple CMSs, because it provides accurate demands with minimal computational effort and can be easily constructed using existing PSHA tools.