The present article introduces a new approach to the Old Russian texts by revealing metrical patterns underlying seemingly prose texts of the chronicle Povest vremennykh let. These patterns proved to be a shared feature of Eastern Slavic oral epic traditions. Thus, ideas of Ivan Franko about metrical character of the chronicles and Ivan Nikiforov’s claim about metrical affi nities of Eastern Slavic epic traditions are developed and enriched by up to date linguistic as well as ethnomusicological observations. Metrical affi nities of certain fragments of the chronicle Povest vremennykh let and Eastern Slavic epic give new clues to the possible persistence of oral epic in written form and consequently broaden the range of Old Russian texts that can be regarded as epic. Poetical epic corpus, enlarged in this way, gives a new relevant context to Slovo o polku Igoreve, authenticity of which can be proven now with more certainty on the basis of metrical affi nities with the fragments of chronicle of presumably oral origin.
In this work, simulation techniques have been implemented to study the sound fields of a multi-configurable performance enclosure by creating computer acoustic 3D-models for each room configuration. The digital models have been tuned by means of an iterative fitting procedure that uses the reverberation times measured on site for unoccupied conditions with the orchestra shell on the stage. The initial virtual acoustic model is validated by comparing the other monaural and binaural acoustic parameters measured in the room in terms of their perception differential threshold. The procedure is applied to the Maestranza Theatre of Seville, built for the Universal Exhibition in 1992. The spatial distribution of the acoustic parameters in the audience area of the venue by measured parameters and simulation mappings enables the establishment of three zones of acoustic comfort, and are corroborated by the values of the Ando-Beranek function which provide a global quality coefficient of each zone.
Noise induced hearing loss (NIHL) as one of the major avoidable occupational related health issues has been studied for decades. To assess NIHL, the excitation pattern (EP) has been considered as one of the mechanisms to estimate the movements of the basilar membrane (BM) in the cochlea. In this study, two auditory filters, dual resonance nonlinear (DRNL) filter and rounded-exponential (ROEX) filter are applied to create two EPs, the velocity EP and the loudness EP respectively. Two noise hazard metrics are proposed based on two proposed EPs to evaluate hazardous levels caused by different types of noise. Moreover Gaussian noise and tone signals are simulated to evaluate performances of the proposed EPs and the noise metrics. The results show that both EPs can reflect the responses of the BM to different types of noise. For Gaussian noise there is a frequency shift between the velocity EP and the loudness EP. The results suggest that both EPs can be used for assessment of NIHL.
A comprehensive characterization of four selected fault distinguishability methods is presented herein. All considered methods are derived from structural residual approaches referring to model-based diagnostics. In particular, these methods are based on a binary diagnostic matrix, fault isolation system, sequences of symptoms, and their combinations. Fault distinguishability issues are discussed based on an example of four pressure vessel system. Substantial benefits are shown in fault distinguishability figures obtained by utilising extended knowledge regarding fault-symptom relation. Finally, the values of three fault distinguishability metrics are calculated for each method. For the case study, the highest score is achieved using the multivalued fault isolation method combined with a diagnosis utilising information regarding the antecedence of symptoms.
In this paper, a modified sound quality evaluation (SQE) model is developed based on combination of an optimized artificial neural network (ANN) and the wavelet packet transform (WPT). The presented SQE model is a signal processing technique, which can be implemented in current microphones for predicting the sound quality. The proposed method extracts objective psychoacoustic metrics including loudness, sharpness, roughness, and tonality from sound samples, by using a special selection of multi-level nodes of the WPT combined with a trained ANN. The model is optimized using the particle swarm optimization (PSO) and the back propagation (BP) algorithms. The obtained results reveal that the proposed model shows the lowest mean square error and the highest correlation with human perception while it has the lowest computational cost compared to those of the other models and software.
The paper presents the results of experimental validation of a set of innovative software services supporting processes of achieving, assessing and maintaining conformance with standards and regulations. The study involved several hospitals implementing the Accreditation Standard promoted by the Polish Ministry of Health. First we introduce NOR-STA services that implement the TRUST-IT methodology of argument management. Then we describe and justify a set of metrics aiming at assessment of the effectiveness and efficiency of the services. Next we present values of the metrics that were built from the data collected. The paper concludes with giving the interpretation and discussing the results of the measurements with respect to the objectives of the validation experiment.