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The differences between Narrow Line Seyfert 1 galaxies (NLS1s) and Broad Line AGNs (BLAGNs) are not completely understood; it is thought that they may have different inclinations and/or physical characteristics. The FWHM(H β)–luminosities correlations are found for NLS1s and their origin is the matter of debate. Here we investigated the spectroscopic parameters and their correlations considering a dusty, cone model of AGN. We apply a simple conical dust distribution (spreading out of broad line region, BLR), assuming that the observed surface of the model is in a good correlation with MIR emission. The dusty cone model in combination with a BLR provides the possibility to estimate luminosity dependence on the cone inclination. The FWHM(H β)–luminosities correlations obtained from model in comparison with observational data show similarities which may indicate the influence of AGN inclination and structure to this correlation. An alternative explanation for FWHM(H β)–luminosities correlations is the selection effect by the black hole mass. These FWHM(H β)–luminosities correlations may be related to the starburst in AGNs, as well. The distinction between spectral properties of the NLS1s and BLAGNs could be caused by multiple effects: beside physical differences between NLS1s and BLAGNs (NLS1s have lighter black hole mass than BLAGNs), inclination of the conical AGN geometry may have important role as well, where NLS1s may be seen in lower inclination angles. [ABSTRACT FROM AUTHOR]

This paper explores predicting early signals of business failure using modern models for bankruptcy prediction. It reviews how continuous operations enhance market value, strengthening competitiveness and reputation among stakeholders. The study involves medium and large companies in the Montenegrin market from 2015 to 2020, comprising 30 bankrupt and 70 financially stable firms. Logistic regression is also employed to create a logit model for early detection of bankruptcy signals in companies. This research establishes the empirical validity of modern models in predicting business failure in the Montenegrin market, particularly through logistic regression. Significant indicators, such as the Degree of Indebtedness (DI) and turnover ratio of business assets (TR), exhibit strong predictive power with a p-value less than 0.001 according to Likelihood ratio tests. The paper underscores the potential benefits of bankruptcy prediction for both internal and external stakeholders, especially investors, in enhancing the competitiveness of Montenegro's large and medium-sized companies. Notably, the research contributes by bridging the gap between theory and practice in Montenegro, as bankruptcy prediction models have not been extensively applied in the market. The authors suggest the possible applicability of the created logit model to neighboring countries with similar economic development levels. In that sense, the concept of predicting bankruptcy is positioned as integral to corporate strategy, impacting the overall reduction of bankruptcies. The paper concludes by highlighting its role as a foundation for future research, addressing the literature gap in the application of bankruptcy prediction models in Montenegro. The created logit model, tailored to the specific needs of Montenegrin companies, is presented as an original contribution, emphasizing its potential to strengthen the competitiveness of companies in the market., Competing Interests: NO authors have competing interests Enter: The authors have declared that no competing interests exist., (Copyright: © 2024 Vukčević et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)