Financial Instruments to Improve the Competitiveness of Enterprises

Against the background of globalization and the deepening struggle for international markets, competitiveness is among the most important indicators describing the performance of an enterprise. The purpose of this study is to create a methodology for assessing the level of competitiveness of enterprises. The proposed methodology is based on a dual model integrating the parametric and nonparametric methods for assessing the effect of using the financial mechanisms of competitiveness management. This study relies on regression and factor analysis. The focus is on the competitiveness level of 12 agricultural enterprises operating in Russia. Among the studied enterprises, the most competitive one is the enterprise that makes the best use of available resources when moving toward the desired amount of income (profit) and minimum costs. The proposed approach allows not only to assess the true level of competitiveness of an enterprise but also to identify reserves it can exploit to improve its competitiveness using financial instruments. At the same time, the stochastic dual approach proved to be a better choice in developing a competitiveness strategy.

Nonparametric extrapolation of extreme quantiles: a comparison study

The extrapolation of quantiles beyond or below the largest or smallest observation plays an important role in hydrological practice, design of hydraulic structures, water resources management, or risk assessment. Traditionally, extreme quantiles are obtained using parametric methods that require to make an a priori assumption about the distribution that generated the data. This approach has several limitations mainly when applied to the tails of the distribution. Semiparametric or nonparametric methods, on the other hand, allow more flexibility and they may overcome the problems of the parametric approach. Therefore, we present here a comparison between three selected semi/nonparametric methods, namely the methods of Hutson (Stat and Comput, 12(4):331–338, 2002) and Scholz (Nonparametric tail extrapolation. Tech. Rep. ISSTECH-95-014, Boeing Information and Support Services, Seattle, WA, United States of America, 1995) and kernel density estimation. While the first and third methods have already applications in hydrology, Scholz (Nonparametric tail extrapolation. Tech. Rep. ISSTECH-95-014, Boeing Information and Support Services, Seattle, WA, United States of America, 1995) is proposed in this context for the first time. After describing the methods and their applications in hydrology, we compare their performance for different sample lengths and return periods. We use synthetic samples extracted from four distributions whose maxima belong to the Gumbel, Weibull, and Fréchet domain of attraction. Then, the same methods are applied to a real precipitation dataset and compared with a parametric approach. Eventually, a detailed discussion of the results is presented to guide researchers in the choice of the most suitable method. None of the three methods, in fact, outperforms the others; performances, instead, vary greatly with distribution type, return period, and sample size.

Reference intervals for electrophoretograms obtained by capillary electrophoresis of dialyzed urine from healthy dogs

Electrophoresis of urine to evaluate protein fractions in dogs with proteinuria to differentiate glomerular from tubular damage has increased in recent years; however, capillary electrophoresis (CE) of urine has not been reported in a study of > 40 healthy animals, to our knowledge. We aimed to establish reference intervals (RIs) for the urine protein fractions obtained by CE of urine from healthy dogs. We obtained urine samples from 123 clinically healthy dogs of both sexes between December 2016 and April 2019; urine was frozen until CE was performed. The electrophoretic patterns obtained were divided into 5 protein fractions, and RIs were established in percentages and absolute values using nonparametric methods. RIs were obtained for the fractions (F) as follows: 5.5 to 56.2% for F1, 3.2 to 16.5% for F2, 3.5 to 16.2% for F3, 17.8 to 69.8% for F4, and 5.1 to 23.9% for F5. These RIs obtained by CE might be useful clinically as a basis for comparison with pathologic samples. Age was a statistically significant factor for F2 ( p = 0.01) and F3 ( p = 0.02), and sex was a statistically significant factor for F1 ( p = 0.03).