Presence Of Negative Values Research Articles

Analysis of Forest Use (Including Forest Income Generation) in the Context of the Existing Regulatory Framework

The relevance. Trends in achieving sustainable development of the forestry economy and determining future prospects for the industry set clear goals for decision-making, which is possible with reliable and complete information about the achieved indicators of the forestry sector. There is a need to analyze the use of Russian forests, as well as consider the main directions of forest management, the dynamics of budget revenues, the ratio of payments for the lease of forest areas and the cost of products of industries that use forest resources in their activities. The purpose is to study and analyze the use of forests in the Russian Federation (including the generation of forest income) under the existing regulatory framework for the period 2016-2021. The objectives is to justify the need to modernize existing approaches to determining the value of forest resources. Methodology. When conducting the study, we used official statistical reference books of the Federal State Statistics Service of the Russian Federation, materials from reports of the Ministry of Economic Development, and materials from periodicals. Results. Based on the analysis described above, despite the steady increase in payments to the budget system from the use of forests, their share relative to the cost of products and services of enterprises using forest resources remains insignificant and weakly correlates with changes in activity volumes. Conclusions. Based on the analysis of the use of forests in the Russian Federation (including the generation of forest income) under the existing regulatory framework, it was established that only in a number of federal districts is there stability in the share of forest leases in the structure of the cost of placement. The current situation demonstrates that there is a discrepancy between the assessment of rental costs and the benefits that forest users receive from the exploitation of recreational resources. The presence of negative values actually demonstrates an increase in the benefits of forest users, which is not commensurate with the fees for the forest plot.

Input/Output Variables Selection in Data Envelopment Analysis: A Shannon Entropy Approach

The purpose of this study is to provide an efficient method for the selection of input–output indicators in the data envelopment analysis (DEA) approach, in order to improve the discriminatory power of the DEA method in the evaluation process and performance analysis of homogeneous decision-making units (DMUs) in the presence of negative values and data. For this purpose, the Shannon entropy technique is used as one of the most important methods for determining the weight of indicators. Moreover, due to the presence of negative data in some indicators, the range directional measure (RDM) model is used as the basic model of the research. Finally, to demonstrate the applicability of the proposed approach, the food and beverage industry has been selected from the Tehran stock exchange (TSE) as a case study, and data related to 15 stocks have been extracted from this industry. The numerical and experimental results indicate the efficacy of the hybrid data envelopment analysis–Shannon entropy (DEASE) approach to evaluate stocks under negative data. Furthermore, the discriminatory power of the proposed DEASE approach is greater than that of a classical DEA model.

Changes in wealth distribution in Italy (2002-2012) and who gained from the Great Recession

The aim of the paper is to analyze changes in families’ assets between 2002 and 2012; to measure changes in the degree of inequality; and to identify which social groups (or classes) have gained from these changes, using the decomposition procedure of the Gini concentration ratio proposed by Dagum (1997). The paper introduces two important methodological innovations. First, the definition of household wealth employed here is net wealth minus the value of the household’s home (if owned). Second, we develop a new method for computing the Gini coefficient in presence of negative values, and for decomposing it. JEL Classification: D10, D31

Changes in Wealth Distribution in Italy (2002-2012): Who Gained from the Great Recession

The aim of the paper is to analyze changes in families’ assets between 2002 and 2012; to measure changes in the degree of inequality; and to identify which social groups (or classes) have gained from these changes, using the decomposition procedure of the Gini concentration ratio proposed by Dagum (1997). The paper introduces two important methodological innovations. First, the definition of household wealth employed here is net wealth minus the value of the household’s home (if owned). Second, we develop a new method for computing the Gini coefficient in presence of negative values, and for decomposing it.

A New Distribution Family for Microarray Data.

The traditional approach with microarray data has been to apply transformations that approximately normalize them, with the drawback of losing the original scale. The alternative standpoint taken here is to search for models that fit the data, characterized by the presence of negative values, preserving their scale; one advantage of this strategy is that it facilitates a direct interpretation of the results. A new family of distributions named gpower-normal indexed by is introduced and it is proven that these variables become normal or truncated normal when a suitable gpower transformation is applied. Expressions are given for moments and quantiles, in terms of the truncated normal density. This new family can be used to model asymmetric data that include non-positive values, as required for microarray analysis. Moreover, it has been proven that the gpower-normal family is a special case of pseudo-dispersion models, inheriting all the good properties of these models, such as asymptotic normality for small variances. A combined maximum likelihood method is proposed to estimate the model parameters, and it is applied to microarray and contamination data. R codes are available from the authors upon request.

Time‐resolved statistics of photon pairs in two‐cavity Josephson photonics

We analyze the creation and emission of pairs of highly nonclassical microwave photons in a setup where a voltage‐biased Josephson junction is connected in series to two electromagnetic oscillators. Tuning the external voltage such that the Josephson frequency equals the sum of the two mode frequencies, each tunneling Cooper pair creates one additional photon in both of the two oscillators. The time‐resolved statistics of photon emission events from the two oscillators is investigated by means of single‐ and cross‐oscillator variants of the second‐order correlation function and the waiting‐time distribution . They provide insight into the strongly correlated quantum dynamics of the two oscillator subsystems and reveal a rich variety of quantum features of light including strong antibunching and the presence of negative values in the Wigner function.

Investigation of a frequency-selective surface based on a planar mushroom-shaped metamaterial with an electronically tunable stopband

A design of a band-stop frequency-selective surface based on a planar mushroom-shaped metamaterial with an electronically tunable stopband has been proposed and investigated. The results of numerical simulation and experimental measurements of the parameters of a finite section of the metamaterial with mounted varicaps are presented. These results illustrate the shift of the structure stopband occurring simultaneously with a change in the capacitance of the lumped elements. Frequency dependences of the inherent dielectric parameters have been obtained. They indicate the presence of negative values of the effective permittivity and effective permeability of the proposed structure and the presence of the surface impedance that significantly exceeds the free-space characteristic impedance and is frequency-tunable.

Generalized unmixing model for multispectral flow cytometry utilizing nonsquare compensation matrices

Multispectral and hyperspectral flow cytometry (FC) instruments allow measurement of fluorescence or Raman spectra from single cells in flow. As with conventional FC, spectral overlap results in the measured signal in any given detector being a mixture of signals from multiple labels present in the analyzed cells. In contrast to traditional polychromatic FC, these devices utilize a number of detectors (or channels in multispectral detector arrays) that is larger than the number of labels, and no particular detector is a priori dedicated to the measurement of any particular label. This data-acquisition modality requires a rigorous study and understanding of signal formation as well as unmixing procedures that are employed to estimate labels abundance. The simplest extension of the traditional compensation procedure to multispectral data sets is equivalent to an ordinary least-square (LS) solution for estimating abundance of labels in individual cells. This process is identical to the technique employed for unmixing spectral data in various imaging fields. The present study shows that multispectral FC data violate key assumptions of the LS process, and use of the LS method may lead to unmixing artifacts, such as population distortion (spreading) and the presence of negative values in biomarker abundances. Various alternative unmixing techniques were investigated, including relative-error minimization and variance-stabilization transformations. The most promising results were obtained by performing unmixing using Poisson regression with an identity-link function within a generalized linear model framework. This formulation accounts for the presence of Poisson noise in the model of signal formation and subsequently leads to superior unmixing results, particularly for dim fluorescent populations. The proposed Poisson unmixing technique is demonstrated using simulated 8-channel, 2-fluorochrome data and real 32-channel, 6-fluorochrome data. The quality of unmixing is assessed by computing absolute and relative errors, as well as by calculating the symmetrized Kullback-Leibler divergence between known and approximated populations. These results are applicable to any flow-based system with more detectors than labels where Poisson noise is the dominant contributor to the overall system noise and highlight the fact that explicit incorporation of appropriate noise models is the key to accurately estimating the true label abundance on the cells. © 2013 International Society for Advancement of Cytometry.