Sistema Regional de Información
en línea para Revistas Científicas de América Latina,
el Caribe, España y Portugal

In 2015, at the United Nations forum, the 17 Sustainable Development Goals (SDGs) were approved to be met by 2030. This research aims to know what has been the behavior of productivity and efficiency of the manufacturing industry in the province of Pichincha, Ecuador, and the relationship with the achievement of some goals of the SDG 8, 9 and 12. The research methodology has a quantitative, non-experimental, and longitudinal approach. The information from the economic and financial reports of the manufacturing companies between 2010 and 2018 was processed. The values of efficiency were estimated using the data enveloping analysis technique. With the annual information, the evolution of productivity and efficiency was predicted until 2022. In the nine years, the results show that the productivity indicators and the number of companies that are related to goals 8.2 and 8.3 depend on the size of the companies and would maintain a constant trend until 2022. In addition, in the same period, it was shown that efficiency has had variations, and its trend is to remain constant until 2022, which shows that there will be no progress in meeting the targets 9.4, and 12.2 of the SDGs for this industrial sector.

This research analyzes the impact of investment in Research and Development (R&D) and the number of researchers on the economic growth of some of the economies of the Organization for Economic Cooperation and Development (OECD), for the period 1996-2016. A causality analysis in the sense of Granger is performed and a panel data model is estimated. Data are obtained from the World Bank. There is empirical evidence of bidirectional causality between R&D and GDP per capita, but predominantly R&D Granger-causes GDP. Bidirectional causality is also found between the number of researchers and GDP per capita, but predominantly GDP Granger-causes the number of researchers. While the dynamic panel model of the MGM system in one stage shows that economic growth is positively affected by investment in R&D and the number of researchers. This work differs from others in the following aspects: 1) it considers a sample of 25 OECD countries in the period 1996-2016; 2) there is a greater availability of data, and 3) a dynamic panel data analysis is carried out that allows the use of a greater number of countries, variables and periods.

In this survey article we study the propagation of ultrasonic waves in liquids with gas bubbles. These media are extremely nonlinear. A tiny void fraction changes the properties of the medium drastically. The gas bubbles not only produce high nonlinearity, but also introduce dispersive phenomena and attenuation that can be decisive for the ultrasonic behavior. Several results obtained by a numerical model previously developed (based on the finite-volume method in the space dimension and the finitedifference method in the time domain) are presented here, which allow us to analyze some complex effects associated with this problem. The model solves a differential system that couples the nonlinear oscillations of the bubbles and the acoustic field. We mainly focus on understanding how to enhance the generation of new frequencies (harmonics and subharmonics from a one-frequency source and sum and difference frequencies from a two-frequency source) taking into account some aspects such as cavity type, smoothing and optimization of the medium.

In this paper, we develop successive mathematical models for describing the first and second waves of COVID-19 in Brazil, including the origi[1]nal strain, the two variants with the highest circulation (Gamma and Delta variants) and population vaccination. We started with a a simple initial model and using mathematical modelling techniques and the Heaviside step function, we improved it according to the necessities of describing the disease’s behaviour, both considering the new variants or vaccination. In all cases, we performed computer simulations and compared the obtained curve with real data from the active cases, which reinforced the model’s efficiency in describing the behaviour of the pandemic and highlighted the importance of population vaccination in describing the dynam[1]ics of the infections and reducing cases.

We apply different information quantifiers to the study of COVID-19 time series. First, we analyze how the fact of smoothing the curves alters the informational content of the series, by applying the permutation and wavelet entropies to the series of daily new cases using a sliding-window method. In addition, to study how coupled the curves associated with daily new cases of infections and deaths are, we compute the wavelet coherence. Our results show how information quantifiers can be used to analyze the unpredictable behavior of this pandemic in the short and medium terms.

We aim to fit 4-state sequences of DNA characters from three species to a tripod tree, whose evolutionary model is Jukes-Cantor. For this purpose, we adapt the closest tree method used in the fit of 2-state sequences coming from four species to a quartet, where the states are purines and pyrimidines and the evolutionary model is CFN. The adaptation requires a multi stage methodology called ‘reduction process’. We take the frequencies of 2-state character patterns on the quartet as parameters and search for solutions to the fit.

We show that codimension 1 distributions with at most isolated singularities on threefold hypersurfaces Xd ⊂ P4 of degree d provide interesting examples of stable rank 2 reflexive sheaves. When d ≤ 5, these sheaves can be regarded as smooth points within an irreducible component of the moduli space of stable reflexive sheaves. Our second goal goes in the reverse direction: we start from a well-known family of stable locally free sheaves and provide examples of codimension 1 distributions of local complete intersection type on Xd.

Different countries used the growth Gompertz function at the beginning of the COVID-19 pandemic to model the number of cumulative infected cases since it provides reasonable results. Such a model allows only one mode, but the pandemic evolution has exhibited a multimodal behavior due to the different waves and variants of the COVID-19 virus. Thus, Gompertz’s classical growth model is not well suited to describe a long pandemic with different virus variants. This work presents generalizations of the Gompertz model that can reproduce a multimodal behavior to model the dynamics of infected cases. The models are applied to COVID-19 data from Nuevo Leon, Mexico.

A negative solution to Hilbert’s tenth problem for the ring of integers OF of a number field F would follow if Z were Diophantine in OF. Denef and Lipshitz conjectured that the latter occurs for every number field F. In this note we show that the conjecture of Denef and Lipshitz is a consequence of a well-known conjecture on elliptic surfaces.

This research study aims to obtain empirical evidence to the influence of company characteristics (firm size, profitability, leverage and liquidity) and good corporate governance (audit committee, board size, and public ownership structure) toward the risk management disclosure. This research study uses the Index of Enterprise Risk Management (IERM) as the parameter of risk management disclosure. This research study uses secondary data, the population of Property and Real Estate companies listed on the Indonesia Stock Exchange (IDX) in 2013-2015. The sampling technique was conducted using purposive sampling, which produces 70 listed companies’ samples during the years of observation. The analytical method of this study uses multiple regression analysis of Econometric views 8. This research study concludes that partial testing shows that firm size, audit committees and board size are positively and significantly related to risk management disclosure. Profitability, leverage, liquidity have no significant influence on risk management disclosure. Hypothesis testing uses multiple linear regressions. Data are obtained using Eviews version 8 software application. Data analysis model done with multiple regression model aiming to examine the influence of audit committee size, board of commissioner size, firm size, profitability, and leverage toward risk management disclosure Based on the research study result that has been analyzed statistically using multiple linear regressions, it can be concluded that variables of firm size, leverage, audit committee, and board of commissioner size positively and significantly affect corporate risk management disclosure.  Meanwhile, variables of profitability, liquidity, and public ownership do not positively and significantly affect risk management disclosure.