Dimensionless fluctuations balance applied to statistics and quantum physics.

This work presents a new method called Dimensionless Fluctuation Balance (DFB), which makes it possible to obtain distributions as solutions of Partial Differential Equations (PDEs). In the first case study, DFB was applied to obtain the Boltzmann PDE, whose solution is a distribution for Boltzmann gas. Following, the Planck photon gas in the Radiation Law, Fermi-Dirac, and Bose-Einstein distributions were also verified as solutions to the Boltzmann PDE. The first case study demonstrates the importance of the Boltzmann PDE and the DFB method, both introduced in this paper. In the second case study, DFB is applied to thermal and entropy energies, naturally resulting in a PDE of Boltzmann's entropy law. Finally, in the third case study, quantum effects were considered. So, when applying DFB with Heisenberg uncertainty relations, a Schrödinger case PDE for free particles and its solution were obtained. This allows for the determination of operators linked to Hamiltonian formalism, which is one way to obtain the Schrödinger equation. These results suggest a wide range of applications for this methodology, including Statistical Physics, Schrödinger's Quantum Mechanics, Thin Films, New Materials Modeling, and Theoretical Physics.

A general class of trimodal distributions: properties and inference.

The modality is an important topic for modelling. Using parametric models is an efficient way when real data set shows trimodality. In this paper, we propose a new class of trimodal probability distributions, that is, probability distributions that have up to three modes. Trimodality itself is achieved by applying a proper transformation to density function of certain continuous probability distributions. At first, we obtain preliminary results for an arbitrary density function g ( x ) and, next, we focus on the Gaussian case, studying trimodal Gaussian model more deeply. The Gaussian distribution is applied to produce the trimodal form of Gaussian known as normal distribution. The tractability of analytical expression of normal distribution and properties of the trimodal normal distribution are important reasons why we choose normal distribution. Furthermore, the existing distributions should be improved to be capable of modelling efficiently when there exists a trimodal form in a data set. After new density function is proposed, estimating its parameters is important. Since Mathematica 12.0 software has optimization tools and important modelling techniques, computational steps are performed using this software. The bootstrapped form of real data sets are applied to show the modelling ability of the proposed distribution when real data sets show trimodality.

A new family of quantile regression models applied to nutritional data.

This paper introduces a new family of quantile regression models whose response variable follows a reparameterized Marshall-Olkin distribution indexed by quantile, scale, and asymmetry parameters. The family has arisen by applying the Marshall-Olkin approach to distributions belonging to the location-scale family. Models of higher flexibility and whose structure is similar to generalized linear models were generated by quantile reparameterization. The maximum likelihood (ML) method is presented for the estimation of the model parameters, and simulation studies evaluated the performance of the ML estimators. The advantages of the family are illustrated through an application to a set of nutritional data, whose results indicate it is a good alternative for modeling slightly asymmetric response variables with support on the real line.

Modeling tropical tuna shifts: An inflated power logit regression approach.

We introduce a new class of zero-or-one inflated power logit (IPL) regression models, which serve as a versatile tool for analyzing bounded continuous data with observations at a boundary. These models are applied to explore the effects of climate changes on the distribution of tropical tuna within the North Atlantic Ocean. Our findings suggest that our modeling approach is adequate and capable of handling the outliers in the data. It exhibited superior performance compared to rival models in both diagnostic analysis and regarding the inference robustness. We offer a user-friendly method for fitting IPL regression models in practical applications.

Identifying Areas of Invasion Risk and Changes in the Ecological Niche Occupied by the Coffee Leaf Miner Leucoptera coffeella (Lepidoptera: Lyonetiidae).

Insects of economic importance such as Leucoptera coffeella can cause high defoliation in plants and reduce crop yields. We aimed to identify changes in the ecological niche and potential zones of the invasion. Occurrence records were obtained from databases and bibliography. WorldClim V2.0 bioclimatic layers were used. For the modeling of the potential distribution, the kuenm R package was used by executing the Maxent algorithm. The potential distribution models suggested greatest environmental suitability extends from Europe, South Asia, and Central and South Africa, showing the "tropical and subtropical moist broadleaf forests" as the ecoregion that presents the greatest probability of the presence of L. coffeella. The potential distribution model projected in the invaded area agrees with the known distribution in the region (America), although the results show that it is occupying environmental spaces not present in the area of origin. This species presented a large proportion of the invaded niche that overlaps the native niche and is colonizing new environmental conditions in the invaded area relative to its native distribution (Africa). This information could be used in the planning of coffee crops on the American continent.

Changes in Neonatal Intensive Care Unit statistics during the COVID-19 pandemic.

BACKGROUND: With the identification of COVID-19 disease in China, a pandemic began that affected health-care systems. The Neonatal Intensive Care Unit (NICU) of the Hospital de Ginecobstetricia del Centro Médico Nacional de Occidente experienced an increase in patient flow as part of the COVID-19 strategy of the Instituto Mexicano del Seguro Social (IMSS). This study aimed to analyze the impact of the COVID-19 pandemic on neonatal care and mortality indicators in our unit. METHODS: We conducted a retrospective study to compare the number of hospital births, pre-term newborns (PTNB), NICU admissions, and deaths. Changes in frequencies between 2019 and 2021 were analyzed using Poisson distribution. Changes in PTNB births, proportion of admissions, and deaths/NICU discharges were analyzed by z-test for two proportions. RESULTS: Between 2019 and 2021, the number of births increased by more than 2-fold. NICU admissions increased from 770 in 2019 to 1045 in 2021 (p < 0.01). The ratio of deaths/discharge from the service was 16.9% in 2019 and 13.1% in 2021 (p = 0.02). CONCLUSIONS: Mortality indicators in the NICU decreased from 2019 to 2021, even with the increase in the number of patients admitted during the COVID-19 pandemic.

INTRODUCCIÓN: Con la identificación de la enfermedad por COVID-19 en China, inició una pandemia que afectó a los sistemas de salud. La Unidad de Cuidados Intensivos Neonatales (UCIN) del Hospital de Ginecobstetricia del Centro Médico Nacional de Occidente del Instituto Mexicano del Seguro Social (IMSS) vio incrementado su flujo de pacientes como parte de la Estrategia COVID-19 del IMSS. El objetivo fue analizar el impacto de la pandemia COVID-19 en los indicadores de atención y mortalidad neonatal en nuestra unidad. MÉTODOS: Se realizó un estudio retrospectivo para comparar el número de nacimientos en el hospital, nacimientos de recién nacidos prematuros (RNPT), ingresos a UCIN y defunciones. Se analizaron los cambios en frecuencias entre los años 2019 a 2021 mediante la distribución de Poisson. Los cambios en nacimientos de RNPT, proporción de ingresos y defunciones/egreso en UCIN se analizaron mediante prueba Z para dos proporciones. RESULTADOS: Entre los años 2019 a 2021, el número de nacimientos incrementó más de 2 veces. Los ingresos a UCIN aumentaron de 770 en 2019, a 1045 en 2021 (p < 0.01). La proporción de defunciones/egreso del servicio fue de 16.9% en 2019, y 13.1% en 2021 (p = 0.02). CONCLUSIONES: Los indicadores de mortalidad en la UCIN disminuyeron de 2019 a 2021, aun con el incremento en el número de pacientes atendidos durante la pandemia COVID-19.

Blood flow effects in a patient with a thoracic aortic endovascular prosthesis.

This work analyzes hemodynamic phenomena within the aorta of two elderly patients and their impact on blood flow behavior, particularly affected by an endovascular prosthesis in one of them (Patient II). Computational Fluid Dynamics (CFD) was utilized for this study, involving measurements of velocity, pressure, and wall shear stress (WSS) at various time points during the third cardiac cycle, at specific positions within two cross sections of the thoracic aorta. The first cross-section (Cross-Section 1, CS1) is located before the initial fluid bifurcation, just before the right subclavian artery. The second cross-section (Cross-Section 2, CS2) is situated immediately after the left subclavian artery. The results reveal that, under regular aortic geometries, velocity and pressure magnitudes follow the principles of fluid dynamics, displaying variations. However, in Patient II, an endoprosthesis near the CS2 and the proximal border of the endoprosthesis significantly disrupts fluid behavior owing to the pulsatile flow. The cross-sectional areas of Patient I are smaller than those of Patient II, leading to higher flow magnitudes. Although in CS1 of Patient I, there is considerable variability in velocity magnitudes, they exhibit a more uniform and predictable transition. In contrast, CS2 of Patient II, where magnitude variation is also high, displays irregular fluid behavior due to the endoprosthesis presence. This cross-section coincides with the border of the fluid bifurcation. Additionally, the irregular geometry caused by endovascular aneurysm repair contributes to flow disruption as the endoprosthesis adjusts to the endothelium, reshaping itself to conform with the vessel wall. In this context, significant alterations in velocity values, pressure differentials fluctuating by up to 10%, and low wall shear stress indicate the pronounced influence of the endovascular prosthesis on blood flow behavior. These flow disturbances, when compounded by the heart rate, can potentially lead to changes in vascular anatomy and displacement, resulting in a disruption of the prosthesis-endothelium continuity and thereby causing clinical complications in the patient.

Evolution of the ecological niche behind the largest disjunct freshwater fish distribution in the world.

Ecological processes that are behind distributions of species that inhabit isolated localities, complex disjunct distributions, remain poorly understood. Traditionally, vicariance and dispersion have been proposed as explanatory mechanisms that drive such distributions. However, to date, our understanding of the ecological processes driving evolution of ecological niches associated with disjunct distributions remains rudimentary. Here, we propose a framework to deconstruct drivers of such distribution using World's most widespread freshwater fish Galaxias maculatus as a model and integrating marine and freshwater environments where its life cycle may occur. Specifically, we assessed ecological and historical factors (Gondwanan vicariance, marine dispersion) and potential dispersion (niche-tracking) that explain its distribution in the Southern Hemisphere. Estimated distribution was consistent with previously reported distribution and mainly driven by temperature and topography in freshwater environments and by primary productivity and nitrate in marine environments. Niche dynamics of G. maculatus provided evidence of synergy between vicariance and marine dispersion as explanatory mechanisms of its disjunct distribution, suggesting that its ecological niche was conserved since approximately 30 Ma ago. This integrated assessment of ecological niche in marine and freshwater environments serves as a generic framework that may be applied to understand processes underpinning complex distributions of diadromous species.

Los procesos ecológicos que subyacen a las distribuciones de especies que habitan en localidades aisladas, distribuciones disjuntas complejas, siguen siendo poco conocidos. Tradicionalmente, se han propuesto la dispersión y la vicarianza como mecanismos explicativos de tales distribuciones. Sin embargo, hasta la fecha, nuestra comprensión de los procesos ecológicos que impulsan la evolución de los nichos ecológicos de distribuciones disjuntas sigue siendo rudimentaria. Aquí proponemos un marco para de­construir los factores que impulsan dicha distribución, utilizando como modelo el pez de agua dulce con distribución más extendida del mundo, Galaxias maculatus, e integrando los entornos marinos y dulceacuícolas en los que se desarrolla su ciclo vital. En concreto, evaluamos los factores ecológicos e históricos (vicarianza gondwánica, dispersión marina) que explican su distribución en el hemisferio sur. La distribución estimada coincide con la descrita anteriormente para la especie y está determinada principalmente por la temperatura y la topografía en ambientes dulceacuícolas, y la productividad primaria y el nitrato en ambientes marinos. La dinámica de nicho de G. maculatus aportó pruebas de la sinergia entre vicarianza y dispersión marina como mecanismos explicativos de su distribución disjunta, lo que sugiere que su nicho ecológico se conservó desde hace aproximadamente 30 Ma. Esta evaluación integrada del nicho ecológico en ambientes marinos y dulceacuícolas puede aplicarse para comprender los procesos que subyacen a las distribuciones complejas de especies diádromas.

Changes in Neonatal Intensive Care Unit statistics during the COVID-19 pandemic / Cambios en las estadísticas de una Terapia Intensiva Neonatal durante la pandemia COVID-19

Abstract Background: With the identification of COVID-19 disease in China, a pandemic began that affected health-care systems. The Neonatal Intensive Care Unit (NICU) of the Hospital de Ginecobstetricia del Centro Médico Nacional de Occidente experienced an increase in patient flow as part of the COVID-19 strategy of the Instituto Mexicano del Seguro Social (IMSS). This study aimed to analyze the impact of the COVID-19 pandemic on neonatal care and mortality indicators in our unit. Methods: We conducted a retrospective study to compare the number of hospital births, pre-term newborns (PTNB), NICU admissions, and deaths. Changes in frequencies between 2019 and 2021 were analyzed using Poisson distribution. Changes in PTNB births, proportion of admissions, and deaths/NICU discharges were analyzed by z-test for two proportions. Results: Between 2019 and 2021, the number of births increased by more than 2-fold. NICU admissions increased from 770 in 2019 to 1045 in 2021 (p < 0.01). The ratio of deaths/discharge from the service was 16.9% in 2019 and 13.1% in 2021 (p = 0.02). Conclusions: Mortality indicators in the NICU decreased from 2019 to 2021, even with the increase in the number of patients admitted during the COVID-19 pandemic.

Resumen Introducción: Con la identificación de la enfermedad por COVID-19 en China, inició una pandemia que afectó a los sistemas de salud. La Unidad de Cuidados Intensivos Neonatales (UCIN) del Hospital de Ginecobstetricia del Centro Médico Nacional de Occidente del Instituto Mexicano del Seguro Social (IMSS) vio incrementado su flujo de pacientes como parte de la Estrategia COVID-19 del IMSS. El objetivo fue analizar el impacto de la pandemia COVID-19 en los indicadores de atención y mortalidad neonatal en nuestra unidad. Métodos: Se realizó un estudio retrospectivo para comparar el número de nacimientos en el hospital, nacimientos de recién nacidos prematuros (RNPT), ingresos a UCIN y defunciones. Se analizaron los cambios en frecuencias entre los años 2019 a 2021 mediante la distribución de Poisson. Los cambios en nacimientos de RNPT, proporción de ingresos y defunciones/egreso en UCIN se analizaron mediante prueba Z para dos proporciones. Resultados: Entre los años 2019 a 2021, el número de nacimientos incrementó más de 2 veces. Los ingresos a UCIN aumentaron de 770 en 2019, a 1045 en 2021 (p < 0.01). La proporción de defunciones/egreso del servicio fue de 16.9% en 2019, y 13.1% en 2021 (p = 0.02). Conclusiones: Los indicadores de mortalidad en la UCIN disminuyeron de 2019 a 2021, aun con el incremento en el número de pacientes atendidos durante la pandemia COVID-19.

The Emergence of the Normal Distribution in Deterministic Chaotic Maps.

The central limit theorem states that, in the limits of a large number of terms, an appropriately scaled sum of independent random variables yields another random variable whose probability distribution tends to attain a stable distribution. The condition of independence, however, only holds in real systems as an approximation. To extend the theorem to more general situations, previous studies have derived a version of the central limit theorem that also holds for variables that are not independent. Here, we present numerical results that characterize how convergence is attained when the variables being summed are deterministically related to one another through the recurrent application of an ergodic mapping. In all the explored cases, the convergence to the limit distribution is slower than for random sampling. Yet, the speed at which convergence is attained varies substantially from system to system, and these variations imply differences in the way information about the deterministic nature of the dynamics is progressively lost as the number of summands increases. Some of the identified factors in shaping the convergence process are the strength of mixing induced by the mapping and the shape of the marginal distribution of each variable, most particularly, the presence of divergences or fat tails.

Waterborne Toxicity to Neotropical Invertebrates and Hazard of Cigarette Butt Leachates in Marine Environments.

Cigarette butts (CBs) are the most common type of beach litter worldwide and contain a complex mixture of chemicals. Given the recent interest in this emerging problem, it is important to assess the toxicity of CB leachates to a range of species from different regions, sensitivities, and ecological traits. We evaluated the waterborne toxicity of smoked CB to tropical invertebrates. Leachates were prepared in the laboratory and characterized for trace elements (Mn, Fe, Co, Ni, Cu, Zn, As, Cd, and Pb), ammonia nitrogen, and polycyclic aromatic hydrocarbons. Then a set of toxicity tests with marine invertebrates was performed as follows: the brine shrimp Artemia sp. (nontoxic); the amphipod Tiburonella viscana (median lethal concentration [LC50] of 0.038 CB/L); the tanaid Monokalliapseudes schubarti (LC50 of 0.126 CB/L); the copepods Tisbe biminiensis (median effect concentration [EC50] of 0.038 CB/L) and Nitokra sp. (EC50 of 0.009 CB/L); pluteus stage larvae of the sea urchin Echinometra lucunter (EC50 of 0.152 CB/L); the sand dollar Mellita quinquiesperforata (EC50 of 0.054 CB/L); and D-stage larvae of the mussel Perna perna (EC50 of 0.005 CB/L). The predicted no-effect concentration was estimated using species sensitivity distribution, producing a 5th percentile hazard concentration of 0.015 CB/L. This preliminary threshold allowed us to estimate the potential impact of a single CB to 67 L of seawater via leaching, contributing to the advancement of knowledge regarding the contamination, toxicity, and ecological risks of cigarette waste. Environ Toxicol Chem 2024;43:374-384. © 2023 SETAC.

Results for Nonlinear Diffusion Equations with Stochastic Resetting.

In this study, we investigate a nonlinear diffusion process in which particles stochastically reset to their initial positions at a constant rate. The nonlinear diffusion process is modeled using the porous media equation and its extensions, which are nonlinear diffusion equations. We use analytical and numerical calculations to obtain and interpret the probability distribution of the position of the particles and the mean square displacement. These results are further compared and shown to agree with the results of numerical simulations. Our findings show that a system of this kind exhibits non-Gaussian distributions, transient anomalous diffusion (subdiffusion and superdiffusion), and stationary states that simultaneously depend on the nonlinearity and resetting rate.

A Wealth Distribution Agent Model Based on a Few Universal Assumptions.

We propose a new agent-based model for studying wealth distribution. We show that a model that links wealth to information (interaction and trade among agents) and to trade advantage is able to qualitatively reproduce real wealth distributions, as well as their evolution over time and equilibrium distributions. These distributions are shown in four scenarios, with two different taxation schemes where, in each scenario, only one of the taxation schemes is applied. In general, the evolving end state is one of extreme wealth concentration, which can be counteracted with an appropriate wealth-based tax. Taxation on annual income alone cannot prevent the evolution towards extreme wealth concentration.

Asymptotic Distribution of Certain Types of Entropy under the Multinomial Law.

We obtain expressions for the asymptotic distributions of the Rényi and Tsallis of order q entropies and Fisher information when computed on the maximum likelihood estimator of probabilities from multinomial random samples. We verify that these asymptotic models, two of which (Tsallis and Fisher) are normal, describe well a variety of simulated data. In addition, we obtain test statistics for comparing (possibly different types of) entropies from two samples without requiring the same number of categories. Finally, we apply these tests to social survey data and verify that the results are consistent but more general than those obtained with a χ2 test.

Fast inference for robust nonlinear mixed-effects models.

The interest for nonlinear mixed-effects models comes from application areas as pharmacokinetics, growth curves and HIV viral dynamics. However, the modeling procedure usually leads to many difficulties, as the inclusion of random effects, the estimation process and the model sensitivity to atypical or nonnormal data. The scale mixture of normal distributions include heavy-tailed models, as the Student-t, slash and contaminated normal distributions, and provide competitive alternatives to the usual models, enabling the obtention of robust estimates against outlying observations. Our proposal is to compare two estimation methods in nonlinear mixed-effects models where the random components follow a multivariate scale mixture of normal distributions. For this purpose, a Monte Carlo expectation-maximization algorithm (MCEM) and an efficient likelihood-based approximate method are developed. Results show that the approximate method is much faster and enables a fairly efficient likelihood maximization, although a slightly larger bias may be produced, especially for the fixed-effects parameters. A discussion on the robustness aspects of the proposed models are also provided. Two real nonlinear applications are discussed and a brief simulation study is presented.

Land-use intensification systematically alters the size structure of aquatic communities in the Neotropics.

Land-use and land-cover transitions can affect biodiversity and ecosystem functioning in a myriad of ways, including how energy is transferred within food-webs. Size spectra (i.e. relationships between body size and biomass or abundance) provide a means to assess how food-webs respond to environmental stressors by depicting how energy is transferred from small to larger organisms. Here, we investigated changes in the size spectrum of aquatic macroinvertebrates along a broad land-use intensification gradient (from Atlantic Forest to mechanized agriculture) in 30 Brazilian streams. We expected to find a steeper size spectrum slope and lower total biomass in more disturbed streams due to higher energetic expenditure in physiologically stressful conditions, which has a disproportionate impact on large individuals. As expected, we found that more disturbed streams had fewer small organisms than pristine forest streams, but, surprisingly, they had shallower size spectrum slopes, which indicates that energy might be transferred more efficiently in disturbed streams. Disturbed streams were also less taxonomically diverse, suggesting that the potentially higher energy transfer in these webs might be channelled via a few efficient trophic links. However, because total biomass was higher in pristine streams, these sites still supported a greater number of larger organisms and longer food chains (i.e. larger size range). Our results indicate that land-use intensification decreases ecosystem stability and enhances vulnerability to population extinctions by reducing the possible energetic pathways while enhancing efficiency between the remaining food-web linkages. Our study represents a step forward in understanding how land-use intensification affects trophic interactions and ecosystem functioning in aquatic systems.

Pesticides bioassays using neotropical aquatic species: Trends during the last twenty years and future challenges in Argentina.

The presence of pesticides in aquatic ecosystems is one of the most relevant stressors which biota usually face. Laboratory tests using model organisms for pesticides toxicity assessment are employed worldwide. The use of these species has been encouraged in the scientific community due to their advantageous features and their acceptation by regulatory and standardization organizations. However, non-model species as well as those belonging particular ecosystems could contribute in the laboratory-field toxicity extrapolation. In this context, this work aims on exploring the state of the ecotoxicological studies of pesticides in neotropical aquatic species, focusing on bioassays performed in Argentina over the last 20 years as a case of study. Furthermore, we analyzed the possible advantages and disadvantages of these studies, possible differential sensitivities among native and model species, and future challenges to be faced. The analysis of more than 150 publications allowed identify the chemical identity of tested compounds, organisms used for the bioassays, characteristics of the experimental designs, and the toxicity endpoints. Particularly, the studied cases showed that the tested chemicals are related to those most used in the agricultural activity in Argentina, the predilection for particular species in some taxonomic groups (e.g. amphibians), and the wide election of biochemical biomarkers in the studies. Regarding the sensitivity comparison between native and non-native species, the amount of data available indicates that there is not a clear difference beyond some particular cases. However, deeper understanding of toxic effects of pesticides on non-model species could help in a more comprehensive ecological risk assessment in different ecosystems.

Porosity determination of Vienna sausages through digital images analysis.

BACKGROUND: This research aimed to determine the porosity and particle size distribution in canned Vienna-type sausages using digital image analysis (DIA) on photographs captured with a digital camera and applying a Monte Carlo simulation. The methodology determined morphometric parameters (area and Feret diameter) by DIA of transverse and longitudinal sections of canned sausages. Those images were previously contrast enhanced, color threshold adjusted, and binarized. Subsequently, the estimation of the pore volume was carried out from the inverse Gaussian distributions of Feret diameter and area, as well as the porosity, using Monte Carlo simulation. RESULTS: The pores had an average Feret diameter of 0.335 mm and an average area of 0.085 mm2 . The highest estimated bivariate kernel density was presented for the smallest pores (around 0.02 mm2 in area and 0.25 mm in diameter). Simulation average values of pore volume, assumed as a cylinder, and porosity were 1.455 mm3 and 0.737 respectively. The average porosity value was consistent with the value experimentally estimated by the indirect method, in concordance with the definition of porosity, which was 0.715, presenting a mean relative percentage error of 3.08% concerning the estimated experimental value as well. CONCLUSION: This research presents interesting perspectives for the quantitative analysis of the microstructure of food and biological materials through a novel, low-cost, reliable, and fast proposal. Moreover, this is the first study to report the porosity determination in canned sausages by DIA. © 2022 Society of Chemical Industry.

A general class of promotion time cure rate models with a new biological interpretation.

Over the last decades, the challenges in survival models have been changing considerably and full probabilistic modeling is crucial in many medical applications. Motivated from a new biological interpretation of cancer metastasis, we introduce a general method for obtaining more flexible cure rate models. The proposal model extended the promotion time cure rate model. Furthermore, it includes several well-known models as special cases and defines many new special models. We derive several properties of the hazard function for the proposed model and establish mathematical relationships with the promotion time cure rate model. We consider a frequentist approach to perform inferences, and the maximum likelihood method is employed to estimate the model parameters. Simulation studies are conducted to evaluate its performance with a discussion of the obtained results. A real dataset from population-based study of incident cases of melanoma diagnosed in the state of São Paulo, Brazil, is discussed in detail.

Shannon Entropy: An Econophysical Approach to Cryptocurrency Portfolios.

Cryptocurrency markets have attracted many interest for global investors because of their novelty, wide on-line availability, increasing capitalization, and potential profits. In the econophysics tradition, we show that many of the most available cryptocurrencies have return statistics that do not follow Gaussian distributions, instead following heavy-tailed distributions. Entropy measures are applied, showing that portfolio diversification is a reasonable practice for decreasing return uncertainty.