The COVID-19 social media infodemic Cinelli, Matteo; Quattrociocchi, Walter; Galeazzi, Alessandro ...
Scientific reports,
10/2020, Letnik:
10, Številka:
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Journal Article
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We address the diffusion of information about the COVID-19 with a massive data analysis on Twitter, Instagram, YouTube, Reddit and Gab. We analyze engagement and interest in the COVID-19 topic and ...provide a differential assessment on the evolution of the discourse on a global scale for each platform and their users. We fit information spreading with epidemic models characterizing the basic reproduction number Formula: see text for each social media platform. Moreover, we identify information spreading from questionable sources, finding different volumes of misinformation in each platform. However, information from both reliable and questionable sources do not present different spreading patterns. Finally, we provide platform-dependent numerical estimates of rumors' amplification.
In the present study, a method based on the conditional density of vine copulas was used to drought monitoring and predicting the rainfall deficiency signature for a 60‐day duration in Dashband, ...sub‐basin of Lake Urmia basin. The annual rainfall and rainfall deficiency signatures at 10‐, 30‐ and 60‐day durations were considered as variables. D‐, C‐ and R‐vine copulas were used to represent the dependence among the variables, finding that D‐vine copula results to be more accurate for the case of interest. We found that, if the rainfall is less than the long‐term mean in the region, the rainfall deficiency signature for near future can be estimated by acceptable accuracy. Moreover, the results of the conditional probability analysis of rainfall deficiency signature for a 60‐day duration respect to the other variables showed that, on average, the probability of the occurrence of rainfall deficiency signature of 250 mm compared to the long‐term mean in the study area is more than 50% per year. The results showed that the proposed approach may facilitate the meteorological drought management in the considered sub‐basin.
A 4‐D method due to the conditional density of vine copulas was proposed to provide predictive equations and simulate the values of rainfall deficiency signatures for meteorological drought management. The diagonal section of copulas was used to reduce the complexity of the conditional density of pairwise variables. While, examining the accuracy of C‐, D‐, and R‐vine copulas, the proposed method was used to predict short‐term rainfall deficiency signatures in the studied basin.
In response to the coronavirus disease 2019 (COVID-19) pandemic, several national governments have applied lockdown restrictions to reduce the infection rate. Here we perform a massive analysis on ...near–real-time Italian mobility data provided by Facebook to investigate how lockdown strategies affect economic conditions of individuals and local governments. We model the change in mobility as an exogenous shock similar to a natural disaster. We identify two ways through which mobility restrictions affect Italian citizens. First, we find that the impact of lockdown is stronger in municipalities with higher fiscal capacity. Second, we find evidence of a segregation effect, since mobility contraction is stronger in municipalities in which inequality is higher and for those where individuals have lower income per capita. Our results highlight both the social costs of lockdown and a challenge of unprecedented intensity: On the one hand, the crisis is inducing a sharp reduction of fiscal revenues for both national and local governments; on the other hand, a significant fiscal effort is needed to sustain the most fragile individuals and to mitigate the increase in poverty and inequality induced by the lockdown.
Electrochromic effect and molecularly imprinted technology have been used to develop a sensitive and selective electrochromic sensor. The polymeric matrices obtained using the imprinting technology ...are robust molecular recognition elements and have the potential to mimic natural recognition entities with very high selectivity. The electrochromic behavior of iridium oxide nanoparticles (IrOx NPs) as physicochemical transducer together with a molecularly imprinted polymer (MIP) as recognition layer resulted in a fast and efficient translation of the detection event. The sensor was fabricated using screen-printing technology with indium tin oxide as a transparent working electrode; IrOx NPs where electrodeposited onto the electrode followed by thermal polymerization of polypyrrole in the presence of the analyte (chlorpyrifos). Two different approaches were used to detect and quantify the pesticide: direct visual detection and smartphone imaging. Application of different oxidation potentials for 10 s resulted in color changes directly related to the concentration of the analyte. For smartphone imaging, at fixed potential, the concentration of the analyte was dependent on the color intensity of the electrode. The electrochromic sensor detects a highly toxic compound (chlorpyrifos) with a 100 fM and 1 mM dynamic range. So far, to the best of our knowledge, this is the first work where an electrochromic MIP sensor uses the electrochromic properties of IrOx to detect a certain analyte with high selectivity and sensitivity.
The development of satellite sensors and interferometry synthetic aperture radar (InSAR) technology has enabled the exploitation of their benefits for long-term structural health monitoring (SHM). ...However, some restrictions cause this process to provide a small number of images leading to the problem of small data for SAR-based SHM. Conversely, the major challenge of the long-term monitoring of civil structures pertains to variations in their inherent properties by environmental and/or operational variability. This article aims to propose new hybrid unsupervised learning methods for addressing these challenges. The methods in this work contain three main parts: (i) data augmentation by the Markov Chain Monte Carlo algorithm, (ii) feature normalization, and (iii) decision making via Mahalanobis-squared distance. The first method presented in this work develops an artificial neural network-based feature normalization by proposing an iterative hyperparameter selection of hidden neurons of the network. The second method is a novel unsupervised teacher–student learning by combining an undercomplete deep neural network and an overcomplete single-layer neural network. A small set of long-term displacement samples extracted from a few SAR images of TerraSAR-X is applied to validate the proposed methods. The results show that the methods can effectively deal with the major challenges in the SAR-based SHM applications.
Abstract
Extremal dependence describes the strength of correlation between the largest observations of two variables. It is usually measured with symmetric dependence coefficients that do not depend ...on the order of the variables. In many cases, there is a natural asymmetry between extreme observations that cannot be captured by such coefficients. An example for such asymmetry is large discharges at an upstream and a downstream stations on a river network: an extreme discharge at the upstream station will directly influence the discharge at the downstream station, but not vice versa. Simple measures for asymmetric dependence in extreme events have not yet been investigated. We propose the asymmetric tail Kendall's
τ
as a measure for extremal dependence that is sensitive to asymmetric behavior in the largest observations. It essentially computes the classical Kendall's
τ
but conditioned on the extreme observations of one of the two variables. We show theoretical properties of this new coefficient and derive a formula to compute it for existing copula models. We further study its effectiveness and connections to causality in simulation experiments. We apply our methodology to a case study on river networks in the United Kingdom to illustrate the importance of measuring asymmetric extremal dependence in hydrology. Our results show that there is important structural information in the asymmetry that would have been missed by a symmetric measure. Our methodology is an easy but effective tool that can be applied in exploratory analysis for understanding the connections among variables and to detect possible asymmetric dependencies.
Plain Language Summary
Compound events describe situations where the simultaneous behavior of two or more variables lead to severe impacts. For instance, the dependence between climate or hydrological variables can lead to particular conditions that result in extreme events at the same time in different locations. Since the physical processes behind these phenomena are very complex, there can be a stronger influence from one variable on another than the other way around. In such cases, there is asymmetry in the dependence between the extreme observations of the two variables. The traditional measures of dependence are symmetric and cannot detect any asymmetries. We propose a new measure that is sensitive to asymmetric behavior in extremes. It is based on an extension of the Kendall's
τ
coefficient, a classical dependence measure. We derive evidence from theory and simulation experiments for the effectiveness of our new methodology. We then apply it to a case study on river networks in the United Kingdom where we show that our measure detects asymmetric behavior of extreme discharges with a preferred direction from upstream to downstream stations. Our work points out the importance of considering proper tools for analyzing the connections between different variables in particular in the presence of asymmetry in extreme observations.
Key Points
Coefficients that can detect asymmetry in dependence among extremes are crucial since traditional methods rely on assumption of symmetry
We propose a conditional version of Kendall's tau that allows to detect asymmetries between extremes, conditioning on one variable at a time
This new measure can be used for exploratory analysis, model assessment, and to detect directional asymmetries or causal structures in extremes
Environmental variability is a major challenging issue in bridge health monitoring because bridges are more prone to such variability than other civil structures. To deal with this challenge, this ...article proposes a new machine-learning method for early damage detection under environmental variability by means of the
k
-medoids clustering, a new damage indicator, and an innovative approach for selecting a proper cluster number. Estimation of a reliable alarming threshold is another important challenge for early damage detection via most of the machine-learning methods. On this basis, a novel probabilistic approach using the theory of extreme value and a goodness-of-fit measure is proposed to estimate an alarming threshold. The major contributions of this article include proposing a new damage indicator suitable for decision making by clustering-based algorithms, an innovative cluster selection algorithm for dealing with the problem of environmental variability and increasing damage detectability, and a novel probabilistic method for threshold estimation. Modal-based features of the well-known Z24 Bridge are considered to verify the accuracy and effectiveness of the proposed approaches along with several comparative studies. Results show that the methods presented here are highly able to detect early damage even under strong environmental variations and estimate a reliable threshold.
Maximum annual daily precipitation is a fundamental hydrologic variable that does not attain asymptotic conditions. Thus the classical extreme value theory (i.e., the Fisher-Tippett's theorem) does ...not apply and the recurrent use of the Generalized Extreme Value distribution (GEV) to estimate precipitation quantiles for structural-design purposes could be inappropriate. In order to address this issue, we first determine the exact distribution of maximum annual daily precipitation starting from a Markov chain and in a closed analytical form under the hypothesis of stochastic independence. As a second step, we formulate a superstatistics conjecture of daily precipitation, meaning that we assume that the parameters of this exact distribution vary from a year to another according to probability distributions, which is supported by empirical evidence. We test this conjecture using the world GHCN database to perform a worldwide assessment of this superstatistical distribution of daily precipitation extremes. The performances of the superstatistical distribution and the GEV are tested against data using the Kolmogorov-Smirnov statistic. By considering the issue of model's extrapolation, that is, the evaluation of the estimated model against data not used in calibration, we show that the superstatistical distribution provides more robust estimations than the GEV, which tends to underestimate (7-13%) the quantile associated to the largest cumulative frequency. The superstatistical distribution, on the other hand, tends to overestimate (10-14%) this quantile, which is a safer option for hydraulic design. The parameters of the proposed superstatistical distribution are made available for all 20,561 worldwide sites considered in this work.
The authors describe a strategy for rapid and sensitive determination of phenyl carbamate pesticides in environmental samples. It consists of the following steps: (a) Enrichment and clean-up of the ...analytes using a C18 microtip based procedure; (b) alkaline hydrolysis of the carbamates (carbofuran, isoprocarb and carbaryl) to form phenol derivatives; and (c) fast separation and amperometric detection in a microfluidic chip (MCs). The microchips were fabricated by using press-transferred carbon black nanoparticles (CB-NPs) as electrochemical sensing nanomaterial. The excellent electrochemical behavior of the CB-NPs coupled to the microchip warrants good separation and allows for the voltammetric determination (best at a working voltage of +0.70 V vs Ag/AgCl) of the carbamates within < 6 min. The authors also describe a rapid procedure for the clean-up and enrichment of the carbamates from real samples by using a C18 microtip. The procedure allowed a 10-fold enrichment of the analytes, and this led to a detection limits in ̴the 0.7 to 1.2 μM concentration range. The assay was applied to samples of river, lake and irrigation water that were spiked with carbamates at 50 and 100 μM levels. Recoveries are in the 87 to 108 % range, and RSDs (
n
= 3) in the 5 to 11 % range. The exploitation of the such nanomaterials coupled to microfluidics and microextraction procedures for real sample analysis in our preception represents a most viable tool for the analysis of complex real samples, for on-site environmental monitoring, and for rapid diagnosis.
Graphical abstract
Press-printed carbon black nanoparticles films on board of microfluidic chips.
Developing statistical period and simulating the required values in case of data shortage increases certainty and reliability of simulations and statistical analyses, which is very important in ...studies on hydrology and water resources. Therefore, in this study, for simulating values of potential evapotranspiration at Birjand Station located in eastern Iran, contemporaneous autoregressive moving average (CARMA), CARMA-generalized autoregressive conditional heteroskedasticity (GARCH), and Copula-GARCH models were used in statistical period of 1984–2019. The potential evapotranspiration and relative humidity time series were simulated using these three models. CARMA model has acceptable accuracy for simulating potential evapotranspiration values due to the effect of the second parameter on simulations. Nash–Sutcliffe efficiency (NSE) coefficient of CARMA model for simulating potential evapotranspiration values was estimated as 0.85. NSE coefficient of CARMA-GARCH model was obtained as 0.87 through extracting residuals of CARMA model and simulating variance of data using GARCH model. Comparing the CARMA and CARMA-GARCH models with each other, it was concluded that a combination of two linear and non-linear time series models increases simulation accuracy to some extent. Using Clayton copula (the selected copula from the studied copulas), the mentioned values were simulated by Copula-GARCH model. The results showed that among the three models used, Copula-GARCH model reduced root mean square error of bivariate simulation compared to CARMA and CARMA-GARCH models by 15 and 13%, respectively. The results also showed that the proposed model simulates the average, first, and third quarters and range of changes in the data by 5 and 95% better than the two CARMA and CARMA-GARCH models.
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