7 research outputs found

    How do civil society organizations communicate in an authoritarian setting? A narrative analysis of the Russian waste management debate

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    Civil society organizations (CSOs) aim to influence public policy. One way of influencing policy is through communication. In authoritarian contexts, CSOs face restrictions that make criticism of governmental actors less likely. However, to achieve change, CSOs need to highlight public problems that are often created by the inaction of governmental actors. This research examines the communicative strategies of CSOs involved in waste management in Russia. By drawing on the Narrative Policy Framework, it examines narratives used by CSOs on social media. Interviews with these CSO provide explanations of why CSOs select specific narrative strategies. We argue that the narrative strategies of CSOs are determined by their objectives of communication related to the activities they are involved in but are also influenced by their working relationship with the government. Results show that CSOs that are involved in educational activities and service provision mostly pursue an angel-shift-strategy, highlighting policy solutions. Only larger CSOs communicate critically and continue to attempt working with governmental actors to influence policy through awareness-raising and policy advocacy

    COVID-19 pandemic decision support system for a population defense strategy and vaccination effectiveness

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    The year 2020 ended with a significant COVID-19 pandemic, which traumatized almost many countries where the lockdowns were restored, and numerous emotional social protests erupted. According to the World Health Organization, the global epidemiological situation in the first months of 2021 deteriorated. In this paper, the decision-making supporting system (DMSS) is proposed to be an epidemiological prediction tool. COVID-19 trends in several countries and regions, take into account the big data clouds for important geophysical and socio-ecological characteristics and the expected potentials of the medical service, including vaccination and restrictions on population migration both within the country and international traffic. These parameters for numerical simulations are estimated from officially delivered data that allows the verification of theoretical results. The numerical simulations of the transition and the results of COVID-19 are mainly based on the deterministic approach and the algorithm for processing statistical data based on the instability indicator. DMSS has been shown to help predict the effects of COVID-19 depending on the protection strategies against COVID-19 including vaccination. Numerical simulations have shown that DMSS provides results using accompanying information in the appropriate scenario

    Nowcasting of air pollution episodes in megacities: A case study for Athens, Greece

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    The main objective of the present study is to develop a model for the prediction of the extreme events of air pollution in megacities using the concept of so-called "natural time" instead of the "conventional clock time". In particular, we develop a new nowcasting technique based on a statistically significant fit to the law of Gutenberg-Richter of the surface concentration of ozone (O3), particles of the size fraction less than 10 ÎŒm (PM-10) and nitrogen dioxide (NO2). Studying the air pollution over Athens, Greece during the period 2000–2018, we found that the average waiting time between successive extreme concentrations values varied between different atmospheric parameters accounted as 17 days in case of O3, 29 days in case of PM-10 and 28 days in case of NO2. This average waiting time depends on the upper threshold of the maximum extreme concentrations of air pollutants considered. For instance, considering the NO2 concentrations over Athens it was found that the average waiting time is 13 days for 130 ÎŒg/m3 and 2.4 years for 200 ÎŒg/m3. Remarkably, the same behaviour of obedience to the Guttenberg-Richter law characterizing the extreme values of the air pollution of a megacity was found earlier in other long-term ecological and paleoclimatic variables. It is a sign of self-similarity that is often observed in nature, which can be used in the development of more reliable nowcasting models of extreme events.National Natural Science Foundation of Chin

    Scaling Behavior of Peat Properties during the Holocene: A Case Study from Central European Russia

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    A better understanding of past climate change is vital to our ability to predict possible future environmental dynamics. This study attempts to investigate the dynamic features of the temporal variability of peat humification, water table depth and air temperature by analyzing palaeoecological data from the Valdai Uplands region (Central European Russia). The regression analysis revealed the presence of a periodicity of about 6000 years in the reconstructed peat humification timeseries. Nonlinear analysis showed that humification time variability, water table depth and air temperature exhibit persistent long-range correlations of 1/f type. This indicates that a fluctuation in these variables in the past is very likely to be followed by a similar one in the future, but is magnified by 1/f power-law. In addition, it dictates that humification, water table depth and temperature are key parameters of a system that implies the existence of a special structure, such as self-organized criticality, operating close to a minimum stability configuration, and achieves it without any fine adjustment by external forcing. These conclusions point to new avenues for modeling future ecosystem disturbances and, in particular, for predicting relevant extreme events

    Boosting Magnetoelectric Effect in Polymer-Based Nanocomposites

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    Polymer-based magnetoelectric composite materials have attracted a lot of attention due to their high potential in various types of applications as magnetic field sensors, energy harvesting, and biomedical devices. Current researches are focused on the increase in the efficiency of magnetoelectric transformation. In this work, a new strategy of arrangement of clusters of magnetic nanoparticles by an external magnetic field in PVDF and PFVD-TrFE matrixes is proposed to increase the voltage coefficient (alpha ME) of the magnetoelectric effect. Another strategy is the use of 3-component composites through the inclusion of piezoelectric BaTiO3 particles. Developed strategies allow us to increase the alpha ME value from similar to 5 mV/cm.Oe for the composite of randomly distributed CoFe2O4 nanoparticles in PVDF matrix to similar to 18.5 mV/cm.Oe for a composite of magnetic particles in PVDF-TrFE matrix with 5%wt of piezoelectric particles. The applicability of such materials as bioactive surface is demonstrated on neural crest stem cell cultures
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