Birds of a feather petition together? Characterizing e-petitioning through the lens of platform data

E-petitioning platforms are increasingly popular in Western democracies and considered by some lawmakers and scholars to enhance citizen participation in political decision-making. In addition to social media and other channels for informal political communication, online petitioning is regarded as both a useful instrument to afford citizens a more important role in the political process and allow them to express support for issues which they find relevant. Building on existing pre-internet systems, e-petitioning websites are increasingly implemented to make it easier and faster to set up and sign petitions. However, little attention has so far been given to the relationship between different styles of usage and the causes supported by different groups of users. The functional difference between signing paper-based petitions vs. doing so online is especially notable with regard to users who sign large numbers of petitions. To characterize this relationship, we examine the intensity of user participation in the German Bundestag’s online petitioning platform through the lens of platform data collected over a period of five years, and conduct an analysis of highly active users and their political preferences. We find that users who sign just a single petition favor different policy areas than those who sign many petitions on a variety of issues. We conclude our analysis with observations on the potential of behavioral data for assessing the dynamics of online participation, and suggest that quantity (the number of signed petitions) and quality (favored policy areas) need more systematic joint assessment

Basin structure in the two-dimensional dissipative circle map

Fractal basin structure in the two-dimensional dissipative circle map is examined in detail. Numerically obtained basin appears to be riddling in the parameter region where two periodic orbits co-exist near a boundary crisis, but it is shown to consist of layers of thin bands.Comment: published in J. Phys. Soc. Jpn., 72, 1943-1947 (2003

Predicting Thermoelectric Power Plants Diesel/Heavy Fuel Oil Engine Fuel Consumption Using Univariate Forecasting and XGBoost Machine Learning Models

Monitoring and controlling thermoelectric power plants (TPPs) operational parameters have become essential to ensure system reliability, especially in emergencies. Due to system complexity, operating parameters control is often performed based on technical know-how and simplified analytical models that can result in limited observations. An alternative to this task is using time series forecasting methods that seek to generalize system characteristics based on past information. However, the analysis of these techniques on large diesel/HFO engines used in Brazilian power plants under the dispatch regime has not yet been well-explored. Therefore, given the complex characteristics of engine fuel consumption during power generation, this work aimed to investigate patterns generalization abilities when linear and nonlinear univariate forecasting models are used on a representative database related to an engine-driven generator used in a TPP located in Pernambuco, Brazil. Fuel consumption predictions based on artificial neural networks were directly compared to XGBoost regressor adaptation to perform this task as an alternative with lower computational cost. AR and ARIMA linear models were applied as a benchmark, and the PSO optimizer was used as an alternative during model adjustment. In summary, it was possible to observe that AR and ARIMA-PSO had similar performances in operations and lower error distributions during full-load power output with normal error frequency distribution of −0.03 ± 3.55 and 0.03 ± 3.78 kg/h, respectively. Despite their similarities, ARIMA-PSO achieved better adherence in capturing load adjustment periods. On the other hand, the nonlinear approaches NAR and XGBoost showed significantly better performance, achieving mean absolute error reductions of 42.37% and 30.30%, respectively, when compared with the best linear model. XGBoost modeling was 8.7 times computationally faster than NAR during training. The nonlinear models were better at capturing disturbances related to fuel consumption ramp, shut-down, and sudden fluctuations steps, despite being inferior in forecasting at full-load, especially XGBoost due to its high sensitivity with slight fuel consumption variations

The role of the alloy structure in the magnetic behavior of granular systems

The effect of grain size, easy magnetization axis and anisotropy constant distributions in the irreversible magnetic behavior of granular alloys is considered. A simulated granular alloy is used to provide a realistic grain structure for the Monte Carlo simulation of the ZFC-FC curves. The effect of annealing and external field is also studied. The simulation curves are in good agreement with the FC and ZFC magnetization curves measured on melt spun Cu-Co ribbons.Comment: 13 pages, 10 figures, submitted to PR