The Fate of Anthropogenic Organic Pollutants in Soil: Adsorption/Desorption of Pesticides Possessing Endocrine Disruptor Activity by Natural Organic Matter (Humic Substances)

n/

Fitness-dependent topological properties of the World Trade Web

Among the proposed network models, the hidden variable (or good get richer) one is particularly interesting, even if an explicit empirical test of its hypotheses has not yet been performed on a real network. Here we provide the first empirical test of this mechanism on the world trade web, the network defined by the trade relationships between world countries. We find that the power-law distributed gross domestic product can be successfully identified with the hidden variable (or fitness) determining the topology of the world trade web: all previously studied properties up to third-order correlation structure (degree distribution, degree correlations and hierarchy) are found to be in excellent agreement with the predictions of the model. The choice of the connection probability is such that all realizations of the network with the same degree sequence are equiprobable.Comment: 4 Pages, 4 Figures. Final version accepted for publication on Physical Review Letter

Soil Amendment with Biochar, Hydrochar and Compost Mitigates the Accumulation of Emerging Pollutants in Rocket Salad Plants

The uptake of organic pollutants by agricultural plants and their accumulation in edible parts cause serious health problems to animals and humans. In this study, we used carbon-rich materials, such as biochar (BC), hydrochar (HC), and green compost (GC), to reduce the absorption and accumulation of three pesticides, imidacloprid (IMI), boscalid (BOS), and metribuzin (MET) and two endocrine disruptors, 4-tert-octylphenol (OP) and bisphenol A (BPA), in rocket salad plants (Eruca vesicaria L.). After an experimental period of 35 days, compared to unamended soil, the addition of BC, HC, and GC significantly reduced chemical phytotoxicity, increasing the elongation of the aerial plant parts by 26, 25, and 39%, respectively, whereas GC increased the fresh biomass by 21%. The assessment of residual chemicals in both soil and plant tissues indicated that any amendment was very effective in enhancing the retention of all compounds in soil, thus reducing their uptake by plants. Averagely for the five compounds, the reduction of plant absorption followed the trend BC > HC > GC. In particular, the presence of BC decreased the chemical residues in the plants from a minimum of 71% (IMI) to a maximum of 91% (OP). The overall results obtained encourage the incorporation in soil of C-rich materials, especially BC, to protect leafy food plants from the absorption and toxicity of organic pollutants of a wide range of hydrophobicity, with relevant benefits for consumers

Patterns of link reciprocity in directed networks

We address the problem of link reciprocity, the non-random presence of two mutual links between pairs of vertices. We propose a new measure of reciprocity that allows the ordering of networks according to their actual degree of correlation between mutual links. We find that real networks are always either correlated or anticorrelated, and that networks of the same type (economic, social, cellular, financial, ecological, etc.) display similar values of the reciprocity. The observed patterns are not reproduced by current models. This leads us to introduce a more general framework where mutual links occur with a conditional connection probability. In some of the studied networks we discuss the form of the conditional connection probability and the size dependence of the reciprocity.Comment: Final version accepted for publication on Physical Review Letter

FIELD INVERSION AND MACHINE LEARNING STRATEGIES FOR IMPROVING RANS MODELLING IN TURBOMACHINERY

Turbulence and transition modelling are critical aspects in the prediction of the flow field in turbomachinery. Recently, several research efforts have been devoted to the use of machine learning techniques for improving Reynolds-averaged Navier-Stokes (RANS) models. In this framework, a promising technique is represented by field inversion which requires to find an optimal correction field that minimises the error between numerical predictions and experimental data. In this work, Artificial Neural Networks and Random Forests are investigated as tools to generalise the correction provided by field inversion. An approach to automatically identify the regions where the correction model should be computed is proposed: this improves the fitting and reduces the calls to the model during the predictions. Furthermore, a correction-based weighting of the database is introduced in order to improve the training performances. The potential and the issues of the methods are investigated on a high-lift gas turbine cascade at low Reynolds number

The entropy of randomized network ensembles

Randomized network ensembles are the null models of real networks and are extensivelly used to compare a real system to a null hypothesis. In this paper we study network ensembles with the same degree distribution, the same degree-correlations or the same community structure of any given real network. We characterize these randomized network ensembles by their entropy, i.e. the normalized logarithm of the total number of networks which are part of these ensembles. We estimate the entropy of randomized ensembles starting from a large set of real directed and undirected networks. We propose entropy as an indicator to assess the role of each structural feature in a given real network.We observe that the ensembles with fixed scale-free degree distribution have smaller entropy than the ensembles with homogeneous degree distribution indicating a higher level of order in scale-free networks.Comment: (6 pages,1 figure,2 tables