Improvement of surface water quality variables modelling that incorporates a hydro-meteorological factor: a state-space approach

In this work it is constructed a hydro-meteorological factor to improve the adjustment of statistical time series models, such as state space models, of water quality variables by observing hydrological series (recorded in time and space) in a River basin. The hydro-meteorological factor is incorporated as a covariate in multivariate state space models fitted to homogeneous groups of monitoring sites. Additionally, in the modelling process it is considered a latent variable that allows incorporating a structural component, such as seasonality, in a dynamic way

Application of Change-Point Detection to a Structural Component of Water Quality Variables

In this study, methodologies were developed in statistical time series models, such as multivariate state-space models, to be applied to water quality variables in a river basin. In the modelling process it is considered a latent variable that allows incorporating a structural component, such as seasonality, in a dynamic way and a change-point detection method is applied to the structural component in order to identify possible changes in the water quality variables in consideration

Using udometric network data to estimate an environmental covariate

Manyhydrologicalandecologicalstudiesrecognizetheimportanceofcharacterizingthetemporalandspatialvari- ability of precipitation. In this study, geostatistical methodologies were developed in order to estimate a hydro-meteorological factor by (re)building the space-time distribution of the precipitation associated to monthly averages in a certain hydrological river basin that will be used in the modelling of surface water quality. A hydro-meteorological factor is constructed for each water quality monitoring site (WQMS), based on the analysis of the space-time behaviour of the precipitation observed in an udometric network located in a Portuguese river basin

Longitudinal profiles of Extensive Air Showers with inclusion of charm and bottom particles

Charm and bottom particles are rare in Extensive Air Showers but the effect of its presence can be radical in the development of the Extensive Air Showers (EAS). If such particles arise with a large fraction of the primary energy, they can reach large atmospheric depths, depositing its energy in deeper layers of the atmosphere. As a consequence, the EAS observables ($X_{max}$, $RMS$ and $N_{max}$) will be modified, as well as the shape of the longitudinal profile of the energy deposited in the atmosphere. In this paper, we will modify the CORSIKA Monte Carlo by the inclusion of charm and bottom production in the first interaction of the primary cosmic ray. Results for different selections of the typical $x_F$ values of the heavy particles and distinct production models will be presented.Comment: Replacement of tex file by the correct versio

Pseudoclassical model for Weyl particle in 10 dimensions

A pseudoclassical model to describe Weyl particle in 10 dimensions is proposed. In course of quantization both the massless Dirac equation and the Weyl condition are reproduced automatically. The construction can be relevant to Ramond-Neveu-Schwarz strings where the Weyl reduction in the Ramond sector has to be made by hand.Comment: 5 page

Simpler is better: a novel genetic algorithm to induce compact multi-label chain classifiers

Multi-label classification (MLC) is the task of assigning multiple class labels to an object based on the features that describe the object. One of the most effective MLC methods is known as Classifier Chains (CC). This approach consists in training q binary classifiers linked in a chain, y1 → y2 → ... → yq, with each responsible for classifying a specific label in {l1, l2, ..., lq}. The chaining mechanism allows each individual classifier to incorporate the predictions of the previous ones as additional information at classification time. Thus, possible correlations among labels can be automatically exploited. Nevertheless, CC suffers from two important drawbacks: (i) the label ordering is decided at random, although it usually has a strong effect on predictive accuracy; (ii) all labels are inserted into the chain, although some of them might carry irrelevant information to discriminate the others. In this paper we tackle both problems at once, by proposing a novel genetic algorithm capable of searching for a single optimized label ordering, while at the same time taking into consideration the utilization of partial chains. Experiments on benchmark datasets demonstrate that our approach is able to produce models that are both simpler and more accurate

Global analysis of piecewise linear systems using impact maps and surface Lyapunov functions

This paper presents an entirely new constructive global analysis methodology for a class of hybrid systems known as piecewise linear systems (PLS). This methodology infers global properties of PLS solely by studying the behavior at switching surfaces associated with PLS. The main idea is to analyze impact maps, i.e., maps from one switching surface to the next switching surface. Such maps are known to be "unfriendly" maps in the sense that they are highly nonlinear, multivalued, and not continuous. We found, however, that an impact map induced by an linear time-invariant flow between two switching surfaces can be represented as a linear transformation analytically parametrized by a scalar function of the state. This representation of impact maps allows the search for surface Lyapunov functions (SuLF) to be done by simply solving a semidefinite program, allowing global asymptotic stability, robustness, and performance of limit cycles and equilibrium points of PLS to be efficiently checked. This new analysis methodology has been applied to relay feedback, on/off and saturation systems, where it has shown to be very successful in globally analyzing a large number of examples. In fact, it is still an open problem whether there exists an example with a globally stable limit cycle or equilibrium point that cannot be successfully analyzed with this new methodology. Examples analyzed include systems of relative degree larger than one and of high dimension, for which no other analysis methodology could be applied. This success in globally analyzing certain classes of PLS has shown the power of this new methodology, and suggests its potential toward the analysis of larger and more complex PLS