A proactive intelligent decision support system for predicting the popularity of online news

"Lecture notes in computer science series, ISSN 0302-9743, vol. 9273"

A new competitive implementation of the electromagnetism-like algorithm for global optimization

The Electromagnetism-like (EM) algorithm is a population- based stochastic global optimization algorithm that uses an attraction- repulsion mechanism to move sample points towards the optimal. In this paper, an implementation of the EM algorithm in the Matlab en- vironment as a useful function for practitioners and for those who want to experiment a new global optimization solver is proposed. A set of benchmark problems are solved in order to evaluate the performance of the implemented method when compared with other stochastic methods available in the Matlab environment. The results con rm that our imple- mentation is a competitive alternative both in term of numerical results and performance. Finally, a case study based on a parameter estimation problem of a biology system shows that the EM implementation could be applied with promising results in the control optimization area.

Development of a methodology to incorporate risk and uncertainty in electricity power planning

Dissertação de mestrado em Engenharia Industrial

Otimização das características de um chassis tubular de automóvel de competição com tração a duas rodas motrizes

Dissertação de mestrado integrado em Engenharia Mecânica

Poker learner: modelação de jogadores através de data mining

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Seguros Income Protection: análise segundo o sexo e a classe profissional dos segurados

Dissertação de mestrado em Economia Monetária, Bancária e Financeira

Utilização de modelos de simulação para a melhoria de um sistema de restauração: estudo de caso de uma cantina universitária

Dissertação de mestrado em Engenharia Industrial

Adaptive facade systems - review of performance requirements, design approaches, use cases and market needs

The paper reflects the work of COST Action TU1403 Workgroup 3/Task group 1. The aim is to identify research needs from a review of the state of the art of three aspects related to adaptive façade systems: (1) dynamic performance requirements; (2) façade design under stochastic boundary conditions and (3) experiences with adaptive façade systems and market needs.

Real-time monitoring of progression towards renal failure in primary care patients

First published online: December 16, 2014.

proxel-based method : formalisation, analysis and applications

Simulation, modelling, proxels, PDEs, Markov chains, Petri nets, stochastic, performability, transient analysis

Adaptive numerical simulation of reaction - diffusion systems

Fluidized beds, granulation, heat and mass transfer, calcium dynamics, stochastic process, finite element methods, Rosenbrock methods, multigrid methods, parallelization

Tolerance to salinities shocks of the invasive mussel Limnoperma fortunei under experimental conditions

The golden mussel, Limnoperna fortunei (Dunker, 1857), has been found in the estuarine regions of South America, including the Patos Lagoon (Brazil), a huge choked lagoon with an estuarine region that is highly unstable chemically. Limnoperna fortunei space-temporal variability in the lagoon's estuarine region demonstrated the need to evaluate this species' ability to survive under salinity shocks. A set of experiments was conducted under controlled laboratory conditions. Specimens were tested under salinities of 2, 4, 6, 8 and 12 ppt, and were exposed for periods of 24, 48, 72, 96 and 240 hours. The mussel can survive (90%) up to a salinity shock of 2 ppt for periods of at least 10 days. Considering the influence of climatic and stochastic events and the chemical instability of the Patos Lagoon estuarine region, it's unlikely that populations could survive for longer periods (more than a year) in this area.

Daily variation of zooplankton abundance and evenness in the Rosana reservoir, Brazil: biotic and abiotic inferences

The zooplankton community presents stochastic temporal fluctuation and heterogeneous spatial variation determined by the relationships among the organisms and environmental conditions. We predicted that the temporal and spatial zooplankton distribution is heterogeneous and discrete, respectively, and that the daily variation of most abundant species is related to environmental conditions, specifically the availability of resources. Zooplankton samples were collected daily at three sampling stations in a lateral arm of the Rosana Reservoir (SP/PR). The zooplankton did not present significant differences in abundance and evenness among sampling stations, but the temporal variation of these attributes was significant. Abiotic variables and algal resource availability have significantly explained the daily variation of the most abundant species (p<0.001), however, the species distribution makes inferences on biotic relationships between them. Thus, not only the food resource availability is influential on the abundance of principal zooplankton species, but rather a set of factors (abiotic variables and biotic relationships).

Contractive metrics for a Boltzmann equation for granular gases: diffusive equilibriaThe Patterson-Sullivan embedding and minimal volume entropy for outer space

We quantify the long-time behavior of a system of (partially) inelastic particles in a stochastic thermostat by means of the contractivity of a suitable metric in the set of probability measures. Existence, uniqueness, boundedness of moments and regularity of a steady state are derived from this basic property. The solutions of the kinetic model are proved to converge exponentially as t→ ∞ to this diffusive equilibrium in this distance metrizing the weak convergence of measures. Then, we prove a uniform bound in time on Sobolev norms of the solution, provided the initial data has a finite norm in the corresponding Sobolev space. These results are then combined, using interpolation inequalities, to obtain exponential convergence to the diffusive equilibrium in the strong L¹-norm, as well as various Sobolev norms.

Consumers' behavior and the Bertrand paradox: an ACE approach

We analyze the classical Bertrand model when consumers exhibit some strategic behavior in deciding from which seller they will buy. We use two related but different tools. Both consider a probabilistic learning (or evolutionary) mechanism, and in the two of them consumers' behavior in uences the competition between the sellers. The results obtained show that, in general, developing some sort of loyalty is a good strategy for the buyers as it works in their best interest. First, we consider a learning procedure described by a deterministic dynamic system and, using strong simplifying assumptions, we can produce a description of the process behavior. Second, we use nite automata to represent the strategies played by the agents and an adaptive process based on genetic algorithms to simulate the stochastic process of learning. By doing so we can relax some of the strong assumptions used in the rst approach and still obtain the same basic results. It is suggested that the limitations of the rst approach (analytical) provide a good motivation for the second approach (Agent-Based). Indeed, although both approaches address the same problem, the use of Agent-Based computational techniques allows us to relax hypothesis and overcome the limitations of the analytical approach.