University King Juan Carlos

Madrid, Spain

University King Juan Carlos

Madrid, Spain
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Ballesteros-Duperon E.,Environment and Water Agency of Andalusia | Virgos E.,University King Juan Carlos | Moleon M.,Environment and Water Agency of Andalusia | Moleon M.,University of Witwatersrand | And 3 more authors.
Mammalia | Year: 2015

Hybridisation between domestic cats, Felis catus, and wildcats, Felis silvestris, could lead to the genetic extinction of the latter; therefore, checking hybridisation rates in wild populations is of vital conservation importance. However, detecting hybridisation in the field is particularly challenging. Here, we aim to test the success of morphological-based procedures for discriminating wildcats from their hybrids and domestic cats, against genetic methods. We checked 17 putative Spanish wildcats by using two different classification systems based on coat patterns. None of the putative wildcats analysed in this study seemed to have an admixed genotype. Concordance between genetic and pelage approaches was almost total: only one coat classification produced mixed results with detection of one potential hybrid. Assignment was worse when performed in the field after a rapid examination of coat characters. We conclude that classification systems using coat traits could serve as surrogates of genetic approaches, but only after careful examination of those characters with more discriminatory power. Thus, the control of hybrid populations in the field as a management tool to preserve the genetic identity of wild forms is problematic if based on crude approaches or incomplete classification systems. © 2015 by De Gruyter 2015.


Lujak M.,University King Juan Carlos | Giordani S.,University of Rome Tor Vergata | Ossowski S.,University King Juan Carlos
Neurocomputing | Year: 2015

In this paper we study the problem of the assignment of road paths to vehicles. Due to the assumption that a low percentage of vehicles follow the routes proposed by route guidance systems (RGS) and the increase of the use of the same, the conventional RGS might shortly result obsolete.Assuming a complete road network information at the disposal of RGSs, their proposed paths are related with user optimization which in general can be arbitrarily more costly than the system optimum. However, the user optimum is fair for the drivers of the same Origin-Destination (O-D) pair but it does not guarantee fairness for different O-D pairs. Contrary, the system optimum can produce unfair assignments both for the vehicles of the same as of different O-D pairs. This is the reason why, in this paper, we propose an optimization model which bridges this gap between the user and system optimum, and propose a new mathematical programming formulation based on Nash Welfare optimization which results in a good egalitarian and utilitarian welfare for all O-D pairs. To avoid the issues with the lack of robustness related with the centralized implementation, the proposed model is highly distributed. We test the solution approach through simulation and compare it with the conventional user- and system-optimization. © 2014 Elsevier B.V.


Lujak M.,University King Juan Carlos | Ossowski S.,University King Juan Carlos
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2016

In this paper, we present a short overview of the people flow coordination methods and propose a multi-agent based route recommender architecture for smart spaces which considers the influence of stress on human reactions to the recommended routes. The objective of the architecture is to ensure that people can efficiently move in and among smart spaces while at the same time improve the overall system performance. The functioning of the architecture is demonstrated on a case study. The proposed approach can be used, among others, in route recommendation in smart cities, large public events, and emergency evacuations. © Springer International Publishing Switzerland 2016.


Lujak M.,University King Juan Carlos | Giordani S.,University of Rome Tor Vergata | Ossowski S.,University King Juan Carlos
2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014 | Year: 2014

In this paper we study the problem of the assignment of road paths to vehicles. If we assume available the real-time road network information, then (self-concerned) vehicles select paths in a way related to user optimization which results in Wardrop equilibrium. The latter, even though fair for the vehicles of the same Origin-Destination (O-D) pair, in general can be arbitrarily more costly than the system optimum. System optimization, on the other hand, can produce unfair assignments both for the vehicles of the same as of different O-D pairs. To surmount the performance issue of the user- in respect to the system-optimization while considering the fairness issues, we propose a MAS-based distributed optimization model for path assignment to vehicles from the same and different OD pairs at two levels. On the upper level, the proposed model optimizes the overall O-D pairs' Nash Welfare with the fairness related constraints while on the lower level, for every O-D pair separately, paths are assigned to individual vehicles through the auction algorithm. We test the solution approach through simulation, compare it with the conventional user- and system-optimization, and thus demonstrate that it results in fair and globally efficient path-vehicle assignments. © 2014 IEEE.


Lujak M.,University King Juan Carlos | Giordani S.,University of Rome Tor Vergata | Ossowski S.,University King Juan Carlos
CEUR Workshop Proceedings | Year: 2016

In this paper, we treat pedestrian evacuation in emergency scenarios of networked smart spaces. Personal safety may be jeopardized due to natural catastrophes (e.g., hurricanes, earthquakes, etc.) and/or adversarial actions of intentional enemies. During evacuation, the severity of emergency may increase causing partial or complete blockage of some evacuation routes. Thus, it is of the highest importance to (re)route evacuees based on updated real-time structure safety conditions. In this paper, we propose a multi-agent based architecture for dynamic route safety optimization in large smart space evacuation. The objective of the model is to ensure that the smart space network gets evacuated securely while aptly responding to unpredictable contingencies in the network safety.

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