University of Patras is a university established in 1964 in Patras, Greece. Initially housed in the city centre, the university's campus is now located in the adjacent municipality of Rio. Covering an area of 4.5 km², it is one of the largest in the country. Until September 2002, it was the only university in the Peloponnese and Western Greece with the exception of Epirus.In particular it comprises 5 Schools – School of science, School of Engineering, School of Social Studies and Humanities, School of Health science and School of Business Administration. It is the third largest university in the country, with 18,500 undergraduate students, 2000 post-graduate students, 670 teaching staff, 369 administrative personnel and 403 teaching and research assistants. The initial emphasis on science and technology has been extended to other academic areas such as Health science, Humanities and Business studies. Today, its 22 departments, with a large number of sectors and consequently a great range of disciplines, reflect a balanced academic environment.The university is accessible by GR-8A, and now the new bypass with an interchange in the west and a westbound interchange in the northeast. The elevation is around 50 m above sea level. The facilities lie in the western part, the central part and the northern part. The far western part, and the eastern part are empty.The campus has 4 entrances; the Platonos Entrance Eisodos Platonos, the Dionysios Solomos Entrance Eisodos Dionysiou Solomou, lying to the west next to Papandreou Avenue which is also a road linking to Kastritsi, Georgios Seferis Entrance Eisodos Georgios Seferis in the east and G. Ritsou Entrance Eisodos G. Ritsou with the old GR-8A in the north. The mountains are situated in the southeast. The track and field and sporting grounds are in the southern part. Wikipedia.
Kotsiantis S.B.,University of Patras
Artificial Intelligence Review | Year: 2013
Decision tree techniques have been widely used to build classification models as such models closely resemble human reasoning and are easy to understand. This paper describes basic decision tree issues and current research points. Of course, a single article cannot be a complete review of all algorithms (also known induction classification trees), yet we hope that the references cited will cover the major theoretical issues, guiding the researcher in interesting research directions and suggesting possible bias combinations that have yet to be explored. © 2011 Springer Science+Business Media B.V. Source
Yannopapas V.,University of Patras
New Journal of Physics | Year: 2012
We show that topological frequency band structures emerge in two-dimensional electromagnetic lattices of metamaterial components without the application of an external magnetic field. The topological nature of the band structure manifests itself by the occurrence of exceptional points in the band structure or by the emergence of one-way guided modes. Based on an electromagnetic network with nearly flat frequency bands of nontrivial topology, we propose a coupled-cavity lattice made of superconducting transmission lines and cavity QED components, which is described by the Jaynes-Cummings-Hubbard model and can serve as a simulator of the fractional quantum Hall effect. © IOP Publishing and Deutsche Physikalische Gesellschaft. Source
Yannopapas V.,University of Patras
Physical Review B - Condensed Matter and Materials Physics | Year: 2011
We show that a gyrotropic (chiral) medium supporting a longitudinal-wave excitation exhibits a Dirac point in the corresponding photon dispersion lines. By breaking the time-reversal symmetry in such a medium, the dispersion relation resembles the energy dispersion of a spin-polarized two-dimensional electron gas with Rashba spin-orbit coupling. The resulting split bands of the dispersion relation correspond to nonzero Chern numbers implying the existence of nontrivial topological states of the electromagnetic field. © 2011 American Physical Society. Source
Nearchou A.C.,University of Patras
International Journal of Production Economics | Year: 2011
Particle swarm optimization (PSO) one of the latest developed population heuristics has rarely been applied in production and operations management (POM) optimization problems. A possible reason for this absence is that, PSO was introduced as global optimizer over continuous spaces, while a large set of POM problems are of combinatorial nature with discrete decision variables. PSO evolves floating-point vectors (called particles) and thus, its application to POM problems whose solutions are usually presented by permutations of integers is not straightforward. This paper presents a novel method based on PSO for the simple assembly line balancing problem (SALBP), a well-known NP-hard POM problem. Two criteria are simultaneously considered for optimization: to maximize the production rate of the line (equivalently to minimize the cycle time), and to maximize the workload smoothing (i.e. to distribute the workload evenly as possible to the workstations of the assembly line). Emphasis is given on seeking a set of diverse Pareto optimal solutions for the bi-criteria SALBP. Extensive experiments carried out on multiple test-beds problems taken from the open literature are reported and discussed. Comparisons between the proposed PSO algorithm and two existing multi-objective population heuristics show a quite promising higher performance for the proposed approach. © 2010 Elsevier B.V. All rights reserved. Source
Agency: Cordis | Branch: H2020 | Program: RIA | Phase: PHC-24-2015 | Award Amount: 14.94M | Year: 2016
Pharmacogenomics is the study of genetic variability affecting an individuals response to a drug. Its use allows personalized medicine and reduction in trial and error prescribing leading to more efficacious, safer and cost-effective drug therapy. The U-PGx consortium will investigate a pre-emptive genotyping approach (that is: multiple pharmacogenomic variants are collected prospectively and embedded into the patients electronic record) of a panel of important pharmacogenomic variants as a new model of personalised medicine. To meet this goal we combine existing pharmacogenomics guidelines and novel health IT solutions. Implementation will be conducted at a large scale in seven existing European health care environments and accounts for the diversity in health system organisations and settings. Feasibility, health outcome and cost-effectiveness will be investigated. We will formulate European strategies for improving clinical implementation of pharmacogenomics based on the findings of this project.