University of Maribor

About: University of Maribor is a education organization based out in Maribor, Slovenia. It is known for research contribution in the topics: Population & KEKB. The organization has 3987 authors who have published 13077 publications receiving 258339 citations. The organization is also known as: Univerza v Mariboru.

Highly Porous Open-Cellular Monoliths from 2-Hydroxyethyl Methacrylate Based High Internal Phase Emulsions (HIPEs): Preparation and Void Size Tuning

Abstract: Preparation of highly porous (up to 80% pore volume) open-cellular monolithic cross-linked polymers from 2-hydroxyethyl methacrylate is reported. Oil-in-water and water-in-oil high internal phase emulsions are applied as porosity templates, resulting in an interconnected porous structure with void diameters between 550 nm and 18 μm. Significantly larger voids were obtained in the case of oil-in-water emulsions (between 5 and 18 μm) as opposed to water in oil emulsions (approx 600 nm). Controlled coarsening exploiting limited kinetical stability of emulsions was used to obtain monoliths with larger voids, diameters being enlarged 3-fold.

Measurement of inclusive radiative B-meson decays with a photon energy threshold of 1.7 GeV

Abstract: Using 605 fb(-1) of data collected at the Upsilon(4S) resonance we present a measurement of the inclusive radiative B-meson decay channel, B -> X-s gamma. For the lower photon energy thresholds of 1.7, 1.8, 1.9, and 2.0 GeV, as defined in the rest frame of the B meson, we measure the partial branching fraction and the mean and variance of the photon energy spectrum. At the 1.7 GeV threshold we obtain the partial branching fraction BF(B -> X-s gamma)=(3.45 +/- 0.15 +/- 0.40)x10(-4), where the errors are statistical and systematic.

A Discrete and Improved Bat Algorithm for solving a medical goods distribution problem with pharmacological waste collection

Abstract: The work presented in this paper is focused on the resolution of a real-world drugs distribution problem with pharmacological waste collection. With the aim of properly meeting all the real-world restrictions that comprise this complex problem, we have modeled it as a multi-attribute or rich vehicle routing problem (RVRP). The problem has been modeled as a Clustered Vehicle Routing Problem with Pickups and Deliveries, Asymmetric Variable Costs, Forbidden Roads and Cost Constraints. To the best of authors knowledge, this is the first time that such a RVRP problem is tackled in the literature. For this reason, a benchmark composed of 24 datasets, from 60 to 1000 customers, has also been designed. For the developing of this benchmark, we have used real geographical positions located in Bizkaia, Spain. Furthermore, for the proper dealing of the proposed RVRP, we have developed a Discrete and Improved Bat Algorithm (DaIBA). The main feature of this adaptation is the use of the well-known Hamming Distance to calculate the differences between the bats. An effective improvement has been also contemplated for the proposed DaIBA, which consists on the existence of two different neighborhood structures, which are explored depending on the bat's distance regarding the best individual of the swarm. For the experimentation, we have compared the performance of our presented DaIBA with three additional approaches: an evolutionary algorithm, an evolutionary simulated annealing and a firefly algorithm. Additionally, with the intention of obtaining rigorous conclusions, two different statistical tests have been conducted: the Friedman's non-parametric test and the Holm's post-hoc test. Furthermore, an additional experimentation has been performed in terms of convergence. Finally, the obtained outcomes conclude that the proposed DaIBA is a promising technique for addressing the designed problem.

A novel hybrid self-adaptive bat algorithm.

Abstract: Nature-inspired algorithms attract many researchers worldwide for solving the hardest optimization problems. One of the newest members of this extensive family is the bat algorithm. To date, many variants of this algorithm have emerged for solving continuous as well as combinatorial problems. One of the more promising variants, a self-adaptive bat algorithm, has recently been proposed that enables a self-adaptation of its control parameters. In this paper, we have hybridized this algorithm using different DE strategies and applied these as a local search heuristics for improving the current best solution directing the swarm of a solution towards the better regions within a search space. The results of exhaustive experiments were promising and have encouraged us to invest more efforts into developing in this direction.