Mathematical Models and Exact Algorithms for the Colored Bin Packing Problem

This paper focuses on exact approaches for the Colored Bin Packing Problem (CBPP), a generalization of the classical one-dimensional Bin Packing Problem in which each item has, in addition to its length, a color, and no two items of the same color can appear consecutively in the same bin. To simplify modeling, we present a characterization of any feasible packing of this problem in a way that does not depend on its ordering. Furthermore, we present four exact algorithms for the CBPP. First, we propose a generalization of Val\'erio de Carvalho's arc flow formulation for the CBPP using a graph with multiple layers, each representing a color. Second, we present an improved arc flow formulation that uses a more compact graph and has the same linear relaxation bound as the first formulation. And finally, we design two exponential set-partition models based on reductions to a generalized vehicle routing problem, which are solved by a branch-cut-and-price algorithm through VRPSolver. To compare the proposed algorithms, a varied benchmark set with 574 instances of the CBPP is presented. Results show that the best model, our improved arc flow formulation, was able to solve over 62% of the proposed instances to optimality, the largest of which with 500 items and 37 colors. While being able to solve fewer instances in total, the set-partition models exceeded their arc flow counterparts in instances with a very small number of colors

ESTUDO DE TRELIÇAS PLANAS E ESPACIAIS UTILIZANDO A LINGUAGEM DE PROGRAMAÇÃO PYTHON E O SOFTWARE VTK

Resumo. Cada vez mais se tem construído ferramentas computacionais voltadas para o ensino/aprendizagem de análise de estruturas e, variadas são as opções de linguagens de programação e softwares para computação gráfica (inclusive tipo código aberto) utilizados com esta finalidade. Este trabalho tem por objetivo estudar o comportamento de treliças 2D e 3D no âmbito linear-elástico mediante a construção de um código desenvolvido com a linguagem de programação Python e o pacote numérico NumPy. A formulação dos elementos de barra é descrita. Exemplos extraídos de literaturas conhecidas foram analisados e, na visualização gráfica das respostas obtidas se explorou as funcionalidades do programa Visualization Toolkit (VTK). Comparados os resultados fica comprovada a eficiência dos algoritmos construídos para solução dos problemas estudados. Palavras-chave: Ensino, Aprendizagem, Treliça, MEF, Python, NumPy, VT

Gold(I) and Silver(I) Complexes Containing Hybrid Sulfonamide/Thiourea Ligands as Potential Leishmanicidal Agents

Leishmaniasis is a group of parasitic diseases with the potential to infect more than 1 billion people; however, its treatment is still old and inadequate. In order to contribute to changing this view, this work consisted of the development of complexes derived from MI metal ions with thioureas, aiming to obtain potential leishmanicidal agents. The thiourea ligands (HLR) were obtained by reactions of p-toluenesulfohydrazide with R-isothiocyanates and were used in complexation reactions with AgI and AuI, leading to the formation of complexes of composition [M(HLR)2]X (M = Ag or Au; X = NO3− or Cl−). All compounds were characterized by FTIR, 1H NMR, UV-vis, emission spectroscopy and elemental analysis. Some representatives were additionally studied by ESI-MS and single-crystal XRD. Their properties were further analyzed by DFT calculations. Their cytotoxicity on Vero cells and the extracellular leishmanicidal activity on Leishmania infantum and Leishmania braziliensis cells were evaluated. Additionally, the interaction of the complexes with the Old Yellow enzyme of the L. braziliensis (LbOYE) was examined. The biological tests showed that some compounds present remarkable leishmanicidal activity, even higher than that of the standard drug Glucantime, with different selectivity for the two species of Leishmania. Finally, the interaction studies with LbOYE revealed that this enzyme could be one of their biological targets

Fragment-based determination of a proteinase K structure from MicroED data using ARCIMBOLDO_SHREDDER.

Structure determination of novel biological macromolecules by X-ray crystallography can be facilitated by the use of small structural fragments, some of only a few residues in length, as effective search models for molecular replacement to overcome the phase problem. Independence from the need for a complete pre-existing model with sequence similarity to the crystallized molecule is the primary appeal of ARCIMBOLDO, a suite of programs which employs this ab initio algorithm for phase determination. Here, the use of ARCIMBOLDO is investigated to overcome the phase problem with the electron cryomicroscopy (cryoEM) method known as microcrystal electron diffraction (MicroED). The results support the use of the ARCIMBOLDO_SHREDDER pipeline to provide phasing solutions for a structure of proteinase K from 1.6 Å resolution data using model fragments derived from the structures of proteins sharing a sequence identity of as low as 20%. ARCIMBOLDO_SHREDDER identified the most accurate polyalanine fragments from a set of distantly related sequence homologues. Alternatively, such templates were extracted in spherical volumes and given internal degrees of freedom to refine towards the target structure. Both modes relied on the rotation function in Phaser to identify or refine fragment models and its translation function to place them. Model completion from the placed fragments proceeded through phase combination of partial solutions and/or density modification and main-chain autotracing using SHELXE. The combined set of fragments was sufficient to arrive at a solution that resembled that determined by conventional molecular replacement using the known target structure as a search model. This approach obviates the need for a single, complete and highly accurate search model when phasing MicroED data, and permits the evaluation of large fragment libraries for this purpose

Synaptic Plasticity and Spike Synchronisation in Neuronal Networks

This work was possible by partial financial support from the following Brazilian government agencies: CNPq (154705/2016-0, 311467/2014-8), CAPES, Fundacao Araucaria, and Sao Paulo Research Foundation (processes FAPESP 2011/19296-1, 2015/07311-7, 2016/16148-5, 2016/23398-8, 2015/50122-0). Research supported by grant 2015/50122-0 Sao Paulo Research Foundation (FAPESP) and DFG-IRTG 1740/2.Peer reviewedPostprin

Algorithms for the Bin Packing Problem with Scenarios

This paper presents theoretical and practical results for the bin packing problem with scenarios, a generalization of the classical bin packing problem which considers the presence of uncertain scenarios, of which only one is realized. For this problem, we propose an absolute approximation algorithm whose ratio is bounded by the square root of the number of scenarios times the approximation ratio for an algorithm for the vector bin packing problem. We also show how an asymptotic polynomial-time approximation scheme is derived when the number of scenarios is constant. As a practical study of the problem, we present a branch-and-price algorithm to solve an exponential model and a variable neighborhood search heuristic. To speed up the convergence of the exact algorithm, we also consider lower bounds based on dual feasible functions. Results of these algorithms show the competence of the branch-and-price in obtaining optimal solutions for about 59% of the instances considered, while the combined heuristic and branch-and-price optimally solved 62% of the instances considered

Fragment-based determination of a proteinase K structure from MicroED data using ARCIMBOLDO_SHREDDER.

Structure determination of novel biological macromolecules by X-ray crystallography can be facilitated by the use of small structural fragments, some of only a few residues in length, as effective search models for molecular replacement to overcome the phase problem. Independence from the need for a complete pre-existing model with sequence similarity to the crystallized molecule is the primary appeal of ARCIMBOLDO, a suite of programs which employs this ab initio algorithm for phase determination. Here, the use of ARCIMBOLDO is investigated to overcome the phase problem with the electron cryomicroscopy (cryoEM) method known as microcrystal electron diffraction (MicroED). The results support the use of the ARCIMBOLDO_SHREDDER pipeline to provide phasing solutions for a structure of proteinase K from 1.6 Å resolution data using model fragments derived from the structures of proteins sharing a sequence identity of as low as 20%. ARCIMBOLDO_SHREDDER identified the most accurate polyalanine fragments from a set of distantly related sequence homologues. Alternatively, such templates were extracted in spherical volumes and given internal degrees of freedom to refine towards the target structure. Both modes relied on the rotation function in Phaser to identify or refine fragment models and its translation function to place them. Model completion from the placed fragments proceeded through phase combination of partial solutions and/or density modification and main-chain autotracing using SHELXE. The combined set of fragments was sufficient to arrive at a solution that resembled that determined by conventional molecular replacement using the known target structure as a search model. This approach obviates the need for a single, complete and highly accurate search model when phasing MicroED data, and permits the evaluation of large fragment libraries for this purpose