Fast 3-coloring Triangle-Free Planar Graphs

Although deciding whether the vertices of a planar graph can be colored with three colors is NP-hard, the widely known Grötzsch’s theorem states that every triangle-free planar graph is 3-colorable. We show the first o(n ²) algorithm for 3-coloring vertices of triangle-free planar graphs. The time complexity of the algorithm is $\mathcal{O}(n\log n)Although deciding whether the vertices of a planar graph can be colored with three colors is NP-hard, the widely known Gr?tzsch’s theorem states that every triangle-free planar graph is 3-colorable. We show the first o(n ²) algorithm for 3-coloring vertices of triangle-free planar graphs. The time complexity of the algorithm is O(nlogn)\mathcal{O}(n\log n) .     

Fast averaging peer group filter for the impulsive noise removal in color images

In the paper, a new approach to the impulsive noise removal in color images is presented. The new filtering design is based on the peer group concept, which determines the membership of a central pixel of the filtering window to its local neighborhood, in terms of the number of close pixels. Two pixels are declared as close if their distance in a given color space does not exceed a predefined threshold value. A pixel is treated as not corrupted by the impulsive noise process, if its peer group consists of at least two close pixels, otherwise this pixel is replaced by a weighted average of uncorrupted samples from the local neighborhood. The peer group size assigned to each pixel is used for the averaging operation, so that pixels which have many peers are taken with higher weight. The new filtering design proved to restore efficiently color images corrupted by even strong impulsive noise, while preserving tiny image details. The beneficial property of the proposed filter is its very low computational complexity, which allows its application in real-time image processing tasks.     

3D Printing of Microgel-Loaded Modular Microcages as Instructive Scaffolds for Tissue Engineering

Biomaterial scaffolds have served as the foundation of tissue engineering and regenerative medicine. However, scaffold systems are often difficult to scale in size or shape in order to fit defect-specific dimensions, and thus provide only limited spatiotemporal control of therapeutic delivery and host tissue responses. Here, a lithography-based 3D printing strategy is used to fabricate a novel miniaturized modular microcage scaffold system, which can be assembled and scaled manually with ease. Scalability is based on an intuitive concept of stacking modules, like conventional toy interlocking plastic blocks, allowing for literally thousands of potential geometric configurations, and without the need for specialized equipment. Moreover, the modular hollow-microcage design allows each unit to be loaded with biologic cargo of different compositions, thus enabling controllable and easy patterning of therapeutics within the material in 3D. In summary, the concept of miniaturized microcage designs with such straight-forward assembly and scalability, as well as controllable loading properties, is a flexible platform that can be extended to a wide range of materials for improved biological performance.     

Virtual screening and experimental verification to identify potential inhibitors of the ErmC methyltransferase responsible for bacterial resistance against macrolide antibiotics

Methyltransferases from the Erm family catalyze S-adenosyl-L-methionine-dependent modification of a specific adenine residue in bacterial 23S rRNA, thereby conferring resistance to clinically important macrolide, lincosamide, and streptogramin B antibiotics. Thus far, no inhibitors of these enzymes have been identified or designed that would effectively abolish the resistance in vivo. We used the crystal structure of ErmC' methyltransferase as a target for structure-based virtual screening of a database composed of 58,679 lead-like compounds. Among 77 compounds selected for experimental validation (63 predicted to bind to the catalytic pocket and 14 compounds predicted to bind to the putative RNA binding site), we found several novel inhibitors that decrease the minimal inhibitory concentration of a macrolide antibiotic erythromycin toward an Escherichia coli strain that constitutively expresses ErmC'. Eight of them have IC(50) values in the micromolar range. Analysis of docking models of the identified inhibitors suggests a novel strategy to develop potent and clinically useful inhibitors.     

Tetragonal Diiodotetrapyridinedicopper(I): Structure,Luminescence, and Computational Modeling

We report on a new crystal modification of (CuIPy₂) _n (Py?=?pyridine), a compound first reported by White et al. In contrast to White’s orthorhombic structure, which is comprised of rhomboid iodide-bridged dimers, Cu₂I₂Py₄, our new tetragonal crystal structure in space group P4₁2₁2 is disordered and can be interpreted as either iodide-bridged dimers or helical chains. To determine the structure type, variable temperature X-ray diffraction and luminescence measurements were carried out. The photoluminescence spectrum shows a distinct cluster-centered transition at high excitation energies which is consistent with the dimer structure. DFT and TD-DFT calculations were performed to explain the difference between the emission spectrum at high energies compared to low energies. Furthermore, correlation of the luminescence spectrum with the X-ray results as temperature is varied demonstrates that the cluster-centered luminescence band in Cu₂I₂Py₄ arises from close Cu?Cu distances which vary with temperature. A low temperature X-ray crystallographic redetermination of the cubane tetrameric Cu₄I₄Py₄ is also presented. Both Cu₂I₂Py₄ and Cu₄I₄Py₄ structures show distortion of the Cu _n I _n core cluster at low temperature resulting in reduced Cu?Cu distances, but with I?I distances roughly unchanged.     

Assessing the Scaling Subtraction Method for Impact Damage Detection in Composite Plates

The scaling subtraction method (SSM) is a non-destructive measurement approach used to extract nonlinear features from the elastic response of a structure. As such it can be used for damage detection purposes by identifying nonlinearities that may result from the presence of micro cracks or inclusions in granular and metallic materials. The effectiveness of such a technique to detect the presence of damage modes typical of laminated composite materials has not been yet assessed. With the purpose of filling this gap, in this paper the SSM is applied to inspect two laminated composite plates with different sizes, impact positions and sensor arrangement. Intact and damaged specimens are tested under harmonic excitations of different amplitude and frequency (the latter selected among the ultrasonic natural frequencies of the two plates). For each excitation case the recorded vibration signals are subtracted from the linearly rescaled reference signals and the SSM nonlinear indicators are calculated. The sensitivity of the method to the presence of damage is assessed in different sensor-receiver scenarios as well as for different excitation frequency and amplitude levels. Finite element numerical investigations are also performed to make comparisons with the experimental results.     

Mechanical and Tribological Properties of HVOF-Sprayed (Cr<Subscript>3</Subscript>C<Subscript>2</Subscript>-NiCr+Ni) Composite Coating on Ductile Cast Iron

The aim of the investigations was to compare the microstructure, mechanical, and wear properties of Cr₃C₂-NiCr+Ni and Cr₃C₂-NiCr coatings deposited by HVOF technique (the high-velocity oxygen fuel spray process) on ductile cast iron. The effect of nickel particles added to the chromium carbide coating on mechanical and wear behavior in the system of Cr ₃ C ₂ -NiCr+Ni/ductile cast iron was analyzed in order to improve the lifetime of coated materials. The structure with particular emphasis of characteristic of the interface in the system of composite coating (Cr ₃ C ₂ -NiCr+Ni)/ductile cast iron was studied using the optical, scanning, and transmission electron microscopes, as well as the analysis of chemical and phase composition in microareas. Experimental results show that HVOF-sprayed Cr₃C₂-NiCr+Ni composite coating exhibits low porosity, high hardness, dense structure with large, partially molten Ni particles and very fine Cr₃C₂ and Cr₇C₃ particles embedded in NiCr alloy matrix, coming to the size of nanocrystalline. The results were discussed in reference to examination of bending strength considering cracking and delamination in the system of composite coating (Cr ₃ C ₂ -NiCr+Ni)/ductile cast iron as well as hardness and wear resistance of the coating. The composite structure of the coating provides the relatively good plasticity of the coating, which in turn has a positive effect on the adhesion of coating to the substrate and cohesion of the composite coating (Cr₃C₂-NiCr+Ni) in wear conditions.     

Learning of fuzzy cognitive maps using density estimate

Fuzzy cognitive maps (FCMs) are convenient and widely used architectures for modeling dynamic systems, which are characterized by a great deal of flexibility and adaptability. Several recent works in this area concern strategies for the development of FCMs. Although a few fully automated algorithms to learn these models from data have been introduced, the resulting FCMs are structurally considerably different than those developed by human experts. In particular, maps that were learned from data are much denser (with the density over 90% versus about 40% density of maps developed by humans). The sparseness of the maps is associated with their interpretability: the smaller the number of connections is, the higher is the transparency of the map. To this end, a novel learning approach, sparse real-coded genetic algorithms (SRCGAs), to learn FCMs is proposed. The method utilizes a density parameter to guide the learning toward a formation of maps of a certain predefined density. Comparative tests carried out for both synthetic and real-world data demonstrate that, given a suitable density estimate, the SRCGA method significantly outperforms other state-of-the-art learning methods. When the density estimate is unknown, the new method can be used in an automated fashion using a default value, and it is still able to produce models whose performance exceeds or is equal to the performance of the models generated by other methods.     

Depth-averaged Lattice Boltzmann and Finite Element methods for single-phase flows in fractures with obstacles

We use Lattice Boltzmann Method (LBM) MRT and Cumulant schemes to study the performance and accuracy of single-phase flow modeling for propped fractures. The simulations are run using both the two- and three-dimensional Stokes equations, and a 2.5D Stokes–Brinkman approximate model. The LBM results are validated against Finite Element Method (FEM) simulations and an analytical solution to the Stokes–Brinkman flow around an isolated circular obstacle. Both LBM and FEM 2.5D Stokes–Brinkman models are able to reproduce the analytical solution for an isolated circular obstacle. In the case of 2D Stokes and 2.5D Stokes–Brinkman models, the differences between the extrapolated fracture permeabilities obtained with LBM and FEM simulations for fractures with multiple obstacles are below 1%. The differences between the fracture permeabilities computed using 3D Stokes LBM and FEM simulations are below 8% . The differences between the 3D Stokes and 2.5 Stokes–Brinkman results are less than 7% for FEM study, and 8% for the LBM case. The velocity perturbations that are introduced around the obstacles are not fully captured by the parabolic velocity profile inherent to the 2.5D Stokes–Brinkman model.