A characterization of 3-graded Lie algebras generated by a pair

AbstractWe prove that any 3-graded Lie algebra generated by an element of degree −1 and another of degree 1 over a field K of characteristic zero is isomorphic to a 3-graded Lie subalgebra of sl2(K[t]/(p(t)·K[t])) endowed with its usual 3-gradation, for some p(t)∈K[t]. We also give a thorough description of the ideals in the free case

FINITE SIZE SCALING FOR FIRST ORDER TRANSITIONS: POTTS MODEL

The finite-size scaling algorithm based on bulk and surface renormalization of de Oliveira (1992) is tested on q-state Potts models in dimensions D = 2 and 3. Our Monte Carlo data clearly distinguish between first- and second-order phase transitions. Continuous-q analytic calculations performed for small lattices show a clear tendency of the magnetic exponent Y = D - beta/nu to reach a plateau for increasing values of q, which is consistent with the first-order transition value Y = D. Monte Carlo data confirm this trend.Comment: 5 pages, plain tex, 5 EPS figures, in file POTTS.UU (uufiles

Preparation And Characterization Of Maleic Anhydride Grafted Poly (hydroxybutirate-co-hydroxyvalerate)-phbv-g-ma

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)A compatibilizer agent was successfully produced by grafting maleic anhydride (MA) to poly(hydroxybutyrate-co-hydroxyvalerate) (PHBV) chains on a reactive processing by mechanical mixing, using a mixture of PHBV, MA and dicumyl peroxide (DCP) as initiator. The resulting PHBV grafted MA (PHBV-g-MA) was characterized by Fourier transform infrared (FTIR) spectroscopy, thermogravimetric analysis (TGA), differential scanning calorimetry (DSC) and gel permeation chromatography (GPC), and its properties were compared to neat PHBV. FTIR showed an absorption band at 699 cm-1 for PHBV-g-MA, related to CH group of grafted anhydride ring. The initial thermal degradation temperature of the compatibilizer agent was reduced when compared to neat PHBV. DSC analysis showed that after grafting MA onto PHBV the crystallization temperature was about 20°C higher than neat PHBV, and the degree of crystallinity was increased. GPC analysis showed that MA when grafted onto PHBV led to a reduction of molecular weight and polydispersity.191229235CAPES, Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorFAPESP, São Paulo Research FoundationCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP

Fatigue Behavior of Metallic Components Obtained by Topology Optimization for Additive Manufacturing

The main goal of the present research is to propose an integrated methodology to address the fatigue performance of topology optimized components, produced by additive manufacturing. The main steps of the component design will be presented, specially the methods and parameters applied to the topology optimization and the post-smoothing process. The SIMP method was applied in order to obtain a lighter component and a suitable stiffness for the desired application. In addition, since residual stresses are intrinsic to every metallic additive manufacturing process, the influence of those stresses will be also analyzed. The Laser Powder Bed Fusion was numerically simulated aiming at evaluating the residual stresses the workpiece during the manufacturing process and to investigate how they could influence the fatigue behavior of the optimized component. The effect of the built orientation of the workpiece on the residual stresses at some selected potential critical points are evaluated. The final design solution presented a stiffness/volume ratio nearly 6 times higher when compared to the initial geometry. By choosing the built orientation, it is possible impact favorably in the fatigue life of the component

Fatigue Crack Propagation Rates Prediction Using Probabilistic Strain‐Based Models

This chapter proposes an evaluation and extension of the UniGrow model to predict the fatigue crack propagation rate, based on a local strain-based approach to fatigue. The UniGrow model, classified as a residual stress‐based crack propagation model, is here applied to derive probabilistic fatigue crack propagation fields (p-da/dN-ΔK-R fields) for P355NL1 pressure vessel steel, covering distinct stress R-ratios. The results are compared with available experimental data. The required strain-life data are experimentally achieved and evaluated. The material representative element size, ρ*, a key parameter in the UniGrow model, is assessed by means of a trial-and-error procedure of inverse analysis. Moreover, residual stresses are computed for varying crack lengths and minimum-to-maximum stress ratios. Elastoplastic stress fields around the crack apex are evaluated with analytical relations and compared with elastoplastic finite-element (FE) computations. The deterministic strain-life relations proposed in the original UniGrow model are replaced by the probabilistic strain‐life fields (p-ε-N) proposed by Castillo and Canteli. This probabilistic model is also extended by considering a damage parameter to allow for mean stress effects. In particular, a probabilistic Smith-Watson-Topper field (p-SWT-N), alternatively to the conventional p-ε-N field, is proposed and applied to derive the probabilistic fatigue crack propagation fields

GenSeed-HMM: A tool for progressive assembly using profile HMMs as seeds and its application in Alpavirinae viral discovery from metagenomic data

This work reports the development of GenSeed-HMM, a program that implements seed-driven progressive assembly, an approach to reconstruct specific sequences from unassembled data, starting from short nucleotide or protein seed sequences or profile Hidden Markov Models (HMM). The program can use any one of a number of sequence assemblers. Assembly is performed in multiple steps and relatively few reads are used in each cycle, consequently the program demands low computational resources. As a proof-of-concept and to demonstrate the power of HMM-driven progressive assemblies, GenSeed-HMM was applied to metagenomic datasets in the search for diverse ssDNA bacteriophages from the recently described Alpavirinae subfamily. Profile HMMs were built using Alpavirinae-specific regions from multiple sequence alignments using either the viral protein 1 (VP1) (major capsid protein) or VP4 (genome replication initiation protein). These profile HMMs were used by GenSeed-HMM (running Newbler assembler) as seeds to reconstruct viral genomes from sequencing datasets of human fecal samples. All contigs obtained were annotated and taxonomically classified using similarity searches and phylogenetic analyses. The most specific profile HMM seed enabled the reconstruction of 45 partial or complete Alpavirinae genomic sequences. A comparison with conventional (global) assembly of the same original dataset, using Newbler in a standalone execution, revealed that GenSeed-HMM outperformed global genomic assembly in several metrics employed. This approach is capable of detecting organisms that have not been used in the construction of the profile HMM, which opens up the possibility of diagnosing novel viruses, without previous specific information, constituting a de novo diagnosis. Additional applications include, but are not limited to, the specific assembly of extrachromosomal elements such as plastid and mitochondrial genomes from metagenomic data. Profile HMM seeds can also be used to reconstruct specific protein coding genes for gene diversity studies, and to determine all possible gene variants present in a metagenomic sample. Such surveys could be useful to detect the emergence of drug-resistance variants in sensitive environments such as hospitals and animal production facilities, where antibiotics are regularly used. Finally, GenSeed-HMM can be used as an adjunct for gap closure on assembly finishing projects, by using multiple contig ends as anchored seeds

Developing a victorious strategy to the second strong gravitational lensing data challenge

Strong lensing is a powerful probe of the matter distribution in galaxies and clusters and a relevant tool for cosmography. Analyses of strong gravitational lenses with deep learning have become a popular approach due to these astronomical objects’ rarity and image complexity. Next-generation surveys will provide more opportunities to derive science from these objects and an increasing data volume to be analysed. However, finding strong lenses is challenging, as their number densities are orders of magnitude below those of galaxies. Therefore, specific strong lensing search algorithms are required to discover the highest number of systems possible with high purity and low false alarm rate. The need for better algorithms has prompted the development of an open community data science competition named strong gravitational lensing challenge (SGLC). This work presents the deep learning strategies and methodology used to design the highest scoring algorithm in the second SGLC (II SGLC). We discuss the approach used for this data set, the choice of a suitable architecture, particularly the use of a network with two branches to work with images in different resolutions, and its optimization. We also discuss the detectability limit, the lessons learned, and prospects for defining a tailor-made architecture in a survey in contrast to a general one. Finally, we release the models and discuss the best choice to easily adapt the model to a data set representing a survey with a different instrument. This work helps to take a step towards efficient, adaptable, and accurate analyses of strong lenses with deep learning frameworks