Portable Hardness tester for timber classification

Hardness is a property largely used in material specifications, mechanical and metallurgical research and quality control of several materials. Specifically for timber, Janka hardness is a simple, quick and easy test, with good correlations with the compression parallel to grain strength, a strong reference in structural classification for this material. More recently, international studies have reported the use of Brinell hardness for timber assessment which resumes the advantages previously mentioned for Janka hardness and make it easier to be performed in the field, especially because of the lower magnitude of the involved loads. A first generation of an equipment for field evaluation of hardness in wood - Portable Hardness tester for wood - based on Brinell hardness has already been developed by the Research Group on Forest Products from FCA/UNESP, Brazil, with very good correlations between the evaluated hardness and several other mechanical properties of the material when performing tests with different species of native and reforested wood (traditionally used as ties - sleepers - in railways). This paper presents results obtained in the experimental program with the first generation of this equipment and preliminary tests with its second generation, which uses accelerometers to substitute the indentation measurements in wood. For the first generation of the equipment functional and calibration tests were carried out using 16 native and reforestation timber lots, among there E. citriodora, E. tereticornis, E. saligna, E. urophylla, E. grandis, Goupia glabra and Bagassa guianenses, with different origins and ages. The results obtained confirm its potential in the classification of specimens, with inclusion errors varying from 4.5% to 16.6%.

An efficient and versatile synthesis of acylpolyamine spider toxins

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Classificação Estrutural e Qualidade da Madeira do Clone GT 1 de Hevea brasiliensis Muell. Arg

Latex is the main product extracted from rubber trees (Hevea brasiliensis). In Brazil, at the end of the production cycle of latex, the wood of rubber tree is traditionally used for energy purposes, but several international studies have reported consolidated practices of adding value to it. The objective of this paper was to evaluate the quality of wood and classify it structurally based on its mechanical properties. Six 20-year-old trees of the clone GT 1 of rubber tree proceeding from Itajobi, State of Sao Paulo, Brazil were sampled. Reduced dimensions specimens in the radial direction of the wood were produced to evaluate the quality by compression parallel to the grain, static bending and Janka hardness tests. Two specimens, one from the lower log (since the ground up to breast height) and one from the higher log (from breast height up to 2.50 m) were produced for structural classification of the wood based on the characteristic strength in compression parallel to the grain (NBR 7190 norm, 1997). The wood was classified as C40 (fc0k ≥ 40 MPa) class. Results revealed that the strength was not statistically different in the radial direction (except for the Janka hardness), though tending to increase from pith to bark.

Desenvolvimento de equipamento para avaliação em campo da dureza de madeiras para dormente ferroviário

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

O dispositivo analítico no hospital na clínica com transexuais: entre o ser e o sujeito

Pós-graduação em Psicologia - FCLAS

Insights into the Specificity of Thioredoxin Reductase-Thioredoxin Interactions. A Structural and Functional Investigation of the Yeast Thioredoxin System

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Structural FRF acquisition via electric impedance measurement applied to damage location

Continuing development of new materials makes systems lighter and stronger permitting more complex systems to provide more functionality and flexibility that demands a more effective evaluation of their structural health. Smart material technology has become an area of increasing interest in this field. The combination of smart materials and artificial neural networks can be used as an excellent tool for pattern recognition, turning their application adequate for monitoring and fault classification of equipment and structures. In order to identify the fault, the neural network must be trained using a set of solutions to its corresponding forward Variational problem. After the training process, the net can successfully solve the inverse variational problem in the context of monitoring and fault detection because of their pattern recognition and interpolation capabilities. The use of structural frequency response function is a fundamental portion of structural dynamic analysis, and it can be extracted from measured electric impedance through the electromechanical interaction of a piezoceramic and a structure. In this paper we use the FRF obtained by a mathematical model (FEM) in order to generate the training data for the neural networks, and the identification of damage can be done by measuring electric impedance, since suitable data normalization correlates FRF and electrical impedance.

Structural damage detection by fuzzy clustering

The development of strategies for structural health monitoring (SHM) has become increasingly important because of the necessity of preventing undesirable damage. This paper describes an approach to this problem using vibration data. It involves a three-stage process: reduction of the time-series data using principle component analysis (PCA), the development of a data-based model using an auto-regressive moving average (ARMA) model using data from an undamaged structure, and the classification of whether or not the structure is damaged using a fuzzy clustering approach. The approach is applied to data from a benchmark structure from Los Alamos National Laboratory, USA. Two fuzzy clustering algorithms are compared: fuzzy c-means (FCM) and Gustafson-Kessel (GK) algorithms. It is shown that while both fuzzy clustering algorithms are effective, the GK algorithm marginally outperforms the FCM algorithm. (C) 2008 Elsevier Ltd. All rights reserved.

Relationship between global structural parameters and Enzyme Commission hierarchy: Implications for function prediction

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Identification of structural damage in flexible structures using system norm and neural networks

Nowadays there is great interest in damage identification using non destructive tests. Predictive maintenance is one of the most important techniques that are based on analysis of vibrations and it consists basically of monitoring the condition of structures or machines. A complete procedure should be able to detect the damage, to foresee the probable time of occurrence and to diagnosis the type of fault in order to plan the maintenance operation in a convenient form and occasion. In practical problems, it is frequent the necessity of getting the solution of non linear equations. These processes have been studied for a long time due to its great utility. Among the methods, there are different approaches, as for instance numerical methods (classic), intelligent methods (artificial neural networks), evolutions methods (genetic algorithms), and others. The characterization of damages, for better agreement, can be classified by levels. A new one uses seven levels of classification: detect the existence of the damage; detect and locate the damage; detect, locate and quantify the damages; predict the equipment's working life; auto-diagnoses; control for auto structural repair; and system of simultaneous control and monitoring. The neural networks are computational models or systems for information processing that, in a general way, can be thought as a device black box that accepts an input and produces an output. Artificial neural nets (ANN) are based on the biological neural nets and possess habilities for identification of functions and classification of standards. In this paper a methodology for structural damages location is presented. This procedure can be divided on two phases. The first one uses norms of systems to localize the damage positions. The second one uses ANN to quantify the severity of the damage. The paper concludes with a numerical application in a beam like structure with five cases of structural damages with different levels of severities. The results show the applicability of the presented methodology. A great advantage is the possibility of to apply this approach for identification of simultaneous damages.

Classification of lumber of Eucalyptus using non-destructive tests

Classification and standardization of the sawn wood is a usual activity, developed by countries that come as great consumers of this material. Brazil does not practice the classification of sawn wood. This work had the main objective of evaluating the sensibility of most common non-destructive tests in the classification of dimension lumber from fast grown Eucalyptus plantation. Wood was obtained from genetic material cultivated at Minas Gerais State, Brazil. 296 beams of structural dimensions (6 cm × 12 cm × 280 cm) from 10 different clones of Eucalyptus were sampled. Beams were non-destructively (stress wave, ultrasound and transverse vibration) and destructively (static bending and compression parallel to grain) tested. Non-destructive results showed sensibility in the classification of structural dimension lumber, being possible to establish wave velocity intervals that attend to the main strength classes reported by Wooden Structures Brazilian Code.

Automatic classification of enzyme family in protein annotation

Most of the tasks in genome annotation can be at least partially automated. Since this annotation is time-consuming, facilitating some parts of the process - thus freeing the specialist to carry out more valuable tasks - has been the motivation of many tools and annotation environments. In particular, annotation of protein function can benefit from knowledge about enzymatic processes. The use of sequence homology alone is not a good approach to derive this knowledge when there are only a few homologues of the sequence to be annotated. The alternative is to use motifs. This paper uses a symbolic machine learning approach to derive rules for the classification of enzymes according to the Enzyme Commission (EC). Our results show that, for the top class, the average global classification error is 3.13%. Our technique also produces a set of rules relating structural to functional information, which is important to understand the protein tridimensional structure and determine its biological function. © 2009 Springer Berlin Heidelberg.

Phylogenetic and structural studies of a novel equine papillomavirus identified from aural plaques

Papillomaviruses (PVs) infect a wide range of animal species and show great genetic diversity. To date, excluding equine sarcoids, only three species of PVs were identified associated with lesions in horses: Equus caballus papillomavirus 1 (EcPV1-cutaneous), EcPV2 (genital) and EcPV3 (aural plaques). In this study, we identified a novel equine PV from aural plaques, which we designated EcPV4. Cutaneous samples from horses with lesions that were microscopically diagnosed as aural plaques were subjected to DNA extraction, amplification and sequencing. Rolling circle amplification and inverse PCR with specific primers confirmed the presence of an approximately 8. kb circular genome. The full-length EcPV4 L1 major capsid protein sequence has 1488 nucleotides (495 amino acids). EcPV4 had a sequence identity of only 53.3%, 60.2% and 51.7% when compared with the published sequences for EcPV1, EcPV2 and EcPV3, respectively. A Bayesian phylogenetic analysis indicated that EcPV4 clusters with EcPV2, but not with EcPV1 and EcPV3. Using the current PV classification system that is based on the nucleotide sequence of L1, we could not define the genus of the newly identified virus. Therefore, a structural analysis of the L1 protein was carried out to aid in this classification because EcPV4 cause lesion similar to the lesion caused by EcPV3. A comparison of the superficial loops demonstrated a distinct amino acid conservation pattern between EcPV4/EcPV2 and EcPV4/EcPV3. These results demonstrate the presence of a new equine PV species and that structural studies could be useful in the classification of PVs. © 2012 Elsevier B.V.