Evolution of surface properties of ornamental granitoids exposed to high temperatures

Granite submitted to high temperatures may lead to the loss of aesthetic values even before structural damage is caused. Thirteen granitoids were exposed to target temperatures, 200 °C, 400 °C, 600 °C, 800 °C and 1000 °C. Damage characterisation, including roughness, colour and oxidation of chromogen elements by means of X-ray photoelectron spectroscopy (XPS) was assessed. Altered granitoids are more resistant to structural failure but redden rapidly. Black mica-rich granitoids turn into yellow with a maximum at 800 °C. Alkali feldspar-rich granitoids redden progressively due to iron oxidation. Roughness varies progressively in mica-rich granitoids, while in mica-poor granitoids, an increase in roughness precedes catastrophic failure.

MEASURING HEAD IMPACT CONTACT PRESSURE IN COLLEGIATE FOOTBALL GAMES TO CORRELATE HEAD KINEMATICS TO BRAIN KINETICS ELUCIDATING BRAIN INJURY DYNAMICS

Does a brain store thoughts and memories the way a computer saves its files? How can a single hit or a fall erase all those memories? Brain Mapping and traumatic brain injuries (TBIs) have become widely researched fields today. Many researchers have been studying TBIs caused to adult American football players however youth athletes have been rarely considered for these studies, contradicting to the fact that American football enrolls highest number of collegiate and high-school children than adults. This research is an attempt to contribute to the field of youth TBIs. Earlier studies have related head kinematics (linear and angular accelerations) to TBIs. However, fewer studies have dealt with brain kinetics (impact pressures and stresses) occurring during head-on collisions. The National Operating Committee on Standards for Athletic Equipment (NOCSAE) drop tests were conducted for linear impact accelerations and the Head Impact Contact Pressures (HICP) calculated from them were applied to a validated FE model. The results showed lateral region of the head as the most vulnerable region to damage from any drop height or impact distance followed by posterior region. The TBI tolerance levels in terms of Von-Mises and Maximum Principal Stresses deduced for lateral impact were 30 MPa and 18 MPa respectively. These levels were corresponding to 2.625 feet drop height. The drop heights beyond this value will result in TBI causing stress concentrations in human head without any detectable structural damage to the brain tissue. This data can be utilized for designing helmets that provide cushioning to brain along with providing a resistance to shear.

Some problems and errors in cytogenetic biodosimetry

Human radiosensitivity is a quantitative trait that is generally subject to binomial distribution. Individual radiosensitivity, however, may deviate significantly from the mean (by 2-3 standard deviations). Thus, the same dose of radiation may result in different levels of genotoxic damage (commonly measured as chromosome aberration rates) in different individuals. There is significant genetic component in individual radiosensitivity. It is related to carriership of variant alleles of various single-nucleotide polymorphisms (most of these in genes coding for proteins functioning in DNA damage identification and repair); carriership of different number of alleles producing cumulative effects; amplification of gene copies coding for proteins responsible for radioresistance, mobile genetic elements, and others. Among the other factors influencing individual radioresistance are: radioadaptive response; bystander effect; levels of endogenous substances with radioprotective and antimutagenic properties and environmental factors such as lifestyle and diet, physical activity, psychoemotional state, hormonal state, certain drugs, infections and others. These factors may have radioprotective or sensibilising effects. Apparently, there are too many factors that may significantly modulate the biological effects of ionising radiation. Thus, conventional methodologies for biodosimetry (specifically, cytogenetic methods) may produce significant errors if personal traits that may affect radioresistance are not accounted for.

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.

Adaptive filter feature identification for structural health monitoring in an aeronautical panel

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A SequenceSpace analysis of Lys49 phospholipases A2: Clues towards identification of residues involved in a novel mechanism of membrane damage and in myotoxicity

'SequenceSpace' analysis is a novel approach which has been used to identify unique amino acids within a subfamily of phospholipases A2 (PLA2) in which the highly conserved active site residue Asp49 is substituted by Lys (Lys49-PLA2s). Although Lys49-PLA2s do not bind the catalytic co-factor Ca2+ and possess extremely low catalytic activity, they demonstrate a Ca2+-independent membrane damaging activity through a poorly understood mechanism, which does not involve lipid hydrolysis. Additionally, Lys49-PLA2s possess combined myotoxic, oedema forming and cardiotoxic pharmacological activities, however the structural basis of these varied functions is largely unknown. Using the 'SequenceSpace' analysis we have identified nine residues highly unique to the Lys49-PLA2 sub-family, which are grouped in three amino acid clusters in the active site, hydrophobic substrate binding channel and homodimer interface regions. These three highly specific residue clusters may have relevance for the Ca2+-independent membrane damaging activity. Of a further 15 less stringently conserved residues, nine are located in two additional clusters which are well isolated from the active site region. The less strictly conserved clusters have been used in predictive sequence searches to correlate amino acid patterns in other venom PLA2s with their pharmacological activities, and motifs for presynaptic and combined toxicities are proposed.

Adaptive filter feature identification for structural health monitoring in aeronautical panel

This paper presents an approach for structural health monitoring (SHM) by using adaptive filters. The experimental signals from different structural conditions provided by piezoelectric actuators/sensors bonded in the test structure are modeled by a discrete-time recursive least square (RLS) filter. The biggest advantage to use a RLS filter is the clear possibility to perform an online SHM procedure since that the identification is also valid for non-stationary linear systems. An online damage-sensitive index feature is computed based on autoregressive (AR) portion of coefficients normalized by the square root of the sum of the square of them. The proposed method is then utilized in a laboratory test involving an aeronautical panel coupled with piezoelectric sensors/actuators (PZTs) in different positions. A hypothesis test employing the t-test is used to obtain the damage decision. The proposed algorithm was able to identify and localize the damages simulated in the structure. The results have shown the applicability and drawbacks the method and the paper concludes with suggestions to improve it. ©2010 Society for Experimental Mechanics Inc.

Application of Volterra series in nonlinear mechanical system identification and in structural health monitoring problems

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

Identification of intermediate debonding damage in FRP-plated RC beams based on multi-objective particle swarm optimization without updated baseline model

Fiber reinforced polymer composites (FRP) have found widespread usage in the repair and strengthening of concrete structures. FRP composites exhibit high strength-to-weight ratio, corrosion resistance, and are convenient to use in repair applications. Externally bonded FRP flexural strengthening of concrete beams is the most extended application of this technique. A common cause of failure in such members is associated with intermediate crack-induced debonding (IC debonding) of the FRP substrate from the concrete in an abrupt manner. Continuous monitoring of the concrete?FRP interface is essential to pre- vent IC debonding. Objective condition assessment and performance evaluation are challenging activities since they require some type of monitoring to track the response over a period of time. In this paper, a multi-objective model updating method integrated in the context of structural health monitoring is demonstrated as promising technology for the safety and reliability of this kind of strengthening technique. The proposed method, solved by a multi-objective extension of the particle swarm optimization method, is based on strain measurements under controlled loading. The use of permanently installed fiber Bragg grating (FBG) sensors embedded into the FRP-concrete interface or bonded onto the FRP strip together with the proposed methodology results in an automated method able to operate in an unsupervised mode.

Analysis of dynamic process models for structural insight and model reduction .1. Structural identification measures

An important consideration in the development of mathematical models for dynamic simulation, is the identification of the appropriate mathematical structure. By building models with an efficient structure which is devoid of redundancy, it is possible to create simple, accurate and functional models. This leads not only to efficient simulation, but to a deeper understanding of the important dynamic relationships within the process. In this paper, a method is proposed for systematic model development for startup and shutdown simulation which is based on the identification of the essential process structure. The key tool in this analysis is the method of nonlinear perturbations for structural identification and model reduction. Starting from a detailed mathematical process description both singular and regular structural perturbations are detected. These techniques are then used to give insight into the system structure and where appropriate to eliminate superfluous model equations or reduce them to other forms. This process retains the ability to interpret the reduced order model in terms of the physico-chemical phenomena. Using this model reduction technique it is possible to attribute observable dynamics to particular unit operations within the process. This relationship then highlights the unit operations which must be accurately modelled in order to develop a robust plant model. The technique generates detailed insight into the dynamic structure of the models providing a basis for system re-design and dynamic analysis. The technique is illustrated on the modelling for an evaporator startup. Copyright (C) 1996 Elsevier Science Ltd

Damage propagation in composite Materials meso-mechanical models

Composite materials have a complex behavior, which is difficult to predict under different types of loads. In the course of this dissertation a methodology was developed to predict failure and damage propagation of composite material specimens. This methodology uses finite element numerical models created with Ansys and Matlab softwares. The methodology is able to perform an incremental-iterative analysis, which increases, gradually, the load applied to the specimen. Several structural failure phenomena are considered, such as fiber and/or matrix failure, delamination or shear plasticity. Failure criteria based on element stresses were implemented and a procedure to reduce the stiffness of the failed elements was prepared. The material used in this dissertation consist of a spread tow carbon fabric with a 0°/90° arrangement and the main numerical model analyzed is a 26-plies specimen under compression loads. Numerical results were compared with the results of specimens tested experimentally, whose mechanical properties are unknown, knowing only the geometry of the specimen. The material properties of the numerical model were adjusted in the course of this dissertation, in order to find the lowest difference between the numerical and experimental results with an error lower than 5% (it was performed the numerical model identification based on the experimental results).

Identification of structural variation in mouse genomes.

Structural variation is variation in structure of DNA regions affecting DNA sequence length and/or orientation. It generally includes deletions, insertions, copy-number gains, inversions, and transposable elements. Traditionally, the identification of structural variation in genomes has been challenging. However, with the recent advances in high-throughput DNA sequencing and paired-end mapping (PEM) methods, the ability to identify structural variation and their respective association to human diseases has improved considerably. In this review, we describe our current knowledge of structural variation in the mouse, one of the prime model systems for studying human diseases and mammalian biology. We further present the evolutionary implications of structural variation on transposable elements. We conclude with future directions on the study of structural variation in mouse genomes that will increase our understanding of molecular architecture and functional consequences of structural variation.

Structural and Resting State Functional Connectivity of the Subthalamic Nucleus: Identification of Motor STN Parts and the Hyperdirect Pathway.

Deep brain stimulation (DBS) for Parkinson's disease often alleviates the motor symptoms, but causes cognitive and emotional side effects in a substantial number of cases. Identification of the motor part of the subthalamic nucleus (STN) as part of the presurgical workup could minimize these adverse effects. In this study, we assessed the STN's connectivity to motor, associative, and limbic brain areas, based on structural and functional connectivity analysis of volunteer data. For the structural connectivity, we used streamline counts derived from HARDI fiber tracking. The resulting tracks supported the existence of the so-called "hyperdirect" pathway in humans. Furthermore, we determined the connectivity of each STN voxel with the motor cortical areas. Functional connectivity was calculated based on functional MRI, as the correlation of the signal within a given brain voxel with the signal in the STN. Also, the signal per STN voxel was explained in terms of the correlation with motor or limbic brain seed ROI areas. Both right and left STN ROIs appeared to be structurally and functionally connected to brain areas that are part of the motor, associative, and limbic circuit. Furthermore, this study enabled us to assess the level of segregation of the STN motor part, which is relevant for the planning of STN DBS procedures.

Identification of optimal structural connectivity using functional connectivity and neural modeling.

The complex network dynamics that arise from the interaction of the brain's structural and functional architectures give rise to mental function. Theoretical models demonstrate that the structure-function relation is maximal when the global network dynamics operate at a critical point of state transition. In the present work, we used a dynamic mean-field neural model to fit empirical structural connectivity (SC) and functional connectivity (FC) data acquired in humans and macaques and developed a new iterative-fitting algorithm to optimize the SC matrix based on the FC matrix. A dramatic improvement of the fitting of the matrices was obtained with the addition of a small number of anatomical links, particularly cross-hemispheric connections, and reweighting of existing connections. We suggest that the notion of a critical working point, where the structure-function interplay is maximal, may provide a new way to link behavior and cognition, and a new perspective to understand recovery of function in clinical conditions.

Parameter identification of structural dynamic models by inverse statistical analysis

Auf dem Gebiet der Strukturdynamik sind computergestützte Modellvalidierungstechniken inzwischen weit verbreitet. Dabei werden experimentelle Modaldaten, um ein numerisches Modell für weitere Analysen zu korrigieren. Gleichwohl repräsentiert das validierte Modell nur das dynamische Verhalten der getesteten Struktur. In der Realität gibt es wiederum viele Faktoren, die zwangsläufig zu variierenden Ergebnissen von Modaltests führen werden: Sich verändernde Umgebungsbedingungen während eines Tests, leicht unterschiedliche Testaufbauten, ein Test an einer nominell gleichen aber anderen Struktur (z.B. aus der Serienfertigung), etc. Damit eine stochastische Simulation durchgeführt werden kann, muss eine Reihe von Annahmen für die verwendeten Zufallsvariablengetroffen werden. Folglich bedarf es einer inversen Methode, die es ermöglicht ein stochastisches Modell aus experimentellen Modaldaten zu identifizieren. Die Arbeit beschreibt die Entwicklung eines parameter-basierten Ansatzes, um stochastische Simulationsmodelle auf dem Gebiet der Strukturdynamik zu identifizieren. Die entwickelte Methode beruht auf Sensitivitäten erster Ordnung, mit denen Parametermittelwerte und Kovarianzen des numerischen Modells aus stochastischen experimentellen Modaldaten bestimmt werden können.