Meshing highly regular structures: The case of super carbon nanotubes of arbitrary order

Mesh generation is an important step inmany numerical methods.We present the “HierarchicalGraphMeshing” (HGM)method as a novel approach to mesh generation, based on algebraic graph theory.The HGM method can be used to systematically construct configurations exhibiting multiple hierarchies and complex symmetry characteristics. The hierarchical description of structures provided by the HGM method can be exploited to increase the efficiency of multiscale and multigrid methods. In this paper, the HGMmethod is employed for the systematic construction of super carbon nanotubes of arbitrary order, which present a pertinent example of structurally and geometrically complex, yet highly regular, structures. The HGMalgorithm is computationally efficient and exhibits good scaling characteristics. In particular, it scales linearly for super carbon nanotube structures and is working much faster than geometry-based methods employing neighborhood search algorithms. Its modular character makes it conducive to automatization. For the generation of a mesh, the information about the geometry of the structure in a given configuration is added in a way that relates geometric symmetries to structural symmetries. The intrinsically hierarchic description of the resulting mesh greatly reduces the effort of determining mesh hierarchies for multigrid and multiscale applications and helps to exploit symmetry-related methods in the mechanical analysis of complex structures.

A hybrid approach of knowledge-based reasoning for structural assessment

La idea básica de detección de defectos basada en vibraciones en Monitorización de la Salud Estructural (SHM), es que el defecto altera las propiedades de rigidez, masa o disipación de energía de un sistema, el cual, altera la respuesta dinámica del mismo. Dentro del contexto de reconocimiento de patrones, esta tesis presenta una metodología híbrida de razonamiento para evaluar los defectos en las estructuras, combinando el uso de un modelo de la estructura y/o experimentos previos con el esquema de razonamiento basado en el conocimiento para evaluar si el defecto está presente, su gravedad y su localización. La metodología involucra algunos elementos relacionados con análisis de vibraciones, matemáticas (wavelets, control de procesos estadístico), análisis y procesamiento de señales y/o patrones (razonamiento basado en casos, redes auto-organizativas), estructuras inteligentes y detección de defectos. Las técnicas son validadas numérica y experimentalmente considerando corrosión, pérdida de masa, acumulación de masa e impactos. Las estructuras usadas durante este trabajo son: una estructura tipo cercha voladiza, una viga de aluminio, dos secciones de tubería y una parte del ala de un avión comercial.

Innovative use of vodcast (Video-Podcast) to enrich learning experience in structures laboratory

Students may have difficulty in understanding some of the complex concepts which they have been taught in the general areas of science and engineering. Whilst practical work such as a laboratory based examination of the performance of structures has an important role in knowledge construction this does have some limitations. Blended learning supports different learning styles, hence further benefits knowledge building. This research involves an empirical study of how vodcasts (video-podcasts) can be used to enrich learning experience in the structural properties of materials laboratory of an undergraduate course. Students were given the opportunity of downloading and viewing the vodcasts on the theory before and after the experimental work. It is the choice of the students when (before or after, before and after) and how many times they would like to view the vodcasts. In blended learning, the combination of face-to-face teaching, vodcasts, printed materials, practical experiments, writing reports and instructors’ feedbacks benefits different learning styles of the learners. For the preparation of the practical, the students were informed about the availability of the vodcasts prior to the practical session. After the practical work, students submitted an individual laboratory report for the assessment of the structures laboratory. The data collection consisted of a questionnaire completed by the students, follow-up semi-structured interviews and the practical reports submitted by them for assessment. The results from the questionnaire were analysed quantitatively, whilst the data from the assessment reports were analysed qualitatively. The analysis shows that most of the students who have not fully grasped the theory after the practical, managed to gain the required knowledge by viewing the vodcasts. According to their feedbacks, the students felt that they have control over how to use the material and to view it as many times as they wish. Some students who have understood the theory may choose to view it once or not at all. Their understanding was demonstrated by their explanations in their reports, and was illustrated by the approach they took to explicate the results of their experimental work. The research findings are valuable to instructors who design, develop and deliver different types of blended learning, and are beneficial to learners who try different blended approaches. Recommendations were made on the role of the innovative application of vodcasts in the knowledge construction for structures laboratory and to guide future work in this area of research.

Blended learning approach to include the innovative use of vodcasts (video-podcasts) to enrich students learning experience in structures laboratory

Students may have difficulty in understanding some of the complex concepts which they have been taught in the general areas of science and engineering. Whilst practical work such as a laboratory based examination of the performance of structures has an important role in knowledge construction this does have some limitations. Blended learning supports different learning styles, hence further benefits knowledge building. This research involves the empirical studies of how an innovative use of vodcasts (video-podcasts) can enrich learning experience in the structural properties of materials laboratory of an undergraduate course. Students were given the opportunity of downloading and viewing the vodcasts on the theory before and after the experimental work. It is the choice of the students when (before or after, before and after) and how many times they would like to view the vodcasts. In blended learning, the combination of face-to-face teaching, vodcasts, printed materials, practical experiments, writing reports and instructors’ feedbacks benefits different learning styles of the learners. For the preparation of the practical laboratory work, the students were informed about the availability of the vodcasts prior to the practical session. After the practical work, students submit an individual laboratory report for the assessment of the structures laboratory. The data collection consists of a questionnaire completed by the students, and the practical reports submitted by them for assessment. The results from the questionnaire were analysed quantitatively, whilst the data from the assessment reports were analysed qualitatively. The analysis shows that students who have not fully grasped the theory after the practical were successful in gaining the required knowledge by viewing the vodcasts. Some students who have understood the theory may choose to view it once or not at all. Their understanding was demonstrated by the quality of their explanations in their reports. This is illustrated by the approach they took to explicate the results of their experimental work, for example, they can explain how to calculate the Young’s Modulus properly and provided the correct value for it. The research findings are valuable to instructors who design, develop and deliver different types of blended learning, and beneficial to learners who try different blended approaches. Recommendations were made on the role of the innovative application of vodcasts in the knowledge construction for structures laboratory and to guide future work in this area of research.

Sequential genome-wide association studies for monitoring adverse events in the clinical evaluation of new drugs

Pharmacovigilance, the monitoring of adverse events (AEs), is an integral part in the clinical evaluation of a new drug. Until recently, attempts to relate the incidence of AEs to putative causes have been restricted to the evaluation of simple demographic and environmental factors. The advent of large-scale genotyping, however, provides an opportunity to look for associations between AEs and genetic markers, such as single nucleotides polymorphisms (SNPs). It is envisaged that a very large number of SNPs, possibly over 500 000, will be used in pharmacovigilance in an attempt to identify any genetic difference between patients who have experienced an AE and those who have not. We propose a sequential genome-wide association test for analysing AEs as they arise, allowing evidence-based decision-making at the earliest opportunity. This gives us the capability of quickly establishing whether there is a group of patients at high-risk of an AE based upon their DNA. Our method provides a valid test which takes account of linkage disequilibrium and allows for the sequential nature of the procedure. The method is more powerful than using a correction, such as idák, that assumes that the tests are independent. Copyright © 2006 John Wiley & Sons, Ltd.

Monitoring growth of hard corals as performance indicators for coral reefs

Digital videophotography, computer image analysis and physical measurements have been used to monitor sedimentation rates, coral cover, genera richness, rugosity and estimated recruitment dates of massive corals at three different sites in the Wakatobi Marine National Park, Indonesia, and on the reefs around Discovery Bay, Jamaica. Semi-structured interviews with key stakeholders in the Wakatobi Marine National Park indicated that coral mining was extensively practised, and is responsible for the absence of large non-branching corals on the Sampela reef Blast fishing is also practised in the Wakatobi Marine Park, and the authors, together with students, showed that blast fishing resulted in coral bleaching and not mortality of two Porites lutea colonies. In addition, we showed that monitoring of bleaching in Porites colonies induced by blast fishing could be a useful way of monitoring blast fishing practices in susceptible areas in the Indo-Pacific. The techniques used in this study are appropriate for use by volunteers with sufficient training, and provide excellent projects for dissertation students reading undergraduate degrees.

School effectiveness and the displacement of equity discourses in education

An essential aspect of school effectiveness theory is the shift from the social to the organisational context, from the macro- to the micro-culture. The school is represented largely as a bounded institution, set apart, but also in a precarious relationship with the broader social context. It is ironic that at a time when social disadvantage appears to be increasing in Britain and elsewhere, school effectiveness theory places less emphasis on poverty, deprivation and social exclusion. Instead, it places more emphasis on organisational factors such as professional leadership, home/school partnerships, the monitoring of academic progress, shared vision and goals. In this article, the authors evaluate the extent to which notions of effectiveness have displaced concerns about equity in theories of educational change. They explore the extent to which the social structures of gender, ethnicity, sexualities, special needs, social class, poverty and other historical forms of inequality have been incorporated into or distorted and excluded from effectiveness thinking.

Case study on the maintenance of a construction monitoring using USN-based data acquisition

In recent years, there has been an increasing interest in the adoption of emerging ubiquitous sensor network (USN) technologies for instrumentation within a variety of sustainability systems. USN is emerging as a sensing paradigm that is being newly considered by the sustainability management field as an alternative to traditional tethered monitoring systems. Researchers have been discovering that USN is an exciting technology that should not be viewed simply as a substitute for traditional tethered monitoring systems. In this study, we investigate how a movement monitoring measurement system of a complex building is developed as a research environment for USN and related decision-supportive technologies. To address the apparent danger of building movement, agent-mediated communication concepts have been designed to autonomously manage large volumes of exchanged information. In this study, we additionally detail the design of the proposed system, including its principles, data processing algorithms, system architecture, and user interface specifics. Results of the test and case study demonstrate the effectiveness of the USN-based data acquisition system for real-time monitoring of movement operations.

Trust in authorities monitoring the distribution of genetically modified foods: dimensionality, measurement issues, and determinants

Based on a combined internet and mail survey in Germany the independence of indica-tors of trust in public authorities from indicators of attitudes toward genetically modified food is tested. Despite evidence of a link between trust indicators on the one hand and evaluation of benefits and perceived likelihoods of risks, correlation with other factors is found to be moderate on average. But the trust indicators exhibit only a moderate relation with the re-spondents’ preference for either sole public control or a cooperation of public and private bodies in the monitoring of GM food distribution. Instead, age and location in either the New or the Old Lander are found to be significantly related with such preferences.

Selective Wrapping and Supramolecular Structures of Polyfluorene-Carbon Nanotube Hybrids

We report on the photophysical properties of single-walled carbon nanotube (SWNT) suspensions In toluene solutions of poly[9,9-dioctylfluorenyl-2,7-diyl](PFO). Steady-state and time-resolved photoluminescence spectroscopy in the near-infrared and visible spectral regions are used to study the interaction of the dispersed SWNTs with the wrapped polymer. Molecular dynamics simulations of the PFO-SWNT hybrids in toluene were carried out to evaluate the energetics of different wrapping geometries. The simulated fluorescence spectra in the visible region were obtained by the quantum chemical ZINDO-CI method, using a sampling of structures obtained from the dynamics trajectories. The tested schemes consider polymer chains aligned along the nanotube axis, where chirality has a minimal effect, or forming helical structures, where a preference for high chiral angles is evidenced. Moreover, toluene affects the polymer structure favoring the helical conformation. Simulations show that the most stable hybrid system is the PFO-wrapped (8,6) nanotube, in agreement with the experimentally observed selectivity.

Automatic devices for monitoring environmentally induced auto-oxidative degradation of artistic materials in conservation sites

This paper describes an automatic device for in situ and continuous monitoring of the ageing process occurring in natural and synthetic resins widely used in art and in the conservation and restoration of cultural artefacts. The results of tests carried out under accelerated ageing conditions are also presented. This easy-to-assemble palm-top device, essentially consists of oscillators based on quartz crystal resonators coated with films of the organic materials whose response to environmental stress is to be addressed. The device contains a microcontroller which selects at pre-defined time intervals the oscillators and records and stores their oscillation frequency. The ageing of the coatings, caused by the environmental stress and resulting in a shift in the oscillation frequency of the modified crystals, can be straightforwardly monitored in this way. The kinetics of this process reflects the level of risk damage associated with a specific microenvironment. In this case, natural and artificial resins, broadly employed in art and restoration of artistic and archaeological artefacts (dammar and Paraloid B72), were applied onto the crystals. The environmental stress was represented by visible and UV radiation, since the chosen materials are known to be photochemically active, to different extents. In the case of dammar, the results obtained are consistent with previous data obtained using a bench-top equipment by impedance analysis through discrete measurements and confirm that the ageing of this material is reflected in the gravimetric response of the modified quartz crystals. As for Paraloid B72, the outcome of the assays indicates that the resin is resistant to visible light, but is very sensitive to UV irradiation. The use of a continuous monitoring system, apart from being obviously more practical, is essential to identify short-term (i.e. reversible) events, like water vapour adsorption/desorption processes, and to highlight ageing trends or sudden changes of such trends. (C) 2007 Elsevier B.V. All rights reserved.

ANALYSIS & STUDY OF AI TECHNIQUES FORAUTOMATIC CONDITION MONITORING OFRAILWAY TRACK INFRASTRUCTURE : Artificial Intelligence Techniques

Since the last decade the problem of surface inspection has been receiving great attention from the scientific community, the quality control and the maintenance of products are key points in several industrial applications.The railway associations spent much money to check the railway infrastructure. The railway infrastructure is a particular field in which the periodical surface inspection can help the operator to prevent critical situations. The maintenance and monitoring of this infrastructure is an important aspect for railway association.That is why the surface inspection of railway also makes importance to the railroad authority to investigate track components, identify problems and finding out the way that how to solve these problems. In railway industry, usually the problems find in railway sleepers, overhead, fastener, rail head, switching and crossing and in ballast section as well. In this thesis work, I have reviewed some research papers based on AI techniques together with NDT techniques which are able to collect data from the test object without making any damage. The research works which I have reviewed and demonstrated that by adopting the AI based system, it is almost possible to solve all the problems and this system is very much reliable and efficient for diagnose problems of this transportation domain. I have reviewed solutions provided by different companies based on AI techniques, their products and reviewed some white papers provided by some of those companies. AI based techniques likemachine vision, stereo vision, laser based techniques and neural network are used in most cases to solve the problems which are performed by the railway engineers.The problems in railway handled by the AI based techniques performed by NDT approach which is a very broad, interdisciplinary field that plays a critical role in assuring that structural components and systems perform their function in a reliable and cost effective fashion. The NDT approach ensures the uniformity, quality and serviceability of materials without causing any damage of that materials is being tested. This testing methods use some way to test product like, Visual and Optical testing, Radiography, Magnetic particle testing, Ultrasonic testing, Penetrate testing, electro mechanic testing and acoustic emission testing etc. The inspection procedure has done periodically because of better maintenance. This inspection procedure done by the railway engineers manually with the aid of AI based techniques.The main idea of thesis work is to demonstrate how the problems can be reduced of thistransportation area based on the works done by different researchers and companies. And I have also provided some ideas and comments according to those works and trying to provide some proposal to use better inspection method where it is needed.The scope of this thesis work is automatic interpretation of data from NDT, with the goal of detecting flaws accurately and efficiently. AI techniques such as neural networks, machine vision, knowledge-based systems and fuzzy logic were applied to a wide spectrum of problems in this area. Another scope is to provide an insight into possible research methods concerning railway sleeper, fastener, ballast and overhead inspection by automatic interpretation of data.In this thesis work, I have discussed about problems which are arise in railway sleepers,fastener, and overhead and ballasted track. For this reason I have reviewed some research papers related with these areas and demonstrated how their systems works and the results of those systems. After all the demonstrations were taking place of the advantages of using AI techniques in contrast with those manual systems exist previously.This work aims to summarize the findings of a large number of research papers deploying artificial intelligence (AI) techniques for the automatic interpretation of data from nondestructive testing (NDT). Problems in rail transport domain are mainly discussed in this work. The overall work of this paper goes to the inspection of railway sleepers, fastener, ballast and overhead.

Non-destructive evaluation tool for monitoring and detection of structural damage by using neural network.

This work studies the capability of generalization of Neural Network using vibration based measurement data aiming at operating condition and health monitoring of mechanical systems. The procedure uses the backpropagation algorithm to classify the input patters of a system with different stiffness ratios. It has been investigated a large set of input data, containing various stiffness ratios as well as a reduced set containing only the extreme ones in order to study generalizing capability of the network. This allows to definition of Neural Networks capable to use a reduced set of data during the training phase. Once it is successfully trained, it could identify intermediate failure condition. Several conditions and intensities of damages have been studied by using numerical data. The Neural Network demonstrated a good capacity of generalization for all case. Finally, the proposal was tested with experimental data.

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

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Identification of hybrids between Neotropical fish Leporinus macrocephalus and Leporinus elongatus by PCR-RFLP and multiplex-PCR: Tools for genetic monitoring in aquaculture

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