939 resultados para Plates (structural components)
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Graphitic like layered materials exhibit intriguing electronic structures and thus the search for new types of two-dimensional (2D) monolayer materials is of great interest for developing novel nano-devices. By using density functional theory (DFT) method, here we for the first time investigate the structure, stability, electronic and optical properties of monolayer lead iodide (PbI2). The stability of PbI2 monolayer is first confirmed by phonon dispersion calculation. Compared to the calculation using generalized gradient approximation, screened hybrid functional and spin–orbit coupling effects can not only predicts an accurate bandgap (2.63 eV), but also the correct position of valence and conduction band edges. The biaxial strain can tune its bandgap size in a wide range from 1 eV to 3 eV, which can be understood by the strain induced uniformly change of electric field between Pb and I atomic layer. The calculated imaginary part of the dielectric function of 2D graphene/PbI2 van der Waals type hetero-structure shows significant red shift of absorption edge compared to that of a pure monolayer PbI2. Our findings highlight a new interesting 2D material with potential applications in nanoelectronics and optoelectronics.
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This article reports the main features of an innovative full-scale Structural Health Monitoring (SHM) system which has been implemented onto a landmark building on QUT Gardens Point Campus and its efficacy in capturing the recent Queensland earthquakes although they occurred almost 300 km away from where the system is located.
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This paper investigates the influence of structural sealant joints on the blast performance of laminated glass (LG) panels, using a comprehensive numerical procedure. A parametric study was carried out by varying the width, thickness and the Young’s modulus (E) of the structural silicone sealant joints and the behavior of the LG panel was studied under two different blast loads. Results show that these parameters influence the blast response of LG panels, especially under the higher blast load. Sealant joints that are thicker, have smaller widths and lower E values increase the flexibility at the supports and hence increase the energy absorption of the LG panel while reducing the support reactions. Results also confirmed that sealant joints designed according to current standards perform well under blast loads. Modeling techniques presented in this paper could be used to complement and supplement the guidance in existing design standards. The new information generated in this paper will contribute towards safer and more economical designs of entire facade systems including window glazing, frames and supporting structures.
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Background Although there are many structural neuroimaging studies of attention-deficit/hyperactivity disorder (ADHD) in children, there are inconsistencies across studies and no consensus regarding which brain regions show the most robust area or volumetric reductions relative to control subjects. Our goal was to statistically analyze structural imaging data via a meta-analysis to help resolve these issues. Methods We searched the MEDLINE and PsycINFO databases through January 2005. Studies must have been written in English, used magnetic resonance imaging, and presented the means and standard deviations of regions assessed. Data were extracted by one of the authors and verified independently by another author. Results Analyses were performed using STATA with metan, metabias, and metainf programs. A meta-analysis including all regions across all studies indicated global reductions for ADHD subjects compared with control subjects, standardized mean difference equal to .408, p less than .001. Regions most frequently assessed and showing the largest differences included cerebellar regions, the splenium of the corpus callosum, total and right cerebral volume, and right caudate. Several frontal regions assessed in only two studies also showed large significant differences. Conclusions This meta-analysis provides a quantitative analysis of neuroanatomical abnormalities in ADHD and information that can be used to guide future studies.
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Any kind of imbalance in the operation of a wind turbine has adverse effect on the downstream torsional components as well as tower structure. It is crucial to detect imbalance at its very inception. The identification of the type of imbalance is also required so that appropriate measures of fault accommodation can be performed in the control system. In particular, it is important to distinguish between mass and aerodynamic imbalance. While the former is gradually caused by a structural anomaly (e.g. ice deposition, moisture accumulation inside blade), the latter is generally associated to a fault in the pitch control system. This paper proposes a technique for the detection and identification of imbalance fault in large scale wind turbines. Unlike most other existing method it requires only the rotor speed signal which is readily available in existing turbines. Signature frequencies have been proposed in this work to identify imbalance type based on their physical phenomenology. The performance of this technique has been evaluated by simulations using an existing benchmark model. The effectiveness of the proposed method has been confirmed by the simulation results.
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The construction industry accounts for a significant portion of the material consumption of our industrialised societies. That material consumption comes at an environmental cost, and when buildings and infrastructure projects are demolished and discarded, after their useful lifespan, that environmental cost remains largely unrecovered. The expected operational lifespan of modern buildings has become disturbingly short as buildings are replaced for reasons of changing cultural expectations, style, serviceability, locational obsolescence and economic viability. The same buildings however are not always physically or structurally obsolete; the materials and components within them are very often still completely serviceable. While there is some activity in the area of recycling of selected construction materials, such as steel and concrete, this is almost always in the form of down cycling or reprocessing. Very little of this material and component resource is reuse in a way that more effectively captures its potential. One significant impediment to such reuse is that buildings are not designed in a way that facilitates easy recovery of materials and components; they are designed and built for speed of construction and quick economic returns, with little or no consideration of the longer term consequences of their physical matter. This research project explores the potential for the recovery of materials and components if buildings were designed for such future recovery; a strategy of design for disassembly. This is not a new design philosophy; design for disassembly is well understood in product design and industrial design. There are also some architectural examples of design for disassembly; however these are specialist examples and there is no significant attempt to implement the strategy in the main stream construction industry. This paper presents research into the analysis of the embodied energy in buildings, highlighting its significance in comparison with operational energy. Analysis at material, component, and whole-of-building levels shows the potential benefits of strategically designing buildings for future disassembly to recover this embodied energy. Careful consideration at the early design stage can result in the deconstruction of significant portions of buildings and the recovery of their potential through higher order reuse and upcycling.
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Background The subtropical Bahia grass (Paspalum notatum) is an important source of pollen allergens with an extended season of pollination and wide distribution in warmer climates. The immunological relationship between pollen allergens of Bahia grass and temperate grasses is unresolved. Methods Serum IgE reactivity of grass pollen-allergic patients with Bahia, Ryegrass and Bermuda grass pollen extracts and their purified group 1 allergens, Pas n 1, Lol p 1 and Cyn d 1, were compared by immunoblotting, ELISA, inhibition ELISA, basophil activation by flow cytometry and molecular modeling. Results Differences in antibody recognition of allergenic components between Bahia grass and Ryegrass pollen were observed by immunoblotting. Eight grass pollen-allergic patients from a temperate region showed greater serum IgE reactivity with Ryegrass pollen than Bahia grass by ELISA. For seven of these sera, Ryegrass pollen inhibited IgE reactivity with Bahia grass pollen but not the converse. For 51 sera from grass pollen-allergic patients in this temperate region, IgE reactivity with Lol p 1 was greater than Pas n 1 or Cyn d 1. IgE reactivity with Lol p 1 was not inhibited by Pas n 1 or Cyn d 1, but Pas n 1 IgE reactivity was inhibited by Lol p 1. Two group 1 grass pollen allergen-specific mAb distinguished between temperate and subtropical grass pollens. Basophil activation for three patients tested was greater by Ryegrass pollen than Bahia or Bermuda grass, and by Lol p 1 than Pas n 1 or Cyn d 1. In contrast, two patients from a subtropical region had higher serum IgE reactivity with Bahia grass pollen than Ryegrass and Bahia grass pollen inhibited IgE reactivity with Ryegrass. A structural model of Pas n 1 showed amino acids implicated in IgE epitopes of other group 1 allergens were juxtaposed on the surface. Conclusion Allergens from subtropical Bahia grass pollen, including Pas n 1, share antigenic determinants with temperate grass pollen allergens, but patients exhibit higher serum IgE reactivity to their locally predominant grass pollen. Basophil activation by Bahia grass pollen and Pas n 1 in patients from a temperate climate indicates clinically relevant cross-sensitization between temperate and subtropical grass pollens.
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Living cells are the functional unit of organs that controls reactions to their exterior. However, the mechanics of living cells can be difficult to characterize due to the crypticity of their microscale structures and associated dynamic cellular processes. Fortunately, multiscale modelling provides a powerful simulation tool that can be used to study the mechanical properties of these soft hierarchical, biological systems. This paper reviews recent developments in hierarchical multiscale modeling technique that aimed at understanding cytoskeleton mechanics. Discussions are expanded with respects to cytoskeletal components including: intermediate filaments, microtubules and microfilament networks. The mechanical performance of difference cytoskeleton components are discussed with respect to their structural and material properties. Explicit granular simulation methods are adopted with different coarse-grained strategies for these cytoskeleton components and the simulation details are introduced in this review.
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In this article, we report the crystal structures of five halogen bonded co-crystals comprising quaternary ammonium cations, halide anions (Cl– and Br–), and one of either 1,2-, 1,3-, or 1,4-diiodotetrafluorobenzene (DITFB). Three of the co-crystals are chemical isomers: 1,4-DITFB[TEA-CH2Cl]Cl, 1,2-DITFB[TEA-CH2Cl]Cl, and 1,3-DITFB[TEA-CH2Cl]Cl (where TEA-CH2Cl is chloromethyltriethylammonium ion). In each structure, the chloride anions link DITFB molecules through halogen bonds to produce 1D chains propagating with (a) linear topology in the structure containing 1,4-DITFB, (b) zigzag topology with 60° angle of propagation in that containing 1,2-DITFB, and (c) 120° angle of propagation with 1,3-DITFB. While the individual chains have highly distinctive and different topologies, they combine through π-stacking of the DITFB molecules to produce remarkably similar overall arrangements of molecules. Structures of 1,4-DITFB[TEA-CH2Br]Br and 1,3-DITFB[TEA-CH2Br]Br are also reported and are isomorphous with their chloro/chloride analogues, further illustrating the robustness of the overall supramolecular architecture. The usual approach to crystal engineering is to make structural changes to molecular components to effect specific changes to the resulting crystal structure. The results reported herein encourage pursuit of a somewhat different approach to crystal engineering. That is, to investigate the possibilities for engineering the same overall arrangement of molecules in crystals while employing molecular components that aggregate with entirely different supramolecular connectivity.
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Impulse propagation in biological tissues is known to be modulated by structural heterogeneity. In cardiac muscle, improved understanding on how this heterogeneity influences electrical spread is key to advancing our interpretation of dispersion of repolarization. We propose fractional diffusion models as a novel mathematical description of structurally heterogeneous excitable media, as a means of representing the modulation of the total electric field by the secondary electrical sources associated with tissue inhomogeneities. Our results, analysed against in vivo human recordings and experimental data of different animal species, indicate that structural heterogeneity underlies relevant characteristics of cardiac electrical propagation at tissue level. These include conduction effects on action potential (AP) morphology, the shortening of AP duration along the activation pathway and the progressive modulation by premature beats of spatial patterns of dispersion of repolarization. The proposed approach may also have important implications in other research fields involving excitable complex media.
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Structural equation modeling (SEM) is a powerful statistical approach for the testing of networks of direct and indirect theoretical causal relationships in complex data sets with intercorrelated dependent and independent variables. SEM is commonly applied in ecology, but the spatial information commonly found in ecological data remains difficult to model in a SEM framework. Here we propose a simple method for spatially explicit SEM (SE-SEM) based on the analysis of variance/covariance matrices calculated across a range of lag distances. This method provides readily interpretable plots of the change in path coefficients across scale and can be implemented using any standard SEM software package. We demonstrate the application of this method using three studies examining the relationships between environmental factors, plant community structure, nitrogen fixation, and plant competition. By design, these data sets had a spatial component, but were previously analyzed using standard SEM models. Using these data sets, we demonstrate the application of SE-SEM to regularly spaced, irregularly spaced, and ad hoc spatial sampling designs and discuss the increased inferential capability of this approach compared with standard SEM. We provide an R package, sesem, to easily implement spatial structural equation modeling.
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Pattern recognition is a promising approach for the identification of structural damage using measured dynamic data. Much of the research on pattern recognition has employed artificial neural networks (ANNs) and genetic algorithms as systematic ways of matching pattern features. The selection of a damage-sensitive and noise-insensitive pattern feature is important for all structural damage identification methods. Accordingly, a neural networks-based damage detection method using frequency response function (FRF) data is presented in this paper. This method can effectively consider uncertainties of measured data from which training patterns are generated. The proposed method reduces the dimension of the initial FRF data and transforms it into new damage indices and employs an ANN method for the actual damage localization and quantification using recognized damage patterns from the algorithm. In civil engineering applications, the measurement of dynamic response under field conditions always contains noise components from environmental factors. In order to evaluate the performance of the proposed strategy with noise polluted data, noise contaminated measurements are also introduced to the proposed algorithm. ANNs with optimal architecture give minimum training and testing errors and provide precise damage detection results. In order to maximize damage detection results, the optimal architecture of ANN is identified by defining the number of hidden layers and the number of neurons per hidden layer by a trial and error method. In real testing, the number of measurement points and the measurement locations to obtain the structure response are critical for damage detection. Therefore, optimal sensor placement to improve damage identification is also investigated herein. A finite element model of a two storey framed structure is used to train the neural network. It shows accurate performance and gives low error with simulated and noise-contaminated data for single and multiple damage cases. As a result, the proposed method can be used for structural health monitoring and damage detection, particularly for cases where the measurement data is very large. Furthermore, it is suggested that an optimal ANN architecture can detect damage occurrence with good accuracy and can provide damage quantification with reasonable accuracy under varying levels of damage.
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A novel near-infrared spectroscopy (NIRS) method has been researched and developed for the simultaneous analyses of the chemical components and associated properties of mint (Mentha haplocalyx Briq.) tea samples. The common analytes were: total polysaccharide content, total flavonoid content, total phenolic content, and total antioxidant activity. To resolve the NIRS data matrix for such analyses, least squares support vector machines was found to be the best chemometrics method for prediction, although it was closely followed by the radial basis function/partial least squares model. Interestingly, the commonly used partial least squares was unsatisfactory in this case. Additionally, principal component analysis and hierarchical cluster analysis were able to distinguish the mint samples according to their four geographical provinces of origin, and this was further facilitated with the use of the chemometrics classification methods-K-nearest neighbors, linear discriminant analysis, and partial least squares discriminant analysis. In general, given the potential savings with sampling and analysis time as well as with the costs of special analytical reagents required for the standard individual methods, NIRS offered a very attractive alternative for the simultaneous analysis of mint samples.
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In this paper, we aim at predicting protein structural classes for low-homology data sets based on predicted secondary structures. We propose a new and simple kernel method, named as SSEAKSVM, to predict protein structural classes. The secondary structures of all protein sequences are obtained by using the tool PSIPRED and then a linear kernel on the basis of secondary structure element alignment scores is constructed for training a support vector machine classifier without parameter adjusting. Our method SSEAKSVM was evaluated on two low-homology datasets 25PDB and 1189 with sequence homology being 25% and 40%, respectively. The jackknife test is used to test and compare our method with other existing methods. The overall accuracies on these two data sets are 86.3% and 84.5%, respectively, which are higher than those obtained by other existing methods. Especially, our method achieves higher accuracies (88.1% and 88.5%) for differentiating the α + β class and the α/β class compared to other methods. This suggests that our method is valuable to predict protein structural classes particularly for low-homology protein sequences. The source code of the method in this paper can be downloaded at http://math.xtu.edu.cn/myphp/math/research/source/SSEAK_source_code.rar.
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STEM education faces an interesting conundrum. Western countries have implemented constructivist inspired student centred practices which are argued to be more engaging and relevant to student learning than the traditional, didactic approaches. However, student interest in pursuing careers in STEM have fallen or stagnated. In contrast, students in many developing countries in which teaching is still somewhat didactic and teacher centred are more disposed to STEM related careers than their western counterparts. Clearly, factors are at work which impact the way students value science and mathematics. This review draws on three components that act as determinants of science education in three different countries – Australia, India and Malaysia. We explore how national priorities and educational philosophy impacts educational practices as well as teacher beliefs and the need for suitable professional development. Socio-economic conditions for science education that are fundamental for developing countries in adopting constructivist educational models are analysed. It is identified that in order to reduce structural dissimilarities among countries that cause fragmentation of scientific knowledge, for Malaysia constructivist science education through English medium without losing the spirit of Malaysian culture and Malay language is essential while India need to adopt constructivist quality indicators in education. While adopting international English education, and reducing dominance of impact evaluation, India and Malaysia need to prevent losing their cultural and social capital vigour. Furthermore the paper argues that Australia might need to question the efficacy of current models that fail to engage students’ long term interest in STEM related careers. Australian and Malaysian science teachers must be capable of changing the personal biographies of learners for developing scientific conceptual information. In addition both Malaysia and Australia need to provide opportunities for access to different curricular programmes of knowledge based constructivist learning for different levels of learner competencies.