925 resultados para vibration-based damage detection (VBDD)
Resumo:
Earthquakes occurring around the world each year cause thousands ofdeaths, millions of dollars in damage to infrastructure, and incalculablehuman suffering. In recent years, satellite technology has been asignificant boon to response efforts following an earthquake and itsafter-effects by providing mobile communications between response teamsand remote sensing of damaged areas to disaster management organizations.In 2007, an international team of students and professionals assembledduring theInternational Space University’s Summer Session Program in Beijing, Chinato examine how satellite and ground-based technology could be betterintegrated to provide an optimised response in the event of an earthquake.The resulting Technology Resources for Earthquake MOnitoring and Response(TREMOR) proposal describes an integrative prototype response system thatwill implement mobile satellite communication hubs providing telephone anddata links between response teams, onsite telemedicine consultation foremergency first-responders, and satellite navigation systems that willlocate and track emergency vehicles and guide search-and-rescue crews. Aprototype earthquake simulation system is also proposed, integratinghistorical data, earthquake precursor data, and local geomatics andinfrastructure information to predict the damage that could occur in theevent of an earthquake. The backbone of these proposals is a comprehensiveeducation and training program to help individuals, communities andgovernments prepare in advance. The TREMOR team recommends thecoordination of these efforts through a centralised, non-governmentalorganization.
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Report for the scientific sojourn carried out at the Paul Drude Institut für Festkörperelektronik of the Stanford University, USA, from 2010 to 2012. The objective of this project is the transport and control of electronic charge and spin along GaAs-based semiconductor heterostructures. The electronic transport has been achieved by taking advantage of the piezolectric field induced by surface acoustic waves in non-centrosymmetric materials like GaAs. This piezolectric field separates photogenerated electrons and holes at different positions along the acoustic wave, where they acummulate and are transported at the same velocity as the wave. Two different kinds of structures have been studied: quantum wells grown along the (110) direction, both intrinsic and n-doped, as well as GaAs nanowires. The analysis of the charge acoustic transport was performed by micro-photoluminescence, whereas the detection of the spin transport was done either by analyzing the polarization state of the emitted photoluminescence or by Kerr reflectometry. Our results in GaAs quantum wells show that charge and spin transport is clearly observed at the non-doped structures,obtaining spin lifetimes of the order of several nanoseconds, whereas no acoutically induced spin transport was detected for the n-doped quantum wells. In the GaAs nanowires, we were able of transporting successfully both electrons and holes along the nanowire axis, but no conservation of the spin polarization has been observed until now. The photoluminescence emitted by these structures after acoustic transport, however, shows anti-bunching characteristics, making this system a very good candidate for its use as single photon emitters.
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Nanomotors are nanoscale devices capable of converting energy into movement and forces. Among them, self-propelled nanomotors offer considerable promise for developing new and novel bioanalytical and biosensing strategies based on the direct isolation of target biomolecules or changes in their movement in the presence of target analytes. The mainachievements of this project consists on the development of receptor-functionalized nanomotors that offer direct and rapid target detection, isolation and transport from raw biological samples without preparatory and washing steps. For example, microtube engines functionalized with aptamer, antibody, lectin and enzymes receptors were used for the direct isolation of analytes of biomedical interest, including proteins and whole cells, among others. A target protein was also isolated from a complex sample by using an antigen-functionalized microengine navigating into the reservoirs of a lab-on-a-chip device. The new nanomotorbased target biomarkers detection strategy not only offers highly sensitive, rapid, simple and low cost alternative for the isolation and transport of target molecules, but also represents a new dimension of analytical information based on motion. The recognition events can be easily visualized by optical microscope (without any sophisticated analytical instrument) to reveal the target presence and concentration. The use of artificial nanomachines has shown not only to be useful for (bio)recognition and (bio)transport but also for detection of environmental contamination and remediation. In this context, micromotors modified with superhydrophobic layer demonstrated that effectively interacted, captured, transported and removed oil droplets from oil contaminated samples. Finally, a unique micromotor-based strategy for water-quality testing, that mimics live-fish water-quality testing, based on changes in the propulsion behavior of artificial biocatalytic microswimmers in the presence of aquatic pollutants was also developed. The attractive features of the new micromachine-based target isolation and signal transduction protocols developed in this project offer numerous potential applications in biomedical diagnostics, environmental monitoring, and forensic analysis.
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The research presented in this report provides the basis for the development of a new procedure to be used by the Iowa DOT and cities and counties in the state to deal with detours. Even though the project initially focused on investigating new tools to determine condition and compensation, the focus was shifted to traffic and the gas tax method to set the basis for the new procedure. It was concluded that the condition-based approach, even though accurate and consistent condition evaluations can be achieved, is not feasible or cost effective because of the current practices of data collection (two-year cycle) and also the logistics of the procedure (before and after determination). The gas tax method provides for a simple, easy to implement, and consistent approach to dealing with compensation for use of detours. It removes the subjectivity out of the current procedures and provides for a more realistic (traffic based) approach to the compensation determination.
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Demosaicking is a particular case of interpolation problems where, from a scalar image in which each pixel has either the red, the green or the blue component, we want to interpolate the full-color image. State-of-the-art demosaicking algorithms perform interpolation along edges, but these edges are estimated locally. We propose a level-set-based geometric method to estimate image edges, inspired by the image in-painting literature. This method has a time complexity of O(S) , where S is the number of pixels in the image, and compares favorably with the state-of-the-art algorithms both visually and in most relevant image quality measures.
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To detect directional couplings from time series various measures based on distances in reconstructed state spaces were introduced. These measures can, however, be biased by asymmetries in the dynamics' structure, noise color, or noise level, which are ubiquitous in experimental signals. Using theoretical reasoning and results from model systems we identify the various sources of bias and show that most of them can be eliminated by an appropriate normalization. We furthermore diminish the remaining biases by introducing a measure based on ranks of distances. This rank-based measure outperforms existing distance-based measures concerning both sensitivity and specificity for directional couplings. Therefore, our findings are relevant for a reliable detection of directional couplings from experimental signals.
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The problem of jointly estimating the number, the identities, and the data of active users in a time-varying multiuser environment was examined in a companion paper (IEEE Trans. Information Theory, vol. 53, no. 9, September 2007), at whose core was the use of the theory of finite random sets on countable spaces. Here we extend that theory to encompass the more general problem of estimating unknown continuous parameters of the active-user signals. This problem is solved here by applying the theory of random finite sets constructed on hybrid spaces. We doso deriving Bayesian recursions that describe the evolution withtime of a posteriori densities of the unknown parameters and data.Unlike in the above cited paper, wherein one could evaluate theexact multiuser set posterior density, here the continuous-parameter Bayesian recursions do not admit closed-form expressions. To circumvent this difficulty, we develop numerical approximationsfor the receivers that are based on Sequential Monte Carlo (SMC)methods (“particle filtering”). Simulation results, referring to acode-divisin multiple-access (CDMA) system, are presented toillustrate the theory.
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Student guidance is an always desired characteristic in any educational system, butit represents special difficulty if it has to be deployed in an automated way to fulfilsuch needs in a computer supported educational tool. In this paper we explorepossible avenues relying on machine learning techniques, to be included in a nearfuture -in the form of a tutoring navigational tool- in a teleeducation platform -InterMediActor- currently under development. Since no data from that platform isavailable yet, the preliminary experiments presented in this paper are builtinterpreting every subject in the Telecommunications Degree at Universidad CarlosIII de Madrid as an aggregated macro-competence (following the methodologicalconsiderations in InterMediActor), such that marks achieved by students can beused as data for the models, to be replaced in a near future by real data directlymeasured inside InterMediActor. We evaluate the predictability of students qualifications, and we deploy a preventive early detection system -failure alert-, toidentify those students more prone to fail a certain subject such that correctivemeans can be deployed with sufficient anticipation.
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We propose an edge detector based on the selection of wellcontrasted pieces of level lines, following the proposal ofDesolneux-Moisan-Morel (DMM) [1]. The DMM edge detectorhas the problem of over-representation, that is, everyedge is detected several times in slightly different positions.In this paper we propose two modifications of the originalDMM edge detector in order to solve this problem. The firstmodification is a post-processing of the output using a generalmethod to select the best representative of a bundle of curves.The second modification is the use of Canny’s edge detectorinstead of the norm of the gradient to build the statistics. Thetwo modifications are independent and can be applied separately.Elementary reasoning and some experiments showthat the best results are obtained when both modifications areapplied together.
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Malignant melanoma, the deadliest form of skin cancer, is characterized by a predominant mutation in the BRAF gene. Drugs that target tumours carrying this mutation have recently entered the clinic. Accordingly, patients are routinely screened for mutations in this gene to determine whether they can benefit from this type of treatment. The current gold standard for mutation screening uses real-time polymerase chain reaction and sequencing methods. Here we show that an assay based on microcantilever arrays can detect the mutation nanomechanically without amplification in total RNA samples isolated from melanoma cells. The assay is based on a BRAF-specific oligonucleotide probe. We detected mutant BRAF at a concentration of 500 pM in a 50-fold excess of the wild-type sequence. The method was able to distinguish melanoma cells carrying the mutation from wild-type cells using as little as 20 ng µl(-1) of RNA material, without prior PCR amplification and use of labels.
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In this work we present the results of experimental work on the development of lexical class-based lexica by automatic means. Our purpose is to assess the use of linguistic lexical-class based information as a feature selection methodology for the use of classifiers in quick lexical development. The results show that the approach can help reduce the human effort required in the development of language resources significantly.
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PDMS-based microfluidic devices combined with lanthanide-based immunocomplexes have been successfully tested for the multiplex detection of biomarkers on cancerous tissues, revealing an enhanced sensitivity compared to classical organic dyes.
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MicroRNAs (miRNAs) are small non-coding RNAs that regulate various biological processes. Cell-free miRNAs measured in blood plasma have emerged as specific and sensitive markers of physiological processes and disease. In this study, we investigated whether circulating miRNAs can serve as biomarkers for the detection of autologous blood transfusion, a major doping technique that is still undetectable. Plasma miRNA levels were analyzed using high-throughput quantitative real-time PCR. Plasma samples were obtained before and at several time points after autologous blood transfusion (blood bag storage time 42 days) in 10 healthy subjects and 10 controls without transfusion. Other serum markers of erythropoiesis were determined in the same samples. Our results revealed a distinct change in the pattern of circulating miRNAs. Ten miRNAs were upregulated in transfusion samples compared with control samples. Among these, miR-30b, miR-30c, and miR-26b increased significantly and showed a 3.9-, 4.0-, and 3.0-fold change, respectively. The origin of these miRNAs was related to pulmonary and liver tissues. Erythropoietin (EPO) concentration decreased after blood reinfusion. A combination of miRNAs and EPO measurement in a mathematical model enhanced the efficiency of autologous transfusion detection through miRNA analysis. Therefore, our results lay the foundation for the development of miRNAs as novel blood-based biomarkers to detect autologous transfusion.
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To target pharmacological prevention, instruments giving an approximation of an individual patient's risk of developing postoperative delirium are available. In view of the variable clinical presentation, identifying patients in whom prophylaxis has failed (that is, who develop delirium) remains a challenge. Several bedside instruments are available for the routine ward and ICU setting. Several have been shown to have a high specificity and sensitivity when compared with the standard definitions according to DSM-IV-TR and ICD-10. The Confusion Assessment Method (CAM) and a version specifically developed for the intensive care setting (CAM-ICU) have emerged as a standard. However, alternatives allowing grading of the severity of delirium are also available. In many units, the approach to delirium follows a three-step strategy. Initially, non-pharmacological multicomponent strategies are used for primary prevention. As a second step, pharmacological prophylaxis may be added. Perioperative administration of haloperidol has been shown to reduce the severity, but not the incidence, of delirium. Perioperative administration of atypical antipsychotics has been shown to reduce the incidence of delirium in specific groups of patients. In patients with delirium, both symptomatic and causal treatment of delirium need to be considered. So far symptomatic treatment of delirium is primarily based on antipsychotics. Currently, cholinesterase inhibitors cannot be recommended and the data on dexmedetomidine are inconclusive. With the exception of alcohol-withdrawal delirium, there is no role for benzodiazepines in the treatment of delirium. It is unclear whether treating delirium prevents long-term sequelae.
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We present a silicon chip-based approach for the enhanced sensitivity detection of surface-immobilized fluorescent molecules. Green fluorescent protein (GFP) is bound to the silicon substrate by a disuccinimidyl terephtalate-aminosilane immobilization procedure. The immobilized organic layers are characterized by surface analysis techniques, like ellipsometry, atomic force microscopy (AFM) and X-ray induced photoelectron spectroscopy. We obtain a 20-fold enhancement of the fluorescent signal, using constructive interference effects in a fused silica dielectric layer, deposited before immobilization onto the silicon. Our method opens perspectives to increase by an order of magnitude the fluorescent response of surface immobilized DNA- or protein-based layers for a variety of biosensor applications.