Operational Modal Analysis of Super Tall Buildings by a Bayesian Approach

Structural health monitoring (SHM) has attracted increasing attention in the past few decades. It aims at monitoring the existing structures based on data acquired by different sensor networks. Modal identification is usually the first step in SHM, and it aims at identifying the modal parameters mainly including natural frequency, damping ratio and mode shape. Three different field tests can be used to collect data for modal identification, among which, ambient vibration test is the most convenient and economical one since it does not require to measure input information. This chapter will focus on the operational modal analysis (OMA), i.e. ambient modal identification of four super tall buildings by a Bayesian approach. A fast frequency domain Bayesian fast fourier transform (FFT) approach will be introduced for OMA. In addition to the most probable value (MPV) of modal parameters, the associated posterior uncertainty will be also investigated analytically. The field tests will be presented and the difficulties encountered will be discussed. Some basic dynamic characteristics will be investigated and discussed. The studies will provide baseline properties of these super tall buildings and provide a reference for future condition assessments

Hybrid quantum device based on NV centers in diamond nanomechanical resonators plus superconducting waveguide cavities

We propose and analyze a hybrid device by integrating a microscale diamond beam with a single built-in nitrogen-vacancy (NV) center spin to a superconducting coplanar waveguide (CPW) cavity. We find that under an ac electric field the quantized motion of the diamond beam can strongly couple to the single cavity photons via dielectric interaction. Together with the strong spin-motion interaction via a large magnetic field gradient, it provides a hybrid quantum device where the dia- mond resonator can strongly couple both to the single microwave cavity photons and to the single NV center spin. This enables coherent information transfer and effective coupling between the NV spin and the CPW cavity via mechanically dark polaritons. This hybrid spin-electromechanical de- vice, with tunable couplings by external fields, offers a realistic platform for implementing quantum information with single NV spins, diamond mechanical resonators, and single microwave photons.Comment: Accepted by Phys. Rev. Applie

Expressions of ECE-CYC2 clade genes relating to abortion of both dorsal and ventral stamens in Opithandra (Gesneriaceae)

<p>Abstract</p> <p>Background</p> <p>ECE-CYC2 clade genes known in patterning floral dorsoventral asymmetry (zygomorphy) in <it>Antirrhinum majus </it>are conserved in the dorsal identity function including arresting the dorsal stamen. However, it remains uncertain whether the same mechanism underlies abortion of the ventral stamens, an important morphological trait related to evolution and diversification of zygomorphy in Lamiales <it>sensu lato</it>, a major clade of predominantly zygomorphically flowered angiosperms. <it>Opithandra </it>(Gesneriaceae) is of particular interests in addressing this question as it is in the base of Lamiales <it>s.l</it>., an early representative of this type zygomorphy.</p> <p>Results</p> <p>We investigated the expression patterns of four ECE-CYC2 clade genes and two putative target <it>cyclinD3 </it>genes in <it>Opithandra </it>using RNA <it>in situ </it>hybridization and RT-PCR. <it>OpdCYC </it>gene expressions were correlated with abortion of both dorsal and ventral stamens in <it>Opithandra</it>, strengthened by the negatively correlated expression of their putative target <it>OpdcyclinD3 </it>genes. The complement of <it>OpdcyclinD3 </it>to <it>OpdCYC </it>expressions further indicated that <it>OpdCYC </it>expressions were related to the dorsal and ventral stamen abortion through negative effects on <it>OpdcyclinD3 </it>genes.</p> <p>Conclusion</p> <p>These results suggest that ECE-CYC2 clade TCP genes are not only functionally conserved in the dorsal stamen repression, but also involved in arresting ventral stamens, a genetic mechanism underlying the establishment of zygomorphy with abortion of both the dorsal and ventral stamens evolved in angiosperms, especially within Lamiales <it>s.l</it>.</p

Incorporation of covariates in simultaneous localization of two linked loci using affected relative pairs

<p>Abstract</p> <p>Background</p> <p>Many dichotomous traits for complex diseases are often involved more than one locus and/or associated with quantitative biomarkers or environmental factors. Incorporating these quantitative variables into linkage analysis as well as localizing two linked disease loci simultaneously could therefore improve the efficiency in mapping genes. We extended the robust multipoint Identity-by-Descent (IBD) approach with incorporation of covariates developed previously to simultaneously estimate two linked loci using different types of affected relative pairs (ARPs).</p> <p>Results</p> <p>We showed that the efficiency was enhanced by incorporating a quantitative covariate parametrically or non-parametrically while localizing two disease loci using ARPs. In addition to its help in identifying factors associated with the disease and in improving the efficiency in estimating disease loci, this extension also allows investigators to account for heterogeneity in risk-ratios for different ARPs. Data released from the collaborative study on the genetics of alcoholism (COGA) for Genetic Analysis Workshop 14 (GAW 14) were used to illustrate the application of this extended method.</p> <p>Conclusions</p> <p>The simulation studies and example illustrated that the efficiency in estimating disease loci was demonstratively enhanced by incorporating a quantitative covariate and by using all relative pairs while mapping two linked loci simultaneously.</p

Structured Attention for Unsupervised Dialogue Structure Induction

Inducing a meaningful structural representation from one or a set of dialogues is a crucial but challenging task in computational linguistics. Advancement made in this area is critical for dialogue system design and discourse analysis. It can also be extended to solve grammatical inference. In this work, we propose to incorporate structured attention layers into a Variational Recurrent Neural Network (VRNN) model with discrete latent states to learn dialogue structure in an unsupervised fashion. Compared to a vanilla VRNN, structured attention enables a model to focus on different parts of the source sentence embeddings while enforcing a structural inductive bias. Experiments show that on two-party dialogue datasets, VRNN with structured attention learns semantic structures that are similar to templates used to generate this dialogue corpus. While on multi-party dialogue datasets, our model learns an interactive structure demonstrating its capability of distinguishing speakers or addresses, automatically disentangling dialogues without explicit human annotation.Comment: Long paper accepted by EMNLP 202

Bayesian Model Updating of a Simply-Supported Truss Bridge Based on Dynamic Responses

Part of the Lecture Notes in Civil Engineering book series (LNCE, volume 224)This study intends to investigate the application of model updating based on forced vibration data to a simply-supported truss bridge. A fast Bayesian FFT method was used to perform the modal identification obtained from field tests, and the Transitional Markov Chain Monte Carlo (TMCMC) algorithm is employed to generate samples. Although updating as many parameters as possible is the ideal model update process, it is not practical to identify all the parameters because of limitation of the experimental data. The bridge was thus divided into several clusters, and the values of the updated parameters of the members in the same cluster are assumed to be equal. Two model updating schemes were discussed as an example to investigate the effect of parameter selection, such as how to model the spring at each support, in model updating process. It was observed that although models with more parameters tend to fit better, the updated result often showed a different trend from the engineering prediction

PLGA-based gene delivering nanoparticle enhance suppression effect of miRNA in HePG2 cells

The biggest challenge in the field of gene therapy is how to effectively deliver target genes to special cells. This study aimed to develop a new type of poly(D,L-lactide-co-glycolide) (PLGA)-based nanoparticles for gene delivery, which are capable of overcoming the disadvantages of polyethylenimine (PEI)- or cationic liposome-based gene carrier, such as the cytotoxicity induced by excess positive charge, as well as the aggregation on the cell surface. The PLGA-based nanoparticles presented in this study were synthesized by emulsion evaporation method and characterized by transmission electron microscopy, dynamic light scattering, and energy dispersive spectroscopy. The size of PLGA/PEI nanoparticles in phosphate-buffered saline (PBS) was about 60 nm at the optimal charge ratio. Without observable aggregation, the nanoparticles showed a better monodispersity. The PLGA-based nanoparticles were used as vector carrier for miRNA transfection in HepG2 cells. It exhibited a higher transfection efficiency and lower cytotoxicity in HepG2 cells compared to the PEI/DNA complex. The N/P ratio (ratio of the polymer nitrogen to the DNA phosphate) 6 of the PLGA/PEI/DNA nanocomplex displays the best property among various N/P proportions, yielding similar transfection efficiency when compared to Lipofectamine/DNA lipoplexes. Moreover, nanocomplex shows better serum compatibility than commercial liposome. PLGA nanocomplexes obviously accumulate in tumor cells after transfection, which indicate that the complexes contribute to cellular uptake of pDNA and pronouncedly enhance the treatment effect of miR-26a by inducing cell cycle arrest. Therefore, these results demonstrate that PLGA/PEI nanoparticles are promising non-viral vectors for gene delivery