Accurate Stress Analysis on Steel Box Girder of Long Span Suspension Bridges Based on Multi-Scale Submodeling Method

This is the published version. Copyright 2010 Multi-Science PublishingThe streamline flat steel box girder is broadly used in long span cable-supported bridges all over the world. As one of the most important part of the bridges, its stress level and distribution under various dynamic loads are especially concerned. However, it is difficult to obtain the accurate stress of the steel box girder by common finite element (FE) calculation. The Runyang Suspension Bridge (RSB) is taken as an example. To increase the accuracy of results, a 3-dimensional FE full model for the RSB is created by ANSYS and a spatial submodel of the steel box girder is particularly built with greater detail. Submodeling method is then employed as a connection technique to link different scale models. After the global analysis and connecting process, the local stress of the steel box girder under various vehicle loads is obtained by submodel analysis. The composite action between the full model and the submodel is analyzed, and the reasonable mesh size for the submodel is specially investigated. The numerical results are proved to be accurate by the field test results. This study demonstrates the reliability and efficiency of the submodeling method; and can provide references for accurately analyzing and designing the steel box girders of other long span bridges

Reconfigurable Intelligent Surface-Assisted Secret Key Generation in Spatially Correlated Channels

Reconfigurable intelligent surface (RIS) is a disruptive technology to enhance the performance of physical-layer key generation (PKG) thanks to its ability to smartly customize the radio environments. Existing RIS-assisted PKG methods are mainly based on the idealistic assumption of an independent and identically distributed (i.i.d.) channel model at both the base station (BS) and the RIS. However, the i.i.d. model is inaccurate for a typical RIS in an isotropic scattering environment and neglecting the existence of channel spatial correlation would possibly degrade the PKG performance. In this paper, we establish a general spatially correlated channel model and propose a new channel probing framework based on the transmit and the reflective beamforming. We derive a closed-form key generation rate (KGR) expression and formulate an optimization problem, which is solved by using the low-complexity Block Successive Upper-bound Minimization (BSUM) with Mirror-Prox method. Simulation results show that compared to the existing methods based on the i.i.d. fading model, our proposed method achieves about $5$ dB transmit power gain when the spacing between two neighboring RIS elements is a quarter of the wavelength. Also, the KGR increases significantly with the number of RIS elements while that increases marginally with the number of BS antennas.Comment: arXiv admin note: text overlap with arXiv:2207.1175

Jasmonate mediates salt-induced nicotine biosynthesis in tobacco (Nicotiana tabacum L.)

AbstractJasmonate (JA), as an important signal, plays a key role in multiple processes of plant growth, development and stress response. Nicotine and related pyridine alkaloids in tobacco (Nicotiana tabacum L.) are essential secondary metabolites. Whether environmental factors control nicotine biosynthesis and the underlying mechanism remains previously unreported. Here, we applied physiological and biochemical approaches to investigate how salt stress affects nicotine biosynthesis in tobacco. We found that salt stress induced the biosynthesis of JA, which subsequently triggered the activation of JA-responsive gene expression and, ultimately, nicotine synthesis. Bioinformatics analysis revealed the existence of many NtMYC2a-recognized G-box motifs in the promoter regions of NtLOX, NtAOS, NtAOC and NtOPR genes. Applying exogenous JA increased nicotine content, while suppressing JA biosynthesis reduced nicotine biosynthesis. Salt treatment could not efficiently induce nicotine biosynthesis in transgenic anti-COI1 tobacco plants. These results demonstrate that JA acts as the essential signal which triggers nicotine biosynthesis in tobacco after salt stress

Enabling Deep Learning-based Physical-layer Secret Key Generation for FDD-OFDM Systems in Multi-Environments

Deep learning-based physical-layer secret key generation (PKG) has been used to overcome the imperfect uplink/downlink channel reciprocity in frequency division duplexing (FDD) orthogonal frequency division multiplexing (OFDM) systems. However, existing efforts have focused on key generation for users in a specific environment where the training samples and test samples obey the same distribution, which is unrealistic for real world applications. This paper formulates the PKG problem in multiple environments as a learning-based problem by learning the knowledge such as data and models from known environments to generate keys quickly and efficiently in multiple new environments. Specifically, we propose deep transfer learning (DTL) and meta-learning-based channel feature mapping algorithms for key generation. The two algorithms use different training methods to pre-train the model in the known environments, and then quickly adapt and deploy the model to new environments. Simulation results show that compared with the methods without adaptation, the DTL and meta-learning algorithms both can improve the performance of generated keys. In addition, the complexity analysis shows that the meta-learning algorithm can achieve better performance than the DTL algorithm with less time, lower CPU and GPU resources

Improving the wind‐induced human comfort of the Beijing Olympic Tower by a double‐stage pendulum tuned mass damper

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154522/1/tal1704_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154522/2/tal1704.pd