332 research outputs found
Prediction of thermal behavior of FRP deck with iSRR- Connector
No full textHybrid structures consisting of FRP(fiber reinforce polymer) composite decksconnected to a steel girder superstructure are gaining more attention by combining the stiffness of steel members with good fatigue endurance and high strength to weight ratio of FRP. However, when composite material FRP is exposed to elevated temperature, delamination and strength reduction could appear due to low transition temperature of fiber and resin. As a result, predicting the temperature of the FRP bridge deck becomes a preliminary requirement for the further study of the FRP bridge deck.This research is trying to simulate the heat transfer process of the FRP bridgedeck and predict the temperature changing process when it is exposed to natural environment. To reach this goal, experiment and FEM(finite element model) are used as main methods. By detecting temperature change history of the FRP bridge deck specimen during hot weathers in Delft, Netherlands, the maximum temperature and heat transfer process of the FRP bridge deck could be studied. On the basis of experimental results, an detailed FEM in Abaqus is built up to simulate the heat transfer process of the specimen used in the experiment. After validating the accuracy of FEM result with experimental data, the FEM is used to predict the temperature of FRP bridge deck that exposed to natural environment in the hottest weather of Netherlands.In conclusion, the results shows that the temperature of FRP structures could bewell predicted which only has 10.5% variance predicting the maximum temperature on the top surface of the specimen and high accuracy of 6% predicting the average temperature of the FRP panels along the thickness. It also has high accuracy of 3.5% predicting the maximum web-core temperature difference. The only insufficient part is that the average temperature difference of web-core has a deviation of 21% with the experimental data. As FRP panels make up the web and flange of the bridge deck which are the main structural parts that bear the stresses, the inaccuracy of predicting temperature of the core is acceptable.Civil Engineering | Structural Engineerin
Low propagation loss Ge-on-Si waveguides and their dependency on processing methods
No full textThe successful fabrication of MIR Ge-on-Si waveguides written by both the high-resolution electron beam lithography (EBL) approach, as well as the wafer scale, high throughput approach using 365 nm i-line stepper lithography is reported. A low propagation loss of ~2.7 dB/cm at a wavelength of 3.8 µm is shown for Ge-on-Si waveguides patterned using the i-line stepper. The waveguide etching technique, using reactive ion or deep-reactive ion etching, is also analyzed. Furthermore, the propagation loss values for both the lithographic techniques are found to be comparable. Therefore the advantage of using a simpler i-line stepper lithography for high volume fabrication is highlighted
A Kudla-Rapoport Formula for Exotic Smooth Models of Odd Dimension
Get PDFIn this article, we prove a Kudla-Rapoport conjecture for
-cycles on exotic smooth unitary Rapoport-Zink spaces of odd
arithmetic dimension, i.e. the arithmetic intersection numbers for
-cycles equals the derivatives of local representation density. We
also compare -cycles and -cycles on these RZ spaces.
The method is to relate both geometric and analytic sides to the even
dimensional case and reduce the conjecture to the results in arXiv:2101.09485.Comment: arXiv admin note: substantial text overlap with arXiv:1604.02419,
arXiv:2312.16906 by other author
DNA display for drug discovery
No full textA novel DNA display strategy, based on a new puromycin modifier, has been developed. The 5'-end puromycin-tethered oligonucleotide was synthesized to hybridize with mRNA, so that it could attack the nascent polypeptides during in vitro translation. The DNA-peptide fusion molecule can tolerate more harsh and stringent selection conditions, therefore, this DNA display may become a useful tool for in vitro display technologies for the selection of peptide drug candidates
Author Correction: The inverted U-shaped relationship between knowledge diversity of researchers and societal impact
No full textNumerical simulation of the dynamic characteristics of the ultra-thin gas flow at the head/disk interface of hard disk drives
No full textEffect of the external shock on the dynamic characteristics of dual sliders at operational state for small form factor hard disk drives
No full textSimulation of the dynamic characteristics for a hard disk drive during load/unload process subject to an external shock
No full textHDI-12 THE ULTRA-THIN FILM LUBRICATION AT THE HEAD-DISK INTERFACE DURING LOAD/UNLOAD PROCESS OF HARD DISK DRIVES(Head/Disk Interface and Tribology III,Technical Program of Oral Presentations)
No full textA lightweight hyperspectral image multi-layer feature fusion classification method based on spatial and channel reconstruction.
No full textHyperspectral Image (HSI) classification tasks are usually impacted by Convolutional Neural Networks (CNN). Specifically, the majority of models using traditional convolutions for HSI classification tasks extract redundant information due to the convolution layer, which makes the subsequent network structure produce a large number of parameters and complex computations, so as to limit their classification effectiveness, particularly in situations with constraints on computational power and storage capacity. To address these issues, this paper proposes a lightweight multi-layer feature fusion classification method for hyperspectral images based on spatial and channel reconstruction (SCNet). Firstly, this method reduces redundant computations of spatial and spectral features by introducing Spatial and Channel Reconstruction Convolutions (SCConv), a novel convolutional compression method. Secondly, the proposed network backbone is stacked with multiple SCConv modules, which allows the network to capture spatial and spectral features that are more beneficial for hyperspectral image classification. Finally, to effectively utilize the multi-layer feature information generated by SCConv modules, a multi-layer feature fusion (MLFF) unit was designed to connect multiple feature maps at different depths, thereby obtaining a more robust feature representation. The experimental results demonstrate that, compared to seven other hyperspectral image classification methods, this network has significant advantages in terms of the number of parameters, model complexity, and testing time. These findings have been validated through experiments on four benchmark datasets
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