Constructing the cultural repertoire in a natural disaster: The role of social media in the Thailand flood of 2011

In 2011, Thailand witnessed its worst flooding catastrophe in half a century. In this study, we explored social media as a new and promising weapon to address the physical and morale challenges caused by the natural disaster. A case study was conducted in the context of crisis response, whichinvestigated the use of social media to contribute to the collective cultural repertoire during the natural disaster. By investigating two paths toward the cultural repertoire construction considering different social groups, this study also identified the roles of social media as an information market and an information threshold in the crisis response

Examination of H-bridge resonant converter using passivity-based control

Author name used in this publication: K. W. E. ChengAuthor name used in this publication: S. L. HoAuthor name used in this publication: J. F. PanPower Electronics Research Centre, Department of Electrical EngineeringRefereed conference paper2004-2005 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

A Sparse Spike Deconvolution Algorithm Based on a Recurrent Neural Network and the Iterative Shrinkage-Thresholding Algorithm

Conventional sparse spike deconvolution algorithms that are based on the iterative shrinkage-thresholding algorithm (ISTA) are widely used. The aim of this type of algorithm is to obtain accurate seismic wavelets. When this is not fulfilled, the processing stops being optimum. Using a recurrent neural network (RNN) as deep learning method and applying backpropagation to ISTA, we have developed an RNN-like ISTA as an alternative sparse spike deconvolution algorithm. The algorithm is tested with both synthetic and real seismic data. The algorithm first builds a training dataset from existing well-logs seismic data and then extracts wavelets from those seismic data for further processing. Based on the extracted wavelets, the new method uses ISTA to calculate the reflection coefficients. Next, inspired by the backpropagation through time (BPTT) algorithm, backward error correction is performed on the wavelets while using the errors between the calculated reflection coefficients and the reflection coefficients corresponding to the training dataset. Finally, after performing backward correction over multiple iterations, a set of acceptable seismic wavelets is obtained, which is then used to deduce the sequence of reflection coefficients of the real data. The new algorithm improves the accuracy of the deconvolution results by reducing the effect of wrong seismic wavelets that are given by conventional ISTA. In this study, we account for the mechanism and the derivation of the proposed algorithm, and verify its effectiveness through experimentation using theoretical and real data

A transformer with adjustable leakage inductance

Author name used in this publication: Y. LuAuthor name used in this publication: S. L. HoAuthor name used in this publication: J. F. PanAuthor name used in this publication: X. D. XueAuthor name used in this publication: K. W. E. ChengRefereed conference paper2005-2006 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

3D Direct Printing of Silicone Meniscus Implant Using a Novel Heat-Cured Extrusion-Based Printer

The first successful direct 3D printing, or additive manufacturing (AM), of heat-cured silicone meniscal implants, using biocompatible and bio-implantable silicone resins is reported. Silicone implants have conventionally been manufactured by indirect silicone casting and molding methods which are expensive and time-consuming. A novel custom-made heat-curing extrusion-based silicone 3D printer which is capable of directly 3D printing medical silicone implants is introduced. The rheological study of silicone resins and the optimization of critical process parameters are described in detail. The surface and cross-sectional morphologies of the printed silicone meniscus implant were also included. A time-lapsed simulation study of the heated silicone resin within the nozzle using computational fluid dynamics (CFD) was done and the results obtained closely resembled real time 3D printing. Solidworks one-convection model simulation, when compared to the on-off model, more closely correlated with the actual probed temperature. Finally, comparative mechanical study between 3D printed and heat-molded meniscus is conducted. The novel 3D printing process opens up the opportunities for rapid 3D printing of various customizable medical silicone implants and devices for patients and fills the current gap in the additive manufacturing industry

3D Printed Silicone Meniscus Implants: Influence of the 3D Printing Process on Properties of Silicone Implants

Osteoarthritis of the knee with meniscal pathologies is a severe meniscal pathology suffered by the aging population worldwide. However, conventional meniscal substitutes are not 3D-printable and lack the customizability of 3D printed implants and are not mechanically robust enough for human implantation. Similarly, 3D printed hydrogel scaffolds suffer from drawbacks of being mechanically weak and as a result patients are unable to execute immediate post-surgical weight-bearing ambulation and rehabilitation. To solve this problem, we have developed a 3D silicone meniscus implant which is (1) cytocompatible, (2) resistant to cyclic loading and mechanically similar to native meniscus, and (3) directly 3D printable. The main focus of this study is to determine whether the purity, composition, structure, dimensions and mechanical properties of silicone implants are affected by the use of a custom-made in-house 3D-printer. We have used the phosphate buffer saline (PBS) absorption test, Fourier transform infrared (FTIR) spectroscopy, surface profilometry, thermo-gravimetric analysis (TGA), X-ray photoelectron spectroscopy (XPS), differential scanning calorimetry (DSC), and scanning electron microscopy (SEM) to effectively assess and compare material properties between molded and 3D printed silicone samples

The ALMaQUEST Survey - V. The non-universality of kpc-scale star formation relations and the factors that drive them

ABSTRACT Using a sample of ∼15 000 kpc-scale star-forming spaxels in 28 galaxies drawn from the ALMA-MaNGA QUEnching and STar formation (ALMaQUEST) survey, we investigate the galaxy-to-galaxy variation of the ‘resolved’ Schmidt–Kennicutt relation (rSK; $\Sigma _{\rm H_2}$–ΣSFR), the ‘resolved’ star-forming main sequence (rSFMS; Σ⋆–ΣSFR), and the ‘resolved’ molecular gas main sequence (rMGMS; Σ⋆–$\Sigma _{\rm H_2}$). The rSK relation, rSFMS, and rMGMS all show significant galaxy-to-galaxy variation in both shape and normalization, indicating that none of these relations is universal between galaxies. The rSFMS shows the largest galaxy-to-galaxy variation and the rMGMS the least. By defining an ‘offset’ from the average relations, we compute a ΔrSK, ΔrSFMS, ΔrMGMS for each galaxy, to investigate correlations with global properties. We find the following correlations with at least 2σ significance: The rSK is lower (i.e. lower star formation efficiency) in galaxies with higher M⋆, larger Sersic index, and lower specific SFR (sSFR); the rSFMS is lower (i.e. lower sSFR) in galaxies with higher M⋆ and larger Sersic index; and the rMGMS is lower (i.e. lower gas fraction) in galaxies with lower sSFR. In the ensemble of all 15 000 data points, the rSK relation and rMGMS show equally tight scatters and strong correlation coefficients, compared with a larger scatter and weaker correlation in the rSFMS. Moreover, whilst there is no correlation between ΔrSK and ΔrMGMS in the sample, the offset of a galaxy’s rSFMS does correlate with both of the other two offsets. Our results therefore indicate that the rSK and rMGMS are independent relations, whereas the rSFMS is a result of their combination.ERC STF

The ALMaQUEST survey IX: The nature of the resolved star forming main sequence

We investigate the nature of the scaling relations between the surface density of star formation rate ($\Sigma _{\rm SFR}$), stellar mass ($\Sigma _*$), and molecular gas mass ($\Sigma _{\rm H_2}$), aiming at distinguishing between the relations that are primary, i.e. more fundamental, and those which are instead an indirect by-product of the other relations. We use the ALMaQUEST survey and analyse the data by using both partial correlations and Random Forest regression techniques. We unambiguously find that the strongest intrinsic correlation is between $\Sigma _{\rm SFR}$ and $\Sigma_{\rm H_2}$ (i.e. the resolved Schmidt-Kennicutt relation), followed by the correlation between $\Sigma _{\rm H_2}$ and $\Sigma _*$ (resolved Molecular Gas Main Sequence, rMGMS). Once these two correlations are taken into account, we find that there is no evidence for any intrinsic correlation between $\Sigma _{\rm SFR}$ and $\Sigma _*$, implying that SFR is entirely driven by the amount of molecular gas, while its dependence on stellar mass (i.e. the resolved Star Forming Main Sequence, rSFMS) simply emerges as a consequence of the relationship between molecular gas and stellar mass.Science and Technology Facilities Council (STFC). ERC Advanced Grant 695671 "QUENCH"