Surface plasmon resonance assisted rapid laser joining of glass

Rapid and strong joining of clear glass to glass containing randomly distributed embedded spherical silver nanoparticles upon nanosecond pulsed laser irradiation (∼40 ns and repetition rate of 100 kHz) at 532 nm is demonstrated. The embedded silver nanoparticles were ∼30–40 nm in diameter, contained in a thin surface layer of ∼10 μm. A joint strength of 12.5 MPa was achieved for a laser fluence of only ∼0.13 J/cm2 and scanning speed of 10 mm/s. The bonding mechanism is discussed in terms of absorption of the laser energy by nanoparticles and the transfer of the accumulated localised heat to the surrounding glass leading to the local melting and formation of a strong bond. The presented technique is scalable and overcomes a number of serious challenges for a widespread adoption of laser-assisted rapid joining of glass substrates, enabling applications in the manufacture of microelectronic devices, sensors, micro-fluidic, and medical devices

On the Nature of X(4260)

We study the property of $X(4260)$ resonance by re-analyzing all experimental data available, especially the $e^+e^- \rightarrow J/\psi\,\pi^+\pi^-,\,\,\,\omega\chi_{c0}$ cross section data. The final state interactions of the $\pi\pi$, $K\bar K$ couple channel system are also taken into account. A sizable coupling between the $X(4260)$ and $\omega\chi_{c0}$ is found. The inclusion of the $\omega\chi_{c0}$ data indicates a small value of $\Gamma_{e^+e^-}=23.30\pm 3.55$eV.Comment: Refined analysis with new experimental data included. 13 page

Reaction-Diffusion-Branching Models of Stock Price Fluctuations

Several models of stock trading [P. Bak et al, Physica A {\bf 246}, 430 (1997)] are analyzed in analogy with one-dimensional, two-species reaction-diffusion-branching processes. Using heuristic and scaling arguments, we show that the short-time market price variation is subdiffusive with a Hurst exponent $H=1/4$. Biased diffusion towards the market price and blind-eyed copying lead to crossovers to the empirically observed random-walk behavior ($H=1/2$) at long times. The calculated crossover forms and diffusion constants are shown to agree well with simulation data.Comment: 4 pages, 3 figure

Enhancing Super-Resolution Networks through Realistic Thick-Slice CT Simulation

This study aims to develop and evaluate an innovative simulation algorithm for generating thick-slice CT images that closely resemble actual images in the AAPM-Mayo's 2016 Low Dose CT Grand Challenge dataset. The proposed method was evaluated using Peak Signal-to-Noise Ratio (PSNR) and Root Mean Square Error (RMSE) metrics, with the hypothesis that our simulation would produce images more congruent with their real counterparts. Our proposed method demonstrated substantial enhancements in terms of both PSNR and RMSE over other simulation methods. The highest PSNR values were obtained with the proposed method, yielding 49.7369 $\pm$ 2.5223 and 48.5801 $\pm$ 7.3271 for D45 and B30 reconstruction kernels, respectively. The proposed method also registered the lowest RMSE with values of 0.0068 $\pm$ 0.0020 and 0.0108 $\pm$ 0.0099 for D45 and B30, respectively, indicating a distribution more closely aligned with the authentic thick-slice image. Further validation of the proposed simulation algorithm was conducted using the TCIA LDCT-and-Projection-data dataset. The generated images were then leveraged to train four distinct super-resolution (SR) models, which were subsequently evaluated using the real thick-slice images from the 2016 Low Dose CT Grand Challenge dataset. When trained with data produced by our novel algorithm, all four SR models exhibited enhanced performance.Comment: 11 pages, 4 figure