Galaxy evolution studies require the measurement of the physical properties of galaxies at different redshifts. In this work, we build supervised machine learning models to predict the redshift and physical properties (gas-phase metallicity, stellar mass, and star formation rate) of star-forming galaxies from the broad-band and medium-band photometry covering optical to near-infrared wavelengths, and present an evaluation of the model performance. Using 55 magnitudes and colors as input features, the optimized model can predict the galaxy redshift with an accuracy of σ(Δz/1+z) = 0.008 for a redshift range of z < 0.4. The gas-phase metallicity [12 + log(O/H)], stellar mass [log(Mstar)], and star formation rate [log(SFR)] can be predicted with the accuracies of σNMAD = 0.081, 0.068, and 0.19 dex, respectively. When magnitude errors are included, the scatter in the predicted values increases, and the range of predicted values decreases, leading to biased predictions. Near-infrared magnitudes and colors (H, K, and H −K), along with optical colors in the blue wavelengths (m425–m450), are found to play important roles in the parameter prediction. Additionally, the number of input features is critical for ensuring good performance of the machine learning model. These results align with the underlying scaling relations between physical parameters for star-forming galaxies, demonstrating the potential of using medium-band surveys to study galaxy scaling relations with large sample of galaxies.

We investigate the evolution of initial fractal clusters at 3 kpc from the Galactic Center (GC) of the MilkyWay and show how red supergiant clusters (RSGCs)-like objects, which are considered to be the result of active star formation in the Scutum complex, can form by 16 Myr. We find that initial tidal filling and tidal over-filling fractals are shredded by the tidal force, but some substructures can survive as individual subclusters, especially when the initial virial ratio is ≤0.5. These surviving subclusters are weakly mass segregated and show a top-heavy mass function. This implies the possibility that a single substructured star cluster can evolve into multiple ‘star clusters’.

A force-free field (FFF) is determined solely by the normal components of magnetic field and current density on the entire boundary of the domain. Methods employing three components of magnetic field suffer from overspecification of boundary conditions and/or a nonzero divergence-B problem. A vector potential formulation eliminates the latter issue, but introduces difficulties in imposing the normal component of current density at the boundary. This paper proposes four different boundary treatment methods within the vector potential formulation. We conduct a comparative analysis of the vector potential FFF solvers that we have developed incorporating these methods against other FFF codes in different magnetic field representations. Although the vector potential solvers with the new boundary treatments do not outperform our poloidal-toroidal formulation code, they demonstrate comparable or superior performance compared to the optimization code in SolarSoftWare. The methods developed here are expected to be readily applied not only to force-free field computations but also to time-dependent data-driven simulations.

Structural inversion refers to the reverse reactivation of extensional faults that influence basin shortening accommodated by contractional faults or folds. On the Korean peninsula, Miocene inversion structures have been found, but the Cretaceous rocks on Geoje Island may have undergone inversion as early as the Upper Cretaceous. To evaluate the structural inversion on Geoje Island, located on the eastern side of South Korea, and to determine the effects of preexisting weakness zones, field-based geometric and kinematic analyses of faults were performed. The lithology of Geoje Island is dominated by hornfelsified shale, siltstone, and sandstone in the Upper-Cretaceous Seongpori formation. NE and NW-oblique normal faults, conjugate strike-slip (NW-sinistral transpressional and E-W-dextral transtensional) faults, and NE-dextral transpressional faults are the most prominent structural features in Geoje Island. Structural inversion on Geoje Island was evidenced by the sinistral and dextral transpressional reactivation of the NW and NE-trending oblique normal faults respectively, under WNW-ESE/NW-SE compression, which was the orientation of the compressive stress during the Late Cretaceous to Early Cenozoic.

Biomass-derived porous carbon is an excellent scientific and technologically interesting material for supercapacitor applications. In this study, we developed biomass-derived nitrogen-doped porous carbon nanosheets (BDPCNS) from cedar cone biomass using a simple KOH activation and pyrolysis method. The BDPCNS was effectively modified at different temperatures of 600 °C, 700 °C, and 800 ℃ under similar conditions. The as-prepared BDPCNS-700 electrode exhibited a high BET surface area of 2883 m2 g− 1 and a total pore volume of 1.26 cm3 g− 1. Additionally, BDPCNS-700 had the highest electrical conductivity (11.03 cm− 1) and highest N-doped content among the different electrode materials. The BDPCNS-700 electrode attained a specific capacitance of 290 F g− 1 at a current density of 1 A g− 1 in a 3 M KOH electrolyte and an excellent longterm electrochemical cycling stability of 93.4% over 1000 cycles. Moreover, the BDPCNS-700 electrode had an excellent energy density (40.27 Wh kg− 1) vs power density (208.19 W kg− 1). These findings indicate that BDPCNS with large surface areas are promising electrode materials for supercapacitors and energy storage systems.