Centrifuge modeling of disconnected piled raft using vertical pushover tests

Acta Geotechnica - A disconnected piled raft (DPR) foundation has been introduced as an effective pile design to reduce the vertical loading experienced by the pile. The characterization of DPRs...     

Photospheric Shear Flows in Solar Active Regions and Their Relation to Flare Occurrence

Solar active regions (ARs) that produce major flares typically exhibit strong plasma shear flows around photospheric magnetic polarity inversion lines (MPILs). It is therefore important to quantitatively measure such photospheric shear flows in ARs for a better understanding of their relation to flare occurrence. Photospheric flow fields were determined by applying the Differential Affine Velocity Estimator for Vector Magnetograms (DAVE4VM) method to a large data set of 2548 coaligned pairs of AR vector magnetograms with 12-min separation over the period 2012?–?2016. From each AR flow-field map, three shear-flow parameters were derived corresponding to the mean (\(\langle S\rangle \)), maximum (\(S_{\mathrm{max}}\)) and integral (\(S_{\mathrm{sum}}\)) shear-flow speeds along strong-gradient, strong-field MPIL segments. We calculated flaring rates within 24 h as a function of each shear-flow parameter and we investigated the relation between the parameters and the waiting time (\(\tau \)) until the next major flare (class M1.0 or above) after the parameter observation. In general, it is found that the larger \(S_{\mathrm{sum}}\) an AR has, the more likely it is for the AR to produce flares within 24 h. It is also found that among ARs which produce major flares, if one has a larger value of \(S_{\mathrm{sum}}\) then \(\tau \) generally gets shorter. These results suggest that large ARs with widespread and/or strong shear flows along MPILs tend to not only be more flare productive, but also produce major flares within 24 h or less.     

Rock Magnetic Approaches Used on Deep-sea Sediments in the Northeastern Equatorial Pacific

Rock magnetic properties of unconsolidated sediments from the Korea Deep Ocean Study area of the northeastern equatorial Pacific were analyzed to trace the time-dependent variations of sedimentary environments. For upper Pleistocene sediments, light-brown sediments predominate, whereas the lower sediments deposited in the late Pliocene are dark brown. Rock magnetic properties also clearly differ among the two distinct color environments. Values of anhysteretic remanent magnetization and low-field magnetic susceptibility are highly responsive to changes in the color of the sediment. For example, comparatively low values denote a dominance of coarse-grained magnetic minerals as observed in the lower dark-brown layers. With respect to the content of magnetic minerals, coarse magnetic grains are highly concentrated in the darker-colored sediment layers. However, both the magnetic mineral dependent parameter (S ratio) and results of the scanning electron microscope observation indicate that magnetic mineral changes did not occur along the core depth, which means that there was no apparent source change. Without distinctive source changes, the variations in the rock magnetic properties likely reflect a process by which the magnetic grains were primarily transported by enhanced wind and bottom currents and affected by diagenetic dissolution as a function of both the time from burial and the extremely low sedimentation rates after deposition.     

Estimation of soil moisture using deep learning based on satellite data: a case study of South Korea

The Korea Meteorological Administration uses soil moisture (SM) observed by the Advanced Microwave Scanning Radiometer-2 (AMSR2) to monitor drought. However, it may not be appropriate for monitoring drought in South Korea due to significant underestimation of SM. In this study, we used a deep learning method that performs better than traditional statistical and physical models for reliable estimation of SM based on remotely sensed satellite data. For estimating SM, we carefully selected input variables that exhibit a feedback loop with SM. To build an effective deep learning model, we examined the influences of sampling criteria and input parameters as well as the accuracy of several deep neural networks. The selected model was cross-validated to determine its stability. The estimated SM using deep learning had a high correlation coefficient (R) of 0.89 and a low root mean square error (RMSE; 3.825%) and bias (?0.039%) compared to in-situ measurements. A time series analysis using dynamic time warping was conducted which showed that the estimated SM was almost similar to the in-situ SM. In order to investigate the improvement in SM estimation using our method, it was compared with the Global Land Data Assimilation System and AMSR2. Significant improvements in R and a reduction in error values by more than half were achieved using our method. The estimated SM has finer spatial resolution at 4 km, and it can be rapidly produced, which will be useful for drought monitoring over the Korean Peninsula in near-real-time.     

Evaluation of ICA and CEM algorithms with Landsat-8/ASTER data for geological mapping in inaccessible regions

Many regions remain poorly studied in terms of geological mapping and mineral exploration in inaccessible regions especially in the Arctic and Antarctica due to harsh conditions and logistic difficulties. Application of specialized image processing techniques is capable of revealing the hidden linear mixing spectra in multispectral and hyperspectral satellite images. In this study, the applications of Independent component analysis (ICA) and Constrained Energy Minimization (CEM) algorithms were evaluated for Landsat-8 and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) remote sensing data for geological mapping in Morozumi Range and Helliwell Hills areas, Northern Victoria Land (NVL), Antarctica. The results of this investigation demonstrate the capability of the two algorithms in distinguishing pixel and subpixel targets in the multispectral satellite data. The application of the methods for identifying poorly exposed geologic materials and subpixel exposures of alteration minerals has invaluable implications for geological mapping and mineral exploration in inaccessible regions.     

Spatial variations of small phytoplankton contributions in the Northern Bering Sea and the Southern Chukchi Sea

Phytoplankton size classes (hereafter, PSCs) were derived from satellite ocean color data using a present phytoplankton abundance-based optical algorithm in the northern Bering and southern Chukchi Seas to characterize the spatial and seasonal variations of the different PSC and investigate the contributions of small phytoplankton to the total phytoplankton biomass. The comparison results showed that the phytoplankton abundance-based method approach could reasonably classify the three PSCs (pico-, nano-, and micro phytoplankton). The satellite maps of the dominant PSCs were derived using long-term satellite ocean color data. The general spatial distribution showed that the large (micro-) phytoplankton were dominant in the coastal waters and the west side of the Bering strait, while the small size (nano- or pico-) phytoplankton were dominant in the open ocean waters. Nano- and microphytoplankton were dominant in May and October in most of the study area, while pico-phytoplankton were dominant in the summer months in the open ocean waters. The annual variation in small phytoplankton dominance had a strong positive relationship with the annual mean sea surface temperature (SST), which is consistent with the increasing dominance of small phytoplankton biomass as water temperature increases. Microphytoplankton have an apparent increasing trend in the southeastern Chukchi Sea but slightly decreasing trends in Chirikov and St. Lawrence Island Polynya (SLIP). In contrast, there were increasing trends in picophytoplankton in Chirikov and SLIP, which seems to be related to increasing annual SST. It is crucial to monitor changes in dominant groups of phytoplankton community in the Bering and Chukchi Seas as important biological hotspots responding to the recent changes in environmental conditions.     

Characterization and Transcriptional Response of Ecdysone Receptor Gene in the Mud Crab Macrophthalmus japonicus: Effects of Osmotic Stress and Endocrine Disrupting Chemicals

Ocean Science Journal - The ecdysone receptor (EcR) induces hormonal pathways by binding ecdysteroids to other nuclear receptors for development, reproduction, and molting processes. To further...     

Assessment of Tidal Stream Energy Resources Using a Numerical Model in Southwestern Sea of Korea

Ocean Science Journal - In this study, tidal stream energy resources of the Southwestern Sea of Korea were assessed using a numerical model, Modelo Hidrodinâmico (MOHID). This numerical model...     

Acidification at the Surface in the East Sea: A Coupled Climate-carbon Cycle Model Study

This modeling study investigates the impacts of increasing atmospheric CO₂ concentration on acidification in the East Sea. A historical simulation for the past three decades (1980 to 2010) was performed using the Hadley Centre Global Environmental Model (version 2), a coupled climate model with atmospheric, terrestrial and ocean cycles. As the atmospheric CO₂ concentration increased, acidification progressed in the surface waters of the marginal sea. The acidification was similar in magnitude to observations and models of acidification in the global ocean. However, in the global ocean, the acidification appears to be due to increased in-situ oceanic CO₂ uptake, whereas local processes had stronger effects in the East Sea. pH was lowered by surface warming and by the influx of water with higher dissolved inorganic carbon (DIC) from the northwestern Pacific. Due to the enhanced advection of DIC, the partial pressure of CO₂ increased faster than in the overlying air; consequently, the in-situ oceanic uptake of CO₂ decreased.