Naver (South Korea)

Anaerobic paddy soils are a major source of methane (CH4) emissions. When the soil is contaminated with arsenic (As), the reducing environments favor the predominance of the highly mobile and toxic As(III). This indicates that flooded paddy soils can simultaneously pose risks associated with greenhouse gas emissions and As toxicity. Sulfate-reducing bacteria (SRB) compete with methanogens for electron donors, while the sulfide produced during sulfate reduction can react with As to form stable As-sulfide phases. Therefore, this study aimed to evaluate the simultaneous mitigation of CH4 emissions and immobilization of As in paddy soils by applying sulfate-based amendments, including ammonium sulfate (NS), iron sulfate (FS), or potassium sulfate (KS). Ten grams of soil were treated with 0.3% NS, FS or KS, with untreated soil used as a control. The soil was amended with either 30 mL of 1000 mg/L deionized water or As(V) solution, and incubated under N2-purged anaerobic conditions at 25°C in the dark. Greenhouse gas emissions were monitored and As concentration and speciation in soil solution were analyzed. Ammonium sulfate, FS, and KS reduced CH4 emissions by 95.6%, 81.4%, and 94.2%, respectively, compared with the control under deionized water conditions, while under As solution conditions, CH4 reduction reached 100% for NS and KS, and 21.3% for FS. At the same time, total As immobilization increased by 26.9%, 31.2%, and 7.23% compared with the control, and the reduction of As(V) to As(III) was suppressed by 55.0%, 19.3%, and 100% in the NS, FS, and KS treatments, respectively. Iron sulfate was less effective at CH4 mitigation than NS and KS because Fe acted as an additional electron donor, enhancing methanogenesis. In addition, FS induced the strongest reduction of As(V) to As(III) through active Fe redox reactions, but showed the highest total As immobilization. Dehydrogenase activity followed the order NS > FS > KS under both deionized water and As solution conditions, likely because NS supplies both sulfate and readily available nitrogen, strongly stimulating microbial metabolic activity. Overall, sulfate-based amendments effectively suppressed CH4 emissions and enhanced As immobilization in paddy soils by stimulating SRB-driven processes. These results indicate that sulfate-based amendments represent a promising strategy for simultaneously mitigating greenhouse gas emissions and As risks in rice paddy systems. Acknowledgments This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (RS-2024-00414790).

Artificial intelligence has advanced global weather forecasting, outperforming traditional numerical models in both accuracy and computational efficiency. Nevertheless, extending predictions beyond subseasonal timescales requires the development of deep learning (DL)–based ocean–atmosphere coupled models that can realistically simulate complex oceanic responses to atmospheric forcing. This study presents KIST-Ocean, a DL-based global three-dimensional ocean general circulation model. Comprehensive evaluations confirmed the model’s robust ocean predictive skill and efficiency. Moreover, it accurately reproduces realistic ocean responses, such as Kelvin and Rossby wave propagation, and vertical motions induced by rotational wind stress, demonstrating its ability to represent key ocean–atmosphere interactions underlying climate phenomena, including the El Niño–Southern Oscillation. These findings reinforce confidence in DL-based global weather and climate models by demonstrating their capacity to capture essential ocean-atmosphere relationships. Building upon this foundation, the present study paves the way for extending DL-based modeling frameworks toward integrated Earth system simulations, thereby offering substantial potential for advancing long-range climate prediction capabilities.

Urban flood defense facilities are facing pressure from climate change, infrastructure aging, and the increasing frequency and intensity of extreme rainfall events. Although resilience has been extensively addressed in urban flood management research, most prior studies depend on static indicators or index-based evaluations at city or regional scales, providing limited understanding of the dynamic responses of individual facilities to disturbances. This study presents a mathematical framework for assessing facility-level resilience in urban flood defense systems and for identifying critical thresholds that drive transitions between functional regimes. The proposed framework shows a composite sigmoid function to capture the nonlinear evolution of facility performance during disturbance and recovery phases. Four resilience dimensions, which are Robustness, Redundancy, Rapidity, and Resourcefulness, are explicitly linked to the parameters of the performance function, representing initial structural performance, availability of functional alternatives, recovery rate, and the resource system over time. To address uncertainty arising from incomplete or partially missing resilience indicators, the framework incorporates a probabilistic treatment of survey-based inputs. Missing or uncertain resilience attributes are modeled using probability distributions, allowing resilience dimensions to be represented as stochastic variables rather than fixed values. A systematic parametric analysis is performed by varying each resilience dimension across feasible ranges, while repeated Monte Carlo simulations are conducted to propagate indicator-level uncertainty into the dynamic performance trajectories. This enables the derivation of empirical cumulative distribution functions and density-based representations of resilience outcomes. The simulation results demonstrate pronounced threshold effects and the presence of alternative performance regimes. When resilience dimensions drop below critical levels, facilities fail to regain their original performance and instead converge toward degraded operational states with distinct probability distributions. Moreover, under scenarios of repeated disturbances, insufficient recovery intervals can trigger irreversible regime shifts, with uncertainty in recovery timing further amplifying the likelihood of such transitions, underscoring the critical role of recovery timing in environments exposed to recurrent extreme rainfall. By directly linking resilience attributes with dynamic performance modeling, and by explicitly accounting for uncertainty associated with incomplete resilience information, this framework enhances understanding of multistability and tipping points in engineered flood defense infrastructure.Acknowledgement This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Ministry of Science and Technology (RS-2024-00356786) and Korea Environmental Industry & Technology Institute grant funded by the Ministry of Environment (RS-2023-00218973).

Urban areas are increasingly facing the limitations of existing drainage systems due to the growing frequency and intensity of extreme rainfall caused by climate change. As underground space becomes more developed and urban infrastructure grows more complex, effective flood mitigation requires a planning tool capable of systematically evaluating large-scale drainage facilities such as deep stormwater tunnels. This study proposes a SaaS-based integrated analysis and design system that automates the entire process of rainfall–runoff simulation, tunnel operation optimization, and surface inundation assessment.The system consists of three major components:(1) a SWMM-based hydrologic and hydraulic simulation engine,(2) an automated calculation module for determining the storage capacity, inlet configuration, pumping requirements, and diversion channel sizing of deep stormwater tunnels, and(3) a cloud-based data environment for storing, managing, and visualizing large hydrologic and topographic datasets.Input data—including terrain, catchment characteristics, sewer networks, and design storm information—are automatically processed in the cloud database. The computation server conducts repeated SWMM simulations for various rainfall scenarios, generating results related to pipe surcharge, water-level variation, and overall drainage performance. A 2D inundation model is integrated to assess surface flooding before and after the construction of a deep stormwater tunnel, enabling spatial comparison of flood reduction effects.The system outputs include required tunnel storage volume, surcharge-prone locations, inundation depth maps, and comparative scenario analyses. Users can easily generate, modify, and store multiple design alternatives and share them within a project team. This integrated modeling environment significantly improves the efficiency of complex drainage analyses and enhances the reliability of decision-making for urban flood mitigation infrastructure.Overall, the proposed system serves as a next-generation digital tool that supports intuitive and comprehensive evaluation of urban drainage conditions. It is expected to markedly improve the practical applicability and planning efficiency of deep stormwater tunnel projects in future urban flood management efforts. AcknowledgementsThis work was supported by Korea Environment Industry & Technology Institute(KEITI) through Technology development project to optimize planning, operation, and maintenance of urban flood control facilities, funded by Korea Ministry of Climate, Energy, Environment(MCEE)(RS-2024-00398012)

Accurate prediction of the state of charge (SoC) of batteries is essential for ensuring the safe, reliable, and uninterrupted operation of electric vehicles (EVs). The prediction fundamentally depends on the ability to accurately predict power consumption. This study investigates the use of GPS-derived information to support SoC prediction, with a particular focus on repeated loop routes such as campus shuttles and closed-circuit EV operations. Real-world driving data are collected using a self-built electric vehicle equipped with a custom battery management system (BMS). These data are used to train three deep learning models, namely gated recurrent unit (GRU), long short-term memory (LSTM), and Transformer, to predict the future SoC of the EV. Experimental results show that the GPS-assisted model consistently outperforms the non-GPS baseline, achieving up to a 23% improvement in prediction accuracy for one-minute-ahead predictions and up to a 76% improvement for ten-minute-ahead predictions. These results demonstrate that GPS-assisted SoC prediction can be effective for forward-looking energy management in practical electric mobility applications.

Gallium-based liquid metals (LMs) are promising materials for stretchable electronics due to their metallic conductivity and deformability. However, the fabrication of large-area stretchable integrated electronics using LMs on various polymers remains challenging due to their high surface tension, fluidity, and poor wettability. Current techniques, such as selective wetting and lift-off processes, face limitations related to substrate compatibility and Ga/metal alloying, hindering their applicability in integrated electronic systems. To address these challenges, we developed a high-resolution top-down etching-based photolithography combined with a universal cryogenic transfer method for transferring patterned LM particles (LMPs) in various polymer substrates. The cryogenic environment modifies the interfacial bonding between the LMPs and substrates, resulting in a universal transfer. The resulting liquid metal particle network embedded polymer (LNEP) exhibits high electrical conductivity (~1.71 × 10⁶ S/m), stability, and strain-insensitive performance across various polymers. This process is scalable to large-area fabrication, overcoming the limitations of existing LM patterning techniques. Leveraging this approach, we demonstrated the use of LNEP ranging from skin-conformal wearable sensors to hybrid stretchable circuits and implantable devices, demonstrating the universality of the method. This technique establishes a scalable pathway for stretchable electronics in advanced applications.

The importance of corneal biomechanics in ocular health and treatments is well-recognized, but reports on associated factors are inconsistent. We established normative data for corneal stiffness using Brillouin spectroscopy in a large cohort of healthy myopic corneas, exploring potential influences on Brillouin modulus (BM) measurements. Data from 1744 healthy corneas of 885 subjects (17–52 years; 422 females) were retrospectively collected, including intraocular pressure (IOP in mmHg), manifest refraction spherical equivalent (MRSE in dioptres [D]), and Pentacam measurements (central corneal thickness [CCT in µm]), K1 and K2 in D). Corneal scans were recorded on a Brillouin Optical Scanning System (BOSS®) using an automated 10-point pattern to measure Central, Mean, Minimum and Maximum BM values in gigapascals (GPa). Monocular data (primarily right eye) were used for correlations; binocular data for interocular comparisons. Parametric analyses included Pearson correlations, t-tests for sex differences, paired t-tests for interocular differences, and multivariate linear regression to isolate independent factors (e.g., age, sex, CCT, IOP) with interaction terms. Normative BM values were Central = 2.856 ± 0.053 GPa, Mean = 2.845 ± 0.038 GPa, Min = 2.819 ± 0.046 GPa, and Max = 2.885 ± 0.042 GPa. Interocular comparisons revealed statistically significant but small increases in OS compared to OD for Mean BM (0.003 GPa [95% CI: 0.001–0.005]; OD = 2.855 ± 0.038 GPa, OS = 2.858 ± 0.038 GPa; P = 0.01) and Max BM (0.004 GPa [95% CI: 0.002–0.006]; OD = 2.886 ± 0.042 GPa, OS = 2.890 ± 0.042 GPa; P = 0.003), as well as in K1 and CCT (P < 0.0001). No significant interocular differences were found in Central or Min BM (P ≥ 0.15). Central, Mean and Min BM values correlated significantly with CCT (P ≤ 0.04) but not with IOP, K1, K2, or MRSE (P ≥ 0.20); Max BM did not correlate with any of these clinical parameters (P ≥ 0.07). Males had higher BM values than females (P ≤ 0.006), with effect sizes (d = 0.21) and r = 0.09 –0.1. Regional mapping showed small overall differences in BM values between zones (P = 0.04), with slightly higher values in the Superior region. Multivariate regression confirmed independent associations: Central BM ~ age (β = 0.0008 [95% CI: 0.0001–0.0015], P = 0.04), sex (β = 0.010 [95% CI: 0.003–0.017], P = 0.008), CCT (β = 0.00015 [95% CI: 0.00005–0.00025], P = 0.003); IOP was non-significant (β = 0.0004 [95% CI: −0.0008–0.0016], P = 0.50, R² = 0.048). Age*sex interaction was non-significant (β=−0.0002, P = 0.62). While statistical significance was observed, the correlation coefficients were not large, suggesting a weak linear relationship. Non-contacting Brillouin scans in a large cohort of healthy myopic eyes established robust normative BM standards, revealing associations between corneal stiffness and age, sex, and CCT. These findings provide reference values that enhance diagnostic accuracy for conditions like keratoconus and inform biomechanical assessments in refractive surgery.

This study aimed to develop a machine-learning-based predictive model for winter traffic-accident risk using meteorological variables and analyze the influence of weather factors on accident occurrence. The study area consisted of five cities in Gyeonggi-do, South Korea—Dongducheon, Paju, Yangpyeong, Icheon, and Suwon—where automated surface observing system (ASOS) stations are located. Winter traffic-accident data from 2015 to 2023 were collected from the traffic accident analysis system (TAAS), and meteorological observations were obtained from the Korea Meteorological Administration. The dependent variable was constructed based on accidentoccurrence levels; ten meteorological variables, including temperature, solar radiation, wind speed, humidity, and cloud cover, were the independent variables. Softmax Regression, Random Forest, and XGBoost models were employed for prediction, and their performances were compared in terms of prediction accuracy and feature importance. The results revealed that the Random Forest model demonstrated the highest predictive performance across all regions. In addition, solar radiation, wind speed, and cloud cover significantly influenced accident occurrence beyond the widely assumed impact of temperature alone. These findings highlight the potential of machine-learning models as decision-support tools for winter road-safety management and proactive accident-prevention policies.