Korea University

Major mining cities worldwide have been suffering from diverse contamination sources such as tailings, mine drainages, geology enriched with toxic metals, and other industrial and domestic sources. This study established a multi-isotopic comprehensive model for elucidating water contamination sources and geochemical reactions in the city of Erdenet in Mongolia. The Erdenet city was contaminated with As, Cu, Mo, Zn, and SO42−, and we used isotopes of Cu, Mo, Zn, and S–O in SO42−. Contamination sources for groundwater and surface water were differentiated as tailings dump, excavated ore, heap leaching, ash pond of a power plant, and argillic alteration zone. Groundwater in the residential area was influenced by the argillic alteration zone, as indicated by low δ34SSO4 and high δ65Cu values, while δ66Zn fingerprints may have been masked by adsorption and mixing. Additionally, δ98Mo fingerprints from two major Mo contamination sources (the ash pond and tailings dump) were evident in the stream. The tailings dump substantially impacted δ65Cu, δ98Mo, and δ66Zn values in the stream, with isotopic fractionation occurring through oxidative dissolution and adsorption. Furthermore, to assess oxidative dissolution of sulfides and adsorption, δ65Cu and δ98Mo+δ66Zn were found to be particularly useful, respectively. This study highlights the effectiveness of multimetal isotopic ratios in tracing contamination sources and geochemical processes in regions with diverse contaminants, presenting a robust spatial model for isotopic fingerprinting.

In this paper, we report the spatiotemporal dynamics of an intraguild predation (IGP)-type predator-prey model incorporating harvesting and prey-taxis. We first discuss the local and global existence of the classical solutions in N-dimensional space. It is found that the model has a global classical solution when controlling the prey-taxis coefficient in a certain range. Thereafter, we focus on the existence of the steady-state bifurcation. Moreover, we theoretically investigate the properties of the bifurcating solution near the steady-state bifurcation critical threshold. As a consequence, the spatial pattern formation of this model can be theoretically confirmed. Importantly, by means of rigorous theoretical derivation, we provide discriminant criteria on the stability of the bifurcating solution. Finally, the complicated patterns are numerically displayed. It is demonstrated that the harvesting and prey-taxis significantly affect the pattern formation of this IGP-type predator-prey model. Our main results of this paper reveal that: (i) The repulsive prey-taxis could destabilize the spatial homogeneity, while the attractive prey-taxis effect and self-diffusion will stabilize the spatial homogeneity of this model. (ii) Numerical results suggest that over-harvesting for prey or predators is not advisable, it can lead to an ecological imbalance due to a significant reduction in population numbers. However, harvesting within a certain range is a feasible approach.

This study focuses on two challenges associated with pancake-wound REBCO magnets: 1) mechanical damage in module coils 2) issues related to electrical joint implementation. Shrink-fitting an external ring structure around the pancake coil can resolve the problems by inducing compressive radial and hoop stress, opposing magnetic stress. Moreover, the ring can serve as an electrical joint between pancake coils, facilitating the easy replacement of malfunctioning module coils within this joint structure. In this study, we fabricated a small pancake wound no-insulation magnet with copper shrink-fit joints. Each pancake is surrounded by a ring -shaped copper structure with a rectangular cross-section, achieved through shrink-fitting. We present detailed manufacturing procedures and measured contact resistances. The result demonstrates that shrink-fitting can be applied to pancake wound magnets and perform novel modular design and mechanical reinforcement. This experiment suggests a new approach to implementing electric connection and reinforcement in REBCO pancake-wound magnets.

This article investigates Nash equilibrium seeking for nonzero-sum games of switched nonlinear systems. A novel cost function is presented that measures the system state cost and control cost while considering the dynamics under different switching modes. Then, a new coupled switching Hamilton–Jacobi (HJ) equation is derived. To address the challenge of directly solving the HJ equation, an event-triggered two-stage reinforcement learning strategy is proposed. Upon event triggering, each player’s switching law determines the optimal subsystem to switch to by minimizing the HJ equation. Subsequently, the corresponding learning law for each player updates its respective input via the determined optimal subsystem. The proposed algorithm achieves Nash equilibrium while ensuring system stability. Furthermore, Zeno behavior is avoided, and the computational and communication loads are reduced. Finally, the proposed algorithm’s efficacy is substantiated through two simulation examples.

Ultrasonication has emerged as a promising technique for modifying physicochemical properties of proteins, enhancing their functionality in food applications. This study evaluated the effects of ultrasonic treatment on the structural and functional properties of mealworm-derived proteins (MPs) and their potential as emulsifiers. Dynamic light scattering revealed a significant reduction in MP particle size from 3464.3 nm (untreated) to 115.5 nm (30 min sonication), along with increased zeta potential, indicating improved colloidal stability. Sonication enhanced oil-holding capacity and solubility, suggesting improved interfacial adsorption and emulsification. Circular dichroism and FT-IR spectroscopy confirmed structural modifications, including increased α-helix content and enhanced hydrogen bonding interactions. Free sulfhydryl content and surface hydrophobicity analyses indicated ultrasound-induced unfolding, exposing functional groups that contribute to emulsifying properties. Sonicated MPs demonstrated superior emulsion stability under varying temperature, pH, and ionic conditions. Furthermore, digestibility analysis showed improved gastric digestion (72.7 % to 82.8 %) and enhanced lipid digestion in the small intestine (36.2 % to 47.9 %), suggesting greater bioavailability. These physicochemical modifications highlight the feasibility of using sonicated MP as natural emulsifiers with enhanced functionality. This study underscores their potential in food formulations, particularly for nutritionally fortified emulsions, contributing to sustainable and functional food ingredient development.

This paper studies a new multi-agent deep reinforcement learning (MADRL) approach for unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) networks, where UAV-mounted servers provide offloading services to mobile users (MUs). We aim to minimize the total energy consumption of MUs by optimizing UAV mobility, UAV-MU association, resource allocation, and task offloading ratios. In the multi-UAV scenario, we model the MEC network as a multi-agent partially observable Markov decision process (POMDP), where each UAV agent operates with limited information for decentralized decision-making. Conventional MADRL methods manually design such UAV interaction messages, thereby incurring performance degradation. To address this issue, we propose a new neural network (NN)-based UAV interaction mechanism that generates autonomously task-oriented messages to minimize energy consumption. Such message-generating NNs are developed under the MADRL framework, which allows for joint optimization of UAV interactions and decentralized decisions in an end-to-end manner. Numerical results demonstrate that our approach outperforms traditional MADRL methods and achieves performance close to ideal centralized schemes while maintaining scalability with varying UAV numbers.

Adjusting the DPTO layer in DPTO/PTO multilayers controls imprint effects, reducing local ferroelectric switching energy. External forces induce flexoelectric phenomena, further lowering switching energy.

The COVID-19 pandemic lockdown provided an unprecedented opportunity to examine changes in India’s air quality following abrupt reductions in anthropogenic emissions, particularly from transportation, industry, and construction. While many studies reported substantial pollution declines during the lockdown, most focused exclusively on this period, neglecting the subsequent ‘unlock’ phase, the influence of transboundary pollution, and the need to distinguish between emission-driven and meteorology-driven changes in PM2.5. Our study addresses these gaps by isolating the contributions of meteorological variability and activity restrictions on PM2.5 across the entire lockdown and unlock phases (February 24-June 30, 2020) using a high-resolution modelling framework and satellite-derived PM2.5 data. Through our WRF-Chem modeling study, we found that PM2.5 concentrations decreased by 29% post-lockdown, compared to a 21% decline over the same period in preceding years, with satellite observations showing similar reductions of 31% and 22%, respectively. However, only an additional 8–9% reduction in 2020, beyond the typical interannual variability, was directly attributable to emission controls, while meteorological factors largely influenced the overall decline. The most pronounced PM2.5 decline occurred in the Indo-Gangetic Plain during the unlock phase. Despite the initial improvements, restrictions on transportation, industry, and construction alone were insufficient to bring PM2.5 levels below the National Ambient Air Quality Standards. A key finding is that persistent emissions from the residential sector, which remained largely unaffected during the lockdown, significantly limited the overall reduction in PM2.5. Without targeted interventions to address household emissions, such as promoting cleaner fuels and improving waste management to prevent garbage burning, India will struggle to achieve sustained air quality improvements. Our results emphasize the urgent need for integrated, regionally tailored, long-term strategies that address all major pollution sources to ensure lasting reductions in PM2.5 levels. Implementing comprehensive measures can significantly improve India’s air quality, ensuring a healthier and more sustainable environment.

Single‐cell RNA sequencing (scRNA‐seq) has gained prominence as a valuable technique for examining cellular gene expression patterns at the individual cell level. In the analysis of scRNA‐seq datasets, it is common practice to visualise a subset of principal components (PCs), obtained via principal component analysis (PCA), using dimensionality reduction techniques such as t‐stochastic neighbour embedding (t‐SNE). Determining the number of PCs (i.e. dimensionality) is a critical step that influences the outcome of single‐cell analysis, and this process typically requires a labour‐intensive manual assessment involving the inspection of numerous projection plots. To address this challenge, we present a visualisation system that assists analysts in efficiently determining the optimal dimensionality of scRNA‐seq data. The proposed system employs two hull heatmaps, a cell type heatmap and a cluster heatmap, which offer comprehensive representations of target cells of multiple cell types across various dimensionalities through the utilisation of a convex hull‐embedded colour map. The cell type heatmap shows overlaps between cell types, and the cluster heatmap compares cell clustering results. The proposed hull heatmaps effectively alleviate the labourious task of manually evaluating hundreds of projection plots for searching for the optimal dimensionality. Additionally, our system offers interactive visualisation of gene expression levels and an intuitive lasso selection tool, thereby enabling analysts to progressively refine the convex hulls on the hull heatmaps. We validated the usefulness of the proposed system through two quantitative evaluations and three case studies.

Developing non‐toxic, efficient, and environment‐friendly microbe‐resistant surfaces for viral infections is crucial, considering the lengthy and complex drug development process. Here, a highly efficient antiviral material is developed with Fe3O4 nanoparticles‐embedded covalent organic framework (COF) thin film. TpAzo‐Fe3O4, the nanoparticles‐embedded thin film, being a cost‐effective, non‐toxic, highly stable, and readily synthesized material, offers a more sustainable approach than the existing antiviral materials. The precise embedding of the nanoparticles on COF thin films provides stability and prevents coagulation in the experimental media. The antiviral potential of TpAzo‐Fe3O4 thin film is evaluated against four viral prototypes, Influenza Type A/PR/8/1934 (H1N1), A/turkey/Wisconsin/1/1966 (H9N2) and Human Corona Virus (HCoV‐OC43 and HCoV‐NL63). Superior antiviral activity is recorded by the TpAzo‐Fe3O4 thin film exhibiting nearly 72% cell viability against H1N1, ≈99% cell viability against H9N2, ≈94% cell viability against HCoV‐OC43, and ≈ 98% against HCoV‐NL63 virus. The importance of the COF thin film is apparent from a control experiment with Fe3O4 nanoparticles‐loaded polymer (TpmXDA‐Fe3O4). Finally, an “antiviral filtration” is performed with TpAzo‐Fe3O4 film, and residual viral activity (H1N1 virus) of the filtrate is assessed. The results revealed nearly ≈95% cell viability, suggesting complete sequestration/containment of the test virus during filtration.

In this research paper, we study body dysmorphic disorder, a psychiatric condition characterized by an intense preoccupation with perceived physical defects. We use functional magnetic resonance imaging data to generate brain networks for individuals diagnosed with body dysmorphic disorder and for the control group. These networks have different brain regions as nodes and functional connections amongst them as edges. We analyze the structural properties, including clustering coefficient and degree, closeness, betweenness, and eccentricity centralities, to identify network properties that could be associated with body dysmorphic disorder. We indeed find significant differences in network properties between the two groups. Our results reveal abnormal brain network organization in individuals with body dysmorphic disorder, and they thus enhance our understanding of the neural underpinnings of this disease and may contribute to better diagnostic and therapeutic strategies.

Low-cycle fatigue (LCF) data involve complex temporal interactions in a strain cycle series, which hinders accurate fatigue life prediction. Current studies lack reliable methods for fatigue life prediction using only initial-cycle data while simultaneously capturing both temporal dependencies and localized features. This study introduces a novel deep-learning-based prediction model designed for LCF data. The proposed approach combines long short-term memory (LSTM) and convolutional neural network (CNN) architectures with an attention mechanism to effectively capture the temporal and localized characteristics of stress–strain data from acquisition through a series of cycle strain-controlled tests. Among the models tested, the LSTM-contextual attention model demonstrated superior performance (R² = 0.99), outperforming the baseline LSTM and CNN models with higher R² values and improved statistical metrics. The analysis of attention weights further revealed the model's ability to focus on critical timesteps associated with fatigue damage, highlighting its effectiveness in learning key features from LCF data. This study underscores the potential of deep-learning-based methods for accurate fatigue life prediction in LCF applications. This study provides a foundation for future research to extend these approaches to diverse materials with varying fatigue conditions and advanced models capable of incorporating non-linear fatigue mechanisms.

In recent years, transforming green resources and managing existing mineral reserves through sustainable policies have become essential for achieving an environmentally sustainable future. In this regard, countries with high biocapacity and abundant mineral reserves have attracted increasing attention, making the implementation of sustainable strategies a critical agenda. This paper examines how green energy use and mineral resource availability relate to environmental sustainability by employing advanced techniques, including the Fourier approach and artificial intelligence algorithms. The results indicate that positive changes in mineral rents do not significantly influence environmental sustainability, while negative variations are linked to worsened ecological outcomes. Moreover, green energy use contributes positively to environmental quality, whereas urbanization has a detrimental effect. The outcomes of this study underscore the necessity for policymakers to account for the nonlinear dynamics between mineral resource abundance and environmental sustainability when formulating effective policy interventions. In particular, the contribution of mineral resources to advancing clean energy transitions should be acknowledged. Promoting investments in environmentally friendly energy and restructuring urban policies toward green infrastructure and sustainable land use are also vital for strengthening environmental sustainability.

Background South Korea is reported to have higher levels of unmet medical needs (UMN) than other countries, particularly among the middle-aged adult population. Considering that this group constitutes a substantial portion of the country’s productive workforce, their health requires continuous management to ensure sustained productivity. The purpose of this study is to investigate the factors associated with UMN in economically active middle-aged adults and to develop a model to predict the occurrence of UMN. Methods In this study, 3,575 middle-aged adults who are economically active were selected from the 2020 Korean Health Panel Survey data. Logistic regression, Random Forest, Naïve Bayes, Gradient Boosting Method, and Neural Network were applied to create the prediction model, and tenfold cross validation was performed by checking the reliability of the analysis. The model was evaluated based on the Area Under Receiver Operating Characteristics (AUROC) as well as accuracy, precision, recall, F-1 score and MCC. Results First, the prevalence of UMN in middle-aged adults was 15.6%. Second, random forest was found to be the model with the highest predictive power. It showed an AUROC of 0.831, Accuracy of 0.862, and F-1 score of 0.820. Third, the main factors influencing the occurrence of UMN were subjective stress and subjective health awareness. Conclusions These findings suggest that psychological support is necessary in order to manage the occurrence of UMN among middle-aged adults, with regular stress management being especially important. However, the lower AUROC suggests that additional variables are needed to enhance the prediction model.

The accurate determination of mycotoxins in food samples is crucial to guarantee food safety and minimize their toxic effects on human and animal health. This study proposed the use of a support vector regression (SVR) predictive model improved by two metaheuristic algorithms used for optimization namely, Harris Hawks Optimization (HHO) and Particle Swarm Optimization (PSO) to predict chromatographic retention time of various food mycotoxin groups. The dataset was collected from secondary sources and used to train and validate the SVR-HHO and SVR-PSO models. The performance of the models was assessed via mean square error, correlation coefficient, and Nash–Sutcliffe efficiency. The SVR-HHO model outperformed existing methods by 4–7% in both the two learning (training and testing) phases respectively. By using metaheuristic optimization, parameter adjustment became more effective, avoiding trapping in local minima and improving model generalization. These results demonstrate how machine learning and metaheuristics may be combined to accurately forecast mycotoxin levels, providing a useful tool for regulatory compliance and food safety monitoring. The SVR-HHO framework is perfect for commercial quality assurance, regulatory testing, and extensive food safety programs because it provides exceptional accuracy and resilience in predicting mycotoxin retention times. In contrast to conventional models, SVR-HHO effectively manages intricate nonlinear interactions, guaranteeing accurate mycotoxin identification and improving food safety while lowering hazards to human and animal health.

Dysregulated lipid metabolism plays a critical role in the pathogenesis of numerous diseases. However, due to the lack of effective solutions, the dynamic monitoring and deep understanding of lipid metabolism in the progression of diseases has remained challenging. Herein, a polarity‐sensitive, lipid droplets (LDs)‐targeted probe was developed via molecular surgery for spatiotemporal super‐resolution imaging of lipid metabolism dynamics. Based on the stimulated emission depletion (STED) and fluorescence lifetime imaging microscopy (FLIM) platform, we achieved the super‐resolution visualization and dynamic tracking of the nonlinear fusion and expansion mechanisms of LDs in orthotopic pathology models—an unprecedented achievement in the field. We also analyzed lipid distribution in zebrafish models using the STED‐FLIM platform to capture lipid metabolism at the organismal level. Crucially, based on the integrated STED‐FLIM platform, this probe accurately monitors lipid metabolism in cellular and pathological tissue contexts of atherosclerosis and fatty liver. This breakthrough provides novel insights for diagnosing lipid metabolism‐related diseases and advancing therapeutic development.

Liver fibrosis is a reversible but complex pathological condition associated with chronic liver diseases, affecting over 1.5 billion people worldwide. It is characterized by excessive extracellular matrix deposition resulting from sustained liver injury, often advancing to cirrhosis and cancer. As its progression involves various cell types and pathogenic factors, understanding the intricate mechanisms is essential for the development of effective therapies. In this context, extensive efforts have been made to establish three-dimensional (3D) in vitro platforms that mimic the progression of liver fibrosis. This review outlines the pathophysiology of liver fibrosis and highlights recent advancements in 3D in vitro liver models, including spheroids, organoids, assembloids, bioprinted constructs, and microfluidic systems. It further assesses their biological relevance, with particular focus on their capacity to reproduce fibrosis-related characteristics. 3D in vitro liver models offer significant advantages over conventional two-dimensional cultures. Although each model exhibits unique strengths, they collectively recapitulate key fibrotic features, such as extracellular matrix remodeling, hepatic stellate cell activation, and collagen deposition, in a physiologically relevant 3D setting. In particular, multilineage liver organoids and assembloids integrate architectural complexity with scalability, enabling deeper mechanistic insights and supporting therapeutic evaluation with improved translational relevance. 3D in vitro liver models represent a promising strategy to bridge the gap between in vitro studies and in vivo realities by faithfully replicating liver-specific architecture and microenvironments. With enhanced reproducibility through standardized protocols, these models hold great potential for advancing drug discovery and facilitating the development of personalized therapies for liver fibrosis.

Hydrogen, while a promising sustainable energy carrier, presents challenges such as the embrittlement of materials due to its ability to penetrate and weaken their crystal structures. Here γ’‐Fe4N nitride layers, formed on iron through a cost‐effective gas nitriding, are investigated as an effective hydrogen permeation barrier. The relatively short process carried out at 570 °C consisted of pre‐nitriding in an atmosphere with higher nitriding potential, followed by treatment in a nitriding potential of 0.0016 Pa−1/2 to obtain a pure γ’ layer. A combination of screening methods, including atom probe tomography, density functional theory calculations, and hydrogen permeation analysis, revealed that the nitride layer reduces hydrogen diffusion (steady‐state hydrogen flux 3.21 x 10⁻⁸ mol/m²·s) by a factor of 20 compared to pure iron, at room temperature. This reduction is achieved by creating energetically unfavorable states due to stronger hydrogen‐binding at the surface and high energy barriers for diffusion. The findings demonstrate the potential of γ’‐Fe4N as a cost‐efficient and easy‐to‐process solution to protect metallic materials exposed to hydrogen at low temperatures, with great advantages for large‐scale applications.

Dysregulated lipid metabolism plays a critical role in the pathogenesis of numerous diseases. However, due to the lack of effective solutions, the dynamic monitoring and deep understanding of lipid metabolism in the progression of diseases has remained challenging. Herein, a polarity‐sensitive, lipid droplets (LDs)‐targeted probe was developed via molecular surgery for spatiotemporal super‐resolution imaging of lipid metabolism dynamics. Based on the stimulated emission depletion (STED) and fluorescence lifetime imaging microscopy (FLIM) platform, we achieved the super‐resolution visualization and dynamic tracking of the nonlinear fusion and expansion mechanisms of LDs in orthotopic pathology models—an unprecedented achievement in the field. We also analyzed lipid distribution in zebrafish models using the STED‐FLIM platform to capture lipid metabolism at the organismal level. Crucially, based on the integrated STED‐FLIM platform, this probe accurately monitors lipid metabolism in cellular and pathological tissue contexts of atherosclerosis and fatty liver. This breakthrough provides novel insights for diagnosing lipid metabolism‐related diseases and advancing therapeutic development.