Korea Maritime and Ocean University

Tropical cyclones (TCs) pose significant threats to coastal communities, primarily through hazardous wind speeds and intense rainfall that drive storm surge and coastal flooding. These wind and water related hazards often occur simultaneously, amplifying impacts on both the built environment and socially vulnerable populations. Despite extensive prior research on individual TC hazards, limited attention has been given to their joint occurrence and evolving risk characteristics over time in relation to changes in social vulnerability.This study proposes a comprehensive, time-evolving TC risk assessment framework that explicitly accounts for the likelihood of coinciding wind and rainfall hazards. The analysis covers the period from 1979 to 2022, incorporating long-term hydroclimatic records to characterize TC-related multi-hazard exposure. In parallel, social vulnerability was evaluated using multiple combinations of vulnerability indicators for the period 2000–2022, allowing temporal changes in population sensitivity and adaptive capacity to be captured. By progressively incorporating newly available data and historical records as time advances, this study reflects how TC risk assessments would have evolved under real-world knowledge constraints in past decades.A total of 29 major TC events impacting the southeastern U.S. coast were examined, and statistical correlations were evaluated between estimated TC risks and observed economic damages. The results indicate that, prior to 2017, fewer than 6% of the cases exhibited stronger correlations when TC risk was quantified using a multi-hazard hurricane index that jointly considers wind and rainfall. In contrast, more recent events demonstrate a growing dominance of wind-based risk metrics in explaining observed damages.These findings suggest a shifting risk regime in which coastal communities are becoming increasingly vulnerable to wind-related TC impacts, including extreme winds, storm surge, and coastal flooding, rather than rainfall-driven hazards alone. The proposed framework highlights the importance of dynamic, multi-hazard risk assessments that integrate evolving social vulnerability, providing critical insights for future coastal resilience planning and disaster risk reduction strategies.Acknowledgments: This work was supported by National Research Foundation of Korea funded by the Ministry of Education (RS-2023-00249547).

Optimizing traffic performance using artificial intelligence (AI) has consistently been a prominent direction in the development of intelligent transportation systems. While numerous studies have proposed methodologies for integrating cooperative connected and autonomous vehicles (CCAVs) with traffic signal systems via V2X communication, they often rely on simplified control strategies or lack effective coordination between signal timing and vehicle behavior. In this study, we propose a novel, integrated traffic signal control strategy combined with CAVs using deep reinforcement learning. Our key differentiation lies in the simultaneous optimization of signal phases using the Soft Actor–Critic (SAC) algorithm and the regulation of CCAVs via cooperative adaptive cruise control and Green Light Optimal Speed Advisory. This dual approach allows the signal controller to leverage rich state information from CAVs and the road infrastructure, enabling more anticipatory and cooperative decisions. The proposed approach is implemented and evaluated through various scenarios using the Simulation of Urban MObility (SUMO) platform. The results demonstrate the superior learning performance and robustness of the proposed model. Specifically, our proposed model achieves a significant reduction in average vehicle waiting time by up to over 80% compared to baseline models under high-demand scenarios (4800–6000 veh/h). These findings underscore the critical importance of joint optimization in future intelligent transportation systems, paving the way for more resilient urban traffic management.

In this study, we examined metal accumulation and biochemical responses of Azolla imbricata (Roxb.) Nakai exposed to water medium (WM) and nutrient medium (NM) under single and combined (co-exposure) treatments with five metals (As, Cd, Cu, Pb, and Zn) at 10% environmentally relevant concentrations. Marked differences between WM and NM highlighted the influence of nutrient availability on plant responses. An inverse relationship was observed, with higher biomass in NM (WM < NM) and greater metal accumulation in WM (WM > NM). Growth inhibition, reflected by reduced photosynthetic pigment contents, was accompanied by elevated stress indicators, including electrolyte leakage, proline, malondialdehyde, and anthocyanins, confirming metal-induced phytotoxicity. Metal interactions under co-exposure were predominantly antagonistic in WM and synergistic in NM. Metal accumulation patterns (mg/kg) varied across media and exposure types: under single exposure, values ranged from 83.8 (As) to 43 881 (Zn) in WM and from 11.7 (Cd) to 12 135 (Cu) in NM; under co-exposure, they ranged from 346 (Cd) to 12 688 (Cu) in WM and from 46 (Cd) to 2859 (Cu) in NM. Accumulation sequences did not correspond to exposure concentrations, indicating metal-specific and media-dependent uptake. Under co-exposure, concurrent accumulation of multiple metals was more frequent in WM than that in NM, suggesting nutrient availability constrained simultaneous uptake. Bioconcentration factor values exceeded common thresholds under certain treatments, indicating strong accumulation potential rather than definitive hyperaccumulation. Overall, these findings highlight the potential of A. imbricata for phytoremediation of metal-contaminated waters while emphasizing cautious application in agroecosystems due to potential soil-crop-food pathway transfer.

Maritime vessel detection in nearshore waters is a fundamental capability for artificial intelligence (AI)-enabled maritime transportation systems, including coastal monitoring, traffic management, and digital maritime services. Although attention mechanisms are widely incorporated into YOLO-based detectors, their relative effectiveness in marine environments under strictly controlled experimental conditions remains insufficiently clarified. This study presents a systematic comparison of Coordinate Attention (CA), Convolutional Block Attention Module (CBAM), and CLIP-based semantic fusion within a unified YOLOv8n framework for binary discrimination between ships and fishing boats in cluttered coastal imagery. All model variants were trained under identical data partitions and optimization settings to isolate architectural effects. The experimental results show that CA achieves the highest localization robustness (mAP@0.5:0.95 = 0.6127) and substantially improves precision (+7.13% over baseline), while CBAM provides the most balanced performance with the highest F1-score. In contrast, CLIP-based semantic fusion consistently degrades detection reliability, indicating limitations of global vision–language representations in small-scale maritime datasets. Precision–Recall and F1 analyses further reveal architecture-specific confidence calibration behaviors relevant to deployment-sensitive maritime applications. The findings provide practical guidance for selecting attention mechanisms in AI-driven maritime perception systems and support reliable AI integration in marine science and engineering applications.

This study investigates the effect of halloysite nanotubes (HNT) on mitigating moisture absorption and the associated mechanical degradation in epoxy-based carbon fiber–reinforced plastics (CFRP) under prolonged exposure to water environments. HNT are nanoadditives known to enhance both moisture resistance and mechanical properties. CFRP specimens containing 1 and 5[Formula: see text]wt.% HNT were fabricated, and their moisture absorption behavior, mechanical property changes and fracture surface morphologies were evaluated. Moisture absorption tests conducted in 70 ∘ C distilled water for 90 days revealed that the incorporation of HNT reduced the moisture absorption rate of CFRP by up to 30%. Mechanical testing showed that the addition of 1[Formula: see text]wt.% HNT improved flexural strength and interlaminar shear strength by approximately 6%, effectively mitigating property degradation following moisture exposure. Fracture surface analysis indicated improved fiber–matrix interfacial bonding due to the presence of HNT, contributing to the retention of mechanical performance. Furthermore, a deep-learning-based prediction model was developed and validated against experimental data to predict the moisture absorption and mechanical property changes of CFRP containing 2–4[Formula: see text]wt.% HNT. These findings demonstrate that incorporating HNT is effective in enhancing the moisture resistance and mechanical durability of CFRP.

Constipation is a prevalent gastrointestinal disorder often associated with impaired intestinal motility and unbalanced gut microbiota. Although probiotics offer therapeutic potential, their clinical efficacy is often limited by low gastric survival and inefficient intestinal release. Herein, we developed a novel smart delivery platform by encapsulating five probiotic strains within a single-layer calcium carbonate coating, designated as Probiotics Encapsulated with Calcium carbonate (PEC). This system is designed to respond to both pH and phosphate levels, enabling targeted intestinal probiotic delivery. Using a loperamide-induced constipation rat model, we evaluated the PEC therapeutic efficacy at three doses (15, 50, and 150mg/kg). PEC administration significantly improved key constipation indicators, including defecation frequency, fecal moisture content, and intestinal transit rate in a dose-dependent manner. Furthermore, PEC selectively modulated harmful microbial enzymes such as β-glucuronidase and nitroreductase without adversely impacting the beneficial β-glucosidase enzyme. It also showed a trend to reduce systemic inflammatory markers, such as TNF-α and IL-1β. Notably, the high-dose PEC group restored most parameters to levels comparable to those of the normal group. These findings suggest that the PEC platform is a promising next-generation probiotic delivery system with potential to address some limitations of conventional probiotic therapy, such as low gastric survival and inefficient intestinal release. However, without dedicated control groups for calcium carbonate alone or uncoated probiotics, the specific contributions of the encapsulation cannot be fully isolated. PEC shows strong potential for development into functional foods and therapeutic agents targeting constipation and related gastrointestinal disorders.