Hongik University

Evogliptin, a dipeptidyl peptidase-4 inhibitor with a glucose-dependent mechanism of action, is widely used as a monotherapy or in combination with other agents. However, observational studies in routine clinical settings remain limited. This study aimed to evaluate the effectiveness and safety of evogliptin in combination with other antidiabetic agents frequently used in clinical practice. This study was conducted in 51 hospitals in South Korea. We evaluated evogliptin in three groups: the evogliptin add-on, evogliptin-switching, and initial combination groups. The primary endpoint was the change in haemoglobin A1c (HbA1c) levels (%) from baseline to 12 weeks. The change in HbA1c (%) levels from baseline to 24 weeks was a secondary endpoint in the extended study. Proportions of patients achieving the HbA1c target, changes in fasting and postprandial glucose levels, and safety endpoints were also evaluated. Between-group comparisons were performed between the add-on and initial combination groups. In the 1596 patients of the effectiveness set, the mean changes in HbA1c levels at 12 weeks were -0.8% ± 1.2%, -0.3% ± 0.8% and -1.9% ± 1.4% for the evogliptin add-on, evogliptin-switching and initial combination groups, respectively. Among the 1920 patients in the safety set, 23 experienced adverse drug reactions, and there was no significant difference among the three groups. Evogliptin reduces blood glucose levels and is well-tolerated when used as an add-on, as a switch from other dipeptidyl peptidase 4 inhibitors, or as part of an initial combination therapy in patients with type 2 diabetes mellitus.

This study aims to investigate the relationship between increasing internal tibial torque (ITT) and the distribution of stress on the anterior cruciate ligament (ACL), with a focus on identifying the location of tear initiation and propagation. In particular, the study emphasizes the femoral enthesis as a potential site of geometric vulnerability during pivot landing. A three-dimensional finite element model of the ACL was developed and combined with an extended finite element method framework to simulate tear initiation and propagation. The model was driven by torque-indexed, three-dimensional knee kinematics obtained from previous in silico knee simulations that were validated against in vitro cadaveric knee tests (15 specimens). ITT was applied in graded steps to reproduce pivot landing-relevant internal rotation loading. Stress/strain fields were evaluated, and tear initiation/propagation were tracked to quantify damage evolution. Maximum von Mises stress increased proportionally with ITT and localized near the femoral enthesis, with a mean increase of 26.56% for each 5Nm increase in torque. Stress concentration consistently occurred at the posterolateral bundle attachment on the femoral side, where tear initiation was predicted. With further ITT increases, the tear propagated from the femoral enthesis, indicating a torque-dependent damage progression mechanism. Increased ITT produced reproducible stress/strain concentration at the femoral enthesis, supporting structural vulnerability during pivot landing. This approach extends stress-only analyses by predicting rupture initiation and tear progression. These findings may inform prevention and rehabilitation by highlighting the roles of ITT and femoral insertion morphology in ACL tear initiation and propagation.

Background: The rapid growth of e-commerce has intensified the need for packaging strategies that reduce logistics costs and environmental impact. Traditional box recommendation methods select the best-fitting box from a fixed set of options, which limits their ability to minimize unused space and total costs. Methods: This study formulates the Shipping Box Configuration Problem (SBCP), which aims to determine an optimal set of box types and dimensions for multi-product orders. To solve this problem, we propose a Particle Swarm Optimization (PSO)-based heuristic that dynamically designs box configuration rather than selecting from predefined sizes. Results: The proposed method is evaluated using real order data from two South Korean e-commerce companies with different product characteristics and existing box configurations. Computational results show that the PSO-based approach reduces total packaging and shipping costs and improves space utilization compared to current box configurations. The analysis also indicates that increasing the number of box types and reducing safety ratios generally lead to cost savings, although these effects must be balanced against operational complexity. Conclusions: The results suggest that adaptive box configuration design can improve both economic efficiency and environmental performance, providing practical guidance for e-commerce logistics managers seeking to optimize packaging strategies under operational constraints.

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).

The resilience and operation of infrastructure systems are shaped not only by physical interdependencies but also by institutional arrangements embedded in laws, regulations, and administrative guidelines. However, institutional documents are typically written in an actor-centric manner, explicitly describing who manages or oversees a given infrastructure, while leaving infrastructure interdependencies largely implicit. This limits our ability to systematically identify potential cascading risks and coordination blind-spots arising from institutional design. This study proposes a network-based framework to reconstruct hidden infrastructure interconnectivity derived from institutional documents. Legal and regulatory texts related to public sewerage management, as an example, are decomposed using the ABDICO framework (Attribute–Object–Deontic–Aim–Condition–Or else), to systematically construct actor–infrastructure heterogeneous network. To capture indirect and semantically meaningful connections between infrastructures, we define a set of infrastructure-to-infrastructure meta-paths that traverse actor chains, including both pure actor-mediated paths and paths that revisit infrastructure nodes. Building on PathSim, we propose a modified similarity measure that (i) is applicable to asymmetric meta-paths and (ii) employs a global normalization scheme based on the total number of meta-path instances associated with each node. Furthermore, similarity scores derived from multiple meta-paths of varying lengths are aggregated using inverse path-length weighting to define a composite Dependency Index. Results show that infrastructure pairs sharing multiple indirect institutional pathways, particularly those involving smaller degrees, exhibit higher dependency scores, indicating potential latent interdependencies not explicitly stated in institutional texts. By treating hidden connectivity detection as an unsupervised problem, this approach provides a scalable means to explore institutional coupling in infrastructure systems where direct infrastructure inter-links are unavailable. The proposed framework contributes to a novel methodology for institutional network analysis and offers insights into governance-induced infrastructure interdependencies, with implications for infrastructure resilience assessment and policy design under increasing climate-related risks.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).

Escalating climate crises, characterized by rising sea levels, alongside excessive groundwater pumping, have severely exacerbated saltwater intrusion, posing a critical threat to coastal aquifers. These combined environmental stressors induce complex, non-linear dynamics in groundwater systems, making the exact prediction of regime shifts driven by tipping points increasingly challenging. To address these uncertainties, this study proposes a comprehensive data-driven approach designed to identify early warning signals (EWS) for approaching tipping points using Electrical Conductivity (EC) time-series data. The primary objective is to investigate the feasibility of utilizing complementary statistical indicators—Variance and Fisher Information (FI)—to assess system instability. We analyzed monitoring data from Incheon and Jeju, South Korea, to validate whether these metrics can effectively filter noise and detect genuine precursor signals. Empirical results demonstrate that our approach achieves significantly enhanced performance in distinguishing critical transitions compared to single-indicator methods. Ultimately, this study serves as a foundational step towards establishing an "Integrated Machine Learning" framework. By validating these statistical metrics as key features, we aim to incorporate them into advanced learning algorithms to further improve the robustness and predictive accuracy of coastal groundwater management systems against climate-induced risks.AcknowledgementThis work was supported by National Research Foundation of Korea(NRF) grant funded by the Ministry of Science and Technology (RS-2024-00356786).

Inspired by the playful, attention-seeking paw gestures of cats, we present PAWSE, a laptop-peripheral robot that encourages short fidgeting-based micro-breaks during digital work. PAWSE integrates a cat-paw-inspired robotic arm with a web-based timer that prompts brief tactile interaction during scheduled breaks. We conducted a within-subjects study comparing three conditions--no break, passive break, and active (PAWSE fidgeting-based) break--using a 2-back task and subjective workload measures (NASA-TLX). Results showed differences in post-task accuracy across conditions, with the highest accuracy observed in the active break condition. Reaction time remained largely comparable. Workload measures indicated reduced mental demand and frustration during rest conditions, with the active break providing the most favorable subjective experience. These preliminary findings offer insight into how fidgeting-based micro-breaks may fit within focused digital work and inform the design of future tactile micro-break systems.

This study introduces a novel multi-sensor unmanned ground vehicle (UGV) system for monitoring ground deformation adjacent to urban excavation sites. In response to limitations in current monitoring practices and public concerns about visible damage, the research developed a UGV platform equipped with three integrated sensor modules: a Global Navigation Satellite System (GNSS)-Simultaneous Localization and Mapping (SLAM) system for localization, a camera-based photogrammetry unit for vertical displacement analysis, and a line laser scanner for measuring block spacing changes. The system was validated on a 12-m testbed simulating sidewalk pavement with controlled deformation scenarios. Results demonstrated the UGV’s ability to accurately detect and quantify settlement-induced deformations with sub-millimeter precision, even under signal interference conditions. The integrated approach enabled real-time processing and zonal analysis, which improved data stability compared to traditional methods. The proposed Excavation Damage Index effectively visualized deformation severity, offering a practical, scalable, and efficient solution to address gaps in adjacent ground monitoring during urban excavation projects.