Cheongju University

ABSTRACT Growing concerns about electronic waste underscore the need for materials that combine high performance with environmental sustainability. Here, we report an organic thin‐film transistor (OTFT) that incorporates a water‐processed gum arabic (GA) dielectric, a natural, biodegradable resin derived from Acacia senegal , to enable eco‐friendly device fabrication. The GA dielectric forms defect‐free films directly from aqueous solution and exhibits a dielectric constant of approximately 27 at 1 kHz. By optimizing GA concentration, we obtain uniform and stable dielectric layers that substantially enhance charge transport in dinaphtho[2,3‐b:2′,3′‐f]thieno[3,2‐b]thiophene (DNTT) semiconductors, yielding p‐type OTFTs operating at ±3 V with high mobilities up to 20.72 cm 2 V −1 s −1 and negligible hysteresis. Comparative analyses show that GA facilitates improved molecular ordering of DNTT and suppresses trap formation, outperforming conventional PMMA dielectrics. Upon immersion in water, the GA layer dissolves rapidly (within 30 s), leaving the substrate pristine and fulfilling key criteria for transient electronics. This combination of outstanding electrical performance and complete aqueous degradability highlights the potential of GA for scalable fabrication of green, high‐performance electronic devices designed to disappear on demand, supporting urgent efforts toward sustainable and transient electronic technologies.

Zero-inflated count data are characterized by an excessive frequency of zeros that cannot be adequately analyzed by a single distribution, such as Poisson or negative binomial. This problem is pervasive in many practical applications, including document–keyword matrix derived from text corpora, where most keyword frequencies are zero. Conventional statistical approaches, such as the zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB) models, explicitly separate a structural zero component from a count component, but they typically assume independent observations and can be unstable when covariates are high-dimensional and sparse. To address these limitations, this paper proposes a graph-based zero-inflated learning framework that combines simple graph convolution (SGC) with zero-inflated count regression heads such as ZIP and ZINB. We first construct an observation graph by connecting similar samples, and then apply SGC to propagate and smooth features over the graph, producing convolutional representations that incorporate neighborhood information while remaining computationally lightweight. The resulting representations are used as covariates in ZIP and ZINB heads, which preserve probabilistic interpretability through maximum likelihood learning. Our experiments on simulated zero-inflated datasets with controlled zero ratios demonstrate that the proposed ZIP+SGC and ZINB+SGC consistently reduce prediction errors compared with their non-graph baselines, as measured by mean absolute error and root mean squared error. Overall, the proposed approach provides an efficient and interpretable way to integrate graph neural computation with zero-inflated modeling for sparse count prediction problems.

With the rise of Urban Air Mobility, vision-based object recognition using ground-fixed observation systems is gaining significance as a key component for ensuring aerial safety. In urban environments, however, consistent and reliable recognition of UAM vehicles remains a challenge due to complex visual backgrounds, the small size of aircraft, and their irregular flight trajectories. To address these issues, this study proposes a visual inference framework based on Behavior Tree. Inspired by human visual cognition, the proposed recognition pipeline mimics the process in which a human observer first applies global attention to a scene and then focuses selectively on uncertain areas for further analysis. Following an initial recognition stage using YOLOv8, ROI are re-examined through SAHI-based slicing to enhance recognition of small or partially missed objects. This modular structure separates the processes of image input, primary recognition, ROI inference, and result storage, while the Behavior Tree framework enables clear execution flow and real-time state tracking through explicit node transitions. Experimental evaluations conducted in a ROS2-based environment demonstrate that the proposed architecture significantly outperforms a conventional single-stage YOLO system, particularly under conditions involving cluttered backgrounds and small-scale aerial targets. Additionally, per-frame state logging and automatic result saving enhance system analysis and debugging capabilities. This work validates Behavior Tree-based visual inference as an effective control framework for object recognition systems and suggests its potential for future integration with real-time autonomous flight and control systems.

Background: As a multidisciplinary field, dental hygiene has seen the emergence of novel research topics that have reshaped its scholarly landscape during the pandemic.This study investigated the evolution of research themes and knowledge structures in the Journal of Dental Hygiene Science (JDHS) by comparing the pre-COVID-19 (20152019) and post-COVID-19 (20202024) periods.Methods: A total of 499 articles (319 from 20152019 and 180 from 20202024) published in JDHS were analyzed.Using NetMiner 5.0, meaningful keywords were extracted from titles, abstracts, and author keywords.Term frequency, term frequency-inverse document frequency, degree centrality, and betweenness centrality were examined.Topic modeling was conducted using the Latent Dirichlet Allocation algorithm.Results: During 20152019, "student," "stress," "health," and "tooth" showed high degree centrality, with eight topics identified: education and clinical practice satisfaction, oral health behaviors, job stress, teeth and oral tissues, professional identity, core competencies, preventive research, and factor analysis.In 20202024, "tooth," "work," and "surface" demonstrated the highest degree centrality, with five reconfigured topics: dental hygienist competencies, infection control, biofilm and material-related experimental research, and oral health behaviors.Notably, "COVID-19," "infection," "control," and "hand hygiene" emerged as an independent topic cluster.Across both periods, "health" and "dental hygienist" maintained high betweenness centrality, serving as bridging concepts across research domains.Conclusion: Over the past decade, JDHS research has shifted from an education-and psychology-oriented focus to clinical and experimental studies, with infection control emerging as an independent research theme after COVID-19.However, internationally prominent topics such as teledentistry and digital dentistry remain underrepresented, suggesting the need for expanded research in these emerging areas.This study provides quantitative evidence of structural changes in Korean dental hygiene research, offering insights to guide future research directions and journal development strategies.

Background: Sodium butyrate, a short-chain fatty acid produced by anaerobic bacteria within periodontal pockets, is traditionally regarded as an anti-inflammatory metabolite in the gut.However, its biological functions in the periodontal environment, particularly during Porphyromonas gingivalis infection, remain unclear.This study aimed to elucidate effects of sodium butyrate on inflammatory and bone-regulating responses in various oral cell types.Methods: Immortalized human oral keratinocytes (IHOKs), Terasaki Human Peripheral blood leukemia (THP)-1 macrophages, and osteoblasts (MC3T3-E1) were treated with various sodium-butyrate concentrations, with or without P. gingivalis infection.Cytotoxicity was assessed using MTT assays (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide).Inflammatory cytokines (interleukin [IL]-8, IL-22, and IL-1) were measured using enzyme-linked immunosorbent assay; osteogenic markers (runt-related transcription factor 2 [RUNX2], osteoprotegerin [OPG], and receptor activator of nuclear factor-B ligand [RANKL]) were assessed using quantitative polymerase chain reaction.Results: Sodium butyrate significantly increased IL-8 (p0.05) and IL-22 (p0.05)production in IHOKs, with IL-8 further elevated by P. gingivalis (p0.05).In THP-1 macrophages, sodium butyrate markedly enhanced IL-1 expression (p0.01),especially under P. gingivalis infection (p0.05).In MC3T3-E1 cells, sodium butyrate reduced RUNX2 (p0.05) and OPG (p0.001) mRNA levels and upregulated RANKL (p0.05)expression, leading to a substantial elevation in the RANKL/OPG ratio (p0.05).These detrimental effects were further aggravated by P. gingivalis (p0.05).Conclusion: Sodium butyrate aggravated inflammatory cytokine production in keratinocytes and macrophages while disrupting osteoblast-mediated bone homeostasis by elevating the RANKL/OPG ratio.These findings indicate that butyrate, which accumulates in dysbiotic periodontal pockets, acts as a pathogenic metabolic factor driving immune dysregulation and an imbalance in bone remodeling within periodontal tissues.

Background: This scoping review examined research on artificial intelligence (AI) published in Korean academic journals in the fields of dental hygiene and dentistry.The Context-Concept-Maturity framework was applied to compare research trends and identify future directions.Methods: Following the Arksey and O'Malley framework, with extensions by Levac et al., the review adhered to Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines.Domestic academic journals published between January 1, 2020, and September 30, 2025, were searched in KISS, RISS, ScienceON, and DBpia.Studies were screened, extracted, and coded according to population, concept, and context criteria, yielding 70 eligible articles (26 in dental hygiene and 44 in dentistry).Results: In dental hygiene, most research focused on education (38.5%) and community/public oral health (38.5%), while clinical applications were limited (7.7%).Major conceptual domains included education/training tools (42.3%), workflow/automation (38.5%), prediction (15.4%), and classification (3.8%).Dentistry studies were predominantly clinically oriented and imagingbased, emphasizing classification, automation, and segmentation.Maturity analysis indicated that dentistry primarily occupied Stage 2 (application; 84.1%), whereas dental hygiene showed a dual distribution across Stage 1 (exploration; 46.2%) and Stage 2 (application; 46.2%), with limited Stage 3 (integration; 7.7%) studies.Conclusion: AI research in Korea exhibits divergent developmental trajectories.Dental hygiene research emphasizes education, prevention, and public oral health, combining exploratory and applied approaches, while dentistry demonstrates greater clinical integration and application-level maturity.Advancing AI maturity in dental hygiene will require expansion of clinical data-driven studies, incorporation of AI and data literacy into curricula, standardized integration of unstructured and clinical data, multicenter long-term validation, and the development of ethical and performance governance frameworks.

ABSTRACT Although previous research has seldom examined the joint influence of urban planning and well-being factors on life satisfaction using advanced econometric techniques, this study investigates their integrated effects on urban residents in Seoul, South Korea, through the application of Structural Equation Modeling (SEM). The analysis focuses on six key domains: environment, transportation, walkability, community, culture, and education. The findings reveal that all domains contribute positively to life satisfaction, with education demonstrating the strongest direct effect (0.615), followed by transportation (0.464), walkability (0.254), culture (0.245), environment (0.079), and community (0.000). Additionally, transportation exerts a substantial indirect influence on other domains, particularly culture (0.594), education (0.475), and environment (0.335). These results underscore the critical interplay between physical infrastructure and subjective well-being in urban contexts. The study offers important implications for policymakers and urban planners, suggesting that strategic investments in transportation, education, and cultural infrastructure can significantly enhance residents’ life satisfaction. Highlights Education demonstrates the strongest positive direct effect on life satisfaction (0.615). Transportation is shown to have a significant positive impact on life satisfaction (0.464). Walkability contributes positively to life satisfaction (0.254). Culture shows a positive association, contributing to life satisfaction (0.245). Environment provides a positive contribution to life satisfaction (0.079).

Reducing iron loss in axial flux permanent magnet (AFPM) motors is critical for improving efficiency. This study proposes a design-optimization procedure that combines 3D finite-element analysis (FEA) data with an artificial neural network (ANN) surrogate. For four design variables—airgap length, rotor back-yoke thickness, stator slot width, and stator slot depth—the search bounds were defined to avoid tooth and back-yoke saturation, and the corresponding space was sampled to construct a dataset. Using this dataset, the ANN was trained and then used to explore low-iron loss solutions. On an independent validation set, ANN predictions showed high agreement with 3D-FEA reference values, enabling rapid evaluation of many design candidates. As a result of the optimization, total iron loss decreased relative to the baseline, and torque increased by 3 Nm. These results demonstrate that the ANN-based surrogate model can reliably perform geometry-dependent iron loss optimization in AFPM motors, providing a fast and accurate alternative to repetitive 3D-FEA evaluations.