While interleukin (IL)-1β is a well-established driver of periodontitis, the specific mechanisms by which the emerging pathogen Filifactor alocis induces this cytokine are largely unknown. We conducted a comprehensive investigation into how F. alocis stimulates IL-1β in THP-1 macrophages, analyzing the response at the mRNA (reverse transcription-polymerase chain reaction), cellular protein (Western blot), and secreted protein (enzymelinked immunosorbent assay) levels. Our results show a clear, dose-dependent increase in all measures, confirming its potent effect. To explore the underlying mechanisms, we first tested the contribution of the NLRP3 inflammasome using the specific inhibitor MCC950. This inhibitor almost completely blocked (~90%) a control response but only partially inhibited (~50%) the F. alocis -stimulated secretion. A similar result was observed with the pan-caspase inhibitor Z-VAD-FMK, which almost completely suppressed the control but only partially inhibited (~50%) the response to F. alocis . Moreover, we found a dominant role for mitogen-activated protein kinase signaling pathways. Specific inhibitors of extracellular signal-regulated kinase (ERK) and c-Jun N-terminal kinase (JNK) significantly attenuated both IL-1β mRNA expression and protein secretion, whereas the p38 inhibitor had no effect. These findings collectively demonstrate that F. alocis robustly induces IL-1β via ERK and JNK signaling with only partial dependence on canonical NLRP3 inflammasome and caspase-mediated pathways. This unique pattern suggests the involvement of other complementary pathways, underscoring the complex inflammatory processes involved in F. alocis -induced periodontitis.

In the production sites of small and medium sized manufacturing enterprises, the increasing proportion of foreign workers has led to frequent difficulties in responding promptly to process defects and equipment setting errors during night and weekend shifts due to the absence of Korean supervisors. If such issues are not addressed in a timely manner, they can lead to large scale defects and reduced production efficiency. In this study, we developed an AI-based defect prediction and prevention system for the bearing machining process to overcome these on site management limitations. Real time machining data, equipment information, and quality inspection results were collected from the production lines of the target company, and the prediction accuracy of three models, RNN(Recurrent Neural Network), LSTM(Long Short-Term Memory), and GRU(Gated Recurrent Unit), was compared. As a result, the LSTM model demonstrated the best performance. The developed system visualizes real time defect prediction results in the form of a dashboard, enabling workers to immediately detect anomalies and adjust the process accordingly. Particularly in bearing machining processes where mass production occurs in short periods, the risk of lot level defects is high, while this system can contribute to improved production quality and efficiency by enabling early defect prediction and immediate response.

The Republic of Korea has experienced rapid economic growth alongside increasing concern for animal welfare. However, progress in improving farm animal welfare remains slow, particularly in slaughterhouses, which are recognized as critical points for animal welfare concerns due to numerous issues observed. Due to the impracticality of assessing all slaughterhouses nationwide, this study conducted a field survey of seven chicken slaughterhouses utilizing the widely adopted WQ® protocol. The evaluation criteria included crate density, rough handling, plumage loss and emaciation, panting or huddling, wing flapping, vocalization, leg fractures, and dead-on arrival (DOA). Given that broilers are transported in crates from farms to the stunning area, the relationship between crate density and these welfare indicators was analyzed. The significance level was set at <0.05. Revealed correlations between crate density and various health and welfare indicators, including rough handling, plumage loss and emaciation, crate height, panting or huddling, pen spacing, lairage temperature and humidity, wing flapping, vocalization, and DOA. This study represents a pioneering effort in the Korean poultry industry, marking the first development of a welfare standard for chicken slaughterhouses.

In the era of big data, where massive volumes of information are collected at high velocity from various sources, data mining has become a crucial tool for organizations seeking competitive advantage. Among its core tasks, clustering plays a key role in uncovering hidden patterns within unlabeled data by grouping similar objects into distinct clusters. Widely used methods such as k-means and its robust counterpart PAM (Partitioning Around Medoids) require the number of clusters, k, to be predefined—a task that remains a major challenge despite extensive research. This study addresses the problem of selecting the optimal number of clusters by proposing three novel enhancements to the widely-used gap statistic method: the 1stDaccSEmax heuristic rule, the recursive gap strategy, and the two-way bootstrapping technique. Collectively termed the new gap, this approach aims to overcome the limitations of the original gap statistic, particularly in datasets with overlapping clusters, hierarchical structures, or large volumes. Extensive experiments on both synthetic and real-world datasets—including Iris, Breast Cancer, Seeds, and Khan gene expression datasets—demonstrate that the new gap method outperforms traditional techniques such as the elbow method, silhouette analysis, and the original gap statistic in both accuracy and computational efficiency. Although PAM was used throughout the experiments for its robustness, the proposed approach is algorithm-agnostic and can be integrated with other clustering methods that require the selection of k. The results suggest that the new gap method provides a more reliable and scalable solution for determining the number of clusters, thereby enhancing the effectiveness of clustering-based data analysis in real-world applications.

Autophagy is a ubiquitous and fundamental catabolic vital process for maintaining cellular homeostasis, achieved by degrading and recycling cytoplasmic components, particularly under conditions of nutrient deprivation or metabolic stress. This mechanism is also integral to the selective clearance of misfolded or aggregated proteins, the removal of dysfunctional organelles (such as mitochondria and the endoplasmic reticulum), and the intracellular degradation of pathogens, including those associated with peroxisomes. In this study, we screened and identified sesamin, a bioactive compound isolated from Hypericum hookerianum extracts, as a novel autophagy activator. Our results demonstrated that sesamin effectively induces autophagy and activates the lysosome biogenesis pathway.

This mixed-methods study investigated language anxiety among Korean military air traffic controllers (ATCOs) when communicating with native English-speaking pilots. The quantitative phase (N = 81) used hierarchical regression analysis examining relationships between individual characteristics and anxiety levels. The qualitative phase (N = 13) employed semi-structured interviews exploring controllers' experiences, anxiety triggers, and coping mechanisms. Results revealed that general communication anxiety (β = .69, p < .001) and English proficiency (β = -.28, p < .05) significantly predicted anxiety with American pilots, with psychological factors explaining 58.8% of variance. These findings suggest that language anxiety stems from broader communication apprehension rather than isolated linguistic difficulties. Qualitative analysis identified five themes: communication breakdown, technical factors, skills hierarchy, informal coping, and institutional gaps. Overall, the study indicates that language anxiety in military ATC contexts is complex, influenced by individual psychological traits, situational demands, and organizational factors. It contributes to a deeper understanding of professional language anxiety in safety-critical contexts and provides evidence-based recommendations for aviation English training reform.

This study aimed to confirm the species identity and characterize two Selenomonas sputigena strains (KCOM 1787 and KCOM 2046) isolated from the oral cavities of Korean individuals using genomic and chemotaxonomic approaches. Whole-genome sequencing was performed with PacBio RSII and Illumina platforms. Species-level classification was assessed using 16S rDNA similarity, average nucleotide identity (OrthoANI), and genome-togenome distance calculation (GGDC). Chemotaxonomic analysis included cellular fatty acid profiling using gas chromatography and polar lipid analysis using two-dimensional thin-layer chromatography. The two strains showed 16S rDNA similarities of 98.85% and 99.53% with the S. sputigena type strain ATCC 35185T. OrthoANI values exceeded the species threshold (95.34% and 95.69%), whereas GGDC values were below the conventional cutoff (61.6% and 63.7%). Despite the low GGDC values, classification as S. sputigena was supported by the combined evidence of high 16S similarity, OrthoANI values above the species demarcation threshold, and minimal differences in genomic GC content (< 1 mol%). Chemotaxonomic analysis revealed that the major fatty acids were C14:0 DMA and C16:1 cis -7, while the polar lipids included phosphatidylethanolamine and several unidentified aminolipids. Although GGDC values were below the 70% species threshold, the high OrthoANI values, 16S rDNA similarity, and genomic GC content supported the classification of KCOM 1787 and KCOM 2046 as S. sputigena. These strains may serve as valuable resources for future studies on intraspecies variation and the pathogenesis of oral Selenomonas species.

This study addresses the limitations of traditional Failure Modes and Effects Analysis (FMEA), which heavily relies on expert judgment and lacks the ability to effectively incorporate unstructured failure history data such as warranty claims and maintenance records into the design stage. To overcome these challenges, we propose an automated FMEA framework based on a Retrieval-Augmented Generation (RAG) architecture integrated with a Local Large Language Model (LLM) in a secure, locally managed environment. The proposed system stores claim and test data in a vector database and leverages the LLM to retrieve and analyze relevant information, enabling automatic extraction of new failure modes and dynamic updates to FMEA documents. Additionally, the system recalculates Risk Priority Number (RPN) by adjusting severity, occurrence, and detection ratings when recurring failures are detected. To improve response quality, we applied prompt engineering and optimized chunking parameters during data retrieval. This research demonstrates the feasibility of achieving a life cycle-integrated quality enhancement framework throughout the product lifecycle while ensuring data security.

The prediction of satisfactory orthodontic treatment outcomes can be very challenging owing to the subjectivity of orthodontists’ judgment, along with the inherent difficulties when considering numerous factors. Therefore, this study introduced a deep learning-based method for predicting orthodontic treatment outcomes based on the image-to-image translation of dental radiographs using the Pix2Pix model. This proposed method addresses the aforementioned issues using a Pix2Pix-based prediction model constructed from adversarial deep learning. Patient datasets and prediction models were separated and developed for extraction and non-extraction treatments, respectively. The patients’ radiographs were pre-processed and standardized for training, testing, and applying the Pix2Pix models by uniformly adjusting the degree of blackness for the region of interest. A comparison of actual with Pix2Pix-predicted images revealed high accuracy, with correlation coefficients of 0.8767 for extraction orthodontic treatments and 0.8686 for non-extraction treatments. The proposed method establishes a robust clinical and practical framework for digital dentistry, offering both quantitative and qualitative insights for orthodontists and patients.

This study aimed to analyze elementary school teachers’ needs regarding the functions of AI-based digital English textbooks. Data were collected from a survey of 96 elementary school teachers. First, a paired sample t-test was first used to examine differences between perceived importance and current implementation. Borich’s needs assessment model was then applied to identify priority items, and the Locus for Focus model was followed visualize them. Finally, a comprehensive analysis was conducted to determine the most urgent needs. Results showed that among five categories—Diagnostic and Evaluation Functions, Support for Personalized Instructional Design, Adaptive Learning Support, AI Tutor and Assistant Teacher, and Dashboard and User Interface—the highest need was in Diagnostic and Evaluation Functions. Personalized Instructional Design and Adaptive Learning Support also showed high demand. These findings suggest that AI-based digital textbooks remain in a developmental phase, requiring further discussion on how to implement them effectively according to subject-specific needs.

The Public Procurement Service was established to ensure efficient supply of necessary goods for public institutions and their quality stability. The Public Procurement Service operates the Excellent Product Designation System for quality improvement. Due to the convenience of sole-source contracts and pricing advantages, suppliers prefer to obtain excellent product certification. However, despite these advantages, the designation does not guarantee customer satisfaction, as consumers pay higher prices without assured quality improvements. The goal of this study is to propose a new evaluation system better reflecting customer satisfaction and repurchase intention. Metal window products were selected as the study subject. Candidate factors were derived through literature review, and surveys were conducted to identify significant items for the new evaluation system. Item weights were then calculated using AHP analysis. The proposed system was validated through case analysis comparing two excellent products with two general products.

본 연구는 장애인의 취미활동이 자기효능감을 매개로 자아존중감에 미치는 영향을 검증하고자 한다. 이를 위해 한국장애인고용공단의 <장애인고용패널> 2차 웨이브 8차 조사를 원자료로 활용하여, 등록 장애인 295명을 대상으로 매개효과 분석과 소벨테스트를 실시하였다. 연구 결과, 취미활동은 자기효능 감에 긍정적인 영향을 미쳤으며, 자기효능감도 자아존중감에 정(+)적인 영향을 미치는 것으로 나타났 다. 취미활동과 자아존중감 사이의 관계에서 자기효능감의 매개효과는 통계적으로 유의미함을 확인하 였다. 본 연구결과는 취미활동이 장애인의 삶의 질을 향상시키는 역할을 할 수 있음을 시사한다. 따라 서 장애인복지 실천 현장에서 취미활동의 가치를 재조명하고, 실패의 부담감이 적은 취미활동 프로그 램을 적극적으로 도입할 것을 제안한다.

‘Efficiency,’ a key performance factor of an organization, is affected by various factors in addition to ‘cost-benefit,’ which can be measured. Data Envelopment Analysis (DEA) is a method to evaluate the relative efficiency of an organization by simultaneously considering various factors that are difficult to measure. The significance of this study is that it presents a ‘method for developing an efficiency performance indicator using DEA’ and provides a practical application plan for inefficient organizations (DMUs) to develop and manage appropriate performance indicators to improve efficiency. It presents a methodology for performing research procedures ranging from selection of input and output variables, correlation analysis, DEA execution, calculation of virtual efficiency units (VEUs) through the latent price of the reference group (DMU), and derivation of efficiency performance indicators of the organization.

This study analyzes the heterogeneous treatment effects of the COVID-19 pandemic on regional tourism demand in South Korea, focusing on the role of geographic distance from the metropolitan area to tourist destinations and the spatial characteristics of tourist destinations. Since a substantial portion of the population resides in the capital region, it can be expected that regional tourism demand is largely driven by residents of the capital region. In addition, the pandemic has particularly discouraged visits to indoor and densely populated areas due to increased perception of infection risk. To estimate these effects, we use a causal machine learning approach using double machine learning, analyzing monthly visitor data from 994 major tourist sites between the years 2019 and 2020. Tourist destinations are classified by spatial characteristics, including indoor, outdoor, and mixed settings as well as by tourism type. The analysis reveals that the impact of COVID-19 was more pronounced for indoor destinations located closer to the metropolitan center, whereas outdoor and mixed destinations showed little variation in treatment effects by distance. These findings highlight the importance of adopting distance-sensitive and space-specific policy measures in tourism planning during pandemics. Our study also demonstrates the practical utility of causal machine learning in tourism analytics, suggesting its potential for enhancing policy precision and resilience against future public health crises.

본 연구는 대표적인 병렬영상기법인 SENSE와 Compressed SENSE의 성능 차이를 알아보기 위해 동일한 조건하에 팬텀 실험을 통해 성능을 비교 분석하였다. 연구방법은 인공물이 발생하지 않는 1.0 가속 인자를 기준으로 5.0까지 0.5 간격씩 두 가지 기법 별 가속인자 변화에 따른 팬텀 영상을 획득한 후, 방사선사 30명이 5점 척도로 영상을 평가하여 유의한 차이가 있는지 판단하였다. 연구결과 Compressed SENSE는 SENSE에 비해 가속 인자를 증가시 킬수록 시간 단축 효과가 크고, 영상 품질 유지 능력이 우수하여, T1 강조영상은 2.5(SENSE는 1.5), T2 강조영상 은 2.0까지(SENSE는 1.5) 인공물이 발생하지 않은 영상을 획득할 수 있었다. 이는, Compressed SENSE가 가속 인자를 증가시킨 고속 촬영 환경에서도 영상 품질의 저하를 상대적으로 억제할 수 있음을 의미한다. 따라서 이러한 Compressed SENSE의 특성을 잘 활용한다면 움직임에 민감한 환자나 촬영 시간이 제한적인 임상 환경에서 유용하 게 활용될 수 있을 것으로 판단된다.

The purpose of this study is to empirically analyze the causal relationship between the influence of the differentiation strategy on the management performance of small and medium-sized business successors, who are shaped by the characteristics of the company and its environment. A survey was conducted on 256 business successors in the metropolitan area, and SPSS 29.0 and AMOS 29.0 programs were used to test the hypotheses of the established research model. The results of the empirical analysis showed that environmental characteristics had a greater influence on business successors than the company's characteristics. Second, the influence of the business successors had a positive effect on the company's differentiation strategy. Third, the differentiation strategy was found to have a strong correlation with the company's financial performance, and it was found to have a positive (+) effect on non-financial performance. Fourth, the financial performance of family businesses was found to have a significant influence on their non-financial performance. This study aims to broaden the understanding of why business successors prefer and choose differentiation strategies by combining theories of strategic management and business succession. Existing research on business succession has focused primarily on succession and management performance, with relatively little empirical research on strategy selection. The novelty of this study lies in its unique focus on strategy selection, which will likely aid in designing customized consulting and support policies for future succession companies. This novel approach is sure to intrigue and interest the audience.

The current study examined the potential language and literacy factors that best explain the L2 reading comprehension abilities of Korean EFL learners. A total of 70 intermediate-level Korean high school students participated in this study and were tested on L1 and L2 reading comprehension, L2 vocabulary, and L2 syntactic knowledge. The findings indicated that, between L1 reading skills and L2 proficiency, L2 proficiency played a more crucial role in predicting L2 reading comprehension. Throughout the analyses, the linguistic threshold hypothesis was supported, demonstrating a threshold level of language proficiency above which learners can effectively transfer their L1 reading skills to L2 reading comprehension. These results highlight the important pedagogical implications for the critical role of L2 proficiency and show the threshold level of proficiency necessary for Korean EFL learners. The insights gained from this study are expected to provide targeted instructional strategies and recommendations, aimed at effectively supporting EFL learners with diverse skills and abilities.

현대 청소년은 경제적 어려움, 가정해체, 정신건강 악화, 학업 스트레스 등 복합적 위기에 직면해 있다. 드림스타트, 교육복지우선지원사업, 청소년동반자사업 등은 일정한 성과를 거두었음에도, 부처 간 칸막이와 서비스 단절 등 일련의 문제점을 드러냈다. 이런 문제를 극복하기 위한 대안으로 2025년 1월 <학생맞춤통합지원법>이 제정되었고, 이 법에 근거해 2026년 1월부터 ‘통합교육맞춤지원사업’이 시행될 예정이다. 본 논문은 이러한 한국의 청소년복지정책의 변화를 분석한다. 통합교육맞춤지원사업 은 교육·복지·보건을 아우르는 생애주기별 통합 지원체계 구축을 목표로 하며, 예방 중심, 통합 중심, 개인 중심의 사례관리 원칙을 강조한다. 본 논문은 통합교육맞춤지원사업의 법적 기반, 전달체계, 정보 시스템, 인력 등을 검토하고, 나아가 이 사업의 정책적·실천적 의의를 분석한다. 통합교육맞춤지원사업 이 청소년복지 전달체계의 현대화와 국가 책임성 강화의 계기가 될 수 있도록 우리 사회가 노력할 필요 가 있다고 본 논문은 제언한다.

To proceed an efficient acquisition program, a variety of factors such as acquirement of excellent weapon system, research and development of defense technology for independent national defense and efficient obtainments are needed to be considered. But present evaluation system of weapon is not enough to include them all. Therefore this study aims to design weapon evaluation system to overcome the cognitive error of decision makers and to cope with the complexity and uncertainty in national acquisition field. To accomplish the goal, the researchers derive 4 factors (compatibility of hierarchy, extensibility, compromise between cost and non-cost factors and aggregation of evaluation criteria and group) based on AHP. And the research intends to present rational weapon evaluation structure which can include national security environment to analysis and guarantee the objective setting of weights through empirical tests.

Anomaly detection is a key technique for ensuring the reliability and stability of systems across various industrial domains. Autoencoder-based reconstruction models are particularly effective in learning normal patterns and detecting deviations. However, conventional loss functions such as Mean Squared Error (MSE) and Mean Absolute Error (MAE) are limited in capturing anomalies that follow heavy-tailed or asymmetric distributions, which are commonly observed in real-world industrial settings. To address this limitation, we propose a Mixture Negative Log-Likelihood (Mixture NLL) loss function based on a combination of Gaussian, Laplace, and Student-t distributions. The loss is constructed using the probability density functions of each distribution, with key parameters such as standard deviation, scale, and degrees of freedom learned during training. The mixture weights representing the contribution of each distribution are also jointly optimized. Experimental results on real-world time-series anomaly detection datasets demonstrate that the proposed MixtureLoss consistently outperforms conventional loss-based Autoencoder models, particularly in detecting tail-end anomalies.