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.

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.

Conductive polymeric composites (CPC) incorporating carbon nanotubes (CNT) and carbon fibers (CF) offer promising potential in self-heating applications due to their superior electrical and thermal properties. This study investigates the synergistic effects of CNT and CF on the electrical conductivity and heat-generation capabilities of CNT/polydimethylsiloxane (PDMS) nanocomposites. Three CF lengths (0.1 mm, 3 mm, and 6 mm) were systematically evaluated to establish hierarchical conductive networks. The incorporation of 6 mm CF into CNT/PDMS composites resulted in a 72% increase in electrical conductivity compared to composites with 0.1 mm CF. Despite these enhancements in electrical performance, the heat-generation capabilities, based on simulations and experimental validation, showed minimal dependence on CF length. A micromechanics-based numerical approach was used to compare and validate the experimental findings, identifying limitations in current analytical models, especially in predicting the heat-generation behavior.

Lithium (Li) metal is a promising anode for next-generation batteries due to its high capacity, low redox potential, and low density. However, dendrite growth and interfacial instability limit its use. In this study, an artificial solid electrolyte interphase layer of LiF and Li-Sn (LiF@Li-Sn) was fabricated by spray-coating SnF2 onto Li. The LiF@Li-Sn anode exhibited improved air stability and electrochemical performance. Electrochemical impedance spectroscopy indicated a charge transfer resistance of 25.2 Ω after the first cycle. In symmetric cells, it maintained a low overpotential of 27 mV after 250 cycles at 2 mA/cm2, outperforming bare Li. In situ microscopy confirmed dendrite suppression during plating. Full cells with NMC622 cathodes and LiF@Li-Sn anodes delivered 130.8 mAh/g with 79.4% retention after 300 cycles at 1 C and 98.8% coulombic efficiency. This coating effectively stabilized the interface and suppressed dendrites, with promising implications for practical lithium metal batteries.

본 연구는 국제경영전략 관점에서 해외자회사가 이전한 지식은 기업 경쟁력 강화에 언제나 긍정 적일 것이라는 가정을 비판하기 위해, 해외자회사의 역지식이전 수준과 기업성과 간 관계를 분석하 였다. 글로벌 기업 직원 256명을 대상으로 설문조사한 결과, 해외자회사가 본사로 이전한 지식 수 준이 높을수록 높은 기업성과를 보이는 것으로 나타났다. 그러나 이러한 효과는 해외자회사의 역량 및 해외자회사와 본사 간 비전공유 수준에 따라 차이를 보이는 것으로 나타났으며, 해외자회사의 역량이 낮거나, 비전공유 수준이 낮은 조직의 역지식이전은 기업성과에 부정적 영향을 제공하는 것 으로 나타났다. 이를 통해 본 연구는 역지식이전 과정에서 글로벌 기업의 형태적 통제기제와 사회 적 통제기제가 요구되고 있음을 제시하였다.

This study investigated the impact of various soil environmental factors on the growth and yield of Actinidia arguta (A. arguta) in Suwon, South Korea. By employing Pearson correlation analysis, we explored the relationships between soil properties such as pH, electrical conductivity (EC), organic matter content, available phosphorus, available silicon, soil moisture, and soil temperature with A. arguta growth and yield. The results revealed that soil temperature and EC had the strongest positive correlations with A. arguta growth, while organic matter content exhibited a strong negative correlation with fruit yield. The high levels of available phosphorus emerged as a potential adverse factor affecting yield. These findings suggest that managing soil temperature, EC, and phosphorus levels is crucial for optimizing A. arguta productivity. Future research should focus on fine-tuning phosphorus levels and further investigating the interactions between soil factors to enhance A. arguta yield and sustainability.