Incorporating nanotechnology into cement composites significantly improves mechanical properties such as strength, toughness, and durability. Graphene, with high tensile strength and large surface area, shows great promise as a nanofiller, but its hydrophobicity complicates its dispersion in cement matrices. This study used a graphene-cellulose nanofiber (G@ CNF) hybrid filler to ensure a highly uniform dispersion within the cement microstructure. The hybrid filler acts as a bridge and efficiently fills voids within the matrix. The planar structure of graphene also provides nucleation sites for hydrated products, leading to a denser microstructure. The cement composite containing 0.01 wt.% graphene exhibited a compressive strength of 72.7 MPa, representing a 47.5% improvement over the plain cement. Furthermore, the resulting cement demonstrated enhanced water resistance compared to graphene oxide-reinforced-cement. This approach offers a cost-effective and sustainable way of producing high-strength, durable cement composite.

This study investigates character collaboration strategies in the context of rising kidult fashion as an emotional and cultural consumption trend. Jo’s kidult consumers typology and Byeon’s character function theory were used to analyze 33 SPAO collaboration cases released between 2022 and 2024. The study adopts a two-stage qualitative content analysis method: 1. categorizing the cases into four types of character collaboration strategies: character-centric, narrative worldview-immersive, fandom-centric, and retro sensibility; and 2. interpreting each type through an integrated framework combining emotional design, nostalgic bonding, and social identity theory. Data were collected from publicly available digital sources and examined with respect to emotional visualization, product design, affective messaging, and participatory/social-media strategies. The findings show that character-centric collaborations were most prevalent (45.5%), emphasizing direct visual cues and everyday product integration. The other three types account for 18.2% each, highlighting narrative immersion, fandom identity, and generational nostalgia, respectively. Character collaboration operated as an affective and symbolic communication mechanism that structures emotional connection, identity expression, and cultural resonance. The results demonstrate that character collaboration is a strategic tool for strengthening consumer engagement, cultural inclusivity, and brand loyalty. The study positions kidult fashion collaboration within an integrated emotional identity and offers a framework for brands to develop emotional comfort products, immersive storytelling designs, fandom-driven engagement, and intergenerational appeal.

This study investigates cataphora in Swahili with a particular emphasis on its socio-pragmatic functions. As can be observed in many other languages, anaphoric references tend to be in more frequent use than cataphoric ones seemingly in view of the fact that anaphoric references designate a word stated in an earlier utterance or sentence. Put it another way, anaphoric reference can be defined as the repetitive arrangement of a word in a linear sequence. This study aims to explore a rather neglected, but significant research topic and inquire into some plausible language-specific factors and motivational aspects that lead to the intermittent occurrence of intra-/inter-sentential cataphora. The structural particularities of internal arrangement and socio-pragmatic functions of cataphora are minutely examined by being based on naturally occurring utterances and those drawn from SNSs. This study aims at being implemental in having a multi-facetted understanding of why speakers use a forward-referring linguistic device and of how language-specific particularities induce speakers to rely on cataphora for anticipatory, emphatic and effective communications.

Cordycepin is the principal bioactive compound produced by Cordyceps militaris and exhibits diverse pharmacological properties. However, cordycepin production is highly sensitive to cultivation conditions, leading to substantially variable production amounts and challenges in process optimization. An interpretable machine learning framework was established in this study to predict the cordycepin produced by C. militaris cultivated on Pinus densiflora sawdust. Three key cultivation parameters—input weight, growth weight, and particle size—were quantified using submerged mycelial culture. The cordycepin content was measured via high-performance liquid chromatography. Four predictive models (random forest, support vector machine, XGBoost, and artificial neural network) were optimized through a randomized hyperparameter search and evaluated using internal validation and Tropsha’s external quantitative structure-activity relationship criteria. The validation accuracy of XGBoost was the highest (root mean square error = 42.67 μg/mL), whereas the external performance of random forest was the most reliable (R² = 0.898). Shapley additive explanations revealed that input weight most strongly influenced cordycepin production, followed by growth weight and particle size, with distinct nonlinear and interaction-driven effects among the cultivation variables. Kernel density and dependence analyses confirmed the occurrence of multimodal production regimes associated with the substrate loading and particle size characteristics. Finally, the best-performing model was deployed through a streamlit-based graphical user interface, enabling the real-time prediction of cordycepin concentration with a 95% confidence interval. The results collectively demonstrate the utility of interpretable AI-driven modeling for unveiling complex biological responses, providing a practical decision-support tool for optimizing cordycepin production in fungal biotechnologies.

The medicinal fungus Cordyceps militaris is recognized for producing cordycepin, a bioactive nucleoside with anticancer, immunomodulatory, and antioxidant properties. However, conventional culture media often entail high production costs and limited sustainability, prompting the search for alternative nutrient sources. This study evaluated onion, green onion, and garlic peel extracts—agricultural by-products rich in flavonoids, phenolics, and sulfur-containing antioxidants—as sustainable substrates for enhancing mycelial biomass and cordycepin biosynthesis in C. militaris. Liquid cultures supplemented with peel extracts (1–5%) were assessed for growth, cordycepin production (HPLC), and antioxidant activity (DPPH assay). Onion peel extract (OPE) showed the strongest growth-promoting effect, yielding 8.2 g/L of biomass at 5% and achieving a 19% increase in cordycepin concentration at 3% compared with the control. Antioxidant activity strongly correlated with cordycepin accumulation (R = 0.96, p < 0.001), indicating that secondary metabolite production contributed significantly to radicalscavenging capacity. Response surface methodology using a Box–Behnken design revealed that extract concentration, pH, and incubation period significantly influenced cordycepin production (p < 0.05), with the quadratic model showing excellent fit (R² = 0.9924). Optimal conditions were identified as 3% extract concentration, pH 6.0, and 12 days of incubation, under which cordycepin reached 0.995 mg/L, substantially higher than the control (0.693 mg/L). These findings demonstrate that agricultural by-product extracts, particularly onion peel, can serve as effective and economical substrates for enhancing cordycepin biosynthesis while supporting sustainable bioprocessing strategies in C. militaris cultivation.

본 연구는 1970년대에 발표된 조세희의 󰡔난장이가 쏘아올린 작은 공󰡕(이하 󰡔난·쏘·공󰡕)이 제기하는 사 회문제를 UN의 지속가능발전목표(SDGs) 관점에서 재해석하고자 한다. 󰡔난·쏘·공󰡕이 제기하는 당시 대한 민국의 사회 문제는 빈곤의 문제, 교육의 불공평 문제, 도시 개발과 철거민의 문제, 노동자의 권리 보장 문제, 환경 오염 문제, 부정부패의 문제 등이다. 󰡔난·쏘·공󰡕에서 도출된 문제를 SDGs 목표 1, 3, 4, 6, 8, 11, 12, 14, 16과 대응시켜 살펴본 결과, 1970년대 한국 산업화 시기의 경험이 오늘날 개발도상국이 직면한 보편적 과제와 깊이 연결되어 있음을 확인하였다. 이를 통해 󰡔난·쏘·공󰡕을 SDGs·ESD 교육과 시민 교육의 핵심 텍스트로 활용할 수 있는 가능성을 제시한다.

Covalent organic framework (COF) membranes have emerged as promising candidates for hydrogen purification due to their tunable pore sizes and robust structures. However, achieving high selectivity and permeability simultaneously remains a challenge due to the inherent pore size distribution of COF materials. In this study, we fabricated two distinct COF membranes, TpPa-1 and TpTGCl, with pore sizes of 1.8 nm and 0.39 nm, respectively, using tailored synthesis methods. The TpTGCl membrane, synthesized via room temperature interfacial polymerization and vacuum-assisted filtration, exhibits an ultrathin nanosheet structure with an interlayer π–π stacking distance of 0.33 nm. This unique architecture, combined with its affinity for CO2 adsorption, enables exceptional hydrogen separation performance, achieving a H2/ CO2 selectivity of 52.5 and a H2 permeability of 3.49 × 10– 7 mol m− 2 s− 1 Pa− 1. Molecular dynamics simulations confirmed the steric hindrance effect as the primary mechanism for the selective permeation of hydrogen. The TpTGCl membrane effectively sieves larger gas molecules ( CO2, N2, CH4, etc.) without the need for material modification or excessive membrane thickness. This study demonstrates the potential of COF membranes with tailored pore sizes for high-performance hydrogen purification and offers valuable insights for the development of advanced separation technologies.

The insulating nature of elemental sulfur has been regarded as a major challenge limiting the electrochemical performance of Li–S batteries. Consequently, previous efforts have focused on developing conductive porous materials to enhance sulfur contact. In this study, we review this conventional assumption and demonstrate that the insulating property of sulfur is not the primary factor affecting Li–S battery performance. Instead, we introduce a novel sulfur host design using polar mesoporous carbon (p-MC), which possesses ultra-low electrical conductivity (6.45 × 10− 7 S cm− 1) and functional groups. Our results demonstrate that all sulfur particles within the nearly insulating p-MC matrix actively participate in electrochemical reduction during the initial discharge. A comparative study with a nonpolar mesoporous carbon host, which features a similar porous structure but higher conductivity (1.07 × 10− 1 S cm− 1), showed that the p-MC host achieved superior cycling stability. This performance is attributed to the strong interaction between the polar functional groups of p-MC and lithium polysulfides, enabling effective and stable confinement of the active materials during cycling. Our findings highlight a paradigm shift in the design of sulfur host materials and the critical role of polar functionalities. This study offers a promising strategy for the development of durable and high-performance Li–S batteries.

CNT/epoxy composite film (CECF) was prepared and used to fabricate the interlayer stiffened and reinforced photothermal synergistic curing glass fiber-reinforced polymer (GFRP) composites, and the influence of the photothermal effects of CECF on compressive strength and failure mechanism of the composite was investigated. Compared to GFRP composite, the uniform and wide temperature distribution in the in-plane and thickness direction was exhibited due to the heat from the lattice vibrations induced by photothermal conversions of CECF, thereby facilitating the decomposition of the thermal initiator and the increase of the curing degree in the CECF/GFRP composite. The in-plane shear modulus and interlaminar shear strength (ILSS) of the CECF/GFRP composite were 12.2% and 13.7% higher than those of the GFRP composite, respectively, indicating the enhanced deformation resistance and interfacial adhesion of the interlayer region. The compressive strength of the CECF/GFRP composite was increased by 14.1% relative to the GFRP composite, which was ascribed to restricted kink-band and delayed delamination damage during the compression process of composite.

This study investigates of repeated freeze-thaw (FT) cycles on the color, pH, and oxidative stability of vacuum-packaged chicken thigh meat. Samples were evaluated at Fresh, Frozen (2 weeks), FT1 (2-times FT, 4 weeks), and FT2 (3-times FT, 6 weeks). FT1 resulted in a higher pH, but the pH was slightly reduced in FT2. Oxidative stability declined with each cycle, as evidenced by significant increases in thiobarbituric acid reactive substances (TBARS), carbonyl content, and peroxide value (POV). Meanwhile, thiol content decreased notably. Color parameters were also affected by FT cycles. Redness (a* ) decreased in the frozen group but increased in subsequent cycles. Lightness (L* ) fluctuated, with a significant increase after FT2, and yellowness (b* ) showed slight increases and subsequent decreases. Chroma (c*) and hue angle (h°) also fluctuated due to repeated freeze-thaw cycles. Furthermore, correlation analysis revealed strong positive associations between TBARS, carbonyls, POV, and pH, while thiol content showed strong negative correlations with these oxidative markers, reinforcing the oxidative degradation trend. This comprehensive analysis illustrates the multifaceted impacts of freeze-thaw processes on the color, pH, and oxidation markers of chicken thigh meat, emphasizing the importance of understanding these effects for proper storage and safety.

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.