Search Results (348)

Ground granulated blast-furnace slag (GGBS), a waste product generated during steel production, can be added as a substitute for cement in concrete to mitigate the environmental impact of the cement and steel industries. However, the use of GGBS is limited because it decreases the early strength development of cement or concrete. This study evaluated the performance of incorporating synthesized C-S-H nanoparticles to enhance the compressive strength, early hydration, and microstructure of cement composite. The synthesized C-S-H nanoparticles were produced at standard atmospheric pressure and room temperature. Heat of hydration, X-ray diffraction, and thermogravimetric analyses were conducted to investigate the hydration and mechanical properties of the cement containing the C-S-H nanoparticles. Further, mercury intrusion porosimetry was conducted to examine the pore structures. The experimental finding demonstrated that adding C-S-H nanoparticles accelerated the early hydration progress in the cement composites, thereby increasing their initial compressive strength. Full article

Background/Objectives: Out-of-hospital cardiac arrest (OHCA) is associated with high mortality, and outcomes may be influenced by underlying conditions such as cancer, hypertension (HTN), and diabetes mellitus (DM). This study aimed to evaluate whether HTN and DM modify the effects of post-resuscitation treatments—specifically targeted temperature management (TTM) and percutaneous coronary intervention (PCI)—on survival and neurological recovery in OHCA patients with a history of cancer. Methods: This retrospective cohort study analyzed data from the Korean national OHCA registry between January 2018 and December 2021. Adults aged ≥18 years with presumed cardiac-origin OHCA and a documented history of cancer—defined as any prior cancer diagnosis recorded in medical records regardless of remission status—were included. Multivariable logistic regression was used to examine associations between treatment and outcomes, and interaction effects were assessed using adjusted p-values to account for multiple testing. Results: Among the 124,916 EMS-assessed OHCA cases, 4115 patients met the inclusion criteria. TTM and PCI were both statistically associated with good neurological recovery (TTM: adjusted odds ratio [aOR], 1.69; 95% confidence interval [CI], 1.12–2.55; p < 0.05; PCI: aOR, 11.35; 95% CI, 7.98–16.14; p < 0.05). In interaction analyses, the benefit of TTM and PCI for achieving good neurological recovery was attenuated in patients with diabetes mellitus (DM; TTM: aOR, 0.59; 95% CI, 0.23–1.49; PCI: aOR, 4.94; 95% CI, 2.69–9.06) and hypertension (HTN; TTM: aOR, 0.94; 95% CI, 0.49–1.82; PCI: aOR, 7.47; 95% CI, 4.48–12.44), with adjusted p-values < 0.05 for all interactions. Conclusions: In OHCA patients with a history of cancer, TTM and PCI are associated with improved survival and neurological outcomes. However, the presence of comorbidities such as HTN and DM may attenuate these benefits. These findings support the need for individualized post-resuscitation care strategies that account for comorbid conditions. Full article

Artificial Intelligence (AI) is transforming smart healthcare by enhancing diagnostic precision, automating clinical workflows, and enabling personalized treatment strategies. This review explores the current landscape of AI in healthcare from two key perspectives: capability types (e.g., Narrow AI and AGI) and functional architectures (e.g., Limited Memory and Theory of Mind). Based on capabilities, most AI systems today are categorized as Narrow AI, performing specific tasks such as medical image analysis and risk prediction with high accuracy. More advanced forms like General Artificial Intelligence (AGI) and Superintelligent AI remain theoretical but hold transformative potential. From a functional standpoint, Limited Memory AI dominates clinical applications by learning from historical patient data to inform decision-making. Reactive systems are used in rule-based alerts, while Theory of Mind (ToM) and Self-Aware AI remain conceptual stages for future development. This dual perspective provides a comprehensive framework to assess the maturity, impact, and future direction of AI in healthcare. It also highlights the need for ethical design, transparency, and regulation as AI systems grow more complex and autonomous, by incorporating cross-domain AI insights. Moreover, we evaluate the viability of developing AGI in regionally specific legal and regulatory frameworks, using South Korea as a case study to emphasize the limitations imposed by infrastructural preparedness and medical data governance regulations. Full article

Background/Objectives: This study aimed to explore the underlying meaning and structure of the experiences of caregivers with lung transplant recipients using phenomenological research methods. Methods: Data were collected between February 2020 and December 2021 via in-depth individual interviews with nine caregivers of lung transplant recipients. The meaning of the participants’ experiences was analyzed using Colaizzi’s phenomenological analysis to ensure methodological rigor. Researchers minimized bias through reflexivity and member checking, and the study adhered to ethical standards to ensure trustworthiness. Results: Participants cared for patients who had not fully crossed the threshold of death without giving up hope for a cure. They did not avoid caregiving as a responsibility to their families but accepted it as their responsibility. The lives of the participants became increasingly immersed as they witnessed the process of the patient’s illness and gained insights into patience and gratitude through the caregiving experience. Conclusions: This study’s findings can help assess the needs of lung transplant recipients and their caregivers and guide interventions that address their reciprocal relationship. It also emphasizes the importance of ongoing education and expanded social care services to reduce caregiver stress and burden. Full article

For sustainable agriculture practices to be achieved as a result of changing climates and growing hazards to the environment, improving resilience in plants is crucial. Stress-Associated Proteins (SAPs) have an important role in helping plants react to abiotic stress conditions such as drought, salinity, and changes in temperature. This study underlines the ability of the SAP gene family to promote stress adaptation mechanisms by presenting a thorough analysis of the gene family across 86 distinct plant species and genera. We present an optimized Hybrid Algorithm for Robust Plant Stress (HARPS), a unique machine learning (ML)-based system designed to efficiently identify and classify plant stress responses. A comparison with conventional ML models shows that HARPS substantially reduces computational time while achieving higher accuracy. This efficiency makes HARPS ideal for real-time agricultural applications, where precise and quick stress detection is essential. With the help of an ablation study and conventional evaluation metrics, we further validated the effectiveness of the model. Overall, by strengthening crop monitoring, increasing resilience, lowering dependency on chemical inputs, and enabling data-driven decision-making, this research advances the objectives of sustainable agriculture production and crop protection. HARPS facilitates scalable, resource-efficient stress detection essential for adjusting to climatic uncertainty and mitigating environmental consequences. Full article

Perovskite solar cells (PSCs) are emerging as leading candidates for sustainable energy generation due to their high power conversion efficiencies and low fabrication costs. However, their performance remains constrained by non-radiative recombination losses primarily at grain boundaries, interfaces, and within the perovskite bulk that are difficult to characterize under realistic operating conditions. Traditional methods such as photoluminescence offer valuable insights but are complex, time-consuming, and often lack scalability. In this study, we present a novel Long Short-Term Memory (LSTM)-based deep learning framework for dynamically predicting dominant recombination losses in PSCs. Trained on light intensity-dependent current–voltage (J–V) characteristics, the proposed model captures temporal behavior in device performance and accurately distinguishes between grain boundary, interfacial, and band-to-band recombination mechanisms. Unlike static ML approaches, our model leverages sequential data to provide deeper diagnostic capability and improved generalization across varying conditions. This enables faster, more accurate identification of efficiency limiting factors, guiding both material selection and device optimization. While silicon technologies have long dominated the photovoltaic landscape, their high-temperature processing and rigidity pose limitations. In contrast, PSCs—especially when combined with intelligent diagnostic tools like our framework—offer enhanced flexibility, tunability, and scalability. By automating recombination analysis and enhancing predictive accuracy, our framework contributes to the accelerated development of high-efficiency PSCs, supporting the global transition to clean, affordable, and sustainable energy solutions. Full article

This study investigates the structural behavior of reinforced concrete slabs and culverts using newly developed tubular rebars as a replacement for conventional deformed rebars. Tubular rebars, which are approximately 50% lighter and exhibit twice the tensile strength of standard deformed rebars, were evaluated through experimental tests and finite element analysis (FEA). Results showed that tubular rebars achieved up to 44.46% higher yield strength and up to 25.31% higher ultimate strength in statically determinate slabs compared to conventional rebars, though with reduced ductility. In statically indeterminate configurations such as fixed slabs and box culverts, the ductility performance improved significantly, with ductility index differences reduced to less than 3%. Hybrid reinforcement combining tubular and deformed rebars also enhanced performance, especially in compression zones. These findings demonstrate that tubular rebars can be a sustainable and structurally efficient alternative to conventional reinforcement when deflection control is ensured. Full article

The purpose of this study was to evaluate the bactericidal effect of 270 nm UV-C light-emitting diode (LED) light delivered through a newly designed prototype device with thin optical fiber against Enterococcus faecalis (E. faecalis). The prototype device, developed to integrate UV-C light into a thin optic fiber (diameter 124 µm) connected to a UV-C LED (Luminous Device; Sunnyvale, CA, USA) via a specialized double-lens system that focuses divergent light to achieve a 65 mm working distance and a numerical aperture of 0.22. E. faecalis, was cultured at 37 °C under aerobic conditions for 24 h. The UV-C LED optical fiber was positioned 10 mm above the bacterial culture prepared in the wells of a 96-well plate. The E. faecalis cells were exposed to UV-C irradiation for 0, 10, 30, 60, 90, 120 and 180 s. Following irradiation, the OD600 values were measured after incubation at 37 °C for an additional 24 h. The data were statistically analyzed using one-way ANOVA, followed by Tukey’s honestly significant difference (HSD) test at a significance level of 0.05. UV irradiation at 270 nm significantly reduced E. faecalis growth in a time-dependent manner (p < 0.05). No significant changes were observed at 0 and 10 s, while peak reductions occurred at 120 and 180 s, with effects beginning at 30 s and increasing over time. The 270 nm UV-C wavelength was highly effective in bactericidal action against E. faecalis. The custom-designed UV-C delivery system effectively integrated the light source into a thin optical fiber, allowing for efficient UV-C light transmission and demonstrating its potential for application in narrow spaces such as root canals. Full article

We aimed to evaluate the therapeutic potential of crizotinib, a broad-spectrum tyrosine kinase inhibitor against bladder cancer (BC) cells, based on a three-dimensional (3D) cell culture system. After proliferating cell masses (spheroids) using T24 cisplatin-naïve and T24R2 cisplatin-resistant human BC cell lines, the spheroids were exposed to various crizotinib concentrations in order to derive an ideal crizotinib concentration to suppress cell survival, migration, and invasion. Crizotinib suppressed cell proliferation, migration, and invasion in both T24 and T24R2 BC cell lines under a 3D spheroid model, which was more appropriate than the conventional two-dimensional cell culture model. Real-time quantitative polymerase chain reaction analysis revealed a reduced expression of E-cadherin and an enhanced expression of vimentin, suggesting EMT suppression and the subsequent suppression of tumor aggressiveness following crizotinib administration. Meanwhile, the expressions of apoptosis-related genes increased. Western blot analysis revealed that the expression levels of phosphorylated mesenchymal–epithelial transition factor (c-Met) and phosphorylated Akt decreased following crizotinib administration, suggesting that the antitumor effect of crizotinib can be associated with the inhibition of the phosphorylated activation of the c-Met/PI3K/Akt pathway. Crizotinib showed a potential antitumor effect on both cisplatin-naïve and cisplatin-resistant human BC cells, likely through c-Met-induced PI3K/Akt pathway inhibition. Full article

Background/Objectives: To develop an automated deep learning-based bone age prediction model using the Tanner–Whitehouse (TW3) method and evaluate its feasibility by comparing its performance with that of pediatric radiologists. Methods: The hand and wrist radiographs of 560 Korean children and adolescents (280 female, 280 male, mean age 9.43 ± 2.92 years) were evaluated using the TW3-based model and three pediatric radiologists. Images with bony destruction, congenital anomalies, or non-diagnostic quality were excluded. A commercialized AI solution built upon the Rotated Single Shot MultiBox Detector (SSD) and EfficientNet-B0 was used. Bone age measurements from the model and radiologists were compared using the paired t-tests. Linear regression analysis was performed and the coefficient of determination (r²), mean absolute error (MAE), and root mean square error (RMSE) were measured. A Bland–Altman analysis was conducted and the proportion of bone age predictions within 0.6 years of the radiologists’ assessments was calculated. Results: The TW3-based model demonstrated no significant differences between bone age measurements and radiologists, except for participants <6 and >13 years old (overall, p = 0.874; 6–8 years, p = 0.737; 8–9 years, p = 0.093; 9–10 years, p = 0.301; 10–11 years, p = 0.584; 11–13 years, p = 0.976; <6 or >13 years, p < 0.001). There was a strong linear correlation between the model prediction and radiologist assessments (r² = 0.977). The RMSE and MAE values of the model were 0.529 (95% CI, 0.482–0.575) and 0.388 (95% CI, 0.361–0.417) years. Overall, 82.3% of bone age model predictions were within 0.6 years of the radiologists’ interpretation. Conclusions: Automated deep learning-based bone age assessment has the potential to reduce radiologists’ workload and provide standardized measurements for clinical decision making. Full article

This study analyzes changes in NO_x concentrations due to the aerodynamic and dry deposition effects of roadside trees in the Jongno area, a central business district of Seoul, Republic of Korea, using a computational fluid dynamics (CFD) model. The simulation results indicate that the on-road NO_x concentration was slightly increased (2.09%) due to the aerodynamic effect of roadside trees. However, the dry deposition effect of roadside trees had a greater impact on reducing NO_x concentrations (−2.77%) along sidewalks. It was observed that the reduction in NO_x concentration due to the dry deposition effect of roadside trees was likely to offset the increase in NO_x concentrations due to the aerodynamic effect of roadside trees, resulting in an overall decrease in NO_x concentrations. Furthermore, sensitivity tests showed that the increase in NO_x concentrations due to the aerodynamic effects of roadside trees was intensified along sidewalks when ambient wind speeds were high, while the decrease in NO_x concentration was proportional to the deposition velocity of roadside trees. Therefore, roadside trees should be planted where aerodynamic effects do not significantly increase NO_x concentrations in order to improve near-road air quality. Full article

Prior studies have shown mixed findings on the role of financial slack. This study examines how environmental factors such as munificence, dynamism, and complexity moderate the relationship between financial slack and innovation activity. Using data from Compustat and the Center for Research in Security Prices (CRSP) database on 578 computer-processing firms in innovation-intensive industries in the United States, our results reaffirm that financial slack is a strategic asset that enhances R&D investment. Further, we find that the positive consequences of financially abundant firms pursuing innovation are attenuated in munificent environments where firms increasingly rely on external resources. Similarly, in dynamic environments, unpredictable market changes divert slack resources from long-term R&D investments, further weakening the effect. However, there is no significant difference in complex environments. Our study contributes to the existing literature by integrating different environments and highlighting the importance of balancing internal resources with external environments in shaping innovation strategies. For managers, these findings provide practical guidance for resource allocation strategies to effectively support innovation in varying external environments. Full article

In the present study, we investigated the anti-inflammatory and anti-melanogenic effects of Leuconostoc mesenteroides subsp. DB-21-derived exosomes (DB-21 exosomes), isolated from Camellia japonica flower in lipopolysaccharide (LPS)-induced RAW 264.7 macrophage cells and melanocyte-stimulating hormone (α-MSH)-induced B16F10 melanoma cells. We confirmed that DB-21 exosomes were not toxic to LPS-induced RAW 264.7 macrophage cells and α-MSH-induced B16F10 melanoma cells. Moreover, we confirmed that DB-21 exosomes inhibit the pro-inflammatory cytokines IL-6, IL-1β, TNF-α, PGE₂, and the expression of inflammatory factors iNOS and COX-2. We also found that DB-21 exosomes have a concentration-dependent ability to inhibit melanin, TRP-1, TRP-2, tyrosinase, and MITF, which are factors involved in melanogenesis. Additionally, it inhibits the phosphorylation of Akt and GSK-3β, and MAP kinase pathway proteins such as ERK, JNK, and p38. We confirmed that DB-21 exosomes inhibit melanin synthesis in B16F10 cells through various pathways, and based on previous results, they may be used as a functional cosmetic material with anti-inflammatory and anti-melanogenic activities. Full article

Background: Pancreatic cancer is a highly aggressive malignancy with a poor prognosis. Neoadjuvant chemotherapy (NAC) is increasingly used to improve survival in patients with pancreatic cancer; however, it often results in nutritional deterioration, which may negatively impact patient outcomes. Therefore, this study aimed to assess the effect of changes in nutritional status on the long-term outcomes of patients with pancreatic cancer who underwent curative surgery after NAC. Methods: This retrospective single-center study included 148 patients with pancreatic cancer who underwent curative surgery after NAC between 2010 and 2020. The Controlled Nutritional Status (CONUT) score was used to determine the nutritional status of the patients. Patients were categorized into worsened, maintained, and improved groups based on the changes in their CONUT scores before and after NAC. We compared differences in overall survival (OS) and disease-free survival (DFS) between the groups. Results: The worsened nutritional status group exhibited the shortest median OS (28 months) compared to the maintained and improved groups (39 and 66 months, respectively; p = 0.01). Additionally, the worsened group demonstrated the shortest DFS compared to the other two groups (13, 22, and 39 months, respectively; p = 0.02). Multivariate analysis identified nutritional deterioration as an independent prognostic factor for OS (hazard ratios (HR), 2.11; 95% confidence intervals (CI), 1.31–3.40; p < 0.01). Conclusions: Nutritional deterioration after NAC is a significant prognostic factor of poor survival outcomes in patients with pancreatic cancer. These findings indicate that serial nutritional assessments and treatment during NAC are crucial for improving patient outcomes. Full article

Drowsy driving significantly impairs drivers’ attention and reaction times, increasing the risk of accidents. Developing effective prevention technologies is therefore a critical task. Previous studies have highlighted several limitations: (1) Most drowsiness-detection methods rely solely on facial features such as eye blinking or yawning, limiting their ability to detect different drowsiness levels. (2) Sensor-based methods utilizing wearable devices may interfere with driving activities. (3) Binary classification of drowsiness levels is insufficient for accident prevention, as it fails to detect early signs of drowsiness. This study proposes a novel drowsiness-detection method that classifies drowsiness into three levels (alert, low vigilant, drowsy) using a non-contact, camera-based approach that integrates physiological signals and visible facial features. Conducted as a feasibility study, it evaluates the potential applicability of this method in driving situations. To evaluate generalizability, experiments were conducted with seen-subject and unseen-subject conditions, achieving accuracies of 96.7% and 75.7%, respectively. This approach provides a more comprehensive and practical solution to drowsiness detection, contributing to safer driving environments. Full article