Search Results (16,251)

A quantitative understanding of methane leakage has become essential for safety design as eco-friendly fuel systems expand in modern ship applications. To address this need, controlled methane-release experiments were conducted in a large indoor chamber (30 × 16 × 20 m) to evaluate how sensor-network resolution (1 m vs. 0.5 m spacing) influences dispersion measurement and 5% Lower Explosive Limit (LEL)-based risk assessment. Initial tests with a 1 m grid showed that most sensors detected only low concentrations except for near the release nozzle, demonstrating that coarse spatial resolution cannot capture the primary dispersion pathway or transient peaks. This limitation motivated the use of a 0.5 m high-density sensor network, which enabled clear identification of the dispersion centerline, concentration-gradient development, early detection behavior, and the evolution of diluted regions, particularly under buoyancy-driven plume rise. Experimental results were compared with CFD simulations using the RNG k–ε and k–ω GEKO turbulence models. Strong agreement was obtained in peak concentration, concentration-rise rates during the accumulation phase, and LEL-based dispersion distances. These findings confirm the suitability of the selected turbulence models for predicting methane behavior in large enclosed spaces and highlight the sensitivity of model–experiment agreement to measurement resolution. The results provide an experimentally grounded reference for sensor layout design and verification of gas-detection strategies in ship compartments, fuel-gas preparation rooms, and modular supply units. Overall, the study establishes a methodological framework that integrates high-resolution experiments with CFD modeling to support safer design and operation of methane-fueled vessels. Full article

Systemic anticancer therapy causes a number of side effects; therefore, local drug release devices may play an important role in this area. In this study, we developed polyurethane-dendrimer foams containing different amounts of third-generation poly (amidoamine) dendrimers (PAMAM G3) to evaluate their ability to encapsulate and release the model anticancer drug doxorubicin (DOX), as well as their biocompatibility and effectiveness against normal and cancer cells in vitro. PU–PAMAM foams containing 10–50 wt% PAMAM G3 were prepared using glycerin-based polyether polyol and castor oil as co-components. Structural and rheological analyses revealed that foams containing up to 20 wt% PAMAM G3 exhibited a well-developed porous structure, while higher dendrimer loadings (≥30 wt%) led to irregular cell shapes, pore coalescence, and thinning of cell walls, and indicated a gradual loss of structural integrity. Rheological creep–recovery measurements confirmed the structural findings: moderate PAMAM G3 incorporation (≤20 wt%) increased both the instantaneous and delayed elastic modulus (E₁ ≈ 130–140 kPa; E₂ ≈ 80 kPa) and enhanced elastic recovery, reflecting improved cross-link density and foam stability. Higher dendrimer contents (30–50 wt%) caused a decline in these parameters and higher viscoelastic compliance, indicating a softer, less stable structure. The DOX loading capacity and encapsulation efficiency increased with PAMAM G3 content, reaching maximum values of 35% and 51% for 30–40 wt% PAMAM G3, respectively. However, the most sustained DOX release profiles were observed for matrices containing 20 wt% PAMAM G3. Analysis of cumulative release and kinetic modeling revealed a transition from diffusion-controlled release at low PAMAM contents to burst-dominated release at higher dendrimer loadings. Importantly, matrices containing 10–20 wt% PAMAM G3 also indicated selective anticancer action against squamous cell carcinoma (SCC-15) compared to non-cancerous human keratinocytes (HaCaT). Moreover, the DOX they released effectively destroyed cancer cells. Overall, PU–PAMAM foams containing 10–20 wt% PAMAM G3 provide the most balanced combination of structural stability, controlled drug release, and cytocompatibility. These materials therefore represent a promising platform as passive carriers in drug delivery systems (DDSs), such as local implants, anticancer patches, or bioactive wound dressings. Full article

Photocatalytic water sterilization has emerged as a promising sustainable technology for addressing microbial contamination across diverse sectors including healthcare, food production, and environmental management. This review examines the fundamental mechanisms and recent advances in photocatalytic water sterilization, with a particular emphasis on the differential bactericidal pathways against Gram-negative and Gram-positive bacteria. Gram-negative bacteria undergo a two-step inactivation process involving initial outer membrane lipopolysaccharide (LPS) degradation followed by inner membrane disruption, whereas Gram-positive bacteria exhibit simpler kinetics due to direct oxidative attacks on their thick peptidoglycan layer. Escherichia coli has long been used as the gold standard in photocatalytic sterilization studies owing to its aerobic nature and suitability for the colony-counting method. In contrast, Lactobacillus casei, a facultative anaerobe, can be cultured statically and evaluated rapidly using turbidity-based optical density measurements. Therefore, both organisms serve complementary roles depending on the experimental objectives—E. coli for precise quantification and L. casei for rapid, practical assessments of Gram-positive bacterial inactivation under laboratory conditions. We also describe sterilization using light alone while comparing it to photocatalytic sterilization and then discuss two innovative suspension-based photocatalyst systems: polystyrene bead-supported TiO₂/SiO₂ composites offering balanced reactivity and separability and magnetic TiO₂-SiO₂/Fe₃O₄ nanoparticles enabling rapid magnetic recovery. Future research directions should prioritize enhancing visible-light efficiency using metal-doped TiO₂ such as Cu-doped systems; improving catalyst durability; developing new applications of photocatalysts, such as protecting RO membranes; and validating scalability across diverse industrial and medical water treatment applications. Full article

Background/Objectives: Thyroid eye disease (TED) can lead to structural and microvascular changes in the orbit and retina. This study aimed to investigate the associations between Clinical Activity Score (CAS), orbital magnetic resonance imaging (MRI) measurements, and retinal microvascular changes in TED patients. Methods: This cross-sectional study included 38 patients (76 eyes) with TED. Each patient underwent a comprehensive ophthalmological evaluation, CAS assessment, and a detailed medical history. Optical coherence tomography angiography (OCTA) was performed to quantify vessel density (VD) in the superficial and deep capillary plexus (SCP and DCP). Exophthalmos, extraocular muscle thickness and orbital fat thickness were measured on MRI scans to evaluate structural changes. Laboratory analyses included thyroid hormone levels, thyrotropin receptor antibodies (TRAb), anti-thyroid peroxidase antibodies (anti-TPO), and lipid profile. Results: Active TED patients (CAS ≥ 3) had significantly higher TRAb levels (p < 0.001), while anti-TPO did not differ between groups. Active eyes showed significantly higher DCP VD in the whole image (p = 0.013), parafovea (p = 0.012), and perifovea (p = 0.009) across all quadrants, with no difference in SCP or the foveal avascular zone (FAZ). In linear mixed model regression analyses, after adjusting for previous glucocorticosteroid therapy, higher triglycerides, greater medial rectus thickness, and whole-image DCP VD independently predicted higher CAS values (R² = 42, p < 0.001). After adjusting for age and sex, CAS remained significantly positive predictor of DCP VD in the parafovea (R² = 0.22, p < 0.001). Conclusions: Changes in DCP VD reflect TED activity and structural orbital involvement. Full article

The financial market correlation structure exhibits dynamic evolution with implications for systemic risk and diversification benefits; yet, conventional approaches using correlation magnitude monitoring may miss fundamental organizational changes. This study develops an integrated geometric–topological framework synthesizing Riemannian manifold geometry with discrete network topology to characterize market structure transformations, applying the methodology to Turkish financial markets spanning May 2015–May 2025. The analysis employs intrinsic dimensionality estimation, curvature tensor computation, and Forman–Ricci curvature calculation alongside correlation network topology measures across seventeen variables, including sectoral equity indices and macroeconomic indicators. The results document an extraordinary mid-2022 structural break featuring dimensional collapse from $2.4$ to $0.43$ dimensions, network density surge to $0.97$, and Ricci curvature spike to $16.0$, establishing a persistent hypersynchronized regime evident through 2024–2025 with density elevated by $44\%$ and the Ricci curvature being amplified $258\%$ above the baseline. A comparative crisis analysis reveals heterogeneous structural signatures distinguishing COVID-19 geometric vulnerability from currency crisis modular reorganization. Macro-financial linkages identify credit growth as primary structural driver, achieving $r=0.504$ correlation with geometric resilience. The findings demonstrate that markets undergo permanent structural transformations responding systematically to monetary–credit conditions, with implications for surveillance frameworks, stress testing design, and macroprudential policy. Full article

Population models offer insights into both theoretical and practical aspects of insect population dynamics. Among the models, stage-structured matrix models are used to describe the population dynamics of insects because the development of insects is by nature stage-structured. Field populations of the pine caterpillar, Dendrolimus spectabilis (Lepidoptera: Lasiocampidae) were monitored in a pine stand located in Dorak-ri, Cheongsan-myeon, Wando-gun, Jeollanam-do, from May 1998 to March 1999, and the pest density was measured as the number of larvae, pupae, or eggs at one-month intervals, excluding the winter season. Life tables and matrix models were constructed based on field observations, and the most vulnerable life stage was identified through sensitivity analysis. The density of the pine caterpillar (number per 1000 cm² branch) was 7.9 on 8 May 1998, and subsequently decreased to 0.5 on 14 March 1999, showing a decreasing trend of caterpillar density. The population growth rate was 0.74, a decreasing trend. The most vulnerable stages were (1) the larvae immediately after hatching and (2) again during overwintering, probably due to indirect mortality caused by humid conditions and activities of natural enemies during winter. Given the significant damage caused by mature larvae in the spring and that the density of the caterpillar after overwintering typically remains stable, forest management requires that the pest density be monitored soon after overwintering to allow decisions about control measures to be taken. Our results showed that a matrix model is useful to describe the population dynamics of the pine caterpillar and to construct suitable management strategies. Full article

Empty fruit bunches (EFBs) and kenaf are two abundant sources of lignocellulosic resource agricultural waste with potential as substrates for mycelium-based composites (MBCs). These composites are lightweight, compostable, low-cost, and suitable for packaging applications. However, their performance is highly dependent on the type of lignocellulosic substrate and the processing conditions applied during production. Despite the promising availability of natural fibers, limited research has focused on the drying process that affects the quality of MBCs. This study investigates the effect of different drying times (12, 18, and 24 h) on the physical and mechanical properties of MBCS produced from EFB and kenaf substrates. Following a 20-day incubation period under controlled conditions, the composites were oven-dried and analyzed for mycelial colonization, density measurement, shrinkage, water loss, shore A hardness, impact resistance, and mold growth. The results demonstrated that a drying time of 24 h yielded the best overall performance. Moisture loss (67.00%) and shrinkage (50.70%) increased with longer drying times (24 h), particularly in kenaf-based composites. Extended drying minimized mold contamination and enhanced the structural integrity of the composites. Overall, EFB-based composites achieved the highest Shore A hardness (44.53 HA). These findings show that optimizing the drying time enhances the durability of MBCs, reinforcing their potential as sustainable, biodegradable alternatives to polystyrene and promoting the development of eco-friendly materials. Full article

Effective mitigation of geotechnical risk and safety management of underground mine requires accurate estimation of rockburst damage potential. The inherent complexity of the rockburst phenomena due to nonlinear, high dimensional, and interdependent nature of the geological factors involved, however, makes predictive modeling a difficult task. The proposed research is based on the use of the K-Nearest Neighbor (KNN) algorithm to predict the risk of rockbursts with the use of microseismic monitoring data. Several key features like the ratio of total maximum principal stress to uniaxial compressive strength, energy capacity of support system, excavation span, geology factor, Richter magnitude of seismic event, distance between rockburst location and microseismic event, and rock density were applied as input parameters to extract critical rockburst precursor activities. In the test stage, the proposed KNN model recorded an accuracy of 75.50%, a precision of 91.3, a recall value of 50.9, and F1 Score of 57.6. The model is reliable with a significant performance indicating its efficacy in practice. The KNN model showed better classification results as compared to recently available models in literature and provided better generalization and interpretability. The model exhibited high prediction in classified low-risk incidents and had strong indicative capabilities towards high-risk situations, attributed to being a useful tool in rockburst hazard measurement. Full article

Background/Objectives: Patients with combined cardiovascular and cerebrovascular disease face poorer prognoses. Early, accurate assessment of the risk of cerebral ischaemic events (including transient ischaemic attacks (TIAs) and ischaemic strokes (ISs)) in patients with coronary artery disease (CAD) is therefore vital for clinical guidance. This study aims to develop a comprehensive risk assessment model for early warning in this population. Methods: In this study, we conducted a retrospective multicentre recruitment of CAD patients undergoing concurrent coronary CTA and cervical CTA (n = 326), with follow-up to observe the occurrence of cerebral ischaemic events. We performed an analysis of high-risk plaque (HRP) characteristics and subcomponent plaque in coronary and cervical arteries, measured the pericoronary fat attenuation index (FAI) and cervical perivascular fat density (PFD), and extracted corresponding radiomic features. Five models were constructed to identify the CAD patients who developed IS/TIA, respectively: Model 1—clinical characteristics; Model 2—coronary CTA parameters + Radscore_coronary; Model 3—cervical CTA parameters + Radscore_cervical; Model 4—Model 1 + Model 2; Model 5—Model 1 + Model 2 + Model 3. Results: In the cerebral ischaemia group, the prevalence of coronary and/or cervical HRP was higher than in the non-ischaemia group (28.0% vs. 26.1%, 57.0% vs. 44.0%, p = 0.02). Multivariate logistic regression confirmed that RCA FAI and PFD remained significant independent risk factors for IS/TIA (all p < 0.05). The model prediction results showed that progressively incorporating coronary and cerebral vascular risk factors into the clinical features gradually improved model performance (Model 4 vs. Model 5, AUC: 0.711 [0.645–0.777] vs. 0.821 [0.769–0.873]). Model 5 achieved a sensitivity of 0.788 [0.485–0.909] and specificity of 0.827 [0.385–0.923], demonstrating the best overall clinical benefit. Conclusions: RCA FAI and PFD are independent predictors of cerebral ischaemic events. By integrating clinical characteristics, coronary CTA and cervical CTA parameters, combined with Radscore_coronary and Radscore_cervical, the risk stratification capability for IS/TIA in CAD patients can be significantly enhanced. Full article

This paper reviews the research progress of supercapacitors (SCs), including the influence of electrode materials on energy storage mechanism and performance, and life prediction. Supercapacitors show application potential in many fields due to their high-power density, fast charge–discharge capability, long cycle life, and environmental protection characteristics. In this paper, the energy storage mechanism of the double-layer capacitor, pseudocapacitor, and asymmetric supercapacitor are discussed. New electrode materials, such as carbon-based materials, metal oxides, and conductive polymers, are reviewed based on the performance optimization measures that are involved in the microstructure design of electrode materials, and integrate the rule prediction of supercapacitors into comprehensive learning. When designing and using supercapacitors, we should not only pay attention to their life but also pay attention to their remaining service life in real time. The paper also mentions the progress of life prediction technology, which is of great significance to improve the reliability and maintenance efficiency of energy storage equipment, and ensure the long-term stable operation of energy storage systems. Future research directions include increasing energy density, extending life, adapting to extreme environments, developing flexible and wearable devices, intelligent management, and reducing costs. Full article

Background: Large-scale production of poultry viral vaccines increasingly requires robust suspension cell platforms. However, most avian cell lines, including DF-1, are strictly anchorage-dependent, limiting scalability. Aquaporin-1 (AQP1) regulates cell–cell adhesion and membrane dynamics, making it a potential target for engineering suspension growth. This study aimed to generate a stable DF-1 suspension cell line via AQP1 disruption and evaluate its potential for enhanced infectious bursal disease virus (IBDV) production. Methodology: DF-1 cells were engineered using a CRISPR/Cas9 ribonucleoprotein system to create a truncated AQP1 gene. DF-1/AQP1⁻ cells were assessed for morphology, tumorigenicity in nude mice, and genetic stability across 20 passages. Suspension growth, cell density, and viability were measured. Cells were infected with IBDV strain BJQ902, and viral titers were compared with wild-type DF-1 and monolayer DF-1/AQP1⁻ cells. Results: DF-1/AQP1⁻ cells maintained normal morphology, were non-tumorigenic, and retained stable AQP1 mutations. They grew as true suspension cultures without adaptation, reaching 4.0 × 10⁶ cells/mL with >95% viability. Suspension DF-1/AQP1⁻ cells cells produced significantly higher viral titers (9.0 log TCID₅₀/mL; 8.63 log EID₅₀/mL) than both monolayer DF-1/AQP1⁻ and wild-type DF-1 cells. Virus production time was shortened in suspension cultures. Conclusions: Targeted AQP1 disruption converts DF-1 cells into a stable, non-tumorigenic suspension cell line with markedly enhanced IBDV production, providing a scalable platform for next-generation avian vaccine manufacturing. Full article

Defect engineering in semiconductor heterojunctions offers a promising route for enhancing the selectivity of photocatalytic CO₂ conversion. In this work, a ZnS/gel-derived TiO₂ photocatalyst featuring sulfur vacancies introduced via hydrazine hydrate (N₂H₄) treatment is developed. XRD, HRTEM, and XPS analyses confirm the formation of a crystalline heterointerface and a defect-rich ZnS surface, enabling effective interfacial electronic modulation. The optimized ZnS/gel-derived TiO₂-0.48 composite achieves CH₄ and CO yields of 6.76 and 14.47 μmol·g⁻¹·h⁻¹, respectively, with a CH₄ selectivity of 31.8% and an electron selectivity of 65.1%, clearly outperforming pristine TiO₂ and the corresponding single-component catalysts under identical conditions. Photoluminescence quenching, enhanced photocurrent response, and reduced charge-transfer resistance indicate significantly improved interfacial charge separation. Mott–Schottky analysis combined with optical bandgap measurements reveals pronounced interfacial charge redistribution in the composite system. Considering the intrinsic band structure of ZnS and gel-derived TiO₂, a Z-scheme-compatible interfacial charge migration model is proposed, in which photogenerated electrons with strong reductive power are preferentially retained on ZnS, while holes with strong oxidative capability remain on gel-derived TiO₂. This charge migration pathway preserves high redox potentials, facilitating multi-electron CO₂ methanation and water oxidation. Density functional theory calculations further demonstrate that sulfur vacancies stabilize *COOH and *CO intermediates and reduce the energy barrier for *COOH formation from +0.51 eV to +0.21 eV, thereby promoting CO₂ activation and CH₄ formation. These results reveal that sulfur vacancies not only activate CO₂ molecules but also regulate interfacial charge migration behavior. This work provides a synergistic strategy combining defect engineering and interfacial electronic modulation to enhance selectivity and mechanistic understanding in CO₂-to-CH₄ photoconversion. Full article

Magnetic Barkhausen noise (MBN) represents a powerful non-destructive testing and material characterization methodology enabling quantitative assessment of microstructural features, mechanical properties, and stress states in ferromagnetic materials. This comprehensive review synthesizes recent advances spanning theoretical foundations, sensor design, signal processing methodologies, and industrial applications. The physical basis rooted in domain wall dynamics and statistical mechanics provides rigorous frameworks for interpreting MBN signals in terms of grain structure, dislocation density, phase composition, and residual stress. Contemporary instrumentation innovations including miniaturized sensors, multi-parameter systems, and high-entropy alloy cores enable measurements in challenging environments. Advanced signal processing techniques—encompassing time-domain analysis, frequency-domain spectral methods, time–frequency transforms, and machine learning algorithms—extract comprehensive material information from raw Barkhausen signals. Deep learning approaches demonstrate superior performance for automated material classification and property prediction compared to traditional statistical methods. Industrial applications span manufacturing quality control, structural health monitoring, railway infrastructure assessment, and predictive maintenance strategies. Key achievements include establishing quantitative correlations between material properties and stress states, with measurement uncertainties of ±15–20 MPa for stress and ±20 HV for hardness. Emerging challenges include standardization imperatives, characterization of advanced materials, machine learning robustness, and autonomous system integration. Future developments prioritizing international standards, physics-informed neural networks, multimodal sensor fusion, and wireless monitoring networks will accelerate industrial adoption supporting safe, efficient engineering practice across diverse sectors. Full article

Magnesium aluminate spinel nanoparticles (MgAl₂O₄-S-NPs) represent a promising class of nanoceramics with potential biomedical applications due to their physicochemical stability and antimicrobial properties. This study aimed to determine the structural characteristics, composition, and biological performance of MgAl₂O₄ spinel nanoparticles that were synthesized via a mechanochemical method. Structural and compositional characterization was performed using X-ray diffraction (XRD) and high-resolution transmission electron microscopy (HR-TEM). Antibacterial activity was evaluated against Helicobacter pylori and Enterococcus faecalis using bacterial viability assays. Structural and morphological analyses confirmed the successful formation of single-phase cubic MgAl₂O₄ with a polyhedral morphology and nanoscale size distribution. Bacterial viability was quantified through optical density measurements following exposure to MgAl₂O₄-S-NPs at different concentrations. The nanoparticles exhibited both bacteriostatic and bactericidal effects, with activity being demonstrated against the tested bacterial strains. Mechanochemically synthesized MgAl₂O₄-S-NPs are promising candidates for biomedical applications, including dental materials, antimicrobial coatings, and infection-control strategies. Overall, the findings highlight the potential of MgAl₂O₄-S-NPs as effective antimicrobial agents that can be produced through an environmentally friendly synthesis route. Full article

Karst regions cover about 12% of Earth’s land surface and exhibit high uncertainty in soil organic carbon (SOC) pools due to strong spatial heterogeneity. This study quantifies the association between rock fissure network morphology and SOC pools across three karst rocky habitat types in the Maolan National Nature Reserve (Guizhou, China): Type I (predominantly sub-horizontal and weakly connected fissures), Type II (oblique and moderately connected fissures), and Type III (predominantly subvertical and highly connected fissures). Fissure network morphology was characterized using quantitative network morphology metrics, and SOC pools (content, density, and stock) were measured from field samples (with long-term sequestration estimated). Type I habitats showed the highest SOC content, density, stock, and sequestration estimates, whereas Type III habitats consistently showed the lowest values. Across habitats, SOC density and stock were negatively associated with metrics reflecting steeper fissure orientation, greater spatial heterogeneity, and higher network connectivity, while SOC content was positively associated with fissure network complexity. These findings highlight fissure network morphology as an important structural dimension for explaining SOC variability in karst rocky habitats and suggest incorporating fissure information into SOC assessment and habitat-specific soil and vegetation management in karst landscapes. Full article