Search Results (4,245)

With the rapid development of the hydrogen economy, Type IV composite pressure vessels have emerged as the core components of on-board hydrogen storage systems. However, accurate fatigue life prediction remains a critical bottleneck limiting their design optimization and safe operation. Existing methods often exhibit prediction errors exceeding ±50% due to the inherent scatter, anisotropy, and complex service environments of composites. This study proposes an improved simulation method for fatigue life prediction of Type IV cylinders. Systematic tension–tension fatigue tests were conducted on carbon fiber-reinforced polymer (CFRP) laminates at four ply angles (0°, ±15°, ±30°, ±45°) and PA6 liner at three temperatures (−30 °C, 25 °C, 82 °C) to establish comprehensive S-N curve databases. The results reveal that ply angle is the predominant factor governing CFRP fatigue performance, while temperature significantly influences PA6 behavior, and failure mode transitions from fiber fracture to matrix-dominated damage as ply angle increases. A fatigue analysis model was developed in nCode, incorporating the ply fatigue Algorithm to characterize the anisotropic fatigue behavior of CFRP overwraps. Full-scale validation on Type IV cylinders under cyclic pressure (2–87.5 MPa) confirmed the method’s effectiveness, achieving prediction errors of 11.5% and 35.3% for the two failed specimens, with failure locations well predicted. This study provides a rapid and reliable engineering calculation method and data support for the anti-fatigue design, safety assessment, and life management of Type IV cylinders. Full article

Femtosecond laser surface texturing offers a promising route to tailor the functionality of bio-based wood coatings, yet the interplay between coating composition and laser processing remains poorly understood. In this study, bio-based epoxy coatings with eventual micronized wax additives were textured using a femtosecond laser to investigate the effects of laser processing parameters on pattern formation and resulting hydrophobicity. The epoxy coatings containing PE, PE/PTFE, HDPE, and rice bran waxes at 1, 5, and 7 wt.-% were characterized in terms of morphology, roughness, wettability, and chemical stability, followed by systematic variation of pulse repetition rate and laser power. The results reveal that the ablation threshold strongly depends on intrinsic coating properties. Ablation resistance increases with surface roughness and wax melting enthalpy, reflecting the role of phase transition energy in laser–matter interaction. The wax-filled coatings exhibit a transition from melting-dominated behavior at low energy input to ablation-dominated behavior at a higher energy. Laser texturing enhances hydrophobicity in parallel with theoretical values calculated from the Cassie–Baxter wetting model, with the highest hydrophobicity achieved for coatings combining intrinsic hydrophobicity and stable pattern formation. Chemical analysis confirms limited degradation of the epoxy matrix without significant carbonization, while wax additives provide partial thermal shielding. Overall, this work demonstrates clear options for tailoring surface morphology and wettability of hydrophobic polymer coatings through controlled femtosecond laser processing. Full article

In this work, we investigated the incorporation of a fluorescent side group, fluorescein octyl ester (FOE), in benzodithiophene-based donor–acceptor terpolymers as a strategy to modulate excited-state behavior. Three FOE-containing terpolymers (P2-iIa-c), obtained at different polymerization times, were systematically evaluated against an analogous material without the fluorescent pendant unit (P1-iI). Thermal analysis revealed good thermal stability and an increase in glass transition temperature upon FOE incorporation, suggesting restricted segmental mobility and increased conformational constraints within the conjugated backbone. Optical characterization showed distinct absorption spectra with reaction time and shorter fluorescence lifetimes for the FOE-containing materials, consistent with the presence of additional excited-state deactivation pathways and intramolecular energy transfer processes within the terpolymer backbone. An approximate estimation of energy transfer efficiencies (≈60–65%) suggested that such processes may be operative within the system. Cyclic voltammetry measurements showed only minor variations in HOMO and LUMO energy levels between P1-iI and P2-iIa-c series, indicating that the conjugated backbone predominantly determined the frontier orbital energies despite side chain modification. Furthermore, photocurrent measurements from the bilayer device configuration exhibited a systematic increase in photocurrent for the FOE-containing material, supporting the role of excitonic modulation, rather than significant changes in interfacial energetic alignment. These results suggest that fluorescent side chain incorporation provides an effective strategy for regulating exciton dynamics while maintaining the electronic structure of the donor–acceptor terpolymer. Full article

Molecularly imprinted polymers (MIPs) offer robust, cost-effective, and highly selective alternatives to fragile biological receptors. Specifically, electropolymerization has emerged as a versatile strategy that enables the precise, in situ formation of uniform MIP films directly on electrode surfaces. This review provides a comprehensive overview of electropolymerized MIPs (eMIPs) supported on advanced carbon-based materials for electrochemical (bio)sensing. We emphasize how the synergistic integration of eMIPs with carbonaceous architectures significantly enhances electron transfer, active surface area, and overall analytical sensitivity. Key fabrication aspects are systematically discussed, including monomer selection, electropolymerization parameters, and efficient template removal. A central aspect of this work is the critical categorization of sensing mechanisms into direct and indirect detection strategies. This distinction elucidates how eMIPs can quantify a broad spectrum of electroactive and non-electroactive targets in complex matrices, while strategically avoiding excessively high applied potentials. Finally, alongside outlining the transition of these systems into portable technologies, we address a critical shortcoming in the current literature: the urgent need for analytical standardization through the rigorous reporting of Imprinting and Selectivity Factors using Non-Imprinted Polymer (NIP) controls. Full article

Higher education is undergoing a transition in which static multimodal resources are giving way to immersive learning environments powered by generative artificial intelligence (GenAI). This PRISMA 2020-compliant systematic review, prospectively registered in INPLASY (202610066), synthesizes evidence on immersive GenAI-based strategies in higher education, examining their reported contributions to sustainability, inclusion, and learning outcomes. Searches across Scopus, ScienceDirect, and ERIC (2022–2026) identified 1364 records; after quality appraisal using an adapted CASP instrument, 25 studies were included in a narrative and descriptive synthesis. Five strategy types emerged—VR-based simulations, virtual patient platforms, adaptive LLM tutoring systems, mixed/augmented reality environments, and 3D/metaverse configurations—with GPT-family models predominating (56%). The central finding is a structural reporting asymmetry: learning outcomes were explicitly documented in 23 studies (92%), whereas sustainability and inclusion were explicitly reported as outcome domains in only one study each (4%). Health sciences (36%) and educational technology (28%) dominated the evidence base, while Latin American, African, and most STEM contexts remained underrepresented. Immersive GenAI strategies are being evaluated for short-term instructional value, while their contribution to sustainable higher education remains underexamined. Advancing SDG 4 requires longitudinal designs, equity-oriented frameworks, and indicators capable of evaluating inclusion and durable learning gains across institutional contexts. Full article

Dental fluorosis (DF) remains a global public health challenge traditionally attributed to elevated water fluoride F⁻. However, the Halo Effect and environmental factors now complicate this dose–response relationship. Following PRISMA 2020 guidelines, this systematic review identified 20 observational studies (n = 21,780) via PubMed, Scopus, and Web of Science. Inclusion logic utilized the PICOS framework, specifically selecting human studies that reported quantitative water F⁻ levels alongside environmental or dietary confounders. Quality was assessed via the Newcastle–Ottawa Scale. Synthesis revealed that in optimal fluoridated areas (0.7 mg/L), mild DF prevalence reached 15–20% in cohorts with high “Halo Effect” exposure (infant formula, processed beverages) a twofold increase over historical benchmarks. High altitude (>2000 m) and arid climates further exacerbated toxicity by altering renal clearance. These factors sustain systemic fluoride levels that inhibit protease activity (MMP-20/KLK4) and induce endoplasmic reticulum stress during enamel maturation, causing hypomineralization. Current water-centric monitoring is insufficient for modern risk assessment. A transition toward Total Daily Intake (TDI) models and context-specific standards accounting for altitude and dietary diffusion is essential to balance caries prevention with systemic safety. Full article

The rapid expansion of artificial intelligence in the educational field has configured a broad, dynamic, and constantly evolving research domain. Nevertheless, there remains a need to systematically analyze the evolution of its pedagogical approaches and to identify the conceptual dimensions that structure recent scientific production. For this purpose, a systematic literature review was conducted following the PRISMA protocol, based on searches in Web of Science and Scopus. The final corpus consisted of 235 articles, analyzed using bibliometric and semantic techniques in R, including bibliometrix, tidyverse, and ggplot2, complemented by co-occurrence maps developed with VOSviewer. The thematic classification was carried out through an inductive analysis based on clusters and emerging patterns. The results reveal a progressive transition from technocentric approaches toward more complex and integrative pedagogical perspectives. The semantic analysis made it possible to identify four structuring dimensions of the field: critical, ethical, literacy-oriented, and humanistic. Recent literature also shows a growing emphasis on teacher education, academic integrity, and cognitive coexistence between humans and intelligent systems. These findings indicate that artificial intelligence not only introduces technological innovations but is also reconfiguring the epistemological and pedagogical foundations of contemporary education, demanding conceptual frameworks capable of articulating its ethical, cognitive, and formative implications. Full article

Introduction: Inequities in access to medicines persist for asylum seekers, refugees, and undocumented migrants in Europe. For vaccines, access gaps not only exist for these groups in childhood routine immunization, but also for life-course and catch-up vaccinations. As part of a broader project examining access to medicines and vaccines for migrants across all stages of the migration cycle, this scoping review synthesizes evidence on the determinants of access to vaccines. Methods: We conducted a scoping review across PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Database of Systematic Reviews, Scopus, and grey literature sources, covering the period 2000–2024. Sources were eligible if they addressed access to vaccines among migrants. We examined access to vaccines along the life course, and across phases of the migratory cycle, including departure, transit, reception and settlement, and return or deportation. Results: A total of 47 research studies and grey literature reports were included. Most studies focused on migrants in reception and settlement (destination) settings, with only twelve sources addressing other phases of the migratory cycle. Across European countries, migrants were frequently reported to have lower uptake of routine vaccines (e.g., measles–mumps–rubella (MMR), polio, diphtheria–tetanus–pertussis (DTP), and human papillomavirus (HPV)) and COVID-19 vaccines than host populations. The most frequently reported barriers were related to migrants’ legal status, administrative requirements, and lack of documentation, alongside poor affordability of vaccination, limited awareness of their rights, and mistrust in the health system. Conclusions: Health systems need to adopt innovative approaches to expand vaccine access for migrant populations. Further, protecting confidentiality is essential for building trust and reducing ethical and legal risks. Flexible and coordinated vaccination strategies are required to address migrants’ mobility across the different migration stages and settings. Our findings appeal for sustained improvements in access to vaccines among migrants in Europe, contingent on strong policy commitments to equity, data protection, and the adoption of life-course and catch-up vaccination strategies. Full article

The construction sector is undergoing a rapid transition toward more resilient, sustainable, and digitally connected systems, creating increasing demand for materials capable of providing functions beyond conventional structural performance. In this context, smart materials have emerged as promising solutions due to their ability to respond to mechanical, thermal, chemical, or electromagnetic stimuli through adaptive behaviors such as self-healing, structural sensing, energy regulation, vibration control, and reversible deformation. Despite growing scientific interest, available knowledge remains fragmented across specific material families and isolated application domains. Therefore, this study presents a PRISMA-based systematic review of smart materials in construction using peer-reviewed journal literature indexed in Scopus during the 2021–2026 period. The review examines the principal smart material families currently applied in construction, including self-healing concretes, self-sensing cementitious systems, Shape Memory Alloys (SMA), piezoelectric materials, phase change materials, adaptive coatings, conductive nanocomposites, and multifunctional geopolymers. Their engineering functions, structural and architectural applications, reported performance characteristics, sustainability contributions, digital integration potential, and implementation barriers are comparatively discussed and qualitatively synthesized based on the reviewed literature. The findings indicate that smart materials can improve durability, structural health monitoring, seismic resilience, thermal efficiency, lifecycle performance, and carbon reduction when properly integrated into buildings and infrastructure. However, large-scale adoption remains constrained by high initial costs, manufacturing scalability, regulatory uncertainty, long-term durability validation, and limited market confidence. The review further shows that the greatest future potential lies in combining material intelligence with IoT platforms, artificial intelligence, BIM environments, and digital twins. Overall, smart materials are positioned as strategic enablers of next-generation low-carbon, adaptive, and intelligent construction systems. Full article

Against the backdrop of the dual-carbon strategy, China’s civil aviation industry, as a high-energy-consumption and high-carbon-emission sector, faces mounting pressure for low-carbon transformation. As the dominant airlines within China’s civil aviation system, Air China, China Eastern Airlines, and China Southern Airlines play a pivotal role in guiding the industry’s high-quality development. Employing the Global Malmquist–Luenberger (GML) index model, this study constructs a global production frontier incorporating undesirable outputs to systematically measure the dynamic evolution of total factor productivity (TFP) for the three major airlines in the period 2005–2023, and further applies a combined static-dynamic regression framework to identify the firm-level heterogeneous mechanisms through which explanatory factors operate. The results reveal significant heterogeneity in TFP trajectories: China Southern Airlines exhibits the most stable efficiency with the lowest volatility; China Eastern Airlines displays the greatest volatility but the strongest post-crisis rebound; and Air China occupies an intermediate position in both efficiency level and volatility. This differentiation stems from fundamental differences in market positioning, strategic orientation, and resource allocation patterns. Market competitiveness exerts a significantly positive effect on TFP for both Air China and China Eastern Airlines. Technological innovation investment generates short-run negative effects across all three airlines, albeit with divergent magnitudes. Human capital accumulation acts as a positive driver for Air China but produces a negative effect for China Southern Airlines, attributable to a structural mismatch between aggressive talent upgrading and organizational absorptive capacity. Shifting the unit of analysis to the firm level, this study identifies three heterogeneous strategic archetypes—market-led, scale-expansion, and regional-deepening—and constructs a differentiated “one firm, one policy” framework to provide targeted policy guidance for improving airline efficiency and facilitating low-carbon transition under carbon constraints. Full article

This study examines the long- and short-run effects of sustainable development, economic growth, energy consumption, urbanization, investment and trade openness on Carbon Dioxide Emissions (CO₂) in the GCC countries utilizing the PMG-ARDL approach by including the data spanning from 2000 to 2022. In the short -run, the sustainable development index demonstrates a positive and substantial impact while it exhibits adverse long-run impact on CO₂ emission. The study also indicates a U-shaped correlation between economic growth and emissions, contrasting with the conventional Environmental Kuznets Curve (EKC) where economic growth at lower income levels often leads to a reduction in emissions; however, income increases beyond around USD 29,942 per capita correlate with higher emissions. Besides, energy use is identified as the primary factor influencing emissions, reflecting global patterns that indicate greater energy usage, particularly from fossil fuels directly boosts emissions. Moreover, the urbanization intensifies this problem, resulting in higher energy demand and greater emissions. Additionally, the study finds that gross capital formation and investments in infrastructure contribute to emissions in the short run, though these effects diminish over time. Our results are robust as it similar to the outcomes obtained from dynamic panel-data System GMM. The GCC policymakers must utilize the sustainable development framework to legally mandate national planning towards low-carbon paths while balancing for short-term transition costs with significant long-run emission reductions. This necessitates the implementation of market-oriented carbon pricing to address the post-threshold U-shaped emissions rebound, the systematic elimination of fossil fuel subsidies to promote renewable energy adoption, and the enforcement of sustainable development regulations to mitigate urbanization pressures. Full article

To reveal the varieties and color genesis of emerald-green tourmaline crystals from Tanzania, a systematic study was conducted using conventional gemological tests, X-ray diffraction, Fourier-transform infrared spectroscopy, polarized ultraviolet–visible spectroscopy (UV–vis), X-ray photoelectron spectroscopy (XPS), low-temperature photoluminescence (PL) spectroscopy, and electron probe microanalysis (EPMA). The results indicate that the tourmaline is dravite. Its UV–vis absorption spectrum shows strong broad absorption bands at approximately 436 and 600 nm, with a pronounced transmission at 520 nm, which directly accounts for its emerald green color. Obvious polarized absorption was observed along and perpendicular to the c-axis. XPS and PL results confirm that chromium is present in the samples in the form of Cr³⁺. EPMA compositional analysis indicated a low Cr₂O₃ content of 0.804 wt.%; combined with crystal structural properties and spectral responses, these results suggest that Cr³⁺ preferentially occupies the Y site in the crystal structure and that d–d electronic transitions represent the underlying mechanism of its color formation. This study comprehensively illustrated the mineralogical and spectral properties of Cr-bearing dravite, providing fundamental data for further research on its genesis and gemological application. Full article

Human pose estimation (HPE) has advanced rapidly with deep learning, enabling a transition from specialized sensing and multi-view systems toward monocular RGB-based approaches. These developments have expanded applications in healthcare, robotics, sports analytics, and human–computer interaction. However, the growing diversity of deep learning paradigms, ranging from convolutional and recurrent models to graph-based and Transformer-based approaches, has resulted in a fragmented literature, making it difficult to systematically compare methods and guide system design. This paper addresses this challenge by providing a comprehensive survey of deep learning-based monocular HPE methods published over the past decade and introducing a unified modular framework. The proposed framework organizes HPE systems into six modular estimation paradigms, including single-image-based estimation, multi-frame-based estimation, Top-Down and Bottom-Up pose estimation strategies, 2D-to-3D pose reconstruction, and direct 3D estimation. Each module is analyzed in terms of representative approaches, design trade-offs, and practical considerations, supported by algorithmic formulations that outline the computational pipeline at each stage. Unlike prior surveys that primarily catalog methods or report benchmark results in isolation, this work emphasizes how component-level design choices relate to overall system performance. The paper summarizes performance trends on benchmarks including Human3.6M, COCO, and MPII, highlighting persistent challenges such as occlusion and viewpoint variation, and outlines future research directions including interaction-aware modeling, efficient deployment, and improved robustness under real-world conditions. Full article

Two-dimensional heterostructures have attracted considerable attention in electrocatalytic hydrogen evolution due to their pronounced interfacial effects, tunable electronic properties, and large specific surface areas. In this work, two representative oxygen-terminated transition metal carbides (MXenes) and three typical transition metal dichalcogenides (TMDs) were selected to construct six heterostructures. Using first-principles density functional theory (DFT) calculations, their binding energies, structural stability, electronic structures, and HER catalytic performance were systematically investigated. The results showed that all heterostructures possessed good thermodynamic stability and favorable electronic properties. In particular, SnS₂/Ti₂CO₂, SnSe₂/Ti₂CO₂, SnTe₂/Ti₂CO₂, and SnTe₂/Zr₂CO₂ exhibited near-optimal hydrogen adsorption Gibbs free energy, indicating excellent HER activity. Moreover, the variation in Gibbs free energy of hydrogen adsorption from isolated monolayers to heterostructures could be effectively correlated with the work function difference. The predicted trends provided a useful descriptor for catalytic performance. Overall, this study provides theoretical insights into the rational design of efficient, advanced HER catalysts and contributes to the advancement of sustainable energy conversion technologies. As this work is based solely on first-principles calculations, the predicted catalytic activity of the heterostructure should be regarded as a theoretical prediction and awaits experimental confirmation. Full article

To systematically examine the impact of climate risks on China’s financial system, this study employs the EGARCH-SGED model to precisely fit financial market volatility based on China’s Climate Change News Index. It then combines the LASSO-CoVaR method to measure tail risk spillover effects within China’s financial system under climate risk shocks, constructs a risk contagion network, and innovatively utilizes the RF-AdaBoost model to establish the risk early warning system. Findings reveal that climate risk is a key driver of dynamic correlation evolution within the financial system, with heterogeneous impacts across different markets. Physical climate risk events intensify short-term risk contagion while generating long-term effects; transition risks undergo a dynamic process, initially amplifying uncertainty before enhancing systemic stability over the long term. The RF-AdaBoost model outperforms traditional machine learning models in risk warning, demonstrating outstanding predictive accuracy and generalization capabilities, thereby providing effective intellectual support for climate risk prevention and financial stability management. Full article