Search Results (6,755)

Background: Parents serve as the primary decision-makers for childhood vaccination while also making decisions regarding their own vaccination, yet vaccination decision drivers are typically studied separately by vaccine type or target population. Methods: This study investigated parental decision-making processes for two self-paid and non-National Immunization Program vaccines in China, childhood rotavirus vaccine and adult influenza vaccine, by utilizing a structured survey grounded in the World Health Organization Behavioral and Social Drivers of Vaccination framework. Spearman’s rank correlation coefficients were used to assess the consistency of parental attitudes toward the two vaccines across behavioral and social driver domains. Structural equation models were conducted separately for childhood and adult vaccines to examine decision-making pathways. Results: The findings indicated that parental drivers related to awareness, social processes, and practical issues showed a high consistency across adult and childhood vaccination decisions (r > 0.7), whereas the consistency in vaccination behaviors remained low (r = 0.21). Compared with adult vaccination, childhood vaccination decisions were more strongly influenced by vaccine safety concerns and healthcare practitioners’ recommendations, which emerged as key drivers. Furthermore, family norms emerged as an effectively shared driver of vaccination decisions for both adult and childhood vaccines (adult: β = 0.784; childhood: β = 0.970). Conclusions: By jointly synthesizing adult and childhood vaccination decisions from a parental perspective, this study provides crucial evidence to support the development of integrated, family-centered strategies to improve vaccine uptake. Full article

Biofouling is an issue of global significance that impairs marine infrastructure, causes increased fuel consumption and greenhouse gas emissions, and threatens biodiversity. Since the year 2000, self-polishing copolymer (SPC) coatings and fouling release coatings (FRCs) dominate the fouling protection coatings market. SPC technology is based on the controlled release of biocides using a mixture of acrylic and natural binders as a delivery system. FRC technology is based on PDMS providing surface properties that resist attachment of fouling organisms. FRCs often contain surface modifying agents, such as free silicone oils, to tune the physicochemical properties of the surface. However, the long-term efficacy of these agents and their migration and distribution in PDMS-based coatings have not been well studied. In this study, we employed time-of-flight secondary ion mass spectrometry (ToF-SIMS) combined with multivariate analysis to examine the distribution of silicone oils as a function of exposure to artificial seawater (ASW). The results show that pure PDMS-based coatings allow uniform distribution of silicone oils with robust behavior upon ASW exposure. In contrast, epoxy–PDMS-based coatings displayed phase separation of the oils, which strongly altered their surface chemistry. Our findings suggest that the modification of mobile oils is critical to the performance of marine antifouling coatings. Furthermore, the presence of other ingredients of commercial coating formulations strongly affected the distribution of mobile oils. This study lays the foundation for future systematic research aimed at developing predictive models to optimize fouling protection coatings for the marine industry. Full article

This study provides novel insights into the gradual development of an algal-bacterial self-flocculent biomass in a 400 L pilot-scale raceway pond for wastewater treatment to enhance sustainability and minimize environmental footprint. The synergetic interaction of algal-bacteria consortia improves nutrient removal while enabling biomass concentration increase. Initially, the microalgae-bacteria biomass was gradually developed by increasing the operating volume from 60 to 400 L. After 80 days, the biomass reached a plateau at a concentration of about 4 g L⁻¹, and exhibited excellent settling characteristics. The initial settling velocity was 14.8 cm min⁻¹ and a settling time of 3 min was required to achieve efficient separation. The reactor achieved high treatment efficiency of about 95% for all nutrients (organic matter, nitrogen and phosphorous) after the 80th day. The kinetic analysis showed that nutrient removal followed first-order kinetics, with soluble chemical oxygen demand and ammonia removal reaching 0.017 and 0.020 h⁻¹, respectively. The results demonstrate high pollutant removal efficiencies and design guidelines for the use of increased concentrations of microalgae–bacteria consortia in urban wastewater treatment practice, an alternative green way for solving present-day wastewater treatment problems. Full article

Precision soil management is fundamental to the sustainable production of high-quality tea, yet the spatial integration of fertility and heavy metal safety remains a significant challenge. This study aimed to delineate multi-dimensional management zones (MZs) in the tea plantations of Tianmuhu, Jiangsu Province, by evaluating three clustering algorithms: K-means (KM), Fuzzy C-means (FCM), and Iterative Self-Organizing Data Analysis Technique (ISODATA). A total of 70 representative soil samples were analyzed for 10 properties. Descriptive statistics revealed pronounced spatial heterogeneity, particularly for Hg (CV = 71.04%) and P (CV = 61.83%). Pearson correlation and Principal Component Analysis (PCA) demonstrated strong synergistic relationships among organic matter (OM), nitrogen (N), and potassium (K) (r = 0.49–0.69, p < 0.01), which formed a distinct Fertility Factor on PC1. Conversely, PCA identified divergent sources for heavy metals, with Cr primarily governed by pedogenic processes (PC2), while Cd were associated with anthropogenic inputs. Guided by these distinct spatial drivers, this study separately delineated fertility and heavy metal safety MZs. The optimal number of clusters was determined by balancing statistical validity with spatial operationality via the Silhouette Coefficient (SC) and Smoothness Index (SI), with results indicating that a 2–3 zone scheme yielded the most favorable scores. Comparative analysis showed that for soil fertility, ISODATA outperformed KM and FCM by effectively capturing the high variability of P and producing statistically distinct zones (p < 0.05). For heavy metal pollution, FCM provided better partitioning by reflecting the continuous gradients of composite contaminants. Validation results showed that while 61% of the area was classified as high-fertility (ISODATA), approximately 63–75% fell into relatively higher heavy metal accumulation categories. This dual-objective zoning framework provides a scientific basis for site-specific fertilization and targeted environmental monitoring in the regional tea industry. Full article

Wind turbine blades are exposed to multiple coupled stressors requiring protective coatings with ultra-low volatile organic compound (VOC) content, thick-film capability, and long-term durability. This review critically evaluates waterborne polyurethane (WPU) coatings as a sustainable solution, benchmarking five synthesis routes—prepolymer emulsification, acetone process, melt dispersion, ketimine/ketazine chemistry, and self-emulsification—with prepolymer emulsification identified as the most industrially mature method. Key modification strategies are systematically compared, including nano-reinforcement, surface energy control, self-healing chemistries, and bio-based approaches. Based on a synthesis of laboratory, wind-tunnel, and field studies, three critical bottlenecks—thick-film formation, nanofiller dispersion, and long-term weatherability—are identified. To address these, a layered coating architecture is proposed, integrating a low-surface-energy topcoat, a lamellar-barrier mid-coat, and a post-crosslinked primer. This framework aims to guide the industrial deployment of WPU thick-film blade coatings in offshore and other extreme environments. Full article

Monocular depth estimation is a central problem in computer vision with applications in robotics, augmented reality, and autonomous driving, yet the self-attention mechanisms used by modern Transformer architectures remain opaque. In this work, we integrate SVD-Inspired Attention (SVDA) into the Dense Prediction Transformer (DPT), introducing a spectrally structured attention formulation for dense prediction that decouples directional alignment from spectral modulation through a learnable diagonal matrix embedded in normalized query–key interactions. Experiments on KITTI and NYU-v2 show that SVDA preserves competitive predictive performance while enabling intrinsic interpretability: on KITTI, AbsRel improves from 0.058 to 0.056 and ${\delta }_1$ from 0.976 to 0.979, while on NYU-v2, AbsRel improves from 0.133 to 0.124 and ${\delta }_1$ from 0.865 to 0.872. This is achieved with only 0.01% additional parameters, at the cost of a measurable runtime overhead associated with the added normalization and spectral modulation. More importantly, SVDA enables six spectral indicators that quantify entropy, rank, sparsity, alignment, selectivity, and robustness, revealing consistent cross-dataset and depth-wise patterns in how attention organizes during training. These properties make the model easier to inspect and better suited to applications where transparency and reliability are important, such as robotics and autonomous navigation. Full article

Conventional chemical gel plugging materials often suffer from poor high-temperature stability and inadequate mechanical properties. To address these issues, this study developed a high-performance composite gel material using a multi-component hybrid crosslinking strategy. The material employs γ-methacryloxypropyltrimethoxysilane (MPTMS) as the silica source, which hydrolyzes in situ to generate SiO₂, thereby enhancing temperature resistance. Laponite nanoplatelets are incorporated as a toughening agent and physical crosslinking points, while a self-synthesized reactive microgel (BWL) serves as the organic crosslinking core. Through copolymerization with monomers such as acrylamide (AM) and methacrylic acid (MAA), a triple-crosslinked network structure is constructed. Compared with conventional gels, the synthesized hybrid crosslinked composite gel maintains a high storage modulus and loss modulus after aging at 140 °C and exhibits excellent tensile and compressive properties. Furthermore, the gel was processed into particle-based lost circulation materials with different particle sizes. High-temperature and high-pressure plugging experiments demonstrate that when using a mixed system of 40–60 mesh, 20–40 mesh, and 10–20 mesh gel particles with a total concentration of 2%, it can effectively seal highly permeable sand beds and fractures with apertures up to 5 mm. This meets the engineering requirements for lost circulation materials with high strength and high stability in deep, high-temperature formations. Full article

Background/Objectives:Human papillomaviruses (HPVs) are the most common sexually transmitted viruses worldwide and are strongly associated with multiple cancers, including cervical cancer. In France, HPV prevention relies on a combination of organized cervical cancer screening and prophylactic vaccination; however, coverage remains below international targets. Methods: This narrative review summarizes recent advances in HPV prevention in France, with a focus on screening strategies, including the integration of high-risk HPV testing and vaginal self-sampling, as well as vaccination policies that now include both girls and boys, notably through school-based programs. Results: International comparisons, particularly with Australia and several European countries, are used to highlight successful strategies and transferable lessons that could enhance the effectiveness of French prevention efforts. The review also discusses persistent barriers to uptake, including social, organizational, and cultural factors, and considers opportunities to reduce inequalities in access to prevention. Conclusions: Overall, this work provides a comprehensive overview of the current landscape of HPV prevention in France and situates national efforts within a global public health context, offering insights for policy development and future research directions. Full article

This study explains how strategy–execution gaps become self-reinforcing during digital process transformation in layered manufacturing organizations. Drawing on an embedded qualitative process study of a large Chinese brewery’s transformation (2020–2024), we triangulate 10 semi-structured interviews across hierarchical levels with longitudinal public disclosures to reconstruct the initiative timeline and trace mechanisms across change phases. The analysis shows that platform-based process governance can scale faster than shared meaning and dialog, producing frontline sensemaking gaps and formalistic, top-down communication. These conditions thin employee voice and weaken feedback closure, which in turn erodes the legitimacy of organizational diagnosis and fragments implementation support. As interface problems are handled through local workarounds, management intensifies visibility-based monitoring, further suppressing voice and reinforcing the execution gap. We develop an organizational development process model that centers feedback closure and diagnosis legitimacy as bridging mechanisms linking soft change dynamics (meaning, trust, voice) with hard digital governance (process standards, data infrastructures, monitoring). The model offers actionable implications for leaders to build closure and legitimate diagnosis as operational capabilities throughout transformation. Full article

This Data Descriptor presents an anonymized, shuffled dataset of creatinine-normalized urinary metabolite measurements from 73 Bulgarian children with autism spectrum disorder (ASD), released to support reuse in secondary analyses and cross-cohort comparisons. The public release represents a pathway-oriented 24-marker subset from a broader urinary diagnostic panel, assembled as a self-contained resource for investigators working in these metabolic domains. Spot urine results are provided as individual-level values after creatinine normalization; for trimethylamine, values below the limit of quantification (LOQ) were replaced with LOQ/2. The deposit contains measurements for 24 urinary markers grouped into three functional classes (neurotransmitters and aromatic amino acid precursors; one-carbon/methylation and vitamin-related metabolites; and energy metabolism/organic acids with microbiome-related amines). The underlying cohort comprised children aged 3–13 years, and no contemporaneous neurotypical control group was enrolled. Second-morning, midstream, acid-stabilized spot urine samples were collected within the provider’s workflow; metabolites were measured by LC–MS/MS, and spot urinary creatinine was measured enzymatically for normalization. The release includes the results table in both XLSX and CSV formats, a reference limits and units file for contextual interpretation, a data dictionary, a README, a changelog, and SHA-256 checksums for integrity verification. The public files contain de-identified analytical variables only and omit individual-level demographics, dates, standalone urinary creatinine, and richer clinical metadata to preserve anonymity. Full article

This study presents a comparative analysis of five gender equality certification schemes alongside the ISO 53800 standard with the aim of distilling shared conceptual foundations and design principles that can inform progress toward Sustainable Development Goal (SDG) 5 on gender equality. The comparative analysis reveals marked heterogeneity in scope, design architecture, indicators, and transparency. Methodologically, the study draws on the relevant literature, documentary evidence, and semi-structured consultations with five experts in gender equality, diversity management, auditing, and ESG reporting. Building on the most effective and robust features across gender equality schemes, the study proposes a universal framework for gender equality certification. Under this framework, an ideal universal certification model should apply the same core requirements to both public and private organizations, while including simplified procedures tailored to small- and medium-sized enterprises (SMEs). Moreover, the model should rely on a limited set of key performance indicators (KPIs), focusing on the most material dimensions and prioritizing quantitative measures. It should also strengthen employee feedback mechanisms and enhance accountability in corporate governance. The framework should also pay attention to intersectional dimensions, extend responsibility across the value chain, and address the gender-related implications of artificial intelligence (AI). Importantly, an ideal universal gender equality certification should ensure a high level of transparency through the public disclosure of certified organizations, assessment criteria, KPIs, and levels or scores achieved. Furthermore, it should be supported by a free digital self-assessment tool and robust auditing arrangements, underpinned by a sufficiently large pool of accredited certification bodies and gender-balanced audit teams. Finally, it should undergo periodic review and align with Environmental, Social, and Governance (ESG) principles and other related SDGs. Full article

Anaerobic digesters operating under neotropical conditions face significant technological constraints. High humidity, intense solar radiation, and pronounced diurnal temperature variations increase conductive, convective, and radiative heat losses. These factors reduce internal thermal stability and directly affect methane production rates and overall energy efficiency. As a result, thermal instability becomes a recurrent operational bottleneck in biogas plants without active temperature control. This review examines the thermal and dynamic behavior of anaerobic reactors from a process-engineering perspective. It integrates energy balances, heat-transfer mechanisms, and computational fluid dynamics (CFD) modeling. The combined effects of temperature gradients, hydrodynamic mixing patterns, and structural material properties are analyzed to determine their influence on thermal homogeneity, microbial stability, and methane yield consistency under mesophilic conditions. Technological strategies to mitigate thermal losses are evaluated. These include passive insulation using low-conductivity materials, geometry optimization supported by numerical modeling, and thermal recirculation schemes, as these factors govern temperature distribution and process resilience. Current limitations are also discussed, particularly the frequent decoupling between ADM1-based kinetic models and transient heat-transfer analysis. This separation restricts predictive capability under real-scale diurnal temperature oscillations. The development and validation of coupled hydrodynamic–thermal–biokinetic models under fluctuating neotropical boundary conditions are proposed as critical steps. Such integrated approaches can enhance operational stability, ensure consistent methane production, and improve energy self-sufficiency in organic waste valorization systems. Full article

Hyperspectral remote sensing provides essential biochemical and structural information for crop disease monitoring, yet its application to cotton Verticillium wilt has largely focused on single-period evaluations or multi-temporal classifications. Such approaches overlook the progressive nature of this vascular disease, whose pigment, water, and mesophyll responses evolve over time, making temporal hyperspectral information critical for reliable severity estimation but still insufficiently utilized. To overcome this limitation, we conducted daily time-series observations on cotton leaves and collected 2895 hyperspectral reflectance measurements and 770 high-resolution RGB images together with disease severity records, generating a temporally dense spectral-severity dataset spanning symptom-free to severe stages. Five categories of disease-related vegetation indices were derived and organized into 5-day spectral–temporal slices. Based on these features, we introduce a dual-branch Transformer-TCN model that integrates global temporal dependencies captured by self-attention with local temporal variations resolved by dilated causal convolutions for severity inversion. The model delivers the strongest performance with an R² of 0.8813, exceeding multiple single and hybrid time-series alternatives by 0.0446–0.1407 in R², equivalent to a relative improvement of 5.33–19.00%. Temporal spectral features also outperform their non-temporal counterparts, highlighting that disease progression dynamics captured by time-series spectra are critical for reliable severity retrieval. Feature contribution analysis indicates that the blue red index BRI provides the highest contribution, consistent with the single-index time-series modelling results. Photosynthesis- and water-related indices provide secondary but complementary support. Collectively, our results demonstrate that the dual-branch Transformer-TCN model can capture complex spectral–temporal relationships between cotton Verticillium wilt and disease severity, providing methodological support for crop disease monitoring and evaluation. Full article

Tight conglomerate reservoirs are characterized by strong heterogeneity, significant in-situ stress differences, and unbalanced fracturing stimulation, which make fracture pressure prediction challenging and severely restrict the effectiveness of reservoir stimulation and ultimate recovery. Although acid pretreatment is an effective means to reduce fracture pressure, its quantitative relationship with fracture pressure remains unclear. There is an urgent need to establish a systematic method that integrates reservoir heterogeneity characterization, data augmentation, and intelligent prediction. Aiming at the tight conglomerate reservoir in the MH Block, this study proposes an intelligent fracture pressure prediction and acid pretreatment optimization method that integrates Self-Organizing Maps (SOMs), Generative Adversarial Networks (GANs), and Transformer models. First, SOM is used to perform unsupervised clustering of logging parameters to identify different geological feature categories and achieve fine-scale characterization of reservoir heterogeneity. Second, to address the issue of limited samples within each cluster, GAN is employed for high-quality data augmentation to expand the training sample set. Finally, a fracture pressure prediction model is constructed based on the Transformer architecture, and the influence of acid treatment parameters on fracture pressure is quantitatively analyzed using the SHAP method and laboratory experiments. The results show that the proposed model achieves a coefficient of determination (R²) of 0.93, a root mean square error (RMSE) of 2.38 MPa, and a mean absolute percentage error (MAPE) of 2.02% on the test set, with prediction accuracy significantly outperforming benchmark models such as BPNN, XGBoost, and LSTM. Ablation experiments verify that both the SOM clustering and GAN augmentation modules effectively enhance model performance. Analysis of acid treatment parameters indicates that hydrofluoric acid (HF) concentration is the dominant factor influencing fracture pressure reduction, and the mud acid system exhibits a stronger synergistic effect compared to the single hydrochloric acid system. Reasonable optimization of acid concentration and dosage can significantly reduce fracture pressure (3.14–5.28 MPa). This method provides a theoretical basis and engineering guidance for accurate fracture pressure prediction and optimal design of acid pretreatment in tight conglomerate reservoirs. Full article

Pavement energy harvesting has been investigated as a means of converting traffic loading, solar radiation, and pavement thermal gradients into usable electricity or heat. This paper reviews 135 publications available through March 2026 and evaluates the field from a pavement engineering perspective. The literature is organized into six technology families: piezoelectric systems, mechanical-electromagnetic systems, triboelectric systems, thermoelectric systems, hydronic/geothermal/solar-thermal pavements, and photovoltaic or pavement-integrated photovoltaic-thermal systems. The review considers not only reported energy output, but also structural compatibility, durability, constructability, maintenance requirements, safety, and deployment conditions. The synthesis shows that the most credible near-term roles of piezoelectric and triboelectric systems are self-powered sensing and other localized low-power functions rather than bulk electricity generation. Mechanical-electromagnetic systems can produce larger event-level output, but their practicality is limited to low-speed and highly controlled settings because they rely on deliberate surface displacement. Thermoelectric systems are mechanically compatible with pavements, yet their performance remains constrained by weak and transient temperature gradients. Hydronic and solar-thermal pavements are presently the most infrastructure-compatible option for large-area energy recovery because they deliver useful heat and align with snow-melting, seasonal storage, and adjacent building-energy applications. Photovoltaic and photovoltaic-thermal pavements offer direct electrical generation, but continued challenges with transparent cover layers, surface friction, durability, fouling, and maintenance still limit broad roadway deployment. Overall, the review indicates that future progress will depend less on maximizing peak output in isolated prototypes and more on integrated pavement-energy design, standardized performance reporting, durability assessment, techno-economic evaluation, and corridor-scale demonstration. Full article