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Background: The nursing profession is recognized as a high-risk occupation, with the emotional toll on healthcare workers reaching a critical point. A complex interplay of anger and cynicism, often stemming from systemic pressures and chronic moral injury, seems to increasingly affect nurses’ professional and personal lives. This psychological strain does not end when the shift ends; rather, it often manifests as insomnia and nightmare distress, creating a vicious cycle of exhaustion and emotional instability. This article explores how anger, cynical distrust, nightmare distress and insomnia are interrelated and jeopardize the well-being of nursing staff and what these “invisible” symptoms reveal about the current state of healthcare by confirming their prevalence rates. Methods: This cross-sectional study was conducted online in October 2025 and included 441 hospital nurses who completed the Dimensions of Anger Reactions-5 (DAR-5), the 8-item Cynical Distrust scale (CDS-8), the Nightmare Distress Questionnaire (NDQ) and the Athens Insomnia Scale (AIS). Results: The prevalence rates of anger, nightmare distress and insomnia were 41.5%, 6.6%, and 62.1%, respectively. Based on the CDS-8 scores, a notable proportion (20.9%) of nurses fell within the highest quartile of CDS-8 scores (CDS-8 > 29), indicating relatively elevated cynical distrust within this sample; this threshold is sample-derived and does not correspond to a validated clinical cut-off. Hierarchical multiple regression analysis indicated that the DAR-5 explained 22.1% of the variance in AIS, while an additional 10.2% was explained by NDQ and another 1.5% by the CDS-8. Both cynical distrust and nightmare distress displayed a chain mediation pattern in the association between anger and insomnia; however, given the cross-sectional design, the temporal order of these variables cannot be confirmed. Conclusions: Anger exhibited significant direct and indirect associations with insomnia, with cynical distrust and nightmare distress acting as serial mediators in this cross-sectional model. Findings from this cross-sectional study tentatively suggest that future intervention efforts targeting insomnia in nurses might benefit from addressing anger alongside nightmare distress and cynical attitudes; however, experimental studies are needed to confirm whether such interventions would be effective. Full article

This article examines how MongoDB optimizes aggregation pipeline queries, focusing on two mechanisms: a trial-based plan selection process that runs candidate execution plans in parallel and picks the one returning the most results for the least work, and rule-based operator rewriting by the Pipeline Optimizer. The study tests nine aggregation query types on a synthetic e-commerce dataset with 50K documents, using MongoDB versions 6.0.3 and 8.2.5 under identical conditions. For each query, all valid operator orderings are evaluated together with the physical execution plan and the Pipeline Optimizer output. Each test runs 20 times with the plan cache cleared before every run. The study also tests scalability with datasets of 150K and 250K documents. Three cases are identified where the rule-based optimizer falls short: IXSCAN preference bias at low selectivity, where the suboptimal plan is up to nine times slower than the optimal (80 ms vs. 699 ms at 250K under MongoDB 8.2.5), unbounded document multiplication after $unwind, and failure to account for $group output cardinality. MongoDB 8.2.5 improves performance in most cases compared to version 6.0.3. $match + $group queries run up to 28% faster. Queries that rely on IXSCAN improve by up to 18%. Unbounded projection operations run slower in MongoDB 8.2.5 at all tested sizes. The slowdown is +23% at 50K, +3% at 150K, and +14% at 250K, pointing to a change in the projection execution path between versions. Full article

Although Graph Neural Networks (GNNs) are widely regarded as an ideal tool for capturing spatial dependencies in river basins, their effectiveness in hydrological forecasting is severely challenged by a topology paradox: under a purely data-driven paradigm, GNNs fail to spontaneously learn physical laws, generating predictions that lack physical interpretability and frequently violate mass conservation. To address this fundamental problem, this paper proposes a physics-informed graph learning framework integrated with an explicit, differentiable water balance constraint (WB-GNN). By reconstructing the continuity equation into a differentiable loss function, we directly embed physical conservation as a strong inductive bias into the neural network’s training objective. We comprehensively evaluated the model on two large-sample datasets (LamaH-CE and CAMELS) against state-of-the-art baselines, including EA-LSTM and unconstrained Pure-GNN. Quantitative results demonstrate that the proposed physical constraint successfully awakens the potential of river network topology. On the LamaH-CE dataset, WB-GNN achieved a Nash-Sutcliffe Efficiency (NSE) of 0.86 and a Root Mean Square Error (RMSE) of 9.2 ${\mathrm{m}}^3/\mathrm{s}$, outperforming both the domain-specific EA-LSTM (NSE: 0.83) and the unconstrained Pure-GNN (NSE: 0.74). Crucially, the introduction of the differentiable constraint reduced the Physical Inconsistency Ratio (PIR) by an order of magnitude-from 39.8% in the unconstrained model to just 4.3%. Similar robust improvements were validated across the highly heterogeneous CAMELS dataset. These quantifiable results confirm that the proposed method not only achieves superior forecasting accuracy but also fundamentally guarantees physical trustworthiness, making it highly robust for critical decision-making in extreme flood events. Full article

This paper studies subset construction in NP-complete problems from the perspective of structured exploration of combinatorial search spaces. Classical approaches rely on exhaustive enumeration of subsets, which leads to exponential growth in time and memory requirements. To address this limitation, we introduce an indexed framework based on the correspondence between a finite set and its associated index set. Within this framework, subsets are represented as ordered index sequences, allowing subset construction to be reformulated as a constraint-guided search process over index space. Candidate subsets are characterized by numerical descriptors derived from their indices (referred to as index certificates), which guide and filter the construction process. Subset generation is further organized through admissible index intervals that restrict feasible transitions and reduce the effective search space. The framework is based on an index-based representation and structured traversal of pairwise index combinations. Computational experiments on representative instances illustrate the behavior of the indexed construction procedure and indicate its efficiency relative to classical enumeration-based methods for small and medium-sized instances. The proposed approach provides a structured perspective on combinatorial search and offers a basis for further development of algorithms based on constrained exploration of subset structures. Full article

Unsafe behaviors among construction workers constitute a major contributing factor to construction accidents, making it critically important to explore their underlying mechanisms and temporal dynamics from a cognitive perspective. This study employed an exploratory sequential mixed-methods approach. Initially, grounded theory was used to conduct three-level coding of in-depth interview data from 35 construction workers, resulting in the development of a cognitive theory model of unsafe behavior among construction workers comprising two main categories: ‘ perceptual recognition’ and ‘cognitive response’. Subsequently, a questionnaire was designed based on this model, and a 10-day longitudinal survey was conducted among 300 workers. Multi-group structural equation modelling was employed to analyze the temporal variation in the relationship between cognitive ability and unsafe behavior. The results indicate that: workers’ cognitive abilities can be decomposed into four dimensions—perceiving danger, identifying hazards, perceptual response, and decision-making response—and further summarized into two higher-order factors: perceptual recognition and cognitive response; (2) cognitive abilities are significantly negatively correlated with unsafe behavior; (3) this relationship exhibits significant temporal variations, with the inhibitory effect on Day 5 (path coefficient −0.95) being stronger than that on Day 1 (−0.88) and Day 10 (−0.50); furthermore, the ‘cognitive response → decision-making response’ path also shows significant differences between Day 5 and Day 10. The study reveals the pattern of fluctuations over time in the inhibitory effects of workers’ cognitive ability on unsafe behavior, providing a theoretical basis for construction companies to implement dynamic and targeted safety interventions. Full article

This work develops an analytical framework for nonlinear fractional partial differential equations that combine Kirchhoff-type terms, double-phase operators, and $\psi$-Hilfer fractional derivatives. This paper investigates two classes of problems involving variable-exponent growth conditions. The first problem analyzes general nonlinear sources and formulates the solution as a fixed point of a nonlinear operator. Precisely, by proving that the functional energy is coercive, hemicontinuous, and strictly monotone, we establish the existence and the uniqueness of weak solutions via monotone operator theory. The second problem incorporates a convection-type nonlinearity, which breaks variational structure and requires the more robust theory of pseudomonotone operators. Under suitable growth and mixed-order assumptions on the nonlinearity, we prove the existence of at least one weak solution. The main tools are grounded in variable-exponent Lebesgue and Musielak–Orlicz–Sobolev spaces, with compact embeddings, modular estimates, and fractional integral identities playing a key role in the proofs. We note that the results contribute to the mathematical modeling of phenomena involving nonlocal elasticity, viscoelastic materials, phase-transition media, and fractional dynamical systems where the stiffness of the medium depends on the total deformation (Kirchhoff effect) and the energy density alternates between distinct growth regimes (double-phase). The $\psi$-Hilfer derivative enhances the scope by enabling models with tunable memory and hereditary effects. Full article

Large-scale hydrogen storage is expected to play a critical role in balancing the variability of renewable energy systems, particularly those driven by wind power. However, the combined influence of storage capacity and deliverability on supply reliability remains insufficiently characterized. This study investigates the reliability limits of hydrogen storage systems operating under variable hydrogen production and time-varying demand. A dimensionless modeling framework is developed to map system performance across a wide range of storage capacities and deliverability levels. The results reveal a clear transition between reliable and unreliable operating regimes. Reliable operation requires a minimum deliverability level approximately equal to the mean hydrogen production rate, corresponding to a value of about 1.05–1.10 times the average production across the range of intermittency conditions considered in this study (from moderate to highly variable production). Below this threshold, increasing storage capacity alone cannot prevent supply shortfalls. Once this threshold is exceeded, further increases in deliverability provide diminishing returns and storage capacity becomes the dominant factor governing reliability. In this regime, the required storage capacity approaches a plateau on the order of 10–30 days of average hydrogen throughput, depending on the level of production variability. The proposed regime-based framework provides a practical tool for evaluating storage feasibility and guiding preliminary capacity design in renewable hydrogen systems. Full article

Aiming at the precise modeling demand of high-gain parabolic antennas for 6G and terahertz wireless communications, this study implements and systematically validates a high-precision, self-developed full-wave electromagnetic analysis framework based on the 3D vector finite element method (VFEM). The weak form of the vector Helmholtz equation is rigorously derived to ensure the discrete system is consistent with Maxwell’s equations physically. First-order tetrahedral edge elements are adopted to suppress spurious modes, and a computationally robust implementation of the Silver–Müller absorbing boundary condition (ABC) is carried out for accurate open-domain truncation. Four progressive test cases (parallel-plate waveguide, free-space dipole, finite planar reflector, and parabolic antenna) validate the algorithm’s performance: the relative error of the parabolic antenna’s gain is only 3.39%, with the L2-norm error well constrained in all cases. The self-developed VFEM achieves precision comparable to commercial software with a transparent underlying architecture. Future research will focus on high-order basis functions, AI-based intelligent ABCs, and the domain decomposition method (DDM) for billion-level-degree-of-freedom simulations. This work lays a solid algorithmic foundation for the forward design of high-throughput communication antennas. Full article

This article examines the emergence of Jamaican Dancehall Gospel (JDG)—a genre that fuses Christian-themed lyrics with dancehall rhythms—during its formative years (1998–2006). Despite its religious content, JDG artists expressed that they were often rejected in religious spaces and their music was excluded from worship spaces, based on debates between gatekeeping religious actors and the artists about the music’s appropriateness and authenticity. Using Koskoff’s concept of musical canon as a framework, the study explores why JDG failed to embody the “philosophical and aesthetic principles” of many ecclesial institutions. Drawing on media discourse, artist interviews, and observations, the analysis addresses four contested elements: artists, music, language, and dance. Findings reveal that resistance stemmed from JDG’s association with secular dancehall culture, its use of Jamaican patois, and its incorporation of dance—practices historically stigmatized as “low class” and incompatible with sacred spaces. While proponents argued for cultural relevance and the neutrality of musical forms, critics viewed JDG as a threat to traditional worship norms and moral order. The paper situates these tensions within broader struggles over identity, authenticity, and cultural hierarchy, highlighting the persistence of colonial attitudes privileging Euro-American aesthetics over indigenous expressions. Ultimately, JDG’s gradual acceptance—facilitated by international recognition and generational shifts—underscores the dynamic interplay between religion, popular culture, and identity politics in Jamaica. This study contributes to scholarship on Caribbean sacred music by documenting the sociocultural negotiations surrounding JDG’s emergence and its implications for redefining worship practices in postcolonial contexts. Full article

Transient analyses of liquid-fueled molten salt reactors involve strong coupling among neutronics, delayed neutron precursor transport, thermal–hydraulics, and solid heat transfer, leading to high computational costs for repeated high-fidelity simulations. To enable fast multi-physics prediction at unseen operating conditions, a parametric non-intrusive reduced-order model (ROM) combining proper orthogonal decomposition (POD) and higher-order dynamic mode decomposition (HODMD) is developed. Coupled full-order snapshots generated from an OpenFOAM-based one-eighth symmetric core model based on a simplified MSRE benchmark configuration are used to construct reduced representations for 11 physical fields. The POD truncation rank, HODMD delay dimension, and interpolation model are selected using leave-one-out cross-validation, with polynomial, radial basis function, and Gaussian process regression models considered as interpolation candidates. For unseen parameter points, the model maintains high accuracy in both the interpolation stage and the temporal extrapolation stage. In the temporal extrapolation stage, the highest mean relative L₂ error for the inlet-temperature-step case is 2.112%, whereas all mean relative L₂ errors for the inlet-velocity-step case remain below 0.177%. The results indicate that, under the present cases and parameter settings, the proposed framework provides an accurate and rapid surrogate for multi-physics transient prediction. Full article

A Fe₃O₄/HNTs composite was synthesized, characterized by SEM, TEM, XPS, adsorption–desorption N₂, XRD, FTIR, VSM and Zeta potential, and was used for an ibuprofen adsorption and Fenton oxidation study. The response surface methodology (RSM) and Box–Behnken experimental designs were employed. The effects of pH, contact time, ibuprofen concentration, and Fe₃O₄/HNTs dosage on ibuprofen adsorption were evaluated. Additionally, adsorption isotherms and a kinetic study were performed. The effects of pH, H₂O₂ concentration, and Fe₃O₄/HNTs dosage for IBU removal were also studied. The results of ibuprofen adsorption on Fe₃O₄/HNTs indicate that adsorption was favored at acidic pH. The adsorption followed pseudo-second-order kinetics and a Freundlich isotherm. Under mild conditions (pH 7, 298.15 K) with a Fe₃O₄/HNTs dosage of 1.5 g L⁻¹ and 0.5 M H₂O₂, the heterogeneous Fenton-like reaction achieved 99% ibuprofen removal and 60% mineralization. The Fe₃O₄/HNTs catalyst demonstrated high efficiency for aqueous ibuprofen removal under environmentally mild pH and temperature conditions, and it was easily recoverable and reusable. Full article

Sustainable management of peatlands is one of the key global strategies for mitigating climate change. The balance between carbon (C) sequestration and emission in peatlands reflects environmental conditions over time and can provide insight into long-term ecosystem dynamics. However, current methods for estimating greenhouse gas (GHG) fluxes are often labor-intensive, costly, and site-specific. In this study, we propose a simplified and cost-efficient method to estimate long-term carbon balance in peatlands based on the inorganic (mineral) content of drill core samples. The approach uses exponential decay equations to approximate peat accumulation and decomposition processes over time. A conceptual model is applied that accounts for both anaerobic transformation of organic matter of varying molecular complexity and enhanced aerobic decomposition resulting from anthropogenic drainage during the last century. The model was applied to more than 100 drill cores from four peatland systems in Estonia. The resulting trends were compared qualitatively with known climatic fluctuations of the last millennium, including periods associated with the Little Ice Age. The results suggest that, in many cases, carbon losses from decomposition in deeper peat layers may exceed carbon accumulation in upper layers, even in peatlands that appear to be well preserved. The proposed method provides a rapid, low-cost, first-order approximation of peatland carbon dynamics and may serve as a complementary tool for large-scale assessments where detailed process-based models are not feasible. Full article

Background. The highly mutable influenza virus causes severe annual infections worldwide and results in substantial socioeconomic losses. The spread of infection could be effectively controlled by cross-protective vaccines and universal diagnostic test systems based on the nucleoprotein (NP) as one of the most conserved viral antigens. However, NP also undergoes slow evolutionary changes, and little is known about the influence of these mutations on its antigenicity and immunogenicity. Methods. We expressed the full-length recombinant 6xHis-tagged NPs of ten evolutionary distant influenza A strains of different subtypes in E. coli BL21(DE3) cells and purified these proteins by immobilized metal affinity chromatography. The obtained antigens were identified by mass spectrometry and serological methods. NPs served as antigens for three immunizations of BALB/c mice (15 µg/animal at 14-day interval) and as capturing proteins in ELISA at 2 µg/mL, in order to study the effect of adaptive mutations on the antigenic and immunogenic properties of NPs. Results. A pronounced cross-reactivity of anti-NP antibodies induced in mice by immunization with different NPs was revealed. At the same time, we observed the differences in the humoral immunogenicity of NP, which are in line with the accumulation of evolutionarily driven NP mutations. In general, antibody affinity to heterologous NPs was reduced, indicating the differences in the specificity of anti-NP immunoglobulins, which may be caused by evolutionarily determined variability of immunogenic epitopes leading to the emergence of escape mutations. Conclusions. Overall, our results reflect the slightly evolving nature of the NP antigen, which influences the specificity spectrum of anti-NP antibodies and should be considered as a limitation for the development of NP-based cross-protective vaccines and test systems. Full article

With the global emphasis on sustainable development, supply chain management is facing new challenges and opportunities. Enterprises often face a large number of suppliers when selecting suppliers, which makes the selection process complex. Considering the crucial role of supplier selection in sustainable supply chains, a sustainable supplier selection model based on multi-attribute utility analysis and a fuzzy approximation ideal solution ranking method is proposed to reduce carbon emissions and environmental pollution. This model helps companies scientifically evaluate and select suppliers by comprehensively considering three aspects: environment, economy, and society. Meanwhile, the study utilizes an optimized genetic algorithm-based order allocation model to raise the efficacy and fairness of order allocation. Reducing procurement costs often relies on improving resource utilization and reducing production waste, which directly lowers the energy consumption and carbon emission intensity per unit of product. At the same time, reducing product damage and delivery delay rates can avoid additional greenhouse gas emissions caused by rework, abandonment, and emergency transportation. By improving supplier productivity and optimizing order allocation, the developed model can not only reduce economic costs but also control environmental pollution and carbon footprints from the source of the supply chain. The outcomes indicate that technological level is a crucial factor influencing supplier selection, with a significant positive impact on supplier willingness to choose, and its standard path coefficient is 0.199, with a significance level of 0.001. Meanwhile, the optimized genetic algorithm exhibits strong stability and convergence in order allocation. This optimization model has high efficiency in handling large-scale orders. This provides strong support for the decision-making of enterprises in sustainable supply chain management and a valuable reference for China’s exploration and practice in the field of sustainable development. Full article

Cryptosporidium spp. is a widely distributed gastrointestinal pathogen in vertebrates, such as the European wild boar. Furthermore, with a fecal–oral pathway, they might spread through tainted food and water or by direct contact. Related to the presence of this parasite in wild boar populations, the handling of hunted carcasses may be a source of zoonotic transmission. This work aims to evaluate the presence of Cryptosporidium spp. in 10 Portuguese hunting areas in two different locations (Northern and Central Portugal) and to preliminarily assess the risk factors of zoonotic transmission to hunting stakeholders. Cryptosporidium spp. antigens were confirmed by an immunochromatography test in the wild boars’ fecal samples from four of the 10 hunting areas analyzed (one in the North and three in the Southeast of Central Portugal). A qualitative assessment of various potential factors contributing to the persistence of infection in this wild population, but also of zoonotic risk factors related to hygiene procedures and handling of carcasses after hunting actions, was also carried out. With these potentially risky practices, it is imperative to raise awareness and establish a surveillance network in the hunting areas in order to mitigate the potential zoonotic transmission of these pathogenic agents to hunting stakeholders. Full article