Search Results (3,068)

Rotating machinery is a core component of modern industry, and its operational state directly affects system safety and reliability. In order to achieve intelligent fault diagnosis of bearings under complex working conditions, the health management of bearings has become an important issue. Although deep learning has shown remarkable advantages, its performance still relies on the assumption that the training and testing data share the same distribution, which often deteriorates in real applications due to variations in load and rotational speed. This study focused on the scenario of domain generalization (DG) and proposed a Meta-Learning with Gradient Alignment and Data Augmentation (MGADA) method for cross-domain bearing fault diagnosis. Within the meta-learning framework, Mixup-based data augmentation was performed on the support set in the inner loop to alleviate overfitting under small-sample conditions and enhanced task-level data diversity. In the outer loop optimization stage, an arithmetic gradient alignment constraint was introduced to ensure consistent update directions across different source domains, thereby reducing cross-domain optimization conflicts. Meanwhile, a centroid convergence constraint was incorporated to enforce samples of the same class from different domains to converge to a shared centroid in the feature space, thus enhancing intra-class compactness and semantic consistency. Cross-working-condition experiments conducted on the Case Western Reserve University (CWRU) bearing dataset demonstrate that the proposed method achieves high classification accuracy across different target domains, with an average accuracy of 98.89%. Furthermore, ablation studies confirm the necessity of each module (Mixup, gradient alignment, and centroid convergence), while t-SNE and confusion matrix visualizations further illustrate that the proposed approach effectively achieves cross-domain feature alignment and intra-class aggregation. The proposed method provides an efficient and robust solution for bearing fault diagnosis under complex working conditions and offers new insights and theoretical references for promoting domain generalization in practical industrial applications. Full article

In the current geopolitical context, sustainable energy development has become one of the pillars of global economic growth. This issue is well recognized in the European Union, which has undertaken a number of measures to achieve sustainable development goals. For these measures to be effective, it is essential to conduct a reliable, multi-variant diagnosis of the state of energy development in the EU-27 countries. This paper addresses this highly topical and important issue. It presents a new proprietary method—the Entropy–Evolutionary Evaluation of Sustainability (E³)—based on a multidimensional approach to researching and evaluating the state of sustainable energy development in the EU-27 countries between 2014 and 2023. Through the integration of 19 indicators representing the adopted dimensions of the study (energy, economic, environmental, and social), the method enabled both a static assessment and a dynamic analysis of energy transition processes across space and time. To determine the weights of the indicators for each dimension of sustainable energy development, the CRITIC, Entropy, and equal weight methods, along with the Laplace criterion, were applied. The Analytic Hierarchy Process method was used to establish the weights of the dimensions themselves. An important component of the approach was the inclusion of scenario studies, which made it possible to assess sustainable energy development under five variants: baseline, level, equilibrium, transformational, and neutral. These scenarios were based on different weight values assigned to three factors: the level of energy development (L), its stability (S), and the trajectory of change ($\tilde{T}$). The results, expressed in the form of a total index value and dimensional indices, reveal significant diversity among the EU-27 countries in terms of sustainable energy development. Sweden, Finland, Denmark, Latvia, and Austria achieved the best results, while Cyprus, Malta, Ireland, and Luxembourg—countries heavily dependent on energy imports, with limited diversification of their energy mix and high energy costs—performed the worst. The developed method and the results obtained should serve as a valuable source of knowledge to support decision-making and the formulation of strategies concerning the pace and direction of actions related to the energy transition. Full article

Background: Self-amplifying mRNA (saRNA) holds promising application prospects. However, due to the inclusion of a replicase sequence, its extended length leads to premature termination during in vitro transcription (IVT), resulting in poor product integrity. This study aims to optimize the IVT process for saRNA vaccines to enhance integrity, thereby addressing the key challenge in saRNA vaccine manufacturing. Method: Guided by the Quality by Design (QbD) framework, Design of Experiment (DoE) methodology was employed to design diverse combinations of process parameters for IVT reactions. Predictive models were established to identify critical process parameters (CPPs) influencing integrity and yield. An optimized parameter set and process design space, meeting predefined yield and integrity standards, were developed. The impact of integrity on the immunogenicity of saRNA vaccines was further investigated. Results: Mg²⁺ concentration exerted the most pronounced effect on saRNA integrity. Under optimized IVT conditions, integrity exceeded 85%. Mathematical modeling simulations defined the IVT design space, meeting the preset criteria of ≥80% integrity and ≥600 μg/100 μL yield while accommodating longer saRNA constructs. Notably, murine model data revealed that higher saRNA integrity significantly enhanced antigen-specific antibody and T-cell responses. Conclusion: This study successfully established a multivariate IVT design space fulfilling preset integrity and yield criteria, providing critical data references for the industrialization and quality specification development of saRNA vaccines. Full article

Magnetron sputtering offers a scalable route to magnetic topological insulators (MTIs) based on Cr-doped Sb₂Te₃. We combine a range of X-ray diffraction (XRD), reciprocal-space mapping (RSM), scanning transmission electron microscopy (STEM), scanning TEM-energy-dispersive X-ray spectroscopy (STEM-EDS), and X-ray absorption spectroscopy, and X-ray magnetic circular dichroism (XAS/XMCD) techniques to study the structure and magnetism of Cr-doped Sb₂Te₃ films. Symmetric $\theta$-2$\theta$ XRD and RSM establish a solubility window. Layered tetradymite order persists up to ∼10 at.-% Cr, while higher doping yields CrTe/Cr₂Te₃ secondary phases. STEM reveals nanocrystalline layered stacking at low Cr and loss of long-range layering at higher Cr concentrations, consistent with XRD/RSM. Magnetometry on a 6% film shows soft ferromagnetism at 5 K. XAS and XMCD at the Cr $L_{2,3}$ edges exhibits a depth dependence: total electron yield (TE; surface sensitive) shows both nominal Cr²⁺ and Cr³⁺, whereas fluorescence yield (FY; bulk sensitive) shows a much higher Cr²⁺ weight. Sum rules applied to TEY give $m_L=(0.20\pm 0.04)$${\mu }_{\mathrm{B}}$/Cr, and $m_S=(1.6\pm 0.2)$${\mu }_{\mathrm{B}}$/Cr, whereby we note that the applied maximum field (3 T) likely underestimates $m_S$. These results define a practical growth window and outline key parameters for MTI films. Full article

Due to its sensitivity to topographic and land use land cover features, air temperature (maximum, minimum, and mean—T_x, T_n, and T_mean) is extremely variable in space and time. The sparse and unevenly distributed meteorological stations observed across remote regions cannot monitor such variability. Freely available, gridded temperature datasets (T-datasets) are positioned as an opportunity to overcome this issue. Still, their coarse spatial resolution (i.e., ≥5 km) does not allow for the observation of air temperature variations on a fine spatial scale. In this context, a set of variables that have a close relationship with daily air temperature (MODIS maximum, minimum, and mean Land Surface Temperature—LST_x, LST_n, and LST_mean; MODIS NDVI; SRTM topographic features—elevation, slope, and aspect) are integrated in three regression machine-learning models (Random Forest—RF, eXtreme Gradient Boosting—XGB, Multiple Linear Regression—MLR) to propose a T-dataset estimates (T_x, T_n, and T_mean) spatial resolution downscaling framework. The approach consists of two main steps: firstly, the machine-learning models are trained at the native 5 km spatial resolution of the studied T-dataset (i.e., CHIRTS); secondly, the application of the trained machine-learning models at a 1 km spatial resolution to downscale CHIRTS from 5 km to 1 km. The results show that the method not only improves the spatial resolution of the CHIRTS dataset, but also its accuracy, with higher improvements for T_n than for T_x and T_mean. Among the considered models, RF performs the best, with an R², RMSE, and MAE improvement of 2.6% (0%), 47.1% (6.1%), and 55.3% (7%) for T_n (T_x). These results will support air temperature monitoring and related extreme events such as heat and cold waves, which are of prime importance in the actual climate change context. Full article

Lung cancer, the leading cause of cancer-related mortality worldwide, necessitates better methods for earlier and more accurate detection. To this end, this study introduces LCxNet, a novel, custom-designed convolutional neural network (CNN) framework for computer-aided diagnosis (CAD) of lung cancer. The IQ-OTH/NCCD lung CT dataset, which includes three different classes—benign, malignant, and normal—is used to train and assess the model. The framework is implemented using five optimizers, SGD, RMSProp, Adam, AdamW, and NAdam, to compare the learning behavior and performance stability. To bridge the gap between model complexity and clinical utility, we integrated Explainable AI (XAI) methods, specifically Grad-CAM for decision visualization and t-SNE for feature space analysis. With accuracy, specificity, and AUC values of 99.39%, 99.45%, and 100%, respectively, the results demonstrate that the LCxNet model outperformed the state-of-the-art models in terms of diagnostic performance. In conclusion, this study emphasizes how crucial XAI is to creating trustworthy and efficient clinical tools for the early detection of lung cancer. Full article

We present a comprehensive information-theoretic evaluation of three widely used rigid water models (TIP3P, SPC, and SPC/$\epsilon$) through systematic analysis of water clusters ranging from single molecules to 11-molecule aggregates. Five fundamental descriptors—Shannon entropy, Fisher information, disequilibrium, LMC complexity, and Fisher–Shannon complexity—were calculated in both position and momentum spaces to quantify electronic delocalizability, localization, uniformity, and structural sophistication. Clusters containing 1, 3, 5, 7, 9, and 11 molecules (denoted 1 M, 3 M, 5 M, 7 M, 9 M, and 11 M) were selected to balance computational tractability with representative scaling behavior. Molecular dynamics simulations validated the force fields against experimental bulk properties (density, dielectric constant, self-diffusion coefficient), while statistical analysis using Shapiro–Wilk normality tests and Student’s t-tests ensured robust discrimination between models. Our results reveal distinct scaling behaviors that correlate with experimental accuracy: SPC/$\epsilon$ demonstrates superior electronic structure representation with optimal entropy–information balance and enhanced complexity measures, while TIP3P shows excessive localization and reduced complexity that worsen with increasing cluster size. The transferability from clusters to bulk properties is established through systematic convergence of information-theoretic measures toward bulk-like behavior. The methodology establishes information-theoretic analysis as a useful tool for comprehensive force field evaluation. Full article

The growing prevalence of short-form video applications among adolescents has drawn increased public and scholarly attention to problematic short-form video use. The current longitudinal study gathered data from adolescents aged 12 to 15 across two waves spaced one year apart. A total of 665 participants provided reports on parental neglect, problematic short-form video use, psychological capital, and demographic details at Time 1 (T1), and reported again on problematic use at Time 2 (T2). After controlling for gender, age, parental education level, parental work status, family socioeconomic status, only-child status, and T1 problematic short-form video use, T1 parental neglect remained a significant predictor of T2 problematic use. Additionally, T1 self-efficacy, T1 resilience, and T1 hope significantly moderated the relationship between T1 parental neglect and T2 problematic use, whereas T1 optimism did not demonstrate a buffering effect. Specifically, the association between T1 parental neglect and T2 problematic use did not vary significantly between adolescents with high and low levels of optimism. However, the predictive effect was significantly weaker, though still statistically significant, among adolescents with higher self-efficacy and hope. Most notably, among those with higher resilience, the effect of parental neglect became non-significant. The study offers valuable evidence-based insights for preventing and addressing adolescent problematic short-form video use in the mobile internet era. Full article

In intuitionistic fuzzy 2-normed spaces, there are numerous symmetries in the topological structures and mapping theorems. In this work, we present the concept of an intuitionistic fuzzy 2-normed space(IF2NS) and demonstrate its structural properties using illustrative examples. This approach unifies and broadens the scope of both classical 2-normed spaces and intuitionistic fuzzy normed spaces when specific conditions are met. We introduce the idea of fuzzy open balls and explore the convergence of sequences with respect to the topology derived from the intuitionistic fuzzy 2-norm. In addition, we define left and right N-Cauchy sequences relative to the topologies ${\tau }_N$ and ${\tau }_{N^{-1}}$ and analyze their convergence characteristics. Special attention is given to the inherent symmetry of the 2-norm, where the magnitude of a pair of vectors remains invariant under exchange of arguments, and to the balanced interaction between membership and non-membership functions in the intuitionistic fuzzy setting. This intrinsic symmetry is further reflected in the proofs of the open mapping and closed graph theorems, which naturally preserve the symmetric structure of the underlying space The paper culminates with the formulation and proof of the open mapping theorem that can be considered for its symmetric properties and the closed graph theorem in the context of IF2NS, thereby generalizing essential theorems of functional analysis to this fuzzy setting. Full article

Surge capacity is the ability to manage sudden patient influxes beyond routine levels and can be evaluated using the 4S Framework: staff, stuff, system, and space. While low-resource settings like Rwanda face frequent mass casualty incidents (MCIs), most surge capacity research comes from high-resource settings and lacks generalisability. This study assessed Rwanda’s hospital surge capacity using a cross-sectional survey of emergency and surgical departments in all referral hospitals. Descriptive statistics, t-tests, Fisher’s exact test, ANOVA, and linear mixed-model regression were used to analyze responses. Of the 39 invited participants, 32 (82%) responded. On average, respondents believed that they could manage 13 MCI patients (95% CI: 10–16) while maintaining routine care, with significant differences between tertiary and secondary hospitals (11 vs. 22; p = 0.016). The intra-class correlation was poor for most variables except for CT availability and ICU beds. Surge capacity perception did not vary significantly by professional category, though less senior staff reported higher capacity. Significantly higher capacity was reported by those with continuous access to imaging (p < 0.01). Despite limited resources, Rwandan hospitals appear able to manage small to moderate MCIs. For larger incidents, patient distribution across facilities is recommended, with critical cases prioritized for tertiary hospitals. Full article

The ultrasound-assisted antisolvent precipitation method was used to prepare lignin nanoparticles from Thlaspi arvense L. rhizomes. The influence of each experimental variable on the average particle size (APS) of the lignin nanoparticles was determined via single-factor experiments. The optimal conditions for the preparation of the lignin nanoparticles were investigated in detail, and the APS of the lignin nanoparticles was 118 ± 3 nm. Compared with those of untreated lignin, the lignin nanoparticles prepared via this method were spherical and evenly distributed, and the structure was not damaged. Ultrasound generated local extreme physical conditions through its cavitation effect to promote nucleation, triggered high-speed turbulence to refine the particle size and improve uniformity, and applied mechanical disturbance to inhibit particle agglomeration, which promoted the preparation of lignin nanoparticles with a small size and good dispersion. A solubility test revealed that the lignin nanoparticles had greater solubility, which was improved 9-fold. The determination of antioxidant capacity revealed that the lignin nanoparticles had high free radical scavenging activity, which provided a broader space for the multifaceted utilization of a kind of grass lignin with the structural characteristics of T. arvense lignin (p-hydroxyphenyl lignin). Full article

The Yellow River Delta is one of China’s most ecologically fragile regions, experiencing prolonged pressures from rapid urbanization and ecological degradation. Existing research, however, has predominantly focused on constructing ecological security patterns under single scenarios, with limited systematic multi-scenario comparisons and insufficient statistical support. To address this gap, this study proposes an integrated framework of “land use simulation—multi-scenario ecological security pattern construction—statistical comparative analysis.” Using the PLUS model, three scenarios were constructed—Business-as-Usual (BAU), Priority Urban Development (PUD), and Priority Ecological Protection (PEP)—to simulate land use changes by 2040. Habitat quality assessment, Multi-Scale Pattern Analysis (MSPA), landscape connectivity, and circuit theory were integrated to identify ecological source areas, corridors, and nodes, incorporating a novel hexagonal grid partitioning method. Statistical significance was evaluated using parametric tests (ANOVA, t-test) and non-parametric tests (permutation test, PERMANOVA). Analysis indicated significant differences in ecological security patterns across scenarios. Under the PEP scenario, ecological source areas reached 3580.42 km² (12.39% of the total Yellow River Delta), corresponding to a 14.85% increase relative to the BAU scenario and a 32.79% increase relative to the PUD scenario. These gains are primarily attributable to stringent wetland and forestland protection policies, which successfully limited the encroachment of construction land into ecological space. Habitat quality and connectivity markedly improved, resulting in the highest ecosystem stability. By contrast, the PUD scenario experienced an 851.46 km² expansion of construction land, resulting in the shrinkage of ecological source areas and intensified fragmentation, consequently increasing ecological security risks. The BAU scenario demonstrated moderate outcomes, with a moderately balanced spatial configuration. In conclusion, this study introduces an ecological restoration strategy of “five zones, one belt, one center, and multiple corridors” based on multi-scenario ecological security patterns. This provides a scientific foundation for ecological restoration and territorial spatial planning in the Yellow River Delta, while the proposed multi-scenario statistical comparison method provides a replicable methodological framework for ecological security pattern research in other delta regions. Full article

Crop–livestock integration is widely adopted as a strategy for recovering degraded pastures. In this system, intercropping crops such as sorghum with tropical grasses enables the harvest of sorghum for silage while simultaneously establishing a new pasture. However, interspecific competition for resources can limit sorghum development and yield, potentially compromise the fermentation process and reduce the nutritional quality of the silage. Therefore, this study aimed to evaluate the agronomic performance, fermentative characteristics, and chemical–bromatological composition of silages produced from different biomass sorghum-grass intercropping systems. The experiment was conducted in a randomized block design with a 3 × 2 factorial arrangement: three cropping systems [sorghum monoculture, sorghum intercropped with Marandu grass (S + M), and sorghum intercropped with Zuri grass (S + Z)] and two sorghum row spacings (45 and 90 cm). The S + Z intercropping system with 90 cm row spacing showed the highest total dry matter yield (16.42 t/ha). It also presented better fermentative parameters, such as pH (4.02) and lactic acid (5.31%DM) and superior nutritional quality, with lower fiber content and higher concentrations of NFC (24.79%DM), TDN (59.75%DM), and digestibility. It is concluded that intercropping biomass sorghum with Zuri grass at 90 cm spacing is the most promising strategy for producing high-quality silage. Full article

In this article, we establish the existence of global mild solutions to the Vlasov–Fokker–Planck equation with local alignment forces under specular reflection boundary conditions in the low-regularity function space $L_k^1{L}_T^{\infty }{L}_v^2$. A key difficulty is that the macroscopic averaged velocity u does not directly possess a dissipative structure in the equation. To overcome this, we rely on the dissipation $u-b$ from the linear part, combined with the dissipation of the macroscopic component b derived from the associated macroscopic equation. Moreover, since no direct energy functional is available for u, we fully exploit the dissipative mechanisms of both $u-b$ and b when handling the estimates for the nonlinear terms. Full article

Rapidly pinpointing the origin of accidental chemical gas releases is essential for effective response. Prior vision pipelines—such as 3D CNNs, CNN–LSTMs, and Transformer-based ViViT models—can improve accuracy but often scale poorly as the temporal window grows or winds meander. We cast recursive backtracking of concentration fields as a finite-horizon, multi-step spatiotemporal sequence modelling problem and introduce Recursive Backtracking with Visual Mamba (RBVM), a Visual Mamba-inspired, directionally gated state-space backbone. Each block applies causal, depthwise sweeps along ${\mathrm{H}}^{\pm }$, ${\mathrm{W}}^{\pm }$, and ${\mathrm{T}}^{\pm }$ and then fuses them via a learned upwind gate; a lightweight MLP follows. Pre-norm LayerNorm and small LayerScale on both branches, together with a layer-indexed, depth-weighted DropPath, yield stable stacking at our chosen depth, while a 3D-Conv stem and head keep the model compact. Computation and parameter growth scale linearly with the sequence extent and the number of directions. Across a synthetic diffusion corpus and a held-out NBC_RAMS field set, RBVM consistently improves Exact and hit $\le 1$ over strong 3D CNN, CNN–LSTM, and ViViT baselines, while using fewer parameters. Finally, we show that, without retraining, a physics-motivated two-peak subtraction on the oldest reconstructed frame enables zero-shot dual-source localization. We believe RBVM provides a compact, linear-time, directionally causal backbone for inverse inference on transported fields—useful not only for gas–release source localization in CBRN response but more broadly for spatiotemporal backtracking tasks in environmental monitoring and urban analytics. Full article