Search Results (21,348)

While continuous-time Hamiltonian dynamics are naturally energy preserving with a symplectic flow, their discrete-time counterparts enhance either geometric or energy preservation properties, but rarely both within a unified framework. It is the object of this paper to more deeply investigate this question. In both linear and nonlinear settings, necessary and sufficient conditions characterizing discrete Hamiltonian dynamics that are conservative and symplectic are derived. The relationship with exact sampled models of continuous-time Hamiltonian dynamics are investigated, showing that such models, that preserve both energy and symplectic structures, do not generally fit into the proposed canonical form. Generalized Hamiltonian structures are, thus, introduced. On these bases, Hamiltonian integrators that preserve both the energy and the symplectic structure up to a prescribed order in the sampling period, are constructed. Some simulations on nonlinear test cases illustrate the theoretical findings. Full article

Classifying benign and malignant lung nodules from computed tomography (CT) images remains difficult because lung nodules can be hard to classify, and unimodal models cannot capture complementary diagnostic information. Despite the success of deep learning, existing methods rely only on image information and miss semantic information that can be obtained from an expert radiologist’s knowledge. Hence, the authors propose a new multimodal lung nodule classification model in this study, named the Graph-Guided Fusion Network (R-GGFN), that combines three-dimensional (3D) CT image features and structured radiologist annotations. The proposed architecture consists of three models. A 3D ResNet-18 network for image feature extraction, an MLP network for encoding semantic information, and a Graph Attention Network (GAT) for capturing inter-nodule relationships and fusing multimodal information with the graph. We add a tabular skip connection to preserve discriminative semantic features and use focal loss to address imbalance during training. To prevent data leakage, we partitioned the publicly available LIDC-IDRI dataset at the patient level. Experimental results on a held-out patient-level test set, accessed only once after model selection was finalized, show that the proposed R-GGFN achieves an accuracy of 85.21%, an AUROC of 0.9147, a PR-AUC of 0.9213, and an F1-score of 0.8609. Among all unimodal and multimodal baselines internally evaluated, R-GGFN achieved the best value on every reported metric, including accuracy, AUROC, PR-AUC, F1-score, Precision, sensitivity, and specificity. Furthermore, the proposed approach enhances model transparency by combining explainable AI techniques (e.g., 3D Grad-CAM, SHAP, and graph visualization) to explain the model at the image, feature, and graph levels. The results show that the graph-guided multimodal fusion method can fully leverage complementary image and semantic information, improving diagnostic accuracy and interpretability. The framework proposed here is a good and understandable computer-aided diagnosis decision-support system for lung cancer and a step towards future external dataset validation. Full article

Electrochromic tungsten oxide (WO₃) films suffer from gradual performance degradation during repeated ion insertion/extraction processes, while the relationship between electrolyte-dependent ion accommodation and structural stability remains insufficiently understood. Herein, magnetron-sputtered WO₃ thin films were systematically investigated in H₂SO₄, LiClO₄, ZnSO₄, and Al₂(SO₄)₃ electrolytes to clarify the coupling relationship among ion accommodation, electronic structure evolution, microstructural retention, and electrochromic durability. Combined electrochemical measurements with UV–visible spectroscopy, atomic force microscopy (AFM), scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), and reflected electron energy loss spectroscopy (REELS) reveal that electrolyte chemistry regulates the balance between electrochemical activation and structural tolerance. Highly mobile H⁺ and strongly interacting Al³⁺ ions promote rapid ion transport and enhanced W⁶⁺ reduction but may induce excessive structural perturbation, defect accumulation, and accelerated degradation during cycling. Zn²⁺ exhibits intermediate behavior, whereas Li⁺ enables balanced ion accommodation through reversible W⁶⁺/W⁵⁺ conversion while preserving the WO₃ framework. Consequently, the LiClO₄ electrolyte achieves superior electrochromic performance with an optical modulation of 80.97% and improved cycling stability. These results demonstrate that durable electrochromic behavior is governed not by maximizing ion transport or reduction degree, but by achieving a reversible ion accommodation regime compatible with the structural tolerance of the host framework. This work provides a microstructure-oriented strategy for designing stable WO₃-based electrochromic devices through electrolyte regulation. Full article

Digital transformation has come to the fore as a pivotal force behind corporate green innovation in the digital economy. Although previous research has documented that digital transformation can promote green innovation, the underlying organizational mechanisms remain theoretically fragmented. Moreover, the external conditions that shape the effectiveness of this relationship are not yet fully understood. To address these gaps, we develop an integrated analytical framework anchored in Organizational Information Processing Theory (OIPT) and the Resource-Based View (RBV). This framework explains how digital transformation promotes green innovation through two complementary organizational mechanisms—resource investment and technology application—and incorporates the moderating role of digital infrastructure. Using panel data on Chinese A-share-listed firms covering 2015–2024, this study examines the proposed relationships through a Bidirectional Fixed Effects Model, mediation analysis, and moderation analysis. The findings demonstrate a significant positive association between digital transformation and green innovation. This effect is partially mediated by increased R&D(Resource and Development) investment and deeper digital organizational embedding, which constitute complementary pathways reflecting resource investment and enhanced resource utilization, respectively. Additionally, digital infrastructure positively moderates this relationship by providing a more supportive external digital environment. This study enriches the existing literature by synthesizing previously dispersed perspectives on mechanisms into an OIPT-RBV framework, offering a more comprehensive account of how and under what circumstances digital transformation facilitates green innovation. Our findings also carry practical implications for managers seeking to strengthen innovation capabilities and for policymakers aiming to advance digital infrastructure and firms’ sustainable development. Full article

To address the capacity shortfall in high-level maintenance of Electric Multiple Units (EMUs) and meet the growing maintenance demand, the location of maintenance bases must be optimized. To avoid resource waste, this paper introduced a repulsion formula between bases based on Coulomb’s Law, aiming to minimize the overlap of maintenance service areas. Only bases with the appropriate qualifications for specific train types and maintenance levels can undertake the corresponding maintenance tasks. Accordingly, a maintenance matching formula was developed to quantify this relationship. From the perspective of multi-project economic evaluation, an investment selection model for EMU maintenance bases was established to minimize the total system cost, which includes the annualized construction investment cost, round-trip empty running cost, high-level maintenance cost, and maintenance capacity loss cost. By minimizing service area overlap and optimizing maintenance task assignments, the model helps to reduce empty running mileage, improve facility utilization, and avoid asset idleness, thereby contributing positively to enhancing resource intensification and operational resilience of the railway maintenance network. Taking the Chinese regional railway network as an example, Gurobi was employed to solve the optimization model. The results show that expanding the bases in Chengdu and Guangzhou, and building a new maintenance base in Nanning minimizes the annualized total cost within the planning period. Cost composition analysis reveals that annualized initial investment accounts for only a small proportion of the total cost, while high-level maintenance cost dominates, indicating that the core benefit of location optimization lies in reducing long-term operating costs and improving resource utilization efficiency. Full article

The development of antiepileptic drugs remains a serious challenge for both academia and industry. Ionotropic glutamate receptors and voltage-gated sodium channels are among the primary targets of antiepileptic agents. Recent studies have revealed a unique property of phenytoin: unlike other sodium-channel blockers, it inhibits calcium-impermeable AMPA receptors at micromolar concentrations. In this study, we explored the structure–activity relationships of eight phenytoin derivatives. The effects of the compounds on neuronal voltage-gated Na⁺ channels and neuronal AMPA receptor channels were examined using the patch-clamp technique. For the Na⁺ channels, we analyzed tonic block, shifts of steady-state inactivation, and frequency-dependent block. For the AMPA receptors, we investigated kinetics, agonist dependence, and trapping effects. NH groups at positions 1 and 3, the carbonyl groups at positions 2 and 4, and the phenyl group at position 5 are important, since their replacement causes a decrease in activity. Replacement of oxygen with sulfur at position 2 results in a significant increase in activity on both types of channels. For the AMPA receptor, this increase is attributable to a more stable drug–channel complex. The enhanced action on sodium channels is due to an increase in tonic block, whereas the effects on inactivated and open channels remain unchanged. These results suggest a new possibility for tuning the activities of phenytoin derivatives against both primary targets to obtain anticonvulsants with novel properties. Full article

Truck–multi-drone collaborative delivery can reduce last-mile costs, but fleet sizing and routing are often optimized separately, making it difficult to match resources with demand under a delivery-period constraint. This study addresses the scenario of collaborative delivery involving multiple trucks and multiple drones by constructing a two-stage optimization framework that integrates fleet sizing and route planning. In the first stage, queueing models and continuous approximation are employed to determine the initial configuration of trucks and drones based on demand intensity and delivery cycle constraints. The second stage introduces continuous drone delivery and cross-vehicle retrieval to enhance the flexibility of truck–drone collaboration; while optimizing collaborative routes, the framework adjusts the allocation of trucks and drones—adding or reducing resources based on route feasibility and equipment utilization—thereby achieving the joint optimization of transport capacity and collaborative routes with the objective of minimizing total system costs. A node–resource–flow-separated three-chain encoding and an adaptive large neighborhood search–simulated annealing algorithm are designed to solve the model. Multi-scale numerical experiments show that, compared with four simplified fleet-sizing strategies, the proposed framework achieves average cost savings of 15.4–15.5% for medium- and large-scale instances. The results reveal an economic saturation point of the delivery period that shifts with node scale and a non-monotonic relationship between fleet size and coordination efficiency. The framework supports demand-driven fleet configuration and provides operational guidance for cost-effective truck–drone last-mile delivery. Full article

Peanut (Arachis hypogaea L.) yield depends on biological nitrogen fixation, but the molecular mechanisms underlying the combined effects of arbuscular mycorrhizal fungi (AMF) and calcium fertilizer on nodulation remain unclear. Here, we used integrated transcriptomic and metabolomic analyses to investigate potential mechanisms in peanut roots. Compared with the non-inoculated control, AMF inoculation alone was associated with a 22.1% higher nodule number per plant. The combined application of AMF and CaO showed a 35.9% higher nodulation than AMF alone, and a 30.9% higher AMF colonization rate than AMF alone was also observed. Mechanistically, AMF colonization was associated with enhanced carbon-nitrogen metabolic profiles and up-regulation of phenylpropanoid metabolism-related pathways, suggesting a potential role in providing energy, carbon skeletons, and signaling molecules for nodule formation. Calcium fertilizer correlated with strengthening of the glyoxylate cycle and pentose phosphate pathway, possibly contributing to the energy supply for nodulation. It also affected genes related to protein secretion and lipid metabolism, with observed changes in membrane lipids and transport metabolites, which may enhance symbiotic interface function. This study reveals the multi-level mechanisms through which AMF and calcium fertilizer collectively promote peanut nodulation, providing a systems-level perspective on plant–microbe–nutrient relationships during symbiosis. Our findings offer new insights for sustainable agriculture by reducing chemical nitrogen inputs and promoting nodulation in legumes. Full article

Digital technologies, particularly artificial intelligence (AI) and big data analytics (BDA), are increasingly recognized as drivers of competitive advantage, yet the mechanisms through which they enhance organizational performance remain insufficiently understood. Grounded in Teece’s dynamic capabilities framework, this scoping review examines how digital technologies enable sensing, seizing, and reconfiguring capabilities and their impact on organizational performance. Following PRISMA-ScR guidelines, 1349 records from Scopus and Web of Science were screened, resulting in 21 eligible studies (2017–2025). A thematic synthesis and a complementary random-effects meta-analysis of 15 quantitative studies (N = 6924) were conducted, representing an integrated scoping review with embedded quantitative synthesis. The findings show that AI and BDA strengthen sensing through advanced data analytics, AI and digital platforms enhance seizing via real-time decision support, and IoT and blockchain facilitate reconfiguring by improving process optimization and organizational flexibility. Dynamic capabilities consistently mediate the relationship between digital technologies and performance. The meta-analysis confirms a significant positive overall effect (β = 0.356, 95% CI: 0.258–0.454), remaining robust after publication bias correction (β = 0.338). This review further identifies three generative tensions, breadth–depth, speed–deliberation, and flexibility–rigidity, that underpin capability development. An integrative framework and future research agenda are proposed, extending dynamic capability theory by conceptualizing digital technologies as active enablers of organizational adaptation and sustained performance. Full article

Polarimetric reflectance can be described using the Stokes parameters with S₀ representing the total intensity of the light beam reflected from a material, S₁ representing the intensity difference between the horizontally {0°, 180°} and vertically {90°, 270°} linearly polarized components, S₂ representing the intensity difference between the +45° and −45° (or 135°) linearly polarized components, and the Degree of Linear Polarization (DoLP) representing the fraction of light that is linearly polarized. Spectral analyses often fail to distinguish between materials that may be spectrally similar, while polarimetric analyses may be able to enhance the distinction. Prior research has demonstrated the utility of polarization for target detection; however, existing studies rarely compare controlled laboratory polarimetric signatures directly with real-world aerial-field measurements. Furthermore, there is a gap in systematically evaluating how both material physical properties, such as metallic versus dielectric structures, and collection geometries affect polarimetric signatures across multiple wavebands. The objective of this research is to test an approach to measure material, object, and land cover separability in visible (VIS), shortwave infrared (SWIR), and longwave infrared (LWIR) polarimetric laboratory and aerial imagery through complementary laboratory and aerial-field experiments, which may aid in differentiating between spectrally similar man-made materials, objects, and land covers. In this study, sensors measure unpolarized and polarized reflectance responses from man-made materials, objects, and land covers in VIS, SWIR, and LWIR bands in laboratory and aerial field imagery at varying collection geometries. The relationship between laboratory samples and aerial-field-collected imagery of man-made materials, objects, and land covers for S₀, S₁, S₂, and DoLP responses was explored. Results show statistically significant separability by material and collection geometry across Stokes parameters and wavelengths. Post hoc pairwise comparisons showed which materials, objects, and land covers were separable and which collection geometries were separable from each other; however, separability differed between the laboratory and field measurements. Ultimately, this research provides a foundational understanding that can assist with spectropolarimetric data collection planning by demonstrating that overall collection geometry is a critical factor for optimizing material, object, and land cover separability. Future work should focus on isolating the effects of individual collection geometry parameters, such as camera angle, flight direction, and time of day, to develop more targeted collection strategies. Full article

High-precision two-dimensional intermolecular potential energy surfaces (PESs) for Rg–CuF (Rg = Ar, Kr, Xe) were constructed at the coupled-cluster singles and doubles with non-iterative triples [CCSD(T)] level by employing aug-cc-pVXZ (X = D, T, Q) basis sets, and the energies were extrapolated to the complete basis set (CBS) limit. All three complexes exhibit a consistent topological pattern: the global minimum corresponds to a collinear Rg–Cu–F configuration, and the local minimum corresponds to an anti-linear Rg–F–Cu configuration. As the atomic number of noble gas increases, the Rg–Cu equilibrium distance lengthens while the binding strength remarkably enhances. Bound state calculations were performed based on these PESs to yield rotational levels, which can be used to derive the intermolecular vibrational frequencies, molecular structures and spectroscopic parameters for all primary isotopologues. The predicted rotational constants B are in excellent agreement with the experimental observations, attaining a sub-MHz accuracy at the AVTZ level for Kr–CuF and at the CBS limit for Ar–CuF and Xe–CuF. Vibrational wavefunction analysis reveals that the intermolecular vibrational modes of Kr–CuF and Xe–CuF are highly localized, consistent with the pronounced molecular rigidity observed experimentally. Isotopic effect analysis reveals a well-defined linear relationship between the changes in the rotational constant B and the intermolecular vibrational frequency in relation to the reduced mass of the complex, which provides a reliable basis for predicting spectroscopic parameters of unobserved isotopologues. Symmetry-adapted perturbation theory (SAPT) energy decomposition further demonstrates that the Rg–Cu interaction is dominated by induction forces, with significant contributions from dispersion and electrostatics, and exhibits notable charge transfer character. This polarization and orbital overlap transcend the conventional van der Waals picture and reveal a partially covalent nature in noble gas transition metal interactions. Full article

Structural fractures and karst cavities are widely developed in deep and ultra-deep carbonate reservoirs, providing preferential pathways for rapid fluid migration while increasing the risk of severe drilling fluid loss. To investigate the lost-circulation mechanism in fractured-vuggy formations, a hydro-mechanically coupled gas–liquid two-phase seepage model was established by considering the multiple-media characteristics of matrix, fractures and cavities, as well as rock deformation and fluid compressibility. We hypothesize that gas–liquid property differences and hydro-mechanical changes in conductivity jointly control drilling fluid loss, with the gas–liquid property contrast exerting the stronger effect under the simulated conditions. In the model, flow in the matrix and fractures is described by Darcy’s law, while high-velocity flow in cavities is characterized using the Forchheimer non-Darcy equation. The coupling between the seepage field and stress field is achieved by incorporating the effective stress relationship, using the Kozeny–Carman porosity–permeability evolution model and the Goodman fracture deformation model. The coupled equations were implemented in COMSOL. Model validation confirms the reliability of the proposed model in predicting drilling fluid loss. The fracture–vug system significantly enhances fluid exchange between the wellbore and formation. Pressure propagates rapidly along fractures and vugs at the early stage and subsequently diffuses into the surrounding matrix, while the loss rate generally decreases with time. After 120 min, hydro-mechanical coupling increased the loss rate from 1.15 × 10⁻³ to 1.23 × 10⁻³ m³/s and the cumulative loss volume from 11.41 to 12.06 m³. Compared with the single-phase model, the gas–liquid two-phase model predicted a 4.82-fold higher loss rate. Fracture aperture, vug size, bottomhole pressure differential, and rock mechanical properties are the principal factors controlling loss intensity and pressure propagation. Through effective stress variations, hydro-mechanical coupling modifies porosity, permeability, and fracture aperture, thereby affecting formation conductivity and dynamic loss behavior. These results provide theoretical guidance for lost-circulation mechanism analysis, risk assessment, and plugging optimization in deep fractured-vuggy carbonate formations. Full article

Fine-grained visual classification (FGVC) aims to distinguish highly similar subcategories, and its performance relies heavily on the accurate modeling of discriminative local parts and their structural relationships. However, existing Vision Transformer-based methods are susceptible to background noise interference, and the relationship modeling approach relying on explicit spatial coordinates struggles to maintain stable structural representations when targets undergo pose variations and non-rigid deformations. To address these issues, this paper proposes a Semantic Topology Part Network (STP-Net). First, a Prior-Guided Part Aggregator (PGA) is designed, which leverages the foreground prior provided by foundation models to guide discriminative part discovery, enhancing target region responses while suppressing background interference. Second, a Topology-Informed Semantic Graph Convolutional Network (TIS-GCN) is designed to dynamically construct topological relationships among parts in an implicit semantic space, achieving robust modeling against complex structural variations. Furthermore, a Semantic–Spatial Cross-Attention (SSCA) mechanism is introduced to establish bidirectional interaction between semantic relationships and spatial features, and combined with a Global-Context Adaptive Gating mechanism to accomplish multi-scale feature fusion. On four mainstream fine-grained visual classification benchmarks, namely CUB-200-2011, Stanford Cars, Stanford Dogs, and NABirds, the proposed model achieves Top-1 accuracies of 92.7%, 94.9%, 95.2%, and 92.3%, respectively. Comprehensive ablation studies and visualization analyses further validate the effectiveness of the proposed method in background suppression, structural relationship modeling, and discriminative feature learning. Full article

Corporate cash holdings provide firms with financial flexibility, yet their economic value depends on the conditions under which liquidity is accumulated and deployed. This study examines how financial risk, corporate governance, and institutional quality jointly shape corporate cash-holding decisions within a dynamic and institutionally heterogeneous setting. Drawing on complementary precautionary, agency, and institutional perspectives, the study uses Liquidity Power and Liquidity Trap as interpretive lenses for understanding when corporate liquidity enhances financial flexibility or becomes associated with inefficient retention. The empirical analysis covers 528 nonfinancial listed firms across nine Asian economies over 2018–2023, yielding 2964 firm-year observations. A two-step System Generalized Method of Moments estimator is employed to account for cash-holding persistence, potential endogeneity, reverse causality, and unobserved firm-specific heterogeneity. The results indicate that financial risk is positively associated with corporate cash holdings, suggesting that firms respond to heightened financial uncertainty by strengthening precautionary liquidity buffers. Growth opportunities do not exhibit a statistically significant direct effect, while leverage and asset tangibility are negatively associated with cash holdings, and intangible intensity is positively associated with liquidity retention. Governance quality is negatively related to cash holdings, consistent with the view that stronger monitoring constrains excessive liquidity accumulation. Institutional quality has no significant direct effect but significantly moderates the relationship between financial risk and cash holdings, indicating that stronger institutional environments attenuate firms’ reliance on internally retained liquidity as financial risk increases. Robustness tests using alternative variable proxies, alternative estimators, and subsample analyses yield broadly consistent evidence. The findings contribute to the corporate liquidity literature by demonstrating that the economic role of cash is conditional on firm-level governance and country-level institutional conditions rather than determined by financial risk alone. Full article

Background: Pathological cardiac remodeling is a major driver of heart failure progression and is closely associated with fibroblast activation, myocardial inflammation, oxidative injury, and impaired autophagic homeostasis. Epidermal growth factor receptor (EGFR) signaling participates in adverse remodeling through activation of MAPK pathways, but the relationship between pharmacologically modulated EGFR turnover and cardiac remodeling remains insufficiently defined. Methods: This study investigated the protective effects of cycloastragenol (CAG), a triterpenoid sapogenin derived from Astragalus membranaceus, in Angiotensin II (Ang II)-induced cardiac remodeling and examined changes in EGFR turnover and associated MAPK signaling and autophagy-related processes. Circulating soluble EGFR was associated with heart failure severity after adjustment for age and sex. Separately, an independent exploratory transcriptomic analysis of left ventricular samples from GSE161472 showed that myocardial EGFR expression distinguished HFrEF from non-failing samples with an AUC of 0.870. Male C57BL/6 mice subjected to continuous Ang II infusion at 500 ng/kg/min for 28 days were used to establish the in vivo cardiac remodeling model and evaluate the effects of CAG on cardiac function, myocardial injury, fibrosis, inflammation, oxidative stress, apoptosis, and autophagy-related changes, with enalapril used as a positive control. Primary cardiac fibroblasts were further used to assess proliferation, migration, collagen production, EGFR membrane accumulation, lysosomal trafficking, MAPK signaling, and autophagic flux. Circulating soluble EGFR increased with heart failure severity and was associated with BNP elevation, ventricular dilation, and reduced ejection fraction. Results: CAG improved cardiac performance, reduced myocardial injury markers, alleviated fibrosis and hypertrophy, and suppressed inflammatory and oxidative responses in Ang II-treated mice. In cardiac fibroblasts, CAG reduced sustained EGFR membrane accumulation, increased EGFR recovery in pan-ubiquitin immunoprecipitates and lysosomal localization, and accelerated EGFR protein turnover, consistent with a predominantly lysosome-associated pathway. The K716R mutation attenuated CAG-associated changes in EGFR turnover, MAPK phosphorylation, and autophagic flux. Conclusions: These findings suggest that CAG attenuates Ang II-induced cardiac remodeling and is associated with enhanced EGFR lysosomal turnover, accompanied by changes in MAPK signaling and autophagy-related processes. Full article