Search Results (12,043)

Ionospheric scintillation represents a disturbance phenomenon induced by irregular electron density variations, predominantly occurring in equatorial, auroral, and polar regions, thereby posing significant threats to Global Navigation Satellite Systems (GNSS) performance. Polar regions in particular confront distinctive challenges, including the sparse deployment of dedicated ionospheric scintillation monitoring receiver (ISMR) equipment, the limited availability of strong scintillation samples, severely imbalanced training datasets, and the insufficient sensitivity of conventional Deep Neural Networks (DNNs) to intense scintillation events. To address these challenges, this study proposes a modeling framework that integrates residual neural networks (ResNet) with the Synthetic Minority Over-sampling Technique for Regression with Gaussian Noise (SMOGN). The proposed model incorporates multi-source disturbance features to accurately estimate phase scintillation indices (σ_φ) in polar regions. The methodology was implemented and validated across multiple polar observation stations in Canada. Shapley Additive Explanations (SHAP) interpretability analysis reveals that the rate of total electron content index (ROTI) features contribute up to 64.09% of the predictive weight. The experimental results demonstrate a substantial performance enhancement compared with conventional DNN models, with root mean square error (RMSE) values ranging from 0.0078 to 0.038 for daytime samples in 2024, and an average coefficient of determination (R²) consistently exceeding 0.89. The coefficient of determination for the Pseudo-Random Noise (PRN) path estimation results can reach 0.91. The model has good estimation results at different latitudes and is able to accurately capture the distribution characteristics of the local strong scintillation structures and their evolution patterns. Full article

To address the challenge of predicting lubrication states and reliability caused by the uncertainty of gear materials and structural parameters, a lubrication reliability analysis method considering the randomness of gear parameters is proposed. Firstly, a nonlinear dynamic model of a gear pair is established to derive the dynamic meshing force. The geometric and kinematic analyses are then performed to determine time-varying equivalent curvature radius and entrainment velocity. The minimum film thickness during meshing is further calculated. Considering gear parameters as random variables, a gear lubrication reliability model is formulated. Monte Carlo Simulation method is employed to accurately analyze the dynamic response, dynamic meshing force, equivalent curvature radius, entrainment velocity, probability distribution of minimum film thickness, and gear lubrication failure probability. Additionally, a specialized wear test device is designed to investigate the evolution of tooth surface roughness with wear and to forecast trends in gear lubrication failure probability as wear progresses. The results indicate that the uncertainty in gear parameters have minimal impact on the equivalent curvature radius and entrainment velocity, but significantly affect the dynamic meshing force. The gear speed and root mean square roughness are critical factors affecting lubrication reliability, and the early wear of the teeth enhances the lubrication reliability. The present work provides valuable insights for the design, maintenance, and optimization of high-performance gear systems in practical engineering applications. Full article

Unconventional natural gas development requires a deeper insight into how CH₄ and CO₂ adsorb and diffuse in the pores of coal seams. Graphene (GRA) is frequently employed in microscopic mechanism simulations on coal surfaces because its structure closely resembles that of the coal seam matrix. In this study, molecular dynamics simulations were conducted to systematically investigate the diffusion, adsorption, and desorption behaviors of CH₄ and CO₂ within the pore system of hydrated graphene under three representative temperature and pressure conditions: 190 K-6 MPa, 298 K-0.1 MPa, and 320 K-8 MPa. The results show that heatinfg and depressurization significantly enhance the diffusion ability of gas molecules and promote their desorption from the graphene surface. Low temperature and high pressure are conducive to the formation of a stable adsorption layer, and more hydrogen bond structures are formed between CO₂ and water. However, under high-temperature conditions, this ordered structure is significantly weakened. The density distribution further reveals the spatial distribution characteristics of water molecules and gases and their evolution trends with changes in temperature and pressure. This research is conducive to a deeper understanding of the multiphase behavior of coalbed methane and its regulatory mechanism, providing theoretical support for the gas storage and displacement processes. Full article

This paper proposes a formal descriptive model for understanding the evolution of characters in detective fiction from the 19th to the 20th century, using methodologies and technologies from the Semantic Web. The integration of Digital Humanities within the theory of comparative literature opens new paths of study that allow for a digital approach to the understanding of intertextuality through close reading techniques and ontological modelling. In this research area, the variety of possible textual relationships, the levels of analysis required to classify these connections, and the inherently referential nature of certain literary genres demand a structured taxonomy. This taxonomy should account for stylistic elements, narrative structures, and cultural recursiveness that are unique to literary texts. The detective figure, central to modern literature, provides an ideal lens for examining narrative intertextuality across the 19th and 20th centuries. The analysis concentrates on character traits and narrative functions, addressing various methods of rewriting within the evolving cultural and creative context of authorship. Through a comparative examination of a representative sample of detective fiction from the period under scrutiny, the research identifies mechanisms of (meta)narrative recurrence, transformation, and reworking within the canon. The outcome is a formal model for describing narrative structures and techniques, with a specific focus on character development, aimed at uncovering patterns of continuity and variation in diegetic content over time and across different works, adaptable to analogous cases of traditional reworking and narrative fluidity. Full article

This paper investigates quiescent solitons in optical fibers and crystals, modeled by the complicated Ginzburg–Landau equation incorporating nonlinear chromatic dispersion and six self-phase modulation structures introduced by Kudryashov. The model is formulated with linear temporal evolution and analyzed using Lie symmetry methods. The study also identified parameter constraints under which solutions exist. Full article

Understanding the evolutionary and geographic trajectories of crop wild relatives is vital for enhancing agro-biodiversity and advancing climate-resilient agriculture. This study focuses on ten Vicia L. taxa—comprising five species, four varieties, and one subspecies—of significant agricultural importance in Türkiye. An integrative molecular framework was applied, incorporating nuclear ITS sequence data, ITS2 secondary structure modeling, phylogenetic network analysis, and time-calibrated biogeographic reconstruction. This approach revealed well-supported clades, conserved secondary structural elements, and signatures of reticulate evolution, particularly within the Vicia sativa L. and V. villosa Roth. complexes, where high genetic similarity suggests recent divergence and possible hybridization. Anatolia was identified as both a center of origin and a dispersal corridor, with divergence events estimated to have occurred during the Late Miocene–Pliocene epochs. Inferred migration routes extended toward the Balkans, the Caucasus, and Central Asia, corresponding to paleoenvironmental events such as the uplift of the Anatolian Plateau and the Messinian Salinity Crisis. Phylogeographic patterns indicated genetic affiliations between Turkish taxa and drought-adapted Irano-Turanian lineages, offering valuable potential for climate-resilient breeding strategies. The results establish a molecularly informed foundation for conservation and varietal development, supporting sustainability-oriented innovation in forage crop systems and contributing to regional food security. Full article

We propose a universal framework for understanding system evolution based on structural complexity, offering a directional signature that applies across physical, chemical, and biological domains. Unlike entropy, which is constrained by its definition in closed, equilibrium systems, we introduce Kolmogorov Complexity (KC) and Fractal Dimension (FD) as quantifiable, scalable metrics that capture the emergence of organized complexity in open, non-equilibrium systems. We examine two major classes of systems: (1) living systems, revisiting Schrödinger’s insight that biological growth may locally reduce entropy while increasing structural order, and (2) irreversible natural processes such as oxidation, diffusion, and material aging. We formalize a Universal Law: expressed as a non-decreasing function Ω(t) = α·KC(t) + β·FD(t), which parallels the Second Law of Thermodynamics but tracks the rise in algorithmic and geometric complexity. This framework integrates principles from complexity science, providing a robust, mathematically grounded lens for describing the directional evolution of systems across scales-from crystals to cognition. Full article

Banking has evolved from ancient times of using grain banks and temple lending to modern banking practices. The transformation of the banking sector has ensured that banks play the crucial role of facilitating faster and efficient service delivery. This paper traced the evolution of banking and examined associated disruptions, opportunities, and challenges. With the specific objective of influencing policy-oriented discussions on the future of banking, this study adopted a literature review methodology of integrating various sources, such as scholarly journals, policy reports, and institutional publications. Public interest theory and disruptive innovation theory underpinned this study. Findings revealed that banking has evolved from Banking 1.0 to Banking 5.0 due to disruptive factors which have been pivotal to the significant structural sector changes: Banking 1.0 (pre-1960s); Banking 2.0 (1960s to 1980s); Banking 3.0 (1980s–2000s); Banking 4.0 (2000s–2020s); and Banking 5.0 (2020s to the future). Despite the existence of opportunities in the transformation, challenges include regulations, skills shortages, legacy systems, and cybersecurity that must be addressed. This calls for a coordinated response from stakeholders, with banking’s future requiring collaborations as cashless economies, digital economies, and digital currencies take centre stage. Full article

With the rapid proliferation of unmanned aerial vehicles (UAVs), reliable identification based on radio frequency (RF) signals has become increasingly important for both civilian and security applications. This paper proposes a spatiotemporal feature extraction and classification framework based on bispectral analysis. Specifically, bispectral estimation is used to convert one-dimensional RF signals into two-dimensional bispectrum feature maps that capture higher-order spectral characteristics and nonlinear dependencies. Based on these characteristics, a two-stage network was constructed for spatiotemporal feature extraction and classification. The first stage utilizes a ResNet18 network to extract spatial structural features from individual bispectrum maps. The second stage employs an LSTM network to learn temporal dependencies across the sequence of bispectrum maps, capturing the continuity and evolution of signal characteristics over time. The experimental results on a public dataset of UAV RF signals show that this method improves recognition accuracy by 6.78% to 13.89% compared to other existing methods across five categories of UAVs. Full article

With the rapid evolution of technology and the continuous deepening of digital transformation in education, personalized and adaptive learning have emerged as inevitable trends in the educational landscape. This study focuses on a Computerized Adaptive Learning System Based on Cognitive Diagnosis (CAL-CDS)—an integrated platform that incorporates multiple technologies for assessment and learning. The study is organized around two dimensions: (1) constructing a foundational cognitive diagnostic assessment framework, and (2) investigating the operational mechanisms of the cognitive diagnosis-based computerized adaptive system. It comprehensively incorporates core components including cognitive modeling, Q-matrix generation, and diagnostic test development. On this basis, this study dissects the system’s operational logic from four aspects: the adaptive testing system, diagnostic system, recommendation system, and empirical case studies. This study effectively addresses two core questions: how to construct a cognitive diagnostic assessment framework that alignes with China’s mathematics knowledge structure, and how to facilitate personalized student learning via cognitive diagnosis. Overall, this study offers a systematic solution for developing mathematics-specific cognitive diagnosis-driven adaptive learning systems. Full article

The energy transition is an issue of fundamental importance in the current global context, as an increasing number of countries are committed to searching for minerals and elements essential for the storage, distribution, and supply of energy derived from new renewable and sustainable sources. In some countries, these elements (such as boron, lithium, and strontium) are considered to be critical raw materials (CRMs) because of their limited occurrence within their own borders and are commonly found in minerals and geothermal–formation waters, especially in brackish to brine waters. In the Italian territory, CRM-rich waters have already been identified by previously published studies (i.e., with mean concentrations in the Salsomaggiore Terme of 390 mg/L of boron, 76 mg/L of lithium, and 414 mg/L of strontium); however, their extraction is hampered by several knowledge gaps. In particular, a comprehensive understanding of the origin, accumulation processes, and migration pathways of these CRM-rich waters is still lacking. These factors are closely linked to the geological framework and evolutionary history of each specific area. To address these gaps, we investigated the Salsomaggiore Structure that is located at the northwestern front of the Apennine in Italy by integrating geological data with hydrogeochemical results. We constructed new preliminary distribution maps of the most significant CRMs around the Salsomaggiore Structure, which can be used in the future for the National Mineral Exploration Program drawn up in accordance with the European Critical Raw Materials Act. These maps, combined with the interpretation of seismic reflection profiles calibrated with surface geology and wells, allowed us to establish a close relationship between water geochemistry/CRM contents and the geological evolution of the Salsomaggiore Structure. This structure can be considered representative of the frontal ranges of the Northwestern Apennine and other mountain chains associated with the foreland basin systems. Full article

Needle structure was analyzed in three Juniperus communis taxa from different localities in central Slovakia. The main aim was to test the hybrid origin hypothesis of J. communis nothovar. intermedia (Schur) Nyman, defined as a cross between J. communis L. ssp. communis and J. communis ssp. nana (Hook.) Syme. While DNA-based analyses remain the most reliable tool for inferring evolutionary history, comparative needle morphology can provide complementary evidence, including diagnostic traits for taxonomic delimitation. In this study, we evaluated three morphometric and sixteen anatomical needle traits, measured via microscopy in ten shrubs per taxon. The analyses indicated that most traits in nothovar. intermedia matched one of the parents, with only two traits proving strongly diagnostic, separating all three taxa: needle length, which showed an intermediate mean phenotype in nothovar. intermedia (R² = 0.824, p = 0.011; between parents), and vascular bundle height, which displayed a transgressive pattern (R² = 0.552, p = 0.031; between parents). Although the diagnostic value of individual traits for hybrid detection was generally weak, a phylogenetic network analysis based on six diagnostic traits that separated individuals of the parental taxa provided evidence for reticulate evolution. These results support the hybrid origin of J. communis nothovar. intermedia and highlight needle traits with potential value for distinguishing ssp. communis and ssp. nana in natural populations, which may assist in taxonomic delimitation and inform future conservation assessments. Full article

Supercritical CO₂ (ScCO₂) injection has emerged as a promising method to enhance shale gas recovery while simultaneously achieving CO₂ sequestration. This research investigates how ScCO₂ interacts with water and shale rock, altering the pore structure characteristics of shale reservoirs. The study examines shale samples from three marine shale formations in southern China under varying thermal and pressure regimes simulating burial conditions at 1000 m (45 °C and 10 MPa) and 2000 m (80 °C and 20 MPa). The research employs multiple analytical techniques including XRD for mineral composition analysis, MICP, N₂GA, and CO₂GA for comprehensive pore characterization, FE–SEM for visual observation of mineral and pore changes, and multifractal theory to analyze pore structure heterogeneity and connectivity. Key findings indicate that ScCO₂–water–shale interactions lead to dissolution of minerals such as kaolinite, calcite, dolomite, and chlorite, and as the reaction proceeds, substantial secondary mineral precipitation occurs, with these changes being more pronounced under 2000 m simulation conditions. Mineral dissolution and precipitation cause changes in pore structure parameters of different pore sizes, with macropores showing increased PV and decreased SSA, mesopores showing decreased PV and SSA, and micropores showing insignificant changes. Moreover, mineral precipitation effects are stronger than dissolution effects. These changes in pore structure parameters lead to alterations in multifractal parameters, with mineral precipitation reducing pore connectivity and consequently enhancing pore heterogeneity. Correlation analysis further revealed that H and D₋₁₀−D₁₀ exhibit a significant negative correlation, confirming that reduced connectivity corresponds to stronger heterogeneity, while mineral composition strongly controls the multifractal responses of macropores and mesopores, with micropores mainly undergoing morphological changes. However, these changes in micropores are mainly manifested as modifications of internal space. Siliceous shale samples exhibit stronger structural stability compared to argillaceous shale, which is attributed to the mechanical strength of the quartz framework. By integrating multifractal theory with multi–scale pore characterization, this study achieves a unified quantification of shale pore heterogeneity and connectivity under ScCO₂–water interactions at reservoir–representative pressure–temperature conditions. This novelty not only advances the methodological framework but also provides critical support for understanding CO₂–enhanced shale gas recovery mechanisms and CO₂ geological sequestration in depleted shale gas reservoirs, highlighting the complex coupling between geochemical reactions and pore structure evolution. Full article

To investigate cohesion degradation in laterite soil under dry–wet cycles—a process exhibiting intrinsic asymmetric evolution in natural systems—direct shear tests were conducted on natural and stabilized soils (guar gum/coconut fiber composites) under simulated cycles. A cohesion prediction model was developed using the gray system GM(1,1) framework, with validation confirming its applicability and reliability. Results indicate the following: (1) Stabilized soils showed significantly increased cohesion and reduced cohesion degradation rates. (2) Compared to coconut fiber-stabilized soil, guar gum-stabilized soil exhibited smaller cohesion decay magnitude and more stable internal structure. (3) Cohesion degradation in both natural and stabilized soils conformed to the GM(1,1) model, achieving >95% fitting accuracy across all groups (peak: 99.84% for natural soil). This model effectively characterizes the strength degradation process under dry–wet cycles, establishing a novel methodology for predicting cohesion in natural/stabilized laterite soils. Full article

Electrochemical CO₂ reduction reaction (CO₂RR) represents a promising pathway for carbon neutralization. Here, we report hierarchical CuO nanorod arrays synthesized via cyclic voltammetry (CV) treatment as freestanding electrodes for selective CO₂RR. The CV activation process generates ultrathin nanosheets on CuO nanorods, creating abundant interfaces that facilitate formate production. Optimized CV-2000-CuO achieves 42% Faradaic efficiency (FE) for formate at −1.4 V vs. RHE while suppressing hydrogen evolution reaction (HER). Comprehensive characterization reveals that CV treatment promotes partial surface reduction to metallic Cu and generates high-density grain boundaries during CO₂RR operation. These structural features enhance CO₂RR activity and stability compared to pristine CuO (P-CuO). This work demonstrates a novel electrode engineering strategy combining freestanding architecture with electrochemical activation for efficient CO₂-to-formate conversion. Full article