Search Results (2,779)

This study investigates the as-cast Mg-8Gd-1Y-2Sm-1.2Zn-0.5Mn (wt.%) alloy with high rare-earth content. Solution treatments were conducted at 480 °C, 520 °C, and 560 °C for 6–10 h. Microstructure and mechanical properties were characterized using OM, XRD, SEM-EDS, and compression testing. The as-cast alloy shows a dendritic structure with continuous grain-boundary phases (Mg₅RE, W, and LPSO), exhibiting a compressive yield strength of 145 MPa, ultimate strength of 238 MPa, and fracture strain of 12.66%. Solution temperature has a critical influence on phase dissolution and grain refinement. Notably, the overall plasticity of the material did not show a significant dependence on the specific solution temperature or holding time within the studied range. Treatment at 520 °C produces the most balanced microstructure: clear grain boundaries, extensive phase dissolution, refined grains, and enhanced solid-solution strengthening. Specifically, 520 °C for 10 h results in the finest and most uniformly distributed residual phases, a homogeneous matrix, the highest compressive strength, and suitable conditions for subsequent aging, thus being identified as optimal. Fractography reveals a transition from quasi-cleavage in the as-cast state toward enhanced ductility after solution treatment. However, small cleavage facets after 10 h are attributed to stress concentrations from rare-earth-rich regions and reduced deformation compatibility due to retained LPSO phases. Full article

Diffuse aurora is a widespread and long-lasting auroral emission that plays an important role in diagnosing magnetosphere-ionosphere coupling and magnetospheric plasma transport. Despite its scientific significance, diffuse aurora remains challenging to identify automatically in all-sky imager (ASI) observations due to its weak optical intensity, indistinct boundaries, and gradual temporal evolution. These characteristics, together with frequent cloud contamination, limit the effectiveness of conventional keogram-based or morphology-driven detection approaches and hinder large-scale statistical analyses based on long-term optical datasets. In this study, we propose an automated framework for the identification and temporal segmentation of diffuse aurora from untrimmed all-sky auroral videos. The framework consists of a frame-level coarse identification module that combines weak morphological information with inter-frame temporal dynamics to detect candidate diffuse-auroral intervals, and a snippet-level segmentation module that dynamically aggregates temporal information to capture the characteristic gradual onset-plateau-decay evolution of diffuse aurora. Bidirectional temporal modeling is employed to improve boundary localization, while an adaptive mixture-of-experts mechanism reduces redundant temporal variations and enhances discriminative features relevant to diffuse emission. The proposed method is evaluated using multi-year 557.7 nm ASI observations acquired at the Arctic Yellow River Station. Quantitative experiments demonstrate state-of-the-art performance, achieving 96.3% frame-wise accuracy and an Edit score of 87.7%. Case studies show that the method effectively distinguishes diffuse aurora from cloud-induced pseudo-diffuse structures and accurately resolves gradual transition boundaries that are ambiguous in keograms. Based on the automated identification results, statistical distributions of diffuse aurora occurrence, duration, and diurnal variation are derived from continuous observations spanning 2003–2009. The proposed framework enables robust and fully automated processing of large-scale all-sky auroral images, providing a practical tool for remote sensing-based auroral monitoring and supporting objective statistical studies of diffuse aurora and related magnetospheric processes. Full article

Curling presents a challenging continuous-control problem in which shot outcomes depend on long-horizon interactions between complex physical dynamics, strategic intent, and opponent responses. Despite recent progress in applying reinforcement learning (RL) to games and sports, curling lacks a unified environment that jointly supports stable, rule-consistent simulation, structured state abstraction, and scalable agent training. To address this gap, we introduce a comprehensive learning framework for curling AI, consisting of a full-sized simulation environment, a task-aligned Markov decision process (MDP) formulation, and a two-phase training strategy designed for stable long-horizon optimization. First, we propose a novel MDP formulation that incorporates stone configuration, game context, and dynamic scoring factors, enabling an RL agent to reason simultaneously about physical feasibility and strategic desirability. Second, we present a two-phase curriculum learning procedure that significantly improves sample efficiency: Phase 1 trains the agent to master delivery mechanics by rewarding accurate placement around the tee line, while Phase 2 transitions to strategic learning with score-based rewards that encourage offensive and defensive planning. This staged training stabilizes policy learning and reduces the difficulty of direct exploration in the full curling action space. We integrate this MDP and training procedure into a unified Curling RL Framework, built upon a custom simulator designed for stability, reproducibility, and efficient RL training and a self-play mechanism tailored for strategic decision-making. Agent policies are optimized using Soft Actor–Critic (SAC), an entropy-regularized off-policy algorithm designed for continuous control. As a case study, we compare the learned agent’s shot patterns with elite match records from the men’s division of the Le Gruyère AOP European Curling Championships 2023, using 6512 extracted shot images. Experimental results demonstrate that the proposed framework learns diverse, human-like curling shots and outperforms ablated variants across both learning curves and head-to-head evaluations. Beyond curling, our framework provides a principled template for developing RL agents in physics-driven, strategy-intensive sports environments. Full article

Creeping landslides constitute the predominant form of long-term, slow-moving geohazards in high mountain gorge regions. Under the combined influence of gravity and external triggering factors, these landslides undergo persistent deformation, posing continuous threats to major transportation corridors, hydropower infrastructures, and nearby settlements. Li County is located within the active tectonic belt along the eastern margin of the Tibetan Plateau, characterized by highly variable topography, intensely fractured rock masses, and dense development of creeping landslides. The slip surfaces are typically deeply buried and concealed. Consequently, conventional drilling and profile-based investigations, limited by high costs, sparse sampling points, and poor spatial continuity, are insufficient for identifying the deep-seated structures of such landslides. To address this challenge, this study applies Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) to obtain ascending and descending deformation rate fields for 2022–2024, revealing pronounced spatial heterogeneity and persistent activity across three types of landslides. Based on the principle of mass conservation, the sliding-surface depths of eight typical landslides were inverted, revealing pronounced heterogeneity. The maximum sliding-surface depths range from 32 to 98 m and show strong agreement with borehole and profile data (R² > 0.92; RMSE ±4.96–±16.56 m), confirming the reliability of the inversion method. The GeoDetector model was used to quantitatively evaluate the dominant factors controlling landslide depth. Elevation was identified as the primary control factor, while slope aspect exhibited significant influence in several landslides. All factor combinations showed either “bi-factor enhancement” or “nonlinear enhancement”, indicating that slip-surface depth is governed by synergistic interactions among multiple factors. Boxplot-based statistical analyses further revealed three typical patterns of slip-surface variation with elevation and slope, based on which the landslides were classified into rotational, push-type translational, and traction-type translational categories. By integrating statistical patterns with mechanical models, the study achieves a transition from “form” to “state”, enabling inference of the internal mechanical conditions and evolutionary stages from the observed surface morphology. The results of this study provide an effective technical approach for deep structural detection, identification of controlling factors, and stability evaluation of creeping landslides in high mountain gorge environments. Full article

Lead-free transparent ferroelectric ceramics with integrated opto-electro-mechanical functionalities are pivotal for next-generation multifunctional devices. In this study, K_0.48Na_0.52NbO₃-xLa₂O₃ (KNN-xLa, x = 0.005 − 0.04) ceramics were fabricated via a conventional solid-state route to investigate the La³⁺-induced multiscale structural evolution and its modulation of optical and electrical properties. La³⁺ substitution drives a critical structural transition from an anisotropic orthorhombic phase (Amm2) to a high-symmetry pseudocubic-like tetragonal phase (P4mm) for x ≥ 0.025, characterized by minimal lattice distortion (c/a = 1.0052). This enhanced structural isotropy, coupled with submicron grain refinement (<1 μm) driven by -mediated solute drag, effectively suppresses light scattering. Consequently, a high-transparency plateau (T₇₈₀ ≈ 53–58%, T₁₇₀₀ ≈ 70–72%) is achieved for 0.025 ≤ x ≤ 0.035. Simultaneously, the system undergoes a crossover from normal ferroelectric (FE) to relaxor (RF) state, governed by an FE–RF boundary at x = 0.015. While x = 0.005 exhibits robust piezoelectricity (d₃₃ ≈ 92 pC/N), the x = 0.015 composition facilitates a transitional polar state with large strain (0.179%) and high polarization (P_m ≈ 33.3 μC/cm², P_r ≈ 15.8 μC/cm²). Piezoresponse force microscopy (PFM) confirms the domain evolution from lamellar macro-domains to speckle-like polar nanoregions (PNRs), elucidating the intrinsic trade-off between optical transparency and piezoelectricity. This work underscores La³⁺ as a potent structural modifier for tailoring phase boundaries and defect chemistry, providing a cost-effective framework for developing high-performance transparent electromechanical materials. Full article

Digitalization is widely heralded as a catalyst for growth, yet its role in achieving the United Nations’ Sustainable Development Goal 10 (Reduced Inequalities) remains deeply contested. Moving beyond linear assumptions of “digital dividends,” this study adopts a complex socio-technical systems perspective to unravel the configurational pathways linking digitalization to national income inequality. We analyze a high-quality balanced panel of 56 major economies from 2012 to 2022. Employing Panel Fuzzy-Set Qualitative Comparative Analysis (Panel fsQCA) and Necessary Condition Analysis (NCA), this study proposes an evidence-based typology of digitalization-inequality pathways. We reveal that the impact of digital transformation is asymmetric and contingent on geo-economic contexts. NCA identifies Digital Infrastructure, Innovation, and Governance as necessary “bottlenecks” for social equity. Sufficiency analysis uncovers three distinct sustainable development modes: an “Open Innovation Mode” in affluent small economies, driven by global integration and technological frontiers; a “Governance-Regulated Industry Mode” in major economies, where strong state capacity regulates digital industrial scale; and an “Open Niche Mode” for transition economies, leveraging openness to bypass domestic structural deficits. Conversely, we identify a critical “Hollow Governance Trap” in the Global South, where digital governance efforts fail to reduce inequality in the absence of real industrial and infrastructural foundations. These findings challenge one-size-fits-all policies, suggesting that bridging the global digital divide requires context-specific strategies—ranging from synergistic integration to asymmetric breakthroughs—that align digital investments with institutional capacity. Full article

Modulating boundary conditions offers a powerful approach to generate and control topological defects, which govern the structure and dynamics of liquid crystals. Here, we employ Langevin dynamics simulations to study defect structure formation in two-dimensional colloidal liquid crystals confined within a square cavity whose walls undergo periodic oscillation. The spatial topology of the driving boundary from single-side to global four-wall actuation directly sets the symmetry of energy input, which in turn determines its spatial gradient and distribution. By controlling boundary vibrations through amplitude and frequency, we demonstrate the emergence of novel steady-state patterns and transformations between distinct defect structures, identified via the local order parameter. Four-wall oscillation generates richer structural diversity due to its higher spatial symmetry. Structural transitions are quantified by tracking a global director angle under two driving regimes: varying amplitude at fixed frequency (f = 2.0), and varying frequency at fixed amplitude (A = 1.0). Our results establish that the manner of energy injection determined by the choice of boundary motion mode governs the emergent defect architectures, providing a general route to engineer non-equilibrium phases under confinement. Full article

To efficiently reuse endless fibre-reinforced composites after their life cycle, the recovery of endless fibres including matrix material with subsequent reprocessing in their original state is desirable. Thanks to their covalent adaptive networks, vitrimers offer ideal properties for enabling new repair and circular strategies for composites. In order to evaluate the detachability—meaning the separation of single laminate layers—and recycling potential for continuous fibre reinforcement, process routes and quality parameters must be established. In this study, the double cantilever beam test is used to test the adhesion based on the detachment of continuous fibre layers, and the interlaminare fracture toughness of mode I (G_IC) is measured as a parameter for the required energy for detachment. It was shown that G_IC increases above the vitrimer transition temperature and is higher than for reference specimens with an epoxy matrix. Surface roughness is measured to determine the mechanical and thermal degradation of the chemical network structure and additionally shows fibre cracking and defects in fibre–matrix interfaces. This allows the recycling process to be evaluated up to the production of a second generation, with the aim of identifying the recycling potential of the vitrimer matrix and implementing it for industrial processes. An efficient recycling strategy of the continuous fibre-reinforced vitrimers was thus demonstrated by hot pressing at 190 °C for 45 min, giving vitrimer samples a second life. Full article

During lunar landing and takeoff, an extremely under-expanded jet from retrorocket engines generates a complex impingement flow, including multiple shocks and a near-field recirculation bubble, posing critical risks to lunar missions. To clarify the formation and evolution of the recirculation bubble, numerical simulations under non-uniform inflow conditions over a range of nozzle heights are performed using a compressible Navier–Stokes solver. The shock structures depend on the distance available for inflow development. Non-uniform total pressure ahead of the surface shock is the primary driver of the adverse pressure gradient that initiates the bubble. This non-uniformity originates from shock interactions at high nozzle heights and directly from the inflow conditions at low heights. Furthermore, the flow stabilizes rapidly at high nozzle heights, while strong unsteadiness persists at low heights. A dimensionless coefficient, C_RB, defined as the ratio of pressure difference to dynamic pressure along the recirculation bubble boundary, is proposed to characterize the interaction between the recirculation bubble and surface shock. Its steady-state variation with nozzle height reveals a distinct threshold below which both bubble size and intensity increase sharply, indicating a flow pattern transition. Full article

With the rapid development of wireless communication systems, the electromagnetic environment in subway stations has become increasingly complex, raising concerns about the long-term safety of station attendants who are chronically exposed to radiofrequency (RF) fields. At present, multiphysics analyses specifically addressing RF antenna exposure scenarios for subway attendants remain limited. To assess occupational electromagnetic exposure risks, this paper establishes a comprehensive electromagnetic–thermal coupling simulation model incorporating RF antennas, station-platform structures, and a realistic human model with organs including the brain, heart, and liver. Using the finite-element software COMSOL Multiphysics (v.6.3), numerical simulations are performed to calculate the specific absorption rate (SAR) in the trunk and major organs of the subway station attendant at RF antennas frequencies of 900 MHz, 2600 MHz, and 3500 MHz, as well as the temperature rise distribution of the human trunk and important tissues and organs under different initial temperatures of the environment. The results show that among the three frequencies, the maximum SAR of 5.55 × ${10}^{-4}$ W/kg occurs in the trunk at 3500 MHz. Tissue temperatures reach thermal steady state after 30 min of exposure, with the maximum temperature rises occurring in the brain at an ambient temperature of 18 °C and an operating frequency of 900 MHz, reaching 0.2123 °C. Across all simulated scenarios, both SAR values and temperature rises remain significantly below the occupational exposure limits established by the International Commission on Non-Ionizing Radiation Protection (ICNIRP). These findings indicate that RF radiation generated by antennas in the subway station environment poses low health risks to female station attendants of similar physical characteristics to the Ella model. This study provides a scientific reference for the occupational RF protection of subway personnel and contributes data for the development of electromagnetic exposure standards in rail transit systems. Full article

Vascular smooth muscle cells (VSMCs) in the tunica media are essential for maintaining the structure and function of the arterial wall. These cells regulate vascular tone and contribute to vasculogenesis and angiogenesis, particularly during development. Proper control of VSMC differentiation ensures the correct size and patterning of vessels. Dysregulation of VSMC behaviour in adulthood, however, is linked to serious cardiovascular diseases, including aortic aneurysm, coronary artery disease, atherosclerosis and pulmonary hypertension. VSMCs are characterised by their phenotypic plasticity, which is the capacity to transition from a contractile to a synthetic, dedifferentiated state in response to environmental cues. This phenotypic switch plays a central role in vascular remodelling, a process that drives the progression of many vascular pathologies. Epigenetic mechanisms, which are defined as heritable but reversible changes in gene expression that do not involve alterations to the DNA sequence, have emerged as key regulators of VSMC identity and behaviour. These mechanisms include DNA methylation, histone modifications, chromatin remodelling, non-coding RNA and RNA modifications. Understanding how these epigenetic processes influence VSMC plasticity is crucial to uncovering the molecular basis of vascular development and disease. This review explores the current understanding of VSMC biology, focusing on epigenetic regulation in health and pathology. Full article

For less-developed, carbon-dependent regions, achieving carbon decoupling while pursuing economic catch-up presents a fundamental challenge. This study investigates this persistent dilemma through the case of Jiangxi Province, China, a typical coal-reliant inland region. Utilizing data from 2000 to 2022, we estimate carbon emissions following IPCC guidelines and employ the Generalized Divisia Index Method (GDIM) to decompose emission drivers, effectively overcoming the limitation of factor independence in conventional decomposition analyses. The results identify economic scale (cumulative contribution: 97.81%) and energy consumption (51%) as the primary drivers of emission growth, while carbon intensity of output (−47.38%) emerges as the strongest inhibiting factor. The application of the Tapio decoupling model reveals that weak decoupling is the dominant state, prevailing in 91% of the study period. This persistent pattern underscores only a partial and unstable separation between economic growth and emissions, highlighting the region’s entrenched carbon lock-in. Our findings demonstrate that transcending this weak decoupling dilemma necessitates a strategic shift beyond efficiency gains. We propose that the resolution lies in accelerating structural transitions within the energy system and fostering low-carbon industrial upgrading. This study not only elucidates the dynamics of the carbon decoupling challenge in catch-up regions but also offers actionable and context-specific pathways, providing a valuable reference for analogous regions, particularly in developing and transition economies. Full article

It is imperative to continuously monitor public awareness, attitudes, and environmental actions to adjust policy to promote and support transition processes given the ongoing phenomenon of climate change. Insights into poorly investigated domains, such as rural areas, are particularly valuable in this context. Responding to this challenge, we aimed to diagnose the efforts in which individuals engage for the benefit of their local communities in rural areas of a selected region of Poland (Małopolskie Voivodeship) in the context of climate change and the energy transition. The study concerns a specific region, one with the most intensive deployment of climate and energy policy in Poland. It is also highly diversified in terms of the environment and population, from the densely urbanised Kraków Metropolitan Area to scattered rural areas where institutional resources are scarce. This diversity affects how local populations engage in climate and energy efforts. The study involves a literature review and an original 2024 survey among 300 people from five rural districts of Małopolskie Voivodeship selected to reflect the region’s diversity. The CAPI (Computer-Assisted Personal Interviewing) survey sample was built with chain referral. The in-depth analyses were performed in IBM SPSS, v.25. We employed statistical analyses, including one-way ANOVA to assess between-group variance, χ2 tests, Sidak tests, and Fisher’s tests. The results show that most respondents recognised an association between energy and climate, but the awareness is fragmented and varied. These conclusions call for amplifying environmental awareness, particularly regarding energy transition. We have also confirmed a significant spatial diversification of environmental attitudes and practices among the public regarding the energy transition. It has been confirmed by all indicators, from the state of the environment to the perceived agency to the structure of home heating systems. Additionally, the importance of local governments in pro-climate activities was indicated. This is particularly important in the context of the ‘Anti-smog resolution for Małopolska’, which has been in force in the Małopolska Province since 2019 and plays a leading role in climate policy in the region. What is particularly important is that the vast majority of respondents from all districts declared their support for these changes, for which local governments are responsible. Full article

Understanding consciousness requires bridging theoretical models and clinically measurable brain dynamics. This review integrates three complementary frameworks that converge on a dynamical view of conscious processing: continuous formulations of Integrated Information Theory (IIT), attractor-landscape modeling of brain-state transitions, and perturbational complexity metrics from transcranial magnetic stimulation combined with electroencephalography (TMS-EEG). Continuous-time IIT formalizes how integrated information evolves across temporal hierarchies, while dynamical-systems approaches show that consciousness emerges near criticality, where metastable attractors enable flexible transitions between partially synchronized states. Perturbational-complexity indices capture these properties empirically, quantifying the brain’s capacity for integration and differentiation even without behavioral responsiveness. Across anesthesia, disorders of consciousness, epilepsy, and neurodegeneration, TMS-EEG biomarkers reveal reduced complexity and altered synchronization consistent with structural and functional disconnection. Integrating multimodal data—diffusion MRI, fMRI, EEG, and causal perturbations—is consistent with individualized modeling of consciousness-related dynamics. Standardized protocols, mechanistically interpretable machine learning, and longitudinal validation are essential for clinical translation. By uniting information-theoretic, dynamical, and empirical perspectives, this framework offers a reproducible foundation for consciousness biomarkers that mechanistically link brain dynamics to subjective experience, paving the way for precision applications in neurology, psychiatry, and anesthesia. Full article

This study examines the influence of social capital, intellectual capital, resource rents, and investment capital on the economic performance of the 18 member states of the European Union from 2005 to 2022. Principal component analysis and factor analysis are employed to construct composite measures of social and intellectual capital. The empirical model integrates static panel estimations with Monte Carlo simulations and Panel Smooth Transition Regression (PSTR) to examine nonlinear and regime-dependent growth functions. Investment capital exerts a greater influence on growth when intellectual capital is above a certain threshold, but social capital and resource rents exhibit diverse effects across various regimes; this is consistent with semi-endogenous growth models. In regimes with low intellectual capital, resource rents adversely influence growth, consistent with the resource curse concept; however, this effect diminishes as intellectual capital rises. Finally, partial least squares structural equation modeling indicates that social capital, investment capital, and resource rents influence economic growth, with this effect mediated by intellectual capital. The findings underscore the necessity for the European Union to cultivate and enhance knowledge-based assets while reducing reliance on resource rents to achieve more resilient and sustainable economic development. Full article