Search Results (3,076)

Understanding the changes in physical and mechanical properties of lignite during dehydration is crucial for its sustainability in coal mining, exploitation of coalbed methane, and carbon dioxide sequestration. Through SEM and Computed Tomography (CT) scanning, combined with fractal theory, this study investigates dynamic dehydration characteristics of macerals in lignite during normal temperature drying (NTD), and their effects on pore–fracture development and physic–mechanical property evolution. The results show that the hard layers of lignite are mainly composed of ulminite (Ul), while the soft layers are primarily composed of fusinite (Fu), densinite (De), and Ul. Ul exhibits low dehydration efficiency but is prone to shrinkage and cracking heavily, whereas Fu has high dehydration efficiency and excellent thermal stability. The layered enrichment of macerals controls the development of the three-dimensional (3D) pore–fracture structures of lignite during NTD and leads to distinct cracking characteristics of fracture structures between hard and soft layers. Unlike soft layers, hard layers tend to form long, straight fracture structures with large apertures and exhibit extremely high fracture connectivity and fractal dimension (FD). In addition, the differential drying behavior of macerals causes the physical parameters of lignite such as moisture ratio (MR), drying rate (DR), and density (ρ) to show a dynamic evolution characteristic of “initial rapid decline (or increase) in the early stage–subsequent gradual decline (or increase) and stabilization in the later stage” during NTD. The unique pore–fracture structure controlled by macerals significantly alters the deformation resistance and failure mode of dehydrated lignite under uniaxial compression but has limited effect on its uniaxial compressive strength. Full article

The evaluation of resources and environmental carrying capacity (RECC) is of great significance for achieving harmony between humans and resources and the environment to realize sustainable development. However, current research has not reached a consensus on the research objects, theories, and methods for RECC evaluation. Therefore, this study defined the research object of regional RECC evaluation and designed an evaluation process for regional RECC based on the mutation progression method developed from the mutation theory. Then, the RECCs of 16 cities in Shandong Province during 2013–2022 were calculated, and their temporal and spatial evolution characteristics were analyzed. The result shows that: (1) the research object of regional RECC evaluation is essentially the concentrated reflection of the interaction between resources, the environment, the economy, and society; (2) the process of “construct a multilevel evaluation index system–determine the mutation types of the evaluation index system–standardize the lowest level indexes–evaluate the comprehensive regional RECC” could provide reference for RECC evaluation; and (3) from 2013 to 2022, the RECC in Shandong Province showed a steady increasing trend, and the RECC in eastern and central areas in Shandong Province was relatively higher. By analyzing these results, we found that the natural background conditions, the mode of production and life, and the decisions of the central government are the important factors affecting regional RECCs. Full article

In the modern era, the green manufacturing innovation ecosystem is vital for promoting sustainable development. It significantly contributes to achieving carbon peak and carbon neutrality goals. Current research regarding the coevolution of the green manufacturing innovation ecosystem predominantly utilizes deterministic models. These models do not account for the inherent stochasticity present in interactions within the innovation ecosystem, and they also neglect quantitative analyses pertaining to ecosystem resilience and stability mechanisms. This study explores the core mechanisms that drive the stability of the green manufacturing innovation ecosystem. It is based on theories of ecology and innovation. This study employs the Lotka–Volterra model to characterize the stochastic evolutionary process of symbiotic interactions among innovation groups. Compared to deterministic models, the stochastic approach has significant advantages. It captures the inherent uncertainties of human behavior and subjective decision-making. Additionally, it accounts for dynamic environmental changes. This approach provides more realistic insights into the evolution of green manufacturing innovation ecosystems amid complex conditions. The findings yield three key conclusions. First, the mutualistic symbiosis model is more stable than other models. This includes independent, competitive, parasitic, and commensal symbiosis models. This stability underscores the mutualistic model’s critical role in sustaining the ecosystem’s development. Second, the return time for a mutualistic symbiosis ecosystem is notably shorter than for a stochastic interaction ecosystem. This indicates that mutualistic symbiosis is more effective in fostering growth within the green manufacturing innovation ecosystem. Third, participant relationships in this ecosystem are complex. They encompass competitive, parasitic, and commensal dynamics, among others. Furthermore, the ecosystem’s resilience improves as the rate of mutually beneficial interactions increases. These findings provide direct policy and management guidance for optimizing the symbiotic mechanisms of green manufacturing innovation ecosystems, enhancing ecosystem resilience, and advancing carbon peaking and carbon neutrality goals. Full article

As nanofabrication advances toward atom-by-atom control of surface morphology, plasmonic electrodes and nanogap devices are being pushed into a regime where atomic-scale protrusions and sub-nanometer separations become accessible. In this extreme limit, classical electrodynamics becomes unreliable because it cannot capture quantum effects. To this end, we compute the optical response of metallic sub-nanometer nanogaps containing atomic-scale protrusions by employing quantum hydrodynamic theory (QHT), and benchmark the predictions against the classical local-response approximation (LRA). We revealed that atomic-scale variations in protrusion can leave the far-field scattering spectrum nearly unchanged while profoundly reshaping tnear-field nanofocusing. Upon a continuous decrease in the nanogap, QHT successfully predicts non-monotonic spectral evolution with a redshift-to-blueshift deflection point accompanied via a suppression of field enhancement, whereas LRA yields a continuous redshift and a monotonic increase in field enhancement. We further demonstrated that such an inflection point is tunable, as determined by the atomic morphology of the electrodes, which provide a theoretical foundation for the experimental observation of varied inflection points. These results provide a practical route to optically diagnose and engineer tunneling-enabled charge exchange and quantum-regulated nanofocusing in extreme plasmonic nanogaps, and offer design guidance for molecular-scale optoelectronic and nanophotonic devices. Full article

With the increasing development and utilization of natural resources, the importance of rock property characterization is becoming increasingly prominent. Artificial intelligence (AI) technology, with its rapid and accurate identification and analysis capabilities, is driving the evolution of a new generation of intelligent rock property analysis technologies. This paper systematically reviews the application and development trends of AI in rock property analysis. Key topics include: using AI methods to identify and analyze rock structure, composition, and texture; introducing commonly used AI models, analytical metrics, and public datasets in this field to help researchers more comprehensively evaluate model performance and match appropriate rock data; and summarizing AI solutions, future challenges, and coping strategies in four key areas of rock property analysis. This study emphasizes that the current application of AI methods to rock property analysis still faces challenges such as data quality, model generalization, and interpretability. To address these challenges, this paper proposes constructive suggestions, including the development of industry standards for intelligent rock analysis, the integration of petrological theory and fluid dynamics equations, and the adoption of weakly supervised learning strategies, in order to overcome existing technical bottlenecks. Full article

The mechanical behavior of loess under varying moisture conditions plays a critical role in the stability of slopes and foundations in loess regions. Owing to its high porosity and metastable structure, loess is particularly sensitive to moisture-induced strength degradation. Although geogrid reinforcement has been widely adopted to improve soil stability, the combined influence of moisture condition, reinforcement characteristics, and confinement on the shear behavior of loess remains insufficiently understood. In this study, consolidated undrained (CU) triaxial tests were conducted on partially saturated loess reinforced with glass fiber geogrids (GFGs) and basalt fiber geogrids (BFGs) under different moisture contents (13–17%) and confining pressures (100–300 kPa). The effects of geogrid type, reinforcement configuration, and confinement on shear strength and deformation behavior were systematically examined. The results indicate that geogrid reinforcement significantly enhances the shear strength, stiffness, and ductility of loess, particularly under low to moderate confining pressures. Increasing the number of reinforcement layers resulted in peak strength improvements of up to approximately 25% and promoted a transition from brittle to ductile behavior. Distinct reinforcement responses were observed: GFG exhibited higher initial stiffness and more rapid mobilization, whereas BFG demonstrated progressive tensile mobilization and superior residual strength. Furthermore, a modified Unified Twin-Shear Strength Theory (UTSST) incorporating a strain-dependent reinforcement mobilization coefficient was proposed, which provided an empirical representation of the observed strength evolution with good agreement with the experimental results (R² > 0.96). Full article

This work is axiomatic and structural in nature and is not intended as a phenomenological physical theory, but as a framework clarifying minimal informational primitives from which geometric and dynamical descriptions may emerge. We present a background-independent framework in which physical geometry, interaction-like forces, and spacetime arise as effective descriptions of constrained relational information rather than as fundamental entities. The only primitive structure is a network of degrees of freedom linked by admissible informational relations, each subject to quantifiable constraints on accessibility or flow. The motivation is to identify whether a single minimal relational primitive can account jointly for the emergence of geometry, forces, and spacetime, without presupposing a manifold, fields, or fundamental interactions. The framework is formalized using weighted relational graphs in which constraint weights encode limitations on information flow between degrees of freedom. Effective geometry is defined operationally through minimal constraint cost along relational paths, yielding an emergent metric without assuming spatial embedding. Relational evolution is modeled via a minimal configuration-space dynamics defined by local rewrite moves, and a statistical description is introduced through an informational action that governs coarse-grained response rather than serving as a fundamental dynamical law. Curvature-like observables are defined using transport-based comparisons of local accessibility structure. Within this setting, metric structure emerges from constrained relational accessibility, while curvature-like behavior arises from heterogeneity in constraint structure. Effective forces appear as entropic or informational action gradients with respect to coarse-grained control parameters that modulate relational constraints, and are interpreted as emergent responses rather than primitive interactions. A finite worked example explicitly demonstrates the emergence of nontrivial distance, curvature proxies, and an effective force via geodesic switching under constraint variation, without assuming fundamental spacetime, fields, or particles. The results support an interpretation in which geometry, forces, and spacetime are representational features of constrained information flow rather than fundamental elements of physical law. The framework clarifies conceptual distinctions and points of compatibility with existing approaches to emergent spacetime, and it outlines qualitative expectations for regimes in which smooth geometric descriptions are expected to break down. The work delineates the scope and limits of geometric description without proposing a complete phenomenological theory. Full article

This study establishes a municipal-level evaluation system for rural revitalization in China, grounded in the five-sphere integrated framework encompassing “prosperous industries, livable ecology, civilized rural customs, effective governance, and affluent life.” Employing methodologies including the entropy weight-coupling coordination model, LISA spatiotemporal analysis, and multi-scale geographically weighted regression (MGWR), it empirically investigates the evolution and driving mechanisms of rural revitalization development across 282 prefecture-level cities from 2011 to 2023. The findings reveal: (1) Nationwide and regional rural revitalization levels demonstrate a consistent upward trajectory, progressing from a state of “Mild Disorder” to being “On the Verge of Disorder,” with a distinct gradient pattern of “Eastern Region > National Average > Central Region > Western Region.” (2) Significant global spatial correlation is observed, manifesting as polarization typified by “high–high” and “low–low” agglomeration, alongside notable volatility in Northeast and Southwest China. (3) Influencing factors display marked spatiotemporal heterogeneity. Agricultural production efficiency (North China) and technological innovation (nationwide, except the Yangtze River Delta) significantly foster rural revitalization. Conversely, economic development level (Northeast, Central, and Western China), government intervention (Northeast China), and industrial structure upgrading (Northwest China) exhibit constraining effects. The localized positive impacts of urbanization (border areas of Yunnan, Heilongjiang, Sichuan, Jilin, and Tibet) and opening up (border ports) are increasingly evident. Building on these insights, the study proposes recommendations—such as implementing differentiated regional policies, innovating spatial governance models, and activating multidimensional drivers—to overcome the “low-level lock-in” predicament and advance comprehensive rural revitalization. Furthermore, this paper reveals the patterns of multidimensional system coupling and the spatial heterogeneity of driving mechanisms. These findings provide a reference for deepening the understanding of geographical complexity within global sustainable development theory. Full article

Against the backdrop of global climate change, frequent public health crises, and escalating geopolitical conflicts, the stable operation of emergency logistics supply chains faces severe challenges. Building a resilient system that combines disturbance resistance and adaptability has become an urgent necessity. This paper, grounded in the evolution of resilience theory, clearly defines the meaning of emergency logistics supply chain resilience. It systematically identifies and constructs an indicator system comprising 17 influencing factors across four dimensions: Resistance, Responsiveness, Adaptability, and Development Capacity. Employing a hybrid fuzzy DEMATEL-ISM-MICMAC approach, the study quantifies causal relationships and hierarchical structures among factors while analyzing their driving forces and dependency attributes. Findings reveal that infrastructure development, emergency plan integrity, talent cultivation, financial safeguards, and regulatory support constitute core critical factors influencing emergency logistics supply chain resilience. Among these, regulatory support and financial safeguards form the fundamental pillars underpinning the system’s operation. The multidimensional influence factor framework and hybrid analytical method developed in this study not only enrich the theoretical research system on emergency logistics supply chain resilience but also provide scientific decision-making references and practical guidance for policymakers and industry practitioners to formulate targeted resilience enhancement strategies. Full article

Compared with conventional synthetic aperture radar (SAR) three-dimensional (3D) imaging systems, distributed unmanned aerial vehicle (UAV) SAR systems offer enhanced flexibility and single-pass capability, enabling rapid 3D imaging. Their performance, however, critically depends on the spatial arrangement of UAVs. Improper configurations result in grating lobes and increase the sidelobe level, thereby degrading elevation reconstruction. Additionally, the coordinated operation of distributed UAVs imposes spatial constraints such as safety separation. To address these challenges, this paper formulates the configuration design as a multi-constraint, multi-objective optimization problem that simultaneously considers both imaging performance and operational feasibility. Based on compressive sensing (CS) theory, the influence of configuration on sparse imaging is analyzed, and practical constraints are integrated, including 3D span limits, safety separation, and mainlobe avoidance. A joint optimization model is established to minimize the cumulative coherence of the sensing matrix while maximizing system spatial compactness. To efficiently solve this high-dimensional problem, a decoupled recursive strategy is proposed. In the first stage, a hybrid algorithm combining particle swarm optimization (PSO) and covariance matrix adaptation evolution strategy (CMA-ES) performs global optimization in the baseline domain. In the second stage, a compact configuration is constructed within the feasible region via analytical spatial recursion. Experimental results demonstrate that the proposed approach effectively reduces sensing matrix coherence and improves 3D reconstruction quality. Full article

The rapid expansion of algorithmic systems has transformed cognition into an increasingly distributed and collective enterprise, giving rise to what can be described as cognitive assemblages, dynamic constellations of humans, institutions, data infrastructures, and artificial agents. This paper traces the historical and conceptual evolution that has led to this shift. First, we show how cognition, once conceived as the property of autonomous individuals, has progressively become embedded in socio-technical networks in which algorithmic processes participate as co-agents. Second, we revisit the progressive awareness of human cognitive limits, from bounded rationality to contemporary theories of extended mind. These frameworks anticipate and help explain today’s hybrid cognitive ecologies. Third, we assess the philosophical implications for Enlightenment ideals of autonomy, rationality, and self-governance, showing how these concepts must be reinterpreted in light of pervasive algorithmic intermediation. Finally, we examine global initiatives that seek to integrate augmented cognitive capacities into large-scale cybernetic forms of societal coordination, ranging from digital platforms and data spaces to AI-driven governance systems. These developments offer new opportunities for steering complex societies under conditions of globalization, environmental disruption, and the rise of autonomous intelligent systems, yet they also raise profound questions regarding control, accountability, and democratic legitimacy. We argue that understanding cognitive assemblages is essential to designing socio-technical systems capable of supporting collective intelligence while preserving human values in an era of accelerating complexity. Full article

Advancements in earth observation technologies are ushering in the big data era, yet this potential is compromised by intrinsic challenges: inherent uncertainty, spatiotemporal heterogeneity, multi-scale character, and pervasive data gaps. Traditional methods often fail to address these issues within a single, coherent system. The main contributions of this review are to systematically establish the Remote Sensing Geostatistical Paradigm (RSGP) as a comprehensive, unified framework. Powered by its core theory, Bayesian Maximum Entropy (BME), RSGP is a broadly designed epistemic framework that transcends a mere conceptual reorganization of established methods. It addresses the above challenges by highlighting two pivotal concepts within a spatiotemporal random field: (1) uncertainty quantification via probabilistic soft data, which redefines observations as probability density functions, representing a fundamental epistemological shift from deterministic scalars to probabilistic entities, and provides a universal interface for rigorous assimilation of heterogeneous remote sensing or in situ observations and synergy with other computational models, such as machine learning; and (2) spatiotemporal structure exploitation, which integrates the underlying structure embedded in remote sensing data of natural attributes, moving beyond mere optical properties to incorporate a broader range of available spatiotemporal information, for robust estimation and mapping purposes. Furthermore, the evolution of key technologies is illustrated by using real-world application cases, guiding how to implement RSGP in terms of different scenarios. Finally, the paradigm’s features and limitations are discussed. This synthesis provides the remote sensing community with a robust foundation for uncertainty-aware analysis and multi-source integration, bridging geostatistical logic with next-generation AI-driven Earth observation. Full article

The evolution of Hybrid Power Supply Systems (HPSSs) has extended from ground-based microgrids to the safety-critical domain of More Electric Aircraft (MEA). This paper presents a comprehensive review of dynamic power allocation strategies, bridging the gap between mature ground-based control theories and the stringent operational requirements of aerospace systems. Strategies are systematically classified into centralized, decentralized, and distributed architectures based on control structures. Evaluations indicate that centralized strategies, while effective in microgrids, achieve global optimality but face reliability constraints in airborne environments. In contrast, decentralized strategies based on virtual impedance ensure the high reliability and “plug-and-play” modularity essential for avionics yet often yield suboptimal coordination. Consequently, distributed cooperative control is identified as the most promising paradigm to bridge this gap, synthesizing optimization with fault tolerance. Finally, critical challenges in adapting these technologies to aviation—spanning algorithmic determinism and airworthiness certification—are discussed, and future trends in hybrid intelligence and digital twin-based verification are outlined for next-generation airborne energy systems. Full article

Thin liquid film flows of nanofluids over porous surfaces are central to applications ranging from microfluidic thermal management to precision coating technologies. This study investigates the hydrodynamic and thermal stability of a nanofluid flowing down a non-uniformly heated inclined porous plane subject to the Beavers-Joseph slip boundary condition. Using the long-wave approximation, a nonlinear evolution equation governing the film thickness is derived. The stability characteristics are systematically analyzed via linear stability theory, weakly nonlinear analysis, and fast Fourier transform (FFT) numerical simulations. Quantitative results indicate that the porous medium permeability, density difference, and Marangoni number act as destabilizing factors; specifically, increasing the porous parameter $\beta$ (from 0 to 0.3), the density ratio ${\zeta }_0$ (from 0 to 5), and the Marangoni number $Mn$ (from 0 to 0.3) significantly reduces the critical Reynolds number and accelerates the onset of interfacial instabilities. In contrast, increasing the nanoparticle volume fraction $\varphi$ from 0 to 0.3 exerts a dominant stabilizing effect by elevating the critical Reynolds number and shrinking the unstable wavenumber domain. Furthermore, nonlinear simulations confirm that higher nanoparticle concentrations effectively suppress the saturation amplitude of disturbances, promoting the eventual stabilization of the liquid film. Full article

We detail a procedure to transform the current empirical stage in the study of complex systems into a predictive phenomenological one. Our approach starts with the statistical-mechanical Landau-Ginzburg equation for dissipative processes, such as kinetics of phase change. Then, it imposes discrete time evolution to explicit back feeding, and adopts a power-law driving force to incorporate the onset of chaos, or, alternatively, criticality, the guiding principles of complexity. One obtains, in closed analytical form, a nonlinear renormalization-group (RG) fixed-point map descriptive of any of the three known (one-dimensional) transitions to or out of chaos. Furthermore, its Lyapunov function is shown to be the thermodynamic potential in q-statistics, because the regular or multifractal attractors at the transitions to chaos impose a severe impediment to access the system’s built-in configurations, leaving only a subset of vanishing measure available. To test the pertinence of our approach, we refer to the following complex systems issues: (i) Basic questions, such as demonstration of paradigms equivalence, illustration of self-organization, thermodynamic viewpoint of diversity, biological or other. (ii) Derivation of empirical laws, e.g., ranked data distributions (Zipf law), biological regularities (Kleiber law), river and cosmological structures (Hack law). (iii) Complex systems methods, for example, evolutionary game theory, self-similar networks, central-limit theorem questions. (iv) Condensed-matter physics complex problems (and their analogs in other disciplines), like, critical fluctuations (catastrophes), glass formation (traffic jams), localization transition (foraging, collective motion). Full article