Search Results (1,483)

Old urban areas face severe spatial supply–demand contradictions as aboveground space nears saturation while public service demand grows. Developing underground public spaces offers a key solution, yet fragmented development limits overall benefits, necessitating coordinated aboveground–underground development to achieve urban renewal and sustainability. Taking Shanghai’s old urban areas as a case, this study constructs an evaluation system with 17 aboveground indicators across 5 dimensions and 9 underground indicators across 3 dimensions. Using the combination of AHP–entropy weight method for weighting, the coupling coordination degree model, and the obstacle degree model, this study identifies the temporal evolution trends in the development levels of the two systems, the characteristics of their coupling coordination stages, and the main constraining factors from 1995 to 2025. The results show: (1) Both systems have shown continuous growth, with underground public space accelerating its development after 2010, and by 2015, it had nearly caught up with the aboveground system in the time-series projection results; (2) The D value of coupling coordination has increased from 0.2431 to 0.9532, experiencing three stages of low coupling coordination, general coupling coordination, and high coupling coordination; (3) The obstacle factors have shown a dynamic evolution path from scale shortage to morphological complexity, and then to the synergy of the aboveground and underground morphologies. In the higher coupling coordination stage, the length of the aboveground bus lines and the landscape shape index of the underground became the dominant obstacles. This study provides a quantitative basis for coordinated planning and decision-making in the renewal of old urban areas. Full article

Artificial intelligence (AI) has rapidly reshaped sports motion analysis through advances in wearable sensing, computer vision, and deep learning. However, existing reviews often focus on isolated techniques and lack a systematic evolutionary perspective. This study presents a 15-year bibliometric and evolutionary review of AI in sports motion analysis (2011–2025), integrating scientometric mapping with quantitative content analysis across modalities, methods, and applications. A comprehensive multi-source dataset of 2602 publications was analyzed using VOSviewer and CiteSpace to examine knowledge structures, collaboration patterns, co-citation networks, and emerging research fronts. In addition, each study was categorized by modality (wearable, visual, and multi-modal), AI method (traditional machine learning, deep learning, and transformer-based), and task type (activity recognition, performance analysis, rehabilitation, and others). The results reveal exponential growth and a clear three-stage evolution: a sensor-driven phase dominated by wearable devices and traditional machine learning (2011–2015), a deep learning expansion phase centered on vision-based modeling (2016–2019), and a recent deep learning consolidation phase characterized by emerging transformer-based methods, increasing multimodal integration, real-time monitoring, and application-oriented sports analytics (2020–2025). The field has shifted from signal-based recognition toward vision-centered performance evaluation, rehabilitation-related movement assessment, and injury-informed applications. This data-driven review provides an integrated evolutionary framework and future research roadmap to support the continued development of AI-driven analytics in sports science, athlete monitoring, and health-related human movement analysis. Full article

We investigate a fractional gravity model in which both the Hubble parameter and the gravitational constant evolve dynamically due to fractional renormalization-group effects. The model incorporates a scalar field coupled to a time-varying G, generating nonlocal corrections characteristic of fractional-action cosmology. Analytical and numerical solutions reveal oscillatory regimes, cyclic phases, and rapid variations with implications for BBN and early-universe evolution. A robust numerical framework is developed to integrate the regularized system and compare the resulting $H\left(z\right)$ evolution with observational data from the Hubble parameter, baryon acoustic oscillations, Type Ia supernovae, gravitational lensing, and black-hole shadows, thereby enabling a consistent reconstruction of cosmographic quantities. A Bayesian analysis shows that the fractional model with $\mu =0$ is the only statistically viable variant. The inferred Hubble parameter is stable across models ($h\simeq 0.72$), while the fractional parameters are significantly better constrained in the $\mu =0$ case ($\alpha ={1.20}_{-0.14}^{+0.25}$, $\zeta ={0.43}_{-0.29}^{+0.39}$). The dynamical sector yields $m={30.8}_{-20.9}^{+28.0}$ and $\mathrm{\Gamma }=108.3\pm 1.1$, leading to a positive discriminant and a relaxation timescale ${\tau }_{+}={219}_{-162}^{+1980}$ Gyr, confirming an overdamped regime. Although the $\mu =0$ model attains a slightly lower ${\chi }_{\min }^2$ than $\mathrm{\Lambda }$CDM, the BIC strongly favors $\mathrm{\Lambda }$CDM due to its smaller parameter space. Overall, the model reproduces late-time acceleration and mimics $\mathrm{\Lambda }$CDM while introducing distinctive cosmographic signatures. The dynamical systems analysis clarifies the stability structure and parameter dependence, indicating that fractional nonlocal corrections may offer new pathways toward addressing the $H_0$ and $S_8$ tensions. Full article

Rapid urbanization has led to habitat fragmentation in peri-urban areas, continuously eroding the ecological foundation of sustainable food systems in urban–rural transition zones and posing a real threat to regional food security. Against the backdrop of urbanization disturbances, traditional nature-based solutions have limitations in addressing socioecological nonlinear responses, whereas digital tools offer new governance pathways for enhancing food system resilience. To elucidate the intrinsic mechanisms through which digital technology empowers the resilience of peri-urban food systems, this study, which is grounded in ecological wisdom theory, constructs a system dynamics model that integrates “digital technology-ecological perception-ecological wisdom capital” in a three-dimensional linkage. This model simulates the dynamic process through which sustainable food systems in urban–rural transition zones resist the risks of habitat fragmentation and achieve synergistic steady-state evolution. According to the simulation results, a synthesized steady-state transition in sustainable food systems can be regarded as a self-organizing phase transition process. During resource metabolism, system elements show strong nonlinear symbiotic and mutually beneficial features. Further, there is a significant time-lag effect on improving food system resilience through digital technology empowerment and policy coordination. Also, the effects of governance are not immediately visible. Further, as an important instrumental empowerment carrier, urban–rural spatial and information barriers can be broken through means like digital ecological monitoring. Moderate investment in this regard can promote the acceleration of the system’s self-organizing phase transition. Also, this can enhance resilience against disturbance from habitat fragmentation while ensuring food production and supply. Finally, the ecological carrying capacity of core food production spaces does not increase monotonically. This means that the system possesses an adaptive cyclical fluctuation mechanism, with a periodic oscillatory evolution of carrying capacity. This study breaks through static analytical paradigms, fills the quantitative research gap on the resilience evolution of peri-urban food systems driven by the integration of digital technology and ecological wisdom, and can provide scientific evidence and decision-making support for food–ecological collaborative governance in China’s urban–rural transition zones. Full article

Multiphase swirling flows in confined spaces induce highly destructive, nonlinear fluid–structure interaction (FSI) vibrations. Understanding the underlying physical mechanisms is critical for ensuring the safety of industrial operations. This study proposes a mesoscopic multiscale framework coupling the Multi-Relaxation Time Lattice Boltzmann Method with Large Eddy Simulation (MRT-LBM-LES) and the Flügge thin-walled cylindrical shell equations to analyze two-way FSI responses. Variational Mode Decomposition (VMD) and the Hilbert–Huang Transform (HHT) are employed to decouple non-stationary broadband excitation signals. The macroscopic topological evolution of the swirling air core—from initial depression to critical breakthrough—is accurately captured. Dynamic mapping reveals a strict time-domain phase-locking mechanism between macroscopic flow instability and microscopic high-frequency structural excitation caused by cavitation bubble collapse. Furthermore, a dimensionless cross-scale energy cascade index is defined to quantify energy transfer. Results indicate that while higher discharge flow rates delay the critical breakthrough, they trigger a delayed, high-amplitude step mutation in the energy cascade, amplifying the global cumulative excitation energy by nearly 75%. Notably, the dominant high-frequency excitation consistently converges within a narrow band of 760 Hz to 790 Hz, independent of flow rate variations. These findings provide a theoretical foundation for unsteady excitation source localization and targeted vibration reduction in complex industrial pipeline networks. Full article

As urban underground spaces and infrastructure development accelerate, subsurface elements such as buried pipelines, integrated utility tunnels, subway tunnels, cavity defects, and deep-seated hidden hazards become increasingly intertwined. Consequently, urban target detection is characterized by pronounced scale discrepancies, intense environmental interference, and severely confined operational spaces. The transient electromagnetic method (TEM) is highly valuable for rapid surveys and hazard identification in urban underground spaces owing to its inherent advantages, including non-contact operation, adaptability to hardened pavements, high sensitivity to low-resistivity anomalies, and the ability to probe a broad range of depths. In recent years, research has shifted from improving isolated instrumentation to synergistically optimizing sensing platforms, transmitter–receiver systems, anti-interference methodologies, and imaging interpretation workflows. Specifically, small-loop configurations and high-frequency excitation technologies have improved shallow-sounding capabilities in confined urban spaces; anti-interference techniques have increased data reliability in complex noise environments; and apparent resistivity mapping, virtual wave-field migration, and rapid inversion methodologies have enabled profiling results to transition from qualitative identification to fine-scale interpretation. Concurrently, the evolution of ground-towed, UAV-borne, helicopter-borne, and semi-airborne platforms has progressively endowed urban TEM profiling with continuous, mobile, and scenario-specific operational capabilities. Looking to the future, further technical breakthroughs in urban TEM technology are required to improve shallow-resolution, deep-seated penetration, multi-source interference decoupling, and real-time concurrent imaging. Full article

Coastal dune fields provide important ecological and geomorphic functions, while their evolution is strongly influenced by groundwater-controlled surface moisture. However, the effects of seasonal groundwater-level fluctuations and moisture-affected sand on long-term dune development remain insufficiently represented in numerical models. In this paper, a time-varying groundwater-level field and moisture-dependent entrainment thresholds are incorporated into a real-space cellular automaton model to investigate the coupled evolution of coastal dunes and interdune lakes. The results show that rising groundwater levels reduce the wind-erodible surface area, inundate interdune depressions, and delay the growth of peak dune height. Periodic water-level fluctuations also produce a sediment storage–release cycle, in which sand is temporarily stored on inundated interdune surfaces during high-water stages and is progressively remobilized during subsequent low-water stages, while newly exposed moisture-affected sand remains subject to an elevated entrainment threshold. In addition, increasing the critical threshold shear stress within the groundwater-controlled moisture-affected layer suppresses dune development and reduces final peak dune height by approximately 8–17%. Comparison with the observed wet-season water-pond area further shows that incorporating the moisture-affected layer brings the simulated relative water-pond area closer to the observed value in the Lençóis Maranhenses dune field. Overall, the results demonstrate that groundwater regulates dune evolution through both direct inundation and an enhanced entrainment resistance of moisture-affected sand above the water table. Full article

This paper proposes an annular suction caisson specifically designed to reinforce in-service monopiles and upgrade existing offshore wind farms to accommodate larger-capacity wind turbines. During penetration of the annular suction caisson into clay, the existing monopile restricts the inward migration of soil into the internal space of the caisson, promoting upward soil displacement and consequently increasing the height of the soil plug formed inside the caisson. In addition, the strain-softening behavior causes varying degrees of strength degradation in the clay along the caisson wall. The softened zones extend approximately one caisson wall thickness on the inner side and 1.2 times the wall thickness on the outer side of the caisson. Both effects should be considered for accurately predicting the penetration resistance of annular suction caissons. Therefore, three-dimensional large-deformation finite element analyses were performed to investigate the penetration behavior of annular suction caissons in strain-softening clay. A comprehensive parametric study was conducted to quantify the soil plug heave and overall penetration resistance. Meanwhile, the soil flow mechanism at the caisson tip, the evolution of clay strength along the caisson wall, and the formation characteristics of the internal soil plug were systematically examined. Based on the numerical results, a theoretical approach was developed to evaluate the penetration resistance of annular suction caissons. Full article

Construction equipment monitoring has shifted from manual logbooks and early telematics toward intelligent digital systems, driven by the need to reduce delays in obtaining equipment data and improve decision-making on construction sites. This study examines research on construction equipment monitoring and tracking published between 2000 and 2026 to identify the evolution, major themes, and emerging directions in the field. A scientometric and bibliometric analysis was conducted on 1093 bibliographic records retrieved from Scopus and Web of Science on 20 May 2026. The annual publication trend was evaluated through 2025, the last complete publication year, while records indexed in 2026 were retained for the remaining corpus-level analyses. The datasets were preprocessed and analyzed using Bibliometrix, VOSviewer, and CiteSpace to examine publication trends, keyword networks, collaboration patterns, citation structures, and research clusters. The keyword network was interpreted through six major thematic clusters, which were synthesized into four broader knowledge streams covering operations and sensing, AI-based perception, safety monitoring, and BIM/digital-twin integration. The results show a substantial increase in the representation of AI- and perception-related research across the later publication periods, reflecting a transition from basic sensor-based approaches toward more intelligent and connected site systems. The study identifies leading contributors and comparatively underdeveloped research priorities, particularly real-time idle-state detection, multimodal data fusion, multi-site validation, and the integration of monitoring outputs with BIM and digital-twin decision-support environments. Full article

Cross-condition bearing fault diagnosis requires models that remain reliable under load-induced distribution shifts while providing transparent and traceable reasoning. Conventional belief rule bases (BRBs) may repeatedly use correlated vibration evidence during inference, and their Cartesian-product rule construction can rapidly increase rule-base complexity. This study proposes a correlation-decoupled interval belief rule base (CD-IBRB) for cross-condition bearing fault diagnosis. Seven diagnostically relevant time-domain features are selected using XGBoost and transformed into a less-correlated feature space through a Kendall-rank-correlation-guided matrix estimated exclusively from the source training data. Attribute-wise referential points and intervals are then constructed from the transformed training attributes, allowing the rule base to grow additively rather than combinatorially. Initial belief distributions are obtained from interval-level class distributions. The projection covariance matrix adaptation evolution strategy (P-CMA-ES) jointly optimizes the belief degrees, rule reliabilities, and rule weights, while evidential reasoning aggregates the activated interval rules to produce the final diagnostic result. In the primary cross-load bearing experiment, CD-IBRB achieved an accuracy of 0.9702 and a macro-averaged F1 score of 0.9703. It outperformed the strongest BRB variant and data-driven baseline by 7.70 and 6.10 percentage points in accuracy, respectively. Ablation experiments confirmed that removing parameter optimization or attribute decoupling reduced accuracy to 0.9053 and 0.9303, respectively. Additional cross-load and noise-injection experiments further demonstrated the stability of CD-IBRB under load shifts and input disturbances. Across five public multiclass datasets, CD-IBRB achieved a mean accuracy of 0.9004 and consistently outperformed the compared BRB variants. These results demonstrate that CD-IBRB provides a compact, uncertainty-aware, and traceable framework for cross-condition bearing fault diagnosis. Full article

Employing a space–time perspective, this study develops a CA-SHIRS model of corporate resonance diffusion of food safety risk, drawing on complex network theory and cellular automata theory. The study then examines the mechanisms and spatial–temporal evolution characteristics of this diffusion, considering the interplay among food firm heterogeneity, media communication strategy, and government regulatory strategy. The study reaches the following conclusions: (1) Higher probabilities of infection, conversion, and immune failure speed up risk transmission within the spatial–temporal association network of food firms. By contrast, raising the immune probability and direct immune probability helps contain the scale of risk spread. (2) The intensity of corporate resonance diffusion is positively correlated with corporate influence and media influence, and negatively correlated with corporate social responsibility, media information disclosure intensity, government penalty intensity, and government regulatory information transparency. It exhibits an inverted U-shaped relationship with corporate risk preference and a positive U-shaped relationship with media reporting preference. (3) Both corporate influence and media influence reinforce corporate resonance diffusion, while government regulation effectively mitigates it. Firms with moderate risk preference are most significantly affected by extreme media coverage; corporate social responsibility and media information disclosure intensity can jointly suppress diffusion at these nodes. Government regulation exerts a stronger inhibitory effect on corporate resonance diffusion than the amplifying effect exerted by media communication. Full article

In this paper, we investigate the real reverse-space–time nonlocal modified Korteweg–de Vries equation using the $\overline{\mathsf{\partial }}$-dressing representation established by Luo and Fan. By specializing the purely imaginary discrete spectral data, we obtain explicit one-, two- and three-soliton expressions from the general determinant formula. We then examine their profiles and parameter-dependent evolution. For regular one-soliton branches, the characteristic velocity and exponential amplitude rate are derived analytically. Depending on the spectral parameters, the amplitude may grow in one temporal direction and attenuate in the reverse direction, while the solution remains spatially localized at each finite time. The nonlocal product $q(x,t)q(-x,-t)$ is shown to be invariant under joint space–time reflection, highlighting the dynamical role of the reverse-space–time coupling and its difference from the real local mKdV equation. Full article

Accurate prediction of the state trend of pumped storage units (PSUs) is essential for timely anomaly detection and preventive maintenance to improve the overall economic performance of power plants. Nevertheless, the complex and time-varying characteristics of PSU vibration data increase the difficulty of accurately modeling their dynamic evolution. In response to this problem, an integrated vibration trend prediction (VTP) method for PSUs is developed by combining a temporal-enhanced generative adversarial network (TEGAN) with an improved bidirectional long short-term memory network (IBiLSTM). Firstly, TEGAN expands the original dataset by synthesizing artificial samples, thereby improving the structural diversity and representativeness of vibration data. Within TEGAN, a temporal characterization (TC) module is designed to collaboratively guide the generator and the discriminator, while a data processing module is adopted to incorporate structural priors into the learning process. Secondly, variational mode decomposition (VMD) is applied to decompose the original vibration data into intrinsic modes, followed by PSR to reconstruct the components of each modality into a higher-dimensional state space representation. Subsequently, by incorporating the proposed multi-order Kolmogorov–Arnold network (M-KAN) for high-order nonlinear fitting, IBiLSTM is employed to model each reconstructed sub-sequence. Finally, the outputs of all sub-sequences are aggregated to produce the final VTP results. The comparative evaluation verifies the advantages of the developed method in terms of prediction accuracy and robustness for PSU vibration trend forecasting. Full article

Scientific visualization is changing from passive observation to active, AI-assisted collaboration. While Extended Reality (XR) has proven valuable for comprehending dense 3D arrays, traditional VR applications are typically deployed in rigid, single-purpose, and monolithic architectures. In this paper, we present the evolution of ASCRIBE-XR: a virtual reality platform backed by remote computation that has been re-engineered into a dynamic, service-oriented ecosystem. We introduce three core innovations that make immersive data analysis easier, faster, and more flexible when using multimodal scientific imaging. First, a lightweight Python REST interface decouples XR logic from the rendering engine, enabling real-time, programmable scene customization and on-demand data generation. Second, we present a Specimen Catalog architecture that lets the platform pivot between radically different disciplines, ranging from archaeological heterogeneous concrete and fuel-cell membranes to the root system of a bioenergy grass, by describing each dataset through portable metadata rather than hard-coded application logic. Finally, we introduce a prompt-driven layer powered by the Claude Agent SDK, allowing researchers to generate, segment, and manipulate volumetric and mesh data through natural language dialogue within the virtual space. For example, applying foundation models such as the Segment Anything Model (SAM) to perform zero-shot segmentation on demand. By bridging human intent with remote computation, ASCRIBE-XR relaxes the constraints of conventional visualization tools, offering a highly adaptable, conversational platform for scientific discovery with human auditing. Full article

Nonlinear electromagnetic wave propagation in complex plasma environments has attracted considerable attention due to its important applications in nonlinear optics, plasma physics, space science, and communication technologies. In the present study, a time-fractional Drinfel’d–Sokolov–Wilson equation (DSWE) is investigated under the influence of electromagnetic wave perturbations. The fractional-order formulation incorporates memory and hereditary effects, providing a more realistic description of wave propagation in nonlinear dispersive media. By employing an appropriate fractional traveling-wave transformation, the governing nonlinear fractional partial differential equation is reduced to a nonlinear ordinary differential equation. Exact solitary wave solutions are subsequently constructed using the ${\displaystyle \frac{G^{\prime }}{G^2}}$-expansion technique. Furthermore, the nonlinear dynamical behavior of the reduced system is examined through phase portraits, bifurcation diagrams, Lyapunov exponents, sensitivity analysis, and multistability investigations. Particular attention is devoted to understanding the emergence of chaotic dynamics induced by electromagnetic wave effects and fractional-order interactions. The obtained results reveal that the fractional-order parameter significantly influences the stability, propagation characteristics, and dynamical evolution of nonlinear wave structures. The coexistence of multiple attractors, transitions between stable states, and chaotic regimes is identified for various parameter configurations. These findings provide deeper insight into the complex dynamics governed by the time-fractional DSWE and contribute to the understanding of nonlinear electromagnetic wave propagation in plasma and other nonlinear dispersive media. Full article