Search Results (275)

This study used Global Navigation Satellite System (GNSS) observations from the China Crustal Movement Observation Network (CMONOC) and the Kunming Continuously Operating Reference Station (KMCORS) network to investigate ionospheric response characteristics over China during the geomagnetic storm of 4–6 November 2023, and to assess their impacts on CORS-based real-time kinematic (RTK) positioning performance in the low-latitude Kunming region. A quantitative assessment was conducted by integrating regional two-dimensional dTEC (%) maps over China, BeiDou Navigation Satellite System (BDS) Geostationary Earth Orbit (GEO) total electron content (TEC), the rate of TEC index (ROTI), and RTK positioning solutions to evaluate ionospheric disturbances, irregularity activity, and associated degradation in positioning performance. Results indicate that, during geomagnetic storms, ionospheric responses over China exhibit pronounced phase-dependent and latitudinal variations. During the second geomagnetic storm on 5–6 November, positive responses were dominant at mid-to-high latitudes, whereas alternating positive and negative responses were observed at low latitudes. During the recovery phase, the Kunming region successively experienced a positive ionospheric storm lasting approximately 10 h, followed by a negative ionospheric storm lasting about 7 h, with relative TEC variations reaching a maximum of approximately 90%. The GEO TEC time series was consistent with the temporal evolution of the two-dimensional dTEC (%), while ROTI increased markedly during the disturbance enhancement period (21:00 UT on 5 November to 07:00 UT on 6 November 2023). During periods of enhanced ionospheric response and irregularities, RTK positioning performance was observed to deteriorate markedly. The fixed-solution rate at medium-to-long baseline stations decreased from nearly 100% to close to 0%, accompanied by an increase in vertical positioning errors to approximately 20 cm, whereas short-baseline stations were only minimally affected. These results indicate that ionospheric disturbances during geomagnetic storms exert a pronounced impact on CORS-based RTK positioning services in the Kunming region, with the magnitude of this impact being closely related to baseline length. Full article

This study presents a comprehensive 35-year (1990–2025) shoreline change assessment along the southeast coast of Ireland, integrating multi-decadal Landsat satellite archives with GIS-based Digital Shoreline Analysis System (DSAS) metrics to quantify both spatial and temporal coastal dynamics. Unlike previous studies that focus on shorter timeframes or localized sectors, this research provides a regional-scale, orientation-specific comparison between the eastern-facing (SE1; County Wexford) and southern-facing (SE2; County Waterford) shorelines. Shoreline evolution was quantified using four complementary DSAS indicators—Shoreline Change Envelope (SCE), Net Shoreline Movement (NSM), End Point Rate (EPR), and Linear Regression Rate (LRR), allowing robust discrimination between short-term variability and multi-decadal trends. The results reveal noticeable spatial variability in shoreline behavior with 57% accretion and 42% erosion across the eastern-facing coast (SE1) in County Wexford and the southern-facing coast (SE2) in County Waterford. SCE values ranging from 2.26 m to 663.83 m indicate considerable short-term shoreline variability, particularly within dynamic barrier and embayed systems. NSM values between −216.65 m and +663.83 m indicate erosional hotspots, particularly along soft-sediment coasts and exposed southern-facing sectors, whereas accretion is limited to embayments, sandy beaches, and zones of effective sediment trapping. Rate-based analyses show EPR values between −14.82 and +20.38 m/yr and LRR values between −5.27 and +20 m/yr, with LRR providing more reliable estimates of multi-decadal trends in highly dynamic environments. The findings highlight the strong influence of coastal orientation, sediment availability, geological controls, and human activities on shoreline change in southeastern Ireland. These findings provide valuable evidence to support coastal management, hazard mitigation, and climate adaptation planning, with the assistance of policymakers, to develop effective strategies that enhance the resilience and quality of life of coastal communities. Full article

Rock bursts frequently occur in the fault group area in China, seriously restricting the safe and efficient production of coal mines. Based on field investigation, physical experiments, and numerical simulation, this study investigates the rupture types and spatial evolution of microseismic events during the excavation of working face through fault group areas in the TB Coal Mine, where the hard roof asymmetric is cut by faults. It reveals the cooperative instability mechanism of faults and hard roof, as well as the mechanisms of rock burst. Targeted rock burst prevention measures are proposed, including “roof blasting to cut off dynamic and static load transfer” and “coal blasting to reduce abutment stress”. The results demonstrate the following: (1) during mining in fault group areas, the synchronous activation of faults induces shear-type and high-energy microseismic events and the subsequent movement of hard roof, which has been cut by faults, forms asymmetric parallelograms and symmetric inverted trapezoids, and induces tensile-type and high-energy microseismic events. The synchronous activation of faults and the breaking of the hard roof are identified as the primary reason for high-energy microseismic events. (2) As the fault dip angle approaches 90º, the compressive strength of the fault-segmented hard roof strata decreases. Under synchronous activation of faults, roof failure concentrates in the central, right, and left sections for fault combinations with dip angles of 70° + 70°, 90° + 70°, and 110° + 70°, respectively. (3) Numerical simulations reveal two rock burst mechanisms in faults—hard roof systems: a forward “high dynamic stress and high static stress” type and a rear “low dynamic stress and high static stress “ type, which is consistent with in situ monitoring data. (4) For the three stages in which the 502 working face approaches, passes through, and mines away from the fault group area, a stress relief scheme combining roof blasting and coal blasting is proposed. Compared with the 501 working face, during the mining of the 502 working face, the total microseismic frequency and energy decreased by 71.9% and 87.9%, respectively, and the effectiveness of these measures is verified. Full article

Martial arts have evolved from self-defense practices into structured competitive sports that demand high levels of neuromotor control, where improper execution remains a major source of injury. This study evaluates lower-limb control during the execution of the karate lateral kick using videogrammetry biomechanical analysis. Three participants were recorded during regular training sessions and selected according to their level of expertise. Each participant performed lateral kicks at three predefined distances (close, comfortable, and long), selected based on common training practice and individual biomechanical considerations. Videogrammetry data were generated using Kinovea version 0.9.5 software to extract sagittal ankle trajectories. Statistical analyses were carried out in MATLAB version 2025b using spatial coordinates to obtain kinematic data on the practitioner’s performance. The results revealed skill-dependent differences in movement control, characterized by temporal evolution of kinematic variables and their corresponding time–frequency representations. Novice practitioners exhibited limited control during the raising and recovery phases, despite reaching the target. In contrast, expert practitioners demonstrated consistent posture, controlled acceleration during impact, and stable limb trajectories during descent. These observations provide a foundation for data-driven classification of kick execution quality and outline potential applications in supervised learning, real-time feedback systems, and injury risk reduction during karate training. Full article

Stray currents pose a significant threat to the structural health and resilience of subway shield tunnels through the destructive effects of electrochemical corrosion, which is broadly recognized as one of the main obstacles to ensuring the sustainability of urban rail transit systems. Environmental humidity can lead to variations in the pore water saturation of concrete structures. In the coupled environment of stray currents and pore water saturation, this condition exacerbates the corrosion of reinforced concrete, shortening its service life and jeopardizing the normal operation of subway systems. Given this, a combined study is carried out to explore the effect of pore water saturation on stray current corrosion of reinforced concrete through FEM-based simulation and experiment tests. The effect of pore water saturation on stray current corrosion is studied by varying applied potential and porosity. The study validates the influence of concrete porosity and voltage on the control ranges of pore water saturation corresponding to the various stages of stray current corrosion in reinforced concrete. Based on the simulation and experimental results, it is concluded that, under the same voltage conditions, an increase in the porosity of the reinforced concrete correlates with a greater severity of corrosion as pore water saturation increases. As the applied voltage increased from 2 V to 10 V, the pore water saturation range for iron oxidation shrank from 0–0.6 to 0–0.4, while the hydrogen evolution range expanded from 0.7–1 to 0.5–1. Pore water saturation influences the control mechanisms of electrochemical corrosion at various stages in reinforced concrete. Moreover, under each control mechanism, the control ranges of pore water saturation corresponding to the corrosion stages demonstrate sequential trends of contraction, movement towards lower saturation regions, and expansion as the applied voltage increases. The findings of the study contribute to the understanding of the intrinsic mechanisms underlying the service life extension of buried foundation structures. Full article

Human physiology is among the most complex systems in nature, characterized by intricate structural and functional networks and rich temporal dynamics. Electrophysiological signals produced by different tissues/organs reflect physiological activity, and are inherently non-stationary, non-linear, and noisy. This work focuses on fractal analysis, a framework that captures the self-similar and scale-free properties of electrophysiological signals, which is considered to act as an output of complex physiological structures that generate complex processes. Central to this approach is the principle of ‘decomplexification’, whereby aging and disease are associated with a loss of physiological complexity. We discuss key algorithms, particularly Higuchi’s fractal dimension, which is often combined with other nonlinear measures and machine-learning models for real-time analysis of electrophysiological signals. Evidence shows that fractal metrics enable the early detection and monitoring of neurological and psychiatric disorders, outperforming traditional spectral measures. In movement disorders and mood disorders, fractal and nonlinear features show high diagnostic accuracy. Beyond diagnostics, we discuss therapeutic applications, including the prediction of responsiveness to non-invasive brain stimulation. Here, we envisage the evolution of one fractal or nonlinear measure use, to several measures applied, then use it as a feature for machine learning, and then realize that a whole cluster of biomarkers must be used to reflect the state of autonomic profile, which then can be used for ontology-based application profiles that can be machine-actionable. In addition, we discuss the fractal and fractional description of transport processes, which offer innovative improvement for a much more accurate description of physiological reality as a prerequisite for further modeling: for example, this is needed for digital twins to support the clinical translation of fractal analysis for personalized medicine. In essence, if one is trying to mathematically describe or quantify structures or processes in human physiology, fractal and fractional are the supreme and adequate approach to accurately model that reality. Full article

The Tinjar–West Baram Fault in the southern South China Sea is a major NW-trending strike-slip fault that has remained tectonically active since the Oligocene. It forms a key structural boundary between the Zengmu, Beikang, and Nansha Trough basins. Multi-phase strike-slip movements have strongly controlled sediment provenance dispersal pathways, and reservoir development in the Zengmu Basin, yet the sedimentary response to these tectonic processes remains poorly understood. This study integrates 2D seismic profiles to analyze the fault geometry, kinematics, and impact on deep-water sedimentary systems. Results indicate that Oligocene right-lateral motion directed sediment supply from the southwest, mainly sourced from Kalimantan, forming fluvial–deltaic systems with depocenters in the southern basin. Since the Late Miocene, a transition to left-lateral motion reoriented sediment provenance toward the southeast, leading to delta-front complexes and northward migration of depocenters. Strike-slip activity deformation enhanced rock fragmentation and sediment supply, producing fan delta, fluvial, and shallow lacustrine facies near the fault. Associated uplift and subsidence induced relative sea-level fluctuations, resulting in alternating transgressive–regressive sequences. From the Late Eocene to Miocene, the basin evolved from a land–sea transitional system to a deltaic–carbonate complex controlled by the paleo-Sunda River. During the Pliocene–Quaternary, sedimentation was dominated by shallow-marine shelf and semi-deep-marine deposits. Fault-related fracturing significantly enhanced porosity and permeability, creating favorable conditions for hydrocarbon migration and entrapment in both sandstone and carbonate reservoirs. These findings demonstrate a strong coupling between strike-slip fault activity and sedimentary system evolution, providing important insights into sedimentary processes and hydrocarbon potential in strike-slip fault-bounded basins globally. Full article

The expansion of carbon pricing instruments, such as carbon taxes and emissions trading systems (ETS), has been rapid over the last three decades. However, the global quantitative evidence is often presented in descriptive reports rather than in a unified empirical framework. The present study documents the long-run co-evolution between three factors: firstly, the global diffusion of carbon pricing mechanisms, secondly, the share of global greenhouse gas emissions covered by an explicit carbon price, and thirdly, global carbon-pricing revenues. The present study utilises annual global time-series data spanning the period 1990–2024 (mechanisms) and overlapping samples for coverage and revenues (2005–2024; 2006–2023). Employing correlation analysis, trend modelling and robustness checks tailored to trending series, the study offers a transparent and replicable quantitative synthesis of the data. The findings suggest a robust positive long-term correlation between the number of mechanisms in operation and emissions coverage. Revenues manifest a pronounced non-linear scaling over time; nevertheless, given the aggregate nature of the dataset, the estimates are interpreted as co-movement patterns rather than causal effects of specific instruments. The paper makes a significant contribution to the field by offering a transparent and replicable quantitative synthesis of global carbon-pricing diffusion and fiscal scaling. It is important to note, however, that the paper also explicitly states the limits of causal inference and outlines panel-data extensions for future research. Full article

Eye tracking in virtual reality (VR) head-mounted displays poses substantial engineering challenges, particularly under immersive display configurations with large fields of view (FOV), where optical layout, illumination, and image acquisition impose nontrivial system constraints. To address these design constraints, we present an integrated near-eye eye-tracking prototype tailored for immersive VR headsets, combining customized hardware components and a real-time software pipeline. The proposed system integrates optimized near-eye illumination and image acquisition with a pupil detection module and a deep learning-based gaze-vector estimation model, forming a real-time software pipeline for stable end-to-end gaze mapping under fixed calibration conditions. Under identical system settings, calibration procedures, and gaze-point mapping conditions, we evaluate the proposed gaze-vector estimation model through a controlled model-level ablation. The attention-enhanced model achieves an average angular deviation of 1.15°, corresponding to a 61.4% relative reduction compared with a baseline ResNet-152 model without attention. To demonstrate the usability of the system outputs at the application level, we further implement a real-time visualization example that integrates pupil diameter, gaze vectors, and blink events to depict the temporal evolution of eye-movement signals. This work provides a cost-effective and reproducible engineering reference for near-eye eye-movement acquisition and visualization in immersive VR settings and serves as a technical foundation for subsequent interaction design or behavioral analysis studies. Full article

Intangible cultural heritage (ICH) is an essential component of China’s outstanding traditional culture, serving as a living testament to the continuity of Chinese civilization and as a crucial foundation for fostering national identity and maintaining social cohesion. The Yangtze River Delta (YRD) region is one of the areas in China with the highest concentration and the most comprehensive range of traditional ICH. However, its spatiotemporal patterns and influencing factors have not yet been systematically examined. In this study, 593 national-level ICH items in the YRD were selected as the research objects. Based on geographic information systems (GIS), spatiotemporal analyses were conducted using the nearest neighbor index, geographic concentration index, imbalance index, kernel density analysis, standard deviation ellipse, and geographic detector methods. The spatial characteristics of ICH were investigated from three perspectives: spatial structure, spatiotemporal evolution, and driving factors. The results indicate that: (1) Shanghai serves as the core agglomeration area of ICH and exhibits the highest kernel density; (2) from a spatiotemporal perspective, the spatial center of ICH distribution shows an overall movement trajectory that first shifts southward and then northward; and (3) driving factor analysis reveals that sociocultural factors exert the most significant influence on the spatial distribution of ICH, followed by economic factors. Natural geographic factors show the weakest explanatory power, but their influence is significantly enhanced through interactions with sociocultural and economic factors. This study develops an integrated analytical framework to examine the spatial patterns and driving mechanisms of ICH in the YRD. It enriches the quantitative methodological system of cultural geography and heritage studies, provides a scientific basis for the protection, transmission, and governance of cultural heritage against the background of regional integration in the YRD, and offers a transferable analytical approach for ICH studies in other urban agglomerations. Full article

Locust outbreaks cause a significant threat to global food security and ecosystem stability, with particularly severe consequences in grassland regions, where grasshoppers also exert considerable ecological pressure. In comparison to grasshoppers, locusts typically occur at much larger spatial scales, as their strong migratory ability and collective movement behavior lead to greater spatial connectivity and autocorrelation. The forecasting of both locust and grasshopper outbreaks remains a formidable scientific challenge, primarily due to the complex, nonlinear spatiotemporal interactions among environmental drivers such as weather, vegetation, and soil conditions. This review compares the evolution of prediction methodologies for locust and grasshopper outbreaks, focusing on the application of deep learning (DL) methods to ecological forecasting tasks. It traces the development from traditional statistical models to classical machine learning, and ultimately to DL, assessing the strengths and limitations of key DL architectures—including Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRUs)—in modeling the intricate dynamics of locust populations. While most studies have concentrated on locust outbreaks, this review emphasizes the adaptation of these models to grassland ecosystems, such as those in Inner Mongolia, where grasshopper outbreaks exhibit similarities to locust plagues but have been largely overlooked in DL research. Despite the potential of DL, challenges such as data scarcity, limited model generalizability across regions, and the “black box” issue of low interpretability remain. To address these issues, we propose future research directions that integrate Explainable AI (XAI), transfer learning, and generative models like GANs to development more robust, transparent, and ecologically grounded forecasting tools. By promoting the use of efficient architectures like GRUs within customized frameworks, this review aims to guide the development of effective early warning systems for sustainable locust management in vulnerable grassland ecosystems. Full article

The performance of Zr-2.5Nb alloy pressure tubes in nuclear reactors is critically dependent on the behavior of precipitated hydrides. In this study, a hydrogen-charged Zr-2.5Nb alloy pressure tube was subjected to in situ tensile testing combined with electron backscatter diffraction to elucidate microcrack evolution and microstructural adaptation. Initially, longitudinal hydride–hydride interface cracks nucleated at non-coherent interfaces of two types of hydrides due to the inherent brittleness. Subsequently, stress redistribution by a small proportion of hydride–hydride interface cracks resulted in the emergence of microcracks at the transverse hydride–matrix interfaces, accompanied by partial hydride phase transformation. Finally, under high strain conditions, increased dislocation movement in the matrix triggered a single slip system, leading to the formation of numerous low-angle grain boundaries. As strain further increased, multiple slip systems were activated, and longitudinal matrix–matrix interface cracks began to nucleate at certain grain boundary locations. Full article

Sandstorm is the most serious disaster suffered by human settlements in arid areas. From the perspective of human activities, this paper analyzes the influence of artificial oasis change on spatial variation in sandstorm disaster and its driving mechanism, and summarizes the evolution of sand control policy centered on human activities, so as to provide a reference for sandstorm prevention and ecological environment control in arid areas. The results show the following: (1) The spatial distribution of sandstorm disasters in Tarim Basin presents a clear pattern of “two core source areas dominate, spread along mountains and basins, and weaken significantly in oasis”. Artificial oasis scale and green vegetation area showed significant spatial inhibition effects on sandstorm disasters. (2) With the strengthening of human activities and sand control policies and systems, the intensity of sandstorms in Tarim Basin showed a significant trend of westward movement and contraction. (3) Human activities, such as population scale, economic level, artificial green vegetation and grassland area, have significant correlation effects on the intensity of sandstorms. Full article

Financial crises increasingly exhibit complex, interconnected patterns that traditional risk models fail to capture. The 2008 global financial crisis, 2020 pandemic shock, and recent banking sector stress events demonstrate how systemic risks propagate through multiple channels simultaneously—e.g., network contagion, extreme co-movements, and information cascades—creating a multidimensional phenomenon that exceeds the capabilities of conventional actuarial or econometric approaches alone. This paper addresses the fundamental challenge of modeling this multidimensional systemic risk phenomenon by proposing a mathematically formalized three-tier integration framework that achieves 19.2% accuracy improvement over traditional models through the following: (1) dynamic network-copula coupling that captures 35% more tail dependencies than static approaches, (2) semantic-temporal alignment of textual signals with network evolution, and (3) economically optimized threshold calibration reducing false positives by 35% while maintaining 85% crisis detection sensitivity. Empirical validation on historical data (2000–2023) demonstrates significant improvements over traditional models: 19.2% increase in predictive accuracy (R² from 0.68 to 0.87), 2.7 months earlier crisis detection compared to Basel III credit-to-GDP indicators, and 35% reduction in false positive rates while maintaining 85% crisis detection sensitivity. Case studies of the 2008 crisis and 2020 market turbulence illustrate the model’s ability to identify subtle precursor signals through integrated analysis of network structure evolution and semantic changes in regulatory communications. These advances provide financial regulators and institutions with enhanced tools for macroprudential supervision and countercyclical capital buffer calibration, strengthening financial system resilience against multifaceted systemic risks. Full article

Here we present a retrospective, integrative review of the approaches and discoveries of our “collaboratory”, a meta-laboratory comprising cross-disciplinary collaborations across laboratories at fourteen different universities and clinics in seven different countries with shared lead investigators. By tying together insights from four decades of research and discovery, applied across cell types, as well as different tissues, organ systems, and organisms, we have aimed to elucidate the interplay between organisms’ movement and the physiology of their tissues, organs, and organ systems’ resident cells. We highlight the potential of increasing imaging and computing power, as well as machine learning/artificial intelligence approaches, to delineate the Laws of Biology. Codifying these laws will provide a foundation for the future, to promote not only the discovery of underpinning mechanisms but also the sustainability of our natural resources, from our brains to our bones, which serve as veritable “hard drives”, physically rendering a lifetime of cellular experiences and millennia of evolution. Full article