Search Results (198)

In the Anthropocene, the environmental crisis necessitates a radical repositioning of the human-nature relationship. This paper examines the sijo poetry in Musan Cho Oh-hyun’s For Nirvana through an interdisciplinary framework bridging Zen philosophy with material ecocriticism. The study elucidates how Musan deconstructs anthropocentric exceptionalism by restoring agency to the non-human world. Textual analysis reveals three arguments. First, elemental forces like wind and waves are subjectified as primordial teachers through mujō-seppō (non-sentient beings preaching the Dharma), dismantling sovereign human scriptural authority. Second, visceral encounters with animals and insects critique logocentric domination, proposing “epistemological silence” and “radical humility” as alternative eco-politics. Finally, bodily decay and trans-corporeal porosity are reframed as generative pathways toward a radical “ecological Nirvana”—a physical matrix of cyclical renewal. By synthesizing Jane Bennett’s vital materialism with Dōgen’s Zen vision of “walking mountains”, this study deploys a Zen materialism lens that enriches Western theory with the Buddhist soteriology of compassion (karuna). Ultimately, Musan reconfigures Nirvana not as an escapist transcendence, but as a profound somatic descent into the material mesh, where ultimate spiritual realization lies in the ego’s total dissolution into the “walking, talking minerals” of a sacred, suffering ecosystem. Full article

The response of temperature extremes to recent warming at the local scale remains uncertain because changes in mean temperature may be accompanied by changes in the shape of the temperature distribution. While higher mean temperatures generally lead to more frequent heat waves and fewer cold events, variations in higher-order statistical moments can either amplify or moderate these effects. This study examines how the probability distribution of 2 m temperature has evolved during the last 80 years in Greece using the ERA-5 reanalysis dataset. The evolution of the first four statistical moments (mean, standard deviation, skewness and kurtosis) and of the 5th and 95th percentiles of daily mean temperature is calculated by splitting the time series into eight decades, with each decade representing a separate climatology. A clear increase in mean temperature is observed across Greece. However, trends in the higher-order moments are more complex: the standard deviation and skewness exhibit positive and negative trends that depend on the region and the season, while kurtosis trends are weaker with a few regional exceptions. These changes alter the response of temperature extremes to warming, resulting in non-uniform shifts of the 5th and 95th percentiles. In mountainous regions, extreme cold events during winter and autumn have decreased more strongly than expected from mean warming alone, while in marine regions extreme warm events during summer and autumn have increased beyond what would be expected by a shift in the mean. In other areas, changes in the distribution shape lead to weaker extremes than those predicted by mean warming alone. These results highlight the role that changes in temperature variability have in modulating the evolution of temperature extremes under climate warming. Full article

Slope displacements (δ) have been shown to correlate uniquely with the earthquake energy (E_eq) contributing to slope sliding, regardless of input motion characteristics. Based on this principle, this study applies the Energy-Based Newmark Method to infinitely long slopes subjected to ten diverse earthquake records with stepwise scaled amplitudes. As the earthquake wave energy (Eᵤ) increases, the energy ratio (E_eq/Eᵤ) exhibits a distinct peak followed by a monotonic decrease. The peak values and corresponding Eᵤ levels strongly depend on the predominant frequencies (f_p) of the motions, consistent with results from harmonic wave analyses. A unified design diagram is developed to correlate E_eq/Eᵤ with Eᵤ, incorporating f_p and slope parameters. Since both Eᵤ and f_p can be determined from design motions or empirically predicted using earthquake magnitudes and source distances, the slope displacement δ can be directly obtained from the diagram, eliminating the need for time-domain numerical simulations used in the conventional Newmark approaches. This method is recommended to conduct seismic zonation and hazard mapping in mountainous and hilly regions for regional authorities and infrastructure planners. Full article

The accurate classification of infrasound events is significant in natural disaster warning, verification of nuclear test bans and geophysical research. Current deep learning-based classification methods mostly focus on denoised and filtered signals. To simplify the process, avoid information loss, and address the issues of incomplete feature extraction by single-scale convolution kernels and the potential loss of physical information by single models, this paper directly utilizes raw infrasound signals and proposes two fusion classification models based on multi-scale Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). Experiments were conducted on a typical infrasound signal dataset (comprising four signal types: mountain-associated waves, auroral infrasound waves, volcanic eruptions, and microbaroms). The performances of the two models were compared in terms of accuracy, convergence speed, and stability. The results indicate that both models achieve classification accuracies exceeding 99% with optimal parameter combinations. The dual-branch multi-scale CNN-LSTM model generally outperforms the multi-scale CNN-LSTM model in classification accuracy, while also demonstrating faster convergence speed and better stability. Addressing the class imbalance in the dataset, evaluations using precision, recall, and F1-score further validated the effectiveness of the proposed models. This study demonstrates that the proposed methods can effectively achieve end-to-end classification of raw infrasound signals and are competitive with existing techniques. Full article

With its excellent physical and mechanical properties, granite is often the first choice for the foundation material for dams in water conservancy engineering. However, alteration can profoundly change the mineral composition, structure, and mechanical behavior of deep granite, posing critical challenges to project safety. The Quwu Mountain area in Baiyin, Gansu Province, a proposed pumped storage reservoir, exposes extensive Silurian granite. Engineering investigation shows that different levels of clay and hydrothermal alteration have taken place in the granite rock mass, and the level of alteration exhibits a distinct vertical zonation as revealed by borehole core logging. In this study, we quantitatively characterize the porosity, compressive strength, wave velocity, and shear parameters of altered granite of different degrees through mineralogical analysis, laboratory tests, and in situ testing. In order to guide the construction in this area, we establish a classification system that distinguishes weak, moderate, and strong alteration degree, based on macroscopic features, RQD, and clay mineral content. Results of this paper show that alteration is dominated by potassium feldspathization and kaolinitization, leading to increased porosity (4–10%) and structural loosening. Strongly altered granite exhibits severe mechanical degradation, moderately altered granite retains medium strength, and weakly altered granite approaches the properties of fresh rock. This research can provide technical support for engineering safety design and risk prevention in the Quwushan reservoir area, but its applicability to other regions requires further validation. Full article

In high-density expressway environments, lane-changing (LC) maneuvers act as stochastic perturbations that compromise the hydrodynamic stability of traffic flow, leading to safety hazards and operational delays. While existing literature has extensively modeled crash severity in static complex environments (e.g., tunnels and mountainous terrains), there remains a critical deficiency in quantifying the dynamic, systemic risks induced by LC maneuvers under saturation conditions. To address this gap, this study proposes a novel Systemic Risk Assessment Framework. First, a Hidden Markov Model (HMM) is employed to decode the latent state transitions of following vehicles, quantifying the systemic consequence of LC maneuvers as “operational delay” based on traffic wave theory. Second, a Bayesian Network (BN) is constructed to infer the causal probability of risk, integrating geometric proxies such as insertion angle with kinematic variables. Validated with real-world trajectory data, the model achieves high accuracy in identifying risk accumulation precursors. This research contributes to the field of transportation systems by shifting the risk paradigm from static collision prediction to dynamic system reliability analysis, offering theoretical support for Connected and Autonomous Vehicle (CAV) decision logic. Full article

This study presents an integrated framework that couples three-dimensional geotechnical ground modeling with a HAZUS-based urban seismic vulnerability assessment for Seoul, Korea. Over 63,000 boreholes, in situ seismic tests, and building inventory records were compiled into a unified relational database following rigorous multi-stage quality control. A multi-parameter N–Vs regression model was calibrated to supplement missing shear-wave velocity (Vs) data, reducing prediction errors by more than 20% relative to conventional empirical equations. Based on the quality-controlled Vs dataset, a high-resolution three-dimensional Vs–ground model was constructed to represent subsurface heterogeneity and associated uncertainty across the metropolitan area. The building inventory, comprising approximately 700,000 structures, was standardized according to the HAZUS structural taxonomy and mapped to Korean seismic design eras, enabling a Seoul-adapted vulnerability assessment in which exposure characterization and seismic demand are localized. Site-specific ground-motion amplification and response spectra derived from the 3D ground model were used to modify the spectral acceleration input to the HAZUS fragility functions. Results reveal pronounced spatial variability in site conditions, with northern mountainous zones corresponding primarily to NEHRP Site Class B, central districts to Class C, and southern alluvial basins to Classes D–E, producing amplification differences of up to 1.7 under identical input spectral accelerations. High-risk zones such as Gangnam, Songpa, and Yeouido exhibit concentrated expected damage where thick alluvial deposits coincide with dense stocks of mid-rise reinforced-concrete buildings. Overall, the study demonstrates that integrating high-resolution 3D geotechnical ground models with HAZUS-based fragility analysis provides a physically consistent and data-driven basis for urban-scale seismic risk assessment and resilience planning. Full article

Impulsive overtopping generated by dam-break surges is a critical hazard for dikes and flood-protection embankments, especially in reservoirs and mountainous catchments. Unlike classical coastal wave overtopping, which is governed by long, irregular wave trains and usually characterized by mean overtopping discharge over many waves, these dam-break-type events are dominated by one or a few strongly nonlinear bores with highly transient overtopping heights. Accurately predicting the resulting overtopping levels under such impulsive flows is therefore important for flood-risk assessment and emergency planning. Conventional cluster-then-predict approaches, which have been proposed in recent years, often first partition data into subgroups and then train separate models for each cluster. However, these methods often suffer from rigid boundaries and ignore the uncertainty information contained in clustering results. To overcome these limitations, we propose a GMM+MoE framework that integrates Gaussian Mixture Model (GMM) soft clustering with a Mixture-of-Experts (MoE) predictor. GMM provides posterior probabilities of regime membership, which are used by the MoE gating mechanism to adaptively assign expert models. Using SPH-simulated overtopping data with physically interpretable dimensionless parameters, the framework is benchmarked against XGBoost, GMM+XGBoost, MoE, and Random Forest. Results show that GMM+MoE achieves the highest accuracy ($R^2=0.9638$ on the testing dataset) and the most centralized residual distribution, confirming its robustness. Furthermore, SHAP-based feature attribution reveals that relative propagation distance and wave height are the dominant drivers of overtopping, providing physically consistent explanations. This demonstrates that combining soft clustering with adaptive expert allocation not only improves accuracy but also enhances interpretability, offering a practical tool for dike safety assessment and flood-risk management in reservoirs and mountain river valleys. Full article

Land seismic data are always contaminated by surface waves, which demonstrate strong energy, low velocity, and long vibrations. Such noises often mask deep effective reflections, seriously reducing the data’s signal-to-noise ratio while limiting the imaging accuracy of complex deep structures and the efficiency of hydrocarbon reservoir identification. To address this critical technical bottleneck, this paper proposes a surface wave joint reconstruction method based on stationary phase analysis, combining the cross-correlation seismic interferometry method with the convolutional seismic interferometry method. This approach integrates cross-correlation and convolutional seismic interferometry techniques to achieve coordinated reconstruction of surface waves in both shot and receiver domains while introducing adaptive weight factors to optimize the reconstruction process and reduce interference from erroneous data. As a purely data-driven framework, this method does not rely on underground medium velocity models, achieving efficient noise reduction by adaptively removing reconstructed surface waves through multi-channel matched filtering. Application validation with field seismic data from the piedmont regions of western China demonstrates that this method effectively suppresses high-energy surface waves, significantly restores effective signals, improves the signal-to-noise ratio of seismic data, and greatly enhances the clarity of coherent events in stacked profiles. This study provides a reliable technical approach for noise reduction in seismic data under complex near-surface conditions, particularly suitable for hydrocarbon exploration in regions with developed scattering sources such as mountainous areas in western China. It holds significant practical application value and broad dissemination potential for advancing deep hydrocarbon resource exploration and improving the quality of complex structural imaging. Full article

Gravity waves (GWs) are an important dynamic process in the planetary atmosphere. They are typically excited by convection, topography, or other sources from the lower atmosphere and propagate upwards. The GWs have a significant effect on the global atmospheric circulation on Mars. However, the lack of high-resolution data from previous observations has resulted in an insufficient understanding of GWs in the Martian atmosphere, particularly in terms of its global distribution and long-term evolution characteristics at different altitudes. Based on multiple years of Mars Climate Sounder (MCS) limb observations on board the Mars Reconnaissance Orbiter (MRO), we conducted a detailed study of the global distribution, seasonal and interannual variations in Martian atmospheric GWs with vertical wavelengths ranging from 9 to 15 km at three different altitude ranges, i.e., the low-altitude range of 200–20 Pa (Lp, ~10–30 km), the mid-altitude range of 20–2 Pa (Mp, ~30–50 km), and the high-altitude range of 2–0.2 Pa (Hp, ~50–70 km). The results indicate complex regional and north–south differences, as well as night–day variations, in the spatial distribution of GWs. Particularly, a three-wave structure of the GW activity is observed over mountainous regions in the mid-to-low latitudes of the Northern Hemisphere. The peak longitude range of this structure closely matches the mountainous terrain. In addition, our results reveal the presence of bands of GW aggregations in the mid- to-high latitudes of the Northern Hemisphere in the Mp and Hp layers, which may be caused by the instability of the polar jet. There are also obvious seasonal and interannual variations in GW activities, which are related to topography, polar jets, and large dust storms. The interannual variations in GWs imply that, in addition to the well-known large seasonal dust storms, complex interannual variations in atmospheric activity over the polar jets and in the complex topography at mid-to-low latitudes on Mars may also exist, which deserve further studies in the future. Full article

A macrotidal estuary with mountain-stream inputs (MEMSs) is characterized by strong hydrodynamic forcing, high turbidity, and complex channel morphology. This study combines field measurements (2005–2020) with a 2D hydrodynamic–sediment model to examine estuarine turbidity maximum (ETM) dynamics, erosion–deposition patterns, and the effects of engineering interventions in the Jiaojiang Estuary (JJE). Results show that the coupled influence of upstream floods and downstream macrotides produces highly seasonal and spatially variable water–sediment processes: mountain-stream floods exhibit sharp hydrodynamic fluctuations, and the estuary displays pronounced tidal-wave deformation. Over the 15-year observation period, the riverbed experienced alternating erosion (up to −3.5 m) and deposition (up to +4.2 m), with net erosion of 0.5–1.2 m occurring in most Ling River sections during high-discharge years. The ETM migrated about 30 km during spring tides, with near-bed suspended sediment concentrations reaching 50–60 kg/m³. Human activities—particularly historical sand mining—modified channel geometry and sediment composition, intensifying the exchange between bed material and suspended sediment and facilitating the formation and migration of the ETM. Extreme events further enhanced geomorphic adjustment: the post-Lekima (2019) flood produced maximum scour of −5.8 m in the upper Ling River and deposition of +3.2 m in the Jiaojiang main channel within weeks. Channel curvature and junction morphology strongly controlled flood-level distribution. Model experiments indicate that lowering shoal elevations and widening the cross-section at key constrictions can effectively reduce flood levels. Collectively, these findings clarify the morphodynamic evolution mechanisms of a MEMS system and provide quantitative guidance for flood-mitigation and estuarine-management strategies. Full article

Cape Town, South Africa, experiences coastal upwelling during austral summer. In this study, the effects of kinematic and thermal ‘roughness’ on wind stress are analyzed using 5–25 km resolution multi-satellite and coupled reanalysis datasets in the period 2010–2024. Average conditions for austral summer (December–February) are calculated to identify east–west gradients in sensible heat flux, wave height, and equatorward winds and to assess their consequences for the drag coefficient, wind-driven Ekman transport, and entrainment over the shelf from 16.9 to 18.7° E, north of Cape Town (33.7° S). Statistical and numerical outcomes are compared for austral summer and during active coastal upwelling in January 2018 with chlorophyll concentrations > 3 mg/m³. A subtropical anticyclone generated shallow equatorward winds next to a wind shadow north of Cape Town. Sharp cross-shore gradients in momentum flux were amplified by shoreward reductions in sensible heat flux and wave height, which suppressed the drag coefficient 10-fold. The inclusion of kinematic and thermal roughness in wind stress calculations results in a higher average cyclonic curl (−2.4 × 10⁻⁶ N/m³), which translates into vertical entrainment > 3 m/day at 33.7° S, 18° E. The research links coastal upwelling leeward of a mountainous cape with cross-shore gradients in air–sea fluxes that support recirculation and phytoplankton blooms during austral summer. Full article

Ground-penetrating radar (GPR), due to its efficiency and non-invasive nature, has become an important tool for detecting the permafrost table, overcoming the limited spatial coverage and high costs associated with drilling and in situ temperature monitoring. Compared with the commonly used 50–100 MHz antennas, the potential of high-frequency antennas to improve detection accuracy and interface resolution has not been fully explored. To address this gap, this study introduces a multi-strategy interface identification method incorporating envelope analysis. Field experiments were conducted in the island-like permafrost zone of the Da Xing’anling Mountains, Heilongjiang Province, using shielded GPR systems operating at 250 MHz and 500 MHz to detect the permafrost table. Potential interfaces were extracted using centroid and edge-detection algorithms and validated against ground temperature observations. The results indicate that: (1) integrating GPR with multi-source data enables accurate estimation of active layer thickness, and the envelope-based multi-strategy approach is effective for interface identification; (2) the 250 MHz antenna is better suited for capturing broader subsurface structures, while the 500 MHz antenna provides higher resolution for shallow layers—combining the two enhances overall interpretive quality; and (3) snow cover significantly affects electromagnetic wave propagation, reducing the accuracy of radar detection. This study provides valuable guidance for engineering investigations, site selection, and foundation design in permafrost regions, contributing to improved precision and efficiency in GPR-based detection of the permafrost table. Full article

Under the complex geological structural stress of the Western Himalayan syntaxis, the widespread distribution of hard and brittle rocks (such as sandstone and limestone) makes them prone to the formation of conjugate joints, also known as X-joints. These joints create weak structural planes in the slope rock mass, and when combined with weak interlayers within the slope, they result in a complex dynamic response and hazard situation in this region, which is further exacerbated by frequent seismic activity. This poses a serious threat to the planning, construction, and safe operation of the Belt and Road Initiative. To study the slope vibration response and instability mechanisms under these conditions, we conducted a shaking table test using the Iymek avalanche as a case study and performed Hilbert–Huang Transform (HHT) analysis. We also compared the results of the shaking table test on slope models without X-joints but containing weak interlayers. The findings show that the presence of X-joints leads to an earlier onset of plastic failure in the slope. During the failure development, X-joints cause stress concentration and the diversification of stress redistribution paths, delaying energy release. Ultimately, the avalanche failure mode in the X-joint slopes is more dispersed compared to the landslide failure mode in the model without X-joints. At the toe of the slope beneath the weak interlayer, low-frequency seismic waves can cause a significant amplification of acceleration, and the weak interlayer is often the shear outlets of the slope. These findings provide new insights into the seismic failure evolution of jointed slopes with weak interlayers and offer practical references for seismic hazard mitigation in mountainous infrastructure. Full article

Severe Typhoon Danas over the northern part of the South China Sea and seas near Taiwan in early July 2025 had an erratic path that had not been observed before, according to historical data in the region. Its formation, movement, and intensification posed significant challenges to the timely tropical cyclone (TC) warning services. This paper documents the observational aspect and forecasting aspect of this cyclone. There are key findings: (a) when Danas interacted with the Central Mountain Range of Taiwan, a “secondary cyclone” appeared over the northeastern part of Taiwan, which was observed by both weather radars and meteorological satellite winds, and was simulated to a certain extent by a mesoscale numerical weather prediction (NWP) model; (b) data-driven AI global models performed better than physics-based global NWP models in capturing the formation and the rather erratic track of Danas a couple of days earlier, although AI models generally underestimate the intensity forecasts; and (c) an atmosphere–ocean–wave coupled model was found to perform the best in capturing both the track changes of Danas (because of being driven by an AI global model) and its intensity changes (because of better physical representation of the oceanic impact on the intensity of this TC), whereas AI global models, though with various recent enhancements, still tended to underestimate the strength of Danas. This paper serves as a reference of this rather unusual TC for the weather forecasting services in the region. Full article