Search Results (758)

The Draa Sfar polymetallic mine, located near Marrakech in Morocco, represents the deepest currently operating underground mine in North Africa, with workings extending beyond depths of −1200 m. At such depths, mining activities are conducted within weak, highly anisotropic foliated black pelites, where recurrent instability mechanisms, most notably rib buckling and crown deterioration, are frequently observed, especially in drifts developed parallel to the foliation planes. In this context, the present study integrates detailed structural field observations with two-dimensional finite-element modelling using RS2 in order to analyse excavation-scale stability within these schistose pelitic rocks. Both numerical simulations and field evidence indicate that increasing depth-related confinement, together with a dominant in situ stress regime, favours stress channelling and localized damage development, while the pronounced transverse weakness of the pelites exerts a primary control on failure kinematics, including schistosity-parallel spalling, asymmetric rib buckling, and shear along inclined foliation intersecting the excavation back. Instability processes are further intensified by excavation geometry and mine layout: angular, square-shaped profiles and foliation-parallel drift orientations generate steeper stress gradients and greater convergence compared to arched sections, while proximity to stopes and adjacent openings enhances mining-induced stress redistribution and associated deformation. Intersection areas emerge as the most critical configurations, where the superposition of stress perturbations and structurally controlled damage mechanisms accelerates wall convergence and roof sagging. Overall, these findings demonstrate that drift stability cannot be adequately evaluated using generic design criteria when excavation geometry, interaction effects, and structural anisotropy exert a dominant influence on mechanical behaviour. Consequently, a fully integrated approach that combines drift geometry optimisation, detailed structural mapping, site-calibrated numerical modelling, and in situ monitoring is required to achieve reliable stability assessment and control. Full article

Benefiting from their outstanding porosity, considerable specific surface area, and natural flexibility, cellulose nanofibers (CNFs)/MOF materials have emerged as competitive candidates for advanced flexible energy storage devices. However, conventional CNFs/MOFs aerogels or films often suffer from poor recoverability under compression, bending, and folding, accompanied by severe plastic deformation that compromises the cycling and structural stability of devices. To address this issue, we report a rationally designed flexible PU/CNFs/ZIF-8/PANI composite foam with an interconnected micro-mesoporous structure. Using polyurethane foam as a soft substrate and CNFs/ZIF-8 as building blocks, the composite was fabricated through a combined strategy of impregnation, in situ ZIF-8 growth, hot-pressing, and in situ aniline polymerization with simultaneous etching of the ZIF-8. The incorporation of carboxylated CNFs enhances the hydrophilicity of the PU skeleton. This, in combination with the hot-pressed framework, establishes an interconnected 3D network, thereby effectively preventing the agglomeration of active materials. Meanwhile, the hierarchical pores derived from the sacrificial ZIF-8 template provide abundant electroactive sites, accelerate ion transport, and facilitate high PANI loading. By virtue of this synergistic architectural effect, the resultant electrode achieves a high specific capacitance of 449 F/g at 0.2 A/g, with 97% capacitance retention after 2000 cycles at 5 A/g. Furthermore, the composite foam demonstrates excellent mechanical flexibility, with a tensile strength of 0.87 MPa and an elongation at break of 230%. This work offers a feasible approach for developing high-performance flexible supercapacitors and provides novel perspectives for the rational design of portable energy storage devices. Full article

The fabrication of NiTiZr alloys by solid-state routes remains challenging due to limited atomic diffusion and the high reactivity of Ti and Zr. Mechanical alloying offers a potential pathway for synthesising such systems; however, complete alloy formation is not always achieved under practical milling conditions. Researchers have infrequently explored the mechanical alloying of NiTiZr, and this study systematically investigates the effect of milling time on microstructural evolution rather than claiming complete alloy synthesis. A high-energy planetary ball mill was used to mechanically process elemental powders of Ni, Ti, and Zr for 5–28 h. The examination revealed that longer milling times resulted in progressive crystallite refinement and increased lattice strain, while particle morphology evolved from irregular to more globular shapes due to repeated fracture and cold welding. After 28 h of milling, limited reacted regions containing Ni, Ti, and Zr were observed (~4.6% area fraction), while most of the powder remained heterogeneous and polyphasic, with no evidence of complete Ni₅₀Ti₃₀Zr₂₀ alloy formation. X-ray diffraction showed significant peak broadening without systematic 2θ peak shifts, indicating severe plastic deformation and crystallite refinement rather than definitive solid-solution formation of the allot. Differential scanning calorimetry revealed exothermic thermal events between 300 °C and 470 °C, which are attributed to defect recovery and thermally activated structural rearrangements rather than confirmed martensitic or crystallisation transformations. These results demonstrate that high-energy ball milling alone is effective for particle size reduction and defect generation but insufficient for producing a fully homogeneous Ni₅₀Ti₃₀Zr₂₀ alloy within 28 h. Additional activation energy, such as post-milling heat treatment or extended processing, is required to promote complete alloying in this system. Full article

The Bayan Obo Mining District, recognized as the largest rare-earth resource base worldwide, has experienced significant surface instability due to intensive mining and large-scale dumping activities. To address the challenges posed by complex geological conditions and mining-induced disturbances, this study employs dual-ascending Sentinel-1A C-band Synthetic Aperture Radar (SAR) datasets (Path 11 and Path 113) and applies the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to retrieve time-series deformation along the line-of-sight (LOS) direction for each track. Through temporal normalization and spatial matching, paired LOS observations from the two tracks were established. Based on the SAR observation geometry and under the assumption that the north–south component is negligible, a LOS projection model was constructed and a geometric decomposition was performed to derive the east–west and vertical two-dimensional deformation fields. The results indicate that the study area is generally stable, while significant subsidence occurs in the northern pit and adjacent waste-dump zones, with local maximum rates approaching 50 mm/year, predominantly controlled by the vertical component. The two-dimensional deformation analysis reveals that vertical displacement dominates surface motion, whereas east–west movement shows smaller amplitudes but clear directional concentration. In particular, the east–west slopes exhibit slightly higher velocities, suggesting a lateral adjustment tendency along this direction, likely related to the overall east–west geometric configuration of the open-pit and waste-dump areas. Time-series observations further reveal that precipitation-related surface deformation occurs with an approximate two-month delay, reflecting the hydrological–mechanical coupling processes of rainfall infiltration, pore-water pressure propagation, and dump-material consolidation. Overall, this study reveals the multi-dimensional deformation characteristics and precipitation-driven stage-wise response of the mining area, demonstrating the effectiveness of the dual-ascending SBAS-InSAR for two-dimensional deformation monitoring in highly disturbed environments, and providing a scientific basis for surface stability assessment and geohazard prevention. Full article

Slow-moving landslides can trigger severe disasters when activated by earthquakes, torrential rains, or typhoons. Early detection is crucial for mitigating loss of life and property damage. Interferometric Synthetic Aperture Radar (InSAR) technology is among the most effective techniques for detecting slow-moving landslides, though its accuracy can be further improved through integration with optical imagery and Digital Elevation Models (DEM). Current machine learning approaches that combine InSAR and optical data suffer from limited efficiency, poor transferability, and challenges in regional-scale application. To address these limitations, this study proposes a multimodal dual-path network that integrates InSAR products with textural information from optical imagery to detect slow-moving landslides. One path processes InSAR deformation rates and topographic factors, while the other incorporates texture information and auxiliary data. Together, these paths extract semantic information from high-dimensional spatial features and condense it into low-dimensional representations. A pyramid pooling module is employed to capture multi-scale features during low-level semantic extraction. For feature fusion, a rate-constrained adaptive module is introduced to enhance the contribution of deformation rates to slow-moving landslides. According to the results, the proposed method improves the F1-score for landslide detection by 6% compared to using InSAR products alone. These results provide reliable technical support for regional landslide inventory compilation and disaster management, as well as new insights for regional-scale surveys in slow-moving landslide-prone areas. Full article

Landslide susceptibility assessment (LSA) heavily depends on the completeness of landslide inventories and the interpretability of predictive models. Conventional inventories, based solely on historical records, often fail to identify newly occurring or slow-moving landslides, leading to biased susceptibility estimates. To address this limitation, this study proposes a dynamic LSA framework that integrates multi-source remote sensing data, Extreme Gradient Boosting (XGBoost) modeling, and Shapley Additive Explanations (SHAP), with a case study in Yongsheng County, Yunnan Province, China. This study jointly uses multi-temporal optical remote sensing imagery and Sentinel-1 InSAR (Interferometric Synthetic Aperture Radar) deformation data to update the landslide inventory. Compared with the historical inventory containing 334 landslide points, the updated inventory incorporates an additional 140 deformation-related landslide hazard points. XGBoost models were developed using conditioning factors selected through multicollinearity analysis to evaluate the influence of inventory completeness on model performance. Results show that the model based on the updated inventory achieves a significant improvement in predictive accuracy. SHAP-based interpretation reveals that distance to roads and maximum deformation rate are the dominant factors controlling landslide occurrence, reflecting the combined effects of human activities and dynamic ground deformation. The resulting susceptibility map shows that the Area Under the Curve (AUC) value for susceptibility zoning of the updated sample increases from 0.857 to 0.928, with high and very high susceptibility zones occupying 8.28% of the study area. Overall, the proposed framework improves both the accuracy and interpretability of LSA and demonstrates the effectiveness of multi-source remote sensing data for dynamic landslide hazard assessment in mountainous regions. Full article

During the early pupal stage in butterflies, the peripheral portion of wing tissue undergoes apoptosis to finalize adult wing morphology, and wing color patterns are determined coordinately. We hypothesized that the development of wing morphology and color patterns may involve NADPH oxidase (NOX). To test this hypothesis, we treated pupae of the blue pansy butterfly Junonia orithya with NOX inhibitors. When VAS2870, isuzinaxib, or diphenyleneiodonium chloride (DPI) in dimethyl sulfoxide (DMSO) was topically applied to the pupal wing tissue via the sandwich method, wing morphology and color pattern elements, including eyespots, parafocal elements, submarginal bands, and marginal bands, were severely deformed as if the marginal area were surgically removed. The topical application of DMSO alone mildly deformed and enlarged eyespots without affecting other color patterns and wing morphology. When systemically injected into pupae, VAS2870 increased eyespots in males but decreased eyespots in females, likely due to the sexual dimorphism of this species. These results suggest that NOX and probably hydrogen peroxide play important roles in wing morphogenesis and color pattern fate determination in butterfly wings. Sexually dimorphic eyespot size in this species may also be explained by the sexually differential activities of NOX. Full article

The identification of potential landslides and targeted risk analysis is crucial for the warning and prevention of geological landslide disasters. This article presents a time series deformation prediction framework based on a Long Short-Term Memory (LSTM) network deep learning model for analyzing Interferometric Synthetic Aperture Radar (InSAR) data. By employing an advanced stacked LSTM network model, we effectively capture temporal dependencies and move beyond traditional methods that depend on explicit deformation. This approach enables short- to medium-term deformation prediction through structured time dynamic modeling, identifies potential landslide targets in the high-altitude regions upstream of Lake Sarez, and classifies associated risk levels. The results indicate that: (1) In short-term forecasting, the stacked LSTM model effectively captures trend turning points, producing stable and reliable predictions with a Mean Absolute Error (MAE) of 0.164 mm and a Root Mean Square Error (RMSE) of 0.194 mm; (2) From 2019 to 2022, regional surface deformation characteristics exhibited significant spatial heterogeneity, with the potential landslide on the right bank identified as the most critical settlement center, demonstrating a line of sight (LOS) deformation rate consistently exceeding 49 mm per year, while the Usoi Dam displayed relatively good stability during this period; (3) By integrating InSAR deformation rate maps with Sentinel-2 optical images, we identified a total of 72 potential landslide targets in the region, four of which exhibited deformation rates exceeding −30 mm per year, indicating significant activity and classifying them as high-risk areas requiring attention. This provides a targeted reference list for the prevention and control of geological landslides around Lake Sarez and establishes a reliable technical pathway for the early identification of landslides under complex geological conditions in high-altitude mountainous areas. Full article

Flexible skin electronics are increasingly sought after for their potential in sensing and drug delivery within wearable human–machine interfaces. However, developing multifunctional applications that maintain biocompatibility and stable electrical performance under various mechanical deformations remains a challenge. Here, we introduce tattoo paper-based graphene–gold conductors that are approximately 0.04 mm thick and feature a dual conductive pathway within the graphene–gold film. By integrating a folding structure with this dual conductive pathway, we can mitigate the strain effects on the electrical resistance of film-based conductors, resulting in wider areas of stable resistance. In addition, we have designed film conductors with a kirigami structure, which achieves a high initial conductivity of 1.5 × 10³ S cm⁻¹ and exhibits negligible resistance changes across a broad strain range of 0 to 130%. We utilize these conductors to develop waterproof on-skin patches that incorporate electrically and optically active heaters for body heating and drug delivery. Furthermore, we have created an on-skin dialing interface using these conductors, which enables users to make telephone calls based on triboelectric nanogenerators. Full article

High-intensity mining activities in coal mining areas have produced large-gradient surface deformation, posing severe challenges to deformation monitoring using Interferometric Synthetic Aperture Radar (InSAR) techniques based on C-band Synthetic Aperture Radar (SAR) data. This study systematically evaluated the applicability of L-band LuTan-1 SAR (L-SAR) data versus C-band Sentinel-1A data for monitoring mining-induced surface deformation, using the Guqiao Coal Mine in Huainan as the study area. Based on 10 ascending-track and 13 descending-track L-SAR images and 42 Sentinel-1A images, deformation retrievals were performed using Differential InSAR (DInSAR) and the Small Baseline Subset (SBAS) InSAR approach, respectively, and the results were validated against independent levelling measurements. Results indicate that the mean coherence of descending- and ascending-track L-SAR interferometric pairs are 0.42 and 0.45, respectively, substantially higher than Sentinel-1A’s 0.25. In the DInSAR analysis along profile A–A′, the maximum line-of-sight (LOS) displacement obtained from descending- and ascending-track L-SAR are −0.40 m and −0.43 m, respectively, compared with −0.25 m from Sentinel-1A. In the SBAS-InSAR time-series analysis, descending- and ascending-track L-SAR yield 209,418 and 228,388 coherent points, respectively, clearly revealing the temporal evolution of surface deformation; their maximum LOS deformation rates are approximately −1.54 m·yr⁻¹ and −2.0 m·yr⁻¹, respectively. By contrast, Sentinel-1A selects only 81,669 coherent points, with severe loss of coherence in the subsidence center and a maximum LOS deformation rate of about −0.48 m·yr⁻¹. Accuracy validation shows that the Root Mean Square Error (RMSE) of vertical displacements obtained from DInSAR monitoring results based on descending and ascending L-SAR data is 16.1 mm, satisfying the requirement of centimeter-level accuracy for mining area surface subsidence monitoring. The study demonstrates the pronounced advantages of L-SAR for monitoring large-gradient, nonlinear deformation in mining environments. L-band data outperform C-band Sentinel-1A across coherence preservation, deformation sensitivity, and monitoring accuracy, providing a scientific basis for the broader application of domestic L-band SAR satellites in disaster risk assessment and long-term time-series monitoring of mining-induced subsidence. Full article

This study presents the first integrated quantification of mud diapirism susceptibility in the Membrillal sector of Cartagena de Indias, Colombia, through a multidisciplinary approach combining geospatial, geotechnical, hydrogeochemical, and socio-structural analyses. Using GIS-based multicriteria modeling, household surveys (n = 240), and temporal satellite imagery from 2013 to 2024, the research identifies spatial and temporal dynamics of active mud volcano reactivation. Field sampling of vent waters and gases followed ISO/IEC 17025 and APHA–AWWA–WEF standards, revealing high-salinity fluids (TDS = 13,220 mg/L; EC = 20.4 mS/cm; pH = 8.0) with elevated chloride (6996 mg/L) and low sulfate (1.67 mg/L) under reducing conditions, though a significant charge-balance discrepancy (Na⁺ = 8 mg/L) indicates either sample dilution during the collection or presence of unmeasured cationic species, and low free-gas flux constrained by high-density brine sealing. Principal component analysis of 240 georeferenced dwelling surveys yielded dimension-specific reliability (α = 0.68–0.76) and strong spatial correlation (Spearman ρ = 0.61–0.87) between vent proximity and structural damage—46.9% of dwellings exhibited visible cracking, with 27.2% severe (width > 1.5 mm). Satellite differencing documented 233% increase in active vents (3→10) and 35% vegetation reduction correlated with informal settlement expansion into moderate-to-high susceptibility zones. Weighted overlay GIS modeling (validated Kappa = 0.82) classified four hazard classes; high-susceptibility zones (18% of the study area) encompassed all ten active vents. Findings underscore anthropogenic pressurization drivers—primarily surface loading from settlement densification—and the need for continuous InSAR deformation monitoring, piezometric observation, complete hydrogeochemical characterization (including alkalinity and unmeasured cations), and establishing early-warning thresholds for community risk mitigation. Full article

Electrophysiology, mechanobiology, and the study of soft matter within cells demonstrate increasing amounts of evidence that neuronal signaling arises from interactions between membrane potential, force, and phase. Herein, we have attempted to collect and organize the evidence for each of these areas of study into an approximate structure called the electromechanical connectome: a three-way state–space (membrane potentials, nanoscale mechanical forces, and cytoplasmic rheology, including phase-separated liquid–liquid droplets) where membrane potentials, nanoscale mechanical forces, and cytoplasmic rheology, and phase-separated liquid–liquid droplets are likely to influence one another, influencing synaptic processing, plasticity and network stability. We will also attempt to illustrate the following: how changes in electrostatic fields can be used to alter the arrangement of lipids, hydration, and dielectric microdomains, and the contact geometry between organelles and activity dependent transcription; how mechanical dynamics associated with spines, axons, and the active zone of synapses may be used to modify the energy landscape of channels, the docking and priming of vesicles, and the transport of cytoskeletons; and how viscosity corridors, along with phase-separated micro-reactors, can be used to regulate the kinetics of signaling, molecular trafficking and metabolic processes in local environments. With these connections in mind, we will propose a multiphysical attractor model in which cognition is the result of navigating through metastable manifolds, while neurodegenerative disease may be a result of the progressive loss of electromechanical coherence, phase boundary control and energetic flexibility. Finally, we will present testable hypotheses and use AI-enabled digital twin methods to potentially quantify the early deformation of manifolds and provide precision biomarkers and therapeutic options. Full article

Coal mining in plain regions and its related surface subsidence and geological hazards have been extensively studied, whereas research on mining-induced hazards in mountainous areas remains limited. This knowledge gap has contributed to the frequent occurrence of mining disasters, particularly under steep ridge-type mountain geometry, where deformation characteristics, large-scale slope failure risks, and mining-induced hazard mechanisms remain poorly understood. In this study, a mining area in Zhenxiong, Zhaotong, Yunnan Province, China, is investigated using SBAS-InSAR, GNSS observations, UAV surveys, optical satellite imagery, and detailed field investigations. Surface hazards triggered by coal extraction are identified, and the response relationship between surface subsidence and mining activities is analyzed to reveal the development mechanisms of surface deformation beneath steep ridge-type mountain geometry. The results show that: (1) deep coal mining can still induce significant surface deformation due to the combined amplification effects of steep slopes and lithological conditions; (2) mining-induced deformation does not necessarily evolve into large-scale slope collapse and may gradually stabilize through natural adjustment processes; (3) SBAS-InSAR, validated by GNSS and field observations, provides an effective approach for detecting mining-related subsidence; (4) surface deformation in the study area is jointly influenced by multiple working faces; and (5) strong coupling between the unique steep ridge-type mountain geometry and underlying coal extraction leads to a compound disaster chain under multi-source interactions. These findings offer a critical scientific understanding of mining-induced deformation beneath steep ridge-type mountain geometry and provide important guidance for geological hazard prevention and control in similar mountainous mining areas. Full article

Landslide susceptibility mapping (LSM) is a fundamental approach for identifying and predicting areas prone to slope failure. However, most conventional LSM methods are based on time-invariant conditioning factors or long-term-averaged predictors and seldom incorporate slope-kinematic information from deformation observations, thereby limiting their ability to capture evolving slope instability. Moreover, the black-box nature of many models limits interpretability and confidence in their predictions. In this study, we integrate small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) with interpretable machine learning (ML) methods to develop a dynamic LSM framework that improves the accuracy and reliability of susceptibility assessment. First, static LSM was performed using ML algorithms, and SHapley Additive exPlanations (SHAP) was used to quantify and visualize feature importance. Subsequently, SBAS-InSAR was applied to retrieve surface deformation rates. Finally, a dynamic LSM matrix was constructed to integrate InSAR-derived deformation with static susceptibility classes, producing time-varying landslide susceptibility maps. Application of the framework in the Baihetan Reservoir area, Southwest China, demonstrates its practical value. During the static LSM phase, the extreme gradient boosting (XGBoost) model achieved strong predictive performance (the area under the receiver operating characteristic curve (AUC) = 0.8864; accuracy = 0.8315; precision = 0.8947), outperforming the alternative models. SHAP analysis indicates that elevation and distance to rivers are the primary controls on landslide occurrence. Incorporating SBAS-InSAR deformation data into the dynamic LSM matrix effectively captures the spatiotemporal evolution of slope instability. Susceptibility upgrades are observed for multiple inventoried landslides, and the actively deforming Xiaomidi and Gantianba landslides are presented as representative case studies, further supported by multisource observations from satellite imagery, unmanned aerial vehicle (UAV) surveys, and ground-based global navigation satellite system (GNSS) monitoring. Consequently, the proposed dynamic LSM framework overcomes limitations of static approaches by integrating deformation information and enhancing interpretability through explainable artificial intelligence. Full article

Landslide susceptibility maps (LSMs) are crucial for risk mitigation, but integrating Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) data is often hampered by a lack of physical interpretation. To address this issue, this study proposes an enhanced modeling framework that integrates multi-source monitoring data by coupling dynamic deformation features. Ground deformation velocity is obtained using MT-InSAR and embedded as dynamic physical constraints into the loss function of a Multi-Layer Perceptron (MLP) model. This approach enables the joint optimization of static geological factors and dynamic deformation characteristics in landslide susceptibility prediction. The proposed framework was applied to Zunyi City, Guizhou Province, China, utilizing an inventory of landslide hazard sites and a dataset of 16 susceptibility factors for model training and evaluation. The results demonstrated that the dynamically constrained model significantly improved predictive performance (AUC = 0.976, an increase of 0.032 compared to the baseline model), and enhanced spatial consistency, reflected by an average increase of 0.0184 in predicted susceptibility for inventoried landslide hazard sites. The framework also outperformed other conventional machine learning models across multiple evaluation metrics. Furthermore, SHAP (SHapley Additive exPlanations) analysis revealed that slope (18.68%), DEM (13.26%), rainfall (11.57%), and mining activities (8.79%) were the primary contributing factors in high-susceptibility areas. This study offers a physically interpretable and robust methodology that advances landslide risk assessment and contributes to disaster prevention strategies. Full article