Search Results (355)

Fracture orientation data in structural geology are commonly affected by non-negligible angular uncertainty, which can significantly impact the reliability of classification and interpretation of deformation patterns. In this work, we address the problem of discriminating between two groups of directional observations. To account for measurement uncertainty inherent in field data, we adopt a deconvolution-based circular kernel discriminant rule specifically designed for noisy angular observations. This approach explicitly incorporates the measurement-error mechanism into the estimation process, allowing for more robust classification in the presence of observational noise. The methodology is applied to measurements arising in structural geology, where the discrimination of fracture orientations is relevant to the interpretation of deformation patterns and to applications in rock engineering. Specifically, we consider two datasets from Ordovician turbidites, involving different types of orientation data. The first dataset consists of $L_0^1$ axes, representing linear features described by Plunge–Azimuth coordinates, while the second dataset concerns axial-plane cleavage surfaces, expressed in terms of Dip and Dip direction. We assess the performance of the estimator under varying levels of angular uncertainty and alternative error distributions, with a focus on its ability to correctly separate the two geological groups. Results show that explicitly modeling measurement error leads to improved discrimination accuracy and more reliable identification of structural patterns compared to standard methods that neglect noise. Full article

Large-diameter rescue shafts serve as critical infrastructure for emergency response in mining disaster scenarios, and their structural deformation directly affects the safe passage of rescue capsules. In this paper, we investigate three-dimensional (3D) reconstruction techniques for large-diameter rescue shaft environments and develop a Neural Radiance Fields (NeRF)-based reconstruction and deformation assessment scheme. The proposed workflow integrates no reference signal-to-noise-ratio (NR-SNR), image-quality filtering, SfM-based camera-pose estimation, Nerfacto reconstruction, point-cloud export, and circular-section fitting. The NR-SNR retention-ratio experiment shows that retaining approximately 35% high-quality images provides a practical efficiency–quality trade-off for the present dataset, reducing the computational burden of SfM pose estimation while preserving sufficient geometric information for subsequent reconstruction. The reconstructed radiance field is further exported as a dense point cloud and evaluated using relative radius error, circle-fitting residuals, and image-level rendering metrics. Experiments on a simulated large-diameter rescue shaft platform show that the proposed NeRF-based scheme provides favorable geometric measurement applicability and visual reconstruction quality under weak-texture and low-illumination conditions. Compared with conventional MVS and the tested 3DGS baseline, the proposed scheme produces a point-cloud output that is more suitable for subsequent circular-section fitting and deformation-related assessment. In addition, comparison with a representative SDF-based baseline indicates that direct implicit surface recovery remains challenging for the tested hollow cylindrical shaft-wall scene. The results demonstrate the potential of the proposed NeRF-based workflow for rescue-shaft inner-wall reconstruction and engineering-oriented deformation evaluation. Full article

Background: Lateral cephalometric radiographs and large-field cone-beam computed tomography (CBCT) routinely used in orthodontics and maxillofacial radiology can reveal incidental pituitary tumors in the sellar region. Given the regular use of these imaging modalities, a structured overview of how pituitary tumors present on dental radiographs and how often they occur is clinically relevant. Methods: A scoping review was conducted according to PRISMA-ScR. MEDLINE via PubMed, Livivo, and Google Scholar were searched up to 20 January 2026 using MeSH terms and keywords for pituitary tumors and dental radiology. Human studies in English or German reporting on radiological presentation, clinical manifestation and epidemiology of pituitary tumors in the context of dental imaging were included. Study selection was performed independently by two reviewers. Results: Of 150 records, 15 studies were included: 2 case–control studies, 5 observational studies, 6 case reports, 1 questionnaire-based study and 1 neurosurgical guideline. Pituitary tumors most frequently presented with enlargement, deformation, or double contour of the sella turcica; growth hormone-producing adenomas additionally showed cephalometric changes such as mandibular and frontal sinus enlargement. The evidence is largely descriptive and does not permit robust estimates of prevalence or diagnostic accuracy but consistently identifies radiological “red flags” and recurrent clinical constellations, especially in acromegaly or unexplained craniofacial changes. Conclusions: Pituitary tumors, among the most common brain tumors, may first be suspected on routine dental radiographs. Distinct radiographic abnormalities combined with suggestive clinical features should prompt timely endocrine and neuroradiological evaluation, underscoring the need for heightened awareness among dental professionals. Full article

Precise Point Positioning with Ambiguity Resolution (PPP-AR) has emerged as a viable alternative to traditional network-based GNSS processing for crustal deformation monitoring and velocity field estimation. It provides high-precision daily coordinate solutions with simpler logistics, particularly for densifying velocity fields in regions lacking dense GNSS infrastructure. This study evaluates whether long-term velocity estimates derived from independent operational GNSS processing chains remain mutually consistent for regional geodynamic applications. We applied four processing strategies to 79 high-quality continuous GNSS stations in Southern Italy over the period 2017–2024: a Bernese double-difference network solution used as reference, Bernese PPP-AR, PRIDE PPP-AR, and the Nevada Geodetic Laboratory (NGL) PPP-AR solution derived from the GipsyX processing pipeline. The daily coordinate series preserve the realistic differences among the processing chains, while the subsequent velocity estimation was performed with a common HectorP workflow. A Bland–Altman screening identified 10 outlier stations, and the final inter-comparison is based on the remaining 69 stations (87.3% of the network). The results show that horizontal velocity components derived from PPP-AR agree with the network solution at sub-millimeter-per-year levels, with correlation coefficients exceeding 0.95, indicating strong coherence between the PPP-AR and network-derived horizontal velocity fields. In addition, vertical velocity estimates exhibit processing-strategy-dependent differences on the order of 1 mm ${\mathrm{yr}}^{-1}$ among PPP-AR solutions and relative to the network, indicating that careful interpretation is required for vertical rates. This study presents a systematic comparison of operational PPP-AR velocity solutions and a double-difference reference solution, demonstrating that complete processing-chain differences can introduce vertical effects comparable to those between PPP-AR and network processing. The findings support the practical maturity of PPP-AR for horizontal velocity field densification, while showing that vertical rates remain sensitive to processing strategy at the ∼1 mm ${\mathrm{yr}}^{-1}$ level. Full article

Conventional settlement monitoring techniques are inadequate for seawall construction environments due to severe physical impacts, the absence of terrestrial communication networks, and highly dynamic disturbances. This research proposes a multi-source fusion settlement monitoring system designed specifically for the construction phase to overcome these constraints. An integrated inclinometer–magnetoresistive sensing unit is the central component of this system. The unit achieves physical isolation from the severe impact loads of rock backfilling, guarantees protection in high-salinity and high-humidity environments, and accommodates the large deformations typical of soft foundations by utilizing a structural design that includes a rigid channel steel sheath, anti-corrosion sealing, and flexible joints. In terms of computation, a cascaded attitude fusion framework is developed that combines a Multiplicative Extended Kalman Filter (MEKF) with Quaternion Estimator (QUEST) initialization. High-precision displacement inversion via quaternion rotation is made possible by the introduction of an adaptive mechanism based on the Mahalanobis distance that precisely detects and suppresses transient acceleration disturbances induced by construction machinery and waves. Additionally, data transmission issues in remote offshore areas are resolved by combining solar power and BeiDou short-message communication technologies. This adaptive technique minimizes attitude estimate errors in dynamic situations by approximately 84.56%, as demonstrated by experimental and field validation. The system was deployed as a 165 m array comprising 49 sensing units and monitored continuously for 458 days, achieving a normalized RMSE of 9.44–11.02% compared to reference settlement tubes and capturing a maximum settlement of 1.7 m in the core high-fill section. These results confirm the system’s high monitoring accuracy and resilience in harsh construction conditions. Full article

The detection and degradation analysis of subsurface microcracks in mural paintings remain challenging due to their inhomogeneous multilayered structure and complex deterioration mechanisms. In this study, we propose a multimodal stepwise method for three-dimensional characterization and cross-scale degradation analysis by integrating digital holography (DH), infrared thermography (IRT), acoustic excitation (AE), and molecular dynamics (MD) simulations. In the first step, an adjustable field-of-view (FOV) digital holographic system is developed to capture subsurface deformation under acoustic excitation, enabling high-resolution planar characterization of subsurface microcracks. Infrared thermography is then employed to estimate crack depth through an inverse thermal model, achieving full three-dimensional reconstruction of crack geometry. Based on the reconstructed structures, MD simulations are conducted to investigate the evolution of stress, bond breaking, and crack propagation under varying temperature and humidity conditions, with particular emphasis on water molecule migration and chemically induced degradation. The results demonstrate that environmental factors promote stress concentration and material embrittlement at crack tips, leading to secondary microcrack formation and progressive deterioration. Experimental aging tests show strong agreement with simulation results, validating the proposed methodology. This work establishes a unified “characterization–simulation–validation” paradigm, providing new insights into the mechanisms of mural degradation and offering a robust framework for non-destructive evaluation and preventive conservation of multilayer cultural heritage materials. Full article

In this paper, we consider the one-dimensional liquid crystal flow with a vacuum free boundary and a degenerate viscosity coefficient. The global existence and long-time dynamics of strong solutions are established under a smallness condition on the initial energy at the basic level. The main challenges come from the degeneracy near the moving boundary and the strong nonlinear coupling between the velocity and the director field. To overcome these, we obtain uniform-in-time and space point-wise bounds of the deformation variable, and we construct uniform-in-time weighted energy estimates via singular multiplier techniques. Unlike previous works, the density is allowed to vanish and the viscosity coefficient is taken to be density-dependent rather than constant. Full article

Releasing a thin film adhered to a rigid substrate by peeling is a fundamental issue in interfacial mechanics and is of practical significance in many fields including flexible electronics, heterogeneous integration, and advanced packaging. While classical peeling theories have established the relationship between peeling force and interfacial adhesion, the in-plane strain evolution that governs film deformation and possible damage remains underexplored. This paper presents a numerical investigation of the in-plane strain in thin film peeling using an energy-variational framework. The results show that the strain response cannot be inferred solely from the peeling force response. Moreover, the dependence of the global maximum strain on film thickness $h$, Young’s modulus $E$, interfacial adhesion energy $\gamma$, peeling angle ${\theta }_F$, and the characteristic length l of the cohesive zone is systematically examined. To distinguish between two-stage and three-stage strain responses, a unified classification criterion is established based on these parameters. A closed-form polynomial decision boundary is obtained, which enables direct identification of the applicable regime and facilitates appropriate strain estimation in peeling processes. Full article

Surface rock movement can lead to geological or environmental problems such as surface subsidence, ground fissure development, and deformation of engineering structures, and its evolution process exhibits significant spatiotemporal heterogeneity. Therefore, conducting high-precision, spatiotemporally continuous monitoring of surface deformation is of great significance for revealing subsidence mechanisms, assessing potential risks, and guiding disaster reduction decisions. GNSS and InSAR have become mainstream methods for monitoring surface deformation, but they still have limitations in terms of spatial sparsity, 3D deformation inversion capability, and data gaps in areas of strong deformation. To address these issues, this paper takes the Jinchuan copper-nickel mine’s No. 2 mining area as the research object and comprehensively utilizes multi-source monitoring data from GNSS and InSAR to construct a joint inversion model of the surface 3D deformation field based on posterior variance component estimation, achieving adaptive optimization of weight allocation and collaborative solution of 3D deformation. To address the issue of InSAR decorrelation in areas of strong deformation, which leads to missing deformation information, a fitting and estimation approach was applied to supplement six decorrelated points that spatially coincide with GNSS stations. These points are located in key deformation areas, and their reconstruction effectively improves the completeness and reliability of the deformation field in critical regions. Based on this, an automated solution process for the fusion model is implemented using MATLAB R2022b, and the joint inversion yields spatiotemporally continuous 3D deformation fields in the northward, eastward, and vertical directions. The results show that compared with traditional monitoring methods, the proposed fusion model exhibits higher inversion accuracy and stability under different InSAR technology conditions, effectively suppressing the impact of single data source errors on the overall solution results. Among them, SBAS-InSAR shows slightly higher accuracy in the vertical direction, while PS-InSAR achieves higher accuracy in the planar direction, as indicated by lower RMSE and MAE values. The research results improve the accuracy and reliability of surface deformation monitoring in mining areas, providing important technical support for safe mining and refined management. Full article

This study presents a probabilistic seismic fragility assessment of a jointed rock slope by integrating field characterization, incremental dynamic analysis (IDA), and numerical modeling. Dominant joint sets are identified through field mapping, and key discontinuity parameters are estimated for the Barton–Bandis non-linear shear strength criterion. Dynamic simulations are performed using the distinct element method with the continuously yielding (C-Y) joint model to capture progressive shear degradation. Twenty real earthquake ground-motion records are scaled incrementally to perform IDA, with critical block displacement and cumulative joint slip adopted as engineering demand parameters (EDPs). A probabilistic seismic demand model (PSDM) is developed to correlate peak ground acceleration (PGA) with EDPs. Kinematic analysis indicates that planar failure along joint set 1 is the most likely failure mechanism (90% probability), followed by wedge failure along the intersection of joint sets 1 and 2 (52%). Fragility curves are derived for three displacement-based damage states: minor (1 cm), moderate (5 cm), and severe (15 cm). The results demonstrate that seismic deformation is strongly controlled by discontinuity geometry and progressive joint slip, with the slope exceeding the severe damage state at PGA levels as low as 0.4 g, indicating high seismic vulnerability. This highlights the importance of integrating field characterization with dynamic numerical modeling for reliable seismic stability assessment of such discontinuous rock mass. Future work should incorporate larger datasets, in situ testing, and 3D modeling to enhance assessment reliability. Full article

The precise spatiotemporal monitoring of dopamine is critical for understanding neurotransmission and neurodegenerative pathologies. While traditional electrochemical methods offer excellent temporal resolution, they lack the spatial resolution required to map network-wide dynamic events. To address this, we adapted a wide-field plasmonic electrochemical microscopy (PEM) platform to spatially image localized electrochemical reactions. Specifically, we leveraged the anodic electropolymerization of dopamine into a surface-confined polydopamine nanofilm to enable label-free, pixel-level optical quantification. Bulk solution testing demonstrated highly uniform sensor sensitivity, yielding an estimated single-pixel limit of detection of 14 pM. Furthermore, utilizing a custom injection system, we successfully imaged the real-time localized delivery of micromolar dopamine concentrations and demonstrated qualitative responsiveness of the integrated optical signal to delivered dopamine as a proof-of-concept for the platform. The platform functions as a spatially resolved mass integrator while simultaneously decoupling this chemical signal from transient hydrodynamic mechanical deformations caused by dopamine injection flow. Ultimately, this platform establishes the fundamental methodology required for future high-throughput spatial monitoring of complex neurotransmitter release dynamics across cellular networks. Full article

Offset tracking based on normalized cross-correlation (NCC) of synthetic aperture radar (SAR) intensity imagery serves as a critical technique for monitoring large-scale ground deformations. However, traditional NCC of SAR intensity imagery is susceptible to isolated high-intensity points, which can induce patch-like errors and compromise the reliability of the derived deformation fields. Existing suppression methods do not differentiate between isolated high-intensity points and those constituting structural features, which are beneficial for NCC, resulting in a substantial loss of valid offset measurements concurrent with errors mitigation. Regarding this, we proposed a method for suppressing patch-like errors of SAR intensity offset tracking. The new method initially employs Z-score standardization to rapidly screen high-intensity points; subsequently, Influenced Outlierness (INFLO) structural density analysis is utilized to identify isolated high-intensity points (classified as outliers), which are then replaced by the median values of their local neighborhood prior to the NCC computation. A method for detecting patch-like errors was also designed based on the spatial characteristics of patch-like errors, defined by abrupt boundary discontinuities and high internal homogeneity. On this basis, quantitative metrics including the patch-like errors removal rate and the valid offset coverage rate were further designed to evaluate the approach’s capability in eliminating patch-like errors while retaining valid offset measurements. Comparative experiments were conducted using simulated and real SAR data. Results demonstrate that the proposed method achieves patch-like errors suppression comparable to existing methods while significantly enhancing the retention of valid offset measurements and improving overall estimation accuracy. Specifically, in the real data experiments over the Amnye Machen and Central Tianshan test areas, compared to the logarithmic weighted NCC, the proposed method increased the valid offset coverage rates by 0.272 and 0.264, and improved the comprehensive quality indices by 0.191 and 0.184, respectively. This study represents a refinement of classical deformation estimation methodologies, offering a more robust option for monitoring large-scale ground deformation. Full article

Deformable medical image registration aims to spatially align anatomical structures across volumetric scans. Recent transformer-based methods achieve high overlap accuracy but often produce deformation fields with topological violations. We propose CTCF, a Cascade Transformer for Coarse-to-Fine registration that wraps a lightweight coarse-and-refined envelope around a core registration module. Level 1 provides a coarse displacement estimate at quarter resolution, Level 2 performs the main registration via a Swin Transformer encoder with deformable cross-attention and a learned super-resolution decoder, and Level 3 applies error-driven flow refinement at half resolution. The two outer levels add only 3.0% parameter overhead yet improve registration accuracy while maintaining competitive deformation regularity relative to external baselines. The model is trained end-to-end with a composite unsupervised loss combining local normalized cross-correlation, diffusion regularization, inverse-consistency, and Jacobian-based topology preservation. On the OASIS brain MRI benchmark, CTCF achieves the highest Dice score of 0.8208 among the compared unsupervised methods while maintaining competitive SDlogJ, with all Dice improvements statistically significant at $p<0.001$ by the Wilcoxon signed-rank test. On IXI, CTCF also achieves the best Dice, HD95, SDlogJ, and fold percentage among the compared methods. A five-round ablation study validates each component: cascade decomposition isolates each level’s contribution, and resolution scaling experiments confirm the framework’s scalability, yielding further accuracy gains with zero parameter overhead. Full article

In landslide-prone areas, spatial gaps in InSAR-derived deformation maps caused by incomplete SAR coverage hinder continuous surface deformation assessment and limit reliable landslide analysis. To address this problem, we propose an explainable AI (XAI) framework that integrates SBAS-InSAR, ensemble machine learning, and Shapley Additive exPlanations (SHAP) to estimate surface deformation in SAR-scarce regions. Geological and engineering factors, including protective measures, distance to roads, and land use, were combined with remote sensing and field data to build a comprehensive dataset. Four ensemble models (LightGBM, XGBoost, Random Forest, and CatBoost) were trained and evaluated, with XGBoost achieving the best performance (R² = 0.816, RMSE = 6.85 mm, MAE = 4.27 mm). Validation against two GNSS benchmarks confirmed sub-millimeter accuracy (0.6 mm and 0.3 mm). Both XGBoost and CatBoost delineated continuous deformation patterns consistent with field-observed damage. SHAP analysis provided model interpretability, highlighting elevation and human-engineering factors as key drivers: areas farther from roads and under cultivation were more prone to downslope movement, while damaged protective works exhibited greater deformation. By coupling InSAR with XAI, this study achieves accurate and interpretable surface deformation estimation in data-scarce regions, advancing landslide assessment and early warning applications. Full article

Inherent defects in engineering rock masses inevitably lead to randomness in mechanical parameters and uncertainty in tunnel deformation and failure. To address these challenges, this study proposes a novel coupled analysis method that integrates complex function theory, physical model testing, and Monte Carlo simulation (MCS) for the deformation estimation and failure probability analysis of non-circular tunnels. Theoretically, this method provides a high-speed, high-accuracy analytical framework that overcomes the limitations of purely numerical approaches, particularly in handling continuous–discontinuous failure processes. Practically, it enables a more reliable and efficient stability assessment of tunnel systems under uncertain geological conditions. The proposed method is applied to a traffic tunnel at the Baihetan Hydropower Station. A series of uniaxial compression tests on 40 rock specimens are conducted to obtain statistical distributions of rock deformation parameters. An analytical solution for tunnel displacement is derived using plane elastic complex function theory, and the random displacement field is estimated via MCS. Physical model tests reveal that the elastic stage accounts for 83% of the overload failure process, based on which an elastic limit displacement function is established for tunnel arch settlement and surrounding rock convergence. The failure probability of the tunnel is then calculated, and the effects of the mean, coefficient of variation, and cross-correlation coefficient of rock deformation parameters on failure probability are discussed. The entire computational process is characterized by high speed and precision, offering a new and practical tool for tunnel stability evaluation and reliability-based design. Full article