Search Results (1,658)

Geological hazards induced by large-scale and high-intensity mining activities worldwide are primary drivers of regional ecological degradation and pose significant threats to human safety and property. To construct efficient monitoring systems and enhance early warning capabilities, it is essential to clarify the formation mechanisms of various hazards and the suitability of corresponding technologies. Focusing on four typical geological hazards prevalent in mining areas (surface subsidence, ground fissures, landslides, collapses, and sinkholes), this paper characterizes their specific features and monitoring requirements. It systematically analyzes the physical principles, accuracy levels, and technical advantages and limitations of ground-based, aerial, and spaceborne monitoring, as well as multi-source remote sensing data fusion and emerging technologies (e.g., distributed optical fiber, light detection and range, microseismical monitoring, and deep learning). Utilizing case studies from an open-pit coal mine in Turkey and a loess gully mining area in China, the paper evaluates the effectiveness of methods like multi-temporal InSAR and UAV photogrammetry in identifying the evolution of these hazards. The findings indicate that the technological framework for mining area monitoring is transitioning from single-method approaches to integrated systems. However, given the complex mining environment, several bottleneck challenges remain, including single data dimensions, the limited environmental adaptability of aerospace remote sensing, insufficient stability of deep monitoring equipment, and weak anti-interference capabilities under extreme operating conditions. Consequently, this paper proposes that future innovations in geological hazard monitoring in mining areas will focus on multi-platform hierarchical collaboration, the development of multi-parameter fusion early warning criteria, and the construction of digital and visual platforms. Constructing a comprehensive monitoring system characterized by multi-scale collaboration and dynamic prediction capabilities is vital for improving safety standards in mining areas and achieving coordinated development between resource exploitation and environmental protection. The findings provide a theoretical foundation for the precise prevention and control of mining hazards, as well as for land ecological restoration. Full article

Milltailings deposition areas in abandoned mines are inherently unstable and spatially extensive and heterogeneous, making regional-scale field investigations challenging under intense rainfall. With the advancement of space–airborne remote sensing technologies, large-scale surface deformation monitoring has become feasible. In this study, a 22.02 km² abandoned mine in Lingqiu County, Shanxi Province, was selected as a case site; during the late-July 2023 extreme rainfall event, the site experienced large-scale surface displacements. Surface deformation was interpreted using Sentinel-1 SBAS-InSAR data, combined with differential digital elevation models (DEMs) derived from UAV surveys before and after heavy rainfall. A bivariate spatial autocorrelation analysis was conducted to evaluate the spatial relationship between differential DEMs and InSAR-derived deformation. The results indicate that: (1) SBAS-InSAR revealed significant spatial heterogeneity of ground deformation, with pronounced subsidence observed in the milltailings deposits; (2) the bivariate spatial autocorrelation analysis yielded a Moran’s I value of 0.2, suggesting a weak but positive spatial correlation between the DEM differences and InSAR results, with dispersed correlation patterns; (3) hotspot analysis highlighted notable clustering of deformation, with approximately 27.84% of the study area showing strong deformation responses, while 25.81% represented low–low clusters with limited deformation. Beyond tailings-deposit settings, this workflow is also applicable to the regional investigation of rainfall-responsive deformation and debris-flow-related terrain change on natural slopes under global change, providing technical support for surface investigations and offering insights for disaster early warning and ecological restoration in similar regions. Full article

Quantum image representation (QIR) is the basic idea behind quantum image processing. It explains how a normal image is converted into quantum states so that it can be processed using quantum computers. The commonly used models for QIR are Flexible Representation of Quantum Images (FRQIs) and Novel Enhanced Quantum Representation (NEQR). Though these approaches highlight the potential of quantum-based image encoding, the limitation of practical applicability on Noisy Intermediate-Scale Quantum (NISQ) devices exists. In this paper, we propose an intensity-preserving quantum image representation (IP-QIR) scheme that aims to maintain accurate grayscale intensity information while significantly reducing quantum resource usage. The proposed method employs a controlled rotation-based encoding strategy, where pixel intensities are embedded into the measurement probability of a single intensity qubit, and spatial information is represented using position qubits. To further enhance feasibility on near-term quantum hardware, the framework operates on small image patches instead of full-resolution images, thereby reducing circuit depth and overall complexity. The performance of the proposed IP-QIR approach is evaluated through IBM Qiskit simulations on three types of grayscale images: synthetic image patches, synthetic aperture radar (SAR) images, and medical tuberculosis (TB) chest X-ray images. Experimental results demonstrate that IP-QIR achieves better intensity preservation than FRQIs and NEQR, with fidelity values reaching up to 84.12% for both SAR and medical datasets. In addition, IP-QIR represents a $4\times 4$ image patch using only five qubits, which significantly reduces the qubit requirement when compared to NEQR, while still preserving high reconstruction accuracy. Full article

Effective crop monitoring during monsoon growing seasons in Central India faces challenges from persistent cloud cover that limits optical remote sensing during critical agricultural periods. This study presents the first attempt to develop a novel set of SAR-derived phenological metrics organized into five thematic categories for monsoon crop discrimination in smallholder agricultural systems. Five major monsoon crops (cotton, rice, maize, soybean, and urad) were analyzed across five different agroclimatic zones in Central India using Sentinel-1 data for the 2021 growing season. Phenological features were extracted from VV, VH polarizations, and their ratio, including seasonal extrema, threshold crossings, duration measures, curve shape descriptors, and area under the curve. Distinct crop-specific signatures were observed, with cotton showing extended phenology and cereal–legume crops displaying compressed, overlapping growth patterns. VV polarization achieved the highest statistical discrimination for intensity-based metrics, with 75% thresholds (VV_HP75V: F = 1287) providing higher separability than other thresholds by capturing near-peak biomass differences. VH performed best for duration and integration-based metrics, while VH/VV provided limited additional separability across metric types. For area-under-the-curve metrics, AUC25 outperformed AUC50 and AUC75 by capturing cumulative backscatter across the broader growing season while remaining robust to soil- and residue-dominated backscatter variability at sowing and harvest. Multiclass classification achieved 48.3% overall accuracy with systematic cereal–legume confusion, reflecting fundamental phenological convergence among monsoon-aligned crops. Cotton achieved the highest performance (F1: 0.79), with VH polarization dominating feature importance (65% of top 20 features). Binary classification revealed crop-specific discrimination patterns: cotton was best separated using VV intensity metrics, maize using the VH/VV ratio, and rice using timing-based features. Cross-district transferability showed the highest mean overall accuracy for rice (74%) and cotton (72%), while the remaining crops showed lower accuracy due to their phenological similarity. These findings highlight both the potential and limitations of SAR phenological metrics for monsoon crop discrimination, with effective results for structurally distinct crops but persistent cereal–legume confusion, requiring further investigation with multi-sensor approaches. Full article

In the semi-arid Batna Belezma region of northeastern Algeria, groundwater is a vital resource for agriculture and drinking water. However, the climate leads to intense evaporation, which affects its quality. This study aims to identify the key hydrogeochemical processes that control groundwater composition in the Merouana Basin and to evaluate the predictive performance of machine learning (ML) models. A total of 30 groundwater samples were analyzed using multivariate statistical techniques, including Principal Component Analysis (PCA), and were modeled using PHREEQC to assess mineral saturation states. Additionally, ML-based regression models, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGB),were employed to predict groundwater chemistry. The results indicate that the dominant ion distribution follows the following trend: Ca²⁺ > Mg²⁺ > Na⁺ and HCO₃⁻ > SO₄²⁻ > Cl⁻. Alkaline earth metals (Ca²⁺ and Mg²⁺) constitute the major fraction of total dissolved cations, reflecting carbonate equilibrium and dolomite dissolution processes. In contrast, Na⁺ represents a smaller proportion of the cationic load; however, its hydro-agronomic significance is substantial due to its influence on sodium adsorption ratio (SAR) and soil permeability. The PHREEQC modeling showed that calcite and dolomite precipitation promote evaporite dissolution, while most samples remain undersaturated with respect to gypsum. The PCA results reveal high positive loadings of Mg²⁺, Cl⁻, SO₄²⁻, HCO₃⁻, and EC, suggesting that ion exchange and seawater mixing are the primary controlling processes, with carbonate weathering playing a secondary role. To enhance predictive assessment, several supervised machine learning models were tested. Among them, the Random Forest model achieved the highest predictive performance (R² = 0.96) with low RMSE and MAE values, confirming its robustness and reliability. The results indicate that silicate weathering and mineral dissolution are the primary mechanisms governing groundwater chemistry. The integration of multivariate statistics and machine learning provides a comprehensive understanding of groundwater evolution and offers a reliable predictive framework for sustainable water resource management in semi-arid environments. Geochemical model performance showed a high global accuracy (GPI = 0.91), confirming a strong agreement between observed and simulated chemical data. However, the HH value (0.81) indicates some discrepancies, particularly for specific ions or extreme conditions. Full article

Background: Neuropsychiatric manifestations are a recognized complication of COVID-19, yet their temporal evolution across pandemic waves remains poorly characterized in hospitalized cohorts. This study examined whether their prevalence and composition changed across five successive waves. Methods: We conducted a retrospective observational study of 1471 hospitalized adults with confirmed Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection at Sibiu County Emergency Clinical Hospital, Romania (March 2020–January 2025), spanning ancestral through Omicron variants. A custom natural language processing pipeline extracted symptoms, medications, and International Classification of Diseases, 10th Revision (ICD-10) codes from electronic medical records. Nine hierarchical clinical clusters were defined; temporal trends were assessed using multivariable logistic regression with age-stratified replication. Results: Severe neurological presentations (stroke, seizures, hemiparesis) increased six-fold from 3.5% in Wave 1 to 20.1% in Wave 5, while psychiatric symptoms (anxiety, insomnia) declined from 13.3% to 4.3%. Overall, neuropsychiatric burden remained stable (~40–45%), revealing a compositional shift. This neurological trend persisted after multivariable adjustment (adjusted odds ratio 4.34, for Wave 5 vs. Wave 1) and within age-stratified subgroups, was inversely associated with respiratory severity and could not be attributed to vaccination status. The composite neurological severity index independently predicted mortality and intensive care unit admission. Conclusions: Neuropsychiatric manifestations in hospitalized Coronavirus disease of 2019 (COVID-19) patients underwent a compositional shift from psychiatric dominance in early waves to severe neurological dominance in later waves, consistent with a transition from reactive psychiatric presentations toward progressive neurological injury. This pattern, largely independent of measured confounders, underscores the need for sustained neurological surveillance beyond the acute respiratory phase. Full article

The disproportionately severe disease course of diabetic patients with SARS-CoV-2 infection was repeatedly observed by clinicians during the COVID-19 pandemic. The overlap between metabolic impairment, viral pathophysiology, and chronic inflammation created a pattern that urged deeper examination. The aim of this paper was to review and synthesize evidence regarding the interaction between diabetes mellitus and COVID-19. We synthesized evidence across mechanistic pathways (immune dysregulation, chronic inflammation, ACE2/DPP-4-related signaling, endothelial dysfunction, and pancreatic involvement) and key clinical outcomes (severity, intensive care unit (ICU) admission, mortality, dysglycaemia/new-onset diabetes, and DKA). This systematic search was conducted in PubMed, Clinical Key, and Google Scholar. The eligibility criteria included papers on adults (≥18 years) with pre-existing diabetes mellitus (type 1 or type 2) or newly diagnosed diabetes/hyperglycemia and confirmed SARS-CoV-2 infection, published between January 2020 and October 2025, in English language. The PRISMA guidelines were used for data extraction. We identified 412 articles, out of which only 30 met all the inclusion criteria. Diabetes was consistently evoked as a major risk factor for severe COVID-19, being associated with higher susceptibility to pneumonia, respiratory failure, ICU admission, and mortality. The explanation lies in the impaired immune system, endothelial dysfunction, and metabolic repercussions imposed by hyperglycemia. Several antidiabetic drugs appeared protective in multiple cohorts. In conclusion, the accumulated evidence underscores the tight interplay between metabolic disease and COVID-19. Essentially, the clinical management of these patients would be a thoughtful selection of antidiabetic therapy and close metabolic monitoring. Full article

Alpine wetlands in the Qinghai Lake Basin, located on the northeastern Qinghai–Tibetan Plateau, are ecologically important but highly vulnerable to climate change and anthropogenic disturbance. Traditional field-based surveys are labor-intensive and spatially constrained, underscoring the need for automated remote sensing approaches for large-scale wetland mapping. In this study, an object-based image analysis (OBIA) framework was developed by integrating Sentinel-2 optical imagery with Sentinel-1 synthetic aperture radar (SAR) data to classify two representative plateau wetland types: marsh meadows and inland tidal flats. Seven categories of features were evaluated, including spectral features, vegetation indices, water indices, red-edge features, topographic variables, radar backscatter, and geometric-textural metrics. The Separability and Thresholds (SEaTH) algorithm was employed for feature selection and optimization prior to classification using a Random Forest model. The results indicate that the incorporating geometric and textural features significantly improved classification performance, achieving an overall accuracy (OA) of 82.53% and a Kappa coefficient of 0.74. Moreover, the SEaTH-based feature optimization scheme yielded the best performance, with an OA of 86.24% and a Kappa coefficient of 0.79. Compared with the full feature set, this approach improved producer’s accuracy by 3.96–6.11% and increased overall accuracy by 1.48%. The proposed framework provides an effective and computationally efficient approach for mapping ecologically fragile alpine wetlands and offers valuable support for wetland conservation in the Qinghai Lake Basin. Full article

Synthetic aperture radar (SAR) ship detection is severely limited by the artifacts caused by motion. Due to the complex six-degree-of-freedom (6-DOF) motion of ships, the ship imaging exhibits aberration phenomena including spatial blurring, discrete ghosting, and Lorentz linear blurring. Traditional detectors rely on the identification of static spatial features. When the phase coherence is disrupted, they tend to fail. To overcome this problem, we propose a multimodal fusion framework based on physical principles. This framework establishes a theoretical connection between the ship hydrodynamic response and imaging degradation through short, long, and ultra-long coherence processing intervals (CPI). The framework adopts a cascaded architecture: first, a lightweight YOLOv8 performs rapid global screening, followed by a signal backtracking mechanism that extracts high-fidelity time-frequency domain (TFD) and range instantaneous Doppler (RID) features from the original distance compressed data. In the second-level detection, these physical features are adaptively fused with spatial intensity through a YOLOv8 network integrated with the convolutional block attention module (CBAM) to reduce the false detection rate. The validation on high-fidelity simulations and real GF-3 datasets shows that this method consistently achieves an average precision (mAP) of over 95%, outperforming several widely used detectors, and demonstrates strong generalization ability in extreme imaging conditions, suitable for maritime detection scenarios. Full article

Context/Objectives: In patients with COPD (chronic obstructive pulmonary disease), SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) infection represents an overlap of viral injury on a lung already affected by pathological mucus, altered mucociliary clearance, chronic inflammation, and impaired antiviral immunity. Methods: A focused narrative review (2020–2025) was conducted using clinical, experimental, and consensus evidence. The evidence was synthesized qualitatively, with priority given to cohort studies, meta-analyses, and mechanism-focused studies with clinical relevance. Results: Mucus obstruction (“mucus plugs”) is frequent in COPD (41–67%) and is associated with unfavorable outcomes. COPD also increases the risk of post-COVID respiratory sequelae. Bacterial coinfection at presentation is uncommon (3–5%), whereas secondary bacterial infections are more frequent (14–18%), especially in severe disease requiring intensive care, where VA-LRTI/VAP (ventilator-associated lower respiratory tract infection/ventilator-associated pneumonia) become predominant. Sepsis, whether viral or mixed, reflects disease severity and may contribute to functional decline and susceptibility to reinfections; however, the concept of a post-acute “sepsis legacy” in COPD after COVID-19 should currently be regarded as a clinically plausible but still emerging hypothesis rather than an established COPD-specific outcome. During recovery, acute exacerbation risk rises to 5.6% versus 3.9%, peaking in the first 30 days after severe disease (aHR ≈ 8.14). Persistent dyspnea and reduced DLCO (diffusing capacity for carbon monoxide) suggest ARDS-related injury, tissue remodeling, and microvascular dysfunction. Conclusions: In COPD, post-COVID respiratory sequelae result from the interaction of mucus, immunity, and infectious/sepsis-related complications. The first post-discharge month is a critical period requiring careful risk stratification and targeted follow-up. Full article

Background/Objectives: Diuretics are recommended for hemodynamically stable patients with COVID-19-associated acute respiratory distress syndrome (ARDS) who have a positive fluid balance. However, furosemide use may be limited by hypokalemia in this population. We aimed to evaluate the clinical and biochemical effects of spironolactone in critically ill patients with COVID-19-associated ARDS. Methods: In this retrospective cohort study, 60 patients with COVID-19-associated ARDS admitted to the intensive care unit (ICU) between March and May 2020 were grouped according to diuretic therapy (furosemide vs. spironolactone). Patients were followed for five days (T0–T4). Demographic characteristics and clinical/laboratory parameters were recorded. A two-sided p value < 0.05 was considered statistically significant. Results: Thirty-one patients received furosemide (F group) and 29 received spironolactone (S group). On day 5, in the F group, cumulative fluid balance and serum sodium increased significantly over time (p < 0.05). Lactate increased significantly over time in both groups (p < 0.05). N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels increased significantly from T0 to T4 in the F group (p < 0.05). Conclusions: Spironolactone use was associated with a more favorable trajectory of organ dysfunction and improved volume, electrolyte, and cardiac stress marker dynamics compared with furosemide in patients with COVID-19-associated ARDS. Although confirmation in larger prospective studies is needed, spironolactone may be considered a reasonable diuretic alternative in selected patients, particularly when potassium preservation and avoidance of hypernatremia are clinical priorities. Full article

Traditional image feature matching methods often fail to achieve satisfactory performance on multimodal remote sensing images (MRSIs), mainly due to significant nonlinear radiometric distortion (NRD) and complex geometric deformation caused by different imaging mechanisms. The key to successful MRSI matching lies in preserving high-frequency edge structures that are robust to geometric deformation, while overcoming nonlinear intensity mappings induced by NRD. To address these challenges, this paper proposes a novel high-precision matching framework, termed structure-aware feature descriptor with multi-scale side window filtering (SA-SWF). The proposed framework consists of three stages: (1) an anisotropic morphological scale space is constructed based on multi-scale side window filtering to strictly preserve geometric edges, and feature points are extracted using a multi-scale adaptive structure tensor with sub-pixel refinement to ensure high localization precision; (2) a structure-aware feature descriptor is constructed by integrating gradient reversal invariance and entropy-weighted attention mechanisms, rendering the multi-modal description highly robust against contrast inversion and noise; and (3) a coarse-to-fine robust matching strategy is established to progressively refine correspondences from descriptor-space matching to strict sub-pixel geometric verification, thereby minimizing alignment errors. Experiments on 60 multimodal image pairs from six categories, including infrared-infrared, optical–optical, infrared–optical, depth–optical, map–optical, and SAR–optical datasets, demonstrate that SA-SWF consistently outperforms seven state-of-the-art competitors. Across all six dataset categories, SA-SWF achieves a 100% success rate, the highest average number of correct matches (356.8), and the lowest average root mean square error (1.57 pixels). These results confirm the superior robustness, stability, and geometric accuracy of SA-SWF under severe radiometric and geometric distortions. Full article

Wildfires have become more frequent and intense worldwide due to climate change and anthropogenic activities, which is why accurate and timely burned area mapping is essential for estimating damage and effective post-fire recovery planning. Synthetic Aperture Radar (SAR) data, which operates under all weather conditions and day-night cycles, offers a reliable source for burned area mapping. In this context, several studies have explored the use of dual-polarization SAR imagery and machine learning, yet the influence of multi-date, dual-orbit pass data and texture features remained unexplored. Therefore, this study aims to assess the Sentinel-1 acquisition configurations, varying in temporal depth and orbital direction, for wildfire burned area mapping, considering the recent Palisades wildfire event as a study area. A comparative study was conducted across different scenarios to evaluate the effectiveness of using single-date versus multi-date SAR imagery, the integration of ascending and descending orbit passes, and the contribution of Grey-Level Co-occurrence Matrix texture features. The performance of Random Forest (RF) and Extreme Gradient Boosting classifiers was analyzed through the scenarios mentioned above. The single-date configuration using RF achieved an accuracy of 82.34%, F1-score of 81.43%, precision of 83.07%, recall of 80.84%, and ROC-AUC of 90.88%, whereas the multi-date approach reached 85.78%, 85.15%, 86.45%, 84.56%, and 93.28%, respectively. Our study highlights the importance of acquisition configuration and texture information for reliable SAR-based wildfire burned area assessment. 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

Human engineering activity, such as cross-regional transportation construction, often disturbs the geological environment and triggers landslides. This study investigated a landslide induced by tunnel excavation in the northeastern region of the Qinghai–Tibet Plateau, exploring how a seemingly low-risk local small-scale landslide can trigger an engineering disaster. Based on field geological and geomorphological surveys, unmanned aerial vehicle (UAV) remote sensing photography, and SBAS-InSAR data analysis (time-series monitoring from 2021 to 2023), the spatiotemporal evolution patterns and causative mechanisms of landslide deformation were systematically elucidated. The results indicate the following: (1) The landslide evolved from initial multiple small local slides, gradually expanding and connecting to form a larger and deep-seated landslide. (2) SBAS-InSAR analysis revealed that the landslide deformation rate ranged from −38.13 to 12.01 mm/a, with a maximum cumulative deformation of 121.91 mm. Substantial deformation was concentrated in April–June 2021, June–August 2022, and April–July 2023. Spatially, the deformation intensity exhibited a pattern of middle section > front > rear, with greater deformation closer to the tunnel construction point. (3) The landslide deformation is primarily related to tunnel construction disturbance. The topography, geological structure, and frozen ground thawing exerted certain influences. The deformation mechanism is summarized as follows: Slope toe excavation initially triggers local sliding, leading to tension cracking at the rear edge. Subsequently, tunnel construction further promotes landslide expansion, resulting in the formation of a deep-seated landslide. This study showed that the landslide resulted from the combined effects of engineering activity and natural conditions. The results reveal that, under disturbances from inappropriate engineering activities, local small landslides may develop into major disasters. Therefore, the construction plan for the tunnel must be revised to mitigate such risks. Full article