Search Results (329)

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

Global Positioning System (GPS)-derived ionospheric electron concentration measurements provide a powerful observational framework for seismo-electromagnetic studies, enabling quantitative investigation of lithosphere–atmosphere–ionosphere coupling processes through statistically detectable perturbations in ionospheric electron concentration. We analyze GPS-derived Vertical Total Electron Content (VTEC) variations associated with the 14 August 2021 Haiti earthquake ($M_w$ 7.2) and the 11 January 2022 Cyprus earthquake ($M_w$ 6.6) using data from nearby International GNSS (Global Navigation Satellite System) Service (IGS) stations located within their respective earthquake preparation zones. VTEC time series spanning 45 days before and 7 days after each event are processed to remove the diurnal component, yielding residuals that isolate short-term ionospheric variability. Anomaly detection is performed using three statistical frameworks: a Gaussian mean, standard deviation model, a robust median/median absolute deviation (MAD) model, and a distribution-free quantile-based model. Daily “occurrence” and “energy” indices are constructed to quantify the frequency and cumulative strength of detected anomalies, respectively. While the indices exhibit similar temporal patterns across all methods, they indicate frequent anomaly detection, limiting statistical selectivity. To address this, both indices are normalized by their median values and filtered using a 95% quantile threshold, retaining only extreme deviations. This procedure substantially reduces background fluctuations and isolates a small number of statistically significant anomaly peaks. For both earthquakes, enhanced anomaly activity is identified in the weeks preceding the events, whereas post-event peaks coincide with periods of elevated meteorological and geomagnetic activity. The results demonstrate that normalization combined with robust statistical methods is essential for discriminating significant ionospheric TEC anomalies from background variability. Full article

The deep integration of machine learning technology with geological prospecting has brought to the forefront a key challenge: how to construct geological-mineralization models by fusing multi-source data, select model features with guidance from metallogenic factors, build multi-source metallogenic prediction models with geological constraints, and ultimately achieve a thorough integration of domain knowledge and machine intelligence. The Eastern-Central Tianshan region is one of China’s most important copper–nickel mineral resource bases, predominantly hosting magmatic copper–nickel sulfide deposits with significant resource potential. In this context, this paper proposes a metallogenic prediction model based on multi-modal feature fusion technology. The model employs a Residual Neural Network (ResNet) incorporating a Squeeze-and-Excitation (SE) attention mechanism and a Multi-Layer Perceptron (MLP) to extract features from different modalities. It integrates multi-source data, including geochemical information, geological metallogenic factors, and aeromagnetic data. A cross-modal feature interaction module, constructed using attention weighting and a gating mechanism, enables deep fusion of the features. After training, the model achieved a prediction accuracy of 97% on the test set. Compared to a unimodal model constructed using Random Forest, the confidence and discriminative capability of the training results were significantly enhanced, validating the effectiveness of multi-modal feature fusion. Applying the trained model to the study area, a total of 11 prospective metallogenic zones were delineated. These include 4 zones in the peripheries of known deposits and 7 zones in previously unexplored (blank) areas. Notably, some known mineral occurrences fall within the predicted blank-area targets, validating the feasibility and significant value of multi-modal feature fusion in mineral prediction. This work provides a novel methodology for the subsequent integrated processing of multi-source data. Full article

Thermal contextual algorithms for 375 m VIIRS active fire detection can produce substantial commission errors over persistent non-wildfire heat sources (e.g., refineries, gas flares, and volcanoes), and globally fixed thresholds may be suboptimal under heterogeneous thermal backgrounds. We present a lightweight spatiotemporal prior layer that augments by applying prior-guided, pixel-level parameter switching during the discrimination stage. The layer combines: (i) a persistent non-wildfire thermal anomaly mask (PFM) derived from multi-year VNP14IMG recurrence and seasonality statistics on a 0.004° grid, and (ii) a short-term heat-source mask (SHM) based on nighttime VIIRS I4/I5 brightness temperature stability to capture newly emerged or rapidly intensifying static sources. Pixels flagged by either prior are processed with a stricter parameter set, while other pixels follow the baseline setting. We evaluate the method using a stratified validation dataset (N = 3435) spanning industrial/urban clusters, volcanic regions, forest/grassland wildfires, and fragmented crop residue burning, with validation supported by independent high-resolution imagery (Sentinel-2/Landsat) and external POI datasets. The framework markedly reduces false positives in high-interference zones (industrial/urban false positive rate from 88.6% to 22.7%; volcanic from 100.0% to 57.3%) while preserving high performance for forest/grassland wildfires (F1 ≈ 0.999). For fragmented residue burning, omission error decreases from 11.2% to 1.3%, improving detection completeness without an apparent increase in commission errors. Overall, the results suggest that integrating long- and short-term spatiotemporal priors via threshold switching can improve the robustness and interpretability of contextual VIIRS fire detection under complex thermal backgrounds in the evaluated scenarios. Full article

Sand production in an offshore hydrocarbon wells poses significant operational and integrity challenges, particularly in deviated wells, where complex flow geometries intensify particle transport and erosion risks. The traditional sand-monitoring method utilizes stationary acoustic sensors attached to the production flowline at the surface. However, these sensors provide limited spatial coverage and intermittent measurements, restricting their ability to detect early sanding onset or precisely localize sanding intervals. By combining with vertical seismic profiling (VSP), Distributed Acoustic Sensing (DAS) delivers continuous, high-density data along the entire length of the wellbore and is increasingly recognized as a powerful diagnostic tool for real-time downhole monitoring. This study presents a field application of DAS-VSP for detecting and characterizing sand transport in a deviated offshore production well equipped with 350 distributed fiber-optic channels spanning 0–1983 m true vertical depth (TVD) at 8 m spacing. A multistage workflow was developed, including SEGY ingestion and shot merging, channel and time window selection, trace normalization, and low-pass filtering below 20 Hz. Multi-domain signal analysis, such as RMS energy, spike-based time-domain attributes, FFT, PSD spectral characterization, and time–frequency decomposition, were used to isolate the characteristic im-pulsive low-frequency (<20 Hz) signatures associated with sand impact. An adaptive thresholding and event-clustering scheme was then applied to discriminate sanding bursts from background noise and integrate their acoustic energy over depth. The processed DAS section revealed distinct, depth-localized sand ingress zones within the production interval (1136–1909 m TVD). The derived sand log provided a quantitative measure of sand intensity variations along the deviated wellbore, with normalized RMS amplitudes ranging from 0.039 to 1.000 a.u., a mean value of 0.235 a.u., and 137 analyzed channels within the production interval. These results indicate that sand production is highly clustered within discrete depth intervals, offering new insights into sand–fluid interactions during steady-state flow. Overall, the findings confirm that DAS-VSP enables continuous real-time monitoring of the sanding behavior with a far greater depth resolution than conventional tools. This approach supports proactive sand management strategies, enhances well-integrity decision-making, and underscores the potential of DAS to evolve into a standard surveillance technology for hydrocarbon production wells. Full article

Magnetite is extensively developed within various alteration zones of the mining district. Some magnetite is closely associated with copper mineralization, possessing significant research value. The Duobuza Cu (Au) deposit is a typical porphyry-type deposit within the Bangong Co-Nujiang metallogenic belt and was the first porphyry Cu-Au deposit discovered in the Duolong copper–gold ore district. Currently, this deposit contains copper resources exceeding 3 million tons @0.46%, with associated gold resources exceeding 80 tons @0.19 g/t. This study focuses on magnetite from the Duobuza deposit. Through field geological logging and microscopic identification combined with electron microprobe analysis (EMPA) and in situ LA-ICP-MS testing, mineralogical and mineral chemical research on magnetite is conducted. This research aims to elucidate the genesis of magnetite in the Duobuza deposit and its implications for mineral exploration. Five magnetite types with different occurrences can be distinguished in the Duobuza deposit: Mt₁ is magmatic magnetite; Mt₂, Mt₃, Mt₄, and Mt₅ are hydrothermal magnetite, with Mt₅ being closely associated with copper mineralization. Mt₁ is relatively enriched in Ti, V, Al, and Cr but depleted in Mn and Si; Mt₂ is relatively enriched in Ti and Al but depleted in Si and Cr; Mt₃ is relatively enriched in Al but depleted in Mg; Mt₄ is relatively enriched in Ti, Al, V, Zn, and Mn; and Mt₅ is relatively enriched in Mg, Si, Ti, Al, Mn, and Zn but depleted in Cr. Based on the Al + Mn vs. Ti + V discrimination diagram, magnetite formed in a medium- to high-temperature environment, with hydrothermal magnetite Mt₄ forming at the lowest temperature. Vanadium (V) content can be used to estimate the oxygen fugacity (fO₂) during mineralization. Mt₁ exhibits the highest V content, indicating relatively low oxygen fugacity, whereas Mt₄ shows the lowest V content, suggesting relatively high oxygen fugacity. Mt₅ has a higher V content compared to other early-stage hydrothermal magnetites, suggesting that a lower fO₂ formation environment favors the precipitation of metal sulfides in the mining district. Trace element analysis of magnetite from the Duobuza, Bolong, and Naruo mining districts reveals that magnetite from all three deposits is enriched in Si and Al and depleted in Ca and Ni. Magmatic magnetite from the Naruo and Duobuza deposits exhibits similar elemental distribution patterns. Hydrothermal magnetite from the Duobuza deposit shows significantly higher Ti and V contents compared to magnetite from the Bolong and Naruo deposits. Full article

Vegetation indices are used to analyze vegetation dynamics and primary productivity. However, most studies rely on short time series and peak or integral metrics, which limit the understanding of long-term vegetation dynamics in heterogeneous areas. This study aimed to classify a subarea of northeastern Mexico using a 25-year EVI time series and to characterize the resulting groups using growth parameters derived from temporal analysis. MODIS EVI mosaics from 2000 to 2024 were averaged and classified using the ISODATA algorithm, resulting in 16 groups. Smoothed EVI time series were analyzed with TIMESAT to extract growth parameters, which were compared among groups using Discriminant Function Analysis with cross-validation. Minimum primary productivity expressed as EVI base value (BVAL) explained most of the observed variance among groups (70.7%). The classification exhibited robust statistical separability, achieving a cross-validated accuracy of 75.1% (κ = 0.73), and showed mesoscale spatial structure (~12.5 km). The groups had moderate but significant associations (Cramer’s V = 0.33) with existing vegetation and climate cartography. The results suggest that long-term BVAL is a stable and ecologically meaningful descriptor of landscape functioning. Overall, the proposed classification captures gradients and transition zones not represented in static cartographic products, revealing vegetation dynamics across heterogeneous landscapes. Full article

Local Climate Zone (LCZ) classification provides a standardized framework for characterizing urban morphology and its climatic implications. However, most existing remote sensing-based LCZ mapping methods rely on pixel-level classification and multispectral data alone, which limits their ability to capture urban scene heterogeneity and to distinguish structurally similar LCZ classes. In this paper, we propose LCZ-HMSSNet, a deep learning framework for scene-level LCZ classification that integrates PRISMA hyperspectral images with Sentinel-2 multispectral data. The proposed approach exploits both the spectral richness of hyperspectral data and the spatial context provided by multispectral observations, and incorporates a spatial–spectral feature separation mechanism to enhance the discriminability of the fused representations. Experiments conducted across six representative European cities evaluate the proposed method from multiple perspectives, including comparisons with different classification models, data contribution analysis, and structural ablation studies. The results demonstrate that the proposed method consistently outperforms MSI-only and existing LCZ classification approaches, achieving an overall accuracy (OA) of 0.988 and a Kappa of 0.985. In addition, the small-sample experiments indicate the robustness and potential of the proposed model, providing a practical reference for future LCZ mapping in data-scarce scenarios. Full article

Background: Continuous non-invasive hemoglobin monitoring (SpHb) may provide real-time information during surgery, but its accuracy in neurosurgery remains uncertain. We evaluated the agreement, trending ability, and diagnostic performance of SpHb compared with arterial blood gas hemoglobin during elective intracranial neurosurgery. Methods: In this prospective observational study, 60 adults undergoing elective neurosurgery with invasive arterial monitoring were included. SpHb (Masimo Radical-7) was compared with paired arterial hemoglobin values. Agreement was assessed using repeated-measures Bland–Altman analysis and mixed-effects modeling. Trending ability was evaluated using four-quadrant concordance with an exclusion zone of ±0.5 g/dL. Discrimination for severe anemia (Hb < 8 g/dL) was assessed using ROC analysis with patient-level cluster bootstrapping. Results: A total of 190 paired measurements were analyzed. Mean bias was +0.23 g/dL, with wide limits of agreement (−3.26 to +3.72 g/dL). Agreement was worse under low-perfusion-index conditions. Trending performance was preserved, with an overall concordance rate of 85.5%. SpHb showed moderate discrimination for severe anemia (AUC 0.78; 95% CI 0.61–0.93), although severe anemia events were infrequent. Conclusions: SpHb showed limited reliability for absolute hemoglobin quantification during neurosurgery but provided useful trend information. SpHb should not replace invasive hemoglobin measurements for clinical decision-making. Full article

Background/Objectives: The Casablanca-Settat region of Morocco, located at the interface between Arab and Amazigh cultural zones, has only recently been investigated using autosomal short tandem repeat (STR) markers. The objective of this study was to characterize the genetic diversity and forensic efficiency of 15 autosomal STR loci in the Casablanca-Settat population and to evaluate its genetic relationships with other Moroccan populations. Methods: Fifteen autosomal STR loci were genotyped in 138 unrelated Arabic-speaking individuals from the Casablanca-Settat region. Allele frequencies, Hardy–Weinberg equilibrium, and standard forensic parameters were calculated. The genetic structure of the population was further examined through comparative analyses with 12 previously published Moroccan reference populations using multivariate and phylogenetic approaches. Results: A total of 146 distinct alleles were identified across the 15 loci. D18S51 was the most polymorphic marker (Ho = 0.9203), whereas D3S1358, TPOX, D5S818, and D16S539 exhibited lower allelic diversity. No statistically significant deviation from Hardy–Weinberg equilibrium was detected after correction for multiple testing. The combined power of discrimination exceeded 0.99, and the combined power of exclusion reached 0.99999965, demonstrating the high forensic efficiency of the STR panel. Population structure analyses positioned the Casablanca-Settat population within an intermediate genetic cluster, closely related to central Moroccan populations, consistent with historical gene flow and admixture. Conclusions: This study provides robust autosomal STR reference data for the Casablanca-Settat population, confirming the suitability of these markers for forensic identification in Morocco and offering valuable insights into regional population structure and genetic diversity. Full article

We have investigated the major- and trace-element composition of hydrothermal pyrite, magnetite, and Ti-magnetite, and of the principal Cu-minerals chalcopyrite and chalcocite, to constrain ore-forming processes in the northeastern Saveh district (central Urumieh–Dokhtar magmatic arc, Iran). Our data provide new constraints on the magmatic–hydrothermal evolution and subsequent hydrothermal–supergene modification of the ore system. Ti-magnetites hosted in monzodioritic intrusions are enriched in Ti–V–Al, plot below the magnetite–ulvöspinel join and record high crystallization temperatures (<500 °C) under relatively low oxygen fugacity. By contrast, magnetite from silica-rich hydrothermal veins is Fe-rich with very low TiO₂; it formed at intermediate temperatures (~200–300 °C) under higher fO₂ and is markedly depleted in Ti and V compared with the intrusive oxides. Textures and oxide systematics (Al + Mn vs. Ti + V; V/Ti–Fe) document repeated hydrothermal pulses, Fe²⁺ leaching and element redistribution during cooling and fluid–rock interaction. Geochemical trends indicate progressive evolution from a magmatic fluid to later meteoric water overprint, with increasing As contents reflecting cooling and mixing with meteoric waters. Vertical elemental zoning suggests that most samples represent mid- to deep-level sections of the epithermal system. Elevated Cu contents (up to 0.95 wt.%) highlight pyrite as a significant Cu host. Co/Ni ratios between 1 and 10 further corroborate a magmatic–hydrothermal origin. Chalcopyrite is the principal economic Cu carrier at Northeast Saveh. Replacement follows a temperature- and fluid-controlled pathway (chalcopyrite → covellite → chalcocite). At lower temperatures (<~200 °C) replacement proceeds more slowly, producing chalcocite/digenite under prolonged reaction conditions. Chalcocite commonly occurs as thin replacement rims and fracture fills that concentrate remobilized copper. Collectively, the investigated oxide and sulfide proxies provide robust discriminants for separating magmatic versus hydrothermal domains and for vectoring toward higher-temperature feeders and zones of remobilized copper. Full article

The rapid rise in the use of Liquefied Petroleum Gas (LPG) as an alternative vehicle fuel in Ghana presents both opportunities and risks within the national energy transition agenda. This study investigates LPG safety as well as environmental and regulatory implications using a multi-method quantitative approach that combines structured survey data, exploratory multivariate analysis (MCA), and machine learning classification (Random Forest) to uncover emerging associations and patterns in LPG safety practices. Primary data were obtained from 384 respondents, including vehicle operators, auto-technicians, regulatory officials, and LPG station attendants across five major transport zones: Kejetia, Asafo, Ahodwo, Bantama, and Suame Magazine. The MCA identified four distinct behavioural and safety profiles—At-Risk, Proactive Safety, Compliant and Equipped, and Formal and Reported—reflecting diverse compliance and risk patterns across socio-occupational groups. The Random Forest classifier achieved a predictive accuracy of 96.5% based on cross-validated performance. Sensitivity and specificity values were high, indicating reliable discrimination among incident types. To reduce the risk of overfitting, k-fold cross-validation and monitored error convergence were performed across increasing numbers of trees. While the model shows strong predictive capability, we present these results cautiously and emphasize observed associations and emerging patterns rather than definitive predictive conclusions. The findings reveal that while economic motivations underpin LPG adoption, weak institutional enforcement and widespread informal installations heighten safety vulnerabilities. Comparisons with sub-Saharan and Asian contexts underscore the need for a structured regulatory framework, mandatory certification of installers, and periodic vehicle inspections. The study contributes to the broader discourse on informal energy transitions in developing economies by demonstrating how technical and behavioural determinants interact within weak regulatory systems. Policy recommendations emphasize the integration of data-driven risk assessment tools into regulatory oversight to enhance vehicular LPG safety and sustainability. Full article

This study systematically investigates the mineralogical, spectral, and geochemical characteristics of Guatemalan lavender jadeite and black omphacite to elucidate their coloration mechanisms and genetic origins. Lavender samples are primarily composed of jadeite, which derives its color from synergistic effects involving Mn³⁺ and Fe²⁺-Ti⁴⁺ charge transfer (554–614 nm). In contrast, black samples are dominated by omphacite, which owes its dark hue to Cr³⁺ (670 nm) and Fe²⁺-Fe³⁺ charge transfer (857 nm). Chemically, lavender jadeite exhibits higher Na₂O and Al₂O₃, approaching the jadeite end-member composition, whereas black omphacite is enriched in CaO, MgO, and FeO. Trace element analyses reveal low overall abundances, with black omphacite showing synchronous LREE and HREE depletion forming a “bulge-shaped” pattern, while lavender jadeite displays N-MORB-like REE distributions. Guatemalan jadeites are distinguished from Myanmar counterparts by Y enrichment. The identification of graphite and CH₄ and CO₂ fluid inclusions indicates formation in an organic-rich reducing environment. Cathodoluminescence zoning and abundant fluid inclusions support a direct crystallization genesis from high-pressure fluids (P-type) in subduction zones. This study establishes key constraints for origin discrimination and genetic classification of Guatemalan lavender jadeite and black omphacite. Full article

Wildfire susceptibility mapping supports proactive forest management, and estimated predictive performance may vary with spatial dependence and the control-point sampling strategy. We developed an interpretable random-forest framework to map wildfire occurrence probability across Hubei Province, China, by integrating multi-source environmental (meteorological, topographic, and vegetation) and socio-economic predictors. To enhance methodological robustness and address high-dimensional data complexity, the Boruta algorithm was employed for rigorous feature selection, identifying the most significant drivers while filtering out random noise. The model showed strong discrimination on held-out data (AUC = 0.942, accuracy = 87.9%), and variable importance highlighted sunshine duration, elevation, relative humidity, and maximum temperature as dominant predictors. Predicted wildfire probability exhibited a clear east–west gradient; high and very high susceptibility classes covered 22% of forested land while containing 82% of historical fires, indicating priority zones for targeted prevention and resource allocation. These results demonstrate that combining multi-source predictors with machine-learning interpretability can produce actionable susceptibility maps for regional fire-risk management. Full article

The current state-centric analysis of Official Variety Trials (OVTs) restricts the identification of stable performance zones across political boundaries. This study employed multivariate statistical learning techniques to delineate soybean (Glycine max L.) “mega-environments” using yield data from 2269 varieties collected across seven U.S. states (2019–2022). Utilizing Quadratic Discriminant Analysis (QDA), Principal Component Analysis (PCA), and Agglomerative Hierarchical Clustering (AHC), we examined the edaphoclimatic factors influencing yield stability. QDA classified over 79% of environments into distinct temporal categories, highlighting significant inter-annual climatic variability driven by Growing Degree Days (GDD) and latitude. PCA distinguished broad climatic drivers (PC1) from localized soil texture constraints (PC2). AHC identified optimal production clusters that frequently diverged from geographic proximity, indicating that distant sites often share more critical yield-determining factors than neighboring counties. By operationalizing these latent environmental patterns, this study provides a data-driven framework for cross-state environmental zoning that can support more precise variety placement once genotype performance has been evaluated within these zones. Full article