Search Results (239)

This study investigates the forecast bust of Typhoon SHANSHAN (2024) characterized by large track errors using the four major interactive grand global operational ensemble data and the atmospheric reanalysis data. Ensemble space empirical orthogonal function (EOF) analysis is applied to 850, 500, and 300 hPa geopotential heights at three target times to diagnose how synoptic-scale uncertainty contributed to the erroneous motions of SHANSHAN. We align the multi-level EOF bases to a reference-time basis via a weighted Procrustes rotation and evaluate similarity to the atmospheric reanalysis data in the aligned principal component (PC) space, enabling robust, distance-based conditioning of ensemble members. Results show that ensemble spread is consistently larger in the mid-latitudes, with relatively large uncertainty concentrated around the upper-tropospheric trough and lower-tropospheric structure near SHANSHAN. The dominant EOF modes differ by phase of SHANSHAN: lower-tropospheric modes govern the westward-moving stage, whereas mid- and upper-tropospheric modes dominate after recurvature. Selecting members whose EOF-based PC structures most closely match the atmospheric reanalysis effectively suppresses large-error outliers and yields improved conditional track predictions. These findings highlight phase-dependent synoptic controls and demonstrate that adaptive, reference-consistent conditioning can enhance the track guidance of tropical cyclones during difficult forecast situations. Full article

Introduction: International studies have demonstrated a positive impact on air quality associated with the presence of green areas in urban conglomerates. However, in Brazil, studies addressing the impacts of urban green areas on air quality are still incipient and are predominantly focused on large urban centers. The objective of this study was to investigate the relationship between urban green areas, surface temperature (LST), and air quality across 15 medium-sized Brazilian cities. Methods: Concentrations of particulate matter fractions (PM₁, PM_2.5, and PM₁₀) were monitored from January 2023 to May 2024 using second data from low-cost sensors. The NDVI and both daytime and nighttime LST profiles were extracted via Google Earth Engine within a 1 km buffer zone surrounding each station via the Sentinel-2 and MODIS 11A1 satellite data, respectively. Spatial–temporal co-variation patterns were explored using principal component analysis (PCA). To model these dynamics while controlling for spatial dependencies, a multi-criteria framework compared linear models (simple linear regression (LM) and linear mixed (LMM)) and generalized models (generalized additive (GAM) and generalized additive mixed (GAMM)). Results: The results revealed a positive relationship between NDVI and PM_2.5 and PM₁₀ fractions in specific regions, while surface temperatures showed a direct association with finer particles (PM₁ and PM_2.5). The regression coefficient showed the significant association of PM2.5 with NDVI and nighttime LST (β = 1.330; IC 95%: [0.397; 2.270]; p = 0.005). The GAMM was the best-fitting model for all particle fractions, demonstrating that incorporating monitoring stations as random intercepts successfully controls for unmeasured local heterogeneity, while penalized splines accurately capture non-linear environmental factors. Conclusions: Although many studies have shown that green areas in temperate regions typically act as consistent sinks for particulate matter, our study revealed localized and seasonal responses in tropical urban landscapes. It should be noted that our study is conducted on a national scale and that the use of low-cost sensors and remote sensing does not allow us to distinguish between the localized microclimatic benefits of vegetation and the long-range transport of regional pollutants. Full article

Extracting reliable damage-sensitive features from sparse sensor networks under Environmental and Operational Variations (EOV) remains a critical challenge in Structural Health Monitoring (SHM). The purpose of this study is to overcome this limitation by proposing a novel, data-driven framework utilizing a cost-effective network of high-sensitivity triaxial roving accelerometers. The methodology integrates an AutoRegressive with eXogenous inputs (ARX) model and Wavelet Packet Decomposition (WPD) to extract robust, damage-sensitive features from complex vibration data. To handle the high-dimensionality of the extracted signals and achieve optimal multi-sensor data fusion, Block-wise Principal Component Analysis (PCA) is employed as a signal sanitation and feature reduction tool. This algorithmic pipeline is applied to a full-scale bridge pier subjected to RC jacketing. The structural enhancements and dynamic behavior shifts post-retrofitting were statistically quantified using the Mahala Nobis distance. The analysis revealed a 41.2% attenuation in median vibration intensity and successfully verified the structural improvements at a 99% confidence interval, clearly distinguishing the retrofitting effects from ambient noise. The proposed framework successfully isolates true structural changes from EOV, providing a reliable non-destructive evaluation tool for continuous monitoring in practical civil engineering applications. Full article

Coastal basins are systems highly susceptible to flooding and erosion, processes that intensify during extreme cyclonic events. This study aims to develop an integrated physical–geographic framework to characterize the geomorphological and multi-hazard dynamics of the La Sabana River basin in Acapulco, Guerrero, in southeastern Mexico. The methodology integrates the analysis of natural and anthropogenic landscape components, 19 morphometric indicators, and Principal Component Analysis (PCA) at the sub-basin scale. The results reveal a high drainage network density (3.8–5.4 km/km²) and short concentration times (0.98–2.75 h), indicating a rapid hydrological response and high susceptibility to flash floods and active erosion. Six critical sub-basins with concentration times ≤ 1.5 h have been identified, spatially coinciding with areas of high anthropogenic exposure. The hypsometric index values (0.04–0.388) indicate advanced geomorphological evolution in most sub-basins. Principal component analysis (PCA) explained 65.8% of the total variance in the first two components: component 1 (52.7%) is linked to basin size and drainage network organization, and component 2 (13.1%) is associated with basin shape. The findings of this research have provided a spatially explicit, robust, and replicable framework that helps strengthen risk governance and guide land-use planning in tropical coastal basins exposed to hydrometeorological hazards. Full article

Optical and Synthetic Aperture Radar (SAR) image registration is a fundamental task in remote sensing information fusion, yet it remains challenging due to significant differences in imaging mechanisms, radiation characteristics, and noise properties between the two modalities. Existing public datasets suffer from limited resolution, small scale, and insufficient scene diversity, and these limitations have hindered algorithm development. This paper constructs a large-scale, high-resolution optical–SAR registration dataset based on the HongTu-1 satellite 3-m SAR imagery and Google Earth optical imagery at zoom level 17, covering diverse scenes across China with a standardized pipeline including terrain correction, geometric alignment, standardized slicing, and quality filtering. Building upon this dataset, a hand-crafted keypoint-based cross-modal registration method is proposed, incorporating multi-level edge filtering and hybrid feature detection. Unlike conventional hand-crafted methods such as RIFT, SRIF, and LNIFT, which mainly refine keypoint detection, description, or matching within a SIFT-style pipeline, the core novelty of this work lies in SAR-specific preprocessing and multi-level hybrid filtering. These components are designed to suppress speckle while extracting more stable and discriminative shared edge responses for cross-modal registration. An improved Log-domain Total Variation (Log-TV) denoising model is introduced for SAR preprocessing. A hybrid edge filtering framework combining phase congruency analysis and Structured Random Forest (SRF) edge detection is constructed within a Gaussian scale space. A dual-branch feature detection scheme integrating blob and corner features is designed with a robust orientation assignment strategy. Feature description uses the Gradient Location–Orientation Histogram (GLOH) descriptor with Principal Component Analysis (PCA) reduction, while geometric estimation employs the Fast Sample Consensus (FSC) algorithm. Experiments on the self-constructed HT dataset and on the public OSdataset and SAR2Opt benchmarks show that the proposed method consistently achieves low RMSE and high success rates. It also maintains competitive efficiency among hand-crafted methods while retaining strong robustness to scale and rotation variations. Full article

Urban river networks face significant ecological challenges due to intensive urbanization. Traditional assessment methods focus mainly on individual rivers and overlook cross-scale connections. To fill this research gap, the study refined the Urban Riverscape Conditions-based Assessment for Management Needs (URBAN) framework and developed a dual-scale assessment system covering the entire river network and individual rivers. It evaluates hydrology, geomorphology, ecology, and the waterfront public service dimension. Taking the Qingxi area of Shanghai as a case study, this study integrated multi-source data and adopted field investigations, the analytic hierarchy process (AHP) and principal component analysis (PCA) to collect field data, calculate indicator weights, and extract dominant functional factors. The results show that the overall comprehensive health score of the study area is 59.39, classified as average; the river network scale scores 58.34, and the 21 monitored rivers achieve an average score of 61.80. The assessment identifies clear advantages in hydrological and geomorphological conditions, whereas waterfront public services and river morphological diversity are still deficient. Overall, this system demonstrates good operability and scientific validity, providing practical technical approaches for sustainable urban river network management and supporting refined watershed governance. Full article

Chilacayote (Cucurbita ficifolia Bouché) is recognized as a rich source of nutrients and bioactive compounds, making it a promising ingredient for fortifying staple foods such as corn tortillas. While fortification can improve nutritional properties, it may also alter sensory characteristics that determine consumer acceptance. Therefore, a rigorous and structurally grounded assessment of these sensory modifications is required. In this study, sensory evaluations were conducted with regular tortilla consumers using Check-All-That-Apply (CATA) questionnaires to examine six attributes (color, smell, texture, taste, mouthfeel, and aftertaste) in tortillas made with nixtamalized dough and commercial flour, both with and without chilacayote powder. Then, a structured framework for dimensionality reduction and sensory profile identification of tortillas is proposed. In this framework, three classical feature extraction methods (Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and a combination of both (PCA+LDA)) were compared with an evolutionary discriminant approach (Differential Evolutionary Linear Discriminant Analysis for Feature Extraction and Visualization (DE-${\mathrm{LDA}}_{FE}$)). The projection quality of these methods was evaluated using a multi-scale separability index that integrates global, semi-global, and local metrics, and the experiments were conducted considering global and attribute-based analyses. Beyond quantitative discrimination, the optimized projections enabled a geometric interpretation that allows the identification of sensory profiles for the tortilla variants. The proposed methodology bridges evolutionary optimization, structural separability assessment, and interpretable sensory characterization, offering a robust and adaptable strategy for multivariate food analysis and other complex discrimination problems and insights into the sensory impact of chilacayote fortification for the development of nutritionally enhanced tortillas that preserve consumer appeal. Full article

This study investigates the physiological trade-off between biomass yield and sugar concentration in five sweet sorghum genotypes to evaluate how carbon partitioning influences bioethanol potential. Field experiments were conducted over the 2019–2020 seasons in the East Marmara transitional zone of Türkiye, under irrigated and rain-fed regimes. Results revealed a highly significant genotype × water regime interaction (p < 0.001). A distinct trade-off was identified: while the hybrid ‘Teide’ maximized juice volume under irrigation (2427.67 L ha⁻¹), ‘Leoti’ maintained superior sugar stability (18.38 °Brix) under moisture deficit. Genotype plus Genotype × Environment Interaction (GGE) biplot analysis indicated that ‘Early Sumac’ provided the highest environmental buffering, balancing productivity and sugar density across water regimes. Principal Component Analysis (PCA) demonstrated that plant height (averaging 214.2 cm) was positively associated with juice yield and concentration. Under irrigation, ‘Teide’ produced the highest bioethanol yield (1690.7 L ha⁻¹), whereas ‘Nutrihang’ led output under rain-fed conditions. While these site-specific trends offer valuable insights into local bioenergy stability, further multi-location trials are necessary to confirm these patterns on a broader scale. The findings conclude that feedstock selection must be categorized by water availability to optimize sweet sorghum-based bioenergy systems in water-limited environments. Full article

Mean grain size (Mz) is a key parameter for characterizing depositional environments and hydrodynamic conditions and fundamentally controls reservoir properties and flow capacity. Conventional grain-size analysis is mainly based on core or cuttings measurements, which are spatially discrete and insufficient to capture continuous Mz variations along the wellbore. To address this limitation, this study presents a novel method for inverting Mz from conventional well logs by integrating Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Principal Component Analysis (PCA). Based on the inherent correlation between density and neutron log responses, a density-neutron separation parameter (DC) is constructed to quantify lithological and grain-structure differences. The DC is decomposed using CEEMDAN to extract multi-scale components capturing variations in Mz. PCA is then applied to identify the dominant factors governing Mz variations. A continuous quantitative Mz prediction model is established based on correlations with measured data. Comparison between predicted results and laboratory measurements demonstrates that the proposed method reliably captures vertical Mz variations and exhibits strong stability. This approach enables continuous quantitative estimation of Mz from conventional well logs, offering reliable support for detailed reservoir characterization and geological modeling. Full article

To address the challenges of accelerated deterioration of concrete bridges caused by overloaded vehicles, this paper proposes a multi-stage heterogeneous visual framework for overloaded vehicle identification. First, a block-wise foreground–background separation method based on two-dimensional correlation coefficients is introduced and integrated with an improved Gaussian Mixture Model (GMM) to achieve dynamic background modeling and robust foreground extraction from images. Next, the Fuzzy C-Means (FCM) clustering algorithm is employed to automatically localize vehicle regions. Subsequently, Histogram of Oriented Gradients (HOG) features of vehicle candidate regions, reduced by Principal Component Analysis (PCA), are extracted and combined with a Support Vector Machine (SVM) to eliminate non-vehicle objects. Finally, an enhanced YOLOv8 model is constructed for axle-count-based overloaded vehicle detection, in which Inception modules are embedded into the CSP Darknet backbone to capture multi-scale deep hierarchical features. Meanwhile, Canny edge detection and affine transformation are fused to optimize axle-counting recognition, and overloaded vehicles are classified in accordance with the Chinese national standard GB1589-2016. Experimental results on real-world concrete bridge surveillance scenarios show that the proposed method can significantly suppress noise in vehicle foreground extraction. After SVM post-processing, the vehicle purification accuracy reaches 98.75%, with a precision of 100% for the non-vehicle category. Compared with the vanilla YOLOv8, the proposed multi-stage heterogeneous visual framework improves the precision, recall, and mAP@50 by 8%, 12.5%, and 7.2%, respectively, for heavy-duty vehicle axle recognition. The axle-feature-based heavy vehicle recognition method achieves an overall identification accuracy of 92%. Full article

Microscopic pore structure strongly controls hydrocarbon storage and flow in carbonate reservoirs, but objective and continuous pore-type classification remains difficult because carbonate pore systems are multiscale, heterogeneous, and commonly interpreted using experience-based criteria. This study develops a reproducible workflow that integrates 912 mercury-intrusion capillary pressure (MICP) datasets from 34 wells with 474 paired thin-section and core-photograph observations from the S oilfield. Principal component analysis (PCA) reduces eight pore-structure parameters to three interpretable components that describe pore-throat scale, distribution uniformity, and connectivity/displacement behavior, retaining 87.63% of the total variance. K-means clustering identifies four pore types for dolomite and four for limestone, with k = 4 selected using the elbow criterion, silhouette coefficient, centroid interpretability, and petrographic consistency. Modified injection-to-final-state analysis (MIFA) is used as an internal MICP-based consistency check rather than as a fully independent validation; paired micro-observations provide cross-scale validation with 81.22% agreement. Lithology-constrained GR, SP, and AC response windows are then used for intra-field upscaling to uncored intervals, and field-scale back-checking shows 87% agreement with existing geological interpretations. The workflow reduces interpreter subjectivity, provides physically interpretable pore-type criteria, and is applicable to carbonate reservoirs with comparable MICP, petrographic, and logging constraints. Full article

Tree architecture is a critical determinant of plant performance, light capture, biomechanical stability, and resource allocation. However, the multidimensional functional trait space and multiscale allometric scaling mechanisms underlying different architectural types in Malus remain poorly understood. This study investigates the multidimensional functional trait space and multiscale allometric scaling relationships among three typical architectural types (weeping, upright, and spreading) in Malus. A total of 206 germplasm accessions were analyzed by integrating nine core functional traits spanning macro-architectural, branch biomechanical, and leaf economic dimensions. Principal component analysis revealed that architectural differentiation is primarily driven by macro-architectural and branch biomechanical traits, alongside coordinated contributions from leaf economic traits. Functional diversity analysis indicated that the upright and spreading types exhibited higher functional richness, while the weeping type displayed the highest functional divergence but minimal or no functional overlap with the upright and spreading type, reflecting strong niche specialization under artificial selection. Multiscale allometric analyses demonstrated significant divergence in resource allocation strategies across hierarchical levels. At the whole-tree level, architectural types differed markedly in height–diameter and height–crown scaling relationships. At the branch level, conserved positive allometric scaling was observed, with the weeping type showing higher intercepts indicative of increased mechanical investment. At the leaf level, consistent negative allometry between petiole length and leaf area suggested optimized resource allocation for light capture. These pronounced differences suggest distinct ecological adaptation strategies: the weeping type prioritizes biomechanical compensation for pendulous branches and optimized light capture in loose canopies; the upright type emphasizes vertical light competition and mechanical compactness; the spreading type balances lateral expansion and spatial filling efficiency, reflecting differentiated resource allocation patterns shaped by artificial selection. Overall, this study reveals that tree architecture in Malus is shaped by coordinated trait interactions across multiple scales, leading to distinct ecological strategies and resource allocation patterns. These findings provide new insights into the structure–function co-evolution of woody plants and offer a theoretical framework for functional trait-assisted breeding of ornamental tree architectures. Full article

Drought is a recurrent hazard in the Vietnamese Mekong Delta (VMD), with major implications for agriculture, water resources, and rural livelihoods. This study assesses drought variability in Kien Giang Province, Vietnam, from 1992 to 2024 using two multiscale indicators: the Multivariate Standardized Precipitation Index (MSPI) and the Multivariate Standardized Precipitation Evapotranspiration Index (MSPEI). Principal Component Analysis (PCA) was applied to Standardized Precipitation Index (SPI)- and Precipitation Evapotranspiration Index (SPEI)-based time series spanning multiple accumulation periods (3–48 months) to derive integrated drought signals and to reduce redundancy across timescales. The results show that the first principal component (PC1) captured a high proportion of total variance across stations, indicating strong coherence in drought dynamics across the province. Both MSPI and MSPEI successfully identified major historical drought episodes, particularly the severe events of 2004–2005 and 2015–2016. However, the two indices differed in their temporal behaviour: MSPI responded more directly to precipitation deficits, whereas MSPEI showed slower post-drought recovery in recent years, suggesting greater sensitivity to evaporative demand and climatic water-balance stress. These differences indicate that evapotranspiration-sensitive indices may provide added analytical value in warming coastal environments. Overall, the combined multiscale framework offers a robust basis for drought monitoring, comparative assessment, and water-resource planning in Kien Giang and other drought-prone coastal delta settings. Full article

Surface subsidence induced by underground coal mining is a primary driver of ecological degradation. The traditional Remote Sensing Ecological Index (RSEI), however, struggles to capture surface deformation constraints and vegetation response lags. To address this, we developed a Mining Deformation–Ecology Coupling Index (MDECI). This index integrates Interferometric Synthetic Aperture Radar (InSAR)-monitored surface stability with multi-spectral indicators via Principal Component Analysis (PCA). We applied this method to the Datong Coalfield, China, using 231 Sentinel-1A SAR scenes and 8 Landsat images (2017–2024) to validate the effectiveness of the index. Meanwhile, we systematically analyzed non-linear response mechanisms, the Ecological Turning Point (ETP), and spatial clustering characteristics. The results demonstrate the following: (1) InSAR and MDECI effectively identified patterns of surface subsidence and ecological decline. Subsidence centers expanded to a maximum of −2085 mm, causing the mean MDECI in these areas to drop to 0.185 (<−1800 mm). This represents a 57.4% decrease relative to the regional average (0.434). (2) MDECI outperformed traditional models with a stable Average Correlation Coefficient (ACC) (0.63–0.75) and high cross-correlation coefficients with RSEI (0.906) and the Mine-specific Eco-environment Index (MSEEI) (0.931). During the 2018 drought, MDECI maintained a robust ACC of 0.628 while RSEI dropped to 0.482. (3) Multi-scale analysis revealed a unimodal MDECI response with an ETP at −100 mm. Initial ‘micro-disturbance gain’ (0.371 to 0.471) is followed by a progressive decline to a minimum of 0.185 under severe deformation. (4) Local Indicators of Spatial Association (LISA) spatial clustering characterized the distribution patterns of ecological damage and localised high-maintenance areas. High–Low damaged areas accounted for 5.09%, while High–High high-maintenance areas reached 9.00%. The scale of High–High areas was approximately 1.77 times that of the damaged areas. The MDECI addresses the deficiencies of traditional indices in high-disturbance areas and isolates the impact of mining on the ecology, providing a quantitative basis for risk identification and differentiated restoration. Full article

Crop planting structure adjustments in irrigated agricultural regions alter irrigation and drainage regimes, with potential consequences for regional surface water dynamics. However, the nature and scale dependence of these linkages remain insufficiently understood. This study investigates the spatiotemporal dynamics of crop planting structure and surface water bodies in the Ningxia Plain from 2004 to 2023, and systematically quantifies their scale-dependent coupling mechanisms. Annual crop maps were generated using a Random Forest classifier (Sentinel-2, 2019–2023) and a Transformer-based model applied to multi-source satellite imagery (2004–2018). Surface water bodies were derived from long-term remote sensing datasets covering the full study period. Results show that the agricultural system underwent a pronounced transition toward maize dominance. Maize area expanded by 50.8%, whereas wheat and rice declined by 74.3% and 44.6%, respectively. Crop diversity also decreased, with the Shannon Diversity Index declining from 1.41 to 1.06 in 2023, indicating progressive system simplification. Meanwhile, surface water bodies exhibited a sustained downward trend, decreasing at an average rate of −5.32 km² per year after 2013 and reaching a minimum in 2022. The Yellow River water surface area also contracted by 14.41% (p = 0.001), indicating a basin-scale reduction in surface water extent. Lake classification results reveal strong scale-dependent hydrological responses. Small lakes (≤18 ha), accounting for 73.2% of lake numbers, are primarily controlled by local irrigation–drainage processes. Medium lakes (18–80 ha) are influenced by both anthropogenic regulation and natural variability. Large lakes (>80 ha), although representing only 4.9% of lake numbers but 62.9% of total water area, are mainly sustained by climatic variability and ecological water supplementation. Principal component analysis explains 84.44% of total variance, highlighting agricultural structural change and irrigation–drainage dynamics as key system drivers. Correlation analysis further reveals strong climate sensitivity of large lakes and the Yellow River (ρ = 0.50, p = 0.031), while small lakes are predominantly influenced by agricultural drainage processes. Overall, crop planting structure affects regional water dynamics through scale-dependent processes, with maize expansion altering irrigation and diversion patterns and local irrigation–drainage processes controlling small water bodies. Full article