Search Results (2,758)

Climate during the Late Cretaceous is characterized by a long-term cooling trend interrupted by several periods of increased warming. This study focuses on the terrestrial response to two rapid climate events just prior to the K-Pg boundary marked by the Chicxulub impact: the Mid-Maastrichtian Event (MME) and the Late Maastrichtian Warming Event (LMWE). These hyperthermals caused widespread biotic and greenhouse gas-related disturbances, and clarification about their timing and environmental character reveals the independent nature of all three events. Using element concentrations in bulk paleosols, as well as element concentrations in pedogenic calcite from paleosols in the Tornillo Basin of West Texas, we reconstruct mean annual precipitation (MAP) and the character of soil weathering across the K-Pg boundary. Modelled MAP indicates increased precipitation during the first half of the MME and rapid high amplitude changes in precipitation during the second half of the MME. The Tornillo Basin became increasingly dry during the LMWE followed by wet conditions that continued across the K-Pg boundary. This study documents the co-occurrence of sedimentation patterns, sea level change, and climate change caused by separate tectonic events prior to the K-Pg boundary. Full article

Eurasian drylands are vital for the global climate and ecological balance. Quantifying spatiotemporal variations in surface soil moisture (SSM) is essential for monitoring water, energy, and carbon cycles. The suitability of recent global-scale surface soil moisture datasets for Eurasian arid and semi-arid regions has not been comprehensively evaluated. This study investigates spatiotemporal trends of five SSM products—MERRA-2, ESACCI, GLEAM, GLDAS, and ERA5—from 1980 to 2023. The performance of these products was evaluated using in situ station data and the three-cornered hat (TCH) method, followed by partial correlation analysis to assess the influence of environmental factors, including mean annual temperature (MAT), mean annual precipitation (MAP), potential evapotranspiration (PET), vapor pressure deficit (VPD), and leaf area index (LAI), on SSM from 1981 to 2018. The results showed consistent SSM patterns: higher values in India, the North China Plain, and Russia, and lower values in the Arabian Peninsula, the Iranian Plateau, and Central Asia. Regionally, MAT, PET, VPD, and LAI increased significantly (0.04 °C yr⁻¹, 1.66 mm yr⁻¹, 0.004 kPa yr⁻¹, and 0.003 m² m⁻² yr⁻¹, respectively; p < 0.05), while MAP rose non-significantly (0.29 mm yr⁻¹). ERA5 exhibited the strongest correlation with in situ station data (R² = 0.42), followed by GLEAM (0.37), ESACCI (0.28), MERRA2 (0.19), and GLDAS (0.17). Additionally, ERA5 showed the highest correlation (correlation = 0.72), while GLEAM had the lowest bias (0.03 m³ m⁻³) and ESACCI exhibited the lowest ubRMSE (0.03 m³ m⁻³). The three-cornered hat method identified ERA5 and GLDAS as having the lowest uncertainties (<0.03 m³ m⁻³), with ESACCI exceeding 0.05 m³ m⁻³ in northern regions. Across land cover types, cropland had the lowest uncertainty among the five SSM products, while forest had the highest. Partial correlation and dominant factor analysis identified MAP as the primary driver of SSM. This study comprehensively evaluated SSM products, highlighting their strengths and limitations. It underscored MAP’s crucial role in SSM dynamics and provided insights for improving SSM datasets and water resource management in drylands, with broader implications for understanding the hydrological impacts of climate change. Full article

Against the background of globalization, the circulation range of traditional Chinese medicinal materials is constantly expanding, and the phenomena of mixed origins and counterfeiting are becoming increasingly serious. Tracing the origin of traditional Chinese medicinal materials is of great significance for ensuring their quality, safety, and effectiveness. Laser-induced breakdown spectroscopy (LIBS), as a rapid and non-destructive element analysis technique, can be used for the origin tracing of traditional Chinese medicinal materials. Deep learning can not only handle non-linear relationships but also automatically extract features from high-dimensional data. In this paper, LIBS is combined with deep learning, and a Double-Multi Convolutional Neural Network LIBS Analysis System (DMC-LIBSAS) is proposed for the origin tracing of the traditional Chinese medicinal material Angelica dahurica. The system consists of a LIBS signal generation module, a spectral preprocessing module, and an algorithm analysis module—Double-Multi Convolutional Neural Network (DMCNN)—achieving a direct mapping from input data to output results. And the ability of DMCNN to extract characteristic peaks is demonstrated by the 1D Gradient-weighted Class Activation Mapping (1D-Grad-CAM) method. The tracing accuracy of DMC-LIBSAS for Angelica dahurica reaches 95.25%. To further verify the effectiveness of the system, it is compared with six classic methods including LeNet, AlexNet, Resnet18, K-nearest neighbors (KNN), Random Forest (RF), and Decision Tree (DT) (with accuracies of 68%, 75%, 72.5%, 79.7%, 86.7%, and 75.5%, respectively), and the tracing effects are all much lower than that of DMC-LIBSAS. The results show that DMC-LIBSAS can effectively and accurately trace the origin of Angelica dahurica, providing a new technical support for the quality supervision of traditional Chinese medicinal materials. Full article

The Herrera township (86.0 km²), located in La Chorrera, is Panama’s leading pineapple production area. Ensuring sustainable agricultural management in this region is crucial for long-term productivity, resource conservation, and environmental protection. This study evaluates soil and irrigation water quality to provide insights into improved management practices. Soil samples were analyzed for pH, EC, OM, SM, CEC, texture, and content of Al, Ca, Cu, Fe, K, Mg, Mn, N, P, Si, Sr, and Zn. Water samples, including surface water and groundwater, were assessed for Ca, Fe, K, Mg, Mn, Na, N, HCO₃, SO₄, PO₄, NO₃-N, and salinity. Soil quality was evaluated using the Igeo, and geospatial techniques were applied to map the soil parameter distribution. The water quality analysis confirmed its suitability for irrigation, though groundwater in the central area requires caution due to elevated Na levels and a moderate risk of salinization. Soil maps indicate adequate levels of essential nutrients but highlight the need for N amendments. This study is the first comprehensive assessment of an agricultural township in Panama, providing critical data for decision-making and the adoption of sustainable agricultural practices that enhance resource management and mitigate climate change impacts. Full article

Estimates of tree species diversity via traditional optical remote sensing are based only on the spectral variation hypothesis (SVH); however, this approach does not account for the vertical structure of a forest. The relative height (RH) indices derived from GEDI spaceborne LiDAR provide vertical vegetation structure information through waveform decomposition. Although RH indices have been widely studied, the optimal RH index for tree species diversity estimation remains unclear. This study integrated GF-1 optical imagery and GEDI LiDAR data to estimate tree species diversity in a warm temperate forest. First, random forest plus residual kriging (RFRK) was employed to achieve wall-to-wall mapping of the GEDI-derived indices. Second, recursive feature elimination (RFE) was applied to select relevant spectral and LiDAR features. The random forest (RF), support vector machine (SVM), and k-nearest neighbor (kNN) methods were subsequently applied to estimate tree species diversity through remote sensing data. The results indicated that multisource data achieved greater accuracy in tree species diversity estimation (average R² = 0.675, average RMSE = 0.750) than single-source data (average R² = 0.636, average RMSE = 0.754). Among the three machine learning methods, the RF model (R² = 0.760, RMSE = 2.090, MAE = 1.624) was significantly more accurate than the SVM (R² = 0.571, RMSE = 2.556, MAE = 1.995) and kNN (R² = 0.715, RMSE = 2.084, MAE = 1.555) models. Moreover, mean_mNDVI, mean_RDVI, and mean_Blue were identified as the most important spectral features, whereas RH30 and RH98 were crucial features derived from LiDAR for establishing models of tree species diversity. Spatially, tree species diversity was high in the west and low in the east in the study area. This study highlights the potential of integrating optical imagery and spaceborne LiDAR for tree species diversity modeling and emphasizes that low RH indices are most indicative of middle- to lower-canopy tree species diversity. Full article

Background/Objectives: Early-onset colorectal cancer (EOCRC), defined as colorectal cancer (CRC) diagnosed before the age of 50, has been increasing in incidence, particularly among Hispanic/Latino (H/L) populations. Despite this trend, the underlying molecular mechanisms driving EOCRC disparities remain poorly understood. The MAPK and JAK/STAT pathways play critical roles in tumor progression, proliferation, and treatment response; however, their involvement in ethnicity-specific differences in EOCRC remains unclear. This study aims to characterize molecular alterations in MAPK and JAK/STAT pathway genes among EOCRC patients, focusing on differences between H/L and Non-Hispanic White (NHW) patients. Additionally, we assess whether these pathway-specific alterations contribute to survival outcomes in H/L EOCRC patients. Methods: We conducted a bioinformatics analysis using publicly available CRC datasets to assess mutation frequencies in MAPK and JAK/STAT pathway genes. A total of 3412 patients were included in the study, comprising 302 H/L patients and 3110 NHW patients. Patients were stratified by age (EOCRC: <50 years, late-onset colorectal cancer—LOCRC: ≥50 years) and ethnicity (H/L vs. NHW) to evaluate differences in mutation prevalence. Chi-squared tests were performed to compare mutation rates between groups, and Kaplan–Meier survival analysis was used to assess overall survival differences based on pathway alterations among both H/L and NHW EOCRC patients. Results: Significant differences were observed in MAPK pathway-related genes when comparing EOCRC and LOCRC in H/L patients. NF1 (11.6% vs. 3.7%, p = 0.01), ACVR1 (2.9% vs. 0%, p = 0.04), and MAP2K1 (3.6% vs. 0%, p = 0.01) were more prevalent in EOCRC, while BRAF mutations (18.3% vs. 5.1%, p = 9.1 × 10⁻⁴) were significantly more frequent in LOCRC among H/L patients. Additionally, when comparing EOCRC in H/L patients to EOCRC in NHW patients, key MAPK pathway genes such as AKT1 (5.1% vs. 1.8%, p = 0.03), MAPK3 (3.6% vs. 0.7%, p = 6.83 × 10⁻³), NF1 (11.6% vs. 6.1%, p = 0.02), and PDGFRB (5.8% vs. 2.1%, p = 0.02) were significantly enriched in H/L EOCRC patients. However, no significant differences were observed in JAK/STAT pathway-related genes when comparing EOCRC and LOCRC in H/L patients, nor when comparing EOCRC in H/L vs. NHW patients. Survival analysis revealed borderline significant differences in H/L EOCRC patients, whereas NHW EOCRC patients with no alterations in the JAK/STAT pathway exhibited significant survival differences. In contrast, MAPK pathway alterations were not associated with significant survival differences. These findings suggest that MAPK and JAK/STAT pathway alterations may have distinct prognostic implications in H/L EOCRC patients, justifying further investigation into their potential role in cancer progression and treatment response. Conclusions: These findings suggest that MAPK pathway dysregulation plays a distinct role in EOCRC among H/L patients, potentially contributing to disparities in CRC development and treatment response. The higher prevalence of MAPK alterations in H/L EOCRC patients compared to NHW patients underscores the need to explore ethnicity-specific tumor biology and therapeutic targets. Conversely, the lack of significant differences in JAK/STAT pathway alterations suggests that this pathway may not play a major differential role in EOCRC vs. LOCRC within this population. Survival analysis highlighted the prognostic relevance of pathway-specific alterations. These insights emphasize the importance of precision medicine approaches that consider genetic heterogeneity and pathway-specific alterations to improve outcomes for H/L CRC patients. Full article

Accurate area estimation of specific land cover/use types in administrative or natural units is crucial for various applications. However, land cover areas derived directly from classification maps of remote sensing via pixel counting often exhibit non-negligible bias. Thus, various design-based area estimators (e.g., bias-adjusted estimator, model-assisted difference estimator, model-assisted ratio estimator derived from confusion matrix), which combine the information of ground truth samples and the classification map, have been applied to provide more accurate area estimates and the uncertainty inference. These estimators work well for estimating areas in a region with sufficient ground truth samples, whereas they encounter challenges when estimating areas in multiple subregions where the samples are limited within each subregion. To overcome this limitation, we propose a novel Bias-Adjusted Estimator based on the Synthetic Confusion Matrix (BAESCM) for estimating land cover areas in subregions by downscaling the global sample information to the subregion scale. First, several clusters were generated from remote sensing data through the K-means method (with the number of clusters being much smaller than the number of subregions). Then, the cluster confusion matrix is estimated based on the samples in each cluster. Assuming that the classification error distribution within each cluster remains consistent across different subregions, the confusion matrix of the subregion can be synthesized by a weighted sum of the cluster confusion matrices, with the weights of the cluster abundances in the subregion. Finally, the classification bias at the subregion scale can be estimated based on the synthetic confusion matrix, and the area counted from the classification map is corrected accordingly. Moreover, we introduced a semi-empirical method for inferring the confidence intervals of the estimated areas, considering both the sampling variance due to sampling randomness and the downscaling variance due to the heterogeneity in classification error distribution within the cluster. We tested our method through simulated experiments for county-level area estimation of soybean crops in Nebraska State, USA. The results show that the root mean square errors (RMSEs) of the subregion area estimates using BAESCM are reduced by 21–64% compared to estimates based on pixel counting from the classification map. Additionally, the true coverages of the confidence intervals estimated by our method approximately matched their nominal coverages. Compared with traditional design-based estimators, the proposed BAESCM achieves better estimation accuracy of subregion areas when the sample size is limited. Therefore, the proposed method is particularly recommended for studies regarding subregion land cover areas in the case of inadequate ground truth samples. Full article

Forest mapping using remote sensing has made considerable progress over the past decade, but substantial uncertainties remain in complex regions, particularly where terrain and climate vary dramatically. Yunnan Province, China, represents such a challenging case, with its diverse climatic zones ranging from tropical to temperate and its topography spanning over 6500 m in elevation. These factors contribute to substantial variation in vegetation types, complicating the accurate identification of forest cover through remote sensing. This study aims to enhance forest mapping in Yunnan by leveraging multi-temporal remote sensing data from Sentinel-2 and Landsat 8/9 imagery, incorporating key phenological stages—such as the leaf greening (GRN) period, as well as the senescence, defoliation, and foliation (SDF) stages of deciduous forests—along with kNDVI and terrain factors. A random forest (RF) classifier was applied on the Google Earth Engine (GEE) platform to create a 10 m resolution forest map (LS2-RF). This map achieved an overall accuracy of 96.35% when validated with 1572 ground samples, significantly outperforming existing global datasets, such as Dynamic World (73.88%) and WorldCover (87.66%). These maps agreed well in extensive forested areas; discrepancies were noted in mixed land types, including farmland, urban areas, and regions with fragmented landscapes. In 2022, Yunnan’s forest cover was 60.40%, with higher coverage in the southwestern region and lower in the northeast. The largest forested area was found in Pu’er City, while the smallest was in Yuxi City. Forests were most abundant at elevations between 1500 and 2500 m (occupying 52.29% of the total forest area) and slopes of 15° to 25° (occupying 39.19% of the total forest area). Conversely, forest cover was lowest in areas below 500 m elevation (occupying 0.64% of the total forest area) and on slopes less than 5° (occupying 2.40% of the total forest area). The analysis also revealed a general trend of increasing forest cover with decreasing latitude and longitude, with peak forest coverage at mid-elevations and slopes, followed by a decline at higher elevations. The resultant forest map provides valuable data for ecological assessments, forest conservation initiatives, and informed policy decision-making. Full article

With the rapid development of remote sensing technology, the question of how to leverage large amounts of unlabeled remote sensing data to detect changes in multi-temporal images has become a significant challenge. Self-supervised methods (SSL) for remote sensing image change detection (CD) can effectively address the issue of limited labeled data. However, most SSL algorithms for CD in remote sensing image rely on convolutional neural networks with fixed receptive fields as their feature extraction backbones, which limits their ability to capture objects of varying scales and model global contextual information in complex scenes. Additionally, these methods fail to capture essential topological and structural information from remote sensing images, resulting in a high false positive rate. To address these issues, we introduce a graph diffusion model into the field of CD and propose a novel network architecture called CGD-CD Net, which is driven by a structure-sensitive SSL strategy based on contrastive learning. Specifically, a superpixel segmentation algorithm is applied to bi-temporal images to construct graph nodes, while the k-nearest neighbors algorithm is used to define edge connections. Subsequently, a diffusion model is employed to balance the states of nodes within the graph, enabling the co-evolution of adjacency relationships and feature information, thereby aggregating higher-order feature information to obtain superior feature embeddings. The network is trained with a carefully crafted contrastive loss function to effectively capture high-level structural information. Ultimately, high-quality difference images are generated from the extracted bi-temporal features, then use thresholding analysis to obtain a final change map. The effectiveness and feasibility of the suggested method are confirmed by experimental results on three different datasets, which show that it performs better than several of the top SSL-CD methods. Full article

Urbanization has intensified traffic loads, posing significant challenges to the efficiency and stability of urban road networks. Overloaded nodes risk congestion, thus making accurate intersection importance classification essential for resource optimization. This study proposes a hybrid clustering method that combines Self-Organizing Maps (SOMs), K-Means, and the Gaussian Mixture Model (GMM), which is supported by the Traffic Flow–Network Topology–Social Economy (TNS) evaluation framework. This framework integrates three dimensions—traffic flow, road network topology, and socio-economic features—capturing six key indicators: intersection saturation, traffic flow balance, mileage coverage, capacity, betweenness efficiency, and node activity. The SOMs method determines the optimal k value and centroids for K-Means, while GMM validates the cluster membership probabilities. The proposed model achieved a silhouette coefficient of 0.737, a Davies–Bouldin index of 1.003, and a Calinski–Harabasz index of 57.688, with the silhouette coefficient improving by 78.1% over SOMs alone, 65.2% over K-Means, and 11.5% over SOM-K-Means, thus demonstrating high robustness. The intersection importance ranking was conducted using the Mahalanobis distance method, and it was validated on 40 intersections within the road network of Zibo City. By comparing the importance rankings across static, off-peak, morning peak, and evening peak periods, a dynamic ranking approach is proposed. This method provides a robust basis for optimizing resource allocation and traffic management at urban intersections. Full article

Land cover classification (LCC) using satellite images is one of the rapidly expanding fields in mapping, highlighting the need for updating existing computational classification methods. Advances in technology and the increasing variety of applications have introduced challenges, such as more complex classes and a demand for greater detail. In recent years, deep learning and Convolutional Neural Networks (CNNs) have significantly enhanced the segmentation of satellite images. Since the training of CNNs requires sophisticated and expensive hardware and significant time, using pre-trained networks has become widespread in the segmentation of satellite image. This study proposes a hybrid synergistic semantic segmentation method based on the Deeplab v3+ network and a clustering-based post-processing scheme. The proposed method accurately classifies various land cover (LC) types in multispectral satellite images, including Pastures, Other Built-Up Areas, Water Bodies, Urban Areas, Grasslands, Forest, Farmland, and Others. The post-processing scheme includes a spectral bag-of-words model and K-medoids clustering to refine the Deeplab v3+ outputs and correct possible errors. The simulation results indicate that combining the post-processing scheme with deep learning improves the Matthews correlation coefficient (MCC) by approximately 5.7% compared to the baseline method. Additionally, the proposed approach is robust to data imbalance cases and can dynamically update its codewords over different seasons. Finally, the proposed synergistic semantic segmentation method was compared with several state-of-the-art segmentation methods in satellite images of Italy’s Lake Garda (Lago di Garda) region. The results showed that the proposed method outperformed the best existing techniques by at least 6% in terms of MCC. Full article

The current methods for detecting coal gangue face several challenges, including low detection accuracy, a high probability of missed detections, and inadequate real-time performance. These issues stem from the complexities associated with diverse industrial environments and mining conditions, such as the mixing of coal gangue and insufficient illumination within coal mines. A detection model, referred to as EBD-YOLO, is proposed based on YOLOv11n. First, the C3k2-EMA module is integrated with the EMA attention mechanism within the C3k2 module of the backbone network, thereby enhancing the model’s feature extraction capabilities. Second, the introduction of the BiFPN module reduces computational complexity while enriching both semantic information and detail within the model. Finally, the incorporation of the DyHead detector head further enhances the model’s ability to express features in complex environments. The experimental results indicate that the precision (P) and recall (R) of the EBD-YOLO model are 88.7% and 83.9%, respectively, while the mean average precision (mAP@0.5) is 91.7%. These metrics represent increases of 3.4%, 3.7%, and 3.9% compared to those of the original model, respectively. Additionally, the frames per second (FPS) improved by 10.01%. Compared to the mainstream YOLO target detection algorithms, the EBD-YOLO detection model achieves the highest mAP@0.5 while maintaining superior detection speed. It exhibits a slight increase in computational load, despite an almost unchanged number of parameters, and demonstrates the best overall detection performance. The EBD-YOLO detection model effectively addresses the challenges of missed detections, false detections, and real-time detection in the complex environment of coal mines. Full article

The $(k,\psi )$-fractional derivative based on the k-gamma function is a more general version of the Hilfer fractional derivative. It is widely used in differential equations to describe physical phenomena, population dynamics, and biological genetic memory problems. In this article, we mainly study the $4m+2$-point symmetric integral boundary value problem of nonlinear $(k,\psi )$-fractional differential coupled Laplacian equations. The existence and uniqueness of solutions are obtained by the Krasnosel’skii fixed-point theorem and Banach’s contraction mapping principle. Furthermore, we also apply the calculus inequality techniques to discuss the stability of this system. Finally, three interesting examples and numerical simulations are given to further verify the correctness and effectiveness of the conclusions. Full article

Let K be a closed convex subset of a real Banach space X, and let F be a map from X to its dual $X^{*}$. We study the so-called variational inequality problem: given $y\in X^{*,},$ does there exist $x_0\in K$ such that (in standard notation) $⟨F\left(x_0\right)-y,x-x_0⟩\ge 0$ for all $x\in K?$ After a short exposition of work in this area, we establish conditions on F sufficient to ensure a positive answer to the question of whether F is a gradient operator. A novel feature of the proof is the key role played by the well-known Ekeland variational principle. Full article

In the argillic alteration zone of the SinAbad area of the Urumieh–Dokhtar magmatic belt (Iran), Mg-rich, Fe-poor chlorites, which crystallised at temperatures between 160 °C and 260 °C, were affected by extensive alteration to smectite mixed-layering at the micro- and nano-scales during the retrograde evolution of the hydrothermal system. Chlorites retain their usual optical aspect and properties, except for the index of refraction perpendicular to the (001) layers, which becomes lower than those parallel to the layers, producing an increase in birefringence and change in the optic and elongation signs, in comparison to the ordinary ones for Mg chlorites. Scanning electron microscopy (SEM) maps and compositions, and electron microprobe (EMP) analyses indicate minor but ubiquitous Ca (and K) content. X-ray diffraction (XRD) of chloritic concentrates allowed the identification of chlorite and tosudite. High-resolution transmission electron microscopy (HRTEM) images show major 14 Å (chlorite), with the frequent presence of 24 Å (contracted tosudite) individual layers and small packets up to five layers thick. Lateral change from 14 Å to 24 Å individual layers has been visualised. High-resolution chemical maps obtained in high-angle annular dark-field (HAADF) mode confirm the existence of areas preferentially dominated by chlorite or tosudite. The overall chemical compositions obtained by SEM, EMP, and transmission electron microscopy (TEM) align from the chlorite to the tosudite end-members, whose pure compositions could be determined from extreme analytical electron microscopy (AEM) analyses. The described intergrowths and interlayers, under the optical resolution, could provide a clue to explain changes in the normal optic properties of chlorite, which are mentioned, but not explained, in the literature. Full article