Search Results (15,945)

The study presents new data on the distribution, mapping, and morphostratigraphic characteristics of pediment deposits in the Montefeltro region (Italian Apennines), within the Val Marecchia Nappe. The Montefeltro landscape represents a clear example of morphology controlled by lithostructural features, with reliefs emerging from the surrounding terrain due to selective erosion. Its evolution has also been strongly influenced by climatic variations during the Middle–Late Pleistocene and the Holocene. Broad, gently sloping surfaces at the base of structural reliefs, together with associated debris deposits, are interpreted as erosional–depositional pediments formed under cold-climate, periglacial conditions during major Pleistocene glacial phases. Stratigraphic data from boreholes allowed the identification of pediment boundaries, thicknesses, and spatial extent, enabling reconstruction of the relict paleotopography and correlation with fluvial terraces. Two distinct lithological assemblages indicate different sediment sources and slope evolution pathways. Over time, pediments became disconnected from the present topography and were progressively dissected and terraced by fluvial incision, while recent slope adjustment is limited to modern drainage systems. This evolution reflects the combined influence of tectonic structure, lithology, and Quaternary climate change, confirming a regional trend of intensified fluvial deepening in the Marche Apennines. The study focuses on three representative areas: San Marino, Montecopiolo and Sassi Simone and Simoncello. Full article

As urbanization intensifies, improving street thermal comfort has become a critical issue in urban renewal. While existing studies generally assume that increasing the Green View Index (GVI) linearly improves pedestrian thermal comfort, this study identifies a significant “Decoupling Effect” in high-density commercial areas through field measurements and numerical simulations of three typical street types (commercial–service, ecological–recreational, and historical–cultural) in Shenyang. Integrating DeepLab V3 semantic segmentation with ENVI-met version 5.1.1 microclimate simulation, the results demonstrate a robust monotonic negative correlation between GVI and Physiological Equivalent Temperature (PET) in ecological streets (Spearman’s ρ = −0.692, p < 0.001), confirming the consistent cooling benefit of greenery in nature-dominated environments. However, a distinct “Threshold Effect” was identified in commercial streets using Piecewise Linear Regression (PLR). A critical breakpoint was detected at GVI = 22.08%. Below this threshold, visual greenery effectively contributes to cooling (slope = −0.454); yet, once GVI exceeds 22.08%, the cooling efficacy diminishes significantly (slope = −0.109), marking the onset of a “decoupling” phase. Specifically, despite Wenhua Road achieving a GVI of ~24.5% with a complex “three-board, four-belt” structure, its PET peak reaches 46.15 °C, approximately 5.5 °C higher than ecological streets. Mechanism analysis reveals that under peak thermal stress (Traffic Heat ≈ 75 W/m²), the high-intensity anthropogenic heat and hardscape radiation exceed the evaporative cooling threshold of vegetation. This study reveals the non-linear relationship between visual greenery and the physical thermal environment, suggesting that simply pursuing visual green quantity is ineffective in commercial canyon renewal; instead, a threshold-based synergistic optimization of canopy shading and pavement thermal performance is required. These findings provide a quantitative basis for sustainable street landscape planning and urban climate adaptation strategies in high-density cities. Full article

The Camino de Santiago is one of the most important cultural routes in the world and a privileged laboratory for analysing the challenges of sustainability in long-distance heritage destinations. The aim of this research is to understand the underlying dynamics of the Way, as well as its degree of sustainability. To achieve this, we examine the recent evolution of tourist demand for the Way from a territorial and sustainability perspective, integrating official statistical data with digital interest indicators from Google Trends (2004–2025). The methodology combines quantitative analyses of trends, seasonality, spatial diversification and internationalisation of demand, applying robust techniques such as the Theil–Sen slope and the Mann–Kendall test. The results show structural growth and high resilience of the Jacobean tourism system, even after the disruption caused by COVID-19, together with a growing internationalisation of flows. However, this tourism success is accompanied by strong spatial and temporal imbalances, with a marked concentration on the French Way and in the summer months, which increases environmental and social pressure on the most travelled territories. The analysis of digital interest also reveals a progressive decline in the importance of Holy Years as a driving force for attraction, especially in international markets. Full article

The development of Smart Tourism often overlooks the “Wilderness Last Mile”, leading to the spatial exclusion of people with disabilities in mountain areas. This problem exists because standard tourist maps and urban-centric accessibility models rely on averaged terrain data, failing to identify critical micro-scale barriers (e.g., short, sudden steep ascents) that pose severe safety and traction risks for off-road wheelchair users. To address this gap, this article presents a novel GIS methodology for planning accessible off-road tourism for electric Specialized Off-Road Wheelchairs. The proposed four-stage analytical model includes (1) graph-based trail network topologization to enable precise routing; (2) traction safety verification utilizing high-resolution (1 × 1 m) Digital Elevation Model (DEM) micro-segmentation to detect hidden slope barriers; (3) multi-criteria evaluation combining a user-calibrated Difficulty Index (EDI) and a Tourism Quality Index (TQI); and (4) a hub optimization algorithm that prioritizes locations maximizing the diversity of accessible routes. The method was empirically tested in a case study of the Bieszczady Mountains (Poland), calibrating the model with the technical limits (25% max slope) of a prototype wheelchair. The experimental results clearly validate the model’s superiority over traditional approaches: the micro-segmentation successfully identified hidden terrain traps, disqualifying 55% of the standard trail network that would have otherwise been deemed safe by average-slope assessments. Furthermore, the model identified a contiguous safe network of 153 km and pinpointed the optimal rental hub location, ensuring the highest inclusivity and route variety. Ultimately, this approach transforms raw spatial data into safe, ready-made tourism products, providing a precise tool with which to implement Universal Design in natural environments. Full article

Hydropower development in high-altitude regions increasingly confronts a challenging “trilemma”: high hydraulic heads, large unit discharges, and spatially constrained narrow valleys. Under such conditions, conventional energy dissipation measures frequently fail to prevent downstream riverbed scour, thereby threatening both ecological integrity and infrastructure safety. This study aims to propose, parametrically optimize, and physically validate a novel composite energy dissipation structure designed to resolve this specific trilemma based on a pressure-dividing transition mechanism. Using the Louli Hydropower Project as a case study (Q_max = 6944 m³/s, unit discharge q = 119 m³/(s·m), available basin length L = 78 m), we conducted systematic 1:100 scale physical model tests. The results demonstrate that conventional optimizations, such as secondary stilling basins and dentated sills, are ineffective under these boundary conditions, leading to incomplete hydraulic jumps and extended high-velocity zones. In contrast, the proposed composite structure, which integrates a deepened stilling basin (depth = 9 m), asymmetric sidewall widening (20 m offset), and a gentle slope transition (1:20 gradient), achieved superior performance. Under the 50-year design flood with controlled discharge operation, the energy dissipation rate increased significantly from 32.11% (baseline) to 63.49% (composite) at the end sill. Furthermore, the structure reduced comprehensive turbulence intensity by 17.8% and floor slab impact stress by 23.4%. These findings validate the composite system as a sustainable solution for high-head dams in constrained settings, offering benefits for riverbed protection and structural durability. Full article

Codon usage bias (CUB) in mitochondrial genomes reflects evolutionary forces such as mutation, selection, and genetic drift, yet its dynamics in early-diverging fungal lineages like Conidiobolus (Zoopagomycota) remain unclear. This study systematically analyzed mitochondrial core protein-coding genes (PCGs) from eight Conidiobolus species to elucidate the drivers of CUB and phylogenomic patterns. Nucleotide composition revealed pronounced AT richness (73.32% ± 3.38%) and low GC3 (13.40% ± 5.11%), indicating a preference for A/T-ending codons. Neutrality and ENC-GC3s plots demonstrated that natural selection, rather than mutation pressure, predominantly shaped codon bias, supported by weak GC12-GC3 correlations (slopes: 0.037–0.335) and significant ENC deviations from mutation-driven expectations. PR2-bias analysis further highlighted a strong bias toward A over T and C over G. Correspondence analysis linked major codon usage variations to GC3s, CAI, and FOP indices. Phylogenetic reconstructions based on relative synonymous codon usage (RSCU) and concatenated mitochondrial sequences revealed discordant topologies, particularly in the placement of C. polytocus and C. polyspermus, suggesting divergent evolutionary trajectories. Optimal codon analysis identified species-specific preferences dominated by A/T termini. These findings underscore natural selection as the primary force driving AT-biased mitochondrial CUB in Conidiobolus, while phylogenomic discordance highlights complex evolutionary pressures in this ecologically diverse fungal genus. This study provides foundational insights into mitochondrial genome evolution and codon adaptation mechanisms in early-diverging fungi. Full article

A deeper understanding of the relationships between the local and landscape scales in herbivore foraging should place the management of rangeland production systems on a firmer footing. The objective was to test whether local-scale landscape features modulate the coupling between locomotion and eating, thereby altering the pattern of landscape-scale grazing pressure. We studied shepherded small-ruminant herds on hilly semiarid rangeland by integrating acoustic monitoring to detect jaw movements, GPS to track location and movement, and GIS to link location to landscape attributes. Based on 69 one-day foraging routes, minutely rate of jaw movement (RJM) as a function of time-into-foraging-route showed a unimodal concave shape but did not respond to path angle. Minutely movement velocity responded convexly to time-into-foraging-route, and the quadratic term for path angle was negative and highly significant. The response to path angle was concave and symmetrical for uphill and downhill travel. Based on the empirical evidence that increasing path angle reduces velocity but not RJM and a set of reasonable associated assumptions, it is inferred that more jaw movements are performed per unit area scanned by the animal. It is further inferred abductively that more bites are removed per unit area and that more mass is removed per unit area, and hence, grazing pressure is more intense on sloping terrain than on level areas. For a given duration of foraging route, an increase in density of bite placement at the local behavioral scale implies a contraction in the surface area of the daily herd footprint at the landscape scale. This has implications for how carrying capacity of such areas should be defined. Full article

This work presents a simulation-based approach to support the planning of Terrestrial Laser Scanning (TLS) surveys for landslide monitoring. Starting from an approximate digital model of the slope, the method estimates the spatial distribution of positional error induced by scanner characteristics, laser beam divergence and, critically, by the incidence angle between the laser beam and the local surface normal. Because complex morphologies cause rapid local variations in incidence angle, neighbouring points may exhibit markedly different error magnitudes, making a direct classification of raw error values insufficient to delineate homogeneous areas. To address this, a multidimensional variable is defined for each simulated point, combining position, estimated error, distance from the scanner and incidence angle. After dimensionality reduction through PCA, the dataset is clustered using K-means with a sufficiently large number of clusters to preserve spatial resolution. Each cluster is associated with a representative error level, and clusters are then merged into broader error classes that delineate zones of comparable expected precision. The procedure is repeated for alternative scanner positions, enabling a comparative evaluation of achievable accuracy across the slope and the identification of areas requiring multiple scans. The method provides a quantitative, reproducible framework to guide TLS station selection and optimize survey design in complex morphological settings. Full article

Rainfall-induced shallow loess landslides pose a significant threat to human life and property. Early warning and risk assessment of these landslides are critical prerequisites for engineering control and disaster loss reduction. The Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Model (TRIGRS)-Three-dimensional Slope Stability Analysis Tool (Scoops 3D) joint model can overcome the shortcomings of using a single TRIGRS model for hydrological analysis and a single Scoops 3D model for slope stability analysis. Landslide risk assessment based on expected economic loss, on the other hand, can overcome the issue of maintaining the risk level edge and sorting at the same level. In this paper, the TRIGRS model’s head pressures were put into the Scoops 3D model, with the southeast of Fangta, a town in Shaanxi province, China, as the study area. The relationship between the slope gradient and the number of grids in each stable grade was certified. The rainfall thresholds for landslides, based on both rainfall intensity and rainfall duration, were obtained by rerunning the TRIGRS-Scoops 3D joint model. The landslide range and land uses of each dangerous slope were determined by maximum likelihood classification, and then the expected economic loss was calculated. To verify the reliability of the TRIGRS-Scoops 3D joint model, the identified dangerous slopes were compared with the results from landslide susceptibility mapping. The results show that the unstable grids are concentrated within a slope gradient of 30° to 35°, and the landslide early warning levels are divided into Tier 3, Tier 2, and Tier 1 Warnings. The occurrence of shallow loess landslides is affected by both rainfall intensity and rainfall duration, and the combined effect should be considered in early warning. The distribution of both extreme susceptible grids and high susceptible grids across all 23 dangerous slopes demonstrates the reasonableness of the TRIGRS-Scoops 3D joint model. The landslide susceptible probability within some dangerous slopes exhibits spatial variability. The mapping relationship between the slope gradient and loess landslides is extremely complex. This paper can provide a theoretical basis for the early warning and risk management for rainfall-induced shallow loess landslides; the proposed method is also applicable to other regions with similar geological and meteorological conditions. Full article

Background: Gestational diabetes mellitus (GDM) affects many pregnancies worldwide and is associated with adverse maternal and fetal outcomes. Current screening at 24–28 weeks limits opportunities for early intervention. We evaluated whether machine learning (ML) models using first-trimester clinical and dietary data can predict GDM risk before the standard oral glucose tolerance test. Methods: We analyzed data from 797 pregnant women enrolled in the BORN2020 prospective cohort study (Thessaloniki, Greece). Ten ML algorithms were evaluated across five class-imbalance handling strategies using stratified 5-fold cross-validation, with final evaluation on an independent 20% held-out test set. Features included maternal demographics, obstetric history, lifestyle factors, and 22 dietary micronutrient intakes from the pre-pregnancy period assessed by Food Frequency Questionnaire. Results: The best-performing model (Logistic Regression without resampling) achieved an AUC-ROC of 0.664 (95% CI: 0.542–0.777), with sensitivity of 0.783 and NPV of 0.932 at the pre-specified threshold. The high NPV should be interpreted in the context of the low GDM prevalence (14.7%), as NPV is mathematically dependent on disease prevalence. A reduced nine-feature model using only routine clinical and demographic variables achieved a numerically higher AUC of 0.712 (95% CI: 0.589–0.825), with overlapping confidence intervals, indicating that detailed FFQ-derived micronutrient data did not improve prediction. Maternal age and pre-pregnancy BMI were the strongest individual predictors by SHAP analysis. No model reached the AUC >0.80 threshold for good discrimination. Substantial miscalibration was observed (slope: 0.56; intercept: −1.83), limiting use for absolute risk estimation. Conclusions: This exploratory study demonstrates that first-trimester ML models achieve modest discriminative ability for early GDM prediction, with routine clinical variables performing comparably to models incorporating detailed dietary assessment. These findings should be interpreted with caution, as no external validation cohort was available and the low events-per-variable ratio (~3.8) constrains the reliability of individual model estimates. Substantial miscalibration further limits use for absolute risk estimation. Accordingly, these models should be regarded as exploratory risk-ranking tools only and require external validation and recalibration before any clinical implementation. Full article

Digital Elevation Models (DEMs) constitute a core data source for Archeological Predictive Modeling. However, how quality differences among multi-source DEM propagate through complex models and subsequently affect predictive accuracy and geographic interpretation remains insufficiently understood. This study aims to develop an integrated evaluation framework that combines machine learning with SHAP-based interpretability analysis to systematically compare the suitability of mainstream open access DEM products for archeological site prediction. The results indicate that (1) in terms of vertical accuracy, Copernicus DEM and TanDEM-X achieved the best performance, with RMSE values of 2.19 m and 2.31 m, respectively, whereas ASTER exhibited the lowest accuracy (RMSE = 6.44 m) and exaggerated terrain. (2) Regarding model performance, Copernicus DEM-driven models demonstrated the highest robustness, achieving an AUC of 0.966 under the XGBoost algorithm. (3) Interpretability analysis revealed that different DEM products significantly reallocate the importance of key variables such as slope and the Topographic Wetness Index, potentially distorting scientific interpretations of ancient military defensive site-selection patterns. Copernicus DEM is recommended as a priority data source. Moreover, while pursuing higher spatial resolution, equal attention must be paid to vertical accuracy and consistency with geomorphological logic. Full article

Indonesia is among the most landslide-prone countries in the world, with thousands of fatalities and widespread infrastructure damage recorded over recent decades. Despite this high hazard level, regional-scale landslide monitoring remains constrained by the limitations of conventional bitemporal satellite imagery, which is susceptible to cloud contamination, dependent on precise acquisition timing, and unable to capture the full temporal dynamics of landslide occurrence and recovery. While the LandTrendr (Landsat-based Detection of Trends in Disturbance and Recovery) algorithm has been widely applied for detecting vegetation disturbances such as forest loss and land-use change, its potential for landslide detection in tropical environments has not been sufficiently explored. This study aims to evaluate the applicability of LandTrendr applied to long-term Landsat time series imagery for automated regional-scale landslide detection and mapping in Indonesia. The method integrates temporal segmentation of the Normalized Difference Vegetation Index (NDVI) derived from Landsat imagery spanning 2000–2022 with slope information from the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) to identify the characteristic drop-recovery spectral signature associated with landslide events. The algorithm was applied and evaluated in two geologically distinct study areas: Lombok, West Nusa Tenggara, and Pasaman, West Sumatra. Detection accuracies of 25.9% by location and 20.3% by area were achieved in Lombok and 76.3% by location and 85.3% by area in Pasaman. The lower accuracy in Lombok is primarily attributed to the predominance of small landslides below the sensor’s spatial resolution and rapid vegetation recovery. The proposed approach demonstrates the unique capability of LandTrendr to model the entire life cycle of a mass movement event, from pre-event stability through abrupt disturbance to ecological recovery within a single unified framework, providing a scalable and cost-effective tool for long-term landslide monitoring applicable to other tropical, landslide-prone regions. Full article

The Shaanxi section of the Qinling Mountains serves as a critical ecological transition zone and security barrier between northern and southern China. Monitoring the dynamics of its vegetation Net Primary Productivity (NPP) is essential for understanding regional carbon cycling and informing ecological management strategies. This study integrates three complementary analytical frameworks: the Mann–Kendall test combined with the Theil–Sen slope for linear trend extrapolation (MK-Theil-Sen), mechanistic simulation (CASA model), and machine learning (random forest). First, we analyzed the spatiotemporal evolution of NPP from 2000 to 2023. Then, based on three CMIP6 scenarios (SSP119, SSP245, SSP585), we projected NPP changes for 2030–2050 and compared results across different models and scenarios. The key findings are as follows: ① From 2000 to 2023, NPP in the Shaanxi section of the Qinling Mountains exhibited a fluctuating upward trend with a cumulative increase of 16.7%. Spatially, it showed a pattern of “higher in the south, lower in the north; higher in the west, lower in the east”. ② Multiple models predict continued NPP growth, though the magnitude remains uncertain. Mechanistic models, incorporating climate stress factors, yield relatively conservative projections. ③ Emission scenarios significantly influence future trends, with low-emission pathways (SSP119) favoring NPP enhancement and extended growing seasons. ④ Different vegetation types exhibit varying responses to scenario changes: broadleaf forests show the highest sensitivity, while grasslands and meadows demonstrate strong climate stability across models, with cultivated vegetation exhibiting intermediate sensitivity. This study provides comprehensive scientific references for regional ecological security assessment and adaptive management through historical analysis and multi-model, multi-scenario projections of NPP in the Shaanxi section of the Qinling Mountains. Full article

Benggang (Benggang), a typical landform characterized by severe erosion and a geohazard in the red-soil hilly regions of southern China, is characterized by a fragmented texture, irregular boundaries, and high similarity to background objects such as bare soil and roads, which poses a dual challenge of “multiscale variability + strong noise” for automated identification at regional scales. To address insufficient information from a single modality and the limited representation of cross-scale features, this study proposes a dual-stream feature-fusion network (DF-Net) for multisource data consisting of a digital orthophoto map (DOM) and a digital elevation model (DEM). The method adopts ResNeSt50d as the backbone of the two branches: on the DOM side, a Canny-edge channel is stacked to enhance high-frequency boundary information; on the DEM side, derived terrain factors, including slope, aspect, curvature, and hillshade, are introduced to provide morphological constraints. In the cross-modal fusion stage, a multiscale sparse attention fusion module is designed, which acquires contextual information via multiwindow average pooling and suppresses noise interference through top-K sparsification. In the decision stage, a multibranch ensemble is employed to improve classification stability. Taking Anxi County, Fujian Province, as the study area, a coregistered dataset of GF-2 (1 m) DOM and ALOS (12.5 m) DEMs is constructed, and a zonal partitioning strategy is adopted to evaluate the model’s generalization ability. The experimental results show that DF-Net achieves 97.44% accuracy, 85.71% recall, and an 82.98% F1 score in the independent test zone, outperforming multiple mainstream CNN/transformer classification models. This study indicates that the strategy of “multimodal feature enhancement + sparse attention fusion” tailored to Benggang erosional landforms can significantly improve recognition performance under complex backgrounds, providing technical support for rapid Benggang surveys and governance-effectiveness assessments. Full article

Using perfluorooctanoic acid (PFOA) as a representative highly mobile solute to isolate hydrological controls, we investigated how slope influences the partitioning of vertical and lateral transport pathways. While vertical percolation has been widely examined using conventional column leaching tests, lateral transport driven by topographic gradients remain insufficiently quantified under controlled conditions. Here, laboratory-scale inclined leaching experiments were conducted to resolve the distribution of solute transport among vertical leachate, lateral runoff, and solid-phase retention under systematically varied slope angles (0°, 4°, 9°, and 20°), flow regimes, and leaching volumes. Results show that solute migration shifted from vertical-dominated transport under flat conditions (91% at 0°) to lateral-dominated export at moderate slopes, with lateral pathways accounting for up to 75% of the recovered mass at 9°. This pathway shift was well described by an exponential partitioning model, f₁(α) = f_max (1 − e^−kα), where f_max = 0.80 and k = 0.34°⁻¹ (R² = 0.97), indicating a critical crossover threshold at approximately 4° slope. Flow regime interacted with slope angle to modulate lateral transport efficiency: slower flow enhanced lateral export at moderate slopes, whereas faster flow promoted peak lateral transport under steeper conditions. In contrast, solid-phase retention remained consistently low (5–9%) across all treatments, indicating that the observed redistribution patterns were primarily governed by hydrological pathway partitioning rather than sorption processes. These results demonstrate that even modest topographic gradients can fundamentally alter solute transport pathways in sloped soils. The slope-dependent pathway partitioning framework developed here provides a process-based basis for incorporating lateral transport into hillslope hydrological models and for improving assessments of contaminant redistribution in both managed and natural landscapes. Full article