Search Results (6)

This study reconstructs nearly five decades of end-of-ablation-season snowline altitudes (SLA_EoA) for 106 tongue-shaped glaciers in the Himalayan Climatological transitional zone using consistent Landsat observations. The regional mean SLA_EoA (~4938 m asl) has risen by ~486 m, a robust and statistically significant increasing trend (Theil–Sen slope = 15.34 m year⁻¹; Kendall’s τ = 0.659, p = 7.14 × 10⁻⁶), indicating a persistent reduction in glacier accumulation areas and increasingly negative mass balance under ongoing warming. Spatial patterns reveal strong control of Indian Summer Monsoon (ISM) moisture, elevation, glacier aspect, and morpho-topographic setting. Snowlines are generally lower in the south-eastern sector and higher in the north-west, while rates of snowline rise are greater in the southern and eastern regions, suggesting enhanced climatic sensitivity. Elevation-dependent trend analysis further shows that the rate of snowline rise decreases systematically with increasing glacier elevation, from 39.37 m year⁻¹ for glaciers below 4500 m asl to 7.89 m year⁻¹ for glaciers above 5500 m asl, indicating substantially greater sensitivity of lower-elevation glaciers to ongoing climatic change. Lower-elevation glaciers exhibit disproportionately rapid SLA_EoA rise, suggesting that lower-elevation glaciers may be approaching critical thresholds at which accumulation zones cannot be sustained. Morphometric analysis shows that smaller, lower-elevation, and gently sloping glaciers respond more rapidly than larger and steeper ones. Aspect further influences variability, with south-facing glaciers showing higher and faster-rising snowlines than north-facing glaciers, though this contrast is diminishing over time. Climatic trends from ERA5-Land data indicate rising air temperatures and declining snowfall, identifying warming and reduced accumulation as primary drivers. The results support the hypothesis that the long-term rise in SLA_EoA across the Himalayan climatological transition zone is primarily driven by regional warming and declining snowfall, while the magnitude of glacier response is further modulated by geometry and topography. Overall, SLA_EoA emerges as a robust indicator of glacier health, highlighting accelerated cryospheric change in the transitional Climatological Himalaya with important implications for regional water resources. Full article

Accurate perception of tugboat operational status is essential for optimising port scheduling efficiency and ensuring operational safety. However, existing AIS-based methods often struggle to capture the fine-grained and asymmetric manoeuvring characteristics of tugboats, particularly in distinguishing assisted berthing from unberthing operations. To address these limitations, this study proposes a hybrid recognition framework integrating multidimensional feature engineering with spatiotemporal dynamics. First, a speed-threshold-based sliding window algorithm segments trajectories into sailing and berthing states. Second, a 15-dimensional feature vector—comprising statistical and descriptive features from speed, heading, and trajectory morphology—is constructed to characterise tugboat behaviour. Notably, morpho-logical descriptors such as the ‘Overlap Ratio’ serve as implicit spatial proxies, capturing geographical constraints without reliance on Electronic Navigational Charts. A three-layer fully connected neural network (FCNN) is then developed to classify segments into “Cruising” and “Assisting in Berthing/Unberthing.” Finally, a speed-dynamics rule further distinguishes berthing from unberthing based on opposing temporal evolution patterns. Experiments on real AIS data from Ningbo–Zhoushan Port demonstrate that the model achieves an F1-score of 0.90 and a recall of 0.93 for assistance-related operations. Permutation importance analysis confirms that integrating kinematic and morphological features enables interpretable and precise intent inference. This study offers a high-precision, low-dependency solution for tugboat operation identification, supporting intelligent port surveillance and sustainable maritime management. Full article

Background/Objectives: Hepatocellular carcinoma (HCC) shows marked molecular and histopathological heterogeneity. Among the alterations most strongly associated with clinical outcome are mutations in TP53 and CTNNB1, as well as the presence of microvascular invasion (MVI). Although these factors are well established as prognostic indicators, how their molecular effects relate to tumor morphology remains unclear. In this work, we studied transcriptomic changes linked to TP53 and CTNNB1 mutational status and to MVI, and examined whether these changes are reflected in routine histology. Methods: RNA sequencing data from HCC samples annotated for mutations and vascular invasion were analyzed using differential expression analysis combined with machine learning-based feature selection to characterize the underlying transcriptional programs. In parallel, we trained a weakly supervised multitask deep learning model on hematoxylin and eosin-stained whole-slide images using slide-level labels only, without spatial annotations, to assess whether these features could be inferred from global histological patterns. Results: Distinct gene expression profiles were observed for TP53-mutated, CTNNB1-mutated, and MVI-positive tumors, involving pathways related to proliferation, metabolism, and invasion. Image-based models were able to capture morphological patterns associated with these states, achieving above-random discrimination with variable performance across tasks. Conclusions: Taken together, these results support the existence of coherent biological programs underlying key risk determinants in HCC and indicate that their phenotypic effects are, at least in part, detectable in routine histopathology. This provides a rationale for integrative morpho-molecular approaches to risk assessment in HCC. Full article

In the context of increasing climate variability and flood risk, this study explores how long-standing agricultural practices in the Hunter Valley, New South Wales, Australia, have fostered flood resilience through the integration of local agro-environmental knowledge and geomorphologic conditions. Employing a morpho-typological framework, the research identifies three flood adaptation landscape types (FALTs)—rolling hills, foot slopes, and flood plains—each reflecting distinct interactions between landform, soil, biodiversity, hydrology, and viticultural management. Through geospatial analysis, field surveys, and interviews with local farmers, the study reveals how adaptive strategies—ranging from flood avoidance to attenuation and acceptance—have evolved in response to site-specific hydrological and ecologic dynamics. These strategies demonstrate a form of ‘sponge landscape’ design, where agricultural systems are co-shaped with natural processes to enhance systemic resilience and long-term productivity. The findings underscore the value of preserving biocultural legacies and suggest that spatially explicit, context-based approaches to flood adaptation can inform sustainable landscape planning and climate resilience strategies in other rural regions. The FALT framework offers a replicable methodology for identifying flood adaptation patterns across diverse agricultural systems in Australia, supporting proactive land use planning and nature-based solutions. This research contributes to the discourse on climate adaptation by bridging traditional environmental knowledge with contemporary planning frameworks, offering practical insights for policy, landscape management, and rural development. Full article

The construction of green infrastructure (GI) plays an important role in improving the rural ecological functions and building a green livable environment. In this paper, the methods of morpho spatial pattern analysis (MSPA) and space syntax analysis are used to study the GI network construction in Suining County, Jiangsu Province. The results show that: (1) In 2018, the area of ecological patches increased by 110% compared with 1998, and the utilization rate of the GI network was significantly improved. (2) A total of 66 ecological corridors were analyzed in the county, and the main corridors were distributed in the central and western regions. The correlation analysis of core ecological patches in 1998, 2008, and 2018 proved that location factors had the greatest impact on the results of function and connectivity. (3) According to the optimization results, ecological benefits can be improved through engineering measures to realize the revitalization and development of regional rural areas. Full article

The morphometric characteristics of the Kalvārī basin were analyzed to prioritize sub-basins based on their susceptibility to erosion by water using a remote sensing-based data and a GIS. The morphometric parameters (MPs)—linear, relief, and shape—of the drainage network were calculated using data from the Advanced Land-observing Satellite (ALOS) phased-array L-type synthetic-aperture radar (PALSAR) digital elevation model (DEM) with a spatial resolution of 12.5 m. Interferometric synthetic aperture radar (InSAR) was used to generate the DEM. These parameters revealed the network’s texture, morpho-tectonics, geometry, and relief characteristics. A complex proportional assessment of alternatives (COPRAS)-analytical hierarchy process (AHP) novel-ensemble multiple-criteria decision-making (MCDM) model was used to rank sub-basins and to identify the major MPs that significantly influence erosion landforms of the Kalvārī drainage basin. The results show that in evolutionary terms this is a youthful landscape. Rejuvenation has influenced the erosional development of the basin, but lithology and relief, structure, and tectonics have determined the drainage patterns of the catchment. Results of the AHP model indicate that slope and drainage density influence erosion in the study area. The COPRAS-AHP ensemble model results reveal that sub-basin 1 is the most susceptible to soil erosion (SE) and that sub-basin 5 is least susceptible. The ensemble model was compared to the two individual models using the Spearman correlation coefficient test (SCCT) and the Kendall Tau correlation coefficient test (KTCCT). To evaluate the prediction accuracy of the ensemble model, its results were compared to results generated by the modified Pacific Southwest Inter-Agency Committee (MPSIAC) model in each sub-basin. Based on SCCT and KTCCT, the ensemble model was better at ranking sub-basins than the MPSIAC model, which indicated that sub-basins 1 and 4, with mean sediment yields of 943.7 and 456.3 ${\mathrm{m}}^3{\mathrm{km}}^{-2}{\mathrm{year}}^{-1}$ , respectively, have the highest and lowest SE susceptibility in the study area. The sensitivity analysis revealed that the most sensitive parameters of the MPSIAC model are slope (R² = 0.96), followed by runoff (R² = 0.95). The MPSIAC shows that the ensemble model has a high prediction accuracy. The method tested here has been shown to be an effective tool to improve sustainable soil management. Full article