Search Results (16,064)

Frost heave in seasonally frozen loess regions is fundamentally governed by pore water migration towards the freezing front driven by temperature gradients, forming ice lenses that damage engineered infrastructure. Because both freezing intensity and moisture availability evolve with climate, frost heave susceptibility is itself dynamic, yet existing assessments remain static and ignore future climate trajectories. This paper presents a physics-constrained machine learning framework that couples soil freezing characteristic curve (SFCC) prior knowledge with multi-source open data and CMIP6 climate projections to achieve dynamic frost heave susceptibility mapping for the Loess Plateau. Monotonicity constraints derived from the coupled phase-transition and cryosuction mechanisms described by the SFCC and the segregation potential theory are enforced during gradient-boosted tree training, ensuring that predictions respect the established relationships among freezing intensity, fine-grained content and ice segregation potential. An ordinal decomposition strategy is adopted to guarantee that the monotonicity constraint on each binary sub-model translates into monotonicity of the predicted ordinal susceptibility level. The best performer, physics-constrained XGBoost, reaches an overall accuracy of 88.7% and an AUC of 0.942 on a four-class susceptibility scheme. Independent validation against 156 field records and Sentinel-1 InSAR observations confirms that the model captures genuine frost heave patterns. Under SSP5-8.5, the area classified as high or very-high susceptibility contracts by approximately 38% by the 2080s owing to warming, while under SSP1-2.6 the reduction is only 12%, and transitional zones of moderate risk expand in both scenarios. These findings provide a temporally explicit and physically grounded basis for climate-adaptive infrastructure planning in cold loess regions. Full article

Hyperspectral (HS) sensors offer high spectral discrimination but generally limited spatial coverage, whereas multispectral (MS) sensors provide broad coverage with lower spectral detail. HS-MS complementary mapping aims to reduce coverage gaps in HS sensors by training classifiers within HS-MS overlap regions and applying them to surrounding MS-only areas. However, comprehensive comparisons across classifiers remain scarce in this complementary mapping setting. Furthermore, lightweight pixel-wise classifiers often produce spatially inconsistent predictions, whereas spatial–spectral deep learning models demand substantial computational resources. In this study, we propose a flexible HS-MS complementary mapping framework by systematically evaluating 13 classifiers across mineral and land-use/land-cover (LULC) mapping tasks and introducing class-adaptive uncertainty revocation (CAUR), a lightweight, classifier-independent post-processing module. Performance was evaluated via spatial holdout cross-validation within the HS-MS overlap area. When computational resources are sufficient, 3D convolutional neural networks (3D-CNN) achieve the highest accuracy. Conversely, lightweight models such as random forest (RF) and k-nearest neighbors (kNN) provide computationally efficient and robust baselines. Applying CAUR consistently improves spatial consistency and overall classification accuracy without model retraining, with the largest improvements observed for coarser-resolution HS reference data. These findings provide practical design guidelines for constructing efficient HS-MS complementary mapping pipelines tailored to application demands and sensor characteristics. Full article

Mapping debris flow susceptibility is essential for disaster risk reduction in mountainous regions. This study proposes a spatially enhanced modeling framework to evaluate these mass-wasting hazards. The framework integrates conventional topographic parameters with localized micro-geomorphic indicators to assess susceptibility in Bomi County, Tibet. Extracted geomorphological variables include the Topographic Position Index (TPI), surface roughness, local relief, and flow accumulation. We applied multi-scale moving window operations to explicitly quantify spatial heterogeneity and sediment connectivity. This approach systematically evaluates the influence of localized landscape variations on debris flow kinematics. The Random Forest (RF) algorithm was utilized to construct the spatial susceptibility model, and the area under the receiver operating characteristic curve (AUC) quantified its predictive capability. The proposed framework achieves a high predictive accuracy with an AUC of 0.9434. Feature importance analysis demonstrates that micro-geomorphic variables contribute significantly to the predictions; specifically, TPI and local relief primarily drive the overall classification performance. Furthermore, the multi-scale spatial enrichment enables the model to effectively capture complex physical interactions across the terrain. Ultimately, this methodology provides an objective, spatially explicit tool for mass-wasting susceptibility mapping, offering reliable data to support disaster prevention and risk management in the Tibetan Plateau and similar highly incised alpine ecosystems. Full article

The height of the first fruiting branch node (HFFBN) is a core indicator for mechanical harvesting of cotton, and the development of molecular markers for this trait is important for accelerating the breeding process. In this study, using bulked segregant analysis coupled with whole-genome sequencing (BSA-seq), one quantitative trait locus (QTL) associated with the HFFBN was mapped; a molecular marker, qFBH7, associated with the HFFBN of cotton was developed; and its application value was systematically evaluated. A total of 20 lines with extreme phenotypes were selected from the recombinant inbred lines constructed using upland cotton Z3-146 and Z3-147 as parental lines. The screened lines with extreme phenotypes were used to construct the extreme high-HFFBN pool and the extreme low-HFFBN pool, which were subsequently used for BSA-seq. Using the upland cotton genome as a reference, relevant QTLs were mapped by BSA-seq. One relevant candidate region was identified, with a total length of 2.25 Mb. The validation experiments revealed that the genotyping results of the KASP_FBH7_03 molecular marker in the parental lines Z3-146 and Z3-147 were completely consistent with the BSA-seq data: Z3-146 had the TT genotype, and Z3-147 had the CC genotype. Among the 66 samples from the natural population, there was a significant difference (p < 0.05) in the HFFBN between the CC and TT genotypes, and the mean HFFBN of the TT genotype was greater than that of the CC genotype. In summary, the KASP_FBH7_03 molecular marker can be effectively used for selective breeding for the HFFBN of cotton, and the TT genotype has a positive regulatory effect on the HFFBN. This study not only provides resources for breeding cotton varieties suited to mechanical harvesting but also offers a robust tool for molecular marker-assisted selection. Full article

Background: Population aging has led to an increasing proportion of older adults (aged 65 and older) acting as informal caregivers. These caregivers face risks of burden, stress, and depression. While research frequently documents negative psychological outcomes, resilience represents a crucial protective process. This systematic review synthesizes and maps the empirical evidence regarding the association between the socio-ecological resilience process and the well-being of older informal caregivers. Methods: Following Joanna Briggs Institute (JBI) mixed-methods guidelines, a convergent segregated mixed studies systematic review was conducted. A systematic search of Medline (PubMed), CINAHL, PsycINFO, and Scopus identified primary articles (2014–2025) in English and Spanish. Methodological quality was appraised using JBI critical appraisal checklists and the Mixed Methods Appraisal Tool. PROSPERO registration number: CRD420251142190. Results: Thirty-one studies were included. Elevated caregiver resilience was associated with lower reported symptoms of depression and anxiety, higher self-rated health, and positive psychological adaptation. Framed within a social ecological model, personal resources such as spirituality, hope, and self care, alongside relational assets like dyadic relationship quality and mutual coping, demonstrated a positive association with resilience and a reduction in subjective burden. Within the community context, informal peer networks and Online Health Communities functioned as essential supportive resources. On a structural level, both household wealth and the regional availability of long-term care beds acted as moderators for spousal well-being, whereas exceeding 30 weekly caregiving hours acted as a temporal threshold for positive adaptation. Conclusions: These findings suggest that resilience in older caregivers may be best conceptualized not as a static individual trait, but as a multi-level, dynamic socio-ecological process. Rather than relying on individual coping alone, public policies and clinical practice should prioritize systemic, relational, and structural environmental support, including formal respite services and long-term care infrastructure, to preserve the well-being of older spousal caregivers. Full article

Inventory and condition assessment of vertical traffic signs constitutes an essential activity for road safety management, as these signs serve as the primary mechanism for regulating vehicular flow and alerting drivers to prevailing road conditions. However, current inspection procedures in Peru rely on field crews that evaluate each sign manually, thereby constraining the frequency, objectivity, and scalability of the process. This paper presents SISEVIR (Sistema de Supervisión de Señales Verticales en Infraestructura Vial), a three-stage deep learning pipeline for the automated diagnosis of vertical traffic sign condition. The first stage employs YOLO26s-seg for instance segmentation of 31 sign classes, achieving a test mAP50 of 0.9305 (box) and 0.9224 (mask). The second stage classifies each detected sign into seven deterioration states using EfficientNet-B0, optimized through a five-experiment ablation study that identified progressive offline augmentation as the most effective strategy for handling a 147:1 class imbalance (macro F1 = 0.8252; pairwise McNemar’s tests with Holm–Bonferroni correction did not confirm significance at the family-wise $\alpha =0.05$ level). The third stage integrates Qwen2-VL-2B-Instruct, a vision–language model, to generate natural-language descriptions of sign condition aligned with the MTC Manual of Traffic Control Devices for Streets and Highways. A structured evaluation by two independent raters on 35 descriptions yielded a correctness rate of 93.5% among valid responses (95% CI: 79.3–98.2%, Cohen’s $\kappa =1.00$). The system was trained and validated on a proprietary dataset of 5935 images and 6412 labeled crops collected along three routes in the Ayacucho Region (246.6 km total), with an inter-rater reliability of $\kappa =0.802$ (95% CI: 0.676–0.928). SISEVIR processes vehicular video at 30.7 FPS on an NVIDIA RTX 5080 GPU and assigns each sign a level within a four-tier condition scale (Optimal through Critical) linked to specific maintenance interventions, significantly reducing the time, cost, and personnel required compared with the manual inspection method established in the MSV-2016 Road Safety Manual. Full article

Mineral prospectivity mapping (MPM) increasingly relies on machine learning classifiers, but model performance may be overstated when spatial dependence, extrapolation beyond the training domain, and poorly calibrated prediction scores are not adequately addressed. This study presents a reproducible workflow for regional Au-Ag prospectivity mapping in Nevada that integrates positive–unlabeled learning with Random Forest models and spatial GroupKFold cross-validation. Predictions were restricted to the Area of Applicability (AOA), defined using the 0.95 quantile of a training-derived feature-space dissimilarity index, resulting in areal coverage of 54.331% for Ag and 73.118% for Au. Discrimination based on pooled out-of-fold (OOF) predictions was strong for both commodities (Ag: AP = 0.745, ROC-AUC = 0.908; Au: AP = 0.777, ROC-AUC = 0.912). Post hoc OOF calibration with isotonic regression reduced Brier scores and improved probability reliability within the supported prediction domain. Area-based validation showed that the highest-ranked 10% of the AOA captured 85.6% of the Ag and 84.2% of the Au pooled-OOF positives, corresponding to targeting efficiencies of 8.6-fold and 8.4-fold relative to random spatial selection. Ensemble dispersion was used to map predictive uncertainty and to identify areas where additional data may be most useful for reducing uncertainty. SHAP importance and partial-dependence diagnostics consistently highlighted proximity to Quaternary faults, elevated slip and dilation tendency, and potential-field edges associated with intrusive or volcanic contacts, while surface heat flow acted mainly as a permissive control. The resulting AOA-masked prospectivity maps, uncertainty layers, and top-k targeting products provide a transparent regional framework for prioritizing follow-up exploration and allocating reconnaissance effort. Full article

Prolonged underwater exposure degrades reinforced concrete (RC) structures, causing chloride-induced corrosion, spalling, cracking, and rebar exposure. Timely damage identification is critical for structural safety, but conventional non-destructive testing methods face severe limitations underwater due to restricted accessibility, image degradation, and weak-textured, irregular crack boundaries. Vision-based inspection offers a promising alternative but remains constrained by underwater optical degradation. This study proposes a two-stage detection framework for underwater RC based on YOLOv13 (YOLOv13-TSDD). First, an underwater color-detail enhancement network (UCDEN) performs color correction, detail recovery, and contour reconstruction through multi-channel color enhancement and multi-level feature refinement. Second, two detection modules are introduced: a pinwheel-shaped receptive field convolution (PRFConv), improving sensitivity to directional textures and local linear structural responses in shallow layers, and a crack-aware efficient multi-scale attention (CEMA) mechanism, enabling joint channel-spatial recalibration and multi-scale focus on crack-relevant regions. A fine-grained irregular crack IoU (FID-IoU) loss function is also developed, using auxiliary boundary boxes and piecewise weighted mapping to improve bounding-box regression for irregular cracks. Experimental results demonstrate that YOLOv13-TSDD not only achieves the best overall image enhancement performance among the evaluated methods but also delivers the highest detection performance. On the constructed underwater dataset, YOLOv13-TSDD achieves Precision, Recall, and mAP@0.50 of 93.63%, 88.97%, and 94.52%, respectively, demonstrating improved performance under complex underwater conditions. Full article

Recent image diffusion models have enabled automatic 3D object creation from text or image guidance, but their 2D generative priors often bake illumination and shadow into textures, making relighting and physically based rendering (PBR) difficult. To address this issue, we propose MaterialSeg3D, a framework that predicts surface materials for 3D assets by leveraging 2D material semantics. Given a mesh and its albedo UV map, MaterialSeg3D renders multi-view images, performs material segmentation using a 2D prior model, projects the predictions back to UV space, and fuses them through weighted voting and region unification to obtain coherent material maps. To train the prior model, we construct MIO++, a large-scale single-object material segmentation dataset containing 115,542 images, 12 object themes, and 30 fine-grained material categories, substantially extending the previous MIO dataset. Each MIO++ material category is associated with an independent pair of roughness and metallic values for PBR assignment. We further observe that poor topology in AI-generated assets can degrade PBR quality even when plausible materials are assigned, and introduce a plane-simplification strategy as an auxiliary preprocessing step for such meshes. Experiments show that MIO++ improves material segmentation and that the resulting material maps support more consistent relightable renderings for both human-crafted and AI-generated 3D assets. Full article

West Nile virus (WNV) transmission has intensified and expanded in Italy, but quantitative evidence on how local climatic and environmental conditions influence human risk in southern regions remains limited. This study examined the association between meteorological, environmental, and host-related factors and West Nile virus (WNV) cases at the municipal level in Apulia in 2023. Human WNV cases were georeferenced at the municipal level and linked to monthly indicators (minimum and maximum temperature, precipitation, surface water extent, green area coverage, land-use change, avian occurrence). A Bayesian spatio-temporal Poisson model with a conditional autoregressive structure was fitted to monthly counts of human WNV cases, with equine WNV cases, climatic, environmental and avian indicators included as covariates. Eight human WNV cases were reported between August and October and four equine WNV cases between September and November, with partial spatial and temporal overlap. In univariable analyses, the strongest associations were observed for meteorological variables, particularly temperature. In the final multivariable model, higher maximum temperature at a two-month lag was associated with increased WNV risk (RR = 1.53; 95% CrI: 1.18–2.23), while minimum temperature was excluded due to collinearity with maximum temperature. Green area coverage and water body extent showed uncertain effects. Model-based maps indicated that elevated fitted risk was concentrated in a narrow temporal window between August and October, expanding sharply across the region in September before receding, rather than describing a stable, spatially fixed hotspot. This exploratory analysis suggests that reported human WNV infections in Apulia in 2023 were temporally concentrated during the late summer/early autumn period and that maximum temperature at a two-month lag was positively associated with the outcome in the selected model. The findings are hypothesis-generating and should be interpreted with caution given the very small number of events, but they illustrate the feasibility of integrating multisource epidemiological, climatic, environmental, and veterinary data to support locally tailored early-warning efforts in southern Italy. Full article

Lake Sevan, 1242 km² large, is the largest lake in the Caucasus and the largest lake in Armenia. This mountain lake serves as a natural water reservoir for the Hrazdan River, which is critical for the water supply to the capital, Yerevan, and the Ararat Valley, the most important agricultural region of Armenia. The annual outflow from the lake shall not exceed 170 million m³. Agriculture is an important economic sector within the lake’s basin, with summer and perennial crops partly depending on irrigation. This study used remote sensing to map the evapotranspiration of agriculture and other vegetation types to assess the extent to which agriculture poses pressure on the lake’s water balance. From 2023 to 2025, the evapotranspiration averaged across all the cropland in the study area ranged between 204 mm and 307 mm, which coincides with low yields of agriculture. During the growing season, evapotranspiration of summer and permanent crops exceeded precipitation by 4.2 to 4.5 million m³, which is small compared with the annual outflow of 170 million m³. Overall, the annual precipitation exceeded evapotranspiration of cropland, including irrigated cropland. The findings suggest that, under current land use and climatic conditions, agriculture around Lake Sevan exerted limited pressure on the lake’s water balance for the years 2023–2025 and is unlikely to substantially affect its capacity to sustain the required outflow into the Hrazdan River. Full article

Sedimentation is eroding the water security of reservoirs in hydrologically active basins: the Poechos reservoir (Peru) has lost 62% of its original 887.7 hm³ capacity in 48 years, yet its Elevation–Area–Volume (EAV) curve is refreshed only by bathymetric surveys at decade-plus intervals, compromising flood regulation and the water supply for over 100,000 ha of farmland. To close this gap, we propose an integrated, low-cost, fully reproducible framework that reconstructs the EAV curve from freely available satellite data: Sentinel-1 SAR (287 acquisitions, 2021–2026), PlanetScope imagery as ground truth (23 dates), and Surface Water and Ocean Topography (SWOT) altimetry (53 validated passes, 2023–2026). Water surfaces were delineated with a deep learning segmentation model (Feature Pyramid Network with an InceptionV4 encoder), selected among nine architecture–encoder combinations and calibrated to a 0.64 decision threshold, achieving a 90.66% Intersection over Union (IoU) and a 95.10% F1 score; a stochastic quantile mapping algorithm then asynchronously coupled the area and elevation series. The resulting EAV curve matched daily operational records from Peru’s National Water Authority (ANA) with high precision (NSE = 0.94, R² = 0.96, and RMSE = 25.93 hm³); the residual bias (BIAS = −11.23 hm³) reflects active sedimentation unaccounted for in the official curve. This bias peaked at an accumulated deficit of 24.5 hm³ during the 2023–2024 hydrological year (3.5 hm³/year), of which up to 19.6 hm³ is attributed to the 2023 Yaku cyclone as a phenomenologically scaled upper-bound estimate (9.8–19.6 hm³ across 40–80% attribution fractions), since SWOT was not yet operational during the event. Updating every 21 days under any weather and requiring no new field campaigns beyond the baseline bathymetric anchor, the trained ensemble was further transferred zero-shot to three additional reservoirs (San Lorenzo, Tinajones, and Gallito Ciego), demonstrating a scalable path from infrequent static assessments to near-continuous, dynamic monitoring of water storage. Full article

Understanding how climate change may alter river discharge in semi-arid regions is essential for sustainable water-resource management. This study assesses future streamflow in the Zat River Basin (High Atlas Mountains, Morocco) using a hybrid framework that combines machine-learning rainfall–runoff modeling, CMIP6 multi-model climate forcing, monthly quantile-mapping post-processing of simulated discharge, and an exploratory temperature-sensitivity assessment. Monthly hydroclimatic observations of precipitation, air temperature, reference evapotranspiration, and discharge were compiled from February 1962 to August 2024. The period 1962–2005 was used for model development, the 2006–2014 window for chronological validation, and the more recent observations for supplementary evaluation of climate-driven simulations. Four algorithms were compared: Gradient Boosting Regressor (GBR), Histogram-based Gradient Boosting Regressor (HGBR), Random Forest (RF), and Multi-Layer Perceptron (MLP). Performance was assessed using NSE, KGE, RMSE, MAE, and R². GBR provided the best validation performance (NSE = 0.71, KGE = 0.80, and R² = 0.72). The selected model was then forced with CMIP6 projections under SSP2-4.5 and SSP5-8.5 to simulate streamflow to 2100. Quantile mapping was applied to the simulated discharge, rather than separately to precipitation, temperature, and reference evapotranspiration. The multi-model ensemble indicates a persistent drying tendency: relative to the historical baseline and without an additional temperature-sensitivity adjustment, mean annual discharge is projected to decline by approximately 12.1% under SSP2-4.5 and 27.3% under SSP5-8.5 by 2081–2100. Under an exploratory sensitivity case using a runoff-temperature-sensitivity coefficient of 0.04 °C⁻¹, the projected declines increase to approximately 23.3% and 44.1%, respectively. Episodic high-flow events nevertheless remain possible, suggesting a shift toward lower mean flows combined with persistent hydrological extremes. Full article

Induced pluripotent stem cells (iPSCs) are prone to genomic instability during prolonged culture, with recurrent chromosomal aberrations conferring selective advantages. Replication stress is a major driver of this instability, yet the repertoire of replication stress-sensitive loci in iPSCs remains largely unexplored. Here, we mapped aphidicolin-sensitive fragile sites (asFS) in three independent iPSC lines using classical cytogenetic break analysis combined with Monte Carlo simulation and MiDAS mapping directly on banded metaphase chromosomes. We identified 28 asFS, which segregated into a highly active Major cluster (8 sites, accounting for 59% of breaks among asFS) and a less active Minor cluster (20 sites). Five universal asFS (9p21, 6q25-26, 20p11-12, 10q22, Xq25) were present in all three lines, representing a fragility signature associated with the pluripotent state, with Xq25 shifting into the Major cluster after correction for X chromosome dosage. Minor asFS showed preferential co-localization with physical breakpoints or minimal overlapping regions of recurrent culture-acquired aberrations, including 20q11.21 (BCL2L1), 1q32 (MDM4), 8q24 (MYC), 17q21 (WNT3-WNT9B), and 18q21 (DCC/FRA18B). MiDAS mapping validated most asFS and revealed additional replication stress-sensitive loci in pericentromeric and subtelomeric regions that are difficult to score by conventional G-banding. Comparison with fragile site maps from other cell types revealed that the iPSC asFS repertoire is distinct in rank order and relative activity, characteristic of the pluripotent state. Collectively, our findings indicate that the asFS repertoire in iPSCs is hierarchically organized into a stable universal core and a variable peripheral component, and suggest that Minor asFS may contribute to, or be associated with, the genesis of culture-acquired rearrangements. This work provides a framework for understanding how replication stress and clonal selection shape the mutational landscape of pluripotent stem cells. Full article

Poland’s rapid expansion of renewable energy sources (RES) and of distributed photovoltaics (PVs) in particular has increasingly collided with a finite and unevenly distributed resource: available capacity in the electricity distribution grid. This paper examines grid-connection capacity as a spatial constraint on RES development in Poland, combining a national overview of grid congestion (2023–2026) with a quantitative case study of the 52 coherent 110 kV node groups administered by ENERGA-OPERATOR S.A. under the statutory reporting regime of Article 7(8l) of the Polish Energy Law. The node-group figures are not an observed annual series: the operator’s disclosure gives the capacity available in the base year of 2018, together with the capacity it planned to make available in each year up to 2023, so the analysis characterises the spatial distribution implied by the operator’s own five-year development plan rather than realised outcomes. Using descriptive statistics, a Gini coefficient, Lorenz curve analysis, and an original growth index (the ENERGA Grid-Connection Growth Index, EGCI) developed for this study, we show that available connection capacity is markedly unequally distributed across node groups (Gini = 0.48 in 2018, rising to 0.51 by the 2023 planning horizon) and that this inequality has a distinct regional pattern: the Olsztyn branch, corresponding to the Warmia–Mazury region (Warmińsko-mazurskie voivodeship), more than doubles its share of the operator’s total available capacity under the plan (from 12.4% to 29.3%, or from 75 MW to 365 MW in absolute terms), moving from the third-lowest to the highest planned capacity among the operator’s six branches, while two of its nine node groups—including the regional capital’s own—receive no increase at all under the plan. A voivodeship-level spatial analysis, mapped using verified administrative boundary data, shows a pattern consistent with this at a national scale: Warmia–Mazury has the second-lowest installed generation capacity of Poland’s 16 voivodeships despite favourable land and irradiation conditions for photovoltaic development. Bootstrap analysis confirms this robust level of inequality while indicating that, with 52 units, the five-year increase is best read as a consistent tendency rather than as a statistically established widening; the operator-level values are specific to ENERGA-OPERATOR and are not numerically generalisable to Poland’s other four operators. We discuss the implications of these findings for grid-investment planning, RES-integration policy (cable pooling, storage co-location, curtailment reduction), and the energy-security dimension of an increasingly decentralised, weather-dependent generation system, and we identify concrete directions for future quantitative and spatial research. Full article