Search Results (1,852)

Nepal is highly susceptible to natural hazards, including earthquakes, flooding, and landslides, all of which may occur independently or in combination. Climate change is projected to increase the frequency and intensity of these natural hazards, posing growing risks to Nepal’s infrastructure and development. To the authors’ knowledge, the majority of existing geohazard research in Nepal is typically limited to single hazards or localised areas. To address this gap, MiMapper was developed as a cloud-based, open-access multi-hazard mapping tool covering the full national extent. Built on Google Earth Engine and using only open-source spatial datasets, MiMapper applies an Analytical Hierarchy Process (AHP) to generate hazard indices for earthquakes, floods, and landslides. These indices are combined into an aggregated hazard layer and presented in an interactive, user-friendly web map that requires no prior GIS expertise. MiMapper uses a standardised hazard categorisation system for all layers, providing pixel-based scores for each layer between 0 (Very Low) and 1 (Very High). The modal and mean hazard categories for aggregated hazard in Nepal were Low (47.66% of pixels) and Medium (45.61% of pixels), respectively, but there was high spatial variability in hazard categories depending on hazard type. The validation of MiMapper’s flooding and landslide layers showed an accuracy of 0.412 and 0.668, sensitivity of 0.637 and 0.898, and precision of 0.116 and 0.627, respectively. These validation results show strong overall performance for landslide prediction, whilst broad-scale exposure patterns are predicted for flooding but may lack the resolution or sensitivity to fully represent real-world flood events. Consequently, MiMapper is a useful tool to support initial hazard screening by professionals in urban planning, infrastructure development, disaster management, and research. It can contribute to a Level 1 Integrated Geohazard Assessment as part of the evaluation for improving the resilience of hydropower schemes to the impacts of climate change. MiMapper also offers potential as a teaching tool for exploring hazard processes in data-limited, high-relief environments such as Nepal. Full article

The Rif Mountains in northern Morocco are highly exposed to geohazards, particularly earthquakes and mass movements. In this context, the Zoumi region is most affected, showing various mass movement types involving both unconsolidated and solid materials. This study evaluates the region’s susceptibility to mass movements using logistic regression (LR), applied for the first time in this area. The model incorporates eight key predisposing factors known to influence mass movement: slope gradient, slope aspect, land use, drainage density, elevation, lithology, fracturing density, and earthquake isodepths. Historical mass movements were mapped using remote sensing and field surveys, and statistical analysis calculation was conducted to analyze their spatial correlation with these environmental conditioning factors. A mass movement susceptibility (MMS) map was produced, classifying the region into four susceptibility levels, ranging from low to very high. Landslides were the most frequent movement type (36%). The LR model showed strong predictive performance, with an AUC of 88%, confirming its robustness. The final map reveals that 42% of the Zoumi area falls within the high to very high susceptibility zones. These results highlight the importance of using advanced modeling approaches to support risk mitigation and land use planning in environmentally sensitive mountain regions. Full article

As climate extremes increasingly threaten global food security, precision tools for early detection of crop stress have become vital, particularly for root crops such as potato (Solanum tuberosum L.) and sweet potato (Ipomoea batatas L. Lam.), which are especially susceptible to environmental stressors throughout their life cycles. In this study, plants were monitored from the initial onset of seasonal stressors, including spring drought, heat, and episodes of excessive rainfall, through to harvest, capturing the full range of physiological and biochemical responses under seasonal, simulated conditions in greenhouses. The spectral data were obtained from regions of interest (ROIs) of each cultivar’s leaves, with over 3000 data points extracted per cultivar; these data were subsequently used for model development. A comprehensive classification framework was established by employing machine learning models, Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Partial Least Squares-Discriminant Analysis (PLS-DA), to detect stress across various growth stages. Furthermore, severity levels were objectively defined using photoreflectance indices and principal component analysis (PCA) data visualizations, which enabled consistent and reliable classification of stress responses in both individual cultivars and combined datasets. All models achieved high classification accuracy (90–98%) on independent test sets. The application of the Successive Projections Algorithm (SPA) for variable selection significantly reduced the number of wavelengths required for robust stress classification, with SPA-PLS-DA models maintaining high accuracy (90–96%) using only a subset of informative bands. Furthermore, SPA-PLS-DA-based chemical imaging enabled spatial mapping of stress severity within plant tissues, providing early, non-invasive insights into physiological and biochemical status. These findings highlight the potential of integrating hyperspectral imaging and machine learning for precise, real-time crop monitoring, thereby contributing to sustainable agricultural management and reduced yield losses. Full article

Highly Hazardous Pesticides (HHPs) pose severe risks to human health and the environment, making it essential to understand their molecular stability and degradation pathways. In this study, the Quantum Theory of Atoms in Molecules (QTAIM) was applied to four representative organophosphate pesticides, allowing the identification of electronically weak bonds as intrinsic sites of lability. These findings are consistent with reported hydrolytic, oxidative, enzymatic, and microbial degradation routes. Importantly, QTAIM descriptors proved largely insensitive to solvation, confirming their intrinsic character within the molecular electronic structure. To complement QTAIM, conceptual DFT (Density Functional Theory) reactivity indices were analyzed, revealing that solvent effects induce more noticeable variations in global and local descriptors than in topological parameters. In addition, a Topological Analysis of the Fukui Function (TAFF) was performed, which mapped nucleophilic, electrophilic, and radical susceptibilities directly onto QTAIM basins. The TAFF analysis confirmed that bonds identified as weak by QTAIM (notably P–O, P–S, and P–N linkages) also coincide with the most reactive sites, thereby reinforcing their mechanistic role in degradation pathways. This integrated framework highlights the robustness of QTAIM, the sensitivity of global and local reactivity descriptors to solvation revealed by conceptual DFT, and the complementary insights provided by TAFF, contributing to risk assessment, remediation strategies, and the rational design of safer pesticides. Full article

Artificial intelligence (AI) offers several opportunities in wildfire management, particularly for improving short- and long-term fire occurrence forecasting, spread modeling, and decision-making. When properly adapted beyond research into real-world settings, AI can significantly reduce risks to human life, as well as ecological and economic damages. However, despite increasingly sophisticated research, the operational use of AI in wildfire contexts remains limited. In this article, we review the main domains of wildfire management where AI has been applied—susceptibility mapping, prediction, detection, simulation, and impact assessment—and highlight critical limitations that hinder practical adoption. These include challenges with dataset imbalance and accessibility, the inadequacy of commonly used metrics, the choice of prediction formats, and the computational costs of large-scale models, all of which reduce model trustworthiness and applicability. Beyond synthesizing existing work, our survey makes four explicit contributions: (1) we provide a reproducible taxonomy supported by detailed dataset tables, emphasizing both the reliability and shortcomings of frequently used data sources; (2) we propose evaluation guidance tailored to imbalanced and spatial tasks, stressing the importance of using accurate metrics and format; (3) we provide a complete state of the art, highlighting important issues and recommendations to enhance models’ performances and reliability from susceptibility to damage analysis; (4) we introduce a deployment checklist that considers cost, latency, required expertise, and integration with decision-support and optimization systems. By bridging the gap between laboratory-oriented models and real-world validation, our work advances prior reviews and aims to strengthen confidence in AI-driven wildfire management while guiding future research toward operational applicability. Full article

Urban Waterlogging Susceptibility Assessment (UWSA) is vital for resilient urban planning and disaster preparedness. Conventional methods depend heavily on Historical Waterlogging Records (HWR), which are limited by their reliance on extreme rainfall events and prone to human omissions, resulting in spatial bias and incomplete coverage. While hydrodynamic models can simulate waterlogging scenarios, their large-scale application is restricted by the lack of accessible underground drainage data. Recently released flood control plans and risk maps provide valuable physics-informed priors (PI-Priors) that can supplement HWR for susceptibility modeling. This study introduces a dual-source integration framework that fuses HWR with PI-Priors to improve UWSA performance. PI-Priors rasters were vectorized to delineate two-dimensional waterlogging zones, and based on the Three-Way Decision (TWD) theory, a Multi-dimensional Connection Cloud Model (MCCM) with CRITIC-TOPSIS was employed to build an index system incorporating membership degree, credibility, and impact scores. High-quality samples were extracted and combined with HWR to create an enhanced dataset. A Maximum Entropy (MaxEnt) model was then applied with 20 variables spanning natural conditions, social capital, infrastructure, and built environment. The results demonstrate that this framework increases sample adequacy, reduces spatial bias, and substantially improves the accuracy and generalizability of UWSA under extreme rainfall. Full article

Landslide runout distance is governed not only by collapsed magnitude but also by site-specific geoenvironmental conditions. While remote sensing techniques has advanced landslide susceptibility mapping, its application to runout modeling remains limited. This study examined the role of collecting data on various site conditions through remote sensing and field surveys datasets in predicting the landslide travel distance from the 2022 disaster in Petrópolis, Rio de Janeiro. A total of 218 multivariate linear regression models were developed using morphometric, remote sensing, and field survey variables collected across collapse, transport, and deposition zones. Results show that predictive accuracy was limited when based solely on landslide scale (R² = 0.06–0.10) but improved substantially with the inclusion of site condition data across collapse, transport, and deposition zones (R² = 0.49–0.51). Additionally, model performance was strongly influenced by runout path typology, with channelized flows producing the most stable and accurate predictions (R² = 0.73–0.90), while obstructed and open-slope paths performed worse (R² = 0.39–0.61). These findings demonstrate that empirical models integrating multizonal site-condition data and runout path typology offer a scalable, reproducible framework for landslide hazard mapping in data-scarce, complex mountainous urban environments. Full article

With climate change, the Qinghai–Tibet Highway (QTH) is facing increasingly severe risks of natural hazards, posing a significant threat to its normal operation. However, the types, distribution, and future risks of hazards along the QTH are still unclear. In this study, we established an inventory of multi-hazards along the QTH by remote sensing interpretation and field validation, including landslides, debris flows, thaw slumps, and thermokarst lakes. The QTH was segmented into three sections based on hazard distribution and environmental factors. Susceptibility modelling was performed for each hazard within each section using machine learning models, followed by further evaluation of hazard susceptibility under future climate change scenarios. The results show that, at present, approximately 15.50% of the area along the QTH exhibits high susceptibility to multi-hazards, with this proportion projected to increase to 20.85% and 23.32% under the representative concentration pathways (RCP) 4.5 and RCP 8.5 distant future scenarios, respectively. Variations in hazard-prone environments dominate the spatial heterogeneity of multi-hazard distribution. Gravity hazards demonstrate limited sensitivity to climate change, whereas thermal hazards exhibit a more pronounced response. Our geomorphology-based segmented assessment framework effectively enhances evaluation accuracy and model interpretability. The results can provide critical insights for the operation, maintenance, and hazard risk management of the QTH. Full article

The object detection model for synthetic aperture radar (SAR) images needs to have strong generalization ability and more stable detection performance due to the complex scattering mechanism, high sensitivity of the orientation angle, and susceptibility to speckle noise. Visual large models possess strong generalization capabilities for natural image processing, but their application to SAR imagery remains relatively rare. This paper attempts to introduce a visual large model into the SAR object detection task, aiming to alleviate the problems of weak cross-domain generalization and poor adaptability to few-shot samples caused by the characteristics of SAR images in existing models. The proposed model comprises an image encoder, an attention module, and a detection decoder. The image encoder leverages the pre-trained Segment Anything Model (SAM) for effective feature extraction from SAR images. An Adaptive Channel Interactive Attention (ACIA) module is introduced to suppress SAR speckle noise. Further, a Dynamic Tandem Attention (DTA) mechanism is proposed in the decoder to integrate scale perception, spatial focusing, and task adaptation, while decoupling classification from detection for improved accuracy. Leveraging the strong representational and few-shot adaptation capabilities of large pre-trained models, this study evaluates their cross-domain and few-shot detection performance on SAR imagery. For cross-domain detection, the model was trained on AIR-SARShip-1.0 and tested on SSDD, achieving an mAP50 of 0.54. For few-shot detection on SAR-AIRcraft-1.0, using only 10% of the training samples, the model reached an mAP50 of 0.503. Full article

Stone cultural heritage structures built from pyroclastic rocks are susceptible to deterioration due to their sensitivity to atmospheric processes. Detecting such deterioration and periodically examining it using non-destructive testing (NDT) techniques is one of the most critical measures for ensuring its transmission to future generations. In recent years, assessing the properties of building stones through NDT methods has been widely applied in planning the preservation of stone cultural heritages. In this study, deterioration observed on the interior walls of the Emir Ali Tomb, a structure distinguished from other tombs in the region by its exceptional architecture, was investigated through laboratory tests and NDT techniques, including deep moisture measurement, P-wave velocity, and infrared thermography. It was determined that the monument was constructed from four different types of pyroclastic rock, classified according to their textural and geomechanical characteristics. Using data obtained from in situ tests, NDT distribution maps were generated. The deep moisture, P-wave velocity, and infrared thermography maps revealed that the primary cause of deterioration in the monument was related to capillary water rise. Full article

Forest fire risk assessment methodologies vary considerably, presenting challenges for adaptation to specific local contexts. This study provides a systematic analysis of forest fire assessment approaches across the Mediterranean basin, American, African, and Asian regions through a comprehensive review of 112 peer-reviewed studies published from 2015 to 2025. Statistical significance testing (Chi-square tests, p < 0.05) confirmed significant regional variation in methodological preferences and indicator usage patterns. Key findings revealed that Multi-Criteria Decision Analysis dominates the field (44% of studies, n = 49), with Analytical Hierarchical Process being the most utilized method (39 studies). Machine learning approaches represent 25% (n = 28), with Random Forest leading significantly (22 applications). The analysis identified 67 indicators across seven major categories, with topographic factors (slope: 105 studies) and anthropogenic indicators (road networks: 92 studies) showing statistically significantly highest usage rates (p < 0.001), representing a statistically significant critical gap in vulnerability assessment (p < 0.01). Organizational factors remain severely underrepresented (a maximum of 14 studies for any factor), representing a statistically significant critical gap in risk assessments (p < 0.01). Statistical analysis revealed that while Mediterranean approaches excel in integrating historical and cultural factors, American methods emphasize advanced technology integration, while Asian approaches focus on socio-economic dynamics and land-use interactions. This study serves as a foundation for developing tailored assessment frameworks that combine remote sensing analysis, ground-based surveys, and community input while accounting for local constraints in data availability and technical capacity. The study concludes that effective forest fire risk assessment requires a balanced integration of global best practices with local environmental, social, and technical considerations, offering a roadmap for future forest fire risk assessment approaches in different regions worldwide. Full article

Kyoho grapes are rich in nutrients, yet their susceptibility to spoilage poses a significant challenge for postharvest preservation. While light treatment can improve fruit quality and carbohydrate metabolism in postharvest grapes, the potential benefits of combining light treatment with modified atmosphere packaging (MAP) remain unexplored. A preservation method that combined red and blue light treatments with MAP has been developed to enhance postharvest fruit quality and carbohydrate metabolism in Kyoho grapes. Our study showed that this combined treatment significantly increased postharvest fruit hardness, as well as total soluble solids (TSS) and fruiting pedicel water content. It also improved the activities of superoxide dismutase (SOD) and phenylalanine ammonialyase (PAL) and increased the antioxidant, anti-browning capacity. This composite treatment slowed down sucrose decomposition by regulating the activities of key enzymes of carbohydrate metabolism (sucrose synthase (SS), sucrose phosphate synthase (SPS), neutral invertase (NI) and acid invertase (AI)). After 60 days of storage, the glucose, fructose, and sucrose contents of the RP group increased by 13.4%, 30.2%, and 18.1%, respectively, compared to the CK group (p < 0.05). In summary, light combined with modified atmosphere packaging significantly improved the physicochemical properties and sugar metabolism of postharvest grapes. The results indicated that the optimal treatment condition was continuous red-light irradiation combined with MAP. The hardness, TSS content, VC content and glucose content of Kyoho grapes in this treatment group were the best in all treatment groups. Full article

The umbilical coiling index (UCI), which quantifies the degree of vascular coiling in the umbilical cord, is a crucial indicator for assessing fetal intrauterine development and predicting perinatal outcomes. However, the existing methods for measuring the UCI primarily rely on manual assessment, which suffers from low efficiency and susceptibility to inter-observer variability. In response to the challenges in measuring the umbilical coiling index during obstetric ultrasound, we propose UCINet, a multi-task neural network engineered explicitly for this purpose. UCINet demonstrates enhanced operational efficiency and significantly improved accuracy in detection, catering to the nuanced requirements of obstetric imaging. Firstly, this paper proposes a Frequency–Spatial Domain Downsampling Module (FSDM) to extract features in both the frequency and spatial domains, thereby reducing the loss of umbilical cord features and enhancing their representational capacity. The proposed Multi-Receptive Field Feature Perception Module (MRPM) employs receptive fields of varying sizes across different stages of the feature maps, enhancing the richness of feature representation. This approach allows the model to capture a more diverse set of spatial information, contributing to improved overall performance in feature extraction. A Multi-Scale Feature Aggregation Module (MSAM) comprehensively leverages multi-scale features via a dynamic fusion mechanism, optimizing the integration of disparate feature scales for enhanced performance. In addition, the UCI dataset, which consisted of 2018 annotated ultrasound images, was constructed, each labeled with the number of vascular coils and keypoints at both ends of the umbilical cord. Compared with state-of-the-art methods, UCINet achieves consistent improvements across two tasks. In object detection, UCINet outperforms Deformable DETR-R50 with an improvement of 1.2% points in mAP@50. In keypoint localization, it further exceeds YOLOv11 with a 3.0% gain in mAP@50, highlighting its effectiveness in both detection accuracy and fine-grained keypoint prediction. Full article

Background: The use of antibiotics in livestock has contributed to the spread of antimicrobial resistance (AMR) and biofilms can play a role in its emergence and dissemination. This review aimed to map the literature on AMR, antimicrobial resistance genes (ARGs), and antibiotic residues (ARs) in biofilms from drinking water distribution systems in poultry, swine, and cattle farms. Methods: The review was conducted according to the PRISMA-ScR extension. Four databases (PubMed, Scopus, Agricola, and Web of Science) were searched. Studies were screened in Rayyan. Results: The search yielded 1242 studies. After screening 732 studies, only 4 met the inclusion criteria. These studies focused on poultry (n = 3) and dairy cattle (n = 1), not on swine. Isolation relied on plating methods. Two studies complemented culturing with 16S rRNA sequencing. No studies applied culture-independent techniques. The number of biofilm-derived isolates across studies ranges from 6 to 102. Three studies performed antimicrobial susceptibility testing, using a wide range of antibiotics (16 to 31). One study analyzed ARGs; none quantified ARs. Conclusions: The limited number of studies and lack of standardized methods hinder the generalizability of the findings, underscoring the need for research to clarify biofilms’ role in AMR dissemination in livestock farms. Full article

Scrapie (classical and atypical) susceptibility in sheep is strongly influenced by PRNP gene polymorphisms. In Portugal, limited data exist for native breeds such as Serra da Estrela, despite their relevance to animal conservation and food production. The full coding region of PRNP gene of 92 Serra da Estrela sheep was sequenced and SNP frequencies were analysed. The predicted functional impact of nonsynonymous SNPs was assessed using PolyPhen-2 and AMYCO. A total of 27 SNPs were identified, including 20 nonsynonymous variants. Thirteen major haplotypes were observed. The ARR allele, which provides resistance to classical scrapie, was present in 58.7% of the population, with 18.5% of animals being homozygous. Several previously unreported SNPs were identified, and their impact on prion protein aggregation propensity and structure was explored. The high frequency of the ARR allele without full ARR fixation suggests that no selective breeding for scrapie resistance has been applied. These results support the adoption of gradual selection strategies that preserve genetic variability and promote farmer compliance, while increasing classical and atypical scrapie resistance. Full article