Search Results (133)

To address the challenge of bearing load monitoring in elastically supported marine shafting systems, a multi-data fusion-based prediction model is constructed. In view of the small-sample nature of measured bearing load data, transfer learning is adopted to migrate the physical relationships embedded in finite element simulations to the measurement domain. A limited number of actual samples are used to correct the simulation data, forming a high-fidelity hybrid training set. The system—supported by air-spring isolators mounted on the raft—is divided into multiple sub-regions according to their spatial layout, establishing local mappings from air-spring pressure variations to bearing load increments to reduce model complexity. On this basis, a Stacking ensemble learning framework is further incorporated into the prediction model to integrate multi-source information such as air-spring pressure and raft strain, thereby enriching the model’s information acquisition and improving prediction accuracy. Experimental results show that the proposed transfer learning-based multi-sub-region bearing load prediction model performs significantly better than the full-parameter input model. Furthermore, the strain-enhanced Stacking-based multi-data fusion bearing load prediction model improves the characterization of shafting features and reduces the maximum prediction error. The proposed multi-data fusion modeling strategy offers a viable approach for condition monitoring and intelligent maintenance of marine shafting systems. Full article

Floods rank among the most destructive natural hazards worldwide. In Canada’s capital region—Ottawa and its surrounding areas—flood prediction is crucial, especially in flood-prone zones, to improve flood mitigation strategies, given its historical record-breaking events in 2017 and 2019, which resulted in substantial damage to homes and infrastructure in the region. Previous studies in these regions typically did not use remote sensing techniques or advanced methods to enhance flood susceptibility prediction and extent mapping. This study addressed the gap by incorporating 18 flood conditioning factors and integrating high-performance machine learning algorithms such as Random Forest, Support Vector Machines and XGBoost to develop ensemble flood susceptibility models. The HEC-RAS 2D model was used to simulate hydrodynamic variables based on a 100-year flood scenario. The developed ensemble model for flood susceptibility prediction achieved strong performance (Kappa, F1-score, and AUC all above 0.979) and demonstrated model transferability, maintaining high accuracy (Kappa > 0.850, F1-score > 0.920, AUC > 0.990) when applied to other sub-regions. The hydraulic model reveals that flood velocity and depth differ across sub-regions, reaching maximums of 15 m/s and 15 m, respectively. SHAP analysis indicates Elevation, Handmodel, MNDWI, NDWI, and Aspect are key factors influencing floods. These findings and methods help Natural Resources Canada develop tools and policies for effective flood risk reduction in the Ottawa River watershed and similar regions. Full article

Nature reserves serve as core spatial units for maintaining regional ecological security and biodiversity. Owing to their high ecosystem integrity, extensive vegetation cover, and low levels of disturbance, they play a crucial role in sustaining ecological processes and ensuring functional stability. Taking the Giant Panda National Park (GPNP), which spans the provinces of Gansu, Sichuan, and Shaanxi in China, as the study region, the vegetation net primary productivity (NPP) during 2001–2023 was simulated using the Carnegie–Ames–Stanford Approach (CASA) model. Spatial and temporal variations in NPP were examined using Moran’s I, Getis-Ord Gi* hotspot analysis, Theil–Sen trend estimation, and the Mann–Kendall test. In addition, the Optimal Parameters-based Geographical Detector (OPGD) model was applied to quantitatively assess the relative contributions of natural and anthropogenic factors to NPP dynamics. The results demonstrated that: (1) The mean annual NPP within the GPNP reached 646.90 gC·m⁻²·yr⁻¹, exhibiting a fluctuating yet generally upward trajectory, with an average growth rate of approximately 0.65 gC·m⁻²·yr⁻¹, reflecting the positive ecological outcomes of national park establishment and ecological restoration projects. (2) NPP exhibits significant spatial heterogeneity, with higher NPP values in the northern, while the central and western regions and some high-altitude areas remain at relatively low levels. Across the four major subregions of the GPNP, the Qinling has the highest mean annual NPP at 758.89 gC·m⁻²·yr⁻¹, whereas the Qionglai–Daxiaoxiangling subregion shows the lowest value at 616.27 gC·m⁻²·yr⁻¹. (3) Optimal NPP occurred under favorable temperature and precipitation conditions combined with relatively high solar radiation. Low elevations, gentle slopes, south facing aspects, and leached soils facilitated productivity accumulation, whereas areas with high elevation and steep slopes exhibited markedly lower productivity. Moderate human disturbance contributed to sustaining and enhancing NPP. (4) Factor detection results indicated that elevation, mean annual temperature, and land use were the dominant drivers of spatial heterogeneity when considering all natural and anthropogenic variables. Their interactions further enhanced explanatory power, particularly the interaction between elevation and climatic factors. Overall, these findings reveal the complex spatiotemporal characteristics and multi-factorial controls of vegetation productivity in the GPNP and provide scientific guidance for strengthening habitat conservation, improving ecological restoration planning, and supporting adaptive vegetation management within the national park systems. Full article

Proactive management of groundwater resources is hindered by the static nature of conventional vulnerability assessments, which provide only a single temporal snapshot and lack predictive capability. To address this limitation, we developed a coupled dynamic–spatial modeling framework to forecast the spatio-temporal evolution of groundwater vulnerability. The framework integrates a βSARMA time-series model for precipitation forecasting with an enhanced M-DRASTIC-LAaRd model, which incorporates Land use, Anthropogenic activity, and River network density, weighted via the Analytical Hierarchy Process (AHP) to better capture hydrogeological complexity. The βSARMA model consistently outperformed conventional SARIMA models across the five subregions of Beijing, achieving the lowest RMSE values (0.0832–0.1617) and MAE values (0.0922–0.1372), with an average RMSE reduction of 15.3% relative to the best SARIMA baseline. These results ensure highly reliable dynamic precipitation inputs for the time-varying Net Recharge (R) parameter. Model validation against historical observations yielded a coefficient of determination (R²) of 0.87, confirming the framework’s robustness and predictive accuracy. Applied to the Beijing metropolitan area (1980–2027), the model projects a marked spatial restructuring of groundwater vulnerability: high-vulnerability zones are expected to expand from 38.65% to 46.18%, while low-vulnerability areas will decline from 42.53% to 34.63%. Emerging “hotspots” are concentrated in the southern urban plains, where urbanization and reduced recharge converge. Overall, 27.9% of the region is predicted to experience intensified vulnerability, whereas only 11.5% will show improvement. This study advances groundwater vulnerability assessment from static mapping toward dynamic forecasting, providing a quantitatively validated and spatially explicit framework that supports more informed groundwater management under future environmental change. Full article

Agricultural soils near industrial parks in the Yellow River bend region face severe heavy metal pollution, posing a significant to human health. This study integrated field sampling with laboratory analysis and applied geostatistical analysis, positive matrix factorization (PMF) modeling, and health risk assessment models to systematically investigate the pollution levels, spatial distribution, sources, and ecological health risks of heavy metals in the area. The main findings are as follows: (1) The average concentrations of the eight heavy metals (Hg, Cr, Cu, Pb, Zn, As, Cd, and Ni) in the study area were 0.04, 48.3, 54.3, 45.7, 70.0, 22.9, 0.4, and 35.7 mg·kg⁻¹, respectively. The concentrations exceeded local background values by factors ranging from 1.32 to 11.2. Exceedances of soil screening and control values were particularly pronounced for Cd and As. Based on the geoaccumulation index, over 75% of the sampling sites for Cr, Pb, Zn, and Cd were classified as moderately to heavily polluted. Potential ecological risk assessment highlighted Cd as the significant ecological risk factor, indicating considerable heavy metal pollution in the region. (2) Kriging interpolation demonstrated elevated concentrations in the western (mid-upper) and eastern (mid-lower) subregions. Pearson correlation analysis suggested common sources for Cu-Pb-As-Cd and Cr-Zn-Ni. (3) PMF source apportionment identified four primary sources: traffic emissions (38.19%), natural and agricultural mixed sources (34.55%), metal smelting (17.61%), and atmospheric deposition (10.10%). (4) Health risk assessment indicated that the non-carcinogenic risk for both adults and children was within acceptable limits (adults: 0.065; children: 0.12). Carcinogenic risks were also acceptable (adults: 5.67 × 10⁻⁵; children: 6.70 × 10⁻⁵). In conclusion, priority should be given to the control of traffic emissions and agriculturally derived sources in the management of soil heavy metal contamination in this region, while the considerable contribution of smelting activities warrants heightened attention. This study provides a scientific basis for the prevention, control, and targeted remediation of regional soil heavy metal pollution. Full article

Background: Gliomas are among the most complex and lethal primary brain tumors, necessitating precise evaluation of both anatomical subregions and molecular alterations for effective clinical management. Methods: To find a solution to the disconnected nature of current bioimage analysis pipelines, where anatomical segmentation based on MRI and molecular biomarker prediction are done as separate tasks, we use here Molecular-Genomic and Multi-Task (MGMT-Net), a one deep learning scheme that carries out the task of the multi-modal MRI data without any conversion. MGMT-Net incorporates a novel Cross-Modality Attention Fusion (CMAF) module that dynamically integrates diverse imaging sequences and pairs them with a hybrid Transformer–Convolutional Neural Network (CNN) encoder to capture both global context and local anatomical detail. This architecture supports dual-task decoders, enabling concurrent voxel-wise tumor delineation and subject-level classification of key genomic markers, including the IDH gene mutation, the 1p/19q co-deletion, and the TERT gene promoter mutation. Results: Extensive validation on the Brain Tumor Segmentation (BraTS 2024) dataset and the combined Cancer Genome Atlas/Erasmus Glioma Database (TCGA/EGD) datasets demonstrated high segmentation accuracy and robust biomarker classification performance, with strong generalizability across external institutional cohorts. Ablation studies further confirmed the importance of each architectural component in achieving overall robustness. Conclusions: MGMT-Net presents a scalable and clinically relevant solution that bridges radiological imaging and genomic insights, potentially reducing diagnostic latency and enhancing precision in neuro-oncology decision-making. By integrating spatial and genetic analysis within a single model, this work represents a significant step toward comprehensive, AI-driven glioma assessment. Full article

High-resolution (HR) remote sensing images contain rich, sensitive information regarding the distribution of geospatial objects and natural resources. With the widespread application of HR remote sensing images, there is an urgent need to protect the data security of HR remote sensing images during transmission and sharing. Existing encryption approaches typically employ a global encryption strategy, overlooking the varying texture complexity across different sub-regions in HR remote sensing images. This oversight results in low efficiency and flexibility for encrypting large-scale remote sensing image data. To address these limitations, this paper presents a texture-adaptive hierarchical encryption method that combines region-specific security levels. The method first decomposes remote sensing images into grid-based sub-blocks and classifies them into three texture complexity types (i.e., simple, medium, and complex) through gradient and frequency metrics. Then, chaotic systems of different dimensions are adaptively adopted to encrypt the sub-blocks according to their texture complexity. A more complex chaotic system encrypts a sub-block with a more complex texture to ensure security while reducing computational complexity. The experimental results on publicly available high-resolution remote sensing datasets demonstrate that the proposed method achieves adequate information concealment while maintaining an optimal balance between encryption security and computational efficiency. The proposed method is more competitive in encrypting large-scale HR remote sensing data compared to conventional approaches, and it shows significant potential for the secure sharing and processing of HR remote sensing images in the big data era. Full article

The long-horned caddisfly Brachysetodes Schmid, 1955 (Leptoceridae: Leptocerinae) is a small genus endemic to the Andean region, especially Chile. Four decades after the last taxonomic revision, a new species, Brachysetodes tricahue Desidério, Santana & Hamada sp. nov., is described and illustrated based on adult males collected from Parque Natural Tricahue in the central subregion of the Chilean Andes. Specimens were examined through genital dissection using heated KOH, photographed with a digital camera mounted on microscopes, and described using the DELTA system. A distribution map was produced in QGIS based on GBIF data and literature records, and an updated identification key for males of the ten known species of Brachysetodes sensu stricto is presented. B. tricahue sp. nov. closely resembles B. bifurcatus and B. nublensis, sharing key features such as paired lateral processes on tergum X and tripartite inferior appendages. However, it can be distinguished by its unique combination of genital features, including unequal lengths of the three processes of the inferior appendage. This discovery emphasizes the underexplored diversity of the Southern Andes and contributes to refining the taxonomy and biogeography of the genus. It also provides a framework for future phylogenetic studies incorporating immature stages and molecular data. Full article

Climate change is jeopardizing global food security, with at least 713 million people facing hunger. To face this challenge, legumes as common beans could offer a nature-based solution, sourcing nutrients and dietary fiber, especially for rural communities in Latin America and Africa. However, since common beans are generally heat and drought susceptible, it is imperative to speed up their molecular introgressive adaptive breeding so that they can be cultivated in regions affected by extreme weather. Therefore, this study aimed to couple an advanced panel of common bean (Phaseolus vulgaris L.) × tolerant Tepary bean (P. acutifolius A. Gray) interspecific lines with Bayesian regression algorithms to forecast adaptation to the humid and dry sub-regions at the Caribbean coast of Colombia, where the common bean typically exhibits maladaptation to extreme heat waves. A total of 87 advanced lines with hybrid ancestries were successfully bred, surpassing the interspecific incompatibilities. This hybrid panel was genotyped by sequencing (GBS), leading to the discovery of 15,645 single-nucleotide polymorphism (SNP) markers. Three yield components (yield per plant, and number of seeds and pods) and two biomass variables (vegetative and seed biomass) were recorded for each genotype and inputted in several Bayesian regression models to identify the top genotypes with the best genetic breeding values across three localities on the Colombian coast. We comparatively analyzed several regression approaches, and the model with the best performance for all traits and localities was BayesC. Also, we compared the utilization of all markers and only those determined as associated by a priori genome-wide association studies (GWAS) models. Better prediction ability with the complete SNP set was indicative of missing heritability as part of GWAS reconstructions. Furthermore, optimal SNP sets per trait and locality were determined as per the top 500 most explicative markers according to their β regression effects. These 500 SNPs, on average, overlapped in 5.24% across localities, which reinforced the locality-dependent nature of polygenic adaptation. Finally, we retrieved the genomic estimated breeding values (GEBVs) and selected the top 10 genotypes for each trait and locality as part of a recommendation scheme targeting narrow adaption in the Caribbean. After validation in field conditions and for screening stability, candidate genotypes and SNPs may be used in further introgressive breeding cycles for adaptation. Full article

Video temporal grounding (VTG) aims to localize a semantically relevant temporal segment within an untrimmed video based on a natural language query. The task continues to face challenges arising from cross-modal semantic misalignment, which is largely attributed to redundant visual content in sensor-acquired video streams, linguistic ambiguity, and discrepancies in modality-specific representations. Most existing approaches rely on intra-modal feature modeling, processing video and text independently throughout the representation learning stage. However, this isolation undermines semantic alignment by neglecting the potential of cross-modal interactions. In practice, a natural language query typically corresponds to spatiotemporal content in video signals collected through camera-based sensing systems, encompassing a particular sequence of frames and its associated salient subregions. We propose a text-guided visual representation optimization framework tailored to enhance semantic interpretation over video signals captured by visual sensors. This framework leverages textual information to focus on spatiotemporal video content, thereby narrowing the cross-modal gap. Built upon the unified cross-modal embedding space provided by CLIP, our model leverages video data from sensing devices to structure representations and introduces two dedicated modules to semantically refine visual representations across spatial and temporal dimensions. First, we design a Spatial Visual Representation Optimization (SVRO) module to learn spatial information within intra-frames. It selects salient patches related to the text, capturing more fine-grained visual details. Second, we introduce a Temporal Visual Representation Optimization (TVRO) module to learn temporal relations from inter-frames. Temporal triplet loss is employed in TVRO to enhance attention on text-relevant frames and capture clip semantics. Additionally, a self-supervised contrastive loss is introduced at the clip–text level to improve inter-clip discrimination by maximizing semantic variance during training. Experiments on Charades-STA, ActivityNet Captions, and TACoS, widely used benchmark datasets, demonstrate that our method outperforms state-of-the-art methods across multiple metrics. Full article

The accurate mapping of crop planting patterns is vital for sustainable agriculture and food security, particularly in regions with complex cropping systems and limited cloud-free observations. This research focuses on the Jianghan Plain in southern China, where diverse planting structures and persistent cloud cover make consistent monitoring challenging. We integrated multi-temporal Sentinel-2 and Landsat-8 imagery from 2017 to 2021 on the Google Earth Engine platform and applied a sample migration strategy to construct multi-year training data. A random forest classifier was used to identify nine major planting patterns at a 10 m resolution. The classification achieved an average overall accuracy of 88.3%, with annual Kappa coefficients ranging from 0.81 to 0.88. A spatial analysis revealed that single rice was the dominant pattern, covering more than 60% of the area. Temporal variations in cropping patterns were categorized into four frequency levels (0, 1, 2, and 3 changes), with more dynamic transitions concentrated in the central-western and northern subregions. A multiscale geographically weighted regression (MGWR) model revealed that economic and production-related factors had strong positive associations with crop planting patterns, while natural factors showed relatively weaker explanatory power. This research presents a scalable method for mapping fine-resolution crop patterns in complex agroecosystems, providing quantitative support for regional land-use optimization and the development of agricultural policies. Full article

Vulvar hygiene is an important part of general hygiene: the goals are to clear the vulvar area of microbial and cellular debris and vaginal and fecal secretions, ensure local comfort, provide natural levels of hydration, and protect the vulvar microbiota. There are few national and international guidelines on vulvar hygiene. We searched the PubMed database up until 30 November 2024, using logical combinations of the following terms: hygiene, washing, vulva, vulvar, microbiota, hydration, syndet, soap, detergent, water, and customs. The abstracts were reviewed, and potentially relevant full-text articles were retrieved and examined. The subregions of the vulva vary with regard to the presence of sweat and sebaceous glands, the keratin content, the water content, the pH, and the microbiota (notably Lactobacillus, Corynebacterium, Staphylococcus, and Prevotella). An alteration in the vulvar microbiota can cause an imbalance in the vaginal microbiota, and vice versa. Vaginal douching may have negative effects on vulvar microbiota. Hair removal might increase the risk of long-term dermatological complications. Repeated washing with water alone exposes the stratum corneum to damage, and washing with soap alters the stratum corneum proteins and lipids, increases skin water loss, and accentuates the risk of irritation. Syndet-based products have a mild detergent effect, promotion of hydration, a suitable pH for the vulvar area, and protection of the vulvar microbiota. Syndet-based products (containing a blend of surfactants, emollients, antioxidants, and buffering agents) appear to be the most appropriate for vulvar care. Full article

Between 2017 and 2023, three sub-regional spatial plans were developed for specific areas of the Valencian Autonomous Region: the Alicante–Elche Metropolitan Area, Vega Baja del Segura County, and the Central Valencian Counties. Their main aim was to develop an envisaged territorial model as a binding framework for municipal urban master plans. During the elaboration of these plans, a set of activities was developed to understand the main consensus among stakeholders. The main axes of the planning process were addressed during territorial working groups conducted with relevant stakeholders, including those focused on green infrastructure, settlement systems, and infrastructure systems. Participants were selected from the public administration, non-governmental organizations, the economic sector, and the university. Drawing on the outcomes of the participatory activities, various factors are analyzed, including the ratio between invited stakeholders and actual participants in the territorial workshops, the contributions made by participants in each main axis of the plan, the inputs provided according to stakeholder type, the nature of these contributions, and the degree of alignment between the inputs and the objectives of the PAT. The present study reveals how contextual factors can influence the orientation of the participatory process. At times, contingency may emerge as an opportunity to energize a governance process. Similarly, the participatory technique is validated for its potential to enrich the process, while also highlighting the absence of voices not aligned with spatial planning in the participatory settings. Full article

Due to ongoing climate change, urban areas face increasing challenges associated with rising temperatures and growing energy demand. Green roofs have emerged as a sustainable, nature-based solution to enhance urban resilience. This study presents a systematic review of the thermal and energy performance of green roofs in the Mediterranean region, and was conducted following the PRISMA framework. By identifying targeted research questions formulated using the PICO(C) structure, this review systematically evaluates the potential of green roofs to promote sustainable urban environments in Mediterranean regions. The findings highlight their effectiveness in mitigating heat stress, enhancing building energy efficiency, and counteracting urban temperature fluctuations, reinforcing their role as a key climate adaptation strategy in densely populated areas. The review also identifies critical research gaps that must be addressed to facilitate the large-scale adoption of green roofs. Specifically, the lack of long-term performance monitoring, the need for standardized assessment protocols, and the necessity of optimizing green roof configurations for Mediterranean subregions emerge as key areas for future investigation. This study bridges a crucial gap in the literature by providing a systematic, PRISMA-compliant evaluation. It offers the scientific community a robust knowledge base to inform policy, urban planning, and future research directions. Full article

Floods are among the most destructive natural hazards globally, necessitating the identification of flood-prone areas for effective disaster risk management and sustainable urban development. Advanced data-driven techniques, including machine learning (ML), are increasingly used to map and mitigate flood risks. However, ML applications for flood risk assessment remain limited in Sorocaba, a sub-region of São Paulo, Brazil. This study employs four ML algorithms—differential evolution (DE), naïve Bayes (NB), random forest (RF), and support vector machines (SVMs)—to develop flood susceptibility models using 16 predictor variables. Key categorical factors influencing flood susceptibility included topographical, anthropogenic, and hydrometeorological, particularly elevation, slope, NDVI, NDWI, and distance to roads. Performance metrics (F1-score and AUC) showed strong results, ranging from 0.94 to 1.00, with the DE and RF models excelling in training, testing, and external datasets. The study highlights model transferability, demonstrating applicability to other regions. Findings reveal that 41% to 50% of Sorocaba is at high flood risk. The explainable artificial intelligence technique Shapley additive explanations (SHAP) further identified moisture and the stream power index (SPI) as significant factors influencing flood occurrence. The study underscores the ML-based model’s potential in highlighting flood-vulnerable areas and guiding flood mitigation strategies, land-use planning, and infrastructure resilience. Full article