Search Results (1,382)

Welded joints in API 5L X70 pipeline steel represent critical locations for pipelines intended for hydrogen service because welding can create microstructural inhomogeneity, stress concentrations, and uneven mechanical properties that can promote hydrogen-assisted degradation. In hydrogen-containing environments, these effects may manifest as reduced ductility, loss of fracture resistance, and increased cracking susceptibility, particularly in the weld metal and heat-affected zone. Therefore, welding procedures for X70 intended for hydrogen applications must be evaluated using systematic mechanical testing and microstructural characterization under defined hydrogen exposure conditions. The study investigates the detrimental effects of hydrogen on the mechanical integrity of pipeline materials, focusing on welded joints of the API 5L X70 steel, a candidate material for use in hydrogen-containing environments. The weldability and structural performance of the X70 pipeline steel joints in hydrogen environments, produced using automated multi-pass metal active gas (MAG) welding, was experimentally studied. Welding was performed on a DN800 pipe with precise control over welding parameters. Comprehensive analyses were conducted on the welded joints, including microstructure examinations, hardness measurements, slow strain rate testing in high-pressure gaseous H₂ with a N₂ baseline and fracture toughness testing. In high-pressure hydrogen SSRT showed a moderate reduction in ductility relative to nitrogen, with reduction of area decreasing from 81.2% (N₂) to 69.1 and 71.5% (H₂), while time-to-failure remained comparable (475 min in N₂ vs. 497 and 496 min in H₂) Ultimate tensile strength was not reduced (579 MPa in N₂ vs. 609 and 597 MPa in H₂). Secondary surface cracks were observed only on specimens tested in hydrogen. Fracture mechanics testing after hydrogen exposure yielded K_IH values of 58–59 MPa√m in the weld metal and 57–61 MPa√m in the HAZ, exceeding the 55 MPa√m acceptance threshold applied in this study. The results highlight the necessity of optimized welding techniques and targeted material analyses to ensure the safety and durability of pipelines in hydrogen-rich environments, thereby contributing to the development of reliable infrastructure for sustainable energy systems. Full article

Sleep abnormalities and circadian rhythm disruptions are frequently observed in psychiatric disorders such as depression and schizophrenia. However, most previous studies have examined circadian rhythms and sleep separately, limiting understanding of how these processes interact within individuals. This study examined circadian and sleep characteristics in depression and schizophrenia compared with healthy controls using multi-day wrist actigraphy. Circadian rhythms were assessed using parametric and non-parametric measures of rest–activity patterns, and sleep metrics were derived using a validated actigraphy-based algorithm. Distinct patterns were observed across diagnostic groups. Schizophrenia showed widespread disruption in daily activity patterns, with altered timing and reduced rhythm strength. Sleep was longer but highly fragmented, with frequent awakenings despite increased time in bed. In contrast, depression showed more limited changes, mainly in activity timing and overall activity levels, while sleep and daily patterns remained closer to controls. A key finding was the identification of distinct circadian–sleep profiles for each condition, with global disruption in schizophrenia and more selective alterations in depression. These findings show that combining circadian and sleep measures provides a clearer understanding of psychiatric disorders and may support monitoring and targeted interventions based on daily behavioral rhythms. Full article

Background: Radiotherapy (RT) is a core modality for cancer treatment, yet it is plagued by inter-observer variability in target delineation, inefficient manual workflows, and challenges in fusing multi-type clinical data. Large language models (LLMs), with their superior semantic understanding and cross-modal fusion capabilities present novel solutions to these challenges. Scope: This narrative review provided a comprehensive overview of the current landscape and emerging trends of LLM applications across the entire RT workflow. Findings: LLMs demonstrated substantial clinical utility in key RT domains, including automated target volume delineation (e.g., Medformer, Radformer), dose prediction (e.g., DoseGNN), treatment planning automation (e.g., GPT-Plan), patient education, clinical decision support, medical information extraction, and prognosis assessment. These applications not only have the potential to enhance the accuracy and efficiency of RT but also facilitate the standardization of clinical pathways. However, widespread clinical adoption was impeded by critical limitations, including model hallucinations, insufficient generalizability, and unresolved issues regarding data privacy and ethical governance. Conclusions: LLMs possessed transformative potential to revolutionize radiation oncology. Future endeavors should prioritize technical refinements to mitigate model deficiencies, establish standardized evaluation benchmarks, and develop robust ethical frameworks. These concerted efforts are crucial for translating LLM research into clinical practice and advancing the era of intelligent, precision RT. Full article

Sperm cryopreservation is pivotal for conserving fish germplasm, yet cryodamage-induced quality decline limits its application. This study focused on Sichuan bream (Sinibrama taeniatus), an endemic and economically important fish species in the upper Yangtze River. Based on an established cryopreservation protocol, we evaluated sperm quality using computer-assisted sperm analysis (CASA) and fertility assays, followed by a systematic assessment of structural and functional damage via flow cytometry (membrane integrity, mitochondrial potential, reactive oxygen species, and DNA fragmentation), enzymatic assays (energy metabolism and antioxidant enzymes), Western blotting, and ultrastructural observation. Finally, integrated proteomic and metabolomic analyses were employed to elucidate the underlying physiological mechanisms. The results demonstrated that freeze–thawing significantly impaired sperm motility, fertility, and ultrastructure, concurrently disrupting energy metabolism and the antioxidant system. Crucially, multi-omics revealed that these functional declines were linked to dysregulation in key pathways involving cytoskeleton organization, lipid metabolism, energy homeostasis, and oxidative stress, forming a coherent network from initial molecular perturbation to phenotypic dysfunction. This study provides a comprehensive characterization of sperm cryodamage in Sichuan bream, advancing the understanding of fish sperm cryobiology and informing targeted cryoprotection strategy development. Full article

Background: Micronutrient deficiencies (MD) are highly prevalent among candidates for bariatric surgery (BS) and are associated with adverse perioperative and postoperative outcomes. Although guidelines recommend systematic preoperative screening and correction, conventional targeted supplementation (CTS) often requires multiple products, potentially limiting adherence and delaying surgical readiness. Bariatric-specific multivitamins (BSM) may simplify nutritional management, but their real-world effectiveness for preoperative correction of multiple MD remains insufficiently investigated. Objective: To compare the effectiveness, efficiency, and adherence of a BSM versus CTS for preoperative correction of multiple MD in BS candidates. Methods: This retrospective multicenter cohort study included 1560 adults with obesity evaluated for BS between 2020 and 2024 across three Italian bariatric centers. The primary efficacy analysis was restricted to patients presenting with ≥3 laboratory-confirmed MD at baseline. Patients treated between 2020 and 2022 received individualized CTS using multiple products, whereas those treated between 2023 and 2024 received a single BSM. Biochemical follow-up was scheduled at 4 and 8 weeks. The primary outcome was the achievement of complete biochemical correction of all baseline deficiencies at the predefined 4-week follow-up assessment (composite endpoint). Secondary outcomes included supplementation burden and self-reported adherence. Early correction rates were compared using absolute risk differences and risk ratios; adjusted associations were evaluated using multivariable regression models including center and baseline deficiency burden. As a supplementary analysis, the patient-level proportion of baseline deficiencies corrected at 4 weeks was also evaluated. Results: Among patients with ≥3 baseline deficiencies (n = 216), complete biochemical correction at 4 weeks was achieved in 55/134 patients (41.0%) in the BSM group and in 13/82 patients (15.9%) in the CTS group, corresponding to an absolute risk difference of 25.2 percentage points (95% CI 7.8–40.0) and a risk ratio of 2.59 (95% CI 1.51–4.44). In adjusted analyses accounting for center and baseline deficiency pattern, BSM use remained independently associated with early complete correction (adjusted absolute risk difference 26.3 percentage points; adjusted risk ratio 2.69). Sensitivity analyses restricting follow-up timing and excluding early calendar periods yielded consistent results. The mean proportion of baseline deficiencies corrected per patient at 4 weeks was higher in the BSM group compared with CTS (0.74 ± 0.25 vs. 0.54 ± 0.30). Compared with CTS, BSM was associated with lower supplementation burden (1 vs. 3.5 supplements on average) and higher adherence (92% vs. 70%). Conclusions: In a real-world multicenter cohort of BS candidates with ≥3 baseline MD, a simplified preoperative supplementation strategy based on a BSM was associated with a significantly higher probability of complete biochemical correction at 4 weeks, lower supplementation burden, and higher reported adherence compared with CTS. Although complete correction was not universal at 4 weeks, BSM significantly increased the likelihood of achieving early multi-deficiency normalization. Given the non-concurrent observational design, these findings should be interpreted as hypothesis-generating and warrant confirmation in prospective studies with concurrent cohorts. Full article

The dynamic nature of acoustic environments—particularly the fluctuation of underwater channels and time-varying target observation angles—poses significant challenges for active sonar target recognition, a problem further aggravated by the scarcity of labeled training samples. To address these limitations, this paper proposes a novel recognition method enabling deep fusion of multi-domain temporal features extracted from target echoes. First, complementary features are extracted across spatial, time–frequency, and Doppler domains to achieve a comprehensive and discriminative representation of targets. Subsequently, we introduce a feature vector-level fusion mechanism designed specifically for few-shot learning, integrating a meta-knowledge-driven multi-stream feature extractor with an internal memory module within the feature tensor framework. This architecture constitutes the Multi-domain Temporal Feature Fusion Recognition Network (MTFF-RNet). The proposed approach is evaluated on a hybrid dataset combining simulated and experimental data, achieving a high recognition accuracy of 96.2% for both targets and interferents. Experimental results demonstrate that MTFF-RNet significantly enhances robustness and adaptability under varying underwater acoustic conditions and dynamic viewing geometries. Full article

Cardiovascular-kidney-metabolic (CKM) syndrome integrates excess adiposity, metabolic dysfunction, kidney impairment, subclinical cardiovascular diseases, and clinical events along a staged continuum that invites unified prevention and treatment. Ultra-processed foods (UPFs) are a complex, high-prevalence exposure that may influence risk across CKM stages through nutrient profiles, additives, processing-induced compounds, and packaging-related contaminants. This review synthesizes epidemiologic, mechanistic, and translational evidence with attention to exposure definition and analytic rigor. We summarize NOVA-based UPF operationalization across dietary assessment tools, highlighting misclassification of mixed dishes, brand heterogeneity, and energy under-reporting, and we propose further examination of energy-adjusted models, calibration, and harmonized metrics. Observational studies consistently associate higher UPF intake with adiposity, diabetes, chronic kidney disease, cardiovascular events, and mortality, with modest to moderate effect sizes that are heterogeneous across populations. Mechanistic data from metabolomics, lipidomics, proteomics, and the gut microbiome converge on pathways of inflammation, lipid metabolism, oxidative and metabolic stress, and intestinal barrier dysfunction; in selected cohorts, multi-omics modules account for a substantial minority of UPF-outcome associations. We outline quality-control pipelines, batch-effect prevention/correction, and multiple-testing control necessary for reproducible diet-omics. Translationally, targeted lipidomic and proteomic panels show promise for CKM risk stratification and monitoring but require validation, clinical thresholds, and guideline endorsement. Equity and global context, including differences in product mix, food systems, and care capacity, modify population impact. We conclude with a research agenda prioritizing harmonized exposure metrics, error-aware modeling, standardized multi-omics workflows, and adequately powered, stage-specific interventions capable of testing mediation and prognostic utility. Full article

Ginkgetin (GK) is a naturally occurring biflavonoid predominantly isolated from Ginkgo biloba and has attracted increasing attention because of its broad pharmacological activities. Structurally, GK belongs to the 3′-8″-linked biflavone subclass, which distinguishes it from other biflavonoids like amentoflavone (the parent compound of this subclass) and its monomeric counterparts such as apigenin. This unique C-C linked dimeric architecture confers distinct molecular planarity and lipophilicity, contributing to its enhanced membrane permeability and multitarget engagement capabilities. GK has been shown to exert pleiotropic biological effects in preclinical studies, including anti-inflammatory, antioxidant, antifibrotic, anticancer, neuroprotective, cardioprotective, metabolic regulatory and antibacterial activities. Mechanistically, preclinical evidence indicates that GK functions as a multitarget modulator of key signaling pathways involved in oxidative stress, inflammation, cell death and tissue remodeling, such as nuclear factor erythroid 2–related factor 2/heme oxygenase-1 (Nrf2/HO-1), nuclear factor kappa-B(NF-κB), Janus kinase/signal transducer and activator of transcription(JAK/STAT), mitogen-activated protein kinases(MAPKs), AMP-activated protein kinase/mechanistic target of rapamycin(AMPK/mTOR), phosphoinositide 3-kinase/protein kinase B(PI3K/Akt) and cyclic GMP-AMP synthase–stimulator of interferon genes(cGAS–STING). Notably, GK has been observed to display context-dependent regulation of cell fate decisions, including apoptosis, autophagy and ferroptosis, thereby enabling the selective elimination of pathological cells while preserving normal tissue function. Preclinical studies further demonstrate that GK exhibits therapeutic potential across diverse disease systems, including cancer, metabolic disorders, cardiovascular diseases, neurological disorders and musculoskeletal diseases. In addition, emerging evidence highlights its antibacterial and antivirulence properties through the inhibition of biofilm formation and quorum sensing. It is crucial to note, however, that this promising profile is predominantly derived from preclinical studies, and clinical evidence in humans remains to be established. Despite these promising findings, the clinical translation of GK remains limited by challenges related to pharmacokinetics, bioavailability and druggability. This review systematically summarizes the chemical characteristics, pharmacological activities and molecular mechanisms of GK, with an emphasis on its multitarget actions and therapeutic potential across disease systems, and discusses current limitations and future perspectives to facilitate the rational development of GK-based interventions. Full article

Multimodal fusion of synthetic aperture radar (SAR) and optical imagery is widely used in Earth observation for applications such as land-cover mapping and surface-water mapping (including post-event flood mapping under near-synchronous acquisitions) and land-use inventory. Optical images provide rich spectral and texture cues, whereas SAR offers all-weather structural information that is complementary but heterogeneous. In practice, this heterogeneity often introduces fusion conflicts in multi-class segmentation, causing critical categories such as water bodies to be under-optimized. To address this issue, this paper presents a SAR-guided class-aware knowledge distillation (SGCAD) method for multimodal semantic segmentation. First, a SAR-only HRNet is trained as a water-expert teacher to learn discriminative backscattering and boundary priors for water extraction. Second, a lightweight multimodal student model (LightMCANet) is optimized using a class-aware distillation strategy that transfers teacher knowledge only within high-confidence water regions, thereby suppressing noisy supervision and reducing interference to other classes. Third, a SAR edge guidance module (SEGM) is introduced in the decoder to enhance boundary continuity for slender structures such as water bodies and roads. Overall, SGCAD improves targeted category learning while maintaining stable performance across the remaining classes. Experiments on a self-built dataset from GF-1 optical and LuTan-1 SAR imagery demonstrate higher overall accuracy and more coherent water/road predictions than representative baselines. Future work will extend the proposed distillation scheme to additional categories and broader geographic scenes. Full article

The proliferation of Internet of Things (IoT) devices has intensified the need for efficient real-time anomaly and intrusion detection, making the selection of an appropriate Complex Event Processing (CEP) engine a critical architectural decision for security-aware data pipelines. Python-based CEP frameworks offer compelling advantages through the seamless integration with data science and machine learning ecosystems; however, rigorous comparative evaluations of such frameworks under realistic IoT security workloads remain absent from the literature. This study presents the first systematic comparative evaluation of Faust and Streamz—two Python-native CEP engines representing fundamentally different architectural philosophies—specifically in the context of IoT network intrusion detection. Faust was selected for its actor-based stateful processing model with native Kafka integration and distributed table support, while Streamz was selected for its reactive, lightweight pipeline design targeting high-throughput stateless processing, making them representative of the two dominant paradigms in Python stream processing. Although both engines target different application niches, their performance characteristics under realistic CEP workloads have never been rigorously compared, leaving practitioners without empirical guidance. The primary evaluation employs an IoT network intrusion dataset comprising 583,485 events from 83 heterogeneous devices. To assess whether the observed performance characteristics are specific to this single dataset or generalize across different workload profiles, a secondary IoT-adjacent benchmark is included: the PaySim financial transaction dataset (6.4 million records), selected because its event schema, fraud-pattern temporal structure, and volume differ substantially from the intrusion dataset, providing a stress test for cross-workload robustness rather than a claim of domain equivalence. We acknowledge the reviewer’s valid point that a second IoT-specific intrusion dataset (such as TON_IoT or Bot-IoT) would constitute a more directly comparable validation; this is identified as a priority for future work. The load levels used in scalability experiments (up to 5000 events per second) intentionally exceed the dataset’s natural rate to stress-test each engine’s architectural ceiling and identify saturation thresholds relevant to large-scale or multi-sensor IoT deployments. We conducted controlled experiments with comprehensive statistical analysis. Our results demonstrate that Streamz achieves superior throughput at 4450 events per second with 89% efficiency and minimal resource consumption (40 MB memory, 12 ms median latency), while Faust provides robust intrusion pattern detection with 93–98% accuracy and stable, predictable resource utilization (1.4% CPU standard deviation). A multi-framework comparison including Apache Kafka Streams and offline scikit-learn baselines confirms that Faust achieves detection quality competitive with JVM-based alternatives (Faust: 96.2%; Kafka Streams: 96.8%; absolute difference of 0.6 percentage points, not statistically significant at $p=0.318$) while retaining the Python ecosystem advantages. Statistical analysis confirms significant performance differences across all metrics ($p<0.001$, Cohen’s $d>0.8$). Critical scalability thresholds are identified: Streamz maintains efficiency above 95% up to 3500 events per second, while Faust degrades beyond 2500 events per second. These findings provide IoT security engineers and system architects with actionable, empirically grounded guidance for CEP engine selection, establish reproducible benchmarking methodology applicable to future Python-based stream processing evaluations, and advance theoretical understanding of the accuracy–throughput trade-off in stateful versus stateless Python CEP architectures. Full article

The unique ecological gradients of Xinjiang have fostered a rich reservoir of genetic resources in local sheep populations. However, the population genetic structure, adaptive mechanisms to extreme environments, and the genetic basis underlying key economic traits of these breeds remain poorly understood. To address this gap, we performed whole-genome resequencing of 140 individuals from seven indigenous sheep populations—Altay, Bayinbuluke, Kazakh, Kirgiz, Bashibai, Turpan Black, and Yemule White—identifying 18,700,507 high-quality SNPs. Genetic diversity analyses revealed that all populations exhibited comparable levels of genetic diversity, with modest variation across breeds, with Turpan Black sheep exhibiting the highest observed heterozygosity (Ho = 0.3110) and proportion of polymorphic sites, whereas Kirgiz sheep showed comparatively lower values. Population structure analyses consistently indicated that geographic isolation is the primary driver of genetic differentiation, with Kirgiz sheep from the Pamir Plateau in southern Xinjiang displaying the greatest genetic distance relative to northern Xinjiang populations. By integrating multiple selection signature detection methods—including F_ST, π ratio, and XP-CLR—we found that genes under selection in Kirgiz sheep were significantly enriched in biological pathways related to stem cell pluripotency regulation (e.g., BMPR1B), DNA repair (e.g., DDB2), and neural development, thereby elucidating their unique genetic adaptations to high-altitude environments. In contrast, Turpan Black sheep appear to cope with heat stress through mechanisms involving basal transcriptional regulation (e.g., GTF2I), maintenance of protein homeostasis (e.g., DNAJB14), and melanin biosynthesis (e.g., MC1R). Furthermore, comparative analysis of body size identified a suite of candidate genes associated with growth and development (e.g., CUX1, KIT), which are primarily involved in transcriptional regulation, protein kinase activity, and the ubiquitin-mediated proteolytic system, thereby revealing a multi-layered genetic regulatory network governing body conformation. Collectively, this study provides a comprehensive genomic framework for understanding the genetic structure, adaptive evolution, and molecular basis of economically important traits in indigenous sheep breeds from Xinjiang, offering valuable candidate targets for future functional validation and precision breeding programs. Full article

Classical Be stars are key laboratories for investigating how rapid rotation, pulsations, and mass loss couple to the formation and evolution of circumstellar decretion disks. However, few studies have combined Kepler/K2 photometry with multi-epoch H$\alpha$ monitoring. Here we present four previously unclassified Be-type variable stars observed by K2 (three in Campaign 11 and one in Campaign 15) and followed up with ground-based spectroscopy. We analyzed public PDC light curves and extracted variability frequencies using Lomb–Scargle periodograms and iterative prewhitening with a conservative detection threshold of S/N ≥ 5. Optical spectra obtained at the Observatório Pico dos Dias (Brazil) over a multi-year baseline (2017–2025) include repeated H$\alpha$ observations and blue-region spectra for photospheric characterization. All targets show detectable K2 variability on timescales from hours to days, with frequency spectra ranging from close multi-periodic components producing beating patterns to power dominated by low frequencies. Each star exhibits H$\alpha$ emission at multiple epochs, with long-term changes in line-profile morphology and equivalent width, indicating disk variability on year-long timescales. These results demonstrate that disk evolution can occur without conspicuous photometric outbursts over the time span of space-based observations, highlighting the diagnostic value of combining high-precision space photometry with long-term spectroscopy to characterize multiscale variability in Galactic Be stars. Full article

Cross-view image geolocalization aims to achieve accurate localization of geo-tagged images without geo-tagging by matching ground-view images with satellite images. However, there are huge imaging differences between ground and satellite viewpoints, and existing methods usually rely on a large number of accurately labeled cross-view image pairs. Therefore, to address issues such as significant perspective differences, high annotation costs, and low utilization of unpaired data, this paper proposes a cross-view generation model that integrates multi-scale contrastive learning and dynamic optimization, designs a multi-scale contrast loss function to strengthen the semantic consistency between the generated images and the target domain, adaptively balances the quality and quantity of pseudo-labels according to a dynamic threshold screening mechanism, and introduces a hard-sample triplet loss to enhance the model discriminative ability. Ablation experiments on the CVUSA and CVACT datasets show that the BEV-CycleGAN+CL (Bird’s-Eye View Cycle-Consistent Generative Adversarial Network with Contrastive Learning) model proposed in this paper significantly outperforms the comparative models in PSNR, SSIM, and RMSE metrics. Specifically, on the CVACT dataset, compared with the BEV-CycleGAN, BEV, and CycleGAN baselines, PSNR increased by 2.83%, 16.02%, and 42.30%, SSIM increased by 6.12%, 8.00%, and 18.48%, and RMSE decreased by 9.28%, 15.51%, and 25.35%, respectively. Similar advantages are observed on the CVUSA dataset. Compared with current state-of-the-art models, the dynamic threshold pseudo-label localization method in this paper demonstrates overall superiority in recall metrics such as R@1, R@5, R@10, and R@1%, for example achieving an R@1 of 98.94% on CVUSA, outperforming the best comparative model, Sample4G^†, which reached 98.68%. This study provides innovative methodological support for disaster emergency response, high-precision map construction for autonomous driving, military reconnaissance, and other applications. Full article

The data plane and the control plane are targets for Distributed Denial of Service (DDoS) attacks in the Software-Defined Internet of Things (SDIoT). Currently available studies rely on observations from a single network layer which limits the cross-layer attack analysis. This paper presents a synchronized, phase-aware, and a multi-layer traffic collection framework mimicking SDIoT environments under diverse DDoS attack scenarios. The data collected are the metrics captured at host, switch, and controller layers during normal, attack, and post-attack phases with strict temporal alignment. For capturing diverse DDoS attack behaviors in SDIoT environments, representative data plane attacks including volumetric flooding and switch-level flow table saturation were used. Control plane level attack targeting the SDN controller was implemented. The evaluation was done using a Mininet-based SDIoT testbed with a POX controller. Each scenario is executed across five independent runs with statistical validation. The proposed framework enables reproducible and time-aligned multi-layer analysis through standardized orchestration and automated logging. Results indicate that SDIoT DDoS behavior demonstrates differently across traffic, state, and resource-level metrics, and that accurate characterization benefits from temporally aligned multi-layer monitoring rather than relying solely on packet rate analysis. Full article

Rapid urbanization, environmental degradation and climate variability are intensifying the exposure of urban populations to multiple, interacting hazards in megacities. In India’s capital, Delhi, extreme heat, worsening air quality and flood-related stress overlap in impacted areas, exacerbated by high population density in low-lying zones and extensive built-up cover. This study develops an integrated spatial framework for assessing relative multi-hazard risk potential in Delhi by combining remote sensing, climate reanalysis, land use and demographic datasets into a predictive modeling system to support urban resilience planning. A comprehensive suite of twenty-two predictors representing thermal stress, air quality, surface indices, topography, hydrology, land use land cover (LULC), and demographic data was derived from diverse Earth observation sources. A cloud-native workflow leveraging Google Earth Engine (GEE) and Python 3 harmonized these predictors to train a Light Gradient Boosting Machine (LightGBM) model with five-fold spatial cross-validation. Quantile regression was used to estimate lower (P10) and upper (P90) predictive bounds, which are interpreted here as empirical predictive intervals around the modeled risk surface rather than as a strict separation of different uncertainty types, while SHapley Additive exPlanations (SHAP) decomposed the non-linear contributions of individual features. The model achieved predictive accuracy (R² = 0.98, MAE = 0.01), with residuals centered near zero and consistent performance across spatial folds, demonstrating strong generalizability. Road density (63.4%) and population density (25.9%) emerged as the primary predictors of the modeled risk surface, followed by building density and NO₂ concentration. Conversely, vegetation cover (NDVI) functioned as a critical mitigating buffer. Spatial risk maps identified persistent high-risk clusters in eastern and northeastern Delhi, coinciding with dense transport networks and industrial zones. The integrated P90 mapping framework provides spatially explicit and uncertainty-aware information on relative multi-hazard risk potential to guide targeted interventions, such as transport corridor mitigation and urban greening in Delhi and other rapidly urbanizing cities. Full article