Search Results (18,121)

This study presents an economic risk evaluation of buildings in Samoa exposed to extreme sea level (ESL)-driven episodic flooding and permanent inundation from relative sea level (RSL) rise. A spatiotemporal risk analysis framework was applied at the building object level to calculate monetary loss, expressed as the exceedance probability loss (EPL) and average annual loss (AAL). Economic risk was enumerated at national and district levels between the period 2020 and 2140 based on RSL projections for medium confidence Shared Socioeconomic Pathways (SSPs). Over this century, national AAL for buildings from ESL flooding in 2020 is expected to double by 2100 (USD 47–51 million). Under high emissions scenarios SSP3-7.0 and SSP5-8.5, AAL rates decelerate after 2100 as permanent inundation loss increases. District level risk variability is evident. For example, Tuamasaga on Upolu Island accounted for 44% of national 100-year annual recurrence interval losses, while AAL for Aiga-i-le-Tai and Va’a-o-Fonoti over this century reaches 8% of total district building replacement values. Our model approach has potential future applications to evaluate spatiotemporal risk distribution for a broader range of socioeconomic impacts that may occur beyond directly affected flood inundation areas. Full article

With the continuous expansion in China’s vehicle fleet, emissions of CO₂ and air pollutants from the on-road transportation sector are widely projected to be rising, posing a challenge to realizing China’s targets of carbon peaking in 2030 and carbon neutrality in 2060, as well as the national target for air quality improvement. Therefore, vehicle electrification in the on-road transportation sector is urgently needed to reduce emissions of CO₂ and air pollutants, as it serves as a key pathway to align transportation development with sustainability goals. While vehicle electrification is supposed to be the primary solution, there is a research gap in quantifying the provincial, environmental, and economic impacts of implementing such a policy in China. To bridge this gap, we projected the provincial-level ownership of different types of vehicles based on historical trends, assessed the emission reduction potential for CO₂ and air pollutants using the LEAP model from 2021 to 2060, and predicted the provincial marginal abatement costs at different mitigation stages under various scenarios with different strategies of vehicle electrification and development patterns of electricity structure. Our results show that the implementation of vehicle electrification lowers the national carbon peak by 0.2–0.6 Gt yr⁻¹ and advances its achievement by 1–3 years ahead of 2030. The marginal abatement cost ranges from $532 to $3466 per ton CO₂ (tCO₂⁻¹) in 2025 and from -$180 to -$113 tCO₂⁻¹ in 2060 across scenarios. The provincial marginal abatement cost curves further indicate that China’s vehicle electrification should be prioritized in cost-effective regions (e.g., Shanghai and Guangdong), while concurrently advancing nationwide grid decarbonization to guarantee the supply of low-carbon electricity across the country. This optimized pathway ensures that transportation decarbonization aligns with both environmental and economic requirements, providing actionable support for China’s sustainable development strategy. Full article

Background/Objective: Dendritic cells (DCs) act as sentinels bridging innate and adaptive immunity, and their functions are strongly influenced by dietary and environmental factors. Phytochemicals such as α-Mangostin (A phytochemical, a xanthone derivative from Garcinia mangostina, known for its anti-inflammatory and antioxidant properties) are widely recognized for their antioxidant and anti-inflammatory effects, but their potential to modulate antiviral pattern recognition pathways remains unclear. This study investigated whether phytochemicals activate retinoic acid–inducible gene I (RIG-I: DDX58, a cytosolic receptor recognizing viral RNA and inducing antiviral responses)–dependent signaling in human monocyte-derived dendritic cells (MoDCs) and affect downstream T cell responses. Methods: MoDCs were generated from peripheral blood and stimulated with selected phytochemicals. RIG-I pathway–related transcripts were quantified by qPCR, and protein expression was assessed by Western blotting, intracellular flow cytometry, and immunofluorescence staining. Functional outcomes were evaluated by co-culturing MoDCs with T cells, followed by phenotypic analysis via flow cytometry and measurement of IFN-γ production by ELISA. Results: α-Mangostin stimulation increased RIG-I (DDX58) mRNA levels in MoDCs and induced time-dependent changes in intracellular protein expression. In co-culture, α-Mangostin–treated MoDCs tended to increase the proportion of OX40⁺ 4-1BB⁺ CD4⁺ T cells, accompanied by a significant elevation of IFN-γ levels in supernatants. Experiments with CpG-ODN (synthetic oligodeoxynucleotides mimicking bacterial DNA that activate TLR9) suggested context-dependent crosstalk between the TLR9 and RIG-I signaling axes. Conclusions: Phytochemicals, exemplified by α-Mangostin, prime antiviral responses in human DCs through upregulation of RIG-I and promote Th1-dependent immune responses. These findings suggest that phytochemicals may represent promising nutritional strategies to enhance antiviral immunity while mitigating excessive inflammation under infectious conditions. Full article

Accurate remote sensing scene classification is essential for applications such as environmental monitoring and disaster management. In real-world scenarios, particularly during emergency response and disaster relief operations, acquiring nadir-view satellite images is often infeasible due to cloud cover, satellite scheduling constraints, or dynamic scene conditions. Instead, off-nadir images are frequently captured and can provide enhanced spatial understanding through angular perspectives. However, remote sensing scene classification has primarily relied on nadir-view satellite or airborne imagery, leaving off-nadir perspectives largely unexplored. This study addresses this gap by introducing Off-nadir-Scene10, the first controlled and comprehensive benchmark dataset specifically designed for off-nadir satellite image scene classification. The Off-nadir-Scene10 dataset contains 5200 images across 10 common scene categories captured at 26 different off-nadir angles. All images were collected under controlled single-day conditions, ensuring that viewing geometry was the sole variable and effectively minimizing confounding factors such as illumination, atmospheric conditions, seasonal changes, and sensor characteristics. To effectively leverage abundant nadir imagery for advancing off-nadir scene classification, we propose an angle-aware active domain adaptation method that incorporates geometric considerations into sample selection and model adaptation processes. The method strategically selects informative off-nadir samples while transferring discriminative knowledge from nadir to off-nadir domains. The experimental results show that the method achieves consistent accuracy improvements across three different training ratios: 20%, 50%, and 80%. The comprehensive angular impact analysis reveals that models trained on larger off-nadir angles generalize better to smaller angles than vice versa, indicating that exposure to stronger geometric distortions promotes the learning of view-invariant features. This asymmetric transferability primarily stems from geometric perspective effects, as temporal, atmospheric, and sensor-related variations were rigorously minimized through controlled single-day image acquisition. Category-specific analysis demonstrates that angle-sensitive classes, such as sparse residential areas, benefit significantly from off-nadir viewing observations. This study provides a controlled foundation and practical guidance for developing robust, geometry-aware off-nadir scene classification systems. Full article

In disaster relief operations, Unmanned Aerial Vehicles (UAVs) equipped with base stations (UAV-BS) are vital for re-establishing communication networks where conventional infrastructure has been compromised. Optimizing their trajectories and coverage to ensure equitable service delivery amidst obstacles, wind effects, and energy limitations remains a formidable challenge. This paper proposes an innovative reinforcement learning framework leveraging a Long Short-Term Memory (LSTM)-based Advantage Actor–Critic (A2C) model enhanced with an attention mechanism. Operating within a grid-based disaster environment, our approach seeks to maximize fair coverage for randomly distributed ground users under tight energy constraints. It incorporates a nine-direction movement model and a fairness-focused communication strategy that prioritizes unserved users, thereby improving both equity and efficiency. The attention mechanism enhances adaptability by directing focus to critical areas, such as clusters of unserved users. Simulation results reveal that our method surpasses baseline reinforcement learning techniques in coverage fairness, Quality of Service (QoS), and energy efficiency, providing a scalable and effective solution for real-time disaster response. Full article

The influence of CO₂ on the mechanical properties of shale is one of the key factors to consider for enhancing shale oil and gas exploitation and realizing CO₂ geological storage. In this paper, triaxial mechanical experiments of rock under real-time contact with CO₂ under different conditions were carried out for the Chang 7 shale of the Yanchang Formation in the Ordos Basin. The results show that under the influence of real-time contact with CO₂, the triaxial compressive strength of shale decreases with an average decrease of 3.77% and a maximum decrease of 6.58% under the experimental conditions. The elastic modulus increased with an average increase of 8.54% and a maximum increase of 11.95%. The core compression failure presents a small degree of multi-fracture complex failure. With an increase in CO₂ exposure time, temperature, and pressure, the triaxial compressive strength gradually decreases, the elastic modulus gradually increases, and the compression failure of shale core is gradually complicated. The variation in mechanical parameters with time, temperature, and pressure under the influence of CO₂ real-time contact was quantitatively described. The effect of gaseous CO₂ on shale mechanical parameters and core compression failure is significantly weaker than that of supercritical CO₂. This research provides theoretical and data support for supercritical CO₂-enhanced shale oil and gas recovery and carbon geological storage from a rock mechanics perspective. Full article

Deforestation and forest degradation in the Brazilian Amazon remain critical threats to ecosystem integrity and local livelihoods. Existing approaches often overlook the nuanced perspectives of different regional actors, limiting our understanding of deforestation drivers and conservation policy effectiveness. This study compared perceptions of deforestation drivers and policy instruments between two major development hubs, Porto Velho and Manaus, using Likert-scale questionnaires administered to 49 villagers and 27 experts. Villagers across both areas identified Natural Disasters (RII = 0.79) and Forest Fires (RII = 0.63) as the most influential drivers, with these ranking particularly high in Porto Velho. Contrastingly, Cattle Ranching Expansion (RII = 0.89) and Political Intervention (RII = 0.86) were prominent in Porto Velho, while Forest Fires (RII = 0.84) and Illegal Logging (RII = 0.73) dominated in Manaus, highlighting distinct governance and economic priorities. Experts and locals both highlighted strong connections between agricultural expansion, land tenure insecurity, and policy deficiency. Conservation units (RII = 0.95) were considered the most important policy instrument according to experts in both areas and governance levels. These results highlight the need for context-specific, participatory solutions tailored to regional realities in Amazonian forest management. Full article

The 2011 Great Flood in Thailand exposed critical deficiencies in water management across the Chao Phraya River Basin, particularly in controlling inflows and discharges from major reservoirs such as Sirikit and Bhumibol. Inadequate rainfall monitoring at the Nakhon Sawan station further intensified the disaster’s impact. As climate change continues to amplify extreme weather events, this study aims to improve flood forecasting accuracy and promote sustainable water resource management aligned with the Sustainable Development Goals (SDGs 6, 11, and 13). Advanced climate data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) were spatially refined and integrated with hydrological models to enhance regional accuracy. The Discrete Wavelet Transform (DWT) was applied for feature extraction to capture hydrological variability, while the Nonlinear Autoregressive Model with Exogenous Factors (NARX) was employed to model complex temporal relationships. A multi-model ensemble framework was developed to merge climate forecasts with real-time hydrological data. Results demonstrate significant model performance improvements, with DWT-NARX achieving 55–98% lower prediction errors (RMSE) compared to baseline methods and correlation coefficients exceeding 0.91 across all forecasting scenarios. Marked seasonal variations emerge, with higher inflows during wet periods and reduced inflows during dry seasons. Under RCP8.5 climate scenarios, wet-season inflows are projected to increase by 15.8–17.4% by 2099, while dry-season flows may decline by up to 33.5%, potentially challenging future water availability and flood control operations. These findings highlight the need for adaptive and sustainable water management strategies to enhance climate resilience and advance SDG targets on water security, disaster risk reduction, and climate adaptation. Full article

Owing to their high vertical resolution, remote sensing data from meteorological satellite hyperspectral infrared sounders are well-suited for the identification, monitoring, and early warning of high-impact weather events. The effective utilization of full field-of-view (FOV) observations from satellite infrared sounders in high-impact weather applications remains a major research focus and technical challenge worldwide. This study proposes a generalized variational retrieval framework to estimate full FOV cloud fraction and precipitable water vapor (PWV) from observations of the Geostationary Interferometric Infrared Sounder (GIIRS) onboard the Fengyun-4A (FY-4A) satellite. Based on this method, experiments are performed using high-frequency FY-4A/GIIRS observations during the landfall periods of Typhoon Lekima (2019) and Typhoon Higos (2020). A three-step channel selection strategy based on information entropy is first designed for FY-4A/GIIRS. A constrained generalized variational retrieval method coupled with a cloud cost function is then established. Cloud parameters, including effective cloud fraction and cloud-top pressure, are initially retrieved using the Minimum Residual Method (MRM) and used as initial cloud information. These parameters are iteratively optimized through cost-function minimization, yielding full FOV cloud fields and atmospheric profiles. Full FOV brightness temperature simulations are conducted over cloudy regions to quantitatively evaluate the retrieved cloud fractions, and the derived PWV is further applied to the identification and analysis of hazardous weather events. Experimental results demonstrate that incorporating cloud parameters as auxiliary inputs to the radiative transfer model improves the simulation of FY-4A/GIIRS brightness temperature in cloud-covered areas and reduces brightness temperature biases. Compared with ERA5 Total Column Water Vapour (TCWV) data, the PWV derived from full FOV profiles containing cloud parameter information shows closer agreement and, at certain FOVs, more effectively indicates the occurrence of high-impact weather events. The simplified methodology proposed in this study provides a robust basis for the future assimilation and operational utilization of infrared data over cloud-affected regions in numerical weather prediction models. Full article

Global warming is recognized as a climate crisis that extends beyond a mere increase in the Earth’s temperature, triggering rapid and widespread climatic changes worldwide. In particular, the frequency and intensity of extreme rainfall events have increased in Korea and the Association of Southeast Asian Nations (ASEAN) region, leading to a significant increase in flood damage. The growing number of large-scale hydrological disasters underscores the urgent need for accurate and rapid flood-forecasting systems that can support disaster preparedness and mitigation. Compared with conventional physics-based forecasting systems, artificial intelligence (AI) models can provide faster predictions using limited observational data. In this study, a river water-level prediction model was constructed using real-time observation data and a long short-term memory (LSTM) algorithm, which is a recurrent neural network-based deep learning approach suitable for hydrological time-series forecasting. A repeated k-fold cross-validation technique was applied to enhance model generalization and prevent overfitting. In addition, water-level differencing was employed to convert nonstationary water-level data into stationary time-series inputs, thereby improving the prediction stability. Water-level observation stations in the Philippines, Indonesia, and the Republic of Korea were selected as study sites, and the model performance was evaluated at each location. The differenced LSTM model achieved a root mean square error of 0.13 m, coefficient of determination (R²) of 0.866, Nash–Sutcliffe efficiency (NSE) of 0.844, and Kling–Gupta efficiency of 0.893, thus outperforming the non-differenced baseline by approximately 17%. The repeated k-fold validation approach was particularly effective when the training data period was short or the number of input variables was limited. These results confirm that ensuring temporal stationarity and applying repeated cross-validation can significantly enhance the predictive accuracy of real-time flood forecasting. The proposed framework exhibits strong potential for implementation in regional early warning systems across data-limited flood-prone areas in the ASEAN region. Ongoing studies that apply and verify this approach in diverse hydrological contexts are expected to further improve and expand AI-based flood prediction models. Full article

The Domain Name System (DNS) is a fundamental component of the Internet, yet its distributed and caching nature makes it susceptible to various attacks, especially cache poisoning. Although the use of random port numbers and transaction IDs has reduced the probability of cache poisoning, recent developments such as DNS Forwarder fragmentation and side-channel attacks have increased the possibility of cache poisoning. To counteract these emerging cache poisoning techniques, this paper proposes the DNS Cache Sensor (DNS-Sensor) system, which operates as a distributed sensor network for DNS security. Like environmental sensors monitoring physical parameters, DNS-Sensor continuously scans DNS cache records, comparing them with authoritative data to detect anomalies with sensor-grade precision. It involves checking whether the DNS cache is consistent with authoritative query results by continuous observation to determine whether cache poisoning has occurred. In the event of cache poisoning, the system switches to a disaster recovery resolution system. To expedite comparison and DNS query speeds and isolate the impact of cache poisoning on the disaster recovery resolution system, this paper uses a local top-level domain authoritative mirror query system. Experimental results demonstrate the accuracy of the DNS-Sensor system in detecting cache poisoning, while the local authoritative mirror query system significantly improves the efficiency of DNS-Sensor. Compared to traditional DNS, the integrated DNS query and DNS-Sensor method and local top-level domain authoritative mirror query system is faster, thus improving DNS performance and security. Full article

Accurate early warning of rainfall-induced landslides poses a critical challenge in geological disaster risk management. Conventional deterministic rainfall threshold models often overlook the heterogeneity of regional geological conditions, while landslide susceptibility assessment is plagued by uncertainties in selecting non-landslide samples. To address these issues, this paper took Zhushan County in Hubei Province as the study area, and the semi-supervised random forest (SRF) model was adopted to conduct landslide susceptibility assessment. The critical rainfall (Effective Rainfall-Duration, EE-D) threshold curves were constructed based on the antecedent effective rainfall (EE) and rainfall duration (D). Furthermore, EE-D threshold curves with different geological condition characteristics were established and analyzed according to the thickness, slope, and area of the landslides, respectively. By coupling the landslide susceptibility results with a classified multi-level rainfall threshold model, a spatiotemporally refined regional framework for tiered landslide early warning was developed. The results show that the SRF model solves the problem of non-landslide sample selection error in traditional supervised learning. The Area Under Curve (AUC) value reaches 0.91, which is better than the analytic hierarchy process, logistic regression, etc. Moreover, the models of landslide susceptibility and EE-D threshold can effectively achieve the hierarchical early warning of rainfall-induced landslide hazards. Full article

Accurate simulation of boundary layer wind field structures is essential for evaluating tropical cyclone (TC) wind hazards and supporting engineering design in coastal regions. However, existing models often assume radially symmetric and homogeneous surface conditions, leading to limited accuracy near landfall where surface roughness varies significantly. This study conducts a comprehensive evaluation of four representative TC boundary layer models of M95, K01, Y21a, and Y21b, under both idealized and real TC case conditions. The idealized experiments are used to clarify the role of vertical advection and turbulent diffusion in shaping the TC boundary layer, while the landfalling case of Typhoon Mangkhut (2018) is simulated to examine the impacts of surface roughness parameterization. Results show that Y21a, which incorporates nonlinear vertical advection, produces stronger and more realistic super-gradient phenomenon than linear models of M95 and K01. Furthermore, the model of Y21b, which accounts for spatially varying drag coefficients and using a terrain-following coordinate system, successfully reproduces the asymmetric wind patterns observed in the WRF simulations during landfall, achieving the highest correlation (R = 0.93). When the spatially varying drag coefficients incorporated into the linear models, their correlation with WRF improved markedly by about 37%. These findings highlight the necessity of incorporating nonlinear advection, dynamic turbulence, and surface heterogeneity for physically consistent TC boundary layer simulations. The results provide valuable guidance for improving parametric wind field models and enhancing TC wind hazard assessments over complex coastal terrains. Full article

The accuracy of historical landslide data is a key factor affecting the precision of landslide susceptibility mapping. The degree of conformity between mathematical models and disaster-prone environments cannot be predetermined, and the optimal model needs to be determined through comparative studies. In this paper, SBAS-InSAR and the object-oriented classification method were integrated to provide data for landslide susceptibility mapping: SBAS-InSAR was used to process Sentinel-1 images, while the object-oriented classification method was applied to interpret Landsat 8 images. Eleven hazard factors were selected for landslide susceptibility modeling, and the best-performing model was determined. The influences of single and multiple hazard factors on landslide susceptibility were analyzed using Geodetector. The results showed that 246 potential landslides were identified, with a total area of 0.427 km² and a total volume of 2.161 × 10⁶ m³. The Blending-XGBoost-CNN model achieved the highest AUC and Precision, outperforming the XGBoost model and CNN model. The extreme high susceptible areas, high susceptible areas, moderate susceptible areas, minor susceptible areas and extreme minor susceptible areas accounted for 6.24% (91.4 km²), 15.07% (220.6 km²), 29.15% (426.8 km²), 30.58% (447.7 km²), and 18.96% (277.8 km²) of the total area, respectively. NDVI and gradient were key factors determining landslide occurrence. Elevation, slope aspect, distance from river, and land use also played significant roles in landslide occurrence. The contributions of TWI and lithology to landslide occurrence were relatively small, while those of plane curvature and distance from road were minimal. The interaction of hazard factors exhibited NE or BE relationships, not only increasing landslide risk but also potentially leading to more complex disaster patterns. This study can provide a theoretical basis for landslide prevention-oriented land use planning. Full article

The Mapeng pluton in the central Taihang Mountains hosts significant gold mineralization; however, the magmatic processes controlling its emplacement, crystallization, and potential role in ore genesis remain debated. Previous petrological and geochemical studies have identified three internal lithofacies zones and suggested magma mixing. However, it remains uncertain whether these zones formed through in situ fractional crystallization or multiple intrusive pulses, and how magmatic dynamics contributed to gold enrichment. To address these questions, we applied quantitative crystal size distribution (CSD) analysis to constrain the intrusion history and evaluate its implications for mineralization. The CSD curves of quartz in the Mapeng granite are typically concave, with characteristic lengths (CLs) ranging from 0.78 to 1.43 mm, slopes from −1.29 to −0.70, and intercepts from −2.10 to 0.95. These variations indicate strong fluctuations in crystal growth and nucleation rates, suggesting a major influence of magma mixing. For plagioclase, the CL values range from 0.56 to 2.50 mm, slopes from −4.40 to −1.33, and intercepts from −1.21 to 3.48, further supporting the idea of multistage magma injection and crystal coarsening. Regarding crystal spatial distribution and alignment, the crystal aggregation degree (R value) ranges from 0.79 to 1.14, and the alignment factor (AF value) ranges from 0.01 to 0.19. These values suggest that the crystals tend to aggregate spatially, with their alignment degree being extremely weak, which indicates rapid magma flow disturbed by mixing processes. Notably, the R value and AF value show a negative correlation (R² > 0.6) in the central facies and a positive correlation in the transitional facies, revealing differences in crystal accumulation mechanisms among different lithofacies zones. By synthesizing the covariance of CSD parameters and texture indices, this study infers that the Mapeng pluton experienced multiple batches of magma injection during its emplacement and consolidation. These injection events accelerated crystal dissolution and regrowth, thereby promoting crystal coarsening and textural reorganization. This study provides new quantitative mineral–textural evidence. Full article