Search Results (406)

To address the susceptibility of traditional concrete to explosive spalling and the lack of in situ damage-monitoring methods at high temperatures, in this study, a novel self-sensing, high-temperature-resistant Engineered Cementitious Composite (ECC) was developed. The matrix contains eco-friendly glass sand reinforced with a hybrid system of polypropylene fibers (PPFs) and carbon fibers (CFs). The evolution of mechanical properties and the multimodal self-sensing characteristics of the ECC were systematically investigated following thermal treatment from 20 °C to 800 °C. The results indicate that the hybrid system exhibits a significant synergistic effect: through PFFs’ pore-forming mechanism, internal vapor pressure is effectively released to mitigate spalling, while CFs provide residual strength compensation. Mechanically, the compressive strength increased by 51.32% (0.9% CF + 1.0% PPF) at 400 °C compared to ambient temperature, attributed to high-temperature-activated secondary hydration. Regarding self-sensing, the composite containing 1.1% CF and 1.5% PPF displayed superior thermosensitivity during heating (resistivity reduction of 49.1%), indicating potential for early fire warnings. Notably, pressure sensitivity was enhanced after high-temperature exposure, with the 0.7% CF + 0.5% PPF group achieving a Fractional Change in Resistivity of 31.1% at 600 °C. Conversely, flexural sensitivity presented a “thermally induced attenuation effect” primarily attributed to high-temperature-induced interfacial weakening. This study confirms that the “pore-formation” mechanism, combined with the reconstruction of the conductive network, governs the material’s macroscopic properties, providing a theoretical basis for green, intelligent, and fire-safe infrastructure. Full article

Carposina sasakii Matsumura is a significant lepidopteran pest in the Carposinidae family, inflicting substantial damage on stone and pome fruit trees such as jujube, peach, and apple. Using MaxEnt, we assessed the worldwide climatic suitability for C. sasakii and its key environmental drivers, evaluating how climate change impacts dispersal risks. Integrating global occurrence records with 37 environmental variables, the model (AUC = 0.982) quantitatively identifies July precipitation (prec7), minimum average temperatures in April and August (tmin4 and tmin8, respectively), and maximum average temperature in May (tmax5) as critical distribution determinants. Among these, prec7 exhibits the highest contribution (threshold approximately 370 mm). The current suitable habitat spans 10.39 × 10² km², concentrated predominantly in East Asia’s temperate monsoon zone (eastern China, the Korean Peninsula, and Japan) and southern North America. Under future climate scenarios, the high-emission pathway (SSP585) will reduce highly suitable areas, while moderately suitable zones expand coastward. In contrast, SSP370 projects a significant, albeit phased, habitat increase with a 19.61% growth rate. Precipitation regimes and extreme temperatures jointly regulate niche differentiation in C. sasakii, whose range shifts toward Southeast Asia and suboptimal regions in Europe and America, underscoring cascading climate change effects. These findings provide a scientific basis for transnational monitoring, early warning systems, and regional ecological governance. Full article

The Water Framework Directive 2000/60/EC requires the assessment of the ecological quality in all surface waters using biological indices, yet the effective application of these indices often demands extensive and long-term monitoring data. Oxidative stress biomarkers offer a promising complementary approach, as they can detect early biochemical responses of organisms to environmental degradation. In this study, we evaluated the suitability of two oxidative stress biomarkers—malondialdehyde (MDA) levels and DNA damage—in the gonads of a freshwater fish species, the Prussian carp Carassius gibelio (Bloch, 1782) as indicators of ecological condition in lakes of differing environmental quality. Fish were sampled from four lakes (Doirani, Vegoritida, Volvi, Petron; Northern Greece) representing a gradient of physicochemical and ecological quality. Both MDA concentrations and DNA damage showed significant (p < 0.05) differences among lakes. However, only DNA damage in the gonads was significantly (p < 0.05) associated with lake ecological quality as determined by the Greek Lake Fish Index (GLFI), with higher biomarker responses observed in lakes of poorer status. These findings demonstrate that oxidative stress biomarkers in C. gibelio reflect variations in lake ecological quality and may serve as sensitive, early-warning tools for biomonitoring and pollution assessment in freshwater ecosystems. Full article

Salt permeation erosion is a key factor leading to the deterioration of service performance and shortening the lifespan of asphalt pavement in salt-rich areas. In this environment, the combined action of water and salt accelerates the decline in the asphalt–aggregate interface, leading to distress, such as raveling and loosening, which severely limit pavement durability. The authors systematically reviewed the research progress on asphalt–aggregate adhesion in a saline corrosion environment and discussed the complex mechanisms of adhesion degradation driven by intrinsic factors, including aggregate chemical properties, surface morphology, asphalt components, and polarity, as well as environmental factors, such as moisture, salt, and temperature. We also summarized multi-scale evaluation methods, including conventional macroscopic tests and molecular dynamics simulations, and revealed the damage evolution patterns caused by the coupled effects of water, salt, heat, and mechanical forces. Based on this, the effectiveness of technical approaches, such as asphalt modification and aggregate modification, is explored. Addressing the current insufficiency in research on asphalt adhesion under complex conditions in salt-rich areas, this study highlights the necessity for further research on mechanisms of multi-environment interactions, composite salt erosion simulation, development of novel anti-salt erosion materials, and intelligent monitoring and early warning, aiming to provide a theoretical basis and technical support for the weather-resistant design and long-term service of asphalt pavement in salt-rich regions. Full article

This study presents an integrated analysis of climate-driven phenology and infestation dynamics of the invasive oak lace bug (Corythucha arcuata) in foothill oak ecosystems of Rășinari, Romania. Using reconstructed microclimatic data for 2024–2025, systematic field monitoring, degree-day (GDD) modeling, and the De Martonne aridity index, we assessed the combined effects of thermal accumulation and hydric stress on multigenerational development. Results indicate that warm springs and sustained summer temperatures enabled the completion of two full generations (G₁–G₂) in both years, while recurrent late-summer aridity intensified foliar vulnerability and accelerated nymphal development. A third generation (G₃) was initiated but remained incomplete due to declining autumn temperatures and photoperiod constraints. Strong habitat-specific differences were observed: exposed forest-edge stands exhibited the highest damage levels (up to 90%), whereas closed-canopy stands benefited from microclimatic buffering. The combined GDD–aridity framework showed close agreement with observed phenological transitions, providing a robust tool for identifying high-risk infestation periods. Climatic projections for 2026 suggest further advancement of generational timing under continued warming and increasing aridity. These findings highlight the growing climatic suitability of foothill oak ecosystems for C. arcuata and support the development of early-warning systems and adaptive strategies for sustainable oak forest management. Full article

To address the stochastic, nonlinear, and strongly coupled characteristics of multivariate long-term structural response in bridge health monitoring, this study proposes the Informer-SEnet prediction model. The model integrates a Squeeze-and-Excitation (SE) channel attention mechanism into the Informer framework, enabling adaptive recalibration of channel importance to suppress redundant information and enhance key structural response features. A sliding-window strategy is used to construct the datasets, and extensive comparative experiments and ablation studies are conducted on one public bridge-monitoring dataset and two long-term monitoring datasets from real bridges. In the best case, the proposed model achieves improvements of up to 54.67% in MAE, 52.39% in RMSE, and 7.73% in R². Ablation analysis confirms that the SE module substantially strengthens channel-wise feature representation, while the sparse attention and distillation mechanisms are essential for capturing long-range dependencies and improving computational efficiency. Their combined effect yields the optimal predictive performance. Five-fold cross-validation further evaluates the model’s generalization capability. The results show that Informer-SEnet exhibits smaller fluctuations across folds compared with baseline models, demonstrating higher stability and robustness and confirming the reliability of the proposed approach. The improvement in prediction accuracy enables more precise characterization of the structural response evolution under environmental and operational loads, thereby providing a more reliable basis for anomaly detection and early damage warning, and reducing the risk of false alarms and missed detections. The findings offer an efficient and robust deep learning solution to support bridge structural safety assessment and intelligent maintenance decision-making. Full article

Massive phytoplankton blooms threaten lake ecosystems, causing significant ecological and socio-economic damage. While remote sensing is vital for monitoring, the vertical stratification of algae influences light propagation and distorts remote sensing reflectance signals. This effect is particularly understudied in high-latitude lakes, leaving a gap in understanding phytoplankton biomass patterns. To address this, our study investigated three high-latitude water bodies: Lake Hulun, Fengman Reservoir, and Lake Khanka. We collected water samples from three depths based on total and euphotic zone depth and developed layer-specific inversion models for chlorophyll-a (Chal) and phycocyanin (PC) using a random forest algorithm. These models demonstrated strong performance and were applied to Sentinel-3 OLCI imagery from 2016–2024. Our results show that Chla generally decreases exponentially with depth, whereas PC exhibits a Gaussian-like vertical distribution with a pronounced subsurface maximum at approximately 1 m. In addition, a significant positive correlation between Chla and PC was observed in surface waters. In Lake Khanka, the northern basin exhibited a significant interannual increase in phytoplankton biomass. At 3 m, PC correlated negatively with turbidity and responded strongly to cyanobacterial blooms, while organic suspended matter correlated positively with Chla. This work establishes a robust framework for multilayer water quality monitoring in high-latitude lakes, providing critical insights for eutrophication management and cyanobacterial bloom early warning. Full article

Flooding in Bengaluru areas such as Kodigehalli, Hebbal, and Nagavara has led to severe disruptions, including traffic congestion, infrastructure damage, and health risks. To address this issue, we have proposed a smart flood alert and communication system, integrating Internet of things (IoT), artificial intelligence (AI), and smart infrastructure solutions. The system helps by giving information about real-time water level sensors, AI-driven flood prediction models, automated emergency coordination, and a mobile-based citizen reporting platform. Through cloud-based data processing, predictive analytics, and smart drainage management, this solution aims to enhance early warnings, reduce emergency response time, and improve urban flood resilience. It yields up to an 80% reduction in alert delays, a 50% faster emergency response, and improved community safety. This project seeks collaboration with government agencies, technology firms, and community stakeholders to implement a pilot plan, ensuring a scalable and sustainable flood mitigation strategy for Bengaluru. Full article

Effective monitoring and early warning of the instability of anti-slide piles in loess landslides depend on identifying the precursory signs of anti-slide pile failure. The acoustic emission (AE) characteristics of concrete anti-slide piles under cyclic loading were studied by using the model box test of the loess landslide–pile system. Cyclic graded loading simulates natural landslide sliding. The synergistic relationship between AE signal characteristics and pile bending moment is established, which reveals the evolution law from micro-damage to macro-damage. The results show that (1) AE ringing count and energy count change in the same way, first stable and then a sudden increase. The evolution of AE dominant frequency and amplitude experiences four stages: low frequency and low amplitude (initial damage), high frequency and low amplitude (stable development), medium frequency and high amplitude (accelerated development), and low frequency and high amplitude (failure). Each stage obviously corresponds to the change in bending moment. (3) The significant increase in the proportion of low-frequency AE energy effectively indicates that the landslide–pile system has entered the state of accelerated deformation and instability, which provides a quantifiable, real-time early warning criterion. This study verifies the feasibility and effectiveness of acoustic emission technology in anti-slide pile damage monitoring and landslide early warning and provides a new technical way for the precursor’s identification and early warning of anti-slide pile instability. Full article

Precise monitoring of damage evolution in coal-rock mass during mining emerges as a paramount requirement for developing accurate early warning systems for coal burst hazards. However, limited research has demonstrated the integrated damage characteristics of the coal-rock mass under static and dynamic loads during longwall mining. Therefore, in this paper, two novel seismic monitoring approaches, the Seismic Cluster Index (CI) and the Number of High Ground Motions (NHGMs), are developed to study the evolution of coal-rock mass damage during longwall mining under static and dynamic loads, respectively. Two months of monitored seismic data from a burst-prone longwall are used for analysis. The results show that CI can depict coal-rock damage conditions under static load, which identifies coalescence of fractures based on seismic source sizes and inter-event distances. Ground motion intensity has a positive correlation with seismic energy. The induced dynamic disturbance to roadways can further weaken the coal-rock mass, depending on the distance from the seismic sources. High-intensity dynamic disturbances, as indicated by elevated NHGMs and accelerated increments, strongly correlate with coal-burst damage. The proposed CI and NHGMs framework evaluate coal-rock mass damage and forecasts coal burst hazards, validated by the correlation between high CI/NHGMs values and actual burst locations. Full article

This study assessed the effectiveness of vegetation index change metrics (ΔVI = Post − Pre) derived from Sentinel-2 imagery for detecting landslide-affected areas and evaluating their relationship with rainfall intensity, thereby enhancing the early-warning potential. The analysis focused on Sancheong-gun, Gyeongsangnam-do, South Korea, where intense rainfall in July 2025 triggered multiple landslides. Pre- and post-event Sentinel-2 Level-2A images (10 m spatial resolution) were used to compute changes in the Normalized Difference Vegetation Index (ΔNDVI), Soil-Adjusted Vegetation Index (ΔSAVI), Modified Soil-Adjusted Vegetation Index (ΔMSAVI), Normalized Difference Moisture Index (ΔNDMI), and Global Vegetation Moisture Index (ΔGVMI) over the landslide-affected post-disaster (PD) and non-damaged (ND) areas. Sensitivity was assessed based on the differences in mean ΔVI between the PD and ND areas, Welch’s t-statistics, and Cohen’s d values. All indices exhibited significant differences between the PD and ND areas (p < 0.001), with ΔMSAVI showing the highest sensitivity (MSAVI > GVMI ≈ SAVI > NDVI > NDMI). Correlation analysis revealed that ΔMSAVI had the strongest positive association with rainfall accumulation (72 h: r = 0.54; 7 days: r = 0.49), indicating that greater rainfall corresponded to stronger vegetation degradation signals. These findings highlight ΔMSAVI as a robust and responsive indicator of rainfall-triggered landslides, supporting its integration into satellite-based early-warning and rapid damage detection systems for improved landslide monitoring and response. Full article

Monitoring landslide displacement and anti-slide pile damage is critical for assessing the stability of progressive loess landslides. To address the challenge of capturing precursor information for loess landslide instability under anti-slide pile reinforcement, this study systematically investigates the damage evolution process of slides (through their “slide-stability-reslide” cycles) and anti-slide piles under acoustic emission (AE) monitoring. Cyclic loading tests were employed to simulate the movement of progressive loess landslides. Based on the core causal logic that “slide displacement induces pile damage, damage generates AE signals, and signals invert displacement status”, a laboratory-scale physical model was designed to simultaneously monitor slide displacement, pile stress, and AE signals. The research results indicate that the dominant frequency and amplitude of AE signals are significantly correlated with slide displacement: with cyclic loading, both the dominant frequency and amplitude exhibit a “low → high → low” characteristic, corresponding to “low/medium-frequency low-amplitude”, “medium/high-frequency medium-high-amplitude” and “low-frequency medium-high-amplitude” signals in the three stages of slide deformation, respectively. The Kaiser and Felicity effects effectively monitor pile damage, and the decrease in Felicity ratio serves as a precursor for landslide early warning. Research results can provide a new methodological framework for early warning systems in pile-reinforced loess landslides. Full article

Microplastics (MPs) are ubiquitous coastal contaminants that can induce oxidative stress, detoxification responses and inflammation in marine species. We evaluated MP occurrence and associated physiological responses in the digestive tract of the peacock wrasse Symphodus tinca (N = 28) from the northeastern coast of Ibiza (Balearic Islands, Spain). MPs occurred in 60.7% of the fish (58 items in total; mean 2.1 ± 0.5 items·fish⁻¹), dominated by fibres (75.9%). Polyester (38.1%) and polypropylene (23.8%) were the most frequent polymers in the subset of MPs analysed. Fish were grouped by median MP count (<2 vs. ≥2), and statistical differences and correlations were assessed. Individuals with ≥2 MPs showed significantly elevated activities of antioxidant enzymes (catalase, CAT; superoxide dismutase, SOD), the phase-II detoxification enzyme glutathione S-transferase (GST), and the pro-inflammatory enzyme myeloperoxidase (MPO). Production of reactive oxygen species (ROS) and oxidative-damage biomarkers, malondialdehyde (MDA) and protein carbonyls tended to be higher in the high-MP group, but differences were not statistically significant. MP exposure correlated positively with all biomarkers except protein carbonyls. In conclusion, higher MP loads in S. tinca are associated with activation of antioxidant, detoxification and inflammatory pathways, without clear evidence of widespread oxidative damage under the sampled conditions. These physiological responses suggest potential impacts on individual fitness that may signal early ecological effects in coastal fish populations, highlighting their value as early-warning indicators in coastal monitoring and environmental management. Full article

Background: Urban air pollution, particularly fine particulate matter (PM2.5 and PM10), poses health risks, including damage to the ocular surface. This pilot study (BIKE-EYE) aimed to assess ocular exposure to airborne pollutants during bicycle commuting and to evaluate particle presence in human tear fluid. Methods: Fifteen healthy volunteers wore portable sensors measuring PM2.5 and PM10 during daily bike commutes over six months. Exposure was calculated as time-weighted integrals over the ten days preceding an ophthalmologic exam assessing conjunctival hyperemia, epithelial damage, tear film quality, and meibomian gland function. Ocular symptoms were assessed via the Ocular Surface Disease Index (OSDI). Tear samples were analyzed using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS). Results: Higher pollutant exposure was significantly associated with conjunctival hyperemia and corneal epithelial damage, while temperature and humidity showed no effect. OSDI scores moderately correlated with PM levels. SEM/EDS analysis confirmed airborne particles in post-exposure tear samples, including carbonaceous material, aluminosilicates, iron, and sulfur compounds. Conclusions: Ocular surface alterations and conjunctival hyperemia were significantly associated with air pollution exposure, while subjective symptoms showed weaker trends. The detection of particulate matter in human tear fluid supports the use of the ocular surface as a sensitive, non-invasive tool for biomonitoring. These findings highlight its potential role in early warning systems for pollution-related health effects, with implications for public health surveillance and urban planning. Full article

Accurately predicting the spatiotemporal evolution of rock-damage zones is vital for underground engineering safety. Using three-dimensional data obtained from uniaxial compression–acoustic emission tests, this study addresses the key limitations of existing data-driven methods, which struggle with spatial heterogeneity and often yield predictions that deviate from fundamental fracture-mechanics principles. To overcome these challenges, we propose a physics-constrained spatiotemporal STConvLSTM framework that integrates a density-adaptive point cloud–voxel conversion mechanism for improved 3D representation, a composite loss incorporating structural and physics-based constraints, and a multi-level encoder–processor–decoder architecture enhanced by 3D convolutions, attention modules, and residual connections. Experimental results demonstrate superior accuracy and physical consistency, achieving 92.6% accuracy and an F1-score of 0.947, outperforming ConvLSTM and UNet3D baselines. The physics-aware constraints effectively suppress non-physical divergence and yield damage morphologies that better align with expected fracture-mechanics behavior. These findings show that coupling data-driven learning with physics-based regularization substantially enhances model reliability and interpretability. Overall, the proposed framework offers a robust and practical paradigm for 3D damage-evolution modeling, supporting more-dependable early-warning, stability assessment, and intelligent support-design applications in underground engineering. Full article