Search Results (550)

The resilience concept is well established in engineering, but the quantitative studies of vessel impact resilience for bridge structures remain limited. This paper presents an integrated framework for assessing vessel impact resilience under combined rebar corrosion and vessel collision effects. First, a corroded reinforced concrete bridge is considered for nonlinear static analysis to quantify initial corrosion damage and for nonlinear dynamic analysis to evaluate post-impact function loss. Then, recovery for each damage state is modeled by using both negative exponential and triangular recovery functions to estimate restoration times and to obtain a vessel impact resilience index. The results show that increasing corrosion severity markedly reduces resilience capacity. Furthermore, resilience indices obtained from the negative exponential function generally exceed those from the triangular function, and this improvement becomes more significant at lower resilience levels. Resilience indices calculated by using negative exponential and triangular recovery functions show negligible differences when the concrete bridge is in the uncorroded initial state and the vessel impact velocity is below 1.5 m/s. However, as reinforcement corrosion increases, the maximum discrepancy between these two recovery functions also increases, reaching a value of 67% at a corrosion level of 15.0%. From the numerical results obtained from a case study, it is important to select an appropriate recovery model when assessing vessel impact resilience. For rapid initial restoration followed by slower long-term recovery, the negative exponential model yields greater resilience gains compared to the triangular model. The proposed method thus provides an effective tool for engineers and decision makers to evaluate and improve the vessel impact resilience of aging bridges under the combined corrosion and impact effects. This proposes a quantitative metric for resilience-based condition assessment and maintenance planning. Full article

This article presents a comprehensive assessment of mudflow risk in the Talgar River basin through the application of Multi-Criteria Decision Analysis (MCDA) methods and numerical modeling using the Rapid Mass Movement Simulation (RAMMS) environment. The first part of the study involves a spatial assessment of mudflow hazard and susceptibility using GIS technologies and MCDA. The key condition for evaluating mudflow hazard is the identification of factors influencing the formation of mudflows. The susceptibility assessment was based on viewing the area as an object of spatial and functional analysis, enabling determination of its susceptibility to mudflow impacts across geomorphological zones: initiation, transformation, and accumulation. Relevant criteria were selected for analysis, each assigned weights based on expert judgment and the Analytic Hierarchy Process (AHP). The results include maps of potential mudflow hazard and susceptibility, showing areas of hazard occurrence and risk impact zones within the Talgar River basin. According to the mudflow hazard map, more than 50% of the basin area is classified as having a moderate hazard level, while 28.4% is subject to high hazard, and only 1.8% falls under the very high hazard category. The remaining areas are categorized as very low (4.1%) and low (14.7%) hazard zones. In terms of susceptibility to mudflows, 40.1% of the territory is exposed to a high level of susceptibility, 35.6% to a moderate level, and 5.5% to a very high level. The remaining areas are classified as very low (1.8%) and low (15.6%) susceptibility zones. The predictive performance was evaluated through Receiver Operating Characteristic (ROC) curves, and the Area Under the Curve (AUC) value of the mudflow hazard assessment is 0.86, which indicates good adaptability and relatively high accuracy, while the AUC value for assessing the susceptibility of the territory is 0.71, which means that the accuracy of assessing the susceptibility of territories to mudflows is within the acceptable level of model accuracy. To refine the spatial risk assessment, mudflow modeling was conducted under three scenarios of glacial-moraine lake outburst using the RAMMS model. For each scenario, key flow parameters—height and velocity—were identified, forming the basis for classification of zones by impact intensity. The integration of MCDA and RAMMS results produced a final mudflow risk map reflecting both the likelihood of occurrence and the extent of potential damage. The presented approach demonstrates the effectiveness of combining GIS analysis, MCDA, and physically-based modeling for comprehensive natural hazard assessment and can be applied to other mountainous regions with high mudflow activity. Full article

Background: Acute mountain sickness (AMS) is a prevalent and potentially life-threatening condition caused by rapid exposure to high-altitude hypoxia, affecting pulmonary and neurological functions. Tongqiao Jiuxin Oil (TQ), a traditional Chinese medicine formula composed of aromatic and resinous ingredients such as sandalwood, agarwood, frankincense, borneol, and musk, has been widely used in the treatment of cardiovascular and cerebrovascular disorders. Clinical observations suggest its potential efficacy against AMS, yet its pharmacological mechanisms remain poorly understood. Methods: The chemical profile of TQ was characterized using UHPLC-Q-Exactive Orbitrap HRMS. Network pharmacology was applied to predict the potential targets and pathways involved in AMS. A rat model of AMS was established by exposing animals to hypobaric hypoxia (~10% oxygen), simulating an altitude of approximately 5500 m. TQ was administered at varying doses. Physiological indices, oxidative stress markers (MDA, SOD, GSH), histopathological changes, and the expression of hypoxia- and apoptosis-related proteins (HIF-1α, VEGFA, EPO, Bax, Bcl-2, Caspase-3) in lung and brain tissues were assessed. Results: A total of 774 chemical constituents were identified from TQ. Network pharmacology predicted the involvement of multiple targets and pathways. TQ significantly improved arterial oxygenation and reduced histopathological damage in both lung and brain tissues. It enhanced antioxidant activity by elevating SOD and GSH levels and reducing MDA content. Mechanistically, TQ downregulated the expression of HIF-1α, VEGFA, EPO, and pro-apoptotic markers (Bax/Bcl-2 ratio, Caspase-3), while upregulated Bcl-2, the anti-apoptotic protein expression. Conclusions: TQ exerts protective effects against AMS-induced tissue injury by improving oxygen homeostasis, alleviating oxidative stress, and modulating hypoxia-related and apoptotic signaling pathways. This study provides pharmacological evidence supporting the potential of TQ as a promising candidate for AMS intervention, as well as the modern research method for multi-component traditional Chinese medicine. Full article

Rapid and comprehensive assessment of building damage caused by earthquakes is essential for effective emergency response and rescue efforts in the immediate aftermath. Advanced technologies, including real-time simulations, remote sensing, and multi-sensor systems, can effectively enhance situational awareness and structural damage evaluations. However, most existing methods rely on isolated time snapshots, and few studies have systematically explored the continuous, time-scaled integration and update of building damage estimates from multiple data sources. This study proposes a stepwise framework that continuously updates time-scaled, single-damage estimation outputs using the best available multi-sensor data for estimating earthquake-induced building damage. We demonstrated the framework using the 2024 Noto Peninsula Earthquake as a case study and incorporated official damage reports from the Ishikawa Prefectural Government, real-time earthquake building damage estimation (REBDE) data, and satellite-based damage estimation data (ALOS-2-building damage estimation (BDE)). By integrating the REBDE and ALOS-2-BDE datasets, we created a composite damage estimation product (integrated-BDE). These datasets were statistically validated against official damage records. Our framework showed significant improvements in accuracy, as demonstrated by the mean absolute percentage error, when the datasets were integrated and updated over time: 177.2% for REBDE, 58.1% for ALOS-2-BDE, and 25.0% for integrated-BDE. Finally, for stepwise damage estimation, we proposed a methodological framework that incorporates social media content to further confirm the accuracy of damage assessments. Potential supplementary datasets, including data from Internet of Things-enabled home appliances, real-time traffic data, very-high-resolution optical imagery, and structural health monitoring systems, can also be integrated to improve accuracy. The proposed framework is expected to improve the timeliness and accuracy of building damage assessments, foster shared understanding of disaster impacts across stakeholders, and support more effective emergency response planning, resource allocation, and decision-making in the early stages of disaster management in the future, particularly when comprehensive official damage reports are unavailable. Full article

River embankments are critical flood defense structures, stretching for thousands of kilometers across alluvial plains. They often originated as natural levees resulting from overbank flows and were later enlarged using locally available soils yet rarely designed according to modern engineering standards. Substantially under-characterized, their performance to extreme events provides an invaluable opportunity to highlight their vulnerability and then to improve monitoring, management, and reinforcement strategies. In May 2023, two extreme meteorological events hit the Emilia-Romagna region in rapid succession, causing numerous breaches along river embankments and therefore widespread flooding of cities and territories. These were followed by two additional intense events in September and October 2024, marking an unprecedented frequency of extreme precipitation episodes in the history of the region. This study presents the methodology adopted to create a regional database of 66 major breaches and damages that occurred during May 2023 extensive floods. The database integrates multi-source information, including field surveys; remote sensing data; and eyewitness documentation collected before, during, and after the events. Preliminary interpretation enabled the identification of the most likely failure mechanisms—primarily external erosion, internal erosion, and slope instability—often acting in combination. The database, unprecedented in Italy and with few parallels worldwide, also supported a statistical analysis of breach widths in relation to failure mechanisms, crucial for improving flood hazard models, which often rely on generalized assumptions about breach development. By offering insights into the real-scale behavior of a regional river defense system, the dataset provides an important tool to support river embankments risk assessment and future resilience strategies. Full article

Soluble solids content (SSC) in fruits, as one of the key indicators of fruit quality, plays a critical role in postharvest quality assessment and grading. While traditional destructive methods can provide precise measurements of sugar content, they have limitations such as damaging the fruit’s integrity and the inability to perform rapid detection. In contrast, non-destructive detection technologies offer the advantage of preserving the fruit’s integrity while enabling fast and efficient sugar content measurements, making them highly promising for applications in fruit quality detection. This review summarizes recent advances in non-destructive detection technologies for fruit sugar content measurement. It focuses on elucidating the principles, advantages, and limitations of mainstream technologies, including near-infrared spectroscopy (NIR), X-ray technology, computer vision (CV), electronic nose (EN) technology and so on. Critically, our analysis identifies key challenges hindering the broader implementation of these technologies, namely: the integration and optimization of multi-technology approaches, the development of robust intelligent and automated detection systems, and issues related to high equipment costs and barriers to widespread adoption. Based on this assessment, we conclude by proposing targeted future research directions. These focus on overcoming the identified challenges to advance the development and practical application of non-destructive SSC detection technologies, ultimately contributing to the modernization and intelligentization of the fruit industry. Full article

Emerging economies such as China have benefited from rapid growth but now face acute carbon risk amid worsening environmental conditions. Carbon-intensive firms—major emitters—face rising carbon risk that pervades operations and threatens efficient capital allocation. To advance global climate-change mitigation, help China meet its dual-carbon goals, and enhance corporate financial sustainability, we analyze panel data on 575 Chinese carbon-intensive companies from 2012 to 2022 and estimate OLS models to assess how carbon risk influences capital mismatch. Results show that higher carbon risk significantly widens capital mismatch, whereas higher media attention and better corporate governance each weaken this effect. These findings suggest that regulators and the media should monitor carbon-intensive firms more closely to improve information transparency and guide capital to its most productive uses, while firms themselves need to strengthen governance to limit the damage carbon risk inflicts on capital allocation. Full article

This paper presents an advanced seismic performance evaluation of reinforced concrete (RC) seismically isolated frame structures under the conditions of rare earthquakes. By employing an elastic–plastic analysis in conjunction with a nonlinear multi-degree-of-freedom model, this study innovatively assesses the incremental dynamic vulnerability of isolated structures. A novel equivalent linearization method is introduced for both single- and two-degree-of-freedom isolation structures, providing a simplified yet accurate means of predicting seismic responses. The reliability of the modified Takeda hysteretic model is verified through comparative analysis with experimental data, providing a solid foundation for the research. Furthermore, a multi-degree-of-freedom shear model is employed for rapid elastic–plastic analysis, validated against finite element software, resulting in an impressive 85% reduction in computation time while maintaining high accuracy. The fragility analysis reveals the staggered upward trend in the vulnerability of the upper structure and isolation layer, highlighting the importance of comprehensive damage control to enhance overall seismic performance. Full article

The spotted wing drosophila, Drosophila suzukii is an injurious polyphagous pest threatening worldwide soft fruit production. Its high adaptability to new colonized environments, short life cycle, and wide host range are supporting its rapid spread. The most common techniques to reduce its significant economic damage are based on multiple insecticides applications per season, even prior to the harvest, which reduces agroecosystem biodiversity and affects human and animal health. Environmental concerns and regulatory restrictions on insecticide use are driving the need for studies on alternative biological control strategies. This study aimed to assess the effect of T. drosphilae in controlling D. suzukii infestations and its interaction with P. vindemiae, a secondary parasitoid naturally present in Apulia (South Italy). Field experiments were carried out in organic cherry orchards in Gioia del Colle (Bari, Italy) to test the efficacy and adaptability of T. drosphilae following weekly releases of artificially reared individuals. Additionally, the interaction between P. vindemiae and T. drosphilae was studied under laboratory conditions. Results from field experiments showed that D. suzukii populations were significantly lower when both parasitoids were present. However, T. drosophilae was less prone to adaptation, so its presence and parasitism were limited to the post-release period. Laboratory experiments, instead, confirmed the high reduction of D. suzukii populations when both parasitoids are present. However, the co-existence of the two parasitoids resulted in a reduced parasitism rate and offspring production, notably for T. drosophilae. This competitive disadvantage may explain its poor establishment in field conditions. These findings suggest that the field release of the two natural enemies should be carried out with reference to their natural population abundance to not generate competition effects. Full article

Climate change poses significant challenges to temperate rice production, particularly affecting grain quality and market acceptance. This review synthesizes current knowledge of climate-induced quality changes, with a focus on the Australian rice industry as a case study with comparisons to other temperate regions. Environmental stressors such as extreme temperatures, variable rainfall, elevated CO₂, and salinity disrupt biochemical pathways during grain development, altering physicochemical, textural, and aromatic traits. Different rice classes exhibit distinct vulnerabilities: medium-grain japonica varieties show reduced amylose under heat stress, aromatic varieties experience disrupted aroma synthesis under drought, and long-grain types suffer kernel damage under combined stresses. Temperature is a key driver, with quality deterioration occurring above 35 °C and below 15 °C. Systems biology analyses reveal complex signalling networks underpinning these stress responses, although experimental validation remains limited. The Australian industry has responded by developing cold-tolerant cultivars, precision agriculture, and water-saving practices, yet projected climate variability demands more integrated strategies. Priorities include breeding for stress-resilient quality traits, refining water management, and deploying advanced phenotyping tools. Emerging technologies like hyperspectral imaging and machine learning offer promise for rapid quality assessment and adaptive management. Sustaining high-quality rice in temperate zones requires innovation linking physiology with practical adaptation. Full article

Effective post-disaster damage assessment is crucial for guiding emergency response and resource allocation. This study introduces DamageScope, an integrated deep learning framework designed to detect and classify building damage levels from post-disaster satellite imagery. The proposed system leverages a convolutional neural network trained exclusively on post-event data to segment building footprints and assign them to one of four standardized damage categories: no damage, minor damage, major damage, and destroyed. The model achieves an average F1 score of 0.598 across all damage classes on the test dataset. To support geospatial analysis, the framework extracts the coordinates of damaged structures using embedded metadata, enabling rapid and precise mapping. These results are subsequently visualized through an interactive, web-based platform that facilitates spatial exploration of damage severity. By integrating classification, geolocation, and visualization, DamageScope provides a scalable and operationally relevant tool for disaster management agencies seeking to enhance situational awareness and expedite post-disaster decision making. Full article

The rapid development of underground engineering contributes significantly to achieving China’s “dual carbon” strategic goals. However, during the construction and operation phases, this engineering project faces diverse risks and challenges related to disasters. Consequently, enhancing the evaluation capability for underground engineering resilience is imperative. Based on the characteristics of resilience evaluation and enhancement in underground engineering, this study defines the concept and objectives of resilience evaluation for underground space engineering and analyzes corresponding enhancement methods. By considering aspects such as the magnitude of collapse disaster risk in underground engineering, its vulnerability, resistance capacity, adaptability to disasters, recovery ability, and economic feasibility, a comprehensive index system for evaluating the resilience of collapse disaster risks in underground engineering has been established. This research suggests that disaster risk management should shift from passive to active prevention. Through resilience evaluation case applications, it is possible to improve the design objectives of underground engineering towards “structural recoverability”, “ease of damage repair”, and “controllable consequences after a disaster”. The integration of intelligent static assessment models based on artificial intelligence algorithms can effectively enhance the accuracy of resilience evaluations. Furthermore, dynamic assessments using multiple data fusion techniques combined with numerical simulations represent promising directions for improving the overall resilience of underground engineering. Full article

Accurate and rapid delineation of wildfire-affected areas is essential in the era of climate-driven increases in fire frequency. This study compares and analyzes four techniques for identifying wildfire-affected areas using Sentinel-2 satellite imagery: (1) calibrated differenced Normalized Burn Ratio (dNBR); (2) differenced NDVI (dNDVI) with empirically defined thresholds (0.04–0.18); (3) supervised SVM classifiers applying linear, polynomial, and RBF kernels; and (4) unsupervised ISODATA clustering. In particular, this study proposes an SVM-based classification method that goes beyond conventional index- and threshold-based approaches by directly using the SWIR, NIR, and RED band values of Sentinel-2 as input variables. It also examines the potential of the ISODATA method, which can rapidly classify affected areas without a training process and further assess burn severity through a two-step clustering procedure. The experimental results showed that SVM was able to effectively identify affected areas using only post-fire imagery, and that ISODATA enabled fast classification and severity analysis without training data. This study performed a wildfire damage analysis through a comparison of various techniques and presents a data-driven framework that can be utilized in future wildfire response and policy-oriented recovery support. Full article

Phenotypic traits and phenotypic extraction at the seedling stage of oilseed rape play a crucial role in assessing oilseed rape growth, breeding new varieties and estimating yield. Manual phenotyping not only consumes a lot of labor and time costs, but even the measurement process can cause structural damage to oilseed rape plants. Existing crop phenotype acquisition methods have limitations in terms of throughput and accuracy, which are difficult to meet the demands of phenotype analysis. We propose an oilseed rape segmentation and phenotyping measurement method based on 3D Gaussian splatting with improved PointNet++. The CKG-PointNet++ network is designed to integrate CGLU and FastKAN convolutional modules in the SA layer, and introduce MogaBlock and a self-attention mechanism in the FP layer to enhance local and global feature extraction. Experiments show that the method achieves a 97.70% overall accuracy (OA) and 96.01% mean intersection over union (mIoU) on the oilseed rape point cloud segmentation task. The extracted phenotypic parameters were highly correlated with manual measurements, with leaf length and width, leaf area and leaf inclination R² of 0.9843, 0.9632, 0.9806 and 0.8890, and RMSE of 0.1621 cm, 0.1546 cm, 0.6892 cm² and 2.1144°, respectively. This technique provides a feasible solution for high-throughput and rapid measurement of seedling phenotypes in oilseed rape. Full article

In cold-region rock engineering, freeze–thaw cycle-induced crack propagation in fractured rock masses serves as a major cause of disasters such as slope instability. Existing studies primarily focus on the influence of individual fissure parameters, yet lack a systematic analysis of the crack propagation mechanisms under the coupled action of multiple parameters. To address this, we establish three groups of slope models with different rock bridge distances (d), rock bridge angles (α), and fissure angles (β) based on the PFC2D discrete element method. Frost heave loads are simulated by incorporating the volumetric expansion during water–ice phase change. The Parallel Bond Model (PBM) is used to capture the mechanical behavior between particles and the bond fracture process. This reveals the crack evolution laws under freeze–thaw cycles. The results show that, at a short rock bridge distance of d = 60 m, stress concentrates in the fracture zone. This easily leads to the rapid penetration of main cracks and triggers sudden instability. At a long rock bridge distance where d ≥ 100 m, the degree of stress concentration decreases. Meanwhile, the stress distribution range expands, promoting multiple crack initiation points and the development of branch cracks. The number of cracks increases as the rock bridge distance grows. In cases where the rock bridge angle is α ≤ 60°, stress is more likely to concentrate in the fracture zone. The crack propagation exhibits strong synergy, easily forming a penetration surface. When α = 75°, the stress concentration areas become dispersed and their distribution range expands. Cracks initiate earliest at this angle, with the largest number of cracks forming. Cumulative damage is significant under this condition. When the fissure angle is β = 60°, stress concentration areas gather around the fissures. Their distribution range expands, making cracks easier to propagate. Crack propagation becomes more dispersed in this case. When β = 30°, the main crack rapidly penetrates due to stress concentration, inhibiting the development of branch cracks, and the number of cracks is the smallest after freeze–thaw cycles. When β = 75°, the freeze–thaw stress dispersion leads to insufficient driving force, and the number of cracks is 623. The research findings provide a theoretical foundation for assessing freeze–thaw damage in fractured rock masses of cold regions and for guiding engineering stability control from a multi-parameter perspective. Full article